Pawel Pawlewski

P Golinska
email: paulina.golinska@put.poznan.pl

M Fertsch

PRODUCTION FLOW CONTROL IN AUTOMOTIVE INDUSTRY-QUICK SCAN APPROACH

Keywords: PRODUCTION CONTROL, PRODUCTION disturbances, uncertainty production planning and control, disturbances, uncertainty, automotive industry

whether they are published or not. The documents may come

Introduction

The production flow control in the automotive industry refers mainly to the monitoring of the planned production orders sequence and its maintaining within a given range of capability by feedback and correction. The control of the production flow comprises the following tasks: tracking vehicle flow in the production cycle, identification of serious setbacks on the production line, changing the vehicle sequence, issuing instructions for correction. The production flow control is performed based on the Master Production Schedule (MPS). MPS can be perceived as the main driver of information for production planning and control in MRP logic based systems. It can be treated as a benchmark for the ideal production flow. The car sequence that has been planned (in the MPS) serves as a pattern, and an attempt is made to maintain it in the course of production. However, when production orders start, their actual sequence deviates very often from the plan. Therefore, in order to make sure that the production is stable and delivered on time, it is particularly important to efficiently control the production flow. Due to the complexity of the production systems in the production departments, different programs supporting the flow control are used. Computer programs help to visualize the vehicle sequence; however, often they do not generate solutions that optimize the production flow. The IT tools very often do not trigger alarms of changes, do not prompt any solutions to prevent changes in the sequence, do not offer suggestions how to change the vehicle sequence so as to maintain the stable MPS. According to the Motor & Equipment Manufacturers Association (MEMA), it takes approximately 3,800 different components (identified by unique part numbers) to build an average automobile. Many components, however, are used in multiplicities (e.g., car has five tires); thus, it takes nearly 35,000 separate items to build each car. Without appropriate information on the real production orders sequence, it is difficult to maintain the production on time and to synchronize all needed deliveries. Companies often track an excessive and ever growing number of indicators to avoid missing something important. As a result, a huge number of parameters is monitored by IT tools. It causes the situation when production planners/controllers are overloaded with different data and not able to analyze them on time.

A number of research works that have been conducted in the automotive industry, regarding the improvement of the performance, concentrates mainly on material management within a supply chain and integration of planning activities (e.g. Manson-Jones and [START_REF] Mason-Jones | Shrinking the Supply Chain Uncertainty Circle[END_REF][START_REF] Towill | Integrating the Automotive Supply Chain: Where are we Now?[END_REF][START_REF] Howard | 3DayCar Production Flexibility: Solving the barriers to production and process technology in a build-to-order environment, 3[END_REF]. The state-of-the-art studies conducted at the initial stage of this research project have shown that there is a need to elaborate a set of simple indicators for a quick scan of the production system. This quick scan should enable easy, on-hand evaluation of the sequencing activities ongoing on the production floor. Moreover, the elaborated method should provide a simple tool for the identification of potential serious disturbances that negatively affect the ability to fulfil customers' orders on time. In this paper authors identify the most important sources of uncertainty that appear in the automotive industry and associate common disturbances with them. In the next sections the main assumptions of the quick scan approach and its application in an automotive assembly plant are discussed.

Sources of uncertainty in high stabilized production systems planned by the MRP logic

Traditionally in MRP systems both demand and lead times have been assumed to be deterministic [START_REF] Brennan | A structured analysis of material requirements planning systems under combined demand and supply uncertainty[END_REF]. This assumption does not reflect the business practice. In real-life manufacturing systems the production process is very often not executed according to assumed MPS, because of the uncertainty conditions. Uncertainty can be defined as any unpredictable event that disturbs the production process in a manufacturing system that is planned by MRP [START_REF] Koh | How Uncertainty Disturbs SME Manufacturers[END_REF]. Uncertainty is difficult to cope with, due to the lack of foreknowledge resulting in problems with manufacturing system recovery on time. The underlying causes of uncertainty, as well as the ways how they affect the manufacturing system (based on MRP logic) were described by [START_REF] Koh | Uncertainty under MRP-planned manufacture: review and categorisation[END_REF]. The researchers deployed a system theory to structure uncertainty according to its source. In the above-mentioned studies uncertainty was categorized into:

• input uncertaintyunderstood as uncertainty that occurs in external supplies and external demand, • process uncertaintyunderstood as uncertainty that occurs in internal demand and internal supplies. [START_REF] Brennan | A structured analysis of material requirements planning systems under combined demand and supply uncertainty[END_REF] identified the following sources of uncertainty:

• demand uncertaintyincluding forecast errors and changes in the orders placed by customers, • supply uncertaintyincluding both uncertainty connected with duration of process lead times (production cycles) and delivery lead times. Both of the above-mentioned categorizations can be combined into one. It can be assumed that input uncertainty consists of external demand uncertainty (so called independent demand) and delivery lead times uncertainty. Process uncertainty comprises internal demand (dependent demand) uncertainty and process lead times uncertainty (production cycles). In this paper, the delivery lead times uncertainty is connected with all disturbing events that cause the situation when a particular material/part is not available when needed. Authors do not determine whether a particular delivery lacks appropriate quality (e.g. higher defect rate than anticipated) or quantity (e.g. the quantity delivered by vendor was smaller than the ordered quantity); in both cases this delivery is treated as delayed. Lead times uncertainty is connected with all the disturbing events due to which a particular technical operation cannot be executed on time e.g. lack of capacity, workforce absence, etc. In the automotive industry, the uncertainty problem is especially harmful owing to the fact that manufacturing systems are of high stability. The high stabilized production system (HSPS) is characterized by (Golinska et.al 2006):

• steady production plans,

• Master Production Schedule (MPS) prepared many weeks in advance,

• high similarity of production routines,

• high similarity of product structure (BOM), however they may differ in the number of specific attributes like for example colour, • high automatization of operations, • application of buffers to stabilize production process, • low flexibility. Figure 1 presents the sources of uncertainty that appear in HSPS.

Figure1. Source of uncertainty vs. level of stability

As presented in Figure 1, the high level of the production system stability allows for reducing the uncertainty of external demand and internal demand. It can be assumed that the only uncertainty to appear is that connected to delivery and process lead times. Such assumptions are based on in-field visits to factories and previous studies of other researchers. As a proof of such assumptions the studies conducted within the framework of the '3DayCar' program [START_REF] Holweg | The challenge of building cars to order, can current automotive supply chains cope?[END_REF] can be cited. They show that in the European automotive industry, the forecasts of customers' demand regarding volume and item specification are prepared many months in advance. In order to protect the stability of their production system and the gain of economy of scale the manufacturers combine two policies by production planning, i.e.:

• build-to-forecastaccording to average company standards, customers are served from inventories of final goods, • build-to-ordera demand driven production approach that aims to provide vehicles built according to specification of individual clients in a minimal process lead time.

The automobile manufacturers very often apply "late-fit-strategy". It is an upgraded version of build-to-forecast strategy, whereby forecast orders are amended to customer requirements at the assembly line. Actual customer orders that are received are either fitted into the plan laid out by the production programmed months ahead, or the forecast orders in the system are amended to customer requirements. Due to the long planning horizon in the automotive industry, from the perspective of a production controller it can be assumed that the demand is deterministic. Internal demand uncertainty can be usually ignored in high stabilized production systems due to the fact that they are high automated and all quality requirements are strictly kept. Quality problems usually appear at the late phase of the production and they result mainly in reworks that affect the production cycle duration. Due to a huge amount of added-value within production process in automotive plants, the work-in-progress is very rarely denied by quality control and classified as a scrape. As already mentioned, problems with the quality of parts and components delivered by suppliers influence only delivery lead times (not internal demand).

The previous studies on uncertainty in the MRP planned manufacturing systems, mainly assume the modification of Master Production Schedule when a variation has appeared. The high replanning frequency in order to overcome the uncertainty induces the system nervousness.

In HSPS the changes at MPS level should be avoided due to the fact that usually a fixed number of orders is assigned to each production day/shift. Any changes in Master Production Schedule result in the delays for the next production days. A crucial element of the effective production flow control in the automotive industry is the identification of the disturbances. Moreover, they should be identified as quickly as possible in order to avoid changes in MPS.

In the quick scan approach authors assume that all disturbances in the production flow should be solved at MRP level.

Dampening and buffering methods and techniques

The effect of uncertainty has been examined by a number of researchers [START_REF] Brennan | A structured analysis of material requirements planning systems under combined demand and supply uncertainty[END_REF], Guide and Shivastava 2000[START_REF] Ho | An investigation of alternative dampening procedures to cope with MRP system nervousness[END_REF]. They propose techniques to tackle uncertainty in the production system planned by MRP logic. These researchers examined a variety of buffering or/and dampening techniques to minimize the effect of uncertainty. A comprehensive literature review can be found in Guide and Shivastava (2000) or [START_REF] Koh | How Uncertainty Disturbs SME Manufacturers[END_REF]. Based on the above mentioned studies, the authors of this paper identified the buffering and dampening methods/techniques that might be applied in the high stabilized production systems HSPS (see table 1). Cox and Blackstone (1998) defined buffering and dampening techniques (BAD); they described buffering as a quantity of resource waiting for processing (labour, money, machines). In their opinion dampening is based on planning methodologies (e.g. rescheduling, safety lead time). Dampening techniques are understood here as an information-based approach and buffering techniques are understood as an application of resources-based approach (e.g. safety stock, safety capacity/overcapacity). In Table 1 authors include the results of the assessment of the BAD techniques identified in the cited work. As a criterion for assessment the level of stability is taken into consideration along with sources of uncertainty that are typical in HSPS. Moreover, while making the assessment, the authors took into consideration the results of interviews with shift managers at the assembly plant. In their opinion, in the automotive industry, due to the high stability of the production system, there is no intention to increase the number of buffers. Moreover, the zero inventories approach is reflected in the continuous shift of safety stocks on the suppliers. Justin-time (JiT), just-in-sequence (JiS) deliveries are a standard approach in this industry. Taking into consideration all of the above-mentioned factors, it can be assumed that dampening techniques are more appropriate than buffering techniques in this sector.

In table 1 authors present only these BAD techniques, which in their opinion might be applied for HSPS; herby, they are indicated there as the modification of the previous research.

Automotive Industry -Production Planning and Control

The production process in the automotive industry can be divided into three technological phases: body welding, painting, final assembly. The simplified schema of a production system is presented in Figure 2. The production system typical for the European automotive industry is a mixture of the traditional mass production features in the departments responsible for pre-treatment (D1, D2) and lean (for more see [START_REF] Womack | The machine that change the world, the story of lean production[END_REF]) concept in the area of the final assembly (D3).

