dataesr/scientific-paragraphs-categorization huggingface dataset:
Whole-genome SNP analysis Each assembly file was processed with the ART-Moun-tRainier simulation tool, which generates synthetic paired-end reads with coverage of 50  [87]. These reads were aligned and mapped against the reference sequence of C. psittaci 6BC (CP002549. 1) using the BWA algorithm implemented in BioNumerics v7. 6. 1 (bioMèrieux, Applied Maths, Sint-Martens-Latem, Belgium) with minimum sequence identity of 90%. SNPs were identified using the BioNumerics wgSNP module and then filtered using the following conditions: minimum 5 × coverage to call a SNP, removal of positions with at least one ambiguous base, one unreliable base or non-informative SNP and minimum inter-SNP distance of 25 bp.
Phylogenetic and recombination analyses A whole-genome alignment (WGA) was performed on the 61 C. psittaci genomes using SKA v1. 0 (https:// github. com/ simon rharr is/ SKA). Prior to that alignment, fragmented genomes with more than one scaffold were mapped on the 6BC reference genome (NC_015470. 1) using minimap2 v2. 17  [88]. Variants were called and a consensus sequence was reconstructed using samtools/ bcftools v1. 9  [89]. A phylogenetic tree was then reconstructed from the WGA with RAxML v8. 2. 12  [90]  using the GTRGAMMA model and 1000 bootstrap replicates. The tree was rooted using midpoint. Recombination analysis was performed from the same WGA using Gubbins v3. 1. 6 [91]  with default settings. Figure  1  was generated using Phandango v1. 3. 0  [92]. The tree based on the 904 common genes of 61 C.
3D structures of OmpA OmpA protein structures were obtained using Colab-Fold  [42] , a free platform for prediction of 3D structures from amino acid sequences based on AlphaFold v2. 1. 0 [41, 70]. Graphics were generated using the molecular visualization program ChimeraX v1. 4. dev202201150102, which was developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from National Institutes of Health R01-GM129325 and the Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases  [94].
Plasmid analysis First, we selected all contigs annotated as plasmids from all originally downloaded FASTA files (NCBI GenBank). We checked that all contigs had a length of ~ 7 kb and discarded too fragmented sequences, resulting in 28 plasmids. Second, we built a Blast database from these 28 sequences to query our re-assemblies for additional plasmids that might not have been successfully assembled before or were discarded when the original assemblies were uploaded to the NCBI database. By that, we discovered 19 additional plasmid sequences for strains, where we did not find a plasmid sequence on NCBI, resulting in a total set of 47 plasmids for our 61 C. psittaci strains included in the study. However, during a first analysis and annotation round, we found four plasmids obtained from NCBI with a differing number of annotated CDS in comparison to the usually observed pattern in our C.
psittaci plasmid collection. While for the majority of plasmids we found 8 CDSs and homology-based functionality with high sequence similarity to reference sequences, we observed discrepancies for the following plasmid sequences uploaded to NCBI: 1) strain MN: 10 CDSs, 1 pseudogene, and 2 hypothetical proteins; 2) strain Ho_ Re_lower: 9 CDSs; 3) strain Ho_Re_upper: 9 CDSs and 1 pseudogene; 4) strain Ful127: 9 CDSs and 2 pseudogenes. As we suspected potential assembly errors in those plasmid sequences, we screened again for corresponding plasmids in our own re-assemblies of these strains. In the case of strain Ful127, we did not find any raw read data and did not reconstruct a new assembly. For the other three strains, we found plasmids in our re-assemblies. Our analysis showed that these three plasmids were in better agreement with all the other C.
psittaci plasmids in our data set (based on annotation and alignment). Thus, we used the re-assembled plasmids to replace the original NCBI plasmids for strains MN, Ho_Re_lower, and Ho_Re_upper. More details (pairwise alignments between the NCBI and re-assembled plasmids, differences in the annotation) can be found in the OSF repository (https:// osf. io/ rbca9). Finally, our plasmid panel comprised 25 plasmids from the NCBI database and 22 plasmids from our re-assemblies. Three re-assembled plasmids (MN, Ho_Re_lower, Ho_Re_upper) replaced the NCBI plasmids due to better assembly quality and annotation agreement. We used pLannotate v1. 2. 0  [95]  to obtain gene annotation and confirmed the selected sequences as plasmids. Subsequently, we screened the pLannotate annotations and selected GP3D_CHLT2, a CDS found in all 47 plasmids, to rearrange them in the same strand orientation and order (see Figure  S8 ) using a custom python script (https:// github.
