bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Longitudinal Analysis of Function Annotations of the Human Proteome Reveals Consistently High Biases
Authors: Phan An , Joshi Parnal , Kadelka Claus , Friedberg Iddo 
Published: October 2024

AbstractThe resources required to study gene function are limited, especially when considering the number of genes in the human genome and the complexity of their function. Therefore, genes are prioritized for experimental studies based on many different considerations, including, but not limited to, perceived biomedical importance, such as disease-associated genes, or the understanding of biological processes, such as cell signaling pathways. At the same time, most genes are not studied or are under-characterized, which hampers our understanding of their function and potential effects on human health and wellness. Understanding function annotation disparity is a necessary first step toward understanding how much functional knowledge is gained from the human genome, and toward guidelines for better targeting future studies of the genes in the human genome effectively. Here, we present a comprehensive longitudinal analysis of the human proteome utilizing data analysis tools from economics and information theory. Specifically, we view the human proteome as a population of proteins within a knowledge economy: we treat the quantified knowledge of the protein’s function as the analog of wealth and examine the distribution of information in a population of proteins in the proteome in the same manner distribution of wealth is studied in societies. Our results show a highly skewed distribution of information about human proteins over the last decade, in which the inequality in the annotations given to the proteins remains high. Additionally, we examine the correlation between the knowledge about protein function as captured in databases and the interest in proteins as reflected by mentions in the scientific literature. We show a large gap between knowledge and interest and dissect the factors leading to this gap. In conclusion, our study shows that research efforts should be redirected to less studied proteins to mitigate the disparity among human proteins both in databases and literature.
Development of an on-chip fluorescence anisotropy immunoassay for human C-peptide secretion reveals a general roadmap for tracer optimization
Authors: Wang Yufeng , Gulati Nitya , Regeenes Romario , Migliorini Adriana , Oake Amanda , Nostro Maria Cristina , Rocheleau Jonathan V. 
Published: October 2024

ABSTRACTFluorescence anisotropy immunoassays (FAIAs) are widely used to quantify the concentration of target proteins based on competition with a tracer in binding a monoclonal antibody. We recently designed an FAIA to measure mouse C-peptide secretion from living islets in a continuous-flow microfluidic device (InsC-chip). To develop an assay for human C-peptide, our initial selection of antibody-tracer pairings revealed the need to optimize both the dynamic range and the binding kinetics to measure the assay on-chip effectively. Here, we present strategies for developing an on-chip FAIA using two different monoclonal antibodies to achieve both a large dynamic range and high temporal resolution. The two monoclonal antibodies (Ab1 & Ab2) to human C-peptide initially showed low dynamic range and slow kinetics, preventing them from being used in an on-chip assay. To shorten the time-to-reach equilibrium for Ab1, we reengineered the tracer based on a comparison between the human and mouse C-peptide sequences, resulting in > 30-fold shorter time-to-reach equilibrium. To increase the relatively small dynamic range for Ab2, we used partial epitope mapping and targeted point mutations to increase the dynamic range by 45%. Finally, we validated both FAIAs by measuring depolarization-induced insulin secretion from individual hESC-islets in our InsC-chip. These strategies provide a general roadmap for developing FAIAs with high sensitivity and sufficiently fast kinetics to be measured in continuous-flow microfluidic devices.
The Effect of Psychotherapy in the Management of Obesity: An umbrella review
Authors: Vasquez-Ponce Mariana , Gómez Óscar , González Ballesteros Luisa F. , Peralta-Reich Dina , Bermúdez-Gómez Julieta , Junco Juan David , Alba Maria Alejandra , Suarez-Ordoñez Luisa , Arana Nayeh , Ballesteros Eduardo , Mojica-Sierra Valeria , González Ballesteros Lina María 
Published: October 2024

