bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis
Authors: Yang Fang , Li Chaoqun , Yang Wanting , He Yumei , Wu Liping , Jiang Kui , Sun Chao 
Published: October 2024

Abstract
We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This study enrolled 622 patients hospitalized due to decompensating episodes at a tertiary hospital. The cohort data were randomly divided into training and test sets. External validation was carried out using 131 patients from other tertiary hospitals. The frail phenotype was defined according to a self-reported questionnaire (Frailty Index). The area under the receiver operating characteristics curve was adopted to compare the performance of five ML models. The importance of the features and interpretation of the ML models were determined using the SHAP method. The proportions of cirrhotic patients with nonfrail and frail phenotypes in combined training and test sets were 87.8% and 12.2%, respectively, while they were 88.5% and 11.5% in the external validation dataset. Five ML algorithms were used, and the random forest (RF) model exhibited substantially predictive performance. Regarding the external validation, the RF algorithm outperformed other ML models. Moreover, the SHAP method demonstrated that neutrophil-to-lymphocyte ratio, age, lymphocyte-to-monocyte ratio, ascites, and albumin served as the most important predictors for frailty. At the patient level, the SHAP force plot and decision plot exhibited a clinically meaningful explanation of the RF algorithm. We constructed an ML model (RF) providing accurate prediction of frail phenotype in decompensated cirrhosis. The explainability and generalizability may foster clinicians to understand contributors to this physiologically vulnerable situation and tailor interventions.
tidysbml: R/Bioconductor package for SBML extraction into dataframes
Authors: Paparozzi Veronica , Nardini Christine 
Published: October 2024

Abstract

Summary
We present tidysbml, an R package able to perform compartments, species, and reactions data extraction from Systems Biology Markup Language (SBML) documents (up to Level 3) in tabular data structures (i.e. R dataframes) to easily access and handle the richness of the biological information. Thanks to its output format, the package facilitates data manipulation, enabling manageable construction, and therefore analysis, of custom networks, as well as data retrieval, by means of R packages such as igraph, RCy3, and biomaRt. Exemplar data (i.e. SBML files) are extracted from Reactome.

Availability and implementation
The tidysbml R package is distributed under CC BY 4.0 License and can be found publicly available in Bioconductor (https://bioconductor.org/packages/tidysbml) and on GitHub (https://github.com/veronicapaparozzi/tidysbml).
Pair-EGRET: enhancing the prediction of protein–protein interaction sites through graph attention networks and protein language models
Authors: Alam Ramisa , Mahbub Sazan , Bayzid Md Shamsuzzoha 
Published: October 2024

Abstract

Motivation
Proteins are responsible for most biological functions, many of which require the interaction of more than one protein molecule. However, accurately predicting protein–protein interaction (PPI) sites (the interfacial residues of a protein that interact with other protein molecules) remains a challenge. The growing demand and cost associated with the reliable identification of PPI sites using conventional experimental methods call for computational tools for automated prediction and understanding of PPIs.

Results
We present Pair-EGRET, an edge-aggregated graph attention network that leverages the features extracted from pretrained transformer-like models to accurately predict PPI sites. Pair-EGRET works on a k-nearest neighbor graph, representing the 3D structure of a protein, and utilizes the cross-attention mechanism for accurate identification of interfacial residues of a pair of proteins. Through an extensive evaluation study using a diverse array of experimental data, evaluation metrics, and case studies on representative protein sequences, we demonstrate that Pair-EGRET can achieve remarkable performance in predicting PPI sites. Moreover, Pair-EGRET can provide interpretable insights from the learned cross-attention matrix.

