bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Vasectomy Services’ Models of Care, Training, and Financing Among Title X Family Planning Providers
Authors: Raybon C. Denise , Spitz Stephanie , Teti Michelle , Webb Shelby , Metcalf-Wilson Kristin , Witt Jacki
Published: November 2024

Background and Objective:

Recent evidence suggests an increased demand for vasectomies following the Supreme Court’s Dobbs v. Jackson Women’s Health Organization overturning Roe v. Wade. Limited published information on vasectomies provided by publicly funded agencies exists. This study seeks to understand and document vasectomy services in Title X Family Planning health centers, including models of care, provider training, and financial strategies.

Methods:

We held qualitative interviews with 22 Title X organizations. Through an iterative, thematic analysis, our research team coded the data to produce findings illustrating the landscape of vasectomy service provision in Title X Family Planning health centers.

Results:

This qualitative research study indicated 3 central components of vasectomy care in publicly funded Title X clinics: models of care, finance models, and provider training. Individual approaches varied considerably depending on organization size, funding, provider types, and policy climate.

Conclusions:

This study confirms that a host of barriers exist to providing vasectomy services in publicly funded or “safety net” clinics, including: (1) Limited opportunities for provider training, (2) federal and state 30-day waiting periods for informed consent, and (3) low reimbursement rates. The study informs options for addressing these barriers in publicly funded settings.
Clostridium lapidicellarium sp. nov. and Clostridium moutaii sp. nov., two species isolated from fermentation cellar-producing sauce-flavour Chinese baijiu
Authors: Yang Fan , Wang Hui , Chen Liang-Qiang , Zhou Nan , Lu Jian-Jun , Pu Xiu-Xin , Wan Bo , Wang Li , Liu Shuang-Jiang
Published: November 2024

Clostridium is an important microbial component in pit mud due to its ability to produce alcohol and short-chain fatty acids. This study presents the characterization and taxonomy of two Gram-stain-positive, strictly anaerobic, rod-shaped mesophilic bacterial strains, designated MT-113T and MT-5T, isolated from pit mud in a fermentation cellar used for producing sauce-flavour Chinese baijiu. Phylogenetic analysis based on genome and 16S rRNA gene sequences of strains MT-113T and MT-5T indicates their affiliation with the genus Clostridium sensu stricto (Cluster I of the Clostridia), with C. luticellarii FW431T and C. aromativorans WLY-B-L2T as the closest related species. The major cellular fatty acids (>10.0%) of both strains are C14 : 0 and summed feature 1 (iso-C15 : 1 h and/or C13 : 0 3-OH). The G+C molar contents of the complete genomes for strains MT-113T and MT-5T are 35.84 and 32.74 mol%, respectively. The average nucleotide identity and average amino acid identity values between strains MT-113T, MT-5T, C. aromativorans WLY-B-L2T and C. luticellarii FW431T range from 79 to 85%. The primary products of glucose fermentation by MT-113T are acetic, butyric and isovaleric acids, while those of MT-5T are acetic, isobutyric, butyric and isovaleric acids. Based on their phenotypic, chemotaxonomic and phylogenetic characteristics, strains MT-113T (=CGMCC 1.18018T = JCM 36532T) and MT-5T (=CGMCC 1.18016T = JCM 36530T) are proposed as the type strains of two novel species of the genus Clostridium, namely Clostridium lapidicellarium sp. nov. and Clostridium moutaii sp. nov., respectively.
Unapproved Medicine Use by Paramedics in New Zealand: A Comparative Analysis with Australian and UK Frameworks
Authors: Mordaunt Dylan A
Published: November 2024

Objective

To evaluate the regulation of unapproved medicines and its impact on paramedic practice in out-of-hospital settings by comparing regulatory frameworks in New Zealand, the United Kingdom, and Australia. The objective was to propose actionable policy recommendations to improve New Zealand’s current regulatory approach.

Methods

A comparative analysis was conducted using theoretical frameworks including regulatory theory, public health law, institutionalism, comparative policy analysis, and health crisis management. A technical comparison was also undertaken. Data were collected from legislative texts, policy documents, and secondary sources. The analysis focused on prescribing and administration authority, administrative requirements, flexibility in emergency situations, and the impact on patient care.