In the European automotive industry production scheduling is prepared 2-3 weeks before production starts. Orders are picked from the order bank and are assigned to production weeks at different plants. The scheduled orders for a build week are re-shuffled into a sequence of build orders for the assembly plants. In any case, only after the orders are sequenced, the suppliers actually receive their final call-off of what is required, as only then it is actually defined what parts will be needed [START_REF] Holweg | The challenge of building cars to order, can current automotive supply chains cope?[END_REF]. Sequenced orders are sent to the body shop (welding department). The paint shop is the bottleneck of the production process by the most of manufacturers. In order to improve the efficiency of painting operations, after the body shop, car bodies are accumulated in pre-paint buffers. When the minimal required amount of car bodies is collected, they are to be sprayed in the same colour. In the paint shop, the initial planned production sequence is usually distorted and becomes unpredictable for all subsequent assembly operations. After paint, the cars are generally re-shuffled again before they are sent to the assembly department. It is done to ensure the mix of cars needed for the line balancing activities. In practice, this reshuffling activity causes a lot of problems due to common variations in the duration of painting lead times. A big number of reworks is often required at the paint shop due to the common failures of the painting process (usually undesired objects/materials glued to the fresh paint coat or uneven paint coat). It is very difficult to forecast the real length of the production cycle for particular orders. This uncertainty inherent in the process lead times at the paint shop causes many problems, so that reshuffling operations before assembly line cannot be completed on time.

The line balancing approach leads to a need for a precise control of the production flow and quick response in a situation when the real production sequence is disturbed and differs from the assumed plan. At the same time, in case of automotive industry where just-in-time and just-in-sequence deliveries are common, business practice changes that appear in Master Production Schedule result in problems with on-time customer's orders fulfilment.

Manufacturers often fail to deliver the right product within an acceptable timeframe to the customer.

Changes in Master Production Schedules (MPS) result in due-date changes in open orders, quantity and timing for planned order of end products. The above-mentioned changes are being translated into gross requirements changes for components and timing of their delivery. It leads to the situation when materials needed for a particular order may not be available particularly if there is an increase in ordered quantity and shortened due date. This situation is especially harmful for just-in-sequence deliveries when all the parts/components are delivered directly to the assembly point in the exact sequence. Each part is placed in a container in the particular sequence and signed with VIN number (vehicular identification number) of a automobile in which it is supposed to be mounted. Good examples of JiS deliveries are mirrors, which are prepainted before assembly, so a silver mirror goes to a silver car, a red mirror to a red car etc. Any disturbances, which cause variations in the production sequence, should be immediately announced to the suppliers. Otherwise, materials needed for a particular order may not be available or an unwanted delivery might appear, like for example arrival of the parts that are not needed any more at the particular production shift. Both situations affect the organization of the production flow.

Disturbances in production system

The uncertainty conditions combined with the complexity of a production system increase the risk of disturbance appearance. Disturbance can be defined as an unexpected and unplanned event, which causes the deviation between planned Master Production Schedule MPS and real production flow (production sequence) within the production system. The deviation between the initially planned and real production sequence resulting from intentional planner activities (e.g. change of the initial order priority) will not be treated as a disturbance. Disturbances in a production system can differ a lot: e.g. breakdown of machines, serious delay in deliveries, workers absence. The disturbances can be classified based on two criteria: 1. source of disturbance, 2. effects they have on the production system. Taking into consideration the first criterion, the following types of disturbances (after Lis 1982) are determined:

• disturbances of the production resources: breakout of machines and tooling, absence of the workforce, • disturbances of the production systems inputs: shortages of materials and energy, invalid external services, lacking information or data, financial factors. Based on the second criterion the following types of disturbances can be identified (after [START_REF] Patig | Flexible Produktionsfeinplanung mit Hilfe von Planungsschritten[END_REF]:

1. production control goal disturbances, 2. production capacity disturbances, 3. input disturbances, 4. information flow disturbances. The main goal of the production control is to execute the production flow according to the planned MPS. The disturbances of the production control goal are any deviations in the initial MPS execution appearing at an independent demand level caused by changes in customers' demand or customers' order due dates. The production capacity disturbances are any variations in the initial MPS execution caused by a limited availability of production resources (machines, tooling, and workforce). These disturbances appear at the MRP level. The input disturbances are any variations in initial MPS execution appearing at dependent demand level caused by shortages of raw materials. The input disturbances are any variations in initial MPS execution appearing at dependent demand level, caused by inappropriate information and data on the technology: e.g. outdated Bill of Material, invalid data on maintenances, lead times, and inventory levels. Due to the deterministic demand assumption, only disturbances appearing at MRP level are taken into consideration in the proposed quick scan approach.

Quick Scan Approach

The effective production control requires the appropriate mechanisms for the process flow monitoring and its normalization when fluctuations appear. The scope of fluctuations in the production flow caused by disturbances very often is bigger than the buffering abilities of the manufacturing system. The protection of the initial sequence of production orders (production flow) is essential because the main value adding activities take place at the final assembly department. Most of the automobile manufacturers apply Just-in-Time/Just in Sequence delivery schema at this part of the production process. Inefficient production flow control results in delays in customer's orders fulfilment. Quick scan approach helps to reduce the process lead time uncertainty by speeding-up the information feedback loop. The model for production planning and control in an automobile plant is presented in Figure 3. The final assembly schedule (FAS) is prepared on the basis of MPS usually 2-3 weeks before production due-date. It shows in detail the production sequence for every day (per production shifts). The FAS depends on company capacities constraints (e.g. max 50 automobiles with automatic gear per shift, at least 4 automobiles in the same colour per block etc.) and availability of materials. These schedules are also sent to suppliers and discussed with them. The FAS is supposed to be executed to the unit. It can be observed that in real life almost in every shift some variations in FAS appear. Due to the production system complexity, there are problems to identify the sources of disturbances. On the basis of the case study and the literature review authors elaborated an approach for quick scan based on a few simple indicators. This approach is based on the assumption that due to the low flexibility of the production control system, it is crucial to build up quasi-flexibility into the production system. There are four elements of a production control system determining its total flexibility, namely (modified from Patig 2001):

• flexibility of a production control goalunderstood as an ability to easily change MPS at any time, • flexibility of production resourcesunderstood as a possibility to substitute machines (e.g. overcapacity, alternative machines on hand, multi-skilled workforce etc.), • flexibility of production programmesunderstood as a wide portfolio of products that can be produced at the same production line, • flexibility of production operationsunderstood as a possibility to manufacture the same product in a number of alternative ways/routines. The flexibility of a production control goal in HSPS is very limited. The other three components of flexibility, namely production resources, production programmes and production operations, determine the alternative decision variants for the production planner/controller's. The alternative decision variants should ensure the execution of the same production control goal (the same MPS in the defined time-framework). Simultaneously, the choice of a particular decision variant results in the appearance of particular events. One choice is determining the new alternative decision variants that are still available as the follow-up actions. The availability of appropriate control information as soon as possible, allows the controller to choose these decision variants (potential corrective actions) that provide the lowest deviation between the real MPS and the planned MPS. It can be assumed that the flexibility of a production control system is a space of alternative decision variants, which afterwards can be transformed into corrective actions. In order to reach the quasi flexibility of a control system in the conditions of HSPS, it is crucial to shorten the time between the appearance of events and the time when this information is visible for the controller/planner. The availability of appropriate information (regarding identification of disturbance) allows a quick application by feedback loop corrective actions into the production flow. It is important because a quick assessment of the real production flow (sequence) allows choosing the best corrective action variants. Figure 4 presents the production model that is applied in the quick approach. The production system in the automotive industry is monitored usually by a number of scanners that register the data from bar codes/GS1, codes which are plugged to any car body at the beginning of the production process. The places where scanners are located can be called production control points (PCPs). A number of PCPs differs among particular manufactures. The data from PCPs is sent in real time to production databases. The initial production MPS is divided into orders block. The size of a block can differ and depends on individual features of particular cars. Each car is indicated in the production system as POIN (Production Order Identification Number). The production flow should be executed according to the planed MPS. In the production database, data is stored on the times when particular POINs reach particular PCPs. Based on this information, the third part logistics companies (3PLCs) and the first trier suppliers release their delivery schedules. Any unidentified disturbances in the production flow result in problems with on time deliveries. The structure of data depends on a particular company. It can be assumed that the minimal requirements for data needed to apply quick scan approach are (Table 2):

• (POIN)

• PCP identification code • date • time when particular POIN passes through particular PCP Tab.2. Simplified structure of database [START_REF] Golinska | Quick scan of the production system for disturbances[END_REF] The authors propose defining and implementing an information feedback mechanism, relying on a quick, automated measurement of sequence and pre-defined initial conditions triggering appropriate corrective actions in response to a sequence change. This quasi flexibility of the production control system can be achieved by decreasing the time between appearance of a disturbance within the production process and the moment when this information is available for the controller not as a pure data but as a diagnosis of the production flow problem (control information feedback). This is presented in Figure 5. As presented in Figure 5, disturbances can be monitored and afterwards can be classified into two categories:

• serious disturbance -when a variation between planned and real sequence is bigger than buffering abilities of the production system, • the danger of serious disturbance -when the appearance of a disturbance in nearly future can be forecast. The production flow control model (Figure 5) follows the procedure:

1. Define the block of orders, 2. Observe the defined block of orders and assess the production sequence, a) count the OPS indicator value for a block of orders, b) compare it with the critical value of OPS indicator, 3. If the production sequence is disturbed (the value of OPS for a defined block falls below the critical value), analyze the production flow cycle for this block (indicator PFC_set av ) at this PCP, 4. Identify the type of disturbances based on the assessment of a production sequence and production flow indicator, 5. Choose suitable corrective actions and apply them at MRP level, 6. When corrective actions cannot be applied at MRP level, inform the production planner, 7. When necessary, adjust the MPS (redefine the next blocks). The above-presented simplified procedure is a continuous process that can be automatically executed for the whole production flow. The indicators, which are used in the procedure, are described below. As mentioned in the previous sections of this paper, changes in the MPS should be avoided and all corrective actions should be taken at MRP level.

The quick scan approach is based on following indicators:

• Production flow cycle indicator PFCis counted as a sum of time periods that the analyzed Production Order Identification Number (POIN) needs to pass the following production control points PCPs (equitation 1). The time when POIN enters the production system at PCP1 is counted as 0 (t pcp1 = 0):

PFC = ∑ = n i pcpi t 1 (1)
This indicator is used in order to observe the duration of a production cycle (process lead times) for the particular production order POIN. It also allows finding the particular configuration of car attributes (configuration of components) that has an extraordinary long production cycle and might cause delays in the production flow.

• For a block of k number of POINs the indicator is counted as (equitation 2):

PFC_set av = k t POIN n i pcpi k k ∑ ∑ =1 1 (2)
In order to define the typical and critical values of the production flow cycle indicator for a block of POINs, on the basis of a long-time observation of non-disturbed POINs (with the same key features as analyzed block), the "ideal" values are counted based on equitation 2 and named as PFC_set av ID. • Sequence dispersion range (SDR)measured for a block of POIN as a difference between maximum order identification number in a defined block and minimum identification number observed at a chosen production control point (equitation 3).

