Radiol Cardiothorac Imaging. 2025 Apr;7(2):e240272. doi: 10.1148/ryct.240272.

ABSTRACT

Purpose To develop and evaluate a free-breathing, highly accelerated, multisection, single-beat cine sequence for cardiac MRI. Materials and Methods This prospective study, conducted from July 2022 to December 2023, included participants with various cardiac conditions as well as healthy participants who were imaged using a 3-T MRI system. A single-beat sequence was implemented, collecting data for each section in one heartbeat. Images were acquired with an in-plane spatiotemporal resolution of 1.9 × 1.9 mm2 and 37 msec and reconstructed using resolution enhancement generative adversarial inline neural network (REGAIN), a deep learning model. Multibreath-hold k-space-segmented (4.2-fold acceleration) and free-breathing single-beat (14.8-fold acceleration) cine images were collected, both reconstructed with REGAIN. Left ventricular (LV) and right ventricular (RV) parameters between the two methods were evaluated with linear regression, Bland-Altman analysis, and Pearson correlation. Three expert cardiologists independently scored diagnostic and image quality. Scan and rescan reproducibility was evaluated in a subset of participants 1 year apart using the intraclass correlation coefficient (ICC). Results This study included 136 participants (mean age [SD], 54 years ± 15; 69 female, 67 male), 40 healthy and 96 with cardiac conditions. k-Space-segmented and single-beat scan times were 2.6 minutes ± 0.8 and 0.5 minute ± 0.1, respectively. Strong correlations (P < .001) were observed between k-space-segmented and single-beat cine parameters in both LV (r = 0.97-0.99) and RV (r = 0.89-0.98). Scan and rescan reproducibility of single-beat cine was excellent (ICC, 0.97-1.0). Agreement among readers was high, with 125 of 136 (92%) images consistently assessed as diagnostic and 133 of 136 (98%) consistently rated as having good image quality by all readers. Conclusion Free-breathing 30-second single-beat cardiac cine MRI yielded accurate biventricular measurements, reduced scan time, and maintained high diagnostic and image quality compared with conventional multibreath-hold k-space-segmented cine images. Keywords: MR-Imaging, Cardiac, Heart, Imaging Sequences, Comparative Studies, Technology Assessment Supplemental material is available for this article. © RSNA, 2025.

PMID:40178397 | DOI:10.1148/ryct.240272

Brief Bioinform. 2025 Mar 4;26(2):bbaf126. doi: 10.1093/bib/bbaf126.

ABSTRACT

With the rapid advances in single-cell sequencing technology, it is now feasible to conduct in-depth genetic analysis in individual cells. Study on the dynamics of single cells in response to perturbations is of great significance for understanding the functions and behaviors of living organisms. However, the acquisition of post-perturbation cellular states via biological experiments is frequently cost-prohibitive. Predicting the single-cell perturbation responses poses a critical challenge in the field of computational biology. In this work, we propose a novel deep learning method called coupled variational autoencoders (CoupleVAE), devised to predict the postperturbation single-cell RNA-Seq data. CoupleVAE is composed of two coupled VAEs connected by a coupler, initially extracting latent features for controlled and perturbed cells via two encoders, subsequently engaging in mutual translation within the latent space through two nonlinear mappings via a coupler, and ultimately generating controlled and perturbed data by two separate decoders to process the encoded and translated features. CoupleVAE facilitates a more intricate state transformation of single cells within the latent space. Experiments in three real datasets on infection, stimulation and cross-species prediction show that CoupleVAE surpasses the existing comparative models in effectively predicting single-cell RNA-seq data for perturbed cells, achieving superior accuracy.

PMID:40178283 | DOI:10.1093/bib/bbaf126

Brief Bioinform. 2025 Mar 4;26(2):bbaf134. doi: 10.1093/bib/bbaf134.

ABSTRACT

Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to application domains like computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and improves the generalizability of the DRP models, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy loss function and plugging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make suggestions for further potential application of the work to achieve desirable outcomes in the healthcare field.

PMID:40178282 | DOI:10.1093/bib/bbaf134

Brief Bioinform. 2025 Mar 4;26(2):bbaf115. doi: 10.1093/bib/bbaf115.