Availability and implementation
Pair-EGRET is freely available in open source form at the GitHub Repository https://github.com/1705004/Pair-EGRET.
Learning to explain is a good biomedical few-shot learner
Authors: Chen Peng , Wang Jian , Luo Ling , Lin Hongfei , Yang Zhihao 
Published: October 2024

Abstract

Motivation
Significant progress has been achieved in biomedical text mining using deep learning methods, which rely heavily on large amounts of high-quality data annotated by human experts. However, the reality is that obtaining high-quality annotated data is extremely challenging due to data scarcity (e.g. rare or new diseases), data privacy and security concerns, and the high cost of data annotation. Additionally, nearly all researches focus on predicting labels without providing corresponding explanations. Therefore, in this paper, we investigate a more realistic scenario, biomedical few-shot learning, and explore the impact of interpretability on biomedical few-shot learning.

Results
We present LetEx—Learning to explain—a novel multi-task generative approach that leverages reasoning explanations from large language models (LLMs) to enhance the inductive reasoning ability of few-shot learning. Our approach includes (1) collecting high-quality explanations by devising a suite of complete workflow based on LLMs through CoT prompting and self-training strategies, (2) converting various biomedical NLP tasks into a text-to-text generation task in a unified manner, where collected explanations serve as additional supervision between text-label pairs by multi-task training. Experiments are conducted on three few-shot settings across six biomedical benchmark datasets. The results show that learning to explain improves the performances of diverse biomedical NLP tasks in low-resource scenario, outperforming strong baseline models significantly by up to 6.41%. Notably, the proposed method makes the 220M LetEx perform superior reasoning explanation ability against LLMs.

Availability and implementation
Our source code and data are available at https://github.com/cpmss521/LetEx.
BEATRICE: Bayesian fine-mapping from summary data using deep variational inference
Authors: Ghosal Sayan , Schatz Michael C , Venkataraman Archana 
Published: October 2024

Abstract

Motivation
We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to sample from the space of causal configurations, which we use to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and noise paradigms, as defined by the relative genetic contributions of causal and noncausal variants.

Results
We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer’s disease. In comparison to the baselines, only BEATRICE can successfully find the APOE ϵ2 allele, a commonly associated variant of Alzheimer’s.

Availability and implementation
BEATRICE is available for download at https://github.com/sayangsep/Beatrice-Finemapping.
ROMI: a randomized two-stage basket trial design to optimize doses for multiple indications
Authors: Wang Shuqi , Thall Peter F , Takeda Kentaro , Yuan Ying 
Published: October 2024

Abstract
Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting in varying OBDs. Conversely, indication-specific dose optimization often requires a large sample size. To address this challenge, we propose a Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI). In stage 1, for each indication, response and toxicity are evaluated for a high dose, which may be a previously obtained maximum tolerated dose, with a rule that stops accrual to indications where the high dose is unsafe or ineffective. Indications not terminated proceed to stage 2, where patients are randomized between the high dose and a specified lower dose. A latent-cluster Bayesian hierarchical model is employed to borrow information between indications, while considering the potential heterogeneity of OBD across indications. Indication-specific utilities are used to quantify response-toxicity trade-offs. At the end of stage 2, for each indication with at least one acceptable dose, the dose with highest posterior mean utility is selected as optimal. Two versions of ROMI are presented, one using only stage 2 data for dose optimization and the other optimizing doses using data from both stages. Simulations show that both versions have desirable operating characteristics compared to designs that either ignore indications or optimize dose independently for each indication.
Echo of extinction: The Ivory-billed Woodpecker's tragic legacy and its impact on scientific integrity
Authors: Michalak Pawel 
Published: October 2024

Abstract
Among the current surge in extinction rates, exceeding between 100 and 1000 times the prehuman background rate, a multitude of species quietly fade away, largely escaping the notice of our collective awareness. The extinction of only a select few species has the power to captivate public attention, stir emotions, and ignite debates comparable to the case of the ivory-billed woodpecker (Campephilus principalis). Reports purporting a rediscovery of this long-extinct species periodically surface and find their way into peer-reviewed journals, attracting worldwide attention from the public media. This phenomenon persists despite a glaring lack of evidence supporting such an exceptional claim, in disconnect with principles of population biology or even common sense, thereby risking erosion of public trust in the foundations of nature conservancy and the integrity of scientific methodologies in general. The tragic fate of this iconic species therefore holds significance beyond ecology, providing deep insights into extinction and conservation while highlighting the need for refinement of scientific rigor and standards of evidence in the peer review process and beyond.
Investigating Concomitant RAG-2 and LRBA Mutations in SCID and Autoimmunity
Authors: Spivak Ilia , Frizinsky Shirly , Mandola Amarilla , Lev Atar , Simon Amos J , Barel Ortal , Vishnevskia-Dai Vicktoria , Somech Raz , Somekh Ido 
Published: October 2024

