Ismael Bouassida Rodriguez

Nicolas Van Wambeke

Khalil Drira

Christophe Chassot
email: chassot@laas.frmohamed.jmaiel@enis.rnu.tn

Mohamed Jmaiel

Multi-layer coordinated adaptation based on graph refinement for cooperative activities

Keywords: dynamic re-configuration, self-adaptation, graphgrammar, context awareness, cooperative activities

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Introduction

Cooperative group activities using wireless mobile communicating systems constitute an increasingly evolving application domain. It is likely to be one of the most important directions that may enable reliable and efficient human and machineto-machine cooperation under the current networking systems and software, and may deeply shape their future deployment.

Such activity-support systems have to deal with dynamically evolving activity-level requirements under constantly changing network-level unpredictable constraints. Maintaining reliable connectivity and QoS in such a communication context is difficult. Adaptive service provisioning should help the different provisioning actors for achieving this goal and constitutes a challenge for different research communities.

Ad hoc solutions are likely to be not applicable to solve such a complex problem. Providing a basic framework for automated service and QoS deployment and management may constitute an important contribution towards solving such a problem. Aiming to answering this problem, we propose a formal model-based framework for adaptability management. Our framework has been elaborated in the context of Crisis Management System (CMS) with QoS provisioning at the transport and middleware levels as the final objectives.

This paper is organized as follows. Section 2 describes related work. Section 3 describes the targeted context used for the case study. Section 4 presents the different levels that we consider in our study. Section 5 provides details about the elaborated models, including optimization strategies though an example application. Finally, section 6 provides conclusions and future work.

Related works

Adaptation objectives, actions and properties are among the main facets of adaptability. They are studied and classified in this section.

Two different adaptability views may be distinguished: the design time adaptability [START_REF] Fahmy | Using graph rewriting to specify software architectural transformations[END_REF] and the run time adaptability [START_REF] Chang | Automatic configuration and runtime adaptation of distributed applications[END_REF][START_REF] Bade | Carsaan architecture for the development of context adaptive retrieval systems[END_REF]. For the first view, we can find design support tools like Adaptive Application Architecture (AAA). This tool handles the application development cycle and optimizes the resource value by insuring that the infrastructure answering clearly and in a measurable way to activity requirements. For the run time adaptability [START_REF] Friday | Developing adaptive applications: The most experience[END_REF] presents several adaptation techniques among which use proxy services, change model of interaction and reorganize application structure.

Adaptation approaches are also targeting different architectural levels including Service, Middleware and Transport levels. At the first level, the Service-Oriented Architecture (SOA) paradigm is based on dynamically publishing and discovering services. This kind of architectures provides the possibilities to dynamically compose services for adapting applications to contexts. Service descriptions are published, via the registry, by service providers and dynamically discovered by service requesters.

Other frameworks are proposed to provide adaptability for the middleware level. As an example, an adaptive framework for supporting multiple classes of multimedia services with different QoS requirements in wireless cellular networks is proposed in [START_REF] Nasser | Adaptive bandwidth framework for provisioning connection-level qos for next-generation wireless cellular networks[END_REF].

At the transport level, dynamically configurable protocol architectures provide adaptive stacks based on the protocol module concept. A protocol module is a primitive building block resulting from the decomposition of the protocol's complexity into various successive elementary functions. A protocol is then viewed as the composition of various protocol modules in order to provide a global service. These architectures can be refined into two different categories depending on their internal structure: the event based model (followed by Coyote and Cactus) and the hierarchical model (X-Kernel and AP-PIA). ETP follows a hybrid approach combining both models. These protocol architectures appear as a good choice for future communication protocol's self-adaptation as they are capable of run-time architectural adaptation, meaning that the modules composing them can change during the communica-tion. The adaptation solutions suggested in the literature distinguish behavioural and architectural aspects. The adaptation is behavioural (or algorithmic) when the behaviour of the adaptive service can be modified, without modifying its structure. Standard protocols such as TCP and specific protocols such as [START_REF] Özgür | Atl: an adaptive transport layer suite for next-generation wireless internet[END_REF] provide behaviour-based adaptation mechanisms. Behavioural adaptation is easy to implement but limits the adaptability properties.

The adaptation is architectural when the service composition can be modified [START_REF]IEEE Std 1471-2000, IEEE Recommended practice for architectural description of software-intensive systems[END_REF] dynamically. In self-adaptive applications components are created and connected, or removed and disconnected during the execution. The architectural changes respond to constraints related to the execution context involving, for example, variations of communication networks and processing resources. They may also respond to requirement evolution in the supported activities involving, for example, mobility of users and cooperation structure modification.

Designing and implementing self-adaptive communicating systems is a complex task. To handle this complexity, several studies showed the need to lay on model-based design approaches associated with automated management techniques.

Static architectures are described by instances of components and interconnection links. The dynamic character of architectures requires additional description rules. Several works have addressed the dynamic architecture description, using different approaches [START_REF] Allen | A formal basis for architectural connection[END_REF]. In order to guarantee the architecture evolving, correctness formal techniques are used. In particular, graphs represent a powerful expressive mean to specify respectively static and dynamic architectures aspects [START_REF] Metayer | Describing software architecture styles using graph grammars[END_REF]. For such approaches, graph vertices represent the software components, and the edges represent the links between these components. Dynamic architectures are described as graph grammars and architecture transformation is specified and ruled using graph rewriting models.

Case study

To expose the targeted problems and concepts and to show the usefulness of the graph-based models, we consider the example of crisis management systems (CMS). We introduce this example and give two different execution steps and some related scenarios.

For CMS-like activities, cooperation is based on information exchanges between mobile participants collaborating to achieve a common mission. A CMS team is composed of participants having different roles: The controller of the mission, several coordinators, and several field investigators. Each group of investigators is supervised by a coordinator (Figure 1). Each participant is represented by his identifier, role and by the devices he uses. Each participant performs different functions. The controller's functions include monitoring and authorizing/managing actions to be done by coordinators and investigators. The controller is the entity which supervises the whole mission. The controller waits for data from his coordinators who synthesize the current situation of the mission. The controller has permanent energy resources and high communication and CPU capabilities. Coordinators that are attached to the controller, have to manage an evolving group of investigators during the mission and to assign tasks to each one of them. The coordinator has also to collect, interpret, summarize and diffuse information from and towards investigators. The The controller's function includes supervising the entire mission, i.e. deciding actions to be performed from the analysis of the observation feedbacks D transmitted by the coordinators. Initially, all investigator groups are in the "exploration step" where investigators provide continuous feedbacks D to the coordinator; they also provide periodical feedbacks P. The coordinator sends continuous feedbacks P to the controller.

When an investigator finds a critical situation, its group architecture has to be reconfigured in order to move to an execution step called "action step" where: the investigator that discovers the critical situation keeps sending both feedbacks D and P to the coordinator but also provides feedbacks P to the other investigators of its group. Other investigators report feedbacks P to coordinator on the basis of feedbacks D transmitted by the critical investigator. The coordinator continue sending feedbacks P to the controller.

In this senario, feedbacks D are more important than feedbacks P. When the critical situation is resolved, the investigation group comes back to the exploration step.

Model-based approach for adaptability management

Managing self-adaptability for optimizing QoS in CMS-like activities is complex as requirements and constraints evolve constantly requiring adaptation at multiple levels of the stack. This raises a coordination problem which may lead to sub optimal solutions. Adapting architectural adaptation may potentially be handled at different levels. For example, an energy constraint may be handled by modifying the servers' deployment at one level or by acting on the pull/push mode another level. Considering that servers consume more CPU as they serve many clients, we can suppose that they are less energyefficient than clients. Moreover, considering that puller clients are more active than pushed clients, we can deduce that they need more CPU time and consume more energy. Both servers and puller clients may be placed on wired machines to save en-ergy of mobile machines. However, actions at both these communication levels are not necessarily mandatory for a given energy loss rate and bandwidth constraints may lead to consider acting only on puller clients. Managing architectural adaptations requires defining and modeling abstractions levels dedicated to specific parts of the whole adaptation. This allows designers and developers to respectively master the design of adaptation rules. For a given configuration A n,1 at level n, multiple configurations (A n-1,1 , ..., A n-1,p ) may be implemented at level n -1).

