Comput Biol Med. 2025 May 28;193:110464. doi: 10.1016/j.compbiomed.2025.110464. Online ahead of print.

ABSTRACT

OBJECTIVE: Developing search strategies for synthesizing evidence on drug harms requires specialized expertise and knowledge. The aim of this study was to evaluate ChatGPT's ability to enhance search strategies for systematic reviews of drug harms by identifying missing and generating omitted keywords.

MATERIALS AND METHODS: A literature search in PubMed identified systematic reviews of drug harms from 10 high-impact journals between 1-Nov-2013 to 27-Nov-2023. Sixteen search strategies used in these reviews were selected each with a single error of omission introduced. ChatGPT's (GPT-4) performance was evaluated based on error detection, similarity between the extracted and generated search strategies via strict and semantic keyword matching, and proportion of omitted keywords generated.

RESULTS: ChatGPT identified the introduced errors in all search strategies. Under strict matching, the mean Jaccard's similarity measure was 0.17 (range: 0.00-0.52) and with semantic matching this increased to 0.23 (range: 0.00-0.53). Similarly, the mean proportion of keywords recreated by ChatGPT was 49 % using strict matching increasing to 71 % with semantic matching.

DISCUSSION AND CONCLUSION: ChatGPT effectively detected errors and generated relevant keywords, showing potential as a tool for evidence retrieval on drug harms.

PMID:40441054 | DOI:10.1016/j.compbiomed.2025.110464

Pediatr Infect Dis J. 2025 May 23. doi: 10.1097/INF.0000000000004859. Online ahead of print.

ABSTRACT

BACKGROUND: Dispersible and immediate-release fixed-dose combinations (FDC) of abacavir, dolutegravir, and lamivudine are priority first-line antiretroviral therapy (ART) in children with HIV-1 (CWHIV). We report safety, efficacy and tolerability of these regimens through 48 weeks of treatment.

METHODS: IMPAACT 2019 was a phase I/II, international, multisite, open-label, noncomparative study of dispersible and immediate-release FDC abacavir/dolutegravir/lamivudine (ABC/DTG/3TC) in participants with HIV-1 <12 years of age weighing 6 to <40 kg. At entry, participants were ART-naive or ART-experienced and virally suppressed on stable ART for ≥6 months. Participants received weight-banded dosing and enrolled across 5 weight bands in parallel. Follow-up visits were completed at weeks 1, 4, 12, 24, 36 and 48.

RESULTS: Fifty-seven participants were enrolled; 2 participants withdrew due to poor drug tolerability. Fifty-four of 55 participants on the study at week 48 remained on the study drug. All 54 participants who remained on study drug through week 48 had viral loads of <200 copies/mL. CD4-lymphocyte counts remained stable with age over 48 weeks. Mean change (95% confidence interval) in body mass index Z-scores was 0.4 (0.2-0.6). Nine study drug-related adverse events were reported. One drug-induced liver injury attributed to abacavir and dolutegravir led to the permanent discontinuation of the study drug.

CONCLUSIONS: Dispersible FDC ABC/DTG/3TC is the first dispersible dolutegravir-containing single tablet regimen for CWHIV. Dispersible- and immediate-release ABC/DTG/3TC was observed to be generally safe, effective and well-tolerated in CWHIV through 48 weeks.

PMID:40440679 | DOI:10.1097/INF.0000000000004859

Mol Biol Rep. 2025 May 29;52(1):513. doi: 10.1007/s11033-025-10625-w.

