MD Catia Duarte
email: catiacmduarte@gmail.com

PhD Tore K Kvien

PhD Joe Sexton

PhD Eduardo Santos

PhD Maarten De Wit

PhD Laure Gossec

PhD J A P Da Silva

Affiliations

Cátia Duarte

in disease activity and impact

Keywords: rheumatoid arthritis, patient-reported outcomes, Patient Experienced Symptom State, Responsiveness, Reliability

Introduction:

The Patient Experienced Symptom State (PESS) is a singlequestion, patient-reported outcome which is validated to assess global disease impact in rheumatoid arthritis (RA). This study addresses its sensitivity to change and reliability.

Material and Methods: Disease activity, disease impact in the 7 domains of RAID and PESS were assessed in patients with RA from the NOR-DMARD registry, at two visits, six-months apart. The PESS over the last week was scored in five levels, from "very bad" to "very good".

Disease impact and disease activity were compared between patients who improved, maintained or worsened PESS over time, through one-way analysis of variance (ANOVA), with post-hoc Bonferroni correction. Correlations between changes in these parameters were assessed through Spearman´s correlation coefficient. Sensitivity to change was assessed by standardized response mean (SRM) between the two visits. Reliability was analyzed through intraclass correlation coefficient between the two visits in patients with stable disease activity and impact.

Results:

In 353 patients (76.8% females, 9.9(9.6) years disease duration), patients who improved their PESS level was associated with substantial improvements in mean impact in all domains as well as disease activity.

(p<0.02). PESS change was moderately to strongly correlated with RAID domains and disease activity (rho: 0.4-0.7). PESS was responsive to change (SRM:0.65, 95%CI: 0.54-0.76), particularly among RAID responders (SRM:1.79, 95%CI: 1.54-1.99). PESS was moderately reliable in patients with stable condition (ICC: 0.72, 95%CI: 0.52-0.83).

Conclusion:

PESS is valid, feasible, reliable and responsive, representing an opportunity to improve the assessment of disease impact with minimal questionnaire burden.

Introduction

The current treatment paradigm for Rheumatoid Arthritis (RA) is typified by the treat-to-target strategy and its recommendation that disease activity should be monitored and drug therapy adjusted regularly so as to achieve and maintain a state of remission or low disease activity.(1-3) Such strategies, combined with novel therapeutic agents, have made remission a realistic goal for a majority of patients.

However, the progressive improvement of disease activity under treatment is not always paralleled by similar benefits on the patient's experience of the disease. While physicians and researchers focus on "biological markers" of disease, such as joint counts and inflammatory markers, the experience of the patients is dominated by pain, function and fatigue, among other factors. [START_REF] Fautrel | Call for action: how to improve use of patient-reported outcomes to guide clinical decision making in rheumatoid arthritis[END_REF]. This contributes frequently to the disagreement between patients and physicians regarding disease status [START_REF] Brites | Patient-Physician discordance in assessment of disease activity in Rheumatoid Arthritis patients[END_REF][START_REF] Furu | Discordance and accordance between patient's and physician's assessments in rheumatoid arthritis[END_REF][START_REF] Kaneko | Discordance in global assessments between patient and estimator in patients with newly diagnosed rheumatoid arthritis: associations with progressive joint destruction and functional impairment[END_REF][START_REF] Studenic | Discrepancies between patients and physicians in their perceptions of rheumatoid arthritis disease activity[END_REF]. It is also reflected by the finding that as many of 45 to 60% of patients with RA who are otherwise in remission still endure significant disease impact as reflected by a Patient Global Assessment of Disease Activity (PGA) >1. [START_REF] Ferreira | The patient global assessment in RA precludes the majority of patients otherwise in remission to reach this status in clinical practice. Should we continue to ignore this?[END_REF][START_REF] Ferreira | Revisiting the use of remission criteria for rheumatoid arthritis by excluding patient global assessment: an individual meta-analysis of 5792 patients[END_REF] PGA is actually a measure of disease impact, rather than of disease activity. [START_REF] Ferreira | Drivers of patient global assessment in patients with rheumatoid arthritis who are close to remission: an analysis of 1588 patients[END_REF] These incongruencies hinder patientphysician communication and shared-decision making, cornerstones of personcentred care. [START_REF] Ferreira | Dual-target strategy: fostering personcentered care in rheumatology[END_REF] The patient's perspective must, indeed, remain central to disease management. [START_REF] Ferreira | Dual target strategy: a proposal to mitigate the risk of overtreatment and enhance patient satisfaction in rheumatoid arthritis[END_REF] A wide spectrum of Patient Reported Outcomes (PRO) is available and validated in RA, both generic and specific of domain. [START_REF] Van Tuyl | Patient-Reported Outcomes in Rheumatoid Arthritis[END_REF] Some of these instruments are difficult to understand by patients as they address abstract concepts hard to relate to real life challenges and goals. [START_REF] Steed | A Critique of Coping Scales[END_REF][START_REF] Ferreira | It can't be zero!' Difficulties in completing patient global assessment in rheumatoid arthritis: a mixed methods study[END_REF] Furthermore, covering all the domains of rheumatoid arthritis impact with specific instruments would be too time-consuming for real-world practice. [START_REF] Hiligsmann | The use of routinely collected patient-reported outcome measures in rheumatoid arthritis[END_REF] In this context, feasible instruments and strategies that allow for a better understanding of the patient's perspective and for improved communication are important to foster better patient outcomes and are, thus, an ethical imperative. [START_REF] Fautrel | Call for action: how to improve use of patient-reported outcomes to guide clinical decision making in rheumatoid arthritis[END_REF] We argue that a two-step approach combining a generic tool to screen for patients who perceive high impact followed by a more detailed evaluation, offers an attractive strategy for the clinical setting. [START_REF] Duarte | Treating-to-target in rheumatology: Theory and practice[END_REF] PGA is the most frequently used PRO in rheumatoid arthritis and is part of validated indices to assess disease activity and define remission. [START_REF] Aletaha | Acute phase reactants add little to composite disease activity indices for rheumatoid arthritis: validation of a clinical activity score[END_REF][START_REF] Aletaha | Definition of treatment response in rheumatoid arthritis based on the simplified and the clinical disease activity index[END_REF][START_REF] Aletaha | rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative[END_REF][START_REF] Van Riel | The development of the disease activity score (DAS) and the disease activity score using 28 joint counts (DAS28)[END_REF] This question might be a natural choice to assess the patients perception, if we take it as a measure of disease impact.(11) However, patients report difficulties in scoring PGA as a continuous variable, mainly due to its subjective nature. [START_REF] Ferreira | It can't be zero!' Difficulties in completing patient global assessment in rheumatoid arthritis: a mixed methods study[END_REF][START_REF] Hirsh | Limited Health Literacy and Patient Confusion About Rheumatoid Arthritis Patient Global Assessments and Model Disease States[END_REF][START_REF] Renskers | Comparison of the construct validity and reproducibility of four different types of patient-reported outcome measures (PROMs) in patients with rheumatoid arthritis[END_REF] Also its scoring is affected by health literacy and background culture. [START_REF] Ferreira | It can't be zero!' Difficulties in completing patient global assessment in rheumatoid arthritis: a mixed methods study[END_REF][START_REF] Ferreira | Suppressing Inflammation in Rheumatoid Arthritis: Does Patient Global Assessment Blur the Target? A Practice-Based Call for a Paradigm Change[END_REF] Recently, our group explored the potential of a new tool to assess the patients´ global perception of their status regarding RA: the Patient Experienced Symptom Status (PESS). This question, derived from the Patient Acceptable Symptom State (PASS), ( 27) is composed of a single question on the patient's satisfaction with their current symptoms, offering a five-level Likert scale response (Very-Bad, Bad, Acceptable, Good and Very-Good). In a crosssectional study, PESS was shown to be easy to apply (feasible), valid and well correlated not only with other measures of disease impact, but also with disease activity. Patients who rate their status as Good or Very Good in PESS have disease activities in the range of low disease/remission and low or absent impact of disease as measured by the Rheumatoid Arthritis Impact of Disease score (RAID) and its individual domains (RAID.7).( 28) Moreover, PESS is meaningful for patients and easy to understand and has a single formulation available, which are important strengths over PGA. However, the longitudinal validity of PESS, i.e. its responsiveness and reliability, is yet to be assessed. [START_REF] Beaton | Instrument Selection Using the OMERACT Filter 2.1: The OMERACT Methodology[END_REF] With this study we aimed to evaluate PESS longitudinally, assessing its reliability and sensitivity to change and, especially, its ability to identify patients in need of a more detailed evaluation of impact.

Material and Methods

Study Design and Patients

Data from the Norway DMARD (NOR-DMARD) registry,(30) a five-centre prospective longitudinal register that includes consecutive consenting patients with inflammatory rheumatic joint diseases, were analysed. The NOR-DMARD registry has been approved by the Norwegian Data Inspectorate and Regional Ethics Committee of Eastern Norway. All patients provided written informed consent.

We selected all patients with a clinical diagnosis of RA (fulfilling the ACR 1987 and/or the ACR/EULAR 2010 classification criteria), [START_REF] Aletaha | rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative[END_REF][START_REF] Arnett | The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis[END_REF], included in the NOR-DMARD registry, who started a new biological (first or switch) or targeted synthetic therapy between 2012 and 2020 and were able to fill in the questionnaires. Only patients with available data regarding PESS, RAID and disease activity at baseline and 6 months of follow-up, were analysed.

