bluuebunny/crossref_metadata_2025_split huggingface dataset:
Licensure Actions Against Psychiatric Clinicians: A Cohort Analysis of National Practitioner Database Reports
Authors: Dadlani Akanksha , Mills Jeffrey A. , Strawn Jeffrey R. 
Published: January 2025

Objective:
To examine trends and predictors of administrative actions against psychiatric clinicians’ licenses between 2002 and 2022.

Methods:
Data from the National Practitioner Data Bank (NPDB) identified 6400 disciplinary actions against psychiatric clinicians’ licenses. Linear trend models assessed the trends of disciplinary actions across mental/physical health, licensing/legal issues, and unprofessional conduct. A first-order autoregressive model with a time indicator interaction term evaluated structural breaks (ie, a sudden or gradual change in the characteristics of data in a time series). Predictors of claims were assessed using logistic regression.

Results:
Since 2002, disciplinary actions related to clinicians’ mental/physical health (P=0.004) and licensure/legal issues (P=0.018) have decreased, while actions related to unprofessional conduct remained unchanged (P=0.358). Declines emerged in 2012 for mental/physical health (break coefficient −0.367; P=0.026) and in 2009 for unprofessional conduct (break coefficient −0.199; P=0.013). Clinicians facing disciplinary actions related to physical/mental health were significantly younger than those with legal/licensure issues (P<0.001). While the average age remained stable for mental/physical health-related actions, the average age increased for actions related to licensure/legal claims and unprofessional conduct.

Conclusions:
The decrease in disciplinary actions related to physical/mental health may reflect better access and acceptance of treatment of clinicians, while the reduction in licensure/legal actions may indicate policy shifts. Findings regarding age underscore the need for enhanced support for mid and late-career clinicians to promote lifelong learning and practice.
Antifungal potential of silver nanoparticles stabilized with the flavonoid naringenin
Authors: Luiz Correa Jakeline , Kikuchi Larissa , Ferreira Deisiany Gomes , Gomes da Silva Fabiana , de Oliveira Kelly Mari P. , de Souza Monique , Baesso Mauro Luciano , Yamanishi Gustavo , Urbano Alexandre , Negri Melyssa 
Published: January 2025

Graphical abstract
Biosynthesis and antifungal activity of silver nanoparticles stabilized with naringenin.
Seo1p, a high affinity, plasma membrane transporter of the γ-Glu-met dipeptide in yeasts and fungi
Authors: Dubey Pratiksha , Ahmad Md Shabbir , Laxman Sunil , Bachhawat Anand K 
Published: January 2025

Abstractγ-Glu dipeptides are ubiquitous in nature, and yet their metabolism and transport are poorly understood. Here we investigate this using the dipeptide γ-Glu-met inSaccharomyces cerevisiae. γ-Glu-met was efficiently utilized byS. cerevisiaeand its degradation was dependent on both the glutathione degrading cytosolic Dug2p/Dug3p complex, and the vacuolar Ecm38p. Using a transcriptomics approach, followed by a genetic screen, we identified Seo1p, an orphan transporter of yeast, as the transporter of γ-Glu-met. Uptake studies confirmed Seo1p as a high affinity (Km =48uM), highly specific transporter of γ-Glu-met since other analogs like n-Glu-met, γ-Glu-leu, γ-Glu-cys, γ-Glu-met-gly, methionine and methionine sulfoxide were not transported by Seo1p. Candida spp. also encoded a functional Seo1p. A second transporter, Opt2p, identified in the screen, was also investigated. However, Opt2p was not primarily involved in γ-Glu-met uptake. Its deletion affected vacuolar morphology, that interfered with the degradation of the peptide through Ecm38p. These studies demonstrate how organisms have evolved dedicated pathways for the uptake of these unusual peptides.
Role ofN-glycosylation as a determinant of ATG9A conformations and activity
Authors: Lambrughi Matteo , Utichi Mattia , Borg Christian B. , Marjault Henri-Baptiste , Echeverría Sergio Esteban , Maeda Kenji , Taylor Nicholas M.I. , Fadda Elisa , Jäättelä Marja , Papaleo Elena 
Published: January 2025

