dataesr/scientific-paragraphs-categorization huggingface dataset:
Baseline [ 18 F]FDG PET/CT radiomic features can improve the survival prediction in patients with diffuse large B-cell lymphoma (DLBCL). The purpose of this study was to investigate whether characterizing tumor locations relative to the spleen location in baseline [ 18 F]FDG PET/CT images predicts survival in patients with DLBCL and improves the predictive value of total metabolic tumor volume (TMTV) and ageadjusted international prognostic index (IPI). Methods: This retrospective study included 301 DLBCL patients from the REMARC (NCT01122472) cohort. Physicians delineated the tumor regions, whereas the spleen was automatically segmented using an openaccess artificial intelligence algorithm. We systematically measured the distance between the centroid of the spleen and all other lesions, defining the SD of these distances as the lesion spread (Spread-Spleen).
Patients A retrospective analysis of 301 DLBCL patients with a baseline [ 18 F]FDG PET/CT scan from the REMARC trial (NCT01122472) was conducted. The REMARC trial study started including patients in 2010 and was a double-blind, international, multicenter, randomized phase II study. Patients included in this study were 60-80 y old and had an Ann Arbor stage of I-IV and age-adjusted IPI of at least 1 at diagnosis, with histologically proven CD20-positive DLBCL according to the 2008 World Health Organization criteria  (11). Detailed characteristics of the patients were reported elsewhere  (2). The survival outcomes (PFS and OS) were recorded as defined by the revised National Cancer Institute criteria  (12). Figure  1  summarizes the flow diagram of the data. Patient data were anonymized before any analysis.
A postprocessing method was developed to check the quality of the spleen segmentation and correct it when needed. The postprocessing method included 2 criteria to trigger a warning, ensuring that the spleen segmentation had a single connected component and was not outside the whole-body field of view. If the spleen included more than 1 connected component, the largest one was automatically selected as the spleen. After postprocessing, all segmentations were visually verified and manually adjusted using the LIFEx software whenever needed  (14). As the CT and PET images were aligned from the PET/CT acquisition, for each patient, the spleen segmentation result obtained from the AI was superimposed onto the lesion segmentation obtained from the experts. Figure  2  illustrates the proposed image-processing pipeline.
Spleen Segmentation Among the 295 patients available for PET/CT segmentations (regardless of the availability of survival data; Fig. 1 ), the TotalSegmentator AI method correctly  detected and segmented the spleen in 282 patients (96%); the postprocessing method yielded a warning in 13 patients (4%) and automatically corrected the segmentation in 12 of 13 patients. The spleen segmentation of 1 patient only had to be manually corrected using the LIFEx software. The median size of the spleen was 256. 15 cm 3 (interquartile range, 169-395 cm 3 ). A total of 101 (34%) patients had lesions segmented by the expert in the spleen. Figure  3  shows the coronal maximum-intensity projection views of the PET image of 1 patient, the PET image overlapped with the spleen segmentation by the AI model, and the lesion segmentations by the expert.
The proposed spleen-based features (SpreadSpleen, Dspleen, and sDspleen) are simple to calculate and intuitive to interpret. The spleen was chosen as the reference organ because of its role in the lymphatic system. Lymphoma splenic invasion is also a poor prognosis factor. In subgroups of patients without splenic invasion, the spleen-based features were prognosticators, suggesting that the spleen-based features code more than just a patient's splenic invasion. The bladder and liver were also tested as reference organs for evaluating tumor distribution and dissemination features. The predictive value from the bladder-based features was low for both PFS and OS (AUC , 0. 60, data not shown). Although liver-based features were correlated with spleen-based features because of their relative anatomic proximity and were also predictive (data in the supplemental material), spleen-based features always had significantly higher predictive values (P , 0.
2% agarose gel in a Tris-acetate-EDTA buffer at room temperature, 6 V/cm for 1 h in order to identify and estimate properly the amount of D-loop created. We detected the free and the bound Cy3 labelled oligonucleotides by a Fluor imager (Typhoon trio-Fuji-GE-healthcare) with an Abs/Em of 532/580 nm. Quantification of the proportion of D-loop created in this assay was performed with the Multigauge and Excel softwares.
