bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19–Associated Myocardial Injury and Arrhythmias
Authors: Mughal Mohsin Sheraz , Akbar Hafsa , Mughal Waqar A , Bansal Amit , Mirza Hasan M , Mazzaferro Natale , Niaz Zafar , Wasty Najam 
Published: November 2024

AbstractBackgroundCOVID-19 is a systemic illness with broad organ involvement, including the cardiovascular system. While arrhythmias are commonly observed in COVID-19 patients, reports on their incidence, predictors of sudden cardiac death, therapeutic strategies, and both short-term and long-term outcomes remain heterogeneous.MethodsIn this retrospective, multicenter study, we evaluated the cohort of patients hospitalized for COVID-19 (RT-PCR confirmed) at Newark Beth Israel Medical Center and Monmouth Medical Center between March 1 and April 30, 2020. This study was approved by the institutional review board. We aimed to assess the incidence of new-onset arrhythmias and major adverse cardiovascular events (MACE), comparing these outcomes in patients admitted to the intensive care unit (ICU) versus those in non-ICU settings.ResultsA total of 720 patients were hospitalized for COVID-19 during the study period. The mean age of the cohort was 63.48 years, with 54% male. The incidence of new-onset tachyarrhythmias was significantly higher in the ICU cohort (19%) compared to the non-ICU cohort (8%). The ICU group also experienced a markedly higher incidence of MACE (78% vs. 22%). In-patient mortality was significantly higher in the ICU group (70% vs. 14%). Multivariate regression analysis revealed that ICU admission was an independent risk factor for developing new-onset arrhythmias, with an odds ratio (OR) of 15.92 (95% CI 10.15–24.97; p < 0.001). In patients without MACE, in-hospital mortality was only 1.6%, whereas mortality was 77.4% in those with MACE. ICU admission also emerged as an independent risk factor for MACE, increasing the odds by 15-fold, adjusted for age and gender (OR 15.39, 95% CI 8.36–28.33; p < 0.001).ConclusionICU admission is a significant independent risk factor for both new-onset arrhythmias and MACE in COVID-19 patients. The high rates of these adverse outcomes in the ICU population are likely due to the severity of the systemic inflammatory response in COVID-19, rather than a direct viral-mediated cardiovascular effect.
Artificial Intelligence-Large Language Models (AI-LLMs) for Reliable and Accurate Cardiotocography (CTG) Interpretation in Obstetric Practice
Authors: Gumilar Khanisyah Erza , Wardhana Manggala Pasca , Aldika Akbar Muhammad Ilham , Putra Agung Sunarko , Perjuangan Banjarnahor Dharma Putra , Mulyana Ryan Saktika , Fatati Ita , Yu Zih-Ying , Hsu Yu-Cheng , Dachlan Erry Gumilar , Lu Chien-Hsing , Liao Li-Na , Tan Ming 
Published: November 2024

AbstractBACKGROUNDAccurate interpretation of Cardiotocography (CTG) is a critical tool for monitoring fetal well-being during pregnancy and labor, providing crucial insights into fetal heart rate and uterine contractions. Advanced artificial intelligence (AI) tools such as AI-Large Language Models (AI-LLMs) may enhance the accuracy of CTG interpretation, leading to better clinical outcomes. However, this potential has not yet been examined and reported yet.OBJECTIVEThis study aimed to evaluate the performance of three AI-LLMs (ChatGPT-4o, Gemini Advance, and Copilot) in interpreting CTG images, comparing their performance to junior and senior human doctors, and assessing their reliability in assisting clinical decisions. STUDY DESIGN: Seven CTG images were evaluated by three AI-LLMs, five senior doctors (SHD), and five junior doctors (JHD) and rated by five maternal-fetal medicine (MFM) experts (raters) using five parameters (relevance, clarity, depth, focus, and coherence). The raters were blinded to the source of interpretations, and a Likert scale was used to score the performance of each system. Statistical analysis assessed the homogeneity of expert ratings and the comparative performance of AI-LLMs and doctors.RESULTSChatGPT-4o outperformed the other AI models with a score of 77.86, much higher than Gemini Advance (57.14) and Copilot (47.29), as well as the junior doctors (JHD; 61.57). CG4o’s performance (77.86) was only slightly below that of the senior doctor (SHD; 80.43), with no statistically significant differences between CG4o and SHD (p>0.05). Meanwhile, CG4o had the greatest score in the “depth” category, while the other four parameters were only marginally behind SHD.CONCLUSIONCG4o demonstrated outstanding performance in CTG interpretation, surpassing junior doctors and other AI-LLMs, while senior doctors remain superior in all groups. AI-LLMs, particularly CG4o, showed promising potential as valuable tools in clinical practice to assist obstetricians, enhance diagnostic accuracy, and improve patient care.
Accurate structure prediction of immune proteins using parameter-efficient transfer learning
Authors: Zhu Tian , Ren Milong , He Zaikai , Tao Siyuan , Li Ming , Bu Dongbo , Zhang Haicang 
Published: November 2024