Results

New Zealand’s Section 29 of the Medicines Act 1981 imposes comprehensive reporting requirements and restricts unapproved medicine use to registered medical practitioners, hindering timely interventions by paramedics. The administrative burden and lack of flexibility in emergency situations compromise patient care. In contrast, the UK’s Human Medicines Regulations 2012 and Australia’s Therapeutic Goods Act 1989 provide structured and adaptable pathways. The Therapeutic Products Act 2023 in New Zealand proposed reforms but is currently in the process of being repealed.

Discussion

New Zealand’s framework of Section 29 is ill-suited for pre-hospital emergency care, creating ethical and practical dilemmas for paramedics. Comparative insights reveal that more flexible legal frameworks in the UK and Australia better support paramedics’ ability to provide timely care. Ethical considerations emphasise the need to balance regulatory oversight with patient care imperatives.

Conclusions

Legislative reforms in New Zealand are urgently needed to enable the lawful administration of unapproved medicines by paramedics, reduce administrative burdens, and align its framework with international best practices.
Humanization of Care: A Geospatial Analysis of Key Indicators of Quality and Safety of Health Care and Service in the Centre Region, Cameroon
Authors: Cheuyem Fabrice Zobel Lekeumo , Amani Adidja , Ajong Brian Ngongheh , Boukeng Lionel Berthold Keubou , Mouangue Christian , Tsafack Martine Golda Mekouzou , Bi Mvondo Esther Andriane Bitye , Nouko Ariane , Minkandi Claude Axel , Ngos Christelle Sandrine , Guissana Edwige Omona , Otsali Raissa Katy Noa , Djoulay-Hatou  , Yaya Adam , Sonfack Pamela , Nkongo Florence Kissougle
Published: November 2024

Background

Integrating the principles of humanized care into healthcare systems is critical to promoting optimal well-being throughout the patient care process. In a context of limited resources, improving the quality of care and health services means identifying priority sites to optimize cost-effectiveness. The objective was to measure key indicators of quality and safety of care in health facilities (HFs) and to map priority health districts (HDs) for effective implementation of high-impact interventions in the Centre Region.

Methods

We conducted a descriptive cross-sectional analysis using retrospective data from 2018 to 2022 from 32 HDs comprising the Centre Region were extracted in April 2023. Data were retrieved from the national database using DHIS 2 and checked for completeness. The health indicators assessed were those defined in the Cameroon Health Sector Strategy for 2020-2030.

Results

The density of the qualified HCWs was 13.7 per 10 000 inhabitants in 2022. The density of doctors in rural areas has fallen sharply over the five-year study period, with a decline of almost 20% by 2020 in favor of urban areas. The Odza HD had the highest inpatient mortality rate over the five-year study period (49%). In rural areas, the Esse HD had the highest mortality rate (32.4%). Esse (18.8%) and Odza (6.7%) HDs had the highest perioperative mortality rates in rural and urban areas, respectively. Most of the urban HDs (5/8=62.5%) had a neonatal mortality rate higher than the regional median (RM=3.1). Half of the rural HDs had a maternal mortality ratio higher than the regional median (RM=118 per 1000 live births). The HDs of Mbandjock (357/1,000) and Mvog-Ada (285/1,000) were the most affected in rural and urban areas respectively. More than two thirds of urban HDs recorded a proportion of free malaria treatment cases below the regional median (14%).

Conclusions

Efforts to humanize healthcare in the Centre Region are hindered by persistent disparities in care quality and inconsistent policy implementation. Addressing these challenges requires timely implementation of a National Strategic Plan to Improve the Quality of Healthcare and Service Delivery, emphasizing on capacity building through training and supervision.
Standards for reporting norms in the scientific literature and the development of free-access tools to apply them in practice
Authors: Blagus Rok , Leskošek Bojan , Ortega Francisco B. , Tomkinson Grant R. , Jurak Gregor
Published: November 2024