Cryo-EM specimen preparation and electron microscopy data acquisition For cryo-EM analyses, 3. 5 l of sample were deposited on glow-discharged Lacey carbon grids, blotted with filter paper to remove excess sample for 4 s, and plunge-frozen in liquid ethane using a FEI Vitrobot Mark IV (FEI/Thermo Fischer) with a blotting force of 0 in an environment with 100% humidity and 4 • C temperature. Cryo-EM images were acquired on a Falcon 3 direct detector in counting mode for the presynaptic complex and in linear mode for the postsynaptic complex on a FEI Talos Arctica at 200 kV. For the presynaptic complex, a magnification of 190 000× was applied to record 40 movie frames with an exposure time of 0. 8 s using a dose rate of 0. 9 electrons per Å2 per frame for a total accumulated dose of 36 electrons per Å2 at a pixel size of 0.
76 Å. For the postsynaptic complex, a magnification of 120,000 x was applied to record 20 movie frames with an exposure time of 1 s using a dose rate of 3 electrons per Å2 per frame, resulting in a total accumulated dose of 60 electrons per Å2 at a pixel size of 1. 24 Å. The final datasets were composed of 2896 (for the presynaptic complex) and 2364 (for the postsynaptic complex) micrographs with defocus values ranging from -0. 8 to -2. 5 m.
Helical reconstruction Similar procedures were applied to the presynaptic complex and the postsynaptic complex datasets using helical reconstruction methods in RELION 2. 1  (31). All frames were corrected for gain reference, binned by a factor of 2 only for the presynaptic complex, motion-corrected and dose-weighted using MOTIONCOR2  (32). Contrast transfer function (CTF) parameters were estimated by CTFind-4. 1  (33). Particles on micrographs of the presynaptic complex were picked manually in box sizes of 180 pixels and with an inter-box distance of 100 Å. Then, picked particles were classified into two-dimensional class averages to identify homogeneous subsets using a regularization value of T = 2. Selected classes were used as references for autopicking in RELION 2. 1  (31). The total number of initial extracted segments (25653) was reduced to 7254 by subsequent rounds of two-dimensional classifications.
46 • and 58. 62 • respectively) and rise (15. 38 and 14. 97 Å, respectively). The final resolution was 3. 9 Å for the presynaptic complex and 3. 8 Å for the postsynaptic complex, calculated with two masked halfmaps refined independently, according to the gold standard Fourier shell correlation (FSC) 0. 143 criterion using RE-LION. Local resolution, calculated with RELION with a Bfactor applied of -141. 9 and -153. 07 respectively, retrieved a range between 3. 7 and 7. 7 Å. All of the densities obtained were subjected to Auto-sharpening in the Phenix software package  (34).
Model building and refinement The initial atomic model of SpRecA protomers in both presynaptic and post-synaptic complexes were generated from the crystal structure of E. coli RecA (PDB ID: 3cmw and 3cmx) by SWISS-MODEL  (35). Rigid-bodies, comprising four molecules of ATP␥ S and the DNA, were docked into the autosharpened electron density map in UCSF-Chimera  (36). The coordinates of the obtained single-chain model were modified manually using Coot and refined with repeated rounds of Phenix real-space refine function. The structure was further refined in real-space in PHENIX with secondary structure restraint  (34). The atomic models were validated using the Cryo-EM validation tools of Phenix  (37). Briefly, each model was firstly refined against the sharpened map (Supplemental data). To monitor the refinement of the model and avoid over-fitting, the final model was refined against one half map and tested against the other half map by calculating the Fourier Shell Correlation curves (not reported), which indicated that the refinement of the atomic coordinates did not suffer from over-fitting.
sity  Paul Sabatier ;  Agence Nationale de la Recherche  [ ANR-10-BLAN-1331 ]; IdeX Toulouse funding for Emergence with the  SMART  project ' Single Molecule Analysis of Homologous Recombination ' attributed to M. H. and for Equipment with the ' Go ahead in life sciences in Toulouse ' project;  Fondation pour la Recherche Médicale (FRM)  [ ING20150532556 ] for the salary of S. S. ;  European Research Council (ERC)  consolidator grant Trans-foPneumo (725554) attributed to R. F. ; S. C. K. was supported by grants from  NIH  ( GM62653 ,  GM64745 , and  R35 GM131900 ) and  DOD-CDMRP  ( BC171869 );  France Bio Imaging (FBI)  funding attributed to E. M. & M. H. ; We thank  Chantal Prevost  for helpful discussions. Funding for open access charge:  ERC PneumoTransfo.