AbstractAccurate prediction of immune protein structures is crucial for understanding the immune system and advancing immunotherapy development. While deep learning methods have significantly advanced protein structure prediction by extracting evolutionary constraints from homologous sequences of a target protein, they struggle with immune proteins due to the limited number of known structures and the lack of homologous sequences in hypervariable regions. To address this challenge, we propose ImmuneFold, a transfer learning approach that fine-tunes ESMFold specifically for immune proteins. We leverage low-rank adaption (LoRA), a parameter-efficient fine-tuning technique that requires considerably less memory and substantially fewer parameters. Evaluations on various immune proteins, including T-cell receptors, antibodies, and nanobodies, demonstrate that ImmuneFold outperforms existing methods in prediction accuracy. Furthermore, we apply ImmuneFold to develop a zero-shot protocol for TCR-epitope binding prediction. Unlike previous supervised methods suffering from severe overfitting due to limited experimental binding data, our approach first predicts TCR-epitope structure using ImmuneFold and then directly estimates the binding affinity by calculating Rosseta energy. Evaluations on experimental binding datasets suggest that our method is robust and accurate in predicting TCR-epitope binding. In summary, ImmuneFold demonstrates accurate predictions of immune protein structures and TCR-epitope binding, highlighting its potential to advance the development of immunotherapies.
Deciphering epigenetic influence of curcumin on histone signatures in breast cancer using 3D network
Authors: Tang Tina , Berezin Mikhail , Mollah Shamim 
Published: November 2024

AbstractAberrant epigenetic alterations are known to lead to cancer arising from normal non-cancerous cells. Unlike genetic mutations, these dysregulations can be reversible and are potential targets for anticancer drugs. Curcumin, a natural plant-derived compound, has been shown to have an anticancer effect via its influence on epigenetic regulation. However, the exact identity of those epigenetic changes, as well as their mechanisms of action, remain largely unknown. The goal of this study is to use a 3D network model to identify what histone codes are being modified by curcumin and hypothesize the enzymatic pathways through which those changes occur in breast cancer. We analyzed multi-omic data from the NIH LINCS program and identified two novel histone marks associated with curcumin that are influenced by twenty-three phosphoproteins involved in cell signaling which are profiled in this paper. These histone and phosphoprotein signatures can be used as potential biomarkers for future chromatin-based drug therapies in breast cancer.GitHub URLSource code is available athttps://github.com/mollahlab/ECHC
Human heart assembloids with autologous tissue-resident macrophages recreate physiological immuno-cardiac interactions
Authors: O’Hern Colin , Caywood Sammantha , Aminova Shakhlo , Kiselev Artem , Volmert Brett , Wang Fei , Sewavi Merlinda-Loriane , Cao Weiheng , Dionise Mia , Muniyandi Priyadharshni , Popa Mirel , Basrai Hussain , Skoric Milana , Boulos George , Huang Amanda , Nuñez-Regueiro Isabel , Chalfoun Nagib , Park Sangbum , Ashammakhi Nureddin , Zhou Chao , Contag Christopher , Aguirre Aitor 
Published: November 2024

