bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic pressure ulcer risk predictions for hospitalized patients: development and validation of a machine learning model with an expert group of nurses
Authors: Greup Steffen , Spoon Denise , de Vroed Annemarie , Werkhoven Ben , Timmerman Enrico , Ista Erwin , van Dijk Monique 
Published: December 2024

AbstractBackgroundPressure ulcers (PU) are a source of harm and discomfort in hospitalized patients. To identify patients at risk for developing PU nurses use validated risk assessment instruments which have insufficient predictive power, also leading to increased nursing workload. Machine learning on electronic health records (EHR) has the potential to provide better risk predictions while also reducing nursing workload.ObjectiveTo develop and validate a dynamic prediction model for daily PU risk predictions together with an expert group of nurses, while considering factors important to successful clinical implementation like explainability and robustness during the modelling process.MethodsAll admissions to general wards in a single-center tertiary university hospital in the Netherlands were included. A retrospective dataset with candidate predictors was collected from the EHR and split into a training set (December 2021 – November 2022, N=19931) and a validation set (January 2023 – June 2023, N=11387). Separate models were developed for the first 72 hours of admission, and >72 hours. The PU outcome was identified from both structured and free text registration. Predictor definition, selection and modelling choices were discussed with an expert group of nurses. Several candidate machine learning models were compared using cross-validation on the training set. The same final model and predictor set were selected for both ≤72h and >72h, and predictive performance on the validation set was compared to the Waterlow score.ResultsThe difference in performance across candidate models was small on the training set. Logistic regression with an L2 penalty and a spline transformation applied to a limited set of predictors was chosen as the final model, and the candidate predictor set was reduced to a final predictor set. The AUROC for the final model was 0.790 (≤72h) and 0.795 (>72h) on the validation dataset. The model clearly outperformed the Waterlow score (0.816 vs. 0.702 (≤72h) and 0.800 vs. 0.677 (>72h)) for the subset of admissions with a registered Waterlow score. Expected remaining length of stay, age and ADL (activities of daily living) score were the predictors with the strongest contribution.ConclusionsWe developed and validated a dynamic prediction model for daily PU risk predictions, which outperformed the traditional Waterlow risk assessment score. During the development process special care was given to considerations for implementation and user acceptance. The model was named DRAAI (Decubitus Risk Alert based on AI), which means ‘turn’ in Dutch and is a common PU preventive measure.
Imaging Clusters of Pediatric Low-Grade Glioma are Associated with Distinct Molecular Characteristics
Authors: Fathi Kazerooni Anahita , Kraya Adam , Rathi Komal S. , Kim Meen Chul , Kesherwani Varun , Corbett Ryan , Vossough Arastoo , Khalili Nastaran , Gandhi Deep , Khalili Neda , Familiar Ariana M. , Jin Run , Huang Xiaoyan , Zhu Yuankun , Sickler Alex , Lueder Matthew R. , Phul Saksham , Storm Phillip B. , Ware Jeffrey B. , Foster Jessica B. , Mueller Sabine , Rokita Jo Lynne , Fisher Michael J. , Resnick Adam C. , Nabavizadeh Ali 
Published: December 2024

