dataesr/scientific-paragraphs-categorization huggingface dataset:
The fluid was then removed by vacuum, and the wells were washed twice. Analysis of each sample was performed in duplicate. The limit of sensitivity was 1. 95 pg/mL, and the linear range of detection was 1. 95 to 32,000 pg/mL for all the cytokines analyzed in this study. Data were collected and analyzed using a Bio-Plex 200 instrument equipped with BioManager analysis software (Bio-Rad, Bio-Plex manager v5. 0, Segrate, Milano, Italy).
Statistical Analysis The lipid oxidation, fatty acid profile, and sperm kinetic traits were analyzed using a linear model to evaluate the fixed effect of diet, time, and their interaction (CNT, HA, HO; SPSS v28). Free isoprostanoids, testosterone, and cytokines were analyzed using a linear model with diet as a fixed effect. The least squares (LS) mean, pooled standard error (SE), and Root Mean Square Error (RMSE) are reported. The Bonferroni correction was applied for multiple comparisons. The significance was set at p ≤ 0. 05. Regression curves were estimated with linear and non-linear models using R 2 as an estimate of the goodness-of-fit.
Fatty Acid Profile of Rabbit Semen Table  3  reports the fatty acid profile of raw semen. The data showed that the HO group had a higher ALA content compared to the other groups at every time point; however, this concentration seemed to decrease over time. Significance was also found for C20:2n-6 and total n-6 PUFA where the highest proportion was recorded in CNT. DHA and n-3 PUFA were also higher (p > 0. 05) in the HO group compared to the other diets, but the high variability of the analytical determinations rendered the differences non-significant.
Data sources 2. 2. 1. Data sources used to build the genetic instruments A list and a description of the GWAS summary statistics used are provided in Supplementary Table  S1. Genetic association estimates for diastolic and systolic blood pressure (BP) were obtained from a GWAS meta-analysis of 757,601 individuals with European ancestry drawn from the UK Biobank  (10)  and the International Consortium of BP GWAS meta-analysis  (11). Genetic association estimates for resting heart rate were obtained from a GWAS of 458,969 individuals with European ancestry drawn from the UK Biobank, where association analysis was adjusted for age, sex, smoking, genotyping array, and 20 ancestry principal components  (12). Expression quantitative trait loci (eQTL) were obtained from the Genotype-Tissue Expression (GTEx) portal (release version 8) that includes 15,201 RNA-sequencing samples from 54 nondiseased tissues of 838 postmortem donors (85.
3% European American, 66. 4% male)  (13). As 49 tissue types are included in the eQTL analyses from GTEx portal, we restricted the selection for our study to the eQTLs data from the heart's left ventricle (LV). Protein quantitative trait loci (pQTL) data were obtained from a cohort of 3,301 participants of European descent from the INTERVAL study that includes about 50,000 healthy participants nested within a randomized trial of varying blood donation intervals  (14). The relative concentrations of 3,622 blood proteins or protein complexes were assessed for each donor by modified aptamers.
Outcome data sources The Heart Failure (HF) risk GWAS comprising 40,805 HF cases and 542,362 controls was derived from a GWAS metaanalysis of HF of the HERMES consortium of European ancestry, which includes 68,157 HF cases and 949,888 controls  (15). The two-sample MR study design used for our analysis requires avoiding an important overlap of participants between the exposure and outcome GWAS. As the BP and heart rate GWAS we used included a large proportion of subjects from the UK Biobank cohort, the genetic association estimates for HF risk were obtained from the HERMES GWAS after the exclusion of   (16). All studies of this meta-analysis included age and sex (except for single-sex studies) as covariates in the regression models. Principal components were included as covariates for individual studies as appropriate.
Selection of genetic instruments To build the genetic instrument, the gene encoding regions of the nine adrenergic receptors, ADRA1A (α1A), ADRA1B (α1B), ADRA1D (α1D), ADRA2A (α2A), ADRA2B (α2B), ADRA2C (α2C), ADRB1 (ß1), ADRB2 (ß2) and ADRB3 (ß3), as well as their promoter and cis-enhancer regions were first selected (Figure  1  and Supplementary Figure  S2 ). This tailored approach, as contrary to the use of a fixed region upstream and downstream (+/-× kbp) of a gene, minimizes the risk of including non-relevant genetic variants that could bias the MR analysis. The cis-coding regions of the genes encoding the nine adrenergic receptors were defined using the Ensembl database  (20). Promoter and cis-enhancer regions were identified using the GeneHancer database in the GeneCards online platform (version 4.