Outcome of Interest

The Patient Experienced Symptom State (PESS) was assessed through the following question "Consider how your rheumatic disease has affected you during the last week. If you remain in the coming months as you have been the last week, how would you rate your condition?" offering a five-level Likert scale response (Very Bad, Bad, Acceptable, Good and Very Good).( 28) PESS was evaluated at baseline (start of a biological agent) and after 6 months.

Data Collection and handling

Socio-demographic and clinical variables included gender, age, educational level, disease duration, and registered comorbidities of interest (chronic low back pain, malign neoplasia and cardiovascular disease) were considered. Swollen (SJC) and tender 28-joint counts (TJC) performed by rheumatologists or research nurses, erythrocyte sedimentation rate (ESR, mm/h), C-Reactive Protein (CRP, mg/dL) and medication were recorded. PGA ("Considering all the way that your illness and health conditions affect you at this time, how do you feel?") and Physician Global Assessment of Disease Activity (PhGA) were scored through a visual analogic scale (VAS) 0-100 mm (where 0 corresponds to the best possible status and 100 to the worst).

Disease activity was categorized according to the 3-variable Disease Activity Score 28 (DAS28-3vESR): High disease activity (HAD) >5.1; Moderate Disease Activity if >3.2 and ≤ 5.1; Low disease activity (LDA) if ≥2.6 and ≤3.2 and Remission <2.6. Patients with DAS28-3v improvement from baseline >1.2 or >0.6 plus at least moderate disease activity at 6 months of follow-up were classified as responders according to the EULAR criteria. [START_REF] Van Riel | The development of the disease activity score (DAS) and the disease activity score using 28 joint counts (DAS28)[END_REF] Simplified Disease Activity Index (SDAI) and Clinical Disease Activity Index (CDAI) [START_REF] Aletaha | Acute phase reactants add little to composite disease activity indices for rheumatoid arthritis: validation of a clinical activity score[END_REF] were also used to assess and categorize disease activity.

Established thresholds for remission, low, moderate, and high disease activity were considered for each index. Patients with an improvement of at least 50% from baseline were considered responders. [START_REF] Aletaha | Definition of treatment response in rheumatoid arthritis based on the simplified and the clinical disease activity index[END_REF] Patient-perceived impact of RA was evaluated through the RAID score [START_REF] Gossec | Elaboration of the preliminary Rheumatoid Arthritis Impact of Disease (RAID) score: a EULAR initiative[END_REF][START_REF] Gossec | Finalisation and validation of the rheumatoid arthritis impact of disease score, a patientderived composite measure of impact of rheumatoid arthritis: a EULAR initiative[END_REF] at every visit: patients were asked to score the intensity of the impact of their disease in each of seven domains (pain, fatigue, physical function, sleep disturbance, emotional and physical well-being and coping). Each domain was assessed on a 0 to 10 numerical rating scale and a combined score was No imputation of missing data was performed. Longitudinal construct validity and reliability were analysed according to the OMERACT filter.( 29)

Longitudinal Construct Validity

To assess longitudinal construct validity, we first analysed if changes in PESS over time reflect changes in disease impact and activity. Patients were classified as "PESS worsened", "PESS stable" or "PESS improved" according to change of at least one PESS category between the two visits. The mean changes in disease activity scores, RAID score and individual items of RAID in the three groups were compared through one-way analysis of variance (ANOVA), with post-hoc Bonferroni correction for pairwise comparisons.

Furthermore, the correlation between the degree of improvement in each of the individual domains of impact and the degree of change in PESS (number of categories) was analysed through Spearman´s correlation coefficient.

Correlations from 0.3 to 0.5 were considered weak, moderate from 0.5 to 0.7, and strong if >0.7.

Responsiveness of PESS was also assessed by the standardised response mean (SRM) (mean change in scores / SD of the change scores). SRMs were categorized as large (>0.80), moderate (0.5-0.80) and small (0.2 to 0.5).( 36) Furthermore, we compared the change of PESS in EULAR and RAID responders. SRM was calculated and compared for both groups, with an a priori hypothesis that it would be higher in the responder group. For responsiveness, patients considering themselves in "very good" level at baseline were excluded from analyses.

Reliability

Reliability of PESS over time was evaluated between the 2 visits in the subgroup of patients with stable disease activity and impact, defined as -0.6<ΔDAS283vESR<0.

Performance of PESS to detect responders

The ability of an improvement of at least one PESS category to identify RAID "responders" was evaluated through a two-by-two cross-tabulation and calculation of the Crude Agreement given by (("True positive" + "True Negative")/ Total of patients)) and through Kappa statistics (k). k values ˂ 0 were considered poor, 0-0.20 weak, 0.21-0.40 fair, 0.41-0.60 moderate, 0.61-0.80 good and 0.81-1.00 excellent. [START_REF] Mchugh | Interrater reliability: the kappa statistic[END_REF]. Sensitivity, Specificity, Negative and Positive Predictive Value were also calculated. A similar analysis was performed for responders according to the different scores of disease activity.

For these purposes, patients considering themselves in "very good" level at baseline were excluded from analyses.

Ability of PESS to detect patients with high disease impact despite remission or LDA

The levels of impact, according to RAID.7 associated with different PESS states in patients who achieved at least LDA in the last visit were analysed and compared through the non-parametric Kruskall-Wallis test (<Acceptable, Acceptable, > Acceptable).

For all analyses, only patients with available data at baseline and follow-up were included. Statistical analysis was performed using the SPSS® software, version 24 and MedCalc. Statistically significant results were assumed when p<0.05 or p<0.02 in case of Bonferroni correction for pairwise comparisons use.

RESULTS:

In total, 353 patients were included in the analyses (Table 1). Three-quarters were women (76.8%), with a mean (S.D.) age of 51.9 (13.9) years, and most had longstanding disease [mean (S.D.) 9.9 (9.6) years)]. 96.6% were receiving a bDMARD (Table 1). Included and excluded patients were similar regarding socio-demographic characteristics, disease duration and disease activity at baseline.

Over the six months of follow-up, the mean SDAI score decreased from 18.1±12.7 to 8.1±5.5. The proportion of patients in high or moderate disease activity (SDAI) decreased from 68.2% to 25.1% while the proportion of those in remission or LDA increased from 31.8% to 74.9%. The mean global RAID score decreased from 4.0±2.2 to 2.9±2.1, with similar changes being observed in all 7 domains of impact (Table 1) .

Regarding PESS, the proportion of patients in Good/Very Good status increased during the six months of follow-up from 18.9% to 42.5%. At the end of follow-up 54.1% of the patients improved, while 33.1% maintained the same status and 12.7% of the patients worsened. (Table 2)

Longitudinal Construct Validity

Patients with improved, stable and worsened PESS over time, differed significantly in terms of change in disease activity, and change in combined RAID and each of the RAID.7 domains. Patients who improved their PESS category over time described, on average, a marked reduction of the impact in all domains as well as in disease activity. Patients who got worse in PESS reported an increased impact in all domains in comparison with baseline, despite there being a small reduction in disease activity. (Figure 1) Changes in PESS, in disease activity and in each of the measures of impact were significantly correlated (Figure 2). Moderate to strong correlations were observed (rho=0.7) between PESS and RAID combined score, RAID-Pain, Function and Physical well-being as well as PGA. Correlations were moderate with RAID-Fatigue (rho=0.6) and RAID-Emotional Well-Being and Coping (rho=0.5). The weakest correlations (rho=0.4), but still significant, were found for RAID-Sleep Disturbance and Disease Activity (Supplementary table 1).

PESS was sensitive to change, with an overall SRM of 0.65 (95%CI: 0.54 to 0.76). PESS was clearly more responsive among EULAR (disease activity) responders than non-responders (SRM:0.91 vs 0.26, respectively).

Responsiveness was higher for impact, with SRMs of 1.79 among RAID responders vs 0.26 in non-responders.

Overall responsiveness measured by SRM was similar for PESS and PGA (SRM: 0.63, 95%CI:0.51 to 0.74). Among RAID responders the performance was better for PESS (SRM: 1.79, 95%CI: 1.54 -1.99 vs 1.65, 95%CI:1.42 -1.85). Among EULAR responders, PESS and PGA showed similar responsiveness (0.91, 95%CI:0.74 to 1.03 vs 0.90, 95%CI: 0.71 to 1.04)).

Reliability

Among patients with a stable condition (-0.6<ΔDAS283vESR<0.6 and -2<ΔRAID<2) (n=78), around 50% remained in the same PESS level during the follow-up period and 6 patients had a change >1 level (Table 3.) PESS was moderately reliable in this sample according to ICC (0.72, 95% CI: 0.52-0.83).

Performance of PESS in detecting responders and non-responders

The performance of an improvement of at least one PESS category to identify RAID (non-)responders is presented in table 4. The diagnostic accuracy ("True positive" + "True Negative" rates)/ Total of patients) was 73%. Over 90% of patients who were RAID responders were also PESS responders (sensitivity), specificity is low: 37.8% of RAID non-responders were PESS responders.

Conversely, NPV indicates that over 90% of those who did not improve at least one level in PESS were also RAID non-responders (NPV).

A similar analysis was performed considering disease activity responders. As expected, the diagnostic accuracy (65.4-70.2%) was lower than for impact, with around 70% of responders being also PESS responders. Among different responder criteria considered, the performance was lower for EULAR response criteria.

Ability of PESS to detect patients with high disease impact despite remission or LDA

Among patients who achieved low disease activity or remission at 6 months (n=248), 40 maintained a PESS state of Very Bad or a Bad. This reflects high disease impact in the seven domains of RAID (Supplementary Figure 1), with majority of patients scoring above 5 in most domains.