Abstract
Inborn errors of immunity (IEI) are a large heterogenous group of diseases characterized by immunodeficiency, immune dysregulation, allergy, auto-inflammation and predisposition for malignancies. Most are inherited in an autosomal recessive trait. We studied a patient with severe combined immunodeficiency (SCID) and immune dysregulation who harbored two distinct bi-allelic IEI-associated genetic mutations. Clinical, immunological and genetic data were collected. Genetic investigation included whole exome sequencing on DNA extracted from skin fibroblasts. Family segregation was performed by Sanger sequencing. Immunological evaluation included absolute and functional evaluation of lymphocytes and chimerism analysis post hematopoietic stem cell transplantation (HSCT). Treg subsets, LRBA and CTLA4 expression levels were measured by flow-cytometric analysis. A nineteen-year-old female patient from a consanguine background underwent unconditioned matched sibling related HSCT during infancy due to clinical presentation of SCID with an Omenn phenotype. At that time her underlying genetic defect was not defined. Years after HSCT, severe auto-immune phenomena were noted, including a systemic lupus erythematosus-like syndrome and ophthalmic manifestations. Genetic evaluation revealed bi-allelic homozygous mutations in RAG-2 (c.685C&gt;T, p.Arg229Trp) and a previously undescribed mutation in LRBA (c.3325G&gt;T, p.Asp1109Tyr). LRBA and CTLA4 expression levels were normal, suggesting that the LRBA variant identified in these kindred is unlikely to be pathogenic. Multiple genetic defects causing complex IEIs may be identified in the same individual in highly consanguineous populations. Functional immunological testing is essential for evaluation of novel genetic variants.
Viral Suppression Rates at 48 Weeks in People With Human Immunodeficiency Virus Starting Long-Acting Cabotegravir/Rilpivirine With Initial Viremia
Authors: Hickey Matthew D , Gistand Nathanael , Grochowski Janet , Mayorga-Munoz Francis , Imbert Elizabeth , Szumowski John D , Oskarsson Jon , Shiels Mary , Dilworth Samantha , Appa Ayesha , Havlir Diane V , Gandhi Monica , Christopoulos Katerina 
Published: October 2024

Abstract

Background
We previously demonstrated at the Ward 86 human immunodeficiency virus (HIV) clinic in San Francisco that long-acting (LA) cabotegravir (CAB)/rilpivirine (RPV) (LA-CAB/RPV) can rapidly lead to viral suppression in people with HIV (PWH) with viremia due to adherence challenges. We now evaluate the durability of viral suppression in this population.

Methods
We conducted a retrospective cohort study of PWH who started LA-CAB/RPV with viremia (HIV RNA viral load ≥50 copies/mL) before December 2022. Our primary outcome was viral suppression (viral load &lt;50 copies/mL) with LA-CAB/RPV persistence (not discontinued or late by &gt;14 days) at 48 weeks, using the viral load closest to 48 ± 8 weeks. We also describe viral failure, defined as a &lt;2-log decline in viral load at 4 weeks or a viral load ≥200 copies/mL after initial viral suppression with emergent CAB- or RPV-associated resistance mutations; overall 48-week viral suppression including those switched to alternative antiretroviral therapy (ART).