AbstractThis study investigates the effects of N99 glycosylation on the structural dynamics and lipid scrambling activity of ATG9A, a key autophagy protein, using microsecond all-atom molecular dynamics (MD) simulations. ATG9A is an integral membrane protein involved in autophagosome biogenesis, and its N-glycosylation at N99 plays a critical, yet poorly understood role in its function. The simulations revealed that the hydrophilic central cavity of ATG9A supports lipid reorientation and partial transbilayer movements, consistent with its lipid scrambling activity observed experimentally. N-glycosylation at N99 was found to enhance cooperative interactions between protomers, facilitating lipid insertion and traversal within the central cavity. These findings align with the proposed mechanism of ATG9A’s role in lipid redistribution across the phagophore membrane during autophagy. However, mutagenesis experiments that abolished N-glycosylation in ATG9A (ATG9AN99Aand ATG9AN99Dmutants) did not show a marked change in autophagy flux, suggesting that further experimental approaches, such as lipid scramblase assays, may be needed to more clearly define the glycosylation’s impact. The study also observed asymmetric protomer conformations in ATG9A, contrasting with symmetric structures seen in cryo-EM, suggesting that the structural heterogeneity of the protein could be further explored in cryo-EM datasets. Overall, the study highlights the importance of incorporating glycosylation in computational studies of membrane proteins and offers valuable insights into the molecular mechanisms of lipid transport in autophagy, with potential implications for other lipid scramblases and flippases.
Tryptophan metabolism reprogramming contributes to the prothrombotic milieu in mice and humans infected with SARS-CoV-2
Authors: Subramaniam Saravanan , Napoleon Marc Arthur , Lotfollahzadeh Saran , Kamal Mohamed Hassan , Kurniawan Helena , Elsadawi Murad , Kenney Devin , Douam Florian , Bosmann Markus , Whelan Stephen , Cabral Howard , Burks Eric J , Zhao Grace , Kolachalama Vijay , Ravid Katya , Chitalia Vipul C. 
Published: January 2025

AbstractSARS-CoV-2 infection disturbs the coagulation balance in the blood, triggering thrombosis and contributing to organ failure. The role of prothrombotic metabolites in COVID-19-associated coagulopathy remains elusive. Leveraging K18-hACE2 mice infected with SARS-CoV-2, we observed higher levels of the tryptophan metabolite, kynurenine, compared to controls. SARS-CoV-2 infected mice showed a significant upregulation of enzymes controlling Kynurenine biogenesis, such as indoleamine 2,3-dioxygenase (IDO-1) and tryptophan 2,3-dioxygenase levels in kidneys and liver, respectively, as well as changes in the enzymes involved in kynurenine catabolism, including kynurenine monooxygenase and kynurinase. Consistent with the agonistic role of these metabolites in Aryl Hydrocarbon Receptor (AHR) signaling, AHR activation and its downstream mediator, tissue factor (TF), a highly potent procoagulant factor, was observed in endothelial cells (ECs) of lungs and kidneys of infected mice. These findings were validated in humans, where compared to controls, sera of COVID-19 patients showed increased levels of Kynurenine, kynurenic acid, anthranilic acid, and quinolinic acid. Activation of the AHR-TF axis was noted in the kidneys and lungs of COVID-19 patients, and COVID-19 sera showed higher IDO-1 activity than controls. Levels of Kyn in COVID-19 patients correlated strongly with the TF-inducing activity of COVID-19 sera on ECs. A specific IDO-1 inhibitor or AHR inhibitor separately or in combination suppressed COVID-19 sera-induced TF activity in ECs. Together, we identified IDO-1 as upregulated by SARS-CoV-2 infection, resulting in augmented Kyn and its prothrombotic catabolites, thereby suggesting the Kyn-AHR-TF axis as possibly a new diagnostic and/or therapeutic target.Key pointsSARS-CoV-2-infection upregulates kynurenine biogenesis in the liver and diminishes kynurenine catabolism in the lungs and kidneys.An increase in kynurenine stimulates the AHR-TF axis in the microvasculature in COVID-19 patients, which is inhibited by pharmacological manipulation.
ORAKLE: Optimal Risk prediction for mAke30 in patients with acute Kidney injury using deep Learning
Authors: Oh Wonsuk , Veshtaj Marinela , Sawant Ashwin , Agrawal Pulkit , Gomez Hernando , Suarez-Farinas Mayte , Oropello John , Kohli-Seth Roopa , Kashani Kianoush , Kellum John A. , Nadkarni Girish , Sakhuja Ankit 
Published: January 2025