ABSTRACT

The rapid advancement of next-generation sequencing (NGS) technology and the expanding availability of NGS datasets have led to a significant surge in biomedical research. To better understand the molecular processes, underlying cancer and to support its development, diagnosis, prediction, and therapy; NGS data analysis is crucial. However, the NGS multi-layer omics high-dimensional dataset is highly complex. In recent times, some computational methods have been developed for cancer omics data interpretation. However, various existing methods face challenges in accounting for diverse types of cancer omics data and struggle to effectively extract informative features for the integrated identification of core units. To address these challenges, we proposed a hybrid feature selection (HFS) technique to detect optimal features from multi-layer omics datasets. Subsequently, this study proposes a novel hybrid deep recurrent neural network-based model DOMSCNet to classify stomach cancer. The proposed model was made generic for all four multi-layer omics datasets. To observe the robustness of the DOMSCNet model, the proposed model was validated with eight external datasets. Experimental results showed that the SelectKBest-maximum relevancy minimum redundancy-Boruta (SMB), HFS technique outperformed all other HFS techniques. Across four multi-layer omics datasets and validated datasets, the proposed DOMSCNet model outdid existing classifiers along with other proposed classifiers.

PMID:40178281 | DOI:10.1093/bib/bbaf115

J Chem Inf Model. 2025 Apr 3. doi: 10.1021/acs.jcim.4c02293. Online ahead of print.

ABSTRACT

This study investigates the application of a deep learning (DL) model, specifically a message-passing neural network (MPNN) implemented through Chemprop, to predict the persistence, bioaccumulation, and toxicity (PBT) characteristics of compounds, with a focus on pharmaceuticals. We employed a clustering strategy to provide a fair assessment of the model performances. By applying the generated model to a set of pharmaceutically relevant molecules, we aim to highlight potential PBT chemicals and extract PBT-relevant substructures. These substructures can serve as structural flags, alerting drug designers to potential environmental issues from the earliest stages of the drug discovery process. Incorporating these findings into pharmaceutical development workflows is expected to drive significant advancements in creating more environmentally friendly drug candidates while preserving their therapeutic efficacy.

PMID:40178174 | DOI:10.1021/acs.jcim.4c02293

Cancer Invest. 2025 Apr 3:1-19. doi: 10.1080/07357907.2025.2483302. Online ahead of print.

ABSTRACT

Colon Cancer (CC) arises from abnormal cell growth in the colon, which severely impacts a person's health and quality of life. Detecting CC through histopathological images for early diagnosis offers substantial benefits in medical diagnostics. This study proposes NalexNet, a hybrid deep-learning classifier, to enhance classification accuracy and computational efficiency. The research methodology involves Vahadane stain normalization for preprocessing and Watershed segmentation for accurate tissue separation. The Teamwork Optimization Algorithm (TOA) is employed for optimal feature selection to reduce redundancy and improve classification performance. Furthermore, the NalexNet model is structured with convolutional layers and normal and reduction cells, ensuring efficient feature representation and high classification accuracy. Experimental results demonstrate that the proposed model achieves a precision of 99.9% and an accuracy of 99.5%, significantly outperforming existing models. This study contributes to the development of an automated and computationally efficient CC classification system, which has the potential for real-world clinical implementation, aiding pathologists in early and accurate diagnosis.

PMID:40178023 | DOI:10.1080/07357907.2025.2483302

J Forensic Sci. 2025 Apr 3. doi: 10.1111/1556-4029.70033. Online ahead of print.

ABSTRACT

The practice of cutting and pasting portions of one image into another, known as "image splicing," is commonplace in the field of image manipulation. Image splicing detection using deep learning has been a hot research topic for the past few years. However, there are two problems with the way deep learning is currently implemented: first, it is not good enough for feature fusion, and second, it uses only simple models for feature extraction and encoding, which makes the models vulnerable to overfitting. To tackle these problems, this research proposes CMV2U-Net, an edge-weighted U-shaped network-based image splicing forgery localization approach. An initial step is the development of a feature extraction module that can process two streams of input images simultaneously, allowing for the simultaneous extraction of semantically connected and semantically agnostic features. One characteristic is that a hierarchical fusion approach has been devised to prevent data loss in shallow features that are either semantically related or semantically irrelevant. This approach implements a channel attention mechanism to monitor manipulation trajectories involving multiple levels. Extensive trials on numerous public datasets prove that CMV2U-Net provides high AUC and F1 in localizing tampered regions, outperforming state-of-the-art techniques. Noise, Gaussian blur, and JPEG compression are post-processing threats that CMV2U-Net has successfully resisted.

