J Med Syst. 2025 Apr 10;49(1):47. doi: 10.1007/s10916-025-02182-3.

ABSTRACT

Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has greatly contributed to addressing DRP challenges. Notably, the prediction of DRP for cancer cell lines benefits significantly from data availability for model development. However, an effective predictive model is still challenging due to issues with data quality, high-dimensional data, and multi-omics data integration. In this study, we introduce MLG2Net, a deep-learning model inspired by graph neural networks designed to predict DRP in lung cancer cell lines based on pharmacogenomics data. Our model comprises two key components: drug SMILES described by local and global graph networks and cell line genomics are illustrated as a map. Our results show that MLG2Net outperforms three reference graph networks. MLG2Net performance reached a Pearson coefficient correlation ( C C p ) of 0.8616 and a root mean square error (RMSE) of 2.94e-6 in predicting drug responses for Lung Adenocarcinoma (LUAD) cell lines. Subsequent testing on the Lung Squamous Cell Carcinoma (LUSC) dataset reveals lower performance ( C C p : 0.7999, RMSE: 4.08e-6), attributed to the dataset's smaller size influencing model capacity. Moreover, we assessed the model's architecture by isolating its components, with results indicating that the global network is particularly effective in this task. In conclusion, MLG2Net exhibited promising applications in DRP for cancer cell lines, with potential advancements by incorporating larger datasets.

PMID:40208442 | DOI:10.1007/s10916-025-02182-3

Ann Am Thorac Soc. 2025 Apr 10. doi: 10.1513/AnnalsATS.202410-1048OC. Online ahead of print.

ABSTRACT

RATIONALE: Incidental features of interstitial lung disease (ILD) are commonly observed on chest computed tomography (CT) scans and are independently associated with poor outcomes. While most studies to date have relied on qualitative assessments of ILD, quantitative imaging algorithms have the potential to effectively detect ILD and assist in risk stratification for population-based cohorts.

OBJECTIVES: To determine whether quantitative measures of ILD are associated with clinically relevant outcomes in the National Lung Screening Trial (NLST).

METHODS: Quantitative measures of ILD were generated using low dose CT (LDCT) data collected as part of the NLST and processed with Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) and deep learning-based usual interstitial pneumonia (DL-UIP) algorithms (Imbio Inc., Minneapolis, MN). A multivariable Cox proportional hazard regression model was used to test the association between ILD measures (percent ground glass opacity, reticular opacity and honeycombing of total lung volume and binary DL-UIP classification) and all-cause mortality. Secondary outcomes of incident lung cancer and lung cancer mortality were also explored.

RESULTS: Quantitative CT data were generated in 11,518 individuals. Mean age was 61.5 years and 58.7% were male. An increased risk of all-cause mortality was observed for each percent increase in CALIPER-derived ground glass opacity (hazard ratio (HR) 1.02, 95% confidence interval (CI) 1.01 - 1.02), reticular opacity (HR 1.18, 95% CI 1.12 - 1.24), and honeycombing (HR 6.23, 95% CI 4.23 - 9.16). Individuals with a positive DL-UIP classification pattern had a 4.8-fold increased risk of all-cause mortality (HR 4.75, 95% CI 2.50 - 9.04). CALIPER derived reticular opacity was also associated with increased lung cancer specific mortality. No quantitative measures of ILD were associated with incident lung cancer.

CONCLUSIONS: Quantitative measures of ILD on LDCT are associated with clinically relevant endpoints in a large at-risk population of individuals with tobacco use history. Primary Source of Funding: This work was supported by the National Institutes of Health Grants K24HL138188 (MKH), F32HL175973 (JMW), T32HL007749 (JMW), R01HL169166 (JMO), R01HL166290 (JMO). Word Count: 324/350.

PMID:40208581 | DOI:10.1513/AnnalsATS.202410-1048OC

J Med Syst. 2025 Apr 10;49(1):47. doi: 10.1007/s10916-025-02182-3.