Adapting the architecture to constraint and requirement changes at level n -1 by switching among these multiple configurations allows maintaining unchanged the n-level configuration. Moreover, when adaptation requires changes at level n, this may need no changes at level n -1 if initial and new configurations of level n (e.g. changes from A n,1 to A n,2 ) have common implementations (e.g. A n-1,p ) at level n -1.

We consider three main abstraction levels for adaptability management which allow describing process-to-process, component-to-component and service-to-service architectural properties. From a communication point of view they represent respectively, the Transport layer, the Middleware layer and the upper users-oriented service layers. In the following, we will refer at these three levels as: the Transport-level adaptation (T-Adapt), the Middleware-level adaptation (M-Adapt) level and the Service-level adaptation (S-Adapt).

The S-Adapt level constitutes the highest level of the communication. It describes the services and their associated requirements and constraints provided by entities exchanging high level information. S-Adapt entities can for example represent the different roles the human participants may have within the considered activity. For CMS-like activities, depending on its role in the mission (e.g. controller, coordinators, investigators...) each participant has to perform a set of given functions (e.g. observe, report...).

The M-Adapt level is viewed as a component-to-component communication level aiming at supporting a given S-Adapt architecture, considering resource-related constraints. Three roles are distinguished: "event producers" (EP), "event consumers" (EC) and "channel manager" (CM).

The T-Adapt level constitutes the lowest level that we consider. It handles the process-to-process communications, i.e. the Transport level connections supporting the component-tocomponent communications of the M-Adapt level.

We consider distributed component-based applications deployed on mobile communication nodes. The communication has to be maintained adapted to the context change factors. These factors are given according to the application and the node properties. Being aware of these factors, that we call context, provides adaptability. The application and the node properties are: the mobile nodes move in a limited perimeter and each node has limited resources in term of energy and memory. The transport connections evolve in an open environment in which they will have to enforce "friendliness" and "fairness" [START_REF] Floyd | Equation-based congestion control for unicast applications[END_REF] with regards to other non-cooperating connections. We drive the evolutions between levels by considering the context factors.

Graph-based refinement framework

We introduce our general framework of models. We use models based on graph grammar [START_REF] Chomsky | Three models for the description of language. Information Theory[END_REF] to handle the architectural adaptation refinement management.

Graph grammars constitute a powerful and very expressive formalism for style description. Moreover, theoretical work on this field provides formal means to specify and check constraints on these architectures [START_REF] Rozenberg | Handbook of Graph Grammars and Computing by Graph Transformations[END_REF][START_REF] Ehrig | Graph Grammars and Their Application to Computer Science: 4th International Workshop[END_REF]. We use productions of type (L; K; R; C) where (L; K; R) corresponds to the structure of a Double PushOut (DPO) production [START_REF] Ehrig | Tutorial introduction to the algebraic approach of graph grammars[END_REF] and where C is a set of connection instructions. The instructions belonging to C are of the edNCE type [START_REF] Rozenberg | Handbook of Graph Grammars and Computing by Graph Transformations[END_REF]. They are specified by a system (n, δ, d, d ′ ) where n corresponds to a vertex belonging to the daughter graph R, δ is a vertex label, and d and d ′ are elements of the set in, out. For example, a production defined by the system (L; K; R; (n, δ, d, d ′ )) is applicable to a graph G if it contains an occurrence of the mother graph L. The application of this production involves transforming G by deleting the subgraph (Del = L \ K) and adding the subgraph (Add = R \ K) while the subgraph K remains unchanged. All dangling edges will be removed. The execution of the connection instruction implies the introduction of an edge between the vertex n belonging to the daughter graph R and all vertices n ′ that are p-neighbours1 of and d-neighbours2 . This edge is introduced following the direction indicated by d ′ and labelled by q.

Refining from S-Adapt to M-Adapt

This section presents the steps and formalisms used to automatically refine a given configuration at the S-Adapt level to an optimal configuration at the M-Adapt level.

Graph grammar productions

In the following, we provide an example of graph grammar for our case study to implement architecture refinement. The proposed grammar generalize the use case by considering a variable number of investigators.

Following the commonly used conventions, we consider that vertices represent communicating entities (e.g. services, components) and edges correspond to their related interdependencies (e.g. communication links, composition dependencies).

For our study, we consider an architecture instance that includes a coordinator (coord) that manage three investigators (Inv). The graph edges are labeled by the exchanged data types and the priority of each type (D H /P L ). Each participant have two attributes: the identifier and the deploying machine.

Architectural refinement deals with characterization of all the architectures that may be generated at an abstraction level n (Service) to implement a given architecture of the abstraction level n -1 (Middleware).

In this subsection we give the graph grammar addressing the refinement of any architecture of the S-Adapt level in all possible architectures of the M-Adapt level, during the exploration step. Since this graph grammar transforms S-Adapt architecture into M-Adapt architectures, its non-terminal nodes are S-Adapt entities while terminals nodes are M-Adapt enti- -------------→ EC(ec1, D, m1), CM(cm2, P, m2) <P ull,1 P ush,0> -------------→ EC(ec2, P, m1), Inv(i1, m2) <P ull,1 P ush,0> -------------→ CM(cm1, D, m2), Inv(i1, m2) <P ull,1 P ush,0> -------------→ CM(cm2, P, m3) Inv(i2, m3) <P ull,1 P ush,0> -------------→ CM(cm1, D, m2), Inv(i2, m3) <P ull,1 P ush,0> -------------→ CM(cm2, P, m3)} C = {ic1, ic2}) p2 = (L = {CM(cm1, D, m1), CM(cm2, P, m2), Inv(i, m3); Inv(i, m3) <P ull,1 P ush,0> -------------→ CM(cm1, D, m1), Inv(i, m3) <P ull,1 P ush,0> -------------→ CM(cm2, P, m2)} K = {CM(cm1, D, m1), CM(cm2, P, m2)}; R = {EP (ep1, D, m3), EP (ep2, P, m3), EP (ep1, D, m3) <P ull,1 P ush,0> -------------→ CM(cm1, D, m1), EP (ep2, P, m3) <P ull,1 P ush,0> -------------→ CM(cm2, P, m2)}; C = { }) with: ic1 = (CM(cm1, D, m2), < DH , PL > /P ull, 1 P ush, 0 >, Inv, in/in) ic2 = (CM(cm2, P, m3), < DH , PL > /P ull, 1 P ush, 0 >, Inv, in/in) Table 1. Refinement graph grammar GG S→M,exp ties. GG S→M,exp (Table 1) allows implementing this refinement in the exploration step. Production p1 allows the refinement of the pattern consisting of the coordinator, and the two investigators that host the channel managers CM 1 and CM 2. Connection instructions ic1 and ic2 allow considering the pull/push options. Production p2 allows refining for other investigators. Figure 2 gives the refinement generated by GG S→M,exp and depicts the case of three nodes represented with their communications and their priorities. The application of the sequence p1; p2; p2; p2 generates a configuration containing only terminal nodes (i.e. nodes belonging to the M-Adapt level).

EC(ec1,D,m1) CM(cm1,D,m2) ) pull,1 pull,1 push,0 push,0 EP(ep1,D,m2) EP(ep2,P,m2) EP(ep5,D, m3) Inv(i2,m2) Cont(c,m1) Inv(i3,m3) p1, p2, p2, p2 EC(ec1,D,m1) CM(cm2,P,m3) push,0 pull,1 pull,1 push
<DH,PL> Inv(i4,m4) Fig. 2. Using GG S→M,exp to achieve the refinement from S-Adapt to M-Adapt

Optimization at the Middleware level

The refinement graph grammar GG S→M,exp , for a given configuration A n,i at the service level, produce the set of configurations (A n-1,1 , ..., A n-1,p ) that can implemente at the middelware level. We provide an algorithm (Table 2) that allows to select the optimal implementation of A n,i .

Let C denotes the context Let An,i Let S = {An-1,1, ..., An-1,p} the result of the refinement graph grammar GGS→M,exp for An,i Select S1 = {A n-1,k ∈ S} such that:

Cost(A n-1,k , C) ≤ Cost(X, C), ∀X ∈ S} if card(S1) = 1
Select any configuration from S1.

