Front Pharmacol. 2025 Mar 20;16:1574010. doi: 10.3389/fphar.2025.1574010. eCollection 2025.

ABSTRACT

BACKGROUND/OBJECTIVES: This article reviews some key emerging pharmacogenomic topics in oncology pharmacy practice.

METHODS: Publications selected to review were mainly sourced from the new drug approvals by the Food and Drug Administration and the new regimens listed in the National Comprehensive Cancer Network.

RESULTS: Key pharmacogenomic topics were presented, including genetic alterations influencing drug metabolism, drug efficacy, and changes in therapeutic targeting; Relevant clinical updates and advancements were summarized to provide an in-depth understanding.

CONCLUSION: The abundance of pharmacogenomic measures builds a solid foundation and heralds a paradigm shift toward individualized patient care.

PMID:40183077 | PMC:PMC11965669 | DOI:10.3389/fphar.2025.1574010

South Afr J HIV Med. 2025 Mar 18;26(1):1661. doi: 10.4102/sajhivmed.v26i1.1661. eCollection 2025.

ABSTRACT

BACKGROUND: CYP2B6 slow metabolisers have higher efavirenz concentrations, which are further increased by isoniazid inhibiting efavirenz's accessory metabolic pathway.

OBJECTIVES: We investigated the association between CYP2B6 genotype and toxicity in people living with HIV (PLWH) on isoniazid and efavirenz.

METHOD: We enrolled participants from the efavirenz arm of the ADVANCE trial (reference no.: NCT03122262), who received isoniazid and consented to genotyping. We compared efavirenz concentrations on and off isoniazid, stratified by CYP2B6 genotype. We explored associations between the CYP2B6 genotype and efavirenz concentrations on isoniazid; and changes over 24 weeks in lipids, alanine aminotransferase (ALT), fasting plasma glucose (FPG), sleep quality, and Modified Mini Screen (MMS) scores.

RESULTS: A total of 168 participants, median age 31 years, 57% female, had classifiable CYP2B6 genotypes. Efavirenz concentrations on isoniazid were higher (pseudo-median difference 0.49 µg/mL (95% confidence interval [CI] [0.19-0.91]) and associated with increases in total and high-density lipoprotein (HDL)-cholesterol. CYP2B6 slow metabolisers had higher efavirenz concentrations on isoniazid than extensive metabolisers (β = 1.66 [95% CI 0.98-2.34]). CYP2B6 slow metabolisers had greater increases in total (β = 0.44 mmol/L [95% CI 0.01-0.86]) and HDL-cholesterol (β = 0.39 mmol/L [95% CI 0.21-0.57]) than extensive metabolisers. There were no associations between efavirenz concentrations or CYP2B6 genotype, and change in ALT, FPG, low-density lipoprotein (LDL)-cholesterol, triglycerides, sleep quality, or MMS scores.

CONCLUSION: CYP2B6 slow metabolisers on isoniazid and efavirenz had greater efavirenz concentrations and increases in total and HDL-cholesterol. We found no association between CYP2B6 genotype or efavirenz concentrations and sleep or psychiatric symptoms.

PMID:40182085 | PMC:PMC11966721 | DOI:10.4102/sajhivmed.v26i1.1661

Ther Adv Endocrinol Metab. 2025 Apr 2;16:20420188251328210. doi: 10.1177/20420188251328210. eCollection 2025.

ABSTRACT

Cystic fibrosis-related bone disease (CFBD) is a common endocrinopathy in people living with cystic fibrosis (CF) that is complex and multifactorial in origin. People with CF experience high rates of progressive bone density loss and increased fracture risk. Focus on prevention and treatment of CFBD is of increasing importance in a now aging CF population. This review will discuss current practices in CFBD, gaps in knowledge, and potential future studies with the goal of advancing the clinical care of patients with CFBD.

PMID:40183033 | PMC:PMC11967205 | DOI:10.1177/20420188251328210

Front Genet. 2025 Mar 20;16:1543623. doi: 10.3389/fgene.2025.1543623. eCollection 2025.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) is a common monogenic multisystem disease caused primarily by variants in the CFTR gene. Emerging evidence suggests that some variants, which are described as missense, synonymous or nonsense variants in the literature or databases, may be deleterious by affecting the pre-mRNA splicing process.