= SDR min) max ( POIN POIN - (3)
The value of SDR indicator shows how a block of POINs defined in the initial MPS was distorted by the appearance of a number of POINs belonging to other blocks of orders.

• The overall production sequence indicator presents the number k of POIN in the analyzed block to SDR at an observed production control point, for example if there was "k" POIN and the SDR equals to "k", the quality of operations management regarding sequencing is 100%. The value of this indicator is counted as following (equitation 4).

OPS = SDR k *100% (4)
The low value of OPS indicator informs about a serious disturbance in the production flow for the analyzed block. On the basis of the proposed indicators, it is possible to monitor the production flow and to identify when serious disturbances appear and regulatory activities are needed.

Based on the interviews with the shifts managers, the typical disturbances in the production flow were identified, as presented in Figure 6 (step 4 of the procedure). The above-mentioned problems were transformed into the types of disturbances categorization presented in Section 2. Only types of disturbances that appear at the MRP level are taken into consideration, because demand is assumed to be deterministic (Tab. 3).

Tab. 3. Identification of the type of disturbances

Numerical example

The numerical example presents the application of the proposed quick scan approach. The procedure described in the previous section is implemented. In step one the block of 16 orders (cars) is defined. In step two the production sequence is assessed, so OPS indicator is calculated for this block (Table 4). The following PCPs are taken into consideration:

• MPSsystem entry, the sequence is equal to initial MPS • B1, B2 -body shop (1) entry and (2) exit • P1, P2 -paint shop (1) entry and (2) exit • A1, A2 -final assembly (1) entry and (2) exit • FC -final control The number of PCPs can be individually defined by a particular company (without affecting the effectiveness of the proposed quick scan approach). The next step in the assessment is the comparison of the calculated OPS with its critical value. The critical value is individual for each company. It is calculated as an average value of OPS indicator for this PCP (long-term calculation for a defined period)) minus the tolerance value. The value 25% used in this numerical example was defined based on interviews with shifts managers in the analyzed company and statistical analyses of the production data for a defined period.

The value of OPS indicator equals 100% when the production flow is not disturbed. For the analyzed block (Table 4) it can be observed that the flow of production is seriously disturbed (below the critical value, Table 5) at the body shop and paint shop. At the final assembly department the production sequence (production flow) is finally undisturbed (after corrective actions), but at the end of the production process the sequence is again disturbed.

Following the quick scan procedure described in Section 4, when the value of OPS falls below the critical value, the analysis of indicator the PFC_set av at this PCP is needed. In order to calculate the benchmark for its duration, the first indicator the PFC_set av is counted for the so called ideal block of orders (PFC_set avID) and then the tolerance is defined. The tolerance value is individual for each company. As presented in tables 4 -6 and in Figure 7 the low values of OPS at PCP from B1 to P1 are not followed by the increase of PFC indicator over the critical value. Based on Table 3, it can be assumed that the production sequence is disturbed by: 1. machine breakdowns at the parallel part of the production line, 2. non-standard material requirements, 3. non-standard supply lead times, 4. poor quality, 5. lack of needed components, 6. invalid BOM. The disturbances caused by a non-standard production can be eliminated (2-3) due to the block size. According to the company's organizational rules, the block of non-standard production orders cannot be bigger than 6 POINs. In case of poor quality (4), at this production phase, the orders have to be re-processed so that the production flow indicator would increase. Invalid BOM (6) disturbs the production flow at the final assembly, not at the body and paint shop. The only possible causes of deviations are machine breakdowns at a parallel part of the production line and the lack of needed components. In order to fully identify the cause, a quick interview with production shift management of a particular department is needed.

At the production control point P2, the value OPS indicator falls below 5%. In most of the European automotive companies orders are reshuffled at the paint shop to build up particular colour blocks. For orders no. 8, 9, 10, 14, the production flow cycle indicator increases over the critical value. Based on the data presented in Table 3, it can be assumed that disturbances in the production sequence are potentially caused by: 1. non-standard supply lead times, 2. poor quality, 3. lack of needed components. As mentioned before, the non-standard production can be excluded. All chemicals used at the paint shop are rather standard and repetitive, so shortages of paint or other chemicals would cause delays of a bigger number of POINs (PFC indicator over critical value). It can be identified that disturbances in the production sequence between P1 and P2 (at the paint shop) are caused by poor quality. The paint shop is usually a bottleneck in a number of automotive companies. The peculiarity of the paint process leads to a big amount of failures that need to be reprocessed, e.g. not appropriate paint coat, workers' hairs or fibres from clothing stuck to the paint coat.

The quick identification of disturbances based on a quick scan approach allows the production controller to take the appropriate corrective actions. The value of the OPS indicator comes back to 100% at the final assembly department (A1-A2) and no disturbances are identified. At the production control point FC, the OPS value falls again a bit but not below the critical value. It is a place in the production system where the technological process is finished and a car is ready to be tested. As presented in Figure 7, some cars (POIN 11&15) need to be reworked; the time-consuming re-processing is illustrated by the increase in PFC at A2-FC part of the production system.

The type of production flow disturbance can be found out as soon as possible by the observation of only 2 main indicators (OPS and PFC). Such an approach accelerates the process of production flow control and helps to take appropriate corrective actions. Moreover, the production planners are not overloaded with information (they do not need to monitor many parameters). The uncertainty of deliveries lead times and process lead times is reduced. This reduction is achieved by the improved monitoring of the production flow. Most of the unpredictable events that disturb the production process can be identified based on the OPS and PFC indicators, so the variations in the production flow can be easily identified and corrected before they are too big to be fixed at the MRP level and the adjustment of MPS is needed.

Conclusions

The automotive production system is very complex. Under the pressure of time, it is very often difficult to analyze the production database in order to identify disturbances. The paper presents quick scan approach that can be applied in the European automotive industry. The proposed approach is designed as a help for production controllers in order to easily exclude some of potential types of disturbances by means of observing a few simple indicators. In order to apply this method, the knowledge about the production system, organizational conditions and the main design characteristics, as well as typical disturbances are required. The future research works should enhance the implementation of the proposed indicators into the information management system for production aimed at a continuous (on-line) monitoring of the production plans execution. Also some heuristic methods are needed to define the critical values of the proposed indicators. The presented research is strongly caseoriented. However, due to the fact that manufacturing systems of the European automotive manufacturers are very similar, the proposed solution might be applied to other companies in the sector as well.

F

Figure

  Figure 1 goes here

Figure 2 .

 2 Figure 2. Production system model

Figure

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Figure 3 .

 3 Figure 3. Model for production planning and control (information loops)

Figure

  Figure 3 goes here

Figure 4 .

 4 Figure 4. Quick scanproduction system model

Figure 5 .Figure

 5 Figure 5. Quick scanproduction flow control model

Figure 6 .

 6 Figure 6. Examples of typical disturbances in an automobile plant

Figure

  Figure 6 goes here

Tab. 6 .

 6 Figure 7. PFC indicator for defined block

Figure

  Figure 7 goes here

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  lack of needed components (delays in external supplies -delivery ) Information flow disturbances invalid production data (mainly invalid BOM) changes in design of product, non-standard design Tab. 4. OPS indicator for a defined block in [%] -ex post

  If the production sequence is disturbed (the value of OPS for a defined block falls below the critical value), analyze the production flow cycle for this block (indicator PFC_set av ) at this PCP, Identify the type of disturbances based on the assessment of a production sequence and production flow indicator, 5) Choose suitable corrective actions and apply them at MRP level 6) Corrective action results in increase of indicator OPS over critical value see table point 2b for subsequent PCP

