dataesr/scientific-paragraphs-categorization huggingface dataset:
Antifungal and antibiofilm activity of the Essential oil obtained from the leaves of B. halimifolia B. halimifolia leaves (Bh-L) were extracted by hydrodistillation to obtain the essential oil (EO) (yield: 0. 12 %) as they showed the highest antibiofilm activity and were known for their high EO content. [26] The antifungal activity and the activity against a mature biofilm of C. albicans of EO were thus evaluated. Unfortunately, Bh-L EO displayed a weak antifungal activity (MIC = 5 mg mL À 1 ) (data not shown). This result was in agreement with those reported for other species of Baccharis (B. trimera and B. oreophila) highlighting an inhibition of the growth of Candida strains only when the EO was used at the highest concentrations (IC 50 = 5289. 15 μg mL À 1 and MIC > 2500 μg mL À 1 against C.
Plates were incubated for 3 h at 37 °C without shaking. The absorbance at 492 nm was measured (Infinite M Plex absorbance reader, TECAN, Zürich, Switzerland). All conditions were performed quadruplicate in two independent experiments. For each tested concentration of every sample, the inhibition percentage was calculated according to the following formula: 100À ½ð100 � X absorbance of the treated group Þ=X absorbance of control � The IC 50 (concentration that inhibits 50 % of the biofilm) was determined using a non-linear regression graph obtained with GraphPad Prism 8 software (GraphPad, San Diego, CA).
Acknowledgements The authors would like to thank the staff of the Direction of the green spaces of the city of Poitiers and  Dr. Marion Millot  from  Limoges University  for the scientific discussions and for providing the plants. The authors wish also to thank  Nicolas Pipet  from the  Interdepartmental Institution of the Sèvre Niortaise River Basin  for the indication of the location of Baccharis and  Antoine Baudoin  for his technical support. The authors also thank  D. Debail  and  Dr. Herman Felani , S. S. , M. A. for revising the English text.
Funding This research was funded by  University of Poitiers and University of Limoges  ( AAP ARIC 2019 Research grant ) (France). This study was also supported by the 2015-2020 State-Region Planning Contracts (CPER),  European Regional Development Fund (FEDER) , and intramural funds from the  Center National de la Recherche Scientifique  and the  University of Poitiers (Poitiers, France ).
MRI Sequences A 7 Tesla MRI (Pharmascan 70/16, Bruker, Billerica, MA, USA) equipped with Par-aVision v6. 0. 1 software (Bruker BioSpin GmbH, Rheinstetten, Germany) was used in this study (see Supplementary Information for details). "T2-turbo RARE" sequences were used to obtain bioreactor images. For one bioreactor, two acquisitions were performed. Each acquisition consisted of 135 xy transverse slices (we remind that z-direction is parallel to the bioreactor axis) of 250 µm thickness and 55 µm 2 resolution. The second acquisition was a duplicate of the first one after a translation of 125 µm in the z-direction. The two acquisitions lasted 70 min. A 125 µm resolution in z-direction was then achieved by merging the two acquisitions. After a polynomial interpolation in z-direction, a 3D image with 55 µm resolution in the three directions was obtained.
Liquid entered the bottom of the main chamber via a ball valve and exited at its top. Experiments were typically performed with DPBS supplemented with 1% (mL mL -1 ) penicillin-streptomycin antibiotics. A total of 500 mL DPBS supply was well-stirred and in equilibrium with air. The whole setup was kept at 37 °C using a water bath. An optical fluorescent oxygen sensor (OptiOx, inLab Mettler-Toledo SAS, Viroflay, France) equipped with an acquisition unit (Seven2Go, Mettler-Toledo SAS, Viroflay, France) and Easy Direct Ph software (Mettler-Toledo SAS, Viroflay, France) was used to acquire every 6 s the dissolved oxygen concentration of the liquid phase in the main chamber of the bioreactor. The second chamber was filled with the crosslinked polysaccharidebased hydrogel to be characterized. The hydrogel volume was 𝑉 = 𝜋𝑒(𝑑 2 ⁄ ).
