dataesr/scientific-paragraphs-categorization huggingface dataset:
We observed a dose-dependent reduction of the progeny for both drugs. For instance, the number of infectious particles collected from A2EN 48 hpi was reduced by 60 to 75 % when the culture had been done in the presence of 0. 5 μM oligomycin (Fig. 4B ) or 10 μM phenformin (Fig. 4C ). To directly measure the effect of these drugs on bacterial proliferation, we used qPCR to estimate the relative number of bacterial genomes in the different culture conditions. We observed a similar decrease in the number of genomes as in the number of IFUs, indicating that the drop in IFU when OxPhos was impaired was reflecting a decrease in bacterial numbers at the previous infection round (Fig. 4D ). These data show both glycolysis and OxPhos are required for optimal bacterial growth and that 1 pathway cannot compensate for the other.
The measures of ECAR in A2EN cells indicated a small increase in glycolysis in the second half of the infectious cycle. This was confirmed by measuring lactate production in End1/E6E7 cells. Increase in lactate production was also observed in HeLa cells 48 hpi, in agreement with published data  (14). Interestingly, the increase in lactate production 48 hpi in End1/E6E7 cells became significant after normalizing to cell number, due to a drop in cell numbers in the infected wells compared to their noninfected counterpart (compare Fig. 3, D  and E ). The drop in cell number over the course of infection is likely due to the welldocumented decrease in cell division in cells infected by C. trachomatis  (34, 35) , although some cell death might also occur. Thus, overall, it appears that the infected cells maintain their level of glycolysis, in spite of reducing their own growth.
Images were acquired on an Axio observer Z1 microscope equipped with an ApoTome module (Zeiss) and a 63 × Apochromat lens. Images were taken with an ORCAflash4. OLT camera (Hamamatsu) using the Zen software. Images were analyzed with ImageJ software. CTD and CTDm inclusions size was determined using the cell image analysis software CellProfiler. qPCR Total DNA was isolated with the DNeasy Blood and Tissue Kit (Qiagen). To determine plasmid number and bacterial genome number (gDNA), 100 μl of CTD and CTDm EBs were used for DNA extraction. DNA concentrations were determined with a spectrophotometer NanoDrop (Thermo Fisher Scientific) and normalized to equal contents. qPCR was performed using LightCycler 480 SYBR Green Master I (Roche) following the manufacturer's instructions. To normalize for equal genome number, a standard curve was made using a plasmid containing the target gene glgA used to measure genome number as described in the Applied Biosystem protocol "Creating Standard Curves with Genomic DNA or Plasmid DNA Templates for use in quantitative PCR"  (2003).
Cells were excited at 480 ± 10 nm using a white light laser, and emission was selected using a band-pass filter of 525/50 nm. GFP was used as a FRET donor in all experiments, its decrease in fluorescence lifetime was measured with the FLIM microscopy Suite (Inscoper). To calculate FRET variations using FLIM, the mean lifetime of cells expressing a donor-only version of the mito-ATEAM 2. 0 (mitoGFP-ATEAM2) biosensor was first calculated for each condition. mitoGFP-ATEAM2 mean lifetime values were then used to normalize data in each experimental condition where the mitoGO-ATEAM2 biosensor was expressed. Last, ΔLifetime values were obtained by normalizing each experimental condition to the corresponding "Not infected" 1 for both mitoGO-ATEAM2 and GO-ATEAM2 vectors.
4), and the plates were incubated in a non-CO2 incubator at 37 C. For both tests, basal levels of OCR and ECAR were recorded for 30 min before consecutive addition of drugs that modulate OxPhos or glycolysis. In the Mito stress test, we added 2 μM oligomycin, 1 μM Carbonyl cyanide-4 (trifluoromethoxy) phenylhydrazone (Cayman Chemicals, 10 mM stock in DMSO), and a mix of 0. 5 μM rotenone (Sigma-Aldrich, 10 mM stock in DMSO) and 0. 5 μM antimycin A (Sigma-Aldrich, 10 mM stock in ethanol). In the Glyco Stress test, we added 10 mM glucose (Sigma-Aldrich), 2 μM oligomycin, 50 mM 2-Deoxy-D-glucose (Sigma-Aldrich). To normalize the data to cell number, plates were fixed at the end of the measurements in 2. 5% paraformaldehyde for 10 min, then stained in 5 μg/ml DAPI (Invitrogen #D1306), 3% bovine serum albumin in PBS.
