bluuebunny/crossref_metadata_2025_split huggingface dataset:
Post-Operative Bleeding After Facial Cleft Repair: A Retrospective Review of TriNetX Database
Authors: Isch Emily L. , Duca Aviana , Habarth-Morales Theodore E. , Self D. Mitchell , Caterson Edward J. 
Published: August 2024

AbstractIntroductionThe objective of this article is to assess and describe the incidence of postoperative hemorrhage following cleft palate surgery (palatoplasty), specifically focusing on need for return to the operating room for management of post-operative hemorrhage.MethodsThe TriNetX federated database was used to identify patients with a diagnosis of cleft lip and/or palate undergoing primary cleft palate repair over a twenty-year period from 2003 until2023. Primary endpoints assessed include post operative hemorrhage resulting in blood transfusion and/or return to the operating room; Kaplan-Meier analysis was used for statistical analysis.ResultsA total of 13,161 patients with cleft lip or palate over the last 20 years underwent operative intervention (palatoplasty). Of those patients, ninety-seven patients were found to have diagnosis of post-operative bleeding (confidence interval 1.196 +/-0.606). One hundred and seventy-five patients experienced post operative hemorrhage requiring transfusion of blood product (CI 1.491 +/-0.952). Seventy patients required return to the operating room for post-operative bleeding in the immediate post operative period.ConclusionsHistoric reporting of post-operative bleeding suggests a moderate rate of post-operative hemorrhage rate following palatoplasty, occasionally necessitating transfusion and return to operating room following index palatoplasty. Our retrospective review of a national database demonstrates a lesser incidence of post-operative hemorrhage than previously noted.
Advancing Early Detection of Major Depressive Disorder: A Comparative Analysis of AI Models Using Multi-Site Functional MRI Data
Authors: Mansoor Masab A. , Ansari Kashif H. 
Published: August 2024

AbstractBackgroundMajor Depressive Disorder (MDD) is a prevalent mental health condition with significant public health implications. Early detection is crucial for effective intervention, yet current diagnostic methods often fail to identify MDD in its early stages.ObjectiveThis study aimed to develop and validate machine learning models for the early detection of MDD using functional Magnetic Resonance Imaging (fMRI) data.MethodsWe utilized fMRI data from 1,200 participants (600 with early-stage MDD and 600 healthy controls) across three public datasets. Four machine learning models (Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), and Deep Neural Network (DNN)) were developed and compared. Models were evaluated using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), and F1 score.ResultsThe DNN model demonstrated superior performance, achieving 89% accuracy (95% CI: 0.86-0.92) and an AUC-ROC of 0.95 (95% CI: 0.93-0.97) in detecting early-stage MDD. Key predictive features included altered functional connectivity between the dorsolateral prefrontal cortex, anterior cingulate cortex, and limbic regions. The model showed good generalizability across different datasets and identified 78% (95% CI: 71%-85%) of individuals who developed MDD within a 2-year follow-up period.ConclusionsOur AI-driven approach demonstrates promising potential for early MDD detection, outperforming traditional diagnostic methods. This study highlights the utility of machine learning in analyzing complex neuroimaging data for psychiatric applications. Future research should focus on prospective clinical trials and the integration of multimodal data to enhance the clinical applicability of this approach further.
Facilitators and barriers for healthcare workers’ adherence to the national nutritional guidelines for people living with HIV in Dar-Es-Salaam: A mixed-method study
Authors: Mwakasungura Felistar , Mkumbwa Rebecca , Sunguya Bruno 
Published: August 2024