com/ Marie Latar etu/ rearr angeF asta). Next, we performed a more detailed functional annotation of the re-arranged plasmid sequences using Bakta v1. 6. 0  [96]  with parameters -genus Chlamydia -species psittaci. Finally, we used MAFFT v7. 508  [97]  with the 'linsi' option to calculate a multiple sequence alignment followed by RAxML v8. 2. 12  [90]  with 1000 bootstrap replicates and the GTRGAMMA model to construct a plasmid tree. We used strain M56 (NC_018635) as an outgroup to arrange a topology comparable to the genome-based phylogeny. We visualized the tree using the Newick Utilities software package  [98]  and finalized the plasmid tree figure using Inkscape. Please note that we removed low bootstrap values at the leaves of the tree where no robust arrangement of identical or highly similar plasmid sequences was possible.
For each patient included during the consultation, the physician completed a "treatment and history" document specifying the different treatments under way in the previous month as well as the surgical history and comorbidities. Socio-demographic data collected were sex, age, marital status (single, married/ cohabitating, divorced, partnership, widow(er)), education (certificate of studies, certificate of secondary education, technical school certificate, baccalaureate degree (general or professional), post-baccalaureate degree), employment status (fulltime, part-time, unemployed, retired).
Background: This study aimed at evaluating the health status and healthcare consumption of ≥16-year-old patients with phenylketonuria (PKU), with a focus on early-diagnosed patients. Methods: This retrospective observational study used health insurance claims data from the French SNDS (Système National des Données de Santé) database. Patients with PKU were identified between 2006 and 2018 by ICD-10 diagnosis codes E70. 0 (classic PKU) or E70. 1 (other causes of hyperphenylalaninemia). They were matched to controls by age, sex, and region. Patients with early-diagnosed PKU were defined as patients born after implementation of nationwide newborn screening in France in 1972. Outcomes were analyzed for the year 2018. Results: Overall, 3549 patients with PKU were identified on January 1st, 2018.
Burton et al. did not specifically restrict their retrospective US study published in 2018 to patients with PKU born before the NBS introduction, but the mean age of the included patients suggests that the largest proportion of them was diagnosed via NBS. The study did not focus on neuropsychiatric and neurocognitive comorbidities but revealed significantly increased rates of renal insufficiency, overweight, osteoporosis, urticaria, anemia, asthma, dermatitis, rhinitis, and several gastrointestinal conditions among patients with PKU than matched non-PKU controls  [13]. A German study published in 2019 confirmed that overweight, anxiety, and depression were more prevalent in early-diagnosed adult PKU than matched non-PKU controls  [12]. The aim of this study was to gain insights into the clinical impact of PKU in adult patients in France from the French claims database SNDS (Système National des Données de Santé) and to put a spotlight on comorbidities and pharmaceutical treatment of adult (≥16-year-old) patients born after the introduction of NBS and who were therefore early-diagnosed.
Data source This retrospective observational study of patients with PKU in France utilized data from the French claims database SNDS. This nationwide database covers 98. 8% of the French population (nearly 66 million people) and represents one of the largest medico-administrative databases in the world. It contains anonymous data on inpatient and outpatient healthcare encounters such as hospitalizations with International Classification of Diseases, 10th Version (ICD-10) codes and diagnosis-related groups (DRG), filled drug prescriptions using CIP (Code Identifiant de Présentation) codes, medical devices, medical procedures using CCAM (Classification Commune des Actes Médicaux) codes, laboratory tests excluding results, attendant care (nursing care, physical therapy, medical transport etc.
), and respective dates. All drugs delivered in community pharmacies are available in the database. Drugs and medical devices delivered during hospital stays are only documented when they are registered on a specific formulary named "hors TAA". This formulary includes only innovative and costly products and is updated regularly. Drugs and medical devices used in the inpatient setting that are not registered on this formulary are not available in the SNDS database. In addition, the SNDS contains information about long-term diseases (Affection de Longue Durée, ALD), occupational accidents, and occupational disorders. Comorbidities can be identified by inpatient discharge ICD-10 codes, by ALD status, or in certain cases by prescriptions of specific treatments (such as statins for hypercholesterolemia).