They were then washed 3 times with PBS then before analysis by a Cytation 5 Cell Imaging Multi-Mode Reader (BioTek) for cell count. Images were acquired on a 7 × 7 grid (394 μm × 291 μm for each square) and were analyzed with the cell image analysis software CellProfiler. In summary, the pipeline counted DAPI-stained nuclei and subtracted mCherry-stained inclusions from the nuclei to give the cell count per well. The obtained count was adjusted according to the actual number of pictures obtained per well and the size of the Seahorse 96-well plate well (0,106 cm 2 ). Seahorse data were analyzed with the Wave software (Agilent Technologies), including the normalization to cell numbers, and the data are presented as mpH/min/cell for ECAR and as pmol/min/cell for OCR or as a ratio to the noninfected or nontreated cells.
Acknowledgments-We thank  Dr Jacques Pouysségur  for the LS174T cells and for discussions and advice throughout this study, and  Dr Alison Quayle  for A2EN cells. We thank  Drs Henri Cohen ,  Yael Levy-Zauberan , and  Christine Louis-Sylvestre  et al. at the  Service de Chirurgie gynécologique ,  Institut Mutualiste Montsouris , and  Dr Wu  for the collection and isolation of primary cells. We thank all the members of the  Microscopy-Rennes Imaging Center (Biologie, Santé ,  Innovation Technologique, BIOSIT, Rennes, France ) for assistance with FLIM acquisition. This work was supported by the  Fondation ARC pour la Recherche sur le Cancer, France , the  Institut Pasteur, France (GPF-LINMEC  grant METINFECT), les  Entreprises contre le cancer (GEFLUC, France) , and the  Centre National de la Recherche Scientifique, France.
Author contributions-M. D. N. ' G. and A. S. conceptualization; M. D. N. ' G. , S. P. , M. G. C. , and G. B. investigation; M. D. N. ' G. and A. S. writing-original draft; M. G. C. , G. B. , and G. A. M. writingreview and editing; G. A. M. formal analysis; A. S. supervision. Funding and additional information-M. D. N. 'G. was funded by the  Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation , and by the  Fondation pour la Recherche Médicale  ( FDT202012010504 ). M. G. C. is supported by the  Springboard  to independence grant AirwayStasis, the  Pasteur Foundation  Fellowship, and the  G5 Chromatin and Infection group.
Our analysis confirms Lyme disease prevalence, measured by seropositivity, in many Chinese provinces and populations at risk. This information can be used to focus prevention measures in provinces where seropositivity is high. occupations and age groups, and by using different diagnostic testing approaches  (9). To summarize published human Lyme disease seropositivity data for 2005-2020, we reviewed data from the literature. We provide updated summary estimates of seropositivity for individual exposure risk, by distinct provinces and for China overall, based on diagnostic testing approaches to determine exposure to B. burgorferi.
Search Strategy and Selection Criteria We conducted a global systematic literature review across 5 databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  (10). We tailored the search to each database accessed: PubMed, EMBASE, CABI direct, China National Knowledge Infrastructure, and Wanfang Data (Appendix Table  1 , https://wwwnc. cdc. gov/EID/article/28/12/21-2612-App1. pdf); we limited the search to articles published from January 1, 2005, through December 31, 2020. After performing the keyword search and reviewing the abstracts of retained articles, we assessed full-text articles to confirm their eligibility for inclusion. Articles were included only if they reported numerator (clearly indicating the number of seropositive persons) and denominator (the population tested) and had a diagnostic testing strategy that included an enzyme immunoassay (enzyme-linked assay [EIA] or ELISA), immunofluorescence assay (IFA), or Western immunoblot (WB).
We excluded articles that did not describe the sample population for the study. We used a snowball technique to identify additional eligible articles in the reference lists of excluded literature review articles. The protocol for the English-language literature review was published in the PROSPERO database (registration CRD42021236906, https:// www. crd. york. ac. uk/prospero/display_record. php?ID=CRD42021236906). The search and extraction of the Chinese-language databases (China National Knowledge Infrastructure and Wanfang Data) occurred independently of the English-language literature review.
, https://www. sas. com), in which the response term was the outcome (seropositivity) and the class term was province. We developed odds ratios to estimate the odds of an association between high-exposure risk group seropositivity and low-exposure risk group seropositivity, including corresponding 95% CIs. Given the paucity of data and variables available from each article, we made no adjustment for confounding in the fixed-effects models. We used forest plots to display the distribution of seropositivity and heterogeneity of the summarized seropositivity results. All analyses were conducted by using SAS version 9. 4.