PMID:40177991 | DOI:10.1111/1556-4029.70033

Nano Lett. 2025 Apr 3. doi: 10.1021/acs.nanolett.5c01071. Online ahead of print.

ABSTRACT

Maintaining consistent quality in biomanufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) often fall short in achieving rapid and accurate monitoring of small-molecule critical process parameters and critical quality attributes. Surface-enhanced Raman spectroscopy (SERS) holds great promise but faces challenges like intensity fluctuations, compromising reproducibility. Herein, we propose a deep learning-powered colloidal digital SERS platform. This innovation converts SERS spectra into binary "ON/OFF" signals based on defined intensity thresholds, which allows single-molecule event visualization and reduces false positives. Through integration with deep learning, this platform enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks. Furthermore, we demonstrate its accuracy and reproducibility for studying AMBIC 1.1 mammalian cell culture media. These results highlight its rapidity, accuracy, and precision, paving the way for widespread adoption and scale-up as a novel PAT tool in biomanufacturing and diagnostics.

PMID:40177940 | DOI:10.1021/acs.nanolett.5c01071

Disabil Rehabil Assist Technol. 2025 Apr 3:1-3. doi: 10.1080/17483107.2025.2483300. Online ahead of print.

ABSTRACT

Background: Speech and language impairments, though often used interchangeably, are two very distinct types of challenges. A speech impairment may lead to impaired ability to produce speech sounds whilst communication may be affected due to lack of fluency or articulation of words. Consequently this may affect a person's ability to articulate may affect academic achievement, social development and progress in life. ChatGPT (Generative Pretrained Transformer) is an open access AI (Artificial Intelligence) tool developed by Open AI® based on Large language models (LLMs) with the ability to respond to human prompts to generate texts using Supervised and Unsupervised Machine Learning (ML) Algorithms. This article explores the current role and future perspectives of ChatGPT AI Tool for Speech-Impaired Assistance.

Methods: A cumulative search strategy using databases of PubMed, Google Scholar, Scopus and grey literature was conducted to generate this narrative review.

Results: A spectrum of Enabling Technologies for Speech & Language Impairment have been explored. Augmentative and Alternative Communication technology (AAC), Integration with Neuroprosthesis technology and Speech therapy applications offer considerable potential to aid speech and language impaired individuals.

Conclusion: Current applications of AI, ChatGPT and other LLM's offer promising solutions in enhancing communication in people affected by Speech and Language impairment. However, further research and development is required to ensure affordability, accessibility and authenticity of these AI Tools in clinical Practice.

PMID:40177878 | DOI:10.1080/17483107.2025.2483300

Eur J Phys Rehabil Med. 2025 Apr 3. doi: 10.23736/S1973-9087.25.08778-7. Online ahead of print.

ABSTRACT

INTRODUCTION: The efficacy of pulmonary rehabilitation (PR) in improving health-related quality of life (HRQoL) in patients with interstitial lung disease (ILD) still have some unresolved issues. This study aimed to identify this gap by using the 36-Item Short Form Survey (SF-36) to assess the advantages and disadvantages of PR in improving the HRQoL of patients with ILD.

EVIDENCE ACQUISITION: Self-controlled before-and-after interventional design research related to PR and ILD published in English were retrieved from PubMed, Embase, Web of Science, Scopus, Ovid, and Cochrane Library from inception to May 19, 2024. Data collected from the included studies were general clinical characteristics, study sample size, SF-36 physical component summary (PCS) score, SF-36 mental component summary (MCS) score, scores of the eight domains (physical function, role physical, bodily pain, general health, vitality, social function, role emotional, and mental health), PR time, and main elements of PR. Subgroup analysis was performed based on the PR time and ILD type. Sensitivity analysis was conducted by excluding one study at a time. Publication bias was assessed using Egger's Test, and the reliability of the studies was determined using the funnel plot and trim-and-fill method. Changes in SF-36 domain scores after PR were presented in a radar chart.