SUMMARYInteractions between the developing heart and the embryonic immune system are essential for proper cardiac development and maintaining homeostasis, with disruptions linked to various diseases. While human pluripotent stem cell (hPSC)-derived organoids are valuable models for studying human organ function, they often lack critical tissue-resident immune cells. Here, we introduce an advanced human heart assembloid model, termed hHMA (human heart-macrophage assembloid), which fully integrates autologous cardiac tissue- resident macrophages (MPs) with pre-existing human heart organoids (hHOs). Through multi-omic analyses, we confirmed that these MPs are phenotypically similar to embryonic cardiac tissue-resident MPs and remain viable in the assembloids over time. The inclusion of MPs significantly impacts hHMA development, influencing cardiac cellular composition, boosting cellular communication, remodeling the extracellular matrix, promoting ventricular morphogenesis, and enhancing sarcomeric maturation. Our findings indicate that MPs contribute to homeostasis via efferocytosis, integrate into the cardiomyocyte electrical system, and support catabolic metabolism. To demonstrate the versatility of this model, we developed a platform to study cardiac arrhythmias by chronic exposure to pro-inflammatory factors linked to arrhythmogenesis in clinical settings, successfully replicating key features of inflammasome-mediated atrial fibrillation. Overall, this work introduces a robust platform for examining the role of immune cells in cardiac development, disease mechanisms, and drug discovery, bridging the gap betweenin vitromodels and human physiology. These findings offer insights into cardiogenesis and inflammation-driven heart disease, positioning the hHMA system as an invaluable tool for future cardiovascular research and therapeutic development.
Transcriptomics predicts Artificial Light at Night’s (ALAN) impact on fitness: nightly illumination alters gene expression pattern and negatively affects fitness components in the midgeChironomus riparius(Diptera:Chironomidae)
Authors: Eberhardt Linda , Binde Doria Halina , Bulut Burak , Feldmeyer Barbara , Pfenninger Markus 
Published: November 2024

AbstractThe emission of artificial light at night (ALAN) is rapidly increasing worldwide. Yet, evidence for its detrimental effects on various species is accumulating. While the effects of ALAN on phenotypic traits have been widely investigated, effects on the molecular level are less well understood. Here we aimed to integrate the effects of ALAN at the transcriptomic and the phenotypic level. We tested these effects onChironomus riparius, a multivoltine, holometabolous midge with high ecological relevance for which genomic resources are available. We performed life-cycle experiments in which we exposed midges to constant light and control conditions for one generation. We observed higher EmT50 and reduced fertility under ALAN. From the observed decline in population size due to the reduced fertility, we predicted the population size to decline to 1% after 200 days. The transcriptomic analysis revealed expression changes of genes related to circadian rhythmicity, moulting, catabolism and oxidative stress. From the transcriptomic analysis we hypothesised that under ALAN, oxidative stress is increased, and that moulting begins earlier. We were able to confirm both hypotheses in two posthoc experiments, showing that transcriptomics are a powerful tool in predicting physiological outcomes before they are even observable.
Hormone-induced thrombosis is mediated through non-canonical fibrin(ogen) aggregation and a novel estrogen target in zebrafish
Authors: Yu Xinge , Zhao Queena Y. , Yaman Murat , Emly Sylvia M. , Lee Jacqueline K. , Su Hongyu , Ferguson Allison C. , Nagaswami Chandrasekaran , Chaturantabut Saireudee , Goessling Wolfram , Weisel John W. , Auchus Richard J. , Shavit Jordan A. 
Published: November 2024

ABSTRACTVenous thrombosis is a well-known complication of sex hormone therapy, with onset typically within weeks to months after initiation. Worldwide, more than 100 million pre-menopausal women use combined oral contraceptives, with tens to hundreds of thousands developing thrombosis annually, resulting in significant morbidity and mortality. Although it is known that estrogens can alter expression of coagulation factors, the pathways and mechanisms that connect the two systems, as well as the proteins involved in progression to thrombosis, are poorly understood. Identification of these mediators are central to any comprehensive understanding of hormone-induced pathophysiology, could help ascertain patients at higher risk for thrombosis, and may also pinpoint future therapeutic targets. The zebrafish is a powerful genetic model in which the hemostatic system is almost entirely conserved with humans. Its external development, ability to generate thousands of offspring at low cost, and optical transparency all make it a powerful tool to study the genetics of coagulation disorders. We previously produced a transgenic line (fgb-egfp) that generates GFP-tagged fibrinogen that labels induced and spontaneous fibrin-rich thrombi. Here we show rapid onset of thrombosis after exposure to various estrogens, but not progestins or testosterone. Thrombi are localized to the venous system, develop broadly along the posterior cardinal vein, and show evidence for clot contraction. Thrombosis is only partially impeded by anticoagulants, occurs in the absence of factor X and prothrombin, but is completely blocked in the absence of fibrinogen. Furthermore, although an estrogen receptor antagonist is partially inhibitory, targeted knockout of all known estrogen receptors does not eliminate thrombosis. These data suggest that zebrafish can be used to model human estrogen-induced thrombosis, although the lack of dependence on the canonical coagulation cascade is surprising. The inability to completely inhibit thrombosis through genetic/pharmacologic anticoagulation or estrogen receptor disruption suggests that the mechanisms may be multifactorial. We hypothesize that thrombi are composed of fibrin(ogen) aggregates rather than purely fibrin. Results of further studies could lead to novel therapeutic targets and ascertain patients at higher risk for thrombosis.
Optimization of MR-ARFI for Human Transcranial Focused Ultrasound
Authors: Mohammadjavadi Morteza , Ash Ryan T. , Glover Gary H. , Pauly Kim Butts 
Published: November 2024

