bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural models of genome-wide covariance identify multiple common dimensions in autism
Authors: de Hoyos Lucía , Barendse Maria T , Schlag Fenja , van Donkelaar Marjolein MJ , Verhoef Ellen , Shapland Chin Yang , Klassmann Alexander , Buitelaar Jan , Verhulst Brad , Fisher Simon E , Rai Dheeraj , Pourcain Beate St 
Published: October 2022

AbstractCommon genetic variation has been associated with multiple symptoms in Autism Spectrum Disorder (ASD). However, our knowledge of shared genetic factor structures contributing to this highly heterogeneous neurodevelopmental condition is limited. Here, we developed a structural equation modelling framework to directly model genome-wide covariance across core and non-core ASD phenotypes, studying autistic individuals of European descent using a case-only design. We identified three independent genetic factors most strongly linked to language/cognition, behaviour and motor development, respectively, when studying a population-representative sample (N=5,331). These analyses revealed novel associations. For example, developmental delay in acquiring personal-social skills was inversely related to language, while developmental motor delay was linked to self-injurious behaviour. We largely confirmed the three-factorial structure in independent ASD-simplex families (N=1,946), but uncovered simplex-specific genetic overlap between behaviour and language phenotypes. Thus, the common genetic architecture in ASD is multi-dimensional and contributes, in combination with ascertainment-specific patterns, to phenotypic heterogeneity.
Whole-brain structural and functional neuroimaging of individuals who attempted suicide and people who did not: a systematic review, exploratory coordinate-based meta-analysis and seed-based connectivity study
Authors: Meda Nicola , Miola Alessandro , Cattarinussi Giulia , Sambataro Fabio 
Published: October 2022

AbstractIntroductionSuicide is the cause of death of approximately 800’000 people a year. Despite the relevance of this behaviour, risk assessment tools rely on clinician experience and subjective ratings.MethodsGiven that previous suicide attempts are the single strongest predictors of future attempts, we designed a systematic review and coordinate-based meta-analysis to evince if any neuroimaging features can help distinguish individuals who attempted suicide from subjects who did not. Out of 5659 publications from PubMed, Scopus and Web of Science, we summarised 102 experiments and meta-analysed 23 of them.ResultsA cluster in the right Superior Temporal Gyrus, a region implicated in emotional processing, might be functionally hyperactive in individuals who attempted suicide. Furthermore, we used JuSpace and the Human Brain Connectome dataset to show that this cluster is enriched in 5-HT1Aheteroreceptors, and its connectivity with the left central operculum is significantly correlated with loneliness scores.ConclusionsThis evidence provides a putative neural substrate for suicidal thought-to-attempt transition as hypothesised by Joiner’s Interpersonal Theory of Suicide. Heterogeneity in the analytical techniques and weak or absent power analysis of the studies included in this review currently limit the applicability of the findings, the replication of which should be prioritised.
Initial Insights from a Quality Improvement Initiative to Develop an Evidence-informed Young Adult Substance Use Program
Authors: Halladay Jillian , Stead Victoria , McCarron Catherine , Kennedy Marina , King Kyla , Venantius Michelle , Carter A. , Syan Sabrina , Matthews Mareena , Khoshroo Saba , Massey Myra , Rahman Liah , Burns Jacinda , Punia Kiran , MacKillop Emily , Raymond Holly , MacKillop James 
Published: October 2022