Results Our literature review identified 3,657 articles that focused on China; 48 articles met the selection criteria (Figure  1 ), of which 42 articles met the criteria for the primary analytical strategy. In total, these 42 articles provided 72 estimates of seropositivity that we extracted for analysis. Some articles produced seropositivity estimates for multiple provinces or years (Appendix Table  2 ). Six articles did not meet the criteria for the primary analytical strategy; thus, they contributed data to only the 2 diagnostic sensitivity analyses. From the included studies, we compiled a description of estimates by subgroup (Table  1 ), by exposure group and province (Table  2 ), and by province (Figures  2, 3 ). For the primary analytical strategy, the reported IgG seropositivity estimates based on a single-tier test (EIA or IFA) ranged from 0% to 37%; the fixed-effects modeled summary estimate was 9.
Acknowledgments We thank  Jennifer Moisi  for her critical review of the manuscript. Editorial support was provided by  Judith Kandel  and funded by  Pfizer Inc. This study was supported and jointly funded by  Valneva  and  Pfizer  as part of their co-development of a Lyme disease vaccine. J. H. S. , J. C. Z. , S. R. V. , J. L. , K. P. , M. A. F. , L. J. , and B. D. G. are employees of  Pfizer  and may hold stock or stock options. X. L. declares no conflict of interest. L. B. is an employee of  P95 , which was contracted by  Pfizer  to conduct the research described in the article.
Key Summary Points Why carry out this study? Generalized pustular psoriasis (GPP) is a rare, potentially life-threatening autoinflammatory skin disease; standard treatment guidance for this condition often follows that of plaque psoriasis, despite limited evidence on the efficacy of anti-psoriatic drugs, including biologics, in GPP. Spesolimab is effective in treating GPP flares, but the relapsing nature of GPP means that there is an unmet need for treatments to prevent the occurrence of GPP flares, which the Effisayil TM 2 study (NCT04399837) will assess.
In an open-label, proof-of-concept study (NCT02978690), patients experiencing a GPP flare became clear or almost clear of GPP by Week 4 post treatment with a single dose of spesolimab (a humanized anti-interleukin-36 receptor monoclonal antibody)  [14]. After this, the Effisayil TM 1 study (NCT03782792) was the first randomized clinical trial to investigate a targeted treatment for GPP, and reported that adult patients with a GPP flare treated with spesolimab achieved rapid pustular and skin clearance  [15]. The results from this study were integral to the recent approval of spesolimab by the US FDA as a first treatment option for GPP flares  [16]. The relapsing nature of GPP (recurrent flares or persistent disease with intermittent flares) highlights the need to develop treatments to prevent flares  [3] , with a recent survey revealing that among dermatologists whose patients experience frequent flares, 67% felt that currently available treatments fail to adequately prevent new flares  [13].
Study Design Effisayil TM 2 (NCT04399837) is a phase 2, multicenter, randomized, parallel-group, double-blind, placebo-controlled, dose-finding study to evaluate the efficacy and safety of spesolimab in preventing GPP flares in patients with a history of GPP. Patients will be randomized 1:1:1:1 to three groups receiving a SC 600-mg loading dose of spesolimab followed by a 300-mg maintenance dose administered every 4 weeks (q4w) or every 12 weeks (q12w), or a 300-mg loading dose of spesolimab followed by a 150-mg maintenance dose administered q12w, and one group receiving placebo, for 48 weeks (Fig. 1 ). To maintain treatment blinding, patients in all treatment arms will receive one loading dose, administered as four injections of 150-mg spesolimab or placebo at Week 1/Day 1, followed by a maintenance dose administered as two injections of 150-mg spesolimab or placebo at subsequent dosing visits in the 4-week schedule.
Randomization Randomization of patients will be conducted according to the use of systemic GPP medications at randomization, region (Japan versus outside of Japan) and age at screening (adults aged 18-75 years versus adolescents aged 12-\ 18 years). The randomization list will be generated using a pseudo-random number generator so that the resulting treatment will be both reproducible and non-predictable. An Interactive Response Technology will be used to screen eligible patients, perform drug assignment, manage initial/re-supply ordering of drug supplies and handle emergency un-blinding. The sponsor will remain blinded to the randomized treatment assignments until the last patient has completed or discontinued early from the 48-week treatment period of the trial and the database is considered ready for unblinding to perform the primary analysis of the trial.