ABSTRACT

Several hereditary and environmental variables contribute to an individual's susceptibility to developing asthma. The pathophysiologic underpinnings of asthma are becoming better understood by ongoing genetic investigations. Most risk factors are differences in one or two base pairs or single-nucleotide polymorphisms (SNPs). Moreover, pharmacogenetics is a significant area of study in asthma genetics; this branch of the field examines the interplay between genes and environmental factors, with pharmacologic medication exposure serving as the environmental factor and phenotypic change as the result of interest. Asthma is an obvious candidate for gene therapy (GT) because of the disease's accessibility and the shortcomings of existing treatments. The functional effect of polymorphisms linked with asthma and their translation into disease-relevant pathways have been obfuscated since almost all of these variations are located in non-coding genomic areas. Repurposing current asthma medications and developing novel therapies may be possible with the help of genomics-guided identification of potential therapeutic targets for the condition. Further research using genomics data and tools to map and identify the relevant gene(s) and phenotype-specific SNPs is needed to understand better the processes involved in asthma's etiology and use pharmacogenomics to develop better medications for tailored treatment plans. This study uses fresh research to investigate the link between heredity and asthma. This research aimed to examine the impact of pharmacogenetic variables and gene therapies on the responsiveness to asthma therapy.

PMID:40439783 | DOI:10.1007/s11033-025-10625-w

Mikrochim Acta. 2025 May 29;192(6):386. doi: 10.1007/s00604-025-07158-1.

ABSTRACT

MnO2 nanozyme-based strategy is for the first time exploited for dual-mode colorimetric and fluorescence determination of antioxidant activity and HPLC - UV - MS/MS profiling of antioxidants. MnO2 nanosheets (MnO2 NS) with outstanding oxidase-like property can trigger rhodamine B (RhB) chromogenic reaction, leading to design an accurate, selective, and sensitive dual-mode colorimetric and fluorescence method for total antioxidant capacity (TAC) determination. Additionally, antioxidants in complex extract can react with MnO2 NS catalytic reactive oxygen species (ROS) intermediates (•O2‒ and 1O2), which deduced the HPLC peaks decreased or disappeared. And screened antioxidants can be identified by MS/MS analysis. As proof of concept, antioxidant levels of four flavonoids (quercetin, rutin, hesperidin, and nobiletin) with different substituent groups and six samples (peels, pulps, and juices for two citrus cultivars ChunJian tangerine and Debao navel orange) were successfully measured. Nineteen flavonoids with ROS scavenging activity from citrus samples have been screened out and characterized. Especially, polymethoxyflavones, nonactive in DPPH•/ABTS•+-based assays, presented certain ROS scavenging activity. Together, this study provided a novel and efficient platform to accurately and selectively measure and screen physiological antioxidants in real complex samples, revealing its great promise and convenient applications in future.

PMID:40439768 | DOI:10.1007/s00604-025-07158-1

J Invest Dermatol. 2025 May 29:S0022-202X(25)00468-3. doi: 10.1016/j.jid.2025.05.001. Online ahead of print.

NO ABSTRACT

PMID:40439658 | DOI:10.1016/j.jid.2025.05.001

Neurosurg Rev. 2025 May 29;48(1):455. doi: 10.1007/s10143-025-03597-9.

ABSTRACT

BACKGROUND: Artificial Intelligence (AI) has emerged as a transformative tool in medicine, particularly addressing neurosurgical challenges such as complex anatomical delineation and intraoperative decision-making. Despite advancements in diagnostic and prognostic algorithms, obstacles including algorithmic bias, data privacy, and model interpretability continue to limit its widespread clinical adoption.

OBJECTIVE: This systematic review aims to evaluate the current applications of AI in neurosurgery, compare the performance of various AI models, and examine the ethical challenges associated with their integration into clinical practice.

METHODS: A systematic literature search was conducted in PubMed, Scopus, and Web of Science databases, following PRISMA guidelines. Studies from 2015 to 2025 focusing on AI applications in diagnostic, prognostic, surgical, and intraoperative neurosurgical contexts were included. Statistical outcomes, model performance metrics, and ethical considerations were analyzed.

RESULTS: Thirteen studies met the inclusion criteria. AI models, particularly ML and DL, demonstrated superior diagnostic accuracy (AUC > 0.90) and improved prognostic predictions by up to 15%. AI-assisted surgical planning enhanced precision and reduced complication rates by 10-20%. However, algorithmic bias, limited transparency, and lack of external validation remain key barriers to clinical adoption.

CONCLUSION: AI improves diagnostic accuracy, prognostic predictions, and surgical precision while reducing complication rates. However, challenges such as bias, limited interpretability, and the need for external validation must be addressed for widespread clinical integration.