	Page 21 of 25			International Journal of Production Research
	MPS B1 OPS 100,00 27,78 20,21 20,83 4,53 100,00 100,00 84,45 B2 P1 P2 A1 A2 FC Disturbances Disturbances Disturbances 100,00% 73,33% 68,75% 18,97% 4,42% 4,20% 100,00% P1 A2 100,00% 70,37% 100,00% 100,00% 51,35% 32,76% 43,18% 1. Define the block of orders 21 Appendix 22 Size 'k' of oredrs'block 49 57 23 100,00% 74,07% 28,57% 30,30% 7,52% 100,00% 100,00% 57 24 100,00% 83,33% 62,50% 26,32% 31,25% 5,68% 100,00%	1,41% 6,76% 3,79% 100,00%
	F o r P e e r R e v i e w O n l y Tab. 5. OPS indicator, average values and critical values for the analyzed production system in [%] MPS B1 B2 P1 P2 A1 A2 OPSQ av. 100,00 63,93 42,05 37,04 46,35 47,47 47,88 60,36 FC Tolerance 25% 0,00 15,98 10,51 9,26 11,59 11,87 11,97 15,09 critical value 100,00 47,95 31,53 27,78 34,76 35,60 35,91 45,27 Tab.6. PFC_set avID indicator for block of orders (the 'ideal values' ID) MPS -B1 B1 -B2 B2 -P1 P1 -P2 P2 -A1 A1 -A2 A2 -FC PFC_set avID [h] 22,49 23,75 29,31 45,91 49,59 59,15 69,82 Tolerance [h] 2,25 2,38 2,93 4,59 4,96 5,92 6,98 Critical value [h] 24,74 26,13 32,24 50,50 54,55 65,07 76,80 Quick Scan Inital MPS 25 33 41 41 49 25 100,00% 83,33% 8,62% 100,00% 100,00% 100,00% 100,00% 19,48% 26 100,00% 86,67% 12,75% 11,50% 2,64% 100,00% 2,33% 2,16% F o r P e e r R e v i e w O n l y Process lead times uncertainty i internal demand Delivery lead times uncertainty & external demand Delivery lead times uncertainty Process lead times uncertainty Source of uncertianty internal external high POIN 1 POIN 2 POIN 3 … … .. Production database 15 20 9 17 25 25 27 100,00% 100,00% 8,33% 100,00% 5,98% 5,88% 6,73% 1,58% 33 28 100,00% 100,00% 4,20% 100,00% 1,08% 100,00% 100,00% 100,00% 29 100,00% 75,00% 8,11% 100,00% 100,00% 100,00% 1,96% 5,11% Level of stability low F o r P e e r R e v i e w O n l y 159x38mm (96 x 96 DPI) F o r P e e r R e v i e w O n l y 129x63mm (96 x 96 DPI) F o r P e e r R e v i e w O n l y 151x79mm (96 x 96 DPI) F o r P e e r R e v i e w O n l y …… .. POIN n -1 POIN n POIN 1 POIN 2 POIN 3 … …… ……… POIN n -1 POIN n PCP Body shop (D1) {1; n d1 } PCP {1; n d2 } Paint shop (D2) PCP {1; -n d3 } Final Assembly (D3) Production system Corrective actions MPS … .. … .. 1 17 30 100,00% 80,00% 100,00% 100,00% 3,60% 100,00% 3,57% 2,42% 10 0,00% 20,00% 40,00% 60,00% 80,00% 100,00% 31 100,00% 81,25% 59,09% 31,71% 28,89% 100,00% 100,00% 2,42% 9 Distribution Center F o r P e e r R e v i e w O n l y Fig.A1 Size "k" of the block of orders depending on work consumption at the assembly line 2. Observe the defined block of orders and assess the production sequence a) Count the OPS indicator value B2 25 33 41 49 57 B1 0% 20% 40% 60% 80% 100% 1 9 17 25 33 41 49 57 0 5 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 F o r P e e r R e v i e w O n l y A1 0% 20% 40% 60% 80% 100% 1 9 17 25 33 41 49 57 P2 0% 20% 40% 60% 80% 100% 1 9 17 25 33 41 49 57 F o r P e e r R e v i e w O n l y b) compare it with the critical value of OPS indicator BLOCK MPS B1 B2 P1 P2 A1 A2 FC 1 100,00% 27,27% 17,65% 100,00% 9,53% 23,08% 25,71% 100,00% 2 100,00% 27,78% 20,21% 20,83% 4,53% 100,00% 100,00% 84,45% 3 100,00% 18,67% 15,22% 11,20% 100,00% 100,00% 100,00% 100,00% 4 100,00% 24,14% 4,96% 100,00% 100,00% 100,00% 3,67% 100,00% 5 100,00% 10,83% 10,63% 100,00% 100,00% 100,00% 4,58% 57,43% 6 100,00% 9,17% 9,80% 9,01% 100,00% 100,00% 2,80% 47,62% 7 100,00% 15,79% 20,00% 17,91% 100,00% 100,00% 7,19% 100,00% 8 100,00% 100,00% 9,84% 11,11% 30,00% 25,00% 25,00% 100,00% 9 100,00% 100,00% 29,61% 26,09% 9,14% 7,86% 100,00% 100,00% 10 100,00% 75,00% 6,12% 6,32% 2,90% 2,97% 100,00% 100,00% FC 0% 20% 40% 60% 80% 100% 1 9 17 25 33 41 49 57 0% 20% 40% 60% 80% 100% 1 F o r P e e r R e v i e w O n l y 32 100,00% 81,82% 18,56% 100,00% 100,00% 100,00% 6,00% 4,02% 33 100,00% 78,57% 10,38% 100,00% 4,93% 100,00% 4,93% 3,68% 34 100,00% 75,00% 68,18% 26,32% 100,00% 100,00% 5,30% 4,84% 35 100,00% 83,33% 75,00% 5,79% 100,00% 100,00% 4,04% 100,00% 36 100,00% 77,27% 24,64% 22,37% 12,41% 100,00% 14,66% 10,18% 37 100,00% 80,00% 80,00% 20,00% 100,00% 100,00% 1,99% 100,00% 38 100,00% 85,71% 60,00% 27,27% 13,04% 11,32% 11,32% 31,58% 39 100,00% 70,83% 68,00% 50,00% 100,00% 100,00% 4,76% 12,41% 40 100,00% 85,00% 100,00% 16,35% 100,00% 100,00% 4,22% 100,00% 41 100,00% 78,95% 100,00% 24,59% 100,00% 5,49% 5,49% 100,00% 42 100,00% 100,00% 100,00% 9,38% 100,00% 5,95% 5,95% 100,00% 43 100,00% 77,78% 100,00% 41,18% 100,00% 2,08% 2,08% 100,00% 44 100,00% 100,00% 80,00% 7,21% 100,00% 3,19% 100,00% 9,70% 45 100,00% 71,43% 6,85% 6,33% 100,00% 1,02% 100,00% 100,00% 46 100,00% 4,85% 4,42% 3,49% 100,00% 1,56% 100,00% 10,81% 47 100,00% 80,00% 29,63% 29,09% 100,00% 4,76% 100,00% 5,52% 48 100,00% 60,00% 7,14% 7,89% 100,00% 2,01% 2,01% 100,00% 49 100,00% 85,71% 100,00% 54,55% 1,99% 2,01% 100,00% 100,00% 50 100,00% 61,90% 59,09% 16,05% 3,88% 4,00% 100,00% 100,00% 51 100,00% 64,29% 21,95% 25,00% 8,65% 6,29% 6,12% 100,00% 52 100,00% 71,43% 71,43% 71,43% 17,86% 17,86% 17,86% 100,00% 53 100,00% 4,08% 3,31% 3,05% 2,42% 2,47% 2,47% 100,00% 54 100,00% 83,33% 71,43% 41,67% 12,50% 11,63% 11,63% 5,10% 55 100,00% 3,82% 3,82% 3,60% 1,49% 1,62% 100,00% 100,00% 56 100,00% 66,67% 50,00% 20,00% 2,30% 1,78% 1,78% 100,00% 57 100,00% 9,76% 8,63% 6,82% 100,00% 100,00% 100,00% 21,82% 58 100,00% 11,01% 8,39% 6,78% 2,65% 100,00% 100,00% 100,00% Av. OPSQ 100,00% 63,93% 42,05% 37,04% 46,35% 47,47% 47,88% 60,36% o r e e r R e v i e w O n l y 3) F P 4)
	17 11	100,00%	15,91%	100,00%		6,20%	100,00%	100,00%	1,99%	100,00%
	12		100,00%	100,00%	100,00%	100,00%	2,59%	3,12%	100,00%	100,00%
	13	9	100,00%	27,78%	41,67%	100,00%	41,67%	100,00%	100,00%	100,00%
	14	1	100,00%	26,53%	21,67%		12,75%	4,08%	4,96%	100,00%	100,00%
	15		0% 100,00%	20% 75,00%	40% 100,00%	60%	80% 16,67%	100% 11,54%	6,12%	6,12%	100,00%
	16		100,00%	75,00%	85,71%		5,94%	22,22%	17,14%	17,14%	20,69%
	17		100,00%	83,33%	3,05%		3,07%	5,75%	4,27%	100,00%	3,97%
	18		100,00%	85,71%	10,71%		10,35%	5,09%	5,61%	100,00%	1,67%
	19		100,00%	75,00%	22,06%		10,49%	7,98%	7,04%	100,00%	100,00%
	20		100,00%	75,00%	18,00%		15,52%	10,23%	12,68%	12,50%	100,00%

http://mc.manuscriptcentral.com/tprs Email: ijpr@lboro.ac.uk

http://mc.manuscriptcentral.com/tprs Email: ijpr@lboro.ac.ukInternational Journal of Production Research


Natallia Luksha

Leanid Luksha

Juan Jesús Carrero

Folke Hammarqvist

Peter Stenvinkel

Karolina Kublickiene
email: karolina.kublickiene@ki.se

Impaired resistance artery function in patients with end-stage renal disease

Keywords: arteries, endothelium, nitric oxide, cardiovascular disease, vascular in vitro studies

We investigated an effect of uraemia on structural and functional features of human resistance vasculature. Arteries (≈200 µm) isolated from subcutaneous fat biopsies obtained from 35 end-stage renal disease (ESRD) patients starting peritoneal dialysis and 30 matched controls were studied using isolated small artery bioassays. Flow-mediated dilatation was attenuated in ESRD patients vs. controls. Nitric oxide (NO) contribution to flow was lacking in ESRD patients, but present in the controls. Asymmetrical dimethyl L-arginine (ADMA) levels were higher in the ESRD group vs. control group. Dilatation to acetylcholine was reduced in ESRD patients vs. controls, but response to NOdonor was similar. Expression of nitrotyrosine and heat shock proteins 70 and 27, but not 90, was increased in arteries from ESRD patients vs. controls. Arterial remodelling was absent in ESRD patients. There was no difference between the groups in myogenic tone, vascular reactivity or sensitivity to several vasoconstrictors. Arterial distensibility, reflecting passive properties of the vascular wall, was reduced in ESRD patients vs. controls. Exclusion of ESRD patients with diabetes and/or cardiovascular disease from analyses had no influence on the main findings. Thus, uraemia has a strong impact on endothelial function and passive properties of the arterial wall of human peripheral resistance vasculature. The reduced contribution of NO to flow stimulus via enhanced nitrosative stress and higher plasma concentrations of ADMA may confer potential mechanisms behind endothelial dysfunction in the resistance peripheral circulation in ESRD.

INTRODUCTION

Despite the rapid progress in dialysis treatment, end-stage renal disease (ESRD) patients still die at a markedly accelerated rate, principally from cardiovascular disease (CVD) and infectious complications [START_REF] Go | Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization[END_REF][START_REF] Stenvinkel | Emerging biomarkers for evaluating cardiovascular risk in the chronic kidney disease patient: how do new pieces fit into the uremic puzzle?[END_REF]. Multiple mechanisms mediate the elevated risk for cardiovascular events in ESRD, which are complex and involve changes of the whole cardiovascular system. Endothelial dysfunction is a common phenomenon in ESRD, which constitutes an obligatory prodromal phase in cardiovascular complications [START_REF] Endemann | Endothelial dysfunction[END_REF][START_REF] Stam | Endothelial dysfunction contributes to renal function-associated cardiovascular mortality in a population with mild renal insufficiency: the Hoorn study[END_REF]. However, current evidence for endothelial dysfunction in renal disease patients mainly relies on measurements of circulating biomarkers [START_REF] Stenvinkel | Emerging biomarkers for evaluating cardiovascular risk in the chronic kidney disease patient: how do new pieces fit into the uremic puzzle?[END_REF] and in vivo assessment of endothelial function in larger vessels [START_REF] Sahin | Effect of n-acetylcysteine on endothelial dysfunction in dialysis patients[END_REF][START_REF] Verbeke | Local shear stress and brachial artery functions in end-stage renal disease[END_REF]. In addition, studies on vascular structure in ESRD patients have mainly concentrated on larger and medium-size arteries [START_REF] Safar | Arterial stiffness and kidney function[END_REF][START_REF] Guérin | Arterial structure and function in end-stage renal disease[END_REF]. This leaves unaddressed the issue of whether and/or to what extent functional and structural abnormalities may occur concurrently in the resistance circulation. Resistance vessels generally refer to small arteries with diameters ranging from 100 to 300 µm, which are directly involved in the control of blood flow to target organs and peripheral resistance. Abnormalities in this circulation will directly contribute to the development of hypertension, which is common in ESRD patients. Current evidence that renal diseases affect endothelial function of resistance arteries has been derived mainly from animal studies [START_REF] Savage | Flow induces dilatation in the femoral artery of uraemic rats but constriction in control rats[END_REF][START_REF] Bagi | Chronic renal failure leads to reduced flow-dependent dilation in isolated rat skeletal muscle arterioles due to lack of NO mediation[END_REF]. Only Morris et al. [START_REF] Morris | Impaired endothelial function in isolated human uremic resistance arteries[END_REF] reported a reduced agonist-induced endothelium-dependent dilatation in small arteries isolated from patients with chronic renal failure. The technique of venous occlusion plethysmography has also been used [START_REF] Passauer | Evidence in vivo showing increase of baseline nitric oxide generation and impairment of endotheliumdependent vasodilation in normotensive patients on chronic hemodialysis[END_REF][START_REF] Passauer | Nitric oxide-and EDHF-mediated arteriolar tone in uremia is unaffected by selective inhibition of vascular cytochrome P450 2C9[END_REF], suggesting an impairment of the nitric oxide (NO) component in the abnormal dilatation to acetylcholine (ACh) in the forearm circulation [START_REF] Passauer | Reduced agonist-induced endothelium-dependent vasodilation in uremia is attributable to an impairment of vascular nitric oxide[END_REF], reflecting, though, the combined responses, not only of the resistance vasculature, but also of conduit arteries and venous circulation [START_REF] Chin-Dusting | Human forearm venous occlusion plethysmography: methodology, presentation and analysis[END_REF]. To date, however, in ESRD patients there has been no study to directly assess resistance artery response to a stimulus of flow, which is recognised as the most important physiological regulator of endothelial function and NO release. Furthermore, there has been no study that combines assessment of several parameters that confer resistance artery maintenance in ESRD.