AbstractBackgroundHigh rates of substance misuse during emerging adulthood require developmentally appropriate clinical programs.ObjectivesThis work outlines the development of an evidence-informed emerging adult outpatient substance use program, quality improvement process and protocol, and 1-year program insights.MethodsLiterature reviews, program reviews, environmental scans, and stakeholder consultations (including lived expertise) were used to develop the program. A 12-week emerging adult (17-25) measurement-based care program was developed including: 1) individual measurement-based care and motivational enhancement therapy sessions; 2) group programming focused on cognitive behavioural therapy, mindfulness, distress tolerance, and emotional regulation; 3) consults for diagnostic clarification and/or medication review; and 4) a separate education group for loved ones. A measurement system was concurrently created to collect clinical and program evaluation data at 6 time points.ResultsIn the first year of the program, 96 young adults fully enrolled in the program (Mean age = 21 years old, 48% female gender) primarily reporting treatment targets of alcohol (70%) and cannabis (59%). Almost all patients (97%) surpassed at least one clinical threshold for co-occurring mental health disorder, with the median/mode of positive psychiatric screens being for 5 conditions.ConclusionsThis program demonstrates that developing an integrative evidence-informed measurement-based care young adult substance use program is feasible, though requires flexibility and ongoing monitoring to meet local needs. Patient characteristics reveal very high rates of concurrent psychiatric disorders in addition to substance use disorders.
Prediction of clinically relevant postoperative pancreatic fistula using radiomic features and preoperative data
Authors: Bhasker Nithya , Kolbinger Fiona R. , Skorobohach Nadiia , Zwanenburg Alex , Löck Steffen , Weitz Jürgen , Hoffmann Ralf-Thorsten , Distler Marius , Speidel Stefanie , Leger Stefan , Kühn Jens-Peter 
Published: October 2022

ABSTRACTClinically relevant postoperative pancreatic fistula (CR-POPF) can significantly affect the treatment course and outcome in pancreatic cancer patients. Preoperative prediction of CR-POPF can aid the surgical decision-making process and lead to better perioperative management of patients. In this retrospective study of 108 pancreatic head resection patients, we present risk models for the prediction of CR-POPF that use combinations of preoperative computed tomography (CT)-based radiomic features, mesh-based volumes of annotated intra- and peripancreatic structures and preoperative clinical data. The risk signatures were evaluated and analysed in detail by visualising feature expression maps and by comparing significant features to the established CR-POPF risk measures. Out of the risk models that were developed in this study, the combined radiomic and clinical signature performed best with an average area under receiver operating characteristic curve (AUC) of 0.86 and a balanced accuracy score of 0.76 on validation data. The following pre-operative features showed significant correlation with outcome in this signature (p<0.05)- texture and morphology of the healthy pancreatic segment, intensity volume histogram-based feature of the pancreatic duct segment, morphology of the combined segment, and BMI. The predictions of this pre-operative signature showed strong correlation (Spearman correlation co-efficient,ρ= 0.7) with the intraoperative updated alternative fistula risk score (ua-FRS), which is the clinical gold standard for intraoperative CR-POPF risk stratification. These results indicate that the proposed combined radiomic and clinical signature developed solely based on preoperatively available clinical and routine imaging data can perform on par with the current state-of-the-art intraoperative models for CR-POPF risk stratification.
A novel cuproptosis-related gene signature for the prediction of liver cancer prognosis identified DLAT is a potential therapeutic target
Authors: Xu Dong , Yang Fei , Chen Yang , Zhu Hong , Sun Haijian , Shen Tong , Zhu Yongkang , Zhou Guowei , Chen Dexuan , Yang Xiaojun , Lin Kai , Xia Cunbing 
Published: October 2022

Background and PurposeThe liver cancer (LC) is a highly malignant tumor of the digestive system with a poor prognosis. Cuproptosis is a new type of regulated cell death that has been found by researchers. The expression of cuproptosis-related genes in LC and their relevance to prognosis, on the other hand, remain unknown. This study aimed to explore a gene signature to predict the liver cancer prognosis and identified the vital gene.Experimental approachThe expression patterns of RNA and related clinical data of 371 LC patients were obtained based on The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were acquired by comparing tumors with adjacent normal samples. Genes displaying significant association with OS were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. All cases were classified into the validation or training group to validate the constructed gene signature. We conducted real-time polymerase chain reaction (PCR) and assays for transwell invasion, CCK-8, and colony formation to determine the biological roles of DLAT.Key ResultsThe differential expression of twelve cuproptosis regulators in LC and normal liver tissues was discovered in this investigation. DEGs can be used to distinguish between two forms of LC. Cuproptosis-related genes were evaluated for survival predictive significance using the Cancer Genome Atlas (TCGA) cohort. A 3-gene signature based on least absolute shrinkage and selection operator (LASSO) Cox regression was used to categorize an LC patient cohort from the TCGA into low- and high-risk categories. Patients in the low-risk group had a considerably higher likelihood of surviving (P = 0.05) than those in the high-risk group. When paired with clinical parameters, risk score was an independent predictor in predicting the OS of patients with LC.Conclusions & ImplicationsCuproptosis-related genes thus play an important role in tumor formation and can be used to predict the prognosis of LC patients. DLAT has the best prognostic value and can be a therapeutic target for liver cancer.
Age-dependent topic modelling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk
Authors: Jiang Xilin , Zhang Martin Jinye , Zhang Yidong , Durvasula Arun , Inouye Michael , Holmes Chris , Price Alkes L. , McVean Gil 
Published: October 2022