AbstractMagnetic resonance acoustic radiation force imaging (MR-ARFI) is an exceptionally promising technique to non-invasively confirm targeting accuracy and estimate exposure of low-intensity transcranial focused ultrasound stimulation. MR-ARFI uses magnetic field motion encoding gradients to visualize the MR phase changes generated by microscopic displacements at the ultrasound focus. Implementing MR-ARFI in the human central nervous system has been hindered by 1) phase distortion caused by subject motion, and 2) insufficient signal-to-noise ratio at low (<1.0 MPa) ultrasound pressures. The purpose of this study was to optimize human MR-ARFI to allow reduced ultrasound exposure while at the same time being robust to bulk and physiological motion. We demonstrate that a time series of single-shot spiral acquisitions, while triggering ultrasound on and off in blocks, provides ARFI maps that with correction are largely immune to bulk and pulsatile brain motion. Furthermore, the time series approach allows for a reduction in ultrasound exposure per slice while improving motion robustness with reduced scan time. The focused ultrasound beam can be visualized in an 80 second scan with our protocol, enabling iteration for image-guided targeting. We demonstrated robust ARFI signals at the expected target in 4 participants. Our results provide persuasive proof-of-principle that MR-ARFI can be used as a tool to guide ultrasound-based precision neural circuit therapeutics.
Machine Learning Approaches for Predicting Virus-Human Protein-Protein Interactions: An Evaluation of Retroviral Interaction Networks
Authors: Mahmoudi Omid , Taghvaei Somayye , Salehi Shirin , Khosravi Soheil , Sazgar Alireza , Zareei Sara 
Published: November 2024

AbstractVirus-human protein-protein interactions (VHPPI) are key to understanding how viruses manipulate host cellular functions. This study constructed a retroviral-human PPI network by integrating multiple public databases, resulting in 1,387 interactions between 29 retroviral and 1,026 human genes. Using minimal sequence similarity, we generated a pseudo-negative dataset for model reliability. Five machine learning models—Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF)—were evaluated using accuracy, sensitivity, specificity, PPV, and NPV. LR and KNN models demonstrated the strongest predictive performance, with sensitivities up to 77% and specificities of 52%. Feature importance analysis identified GC content and semantic similarity as influential predictors. Models trained on selected features showed enhanced accuracy with reduced complexity. Our approach highlights the potential of computational models for VHPPI predictions, offering valuable insights into viral-host interaction networks and guiding therapeutic target identification.SignificanceThis study addresses a crucial gap in antiviral research by focusing on the prediction of virus-host protein-protein interactions (VHPPI) for retroviruses, which are linked to serious diseases, including certain cancers and autoimmune disorders. By leveraging machine learning models, we identified essential host-pathogen interactions that underlie retroviral survival and pathogenesis. These models were optimized to predict interactions accurately, offering valuable insights into the complex mechanisms that retroviruses use to manipulate host cellular processes. Our approach highlights key host and viral proteins, such as ENV_HV1H2 and CD4, that play pivotal roles in retroviral infection and persistence. Targeting these specific interactions can potentially disrupt the viral lifecycle while minimizing toxicity to human cells. This study thus opens avenues for the development of selective therapeutic strategies, contributing to more effective and targeted antiviral interventions with fewer side effects, marking a significant step forward in computational virology and drug discovery.
A novel and simple maneuver greatly improves rat liver transplantation
Authors: Chen Yongfeng , Zhang Jiabin , Chen Guoyong , Zhou Shaotang 
Published: November 2024

