bluuebunny/crossref_metadata_2025_split huggingface dataset:
A staged screening protocol identifies people with biomarkers related to neuronal alpha-synuclein disease
Authors: Brown Ethan G. , Chahine Lana M. , Siderowf Andrew , Gochanour Caroline , Kurth Ryan , Marshall Micah J. , Caspell-Garcia Chelsea , Brumm Michael C. , Coffey Christopher S. , Stanley Craig E. , Korell Monica , McMahon Bridget , Kuhl Maggie , Fabrizio Kimberly , Heathers Laura , Foroud Tatiana M. , Concha-Marambio Luis , Soto Claudio , Chowdhury Sohini , Simuni Tanya , Marek Kenneth , Tanner Caroline M. ,   the Parkinson Progression Marker Initiative
Published: June 2024

AbstractImportanceIdentifying individuals in the earliest stages of synucleinopathy is essential to evaluate drugs aimed to slow progression or prevent manifest disease. Remote identification of hyposmic individuals may enable scalable recruitment of participants with underlying alpha-synuclein pathology.ObjectiveTo evaluate the performance of a staged screening paradigm using smell testing to enrich for deficit on dopaminergic transporter (DAT) imaging and pathologic alpha-synuclein aggregation.DesignCross-sectional analysis of data from the Parkinson’s Progression Markers Initiative (PPMI).SettingScreening activities were completed both at home and local PPMI sites.ParticipantsIndividuals aged 60 and older without a diagnosis of Parkinson’s diseaseInterventions or Exposures:Participants were asked to complete a University of Pennsylvania Smell Identification Test (UPSIT) remotely. Participants with hyposmia were invited to complete DAT imaging, which determined eligibility for enrollment in longitudinal assessments and further biomarker evaluation including cerebrospinal fluid synuclein seed amplification assay (synSAA).Main Outcomes and MeasuresWe determined the proportion of people with hyposmia, impaired DAT binding, and positive synSAA and explored determinants of these biomarkers.ResultsAs of January 29, 2024, 49,843 participants were sent an UPSIT and 31,293 (63%) completed it. Of UPSIT completers, 8,301 (27%) scored <15th%ile. Of 1,546 who completed DAT, 19% had DAT binding < 65% expected for age and sex. Self-reported features were independently associated with severe hyposmia (UPSIT <10th%ile), such as REM sleep behavior disorder (RBD) or dream enactment behavior (DEB) (aOR: 1.9, 95% CI 1.7–2.1) and subjective smell loss (aOR: 15.0, 95% CI 13.7–16.3). Participants with an UPSIT <10th%ile (N=1,221) had greater likelihood of low DAT binding compared to participants with an UPSIT in the 10th– 15th%ile (OR 3.01, 95% CI 1.85–4.91). Among remotely recruited participants with synSAA results obtained, 198/363 (55%) had positive synSAA at baseline. This proportion increased when the cohort was limited to an UPSIT<10th%ile (182/257, 71%).Conclusion and RelevanceRemote screening for severe hyposmia identifies participants with a high proportion of positive synSAA and reduced DAT binding. This staged screening protocol is an effective approach to identify cohorts for therapeutic trials aiming to slow progression in alpha-synucleinopathy.
Genetic Evidence for a Causal Link between Intra-Pancreatic Fat Deposition and Pancreatitis: a Mendelian Randomization Study
Authors: Yamazaki Hajime , Heni Martin , Wagner Róbert , Fukuhara Shunichi , Grossman Steven R. , Han Sihao , Wu Lang , Streicher Samantha A. , Huang Brian Z. 
Published: June 2024

AbstractIntroductionRecent associative studies have linked intra-pancreatic fat deposition (IPFD) with risk of pancreatitis, but the causal relationship remains unclear.MethodsUtilizing Mendelian randomization, we evaluated the causal association between genetically predicted IPFD and pancreatitis. This approach utilized genetic variants from genome-wide association studies of IPFD (n=25,617), acute pancreatitis (n=6,787 cases/361,641 controls), and chronic pancreatitis (n=3,875 cases/361,641 controls).ResultsGenetically predicted IPFD was significantly associated with acute pancreatitis (OR per 1-SD increase: 1.40[95%CI:1.12-1.76], p=0.0032) and chronic pancreatitis (OR:1.64[95%CI:1.13-2.39], p=0.0097).DiscussionOur findings support a causal role of IPFD in pancreatitis, suggesting that reducing IPFD could lower the risk of pancreatitis.
Lifestyles and their relative contribution to biological aging across multiple organ systems: change analysis from the China Multi-Ethnic Cohort Study
Authors: Zhang Yuan , Tang Dan , Zhang Ning , Xiang Yi , Hu Yifan , Qian Wen , Baima Yangji , Ding Xianbin , Wang Ziyun , Yin Jianzhong , Xiao Xiong , Zhao Xing 
Published: June 2024

