bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep Learning Model Utilization for Mortality Prediction in Mechanically Ventilated ICU Patients
Authors: Ashrafi Negin , Liu Yiming , Xu Xin , Wang Yingqi , Zhao Zhiyuan , Pishgar Maryam 
Published: March 2024

ABSTRACTBackgroundThe requirement for mechanical ventilation has increased in recent years. Patients in the intensive care unit (ICU) who undergo mechanical ventilation often experience serious illness, contributing to a high risk of mortality. Predicting mortality for mechanically ventilated ICU patients helps physicians implement targeted treatments to mitigate risk.MethodsWe extracted medical information of patients with invasive mechanical ventilation during ICU admission from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. This information includes demographics, disease severity, diagnosis, and laboratory test results. Patients who met the inclusion criteria were randomly divided into the training set (n=11,549, 70%), the test set (n=2,475, 15%), and the validation set (n=2,475, 15%). The Synthetic Minority Over-sampling Technique (SMOTE) was utilized to resolve the imbalanced dataset. After literature research, clinical expertise and an ablation study, we selected 12 variables which is fewer than the 66 features in the best existing literature. We proposed a deep learning model to predict the ICU mortality of mechanically ventilated patients, and established 7 baseline machine learning (ML) models for comparison, including K-nearest Neighbors (KNN), Logistic Regression, Decision Tree, Random Forest, Bagging, XGBoost, and Support Vector Machine (SVM). Area under the Receiver Operating Characteristic Curve (AUROC) was used as an evaluation metric for model performance.ResultsUsing 16,499 mechanically ventilated patients from the MIMIC-III database, the Neural Network model outperformed existing literature by 7.06%. It achieved an AUROC score of 0.879 (95% Confidence Interval (CI) [0.861-0.896]), an accuracy of 0.859 on the test set, and was well-calibrated with a Brier score of 0.0974, significantly exceeding previous best results.ConclusionsThe proposed model demonstrated an exceptional ability to predict ICU mortality among mechanically ventilated patients. The SHAP analysis showed respiratory failure is a significant indicator of mortality prediction compared to other related respiratory dysfunction diseases. We also incorporated mechanical ventilation duration variable for the first time in our prediction model. We observed that patients with higher mortality rates tended to have longer mechanical ventilation times. This highlights the model’s potential in guiding clinical decisions by indicating that longer mechanical ventilation may not necessarily enhance patient survival chances.
Deep learning of antibody epitopes using molecular permutation vectors
Authors: Vardaxis Ioannis , Simovski Boris , Anzar Irantzu , Stratford Richard , Clancy Trevor 
Published: March 2024

ABSTRACTBackgroundThe accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies.ResultsHere we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary molecular permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody, rather than the traditional approach of defining epitopes as scores per amino acid on a protein sequence that pertain to their probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary molecular permutation vectors, the approach also uses the 3D macrostructure features of the unbound 3D protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we demonstrate that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model in training and generalizes in very robust manner on dissimilar data not seen by the model in training.ConclusionsWith the approach described herein, a primary protein sequence with the query molecular permutation vector alone is required to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.
Imaging genomics reveals genetic architecture of the globular human braincase
Authors: Molz Barbara , Eising Else , Alagöz Gökberk , Schijven Dick , Francks Clyde , Gunz Philipp , Fisher Simon E. 
Published: March 2024

AbstractCompared with our fossil ancestors and Neandertal kin, modern humans have evolved a distinctive skull shape, with a rounder braincase and more delicate face. Competing explanations for this rounder skull have either linked it to changes in brain organisation, or seen it as a by-product of gracilization (evolution of thinner and lighter skeletal anatomy). Here, we combined palaeoanthropological data from hominin fossils and imaging genomics data from living humans to gain insight into evolutionary and developmental mechanisms shaping this uniquely modern human phenotype. We analysed endocranial globularity from magnetic resonance imaging (MRI) brain scans and genetic data of more than 33,000 adults. We discovered 28 genomic loci significantly associated with endocranial globularity. There was genetic overlap with the brain’s ventricular system, white matter microstructure, and sulcal morphology, and with multivariate genetic analyses of reading/language skills, but not with general cognition. The associated genes exhibited enriched expression in the brain during prenatal development and early childhood. The connection to the ventricular system hints at a role for cerebrospinal fluid pressure in shaping the endocranium during development. Genes linked to endocranial globularity also showed enhanced expression in the cardiovascular and female reproductive systems. This finding suggests co-evolutionary pathways whereby changes impacting factors such as energy needs, pregnancy, or fertility concurrently shape the brain and its structure.
Development of radiofluorinated MLN-4760 derivatives for PET imaging of the SARS-CoV-2 entry receptor ACE2
Authors: Wang Jinling , Beyer Darja , Vaccarin Christian , He Yingfang , Tanriver Matthias , Benoit Roger , Deupi Xavier , Mu Linjing , Bode Jeffrey W. , Schibli Roger , Müller Cristina 
Published: March 2024