8)  (21). We verified that the identified SNPs were specific to the genes encoding the adrenergic receptors when located in intergenic regions by using the genetic. opentargets. org database where we checked that the SNPs had a high Variants to Genes (V2G) score affiliated to the gene encoding the corresponding adrenergic receptor. The V2G score is a single aggregated score for variant-gene prediction obtained by combining eQTLs and pQTLs, chromatin interaction and conformation datasets, in silico functional predictions, and distance from the canonical transcript start site  (22). We then identified the genetic variants of these regions as single nucleotide polymorphisms (SNPs) that are in both exposure (BP, heart rate, LV eQTLs or blood pQTLs) and outcome GWAS and checked their specific relationship to the corresponding adrenergic receptor.
For the MR analyses using eQTLs, the results are expressed as effect size weighted by the expression level of the gene encoding the corresponding adrenergic receptor. Sensitivity analyses were employed to assess the validity of these findings. They included diagnostic tests for horizontal pleiotropy (Cochran's Q statistic and MREgger test) and SNP outliers (leaveone-out analysis). If horizontal pleiotropy was detected, then the MR Egger method would have been employed to conduct the MR analysis. When, for a given gene, additional SNPs have been selected using alternate thresholds, supplementary MR analyses using only the SNPs at genome-wide significance (P ≤ 5 × 10 -8 ) and with a LD threshold of r 2 < 0. 1 were also performed. All analyses were conducted using the R programming language. The data formatting steps to perform MR analyses, including SNP selection and data harmonization steps, were completed using the R packages "data.
Additional MR analyses The analyses were repeated, but with the genetic instruments weighted by systolic BP as proxies for ß1 and ß2 activities and by heart rate as a proxy for α2B activity. The results, as detailed in the supplemental Results, were consistent with the main analysis (Supplementary Tables S7-S9 and Figures  S6-S9 ). We repeated also these MR analyses using eQTL data which were available for ADRA1A and ABRB2. In general, these eQTL weighted analyses supported our findings, despite a decrease in precision related to the more limited number of available instruments (Supplementary Table  S11 ). We did not find any significant pQTLs for any of the adrenergic receptors studied, preventing any MR analyses with pQTLs.
77 (95% CI 0. 64-0. 93, P = 0. 008). Furthermore, our results are consistent with recent studies that suggest a lack of detrimental or beneficial effect of long-term ß2 modulation on LV volumes and function and the risk of HF  (41). Drug target MRs follow the same principle as the classical MR but they evaluate the effect of the drug target and not the biomarker itself on the disease  (9). As the effect of variants on gene activity cannot usually be directly measured, a range of traits are used as proxies, including gene or protein expression, or downstream physiological biomarkers, such as in our study BP and heart rate, the main cardiovascular biomarkers modulated by the sympathetic nervous system. To be applicable, the method requires either a comprehensive understanding of the pathophysiology of the target such as in the case of our targets or reliable data on expression, available also in our study for ADRA1A and ADRB2.
Funding This study was funded by  Servier  and  BigData@Heart  that received funding from the  Innovative Medicines Initiative2 Joint Undertaking  under grant agreement No  116074 , which received support from the  European Union 's  Horizon 2020 research and innovation program  and the  European Federation of Pharmaceutical Industries and Associations. FA is supported by  University College London Hospitals National Institute for Health Research (NIHR) Biomedical Research Centre. RT is supported by a  UK Research  and  Innovation Rutherford Fellowship  hosted by  Health Data Research UK  ( MR/S003754/1 ). DK is supported by the  NIHR  ( 203326   Birmingham Biomedical Research Centre ;  130280/132974 DaRe2THINK ), and the  British Heart Foundation.