AbstractBackgroundBiological aging exhibits heterogeneity across multi organ systems. However, it remains unclear how is lifestyle associated with overall and organ-specific aging and which factors contribute most in Southwest China.ObjectiveTo examine the associations of healthy lifestyle with comprehensive and organ-specific biological ages and which lifestyle factors contribute most.MethodsThis study involved 8,396 participants who completed two surveys from the China Multi-Ethnic Cohort (CMEC) Study. The healthy lifestyle index (HLI) was developed using five lifestyle factors: smoking, alcohol, diet, exercise, and sleep. The comprehensive and organ-specific biological ages (BAs) were calculated using the Klemera-Doubal method based on longitudinal clinical laboratory measurements, and validation were conducted to select BA reflecting related diseases. Fixed effects model was used to examine associations between HLI or its components and the acceleration of validated BAs. We further evaluated the relative contribution of lifestyle components to comprehension and organ systems BAs using quantile G-computation.ResultsAbout two-thirds of participants changed HLI scores between surveys. After validation, three organ-specific BAs (the cardiopulmonary, metabolic, and liver BAs) were identified as reflective of specific diseases and included in further analyses with the comprehensive BA. The health alterations in healthy lifestyle index showed a protective association with the acceleration of all biological ages, with a mean shift of –0.19 (95%CI: –0.34, –0.03) in the comprehensive biological age acceleration. Diet and smoking were the major contributors to overall negative associations of five lifestyle factors with the comprehensive BA and metabolic BA accounting for 24% and 55% respectively.ConclusionsHealthy lifestyle changes were inversely related to comprehensive and organ-specific biological aging in Southwest China, with diet and smoking contributing most to comprehensive and metabolic BA separately. Our findings highlight the potential of lifestyle interventions to decelerate aging and identify intervention targets to limit organ-specific aging in less-developed regions.
Machine Learning Assisted Web Application for Identifying Beneficial Drug Candidates for Genetic Alterations in Cancer Patients
Authors: Bozcuk Hakan Şat 
Published: June 2024

ABSTRACTBackgroundPrecision medicine in oncology relies heavily on molecular genetic data, primarily obtained from Next-generation sequencing (NGS) tests. However, the complexity of these data and the need to match genetic alterations with specific drug candidates pose significant challenges for clinicians. To simplify this process, a user-friendly web application has been developed. This app facilitates the matching, graphical presentation, and clustering of treatment options for specific genetic alterations, making it easier for clinicians to interpret and apply the results in patient care.Materials and MethodsUtilizing the application programming interface (API) of the Drug-Gene Interaction Database (DGIdb 4.0), a web application was developed in Python to list drugs that interact with specific genetic changes. The application features a user-friendly display achieved through graphical representation and web scraping for gene-related information. Additionally, unsupervised machine learning, specifically K-means cluster analysis, was employed to categorize drug candidates based on their interaction scores with the genetic alteration in question. To enhance the interpretability of the results, the web app also provides key references and web links to the relevant drug interactions.ResultsThe developed web application successfully filtered, listed, and displayed the gene interaction results. Utilizing an unsupervised machine learning algorithm, the app identified three optimal clusters of drug candidates based on their efficacy potentials using the Elbow Method. The cluster analysis demonstrated strong performance, evidenced by the following metrics for BRAF mutations: a Silhouette score of 0.74, a Davies-Bouldin index of 0.44, and a Calinski-Harabasz index of 475.96. Additionally, the web app effectively extracted and defined relevant gene information and identified key references for each genetic alteration within the cloud database.ConclusionThe web application developed in this study provides a user-friendly platform for classifying and interpreting drug candidates based on the presence of specific genetic alterations in cancer patients. This tool is expected to enhance the accessibility and usability of genetic data, aiding clinicians in making informed treatment decisions.
Current-use and legacy contaminants evidence dissolved organic matter transfer and dynamics across a fractured-rock groundwater recharge area
Authors: Zerfaß Christian , Lehmann Robert , Ueberschaar Nico , Totsche Kai Uwe , Pohnert Georg 
Published: June 2024