In the present study we hypothesized that ESRD patients will exhibit several abnormalities at the level of resistance circulation via altered dilatory and constrictor responses, as well as via occurrence of structural changes. The impaired endothelial function will be conferred by changes in NO bioavailability. All these abnormalities will contribute to an increased peripheral resistance in patients with ESRD. We tested this hypothesis by investigating isolated, pressurized subcutaneous resistance arteries from ESRD patients and healthy controls under conditions representative of an in vivo physiological performance. Responses to physical forces created by intraluminal flow and pressure as the most relevant physiological stimulators for the resistance artery tone were investigated. Furthermore, passive properties of the vascular wall (distensibility), remodelling process, as well as the contribution of NO to flow responses, were studied. Wire myography technique was implemented to test vascular reactivity/sensitivity to a number of pharmacological vasoconstrictor agonists and the role of NO in these responses was also assessed. Finally, in these vessels we analysed by immunohistochemistry the expression of heat shock proteins (HSPs) 27, 70 and 90, which, it has been suggested, reflect endothelial stress [START_REF] Ferns | Heat shock protein 27: its potential role in vascular disease[END_REF][START_REF] Bielecka-Dabrowa | Hsp 70 and atherosclerosis-protector or activator?[END_REF][START_REF] Pritchard | Heat shock protein 90 mediates the balance of nitric oxide and superoxide anion from endothelial nitric-oxide synthase[END_REF] and expression of nitrotyrosine [START_REF] Ischiropoulos | Biological tyrosine nitration: a pathophysiological function of nitric oxide and reactive oxygen species[END_REF] to link endothelial abnormalities to a prooxidative phenotype. We also measured the endogenous inhibitor of NO-synthase (NOS) asymmetrical dimethyl L-arginine (ADMA) that has been introduced as an additional cardiovascular risk factor in ESRD [START_REF] Leone | Accumulation of an endogenous inhibitor of nitric oxide synthesis in chronic renal failure[END_REF]. A c c e p t e d M a n u s c r i p t Licenced copy. Copying is not permitted, except with prior permission and as allowed by law.

METHODS

Participants

A subcutaneous fat biopsy was obtained from 35 ESRD patients (25 males; median age 58 yrs, range 22-79 yrs) at the time of peritoneal dialysis catheter insertion. In order to establish homogeneity, only patients starting dialysis treatment de novo were included. Exclusion criteria were clinical signs of acute infection, active vasculitis or liver disease at the time of evaluation. Control tissue was obtained from 30 age-and gender-matched healthy volunteers (23 males; median age 54 yrs, range 29-74 yrs) without documented renal, cardiovascular, mental or diabetic disease, who underwent hernia repair or laparoscopic cholecystectomy. All subjects gave informed consent, and the ethics committee at Karolinska University Hospital, Stockholm, Sweden, approved the protocol.

Baseline Laboratory and Clinical Assessments

Clinical history of CVD or diabetes was obtained from medical records. CVD was defined as the presence of ischemic cardiac disease, peripheral vascular disease and/or cerebrovascular disease. Glomerular filtration rate was estimated by the mean of creatinine and urea clearances in ESRD, whereas cystatin-C was used to estimate glomerular filtration rate in controls. Fasting venous blood samples were taken for generation of plasma and serum and stored at -70°C pending further analyses. Serum interleukin-6 was measured on an Immulite ® analyser (Siemens Medical Solution Diagnostic, Los Angeles, CA, USA). Circulating ADMA was assessed in serum using commercial ELISA assays (DLD Diagnostika GMBH, Germany). Serum concentrations of albumin (by the bromocresol purple method), creatinine, blood lipids and high-sensitivity C-reactive protein were measured by routine procedures at the Department of Clinical Chemistry at Karolinska University Hospital-Huddinge. At the time of surgery, a subcutaneous fat biopsy (≈2 × 1.5 × 1.5 cm) was removed from the anterior abdominal wall and immediately placed into cold physiological salt solution (PSS). Arteries with an internal diameter of ≈200 µm were dissected. Depending on the number of arterial segments obtained from the fat biopsy specimen, one or several experiments (flowmediated dilatation, agonist-induced responses, pressure-induced tonus, distensibility index) were performed.

Pressure myography

The arterial segments were oriented to mimic the direction of flow in vivo and mounted between two glass microcannulae in a pressure myograph chamber (Living Systems Instrumentation Inc., USA). Intraluminal pressure (60 mmHg) was maintained by a servocontrolled pump. The dimensions of the cannulated artery (internal diameter and wall thickness) were continuously monitored via a video dimension analyser. The organ bath was superfused with ≈37°C PSS gassed with 5% CO 2 in O 2 . Each artery was equilibrated for 60 min. The viability tests were carried out by examining the responses to norepinephrine (NE, 1 µmol/l), and endothelial function was confirmed by relaxation to ACh (1 µmol/l).

Assessment of vascular function: flow-mediated dilatation

Arteries from 27 ESRD patients and 22 controls were used to investigate flow-mediated dilatation. After the equilibration period, the intraluminal pressure was gradually increased from 60 to 80 mmHg for arteries with an internal diameter >200 µm, and the internal diameter was recorded after 20 min. In contrast, for arteries with an internal diameter <200 µm, intraluminal pressure was permanently kept at 60 mmHg. A flow response curve to increasing intraluminal flow by stepwise increase from 0 to 180 µl/min every 3 min was performed on the preconstricted artery to ≈50% of the initial diameter. In a separate experimental setup including arteries from ESRD patients (n = 9) and Controls (n = 9), flow response curves were obtained before and after incubation with NO synthase (NOS) inhibitor, N ω -nitro-Larginine-methyl ester (L-NAME) (300 µmol/l, 30 min).

Agonist-induced responses

Endothelium-dependent response to ACh was tested in 13 ESRD patients and 9 controls, whereas endothelium-independent dilatation to sodium nitroprusside (SNP) was performed in 12 ESRD patients and 10 controls. Equilibrated arteries at 60 mmHg were superfused with NE (1µmol/l) for 20 min in order to induce a stable constriction. ACh (3 nM-1 µmol/l) or SNP (0.1 µmol/l -0.1 mmol/l) were added in NE-contained PSS and concentration-response curves were assessed. Each concentration of the agonist was extraluminally perfused for 3 min, while the changes in the diameter were constantly recorded.

Pressure-induced myogenic tone

The response to changes in pressure was evaluated in arteries from 26 patients with ESRD and 24 controls. After a 20 min artery equilibration at 20 mmHg, intraluminal pressure was gradually increased up to 120 mmHg. Internal artery diameter was recorded after each pressure increment, which was maintained for 5-7 min in order to reach a steady state diameter. Thereafter, PSS was replaced with PSS without Ca 2+ (Ca 2+ -free PSS) to determine the passive diameter curve in response to stepwise pressure increase.

Wire myography

The subcutaneous arteries were mounted on a 4-chamber Danish Myotechnology M610 wire myograph, as described elsewhere [START_REF] Luksha | The mechanism of EDHF-mediated responses in subcutaneous small arteries from healthy pregnant women[END_REF]. Cumulative concentration-response curves were constructed for phenylephrine (selective agonist of α 1 -adrenergic receptors, 10 nmol/l -0.03 mmol/l), NE (non-selective agonist of adrenergic receptors, 10 nmol/l -0.03 mmol/l), angiotensin II (Ang II, 0.1 -30 nmol/l) or endothelin-1 (ET-1, 0.1 -30 nmol/l) before and after incubation with the NOS inhibitor L-NAME (300 µmol/l, 30 min).

Immunohistochemistry

Cryosections (7 µm thick) of subcutaneous arteries were immunostained with antibodies against HSPs 90, 70, 27 and nitrotyrosine. Antibody concentrations were 10 µg/ml for anti-HSP90, 0.66 µg/ml for anti-HSP70, 5 µg/ml for anti-HSP27 and 20 µg/ml for antinitrotyrosine. Negative controls with 0.1% Tween in 3% phosphate buffered saline (PBS) and in PBS without primary antibodies were used.

Chemicals and solutions

The composition of PSS (mmol/l) was NaCl 119, KCl 4.7, CaCl 2 2.5, MgSO 4 1.17, NaHCO 3 25, KH 2 PO 4 1.18, ethylenediamine-tetraacetic acid 0.026, and glucose 5.5; pH 7.4. Relaxing solution was Ca 2+ -free PSS supplemented with papaverine (0.1 mmol/l) and ethylene glycolbis-(β-aminoethyl ether) tetraacetic acid (1 mmol/l). NE was dissolved directly in PSS, whereas for ACh, phenylephrine, Ang II, ET-1 and SNP stock solutions were prepared in distilled water and further dissolved in PSS. All chemicals were obtained from Sigma-Aldrich Sweden AB (Sweden). Monoclonal mouse antibodies used for HSP27 (ab8600), HSP70 (ab6535), HSP90 (ab1429) and nitrotyrosine (ab7048), and secondary biotinylated polyclonal goat anti-mouse IgG antibody (ab6788) were all obtained from Abcam plc (UK, Cambridge).