AbstractThe dual burden of HIV and malnutrition is a significant public health issue in Sub-Saharan Africa. In Tanzania, with an HIV prevalence of 4.9% among adults, malnutrition among People Living with HIV (PLHIV) remains high. This study evaluated adherence to nutritional guidelines for PLHIV in Care and Treatment Centers (CTCs) in Dar es Salaam and identified influencing factors. A mixed-method design was used among PLHIV, health facility administrators, and healthcare providers. Data were collected through observational checklists, structured questionnaires, and in-depth interviews. Statistical and qualitative analyses were performed to assess adherence and determinants. Among 478 participants who received care, only 19.46% were managed with fully adherence to the nutritional guidelines. Universally, anthropometric assessments were observed, however, micronutrient supplementation was minimal (1%). Higher education (AOR=5.08, p=0.019) and attendance at referral hospitals (AOR=8.23, p=0.032) positively influenced adherence. Conversely, nurse attendance (AOR=0.34, p=0.038), adequate staffing (AOR=0.31, p=0.010), and urban residence (AOR=0.47, p=0.009) negatively influenced adherence. Key facilitators included consistent training and supportive leadership, while barriers involved financial constraints and high staff turnover. The study reveals a significant gap in adherence to nutritional guidelines among PLHIV in Dar es Salaam, highlight the need for improved resource distribution, staff training, and patient education. Findings from this study aim to bridge the knowledge gap in adherence to nutritional guidelines for PLHIV, support evidence-based decision-making, and ultimately improve health outcomes.
Survey: Accessibility of At-Home COVID-19 Tests for Users with Disabilities
Authors: Farmer Sarah , Peagler Amanda , Sheiner Rebecca , Martinez Adina , Razin Victoria 
Published: August 2024

2AbstractWidespread access to over-the-counter at-home COVID-19 tests was a crucial component of the United States’ public health response to the COVID-19 pandemic. For at-home testing to be effective, it is essential for the tests to be not only available but also usable and accessible to people with disabilities. In the present study, a survey was conducted to collect feedback about people’s experiences using over-the-counter COVID-19 tests, with an aim to identify barriers to the accessibility of COVID-19 tests for people with disabilities. Survey respondents self-identified as having little to no vision, low vision, or limited dexterity. The survey covered ten at-home COVID-19 test brands. For each test that a respondent had used, the survey included questions about whether the user was able to complete the test independently or whether they needed assistance, the ease of use for performing each part of the testing procedure, and any difficulties that the user encountered while using the test. The survey also included questions for each test regarding the use of mobile applications, digital instructions, and customer service. This report presents the results of nearly 400 responses to the accessibility survey. The results offer an improved understanding of accessibility barriers for at-home COVID-19 tests; designers of at-home diagnostic tests can apply this knowledge in order to improve the accessibility of over-the-counter COVID-19 tests and other at-home diagnostic tests to people with disabilities.
Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation
Authors: Kany Shinwan , Rämö Joel T. , Friedman Samuel F. , Weng Lu-Chen , Roselli Carolina , Kim Min Seo , Fahed Akl C. , Lubitz Steven A. , Maddah Mahnaz , Ellinor Patrick T. , Khurshid Shaan 
Published: August 2024

ABSTRACTBackgroundAF risk estimation is feasible using clinical factors, inherited predisposition, and artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis.ObjectiveTo test whether integrating these distinct risk signals improves AF risk estimation.MethodsIn the UK Biobank prospective cohort study, we estimated AF risk using three models derived from external populations: the well-validated Cohorts for Aging in Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) clinical score, a 1,113,667-variant AF polygenic risk score (PRS), and a published AI-enabled ECG-based AF risk model (ECG-AI). We estimated discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic (AUROC) and average precision (AP).ResultsAmong 49,293 individuals (mean age 65±8 years, 52% women), 825 (2.4%) developed AF within 5 years. Using single models, discrimination of 5-year incident AF was higher using ECG-AI (AUROC 0.705 [95%CI 0.686-0.724]; AP 0.085 [0.071-0.11]) and CHARGE-AF (AUROC 0.785 [0.769-0.801]; AP 0.053 [0.048-0.061]) versus the PRS (AUROC 0.618, [0.598-0.639]; AP 0.038 [0.028-0.045]). The inclusion of all components (“Predict-AF3”) was the best performing model (AUROC 0.817 [0.802-0.832]; AP 0.11 [0.091-0.15], p<0.01 vs CHARGE-AF+ECG-AI), followed by the two component model of CHARGE-AF+ECG-AI (AUROC 0.802 [0.786-0.818]; AP 0.098 [0.081-0.13]). Using Predict-AF3, individuals at high AF risk (i.e., 5-year predicted AF risk >2.5%) had a 5-year cumulative incidence of AF of 5.83% (5.33-6.32). At the same threshold, the 5-year cumulative incidence of AF was progressively higher according to the number of models predicting high risk (zero: 0.67% [0.51-0.84], one: 1.48% [1.28-1.69], two: 4.48% [3.99-4.98]; three: 11.06% [9.48-12.61]), and Predict-AF3 achieved favorable net reclassification improvement compared to both CHARGE-AF+ECG-AI (0.039 [0.015-0.066]) and CHARGE-AF+PRS (0.033 [0.0082-0.059]).ConclusionsIntegration of clinical, genetic, and AI-derived risk signals improves discrimination of 5-year AF risk over individual components. Models such as Predict-AF3 have substantial potential to improve prioritization of individuals for AF screening and preventive interventions.
A cleaved cytosolic FOXG1 promotes excitatory neurogenesis by modulation of mitochondrial translation – a new therapeutic target for brain disorders
Authors: Bruce Hannah , Halpern Marnie E. , Monfries Clinton , Suleyman Oniz , Hamid Fursham , Houart Corinne 
Published: August 2024