The sealing of the reactor was ensured by clamping systems with flat seals and fixing screws. Liquid circulated in the main chamber thanks to a peristaltic pump (AxFlow, Dublin, Ireland) and flexible hoses connected with 6 mm automatic push-in fittings (Parker Legris Hannifin Manufacturing France, Rennes, France). Liquid entered the bottom of the main chamber via a ball valve and exited at its top. Experiments were typically performed with DPBS supplemented with 1% (mg mL -1 ) penicillin-streptomycin antibiotics. A total of 500 mL DPBS supply was well-stirred and in equilibrium with air. The whole setup was kept at 37 • C using a water bath. An optical fluorescent oxygen sensor (OptiOx, inLab Mettler-Toledo SAS, Viroflay, France) equipped with an acquisition unit (Seven2Go, Mettler-Toledo SAS, Viroflay, France) and Easy Direct Ph software (Mettler-Toledo SAS, Viroflay, France) was used to acquire every 6 s the dissolved oxygen concentration of the liquid phase in the main chamber of the bioreactor.
4 mm). (B): The cumulative (number-average) distribution of spheroid diameters for an initial seeding of 600. 000 cells, 24 h post-seeding (black diamonds) and after 14 days of dynamic culture (grey diamonds). Data points were fitted by Equation (1) using least squares method. The error bars represent the standard deviation of the mean calculated for 3 scaffolds from the same bioreactor (n = 3). The cell numbers were calculated using stack confocal microscopy images of PKH-26 pre-labeled cells. The numbers counted on 26 sections of 2. 5 µm and extrapolated to the total thickness of the swollen scaffold (1. 4 mm) were 40,000 ± 9000, 100,000 ± 65,000, and 210,000 ± 50,000 cells, respectively. The equivalent diameter of the spheroids ranged from 18 µm (isolated cell) to more than 200 µm. The cumulative distribution (number-average) of spheroid diameters 24 h post-seeding determined for an initial seeding of 600,000 cells per scaffold is shown in Figure  4B.
The cell numbers were calculated using stack confocal microscopy images of PKH-26 pre-labeled cells. The numbers counted on 26 sections of 2. 5 µm and extrapolated to the total thickness of the swollen scaffold (1. 4 mm) were 40,000 ± 9000, 100,000 ± 65,000, and 210,000 ± 50,000 cells, respectively. The equivalent diameter of the spheroids ranged from 18 µm (isolated cell) to more than 200 µm. The cumulative distribution (number-average) of spheroid diameters 24 h post-seeding determined for an initial seeding of 600,000 cells per scaffold is shown in Figure  4B. The first quartile, the median diameter, and the third quartile were equal to 26 µm, 38 µm, and 57 µm, respectively. The data are well described by Equation (  1 ) with δ = 28 µm. In order to evaluate the fraction of isolated cells, we also computed the volume-average distribution of spheroid diameter (Figure  S6 ): we found that the fraction of isolated cells was lower than 1% of the total cell number and 50% of the cells belonged to spheroids greater than 85 µm in diameter.
Digitalization of the Bioreactors by MRI Acquisitions The grayscale MRI images (Figure  9 (A1), resolution = 55 µm) of the 48 porous hydrogel scaffolds stack were segmented by image processing into the fluid phase and the solid phase (hydrogel scaffold) (Figure  9 (A2)). The pores of the scaffolds (poorly interconnected when the scaffolds are in the swollen state) were neglected and assimilated to the solid phase. The digital hydrogel scaffolds were virtually seeded with 135-µm diameter spheroids represented by eight 55-µm voxels (Figure  9 (A3)). The relaxometry curves were obtained after averaging the signal over the whole scaffolds (A1,A2) or for a target voxel (A3). Data points were fitted by Equation (S1) (Supplementary Information) according to Milford et al. [33]. R 2 stands for the linear correlation coefficient.
Background: The core motive of pharmacovigilance is the detection and prevention of adverse drug reactions (ADRs), to improve the risk-benefit balance of the drug. However, the causality assessment of ADRs remains a major challenge among clinicians, and none of the available tools of causality assessment used for assessing ADRs have been universally accepted. Objective: To provide an up-to-date overview of the different causality assessment tools. Methods: We conducted electronic searches in MEDLINE, EMBASE, and the Cochrane database. The eligibility of each tool was screened by three reviewers. Each eligible tool was then scrutinized for its domains (the reported specific set of questions/areas used for calculating the likelihood of cause-and-effect relation of an ADR) to discover the most comprehensive tool.