METHODS: We analyzed 27 exonic variants in the CFTR gene utilizing bioinformatics tools and identified candidate variants that could lead to splicing changes through minigene assays. Ultimately, we selected eight candidate variants to assess their effects on pre-mRNA splicing. The numbering of DNA variants is based on the complementary DNA (cDNA)sequence of CFTR (Ref Seq NM_000492.4).

RESULTS: This study assessed the impact of CFTR variants on exon splicing by combining predictive bioinformatics tools with minigene assays. Among the eight candidate single nucleotide alterations, five variants (c.488A>T,c.1117G>T, c.1209G>T, c.3239A>G and c.3367G>C) were identified as causing exon skipping.

CONCLUSION: Our study employed a minigene system, which offers great flexibility for assessing aberrant splicing patterns when patient mRNA samples are not accessible, to investigate the effects of exonic variants on pre-mRNA splicing. Our experimental outcomes highlight the importance of analyzing exonic variations at the mRNA level.

PMID:40182926 | PMC:PMC11965618 | DOI:10.3389/fgene.2025.1543623

Ophthalmol Sci. 2025 Jan 30;5(4):100721. doi: 10.1016/j.xops.2025.100721. eCollection 2025 Jul-Aug.

ABSTRACT

OBJECTIVE: Code-free deep learning (CFDL) allows clinicians with no coding experience to build their own artificial intelligence models. This study assesses the performance of CFDL in glaucoma detection from fundus images in comparison to expert-designed models.

DESIGN: Deep learning model development, testing, and validation.

SUBJECTS: A total of 101 442 labeled fundus images from the Rotterdam EyePACS Artificial Intelligence for Robust Glaucoma Screening (AIROGS) dataset were included.

METHODS: Ophthalmology trainees without coding experience designed a CFDL binary model using the Rotterdam EyePACS AIROGS dataset of fundus images (101 442 labeled images) to differentiate glaucoma from normal optic nerves. We compared our results with bespoke models from the literature. We then proceeded to externally validate our model using 2 datasets, the Retinal Fundus Glaucoma Challenge (REFUGE) and the Glaucoma grading from Multi-Modality imAges (GAMMA) at 0.1, 0.3, and 0.5 confidence thresholds.

MAIN OUTCOME MEASURES: Area under the precision-recall curve (AuPRC), sensitivity at 95% specificity (SE@95SP), accuracy, area under the receiver operating curve (AUC), and positive predictive value (PPV).

RESULTS: The CFDL model showed high performance metrics that were comparable to the bespoke deep learning models. Our single-label classification model had an AuPRC of 0.988, an SE@95SP of 95%, and an accuracy of 91% (compared with 85% SE@95SP for the top bespoke models). Using the REFUGE dataset for external validation, our model had an SE@95SP, AUC, PPV, and accuracy of 83%, 0.960%, 73% to 94%, and 95% to 98%, respectively, at the 0.1, 0.3, and 0.5 confidence threshold cutoffs. Using the GAMMA dataset for external validation at the same confidence threshold cutoffs, our model had an SE@95SP, AUC, PPV, and accuracy of 98%, 0.994%, 94% to 96%, and 94% to 97%, respectively.

CONCLUSION: The capacity of CFDL models to perform glaucoma screening using fundus images presents a compelling proof of concept, empowering clinicians to explore innovative model designs for broad glaucoma screening in the near future.

FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PMID:40182983 | PMC:PMC11964632 | DOI:10.1016/j.xops.2025.100721

Ophthalmol Sci. 2024 Dec 20;5(4):100689. doi: 10.1016/j.xops.2024.100689. eCollection 2025 Jul-Aug.

ABSTRACT

TOPIC: In ophthalmology, artificial intelligence (AI) demonstrates potential in using ophthalmic imaging across diverse diseases, often matching ophthalmologists' performance. However, the range of machine learning models for epiretinal membrane (ERM) management, which differ in methodology, application, and performance, remains largely unsynthesized.