AbstractModulation of mitochondrial function is at the core of cell fate decisions and tissue homeostasis, yet the mechanisms that govern their activity are not understood. Here, we provide evidence that mitochondrial activity is controlled in a tissue-specific manner through a non-canonical cytoplasmic function of the transcription factor FOXG1. Using zebrafish and human models of the neurodevelopmental disorder, FOXG1 Syndrome, we found thatFOXG1mutations inducing a premature stop codon unexpectedly lead to the production of a short C-terminal peptide. The expression of this truncated protein is responsible for an excess of excitatory neurons and a structural, functional, and translational mitochondrial phenotype in mutants. We demonstrate that this activity is a gain of function, normally carried out by a cleavage product in wildtype. Both peptides promote the translation of mitochondrially-encoded transcripts, are preferentially transported to the mitochondria, and interact with mito-ribosomal proteins. These findings unveil a mechanism that integrates cell fate decisions with metabolic output. Adjusting the dosage of the mutant peptide rescues aspects of FOXG1 Syndrome, offering a new therapeutic avenue for the treatment of disorders involving mitochondrial dysfunctions.Graphic Abstract
Refined Enterotyping Reveals Dysbiosis in Global Fecal Metagenomes
Authors: Keller Marisa Isabell , Nishijima Suguru , Podlesny Daniel , Kim Chan Yeong , Robbani Shahriyar Mahdi , Schudoma Christian , Fullam Anthony , Richter Jonas , Letunic Ivica , Akanni Wasiu , Orakov Askarbek , Schmidt Thomas Sebastian , Marotta Federico , Trebicka Jonel , Kuhn Michael , Van Rossum Thea , Bork Peer 
Published: August 2024

AbstractEnterotypes describe human fecal microbiomes grouped by similarity into clusters of microbial community composition, often associated with disease, medications, diet, and lifestyle. Numbers and determinants of enterotypes have been derived by diverse frameworks and applied to cohorts that often lack diversity or inter-cohort comparability. To overcome these limitations, we selected 16,772 fecal metagenomes collected from 38 countries to revisit the enterotypes using state-of-the-art fuzzy clustering and found robust clustering regardless of underlying taxonomy, consistent with previous findings. Quantifying the strength of enterotype classifications enriched the enterotype landscape, also reflecting some continuity of microbial compositions. As the classification strength was associated with the patient’s health status, we established an “Enterotype Dysbiosis Score” (EDS) as a latent covariate for various diseases. This global study confirms the enterotypes, reveals a dysbiosis signal within the enterotype landscape, and enables robust classification of metagenomes with an online “Enterotyper” tool, allowing reproducible analysis in future studies.Graphical Abstract
Chromosomal curing drives prophage evolution and bacterial epidemiology
Authors: Kwun Min Jung , Ion Alexandru V. , Apagyi Katinka J. , Croucher Nicholas J. 
Published: August 2024