Table 2. Selection alogorithm

We define the context C as the percentage of the remain energy of each node (L E ) and the percentage of the remain memory of each node (L M ). We define also the fonction Cost() (Table 2).We proceed by step. First, for each node (i) for a given deployment configuration A n-1,p , we calculate an evaluation V i = αLE+βLM α+β . Where the values α and β are weights that allow an importance degree to be associated with each factor. For instance, if we know that for a specific node the memory saturation level is the most important factor, we set β to a value higher than α. Second, we calculate

Cost(A n-1,p , C) = 1 N N 1 V i ,
where the value N is the node number of the deployment configuration A n-1,p . In the end of this refinmement, we optain the optimal A n-1,p (Service level) that implements A n,i (Middelware level).

Refining from M-Adapt to T-Adapt

Given the M-Adapt graph obtained by the sucessive p1; p2; p2; p2 productions presented figure 2, it appears that the different connections have different priorities assigned to them, directly derived from the type of media that they transport. In a process similar to what has been described in the previous section to refine an element from S-Adapt into M-Adapt, a graph grammar (GG M→T,exp ) generating a T-Adapt graph (see figure 3) can be written. However, it is not presented here due to space limitations.

The refinement graph grammar GG M→T,exp , for a given configuration A n-1,i at the Middleware level, produces a set of configurations (A n-2,1 , ..., A n-2,p ) at the transport level. A first step in transport provisioning consists in selecting the adequate (A n-2,p ) composition to be instantiated given the media that is being transport as well as the context in which the connection takes place. This step is presented in [START_REF] Van Wambeke | A model-based approach for self-adaptive transport protocols[END_REF]. The approach relies on analytical models that define an optimization problem which's output is the most suitable composition for the modular protocol.

Given the collaborative nature of the case study, the analytical model that leads to the protocol compositions is extended in order to make it compulsory for them to include a congestion control that is "collaborative". A collaborative congestion control is a modified version of the standard algorithm in order to allow 2 or more connections to collaborate meaning that one of them will reduce its sending rate in order for the other to be able to acheive greater throughput. The drop and raise being simultaneous, the good properties of fairness and friendliness [START_REF] Floyd | Equation-based congestion control for unicast applications[END_REF] are maintained towards non-collaborating connections.

In what follows, we describe a method that takes the data available on the M-Adapt graph as input in order to compute the parameters to be provided to a collaborative congestion control module instantiated in the dynamic transport protocol instance supporting the connection. Definition: Let {c i = (S i , D i ), c j = (S j , D j )} two connections, c i and c j are said to be dependant iif:

(S i = S j ∨ S i = D j ) ∧ (D i = S j ∨ D i = D j ).
Definition: Let D the dependency matrix for the activity such that: D i,j = 1 if c i and c j are dependant 0 otherwise Given the above definitions, the ratio of the total available bandwidth between two nodes to be consumed by each connection without taking priorities into account is given (in an activity composed by n connections) by: F i = Di,j P n j=1 Di,j

Let P i the priority associated to connection i. In order to take this priority into account, the F i expression is modified as follows: F i = The priorities vector is: P = (0, 1, 1, 0, 1, 0, 1, 0)

Applying the previously presented heuristics, we obtain F = 1, 1, 1, 1 2 , 1, 1 2 , 1, 1 the parameters vector to be fed to the collaborative congestion control.

As it can be observed, connection's which don't "collaborate" have a fraction of 1. This instructs the collaborative congestion control to use the standard algorithm. On the other hand, c 4 and c 6 have a fraction of 1 2 indicating that they share resources with another collaborating connection of the same priority.

Conclusion

In this paper, we presented a framework for the context-aware dynamic provisioning and automated management of group cooperative activities. All the levels ranging from Service to Transport have been introduced. The graph formalism used to model each of the levels has been illustrated in the context of CMS-like activities. In this context, the graph grammar as well as a the productions required for automated top-down refinement of the models have been explicitly presented. Finally, algorithms and models to further refine the decision at the Middleware and the Transport level have been introduced to take advantage of context information captured in a CMS-like case study.

Future works include the implementation of the models and algorithms presented in this paper and the introduction of a coordination approach to avoid conflict between the adaptation at different levels. Moreover, the integration of the optimization algorithms as well as the graph transformation modules with a monitoring and measurements system would provide all the needed inputs for the deployment of the solution in controlled network environments.

Fig. 1 .

 1 Fig. 1. CMS architecture

  GGS→M,exp = (AX, N T, T, P ) with: T = {CM(cm, D, m), EC(ec, D, m), EP (ep, D, m)}, and N T = {Coord(c, m1), Inv(A, m2)}, and P = {p1, p2} p1 = (L = {Coord(co, m1), Inv(i1, m2), Inv(i2, m3), Inv(i1, m2) <D H ,P L > --------→ Coord(co, m1), Inv(i2, m3) <D H ,P L >--------→ Coord(co, m1)}; K = {Inv(i1, m2), Inv(i2, m3)}; R = {EC(ec1, D, m1), EC(ec2, P, m1), CM(cm1, D, m2), CM(cm2, P, m3), CM(cm1, D, m1) <P ull,1 P ush,0>

Fig. 3 .

 3 Fig. 3. Transport Connections Graph

  Di,j (Pj +1)For the current scenario, the graph presented on figure3leads to the following dependency matrix:

p-neighbours of a vertex n are all vertices n ′ such that there exists an edge labeled by p which connects n and n ′ .

In-neighbours if d = in and out-neighbours otherwise.


Carla Carolina

Ferreira Meneses

Paulo Robson

Monteiro De Sousa

Laine Celestino Pinto

Gerson Maciel Coelho

Tamires Ferreira Da Silva

Oliveira Luan

Kayo Silva Ferreira

Gustavo

Jorge Arnaldo

Kelson Martins-Filho

Carmo Freitas Do

Elizabeth Sumi Faial

Yamada

Carla Carolina Ferreira Meneses

Ferreira

Silva Gustavo

Jorge Martins-Filho

Kelson

Carmo Freitas Faial

Elizabeth Sumi Yamada

Jerônimo Lameira

Jenny Jouin

Philippe Thomas

Olivier Masson

Catrina Dielly

Favacho Lopes

Dr Nahum Cláudio

Chem. D Alves

Dra Dielly

Farm. D Catrina Favacho Lopes

Layered double hydroxide-indomethacin hybrid: A promising biocompatible compound for the treatment of neuroinflammatory diseases

Keywords: Layered double hydroxide, Indomethacin, Nanohybrid, Neuroinflammation

Introduction

Neuroinflammation is a healthful immune response, however, if excessive, it becomes harmful and, when in a chronic state, it seems to play an important role in the onset and/or progression of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease and multiple sclerosis [START_REF] Kempuraj | Neuroinflammation Induces Neurodegeneration[END_REF][START_REF] Réus | The role of inflammation and microglial activation in the pathophysiology of psychiatric disorders[END_REF] . Some studies support that anti-inflammatory drugs may be beneficial if used at the beginning of these diseases, but on the other hand, it may be harmful if given in the late stages [START_REF] Bernardi | Indomethacin-loaded lipid-core nanocapsules reduce the damage triggered by Aβ1-42 in Alzheimer's disease models[END_REF]. In view of the neuroinflammatory scenario of neurodegenerative diseases and other neurological disorders, anti-inflammatory drugs have been proposed as auxiliary drug capable of delaying and/or ameliorating these processes [START_REF] Hamasaki | Animal models of neuroinflammation secondary to acute insults originated outside the brain[END_REF].

Non-steroidal anti-inflammatory drugs (NSAIDs) are a class of aromatic organic compounds with ionizable carboxylic groups. NSAIDs usually show antipyretic, analgesic, and anti-inflammatory activities [START_REF] Wojek | Non-steroidal anti-inflammatory drugs[END_REF]. They constitute a rather heterogeneous group of drugs which share the ability to inhibit cyclooxygenase (COX) isoforms 1 and 2 activity. COX is an enzyme essential for the synthesis of prostaglandins (PGs), which have a strong propensity for inducing inflammation [START_REF] Kata | A novel pleiotropic effect of aspirin: Beneficial regulation of pro-and anti-inflammatory mechanisms in microglial cells[END_REF][START_REF] Tomisato | Membrane permeabilization by non-steroidal anti-inflammatory drugs[END_REF].