Non-PKU controls To assess the additional clinical impact related to PKU, controls from the general population without PKU were selected at random from the SNDS database. For each of the eligible patients with PKU, up to five controls without a recorded PKU diagnosis during the enrolment period were drawn from the database via direct, exact matching without replacement on age, sex, and region. The status of the matching parameters was determined on January 1st, 2018. Like the patients with PKU, controls needed to be alive on January 1st, 2018.
Study population Overall, 3549 patients with PKU who were alive on January 1st, 2018, were identified in the database (Fig. 1 ). Of those, 3469 patients could be matched to 17,170 controls without PKU (96. 6% of patients with PKU were matched to 5 controls, 2. 5% to 4 controls, 0. 4% to 3 controls, 0. 2% to 2 controls, and 0. 3% to 1 control). Of these, 2175 patients with PKU were at least 16 years old on January 1st, 2018, among them 1528 (70. 3%) were born after 1972 and therefore categorized as patients with early-diagnosed PKU. For some outcome analyses, it was required that patients had at least one documented incident of healthcare consumption in 2018. The number of ≥16-year-old patients with PKU with a documented medical resource use was reduced to 1911 patients, and the number of ≥16year-old early-diagnosed patients decreased to 1301 patients (Fig.
Our findings for the overall population of ≥16-year-old patients with PKU are partially in line with results from a recent German study assessing the clinical impact of PKU in adult patients with PKU using claims data from statutory health insurances  [12]. In general, the German study found higher proportions of patients and controls with comorbidities, mainly because the German database included inpatient as well as outpatient diagnosis codes to identify comorbidities, whereas only inpatient ICD-10 codes and specific algorithms combining inpatient with prescription codes could be used in our analysis of the French database. Furthermore, the adult patients with PKU included in the German study were on average older than in our study (mean age 51 years versus 41 years, respectively). The increased rates of ischemic heart disease along with hypertension, dyslipidemia, and obesity in patients with PKU compared with matched controls as shown in our study support previous findings concerning classical risk factors for cardiovascular diseases in PKU patients  [20].
This study has some limitations. First, older patients with PKU in France seem to be underrepresented in this study. Assuming that the age distribution among patients with PKU is similar to the age distribution in the general French population, we compared the proportion of ≥45-year-olds in our ≥16-year-old PKU study population (30%) with the proportion of ≥45-year-olds in the ≥15-year-old general French population (56%)  [25]  which may indicate an underrepresentation of older patients with PKU in our study. A possible reason could be that patients who are treated in nursing homes cannot be identified in the SNDS database. Additionally, patients born before the implementation of nationwide NBS, and by definition older patients, may not have been diagnosed at all. Second, the applied wide definition of PKU (ICD-10 codes E70.
Therefore, it is possible that for some outcomes the results may be skewed. Furthermore, the comorbidity profile may be underestimated in our study because the identification relied on algorithms for specific comorbidities or inpatient diagnoses for any comorbidity. Therefore, diseases that are mainly managed in the outpatient setting could not be detected with this study. The SNDS database does not provide information about the level of education, income and socio-professional category of the insured. Additionally, as all administrative claims databases, the SNDS database only contains information about care that was actually reimbursed, and neither self-treatment nor the use of prescribed medications that are not reimbursed can be measured. Likewise, it is not possible to evaluate the compliance of patients, since, for example, pharmaceuticals that are prescribed but not filled by the patients are not documented in the SNDS.
Study design and population For this analysis, we used baseline data from a multicenter, prospective cohort study. Healthy participants considered at increased risk for HPV-OPC were eligible for enrollment in the Men and Women Offering Understanding of Throat HPV (MOUTH  17  ) study (ClinicalTrials. gov identifier NCT03644563) based on meeting one or more of the following eligibility criteria: men aged 30 years or older who had two or more self-reported lifetime oral sex partners,  10   12  This study was approved by the institutional review boards of both sites, and informed consent was obtained from all participants.