Calculations and statistical analysis

Relaxation to flow, ACh and SNP were calculated as a percentage change in internal diameter in response to stimulation, divided by the difference in internal diameter before and after preconstriction. Wall shear stress (τ, dyn/cm 2 ) was calculated using the Hagen-Poiseuille formula: τ = 4 × η × Q × 10 9 / πr 3 , where η is viscosity of the perfusate (poise -dyn s/cm 2 ), Q is flow rate (µl/s) and r is artery radius (µm). The factor of 10 9 in the equation is to correct the use of µl/s for flow and µm for artery radius (1µl = 10 9 µm 3 ). Viscosity of PSS was assumed as 0.007 poise at 37 o C. Cross-sectional area (CSA) was calculated as: CSA = (π/4) × (D e 2 -D i

2 ), where D e and D i are external and internal diameters, respectively. Wall lumen ratio was defined as: wall thickness / D i . Pressure-induced myogenic tone was calculated as: Myogenic

tone (%) = 100 × (D i Ca 2+ -free PSS -D i PSS ) / D i Ca 2+
-free PSS , where D i is the internal diameter of the artery. Distensibility index was calculated as:

Distensibility index (%) = (D i -D 0 ) × 100,
where D i is the internal diameter in Ca 2+ -free PSS at different steps of pressure and D 0 is the internal diameter in Ca 2+ -free PSS at 5 mmHg. In tables and figures, results are expressed as the mean ± standard error of the mean, and the mean ± standard deviation or median and range, as appropriate. Baseline characteristics of the patients and arteries used were analysed by conventional parametric and non-parametric methods as appropriate. Differences in artery responses between patients and controls were determined by two-way repeated measures analysis of variance (ANOVA). The semiquantitative analysis for immunohistochemistry staining intensity was assessed by the average blind score of three different observers, using a scale between 0 and 3, where 0 was absence of staining and 3 corresponded to maximal intensity. In addition, a computer image analysis software (ImageJ), available at http://rsb.info.nih.gov/ij/index.html, was used for quantitative analysis of immunostaining [START_REF] Takacs | Vaginal smooth muscle cell apoptosis is increased in women with pelvic organ prolapse[END_REF][START_REF] Rangan | Quantification of renal pathology by image analysis (methods in renal research)[END_REF]. The image area, including vascular wall and lumen on each cross-section, was outlined manually and proportional staining (%) within the marked area was quantified. The examiner was blinded and the threshold level for detection was based on ability to distinguish the specific target staining vs. the non-specific background. For a valid comparison between different images, the threshold levels were maintained similarly. The results were evaluated with Mann-Whitney U test for non-parametric comparisons. All calculations and statistical analyses were performed using STATISTICA (ver. 8.0, StatSoft, Uppsala, Sweden,). All comparisons were considered statistically significant if P < 0.05. 1. Age, gender, and smoking status were similar between groups. The body mass index was lower in ESRD group and sub-group versus controls. Whereas inflammation markers (interleukin-6 and high-sensitivity C-reactive protein) and ADMA were elevated in the ESRD group and sub-group, total cholesterol was significantly lower in the ESRD group vs. controls, but similar between the ESRD sub-group and controls. No significant difference in blood pressure was observed between the groups.

Vascular Structure

Baseline characteristic of pressurized subcutaneous resistance arteries used in the functional study are summarized in Table 2. The arteries from all groups had similar diameters, wall thickness, wall-lumen ratio and cross-sectional area.

Vascular Function

Flow-mediated dilatation was significantly attenuated in the ESRD patients (Figure 1a), and increments of intraluminal flow led to a much steeper rise in wall shear stress values compared to the control group (Figure 1b). Incubation with a NOS inhibitor, L-NAME, did not affect flow-mediated dilatation in arteries from ESRD patients (Figure 2a). In contrast, L-NAME significantly reduced flow-mediated responses in arteries from controls (Figure 2b) to a level comparable to that obtained in arteries from ESRD before NOS inhibition (Figure 2a). ACh and SNP induced a robust, concentration-dependent dilatation in both ESRD and control arteries. Whereas a significant decrease in endothelium-dependent dilatation to ACh was observed in the ESRD group versus controls (Figure 3a), the SNP-induced dilatation was similar between the groups (Figure 3b). There was no difference in myogenic tone between the two groups (Figure 4a) or in vascular reactivity or sensitivity to Ang II, ET-1 or agonists of adrenergic receptors (Table 3). Moreover, NOS inhibition had no effect on vascular reactivity or sensitivity to the above-mentioned agonists in isolated arteries from either group studied (Table 3). In contrast, arterial distensibility was significantly reduced in ESRD patients in comparison to controls (Figure 4b). In order to eliminate the possible interference of co-morbidities, ESRD patients without CVD (based on clinical history from medical records) and/or diabetes mellitus were divided in a sub-group and compared with controls. Briefly, we observed similar results as above (Figure 5). Also, no difference in myogenic tone (e.g. at 120 mmHg 13 ± 3% in ESRD, n = 14 vs. 12 ± 2% in controls, n = 24) or SNP response (e.g. % maximum relaxation at 0.1 mmol/l of SNP: 61 ± 7 in ESRD, n = 7 vs. 69 ± 7 in controls, n = 10) was observed between the groups.

Heat shock proteins and nitrotyrosine expression in the vascular wall

The intensity of immunohistochemistry staining for HSP27, HSP70, HSP90 and the oxidative stress marker nitrotyrosine within the vascular wall of resistance subcutaneous arteries was compared between ESRD patients and controls. Both the semi-quantitative and computer imaging analysis (Table 4) showed higher expression patterns of HSP27, HSP70 and nitrotyrosine in arteries from the ESRD group vs. controls (Figure 6).