ABSTRACT

Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has greatly contributed to addressing DRP challenges. Notably, the prediction of DRP for cancer cell lines benefits significantly from data availability for model development. However, an effective predictive model is still challenging due to issues with data quality, high-dimensional data, and multi-omics data integration. In this study, we introduce MLG2Net, a deep-learning model inspired by graph neural networks designed to predict DRP in lung cancer cell lines based on pharmacogenomics data. Our model comprises two key components: drug SMILES described by local and global graph networks and cell line genomics are illustrated as a map. Our results show that MLG2Net outperforms three reference graph networks. MLG2Net performance reached a Pearson coefficient correlation ( C C p ) of 0.8616 and a root mean square error (RMSE) of 2.94e-6 in predicting drug responses for Lung Adenocarcinoma (LUAD) cell lines. Subsequent testing on the Lung Squamous Cell Carcinoma (LUSC) dataset reveals lower performance ( C C p : 0.7999, RMSE: 4.08e-6), attributed to the dataset's smaller size influencing model capacity. Moreover, we assessed the model's architecture by isolating its components, with results indicating that the global network is particularly effective in this task. In conclusion, MLG2Net exhibited promising applications in DRP for cancer cell lines, with potential advancements by incorporating larger datasets.

PMID:40208442 | DOI:10.1007/s10916-025-02182-3

Oral Maxillofac Surg. 2025 Apr 10;29(1):79. doi: 10.1007/s10006-025-01371-1.

ABSTRACT

PURPOSE: Not much is known about the applications of artificial intelligence (AI) in cleft lip and/or palate. We aim to perform a scoping review to synthesize the literature in the last 10 years on integrating AI in the approach to this condition and highlight aspects of research into its prediction, diagnosis and treatment.

METHODS: A search was performed via PubMed, Science Direct, Scopus, and LILACS from 2014 to 2024, in which 649 articles were identified, and 3 studies were identified via the snowball method; the title and abstract were identified, and 35 articles were obtained for full reading. Finally, 25 studies were selected after applying the inclusion and exclusion criteria to execute this review.

RESULTS: The articles reviewed included different types of studies, with observational and experimental studies being frequent and systematic reviews and narratives being less frequent. Similarly, there was evidence of a generalized distribution, with a greater concentration in the United States. These studies were analyzed according to the use of AI applied to cleft lip/palate, obtaining 6 subcategories, including diagnosis, prediction, treatment, and education, in which different types of AI models were included, most frequently using deep learning and machine learning.

CONCLUSION: These technologies promise to optimize the care of patients with this condition. Although current advances are promising, further research is essential to expand and refine their beneficial use. AI has driven significant advances in various stages of the cleft lip and/or palate approach, integrating tools such as assisted algorithms, genetics-based predictive models, and advanced surgical planning.

PMID:40208434 | DOI:10.1007/s10006-025-01371-1

J Autism Dev Disord. 2025 Apr 10. doi: 10.1007/s10803-025-06811-1. Online ahead of print.

ABSTRACT

PURPOSE: The objective of this study is to identify the acoustic characteristics of cries of Typically Developing (TD) and Autism Spectrum Disorder (ASD) children via Deep Learning (DL) techniques to support clinicians in the early detection of ASD.

METHODS: We used an existing cry dataset that included 31 children with ASD and 31 TD children aged between 18 and 54 months. Statistical analysis was applied to find differences between groups for different voice acoustic features such as jitter, shimmer and harmonics-to-noise ratio (HNR). A DL model based on Recursive Convolutional Neural Networks (R-CNN) was developed to classify cries of ASD and TD children.

RESULTS: We found a statistical significant increase in jitter and shimmer for ASD cries compared to TD, as well as a decrease in HNR for ASD cries. Additionally, the DL algorithm achieved an accuracy of 90.28% in differentiating ASD cries from TD.

CONCLUSION: Empowering clinicians with automatic non-invasive Artificial Intelligence (AI) tools based on cry vocal biomarkers holds considerable promise in advancing early detection and intervention initiatives for children at risk of ASD, thereby improving their developmental trajectories.

PMID:40208423 | DOI:10.1007/s10803-025-06811-1

Technol Health Care. 2025 Apr 10:9287329251325740. doi: 10.1177/09287329251325740. Online ahead of print.