ACKNOWLEDGEMENTS The authors would like to thank the patients and their caregivers, in addition to the investigators and their teams, who will be involved in the Effisayil 2 study, which is supported and funded by  Boehringer Ingelheim. The authors would like to acknowledge the meaningful contributions made by  Ming Tang , an employee of  Boehringer Ingelheim , in the development of the manuscript. Funding. The study was supported and funded by  Boehringer Ingelheim. The journal's Rapid Service Fee was funded by  Boehringer Ingelheim. Medical Writing and Editorial Assistance. Derah Saward-Arav, PhD , of  OPEN Health Communications (London, UK ) provided writing, editorial and formatting support, which was contracted and funded by  Boehringer Ingelheim. Akimichi Morita  reports consulting fees from  AbbVie ,  Boehringer Ingelheim ,  Eli Lilly ,  Nichi-Iko ,  Nippon Kayaku , and Novartis; and honoraria from  Eisai ,  Janssen ,  Kyowa Kirin ,  LEO Pharma ,  Maruho ,  Mitsubishi Tanabe ,  Sun Pharmaceutical Industries, Taiho Pharmaceutical ,  Torii Pharmaceutical , and  Ushio.
Study population We used the previously detailed strategies to identify, review, and abstract data from potential SLE cases  [22] [23] [24]. Patients meeting the 2019 European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR) classification criteria for SLE  [25]  were considered incident cases. Those who migrated to the 27-county region after diagnosis (and therefore were under treatment) were included if they had at least 7 EULAR/ ACR points and a physician diagnosis. We included all patients meeting the requirements described above and living in the 27-county region on January 1, 2015 (index date). Patients with SLE were matched (1:1) on sex, age, race, and county to non-SLE comparators. All patients with SLE and non-SLE comparators had at least 1 year of medical history and were followed until February 29, 2020, emigration out of the geographical area, or death.
Data collection Through medical record review, we manually abstracted demographics, disease duration, and clinical and serological SLE features. Smoking status, disease-modifying anti-rheumatic drugs (DMARDs), hyperlipidemia medications, antiosteoporotic drugs, and GC use were electronically retrieved for SLE and non-SLE comparators (see the complete list of medications in Additional file 1, supplemental table 1) for 5 years prior to index date. The patients on GC therapy were then stratified by ≥90 days and <90 days of use. Area Deprivation Index (ADI) scores at the census block group level were obtained using patient addresses  [26]. These variables were evaluated at index date. We used a 5-year lookback period prior to January 1, 2015, using International Classification of Diseases Ninth Revision (ICD-9) and International Classification of Diseases Tenth Revision (ICD-10) diagnosis codes to identify the preexistence of breast, cervical, or other gynecological cancer (endometrial, uterine, placental), hypertension, hyperlipidemia, DM, and osteoporosis (see the complete list of diagnosis codes used in Additional file 1, supplemental table 2).
Statistical analysis Descriptive statistics were used to summarize the data. Chi-square and Wilcoxon rank sum tests were performed to compare the baseline characteristics between patients with and without SLE. The cumulative incidence of screening tests and vaccination was estimated for patients with and without SLE using Kaplan-Meier methods. Cox proportional hazards models with adjustment for age, sex, and race were used to compare screening tests and vaccination rates between the two groups. Breast and cervical cancer models were adjusted for age and race as only females were included in these analyses. A p-value of <0. 05 was considered statistically significant for all analyses. Analyses were performed using SAS version 9. 4 (SAS Institute, Cary, NC, USA) and R 4. 0. 3 (R Foundation for Statistical Computing, Vienna, Austria).
A cross-sectional study from the Georgians Organized Against Lupus (GOAL) cohort reported that 84. 3% of patients with SLE had received a mammogram within the previous 2 years of survey, higher than their comparative community sample  [16]. Some methodological and population-based differences could explain our different findings. We used the REP infrastructure, allowing us to ascertain the documented fulfillment of the testing instead of survey data with inherent reporting limitations. These other studies had different targeted age populations since the recommendations were different; we selected the age range based on the USPSTF recommendations in effect during the period evaluated. Additionally, the racial/ethnical distribution of the LUMEN, LOS, and GOAL cohorts differed. While breast cancer screening is recommended every 1-2 years  [28, 29] , cervical cancer screening, through Pap smear and/or HPV test, is recommended every 3-5 years  [30, 31].