Based on this, these compounds are commonly used in the broad range of diseases associated with inflammatory processes [START_REF] Wongrakpanich | A Comprehensive Review of Non-Steroidal Anti-Inflammatory Drug Use in The Elderly[END_REF]. Indomethacin (INDO), 1-(4-chlorobenzoyl)-5-methoxy-2-methyl-1H-indole-3acetic acid, which belongs to the NSAIDs class, is a potent inhibitor of both COX-1 and COX-2 [START_REF] Lucas | The Pharmacology of Indomethacin[END_REF]. It has been introduced to treat rheumatic inflammatory diseases because of its extensive anti-inflammatory efficacy [START_REF] Banks | Lipopolysaccharide-induced blood-brain barrier disruption: Roles of cyclooxygenase, oxidative stress, neuroinflammation, and elements of the neurovascular unit[END_REF] and is also used in many studies as an auxiliary for the treatment of some diseases that affect the CNS, such as Alzheimer's disease and multiple sclerosis [START_REF] Bernardi | Protective effects of indomethacin-loaded nanocapsules against oxygen-glucose deprivation in organotypic hippocampal slice cultures: Involvement of neuroinflammation[END_REF][START_REF] Preisner | Non-steroidal anti-inflammatory drug indometacin enhances endogenous remyelination[END_REF].

However, most of the NSAID compounds exhibit adverse effects, especially gastrointestinal, cardiac, cerebrovascular and renal problems [START_REF] Hopper | Cardiac effects of non-cardiac drugs[END_REF][START_REF] Vostinaru | Adverse Effects and Drug Interactions of the Non-Steroidal Anti-Inflammatory Drugs[END_REF].

In recent decades, several studies have reported that drug intercalation into layered double hydroxides (LDHs), also known as anionic clays or hydrotalcite like materials reduces adverse effects, such as stomach irritation, and increase the drug solubility [START_REF] Ambrogi | Effect of hydrotalcite-like compounds on the aqueous solubility of some poorly water-soluble drugs[END_REF][START_REF] Gaskell | Ibuprofen intercalation and release from different layered double hydroxides[END_REF][START_REF] Rives | Layered double hydroxides as drug carriers and for controlled release of non-steroidal antiinflammatory drugs (NSAIDs): a review[END_REF]. The LDHs have the advantages of the easy of preparation, low cost, good biocompatibility, low cytotoxicity, and full protection of the loaded drug [START_REF] Hesse | Layered double hydroxides as efficient drug delivery system of ciprofloxacin in the middle ear : an animal study in rabbits[END_REF][START_REF] Silva | Recent advances in layered double hydroxides applied to photoprotection[END_REF]. Moreover, it provide the controlled release these drugs [START_REF] Gaskell | Ibuprofen intercalation and release from different layered double hydroxides[END_REF][START_REF] Hashim | Preparation of zinc layered hydroxide-ferulate and coated zinc layered hydroxide-ferulate nanocomposites for controlled release of ferulic acid[END_REF].

Interestingly, Del Arco et al showed that intercalation of the INDO in the LDH host reduced the ulcerating damage of this drug [START_REF] Del Arco | Mg,Al layered double hydroxides with intercalated indomethacin: Synthesis, characterization, and pharmacological study[END_REF]. Thus, proving that the LDHs are good matrices for formulations intended for application pharmacological of the NSAIDs drugs. In this work, the indomethacin anion (negatively charged, INDO -) was intercalated by coprecipitation method into a biocompatible layered double hydroxide (LDH, MgAl-NO3). In addition, was investigated the effect by which the INDO anion

Material and methods

Chemicals and reagents

The indomethacin (C19H16ClNO4, with a purity of 99%), Mg(NO3)2.6H2O (Sigma-Aldrich, 98%), Al(NO3)3.9H2O (Sigma-Aldrich, 98%), NaOH (Sigma-Aldrich, 98%), Lipopolysaccharide (LPS) derived from Escherichia coli O111:B4 (Sigma-Aldrich Brazil), anti-Iba1 (Sigma-Aldrich Brazil), horse serum (Sigma-Aldrich Brazil), DAB (3,3'diaminobenzidine) chromogen solution (Sigma-Aldrich Brazil) and CH3CH2OH (Alfa Aesar, 94-96%) were purchased from Sigma-Aldrich Brazil Ltds. The streptavidin-HRP (LSAB2 System-HRP) was purchased from Dako North America Inc. (Carpinteria, CA, USA). All were of analytical grade. The water used was purified with an ELGA Purelab Option labwater.

Synthesis

The LDH (Mg/Al-NO -3) were synthesized by a standard coprecipitation method similar to those proposed by Del [START_REF] Del Arco | Mg,Al layered double hydroxides with intercalated indomethacin: Synthesis, characterization, and pharmacological study[END_REF], for comparison purposes, under the same conditions as of the nanohybrid (LDH-INDO) sample [START_REF] Del Arco | Mg,Al layered double hydroxides with intercalated indomethacin: Synthesis, characterization, and pharmacological study[END_REF]. For indomethacin intercalated within the LDH interlayer space, LDH-INDO complex, a mixed aqueous solution containing Mg(NO3)2.6H2O and Al(NO3)3.9H2O salts was slowly added to 45 ml of ethanol-water solution 50% (v/v) with indomethacin (1.67 g), the solution pH was previously adjusted to 8 with dropwise addition of 2 M NaOH solution. A gel was formed and aged with vigorous stirring at 80 °C for 24 hours under N2 gas flow to avoid carbonate contamination. The solid was then isolated by centrifugation (1000 rpm for 5 minutes), filtrated and washed with portions of deionized water and ethanol solution (volume ratio 1:1), and finally dried in a vacuum desiccator for 24 hours.

Physical characterization of the LDH-INDO sample

In this work, different instrumental techniques were used in characterization of the synthesized samples, i.e. powder X-ray diffraction (XRD), Infrared spectroscopy (IR), Raman spectroscopy, Thermogravimetric analyses (TGA-DSC) and Scanning electron microscopy (SEM). The XRD patterns of the powders were recorded with CuKα1 radiation in the 2θ range 20-80° on a θ/2θ D8 advance diffractometer (Bruker, Germany) with a step of 0.04° and exposure time of 2.8 seconds per step. The analysis of the samples were carried out with the Peakoc program [START_REF] Masson | Peakoc Program[END_REF] using the split pseudo-Voigt function [START_REF] Thompson | Rietveld Refinement of Debye-Scherrer Synchrotron X-ray Data from A1203[END_REF][START_REF] Thompson | Rietveld Refinement of Debye-Scherrer Synchrotron X-ray Data from A1203[END_REF][START_REF] Thompson | Rietveld Refinement of Debye-Scherrer Synchrotron X-ray Data from A1203[END_REF] to fit the experimental profiles. The IR spectra were recorded on a Thermo Fisher Nicolet 6700 model spectrometer in air at room temperature (400-4000 cm -1 ) using the KBr pellet technique. The Raman spectroscopic analyses were performed on a T64000 Raman spectrometer at the wavelength of 514,532 nm. The TGA-DSC was measured on Netzsch instrument model STA 449F3 (Germany).

The thermal analyses were carried out in flowing (20 ml/min) argon gas, from 25 °C to 1000 °C at a heating rate of 10 °C/min and morphology of the samples was observed through of the Field-Emission Scanning Electron Microscopes (FE-SEM) with a JEOL-JEM-6700 at 5.0 kV. The Mg and Al metals was quantified on a Varian ICP optical emission spectrometer (OES), as described in the literature [START_REF] Sousa | Ferulate Anion Intercalated into Zn/Al Layered Double Hydroxide: A Promising Intercalation Compound for Inhibition of Leishmania (L.) amazonensis[END_REF]. 1 mg of both pristine LDH and LDH INDO samples were dissolved in an aqua regia solution. The aliquots were left to cool after which 1 mL of aliquot was diluted with 9 mL of deionized water, then measured three times and the average ICP value was recorded.

Drug-loading capacity

The indomethacin (INDO) quantification into LDH (MgAl-NO3) was determined by ultraviolet-visible absorbance (UV-Vis) using Shimadzu UV-2600 model, as described in our previous work [START_REF] Sousa | Structure and analgesic properties of layered double hydroxides intercalated with low amounts of ibuprofen[END_REF]. Initially, 0.5 ml of ethanol and 0.5 ml of 0.1 M HCl solution were used to dissolve a milligram of the LDH-INDO complex then diluted with phosphate buffer solution (PBS) at pH 7.4 ± 0.02 in a 10 ml volumetric flask. The amount of INDO in this solution was determined by measuring the absorbance at λmax = 320 nm, using a calibration curve previously obtained from a series of standard solutions of indomethacin. Furthermore, the solid state UV-Vis spectra for indomethacin and LDH-INDO powder were obtained in a range from 200 nm to 800 nm with BaSO4 background by using a UV-Vis spectroscope (Shimadzu UV-2600 model).