ABSTRACTBackgroundMajor Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered outcome for assessing the impact of acute kidney injury (AKI). The existing prediction models for MAKE30 are static and overlook dynamic changes in clinical status. In this study, we introduce ORAKLE, a novel deep-learning model that utilizes evolving time-series data to predict MAKE30, enabling personalized, patient-centered approaches to AKI management and outcome improvement.MethodsWe conducted a retrospective study using three publicly available critical care databases: MIMIC-IV, SICdb, and eICU-CRD. Among these, MIMIC-IV was divided into 80% training and 20% internal test sets, whereas SiCdb and eICU-CRD were used as external validation cohorts. Patients with sepsis-3 criteria who developed AKI within 48 hours of intensive care unit admission were identified. Our primary outcome was MAKE30, defined as a composite of death, new dialysis or persistent kidney dysfunction within 30 days of ICU admission. We developed ORAKLE using Dynamic DeepHit framework for time-series survival analysis and its performance against Cox models using AUROC and AUPRC. We further assessed model calibration using Brier score.ResultsWe analyzed 16,671 patients from MIMIC-IV, 2,665 from SICdb, and 11,447 from eICU-CRD. ORAKLE outperformed the Cox models in predicting MAKE30, achieving AUROCs of 0.84 (95% CI: 0.83–0.86) vs. in MIMIC-IV internal test set 0.80 (95% CI: 0.78–0.82), 0.83 (95% CI: 0.81–0.85) vs. 0.79 (95% CI: 0.77–0.81) in SICdb, and 0.85 (95% CI: 0.84–0.85) vs. 0.81 (95% CI: 0.80–0.82) in eICU-CRD. The AUPRC values for ORAKLE were also significantly better than that of Cox models. The Brier score for ORAKLE was 0.21 across the internal test set, SICdb, and eICU-CRD, suggesting good calibration.ConclusionsORAKLE is a robust deep-learning model for predicting MAKE30 in critically ill patients with AKI that utilizes evolving time series data. By incorporating dynamically changing time series features, the model captures the evolving nature of kidney injury, treatment effects, and patient trajectories more accurately. This innovation facilitates tailored risk assessments and identifies varying treatment responses, laying the groundwork for more personalized and effective management approaches.
Assessing Large Language Model Performance Related to Aging in Genetic Conditions
Authors: Othman Amna A. , Flaharty Kendall A. , Ledgister Hanchard Suzanna E. , Hu Ping , Duong Dat , Waikel Rebekah L. , Solomon Benjamin D. 
Published: January 2025

AbstractUnlike some health conditions that have been extensively delineated throughout the lifespan, many genetic conditions are largely described in pediatric populations, with a focus on early manifestations like congenital anomalies and developmental delay. An apparent gap exists in understanding clinical features and optimal management as patients age. Generative artificial intelligence is transforming biomedical disciplines including through the introduction of large language models (LLMs). Motivated by these advances, we explored how LLMs handle age with respect to 282 genetic conditions selected based on prevalence. We divided these conditions into five categories: Disorders limited to childhood; Disorders limited to adulthood; Disorders with changes in presentation across ages; Disorders with changes in management across ages; Disorders with no changes across ages. We evaluated Llama-2-70b-chat (70b) and GPT-3.5 (GPT) capabilities at generating accurate medical vignettes for these conditions based on Correctness, Completeness, and Conciseness as graded by 3 clinicians. Using accurately generated vignettes as in-context prompts, we further generated and evaluated patient-geneticist dialogues and assessed LLM performance in answering specific questions regarding age-based management plans for a subset of conditions. Results revealed impressive performances of 70b with in-context prompting and GPT in generating vignettes. We overall did not observe age-based biases, though our experiments identified statistically significant differences in some areas related to LLM output. Despite impressive capabilities, LLMs still have limitations in clinical applications.
Endothelial Sphingosine-1-Phosphate Receptor (S1PR) 1, a Functional S1PR in the Human Cerebrovascular Endothelium, Limits Blood Brain Barrier Permeability and Neuronal Injury following Subarachnoid Hemorrhage in Mice
Authors: Ito Akira , Uchida Hiroki , Dash Sabyasachi , Aburakawa Daiki , Shingai Yuto , Liu Cathy , Avila-Gomez Paulo , Tominaga Keita , Kim Gab Seok , Faraco Giuseppe , Proia Richard L. , Niizuma Kuniyasu , Tominaga Teiji , Anrather Josef , Iadecola Costantino , Kluk Michael J. , Sanchez Teresa 
Published: January 2025