ABSTRACTBackgroundCancers show heterogeneity at various levels, from genome to radiological imaging. This study aimed to explore the interplay between genomic, transcriptomic, and radiophenotypic data in pediatric low-grade glioma (pLGG), the most common group of brain tumors in children.MethodsWe analyzed data from 201 pLGG patients in the Children’s Brain Tumor Network (CBTN), using principal component analysis and K-Means clustering on 881 radiomic features, along with clinical variables (age, sex, tumor location), to identify imaging clusters and examine their association with 2021 WHO pLGG classifications. To determine the transcriptome pathways linked to imaging clusters, we employed a supervised machine learning model with elastic net logistic regression based on the pathways identified through gene set enrichment and gene co-expression network analyses.ResultsThree imaging clusters with distinct radiomic characteristics were identified.BRAF V600Emutations were primarily found in imaging cluster 3, whileKIAA1549::BRAFfusion occurred in subtype 1. The model’s predictive accuracy (AUC) was 0.77 for subtype 1, 0.78 for subtype 2, and 0.70 for subtype 3. Each imaging cluster exhibited unique molecular mechanisms: subtype 1 was linked to oxidative phosphorylation,PDGFRB, and interleukin signaling, whereas subtype 3 was associated with histone acetylation and DNA methylation pathways, related toBRAF V600EpLGGs.ConclusionsOur radiogenomics study indicates that the intrinsic molecular characteristics of tumors correlate with distinct imaging subgroups in pLGG, paving the way for future multi-modal investigations that may enhance understanding of disease progression and targetability.
Associations of Combined Genetic and Lifestyle Risks with Incident Type 2 Diabetes in the UK Biobank
Authors: Zhao Chi , Hatzikotoulas Konstantinos , Balasubramanian Raji , Bertone-Johnson Elizabeth , Cai Na , Huang Lianyun , Huerta-Chagoya Alicia , Janiczek Margaret , Ma Chaoran , Mandla Ravi , Paluch Amanda , Rayner Nigel W , Southam Lorraine , Sturgeon Susan R. , Suzuki Ken , Taylor Henry J , VanKim Nicole , Yin Xianyong , Lee Chi Hyun , Collins Francis , Spracklen Cassandra N. 
Published: December 2024

AbstractBackgroundType 2 diabetes (T2D) results from a complex interplay between genetic predisposition and lifestyle factors. Both genetic susceptibility and unhealthy lifestyle are known to be associated with elevated T2D risk. However, their combined effects on T2D risk are not well studied. We aimed to determine whether unhealthy modifiable health behaviors were associated with similar increases in the risk of incident T2D among individuals with different levels of genetic risk.MethodsWe performed a genetic risk score (GRS) by lifestyle interaction analysis within 332,251 non-diabetic individuals at baseline from the UK Biobank. Multi-ancestry GRS were calculated by summing the effects of 783 T2D-associated variants and ranked into tertiles. We used baseline self-reported data on smoking, BMI, physical activity level, and diet quality to categorize participants as having a healthy, intermediate, or unhealthy lifestyle. Cox proportional hazards regression models were used to generate adjusted hazards ratios (HR) of T2D risk and associated 95% confidence intervals (CI).ResultsDuring follow-up (median 13.6 years), 13,128 (4.0%) participants developed T2D. GRS (P< 0.001) and lifestyle classification (P< 0.001) were independently associated with increased risk for T2D. Compared with healthy lifestyle, unhealthy lifestyle was associated with increased T2D risk in all genetic risk strata, with adjusted HR ranging from 7.11 (low genetic risk) to 16.33 (high genetic risk).ConclusionsHigh genetic risk and unhealthy lifestyle were the most significant contributors to the development of T2D. Individuals at all levels of genetic risk can greatly mitigate their risk for T2D through lifestyle modifications.
Differential metabolic biomarkers between coronary heart disease and diabetes mellitus
Authors: Wang Zhicai , Cang Yan , Shi Fei , Zhang Yi , Cui Hui-Na , He Yun , Liu Li , Li Yan , Xu Yawei , Wen He , Liu Zheng 
Published: December 2024

AbstractBackgroundCoronary heart disease (CHD), the leading cause of death globally, is a complex disease often association with metabolic disorders, particularly when co-occurring with diabetes mellitus (DM). However, the mechanisms underlying the interaction between these two chronic conditions remain poorly understood.MethodsIn this study, we enrolled 320 participants, including 103 healthy controls (HC), 62 individuals with CHD, 44 with DM, and 47 with both CHD and DM (CHDDM)) in the training set, along with 64 participants (20 HC, 18 CHD, 12 DM, and 14 CHDDM) in the test set. Plasma metabolomic profiling was performed using gas chromatograph-mass spectrometry (GC-MS) and ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS)-based multi-platform untargeted metabolomics.ResultsUsing multi-platform metabolomics, we identified 453 distinct metabolites. Through multivariate statistical analysis, we assigned 63, 27, and 56 disease-associated metabolic biomarkers for CHD, DM, and CHDDM, respectively, followed by pathway enrichment analysis. Using an area under the receiver operating characteristic (ROC) curve (AUC) cutoff value > 0.77, we identified 10, 4, and 8 disease-specific biomarkers for CHD, DM, and CHDDM, respectively. Notably, N-Formyl-L-methionine, N-Acetyl-L-methionine, PE 36:1p (18:0p/18:1), PE 36:2p (18:0p/18:2), and PE 34:2p (16:0p/18:2) were predominant in CHD, glycerophosphocholine and turanose were distinctive to DM, and xanthine and PE 38:5p (18:1p/20:4) were unique to CHDDM. These disease-specific metabolites showed superior diagnostic performance in their respective conditions. Additionally, 23 metabolites were identified as potential biomarkers for assessing the risk of CHD in DM patients.ConclusionThis comparative metabolomics approach provided new insights into disease-specific markers and pathogenic pathways, offering potential for improved diagnosis and management of CHD and DM.
Integration of metagenome-assembled genomes with clinical isolates reveals genomic signatures ofKlebsiella pneumoniaein carriage and disease
Authors: Gupta Samriddhi , Almeida Alexandre 
Published: December 2024