ABSTRACTObjectiveTo assess the impact of psychotherapy on obesity treatment in outpatient settings.MethodsThe study targets adults with a BMI of 30 or higher, comparing psychotherapeutic interventions to usual care or no treatment. Key outcomes include weight loss, adverse events, depression, quality of life, and long-term BMI changes. Extensive database searches were conducted.ResultsOut of 1,334 articles reviewed, only 3 studies met the inclusion criteria, emphasizing the scarcity of relevant research. These studies primarily focused on behavioral and cognitive-behavioral strategies. Results showed that combining behavior therapy with diet/exercise led to greater weight loss than diet/exercise alone. Studies on cognitive therapy and general psychotherapy were less frequent and yielded weaker evidence. Despite varied methodologies, the findings suggest that behavioral therapy combined with dietary and exercise interventions offers improved outcomes, though more research is needed to strengthen these conclusions.ConclusionsThe study found that combining behavioral therapy with diet and exercise resulted in greater weight loss than diet and exercise alone. However, research on the impact of psychotherapy on obesity is limited, as only 3 studies met the inclusion criteria. Cognitive therapies and general psychotherapy provided weaker evidence. Therefore, further research is necessary to validate these findings and investigate alternative psychotherapeutic methods for treating obesity.
Metabolic analysis of sarcopenic muscle identifies positive modulators of longevity and healthspan inC. elegans
Authors: Jonk Steffi M , Nicol Alan , Chrysostomou Vicki , Lardner Emma , Yu Shu-Che , Stålhammar Gustav , Crowston Jonathan G , Tribble James R , Swoboda Peter , Williams Pete A 
Published: October 2024

AbstractSarcopenia is the age-related degeneration of skeletal muscle, resulting in loss of skeletal muscle tone, mass, and quality. Skeletal muscle is a source of systemic metabolites and macromolecules important for neuronal health, function and healthy neuronal aging. Age-related loss of skeletal muscle might result in decreased metabolite and macromolecule availability, resulting in reduced neuronal function or increased susceptibility to unhealthy aging and neurodegenerative diseases. We aimed to identify muscle metabolite candidates that regulate healthy aging. C57BL/6J mice were aged to young adult (4 months) and old age (25 months) and skeletal muscle was collected. Age related muscle loss was confirmed by reduced muscle mass, muscle fiber degeneration, reduced myosin intensity, in addition to a metabolic shift and increased DNA damage in skeletal muscle. Using a low molecular weight enriched metabolomics protocol, we assessed the metabolic profile of skeletal muscle from young adult and old mice and identified 20 metabolites that were significantly changed in aged muscle. These candidate metabolites were tested inC. elegansassays of lifespan, health span, muscle-, and mitochondrial morphology under normal and stressed conditions. We identified four candidate metabolites (beta-alanine, 4-guanidinobutanoic acid, 4-hydroxyproline, pantothenic acid) that when supplemented inC. elegansprovided robust gero- and mitochondrial protection. These candidates also affected life-, and health span inC. elegansmodels of amyotrophic lateral sclerosis and Duchenne muscular dystrophy. Our findings support that aging muscle can be used to identify novel metabolite modulators of lifespan and health and may show promise for future treatments of neurodegenerative and neuromuscular disorders.
Single cell transcriptional analysis of human adenoids identifies molecular features of airway microfold cells
Authors: Alvarez-Arguedas Samuel , Mazhar Khadijah , Wangzhou Andi , Sankaranarayanan Ishwarya , Gaona Gabriela , Lafin John T. , Mitchell Ron B. , Price Theodore J. , Shiloh Michael U. 
Published: October 2024

AbstractThe nasal, oropharyngeal, and bronchial mucosa are primary contact points for airborne pathogens likeMycobacterium tuberculosis(Mtb), SARS-CoV-2, and influenza virus. While mucosal surfaces can function as both entry points and barriers to infection, mucosa-associated lymphoid tissues (MALT) facilitate early immune responses to mucosal antigens. MALT contains a variety of specialized epithelial cells, including a rare cell type called a microfold cell (M cell) that functions to transport apical antigens to basolateral antigen-presenting cells, a crucial step in the initiation of mucosal immunity. M cells have been extensively characterized in the gastrointestinal (GI) tract in murine and human models. However, the precise development and functions of human airway M cells is unknown. Here, using single-nucleus RNA sequencing (snRNA-seq), we generated an atlas of cells from the human adenoid and identified 16 unique cell types representing basal, club, hillock, and hematopoietic lineages, defined their developmental trajectories, and determined cell-cell relationships. Using trajectory analysis, we found that human airway M cells develop from progenitor club cells and express a gene signature distinct from intestinal M cells. Surprisingly, we also identified a heretofore unknown epithelial cell type demonstrating a robust interferon-stimulated gene signature. Our analysis of human adenoid cells enhances our understanding of mucosal immune responses and the role of M cells in airway immunity. This work also provides a resource for understanding early interactions of pathogens with airway mucosa and a platform for development of mucosal vaccines.
Transcriptomic profiling identifies breed-specific immune signatures of Tuberculosis susceptibility in cattle
Authors: Kumar Rishi , Gandham Sripratyusha , Maity Hemant Kumar , Sarkar Uttam , Dey Bappaditya 
Published: October 2024