CLINICAL RELEVANCE: Epiretinal membrane management relies on clinical evaluation and imaging, with surgical intervention considered in cases of significant impairment. AI analysis of ophthalmic images and clinical features could enhance ERM detection, characterization, and prognostication, potentially improving clinical decision-making. This scoping review aims to evaluate the methodologies, applications, and reported performance of AI models in ERM diagnosis, characterization, and prognostication.

METHODS: A comprehensive literature search was conducted across 5 electronic databases including Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science Core Collection from inception to November 14, 2024. Studies pertaining to AI algorithms in the context of ERM were included. The primary outcomes measured will be the reported design, application in ERM management, and performance of each AI model.

RESULTS: Three hundred ninety articles were retrieved, with 33 studies meeting inclusion criteria. There were 30 studies (91%) reporting their training and validation methods. Altogether, 61 distinct AI models were included. OCT scans and fundus photographs were used in 26 (79%) and 7 (21%) papers, respectively. Supervised learning and both supervised and unsupervised learning were used in 32 (97%) and 1 (3%) studies, respectively. Twenty-seven studies (82%) developed or adapted AI models using images, whereas 5 (15%) had models using both images and clinical features, and 1 (3%) used preoperative and postoperative clinical features without ophthalmic images. Study objectives were categorized into 3 stages of ERM care. Twenty-three studies (70%) implemented AI for diagnosis (stage 1), 1 (3%) identified ERM characteristics (stage 2), and 6 (18%) predicted vision impairment after diagnosis or postoperative vision outcomes (stage 3). No articles studied treatment planning. Three studies (9%) used AI in stages 1 and 2. Of the 16 studies comparing AI performance to human graders (i.e., retinal specialists, general ophthalmologists, and trainees), 10 (63%) reported equivalent or higher performance.

CONCLUSION: Artificial intelligence-driven assessments of ophthalmic images and clinical features demonstrated high performance in detecting ERM, identifying its morphological properties, and predicting visual outcomes following ERM surgery. Future research might consider the validation of algorithms for clinical applications in personal treatment plan development, ideally to identify patients who might benefit most from surgery.

FINANCIAL DISCLOSURES: The author(s) have no proprietary or commercial interest in any materials discussed in this article.

PMID:40182981 | PMC:PMC11964620 | DOI:10.1016/j.xops.2024.100689

Front Genet. 2025 Mar 20;16:1527300. doi: 10.3389/fgene.2025.1527300. eCollection 2025.

ABSTRACT

Determining drug-target affinity (DTA) is a pivotal step in drug discovery, where in silico methods can significantly improve efficiency and reduce costs. Artificial intelligence (AI), especially deep learning models, can automatically extract high-dimensional features from the biological sequences of drug molecules and target proteins. This technology demonstrates lower complexity in DTA prediction compared to traditional experimental methods, particularly when handling large-scale data. In this study, we introduce a multimodal deep neural network model for DTA prediction, referred to as MDNN-DTA. This model employs Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN) to extract features from the drug and protein sequences, respectively. One notable strength of our method is its ability to accurately predict DTA directly from the sequences of the target proteins, obviating the need for protein 3D structures, which are frequently unavailable in drug discovery. To comprehensively extract features from the protein sequence, we leverage an ESM pre-trained model for extracting biochemical features and design a specific Protein Feature Extraction (PFE) block for capturing both global and local features of the protein sequence. Furthermore, a Protein Feature Fusion (PFF) Block is engineered to augment the integration of multi-scale protein features derived from the abovementioned techniques. We then compare MDNN-DTA with other models on the same dataset, conducting a series of ablation experiments to assess the performance and efficacy of each component. The results highlight the advantages and effectiveness of the MDNN-DTA method.

PMID:40182923 | PMC:PMC11965683 | DOI:10.3389/fgene.2025.1527300

Front Med (Lausanne). 2025 Mar 13;12:1529335. doi: 10.3389/fmed.2025.1529335. eCollection 2025.