DATA AVAILABILITY The cryo-EM densities of RecA nucleofilaments on ss-DNA and dsDNA have been deposited in the Electron Microscopy Data Bank under ID codes EMD-15524 and EMD-15525 respectively. The models for RecA nucleofilaments on ssDNA and dsDNA have been deposited in the PDB under ID codes PDB 8AMD and 8AMF respectively. Raw cryo-EM data are available on request.
Objective: CHAMPION-NMOSD (NCT04201262) is a phase 3, open-label, externally controlled interventional study evaluating the efficacy and safety of the terminal complement inhibitor ravulizumab in adult patients with antiaquaporin-4 antibody-positive (AQP4+) neuromyelitis optica spectrum disorder (NMOSD). Ravulizumab binds the same complement component 5 epitope as the approved therapeutic eculizumab but has a longer half-life, enabling an extended dosing interval (8 vs 2 weeks). Methods: The availability of eculizumab precluded the use of a concurrent placebo control in CHAMPION-NMOSD; consequently, the placebo group of the eculizumab phase 3 trial PREVENT (n = 47) was used as an external comparator. Patients received weight-based intravenous ravulizumab on day 1 and maintenance doses on day 15, then once every 8 weeks.
4, 5 he emergence of C5 inhibition as a treatment strategy represented a crucial advancement in the management of this rare disease. 6 Eculizumab is a humanized monoclonal antibody that specifically binds and inhibits C5, preventing its cleavage. In the phase 3 PREVENT clinical trial (NCT01892345), eculizumab administered intravenously once every 2 weeks was associated with a 94. 2% reduction in NMOSD relapse risk compared with matching placebo. 4 In the second half of 2019, eculizumab was the first, and remains the only, terminal complement inhibitor to receive approval for the treatment of adults with AQP4+ NMOSD. 7 avulizumab is a recombinant, humanized monoclonal antibody structurally related to eculizumab with equivalent high specificity for the same C5 epitope. 8 avulizumab was created by substituting 4 amino acids in the eculizumab frame at the complementary binding region and the neonatal fragment crystallizable (FcRn) region, resulting in efficient recycling and augmented endosomal dissociation of C5.
These changes increase the half-life of ravulizumab compared with eculizumab, thereby extending the duration of C5 inhibition  8  and enabling a prolonged dosing interval (intravenously, once every 8 vs 2 weeks). CHAMPION-NMOSD (NCT04201262) is a pivotal interventional trial designed to minimize placebo exposure in this rare disease population. It aims to assess the efficacy and safety of ravulizumab in patients with AQP4+ NMOSD, using the PREVENT placebo group as an external comparator.
Trial Design and Oversight CHAMPION-NMOSD (NCT04201262, https://www. clinicaltrials. gov/ct2/show/NCT04201262) is a phase 3, externally placebo-controlled, open-label, multicenter study to evaluate the efficacy and safety of ravulizumab in adult patients with AQP4+ NMOSD. Given the potentially devastating consequences of a single NMOSD attack and the availability of eculizumab, use of a concurrent placebo comparator was considered unethical. 9 At the time of study design and initiation, alternative targeted therapies for AQP4+ NMOSD (eg, satralizumab, inebilizumab) were not approved, precluding their use as active comparators in a head-to-head clinical trial. To conduct a noninferiority trial using eculizumab as a comparator for ravulizumab, an estimated 8,600 patients would have been required for a trial with 90% power, 5% type I error, and a 1.
In contrast to PREVENT, patients in CHAMPION-NMOSD could continue to receive the study drug ravulizumab after a physician-determined relapse. All ontrial relapses were evaluated retrospectively, adhering to the same on-trial relapse criteria used by the treating physician, by an independent 3-member RAC. An adjudicated on-trial relapse refers to a relapse that was positively confirmed by the RAC. To ensure that relapses were not overlooked by treating physicians and to mitigate any concerns of bias, cases of interest (COIs) were also adjudicated by the RAC. Patients who were seen by a treating physician for a relapse evaluation visit during which the treating physician determined that the patient had not had an on-trial relapse were considered to be COIs. In addition, to ensure ascertainment of all COIs, the study team conducted a protocol-defined periodic clinical database review of sentinel AEs: for example, back pain, hiccups, hemiparesthesia, and pruritis.
Published registry data support an ARR of 0. 6 in patients with NMOSD receiving commonly used therapies,  16  with another published analysis indicating a range of ARRs from 0. 2 to 0. 63 in patients receiving various first-line therapies for NMOSD. 17 The ARR endpoint would be considered statistically significant if the adjudicated on-trial ARR was <0. 25 with a 2-sided p ≤ 0. 05. Safety endpoints included incidence of treatment-emergent AEs (TEAEs), treatment-emergent serious AEs (TESAEs), and TEAEs leading to discontinuation of study drug. TEAEs presented herein exclude AEs of NMOSD relapse.