AbstractThe analysis of longitudinal data from electronic health records (EHR) has potential to improve clinical diagnoses and enable personalised medicine, motivating efforts to identify disease subtypes from age-dependent patient comorbidity information. Here, we introduce an age-dependent topic modelling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR data sets. The model learns, and assigns to each individual, topic weights for several disease topics, each of which reflects a set of diseases that tend to co-occur within individuals as a function of age. Simulations show that ATM attains high accuracy in distinguishing distinct age-dependent comorbidity profiles. We applied ATM to 282,957 UK Biobank samples, analysing 1,726,144 disease diagnoses spanning all 348 diseases with ≥1,000 independent occurrences in the Hospital Episode Statistics (HES) data, identifying 10 disease topics under the optimal model fit. Analysis of an independent cohort, All of Us, with 211,908 samples and 3,098,771 disease diagnoses spanning 233 of the 348 UK Biobank diseases produced highly concordant findings. In UK Biobank we identified 52 diseases with heterogeneous comorbidity profiles (≥500 occurrences assigned to each of ≥2 topics), including breast cancer, type 2 diabetes (T2D), hypertension, and hypercholesterolemia. For most of these diseases, topic assignments were highly age-dependent, suggesting differences in disease aetiology for early-onset vs. late-onset disease. We defined subtypes of the 52 heterogeneous diseases based on the topic assignments, and compared genetic risk across subtypes using polygenic risk scores (PRS). We identified 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease, including a subtype of T2D characterised by cardiovascular comorbidities and a subtype of asthma characterised by dermatological comorbidities. We further identified specific variants underlying these differences such as a T2D-associated SNP in theHMGA2locus that has a higher odds ratio in the top quartile of cardiovascular topic weight (1.18±0.02) compared to the bottom quartile (1.00±0.02) (P=3 × 10-7for difference, FDR = 0.0002 < 0.1). In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.
IMPROVING CARDIOVASCULAR DISEASE RISK PREDICTION WITH MACHINE LEARNING USING MENTAL HEALTH DATA: A PROSPECTIVE UK BIOBANK STUDY
Authors: Dorraki Mohsen , Liao Zhibin , Abbott Derek , Psaltis Peter J. , Baker Emma , Bidargaddi Niranjan , Wardill Hannah R. , van den Hengel Anton , Narula Jagat , Verjans Johan W. 
Published: October 2022