ABSTRACT

INTRODUCTION: Pathological myopia (PM) is a serious visual impairment that may lead to irreversible visual damage or even blindness. Timely diagnosis and effective management of PM are of great significance. Given the increasing number of myopia cases worldwide, there is an urgent need to develop an automated, accurate, and highly interpretable PM diagnostic technology.

METHODS: We proposed a computational model called PMPred-AE based on EfficientNetV2-L with attention mechanism optimization. In addition, Gradient-weighted class activation mapping (Grad-CAM) technology was used to provide an intuitive and visual interpretation for the model's decision-making process.

RESULTS: The experimental results demonstrated that PMPred-AE achieved excellent performance in automatically detecting PM, with accuracies of 98.50, 98.25, and 97.25% in the training, validation, and test datasets, respectively. In addition, PMPred-AE can focus on specific areas of PM image when making detection decisions.

DISCUSSION: The developed PMPred-AE model is capable of reliably providing accurate PM detection. In addition, the Grad-CAM technology was also used to provide an intuitive and visual interpretation for the decision-making process of the model. This approach provides healthcare professionals with an effective tool for interpretable AI decision-making process.

PMID:40182849 | PMC:PMC11965940 | DOI:10.3389/fmed.2025.1529335

Front Physiol. 2025 Mar 13;16:1522090. doi: 10.3389/fphys.2025.1522090. eCollection 2025.

ABSTRACT

BACKGROUND: Standardised management of chronic sinusitis (CRS) is a challenging but vital area of research. Not only is accurate diagnosis and individualised treatment plans required, but post-treatment chronic disease management is also indispensable. With the development of artificial intelligence (AI), more "AI + medical" application models are emerging. Many AI-assisted systems have been applied to the diagnosis and treatment of CRS, providing valuable solutions for clinical practice.

OBJECTIVE: This study summarises the research progress of various AI-assisted systems applied to the clinical diagnosis and treatment of CRS, focusing on their role in imaging and pathological diagnosis and prognostic prediction and treatment.

METHODS: We used PubMed, Web of Science, and other Internet search engines with "artificial intelligence"、"machine learning" and "chronic sinusitis" as the keywords to conduct a literature search for studies from the last 7 years. We included literature eligible for AI application to CRS diagnosis and treatment in our study, excluded literature outside this scope, and categorized it according to its clinical application to CRS diagnosis, treatment, and prognosis prediction. We provide an overview and summary of current advances in AI to optimize the diagnosis and treatment of CRS, as well as difficulties and challenges in promoting standardization of clinical diagnosis and treatment in this area.

RESULTS: Through applications in CRS imaging and pathology diagnosis, personalised medicine and prognosis prediction, AI can significantly reduce turnaround times, lower diagnostic costs and accurately predict disease outcomes. However, a number of challenges remain. These include a lack of AI product standards, standardised data, difficulties in collaboration between different healthcare providers, and the non-interpretability of AI systems. There may also be data privacy issues involved. Therefore, more research and improvements are needed to realise the full potential of AI in the diagnosis and treatment of CRS.

CONCLUSION: Our findings inform the clinical diagnosis and treatment of CRS and the development of AI-assisted clinical diagnosis and treatment systems. We provide recommendations for AI to drive standardisation of CRS diagnosis and treatment.

PMID:40182690 | PMC:PMC11966420 | DOI:10.3389/fphys.2025.1522090

AI Precis Oncol. 2024 Aug 19;1(4):195-206. doi: 10.1089/aipo.2024.0022. eCollection 2024 Aug.