Study design and search strategy This is a narrative review of the literature. To identify the different published approaches, tools, and scales for causality assessment of ADRs, electronic literature searches were conducted in MEDLINE (through PubMed), EMBASE, and the Cochrane database from 1976 to 2020. The search procedure employed the following keywords: "causality assessment tool", "adverse event", "adverse drug events", "ADRs", "drug side effect", "causality assessment", "causal", "causal algorithm", and "Bayesian scale". The Medical Subject Heading (MeSH) was also searched using "adverse drug reaction" or "adverse event" and "causality" as terms. A thorough search on Google Scholar was also conducted using the same keywords. While searching the databases, no language restriction was applied.
Data extraction and exhaustivity analysis Each tool deemed eligible was scrutinized by two reviewers for the exhaustivity (R2-ML and R3-PP). Specifically, each tool has a specific set of questions that are used to calculate the causal relationship of an ADR. We investigated each set of questions to identify each domain covered -as shown in Table  1. The type of causality assessment tool, name of the author, year of publication, title of the article, and all the questions were listed. When a domain was covered by a tool, it was identified and marked with marked with present (P) (Table  1 ). The percent-age of domains covered by each tool was calculated to quantify the most exhaustive and comprehensive tool. Each question was considered to have the same weight for this exercise, as the goal was to assess the exhaustivity of domains and not to be a proper ADR causality assessment.
Background Disease-modifying drug use necessitates better Alzheimer disease (AD) detection. Mild cognitive impairment (MCI) leverages cognitive decline to identify the risk group; similarly, mild behavioral impairment (MBI) leverages behavioral change. Adding MBI to MCI improves dementia prognostication over conventional approaches of incorporating neuropsychiatric symptoms (NPS). Here, to determine if adding MBI would better identify AD, we interrogated associations between MBI in MCI, and cerebrospinal fluid biomarkers [β-amyloid (Aβ), phosphorylatedtau (p-tau), and total-tau (tau)-ATN], cross-sectionally and longitudinally. Methods Data were from two independent referral-based cohorts, ADNI (mean[SD] follow-up 3. 14[1. 07] years) and MEMENTO (4. 25[1. 40]  years), collected 2003-2021. Exposure was based on three-group stratification: 1) NPS meeting MBI criteria; 2) conventionally measured NPS (NPSnotMBI); and 3) noNPS.
ADNI and MEMENTO Primary testing was in the Alzheimer's Disease Neuroimaging Initiative (ADNI), a partnership involving multiple centers across North America with the goal of tracking participants through periods of cognitive decline and dementia. Launched in 2003, ADNI continues to evaluate biomarker, neuroimaging, and neuropsychological status in participants. All data used in this study were obtained prior to December 2021. Only participants with MCI were included in our study, to represent Stage 3 of the NIA-AA framework. Validation was in the French MEMENTO cohort study  [29] , which recruited 2323 memory clinic patients with subjective cognitive complaints between April 2011 and June 2014. Participants were ≥ 60 years old, and MCI was defined as performance 1. 5 SD below age and education adjusted norms in ≥ 1 cognitive domain (memory, language, praxis, visuospatial or executive function).
Sample The study flowchart is shown in Fig. 1. Participants were included if they had complete Neuropsychiatric Inventory (NPI) or Neuropsychiatric Inventory Questionnaire (NPI-Q)  [30]  data for the first (baseline), and second (1-year) visits. A total of 510 MCI participants were included in the analysis, all with CSF measures at baseline. Biomarkers included β-amyloid42 (Aβ42), β-amyloid40 (Aβ40), p-tau, t-tau, and Aβ42/Aβ40, p-tau/ Aβ42, and t-tau/Aβ42 ratios.
Statistical analysis Basic demographic information was abstracted (age, sex, education, and Mini-Mental State Examination (MMSE) score). NPS groups (MBI, NPSnotMBI) were compared against the noNPS group using Chi-squared tests (categorical variables) and independent t-tests (continuous variables). Linear regressions were fitted to determine cross-sectional associations between NPS status as independent variable, and CSF biomarkers as continuous dependent variables. Logarithmic transformations were applied to CSF biomarker measures due to skewness. All models Fig. 1  Flowchart showing how the sample populations were obtained from the ADNI and MEMENTO datasets adjusted for age, sex, education, MMSE scores and the source of NPS (NPI or NPI-Q). MBI status was coded using dummy variables, corresponding to MBI, NPSnot-MBI, and noNPS groups.