ABSTRACT

BackgroundBrain tumors pose a significant healthcare challenge, necessitating early detection and precise monitoring to ensure effective treatment.ObjectivesThe study proposes an innovative technique with the integration of hybrid transfer learning with improved microwave imaging. The integration of special feature extraction abilities of pre-trained deep learning methods along with the high-resolution imaging capabilities of the patch antenna.MethodsIt was primarily composed of two phases. The initial stage involves the development of a patch antenna and head phantom model, which are then subjected to SAR analysis to extract pertinent features from transmitted signals. In the second stage, an AI-based detection model that utilizes MobileNet V2 is implemented. The images acquired by the patch antenna system are fed into MobileNet V2, which extracts high-level features by employing depth-wise separable convolutions and inverted residual blocks. The fully connected layer is used to classify brain tumors in an effective manner by passing these extracted features.ResultsThe results of the simulation indicate that the model performs exceptionally well, with an accuracy of 98.44%, precision of 98.03%, recall of 99.00%, F1-score of 98.52%, and specificity of 97.82%.ConclusionThis method offers a promising solution for the non-invasive and real-time detection of brain tumors, taking advantage of the electromagnetic properties of brain tissue and the capabilities of AI to address the limitations of current diagnostic methods, such as MRI and CT scans.

PMID:40208040 | DOI:10.1177/09287329251325740

J Int Adv Otol. 2025 Mar 25;21(2):1-9. doi: 10.5152/iao.2025.241693.

ABSTRACT

BACKGROUND: Vestibular schwannoma (VS) management decisions are made within multidisciplinary meetings (MDMs). The improved accuracy of volumetric compared to linear tumor measurements is well-recognized, but current volumetric evaluation methods are too time-intensive. The aim was to determine if the availability of fully automated volumetric tumor measures during MDM preparation resulted in different radiological outcomes compared to a standard approach with linear dimensions, and whether this impacted the clinical management decisions.

METHODS: A prospective cohort study evaluated 50 adult patients (mean age 64.6, SD 12.8; 24 male, 26 female) with unilateral sporadic VS. Two simulated MDMs were convened using different methods to measure tumor size during radiology preparation: MDM-mlm used linear tumor dimensions, while MDM-avm was provided with fully automated deep learning-based volume measurements. Interval changes in VS size from the index to final and penultimate to final magnetic resonance imaging (MRI) studies defined the radiological outcomes. The subsequent clinical MDM outcomes were classified. Wilcoxon signed rank tests compared the radiological classification of VS size change and the management outcomes between the MDM-mlm and the MDM-avm.

RESULTS: The 57 interval MRI comparisons in 33 patients showed a significant difference in the classification of VS size change between the MDM-mlm and MDM-avm for all intervals (z=2.49, P=.01). However, there was no significant difference in the resulting management decisions between the 2 MDMs (z=0.30, P= .76).

CONCLUSION: Provision of fully automated VS volume measurements to "real-world" MDM preparation significantly impacted the radiological classification of VS size change but did not influence management decisions.

PMID:40208025 | DOI:10.5152/iao.2025.241693

J Chem Inf Model. 2025 Apr 10. doi: 10.1021/acs.jcim.5c00238. Online ahead of print.

ABSTRACT

This study focuses on predicting the heat capacity of pure liquid-phase ionic liquids (ILs) using machine learning models from various categories, including support vector machines, instance-based learning, ensemble learning, and neural networks, with linear regression serving as a baseline. A key aim of this work is not only to achieve accurate predictions but also to ensure the interpretability of the results, addressing a gap often overlooked in predictive modeling studies. To accomplish this, we curated and cleaned a comprehensive data set of 13,893 data points covering 322 ILs, using temperature and chemical structure-based features as inputs. We evaluated model performance and conducted a thorough interpretability analysis to reveal the patterns of the top-performing model's predictions, ensuring that they are understandable. All models outperformed the baseline, with XGBoost (eXtreme Gradient Boosting) from the ensemble learning category achieving the best results, with total RMSE, R2, and AARD (%) values of 11.389, 0.997, and 1.212%, respectively. Shallow neural networks also performed competitively, suggesting that complex deep learning architectures may not be necessary. Both 10-fold and leave-one-IL-out (LOILO) cross-validation further validated the robustness of these results. Importantly, the interpretability analysis identified key factors influencing heat capacity predictions, such as anion size (e.g., NTf2 and FAP) and alkyl chain length. These factors were validated by testing the model on previously unseen IL examples. Additionally, a user-friendly web application was developed to make predictions, allowing users to input chemical groups or select compounds from a predefined list of 1633 ILs. This study underscores the importance of combining diverse modeling approaches with robust interpretability techniques to achieve reliable and explainable predictions for IL heat capacity.