Drug release kinetics

The in vitro release of INDO was performed following the method described by [START_REF] Sousa | Structure and analgesic properties of layered double hydroxides intercalated with low amounts of ibuprofen[END_REF] 10 mg of the LDH-INDO was immersed in 100 ml of a phosphate buffer solution (PBS) at pH 4.8 ± 0.02 and 7.4 ± 0.02 with mild shaking at 150 rpm and temperature maintained at 37 ± 0.5 °C. An aliquot of 5 ml was taken from the suspension and immediately replaced by an equal volume of PBS at 37 ± 0.5 °C to keep the temperature and volume constant. The content of INDO in each aliquot was filtered and measured at an absorbance of 320 nm using a UV/Vis spectrophotometer (Shimadzu UV-2600 model) according to a previously determined calibration curve (y = 0,0351x -0,0353; r² = 0,9997).

The released percentage at each time point (1, 5, 10, 15, 20, 25, 30, 45, 60, 75, 90, 120, 150, 180, 240, 300, 360, 420, 480, 540 and 600 min) was expressed as a fraction of the total amount of indomethacin anion (INDO -), obtained by an average of the three determinations . Finally, the release behavior of the indomethacin from the LDH-INDO was modeled applied three types of dissolution-diffusion kinetic models, a pseudo-second order model (Equation 1), the (Equation 2) [START_REF] Sakore | Formulation and evaluation of enalapril maleate sustained release matrix tablets[END_REF]) Korsmeyer-Peppas model and the Higuchi model (Equation 3) [START_REF] Sakore | Formulation and evaluation of enalapril maleate sustained release matrix tablets[END_REF].

• • • = 1 • ! " + • • ! (1) • • • $ = • % (2) • • = √• (3)
Where • • and • are the equilibrium release amounts and the release amounts at any time (!), respectively.

Biological assays

Adults male Wistar rats aged 8 weeks, weighing 200-220 g were assessed in study, all from Instituto Evandro Chagas. All procedures in accordance with National Council for Animal Experimentation Control (CONCEA) and approved by the Ethics Committee in the Use of Animals (Protocol: 1624210617) of the Federal University of Pará. Animals were kept in a temperature-regulated room (22-24 ºC) at 12/12 hours light/dark cycle, with water and food ad libitum. The weight body and intake of food were daily measured and calculated as follows:

ΔBWV = WD -FW (4)
Where ΔBWV, WD, FW are body weight variation, weight of the day, first weight measured on the day before LPS dose or saline, respectively.

ΔFIV = FWD -FWPD (5)

Where ΔFIV, FWD, FWPD are food intake variation, feed weight in the day, feed weight of the previous day, respectively.

The animals received intraperitoneal (i.p.) injections of LPS (0.75 mg/kg) derived from Escherichia coli O111:B4 dissolved in 0.9% saline solution or 0.9% saline solution only (control group) every day for five days in the period from 7:00 to 8:30 a.m. (day 0 to day 4). Pharmacological treatments (LDH, INDO and combinations) were performed 24 hours after the first injection of LPS (day 1) and 48 hours after first doses of the treatments (day 3) in the period from 13:00 to 14:30.

The choice of these days was based on the hypothesis that the LDH formulation would provide drug release-sustained, with full release within 48 hours [START_REF] Gaskell | Ibuprofen intercalation and release from different layered double hydroxides[END_REF][START_REF] Gunawan | Direct Control of Drug Release Behavior from Layered Double Hydroxides through Particle Interactions[END_REF][START_REF] Rives | Intercalation of drugs in layered double hydroxides and their controlled release: A review[END_REF]. The dose of INDO was chosen according to previous studies [START_REF] Boehme | Impact of indomethacin on neuroinflammation and hippocampal neurogenesis in aged mice[END_REF][START_REF] Chao | Indomethacin protects rats from neuronal damage induced by traumatic brain injury and suppresses hippocampal IL-1β release through the inhibition of Nogo-A expression[END_REF]. The drug was dissolved in 0.9% saline solution with 7% sodium bicarbonate and given for gavage [START_REF] Banks | Lipopolysaccharide-induced blood-brain barrier disruption: Roles of cyclooxygenase, oxidative stress, neuroinflammation, and elements of the neurovascular unit[END_REF].

The concentrations tested for pristine LDH and those for LDH containing indomethacin were based on the INDO intercalation efficiency in the nanoparticle, keeping the indomethacin concentration comparable to those used in the experiments with the drug non-intercalated (free drug). The rats were divided into 8 experimental groups, according to table 2.

Immunohistochemistry

After euthanasia, brains were removed and fixed in paraformaldehyde (4%), 30%

sucrose-embedded, and sectioned at 40 µm. Sections of brain tissues were washed with PBS pH 7.4 ± 0.02 and incubated with mouse anti-Iba1 (1:1000), diluted in PBS and 3%

horse serum overnight (4 °C). Sections were rinsed in PBS and exposed to sequential incubations of biotinylated link (goat anti-mouse secondary antibody) and streptavidin-HRP according to manufacturer's instructions. Binding sites were revealed with DAB chromogen solution and 0.5 M nickel plus hydrogen peroxide. The slices, thus processed, were stained with cresyl violet for 10 minutes. The slides were observed in optical microscope Nikon Labophot 2 (Nikon Instruments Inc., NY, USA) and the images were captured by the digital camera and analyzed using the software Stereologer TM 3.0 software (Systems Planning & Analysis, Inc., Alexandria, VA).

Statistical analysis

All analyzes were performed in the program GraphPad Prism 8.0 software (San Diego, California, USA). Comparisons of animal weight and food intake were assessed by two-way analysis of variance (ANOVA) [START_REF] Salkind | Encyclopedia of research design dependent variable[END_REF] followed by Tukey post-test for multiple comparisons. The data were expressed as mean ± standard deviation. In all statistics, values were adopted for p < 0.05 as statistically significant.

Results

X-ray powder diffraction (XRD)

The XRD patterns for LDH, before and after intercalation, are shown in Figure 1.

They exhibit a series of Bragg reflections peaks, all associated to single phase and wellcrystallized structures typical of hydrotalcite-like materials [START_REF] Miyata | Physico-Chemical Properties of Synthetic Hydrotalcites in Relation to Composition[END_REF][START_REF] Ambrogi | Intercalation compounds of hydrotalcite-like anionic clays with antiinflammatory agents -I. Intercalation and in vitro release of ibuprofen[END_REF][START_REF] Kastner | Highthroughput manufacturing of size-tuned liposomes by a new microfluidics method using enhanced statistical tools for characterization[END_REF]. The basal spacing of pure LDH (Figure 1A) is about 8.87 Å, which corresponds to typical values found with intercalated nitrate ions [START_REF] Dupin | Intercalation compounds of Mg-Al layered double hydroxides with dichlophenac: different methods of preparation and physico-chemical characterization[END_REF]. The FWHM (full width at half-maximum) value for LDH is about 0.542° for the (003) reflection which gives an estimated crystallite coherent size of 138.8 Å along the c direction by the Scherrer equation [START_REF] Hu | Unique layered double hydroxide morphologies using reverse microemulsion synthesis[END_REF], indicating a stacking of 22-23 monolayers.

The XRD pattern for hybrid solid (LDH-INDO, shown in Figure 1 B) also exhibit sharp and symmetric Bragg reflections of ( 003), ( 006) and ( 009) planes, but they occur at lower angles and exhibit a larger FWHM. This indicates the formation of LDH-INDO intercalation compound, such as described in the literature [START_REF] Del Arco | Mg,Al layered double hydroxides with intercalated indomethacin: Synthesis, characterization, and pharmacological study[END_REF][START_REF] Mohanambe | Anionic clays containing anti-inflammatory drug molecules: comparison of molecular dynamics simulation and measurements[END_REF].