PESS good or VG, is generally associated with low impact, with levels of RAID.7 scores 2 in at least 75% of patients for most domains, with exception of fatigue and emotional well-being, which have slightly higher impact.

Discussion:

This study brings important information for research and especially for clinical management of patients with RA. Our results demonstrate that PESS is very simple to use but also reliable and sensitive to change in patients with RA.

PESS was responsive to treatment and improvement in PESS status over time is associated with a significant improvement in mean impact in all seven domains of RAID and, to a lesser degree, also of disease activity. This confirms that PESS is primarily a measure of disease impact. Correlations with PESS are stronger for the domains of RAID typically associated with disease activity and of highest priority for patients: pain, function and physical well-being. [START_REF] Heiberg | Preferences for improved health examined in 1,024 patients with rheumatoid arthritis: pain has highest priority[END_REF] Our study also indicates that PESS is reliable, as it remains stable over time in steady conditions of disease activity and impact. PESS also showed a very good performance in screening for patients who do not respond to treatment in terms of impact. The ability to detect responders and non-responders regarding disease activity is smaller, but this is not the intended use of PESS.

Taken together with our prior observations, [START_REF] Duarte | The Patient Experienced Symptom State (PESS): a patient-reported global outcome measure that may better reflect disease remission status[END_REF] these results demonstrate that PESS, is feasible, valid, reliable and responsive to change, thus fulfilling all requirements of the OMERACT filter. [START_REF] Beaton | Instrument Selection Using the OMERACT Filter 2.1: The OMERACT Methodology[END_REF] Our results are supported by the methodological robustness of the study with emphasis on its longitudinal design, large sample size and the observation of significant change of both disease activity and disease impact during follow-up. This is reinforced by the pragmatic clinical practice setting, without constraining inclusion criteria, stiff protocols or dedicated explanations to patients or physicians. No specific guidance was given to clinicians regarding their prior knowledge of PROs when examining the patient and scoring the PhGA. This probably varied among clinicians and centres. It is unlikely that this may have affected the PhGA and other scores of disease activity, since PhGA is mainly influenced by objective aspects of the disease and only slightly affected by impact. [START_REF] Desthieux | Patient-Physician Discordance in Global Assessment in Rheumatoid Arthritis: A Systematic Literature Review With Meta-Analysis[END_REF] We believe, in any case, that this variability provides welcome representation of reality in regular clinics, as opposed to protocolized clinical trials. The stronger sensitivity to changes in impact rather than disease activity reinforces face validity while incorporating the strong connections between these two dimensions when departing from high disease activity. The stronger associations with the physical domains of impact are in line with evidence that these are more modifiable/responsive to immunosuppressive treatment than social and psychological measures. [START_REF] Duarte | Validity and reliability of the EULAR instrument RAID.7 as a tool to assess individual domains of impact of disease in rheumatoid arthritis: a cross-sectional study of 671 patients[END_REF][START_REF] Gossec | Improvement in patientreported outcomes after rituximab in rheumatoid arthritis patients: An open-label assessment of 175 patients[END_REF][START_REF] Smolen | Patientreported outcomes from a randomised phase III study of baricitinib in patients with rheumatoid arthritis and an inadequate response to biological agents (RA-BEACON)[END_REF][START_REF] Boyce | Impact of tofacitinib on patient outcomes in rheumatoid arthritis -review of clinical studies[END_REF] Some limitations should be considered. Patients were recruited in academic centres from a single country (Norway), thus calling into question the generalizability of the results. In fact, sociodemographic, clinical and cultural factors have been shown to influence patients' perception of impact [START_REF] Ferreira | Impact of Patient's Global Assessment on Achieving Remission in Patients With Rheumatoid Arthritis: A Multinational Study Using the METEOR Database[END_REF][START_REF] Hifinger | In rheumatoid arthritis, country of residence has an important influence on fatigue: results from the multinational COMORA study[END_REF][START_REF] Santos | Determinants of happiness and quality of life in patients with rheumatoid arthritis: a structural equation modelling approach[END_REF][START_REF] Duarte | Attainment of the Patient-acceptable Symptom State in 548 patients with rheumatoid arthritis: Influence of demographic factors[END_REF] and of disease activity, [START_REF] Ferreira | Impact of Patient's Global Assessment on Achieving Remission in Patients With Rheumatoid Arthritis: A Multinational Study Using the METEOR Database[END_REF][START_REF] Putrik | Lower education and living in countries with lower wealth are associated with higher disease activity in rheumatoid arthritis: results from the multinational COMORA study[END_REF] suggesting that our results need external validation in other cohorts. The relatively long interval between the two assessments (6 months as opposed to the usual 2 weeks) may have had a negative effect on the assessment of reliability. A significative proportion of patients were in remission, or at least low disease activity at baseline, which may not reflect clinical practice elsewhere. Only one third of patients with RA in NORDMARD were analysed, essentially due to missing data. Excluded patients were not different regarding socio-demographic aspects, disease duration and disease activity at baseline (data not shown). However, health literacy or health care adherence, which may have affected the filling of PROs, were not evaluated, thus opening an opportunity of selection bias that deserves consideration.

We believe that our findings are relevant for clinical practice as they show that PESS provides a dependable summary view of the disease impact and the patient's perspective by means of a single, simple and easily understood, question. The use of the PESS will facilitate communication between patients and physician, potentially increasing their mutual agreement and providing a better support for the shared decision-making process. [START_REF] Barton | English language proficiency, health literacy, and trust in physician are associated with shared decision making in rheumatoid arthritis[END_REF][START_REF] Voshaar | Patient-centred care in established rheumatoid arthritis[END_REF] We believe that PESS is preferable to PGA as a measure of disease impact for several reasons. Different formulations of the question used to assess PGA are available which are not interchangeable, as they are interpreted and scored differently by patients. [START_REF] Ferreira | Influence of the different "patient global assessment" formulations on disease activity score by different indices in rheumatoid arthritis[END_REF][START_REF] Gossec | Phrasing of the patient global assessment in the rheumatoid arthritis ACR/EULAR remission criteria: an analysis of 967 patients from two databases of early and established rheumatoid arthritis patients[END_REF] The designation of PGA of disease activity is misleading to both patients and health care providers, affecting their decisions. Distinguishing between 2 levels of PESS is certainly more meaningful to patients than separating the abstract concepts conveyed by numbers in PGA, making PGA more difficult to understand and, thus, less reliable. [START_REF] Ferreira | It can't be zero!' Difficulties in completing patient global assessment in rheumatoid arthritis: a mixed methods study[END_REF] Furthermore, a given score of PGA does not indicate whether the patient is or not personally satisfied with their current condition and to what degree. Additionally, PESS refers to remaining in a given status over time, which is certainly relevant in conveying the patient's perspective in chronic diseases.

PESS could also facilitate and foster the use of PROs in clinical practice as an integral part of disease evaluation and management. We propose that it might be used as a screening tool to quickly detect patients who have significant burden of the disease and, thus, deserve a detailed domain-driven evaluation, especially after the disease activity has been brought under control. A patient who does not reach at least a "good" PESS level despite disease remission or LDA should be selected for a discriminative assessment of the causes of persistent impact, so that adequate adjunctive measures can be considered and tailored. A similar approach might be considered in clinical trials. This would provide a simple and reliable evaluation of the overall satisfaction of the patients with the treatment results and allow for the needed assessment of unmet needs from the patient's perspective. Naturally, the conditions of practice may advise the application of RAID, without PESS, to all patients in some clinical settings. [START_REF] Duarte | Treating-to-target in rheumatology: Theory and practice[END_REF] In conclusion, PESS showed good psychometric properties, making it suitable to be used in clinical practice and research to assess treatment success from the perspective of the patient, serving person-centred care. Further validation of PESS in other cohorts of RA is warranted.

  derived according to the proposed algorithm. Both the combined RAID score and the individual domain scores (RAID.7) (34) were considered. Based on Minimum Clinically Important Improvement defined for RAID, patients were classified as impact responders if the change observed in the RAID score between baseline and 6 months of follow-up was ≥3 in absolute change or >50% of relative change.(35)Statistical analysesDescriptive characteristics were presented as means [±Standard Deviation (SD)] for continuous variables and as proportions (%) for categorical variables.

  6 and -2<ΔRAID<2. Reliability was assessed through the Intra-Class Correlation Coefficient (ICC, two-way mixed model absolute agreement) with 95% CI. An ICC >0.8 was considered indicative of excellent reliability.[START_REF] Fleiss | The Equivalence of Weighted Kappa and the Intraclass Correlation Coefficient as Measures of Reliability[END_REF]

Table 1 : Socio-demographic and clinical characteristics of 353 patients included at start of bDMARDs and at 6 months of follow-up

 1 : synthetic disease Modifying Anti-rheumatic Drugs; bDMARD: biotechnological disease Modifying Antirheumatic Drugs; tsDMARDs: targeted synthetic disease Modifying Anti-rheumatic Drugs DAS28-ESR-3v, Disease Activity Score-Erythrocyte Sedimentation Rate-3 variables; SDAI, Simplified Disease Activity Index; CDAI, Clinical Disease Activity Index; PGA: Patient Global Assessment of Disease Activity; RAID, Rheumatoid Arthritis Impact of Disease. Values are mean ±SD, unless stated otherwise.