AbstractKlebsiella pneumoniaeis an opportunistic pathogen causing diseases ranging from gastrointestinal disorders to severe liver abscesses. While clinical isolates ofK. pneumoniaehave been extensively studied, less is known about asymptomatic variants colonizing the human gut across diverse populations. Genome-resolved metagenomics has offered unprecedented access to metagenome-assembled genomes (MAGs) from diverse host states and geographical locations, opening opportunities to explore health-associated microbial features. Here we analysed 662 human gut-derivedK. pneumoniaegenomes (319 MAGs, 343 isolates) from 29 countries to investigate the population structure and genomic diversity ofK. pneumoniaein carriage and disease. Only 9% of sequence types were found to be shared between healthy and disease states, highlighting distinct diversity across health conditions. Integrating MAGs nearly doubled gut-associatedK. pneumoniaephylogenetic diversity, and uncovered 86 lineages without representation among >20,000Klebsiellaisolate genomes from various sources. Genomic signatures linked to pathogenicity and carriage included those involved in antibiotic resistance, iron regulation, restriction modification systems and polysaccharide biosynthesis. Notably, machine learning models integrating MAGs and isolates more accurately classified disease and carriage states compared to isolates alone. These findings showcase the value of metagenomics to understand pathogen evolution with implications for public health surveillance strategies.
Poly-ubiquitylated transmembrane proteins outcompete other cargo for limited space inside clathrin-coated vesicles
Authors: Liu Hao-Yang , Ashby Grant , Yuan Feng , Sarkar Susovan , Hayden Carl C. , Huibregtse Jon M. , Stachowiak Jeanne C. 
Published: December 2024

AbstractEndocytic recycling of transmembrane proteins is essential to cell signaling, ligand uptake, protein traffic and degradation. The intracellular domains of many transmembrane proteins are ubiquitylated, which promotes their internalization by clathrin-mediated endocytosis. How might this enhanced internalization impact endocytic uptake of transmembrane proteins that lack ubiquitylation? Recent work demonstrates that diverse transmembrane proteins compete for space within highly crowded endocytic structures, suggesting that enhanced internalization of one group of transmembrane proteins may come at the expense of other groups. Here we show that preferential internalization of poly-ubiquitylated transmembrane proteins results in reduced endocytosis of mono-ubiquitylated and non-ubiquitylated proteins. Using a combination of live-cell imaging and ligand uptake assays, we confirmed that increased ubiquitylation correlates with increased internalization by clathrin-coated vesicles. Further, poly-ubiquitylated receptors significantly outcompeted their mono-ubiquitylated and non-ubiquitylated counterparts for localization to endocytic sites and uptake of extracellular ligands. These findings demonstrate the inherent interdependence of transmembrane protein recycling, suggesting that clathrin-coated vesicles act as selective filters, prioritizing highly ubiquitylated transmembrane proteins for uptake while leaving proteins with little or no ubiquitylation behind. Given that poly-ubiquitylation is thought to signal protein aging and damage, our findings suggest a mechanism for selective internalization of high priority cargo proteins, with simultaneously exclusion and protection of functional proteins that lack poly-ubiquitylation.SignificanceUbiquitylation is essential for maintaining cellular homeostasis by regulating transmembrane protein trafficking, degradation, and signaling. Traditionally, poly-ubiquitylation has been viewed primarily as a signal for protein degradation, while mono-ubiquitylation is considered sufficient to trigger endocytosis. However, our work reveals a previously unrecognized role for poly-ubiquitylation in clathrin-mediated endocytosis, demonstrating that poly-ubiquitylated transmembrane proteins outcompete their non-ubiquitylated counterparts for incorporation into clathrin-coated vesicles, thereby establishing a competitive framework for endocytic cargo sorting. This mechanism reveals a selective sorting mechanism driven by the extent of ubiquitylation, which could regulate the removal of damaged proteins while protecting functional proteins at the plasma membrane.
Staphylococcus aureusgliding comets: formation and observation
Authors: Davies M.C , Pollitt E.J.G. 
Published: December 2024