Statistical analysis Inclusion in the analytic population required at least partial completion of the baseline survey and completion of HPV biomarker testing for one or more specimens at the time of analysis; 65 individuals who were enrolled but did not yet have data collection and/ or testing were excluded. The primary outcome for this analysis was eligibility for the at-risk cohort. A secondary outcome was having HPV16-specific biomarkers. To explore further enrichment by demographic or behavioral characteristics, we compared oral health, medical history, sexual history, and substance use behaviors between at-risk and not-at-risk participants. The χ 2 test was used for categorical variables, and the K-sample equality-of-medians test was used for continuous variables. Cumulative behavioral measures (amount used per day * years used) that were calculated included tobacco pack-years, alcohol drink-years, marijuana joint-years, and sexual partners per 10 years (sex-years).
DISCUSSION This analysis was designed to evaluate how well we identified a subset of the population enriched with oncogenic HPV biomarkers that is at risk for developing of OPC. We successfully identified many individuals with oncogenic oral HPV DNA or serum antibodies to oncogenic HPV, but the prevalence of HPV16-specific biomarkers (which are thought to be most associated with HPV-OPC) was low. The prevalence of oral  27, 28  and serum  29  oncogenic HPV biomarkers was elevated among all enrollment groups compared with the general population (Table  5 ). Of note, the differences in prevalence between    24  ). The median age was 57 years (range, 18-86 years), and 66% were ever-smokers. e NHANES included <20 people living with HIV who had oral HPV data available, so estimates are not presented in this group because they are underpowered.
The diagnosis of HIV was the only medical factor associated with increased biomarker positivity. [33]  Although oral HPV DNA is a recognized precursor to HPV-OPC,  34  screening for it alone would have a poor balance of benefits and harms. The majority of oral HPV DNA clears, which would result in a high false-positive rate and a low positive predictive value. 18 Positivity of multiple markers could suggest increased risk, but concordance of biomarkers was low, consistent with prior studies. 18 Only 17 individuals had both oral and serum HPV markers, and only one was positive for oral HPV16 DNA and HPV16 E6 antibodies. HPV16 E6 may exceed oral HPV in performance characteristics because of its stability over the years between seroconversion and clinical presentation of disease. 35 The Hamburg City Health study (ClinicalTrials.
gov identifier NCT03934957), the largest published screening study to date (n = 4424), used seropositivity against HPV16 E6 and at least one other early protein to identify an at-risk population. Of 35 E6 seropositive individuals (median follow-up, 4. 7 years), two advanced-stage cases and one early stage case were identified, highlighting the limited positive predictive value of serology. 36 e number needed to screen (NNTS) to prevent one death is a metric used to evaluate screening implementation feasibility. 8] [39]  However, If HPV16 E6 seroprevalence were used for screening, its low prevalence would suggest a high NNTS. This may reduce feasibility, despite easier administration of an HPV16 E6 blood-based test compared with more invasive screening practices because one would have to screen a large population to identify those at risk.
ACKNOWLEDGMENTS We thank the  HPV Cancer Cohort Consortium  for sharing data on sex-specific HPV seroprevalence and  Maura Gillison  and  Anil Chaturvedi  for oral HPV testing performed in the NHANES and used as publicly available data for this analysis. This study was supported by grant/contract  R35DE026631  from the  National Institute of Dental and Craniofacial Research/National Institutes of Health  and by grant/ contract  5U01CA195603  from the  National Cancer Institute. Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and do not necessarily represent the decisions, policies, or views of the  International Agency for Research on Cancer/World Health Organization.
3% vs. 0. 4%, T A B L E 3 a Among those at risk, N = 17 had both positive rinse and blood results, whereas 69 were categorized as positive based only on rinse results, and 162 were categorized as positive based only on blood results. b Among those not at risk, N c The p d T A B L E 4 Abbreviations: aOR, adjusted odds ratio; CI, confidence interval; GED, General Educational Development test; HIV, human immunodeficiency virus; HPV, human papillomavirus; MOUTH, the Men and Women Offering Understanding of Throat HPV study (ClinicalTrials. gov identifier NCT03644563); OR, odds ratio; Ref, reference category. respectively; p =. 36).