	Characteristic	Baseline	6 months follow-up
	Female, n (%)	271 (76.8)	--
	Age, years	51.9±13.9	--
	Disease duration years	9.9±9.6	--
	Glucocorticoids intake n (%)	161 (45.6)	119 (33.7)
	csDMARDs n (%)	275 (77.9)	275 (77.9)
	bDMARD intake, n (%)	341 (96.6)	341 (96.6)
	tsDMARD intake, n (%)	12 (5.4%)	12 (5.4%)
	Disease activity, DAS 28-	3.1±1.5	2.7±1.2
	3vESR
	High n (%)	69 (19.5)	10 (2.8)
	Moderate n (%)	165 (46.7)	95 (26.9)
	Low n (%)	48 (13.6)	59 (16.7)
	Remission n (%)	71 (20.1)	189 (53.5)
	SDAI (n=302)	18.1±12.7	8.1±5.5
	High n (%)	61 (20.2)	14 (4.6)
	Moderate n (%)	145 (48)	62 (20.5)
	Low n (%)	81 (26.8)	127 (42.1)
	Remission n (%)	15 (5.0)	99 (32.8)
	CDAI (n=302)	17.0±12.2	7.5±8.1
	High n (%)	73 (24.2)	23 (7.6)
	Moderate n (%)	139 (46)	58 (19.2)
	Low n (%)	76 (25.2)	117 (38.7)
	Remission n (%)	14 (4.6)	104 (34.4)
	PGA (0-100)	43.3±26.8	27.5 25.7
	RAID Score (0-10)	4.0±2.2	2.9±2.1
	Pain (0-10)	4.0±2.5	3.0±2.3
	Function (0-10)	4.0±2.6	2.7±2.1
	Fatigue	4.2±2.6	3.1±2.6
	Sleep Disturbance (0-10)	3.5±2.9	2.5±2.6
	Emotional Wellbeing (0-10)	3.4±2.5	2.6±2.3
	Physical Wellbeing (0-10)	4.2±2.5	3.4±2.4
	Coping (0-10)	3.1±2.4	2.2±2.3

csDMARDs

Table 2 . Distribution of patients per PESS category at baseline and 6 months of follow-up.

 2

	Follow-up	Very Bad	Bad	Acceptable	Good	Very Good	Total
	Baseline
	Very Bad	2	10	6	2	4	24
	Bad	7	47	62	30	21	167
	Acceptable	0	16	34	34	11	95
	Good	1	5	8	21	11	46
	Very Good	0	2	3	3	13	21
	Total	10	80	113	90	60	353

Table 3 . PESS scores at baseline and 6 months in patients with stable disease regarding both disease activity (DAS28-3vESR |0.6|)3 and Impact (RAID score |2|).

 3

	Follow-up	Very Bad	Bad	Acceptable	Good	Very Good Total
	Baseline
	Very Bad	1	2	0	0	0	3
	Bad	3	12	8	2	0	25
	Acceptable	0	3	10	10	2	25
	Good	1	1	2	8	4	16
	Very Good	0	0	0	0	9	9
	Total	5	18	20	20	15	78

Table 4 . Performance of PESS improvement ≥1 as a predictor of RAID and Disease activity response.

 4

		EULAR	CDAI	SDAI	RAID
		Responder	Responder	responder	Responder
	Crude	65.4	70.0	70.2	73
	Agreement
	Kappa	0.3	0.3	0.3	0.5
	Sensitivity	68.1	72.1	72	93.2
	Specificity	60.7	66.4	67.2	62.2
	PPV	74.9	76.8	77.9	57.6
	NPV	52.5	70.8	60	94.3
	+LHR	1.7	2.1	2.2	2.46

RAID Responder: Δ combined RAID score ≥3 and/or ≥50%. EULAR Responder according EULAR response criteria; CDAI Responder: ΔCDAI≥50%; SDAI Responder: ΔSDAI≥50%; PPV: Positive Predictive Value, NPV: Negative Predictive Value, +LHR: Positive Likelihood Ratio. CDAI: Clinical Disease Activity Index; SDAI: Simplified Disease Activity Index; RAID: Rheumatoid Arthritis Impact of Disease.

this study was conducted according the Declaration of Helsinquia. The NOR-DMARD registry has been approved by the Norwegian Data Inspectorate and Regional Ethics Committee of Eastern Norway. All patients provided written informed consent.

Funding: No specific funding was received from any funding bodies in the public, commercial or not-for-profit sectors to carry out the work described in this manuscript. NORDMARD receive funding from Abbvie, Amgen, BMS, MSD, Novartis,

The data underlying this article will be shared on reasonable request to the corresponding author.


S J Wolk

A K Dupree

H M Günther

Céline Reylé

Victor Sanchez Gimenez

Nadège Lagarde

The 20.5th Cambridge Workshop on Cool Stars, Stellar Systems, and the Sun

Ultra-cool dwarfs (UCDs) serve as a link between the lowest mass stars and the brown dwarfs. Gaia DR2 revealed thousands of ultra-cool dwarfs not identified as such before. In this work, we searched for companions to those in the Gaia catalogue, from their common proper motions and parallaxes. We found about 2,300 stars in multiple systems, about 1,300 of them being UCDs out of the original 16,500 sample. Among the companions, there are several tens of young stellar objects and white dwarfs, but also new M7-L2 candidates. Such a sample can be used to better understand the binary properties of UCDs and to better characterize the physics of UCDs thanks to the characterization of higher mass companions.

Introduction

Ultra-Cool dwarfs (UCDs) are the lowest-mass, coldest, and faintest products of star formation, making them difficult to study. They were defined by Kirkpatrick et al. (1997) as objects with spectral types M7 and later, through L, T, and Y types, have masses 0.1 M , and effective temperatures 2700 K. UCDs are of particular interest since they include both very low-mass stars that slowly fuse hydrogen, and brown dwarfs, which have insufficient mass (below about 0.075 M ) to sustain hydrogen fusion in their cores, and cool down with time. These objects are nevertheless abundant (> 20% of all stars) and are key probes in Galactic studies. Moreover, they are known to host exoplanets, and are ideal targets for searches for potentially habitable terrestrial planets (such as the recent discoveries around TRAPPIST-1, Gillon et al. 2017).

The Gaia mission Gaia Collaboration et al. (2016) offers the means to uncover nearby UCDs through astrometric, rather than purely photometric, selection. On its completion, Gaia should provide high precision astrometry and multiepoch photometry for over 1000 L-and T-type dwarfs, and potentially tens of thousands of late M dwarfs [START_REF] Sarro | [END_REF]. Reylé (2018) Nearly 14,000 previously non characterized UCDs candidates have been identified. These are evenly distributed across the sky, filling in "missing" populations in the Southern hemisphere and Galactic plane.

Yet they remain elusive due to their low brightness, and the modeling of their cold and complex atmosphere, and in particular the onset of dust formation, remains challenging. Binaries with UCDs are expected to be relatively common, with binary systems containing M dwarfs accounting for 26.8% of the M dwarf population in the solar neighbourhood according to (e.g. Winters et al. 2019). Systems with a higher mass and well characterized primary are benchmarks to constrain the atmospheric parameters and ages of the UCDs (e.g. Marocco et al. 2020). Improving our knowledge of binary systems including UCDs can bring constraints on their physics and formation mechanisms.

In this work we use the sample of UCDs revealed by Gaia and search for companions from their common parallax and proper motion to investigate their binarity. The sample and the method are described in Sect. 2. The results are given in Sect. 3. Sect. 4 gives the conclusions and perspectives.

Sample and method

We started from a sample of 16,526 UCDs selected from the precise Gaia DR2 colour absolute magnitude diagram (see Fig. 1), using the data filtering and selection described by Reylé (2018).

We query the Gaia DR2 archive1 [START_REF] Salgado | Gaia DR2 documentation Chapter 13: Construction of the archive[END_REF]) for companion stars matching the following criteria:

ρ < 100 ; ∆ < max[1.0, 3σ ]; ∆µ < 0.1µ; ∆θ < 15 • (1)
Where ρ is the query radius, equivalent to a maximum projected separation of 100,000 AU from the source star (a conservative upper limit according to Caballero ( 2009)), ∆ is the parallax range in the LOS direction, ∆µ is the difference in proper motion and ∆θ is the angular difference between proper motion vectors. These criteria are discussed in Smart et al. (2019).

This search results in 3,445 stars in multiple systems. However the method used is likely to find a non-negligible amount of by chance aligned stars. Since no radial velocity measurements are available for most of the stars nor can we calculate the mass of the system in an accurate enough manner to ensure it is gravitationally bound, we must find an alternative way of assessing the probability of a star being a binary. We thus follow a similar method for Bayesian analysis as described by Hartman & Lépine (2020). In order to compute the bayesian probabilities, we perform a "real" and a "spurious" search. The first one is done within a radius of 1 from the original UCD positions at a parallax of ± max [1, 3σ ]. The second one is done the same way but shifting the search by 4200 arcsec in declination. Proper motion difference and separation distributions are then computed in both cases and used to defined the difference Bayes probability of being a true or false binary with proper motion difference and separation as priors.

The selection of stars with probability > 0.8 of being binary returns 1,534 stars in systems. Adding all stars in higher order systems, we finally get 2,332 stars.

Results

Multiplicity frequency

Our sample contains 2,332 stars in systems out of which 1,286 are UCDs distributed in 926:182:178 binary, tertiary and higher order systems, respectively. This yields to a multiplicity rate of 7.8% for the UCDs. In comparison, Winters et al. (2019) searches for companions to M0 to M9.5 dwarfs including close pairs and finds a multiplicity rate of 26.8%. Since our sample is devoid of close binaries and we do not search for companions to the brighter M0 to M6 dwarfs, it is reasonable to expect our sample to exhibit a lower multiplicity rate.