DISCUSSION

To the best of our knowledge, our ex vivo study is the first to extensively characterize changes present in the peripheral resistance vasculature in ESRD patients. Here, we show for the first time that endothelium-dependent dilatation to flow-mediated shear stress is impaired in the resistance arteries from patients with ESRD. The obvious absence of NO contribution to flow response in the ESRD group contrasts with data obtained in controls, in which NO played a predominant role. The presence of endothelial dysfunction in the resistance vasculature of the ESRD subjects was further strengthened by a reduced dilatation to ACh, but by preserved response to a NO donor. Moreover, vascular distensibility was also impaired in arteries from the ESRD group compared to controls. The renal failure had, however, no effect on contractile responses to Ang II, ET-1, NE and phenylephrine, or on pressure-induced myogenic tone and remodelling process. Increased expressions of nitrotyrosine and heat shock proteins 70 and 27, but not 90, were observed in artery walls from the ESRD group in comparison with controls. Elevated plasma levels of ADMA were found in ESRD patients vs. controls. Exclusion of ESRD patients with diabetes and/or CVD had no impact on experimental outcomes. Blunted flow-mediated dilatation and the lack of NO contribution to this response in human peripheral resistance vasculature extend and complement previous reports, but in other vascular beds [START_REF] Verbeke | Local shear stress and brachial artery functions in end-stage renal disease[END_REF][START_REF] Morris | Impaired endothelial function in isolated human uremic resistance arteries[END_REF][START_REF] Passauer | Evidence in vivo showing increase of baseline nitric oxide generation and impairment of endotheliumdependent vasodilation in normotensive patients on chronic hemodialysis[END_REF][START_REF] Passauer | Nitric oxide-and EDHF-mediated arteriolar tone in uremia is unaffected by selective inhibition of vascular cytochrome P450 2C9[END_REF][START_REF] Passauer | Reduced agonist-induced endothelium-dependent vasodilation in uremia is attributable to an impairment of vascular nitric oxide[END_REF][START_REF] Annuk | Endothelial function, CRP and oxidative stress in chronic kidney disease[END_REF], further emphasizing the general state of endothelial dysfunction and the significant role of NO in this high-risk patient group. Our study also suggests potential mechanisms behind small artery dysfunction in ESRD. Thus, the increased expression of nitrotyrosine in arteries from the ESRD group may imply the enhancement of free radical production towards a pro-oxidant environment and NO degradation. The elevated circulating levels of ADMA in ESRD patients will favour decreased NO production. Similar responses to a number of pharmacological agonists tested, as well as similar pressureinduced myogenic tone between arteries from ESRD and control groups, argue against the altered vascular smooth muscle function in resistant arteries from patients with ESRD. However, alterations in the passive properties of the arterial wall (reduced distensibility) strengthen a link between ESRD and increased vascular stiffness [START_REF] Blacher | Impact of aortic stiffness on survival in end-stage renal disease[END_REF][START_REF] Peralta | The differential association of kidney dysfunction with small and large arterial elasticity: the multiethnic study of atherosclerosis[END_REF]. Since exclusion of patients with diabetes and/or CVD had no impact on experimental outcomes, it is likely that uraemia per se, and not existing co-morbidities, is the main cause of endothelial dysfunction and altered passive properties of the resistance vasculature. Thus, our findings may contribute to the elucidation of mechanistic links between uraemia and vascular abnormalities. Wall shear stress is the main determinant of flow-mediated dilatation, which in small arteries is an important physiological regulator of tissue perfusion [START_REF] Davies | Hemodynamic shear stress and the endothelium in cardiovascular pathophysiology[END_REF]. In our study we characterized the effects of increases in intraluminal flow on artery diameter by calculating wall shear stress. In controls, the rise in flow slightly increased wall shear stress. The increments in flow rate resulted in the substantial increases in lumen diameter that prevented further enhancement of wall shear stress, thus providing a functional adaptation to rapid changes in flow rate [START_REF] Bagi | Chronic renal failure leads to reduced flow-dependent dilation in isolated rat skeletal muscle arterioles due to lack of NO mediation[END_REF][START_REF] Davies | Hemodynamic shear stress and the endothelium in cardiovascular pathophysiology[END_REF]. ESRD patients, however, demonstrated a compromised adaptation, as the increments in intraluminal flow increased shear stress values continuously, due to impaired dilatory response. This may suggest that the efficacy of shear stress (higher wall shear stress values for the same flow rate) as a dilatory stimulus is impaired in the uraemic environment. In ex vivo set-up, shear stress values, as a function of changes in flow, are usually assessed at constant fluid viscosity. When extrapolating our data to an in vivo situation, it should be emphasized that shear stress depends on blood viscosity, which is directly related to haemoglobin concentration. Therefore, abnormalities in flow-mediated responses at the level of resistance arteries may be further amplified in vivo by anaemia that is well known to worsen as kidney disease progresses. Taken together, we postulate that in ESRD the reduced efficiency of shear stress to induce relaxation, as presented in our study, along with anaemiarelated low blood viscosity present in the in vivo situation, may further impede small artery wall 'sensitivity' to this physiological stimulus. Indeed, abnormal shear stress values in the brachial artery in vivo have been found in ESRD patients, and anaemia correction led to improved flow responses and enhanced arterial wall sensitivity to this mechanical stimulation [START_REF] Verbeke | Local shear stress and brachial artery functions in end-stage renal disease[END_REF]. The significantly reduced response to flow after NOS inhibition occurred in arteries from controls, further supporting the importance of NO in flow-mediated dilatation of isolated resistance arteries from healthy volunteers [28][START_REF] Paniagua | Role of endothelial nitric oxide in shear stress-induced vasodilation of human microvasculature: diminished activity in hypertensive and hypercholesterolemic patients[END_REF]. In ESRD arteries, however, the contribution of NO to flow responses was lacking. This observation concurs with evidence that so far has only been reported in animal studies [START_REF] Savage | Flow induces dilatation in the femoral artery of uraemic rats but constriction in control rats[END_REF][START_REF] Bagi | Chronic renal failure leads to reduced flow-dependent dilation in isolated rat skeletal muscle arterioles due to lack of NO mediation[END_REF], and is indirectly supported by in vivo investigations in ESRD patients [START_REF] Leone | Accumulation of an endogenous inhibitor of nitric oxide synthesis in chronic renal failure[END_REF][START_REF] Yilmaz | Endothelial functions improve with decrease in asymmetric dimethylarginine (ADMA) levels after renal transplantation[END_REF][START_REF] Baylis | Nitric oxide deficiency in chronic kidney disease[END_REF]. Considering the complexity of NO deficiency in uraemia [START_REF] Himmelfarb | The elephant in uremia: oxidant stress as a unifying concept of cardiovascular disease in uremia[END_REF], we suggest that changes in NO bioavailability merits particular attention. Based on the enhanced pattern of nitrotyrosine staining (marker for peroxynitrite formation) in the resistance artery wall and elevated levels of circulating ADMA (endogenous inhibitor of NO production), our study suggests that decreased bioavailability of NO could serve as a potential mechanism behind impaired flowmediated dilatation in resistance vasculature of ESRD patients. Our suggestion is further strengthened by in vivo studies showing negative relationships between markers of oxidative stress [START_REF] Annuk | Endothelial function, CRP and oxidative stress in chronic kidney disease[END_REF], ADMA and flow-mediated dilatation in ESRD subjects [START_REF] Yilmaz | ADMA levels correlate with proteinuria, secondary amyloidosis, and endothelial dysfunction[END_REF]. In addition, the similar expression of HSP90 in the vascular wall of ESRD patients and controls argues against NO deficiency via uncoupling of endothelial NOS (eNOS) caused by HSP90 insufficiency [START_REF] Pritchard | Heat shock protein 90 mediates the balance of nitric oxide and superoxide anion from endothelial nitric-oxide synthase[END_REF]. However, limited availability of other co-factors may also contribute to eNOS uncoupling, with a subsequent decrease in NO signalling and an increase in eNOSderived superoxide generation [START_REF] Förstermann | Janus-faced role of endothelial NO synthase in vascular disease: Uncoupling of oxygen reduction from NO synthesis and its pharmacological reversal[END_REF]. Finally, despite the observation of higher expression of HSP70 and HSP27 in the arterial walls of the ESRD group, their contribution to impaired endothelial function is not yet clear and as such requires further investigation. However, some evidence suggests that increased expression of HSP 70 and 27 might reflect a compensatory role against endothelial dysfunction [START_REF] Ferns | Heat shock protein 27: its potential role in vascular disease[END_REF][START_REF] Bielecka-Dabrowa | Hsp 70 and atherosclerosis-protector or activator?[END_REF]. Despite existing debate as to whether myogenic tone abnormalities may serve as a cause or a consequence of elevated blood pressure [START_REF] Ibrahim | Sexspecific differences in cerebral arterial myogenic tone in hypertensive and normotensive rats[END_REF][START_REF] Izzard | Myogenic tone in mesenteric arteries from spontaneously hypertensive rats[END_REF], we proposed that enhanced pressuredependent myogenic constriction would contribute to the elevated peripheral resistance in ESRD. However, even if a slight tendency existed for enhanced myogenic tone in ESRD patients, our study did not support this. The observed preservation of myogenic tone concurs with the previous animal report on uraemic hypertension [START_REF] New | The myogenic response in uremic hypertension[END_REF]. It might be anticipated that treatment for blood pressure control could also normalize the level of myogenic tone. This suggestion is supported by a number of studies on hypertensive animals after antihypertensive treatment [START_REF] Osol | Effect of antihypertensive treatment on myogenic properties of brain arteries from the stroke-prone rat[END_REF][START_REF] Vettoretti | Altered myogenic constriction and endothelium-derived hyperpolarizing factor-mediated relaxation in small mesenteric arteries of hypertensive subtotally nephrectomized rats[END_REF]. However, we cannot exclude the possibility that increased peripheral resistance in vivo take place by other means in patients with ESRD. For example, enhanced levels of local and circulating endothelium-derived vasoconstrictors, such as ET-1 [START_REF] Koyama | Plasma endothelin levels in patients with uraemia[END_REF], Ang II [START_REF] Ruiz-Ortega | Angiotensin II: a key factor in the inflammatory and fibrotic response in kidney diseases[END_REF] or the activated sympathetic nervous system [START_REF] Rump | Sympathetic overactivity in renal disease: a window to understand progression and cardiovascular complications of uraemia?[END_REF] could amplify the myogenic tone via constriction. Conversely, our data on wire-mounted arteries indicated similar constriction in response to ET-1, Ang II and agonists of adrenergic receptors, indicating that increased local concentrations of vasoconstrictors, rather than changes in sensitivity of the arterial wall to them, could be involved in ESRD. Our data is in line with a previous report on subcutaneous arteries that showed similar response to vasoconstrictors between controls and chronic intermittent dialysis patients [START_REF] Aalkjaer | Morphological and functional characteristics of isolated resistance vessels in advanced uraemia[END_REF], but is in contrast with the study by Morris et al. [START_REF] Morris | Impaired endothelial function in isolated human uremic resistance arteries[END_REF], in which a more sustained constriction at highest concentrations of ET-1 and NE were observed in ESRD. The reasons for difference between studies are largely unknown and further studies are warranted. As the internal diameter, wall thickness, wall-lumen ratio and cross-sectional area did not differ between ESRD patients and controls, our study fails to show vascular remodelling in the resistance subcutaneous arteries from patients with ESRD. Although our observation agrees with one very early report on isolated subcutaneous arteries in advanced human uraemia [START_REF] Aalkjaer | Morphological and functional characteristics of isolated resistance vessels in advanced uraemia[END_REF], it opposes the observed vascular remodelling in the microcirculation of the heart [START_REF] Amann | Microvascular disease-the Cinderella of uraemic heart disease[END_REF]. Clearly, further studies are needed to evaluate whether regional differences are operative in this process. The lack of remodelling in ESRD arteries could be attributed to antihypertensive treatment, as several studies have provided direct evidence of a corrective effect of different antihypertensive drugs on structural parameters of resistance arteries [START_REF] Agabiti-Rosei | Effects of antihypertensive treatment on small artery remodelling[END_REF]. Because the correction of wall structure by antihypertensive treatments has been shown to be accompanied by normalization of myogenic tone [START_REF] Vettoretti | Altered myogenic constriction and endothelium-derived hyperpolarizing factor-mediated relaxation in small mesenteric arteries of hypertensive subtotally nephrectomized rats[END_REF][START_REF] Rigsby | Spironolactone improves structure and increases tone in the cerebral vasculature of male spontaneously hypertensive stroke-prone rats[END_REF], the simultaneous absence of remodelling and preserved myogenic tone in our ESRD patients seems to be logical. Since the elastic properties of the vessels in this study were tested ex vivo (without flow and active tone in maximally dilated condition), the decreased distensibility is directly related to the vessel wall composition, which is determined by extracellular matrices, such as collagen and elastin [START_REF] Briones | Role of elastin in spontaneously hypertensive rat small mesenteric artery remodelling[END_REF]. Although the mechanisms by which this occurs are less well understood, the renal diseases may exert detrimental effects on collagen and elastin functionality, particularly when an increased exposure to glycation and oxidation products is demonstrated [START_REF] Yamamoto | Possible involvement of increased glycoxidation and lipid peroxidation of elastin in atherogenesis in haemodialysis patients[END_REF][START_REF] Meng | Increased glycoxidation and lipoperoxidation in the collagen of the myocardium in hemodialysis patients[END_REF]. This may lead to the loss of fibre flexibility, altered conformation and elevated susceptibility to enzymatic digestion [START_REF] Vlassara | Recent progress on the biologic and clinical significance of advanced glycosylation end products[END_REF]. The decreased distensibility of the resistance vasculature and the proposed modifications of the passive elements of the vascular wall may also contribute to vascular complications upstream and serve as a possible common pathway to explain the cardiovascular risk linked to ESRD [START_REF] Safar | Arterial stiffness and kidney function[END_REF]. The present study should be considered with the following caveats: although we have studied a relatively homogenous group of incident dialysis patients, the findings regarding remodelling and myogenic tone should be considered with caution, due different pharmacological treatments. Since direct vascular effects of anti-hypertensives could not be ruled out, it should be noted that these drugs may reverse the remodelling process and reduce myogenic tone [51][START_REF] Christensen | Vasodilatation, not hypotension, improves resistance vessel design during treatment of essential hypertension: a literature survey[END_REF][START_REF] Schiffrin | Remodeling of resistance arteries in essential hypertension and effects of antihypertensive treatments[END_REF]. Moreover, as the presence of CVD was based solely on clinical grounds, the true prevalence of CVD in these patients may have been underestimated. A larger patient cohort would possibly have permitted further associations between biochemical markers and functional and structural findings in isolated resistance arteries that would allow us to gather new insights into possible mechanisms. The future aim would be to extend the investigations in the same cohort, but in other resistance vascular beds, particularly to those of critical importance like coronary and cerebral circulations, or to larger elastic and muscular arteries. In summary, our ex vivo study provides new insights into how renal failure affects human resistance artery function. We show for the first time that uraemia primarily targets endothelial maintenance via impaired response to flow-mediated shear stress and absence of NO contribution to this stimulation, as well as via blunted dilatation to endotheliumdependent agonist. Overall, these findings further highlight the general state of endothelial dysfunction in ESRD. The changes in NO bioavailability via enhanced degradation and/or reduced production may serve as a potential mechanism behind endothelial dysfunction in the peripheral resistance circulation in patients with ESRD. Renal failure also affects passive properties of the vascular wall (reduced distensibility), which strengthens an association between ESRD and increased arterial stiffness. Such changes in the peripheral resistance circulation will predispose this patient group to increased cardiovascular risk. Therefore, association between hypertension, cardiovascular diseases, cerebral ischemic attacks and renal failure might be mediated, at least partly, by functional alterations at the level of microcirculation. [START_REF] Bagi | Chronic renal failure leads to reduced flow-dependent dilation in isolated rat skeletal muscle arterioles due to lack of NO mediation[END_REF] 106 ± 8 [START_REF] Savage | Flow induces dilatation in the femoral artery of uraemic rats but constriction in control rats[END_REF] Data are expressed as the mean ± standard error of the mean (SEM). The number of patients in the groups is indicated in parenthesis. ID, internal diameter (µm) of relaxed arteries at pressure 100 mmHg; pEC 50 , negative logarithm of molar concentration of agonist, which is required to induce 50% of the maximum response; Max. response, contractile response of arteries to maximally used concentration of agonists expressed as a percentage of constriction to high K + , 124 mmol/l; L-NAME, responses after NO synthase inhibition ( N ω -nitro-Larginine-methyl ester, 300 µmol/l); NE, Norepinephrine; PE, Phenylephrine; Ang II, Angiotensin II; ET-1, Endothelin-1.