Statistical Analysis Statistical analyses were performed using SAS version 9. 4. A logrank test was used to analyze between-group differences for the primary endpoint; the analysis included all patients who had received at least 1 dose of study drug. Hazard ratios (HRs) and risk reductions were summarized from a Cox proportional hazards model. For consistency with PREVENT, changes in HAI, EQ-5D index, EQ-5D VAS, and EDSS scores were assessed from baseline (day of first dose of study drug or placebo) to either the end of the primary treatment period, or, for patients with physician-determined relapse, the week 6 relapse follow-up visit. Additional statistical information, including statistical tests used for the secondary efficacy endpoints, can be found in the supplement. Because using an external comparator could introduce confounding, a sensitivity analysis was conducted using propensity scores to balance treatment groups, 18 and a tipping-point analysis (E-value) was used to estimate, in terms of a relative risk, the amount of unmeasured confounding that would have been required to account for the treatment effect.
To mitigate potential bias in patients, and to replicate the cadence of patient and study site staff interactions in PREVENT, biweekly phone visits were implemented in this trial to ensure that patients had equivalent opportunities to report AEs and potential relapse symptoms. If potential relapse symptoms were identified during these phone visits, an in-person relapse evaluation visit would have been scheduled as soon as possible. To minimize potential bias further, the database was systematically reviewed per protocol to identify potential missed relapses and other COIs, and both physicianidentified on-trial relapses and COIs were adjudicated by the independent RAC. It is our hope that the innovations in trial design undertaken in CHAMPION-NMOSD will further the advancement of research in rare diseases, especially in situations when the availability of an approved treatment ethically precludes the use of placebo control, and/or limited patient populations prevent the recruitment of sufficient patients to power a noninferiority trial against existing approved treatments.
Acknowledgments The CHAMPION-NMOSD trial was funded by  Alexion ,  AstraZeneca Rare Disease. We thank the patients who participated in the trial and their families, and the members of the  RAC :  Brian Weinshenker, MD  (chair),  Benjamin Greenberg, MD , and  Robert Sergott, MD. We would like to acknowledge  B. Thompson , who provided editorial support based on authors' detailed directions in the production of this article (funded by  Alexion ,  AstraZeneca Rare Disease ).
Fine mapping and functional analyses of GWAS loci To prioritize candidate variants for functional follow-up from each of the LUAD GWAS loci, we performed Bayesian fine mapping using FINEMAP  32  (Methods). Fine mapping of the genome-wide significant loci from the discovery set nominated 95% credible set variants for 9 loci with a median of 63 variants per locus (Supplementary Data 1). For the 12 novel variants identified from the combined discovery and replication datasets as well as conditional analysis, we then performed variant annotation analysis. High-LD variants for these signals (R 2 ≥ 0. 8 with the lead SNP in the 1000 Genomes, phase 3, EA) included those located in predicted promoters or enhancers in lung tissues/cells (RegulomeDB 33 , Haploreg 34 v4. 1, and FORGE2;  35  Supplementary Data 2), which can be tested in future experimental studies.
To further characterize the functionality of the prioritized susceptibility genes that could explain the new GWAS loci, eQTL colocalization and transcriptome-wide association study (TWAS) analyses were conducted. Initial stratified LD score regression  36  using GTEx data (Supplementary Fig. 4 ; Supplementary Data 3) indicated that LUAD heritability drawn from EA populations are enriched in lung tissuespecific genes and chromatin features compared to other tissues (aggregated rank test P = 1. 36 × 10 -2 and 7. 7 × 10 -3 , respectively; Supplementary Data 3). Accordingly, we performed eQTL analyses using the Taiwanese dataset of adjacent normal lung tissues from 115 never-smoking lung cancer patients (LCTCNS) (Methods; Supplementary data 4). We performed colocalization analyses of eQTL genes using eCAVIAR  37  and HyPrColoc  38.
which is a key enzyme in the metabolism of polyunsaturated fatty acids and plays a key role in inflammatory diseases  40. Higher FADS1 levels in the lung tissues were associated with LUAD risk, which is consistent with its role in increasing the proliferation and migration of laryngeal squamous cell carcinoma through activation of the Akt/mTOR pathway  41. Among the known loci, colocalization identified TP63 at 3q28 and ACVR1B at 12q13. 13 (Supplementary Data 5). We then performed a TWAS using LCTCNS eQTL dataset. TWAS identified FADS1 as a susceptibility gene from the 11q12. 2 locus (TWAS P = 3. 01 × 10 -6 ) validating the finding from the colocalization analysis. We further identified ELF5 (TWAS P = 1. 89 × 10 -8 ) as a novel gene from a locus (at 11p13) not originally passing the genome-wide significance threshold based on a single-variant test in our EA discovery GWAS (Supplementary data 6, Methods).