ABSTRACT

In recent years, the emergence of new-generation deep learning-based artificial intelligence (AI) tools has reignited enthusiasm about the potential of computer-assisted detection (CADe) and diagnosis (CADx) for screening mammography. For screening mammography, digital breast tomosynthesis (DBT) combined with acquired digital 2D mammography or synthetic 2D mammography is widely used throughout the United States. As of this writing in July 2024, there are six Food and Drug Administration (FDA)-cleared AI-based CADe/x tools for DBT. These tools detect suspicious lesions on DBT and provide corresponding scores at the lesion and examination levels that reflect likelihood of malignancy. In this article, we review the evidence supporting the use of AI-based CADe/x for DBT. The published literature on this topic consists of multireader, multicase studies, retrospective analyses, and two "real-world" evaluations. These studies suggest that AI-based CADe/x could lead to improvements in sensitivity without compromising specificity and to improvements in efficiency. However, the overall published evidence is limited and includes only two small postimplementation clinical studies. Prospective studies and careful postimplementation clinical evaluation will be necessary to fully understand the impact of AI-based CADe/x on screening DBT outcomes.

PMID:40182614 | PMC:PMC11963389 | DOI:10.1089/aipo.2024.0022

World J Gastroenterol. 2025 Mar 28;31(12):102950. doi: 10.3748/wjg.v31.i12.102950.

ABSTRACT

Current diagnosis of autoimmune pancreatitis (AIP) is challenging and often requires combining multiple dimensions. There is a need to explore new methods for diagnosing AIP. The development of artificial intelligence (AI) is evident, and it is believed to have potential in the clinical diagnosis of AIP. This article aims to list the current diagnostic difficulties of AIP, describe existing AI applications, and suggest directions for future AI usages in AIP diagnosis.

PMID:40182594 | PMC:PMC11962844 | DOI:10.3748/wjg.v31.i12.102950

Front Digit Health. 2025 Mar 13;7:1514757. doi: 10.3389/fdgth.2025.1514757. eCollection 2025.

ABSTRACT

INTRODUCTION: Inflammatory bowel disorders may result in abnormal Bowel Sound (BS) characteristics during auscultation. We employ pattern spotting to detect rare bowel BS events in continuous abdominal recordings using a smart T-shirt with embedded miniaturised microphones. Subsequently, we investigate the clinical relevance of BS spotting in a classification task to distinguish patients diagnosed with inflammatory bowel disease (IBD) and healthy controls.

METHODS: Abdominal recordings were obtained from 24 patients with IBD with varying disease activity and 21 healthy controls across different digestive phases. In total, approximately 281 h of audio data were inspected by expert raters and thereof 136 h were manually annotated for BS events. A deep-learning-based audio pattern spotting algorithm was trained to retrieve BS events. Subsequently, features were extracted around detected BS events and a Gradient Boosting Classifier was trained to classify patients with IBD vs. healthy controls. We further explored classification window size, feature relevance, and the link between BS-based IBD classification performance and IBD activity.

RESULTS: Stratified group K-fold cross-validation experiments yielded a mean area under the receiver operating characteristic curve ≥0.83 regardless of whether BS were manually annotated or detected by the BS spotting algorithm.

DISCUSSION: Automated BS retrieval and our BS event classification approach have the potential to support diagnosis and treatment of patients with IBD.

PMID:40182584 | PMC:PMC11965935 | DOI:10.3389/fdgth.2025.1514757

Front Plant Sci. 2025 Mar 13;16:1545875. doi: 10.3389/fpls.2025.1545875. eCollection 2025.

ABSTRACT

Automated detection of apple leaf diseases is crucial for predicting and preventing losses and for enhancing apple yields. However, in complex natural environments, factors such as light variations, shading from branches and leaves, and overlapping disease spots often result in reduced accuracy in detecting apple diseases. To address the challenges of detecting small-target diseases on apple leaves in complex backgrounds and difficulty in mobile deployment, we propose an enhanced lightweight model, ELM-YOLOv8n.To mitigate the high consumption of computational resources in real-time deployment of existing models, we integrate the Fasternet Block into the C2f of the backbone network and neck network, effectively reducing the parameter count and the computational load of the model. In order to enhance the network's anti-interference ability in complex backgrounds and its capacity to differentiate between similar diseases, we incorporate an Efficient Multi-Scale Attention (EMA) within the deep structure of the network for in-depth feature extraction. Additionally, we design a detail-enhanced shared convolutional scaling detection head (DESCS-DH) to enable the model to effectively capture edge information of diseases and address issues such as poor performance in object detection across different scales. Finally, we employ the NWD loss function to replace the CIoU loss function, allowing the model to locate and identify small targets more accurately and further enhance its robustness, thereby facilitating rapid and precise identification of apple leaf diseases. Experimental results demonstrate ELM-YOLOv8n's effectiveness, achieving 94.0% of F1 value and 96.7% of mAP50 value-a significant improvement over YOLOv8n. Furthermore, the parameter count and computational load are reduced by 44.8% and 39.5%, respectively. The ELM-YOLOv8n model is better suited for deployment on mobile devices while maintaining high accuracy.