PMID:40439939 | DOI:10.1007/s10143-025-03597-9

Jpn J Radiol. 2025 May 29. doi: 10.1007/s11604-025-01806-5. Online ahead of print.

ABSTRACT

PURPOSE: To investigate vessel delineation and image quality of ultra-high-resolution (UHR) CT angiography (CTA) reconstructed using deep learning reconstruction (DLR) optimised for brain CTA (DLR-brain) in moyamoya disease (MMD), compared with DLR optimised for body CT (DLR-body) and hybrid iterative reconstruction (Hybrid-IR).

MATERIALS AND METHODS: This retrospective study included 50 patients with suspected or diagnosed MMD who underwent UHR brain CTA. All images were reconstructed using DLR-brain, DLR-body, and Hybrid-IR. Quantitative analysis focussed on moyamoya perforator vessels in the basal ganglia and periventricular anastomosis. For these small vessels, edge sharpness, peak CT number, vessel contrast, full width at half maximum (FWHM), and image noise were measured and compared. Qualitative analysis was performed by visual assessment to compare vessel delineation and image quality.

RESULTS: DLR-brain significantly improved edge sharpness, peak CT number, vessel contrast, and FWHM, and significantly reduced image noise compared with DLR-body and Hybrid-IR (P < 0.05). DLR-brain significantly outperformed the other algorithms in the visual assessment (P < 0.001).

CONCLUSION: DLR-brain provided superior visualisation of small intracranial vessels compared with DLR-body and Hybrid-IR in UHR brain CTA.

PMID:40439843 | DOI:10.1007/s11604-025-01806-5

Int J Comput Assist Radiol Surg. 2025 May 29. doi: 10.1007/s11548-025-03421-1. Online ahead of print.

ABSTRACT

PURPOSE: Facial recognition of reconstructed computed tomography (CT) scans poses patient privacy risks, necessitating reliable facial de-identification methods. Current methods obscure sinuses, turbinates, and other anatomy relevant for otolaryngology. We present a facial de-identification method that preserves these structures, along with two automated workflows for large-volume datasets.

METHODS: A total of 20 adult head CTs from the New Mexico Decedent Image Database were included. Using 3D Slicer, a seed-growing technique was performed to label the skin around the face. This label was dilated bidirectionally to form a 6-mm mask that obscures facial features. This technique was then automated using: (1) segmentation propagation that deforms an atlas head CT and corresponding mask to match other scans and (2) a deep learning model (nnU-Net). Accuracy of these methods against manually generated masks was evaluated with Dice scores and modified Hausdorff distances (mHDs).

RESULTS: Manual de-identification resulted in facial match rates of 45.0% (zero-fill), 37.5% (deletion), and 32.5% (re-face). Dice scores for automated face masks using segmentation propagation and nnU-Net were 0.667 ± 0.109 and 0.860 ± 0.029, respectively, with mHDs of 4.31 ± 3.04 mm and 1.55 ± 0.71 mm. Match rates after de-identification using segmentation propagation (zero-fill: 42.5%; deletion: 40.0%; re-face: 35.0%) and nnU-Net (zero-fill: 42.5%; deletion: 35.0%; re-face: 30.0%) were comparable to manual masks.

CONCLUSION: We present a simple facial de-identification approach for head CTs, as well as automated methods for large-scale implementation. These techniques show promise for preventing patient identification while preserving underlying sinonasal anatomy, but further studies using live patient photographs are necessary to fully validate its effectiveness.

PMID:40439827 | DOI:10.1007/s11548-025-03421-1

Graefes Arch Clin Exp Ophthalmol. 2025 May 29. doi: 10.1007/s00417-025-06858-x. Online ahead of print.

ABSTRACT

BACKGROUND: Ophthalmic diseases significantly impact vision and quality of life. Early diagnosis using fundus images is critical for timely treatment. Traditional deep learning models often lack accuracy, interpretability, and efficiency for multi-label classification tasks in ophthalmology.