AbstractTuberculosis (TB) remains a major chronic infectious disease in cattle, particularly challenging in India which hosts the world’s largest and most diverse cattle population farmed in close proximity to its human settlements. This study investigates the hypothesis that genetic and immune variations drive differential TB susceptibility in cattle breeds. Using comprehensive transcriptomic analyses, we examined immune responses in peripheral blood mononuclear cells (PBMCs) from Sahiwal and Sahiwal-Holstein Friesian (SHF) crossbred cattle. Responses to both the virulentMycobacterium tuberculosisand the vaccine strainM. bovisBCG were compared. Notably, Sahiwal cattle exhibited a robust early immune response characterized by upregulation of interferon signaling, enhanced cytotoxic activity by CD8+ T and NK cells, and pronounced T cell recruitment and activation pathways compared to SHF crossbreds. PBMCs of this breed also demonstrated superior control ofM. tuberculosisreplicationin vitro. A distinctive immune signature comprising 8 genes, includingCXCL10, ISG15, CTLA4, SELL, TLR3, MYD88, IRF1, and EOMESwas significantly upregulated in Sahiwal cattle PBMCs, potentially driving their reduced TB susceptibility. These findings underscore the importance of breed-specific immune profiling in devising effective TB control strategies, and could lead to targeted interventions that leverage genetic and immunological insights to mitigate TB in regions with high cattle diversity.
Mathematical modeling of the frozen zone dynamics: towards using thermal imagers in cryotherapy
Authors: Ivakhnenko O. V. , Todrin O. F. , Globa V.Yu. , Chyzh M.O. , Kovalov G.O. , Shevchenko S.N. 
Published: October 2024

AbstractUsing thermal imaging for cryoablation and cryotherapy is a convenient technique but it does not allow for visualization of what is inside. We model thermal field distribution which can help relate surface thermal field images to in-depth temperature field dynamics that show freezing and thawing processes dynamics. We consider the spatio-temporal distribution of temperature using examples of hydrogel and living tissue subjected to a cryoprobe. Freezing in the hydrogel is compared with the measurements using visual morphometry. Such studies can be useful for comparingin-vitroandin-vivothermal field dynamics and for estimating the correct timing for cryoapplication.
Correlating Disordered Activation Domain Ensembles with Gene Expression Levels
Authors: Flores Eduardo , Camacho Aleah R. , Cuevas-Zepeda Estefania , McCoy Mary B. , Yu Feng , Staller Max V. , Sukenik Shahar 
Published: October 2024