EVIDENCE SYNTHESIS: Pooled analysis of 15 studies involving 1289 patients with ILD who underwent PR showed that the patients had significantly higher PCS scores (weighted mean difference [WMD]=2.07, 95% CI: 1.06, 3.09) and MCS scores (WMD=4.48, 95% CI: 3.21, 5.76) after PR. According to disease types, subgroup analyses showed that patients with idiopathic pulmonary fibrosis had significantly higher PCS scores (WMD=3.15, 95% CI: 0.05, 6.24) but no change in MCS scores after PR (WMD=1.97, 95% CI: -1.91, 5.85). Additionally, subgroup analysis based on PR time revealed that the PCS scores of patients with ILD were significantly increased after <8 weeks of PR (WMD=2.09, 95% CI: 1.02, 3.17) but not after ≥8 weeks of PR (WMD=1.94, 95% CI: -1.05, 4.93, P=0.204). All included studies were of good quality, and the pooled and subgroup results were robust without publication bias.

CONCLUSIONS: In patients with ILD, PR less than 8 weeks effectively improved the physical and mental HRQoL, but not the social function. Future studies should focus on determining the optimal PR time for enhancing HRQoL in patients with ILD and evaluating the efficacy of PR in different ILD types and other HRQoL domains.

PMID:40178411 | DOI:10.23736/S1973-9087.25.08778-7

Semin Immunopathol. 2025 Apr 3;47(1):24. doi: 10.1007/s00281-025-01047-8.

ABSTRACT

Galectin-1 (Gal-1), a member of the β-galactoside-binding soluble lectin family, is a double-edged sword in immunity. On one hand, it plays a crucial role in regulating diverse immune cell functions, including the apoptosis of activated T cells. These processes are key in resolving inflammation and preventing autoimmune diseases. On the other hand, Gal-1 has significant implications in cancer, where tumor cells and the tumor microenvironment (TME) (e.g., tumor-associated fibroblasts, myeloid-derived suppressor cells) secrete Gal-1 to evade immune surveillance and promote cancer cell growth. Within the TME, Gal-1 enhances the differentiation of tolerogenic dendritic cells, induces the apoptosis of effector T cells, and enhances the proliferation of regulatory T cells, collectively facilitating tumor immune escape. Therefore, targeting Gal-1 holds the potential to boost anti-tumor immunity and improve the efficacy of cancer immunotherapy. This review provides insights into the intricate role of Gal-1 in immune cell regulation, with an emphasis on T cells, and elucidates how tumors exploit Gal-1 for immune evasion and growth. Furthermore, we discuss the potential of Gal-1 as a therapeutic target to augment current immunotherapies across various cancer types.

PMID:40178639 | DOI:10.1007/s00281-025-01047-8

Plant Dis. 2025 Apr 3. doi: 10.1094/PDIS-02-25-0251-PDN. Online ahead of print.