PMID:40208008 | DOI:10.1021/acs.jcim.5c00238

Ergonomics. 2025 Apr 10:1-16. doi: 10.1080/00140139.2025.2487558. Online ahead of print.

ABSTRACT

Retained surgical items (RSIs) can lead to severe complications, and infections, with morbidity rates up to 84.32%. Computer-aided detection (CAD) systems offer potential advancement in enhancing the detection of RSIs. This systematic review aims to summarise the characteristics of CAD systems developed for the detection of RSIs, evaluate their development, effectiveness, and limitations, and propose opportunities for enhancement. The systematic review adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 guidelines. Studies that have developed and evaluated CAD systems for identifying RSIs were eligible for inclusion. Five electronic databases were searched from inception to March 2023 and eleven studies were found eligible. The sensitivity of CAD systems ranges from 0.61 to 1 and specificity varied between 0.73 and 1. Most studies utilised synthesised RSI radiographs for developing CAD systems which raises generalisability concerns. Moreover, deep learning-based CAD systems did not incorporate explainable artificial intelligence techniques to ensure decision transparency.

PMID:40208001 | DOI:10.1080/00140139.2025.2487558

Curr Pharm Des. 2025 Apr 8. doi: 10.2174/0113816128369168250311172823. Online ahead of print.

ABSTRACT

Breast cancer poses a significant global health challenge, necessitating improved diagnostic and treatment strategies. This review explores the role of artificial intelligence (AI) in enhancing breast cancer pathology, emphasizing risk assessment, early detection, and analysis of histopathological and mammographic data. AI platforms show promise in predicting breast cancer risks and identifying tumors up to three years before clinical diagnosis. Deep learning techniques, particularly convolutional neural networks (CNNs), effectively classify cancer subtypes and grade tumor risk, achieving accuracy comparable to expert radiologists. Despite these advancements, challenges, such as the need for high-quality datasets and integration into clinical workflows, persist. Continued research on AI technologies is essential for advancing breast cancer detection and improving patient outcomes.

PMID:40207818 | DOI:10.2174/0113816128369168250311172823

Curr Top Med Chem. 2025 Apr 8. doi: 10.2174/0115680266346722250401191232. Online ahead of print.

ABSTRACT

The field of drug design has evolved from conventional approaches relying on empirical evidence to advanced approaches such as Computer-Aided Drug Design (CADD). It aids in intricate phases of drug discovery, such as target discovery, lead optimization, and clinical trials, establishing a safe, rapid, and cost-effective system. Structure based drug design (SBDD), Ligand based drug design (LBDD), and Pharmacophore modelling, being the most utilized techniques of CADD, play a major role in establishing the road map necessary for the discovery. Artificial intelligence (AI) and Machine learning (ML) have improved the field with the incorporation of big data and, thereby, enhancing the efficacy and accuracy of the CADD. Deep Learning (DL), a part of AI helps in processing complex and non-linear data and thereby decreases complexity, increases resource utilization and enhances drug-target interaction prediction. These approaches have revolutionized healthcare by enhancing diagnostic precision and predicting the behavior of drugs. Currently, AI/ML approach has become crucial for rapidly discovering novel insights and transforming healthcare areas lie diagnostics, clinical research, and critical care. In the case of the drug development area, techniques like PBPK modeling and advanced nano-QSAR enhance drug behavior understanding and predict nano material toxicity if any, leading to safe and effective therapeutic predictions and interventions. The advancement of AI/ML techniques will bring accuracy, efficacy, and more patient-tailored responses to the drug development field.

PMID:40207759 | DOI:10.2174/0115680266346722250401191232

Am J Respir Crit Care Med. 2025 Apr 10. doi: 10.1164/rccm.202502-0465ED. Online ahead of print.

NO ABSTRACT

PMID:40208255 | DOI:10.1164/rccm.202502-0465ED

Am J Respir Crit Care Med. 2025 Apr 10. doi: 10.1164/rccm.202407-1506OC. Online ahead of print.

ABSTRACT

RATIONALE: While idiopathic pulmonary fibrosis (IPF) has been widely studied, progressive non-IPF interstitial lung disease (ILD) remains poorly understood.

OBJECTIVE: To identify and validate proteomic biomarkers of non-IPF ILD survival.