Results
Fifty-nine PWH initiated LA-CAB/RPV with viremia and were included in the analysis; 49% had a CD4 cell count &lt;200/µL, and the median baseline viral load was 42 900 copies/mL (quarter 1–quarter 3, 5272–139 038). At 48 weeks, 47 PWH met the primary outcome of viral suppression with LA-CAB/RPV persistence (80% [95% confidence interval, 67%–89%]). Five had viral failure with resistance (3 with RPV-associated and 2 with CAB- and RPV-associated mutations), and 1 was lost to follow-up. At week 48, 2 of those with viral failure were suppressed on alternative regimens (lenacapavir + bictegravir/tenofovir alafenamide/emtricitabine and CAB + lenacapavir). The overall viral suppression at week 48 with either LA-CAB/RPV or alternative ART was 92% (54 of 59).

Conclusions
In PWH initiating LA-CAB/RPV with initial viremia, 48-week viral suppression (&lt;50 copies/mL) was seen in 92%. LA ART can be an important tool for improving viral suppression among patients who face adherence challenges to oral ART.
Association of hyperandrogenaemia with hypertension and cardiovascular events in pre-menopausal women: a prospective population-based cohort study
Authors: Tuorila Katri , Ollila Meri-Maija , Hurskainen Elisa , Tapanainen Juha , Franks Stephen , Piltonen Terhi , Kaikkonen Kari , Morin-Papunen Laure 
Published: October 2024

Abstract

Objective
The present study aimed to clarify the conflicting association of premenopausal hyperandrogenaemia (HA) with the development of hypertension and cardiovascular disease (CVDs) in women.

Design
A population-based cohort study including 5889 women.

Methods
The association of serum testosterone (T), sex hormone-binding globulin (SHBG), and free androgen index (FAI) at age 31 with blood pressure (BP) and hypertension (BP ≥ 140/90 mmHg and/or use of antihypertensive medication) at ages 31 and 46 and with CVDs (angina pectoris [AP] and/or acute myocardial infarction [AMI] n = 74, transitory cerebral ischaemia and/or stroke n = 150) and combined CVD events (AP, AMI, stroke, heart failure, or CVD mortality n = 160) by age 53 was investigated.

Results
T and FAI were positively associated with systolic and diastolic BP at ages 31 and 46 in the multivariable model. Compared to their lowest quartile, the highest quartiles of T and FAI were positively associated with hypertension at age 31 in the multivariable model. During the 22-year follow-up, FAI was positively associated with increased risk of AP/AMI (hazard ratio [HR]: 2.02, 95% CI: 1.06-3.85) and overall CVD events or mortality (HR: 1.54, 95% CI: 1.02-2.33) in the unadjusted models. However, the significance disappeared after adjusting for body mass index (BMI).

Conclusions
Women with HA at premenopausal age had an elevated risk of hypertension, and together with BMI, increased risk of CVD events and CVD mortality during the 22-year follow-up. However, because of several study limitations regarding ethnicity and BMI characteristics, a longer follow-up of this cohort and future studies in ethnically diverse populations are needed to verify the results.
Antihypertensive, anti-inflammatory, glycaemic, and satiety effects of cocoa-flavoured soy-based powder drink mix enriched with bioactive hydrolysates from nests of Aerodramus fuciphagus
Authors: Aris Hazimah Mohd , Kasim Zalifah Mohd , Zubairi Saiful Irwan 
Published: October 2024

Abstract

Objectives
Edible bird nest hydrolysates (EBNHs) are renowned for their health benefits. The study aimed to determine their functional effects as an ingredient in cocoa-flavoured soy-based powder drink mix (PDM). The glycaemic and satiety effects of the product were also studied.

Materials and Methods
The product was enriched with a reasonably low amount of spray-dried EBNH. The product’s functional effects with respect to in vitro angiotensin-converting enzyme (ACE) inhibitory activity and human red blood cell membrane stabilization (RBCMS) activity were evaluated using its water extract (90–95 °C). The glycaemic and satiety impacts were evaluated in twelve and thirty subjects, respectively, who consumed 250 mL of the product, which contained 25 g of available carbohydrates.