In this work, as expected, after the intercalation of INDO the calculated basal spacing corresponding to diffraction by planes (00l) was much larger than for the LDH, because of the dimensions of the drug molecules (width = 7.5 Å; length = 14.2 Å) [START_REF] Del Arco | Mg,Al layered double hydroxides with intercalated indomethacin: Synthesis, characterization, and pharmacological study[END_REF]. This is reflected by the d-spacing values expanding from 8.87 Å to 25.29 Å, calculated from the average values of the first, second and third order peaks of the XRD patterns. This is equivalent to a gallery height of 20.41 Å (from the width of the brucitelike layers, 4.8 Å [START_REF] Drezdzon | Synthesis of isopolymetalate-pillared hydrotalcite via organicanion-pillared precursors[END_REF], suggesting an arrangement of the drug in the interlayer region in bilayers with the minimal dimension of each anion perpendicular to the brucitelike layers, as stated in the literature [START_REF] Del Arco | Mg,Al layered double hydroxides with intercalated indomethacin: Synthesis, characterization, and pharmacological study[END_REF][START_REF] Mohanambe | Anionic clays containing anti-inflammatory drug molecules: comparison of molecular dynamics simulation and measurements[END_REF]. This confirms that the drug has indeed been intercalated. This process occurs due to the negative charge of the guest anionic INDO, which is intercalated by the electrostatic force of attraction between the anionic INDO and LDH basal layer (Figure 1).

Moreover, the estimated coherent size of the crystallites increased to 170.6 Å, as calculated from the broadening of the peaks. Finally, no great change is observed in the value of the unit cell parameter a for LDH (4.57 Å) and LDH-INDO (4.55 Å), calculated from the position of the (110) reflection, which appears at the same position (~60.5° 2θ).

This indicates that intercalation of the INDO has not changed the structure of the layers but only the interlayer spacing.

Infrared spectroscopy (IR) analysis

The measured FTIR spectra of raw and intercalated LDH (LDH-INDO), shown in Figure 2, are in agreement with the previous observations found in literature, such as bands related to the nitrate, a sharp peak at 1385 cm -1 and a weak peak at 825 cm -1 , which confirms that sample is a pristine LDH. Another important support for pristine LDH is the ѵ(O-H) stretching vibration at around 3554 cm -1 , as well as a peak at 1629 cm -1 characteristic of the δ(H2O) vibrational model attributed to water molecules between layered of the LDH (MgAl-NO3) [START_REF] Andrade | Synthesis and characterization of a diruthenium-ibuprofenato complex[END_REF][START_REF] Kannan | Synthesis and physicochemical properties of cobalt aluminium hydrotalcites[END_REF]. In addition, lattice vibrations of metal-oxygen ѵ(M-O) bonds are also observed between 445 cm -1 and 676 cm -1 [START_REF] Kannan | Synthesis and physicochemical properties of cobalt aluminium hydrotalcites[END_REF].

For pure indomethacin, the infrared spectra show a sharp band at 1717 cm -1 attributed to the ѵ(C=O) stretching of the carboxylic acid, in addition with one from the carbonyl stretch of the non-protonated amide at 1692 cm -1 . However, this vibration changes after the intercalation in the LDH gallery to asymmetric carboxylate stretch ѵas(COO -) in the range 1550-1560 cm -1 , while symmetric stretch ѵas(COO -) was recorded between 1480-1300 cm -1 , thus confirming that the species intercalated into the LDH layers is the anionic form of indomethacin [START_REF] Del Arco | Mg,Al layered double hydroxides with intercalated indomethacin: Synthesis, characterization, and pharmacological study[END_REF][START_REF] Mendieta | Intercalation of Anti-inflammatory Drugs Sodium Indomethacin into Nanocomposites of Mg-Al[END_REF][START_REF] Mohanambe | Anionic clays containing anti-inflammatory drug molecules: comparison of molecular dynamics simulation and measurements[END_REF]. Moreover, other vibrations were detected, for example: CH3 at 2965 cm -1 (asymmetric stretch), CH3 at 2926 cm -1 (symmetric stretch), C=O at 1676 cm -1 (benzoyl), C=C at 1598 and 1480 cm -1 (aromatic rings), C-Cl in range 1061 at 1068 cm -1

and 754 cm -1 (Indole ring vibrations) [START_REF] Del Arco | Mg,Al layered double hydroxides with intercalated indomethacin: Synthesis, characterization, and pharmacological study[END_REF][START_REF] Mohanambe | Anionic clays containing anti-inflammatory drug molecules: comparison of molecular dynamics simulation and measurements[END_REF][START_REF] Zhang | Thermal analysis and FTIR spectral curve-fitting investigation of formation mechanism and stability of indomethacin-saccharin cocrystals via solid-state grinding process[END_REF].

Thermogravimetric analysis (TG-DSC)

Weight loss as a function of temperature for LDH, pure INDO and LDH-INDO were obtained using TG-DSC curves (Figure 3). Three weight-loss steps are observed for LDH (MgAl-NO3), the first one is related to the removal of interlayer water molecules, with an endothermic peak at 160 °C in DSC curve, the second one being due to the dehydroxylation of the inorganic layers and decomposition of intercalated nitrate anions in the temperature range of 300 to 600 °C [START_REF] Whilton | Bioinorganic clays: synthesis and characterization of amino-and polyamino acid intercalated layered double hydroxides[END_REF]. Finally, the total degradation occurs at 900°C (Figure 3). Pure indomethacin exhibits a single-stage thermal degradation, with endothermic a peak at 56 °C attributed to the melting of the drug (Figure 3). The complete thermal decomposition occurs at 300 ºC due to the exothermic effect of decomposition phenomena in its DSC curve [START_REF] Ambrogi | Effect of hydrotalcite-like compounds on the aqueous solubility of some poorly water-soluble drugs[END_REF].

TG-DSC curves for the LDH-INDO shows a first weight loss at 100 ºC, corresponding to the removal of interlayer water molecules (Figure 3). A second weight loss in the range 200 to 450 °C characterized by the presence of a small exothermic peak at 350 ºC could be ascribed to drug degradation, releasing probably CO2, NO2 and H2O. A third step from 500 to 600 ºC, with the presence of an intense exothermic peak at 550 ºC in DSC, was also due to the partial decomposition of intercalated INDO anions and dehydroxylation of the LDH layers [START_REF] Del Arco | Mg,Al layered double hydroxides with intercalated indomethacin: Synthesis, characterization, and pharmacological study[END_REF]. This process ended at 900 °C, corresponding to the total removal of hydroxyl groups from the layers. Therefore, these results indicate that the chemical stability of the drug is increased after the intercalation process. Furthermore, data obtained through the ICP allowed calculating the chemical composition of the samples, as shown in Table 1, and are consistent with those found in the literature [START_REF] Del Arco | Mg,Al layered double hydroxides with intercalated indomethacin: Synthesis, characterization, and pharmacological study[END_REF].

Surface morphology studies

The morphology observed in pure LDH and LDH-INDO are similar to what was previously seen in Magnesium and aluminum based LDH clays, usually platelets-like morphology, as shown in Figure 4 [START_REF] Sousa | Structure and analgesic properties of layered double hydroxides intercalated with low amounts of ibuprofen[END_REF][START_REF] Tao | Synthesis and characterization of layered double hydroxides with a high aspect ratio[END_REF]. The FE-SEM images of LDH suggest a particle size of about 100 at 160 nm and a thickness of a few nanometers (Figure 4A). This thickness indicates that the particles are composed of a small number of layers (typically less than 10). As expected, the intercalation of INDO anions strongly influenced the morphology properties of LDH [START_REF] Ambrogi | Effect of hydrotalcite-like compounds on the aqueous solubility of some poorly water-soluble drugs[END_REF], and consequently gave rise to an irregular structure with aggregated particles. It may be due to the adsorption of INDO anions on positively charged LDH surface, which hiders the full growth of the particles (Figure 4B) [START_REF] Zhang | Colloids and Surfaces B : Biointerfaces Methotrexate intercalated layered double hydroxides with different particle sizes : Structural study and Controlled release properties[END_REF]. Moreover, these results confirmed the conclusions from XRD and IR, and indicated the formation LDH-INDO.

Quantification and release study of LDH-INDO complex

The amount of indomethacin intercalated into LDH gallery was calculated at 50% (w/w) a good result when compared to the 48% obtained by [START_REF] Mendieta | Intercalation of Anti-inflammatory Drugs Sodium Indomethacin into Nanocomposites of Mg-Al[END_REF] after 10 hours when exposed to pH 4.8 ± 0.02 and 7.4 ± 0.02, respectively.