AbstractPurposeThe angiotensin converting enzyme 2 (ACE2) plays a regulatory role in the cardiovascular system and serves SARS-CoV-2 as an entry receptor. The aim of this study was to synthesize and evaluate radiofluorinated derivatives of the ACE2 inhibitor MLN-4760. [18F]F-MLN-4760 and [18F]F-Aza-MLN-4760 were demonstrated to be suitable for non-invasive imaging of ACE2, potentially enabling a better understanding of its expression dynamics.MethodsBased on computational molecular modeling, the ACE2-binding modes of F-MLN-4760 and F-Aza-MLN-4760 were similar to that of MLN-4760. Co-crystallization of the hACE2/F-MLN-4760 protein complex was performed for confirmation. Displacement experiments using [3H]MLN-4760 enabled the determination of the binding affinities of the synthesized F-MLN-4760 and F-Aza-MLN-4760 to ACE2 expressed in HEK-ACE2 cells. Aryl trimethylstannane-based and pyridine-based radiofluorination precursors were synthesized and used for the preparation of the respective radiotracers. [18F]F-MLN-4760 and [18F]F-Aza-MLN-4760 were evaluated with regard to the uptake in HEK-ACE2 and HEK-ACE cells and in vitro binding to tissue sections of HEK-ACE2 xenografts and normal organs of mice. Biodistribution and PET/CT imaging studies of [18F]F-MLN-4760 and [18F]F-Aza-MLN-4760 were performed using HEK-ACE2 and HEK-ACE xenografted nude mice.ResultsCrystallography data revealed an equal ACE2-binding mode for F-MLN-4760 as previously found for MLN-4760 and indicated that the same would hold true for F-Aza-MLN-4760. The IC50values were all in the high nM range, but three-fold lower for F-MLN-4760 and seven-fold lower for F-Aza-MLN-4760 than for MLN-4760. [18F]F-MLN-4760 and [18F]F-Aza-MLN-4760 were obtained in 1.4 ± 0.3 GBq and 0.5 ± 0.1 GBq activity with >99% radiochemical purity in a 5.3% and 1.2% radiochemical yield, respectively. Uptake in HEK-ACE2 cells was higher for [18F]F-MLN-4760 (67 ± 9%) than for [18F]F-Aza-MLN-4760 (37 ± 8%) after 3 h incubation while negligible uptake was seen in HEK-ACE cells (<0.3%). [18F]F-MLN-4760 and [18F]F-Aza-MLN-4760 accumulated specifically in HEK-ACE2 xenografts of mice (13 ± 2% IA/g and 15 ± 2% IA/g at 1 h p.i.) with almost no uptake observed in HEK-ACE xenografts (<0.3% IA/g). This was confirmed by PET/CT imaging, which also visualized unspecific accumulation in the gall bladder and intestinal tract.ConclusionBoth radiotracers showed specific and selective binding to ACE2 in vitro and in vivo. [18F]F-MLN-4760 was, however, obtained in higher yields and the ACE2-binding affinity was superior over that of [18F]F-Aza-MLN-4760. [18F]F-MLN-4760 would, thus, be the candidate of choice for further developlment to enable PET imaging of ACE2 in patients.
Perturbation-aware predictive modeling of RNA splicing using bidirectional transformers
Authors: McNally Colin P , Abdulhay Nour J , Khalaj Mona , Saberi Ali , Zaro Balyn W , Goodarzi Hani , Ramani Vijay 
Published: March 2024