METHODS: We propose HAM-DNet, a hybrid deep learning model combining EfficientNetV2 and Vision Transformers (ViT) for multi-label ophthalmic disease detection. The model includes SE (Squeeze-and-Excitation) blocks for attention-based feature refinement and a U-Net-based lesion localization module for improved interpretability. The model was trained and tested on multiple fundus image datasets (ODIR-5 K, Messidor, G1020, and Joint Shantou International Eye Centre).

RESULTS: HAM-DNet achieved superior performance with an accuracy of 95.3%, precision of 96.2%, recall of 97.1%, AUC of 98.42, and F1-score of 96.75, while maintaining low computational cost (9.7 GFLOPS). It outperformed existing models including Shallow CNN and EfficientNet, particularly in handling multi-label classifications and reducing false positives and negatives.

CONCLUSIONS: HAM-DNet offers a robust, accurate, and interpretable solution for automated detection of multiple ophthalmic diseases. Its lightweight architecture makes it suitable for clinical deployment, especially in telemedicine and resource-constrained environments.

PMID:40439748 | DOI:10.1007/s00417-025-06858-x

Med Phys. 2025 May 29. doi: 10.1002/mp.17912. Online ahead of print.

ABSTRACT

BACKGROUND: Medical image translation has become an essential tool in modern radiotherapy, providing complementary information for target delineation and dose calculation. However, current approaches are constrained by their modality-specific nature, requiring separate model training for each pair of imaging modalities. This limitation hinders the efficient deployment of comprehensive multimodal solutions in clinical practice.

PURPOSE: To develop a unified image translation method using variational autoencoder (VAE) latent space mapping, which enables flexible conversion between different medical imaging modalities to meet clinical demands.

METHODS: We propose a three-stage approach to construct a unified image translation model. Initially, a VAE is trained to learn a shared latent space for various medical images. A stacked bidirectional transformer is subsequently utilized to learn the mapping between different modalities within the latent space under the guidance of the image modality. Finally, the VAE decoder is fine-tuned to improve image quality. Our internal dataset collected paired imaging data from 87 head and neck cases, with each case containing cone beam computed tomography (CBCT), computed tomography (CT), MR T1c, and MR T2W images. The effectiveness of this strategy is quantitatively evaluated on our internal dataset and a public dataset by the mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Additionally, the dosimetry characteristics of the synthetic CT images are evaluated, and subjective quality assessments of the synthetic MR images are conducted to determine their clinical value.

RESULTS: The VAE with the Kullback‒Leibler (KL)-16 image tokenizer demonstrates superior image reconstruction ability, achieving a Fréchet inception distance (FID) of 4.84, a PSNR of 32.80 dB, and an SSIM of 92.33%. In synthetic CT tasks, the model shows greater accuracy in intramodality translations than in cross-modality translations, as evidenced by an MAE of 21.60 ± 8.80 Hounsfield unit (HU) in the CBCT-to-CT task and 45.23 ± 13.21 HU/47.55 ± 13.88 in the MR T1c/T2w-to-CT tasks. For the cross-contrast MR translation tasks, the results are very close, with mean PSNR and SSIM values of 26.33 ± 1.36 dB and 85.21% ± 2.21%, respectively, for the T1c-to-T2w translation and 26.03 ± 1.67 dB and 85.73% ± 2.66%, respectively, for the T2w-to-T1c translation. Dosimetric results indicate that all the gamma pass rates for synthetic CTs are higher than 99% for photon intensity-modulated radiation therapy (IMRT) planning. However, the subjective quality assessment scores for synthetic MR images are lower than those for real MR images.

CONCLUSIONS: The proposed three-stage approach successfully develops a unified image translation model that can effectively handle a wide range of medical image translation tasks. This flexibility and effectiveness make it a valuable tool for clinical applications.

PMID:40439703 | DOI:10.1002/mp.17912

Adv Sci (Weinh). 2025 May 29:e02833. doi: 10.1002/advs.202502833. Online ahead of print.