We thank  Dr Nicolas Gilliers  for his assistance with the statistical analysis, and  Luce Smagghe  for his technical help (financially supported by FILO). This study was supported by grants from  INCA-DGOS-Inserm_12560  (SiRIC CURAMUS is financially supported by the  French National Cancer Institute , the  French Ministry of Solidarity and Health and Inserm  with financial support from  ITMO Cancer AVIESAN) , by prix Force Hémato 2020 and by the  Fondation ARC pour la recherche sur le cancer  (to DRW). Luce Smagghe  and  Jonathan Caron  were financially supported by  SiRIC CURAMUS. NF, VL and DRW designed the research, NF and DRW analysed data and wrote the manuscript. NF, JC, CBG, DK, NG, CB, FNK, EC and DRW performed experiments. NF, MB, CBG, DK, NG,.
Flow cytometry EFluor 450-CD3 (Thermo Fisher Scientific, Waltham, MA, USA, Invitrogen), PE-Cy7-CD4 (Thermo Fisher Scientific, Invitrogen), FITC-CD69 (Becton Dickinson (BD), Franklin Lakes, NJ, USA), PE-CD86 (BD Biosciences), PE-Vio770-CD45 (Miltenyi, Bergisch Gladbach, Germany), PE-IL-12Rβ1 (R&D Systems), and PerCP-IL-23 (R&D Systems) were used for cell surface staining. Cells were incubated for 20 min at 4 °C in staining buffer (PBS 1× + 2% of FBS), washed, and analyzed by flow cytometry (Navios, Beckman Coulter, Brea, CA, USA). Analysis was done with the FlowJo software.
In brief, mRNAs were pre-amplified from 200 pg total RNA using the SMART-Seq V4 Ultra Low RNA kit (Clonetec). Library preparation was performed from 500 pg cDNAs using the Nextera kit (Illumina) following manufacturer instructions. Libraries were sequenced using an Illumina NextSeq500 platform and 75 bp single-end sequencing data were obtained with between 28 and 41 million reads per sample. Perfect trimmed reads were aligned to Mus musculus mm10 reference genome using the TopHat2 software  [31]. The featureCounts tool was used to determine the number of reads mapping to each gene  [32].
qPCR on human postmortem brain samples RNA from human postmortem AD and control brain samples were extracted using the Qiagen RNeasy Plus Mini kit (Qiagen, #74136) following manufacturer protocol. Quality of the total RNA was assessed using the Agilent 2100 Bioanalyzer (Agilent). All samples had RNA Integrity Numbers (RINs) higher than 6. 4. Reverse transcription of 500 ng total RNA was performed using the iScript kit (BioRad). Real-time PCR was performed in 384-well plates in a final volume of 10 µl using SYBR Green dye detection on the LightCycler480 system (Roche-Diagnostic). The primers pairs for CLEC7A, CST7 and CYBB were designed using Primer 3 software. These sequences are reported in the Extended Method section. All three analyzed genes showed detectable expression levels. Because i) these three genes are specifically expressed in microglia and ii) the objective was to assess their potential up-regulation in microglia, HEXB was selected as normalizing gene.
Differential gene expression Samples to be included in the different analyses were selected according to the question addressed. After selection, gene expression normalization was performed using Relative Log Expression (RLE) normalization implemented in the DESeq 2 R package and genes with less than 1 count per million (cpm) in at least 3 out of 4 replicates and at least one condition, were filtered out. To detect the differentially expressed genes (DEG), we applied Generalized Linear Models (GLM) with tagwise dispersion. Both raw-(pv raw ) and adjusted-P values (pv adj ) were computed. Principal component analysis (PCA) was performed using FactoMiner R package  [33].
Gene network representations Gene networks of pre-selected genes were constructed using specific applications (Apps) implemented in the Cytoscape software. First, we used the STRING App, with full STRING network and 0. 7 confidence cutoff as settings to construct the network. At this stage, isolated genes were removed. Large networks were further divided into subnetworks using the MCL tool of the STRING App. To identify hub genes, we then ran the CytoHubba app, employing five calculation methods: Degree, Edge Percolated Component (EPC), EcCentricity, Maximal Clique Centrality (MCC), and Maximum Neighborhood Component (MNC). The intersecting genes derived using these five algorithms encode the most highly connected proteins and may represent key candidate genes with important biological regulatory functions.