Results
The enriched PDM ACE inhibitory activity and RBCMS activity were significantly higher (P&lt;0.05) than those that were devoid of EBNH. Two hundred and forty milliliters (12.5% (125 mg/mL)) of PDM (containing 3.0% EBNH, 75.44% soybean powder, 20% cocoa powder, 0.5% xanthan gum, 1.0% silicon dioxide, and 0.06% sucralose (mass fraction)) provides approximately 3.7 kcal/g energy. The 250 mL meal (consistency index of 14.17 Pa·sn) is high in protein and dietary fibre. The product’s glycaemic index (GI) and glycaemic load (GL) are low, 24 and 6.0, respectively, raising a normal level of 2-h postprandial blood glucose (&lt;6 mmol/L) and satisfying hunger for approximately 2.5 h.

Conclusions
EBNH, as an ingredient, enhances the antihypertensive and anti-inflammatory properties of PDM. The overall findings suggest that the formulated PDM is a wellness food product capable of promoting good human health, including for dieters and individuals with diabetes.
Automated multimodal imaging of Caenorhabditis elegans behavior in multi-well plates
Authors: Ji Hongfei , Chen Dian , Fang-Yen Christopher 
Published: October 2024

Abstract
Assays of behavior in model organisms play an important role in genetic screens, drug testing, and the elucidation of gene-behavior relationships. We have developed an automated, high-throughput imaging and analysis method for assaying behaviors of the nematode Caenorhabditis elegans. We use high-resolution optical imaging to longitudinally record the behaviors of 96 animals at a time in multi-well plates, and computer vision software to quantify the animals’ locomotor activity, behavioral states, and egg-laying events. To demonstrate the capabilities of our system, we used it to examine the role of serotonin in C. elegans behavior. We found that egg-laying events are preceded by a period of reduced locomotion, and that this decline in movement requires serotonin signaling. In addition, we identified novel roles of serotonin receptors SER-1 and SER-7 in regulating the effects of serotonin on egg laying across roaming, dwelling, and quiescent locomotor states. Our system will be useful for performing genetic or chemical screens for modulators of behavior.
The Neighborhood Environment and Handgrip Strength: Longitudinal Findings From the Health and Retirement Study
Authors: Duchowny Kate A , Diaz-Ramirez L Grisell , Boscardin W John , Perera Rohini , Lin-Gomez Scarlett , Cawthon Peggy M , Noppert Grace A , Clarke Philippa J 
Published: October 2024

Abstract

Background
Muscle strength, as measured by handgrip strength (HGS), is associated with physical function and mortality. Yet the environmental context that influences muscle strength is poorly understood. We evaluated built and social neighborhood characteristics and their association with muscle strength over time.

Methods
Using data from the Health and Retirement Study (2006–2018), linear mixed models assessed how 11 built and social neighborhood variables were associated with baseline levels and changes in HGS over time.

Results
Among the 20 045 respondents (mean age = 63 years, standard deviation = 9.7) with up to 4 HGS measures, 8 455 were men and 11 590 were women. Among men, residing in a neighborhood with a 10% increment higher score on neighborhood disadvantage was associated with a ~1 kg lower HGS at baseline (B = −0.96 kg, 95% confidence interval [CI] = −1.39 to −0.53). Similarly, each 1-point increment on the physical disorder scale was associated with a −0.39 kg lower (95% CI = −0.65 to −0.12) baseline HGS value. Among women, each 10% increment in neighborhood disadvantage was associated with a 0.29 kg lower HGS at baseline (B = −0.29 kg for each 10% increment, 95% CI = −0.46, −0.13). Each 1-unit increment in the number of neighborhood gyms at baseline was associated with a 0.50 kg lower HGS (B = −0.50, 95% CI = −0.76 to −0.23). Each 1-point increment in physical disorder was associated with a −0.12 kg lower (95% CI = −0.24 to −0.00) baseline HGS value. None of the neighborhood features were associated with the HGS rate of change.

Conclusions
Findings suggest that residing in neighborhoods with greater disadvantages and physical disorders may pose challenges for HGS among middle-aged adults as they enter into older adulthood.