According to the literature, LDH-drug hybrids materials can exhibit a release at pH 4.8 ± 0.02 and 7.4 ± 0.02 following different molecular mechanisms [START_REF] Ambrogi | Role of mesoporous silicates on carbamazepine dissolution rate enhancement[END_REF][START_REF] Lima | Controlled release of ferulic acid from a hybrid hydrotalcite and its application as an antioxidant for human fibroblasts[END_REF]. A greater stability was demonstrated at pH 7.4 ± 0.02 instead of 4.8 ± 0.02 due to electrostatic interactions between the organic anions and the positively charged LDH layers [START_REF] Tyner | Nanobiohybrids as delivery vehicles for camptothecin[END_REF]. The release at pH 7.4 ± 0.02 indeed occurs via an ion exchange with the ions in the buffer solution, while the release at pH 4.8 ± 0.02 occurs through both dissolution of the LDH layers and ion exchange [START_REF] Kura | Development of a controlled-release anti-parkinsonian nanodelivery system using levodopa as the active agent[END_REF][START_REF] Tyner | Nanobiohybrids as delivery vehicles for camptothecin[END_REF]. From this information, the pseudo-second order equation [START_REF] El Zowalaty | Synthesis, characterization, controlled release, and antibacterial studies of a novel streptomycin chitosan magnetic nanoantibiotic[END_REF][START_REF] Ho | Pseudo-second-order model for lead ion sorption from aqueous solutions onto palm kernel fiber[END_REF], Korsmeyer-Peppas and Higuchi models [START_REF] Sakore | Formulation and evaluation of enalapril maleate sustained release matrix tablets[END_REF] were used as fitting standard release to the experimental data. It can be seen that the pseudo-second order model is the better fitted to the data for INDO release behavior than the other models.

The plots of t/ɋt versus t (pseudo-second order model) for the release of INDO show that the correlation coefficient (r 2 ) value was >0.99 which is slightly higher compared to the other models, however, all data is well adjusted (see Table 1). The burst effect observed in the matrix during the first 2 hours could be due to the release of intercalated ions. The slow release from the second to the 10 hours, can be attributed to the release of indomethacin anion from deeper interlayer sites (see Figure 6), where the effective distance to diffuse up to the edges reduces the release rate, together with a possible increase in the rigidity of the layers [START_REF] Lima | Controlled release of ferulic acid from a hybrid hydrotalcite and its application as an antioxidant for human fibroblasts[END_REF][START_REF] Sousa | Ferulate Anion Intercalated into Zn/Al Layered Double Hydroxide: A Promising Intercalation Compound for Inhibition of Leishmania (L.) amazonensis[END_REF].

Biological assays

Effects of treatment with LDH-INDO on body weight and food intake in animal model of subacute inflammation induced by LPS. In control groups (animals that received 0.9% saline solution i.p.), our results showed a similar food intake, with an increase only between SAL+SAL and SAL+INDO in day 4 and an increase body weight variation from day 3, compared to day 0. Those data confirming that LDH, pure INDO and LDH-INDO did not alter appetite and weight of the animals (Figure 7 and Figure 8).

On the other hand, LPS groups showed a reduction of food intake from day 1 to day 4 when compared to control groups, being that on the day 3 there were only a reduction

Discussion

It has long been argued that inflammation can be controlled and/or reduced with the use of NSAIDs. These drugs are capable of suppressing the inflammatory response triggered by several agents, such as bacteria and endotoxins, as well as attenuating neuroinflammation in the model of neurodegenerative diseases [START_REF] Bastos | Involvement of cyclooxygenase-2 in lipopolysaccharide-induced impairment of the newborn cell survival in the adult mouse dentate gyrus[END_REF][START_REF] Bernardi | Protective effects of indomethacin-loaded nanocapsules against oxygen-glucose deprivation in organotypic hippocampal slice cultures: Involvement of neuroinflammation[END_REF][START_REF] Preisner | Non-steroidal anti-inflammatory drug indometacin enhances endogenous remyelination[END_REF]. In the present study, although we demonstrated that INDO groups had shown a faster recovery, the LDH-INDO outcomes was more effectivity and quickly than pure INDO against the inflammatory process induced by LPS.

These findings about the INDO free are similar to those found by [START_REF] Banks | Lipopolysaccharide-induced blood-brain barrier disruption: Roles of cyclooxygenase, oxidative stress, neuroinflammation, and elements of the neurovascular unit[END_REF]. where the drug inhibited the increase of BBB permeability to sucrose induced by LPS, which also prevented the loss of body weight induced by this endotoxin, suggesting attenuation of sickness behavior, as also observed in our experiments [START_REF] Banks | Lipopolysaccharide-induced blood-brain barrier disruption: Roles of cyclooxygenase, oxidative stress, neuroinflammation, and elements of the neurovascular unit[END_REF].

Recently, it has been reported that peripheral infections accompanied by chronic inflammation represent the main risk factors for the development of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease [START_REF] Holmes | Review: Systemic inflammation and Alzheimer's disease[END_REF][START_REF] Su | Immune responses in parkinson's disease: Interplay between central and peripheral immune systems[END_REF]. During an inflammatory response, the activation of microglia leads to the retraction of their processes and swelling of the cell bodies with subsequent loss of brain monitoring. This may induce neuronal damage, rupture of relevant circuits and, in the case of uncontrolled or chronic neuroinflammation, decline sensory-cognitive [START_REF] Tremblay | The Role of Microglia in the Healthy Brain[END_REF].

In view of the above, the neuroinflammation induced by LPS in the present study was evidenced by the increase of Iba1 + cells (microgliosis) and by the morphological alteration of these cells, characterized by the slightly branched phenotype, with thickening of the projections and increased somatic size (microglial activation), both in the hippocampus and in the somatosensory cortex. Furthermore, we observed that these morphological and functional changes induced by LPS were attenuated after treatment with INDO, both free and LDH-intercalated, in both regions of the brain.

Differently from that observed by [START_REF] Banks | Lipopolysaccharide-induced blood-brain barrier disruption: Roles of cyclooxygenase, oxidative stress, neuroinflammation, and elements of the neurovascular unit[END_REF] believe that further studies should be conducted with regard to the choice of doses administered, as well as when, in the disease process, different formulations should be introduced to improve the outcomes.

Conclusion

In .0001 by 2-way in each day; compared to day 0 # p<0.05, ## p<0.01, ### p<0.001, #### p<0.0001; compared to day 1 $ p<0.05, $$ p<0.01, $$$ p<0.001, $$$$ p<0.0001; compared to day 2 & p<0.05, && p<0.01, &&& p<0.001, &&&& p<0.0001; compared to day 3 α p<0.05, αα p<0.01, ααα p<0.001, αααα p<0.0001 between groups by ANOVA followed by Tukey posttest.

(

  INDO -) released from INDO/MgAl-NO3-LDH hybrid (LDH-INDO) act as in a Lipopolysaccharide (LPS)-induced neuroinflammation model as a potential candidate to assist in the treatment of inflammatory neurological diseases.

  and 36% presented in the work of the Del arco et al. (2004), both for indomethacin intercalated into MgAl-LDH by coprecipitation method. The UV-Vis specters of both pure INDO and INDO released from the LDH-INDO are show in the Figure5. These data were obtained using a PBS at pH 7.4 ± 0.02 as well as in the solid state. In solution, three absorbance peaks are visible at 211 nm, 270 nm and 320 nm, which is in good agreement with the literature data (Sugandha Singh, Rajesh Kumar Maheshwari, 2018). In addition, the UV-Vis solid state spectrum (Figure5) confirmed the effective intercalation of the indomethacin in the LDH through of the increase in intensity and absorption in the UV and in the visible regions.In this analysis it is clear that LDH-INDO possessed higher UV absorption capacity as compared with pure indomethacin, thus, suggesting that the indomethacin was intercalated into LDH gallery instead of degraded in the synthesis process. Finally, the release profiles of indomethacin anion from its LDH-INDO performed in a PBS at pH 4.8 ± 0.02 and pH 7.4 ± 0.02 are shown in the Figure6. The curves obtained indicate that in this pH range, release of indomethacin from LDH-INDO is approximately of 90% and 51%