Norm-referenced tests compare individuals to a group. While norms are often presented in tables and graphs, exact score evaluation relies on model parameters, often undisclosed. These models, like those from the Rgamlsspackage, include individual data protected by law and consent, hindering full transparency. Thus, this paper proposes standards for publishing test norms that allow precise score evaluation while protecting participant privacy. We outline specific requirements for norms publications: a) the exact presentation of the fitted model that contains the estimates of all model parameters and other information required for exact evaluation; b) computer sharable fit of the model that does not contain any sensitive information and can be used by those with programming skills to evaluate scores; and c) a web-based application that can be used by those without programming skills to use the results of the fitted model. To facilitate publication and utilization of norms, we have developed and provided in this manuscript an open-source R package of tools for authors and users alike.
scCompass: An integrated cross-species scRNA-seq database for AI-ready
Authors: Wang Pengfei , Liu Wenhao , Wang Jiajia , Liu Yana , Li Pengjiang , Xu Ping , Cui Wentao , Zhang Ran , Long Qingqing , Hu Zhilong , Fang Chen , Dong Jingxi , Zhang Chunyang , Chen Yan , Wang Chengrui , Liu Guole , Xie Hanyu , Zhang Yiyang , Xiao Meng , Chen Shubai ,   The X-Compass Consortium, Chen Yiqiang , Yang Ge , Zhang Shihua , Meng Zhen , Wang Xuezhi , Feng Guihai , Li Xin , Zhou Yuanchun
Published: November 2024

Emerging single-cell sequencing technology has generated large amounts of data, allowing analysis of cellular dynamics and gene regulation at the single-cell resolution. Advances in artificial intelligence enhance life sciences research by delivering critical insights and optimizing data analysis processes. However, inconsistent data processing quality and standards remain to be a major challenge. Here we propose scCompass, which provides a data quality solution to build a large-scale, cross-species and model-friendly single-cell data collection. By applying standardized data pre-processing, scCompass integrates and curates transcriptomic data from 13 species and nearly 105 million single cells. Using this extensive dataset, we are able to archieve stable expression genes (SEGs) and organ-specific expression genes (OSGs) in human and mouse. We provide different scalable datasets that can be easily adapted for AI model training and the pretrained checkpoints with state-of-the-art (SOTA) single-cell foundataion models. In summary, the AI-readiness of scCompass, which combined with user-friendly data sharing, visualization and online analysis, greatly simplifies data access and exploitation for researchers in single cell biology(http://www.bdbe.cn/kun).
Identifying Alzheimer’s disease-associated genes using PhenoGeneRanker
Authors: Rahman Most Tahmina , Saeed Fahad , Bozdag Serdar ,   Alzheimer’s Disease Neuroimaging Initiative
Published: November 2024

Alzheimer’s disease (AD) is a neurogenerative disease that affects millions worldwide with no effective treatment. Several studies have been conducted to decipher to genomic underpinnings of AD. Due to its complex nature, many genes have been found to be associated with AD. Despite these findings, the pathophysiology of the disease is still elusive. To discover new putative AD-associated genes, in this study, we integrated multimodal gene and phenotype datasets of AD using network biology methods to prioritize potential AD-related genes. We constructed a multiplex heterogeneous network composed of patient and gene similarity networks utilizing phenotypic and omics datasets of AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We applied PhenoGeneRanker to traverse this network to discover potential AD-associated genes. To assess the impact of each network layer and seed gene, we also run PhenoGeneRanker on different variants of the network and seed genes. Our results showed that top-ranked genes captured several known AD-related genes and were enriched in Gene Ontology (GO) terms related to AD. We also observed that several top-ranked genes that are not in AD-associated gene list had literature supporting their potential relevance to AD.
Going with the flow: leveraging reef-scale hydrodynamics for upscaling larval-based restoration
Authors: Gouezo Marine , Langlais Clothilde , Beardsley Jack , Roff George , Harrison Peter , Thomson Damian P. , Doropoulos Christopher
Published: November 2024

Anthropogenic pressures are impacting coastal marine ecosystems, necessitating large-scale interventions to accelerate recovery. Propagule-based restoration holds the potential for restoring shallow coastal systems at hectare scales by harnessing natural dispersal. However, predicting propagule dispersal remains challenging due to the complex hydrodynamic nature of coastal marine ecosystems and the complex behaviours of marine propagules. To improve predictions of fine-scale larval dispersal patterns, we developed a 3D reef-scale (∼30 m resolution) dispersal model for Lizard Island, Australia, with the aim to predict the effect of island scale hydrodynamics on the distribution of coral spawn slicks and larvae. Usingin situfield observations, and dispersal simulations, we assessed the model’s capability to (1) forecast hydrodynamic conditions, (2) predict coral spawn slick convergence zones for collection efforts, and (3) identify optimal locations and timeframes where high particle residence time may enhance local settlement following larval delivery to damaged reefs. Predictions of convergence zones in the upper water column aligned well with field observations of coral spawn slicks. At the reef benthos, the model captured variability in current speed and direction at ∼58% of studied locations. At other locations, the model did not resolve hydrodynamic conditions due to sheltering effects and associated hydrodynamic processes occurring at a scale below 50 m. At locations where the model performed well, propagules could remain within a one-hectare area around the delivery site for 5 to 15 hours depending on locations and the timing of larval release. These high retention conditions were infrequent but occurred at least once at 15 of the 25 studied sites. Observations of local currentsa posterioriconfirmed model predictions, showing periods of little water movement lasting from 6.5 to 15 hours. Overall, our study highlights fine-scale dispersal modelling as a key tool for scaling up larval-based reef restoration, while also acknowledging the need for better predictions of local conditions in complex, shallow environments. Applications of fine-scale modelling, coupled with local knowledge of reproductive timing and larval behavioural ecology, assist with the mass collection of propagules upon release and in identifying areas and times of optimal larval deployment to achieve the greatest impact.
Organization of the Proteostasis Network of Membraneless Organelles
Authors: Lim Christine M. , Bian Yuqi , González Díaz Alicia , Pun Frank , Zhavoronkov Alex , Morimoto Richard I. , Vendruscolo Michele
Published: November 2024