METHODS: High-throughput proteomic data were generated using plasma collected as part of prospective registries at the Universities of California and Texas (discovery cohort, n=676) and PRECISIONS multi-omic study (validation cohort, n=616). Proteins associated with three-year transplant-free survival (TFS) were identified using multivariable Cox proportional hazards regression, and those associated with TFS after adjustment for false discovery were advanced for validation cohort testing. Pathway analysis was performed to identify molecular pathways unique to non-IPF ILD and shared with IPF.

MAIN RESULTS: Of 2925 proteins tested in the discovery cohort, 73 were associated with TFS, with 44 showing sustained TFS association in the validation cohort. The top TFS-associated proteins were amphiregulin (HR 2.51, 95% CI 2.07-3.04), integrin subunit beta 6 (HR 2.46; 95% CI 1.95-3.10) and keratin 19 (HR 1.70, 95% CI 1.47-1.98). All but one validated biomarkers showed consistent TFS association across non-IPF ILD subtypes. Pathway analysis identified several molecular pathways shared with IPF, along with three pathways unique to non-IPF ILD.

CONCLUSIONS: We identified and validated novel prognostic protein biomarkers in non-IPF ILD, most of which showed consistent association across non-IPF ILD subtypes. While most biomarkers and molecular pathways identified were previously linked to IPF, several were unique to non-IPF ILD, suggesting that unique biology may contribute to progressive non-IPF ILD.

PMID:40208180 | DOI:10.1164/rccm.202407-1506OC

Elife. 2025 Apr 10;13:RP97313. doi: 10.7554/eLife.97313.

ABSTRACT

The spike protein is essential to the SARS-CoV-2 virus life cycle, facilitating virus entry and mediating viral-host membrane fusion. The spike contains a fatty acid (FA) binding site between every two neighbouring receptor-binding domains. This site is coupled to key regions in the protein, but the impact of glycans on these allosteric effects has not been investigated. Using dynamical nonequilibrium molecular dynamics (D-NEMD) simulations, we explore the allosteric effects of the FA site in the fully glycosylated spike of the SARS-CoV-2 ancestral variant. Our results identify the allosteric networks connecting the FA site to functionally important regions in the protein, including the receptor-binding motif, an antigenic supersite in the N-terminal domain, the fusion peptide region, and another allosteric site known to bind heme and biliverdin. The networks identified here highlight the complexity of the allosteric modulation in this protein and reveal a striking and unexpected link between different allosteric sites. Comparison of the FA site connections from D-NEMD in the glycosylated and non-glycosylated spike revealed that glycans do not qualitatively change the internal allosteric pathways but can facilitate the transmission of the structural changes within and between subunits.

PMID:40208235 | DOI:10.7554/eLife.97313

Infect Immun. 2025 Apr 10:e0061824. doi: 10.1128/iai.00618-24. Online ahead of print.

ABSTRACT

Bacteriophages are the dominant members of the human enteric virome and can shape bacterial communities in the gut; however, our understanding of how they directly impact health and disease is limited. Previous studies have shown that specific bacteriophage populations are expanded in patients with Crohn's disease (CD) and ulcerative colitis (UC), suggesting that fluctuations in the enteric virome may contribute to intestinal inflammation. Based on these studies, we hypothesized that a high bacteriophage burden directly induces intestinal epithelial responses. We found that filamentous bacteriophages M13 and Fd induced dose-dependent IL-8 expression in the human intestinal epithelial cell line HT-29 to a greater degree than their lytic counterparts, T4 and ϕX174. We also found that M13, but not Fd, reduced bacterial internalization in HT-29 cells. This led us to investigate the mechanism underlying M13-mediated inhibition of bacterial internalization by examining the antiviral and antimicrobial responses in these cells. M13 upregulated type I and III IFN expressions and augmented short-chain fatty acid (SCFA)-mediated LL-37 expression in HT-29 cells. Taken together, our data establish that filamentous bacteriophages directly affect human intestinal epithelial cells. These results provide new insights into the complex interactions between bacteriophages and the intestinal mucosa, which may underlie disease pathogenesis.

PMID:40208028 | DOI:10.1128/iai.00618-24

Environ Health Perspect. 2025 Apr 10. doi: 10.1289/EHP15648. Online ahead of print.