AbstractMany integrative mobile genetic elements (MGEs) are sensitive to RecA-DNA filaments, suggesting their activation may enable them to evade deletion through homologous recombination (HR). This was studied using pneumococcal prophage. Most pneumococci contained prophage that inhibited transformation-mediated HR through chromosomal modification. These included a commonly-targetedattBthat reduced transformation by modifying the small cytoplasmic RNA. The prophage were almost universally regulated by C1-type or ImmAR-type systems. Representatives with either type were efficiently deleted by HR. Mutations that impaired a C1-regulated prophage increased this deletion rate, reflecting this element being activated by RecA-DNA filaments imported during transformation. ImmAR-regulated prophage instead responded to transient stimuli by excising as deletion-resistant pseudolysogens, with varying RecA sensitivity, gradually committing to lysing cells following longer-duration signals. These processes were modulated by interactions with the DNA-binding protein DprA. Therefore bacterial diversification though HR is impeded by MGEs that selfishly prevent themselves from being eliminated from the chromosome.
Sequence-Directed Covalent Protein-RNA Linkages in a Single Step Using Engineered HUH-Tags
Authors: Smiley Adam T. , Babilonia-Díaz Natalia , Krueger August J. , Aihara Hideki , Tompkins Kassidy J. , Lemmex Andrew C.D. , Gordon Wendy R. 
Published: August 2024

AbstractReplication-initiating HUH-endonucleases (Reps) are enzymes that form covalent bonds with single-stranded DNA (ssDNA) in a sequence specific manner to initiate rolling circle replication. These nucleases have been co-opted for use in biotechnology as sequence specific protein-ssDNA bioconjugation fusion partners dubbed ‘HUH-tags’. Here, we describe the engineering andin vitrocharacterization of a series of laboratory evolved HUH-tags capable of forming robust sequence-directed covalent bonds with unmodified RNA substrates. We show that promiscuous Rep-RNA interaction can be enhanced through directed evolution from nearly undetectable levels in wildtype enzymes to robust reactivity in final engineered iterations. Taken together, these engineered HUH-tags represent a promising platform for enabling site-specific protein-RNA covalent bioconjugation in vitro, potentially mediating a host of new applications and offering a valuable addition to the HUH-tag repertoire.
CD24 regulates extracellular vesicle release via an aSMase/PI3K/mTORC2/ROCK/actin pathway in B lymphocytes
Authors: Phan Hong-Dien , Squires Willow R.B. , Mayne Kaitlyn E. , Kelly Grant R. , Jafardoust Rashid , Christian Sherri L. 
Published: August 2024

ABSTRACTCD24 is a glycophosphatidylinositol-linked protein that regulates B cell development. We previously reported that stimulation of CD24 on donor B cells promotes the transport of functional receptors to recipient B cells via extracellular vesicles (EVs). However, the mechanisms regulating EV formation in response to CD24 are unknown. Using bioinformatics, we found a connection between CD24 and the PI3K/AKT and mTOR signaling pathways. To determine if PI3K or mTOR regulates EV release, we made use of our co-culture model, whereby donor B cells carrying the B cell receptor (BCR, IgM) release EVs labeled with palmitoylated GFP upon CD24 stimulation are incubated with recipient B cells that lack IgM and express palmitoylated tdTomato. Using flow cytometry, we are able to follow the transfer of EVs carrying lipid-associated GFP and surface IgM from donor to recipient cells. Using chemical and genetic inhibition, we found that a PI3K/mTORC2/ROCK/actin pathway regulates EV release. We also found that acid sphingomyelinase (aSMase) activates PI3K to induce EV release. Lastly, through live cell imaging, we found that ROCK is required for inducing the membrane dynamics associated with EV release. Overall, our data suggest that these EVs are ectosomes budded from the plasma membrane and not intracellularly-derived exosomes. Importantly, we have uncovered a novel pathway regulating ectosome release.
Subcellular Region Morphology Reflects Cellular Identity
Authors: Román Ángel-Carlos , Diaz-Pizarro Alba , Del Valle-Del Pino Nuria , Olivera-Gómez Marcos , Cumplido-Laso Guadalupe , Benítez Dixan Agustín , Carvajal-González Jose María , Mulero-Navarro Sonia 
Published: August 2024