ABSTRACT

In July 2024, a pooled leaf sample (D760/24) was collected from several plants of three watermelon cultivars (Citrullus lanatus cvs. Crimson Sweet, Asahi Miyako Hybrid F1 and Top Gun) grown in an open field (approx. 0.5ha) in Dombrava, Slovenia. The plants which were included in the pooled sample showed virus-like symptoms, such as leaf mosaic, wilting and necrosis (eXtra Supplementary material Fig. S1). The disease incidence was estimated at 10%. DNA and RNA were extracted following Mehle et al. (2013) and RNeasy Plant Mini Kit (Qiagen, Germany) protocols, respectively. The sample was tested positive by reverse-transcription (RT)-PCR for watermelon crinkle leaf-associated virus 1 (WCLaV-1) and WCLaV-2 ( Hernandez et al. 2021) and negative for other viruses (details on viruses tested and primers used are available in eXtra Table S1). The obtained amplicons of expected sizes of WCLaV-1 and WCLaV-2 movement protein (MP) and RNA-dependent RNA polymerase (RdRp) genes (eXtra Fig S2) were then subjected to Sanger sequencing (Eurofins Genomics, Germany) and BLAST analysis. The MP (PQ570004, PQ570006) and the RdRp (PQ570005, PQ570007) sequences exhibited 100% identity with multiple accessions of WCLaV-1, such as PP792977 and PP792976, and WCLaV-2, such as LC636073 and LC636074. Illumina high-throughput sequencing (HTS, Novogene, Germany, NovaSeq X Plus, PE150) identified WCLaV-1 (PV012703-04) and WCLaV-2 (PV012705-06) reads, along with cucumis melo amalgavirus 1 (CmAV1, PV012707) and solanum nigrum ilarvirus 1 reads (insufficient reads to reconstruct genome segments, it may originate from pollen contamination of nearby infected plants in the field (Rivarez et al. 2023)). HTS data were analyzed in CLC Genomics Workbench v. 24 (Qiagen, USA) using the pipeline by (Pecman et al. 2022). Consensus genome sequences were reconstructed by iterative read mapping to the most similar reference sequence of the virus obtained from NCBI GenBank. To check for WCLaVs in watermelon seeds sold in Slovenia, we tested five seed samples from Sugar Baby, Crimstar F1, and Crimson Sweet (three lots) by RT-PCR. We also tested four leaf samples from plants grown from these seeds at 3-5 true leaves stage. Both viruses were found in all seed and leaf extracts. However, mechanical inoculations with the sap of two samples (plants grown from infected seed sample and sample D760/24) on several commonly used indicator plants including Chenopodium quinoa, Capsicum annuum, Nicotiana clevelandii, Nicotiana glutinosa, Nicotiana benthamiana, Nicotiana tabacum cv. White Burley, Nicotiana rustica, Datura stramonium, Cucurbita pepo cv. Bianca di Trieste, and Cucurbita maxima did not result in their infection. Retrospective analyses of our HTS data of two watermelon and 84 other cucurbits samples from previous years showed WCLaV-1 and WCLaV-2 reads in two pooled samples (containing equal amount of RNA of each sample): one from 2018 and another from 2019. RT-PCR confirmed the presence of WCLaVs only in watermelons. The pool from 2018 was sequenced at GATC (Germany, NovaSeq 6000 S2, PE 150) and from 2019 in-house using Oxford Nanopore Technologies (UK, MinION Mk1B device, SQK-PCS108, R9 flow cell). All HTS reads are deposited in the NCBI Short Reads Archive (PRJNA1202089). This is the first report of WCLaV-1 and WCLaV-2 in Slovenia and Europe, the two viruses which were included to the Alert list of the European and Mediterranean Plant Protection Organization, due to limited knowledge about their epidemiology (EPPO 2023). Further research is necessary to determine the incidence of these viruses in Europe, elucidate their epidemiology, symptoms association and their potential impact on the production of watermelons in the region.

PMID:40178537 | DOI:10.1094/PDIS-02-25-0251-PDN

Plant Cell. 2025 Apr 3:koaf059. doi: 10.1093/plcell/koaf059. Online ahead of print.

ABSTRACT

The significance of research conducted on Arabidopsis thaliana cannot be overstated. This focus issue showcases how insights from Arabidopsis have opened new areas of biology and directly advanced our understanding of crops. Here, experts intimately involved in bridging between Arabidopsis and crops share their perspectives on the challenges and opportunities for translation. First, we examine the translatability of genetic modules from Arabidopsis into maize, emphasizing the need to publish well-executed translational experiments, regardless of outcome. Second, we highlight the landmark success of HB4, the first GM wheat cultivar on the market, whose abiotic tolerance is borne from direct translation and based on strategies first outlined in Arabidopsis. Third, we discuss the decades-long journey to engineer oilseed crops capable of producing omega-3 fish oils, with Arabidopsis serving as a critical intermediary. Fourth, we explore how direct translation of starch synthesizing proteins characterised in Arabidopsis helped uncover novel mechanisms and functions in crops, with potential valuable applications. Finally, we illustrate how shared molecular factors between Arabidopsis and barley exhibit distinct molecular wiring as exemplified in cuticular and stomatal development. Together, these vignettes underscore the pivotal role of Arabidopsis as a foundational model plant while highlighting the challenges of translating discoveries into field-ready, commercial cultivars with enhanced knowledge-based traits.