ABSTRACT

BACKGROUND: Electronic cigarette (e-cigarette) liquids and aerosols contain metals, which can be detrimental to human health. Recently marketed ultrasonic cigarettes (u-cigarettes) claim to be less harmful than e-cigarettes that use heating coils.

OBJECTIVES: We quantified chemical elements/metals in multiple flavors of SURGE u-cigarettes, JUUL e-cigarettes, and "Other Brands" of pod-style e-cigarettes.

METHODS: Elements/metals were identified in atomizers of SURGE using a scanning electron microscope/energy-dispersive X-ray spectrometer. Quantitation of elements/metals in fluids and aerosols from SURGE, JUUL and Other Brands was performed using inductively coupled plasma optical emission spectroscopy.

RESULTS: U-cigarettes contained a sonicator, unlike e-cigarettes which had heated coils. Sixteen elements were identified in at least one fluid or aerosol sample. Generally, u-cigarette fluids and aerosols had more elements/metals at higher concentrations than aerosols from 4th generation e-cigarettes. Element concentrations generally increased in fluids after vaping. All products, including SURGE, had silicon in their fluids and aerosols. Nickel, which was present in low concentrations in all fluids except KWIT Stick (up to 66,050 μg/mL), transferred to the aerosols with low efficiency. SURGE, but not e-cigarettes, also had copper and zinc in their fluids, but little transferred to their aerosols. SURGE fluids and aerosols, unlike e-cigarettes, had relatively high concentrations of arsenic and selenium. Arsenic and selenium, which are on the FDA's Harmful and Potentially Harmful List, likely came from poor quality solvents used to produce the e-liquids in SURGE pods and possibly from the sonicator, which heats during use.

DISCUSSION: SURGE u-cigarettes produce aerosols with metals equivalent to heated coil-style e-cigarettes and had high levels of arsenic and selenium, which are a health concern. Regulations limiting arsenic and selenium in these products are needed, and routine surveillance to identify rogue products, such as Kwit Stick, that have abnormally high levels of nickel or other metals could protect human health. https://doi.org/10.1289/EHP15648.

PMID:40207990 | DOI:10.1289/EHP15648

J Med Virol. 2025 Apr;97(4):e70203. doi: 10.1002/jmv.70203.

ABSTRACT

After the coronavirus disease 2019 (COVID-19) pandemic, the postacute effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have gradually attracted attention. To precisely evaluate the health status of convalescent patients with COVID-19, we analyzed symptom and proteome data of 442 plasma samples from healthy controls, hospitalized patients, and convalescent patients 6 or 12 months after SARS-CoV-2 infection. Symptoms analysis revealed distinct relationships in convalescent patients. Results of plasma protein expression levels showed that C1QA, C1QB, C2, CFH, CFHR1, and F10, which regulate the complement system and coagulation, remained highly expressed even at the 12-month follow-up compared with their levels in healthy individuals. By combining symptom and proteome data, 442 plasma samples were categorized into three subtypes: S1 (metabolism-healthy), S2 (COVID-19 retention), and S3 (long COVID). We speculated that convalescent patients reporting hair loss could have a better health status than those experiencing headaches and dyspnea. Compared to other convalescent patients, those reporting sleep disorders, appetite decrease, and muscle weakness may need more attention because they were classified into the S2 subtype, which had the most samples from hospitalized patients with COVID-19. Subtyping convalescent patients with COVID-19 may enable personalized treatments tailored to individual needs. This study provides valuable plasma proteomic datasets for further studies associated with long COVID.

PMID:40207927 | DOI:10.1002/jmv.70203

Nucleic Acids Res. 2025 Apr 10;53(7):gkaf273. doi: 10.1093/nar/gkaf273.

ABSTRACT

Proteins of the BCL7 family (BCL7A, BCL7B, and BCL7C) are among the most recently identified subunits of the mammalian SWI/SNF chromatin remodeler complex and are absent from the unicellular version of this complex. Their function in the complex is unknown, and very limited structural information is available, despite the fact that they are mutated in several cancer types, most notably blood malignancies and hence medically relevant. Here, using cryo-electron microscopy in combination with biophysical and biochemical approaches, we show that BCL7A forms a stable, high-affinity complex with the nucleosome core particle (NCP) through binding of BCL7A with the acidic patch of the nucleosome via an arginine anchor motif. This interaction is impaired by BCL7A mutations found in cancer. Further, we determined that BCL7A contributes to the remodeling activity of the mSWI/SNF complex and we examined its function at the genomic level. Our findings reveal how BCL7 proteins interact with the NCP and help rationalize the impact of cancer-associated mutations. By providing structural information on the positioning of BCL7 on the NCP, our results broaden the understanding of the mechanism by which SWI/SNF recognizes the chromatin fiber.