We also compared this result with the expected UCD binarity from the Gaia Universe Model Snapshot (Robin et al. 2012, GUMS), a simulator developped for Gaia, and based on a stellar population synthesis model of the Galaxy. We retrieved stars within 100 pc with similar positions in the colour absolute magnitude diagram as our initial UCD sample. 6.2% are in wide binary systems (orbital radius larger than 500 AU. In comparison, our sample shows 5.5% of UCDs being wide binaries, which is consistent with the GUMS result since our Figure 2: Distribution of logarithmic projected separation before (grey) and after (blue) filtering binaries with Bayes probability less than 0.8. The ultra wide binaries are dominated by chance aligned pairs. initial sample is probably not complete due to the strict filters applied by Reylé (2018) in order to get robust candidates.

Projected separations

Fig. 2 shows the projected separation distribution, in astronomical unit, of the binary systems, before (in grey) and after (in blue) the filtering from the bayesian analysis.

It can be divided in two groups: wide binaries (< 10 4 AU, herafter WB), and ultra-wide binaries (> 10 4 AU, hereafter UWB). The distribution for WB follows a similar trend as the one shown by Hartman & Lépine (2020) or Arenou et al. (2018), with increasing binary number as separation decreases, up to some limit likely due to limiting resolution. In terms of angular separation distribution our sample shows a sharp drop at separation < 6 arcsec. This is higher than the 0.4 arcsec limiting resolution found by Arenou et al. (2018), because our sample is dominated by rather faint stars. The change of distribution in the WB and UWB regimes might indicate a different formation mechanism for WB and UWB (see e.g. Tokovinin 2017).

Spectral type distribution

We used 190,000 objects with spectral type from a SIM-BAD2 (Wenger et al. 2000) query to define a spectral type calibration from the median absolute magnitude M G , and used it to estimate the spectral types of the companions. The distribution of primaries and secondaries (and higher order multiplicity objects) is shown in Fig. 3. The secondaries distribution peaks at M7 due to the selection of our starting UCDs sample. The primaries distribution peak around M3 since there are the most numerous stars in the Galaxy.

Fig. 4 shows the systems in the colour absolute magnitude diagram. The lowest spectral type found is a L3 dwarf which might be a wide companion to the already known binary system LP 395-8 A-B at a projected separation of 42,000AU. The total sample contains 57 L type dwarfs (30 L0, 20 L1, 5 We also found 48 white dwarf companions to UCDs. 65% of these systems has a separation lower than 10 4 AU, compared to 48% for the full sample. This is reasonable since wide and ultra-wide binaries are relatively instable and easily disrupted, old systems are less likely to have survived. White dwarf and UCD binaries are unlikely to have formed as binaries (from processes that may result in wide binaries such as core fragmentation), a likely mechanism of formation for these systems is through cluster dissolution (see for instance Kouwenhoven et al. 2010).

Out of all the multiple systems, four were found to belong to star forming regions, with at least one Young Stellar Object (hereafter YSO) candidate in each group. They lie on the right part of the main sequence. A group of twelve stars are found in the Barnard 59 cloud in the Pipe Nebula. Large parts of this region are obscured by dust so it is likely that more sources are missing. The next most populated group with YSOs is located in the Lupus I cloud and has eight members in our sample.

Conclusion

UCDs represent a high percentage of the stars in our neighbourhood, yet they are largely undiscovered because of their typically faint magnitudes. Multiple systems with UCDs offer additional constraints which make it possible to calculate physical properties of each star from orbital dynamical parameters. Since wide systems with UCDs are expected to origin from different formation mechanisms than systems with smaller separations, studying wide and ultra-wide systems can give additional hints on the formation mechanisms of such stars.

Starting from a sample of 16,526 UCDs, we found 1,286 belonging to a multiple system. Most of them were unknown prior to Gaia DR2, and 85 UCDs, not included in our original UCD sample, are found to be companions.

These systems are divided into wide and ultra-wide systems according to their projected separations and follow different distributions with projected separation which are consistent with expectations from literature.

We will confront our findings with recent similar searches (Gaia Collaboration et al. 2020b; El-Badry et al. 2021) using Gaia EDR3. We started spectroscopic follow-up of these pairs (see Low et al. 2021) to learn UCDs physical properties from the higher mass primary.

  ; Smart et al. (2019); Scholz (2020); Gaia Collaboration et al. (2020b); and others have undertaken the systematic searches of UCDs in Gaia second (Gaia DR2, Gaia Collaboration et al. 2018) and Early Third (Gaia EDR3, Gaia Collaboration et al. 2020a) data releases.

Figure 1 :

 1 Figure 1: Colour absolute magnitude diagram from Gaia DR2 of 3,716,407 stars with reliable astrometric and photometric solutions, avoiding Galactic regions with high extinction, and after removing stars showing color excess using G-J (Reylé 2018). The locus of the UCDs is indicated by the blue rectangle. It is defined from the expected position based on a sample of spectroscopically characterized UCDs.

Figure 3 :

 3 Figure 3: Spectral type distribution of the system components.

Figure 4 :

 4 Figure 4: Colour absolute magnitude diagram of the stars in systems. The colour indicates the primary spectral type (white dwarfs are in grey) and the size indicates the secondary spectral type.

https://gea.esac.esa.int/archive/

http://simbad.u-strasbg.fr 2 Zenodo, 2021

Acknowledgments

This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/ web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This work was supported by the "Programme National de Physique Stellaire" (PNPS) of CNRS/INSU co-funded by CEA and CNES.


Inga Tijunelyte

Jeffrey Teillet

Paul Bruand

Rémi Courson

Aurélie Lecestre

Pierre Joseph

Aurélien Bancaud
email: abancaud@laas.fr

§ Affiliation

Hybridization-based DNA

Keywords: Electrohydrodynamic concentration, fluorescence sensor, microfluidics, grayscale lithography

teaching and research institutions in France or abroad, or from public or private research centers.

INTRODUCTION

The worldwide Covid-19 crisis has brought nucleic acid biosensing technologies, particularly the quantitative real-time reverse-transcriptase polymerase chain reaction (qRT-PCR), to the headlines. Enzymatic amplification technologies are the most sensitive with reported limits of detection (LoD) of ~10 3 copies of viral RNA per milliliter of body fluids 1,2 , or equivalently attoMolar concentrations. Considerable efforts have been made to reduce the total analytical time down to 30 minutes, including the purification of nucleic acids prior to amplification 3 . These impressive achievements have complicated the development of competitive amplification-free technologies 4 , which nevertheless remain a driving research direction in the field of bioengineering 5,6 . Amplification-free detection technologies can be roughly cast into two categories depending on whether detection occurs in bulk or on surfaces. Commonly based on mass, electrical, or optical index variation, surface detection strategies reach fM performances 7,8 . The highest sensitivities are generally obtained with nanosensors, which are not competitive in terms of time to response due to the overly long target:probe interaction kinetics limited by diffusion 9 . Using an electric field to drive the targets towards the sensor electrophoretically showed a good compromise between the sensitivity of 30 fM and detection time of 15 minutes 10 . The trade-off is usually the opposite in bulk assays given that nucleic acids detection is rapid but the sensitivity is low. For instance, using a molecular beacon (MB) probe, which changes conformation and becomes fluorescent upon hybridization to the target, the LoD of 1 nM can be reached within 20 to 60 min 11 . However, this approach usually leads to a low dynamic range due to the background signal from unreacted MBs 12 . Strand displacement reactions have been integrated into the detection scheme to associate DNA hybridization events with a subsequent cascade of detectable readouts 13,14 . These assays attained sub-pM to sub-fM LoDs [15][16][17][18] , though generally also at the expense of a long time to result 19 . In another yet non-mutually exclusive direction, a compromise between fast and sensitive detection has been reached with concentration modules. Using conventional MB and isotachophoresis for concentration, an LoD of 5 pM in 3 minutes has been reported [20][START_REF] Bercovici | Proc. Natl. Acad. Sci[END_REF][START_REF] Persat | [END_REF] . In line with this biosensing technology based on a concentration module, we recently developed the µLAS technology (µ-Laboratory for DNA analysis and separation), which enables DNA size separation, concentration, and purification in a microchannel using bi-directional electrohydrodynamic actuation [23][24][25][26] . This technology has been exploited to detect nucleic acids by the selective concentration of the complex formed between the target molecule and a complementary MB 27 . We reported an LoD of 2 pM within 30 seconds. Here, after a description of the operating conditions of the technology, we enhance its biosensing performances by optimizing the microfluidic chip geometry and the target:probe size settings. Specifically, 2.5D microchannels are fabricated by direct grayscale lithography (GSL) to produce funnel shaped geometries with non-uniform width and height. GSL has already been used in microfluidics to obtain complex free-standing shapes 28 or design programmable gradients 29 , valves 30 , and model porous systems 31 , but to the best of our knowledge, it has not been applied to design biosensing systems. An LoD of 4 fM in 30 seconds is achieved with these GSL chips. This performance is comparable to the level detection achieved by nanosensors 7,8 , though the production of our devices is greatly simplified. Moreover, it constitutes a good trade-off between sensitivity and time to result since detection at 0.1 fM typically takes 10 minutes.