  when compared SAL + SAL, SAL + LDH and SAL + LDH-INDO with LPS + LDH-INDO. Also, there was a decrease when SAL + LDH-INDO group was compared to LPS + SAL and LPS + LDH on the day 3. From day 4, all LPS groups increased food intake when compared to day 1. Furthermore, LPS groups exhibited a significant weight loss during the experiment in comparison with control groups (p < 0.0001). However, LPS + LDH-INDO regained weight in day 3 and day 5 when compared with day 0 and day 1, respectively. Whereas LPS + INDO recovered weight only in day 5 in relation to day 1 and day 2, suggesting that LDH-INDO was able to recover the animals more quickly than pure INDO. Therefore, these data suggest that LPS induced a sickness behavior in animals, characterized by decreased appetite and weight loss and treatment with pure INDO and LDH-INDO appear recovery the variables worked in this study. Effects of LDH-INDO on the microglial activation in the somatosensory cortex and hippocampus of rats undergoing LPS-induced neuroinflammation. Microglial activation of somatosensory cortex and hippocampus were performed by immunohistochemistry of anti-Iba1. With regard to the somatosensory cortex, an equivalent number of Iba1 + cells were observed in all control groups, which expressed prolonged and narrow branches, and a reduced somatic size (Figure 9A-L). In the LPS + SAL (Figure 9M-O) and LPS + LDH (Figure 9S-U) groups there was intense microglial activation in the cerebral parenchyma, with qualitative elevation of number of microglia.These presented a slightly branched phenotype, with thickening of the projections and increased somatic size, when visually compared to control groups, respectively, (SAL + SAL and SAL + LDH), suggesting intense microglial activation.The LPS + INDO (Figures9P-R) and LPS + LDH-INDO (Figures9V-Y) exhibited discrete cortical microgliosis. However, these cells presented phenotypic characteristics of a quiescent morphology with reduced cell body and thin projections, although a discrete presence of microglia with activated pattern in the cortex is still visualized, suggesting control of the neuroinflammation induced by LPS.In relation to the hippocampus, control groups were qualitatively observed a number of Iba1 + cells decreased with predominance of quiescent pattern (Figure10A-L).In LPS + SAL (Figure10M-O) and LPS + LDH (Figure 10S-U) groups there were progressive microglial activation (microgliosis), with apparent increase in the number of Iba1 + cells, presenting a phenotypic morphology of activation stage, marked by increase in the somatic body, compared to the SAL + SAL, and decrease in thickening of projections. LPS + INDO (Figure 10P-R) and LPS + LDH-INDO (Figure 10V-Y) presented microgliosis, characterized by visible increase of positively labeled cells. However, these cells have characteristics of a quiescent morphology, with a decrease in soma size and narrowing of the projections, suggesting control of the inflammatory process induced by LPS.

  this work, INDO anion was intercalated into a LDH by a simple coprecipitation method. This anion incorporated within the LDH interlayer space (LDH-INDO) were analyzed by XRD, FT-IR, TGA-DSC, and UV-vis. The spectroscopic studies (FT-IR and UV-vis solid) confirmed that only the unprotonated form of INDO intercalates within the basal spacing. The structural study of the LDH-INDO complex showed that the intercalation of INDO only affects the interlayer area without major modification in the brucite-type layers. Moreover, the results showed an intercalation percentage of 50% (w/w) of the INDO anion intercalated within the LDH interlayer space. The in vivo assays performed on male Wistar rats revealed that INDO groups had shown a faster recovery, the LDH-INDO outcomes was more effectivity and quickly than pure INDO against the inflammatory process induced by LPS. Our results also highlight the importance of LDH as optimal way for as a drug carrier.

Figure captions Figure 1

 captions1 Figure captions

Figure 2

 2 Figure 2 IR spectra of LDH, (B) pure INDO and (C) LDH-INDO complex.

Figure 3

 3 Figure 3 TGA (A) and DSC (B) curves of the LDH, indomethacin pure (B) and nanohybrid (LDH-INDO).

Figure 4

 4 Figure 4 FE-SEM images of LDH and LDH-INDO complex.

Figure 5

 5 Figure 5 Solution UV-Vis spectra of INDO pure (A), released INDO from the LDH-INDO (B). Solid-state UV-Vis spectra of the neutral INDO (C) and LDH-INDO complex (C).

Figure 6

 6 Figure 6 Release profiles for indomethacin from the LDH-INDO complex at pH 7.4 and pH 4.8.

Figure 9

 9 Figure 9 Photomicrography of immunohistochemistry with anti-iba1 antibody for microglia in the somatosensory cortex rats treated with indomethacin free or intercalated in model of neuroinflammation induced by LPS. Cresyl violet staining in the somatosensory cortex. (A-C) SAL + SAL, animals controls. (D-F) SAL + INDO, control animals treated with indomethacin. (G-I) SAL + LDH, control animals treated with LDHempty. (J-L) SAL + LDH-INDO, control animals treated with LDH-indomethacin. (M-O) LPS + SAL, animals injected with LPS and treated with saline solution; (P-R) treated with indomethacin (LPS + INDO); (S-U) treated with LDH-empty (LPS + LDH-empty) or (V-Y) treated with LDH-indomethacin (LPS + LDH-INDO). Arrows in O, U, R pointing microglia on stage activated and arrowheads in C, F, I, L, R, Y point to quiescent microglia. A, D, G, J, M, P, S, V: scale of 1000 μm. B, E, H, K, N, Q, T, X: 100 μm scale. C, F, I, L, O, R, U, V: 50 μm scale.

Figure 10

 10 Figure 10 Photomicrography of immunohistochemistry with anti-iba1 antibody for microglia in the hippocampus rats treated with indomethacin free or intercalated in model of neuroinflammation induced by LPS. Cresyl violet staining in the somatosensory cortex. (A-C) SAL + SAL, animals controls. (D-F) SAL + INDO, control animals treated with indomethacin. (G-I) SAL + LDH, control animals treated with LDH-empty. (J-L) SAL + LDH-INDO, control animals treated with LDH-indomethacin. (M-O) LPS + SAL, animals injected with LPS and treated with saline solution; (P-R) treated with indomethacin (LPS + INDO); (S-U) treated with LDH-empty (LPS + LDH-empty) or (V-Y) treated with LDHindomethacin (LPS + LDH-INDO). Arrows in O, U, R pointing microglia on stage activated and arrowheads in C, F, I, L, R, Y point to quiescent microglia. A, D, G, J, M, P, S, V: scale of 1000 μm. B, E, H, K, N, Q, T, X: 100 μm scale. C, F, I, L, O, R, U, V: 50 μm scale.

  in which treatment with INDO was not able to attenuate the microglial activation in the cortex of mice twenty-four hours after a single injection of LPS, we qualitatively show that groups treated with INDO, both pure INDO and LDH-INDO, was able to decrease microglial activation. As INDO acts by non-selectively blocking COX activity by reducing the production of prostaglandins, here we observe a decrease in LPS-induced neuroinflammation through the administration of INDO (pure INDO and LDH-INDO), by decreasing the activation profile of the microglia cells. We suggest that the breakdown of BBB homeostasis caused by LPS occurs via a COX-dependent pathway. In addition, we suggest that INDO may modulate transcription factors in the microglia, inducing these cells to reduce the release of proinflammatory cytokines. Although INDO and LDH-INDO has shown better effects than free-INDO, we

Table captions Table 1 .

 captions1 Chemical compositions of the synthesized samples, correlation coefficient (R 2 ) obtained by fitting indomethacin release data from LDH-INDO nanohybrid into phosphatebuffered saline at pH 7.4 and pH 4.8, and amount INDO -anions intercalated into LDH.

Table 2

 2 Experimental design.

Table 2 Groups Injectable administration Administration by gavage

 2 SAL: saline solution; b INDO: indomethacin; c LDH: layered double hydroxides matrix; d LDH-INDO: indomethacin anion intercalated into layered double hydroxides matrix in saline; e LPS: lipopolysaccharide.

	Number
	of animals

a

Acknowledgments

This work was supported by the Brazilian agencies CAPES, MCTI/CNPq (nº 42466220167), FAPESPA and PROPESP/UFPA. The work was also partially supported by the French CAPES-COFECUB program (Ph-C 780-13). The authors would like to thanks PhD P. Thomas, PhD P. O. Masson and PhD J. Jouin, for their valuable contributions in this work. Finally, the authors also acknowledge the use of the facilities at Laboratoire IRCER/Université de Limoges-CNRS, Labnano-Amazon/Federal University of Pará and Toxicology Laboratory/Evandro Chagas Institute.