PMID:40207634 | DOI:10.1093/nar/gkaf273

Int J Immunopathol Pharmacol. 2025 Jan-Dec;39:3946320251327618. doi: 10.1177/03946320251327618. Epub 2025 Apr 10.

ABSTRACT

The aim of this study was to evaluate the country-specific reporting status profile of immunomodulatory drugs (IMiDs)-related adverse events (ImrAEs) in real-world clinical practice, using data from the Japanese Adverse Drug Event Report (JADER) and Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) databases. Immunomodulatory drugs, including thalidomide and its derivatives, are a new class of anticancer and anti-inflammatory drugs. IMiD risk management programs have instituted sufficient measures to prevent fetal effects but do not address adverse effects experienced by patients themselves. To date, no study has compared ImrAE profiles across countries. Adverse events were defined using the preferred terms in the Medical Dictionary for Regulatory Activities. The number of reported adverse events related to IMiDs in each country (the United States and Japan) was investigated. In both Japan and the United States, myelosuppression, pneumonia, and neuropathy peripheral have been reported as adverse events suspected to be associated with IMiDs. Adverse event profiles differed between the countries. The number of adverse event reports for thalidomide increased transiently in the United States in 2008 following the multiple myeloma indication, and then exhibited a downward trend. The number of adverse event reports for lenalidomide and pomalidomide has increased in the United States since their launch. The number of transient reports increased in Japan in 2015, when pomalidomide was launched. In this study, the profile of ImrAEs was revealed using the FAERS and JADER databases. Our comparative safety study indicated the importance of comparing the safety profiles of IMiDs using post-marketing real-world data. It is important to focus on the adverse events experienced by patients taking IMiDs, as well as the effects of IMiDs on fetuses.

PMID:40207612 | DOI:10.1177/03946320251327618

Biochem Biophys Rep. 2025 Mar 19;42:101957. doi: 10.1016/j.bbrep.2025.101957. eCollection 2025 Jun.

ABSTRACT

Colorectal cancer (CRC) is a multifaceted disease characterized by abnormal cell proliferation in the colon and rectum. The BCL-2 family proteins are implicated in CRC pathogenesis, yet the impacts of genetic variations within these proteins remains elusive. This in-silico study employs diverse sequence- and structure-based bioinformatics tools to identify potentially pathogenic nonsynonymous single nucleotide polymorphisms (nsSNPs) in BCL-2 family proteins. Leveraging computational tools including SIFT, PolyPhen-2, SNPs&GO, PhD-SNP, PANTHER, and Condel, 94 nsSNPs were predicted as deleterious, damaging, and disease-associated by at least five tools. Stability analysis with I-Mutant2.0, MutPred, and PredictSNP further identified 31 nsSNPs that reduce protein stability. Conservation analysis highlighted highly functional, exposed variants (rs960653284, rs758817904, rs1466732626, rs569276903, rs746711568, rs764437421, rs779690846, and rs2038330314) and structural, buried variants (rs376149674, rs1375767408, rs1582066443, rs367558446, rs367558446, rs1319541919, and rs1370070128). To explore the functional effects of these mutations, molecular docking and molecular dynamics simulations were conducted. G233D (rs376149674) and R12G (rs960653284) mutations in the BCL2 protein exhibited the greatest differences in docking scores with d-α-Tocopherol and Tocotrienol, suggesting enhanced protein-ligand interactions. The simulations revealed that d-α-Tocopherol and Tocotrienol (strong binders) contributed to greater stability of BCL-2 family proteins, while Fluorouracil, though weaker, still demonstrated selective binding stability. This work represents the first comprehensive computational analysis of functional nsSNPs in BCL-2 family proteins, providing insights into their roles in CRC pathogenesis. While these findings demand experimental validation, they hold great promise for guiding future large-scale population studies, facilitating drug repurposing efforts, and advancing the development of targeted diagnostic and therapeutic modalities for CRC.

PMID:40207085 | PMC:PMC11979393 | DOI:10.1016/j.bbrep.2025.101957