ABSTRACTBackgroundRobust and accurate prediction of cardiovascular disease (CVD) risk facilitates early intervention to benefit patients. It is well-known that mental disorders and CVD are interrelated. Nevertheless, psychological factors are not considered in existing models, which use either a limited number of clinical and lifestyle factors, or have been developed on restricted population subsets.ObjectivesTo assess whether inclusion of psychological data could improve CVD risk prediction in a new machine learning (ML) approach.MethodsUsing a comprehensive, long-term UK Biobank dataset (n=375,145), we examined the correlation between CVD diagnoses and traditional and psychological risk factors. An ensemble ML model containing five constituent algorithms [decision tree, random forest, XGBoost, support vector machine (SVM), and deep neural network (DNN)] was tested for its ability to predict CVD risk based on two training datasets: one using traditional CVD risk factors alone, or a combination of traditional and psychological risk factors.ResultsOur ensemble ML model could predict CVD with 71.31% accuracy using traditional CVD risk factors alone. However, by adding psychological factors to the training data, accuracy dramatically increased to 85.13%. The accuracy and robustness of our ensemble ML model outperformed all five constituent learning algorithms. Re-testing the model on a control dataset to predict bone diseases returned random results, confirming specificity of the training data for prediction of CVD.ConclusionsIncorporating mental health assessment data within an ensemble ML model results in a significantly improved, highly accurate, state-of-the-art CVD risk prediction.AUTHOR APPROVALAll authors have seen and approved the manuscript.COMPETING INTERESTSThe authors declare no competing interests.DATA AVAILABILITY STATEMENTAll data needed to evaluate the conclusions in the paper are present in the paper or in the supplementary materials. In addition, we used UK Biobank in this study:www.ukbiobank.ac.uk.FUNDINGNo funding.
CNA Explorer and anaLyzer (CNAEL): an interactive web application and standard operating procedure enabling efficient clinical review and reporting of complex NGS-derived tumor copy number profiles
Authors: Chen Ellen , Wang Jinlian , Kueffner Robert , Al-Kateb Hussam , Silkov Antonina , Uzilov Andrew , Lochovsky Lucas , Li Hui , Newman Scott 
Published: October 2022

AbstractPurposeClinical analysis and reporting of somatically acquired copy number abnormalities (CNAs) detected through next-generation sequencing (NGS) is time consuming and requires significant expertise. Interpretation is complicated by other classes of variants such as coding mutations and gene fusions. Recent guidelines for the clinical assessment of tumor CNAs harmonize and simplify the reporting criteria but did not directly address NGS-specific concerns or the need for a standardized and scalable protocol for CNA analysis.MethodsWe developed a scalable NGS-derived CNA analysis protocol paired with a novel interactive web application, CNA Explorer and anaLyzer (CNAEL), to facilitate the rapid, scalable, and reproducible analysis and reporting of complex tumor-derived CNA profileshttps://CNAEL.sema4.com.ResultsNovel features of CNAEL include on-the-fly data rescaling to account for tumor ploidy, purity, and modal chromosomal copy number; integration of gene expression, coding, and fusion variants into review and automated genome-wide summarization to enable rapid reporting. We found that case curation times were significantly reduced when using CNAEL [median:7 mins, IQR = 4, 10.25] compared with our previous laboratory standard operating procedure [median: 61 mins, IQR = 23.75, 176,25] with p=4.631e-05.ConclusionCNAEL enables efficient and accurate clinical review and reporting of complex NGS-derived tumor copy number profiles.
Responsiveness of the electrically stimulated cochlear nerve in patients with a missense variant inACTG1: Preliminary Results
Authors: Yuan Yi , Yan Denise , Skidmore Jeffrey , Chapagain Prem , Liu Xuezhong , He Shuman 
Published: October 2022

AbstractObjectivesThis preliminary study identified a missense variant inACTG1(NM_001614.5) in a family with autosomal dominant non-syndromic hearing loss (ADNSHL). The responsiveness of the electrically-stimulated cochlear nerve (CN) in two implanted participants with this missense change was also evaluated and reported.DesignGenetic testing was done using a custom capture panel (MiamiOtoGenes) and whole exome sequencing. The responsiveness of the electrically-stimulated CN was evaluated in two members of this family (G1 and G4) using the electrically evoked compound action potential (eCAP). eCAP results from these two participants were compared with those measured three implanted patient populations: children with cochlear nerve deficiency, children with idiopathic hearing loss and normal-sized cochlear nerves, and postligually deafened adults.ResultsSequencing ofACTG1identified a missense c.737A>T (p. Gln246Leu) variant inACTG1(NM_001614.5) which is most likely the genetic cause of ADNSHL in this family. eCAP results measured in these two participants showed substantial variations.ConclusionThe missense c.737A>T (p. Gln246Leu) variant inACTG1(NM_001614.5) co-segregated with hearing loss in this family. The responsiveness of the electrically-stimulated CN can vary among patients with the same genetic variants, which suggests the importance of evaluating the functional status of the CN for individual CI patients.
A comparison of claw removal methods on the post-release survival and claw regeneration of stone crab (Menippe mercenaria)
Authors: Walus Alexandria M. , Schneider Eric V. C. , Parker Erin , Brittain Candice , McGaw Iain J. , Hayes Daniel , Peters Amber , Van Leeuwen Travis E. 
Published: October 2022