ABSTRACTPredicting molecular function directly from DNA sequence remains a grand challenge in computational and molecular biology. Here, we engineer and train bidirectional transformer models to predict the chemical grammar of alternative human mRNA splicing leveraging the largest perturbative full-length RNA dataset to date. By combining high-throughput single-molecule long-read “chemical transcriptomics” in human cells with transformer models, we train AllSplice – a nucleotide foundation model that achieves state-of-the-art prediction of canonical and noncanonical splice junctions across the human transcriptome. We demonstrate improved performance achieved through incorporation of diverse noncanonical splice sites in its training set that were identified through long-read RNA data. Leveraging chemical perturbations and multiple cell types in the data, we fine-tune AllSplice to train ChemSplice – the first predictive model of sequence-dependent and cell-type specific alternative splicing following programmed cellular perturbation. We anticipate the broad application of AllSplice, ChemSplice, and other models fine-tuned on this foundation to myriad areas of RNA therapeutics development.
Bivalent COVID-19 vaccines boost the capacity of pre-existing SARS-CoV-2-specific memory B cells to cross-recognize Omicron subvariants
Authors: Fryer Holly A. , Geers Daryl , Gommers Lennert , Zaeck Luca M. , Tan Ngoc H. , Jones-Freeman Bernadette , Goorhuis Abraham , Postma Douwe F. , Visser Leo G. , Hogarth P. Mark , Koopmans Marion P. G. , GeurtsvanKessel Corine H. , O’Hehir Robyn E. , van der Kuy P. Hugo M. , de Vries Rory D. , van Zelm Menno C. 
Published: March 2024

AbstractBivalent COVID-19 vaccines comprising ancestral Wuhan-Hu-1 (WH1) and the Omicron BA.1 or BA.5 subvariant elicit enhanced serum antibody responses to emerging Omicron subvariants. We characterized the memory B-cell (Bmem) response following a fourth dose with a BA.1 or BA.5 bivalent vaccine, and compared the immunogenicity with a WH1 monovalent fourth dose. Healthcare workers previously immunized with mRNA or adenoviral vector monovalent vaccines were sampled before and one-month after a monovalent, BA.1 or BA.5 bivalent fourth dose COVID-19 vaccine. RBD-specific Bmem were quantified with an in-depth spectral flow cytometry panel including recombinant RBD proteins of the WH1, BA.1, BA.5, BQ.1.1, and XBB.1.5 variants. All recipients had slightly increased WH1 RBD-specific Bmem numbers. Recognition of Omicron subvariants was not enhanced following monovalent vaccination, while both bivalent vaccines significantly increased WH1 RBD-specific Bmem cross-recognition of all Omicron subvariants tested by flow cytometry. Thus, Omicron-based bivalent vaccines can improve recognition of descendent Omicron subvariants by pre-existing, WH1-specific Bmem, beyond that of a conventional, monovalent vaccine. This provides new insights into the capacity of variant-based mRNA booster vaccines to improve immune memory against emerging SARS-CoV-2 variants.
Extracellular vesicles produced during fungal infection in humans are immunologically active
Authors: de Rezende Caroline P. , Santos Patrick W. S. , Piraine Renan A. , Silvestrini Virgínia C. , Barbosa Julio C. J. , Valera Fabiana C. P. , Tamashiro Edwin , Podolski-Gondim Guilherme G. , Quintana Silvana M. , Calado Rodrigo , Martinez Roberto , P. Fill Taicia , Rodrigues Márcio L. , Almeida Fausto 
Published: March 2024