AbstractStaphylococcus aureushas been shown to move across soft agar surfaces by several different mechanisms.S. aureuscan move using either spreading (a form of sliding motility producing generally round/frond like colonies) or by forming comets (slime covered aggregates of cells) that result in long thin dendrites branching out from the central colony. Spreading is agreed to be a form of passive motility whilst the comets are a form of gliding motility (i.e. active). Comets occur under similar conditions to spreading round colonies; however it can be difficult to get the comets to form. Here we examine the variables involved in determining whether comets form as well as report further observations of the comets themselves. We found that the conditions that favoured comet formation (and formed the associated dendrites) occurred over a more limited range than those that enabled spreading motility. Comet formation is very sensitive to the solidifying agents used, the amount of media used and the drying time. We further observed that the comets can propel themselves upwards against gravity unlike spreading motility and thatS. aureusformed unusual dense aggregates and strand-like structures within comets unlike the normal growing arrangement ofS. aureusobserved in spreading. These results may aid others in producing motility assays to study spreading and comet formation inS. aureusand provides further insight into how comets behave.
Reduced trust in bodily sensations predicts suicidal ideation in hospitalized patients with major depression: an observational study
Authors: Eggart Michael , Valdés-Stauber Juan , Müller-Oerlinghausen Bruno 
Published: December 2024

AbstractBackgroundMajor depressive disorder (MDD) is associated with maladaptive self-reported interoception, i.e., abnormal bodily self-experience. Although diminished body trusting predicts suicidal ideation, interoceptive measures have not been considered in depressed inpatients, whose suicide risk regularly peaks post-discharge. This study aims to explore interoceptive characteristics at admission that help identify inpatients at risk for suicidal ideation at discharge, thereby preventing fatal outcomes.MethodsThe observational study included 87 depressed inpatients providing self-ratings at both hospital admission (T0) and discharge (T1) on the following scales: Multidimensional Assessment of Interoceptive Awareness (MAIA-2); Beck Depression Inventory-II (BDI-II). A hierarchical logistic regression analysis estimated the longitudinal association between self-reported interoception (T0) and suicidal ideation (T1). The optimal cutpoints for predicting suicidal ideation were calculated using ROC curve analysis.ResultsSuicidal ideation was found in 17.24% patients at discharge, who reported lower baseline MAIA-2Trustingscores than non-ideators (p=0.01). Diminished body trusting (OR=0.19), somatic comorbidity (OR=16.77), and baseline suicidal ideation (OR=24.01) significantly predicted suicidal ideation (T1). For body trusting, we estimated an optimal classification of subsequent suicidal ideation for the cutpoint≤2.33 (AUC=0.70 [95% CI 0.57, 0.83], sensitivity=0.87, specificity=0.44, positive predictive value=0.25, negative predictive value=0.94).LimitationsDue to the exploratory nature of the study, the findings should be replicated in pre-registered trials with larger sample sizes.ConclusionsDiminished body trusting is, with acceptable sensitivity, a significant predictor for post-treatment suicidal ideation in depressed inpatients. This finding emphasizes the importance of incorporating body-centered approaches into multimodal treatment strategies especially in inpatients under risk to prevent suicidal incidents.
Evaluating Model Performance with Hard-Swish Activation Function Adjustments
Authors: Pydimarry Sai Abhinav , Khairnar Shekhar Madhav , Palacios Sofia Garces , Sankaranarayanan Ganesh , Nguyen Huu Phong 
Published: December 2024