AbstractBackgroundOrthotopic rat liver transplantation (OLT) is preferentially used to study transplant immunology; it remains challenging due to higher complications associated with 26-minute anhepatic time ceiling, here we safely extended the anhepatic time to improve success of OLT.MethodsInitially we performed OLT including whole graft OLT from inbred Sprague Dawley (SD) rat to SD (whole graft group, control group, n=21) and 30 minute anhepatic time (AHT) group (AHT group, n=11) to master this procedure. For generalization of this maneuver, partial OLT was performed from 50% Lewis allograft to Brown Norway (BN) rat to induce tolerance (half graft group, n=28), Cyclosporine A was injected once daily for 10 days in this group.ResultsFor whole graft group, 30-day survival rate was 85.5% (18/21), the reasons of death were gas embolism due to the missed suturing in 2 cases, blood loss in 1 case. For AHT group, 30-day survival rate was 72.7% (8/11), one recipient died from respiratory failure intra-operatively; two deaths were biliary leakage in 14 days. There were no differences in survival between whole graft group and AHT group (p=0.368). For tolerance group, 30-day survival rate was 82.0% (11/61), and the causes of death were diverse and recorded.ConclusionThe anhepatic time can be extended to 30 minutes simply through the change of clamping the diaphragm, which facilitates LT and improves its success good for the basic research.
Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy
Authors: Eggertsen Taylor G. , Travers Joshua G. , Hardy Elizabeth J. , Wolf Matthew J. , McKinsey Timothy A. , Saucerman Jeffrey J. 
Published: November 2024

ABSTRACTIntroductionCardiomyocyte hypertrophy is a key clinical predictor of heart failure. High-throughput and AI-driven screens have potential to identify drugs and downstream pathways that modulate cardiomyocyte hypertrophy.MethodsHere we developed LogiRx, a logic-based mechanistic machine learning method that predicts drug-induced pathways. We applied LogiRx to discover how drugs discovered in a previous compound screen attenuate cardiomyocyte hypertrophy. We experimentally validated LogiRx predictions in neonatal cardiomyocytes, adult mice, and two patient databases.ResultsUsing LogiRx, we predicted anti-hypertrophic pathways for 7 drugs currently used to treat non-cardiac disease. We experimentally validated that escitalopram (Lexapro) and mifepristone inhibit hypertrophy of cultured cardiomyocytes in two contexts. The LogiRx model predicted that escitalopram prevents hypertrophy through an “off-target” serotonin receptor/PI3Kγ pathway, mechanistically validated using additional investigational drugs. Further, escitalopram reduced cardiomyocyte hypertrophy in a mouse model of hypertrophy and fibrosis. Finally, mining of both FDA and University of Virginia databases showed that patients with depression on escitalopram have a lower incidence of cardiac hypertrophy than those prescribed other serotonin reuptake inhibitors that do not target the serotonin receptor.ConclusionMechanistic machine learning by LogiRx discovers drug pathways that perturb cell states, which may enable repurposing of escitalopram and other drugs to limit cardiac remodeling through “off-target” pathways.
LIN-35 and the DREAM complex promote temperature stress induced increases in germline apoptosis and cytoplasmic streaming inC. elegans
Authors: Compere Frances V. , Quaglia Kristen A. , Crespo Cruz Margaret N. , Lorenzen Hannah N. , Oswald Samantha H. , Uttal Katherine , Petrella Lisa N. 
Published: November 2024

ABSTRACTAs modest increases in temperature become more common due to global climate change, species are being subjected to moderate temperature stress that can disproportionally affect species fertility. Species that can buffer fluctuations in temperature through tissue or cellular responses in the germline will therefore be more likely to survive moderate temperature stress. Currently, what mechanisms are used in the germline to facilitate maintenance of fertility under moderate temperature stress are unknown. To address this, we investigated how germline apoptosis is modulated inCaenorhabditis elegansnematodes in response to moderate temperature stress. We found that wildtype animals increase their germline apoptosis levels from the physiological baseline in response to the moderate temperature stress. This induction of germline apoptosis was dependent on conserved members of the DREAM complex, including LIN-35, theC. eleganshomolog of the retinoblastoma tumor and members of the MuvB core, LIN-54 and LIN-37. We also found that LIN-35, LIN-54, and LIN-37 were necessary for DNA damage induced apoptosis. Additionally, induction of germline apoptosis during moderate temperature stress was dependent on repression CED-9 function, theC. elegansBcl2 ortholog. Finally, we found that changes in cytoplasmic streaming likely lead to changes to oocyte provisioning in wildtype animals but not mutants. Together, these data suggest an expanded role for LIN-35, CED-9, and the DREAM complex in maintaining fertility by activating apoptosis during moderate temperature stress.