AbstractIn multicellular organisms, various cells perform distinct physiological and structural roles. Traditionally, cell identity has been defined through morphological features and molecular markers, but these methods have limitations. Our study explores the potential of subcellular morphology to define cellular identity and predict molecular differences. We developed workflows to identify subcellular regions in different cell lines, using convolutional neural networks (CNNs) to classify these regions and finally quantify morphological distances between cell types. First, we demonstrated that subcellular regions could accurately distinguish between isolated cell lines and predict cell types in mixed cultures. We extended this approach to predict molecular differences by training networks to identify human dermal fibroblast subtypes and correlating morphological features with gene expression profiles. Further, we tested pharmacological treatments to induce controlled morphological changes, validating our approach in order to detect these changes. Our results showed that subcellular morphology could be a robust indicator of cellular identity and molecular characteristics. We observed that features learned by networks to distinguish specific cell types could be generalized to quantify distances between other cell types. Networks focusing on different subcellular regions (nucleus, cytosol, membrane) revealed distinct morphological features correlating with specific molecular changes. This study underscores the potential of combining imaging and AI-based methodologies to enhance cell classification without relying on markers or destructive sampling. By quantifying morphological distances, we provide a quantitative characterization of cell subtypes and states, offering valuable insights for regenerative medicine and other biomedical fields.
Benchmarking of hydrodynamic plasma waveguides for multi-GeV laser-driven electron acceleration
Authors: Miao B. , Rockafellow E. , Shrock J. E. , Hancock S. W. , Gordon D. , Milchberg H. M. 
Published: August 2024

Hydrodynamic plasma waveguides initiated by optical field ionization have recently become a key component of multi-GeV laser wakefield accelerators. Here, we present the most complete and accurate experimental and simulation-based characterization to date, applicable to current multi-GeV experiments and future 100 GeV-scale laser plasma accelerators. Crucial to the simulations is the correct modeling of intense Bessel beam interaction with meter-scale gas targets, the results of which are used as initial conditions for hydrodynamic simulations. The simulations are in good agreement with our experiments measuring evolving plasma and neutral hydrogen density profiles using two-color short pulse interferometry, enabling realistic determination of the guided mode structure for application to laser-driven plasma accelerator design.

Published by the American Physical Society
2024
Exact thermodynamics for weakly interacting normal-phase quantum gases: Equations of state for all partial waves
Authors: Gao Xin-Yuan , Blume D. , Yan Yangqian 
Published: August 2024

While the thermodynamics for bosonic systems with weak s-wave interactions has been known for decades, a general and systematic extension to higher partial waves has not yet been reported. We provide closed-form expressions for the equations of state for weakly interacting systems with arbitrary partial waves in the normal phase. Thermodynamics, including contact, loss rate, and compressibility, are derived over the entire temperature regime. Our results offer an improved thermometer for ultracold atoms and molecules with weak high-partial wave interactions.

Published by the American Physical Society
2024
Assessing the impact of climate change on aircraft engine performance during different flight phases (take-off and cruise) and the environmental consequences
Authors: Kale Utku 
Published: August 2024

Purpose
Climate change significantly impacts global temperatures, posing challenges to various sectors, including aviation. The purpose of this study is to assess the impact of climate change on aircraft engine performance during different flight phases (take-off and cruise) and the environmental consequences.

Design/methodology/approach
This study examines the effects of rising temperatures on aircraft engine performance using real-time data from a Boeing 787-8 equipped with GEnx-1B engines, which are collected via Flight Data Recorder of the engines and were analyzed for the take-off and cruise phases on the ground. Exhaust gas temperature (EGT), fuel flow and take-off weights were evaluated.

Findings
The analysis revealed a significant increase in EGT at the cruising altitude of 38,000 ft during the summer months compared to expected standard atmospheric values. This increase, averaging over 200 °C, is attributed to global warming. Such elevated temperatures are likely to accelerate the degradation of turbine components, resulting in increased fuel consumption: higher EGT signifies inefficient engine operation, resulting in more fuel burned per unit thrust; early engine aging: elevated temperatures accelerate wear and tear on turbine components, potentially reducing engine lifespan and increasing maintenance costs and enhanced atmospheric pollution: incomplete combustion at high EGTs generates additional emissions, contributing to local air quality concerns.

Practical implications
The research findings have practical implications for understanding the potential operational challenges and environmental impacts of climate change on aircraft engine performance. This lets us explore mitigation strategies and adapt operational procedures to ensure sustainable regional aviation practices.

Originality/value
This research enhances environmental consequences by assessing the impact of climate change on aircraft performance.