ABSTRACTHypoxia-induced blood-brain barrier (BBB) permeability has been identified as a key contributor to the progression of ischemic-hypoxic brain injury and neuronal dysfunction in stroke and other cerebrovascular diseases. Emerging clinical evidence highlights that the vasoprotective signaling properties of high-density lipoprotein (HDL), mediated through its bioactive lipid component sphingosine-1-phosphate (S1P), may be impaired in cardiovascular and inflammatory conditions. Nonetheless, the precise contributions and mechanistic roles of S1P signaling within the cerebral microvasculature remain insufficiently characterized.In this study, we aimed to elucidate the role of S1P signaling via its endothelial receptor S1PR1 in the pathophysiology of early brain injury following subarachnoid hemorrhage (SAH), a particularly severe form of stroke. Additionally, we sought to evaluate the relevance of the endothelial S1PR1 pathway in the human cerebrovascular endothelium, its functional role in hypoxia-induced cerebral endothelial barrier dysfunction, and its underlying molecular mechanisms. To address these objectives, we utilized endothelial-specific S1PR1 knockout mice subjected to the endovascular rupture model of aneurysmal SAH, performed mechanistic studies in primary human cerebral microvascular endothelial cells, and characterized S1PR1 expression in human brain tissue using validated protocols.Our findings reveal robust expression of S1PR1 in the cerebrovascular endothelium of both mice and humans. Functional analyses demonstrated that S1PR1 is critical for maintaining BBB integrity and mitigating neuronal injury in the context of SAH. Mechanisticin vitrostudies indicated that S1PR1 exerts a vasoprotective effect by limiting hypoxia-induced BBB dysfunction in human primary brain microvascular endothelial cells through inhibition of Rho-associated kinase (ROCK)-mediated phosphorylation of myosin light chain (MLC), suppression of stress fiber formation and caveolin-1-dependent endosomal trafficking.These results highlight the pivotal role of endothelial S1PR1 signaling in preserving cerebral vascular integrity and provide a strong scientific foundation for developing novel therapeutic approaches targeting the S1P pathway in the endothelium to enhance neurovascular protection.
ESMAdam: a plug-and-play all-purpose protein ensemble generator
Authors: Yu Zongxin , Liu Yikai , Lin Guang , Jiang Wen , Chen Ming 
Published: January 2025

AbstractProteins often adopt multiple ensemble conformations to perform essential functions such as catalysis, transport, and signal transduction. Traditional physics-based methods for generating these conformations, including molecular dynamics and Monte Carlo simulations, are computationally expensive and time-consuming, limiting their practicality for high-throughput applications like screening. Recent advances in machine learning, particularly deep generative models, offer a promising alternative for protein conformation ensemble generation. However, these models are often task-specific or rely on strong assumptions to generalize. Here, we introduce ESMAdam, a versatile and efficient framework for protein conformation ensemble generation. Using the ESMFold protein language model ESMFold and ADAM stochastic optimization in the continuous protein embedding space, ESMAdam addresses a wide range of ensemble generation tasks. In this work, we demonstrate several basic applications of ESMAdam, including conditional ensemble generation and CG-to-all-atom backmapping. In addition, we showcase advanced applications, such as screening alternative binding modes of protein multimers and reconstructing 3D structures from cryo-EM images. Compared to traditional physics-based methods, ESMAdam significantly reduces computational time. Unlike deep-generative-model-based approaches, it requires no retraining and easily adapts to diverse ensemble restraint conditions, making it exceptionally suited for various structure prediction and screening tasks. This plug-and-play framework represents a step toward efficient and flexible protein ensemble generation for applications in structural biology and drug discovery.
Age and Gender-Dependent Comorbidities in UAE Population identifies Coronary Artery Disease with Gene-Environment Interactions
Authors: Iftikhar Hassa 
Published: January 2025