AbstractOf the known 1.5 million fungal species,Candidaspp.,Cryptococcusspp., andParacoccidioidesspp. are the main pathogenic species causing serious diseases with almost two million annual deaths. The diagnosis and treatment of fungal infections are challenging since of the limited access to diagnostic tests and the emergence of antifungal resistance. Extracellular vesicles (EVs) promote the interactions of fungal cells with other organisms and play an important role in the pathogen–host relationship. Owing to the complexity of fungal EVs and the lack of clinical studies on their roles in human infections, we studied the EVs from the serum and urine samples of patients with fungal infections caused byCandida albicans,Cryptococcus neoformans, andParacoccidioides brasiliensisand determined their roles. Steroids, sphingolipids, and fatty acids were identified as the main secondary metabolites via mass spectrometry analysis. We asked whether these metabolites in EVs could play roles in modulating the host immune response. Our findings revealed the polarization of the proinflammatory profile in murine and human macrophages, with the increased production of cytokines, such as the tumor necrosis factor-α, interferon-γ, and interleukin-6, and an increased expression of the inducible nitric oxide synthase gene, a M1 response marker. Therefore, circulating EVs from patients with fungal infections are likely involved in the disease pathophysiology. Our findings provide insights into the roles of EVs in fungal infections in clinical samples and in vitro, suggesting possible targets for systemic mycoses therapy.Significance StatementFungal infections cause approximately 1.6 million deaths annually. Due to therapeutic and diagnostic limitations, it is mandatory to understand and develop new immunological interventions. Despite several in vitro studies on the production of extracellular vesicles (EVs) from fungal pathogens, this study is a pioneer in the identification and characterization of EVs in the course of fungal infection in humans. Our group demonstrated the presence of EVs in clinical samples from patients diagnosed with candidiasis, cryptococcosis, and paracoccidioidomycosis, as well as the EVs interaction produced by host and fungal pathogen with the immune system, resulting in relationships that may be beneficial for the progression or elimination of fungal disease.
Combination of AID2 and BromoTag expands the utility of degron-based protein knockdowns
Authors: Hatoyama Yuki , Islam Moutushi , Bond Adam G. , Hayashi Ken-ichiro , Ciulli Alessio , Kanemaki Masato T. 
Published: March 2024

AbstractAcute protein knockdown is a powerful approach to dissecting protein function in dynamic cellular processes. We previously reported an improved auxin-inducible degron system, AID2, but recently noted that its ability to induce degradation of some essential replication factors, such as ORC1 and CDC6, was not enough to induce lethality. Here, we present combinational degron technologies to control two proteins and enhance target depletion. For this purpose, we initially compared PROTAC-based degrons, dTAG and BromoTag, with AID2 to reveal their key features and then demonstrated control of cohesin and condensin with AID2 and BromoTag, respectively. We developed a double-degron system with AID2 and BromoTag to enhance target depletion and accelerate depletion kinetics and demonstrated that both ORC1 and CDC6 are pivotal for MCM loading. Finally, we found that co-depletion of ORC1 and CDC6 by the double-degron system completely suppressed DNA replication, and the cells entered mitosis with single-chromatid chromosomes, indicating DNA replication was uncoupled from the cell cycle control. Our combinational degron technologies will expand the application scope for functional analyses.
MixtureFinder: Estimating DNA mixture models for phylogenetic analyses
Authors: Ren Huaiyan , Wong Thomas KF , Minh Bui Quang , Lanfear Robert 
Published: March 2024

AbstractIn phylogenetic studies, both partitioned models and mixture models are used to account for heterogeneity in molecular evolution among the sites of DNA sequence alignments. Partitioned models require the user to specify the grouping of sites into subsets, and then assume that each subset of sites can be modelled by a single common process. Mixture models do not require users to pre-specify subsets of sites, and instead calculate the likelihood of every site under every model, while co-estimating the model weights and parameters. While much research has gone into the optimisation of partitioned models by merging user-specified subsets, there has been less attention paid to the optimisation of mixture models for DNA sequence alignments. In this study, we first ask whether a key assumption of partitioned models – that each user-specified subset can be modelled by a single common process – is supported by the data. Having shown that this is not the case, we then design, implement, test, and apply an algorithm, MixtureFinder, to select the optimum number of classes for a mixture model of Q matrices for the standard models of DNA sequence evolution. We show this algorithm performs well on simulated and empirical datasets and suggest that it may be useful for future empirical studies. MixtureFinder is available in IQ-TREE2, and a tutorial for using MixtureFinder can be found here:http://www.iqtree.org/doc/Complex-Models#mixture-models.
Preeclampsia in mice carrying fetuses with APOL1 risk variants
Authors: Yoshida Teruhiko , Latt Khun Zaw , Shrivastav Shashi , Lu Huiyan , Reidy Kimberly J. , Charlton Jennifer R. , Zhao Yongmei , Winkler Cheryl A. , Reznik Sandra E. , Rosenberg Avi Z. , Kopp Jeffrey B. 
Published: March 2024