Participant ID was modeled as a random effect. Fixed effects include age, sex, education, NPS (between-and within-person), MMSE, time, diagnosis, and NPS scale (NPI, NPI-Q, combination). Kaplan-Meier survival curves were generated to compare dementia-free survival probability for the 3 groups (MBI, NPSnotMBI, noNPS). Cox proportional hazards regressions were utilized to compare rates of progression from MCI to dementia as a function of NPS status (MBI, NPSnotMBI, noNPS), while controlling for age, sex, years of education, and MMSE score at baseline. ADNI did not provide information on dementia subtypes, but MEMENTO did. Thus, dementia progressors in MEMENTO were further reported by percentage of participants in each dementia subtype. For the ADNI dataset, statistical analyses were performed using R (version 4.
2. 2). For the MEMENTO validation dataset, analyses were performed using R (version 4. 1. 3) on the Dementias Platform UK Data Portal (DPUK). Assumptions of linear regression were satisfied, assessed using the ggfortify package in R. Linearity was assessed with a Martingale residuals plot. Assumptions for Cox proportional hazards were satisfied, tested using a Schoenfeld test and linearity was assessed with a Martingale residuals plot from the survival and survminer packages in R.
Survival analyses -Kaplan-Meier and Cox regressions Across ADNI and MEMENTO, dementia-free survival was significantly lower in the MBI group versus the NPSnotMBI and noNPS groups. Cox regressions demonstrated that MBI had a significantly and substantially higher rate of incident dementia compared to noNPS, while NPSnotMBI did not differ from noNPS. These survival analysis results conform with the findings from the cross-sectional and longitudinal biomarker analyses. Findings also align with the bulk of the MBI literature comprising epidemiological or cohort studies with MBI as exposure, and outcomes of cognition, function, or risk marker. Consistently, whether in CN, MCI, or mixed dementia-free samples, or whether in clinical, community, or even online cohort studies, the data have consistently shown that selecting NPS in accordance with MBI criteria identifies a group with greater baseline risk  [37, [46] [47] [48] [49] [50] [51] [52] , and/or greater risk for incident MCI and dementia  [12-20, 41, 53, 54]  than the comparator groups.
Acknowledgements Data used in preparation of this article were obtained from the  Alzheimer' s Disease   Neuroimaging Initiative (ADNI)  database (adni. loni. usc. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http:// adni. loni. usc. edu/ wp-conte nt/ uploa ds/ how_ to_ apply/ ADNI_ Ackno wledg ement_ List. pdf. The authors would like to thank the  Memento Study Group , whose author names are listed in a separate file. The MEMENTO cohort was funded through research grants from the  Fondation Plan Alzheimer  ( Alzheimer Plan   2008-2012 ), the  French Ministry of Higher education , and  Research and Innovation  ( Plan Maladies Neurodégénératives   2014-2019 ).
Funding This work was also supported by  CIC1401 -EC ,  Bordeaux University Hospital (CHU Bordeaux , sponsor of the cohort),  Inserm , and the  University of Bordeaux. The  MEMENTO  cohort has received funding support from  FUJIREBIO  through private-public partnerships. ZI is funded by a grant from the  Canadian Institutes for Health Research  ( BCA 2633 ). ZI, BC, and CB are supported by the  National Institute for Health and Care Research (NIHR) Exeter Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the  NIHR  or the  Department of Health and Social Care.
With the recent advent of disease-modifying therapies  [3]  and less invasive diagnostic techniques for the cardiac form of ATTR amyloidosis  [4] , the number of ATTR amyloidosis diagnoses is expected to increase and latency to diagnosis to decrease. To explore this hypothesis, we examined temporal trends in the make-up of patients enrolled in the Transthyretin Amyloidosis Outcomes Survey (THAOS) during four roughly equal time periods  (2007-2010, 2011-2014, 2015-2017 and 2018-2021)  spanning 15 years. THAOS is the largest ongoing, global, longitudinal observational study of patients with ATTR amyloidosis, including both ATTRv and ATTRwt amyloidosis, and asymptomatic carriers of pathogenic TTR variants. The full study design and eligibility criteria of THAOS (NCT00628745) have been published  [5].