Clinical

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  Clinical Science Immediate Publication. Published on 11 Jan 2011 as manuscript CS20100277 THIS IS NOT THE VERSION OF RECORD -see doi:10.1042/CS20100277 A c c e p t e d M a n u s c r i p t RESULTS Participants Baseline clinical and laboratory characteristics of patients in the ESRD group, ESRD subgroup (ESRD patients without CVD and/or diabetes mellitus) and controls enrolled in the study are shown in Table

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Clinical

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Clinical

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FUNDING

  Financial support was provided by grants from the Swedish Society of Medicine, Swedish Heart and Lung Foundation, Loo och Hans Ostermans Foundation, the Swedish Kidney Association and the Karolinska Institute Research Funds. L. Luksha is partly supported by Postdoctoral grants from AFA Insurance and CLINTEC, Sweden. Clinical Science Immediate Publication. Published on 11 Jan 2011 as manuscript CS20100277 THIS IS NOT THE VERSION OF RECORD -see doi:10.1042/CS20100277 A c c e p t e d M a n u s c r i p t

Figure 1 .

 1 Figure 1. Flow-mediated dilatation (a) and wall shear stress (τ, b) in norepinephrine preconstricted resistance subcutaneous arteries from end-stage renal disease (ESRD) patients and controls. The number of participants in the groups is indicated in parenthesis. * P < 0.05.

Figure 2 .

 2 Figure 2. Flow-mediated dilatation in physiological salt solution (PSS) and after incubation with N ω -nitro-L-arginine-methyl ester (L-NAME) in norepinephrine pre-constricted resistance subcutaneous arteries from end-stage renal disease (ESRD) patients (a) and from controls (b).The number of participants in the groups is indicated in parenthesis. * P < 0.05.

Figure 3 .

 3 Figure 3. Concentration response curves to acetylcholine (a, ACh) and sodium nitroprusside (b, SNP) in resistance subcutaneous arteries from end-stage renal disease (ESRD) patients and controls. The number of participants in the groups is indicated in parenthesis. * P < 0.05.

Figure 4 .

 4 Figure 4. Pressure-induced myogenic tone (a) and distensibility (b) in response to changes of intraluminal pressure in resistance subcutaneous arteries from end-stage renal disease (ESRD) patients and controls. The number of participants in the groups is indicated in parenthesis. * P < 0.05.

Figure 5 .

 5 Figure 5. Flow-mediated dilatation (a), wall shear stress (b, τ), concentration response curves to acetylcholine (c, ACh) and distensibility (d) in resistance subcutaneous arteries from endstage renal disease (ESRD) patients without a clinical history of diabetes mellitus and/or cardiovascular disease vs. controls. The number of participants in the groups is indicated in parenthesis. * P < 0.05.

Figure 6 .

 6 Figure 6. Immunohistochemistry of heat shock protein 27 (A, B, C, E), 70 (D, F, G, H) and nitrotyrosine (I, J, K, M) and negative control (L) in resistance subcutaneous arteries from end-stage renal disease (ESRD) patients and controls. The areas denoted by the boxes are magnified and presented in separate pictures (C, D, E, F, K, M). All images are ×40; scale bar: 50µm.

Figure 6 .

 6 Figure 6.

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  Clinical Science Immediate Publication. Published on 11 Jan 2011 as manuscript CS20100277 THIS IS NOT THE VERSION OF RECORD -see doi:10.1042/CS20100277

Table 1 .

 1 Baseline characteristics of end-stage renal disease (ESRD) patients with cardiovascular disease (CVD) and/or diabetes mellitus (DM) (ESRD group), ESRD patients without co-morbidities (ESRD sub-group) and matched controls. Clinical Science Immediate Publication. Published on 11 Jan 2011 as manuscript CS20100277

		Parameters	ESRD group (n = 35)	ESRD sub-group (n = 14)	Controls (n = 30)	P-value
	THIS IS NOT THE VERSION OF RECORD -see doi:10.1042/CS20100277	Age (years) Males, % (n) BMI (kg/m 2 ) SBP (mmHg) DBP (mmHg) Biochemical parameters Total cholesterol (mmol/l) Triglycerides (mmol/l) 1.6 (0.7-5.6) 58 ± 13 71 (25) 25 ± 3 145 ± 20 86 ± 9 4.6 ± 1.2 S-albumin (g/l) 35 ± 4 S-Creatinine (µmol/l) 649 (249-1 069) 688 (440-1 069) 54 ± 17 57 (8) 24 ± 3 139 ± 14 85 ± 11 5.0 ± 1.1 1.5 (0.8-3.7) 35 ± 3 Haemoglobin (g/l) 111 ± 13 108 ± 17 IL-6 (pg/ml) 6.9 (2.6-31.7) 6.9 (2.6-11.9) hsCRP (mg/l) 5 (1.0-24.0) 4 (1.0-24.0) GFR (ml/min) 12 ± 3 11 ± 3 ADMA (µmol/l) 0.56 ± 0.1 0.56 ± 0.1 Calcium (mmol/l) 2.3 ± 0.2 2.3 ± 0.1 Phosphate (mmol/l) 1.8 ± 0.5 1.9 ± 0.7 U-albumin (mg/24h) 804 (6-5 985) 401 (6-5 985) Co-morbidities DM, % (n) 29 (10) 0 CVD, % (n) 40 (14) 0 Treatments Antihypertensives % (n): 91 (32) 91 (32) β-blockers, % (n) 60 (21) 60 (8) Ca-blockers, % (n) 51 (18) 29 (4) ACE-inhibitors, % (n) 80 (28) 86 (12) Statins, % (n) 40 (14) 14 (2) Erythropoietin, % (n) 83 (29) 93 (13) Mean ± standard deviation indicated for normally distributed variables. Median (range) 56 ± 12 NS 77 (23) 27 ± 4 <0.01 140 ± 16 NS 86 ± 10 NS 5.4 ± 1.0 <0.05/NS* 1.3 (0.7-19) NS 39 ± 3 <0.05 80 (55-105) <0.001 142 ± 12 <0.001 2.7 (1.4-18.1) <0.001 1.4 (0.5-42.0) <0.001 89 ± 5 <0.001 0.49 ± 0.1 <0.05 2.3 ± 0.1 NS 1.0 ± 0.2 <0.001 Not determined 0 0 0 0 0 0 0 0 indicated for variables not normally distributed. n, number of patients in the groups. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; hsCRP, high-sensitivity C-reactive protein; IL-6, Interleukin-6; GFR, glomerular filtration rate; β-blockers, beta-adrenergic antagonists; Ca-blockers, calcium channel antagonists; ACE-inhibitors, inhibitors of angiotensin-converting enzyme; ADMA, asymmetrical dimethyl L-arginine; NS, not significant. P values indicate comparison between ESRD group vs. Controls and ESRD sub-group vs. Controls * P < 0.05: ESRD group vs. Controls / NS: ESRD sub-group vs. Controls. A c c e p t e d M a n u s c r i p t

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Table 2 .

 2 Baseline characteristics of pressurized resistance arteries from end-stage renal disease (ESRD) patients with cardiovascular disease (CVD) and/or diabetes mellitus (DM) (ESRD group), ESRD patients without CVD and/or DM (ESRD sub-group) and matched controls. area (µm 2 ) 14 952 ± 1 160 14 130 ± 1 417 17 417 ± 1 383 NS Data are expressed as the mean ± standard error of the mean. NS, not significant.

	Parameters	ESRD group (n = 27)	ESRD sub-group (n = 14)	Controls (n = 27)	P-value
	Internal Diameter (µm)	178 ± 8	175 ± 12	177 ± 10	NS
	Passive diameter in Ca ++ free PSS (µm)	186 ± 8	186 ± 12	186 ± 11	NS
	Wall thickness (µm)	47 ± 2	44 ± 2	54 ± 2	NS
	Wall lumen ratio	0.29 ± 0.02	0.12 ± 0.02	0.37 ± 0.04	NS
	Cross-sectional

Table 3 .

 3 Potency of vasoconstrictor agonists and maximum constriction of resistance arteries from ESRD patients and controls.

	Agonist		ESRD			Controls
		ID	pEC 50 Max.response	ID	pEC 50 Max.response
	NE	215 ± 13	6.7 ± 0.2	84 ± 6 (7)	199 ± 11	6.6 ± 0.2	77 ± 7 (11)
	NE, L-NAME	195 ± 17	6.8 ± 0.4	89 ± 5 (6)	214 ± 17	7.1 ± 0.3	73 ± 10 (7)
	PE	216 ± 15	5.7 ± 0.1	71 ± 13 (8)	214 ± 15	5.9 ± 0.2	79 ± 11 (10)
	PE, L-NAME	184 ± 18	5.5 ± 0.3	78 ± 6 (6)	215 ± 27	5.8 ± 0.2	74 ± 6 (5)
	Ang II	226 ± 18	8.7 ± 0.2	61 ± 9 (8)	199 ± 11	9.2 ± 0.2	79 ± 8 (10)
	Ang II, L-NAME	178 ± 20	8.2 ± 0.5	70 ± 10 (5)	186 ± 16	9.0 ± 0.1	72 ± 8 (6)
	ET-1	197 ± 16	8.7 ± 0.2	110 ± 5 (8)	192 ± 16	8.7 ± 0.2	103 ± 7
	ET-1, L-NAME	187 ± 14	8.7 ± 0.1	118 ± 5 (6)	195 ± 11	8.9 ± 0.3

Table 4 .

 4 Staining levels of heat shock protein and nitrotyrosine in resistance arteries from endstage renal disease (ESRD) patients and controls. = 12, b; n = 13, c; n = 5, d; n = 6; n, number of participants in the groups. NS, not significant; a.u., arbitrary unit; i.a.s., quantified by image analysis software.

			Semi-quantitative assessment of staining (a.u.)	P-value	% of staining (i.a.s.)	P-value
	Heat shock protein 90	Control ESRD	0.9 (0.3-1.7) c 1.1 (0.5-2.2) a	NS	0.6 (0-21) c 2.5 (0-30) a	NS
	Heat shock protein 70	Control ESRD	1.2 (0.2-2.9) c 2.2 (1.7-3.0) a	0.02	1.8 (0-5) c 6.9 (1-38) a	0.008
	Heat shock protein 27	Control ESRD	1.2 (0.7-2.0) c 2.2 (0.9-3.0) b	0.04	0.1 (0-16) c 15.6 (0-54) b	0.006
	Nitrotyrosine	Control ESRD	0.7 (0.0-2.0) d 1.2 (0.5-2.5) a	0.01	0.6 (0-9) d 2.3 (0-30) a	0.01
	a; n

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ACKNOWLEDGEMENTS

We would like to thank the patients and personnel at Karolinska University Hospital-Huddinge involved in the samples collection. Special consideration to Annika Moberg and Christina Bäckmark for surgery planning, to KBC (Annika Nilsson, Anki Emmot and Ulrika Jensen) for sampling protocol and coordination and to John Sandberg, Olof Heimbürger and Enes Efendic for patient recruitment. The authors also wish to express their appreciation to Anton Paier for technical assistance during immunohistochemical analysis. Clinical Science Immediate Publication. Published on 11 Jan 2011 as manuscript CS20100277 THIS IS NOT THE VERSION OF RECORD -see doi:10.1042/CS20100277

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