MATERIALS AND METHODS

Materials and reagents

Chemicals were purchased from Signa-Aldrich, unless mentioned. The neutral polymer polyvinylpyrrolidone (PVP; MW=1.3 MDa) was dissolved in water at 6% (w:v) concentration. This stock solution was mixed with PBS (Dulbecco's Phosphate buffer saline) to obtain a PVP concentration of 5% and 22 mM of NaCl. The viscosity and viscoelastic relaxation time of the solution were 31 mPa.s and 1.1 ms, respectively, as evaluated using the technique described in 32 .

Note that (i) PVP was chosen due to its minimal shear-thinning behavior 33 , and (ii) the addition of salt allowed us to increase the fluorescence signal in comparison to electrophoresis buffers (Supplementary Fig. S1). Prior to use, all solutions were filtered with 0.2 µm filters.

Genomic material

Four oligonucleotides were obtained from Eurogentec (Belgium), dissolved in Tris-HCl 10 mM and EDTA 1 mM (pH = 7.5) at a stock concentration of 100 µM, and stored at -20°C:

2D and 2.5D microfluidic chips

The microfluidic chips with a uniform height of 2, 4, or 6 µm were obtained by conventional photolithography with the protocol described in 23 . Grayscale lithography chips were processed after improving the protocol of 31 . The silicon wafer was first submitted to 800 W O2 plasma for 5 min to promote the adhesion of a 1.1 µm layer of the photoresist AZ ECI 3012 (Microchemicals GmbH) spin-coated at 3600 rpm with an acceleration of 5000 rpm/s. Exposure was operated by laser lithography (Dilase 750, Kloé SA) using a fixed 405 nm laser diode and translating the wafer along lines. The laser beam was focused by a 10X objective (numerical aperture, NA=0.3) to produce a spot diameter of 20 µm. The 100 mW laser beam was attenuated to 10 %, and further modulated by increments of 0.1 % in the range from 10 to 50% (Supplementary Fig. S2). After exposure, the resist was baked at 126 °C for 5 min and finally developed in MFCD-26 solution for 30 s. The maximal and minimal depths of the resulting 2.5D structures were 0.5±0.09 and 1.05±0.07 µm, respectively (Supplementary Fig. S2). These patterns were eventually transferred into silicon by plasma etching, producing gradients from 1.9±0.2 to 5.2±0.1 µm. The etching selectivity, as defined by the ratio of silicon to photoresist transferred depth, was 5 for a plasma etching process time of 22 min.

Fabricated chips were then placed in a custom chip support with inlet and outlet reservoirs sealed by O-rings (Supplementary Fig. S3). The reservoirs contained 0.5 mm diameter platinum wires (GoodFellow) as electrodes. Actuation was operated with a 7 bar pressure controller (Fluigent) and a DC high voltage power supply (Labsmith). Before each experiment, microfluidic chips were filled with ethanol to eliminate air bubbles, then flushed with milli-Q water, and subsequently filled with the PVP working buffer. Finally, the sample solutions containing genomic material were introduced into the chip, and analysis was carried out for one minute. The chip was extensively rinsed with the PVP working buffer between each analysis.

Microscopy and image analysis

Data were acquired using Leica fluorescence microscopes equipped with light engines for cyan excitation (Lumencore), and sCMOS digital cameras from Hamamatsu. 20X air objective (NA=0.8) was used. For time-lapse video acquisitions, the inter-frame interval was set to 2 s with an exposure time of 0.1 s and a binning of 4x4 (512*152 pixels of 1.3 µm in size). The resulting videos were processed using the Fiji software 34 . The position of stagnation of the cloud of concentrated molecules was inferred by measuring the distance to the constriction of its center of mass. In order to extract the LoD, we used the method described in ref. 27 . Briefly, we recorded 10 images before the application of the electric field, and used the corresponding intensity signal to define the background and its standard deviation. For titration, we first applied a 4x4 binning filter, and then measured the fluorescence signal next to the constriction using a region of interest (ROI) selected as a 200 µm line of 5 µm thickness. The fluorescence intensity profile was fitted with a Gaussian to retrieve the maximum intensity. Finally, the maximum intensities were plotted as a function of concentration, and the LoD was obtained by extrapolation of the signal to two times the standard deviation of the background. Data fitting was performed using power-law and Gaussian functions in Igor-Pro.

COMSOL simulations

Finite element simulations were run with COMSOL Multiphysics 5.6. We used AC/DC electric current and Computational Fluid Dynamics modules assuming laminar flow. The fluid conductivity was set to 0.24 S/m and the viscosity to 31 mPa.s. Boundary conditions consisted of electrical isolation and no flux at the lateral walls of the chip (see drawing in Supplementary Materials). An electric potential difference of 100 V and a pressure difference of 1 bar were set between the inlet and outlet to compute the maximum electric field and flow velocity.

RESULTS & DISCUSSION

Operation of the concentration module

In a 2D microfluidic funnel of maximal and minimal width W and w, respectively (Fig. 1A), the hydrodynamic flow velocity at the centerline of the channel typically ranges from 𝑣 0 𝑚𝑎𝑥 × 𝑤/𝑊 to 𝑣 0 𝑚𝑎𝑥 , according to mass conservation laws, and similarly, the electric field spans 𝐸 𝑚𝑎𝑥 × 𝑤/𝑊 to 𝐸 𝑚𝑎𝑥 . Given that 𝑣 0 𝑚𝑎𝑥 is typically 50 mm/s (Table 1), the Reynold number is ~0.01 and the flow is laminar. The use of electric fields neither induces detectable temperature change in the silicon chip nor does it alter DNA hybridization reactions, as we showed in ref. 27 .

The funnel shape is reminiscent of the geometry of dielectrophoretic trapping modules [35][36][37] .

Our actuation strategy is however very different because we apply continuous electric fields and dielectrophoretic trapping is most frequently based on high-frequency electric fields. Notably, a few contributions described DNA concentration by Direct Current insulator-based gradient dielectrophoresis [38][39][40] , but the size of funnels of less than 100 nm and the elimination of hydrodynamic forces to achieve concentration cannot be compared to our experimental settings.

Thus, we claim that DNA trapping in our module is enabled by a transverse force 𝐹 𝑉𝐸 perpendicular to the flow direction and oriented toward the upper and lower walls (shown in the (xz) plane of Fig. 1A). We use a salty and viscoelastic solution (see Methods), implying that 𝐹 𝑉𝐸 arises from the variation of the normal stress in the gradient direction 41 , rather than from hydrodynamic interactions around DNA created by the electric field 42 . As we showed in ref. 43 , this force 𝐹 𝑉𝐸 can be expressed analytically with a linear dependence on the hydrodynamic and electric field 𝑣 0 and

E: 𝐹 𝑉𝐸 ⃗⃗⃗⃗⃗⃗ (𝑧)~- 𝑁 ℎ × 𝑣 0 × 𝐸 × (1 -2 𝑧 ℎ ) × 𝑢 𝑧 ⃗⃗⃗⃗ (1)
with z the distance from the lower wall in Fig. 1A, N the size of the molecule, ℎ the channel height and 𝑢 𝑧 ⃗⃗⃗⃗ the unit vector. Note that this transverse force depends on the equilibrium end-to-end distance of the chain N because hydrodynamic interactions are screened out in viscoelastic solutions and the Rouse model applies, as shown in ref. 43 . Fluctuations of the length of the molecules during their migration are discussed in ref. 41 . Eq. ( 1) is valid for molecules far from the wall because it is derived by the reflection method 43 . In ref. 43 , we suggested an extrapolation for the force profile, considering that additional repulsive forces, including electrostatic and/or shear-induced electroviscous forces 44 and/or lubrication 45 forces, balance the viscoelastic transverse force. The resulting effective force vanishes toward the wall, and may be approximated by a linear drop, following the expression:

𝐹 𝑤𝑎𝑙𝑙 ⃗⃗⃗⃗⃗⃗⃗⃗⃗ (𝑧)~-𝑁 × 𝑣 0 × 𝐸 × 𝑧 ℎ 2 × 𝑢 𝑧 ⃗⃗⃗⃗ (2)
Note that expression (2) is valid for the lower wall in Fig. 1A. Predictions based on this model have been successfully confronted to an extensive set of more than 300 data in ref. 43 .

Let us now define the regime of operation of the concentration module. Far upstream from the constriction, hydrodynamic and electrophoretic velocities are low, and the transverse force can be neglected. Molecules reach the concentration module if the average flow velocity is greater than the electrophoretic velocity, equivalently 𝑣 0 𝑚𝑎𝑥 > 2𝜇 0 𝐸 𝑚𝑎𝑥 with 𝜇 0 the electrophoretic mobility.