Abbreviations: HIV, human immunodeficiency virus; HPV, human papillomavirus; MOUTH, the Men and Women Offering Understanding of Throat HPV study (ClinicalTrials. gov identifier NCT03644563); NHANES, National Health and Nutrition Examination Survey. MOUTH, HPVC3, N = 997 N = 5794 d Overall 7. 3 a 3. 5 1. 8 a 0. 9 a 8. 8 7. 4 2. 2 a 0. 3 a By sex Men 8. 0 a 5. 8 a 2. 0 1. 6 8. 9 7. 6 2. 1 a 0. 3 a Women 1. 7 1. 2 0. 0 0. 25 7. 9 6. 9 3. 0 a 0. 4 a By HIV status Living with HIV d 22. 7 - 5. 2 - 15. 6 4. 4 - Living without 5. 9 a 3. 4 a 1. 5 a 0. 69 a 8. 2 2. 0 - HIV By the no. of people performed oral sex on in lifetime 0-1 e 0. 0 0. 54 0. 0 0. 18 5. 0 5. 0 - 2-5 5. 8 a 3. 9 a 1. 3 0. 54 7. 1 1. 5 - 6-19 6. 9 a 3. 6 a 1. 6 1. 1 7. 7 0. 9 - ≥20 11. 3 a 9. 2 a 3. 1 3. 2 13. 5 4. 9 - a These prevalence estimates were significantly different (p <.
Research in context Evidence before this study We searched PubMed and Google Scholar on Feb 11, 2023, for English-language publications of randomised controlled trials published since database inception using the terms "PD-1 inhibitor" OR "PD-L1 inhibitor" OR "immune checkpoint inhibitor" AND "unresectable" OR "metastatic" AND "biliary tract cancer" OR "BTC" OR "cholangiocarcinoma" OR "gallbladder cancer". Several phase 2 trials of immune checkpoint inhibitors for the treatment of biliary tract cancer were identified. The only phase 3 study was the double-blind TOPAZ-1 trial of durvalumab plus gemcitabine and cisplatin versus placebo plus gemcitabine and cisplatin in patients with previously untreated unresectable or metastatic biliary tract cancer or with recurrent disease. Results of TOPAZ-1 showed that durvalumab plus chemotherapy significantly improved overall survival versus placebo plus chemotherapy and that the two treatment groups had similar safety profiles.
Safety and treatment exposure were assessed in the astreated population (ie, all randomly assigned participants who received one or more doses of any study treatment). An independent data and safety monitoring committee oversaw the study and assessed efficacy and safety at prespecified interim analyses. After reviewing overall survival results from the first and second interim analyses, the independent monitoring committee reported that superiority for the pembrolizumab group was not achieved and recommended the study continue as planned. Sample size and power calculations were done using R (version 3. 6. 1 with gsDesign version 3. 05 and simtrial version 0. 1. 6 packages). Statistical analyses were done using SAS (version 9. 4). This study is registered with ClinicalTrials. gov, NCT04003636, and is ongoing but closed to enrolment.
Overall, 43% of participants in the pembrolizumab group and 39% of participants in the placebo group received nine or more cycles of gemcitabine, including 32% in the pembrolizumab group and 27% in the placebo group who received 12 or more cycles. The different gemcitabine durations in KEYNOTE966 and TOPAZ1 reflect heterogeneity of clinical practice and provide complementary data that accommodate different standards of care worldwide. KEYNOTE966 stratified randomisation by geographical region, whereas TOPAZ1 did not. Furthermore, KEYNOTE966 enrolled more participants (n=1069 vs n=685 in TOPAZ1), including a larger proportion of participants outside of Asia (583 [55%] vs 311 [45%]). 21 It is reassuring that in KEYNOTE966, the relative benefit of pembrolizumab plus gemcitabine and cisplatin in the large nonAsian population was similar to that observed in the intentiontotreat population.
A bias in favour of patients with intrahepatic tumours has been observed in other studies, including TOPAZ1,  21  and might reflect the rising incidence of intrahepatic cholangiocarcinoma worldwide. 1, 7 The requirement for tumour tissue at study entry might have also contributed to a selection bias for patients with intrahepatic tumours because these tumours are more accessible for sampling. Given the low prevalence of microsatellite instabilityhigh biliary tract tumours,  [26] [27] [28] [29] [30]  we were unable to assess outcomes by microsatellite instability status because only ten participants with known microsatellite instabilityhigh tumours were enrolled. Samples for biomarker assessment were collected from consenting participants, and translational analyses are ongoing. A dedicated analysis of complete patient reported outcomes data will be presented in the future.