INTRODUCTION Haemophilia care is rapidly evolving with emerging treatment options including extended half-life (EHL) factor concentrates, non-factor replacement therapies and gene therapy. Emicizumab, a recombinant bispecific monoclonal antibody that mimics the function of factor VIII (FVIII), is the first licensed and now widely available non-factor replacement therapy. 1  In most countries, emicizumab was first used in patients with persisting inhibitors as prophylaxis with good protection from bleeds. 2  The data on emicizumab in previously untreated patients (PUPs) and minimally treated patients (MTPs) is scarce. A case series on four infants on emicizumab was published  3  and, very recently and, very recently, the interim analysis of HAVEN 7 (NCT04431726), were published as an abstract and demonstrated the safety and efficacy of emicizumab in a relatively small number of children under the age of 12 months with severe haemophilia A.
ACKNOWLEDGEMENTS All the authors have contributed to planning of the survey, summarizing the results and writing the manuscript. They thank all the  PedNet  members who shared their current views on treatment with emicizumab. This study is supported by the  PedNet Hemophilia Research Foundation. Unrestricted sponsorship for the  PedNet Hemophilia Research Foundation  is currently received from  Bayer AG ,  Takeda ,  Novo Nordisk ,  CSL Behring ,  Pfizer Inc. ,  Swedish Orphan Biovitrium AB ,  Hoffmann-La Roche ,  LFB Biotechnologies. Supported by  MH CZ-DRO  ( FNBr ,  65269705 ).
In case of inhibitor development, the Future of Immunotolerance Treatment (FIT) group recommended several years ago to attempt ITI at least once to eradicate inhibitors. 17  In our survey, most Ped-Net centres offered ITI and used emicizumab during ITI to prevent bleeds. Evidence on ITI with concomitant emicizumab is emerging, with MOTIVATE (NCT04023019), NAVIGATE (NCT05802836) and the Emicizumab PUPs and Nuwiq ITI Study (NCT04030052) addressing this topic. to a significant reduction in exposure to FVIII leading to children only reaching 20-50 EDs to FVIII at much older ages-hence, emicizumab may delay or modify the development of FVIII inhibitors. Several clin- ical trials (Emicizumab PUPs and Nuwiq ITI Study, NCT04030052 and NAVIGATE trial; NCT05802836) aim to assess these questions. Despite clinicians considering that routine concomitant FVIII treat- ment along with emicizumab may be beneficial to prevent inhibitor development, this approach is only practiced in a minority (4/20; 20%) of PedNet centres.
Data collection This study was approved by the Institut Curie review board; a waiver of informed consent was granted due to the retrospective nature of the work. A synopsis of this retrospective study was sent to French cancer centers, known for having signi cantly contributed to the alpelisib EAP. Eleven of these centers agreed to participate in the study: Institut Curie Paris and Saint Cloud Hospitals, Institut Paoli Calmettes, Centre Eugene Marquis, Institut de Cancérologie de l'Ouest, Gustave Roussy, Centre Antoine Lacassagne, Tenon Hospital, Brest University Hospital Center, Centre Georges-François Leclerc, American Hospital of Paris, Institut Sainte-Catherine. Medical oncologists retrospectively collected data using electronic medical records for all patients enrolled in the EAP at their sites.
Treatment e cacy At data cutoff, follow-up was 7. 1 months (range: 0. 2-19. 5). With 168 PFS events, the median PFS was 5. 3 months (95%CI, 4. 7-6. 0) (Fig. 2 ). Sixty-nine patients (29. 6%) have died at database lock, with a median OS of 16. 8 months (95%CI, 14. 5-NR) (Fig. 3 ). Among 186 evaluable patients, best tumor responses were: complete response in 5 patients (2. 7%), partial response in 66 patients (35. 5%) and stable disease in 53 patients (28. 5%). The overall response rate was 38. 8% (95%CI, 31. 8%-45. 7%) and the 6mCBR was 45. 3% (95%CI, 37. 8%-52. 8%) (Table  3 ). 1 ).
While the small number of patients precludes any de nitive conclusion, mutations located in exon 4 and that impact the C2 domain (C420R and N345K) have been previously reported as associated with shorter OS in the METABRIC dataset (12). In vitro screening experiments also showed that the C420R PIK3CA-mutant EFM192A and JIMT-1 cell lines display limited sensitivity to alpelisib (14). Of note, most centers participating to the EAP did not sequence PIK3CA exon 4, explaining the underrepresentation of exon 4 mutations in our cohort: 1. 7% (4/233 patients), while C420R and N3345K account for 7. 5% of all activating PIK3CA mutations (15). Further research is therefore required to explore the potential impact of these exon 4 mutations on alpelisib e cacy. As part of the EAP, clinicians were requested to document and report treatment-related adverse events in a prospective manner.