ABSTRACT

Genetic testing for pathogenic germline variants is critical for the personalized management of high-risk breast cancers, guiding targeted therapies and cascade testing for at-risk families. In this study, MAIGGT (Multimodal Artificial Intelligence Germline Genetic Testing) is proposed, a deep learning framework that integrates histopathological microenvironment features from whole-slide images with clinical phenotypes from electronic health records for precise prescreening of germline BRCA1/2 mutations. Leveraging a multi-scale Transformer-based deep generative architecture, MAIGGT employs a cross-modal latent representation unification mechanism to capture complementary biological insights from multimodal data. MAIGGT is rigorously validated across three independent cohorts and demonstrated robust performance with areas under receiver operating characteristic curves of 0.925 (95% CI 0.868 - 0.982), 0.845 (95% CI 0.779 - 0.911), and 0.833 (0.788 - 0.878), outperforming single-modality models. Mechanistic interpretability analyses revealed that BRCA1/2-mutated associated tumors may exhibit distinct microenvironment patterns, including increased inflammatory cell infiltration, stromal proliferation and necrosis, and nuclear heterogeneity. By bridging digital pathology with clinical phenotypes, MAIGGT establishes a new paradigm for cost-effective, scalable, and biologically interpretable prescreening of hereditary breast cancer, with the potential to significantly improve the accessibility of genetic testing in routine clinical practice.

PMID:40439693 | DOI:10.1002/advs.202502833

Brief Bioinform. 2025 May 1;26(3):bbaf245. doi: 10.1093/bib/bbaf245.

ABSTRACT

DNA-binding proteins (DBPs) play a crucial role in gene regulation, development, and environmental responses across plants, animals, and microorganisms. Existing DBP prediction methods are largely limited to sequence information, whether through handcrafted features or sequence-based protein language models (PLMs), overlooking structural cues critical to protein function. In addition, most existing tools are trained for general DBP predictions, which are often not accurate for plant-specific DBPs due to the unique structural and functional properties of plant proteins. Our work introduces PLM-DBPs, a deep learning framework that integrates both sequence-based and structure-aware representations to enhance DBP prediction in plants. We evaluated several state-of-the-art PLMs to extract high-dimensional protein representations and experimented with various fusion strategies to validate the complementary information between the various representations. Our final model, a fusion of sequence-based and structure-aware ANN models, achieves a notable improvement in predicting DBPs in plants outperforming previous state-of-the-art models. Although sequence-based PLMs already demonstrate strong performance in DBP prediction, our findings show that the integration of structural information further enhances predictive accuracy. This underscores the complementary nature of structural representations and establishes PLM-DBPs as a robust tool for advancing plant research and agricultural innovation. The proposed model and other resources are publicly available at https://github.com/suresh-pokharel/PLM-DBPs.

PMID:40439671 | DOI:10.1093/bib/bbaf245

Brief Bioinform. 2025 May 1;26(3):bbaf243. doi: 10.1093/bib/bbaf243.

ABSTRACT

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) produces vast amounts of individual cell profiling data. Its analysis presents a significant challenge in accurately annotating cell types and their associated biomarkers. Different pipelines based on deep neural network (DNN) methods have been employed to tackle these issues. These pipelines have arisen as a promising resource and can extract meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets. We have developed a cutting-edge platform known as scaLR (Single-cell analysis using low resource) that efficiently processes data into feature subsets, samples in batches to reduce the required memory for processing large datasets, and running DNN models in multiple central processing units. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. Its novel feature extraction algorithm first trains the model on a feature subset and stores the importance of the features for all the features in that subset. At the end of the training of all subsets, the top-K features are selected based on their importance. The final model is trained on top-K features; its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and computational resources than existing Python-based pipelines. We present scaLR, a Python-based platform, engineered to utilize minimal computational resources while maintaining comparable execution times and analysis costs to existing frameworks.

PMID:40439670 | DOI:10.1093/bib/bbaf243

J Acoust Soc Am. 2025 May 1;157(5):3930-3945. doi: 10.1121/10.0036773.