PMID:40178150 | DOI:10.1093/plcell/koaf059

Int J Clin Pharm. 2025 Apr 3. doi: 10.1007/s11096-025-01904-4. Online ahead of print.

ABSTRACT

BACKGROUND: Methotrexate (MTX) is the cornerstone of rheumatoid arthritis (RA) treatment. However, patients using MTX often experience drug-related problems (DRPs), negatively affecting adherence and persistence.

AIM: To identify the number and type of DRPs experienced by RA patients during the first 3 months of MTX treatment.

METHOD: A longitudinal observational study was conducted in the Sint Maartenskliniek, The Netherlands, between March and August 2023. Adult RA patients were interviewed at 2, 6 and 12 weeks after MTX initiation using the United Kingdom's New Medicines Service interview guide. DRPs were categorized using a classification system for patient-reported DRPs, and analyzed descriptively.

RESULTS: All fifty participants (median age 62 years (IQR 51-68), 66% female) reported a DRP, with a median of 6 (IQR 3-8) DRPs per patient and a total of 301 DRPs. The top 5 most frequently reported DRPs were concerns about (long-term) side-effects, nausea, fatigue, remembering intake and information needs regarding dose instructions. Of the DRPs reported at weeks 2 and 6, 33% were unresolved at week 12.

CONCLUSION: Patients with RA experience numerous DRPs in the first 3 months of MTX use. Resolving DRPs soon after occurrence may reduce the burden of drug treatment and improve adherence and/or persistence.

PMID:40178797 | DOI:10.1007/s11096-025-01904-4

Front Oncol. 2025 Mar 19;15:1450602. doi: 10.3389/fonc.2025.1450602. eCollection 2025.

ABSTRACT

AIM: Statins have been shown to improve the possibility of a pathological complete response (pCR) in patients with locally advanced rectal cancer when given in combination with neo-adjuvant chemo-radiation (NACTRT) in observational studies. The primary objective of this phase II randomized controlled trial (RCT) is to determine the impact of rosuvastatin in improving pCR rates in patients with locally advanced rectal cancer who are undergoing NACTRT. The secondary objectives are to compare adverse events, postoperative morbidity and mortality, disease-free survival (DFS), and overall survival in the two arms and to identify potential prognostic and predictive factors determining outcomes. If the study is positive, we plan to proceed to a phase III RCT with 3-year DFS as the primary endpoint.

METHODS: This is a prospective, randomized, open-label phase II/III study. The phase II study has a sample size of 316 patients (158 in each arm) to be accrued over 3 years to have 288 evaluable patients. The standard arm will receive NACTRT while the intervention group will receive 20 mg rosuvastatin orally once daily along with NACTRT for 6 weeks followed by rosuvastatin alone for 6-10 weeks until surgery. All patients will be reviewed after repeat imaging by a multidisciplinary tumor board at 12-16 weeks after starting NACTRT and operable patients will be planned for surgery. The pathological response rate, tumor regression grade (TRG), and post-surgical complications will be recorded.

CONCLUSION: The addition of rosuvastatin to NACTRT may improve the oncological outcomes by increasing the likelihood of pCR in patients with locally advanced rectal cancer undergoing NACTRT. This would be a low-cost, low-risk intervention that could potentially lead to the refinement of strategies, such as "watch and wait", in a select subgroup of patients.

CLINICAL TRIAL REGISTRATION: Clinical Trials Registry of India, identifier CTRI/2018/11/016459.

PMID:40177244 | PMC:PMC11961435 | DOI:10.3389/fonc.2025.1450602

Front Pharmacol. 2025 Mar 19;16:1565890. doi: 10.3389/fphar.2025.1565890. eCollection 2025.

NO ABSTRACT

PMID:40176905 | PMC:PMC11961997 | DOI:10.3389/fphar.2025.1565890

Brain Commun. 2025 Mar 17;7(2):fcaf113. doi: 10.1093/braincomms/fcaf113. eCollection 2025.