The sector represented in green in Fig. 1B corresponds to 𝑣 0 𝑚𝑎𝑥 < 2𝜇 0 𝐸 𝑚𝑎𝑥 . As molecules migrate toward the funnel, electrophoretic and hydrodynamic velocities increase, and so does 𝐹 𝑤𝑎𝑙𝑙 . The concentration module operates if the regime of migration is dominated by electrophoresis at the apex of the funnel. Applying Boltzmann statistics to the transverse force in Eq. ( 2), we infer the average distance of the molecules to the wall 〈𝑧 𝐷𝑁𝐴 〉 that scales as (𝑁𝑣 0 𝐸/ℎ 2 ) -0.5 . Linearizing the expression of the flow velocity near the bottom wall 4𝑣 0 𝑚𝑎𝑥 〈𝑧 𝐷𝑁𝐴 〉 ℎ ⁄ , the condition of dominant electrophoretic migration at the apex reads

𝑣 0 𝑚𝑎𝑥 < 𝑁𝜇 0 3 16 × 𝐸 𝑚𝑎𝑥 3 (3)
If this condition is unfulfilled, the migration is dominated by hydrodynamics everywhere in the concentration module, and the corresponding sector is depicted in red in Fig. 1B. In the light blue sector, hydrodynamic forces prevail over electrophoresis at the entry of the funnel whereas electrophoresis is predominant at the tip of the apex. Hence, one position of balanced electrophoretic and hydrodynamic forces exists along the funnel, and hybridized DNA molecules, continuously conveyed toward, accumulate at this stagnation position. Moreover, unbound probes, which have a smaller molecular weight than the target:probe complex, undergo transverse forces of lower amplitude. They can thus be flushed out, and selective target detection is achieved, as was demonstrated in ref. 27 . Notably, the resulting theoretical diagram is in qualitative agreement with that reported in ref. 46 13.8 7.9 5.9 12.0 v0 max (mm/s) (@ 5 bar) 57 (@ 2 bar) 61 (@1 bar) 51 (@5 bar) E max (kV/cm) 23 (@ 290 V) 20 (@ 290 V) 12 (@290 V) 17 (@190 V)

Optimization of the concentration module

The phase diagram in Fig. 1B can be investigated experimentally by applying a constant electric field and gradually increasing hydrodynamic forces, or using a constant pressure and increasing the electric field (orange and purple arrows in Fig. 1B, respectively). We first report an experiment performed in a chip of 2 µm in height (Fig. 2A), in which 𝑣 0 𝑚𝑎𝑥 is set to 30 mm/s and 𝐸 𝑚𝑎𝑥 is increased stepwise. For each step indicated by the green dashed lines in Fig. 2A, the position of stagnation shifts away from the constriction (see the selected snapshots recorded at times t1, t2 and t3). Below 21.5 kV/cm, each electric field step is associated with an increase in the maximum intensity (Fig. 2B). At 22 kV/cm and above, the maximum intensity signal decreases, but the total fluorescence signal still rises (Supplementary Fig. S4). This experiment shows that the concentration module is optimal for a set of electrohydrodynamic actuation parameters, which correspond to 𝐸 𝑚𝑎𝑥 of ~21 kV/cm with this microchip geometry. The result is confirmed by the mirror experiment that consists in increasing the pressure at constant electric field while recording fluorescence intensity in the funnel (Fig. 2C; this corresponds to the orange arrow in the diagram of Fig. 1B). The signal is initially null in the concentration module because the migration is dominated by electrophoresis. Fluorescence intensity then increases to reach an optimum for 𝑣 0 𝑚𝑎𝑥 of ~2.3, 2.9, and 3.1 cm/s depending on the salt concentration in the solution, and decreases due to the leaks associated with a regime of concentration dictated by prevalent hydrodynamic forces.

The kinetics of molecular accumulation can be used as a readout to optimize the performance of the concentration module. The inset of Fig. 2B shows that the typical timescale of concentration increases from 1 to 10 s as 𝐸 𝑚𝑎𝑥 increases from 16 kV/cm to 21 kV/cm, respectively. The saturation of the signal after the concentration phase is explained by the occurrence of velocity fluctuations around the stagnation position, as described in ref. 41,43 . For a low electric field, the stagnation position is close to the apex, and fluctuations are likely to disengage T:P from the concentration module. The onset of the electric field forces T:P complexes to shift away from the constriction, reducing their chances to escape, and in turn, increasing their residence time in the module. Hence, the electric field allows us to increase the time of concentration and enhance the detection signal, making a connection between kinetics and performance.

Long targets and short probes enable high sensitivity detection

The technology is expected to concentrate high MW targets more rapidly because they undergo larger transverse forces and can thus be captured with a higher hydrodynamic flow for a given electric field (See Eq. ( 2)). This hypothesis has been tested by measuring the concentration of four different targets of increasing MW of 44, 98, 114, and 120 nt (see the definition of genomic sequences in Methods). Using the same electrohydrodynamic actuation associated to 𝑣 0 𝑚𝑎𝑥 and 𝐸 𝑚𝑎𝑥 of 30 mm/s and 23 kV/cm in a microfluidic chip of 2 µm in height, the maximum intensity after 60 s of actuation increases with the size of the target (Fig. 3A). Moreover, the spatial distribution of concentrated targets becomes more distant from the apex with the size of the target, as indicated by the orange arrow in the snapshots of Fig. 3B. Moreover, the concentration kinetics become slower (Fig. 3C), in agreement with our discussion on the relationship between the time of concentration and the accumulation position (see above). Interestingly, the peak fluorescence signal is enhanced by 27-fold between the 120 and 44 nt target (black and purple curves in Fig. 3A, respectively), and the difference in signal is consistently observed for a broad range of electric fields, as reported in Supplementary Fig. S5.

These experiments also show that the difference of size between the target and the probe is critical to achieve high sensitivity and selective signals. Indeed, the signal of a single strand of 98 nt is 37 % lower than that of a complex formed by pairing 22 nt SP with 92 nt LT, the so-called target of 114 nt (blue and green curves in Fig. 3). Conversely, we do not detect any signal for the 22 nt probe under these actuation conditions (not shown), and collect the maximum signal with the 120 nt complex. Consequently, the concentration module works optimally with a long target of more than ~100 nt and a short hybridization probe of less than ~30 nt.

Grayscale microfluidic chips to enhance the LoD by 100-fold

In our recent study on short targets of 25 nt, we reported an LoD of 2 pM in microchips with a channel height of 2 µm 27 . Here, using the same microfluidic chip, we reach a limit of detection 10-fold enhanced of 0.2 pM with the long target and short probe (blue dataset in Fig. 4A, see details of calculation in Methods). In order to enhance these performances, we aimed to increase the flow rate to accumulate more DNA complexes during the concentration process. For this, we fabricated microfluidic chips of 4 and 6 µm in height. The hydraulic resistance of the chips indeed scales as ℎ -3 , implying that the flow rate sharply increases by 26-fold for a 3-fold increase of the channel vertical dimension (Table 1). The electric field is on the other hand less confined at the apex when the height increases, i.e. the gradient of voltage is more evenly distributed throughout the chip.

𝐸 𝑚𝑎𝑥 decreases by 2-fold as ℎ increases from 2 to 6 µm (Table 1). Notably, the scaling of the electrical resistance of ℎ -1 is not observed because the contribution of the thick lateral channel cannot be neglected (see size specifications and calculations in Supplementary Material). Electrohydrodynamic actuation parameters are specified in Table 1. For the kinetics in 2 µm chips, see 27 .

Using the maximum electric field of 290 V that the oxide layer could withstand, detection is achieved at relatively low pressures of 2 and 1 bar for h of 4 and 6 µm, respectively. Despite the possible application of more pressure (our system can deliver 5 bar), the low value of the electric field in these 2D chips (gray lines in Table 1) prevents us from taking advantage of the reduced hydraulic resistance. The resulting electrohydrodynamic actuation parameters 𝑣 0 𝑚𝑎𝑥 and 𝐸 𝑚𝑎𝑥 are comparable to that used in the 2 µm chip (Table 1). The kinetics of concentration is rapid with a plateau after ~4 s (Fig. 4B-C), indicating a leaky regime due to the insufficient strength of the electrophoretic force. The LoD of 4 and 5 pM determined for chips of 4 and 6 µm, respectively, i.e. an order of magnitude higher than the one obtained with 2 µm chips (Fig. 4A). Albeit the enhanced flow rate in thick microfluidic chips, the electric field becomes limiting to reach a regime of efficient concentration.

In order to deliver high flow rates and high electric fields, we used GSL to produce a channel with a height gradually decreasing from 5.2 to 1.9 µm (Fig. 5A). This geometry indeed enables us to increase 𝑣 0 𝑚𝑎𝑥 by 40% in comparison to the 2 µm chip (Table 1). Furthermore, the spatial extent of the concentration region, as measured by the intensity profile along the symmetry axis of the channel (red arrow in Fig. 5A), can be significantly reduced in GSL chips. Setting 𝑣 0 𝑚𝑎𝑥 to 15, 56, and 51 mm/s, the full width at half maximum decreases from 67, 49 to 24 µm, respectively (light green, dark green, and black curves in Fig. 5B). Moreover, the width of the target band in the 4 µm and 2 µm chips (blue and orange datasets in Fig. 5B, respectively) of 135 µm compare unfavorably to that in the GSL chip. T:P concentration occurs in a region typically reduced by (135/24) 2 ~30 fold, allowing us to reach enhanced detection performances with an LoD of 4 fM (Fig. 5C) i.e.

three-fold lower compared to our previous report 27 . This performance is explained by the use of long targets and the design of GSL chips, which feature equal gains in sensitivity of 27 and 30fold, respectively. Furthermore, the dynamic range using the same electrohydrodynamic actuation settings is 5 decades (Fig. 5C), and the concentration kinetics are insensitive to the target concentration (Fig. 5D) with a time scale of 16 s, as inferred from exponential fitting. Notably, the global turnaround time of our assay, including target:probe incubation, sample loading in the chip, and image analysis is on the order of one hour. Nevertheless, our technology appears as a good trade-off between sensitivity and time to result. While a sub-fM level of detection can be achieved by nanosensors 7,8 , the response time is on the order of 20 minutes for nanopore, nanowire, cantilever, or surface acoustic wave sensors. Record performances, which feature an LoD of 10 -17

M in 4 minutes with quartz microbalance 47 , 10 -15 M in 30 s using nanowires 48 , or 10 -15 M in 4 minutes with nanopores 49 , compare relatively favorably with our technology.