AbstractAnthropogenic activities cause the release of vast amounts of contaminants into the environment which eventually reach even groundwater resources. With usually sparse regulatory monitoring of limited priority compounds, the large spectrum of contaminants, and the intricacies of intra- and inter-annual contaminant dynamics, such as the emergence and mobilisation of contaminants, are easily overlooked. Utilizing an 6-year record of untargeted LC-MS assessment of dissolved organic, we report the detection and tracing of selected environmental chemicals in the Hainich Critical Zone Exploratory (central Germany), representing a model groundwater flow system under different land use. The insect repellent DEET (N,N-diethyl-m-toluamide) and the coniferous resin acid 7-ODAA (7-oxodehydroabietic acids) show phases of seasonal dynamics in line with their expected periods of release. The legacy herbicides simazine, the triazine transformation product hydroxypropazine, and the flame retardant/plasticiser TPP (triphenyl phosphate) occurred episodically at various locations in the fractured sedimentary bedrock. Within the period of monitoring, extreme weather events (i.e., the severe 2018 drought) and extreme subsurface responses (i.e., 5-year groundwater highstand 2018) likely contributed to long-term organic matter dynamics, potentially causing re-emergence of legacy agrochemicals. This investigation points to the persistence and mobilisation of anthropogenic contaminants, and highlights the importance of long-term combined untargeted and targeted analysis of the groundwater dissolved organic matter for understanding subsurface ecosystems processes. The results add a note of caution for regulatory monitoring since also legacy contaminant levels may considerably vary over time.Highlights6 years of monitoring with non-target LC-MS screenings; 4-weekly samplings5 current-use and legacy compounds that evidence dissolved organic matter transferCompounds comprise regularly targeted and non-monitored features in groundwaterPronounced phases of non-detects help to further investigate surface-subsurface couplingThis illustrates the need for spatiotemporally highly resolved non-target monitoring
Loss of FBXO31-mediated γH2AX foci formation impairs initiation of NHEJ and HR repair pathways, and sensitizes breast cancer to therapy
Authors: Sahay Osheen , Barik Ganesh Kumar , Dey Samya , Islam Sehbanul , Paul Debasish , Bhavsar Praneeta Pradip , Nath Somsubhra , Rapole Srikanth , Santra Manas Kumar 
Published: June 2024

AbstractIn response to genotoxic stress, cell initiates complex signalling cascades to combat genomic insults through simultaneous initiation of growth arrest and DNA damage repair process. γH2AX functions as a crucial initiator in DNA double strand damage repair process. Therefore, γH2AX foci formation onto the damage sites is essential to initiate the recruitment of repair proteins involved in NHEJ (Non-homologous DNA-end joining) or HR (Homologous recombination) repair process. However, molecular events associated with γH2AX foci formation onto the DNA damage sites are poorly understood. Here, we show that FBXO31, the first ubiquitin ligase, mediated Lys-63-linked ubiquitination of γH2AX is essential for its foci formation onto the DNA damage sites to initiate recruitment of proteins involved in NHEJ and HR-mediated DNA damage repair. Therefore, tumors with FBXO31 deficiency show enhanced growth suppression following chemotherapeutic drug treatment because of synthetic lethality, indicating that FBXO31 could be used as a marker for predicting the outcome of chemotherapy treatment.
GCN5L1 regulates glucose and ketone body metabolism during cardiac ischemia-reperfusion injury
Authors: Bugga Paramesha , Stoner Michael W , Manning Janet R , Mushala Bellina , Bhattarai Nisha , Sharifi-Sanjani Maryam , Scott Iain 
Published: June 2024