ABSTRACTTranscription factor proteins bind to specific DNA promoter sequences and initiate gene transcription. In eukaryotes, most transcription factors contain intrinsically disordered activation domains (ADs) that regulate their transcriptional activity. Like other disordered protein regions, ADs do not have a fixed three-dimensional structure and instead exist in an ensemble of conformations. Disordered ensembles contain sequence-encoded structural preferences which are often linked to their function. We hypothesize this link exists between the structural preferences of disordered AD ensembles and their ability to induce gene expression. To test this, we used FRET microscopy to measure the ensemble dimensions of two activation domains, HIF-1α and CITED2, in live cells, and correlate this structural information with transcriptional activity. We find that point mutations that expanded the HIF-1α ensemble increased transcriptional activity, while those that compacted it reduced activity. Conversely, CITED2 showed no correlation between ensemble dimensions and activity. Our results reveal a sequence-dependent relationship between AD ensemble dimensions and their transcriptional activity.WHY IT MATTERSTranscription factors have activation domains (ADs) that bind to coactivator complexes to initiate gene transcription. Despite their key role, a comprehensive understanding of what drives their transcriptional activity has remained elusive. Efforts to understand AD activity have largely focused on their amino acid composition. In recent years, it is increasingly realized that the structural ensembles of disordered proteins contain biases that dictate their structural properties. For ADs, ensemble structures remain poorly explored, especially in relation to their activity. Here we report a mutational study of two ADs, HIF-1α and CITED2, that examines how ensemble dimensions correlate with activity. Our findings suggest that ensemble dimensions may drive activity in some ADs, and that AD ensemble dimensions can be modulated not only through mutations, but also through changes in the cellular environment.
Complete genome sequences of nine isolates from microplastics in the Bow River, Calgary, Canada
Authors: Goff Kira L , Suresh Sneha , Stadfeld Jonas M , Bhatnagar Srijak 
Published: October 2024

AbstractWe present the complete genome sequences of nine bacterial strains isolated from microplastics from water or sediments of the Bow River in Calgary, Alberta. These isolates provide insight into the freshwater microplastic microbiome and their plastic biodegradation potential.
Impact of winter enclosures on energy expenditure and movement activity of european reed deer
Authors: Silovský Václav , Pilská Anna , Faltusová Monika , Ježek Miloš 
Published: October 2024

AbstractThe management of red deer enclosures, combined with supplementary feeding, can reduce the energy expenditure of confined animals. These enclosed individuals minimize their spatio-temporal movement and energy usage during their time inside the winter enclosure compared to those living in free range during the same period. The datasets used in this study were based on biologging technology. All measured animals (n=11) were equipped with GPS collars and Daily Dairy sensors to record Vectorial Dynamic Body Acceleration (VeDBA). The study revealed differences in the daily summary and daily mean of VeDBA parameters between enclosed and free-living animals in the study area. Energy expenditure was similar for both groups only at the moment of winter enclosure release. After the enclosure was opened, the released individuals increased their movement and energy expenditure. The use of winter enclosures for red deer management can reduce animal movement and minimize potential damage to young forest stands and seedlings. The opening of the winter enclosure must be synchronized with the availability of natural food resources in the locality. The management of winter enclosures should be carried out with a system of supplementary feeding inside.
Multiscale heterogeneity of functional connectivity in autism
Authors: Ilioska Iva , Oldehinkel Marianne , Llera Alberto , Rovný Maroš , Mei Ting , Kia Seyed Mostafa , Floris Dorothea L. , Tillmann Julian , Holt Rosemary J. , Loth Eva , Charman Tony , Murphy Declan G. M. , Ecker Christine , Banaschewski Tobias , Mennes Maarten , Beckmann Christian F. , Marquand Andre , Buitelaar Jan K. , Fornito Alex 
Published: October 2024

ABSTRACTAtypical functional connectivity (FC) in autism is a common finding, but the results of individual studies are often inconsistent and sometimes contradictory. Classical reliance on case-control comparisons of group means that ignore the inter-individual heterogeneity in autism may be a key drive of this inconsistency. Here, we used normative modelling to examine FC heterogeneity at the level of pair-wise inter-regional connections, specific brain regions, and broader functional networks in 1,824 participants (796 autistic) aged 5-58 years recruited across 32 different sites. Connection-level heterogeneity was high in both groups, with no single connection deviating in more than 4% of participants. However, deviant connections tended to converge on common regions and networks in autistic individuals more than in controls. Autistic individuals showed significantly greater overlap for positive deviations (i.e., atypically increased FC) in transmodal systems and negative deviations (atypically decreased FC) in sensory-motor areas. FC deviation patterns across coarser levels correlated with social functioning symptoms and intellectual ability. This work suggests that clinical variability in autism may be associated with extreme heterogeneity in the specific functional connections, whereas commonalities may be driven by convergence of atypical FC increases in transmodal systems and atypical decreases in sensorimotor networks, pointing to an imbalance in the functional organization of the brain’s sensorimotor-association axis.
Improved Sensitivity For Detection Of Clinical Deterioration When Diagnostic Pathology And Patient Trends Are Included In Machine Learning Models
Authors: Greenberg Jonathan D. , Huberts Leo C.E. , Ritchie Angus , Ooi Sze-Yuan , Flynn Gordon M. , Hart Graeme K. , Gallego Blanca 
Published: October 2024