All study sites received ethical or institutional review board approval prior to patient enrolment, and each patient provided written informed consent. The study followed the Good Pharmacoepidemiology Practice guidelines and the principles of the Declaration of Helsinki. This analysis focussed on symptomatic patients, defined as those with !1 symptom rated by investigator as definitely related to ATTR amyloidosis at enrolment (data cut-off date: 1 January 2022). At the data cut-off date, 6042 patients were enrolled in THAOS at 84 study sites in 23 countries. A total of 4206 patients were symptomatic, of whom 548 enrolled in the period  2007-2010, 1273 in 2011-2014, 924 in 2015-2017 and 1461 in 2018-2021 (    The most striking finding of this THAOS analysis was that time from symptom onset to diagnosis did not decrease as expected, and even increased to >2 years in patients with ATTRv amyloidosis and ATTRwt amyloidosis enrolled in the most recent era.
This occurred despite greater disease awareness through the advent of disease-modifying treatments and availability of minimally invasive diagnostic procedures  [3, 4]. One possible explanation is that these developments are not yet reflected in the THAOS population, and future data will show an eventual decrease in diagnostic delay. Alternatively, ATTR amyloidosis may continue to be misdiagnosed or diagnosed late, highlighting the need for improved education on identifying early symptoms of ATTR amyloidosis. This is especially true for patients with ATTRwt amyloidosis and non-V30M (p. V50M) variants since they are generally older, have more comorbidities, do not live in endemic areas, and seldom have a known family history. Another potential factor for continued late diagnosis is that increased clinical awareness is a dynamic process, and patients with obvious disease with years of progression are often the first to be recognised; as a consequence, these patients receive a late diagnosis when they would otherwise have remained undiagnosed.
This provided the core data to shape the broad snake geometry. Then body stiffness was measured on different segments of dead snakes in the four main bending directions (lateral side left and right, dorsal, and ventral). The deflection angle was measured in function of the force applied at the tip of selected segments of the snake, which results in an equivalent bending moment allowing us to size the compliant vertebrae of the robot. The maximum angle between two vertebrae along the whole snake body, measured using X-rays, allowed us to identify the mechanical stops and to define the maximum bending deflection of compliant vertebrae. Dead snakes were collected opportunistically, for example hit by vehicles (permit issued to XB, DBEC 004/2022). The measured data resulted in respective measurement intervals in which the snake robot skeleton was sized and designed  [48] , represented through elliptical vertebrae in a 3D plot (Figure  3 ).
CAPs were recruited in both public hospitals and private practice, from the networks of both departments through emails and telephone. Stage 4: Data collection, access to experience. One-to-one interviews were conducted in the psychiatric department for the adolescents and at the workplaces for the CAPs by two male psychiatry trainees (RD, CH), and one female psychologist (EM), all public health researchers (introduced as such to the research participants) trained in qualitative methods. Interviewers did not have any contact with the participants prior to the study. They conducted semi-structured interviews using an open-ended approach  48  , structured by areas of exploration (Table  2 ) collectively determined by the group, from listening and reading two pilot interviews, neither included in the final data.
The interviews lasted 50-70 min. They were audio-recorded and transcribed into anonymized transcripts, including the participants' expressive nuances. Stage 5: Data analysis, from the description of the structure of experience to practical implications. The analytic procedure followed the IPSE approach  43. The IPSE analytic process is a rigorous procedure that relies on an inductive, phenomenological method  43. In practice, the analysis had two stages: one stage of independent work by the three researchers, aided by Nvivo software, and the other by the group as a whole, pooling the data collectively. In the individual procedure, the three qualitative researchers independently and simultaneously conducted a systematic descriptive analysis to convey each participant's experience. This involved for each interview: (1) listening to the recorded interview twice and reading it three times; (2) exploring the experience word by word, that is, cutting up the entire text into descriptive units; (3) regrouping the descriptive units into categories.
These stages are carried out with the help of QSR NVivo 12 software. During the group process, the three researchers met regularly with the other group members, who had familiarized themselves with the data by listening to and reading all the interviews as many times as necessary. These two-hour meetings began after the analysis of five interviews. The first set was intended to conduct the structuring phase, that is, to regroup the categories into axes of experience, constructed such that each could be linked to its subjacent categories, and then to determine the structure of lived experience characterized by the central axes. The second set of meetings covered the practical phase, the process of triangulation with the data in the literature that made it possible to identify the original aspects of the results and to suggest potential practical implications of the finding to improve care.