Interestingly, the frequency of any grade hyperglycemia (53%) appears very similar to that observed in SOLAR-1 (64%) and BYLIEVE (58%). Similarly, the proportion grade 3 or 4 rashes was consistent with that observed in BYLieve 8. This may be explained by the application of prophylactic antihistamines recommended for the rst 8 weeks of alpelisib treatment  13. Our real-life study however reports a numerically higher rate of alpelisib discontinuation (37%) than in trials (SOLAR-1: 25%; BYLieve: 21%), which could be explained by the different clinical pro le of treated patients and less stringent monitoring. Overall, our data suggest that alpelisib toxicity management seems feasible in real life, nothwithstanding the fact that our study was conducted in expert cancer centers, which may have participated to prior alpelisib trials.
Yet, regarding the lack of documented signi cant OS and/or quality of life bene t, as illustrated by the score of 3 on ESMO-magnitude of clinical bene t scale (MCBS), such a tolerance pro le makes the actual place of alpelisib in the management of ABC still discussable. Limitations of this study stem from its retrospective nature, preventing any direct comparison with other studies and trials. Nevertheless, a strength of the EAP is that patients were prospectively registered and no patient was lost to follow-up, allowing robust outcomes analyses. This cohort is, to our knowledge, the largest real-life dataset reported so far, and con rms the e cacy and manageability of alpelisib and fulvestrant in PIK3CA-mutant HR+/HER2-ABC patients.
However, the generated mutants frequently appear as a combination of already described single mutations. Moreover, these methods still require experimental testing of many variants because of their low performance in predicting the combinatorial effect of several single mutations. Several in silico methods have been developed to predict protein/protein binding affinity  [18]. These methods are generally pre-determined equations (scoring functions) of energy terms, and the weights of the terms are optimized by machine learning on experimental datasets comprising various protein-protein complex structures. If these methods perform well with the training dataset, they generally show low correlation with a new test set, which is certainly due to the fact that the test set diverges too much from the learning set  [19, 20].
Indeed, as with all machine learning settings, the final performance is highly dependent on the quality and diversity of the learning dataset. Algorithms dedicated to the prediction of Fc/FcRn binding affinity have been developed  [21, 22]. However, the precision of these scoring functions is low, especially for evaluating the impact of multiple mutations. Most of these algorithms suffer from too reduced learning sets. Nevertheless, a lot of data are available regarding Fc/FcRn variants, but they have not been exploited with these methods yet. Indeed, only a selection of variants is usually described in the scientific literature, even in supplementary data, although a larger number of tested variants can be retrieved from patent applications or patents. For example, researchers from Chugai Pharma tested more than 1000 variants, but the comprehensive set of mutated variants can only be found in some patent applications (e.
g. , WO2013046704), whereas only 7 variants are described in the corresponding article  [23]. In the present work, we collected these data in order to constitute a specific Fc/FcRn dataset that could be used in machine learning algorithms. Our dataset of Fc variants was mainly collected from the patent literature. We then trained different algorithms with Fc/FcRn parameters calculated with bioinformatic tools, together with affinity data, and assessed the performance of the different algorithms in a 10-fold cross-validation setting. We also evaluated the algorithms by comparing the distribution of predicted affinities for thousands of in silico randomly generated Fc variants. Finally, to validate the robustness of the models, we produced three new variants with three, five, and seven mutations and compared the predicted affinity with the experimental binding affinities measured by SPR.
Algorithms and Tested Features The 3D structure of the 1323 Fc variants were modeled from the Fc/FcRn co-crystal (4N0U. pdb file  [13] ) with PyMOL v2. 5. 4, and features reported to be relevant in previous studies  [25] [26] [27]  were calculated with the CCP4 software v8. 0. 009. In total, 147 features were initially considered (Table  A1 ) and collected from the 1323 Fc/FcRn 3D models. In our model, variants considered as different in the original patent can lead to duplicates since not all amino acids are used for computing parameters. For example, if a variant has the S239K/T256E substitution and the other variant has the L235R/T256E substitution, it is considered as a duplicate because the influence of the S239K or L235R substitutions is ignored in our model. Including these positions in the study was nevertheless considered.