AbstractIschemic heart diseases are the leading cause of death among cardiovascular diseases. Reperfusion of the ischemic myocardium is the only way to treat IHD. However, reperfusion can damage the ischemic myocardium if it does not happen at the right time. Therefore, understanding the pathophysiology of cardiac ischemic reperfusion injury is essential to identifying new therapeutic targets. We reported the cardioprotective role of GCN5L1 in ex-vivo cardiac ischemic reperfusion injury through the regulation of cytoprotective signaling by reducing oxidative stress. The current study investigated the role of GCN5L1 in in-vivo cardiac ischemic reperfusion injury and in-vitro H/R injury in AC16 cells. The cardiac-specific GCN5L1 KO did not further deteriorate the cardiac function and infarct size. We measured the glucose metabolism-related protein expression among all experimental groups and found that GCN5L1 KO significantly decreases glucose metabolism by PDH phosphorylation. We further evaluated the role of GCN5L1 in in-vitro H/R injury in genetically modified AC16 cells (GCN5L1 KD and overexpressed and control). GCN5L1 KD in AC16 cells promotes phosphorylation of PDH; similarly, overexpression reduces the p-PDH levels. We identified that the regulation of PDH phosphorylation via PDK4/PDP1/PDPR and GCN5L1 regulates the PDK4 expression. We measured the ketone body metabolism related to protein expression in control and KD cells after H/R injury and found the upregulation of ketone body metabolism-related protein expression in KD cells compared to control AC16 cells. Altogether, our in vivo and in vitro studies findings conclude that GCN5L1 is required for glucose metabolism in ischemic reperfusion injury, and in the absence of GCN5L1, ketone body metabolism accomplishes the cardiac energy requirements and prevents I/R induced cardiac damage and infarct size.Graphical AbstractGraphical abstract of the manuscriptAltogether, our results conclude that Cardiomyocyte-specific GCN5L1 KO in-vivo cardiac ischemic reperfusion injury and in-vitro Hypoxia-reoxygenation stress significantly affect glucose metabolism by suppressing glucose metabolism. Cardiac-specific GCN5L1 KO mice in-vivo and knockdown in AC16 cells invitro upregulates the expression of enzymes that are involved in the ketone body metabolism to rescue from in-vivo cardiac ischemic reperfusion injury and invitro H/R stress.
Immunoinformatics design of a novel multiepitope vaccine candidate against non-typhoidal salmonellosis caused bySalmonellaKentucky using outer membrane proteins A, C, and F
Authors: Igomu Elayoni E. , Mamman Paul H. , Adamu Jibril , Muhammad Maryam , Woziri Abubarkar O. , Sugun Manasa Y. , Benshak John A. , Anyika Kingsley C. , Sam-Gyang Rhoda , Ehizibolo David O. 
Published: June 2024

AbstractThe global public health risk posed bySalmonellaKentucky, particularly due to the dissemination of antimicrobial resistance genes in human and animal populations, is rising. This serovar, widespread in Africa, has emerged as a notable cause of non-typhoidal gastroenteritis in humans. In this study, we utilized a bioinformatics approach to design a peptide-based vaccine targeting epitopes from the outer membrane proteins A, C, and F ofSalmonellaKentucky, with thefliCprotein fromSalmonellaTyphimurium serving as an adjuvant. Through this approach, we identified 14 CD8+ and 7 CD4+ T-cell epitopes, which are predicted to be restricted by various MHC class I and MHC class II alleles. When used in vaccine formulations, the predicted epitopes are expected to achieve a population coverage of 94.91%. Additionally, we identified seven highly immunogenic linear B-cell epitopes and three conformational B-cell epitopes. These T-cell and B-cell epitopes were then linked using appropriate linkers to create a multi-epitope vaccine (MEV). To enhance the immunogenicity of the peptide construct, thefliCprotein fromSalmonellaTyphimurium was included at the N-terminal as an adjuvant. The resulting MEV construct demonstrated high structural quality and favorable physicochemical properties. Molecular docking studies with Toll-like receptors 1, 2, 4, and 5, followed by molecular dynamic simulations, suggested that the vaccine-receptor complexes are energetically feasible, stable, and robust. Immune simulation results showed that the MEV elicited significant responses, including IgG, IgM, CD8+ T-cells, CD4+ T-cells, and various cytokines (IFN-γ, TGF-β, IL-2, IL-10, and IL-12), along with a noticeable reduction in antigen levels. Despite these promising in silico results, further validation through preclinical and clinical trials is necessary to confirm the vaccine’s protective efficacy and safety.
Genome-wide mapping of native co-localized G4s and R-loops in living cells
Authors: Liu Ting , Shen Xing , Ren Yijia , Lu Hongyu , Liu Yu , Chen Chong , Yu Lin , Xue Zhihong 
Published: June 2024