For these two loci, we also performed TWAS conditional analysis to assess whether genetically predicted expression of these genes explain most of the GWAS signal. When GWAS signal was conditioned on predicted expression of ELF5, most of the signal disappeared, adding support for ELF5 as the main susceptibility gene in this locus (Supplementary Fig. 5A ). ELF5 encodes E74-like factor 5, a key transcription factor of alveologenesis of mammary glands  42. Lower levels of ELF5 were associated with LUAD risk in the TWAS. Similarly, when GWAS signal was conditioned on predicted expression of FADS1, the strongest part of the signal disappeared (Supplementary Fig. 5B ). We further performed TWAS analysis using GTEx lung eQTL dataset (v8, n = 515, ~85% Europeans) and identified five genes from four loci (Supplementary Data 6).
While identification All p values are nominal and two-sided. a Data from single-variant analysis in GWAS. b Conditional analysis using GCTA, conditioning on the lead variant in each locus. c Joint analysis using GCTA including the lead variant and the significant variants in conditional analysis. of ELF5 was common between two datasets, TWAS using GTEx data identified four unique genes from three known loci (DCBLD1, MPZL3, JAML, and LINC00674). Notably, FADS1 was identified only by ancestrymatched LCTCNS eQTL dataset even with a ~4 times smaller sample size. An investigation of the local environment of susceptibility loci revealed further plausible candidate genes that could be pursued in laboratory follow-up. For instance, rs137884934 on 3q22. 3 maps to PIK3CB encoding an isoform of p110 catalytic subunit of Class IA PI3K  43.
Polygenic risk score and gene-smoking interaction analysis We investigated whether the polygenic risk score (PRS), which was based on the cumulative effect of 25 independent susceptibility loci for LUAD in EA (Supplementary Table  4 ), interacted with smoking status to influence the risk of LUAD, given previous evidence of geneenvironment interaction  61, 62. Since only summary statistics were available for some datasets (instead of individual genotype data), we developed a statistical method for testing the multiplicative smoking-PRS interaction using the summary statistics for the susceptibility variants (Methods). Compared to the middle quintile that represents the average risk in the general population, the top quintile had OR of 2. 07 (95% CI = 1. 99, 2. 15) for never-smokers and 1. 80 (95% CI = 1.
Genetic architecture, performance of PRS and sample size requirements in EA populations To further investigate the underlying genetic architecture of susceptibility (Methods) to LUAD  63  in EA populations, we performed a GENESIS  26  analysis based on the GWAS summary statistics for our larger never-smoker dataset. We estimated that ~2275 (s. e. = 1167) susceptibility variants are independently associated with LUAD, suggesting that LUAD is a highly polygenic disease and most of the susceptibility variants have very small effect sizes. Based on the estimated parameters, we investigated how the performance of a PRS, measured as the area under the receiver operating characteristic curve (AUC), depended on the sample size of the training GWAS (Fig. 5 ). The AUC is predicted to be 60. 7% (95% CI = 56.
Polygenic risk score quinƟle group Never-smokers Individuals with a history of smoking Fig. 4  | A polygenic risk score (PRS) is more strongly associated with risk of lung adenocarcinoma in never-smokers than in individuals with a history of smoking (P = 0. 0058). The PRS was defined based on 25 independent variants that achieved genome-wide significance in EA with weights derived from the metaanalysis of the current study (Supplementary Table  4 ). The odds ratios (ORs additional loci in the combined EA and EUR populations, with consistent effects in both. Our eQTL colocalization and TWAS analyses using an ancestrymatched lung eQTL dataset (EA population) identified novel LUAD susceptibility genes including FADS1 and ELF5. Importantly, FADS1 is regulated by sterol-response element-binding proteins (SREBPs)  65  , which govern lipid metabolism in alveolar type II (ATII) cells  66.