AbstractAfrican-American women have a maternal mortality rate approximately three times higher than European-American women. This is partially due to hypertensive disorders of pregnancy, including preeclampsia. FetalAPOL1high-risk genotype increases preeclampsia risk, although mechanisms remain elusive. We characterized two mouse models to investigate whether fetal-origin APOL1 induces preeclampsia and which cell types contribute. Wein vitrofertilized mice with sperm from two transgenic mouse lines:APOL1transgenic mice carrying human genomic locus constructs from bacterial artificial chromosomes (BAC) containing theAPOL1gene, mimicking expression and function of human APOL1 (BAC/APOL1 mice) and albumin promoterAPOL1transgenic mice expressingAPOL1in liver and plasma (Alb/APOL1 mice). Dams carrying either BAC/APOL1-G1 or Alb/APOL1-G1 fetuses had elevated systolic blood pressure, while dams carrying BAC/APOL1-G0 or Alb/APOL1-G0 fetuses did not. BAC/APOL1-G1 and Alb/APOL1-G1 fetuses weighed less than littermates, indicating intrauterine growth restriction. Single-nucleus RNA-seq of APOL1-G1 placentas showed increased expression of osteopontin/Spp1, most prominently in vascular endothelial cells with robust APOL1 expression. Cell-cell interaction analysis indicated pro-inflammatory signaling between placental cells and maternal monocytes. These models show that fetal origin APOL1-G1 causes preeclampsia, inducing pro-inflammatory response in placenta and maternal monocytes. The APOL1-G1 variant poses a multi-generational problem, causing effects in mothers and offspring.
Comprehensive analysis of the genetic variation in theLPAgene from short-read sequencing
Authors: Betschart Raphael O. , Koliopanos Georgios , Garg Paras , Guo Linlin , Rossi Massimiliano , Schönherr Sebastian , Blankenberg Stefan , Twerenbold Raphael , Zeller Tanja , Ziegler Andreas 
Published: March 2024

AbstractLipoprotein (a) [LP(a)] is a risk factor for cardiovascular diseases and mainly regulated by the complex LPA gene. We investigated the types of variation in the LPA gene and their predictive performance on LP(a) concentration. We determined the Kringle IV-type 2 (KIV-2) copy number (CN) using the DRAGEN LPA Caller (DLC) and a read-depth based CN estimator in 8351 whole genome sequencing samples from the GENESIS-HD study. The pentanucleotide repeat in the promoter region was genotyped with GangSTR and ExpansionHunter. LP(a) concentration was available in 4861 population-based subjects. Predictive performance on LP(a) concentration was investigated using random forests. The agreement of the KIV-2 CN between the two specialized callers was high (r=0.9966; 95% confidence interval [CI] 0.9965–0.9968). Allele-specific KIV-2 CN could be determined in 47.0% of the subjects using the DLC. Lp(a) concentration can be better predicted from allele-specific KIV-2 CN than total KIV-2 CN. Two single nucleotide variants 4925G>A and rs41272114 further improved prediction. The genetically complex LPA gene can be analyzed with excellent agreement between different callers. The allele-specific KIV-2 CN is more important for predicting LP(a) concentration than the total KIV-2 CN. It would be important that the allele-specific KIV-2 CN is determinable in all subjects.
Elevated expression ofB4GALT6,GABRA1,GAD2,GLRA3,HTR2A,PCSK1, andSLC17A6are postmortem markers for the ALS-Ox subtype
Authors: Eshima Jarrett , Pennington Taylor R. , Choudhury Raiyan , Garcia Jordan M. , Fricks John , Smith Barbara S. 
Published: March 2024

AbstractIn this work, we stratify 428 bulk RNA-seq spinal cord transcriptomes from 206 ALS patients to assess concordance with cortical phenotype. We find the postmortem spinal cord generally recaptures the molecular phenotypes observed in the postmortem cortex and observe weaker differences in patient survival after correction for repeat measures. We compare intra-patient subtype assigned in the cortex and spinal cord, finding modest agreement between the cortex and lumbar region specifically, ranging from 46.2 – 66.7%. We leverage differential expression analysis to identify seven marker genes for the oxidative stress (ALS-Ox) subtype that show consistent upregulation in both the cortex and spinal cord and utilize three to construct classifiers that achieve notable predictive power for the ALS-Ox subtype in three different holdout cohorts, with AUCs ranging from 0.81 – 0.89. Our study shows the ALS-Ox subtype is conserved in the spinal cord of the same patients, offering a postmortem foundation for clinical stratification.