Let us determine the number of complexes trapped in the concentration module. Given that the flow rate is 0.2 µL/min (Table 1), we expect to process a volume of ~0.1 µL after 30 s of concentration. Therefore, for a target concentration of 20 fM, ~1200 molecules are accumulated in the concentration volume. The background signal is produced by probes at a concentration of 20 nM, which amounts for 1800 molecules in the concentration volume of 23x3.5x1.9 µm 3 (see the characterization of the detection volume Supplementary Fig. S6). The readout signal is indeed equal to 1.8 times the background in our experiment (Supplementary Fig. S7). Taken together, we conclude that GSL chips enable us to enhance detection by nearly 100-fold with no compromise on the concentration kinetics and on the dynamic range, as well as with electric field settings of 190 V that are less prone to induce oxide breakdown.

DNA detection in biological samples (e.g. body fluids 50 ) raises complications due to the large amounts of background molecules, which lead to non-specific interactions, as well as to ionic conditions. We already studied whether and to what extent background species in purified and unsalted solutions impaired the detection of our technology 27 . Here, we further investigate that detection using GSL chips operates in unpurified plasma, i.e. with a salty solution as well as background protein and nucleic acids. Using equimolar concentrations of target and probes of 20 nM, we spike the running buffer with gradual amounts of plasma of 17, 50, and 100% (Supplementary Fig. S8). Detection is obtained in 17% and 50% plasma-containing solutions with a moderate to an important decrease of the signal by 30% and 60%, respectively. Detection was not achieved in 100% plasma samples due to oxide layer breakdown and irreversible damage of the chip. The salinity of biofluids is a harsh environment for silicon based microfluidic devices due to oxide insulating layer breakdown and to a lesser extent Joule heating. Nevertheless, our results

suggest that DNA detection can be operated in plasma provided that the sample is diluted typically five-fold before analysis.

Conclusion

Using GSL to produce an optimized module for nucleic acid concentration, we demonstrate that target DNA molecules of 100 nt or more can be detected with a complementary linear probe of less than 30 nt at an LoD of 4 fM in 30 s. These performances, which are enhanced by three orders of magnitude in comparison to our previous reports, are explained by (i) the use of a long target and short probe that accounts for a 10-fold improved sensitivity, and (ii) the narrow volume of concentration in GSL chips that enables us to obtain 100-fold enhanced signal to noise ratios.

As future steps, this technology for biosensing should be tested for concrete biological applications, most notably for cfDNA in oncology applications 50 , where background signals are expected to be significantly elevated. cfDNA biosensing could be first tested with abundant genes, such as ALU sequences which are present at concentrations of 60x10 9 copies/mL 50 , so as to delineate the difficulties associated with the use of biofluids and the detection of rare targets. Interestingly, detection sensitivity for rare targets can further be enhanced by integrating strand displacement reactions 15,16 with the concentration module. Whether the strand displacement reaction should be performed before concentration and detection or after concentration of the sample process remains to be clarified in light of the cost in analytical time.

Acknowledgments:

  Short probe (SP, 22 nt) 5'-6-FAM-TCA-ACA-TCA-GTC-TGA-TAA-GCT-A-3' Long probe (LP, 98 nt) 5'-6-FAM-A38-TCA-ACA-TCA-GTC-TGA-TAA-GCT-A38-3' Short target (ST, 22 nt) 5'-TAG-CTT-ATC-AGA-CTG-ATG-TTG-A-3' Long target (LT, 92 nt) 5'-A35-TAG-CTT-ATC-AGA-CTG-ATG-TTG-A-A35-3' 6-FAM corresponds to 6-carboxyfluorescein. Further in the text, we use four complexes, namely SP:ST, LP alone, SP:LT, and LP:ST, that we define as the targets of 44, 98, 114, and 120 nt, respectively. Nucleic acid samples were prepared as follows: (i) the stock of probe and target were diluted in 1X-DPBS at 1 µM; (ii) 1 µL of target and 1 µL of the probe were then mixed, heated at 92°C for 5 min, and cooled down to RT; (iii) PVP based buffer was added to obtain the final probe:target complex concentration. The solution was then extensively vortexed for three minutes and left at room temperature to homogenize for one hour. For calibration experiments, we used a constant probe concentration of 100 nM and diluted the target by consecutive increments of 10-fold before heating to obtain concentrations spanning 20 nM to 20 fM.

Figure 1 .

 1 Figure 1. Principle of the biosensing module. (A) The optical micrograph shows one microfluidic chip in the (xy) plane. The funnel maximum and minimal width W and w, respectively, are represented. The symmetry axis of the channel is highlighted by a red line, and the response of DNA molecules along this line in the (xz) plane is represented below. The hydrodynamic flow field is oriented along the x-axis and characterized by a Poiseuille profile in the xz plane (red arrow). The electrophoretic force is oriented in the opposite direction with a flat profile in the (xz) plane (green arrow). The scale bar corresponds to 200 µm. (B) Actuation parameter space of the concentration module as deduced from the model. Concentration occurs in the blue sector. Silicon oxide breakdown reduces the operating range in electric field (red dashed line), the maximum tension that can be applied in these

Figure 2 :

 2 Figure 2: Kinetic response and optimization of the concentration module. (A) The plot shows the distance of the center of mass of the target:probe (T:P) from the apex as a function of time. We apply a constant pressure associated with 𝑣 0 𝑚𝑎𝑥 of 30 mm/s and a range of electric fields from 0 to 22 kV/cm, as indicated with green vertical dashed lines. The three fluorescence micrographs correspond to snapshots at different time points. (B) The graph presents the maximum fluorescence intensity as a function of time. The inset shows the same fluorescence data normalized in intensity and registered in time for four different electric fields. (C) Normalized fluorescence intensity as a function of time as the pressure is gradually increased from 2 to 4.6 bar as indicated in the upper panel with 𝐸 𝑚𝑎𝑥 set to 20 kV/cm. The viscoelastic solutions were obtained by dissolving PVP in water, electrophoresis buffer, and 0.1X PBS.

Figure 3 .

 3 Figure 3. Optimization of target and probe size for biosensing. (A) The plot shows the concentration kinetics for a series of targets of increasing size of 44, 98, 114, and 120 nt (pink, blue, green and black dataset, respectively).

  𝑣 0 𝑚𝑎𝑥 is set to 30 mm/s and 𝐸 𝑚𝑎𝑥 is 23 kV/cm. (B) The fluorescence micrographs correspond to the signal recorded after 50 s of actuation for the 44, 98, 114, and 120 nt targets from bottom to top. The scale bar corresponds to 200 µm. (C) The graph reports the same curves as in (A) after normalization in intensity.

Figure 4 .

 4 Figure 4. Detection with microfluidic chips of different heights. (A) The plot shows the variation of the maximum intensity as a function of target concentration for a channel height of 2, 4, and 6 µm. The probe concentration is set to 100 nM. (B) The graphs represent normalized intensity as a function of time for the three target concentrations of 1, 10, and 100 nM using a microchannel of 4 µm in height. (C) Same as (B) for a channel height of 6 µm.

Figure 5 .

 5 Figure 5. Detection with GSL chips. (A) Optical micrograph of the GSL chip. The refraction-like pattern arises from the variation in channel height. (B) The graph presents the normalized intensity distribution along the symmetry axis of the channel (red arrow in A) after one minute of concentration for the 2 and 4 µm chips (orange and blue open rectangles, target concentration of 1 nM), as well as for the GSL chip using a tensionof 130, 160, and 190 V (light green, dark green and black dots, respectively, target concentration of 10 pM). Note that the peaks of the curves have been registered and centered at 0 for clarity. Solid curves are Gaussian fits. (C) The graph shows the maximum intensity signal after one minute of concentration as a function of the target concentration. The four fluorescence micrographs in the right correspond to snapshots after one minute of concentration (the concentration as indicated in legend). 𝑣 0 𝑚𝑎𝑥 is 51 mm/s and 𝐸 𝑚𝑎𝑥 17 kV/cm. (D) The plot presents the kinetic response of the GSL concentration module for the same five target concentrations as in (C).

  based on experimental measurements of high MW genomic DNA.

	Chip geometry	2 µm	4 µm	6 µm	GSL
	Hydraulic resistance (10 16 kg/m 4 s)	27.4	3.6	1.1	19.5
	Electric resistance (MOhm)

Table 1 .

 1 Electrohydrodynamic

parameters for the microfluidic chip geometries used in this study as deduced from finite element simulations (see Methods).

Data availability statement:

The data that support the findings of this study are available from the corresponding author upon reasonable request.

This work was partly supported by the LAAS-CNRS micro and nanotechnologies platform, member of the French RENATECH network. This project was partly supported by the ANR μLAS (ANR-16-CE18-0028-01), the ANR Biopulse (ANR-16-ASTR-0020), and the GRAINE-FEDER PURE-TECH grant.

Supplementary Material

See the supplementary material for details about the computation of hydrodynamic and electric fields, and data about (i) the effect of salt on target:probe intensity (S1), (ii) the fabrication protocol (S2-S3), (iii) the validation of the phase diagram of Fig. 1B (S4), (iv) the detection of targets of different sizes (S5), (v) the spatial characterization of the accumulation region in GSL chips (S6-S7), and (vi) biosensing using diluted and spiked plasma solutions (S8).