AbstractCommercial and recreational stone crab (Menippe mercenaria) fisheries primarily occur along the Gulf of Mexico and Atlantic coasts of the southeastern United States and the northeastern Caribbean. The fishery is unique in that only the crabs’ claws are retained and the animal is returned to the water alive. While the fishery is often regarded as sustainable because it is believed to exploit the crabs’ natural ability to voluntarily drop (autotomize) and regenerate lost claws, the post-release survival of de-clawed stone crabs is often low, especially when both claws are harvested. In this study, a controlled laboratory experiment was used to compare a new method of claw removal to the typical method currently used in the fishery. For the two different claw removal methods, we compared crab survival and start time to claw regeneration as a function of harvester and whether one claw or both claws were removed. Overall, we found a significant effect of the removal method, harvester, and whether one claw or both claws were removed on crab survival, but these factors did not influence the time to start of claw regeneration. Although our new method was several seconds slower in processing time than the typical method, it resulted in a 28% increase in survival (up to 92% survival throughout the study) compared to the typical method of claw removal (64% survival throughout the study). Overall, these results suggest that our new method of claw removal significantly increases post-release survival of stone crabs, and most notably does so independent of harvester and whether one claw or both claws are removed.
COOBoostR: an extreme gradient boosting-based tool for robust tissue or cell-of-origin prediction of tumors
Authors: Yang Sungmin , Ha Kyungsik , Song Woojeung , Fujita Masashi , Kübler Kirsten , Polak Paz , de Alba Rivas Carolina Garcia , Pessina Patrizia , de Aja Julio Sainz , Rowbotham Samuel , Bhetariya Preetida , Dost Antonella , Moye Aaron L. , Hiyama Eiso , Nakagawa Hidewaki , Kim Carla F. , Kim Hong-Gee , Lee Hwajin 
Published: October 2022

AbstractWe here present COOBoostR (https://github.com/SWJ9385/COOBoostR), a computational method designed for the putative prediction of tissue-or cell-of-origin of various cancer types. COOBoostR leverages regional somatic mutation density information and chromatin mark features to be applied to an extreme gradient boosting-based machine-learning algorithm. COOBoostR ranks chromatin marks from various tissue and cell types which best explain the somatic mutation density landscape of any sample of interest. Through integrating either ChIP-seq based chromatin data or bulk/single cell chromatin accessibility data along with regional somatic mutation density data derived from normal cells/tissue, precancerous lesions, and cancer types, we show that COOBoostR outperforms existing random forest-based methods in prediction speed with comparable or better tissue or cell-of-origin prediction performance. In addition, our results suggest a dynamic somatic mutation accumulation at the normal tissue or cell stage which could be intertwined with the changes in open chromatin marks and enhancer sites. These results further represent chromatin marks shaping the somatic mutation landscape at the early stage of mutation accumulation, possibly even before the initiation of precancerous lesions or neoplasia.
Representation and quantification Of Module Activity from omics data with rROMA
Authors: Najm Matthieu , Cornet Matthieu , Albergante Luca , Zinovyev Andrei , Sermet-Gaudelus Isabelle , Stoven Véronique , Calzone Laurence , Martignetti Loredana 
Published: October 2022

AbstractThe efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes.One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets.Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package’s capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at:www.github.com/sysbio-curie/rROMA.Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation.Contact:loredana.martignetti@curie.fr