AbstractIn the field of pattern recognition, achieving high accuracy is essential. While training a model to recognize different complex images, it is vital to fine-tune the model to achieve the highest accuracy possible. One strategy for fine-tuning a model involves changing its activation function. Most pre-trained models use ReLU as their default activation function, but switching to a different activation function like Hard-Swish could be beneficial. This study evaluates the performance of models using ReLU, Swish and Hard-Swish activation functions across diverse image datasets. Our results show a 2.06% increase in accuracy for models on the CIFAR-10 dataset and a 0.30% increase in accuracy for models on the ATLAS dataset. Modifying the activation functions in architecture of pre-trained models lead to improved overall accuracy.
Hexosamine Biosynthesis Disruption Impairs GPI Production and ArrestsPlasmodium falciparumGrowth at Schizont Stages
Authors: Alberione María Pía , Avalos-Padilla Yunuen , Rangel Gabriel W , Ramírez Miriam , Romero-Uruñuela Tais , Fenollar Àngel , Crispim Marcell , Smith Terry K , Llinás Manuel , Izquierdo Luis 
Published: December 2024

AbstractUDP-N-acetylglucosamine (UDP-GlcNAc) is a crucial sugar nucleotide for glycan synthesis in eukaryotes. In the malaria parasitePlasmodium falciparum, UDP-GlcNAc is synthesized via the hexosamine biosynthetic pathway (HBP) and is essential for glycosylphosphatidylinositol (GPI) anchor production, the most prominent form of protein glycosylation in the parasite. In this study, we explore a conditional knockout of glucosamine-6-phosphate N-acetyltransferase (PfGNA1), a key HBP enzyme.PfGNA1 depletion led to significant disruptions in HBP metabolites, impairing GPI biosynthesis and causing mislocalization of the merozoite surface protein 1 (MSP1), the most abundant GPI-anchored protein in the parasite. Furthermore, parasites were arrested at the schizont stage, exhibiting severe segmentation defects and an incomplete rupture of the parasitophorous vacuole membrane (PVM), preventing egress from host red blood cells. Our findings demonstrate the critical role of HBP and GPI biosynthesis inP. falciparumasexual blood stage development and underscore the potential of targeting these pathways as a therapeutic strategy against malaria.Author SummaryMalaria remains a major cause of illness and death, particularly in sub-Saharan Africa, with increasing resistance to treatments highlighting the urgent need for new strategies. Malaria parasites rely on the hexosamine biosynthetic pathway to produce UDP-N-acetylglucosamine, an essential metabolite for glycosylphosphatidylinositol synthesis. Glycosylphosphatidylinositol molecules anchor vital proteins to the parasite’s surface and, as free glycolipids, serve as structural components of its membranes. Our study examined the effects of disrupting PfGNA1, a key enzyme in the hexosamine biosynthetic pathway, which is distinct from its human counterparts. Disruption of PfGNA1 blocked the production of glycosylphosphatidylinositol, leading to improper protein localization, developmental arrest, and failure of the parasites to mature or exit infected red blood cells. Our results underscore the central role of the hexosamine biosynthetic pathway and glycosylphosphatidylinositol biosynthesis, which are essential for parasite survival. This pathway represents a promising target for developing novel antimalarial therapies.
Muscle growth by sarcomere divisions
Authors: Rodier Clement , Estabrook Ian D. , Chan Eunice HoYee , Rice Gavin , Loreau Vincent , Raunser Stefan , Görlich Dirk , Friedrich Benjamin M. , Schnorrer Frank 
Published: December 2024

AbstractThe sarcomere is the elementary contractile unit of muscles. Adult muscle cells chain thousands of sarcomeres into long periodic myofibrils that attach to the skeleton. How new sarcomeres are added during muscle growth is unknown. By live imaging and high-throughput image analysis, we have now tracked sarcomeric components duringDrosophilamuscle development and discovered that individual sarcomeres divide along the myofibril tension axis into daughter sarcomeres. This way, new sarcomeres can be inserted into contractile and mechanically intact myofibrils. We propose that sarcomere division is triggered by tension and local sarcomere damage originating from skeletal growth and muscle contractions. Sarcomere divisions repair damaged sarcomeres, ensure their mechanical integrity and synchronise sarcomere addition with skeletal growth during animal development.