PMID:40182549 | PMC:PMC11965912 | DOI:10.3389/fpls.2025.1545875

Front Plant Sci. 2025 Mar 13;16:1492110. doi: 10.3389/fpls.2025.1492110. eCollection 2025.

ABSTRACT

INTRODUCTION: In the natural harvesting conditions of cherry tomatoes, the robotic vision for harvesting faces challenges such as lighting, overlapping, and occlusion among various environmental factors. To ensure accuracy and efficiency in detecting cherry tomatoes in complex environments, the study proposes a precise, realtime, and robust target detection algorithm: the CTDA model, to support robotic harvesting operations in unstructured environments.

METHODS: The model, based on YOLOv8, introduces a lightweight downsampling method to restructure the backbone network, incorporating adaptive weights and receptive field spatial characteristics to ensure that low-dimensional small target features are not completely lost. By using softpool to replace maxpool in SPPF, a new SPPFS is constructed, achieving efficient feature utilization and richer multi-scale feature fusion. Additionally, by incorporating a dynamic head driven by the attention mechanism, the recognition precision of cherry tomatoes in complex scenarios is enhanced through more effective feature capture across different scales.

RESULTS: CTDA demonstrates good adaptability and robustness in complex scenarios. Its detection accuracy reaches 94.3%, with recall and average precision of 91.5% and 95.3%, respectively, while achieving a mAP@0.5:0.95 of 76.5% and an FPS of 154.1 frames per second. Compared to YOLOv8, it improves mAP by 2.9% while maintaining detection speed, with a model size of 6.7M.

DISCUSSION: Experimental results validate the effectiveness of the CTDA model in cherry tomato detection under complex environments. While improving detection accuracy, the model also enhances adaptability to lighting variations, occlusion, and dense small target scenarios, and can be deployed on edge devices for rapid detection, providing strong support for automated cherry tomato picking.

PMID:40182545 | PMC:PMC11965914 | DOI:10.3389/fpls.2025.1492110

Front Med (Lausanne). 2025 Mar 20;12:1543571. doi: 10.3389/fmed.2025.1543571. eCollection 2025.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive and irreversible interstitial lung disease characterized by high mortality rates. An expanding body of evidence highlights the critical role of targeted therapies in the management of IPF. Nevertheless, there is a paucity of bibliometric studies that have comprehensively assessed this domain. This study seeks to examine global literature production and research trends related to targeted therapies for IPF.

METHOD: A literature search was conducted using the Web of Science Core Collection, encompassing publications from 2004 to 2024, focusing on targeted therapies for IPF. The bibliometric analysis utilized tools such as VOSviewer, CiteSpace, and the "bibliometrix" package in R.

RESULTS: A total of 2,779 papers were included in the analysis, demonstrating a general trend of continuous growth in the number of publications over time. The United States contributed the highest number of publications, totaling 1,052, while France achieved the highest average citation rate at 75.74. The University of Michigan Medical School was the leading institution in terms of publication output, with 88 papers. Principal Investigator Naftali Kaminski was identified as the most prolific researcher in the field. The American Journal of Respiratory Cell and Molecular Biology emerged as the journal with the highest number of publications, featuring 98 articles. In recent years, the research has emerged surrounding targeted therapies for IPF, particularly focusing on agents such as TGF-β, pathogenesis, and autotaxin inhibitor.