ABSTRACT

Efficient and accurate prediction of underwater acoustic transmission loss (TL) is important for minimizing noise impacts on marine ecosystems and supporting naval operations. Traditional wave-based solvers are computationally expensive, especially for range-dependent bathymetry, rendering them unsuitable for real-time applications. Recent advances in data-driven models, particularly convolutional and recurrent neural networks, provide a more efficient alternative by substantially reducing the dimensionality of the data. However, these deep-learning models struggle with long-range wave forecasts as they often rely on auto-regressive predictions and lack far-field bathymetry information. This research aims to improve the accuracy of deep learning models for forecasting underwater radiated noise in far-field scenarios. We introduce a range-dependent conditional convolutional neural network that predicts TL fields in a single step by conditioning directly on input bathymetry. The model is trained using a replay-based continual learning strategy, which allows generalization across sequential bathymetric changes without retraining. We evaluate our model using multiple test cases and a benchmark scenario that involves predictions over the Dickins Seamount. Our architecture effectively captures transmission loss over range-dependent bathymetry profiles. The proposed framework provides an efficient deep learning model for digital twins of the ocean soundscape, enabling real-time decision-making for underwater radiated noise.

PMID:40439448 | DOI:10.1121/10.0036773

Bioinformatics. 2025 May 29:btaf323. doi: 10.1093/bioinformatics/btaf323. Online ahead of print.

ABSTRACT

MOTIVATION: Nanopores are highly sensitive sensors that have achieved commercial success in DNA/RNA sequencing, with potential applications in protein sequencing and biomarker identification. Solid-state nanopores, in particular, face challenges such as instability and low signal-to-noise ratios (SNRs), which lead scientists to adopt data-driven methods for nanopore signal analysis, although data acquisition remains restrictive.

RESULTS: We address this data scarcity by augmenting the training samples with traces that emulate Brownian motion effects, based on dynamic models in the literature. We apply this method to a publicly available dataset of a classification task containing nanopore reads of DNA with encoded barcodes. A neural network named QuipuNet was previously published for this dataset, and we demonstrate that our augmentation method produces a noticeable increase in QuipuNet's accuracy. Furthermore, we introduce a novel neural network named YupanaNet, which achieves greater accuracy (95.8%) than QuipuNet (94.6%) on the same dataset. YupanaNet benefits from both the enhanced generalization provided by Brownian motion data augmentation and the incorporation of novel architectures, including skip connections and a soft attention mask.

AVAILABILITY AND IMPLEMENTATION: The source code and data are available at: https://github.com/JavierKipen/browDataAug.

SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

PMID:40439147 | DOI:10.1093/bioinformatics/btaf323

Gigascience. 2025 Jan 6;14:giaf052. doi: 10.1093/gigascience/giaf052.

ABSTRACT

Unsupervised clustering is a powerful machine-learning technique widely used to analyze high-dimensional biological data. It plays a crucial role in uncovering patterns, structures, and inherent relationships within complex datasets without relying on predefined labels. In the context of biology, high-dimensional data may include transcriptomics, proteomics, and a variety of single-cell omics data. Most existing clustering algorithms operate directly in the high-dimensional space, and their performance may be negatively affected by the phenomenon known as the curse of dimensionality. Here, we show an alternative clustering approach that alleviates the curse by sequentially projecting high-dimensional data into a low-dimensional representation. We validated the effectiveness of our approach, named automated projection pursuit (APP), across various biological data modalities, including flow and mass cytometry data, scRNA-seq, multiplex imaging data, and T-cell receptor repertoire data. APP efficiently recapitulated experimentally validated cell-type definitions and revealed new biologically meaningful patterns.

PMID:40440093 | DOI:10.1093/gigascience/giaf052

Mol Biol Rep. 2025 May 29;52(1):514. doi: 10.1007/s11033-025-10607-y.

ABSTRACT

BACKGROUND: Gallbladder cancer (GBC) is a rare and aggressive cancer of the biliary tract with a very low survival rate. The availability of diagnostic biomarkers and targeted therapies for its management is limited. The study identifies potential genetic biomarkers of GBC by analyzing differentially expressed genes (DEGs) through microarray profiling and constructing regulatory networks using systems biology techniques.