ABSTRACTObjectivesThis study aimed to develop and validate a machine learning model to predict deterioration using Australian hospital data, paying particular attention to the role of predictors not included in current scoring systems.DesignRetrospective cohort study using electronic health records from a large metropolitan health service.SettingGeneral hospital wards, excluding the Emergency Department, Intensive Care Unit, or Palliative Care.ParticipantsInpatients over the age of 18.Main Outcome MeasuresThe primary outcomes of deterioration were mortality and ICU transfer within 24 hours of a newly available observation. A Gradient Boosted Tree model was estimated using patient demographics, vital signs, pathology results, and linear trends. Resulting feature importance was investigated using Shapley values. The model performance was validated against existing scoring systems, including Between the Flags (BTF) and the Modified / National Early Warning Score (MEWS/NEWS).ResultsA Gradient Boosted Tree was developed from 121,608 patients and tested in 20,605 patients. The model, named aWARE, demonstrated higher discriminative ability (AUROCmortality=0.93, AUROCICU transfer=0.84), and calibration when compared to baseline scores. Overall, the 10 most influential features unique between both outcomes were age, oxygen saturation to inspired oxygen ratio, respiratory rate, white cell count, venous lactate, heart rate to systolic blood pressure ratio, albumin, oxygen saturation, urea and heart rate. Of these, only 3 are included in BTF.ConclusionThe machine learning model proposed in this study identified more deteriorating patients and produced less false positive alerts than Between the Flags. Feature importance highlighted the deficit between strong predictors of deterioration and the parameters used in current scoring systems.
PERSONALIZED GLYCEMIC RESPONSES TO FOOD AMONG INDIVIDUALS WITH TYPE 2 DIABETES IN INDIA: DEVELOPMENT OF A MACHINE LEARNING PREDICTION MODEL
Authors: Choudhry Niteesh K. , Priyadarshini Shweta , Swamy Jaganath , Mehta Mridul 
Published: October 2024

ABSTRACTObjectiveEmerging global evidence demonstrates marked inter-individual differences in post-prandial glucose response (PPGR) although no such data exists in India and prior studies have primarily evaluated PPGR variation in individuals without diabetes. This study sought to develop a machine learning model to predict individual PPGR responses to facilitate the prescription of personalized diets for individuals with type 2 diabetes.Research Design and MethodsAdults with type 2 diabetes and a hemoglobin A1c (HbA1c) ≥7 were enrolled from 14 sites around India. Subjects wore a continuous glucose monitor and logged meals. PPGR was calculated for each meal, based on the incremental area under the curve, and a machine learning predictor of PPGR was developed using stochastic gradient boosting regression. Model calibration and discrimination was assessed using a Pearson product moment correlation and area under a receiver operating curve (AUC), respectively, and its performance was compared to models based only on meal carbohydrate and calorie content.ResultsThe study included data from 488 patients (mean age 52.5 years, 36% female, mean duration of diabetes 6.4 years, mean hemoglobin A1c 8.16%). Mean PPGR to common foods varied substantially (e.g. PPGR for “aloo paratha with curd” ranged from 10 to 170 mg/dl*h). PPGR values predicted by the machine learning model were highly correlated with observed PPGR (r=0.69) and model calibration was substantially stronger than for a model based only on calorie (r=0.57) or carbohydrate (r=0.39) content. The machine learning model also demonstrated very strong discriminative ability (AUC 0.80).ConclusionsA machine learning model built with nutritional content, health habits, biometric information and common laboratory data produced highly accurate individualized predictions of PPGRs that substantially outperformed predictions based upon calorie and carbohydrate content. These results could be used to facilitate the delivery of personalized medical nutritional therapy as is widely recommended by type 2 diabetes practice guideline in India and globally.