CONCLUSION: In this bibliometric study, we systematically analyze research trends related to targeted therapies for IPF, elucidating recent research frontiers and emerging directions. The selected keywords-idiopathic pulmonary fibrosis, targeted therapy, bibliometric analysis, transforming growth factor β, and autotaxin inhibitor-capture the essential aspects of this research domain. This analysis serves as a reference point for future investigations into targeted therapies.

PMID:40182841 | PMC:PMC11967194 | DOI:10.3389/fmed.2025.1543571

Front Genet. 2025 Mar 20;16:1440364. doi: 10.3389/fgene.2025.1440364. eCollection 2025.

ABSTRACT

BACKGROUND: Coronary heart disease (CHD) represents a substantial global burden in terms of morbidity and mortality. Understanding the causal relationships between serum metabolites and CHD can provide a crucial understanding of disease mechanisms and potential therapeutic targets.

METHODS: We conducted a Mendelian randomization (MR) approach to explore the potential causal associations between serum metabolites and CHD risk. The primary analysis employed the inverse variance weighted (IVW) method, supplemented by additional analyses, including MR-Egger, weighted median, weighted mode, and sample mode. To bolster the robustness and reliability of our findings, we performed sensitivity analyses, which included evaluating, horizontal pleiotropy and leave-one-out analysis. Additionally, pathway enrichment analysis was conducted.

RESULTS: We identified 15 known and 11 unknown metabolites with potential associations to CHD. Among the known, six displayed protective effects, while nine were identified as risk factors. Notably, many of these metabolites are closely related to mitochondrial function, which was further supported by pathways and enrichment analysis. Using multiple statistical models to ensure robust results, we unveiled a significant association between hexadecanedioate, a palmitoyl lipid metabolized in mitochondria, and a ∼18% reduced risk of CHD (OR = 0.82, 95%CI: 0.72-0.93).

CONCLUSION: MR analysis revealed 6 protective molecules, 9 hazardous metabolites associated with CHD. Many of these known metabolites are closely link to mitochondrial function, suggesting a critical role of mitochondria in CHD development. In particular, hexadecanedioate, an essential component for mitochondrial energy production, was inversely associated with CHD risk. This suggests that mitochondrial function, and specifically the role of hexadecanedioate, may be pivotal in the development and progression of CHD.

PMID:40182922 | PMC:PMC11965349 | DOI:10.3389/fgene.2025.1440364

Patterns (N Y). 2025 Feb 25;6(3):101184. doi: 10.1016/j.patter.2025.101184. eCollection 2025 Mar 14.

ABSTRACT

Immunotherapies, including checkpoint blockade and chimeric antigen receptor T cell (CAR-T) therapy, have revolutionized cancer treatment; however, many patients remain unresponsive to these treatments or relapse following treatment. CRISPR screenings have been used to identify novel single gene targets that can enhance immunotherapy effectiveness, but the identification of combinational targets remains a challenge. Here, we introduce a computational approach that uses sgRNA set enrichment analysis to identify cancer-intrinsic paralog pairs for enhancing immunotherapy using genome-wide screens. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features to predict paralog gene pairs that influence immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using CRISPR double knockout (DKO). These data and analyses collectively provide a sensitive approach to identifying previously undetected paralog gene pairs that can significantly affect cancer immunotherapy response, even when individual genes within the pair have limited effect.

PMID:40182179 | PMC:PMC11963098 | DOI:10.1016/j.patter.2025.101184

F1000Res. 2025 Jan 6;14:37. doi: 10.12688/f1000research.160371.1. eCollection 2025.

ABSTRACT

TAF15 (TATA-box binding protein-associated factor 15) is a member of the FET protein family, known for their roles in transcriptional regulation and RNA metabolism. Here we have characterized five TAF15 commercial antibodies for western blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. These studies are part of a larger, collaborative initiative seeking to address antibody reproducibility issues by characterizing commercially available antibodies for human proteins and publishing the results openly as a resource for the scientific community. While use of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies for their specific needs.

PMID:40182019 | PMC:PMC11966097 | DOI:10.12688/f1000research.160371.1

BMC Oral Health. 2025 Apr 3;25(1):476. doi: 10.1186/s12903-025-05847-0.