However, from our dataset, mutations at these two positions do not significantly alter the affinity. Consequently, in this example, only the T256E substitution is taken into account, and the two variants appear as duplicates in our set. We thus eliminated such duplicates, which could bias the training results. As a result, the FLS contains 1048 examples and the SLS 1251 examples. We then tested different machine learning (ML) algorithms using the FLS and SLS learning sets. Among the scikit-learn library  [28] , we chose four different algorithms: support vector regressor (SVR), multi-linear regression (MLR), multi-layer perceptron (MLP), and random forest regressor (RFR). These methods were well suited for the type of data we had and the type of predictions we wanted to obtain. Moreover, they are quite simple in their principles, and we wanted to see if the parameters we had in mind were sufficient for the task.
The performance of SVR, RFR, and MLP remained unchanged (data not shown), but with a net gain in calculation speed. We then removed features that were highly correlated (evaluated by the pandas. DataFrame. corr method) and kept 11 features for the FLS and 6 for the SLS (Figure  1 ). This second step slightly improved the performance of the MLR with the FLS and slightly decreased the performance of the other algorithms with the SLS. However, this further dimension reduction is useful to prevent overfitting. Further dimension reduction (removing of features) negatively impacted the performance of all algorithms. We compared the results obtained for the two learning sets using the optimal number of features: FLS with 11 features and SLS with 6 features. The most important feature of the FLS model (35% relative importance) is the number of atoms interacting between the β chain of FcRn (β2-microglobulin) and the Fc (Figure  1 ).
The accessible surface area of residue at position 255 and buried surface area of residue at position 434 of the Fc come in second and third position, respectively (Figure  1 ). The other features have lower impact but altogether account for about half of the model information (Figure  1 ). The most important feature retained with the SLS model is the buried surface area of the amino acid at position 129 of FcRn, with a relative importance of 0. 7. To ensure that the models were not overfitting, despite good learning performance on the entire training datasets, we used a 10-fold cross-validation scheme. We performed this cross-validation test several times for each algorithm to ensure that scores were consistent between different runs, because each run of the algorithms can produce different results.
We also tested a model that also incorporated the energy terms (60 parameters) calculated from the FoldX suite  [29]  with all variants and following the same procedure of removing duplicates and correlated features, but the performance did not improve, and the best correlation obtained was 0. 89 with 11 parameters with the RFR (Figure  A1 ). To ensure that the models were not overfitting, despite good learning performance on the entire training datasets, we used a 10-fold cross-validation scheme. We performed this cross-validation test several times for each algorithm to ensure that scores were consistent between different runs, because each run of the algorithms can produce different results. With optimized parameters (see Materials and Methods), the consistent regression scores of the M1048/11 model (R 2 ) obtained with MLR, MLP, SVR, and RFR are on average 0.
Randomly Generated Variants Predicted Affinity Comparison with the Four Algorithm For evaluating the capacity of our two models and algorithms to generalize data, we tested both models with the four algorithms with in silico randomly gen Fc variants. We generated two sets of more than 8000 variants containing three (mu and five (mut5 set) random mutations. These mutations were introduced at positio 252,  253, 254, 255, 256, 257, 285, 286, 288, 307, 308, 309, 310, 311, 314, 428, 433, 434, 43  436 because the calculated features of our models only included these positions. W erated one additional set of 1000 Fc variants containing six to eight mutations (mu with not too much destabilizing, or with a positive effect on their own according dataset. The number of mutations was limited to eight because the effect of close tions on the stability and production of the antibody is hard to predict.
Randomly Generated Variants Predicted Affinity Comparison with the Four Algorithms For evaluating the capacity of our two models and algorithms to generalize to new data, we tested both models with the four algorithms with in silico randomly generated Fc variants. We generated two sets of more than 8000 variants containing three (mut3 set) and five (mut5 set) random mutations. These mutations were introduced at positions 251,  252, 253, 254, 255, 256, 257, 285, 286, 288, 307, 308, 309, 310, 311, 314, 428, 433, 434, 435, and 436  because the calculated features of our models only included these positions. We generated one additional set of 1000 Fc variants containing six to eight mutations (mut8 set), with not too much destabilizing, or with a positive effect on their own according to our dataset.