Membraneless organelles (MLOs) are dynamic macromolecular condensates that act as crucibles to modulate biological process within the cell. Since MLOs form in the absence of lipid membranes, it is important to understand how their effective regulation is achieved by the protein homeostasis (proteostasis) system. To address this question, we report a comprehensive mapping of the proteostasis network (PN) of MLOs, comprising over 220,000 protein-protein interactions. The analysis of this PN reveals how the regulatory proteins (PN proteins) occupy central roles in the overall protein-protein interaction network of MLOs. We then investigate which branches of the PN are most important in the regulation of MLOs, and find that the anabolic component, which makes up ∼30% of the PN, is more closely involved with MLOs than the catabolic component, which makes up the remaining ∼70% of the PN. We also find that translation-related PN proteins and molecular chaperones play central roles in the regulation of MLOs. We then analyse the similarity between the PNs of different MLOs, revealing that stress granules and P-bodies share large portions of their PNs. Finally, to understand how specificity is achieved despite these similarities, we further analyse the PN organization, showing that HSP70 chaperones are generic in regulating MLOs, while client-specific HSP70 co-chaperones confer specificity to the chaperone action. Taken together, these results identify the composition of the PN of MLOs, rationalize its organization, and reveal a central role of molecular chaperones in the regulation of protein conformations within MLOs.
RF shimming strategy for an open 60-channel RF transmit 7T head coil for routine use on the single transmit mode
Authors: Sajewski Andrea N , Santini Tales , DeFranco Anthony , Berardinelli Jacob , Jin Hecheng , Li Jinghang , Chu Cong , Berardo Jeremy J , Ibrahim Tamer S
Published: November 2024

Purpose

To develop an RF shimming approach for operating the 2ndGeneration Tic Tac Toe RF Coil System (60 transmit channels integrated with 32-channel receive insert) for routine use in 7T neuro MRI on the single transmit mode.

Methods

RF simulations were performed and used to develop non-subject-specific RF shim cases over three anatomically detailed head models: adult male, adult female, and child female. Multi-ROI shimming strategies were developed and implemented. B1+maps and in-vivo images were acquired on the single transmit mode of a 7T scanner using the RF shim cases derived from the computer simulations.

Results

The availability of 60 transmit channels enables more control over RF power efficiency, SAR efficiency, and B1+homogeneity using RF shimming. On the single transmit mode, the 2ndGeneration Tic Tac Toe RF Coil System consistently provides homogeneous B1+field distribution with extended coverage into the temporal lobes, cerebellum, reaching all the way to C5-C6. Safe levels of SAR are also achieved.

Conclusion

By using a non-subject specific RF shimming approach derived from computer simulations, the 2ndGeneration Tic Tac Toe RF Coil System allows for robust, routine neuroimaging (> 1750 in-vivo scanning sessions over the past 28 months) at 7T in single transmit mode.
Simple Words over Rich Imaging: Accurate Brain Disease Classification via Language Model Analysis of Radiological Reports
Authors: Gao Xin , Zhang Meihui , Chen Longfei , Qiu Jun , Zhao Shanbo , Li Junjie , Hua Tiantian , Jin Ying , Wu Zhiqiang , Hou Haotian , Wang Yunling , Zhao Wei , Li Yuxin , Duan Yunyun , Ye Chuyang , Liu Yaou
Published: November 2024