AbstractThis study investigated the age and sex-dependent comorbidities contributing to coronary artery disease (CAD) within the UAE population. A cohort of 3,000 individuals was analyzed by integrating genetic data, environmental stressors (e.g., PM2.5 exposure), and demographic profiles to identify CAD and nine other diseases with heterogeneous comorbidity patterns. Key genetic markers, including APOE rs429358, PCSK9, and LPA were significantly associated with CAD risk, amplified by environmental exposure and metabolic conditions such as diabetes and obesity. Notably, APOE rs429358 carriers exposed to high PM2.5 levels exhibited a 2.8-fold increase in CAD risk (p < 0.001), emphasizing the synergistic effects of gene-environment interactions. Monte Carlo and Markov Chain Monte Carlo simulations validated the results, enabling the identification of high-risk genetic profiles across various environmental and demographic conditions. Kaplan-Meier survival analyses revealed accelerated disease progression in high-risk groups, whereas Principal Component Analysis and hierarchical clustering identified distinct genetic clusters stratified by age and sex. This study further identified demographic-specific disease subtypes with implications for public health strategies, such as addressing higher environmental susceptibility in males and targeted management of metabolic comorbidities in females such as obesity, diabetes, and stroke. These findings support precision medicine strategies tailored to regional populations, promoting targeted interventions to mitigate CAD risk. This study synthesizes observational findings and computational simulations to establish a comprehensive framework for elucidating the pathogenesis of coronary artery disease (CAD) and enhancing public health interventions in the United Arab Emirates (UAE). The actionable outcomes include the development of sex-specific health interventions and environmental policies to reduce CAD risk in high-susceptibility groups.
High intensity exercise before sleep boosts memory encoding the next morning
Authors: Ramirez Butavand Daniela , Nagel Juliane , Feld Gordon B. , Steib Simon 
Published: January 2025

ABSTRACTThe importance of sleep for memory consolidation has been extensively studied, but its role for memory encoding remains less well characterized. At the molecular and cellular level, the renormalization of synaptic weights during sleep has received substantial support, which is thought to free capacity to encode new information at the behavioral level. However, at the systems level and behaviorally, support for this process playing a major role for memory function remains scarce. In the current study, we investigated the utility of moderate- and high-intensity evening exercise as a low-cost low-tech intervention to modulate sleep and its influence on subsequent encoding in the morning. Our findings indicate that high-intensity interval training (HIIT) improved post-sleep memory performance with effects lasting up to 24 hours after initial encoding. In addition, we show that especially the early parts of the encoding task were affected by the HIIT intervention, which is in line with increases in synaptic homeostasis being targeted by the exercise. Intriguingly, low-performing participants seemed to benefit more from the HIIT intervention suggesting it not only as a tool for basic research but also as a candidate for applications to boost memory performance in mental disorders or in the elderly. These results provide first evidence that acute exercise can affect learning processes even hours after it occurs.
Two stages of substrate discrimination dictate selectivity in theE. coliMetNI-Q ABC transporter system
Authors: Yang Janet G. , Chen Hulda Yuchun , Guardado John H. , Gardner Maile , Foronda Matthew S. 
Published: January 2025

AbstractTheEscherichia coliMetNI-Q importer, an ATP-binding cassette (ABC) transporter, mediates the uptake of both L- and D-enantiomers of methionine. Originalin vivouptake studies show a strong preference for L-Met over D-Met, but the molecular basis of this selectivity is unclear. In this work, we systematically examine substrate discrimination by the MetNI transporter and MetQ substrate binding protein using an array of biophysical and biochemical techniques. Based on the kinetic and thermodynamic parameters of individual intermediates in the transport cycle, we uncover multiple steps in the transport cycle that confer substrate specificity. As in many other ABC importer systems, selectivity is applied at the level of binding to the substrate binding protein: MetQ dictates a 1,000-fold preference for L-Met over D-Met. However, beyond this initial level of selectivity, MetQ displays distinct binding preferences for the MetNI transporter depending on the substrate. We propose that the differences in binding affinities reflect the more favored release of L-Met into the permeation pathway when compared to D-Met. In support of this model, under saturating conditions, MetNI transports L-Met across the lipid bilayer at a faster rate than D-Met. Interestingly, the ATPase activity of the MetNI-Q complex is not modulated by the presence of substrate. Our studies reveal that the MetNI-Q system incorporates two separate steps in tuning methionine uptake to substrate chirality and availability. This method of discrimination ensures the import of the most biologically preferred substrate while also allowing for adaptability to more limiting nutrient conditions.