ABSTRACT

BACKGROUND: Spleen-stomach damp-heat syndrome is one of the most common syndrome types in periodontitis from traditional Chinese medicine theory. However, its pathological mechanism is still uncertain. Tissue metabolism is driven by microbes in the host and its microenvironment. Hostmicrobe-metabolism is an interacting and closely related complex. Lipid metabolomics can find lipid metabolites in disease or healthy state, which is beneficial to explore the metabolic process and change mechanism of lipids that may be involved in organisms in healthy or disease state from the perspective of systems biology.

METHODS: In this study, 10 patients in the periodontitis group (CP), 10 patients in the periodontitis with spleen-stomach dampness-heat syndrome group (SP) and 10 patients in the healthy group (H) were recruited for participation, whose unstimulated saliva was collected. The differential metabolites between the groups were detected by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and screened out based on the variable importance in projection (VIP) combined with the P-value and fold change (FC) value of univariate analysis. Finally, KEEG pathway enrichment analysis was performed on these differential metabolites.

RESULTS: A total of 1131 metabolites were detected in saliva in this study. 497 metabolites were significantly up-regulated in periodontitis, mainly plasma-membrane-associated lipids, unsaturated fatty acids and oxidized lipids. Compared with the healthy group, the lipid metabolism pathways of periodontitis with or without spleen-stomach dampness-heat syndrome group were mainly characterized by significant enrichment of glycerophospholipid metabolism and unsaturated fatty acid metabolism such as arachidonic acid metabolism.

CONCLUSION: Compared with periodontally healthy patients, periodontitis with or without spleen-stomach dampness-heat syndrome can cause changes in lipid metabolism in saliva samples of patients. These metabolites are mainly plasma membrane lipids, unsaturated fatty acids and oxidized lipids quality. These lipids may be potential biomarkers of periodontitis. The downstream metabolites of unsaturated fatty acids in the saliva samples of patients with periodontitis and spleen-stomach dampness-heat syndrome were abnormal, and the oxidized lipid (±)5-HETE was significantly abnormal. We speculate that this may be related to the increased state of oxidative stress in saliva samples in disease states.

PMID:40181453 | DOI:10.1186/s12903-025-05847-0

Front Pharmacol. 2025 Mar 13;16:1542269. doi: 10.3389/fphar.2025.1542269. eCollection 2025.

ABSTRACT

INTRODUCTION: Drug-related problems (DRPs) are a significant concern in many patient populations, including breastfeeding women. This study aimed to identify and characterize those problems in a group of breastfeeding women seeking specialized pharmaceutical care.

MATERIALS AND METHODS: A prospective observational study was conducted among women who registered for a pharmacist's online consultation regarding medication safety in lactation. 200 patients were enrolled. Patient medical history, medication use, breastfeeding practices, and DRPs were assessed. DRPs were classified using the Pharmaceutical Care Network Europe Association (PCNE) classification system. Causality assessment for adverse events was performed using the Naranjo algorithm and the Liverpool Causality Assessment Tool (LCAT).

RESULTS: This study found a high prevalence of DRPs among 190 out of 200 breastfeeding women. Of these, 27 experienced potential DRPs, and 163 manifested actual DRPs. A total of 218 DRPs were identified, with ineffective therapy being the most frequent (63.3%, n = 138). Among all identified causes (n = 265), the most common were patient-related factors (47.5%, n = 126) and dispensing-related issues, particularly regarding the information provided to patients about medication safety during lactation. Pharmacist interventions were accepted by 79.5% (n = 151) of patients, with 70% (n = 133) of DRPs successfully resolved.

CONCLUSION: This study highlights the significant burden of DRPs among breastfeeding women and the potential for medical professionals to improve patient outcomes through evidence-based interventions. Future research should focus on developing evidence-based guidelines for medication use during lactation and improving healthcare provider education to optimize maternal and infant health.

PMID:40183105 | PMC:PMC11965936 | DOI:10.3389/fphar.2025.1542269