Brain diseases exert profound detrimental effects on human health by affecting the central nervous system. Accurate automated diagnosis of brain diseases is imperative to delay the progression of illness and enhance long-term prognosis. However, existing image-based diagnostic approaches struggle to achieve satisfactory performance due to the high dimensionality of imaging data. Radiological reports, which are required in clinical routine to describe image findings, provide a more straightforward comprehension of the imaging data, yet they have been neglected in automated brain disease classification. In this work, we explore automated brain disease classification via radiological reports and language models and compare the results with conventional image-based methods. Specifically, in the report-based diagnostic approach, we fine-tune Pre-trained Language Models (PLMs) and Large Language Models (LLMs) based on the findings part of radiological reports to achieve disease classification. Four clinically relevant brain disease classification tasks were performed in our experiments, involving 12 datasets with a total number of 14,970 patients, including two independent validation sets. The best language model reached an average area under the receiver operating characteristic curve (AUC) of 84.75%, an average accuracy (ACC) of 79.48%, and an average F1-score of 79.45%. Compared with the best image-based model, it achieved an average improvement of 10.34%, 10.75%, and 9.95% in terms of AUC, ACC, and F1-score, respectively. The language model also outperformed junior radiologists by 9.47% in terms of ACC. Moreover, the report-based model exhibited better adaptability to missing image contrasts and cross-site data variability than image-based models. Together, these results show that brain disease classification via language model analysis of radiological reports can be more reliable than image-based classification, and our work demonstrates the potential of using radiological reports for accurate diagnosis of brain diseases.
A Randomized, Double-blind, Placebo-controlled Crossover Pilot Study of the Effect of Metformin on Airway Glucose in COPD: The Metformin and Airway Glucose In COPD (MAGIC) Trial
Authors: Fiorenzo Eva , Tregoning John S. , Fahidi Isaac , Edwards Michael R. , Kebadze Tata , Mallia Patrick , Johnston Sebastian L. , Farne Hugo A.
Published: November 2024

Background and Objective

Patients with COPD have elevated levels of airway glucose. This may increase the risk of bacterial infection leading to exacerbation and disease progression. We aimed to test whether treating COPD patients with metformin, an oral hypoglycaemic used in diabetes, reduced airway glucose.

Methods

In this randomized, double-blind, placebo-controlled crossover pilot study, we assigned patients with COPD to two 3-month periods of treatment with 1g metformin or placebo twice daily, followed by the alternate treatment after a 2-week washout period. Patients were required to be free of infection, antibiotic or oral steroid treatment in the 8 weeks prior to enrolment. The following were collected at baseline and monthly thereafter: sputum (lower airways sample), nasal/upper airway lining fluid samples using synthetic absorptive matrix (SAM), spirometry, and St Georges Respiratory Questionnaire (SGRQ) and COPD Assessment Test (CAT) scores. The primary outcome was a change in sputum glucose following 3 months treatment with metformin compared to 3 months of placebo.

Results

14 patients were randomised of whom 3 completed the study, mainly due to withdrawals for exacerbations (8/11 withdrawals). In the n=4 patients who completed 3 months metformin treatment, there were no significant changes in sputum or nasal glucose compared to baseline. Metformin did not affect COPD symptom scores or spirometry.

Conclusions

Treatment with metformin in this underpowered pilot study did not significantly reduce airway glucose in COPD. Larger studies are required to definitively evaluate this.

SUMMARY AT A GLANCE

Elevated airway glucose is associated with bacterial infection in chronic obstructive pulmonary disease (COPD) exacerbations. We tested whether metformin, a drug used to treat diabetes, reduced airway glucose in patients with COPD. Recruitment/retention difficulties led to an underpowered study, which observed no difference between metformin and placebo on airway glucose.
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

Background

COVID-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.

Methods

In 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.

Results

A 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).

Conclusion

ICU 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

BACKGROUND

Accurate 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.

OBJECTIVE

This 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.

RESULTS

ChatGPT-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.

CONCLUSION

CG4o 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

Accurate 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

Aberrant 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 URL

Source 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

Interactions 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

The 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

Venous 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

Magnetic 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

Virus-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.

Significance

This 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

Background

Orthotopic 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.

Methods

Initially 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.

Results

For 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.

Conclusion

The 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

Introduction

Cardiomyocyte 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.

Methods

Here 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.

Results

Using 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.

Conclusion

Mechanistic 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

As 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.