ABSTRACT

Somatic mosaic variants contribute to focal epilepsy, with variants often present only in brain tissue and not in blood or other samples typically assayed for genetic testing. Thus, genetic analysis for mosaic variants in focal epilepsy has been limited to patients with drug-resistant epilepsy who undergo surgical resection and have resected brain tissue samples available. Stereo-EEG (sEEG) has become part of the evaluation for many patients with focal drug-resistant epilepsy, and sEEG electrodes provide a potential source of small amounts of brain-derived DNA. We aimed to identify, validate, and assess the distribution of deleterious mosaic variants in epilepsy-associated genes in DNA extracted from trace brain tissue on individual sEEG electrodes. We enrolled a prospective cohort of 10 paediatric patients with drug-resistant epilepsy who had sEEG electrodes implanted for invasive monitoring. We extracted unamplified DNA and in parallel performed whole-genome amplification from trace brain tissue on each sEEG electrode. We also extracted DNA from resected brain tissue and blood/saliva samples where available. We performed deep sequencing (panel and exome) and analysis for candidate germline and mosaic variants. We validated candidate mosaic variants and assessed the variant allele fraction in amplified and unamplified electrode-derived DNA and across electrodes. We extracted unamplified DNA and performed whole-genome amplification from >150 individual electrodes from 10 individuals. Immunohistochemistry confirmed the presence of neurons in the brain tissue on electrodes. Deep sequencing and analysis demonstrated similar depth of coverage between amplified and unamplified DNA samples but significantly more potential mosaic variants in amplified samples. We validated four deleterious mosaic variants in epilepsy-associated genes in electrode-derived DNA in three patients who underwent laser ablation and did not have resected brain tissue samples available. Three of the four variants were detected in both amplified and unamplified electrode-derived DNA, with higher variant allele fraction observed in DNA from electrodes in closest proximity to the electrical seizure focus in one case. We demonstrate that mosaic variants can be identified and validated from DNA extracted from trace brain tissue on individual sEEG electrodes in patients with drug-resistant focal epilepsy, from both unamplified and amplified electrode-derived DNA. Our findings support a relationship between the extent of regional genetic abnormality and electrophysiology and suggest that with further optimization, this minimally invasive diagnostic approach holds promise for advancing precision medicine for patients with drug-resistant epilepsy as part of the surgical evaluation.

PMID:40177531 | PMC:PMC11961356 | DOI:10.1093/braincomms/fcaf113

Acta Pharm Sin B. 2025 Feb;15(2):963-972. doi: 10.1016/j.apsb.2024.11.002. Epub 2024 Nov 7.

ABSTRACT

One of the leading therapies for acute lymphoblastic leukemia (ALL) is the chemotherapeutic agent PEGylated E. coli-derived-l-asparaginase (PEG-ASNase). Due to the high risk of dose-limiting liver injury, characterized by clinically elevated levels of hepatic transaminases, PEG-ASNase therapy is generally avoided in adult patients. Our preclinical investigations have indicated that PEG-ASNase-induced liver injury is associated with the release of free fatty acids (FFAs) from white adipose tissue (WAT), suggesting potential lipotoxic effects. However, it remains uncertain whether PEG-ASNase directly induces hepatotoxicity or sensitizes hepatocytes to FFA-induced toxicity. Our results show that PEG-ASNase treatment results in hepatocyte apoptosis and lipid peroxidation. Ex vivo and in vitro studies in mouse and human WAT suggest that PEG-ASNase induces the expression of adipose triglyceride lipase (ATGL), activates the lipase, and stimulates adipose tissue lipolysis, suggesting that the FFAs from WAT may contribute to the observed liver injury. Moreover, treatment with PEG-ASNase sensitizes hepatocytes to FFA-induced lipotoxicity. Mechanistically, our RNA-sequencing (RNA-seq) analyses reveal that PEG-ASNase-induced sensitization to lipotoxicity is accompanied by the induction of Cyp2e1. We demonstrated that this sensitization effect is attenuated by both pharmacological and genetic inhibition of Cyp2e1. Our findings suggest that PEG-ASNase therapy induces WAT lipolysis and sensitizes hepatocytes to hepatic lipotoxicity in a Cyp2e1-dependent manner.