AbstractThe non-B DNA structures can act as dynamic functional genomic elements regulating gene expression. Among them, G4s and R-loops are two of the best studied. The interplay between R-loops and G4s are emerging in regulating DNA repair, replication and transcription. A comprehensive picture of native co-localized G4s and R-loops in living cells is currently lacking. Here, we describe the development of HepG4-seq and an optimized HBD-seq methods, which robustly capture native G4s and R-loops, respectively, in living cells. We successfully employed these methods to establish comprehensive maps of native co-localized G4s and R-loops in human HEK293 cells and mouse embryonic stem cells (mESCs). We discovered that co-localized G4s and R-loops are dynamically altered in a cell type-dependent manner and are largely localized at active promoters and enhancers of transcriptional active genes. We further demonstrated the helicase Dhx9 as a direct and major regulator that modulates the formation and resolution of co-localized G4s and R-loops. Depletion of Dhx9 impaired the self-renewal and differentiation capacities of mESCs by altering the transcription of co-localized G4s and R-loops - associated genes. Taken together, our work established that the endogenous co-localized G4s and R-loops are prevalently persisted in the regulatory regions of active genes and are involved in the transcriptional regulation of their linked genes, opening the door for exploring broader roles of co-localized G4s and R-loops in development and disease.
Chioso: Segmentation-free Annotation of Spatial Transcriptomics Data at Sub-cellular Resolution via Adversarial Learning
Authors: Yu Ji 
Published: June 2024

AbstractRecent advances in spatial transcriptomics technology have produced full-transcriptomic scale dataset with subcellular spatial resolutions. Here we present a new computational algorithm, chioso, that can transfer cell-level labels from a reference dataset (typically a single-cell RNA sequencing dataset) to a target spatial dataset by assigning a label to every spatial location at sub-cellular resolution. Importantly, we do this without requiring single cell segmentation inputs, thereby simplifying the experiments, and allowing for a more streamlined, and potentially more accurate, analysis pipeline. Using a generative neural network as the underlying algorithmic engine, chioso is very fast and scales well to large datasets. We validated the performance of chioso using synthetic data and further demonstrated its scalability by analyzing the complete MOSTA dataset acquired using the Stereo-Seq technology.Abstract Figure
Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata
Authors: Yuan Hao , Hicks Parker , Ahmadian Mansooreh , Johnson Kayla , Valtadoros Lydia , Krishnan Arjun 
Published: June 2024

AbstractReusing massive collections of publicly available biomedical data can significantly impact knowledge discovery. However, these public samples and studies are typically described using unstructured plain text, hindering the findability and further reuse of the data. To combat this problem, we proposetxt2onto 2.0, a general-purpose method based on natural language processing and machine learning for annotating biomedical unstructured metadata to controlled vocabularies of diseases and tissues. Compared to the previous version (txt2onto 1.0), which uses numerical embeddings as features, this new version uses words as features, resulting in improved interpretability and performance, especially when few positive training instances are available.Txt2onto 2.0uses embeddings from a large language model during prediction to deal with unseen-yet-relevant words related to each disease and tissue term being predicted from the input text, thereby explaining the basis of every annotation. We demonstrate the generalizability oftxt2onto 2.0by accurately predicting disease annotations for studies from independent datasets, using proteomics and clinical trials as examples. Overall, our approach can annotate biomedical text regardless of experimental types or sources. Code, data, and trained models are available athttps://github.com/krishnanlab/txt2onto2.0.Key pointsWe developedtxt2onto 2.0, a computational method that combines language models and machine learning to annotate public samples and studies with standardized tissue and disease terms, with a focus on interpretability and explainability.Txt2onto 2.0uses word/phrase occurrence statistics to represent sample/study metadata, train machine learning models, and predict terms in controlled vocabularies to annotate each sample and study. This approach allows the model to keep track of predictive words related to model decisions and easily separate informative from uninformative words.Txt2onto 2.0outperforms its predecessor,txt2onto 1.0, in tissue and disease annotation, especially when training data is limited.The predictive features learned bytxt2onto 2.0are highly interpretable. These features not only include explicit mentions of the actual disease or tissue terms but also related biomedical concepts, including words that are unseen by the model during training.Although trained on metadata of transcriptomes,txt2onto 2.0is capable of annotating disease and tissue for any kind of biomedical metadata, making it a versatile tool for sample and study annotation.