PMID:40177540 | PMC:PMC11959929 | DOI:10.1016/j.apsb.2024.11.002

Front Pharmacol. 2025 Mar 19;16:1538432. doi: 10.3389/fphar.2025.1538432. eCollection 2025.

ABSTRACT

BACKGROUND: Tacrolimus, an approved first-line calcineurin inhibitor, is widely prescribed in organ transplantation to prevent allograft rejection. Its narrow therapeutic index requires precise management to achieve optimal dosing and to minimize adverse drug events (ADEs) while ensuring its therapeutic efficacy. Among several factors, genetic differences contribute significantly to the inter-individual and inter-ethnic variability in pharmacokinetics (PK) of tacrolimus in kidney transplant recipients. As a result, investigating the role of genetic variation in Greek transplant recipients becomes crucial to optimizing therapeutic strategies and enhancing the efficacy of immunosuppressive treatment.

HYPOTHESIS: Genetic variants which are known to influence the activity of enzymes or drug-transporters critical to tacrolimus pharmacokinetics, may significantly affect the required kidney post-transplant tacrolimus daily dose.

AIM: To assess the correlation of ABCB1 genetic variants (rs1128503, rs2229109) and CYP3A4 (rs2242480, rs4986910) with tacrolimus dose-adjusted trough concentration (C0/D), in Greek kidney transplant recipients.

METHODS: Ninety-four unrelated Greek kidney transplant recipients were included in this study from the Department of Nephrology and Kidney Transplantation of the University General Hospital of Patras. Patients' dose-adjusted trough levels were measured at five distinct time points after transplantation and analyzed in relation to the possible influence of CYP3A4 and correlated with the abovementioned ABCB1 genetic variants using standard genotyping analysis and Sanger sequencing.

RESULTS: The genetic variants rs1128503, rs2229109, rs2242480, rs4986910 did not show any significant association with the daily dosing requirements of tacrolimus for at least 1 year, in Greek patients who have undergone kidney transplant.

CONCLUSION: It remains uncertain whether these genetic variants influence the assessment of the appropriate tacrolimus dosing 1 year after transplantation in Greek kidney transplant recipients.

PMID:40176889 | PMC:PMC11962430 | DOI:10.3389/fphar.2025.1538432

Curr Rev Clin Exp Pharmacol. 2025 Mar 28. doi: 10.2174/0127724328356429250315163111. Online ahead of print.

ABSTRACT

INTRODUCTION: Statin-induced myopathy (SIM) is a prevalent adverse event impacting treatment adherence. Despite extensive exploration of genotypes, conflicting evidence obscures their role in SIM incidence, prompting this network meta-analysis.

METHODS: Observational studies meeting eligibility criteria (patients on any statin with reported SNPs and SIM details) were systematically reviewed. Severe SIM was defined as creatine kinase elevations exceeding 10 times the upper limit of normal. Mixed treatment comparison pooled estimates were generated from direct and indirect pooled estimates, represented by odds ratios (OR) with 95% confidence intervals (CI), and validated via bootstrap analysis.

RESULTS: Thirty-four studies (26,152 participants) examining genotypes spanning drug transporters, metabolizing enzymes, reactive oxygen species production, and myopathy-related genes were analyzed. Significant associations were observed with drug transporters (OR: 1.4; 95% CI: 1.04, 1.5). Notably, solute carrier organic anion transporter 1B1 (SLCO1B1) (rs4149056) exhibited a moderate association with SIM (OR: 2.1; 95% CI: 1.7, 2.6), validated by bootstrap analysis (OR: 2.1; 95% CI: 1.7, 2.8). Similar associations were found for severe SIM with SLCO1B1 (rs4149056) (OR: 3.8; 95% CI: 1.4, 10.4) and ATP Binding Cassette Subfamily B Member 1 (ABCB1) (rs2373588) (OR: 2.8; 95% CI: 1.4, 5.4). Intraclass differences in genetic predictor risks were noted among statins.

CONCLUSION: Our meta-analysis underscores the significant association of SLCO1B1 with SIM, supporting its clinical utility. Further research is warranted to clarify additional genetic predictors. These findings endorse current guidelines advocating for SLCO1B1 genotyping in statin therapy decisions.

PMID:40176697 | DOI:10.2174/0127724328356429250315163111