METHODS: We used Clariom™ D Array in gallbladder cancer, cholelithiasis, and normal tissues (10 cases in each group), identifying DEGs and key biological pathways. Functional analysis via Metascape, DisGeNET, and KEGG-SIGNOR network mapping revealed gene-disease relationships and protein interactions.

RESULTS: There were 3,898 significant DEGs (|Fold Change| > 2.0, p < 0.05) identified in GBC compared to normal gallbladder tissue, with 2,575 genes upregulated and 1,323 downregulated. On comparison with cholelithiasis, 2523 DEGs (|Fold Change|>2.0, p < 0.05) were upregulated and 1451 downregulated. The functional analyses have shown that these DEGs were mainly involved in anatomical structure maturation and cell-cycle regulation. Top ten identified hub genes were XAB2, XPA, RPA1, RAD51B, RPS27A, BRCA2, ATR, PDS5B, CCNB2 and RANBP2. The top 3 related pathways were mismatch repair pathway, nucleotide excision repair and homologous recombination.

CONCLUSION: A significantly high differential gene expression was identified in gallbladder cancer compared to control groups. For the first time, we identified key genes-XAB2, XPA, RPA1, RAD51B, RPS27A, BRCA2, ATR, PDS5B, CCNB2, and RANBP2-as crucial players in homologous recombination, mismatch repair, DNA damage repair, and DNA replication processes that contribute to gallbladder carcinogenesis.

PMID:40439781 | DOI:10.1007/s11033-025-10607-y

Philos Trans R Soc Lond B Biol Sci. 2025 May 29;380(1927):20240234. doi: 10.1098/rstb.2024.0234. Epub 2025 May 29.

ABSTRACT

Rising temperatures and heat waves pose a substantial threat to crop productivity by disrupting essential physiological and reproductive processes. While plants have a genetically inherited capacity to acclimate to high temperatures, the thermotolerance capacity of many crops remains limited. This limitation leads to yield losses, which are further intensified by the increasing intensity of climate change. In this review, we explore how thermopriming enhances plant resilience by preparing plants for future heat stress (HS) events and summarize the mechanisms underlying the memory of HS (thermomemory) in different plant tissues and organs. We also discuss recent advances in priming agents, including chemical, microbial and physiological interventions, and their application strategies to extend thermotolerance beyond inherent genetic capacity. Additionally, this review examines how integrating priming strategies with genetic improvements, such as breeding and genome editing for thermotolerance traits, provides a holistic solution to mitigate the impact of climate change on agriculture. By combining these approaches, we propose a framework for developing climate-resilient crops and ensuring global food security in the face of escalating environmental challenges.This article is part of the theme issue 'Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the 'Resilience Revolution'?'.

PMID:40439313 | DOI:10.1098/rstb.2024.0234

Philos Trans R Soc Lond B Biol Sci. 2025 May 29;380(1927):20240241. doi: 10.1098/rstb.2024.0241. Epub 2025 May 29.

ABSTRACT

An increase in the frequency and intensity of heat waves, floods, droughts and other environmental stresses, resulting from climate change, is threatening agricultural food production worldwide. Heat waves are especially problematic to grain yields, as the reproductive processes of almost all our main grain crops are highly sensitive to heat. At times, heat waves can occur together with drought, high ozone levels, pathogen infection and/or waterlogging stress that suppress the overall process of plant cooling by transpiration. We recently reported that under conditions of heat and water-deficit stress combination, the stomata on sepals and pods of soybean (Glycine max) remain open, while the stomata on leaves close. This process, termed 'differential transpiration', enabled the cooling of reproductive organs, while leaf temperature increased owing to suppressed transpiration. In this review article, we focus on the impacts on crops of heat waves occurring in isolation and of heat waves combined with drought or waterlogging stress, address the main processes impacted in plants by these stresses and discuss ways to mitigate the negative effects of isolated heat waves and of heat waves that occur together with other stresses (i.e. stress combination), on crops, with a focus on the process of differential transpiration.This article is part of the theme issue 'Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the 'Resilience Revolution'?'.

PMID:40439306 | DOI:10.1098/rstb.2024.0241

J Exp Bot. 2025 May 29:eraf236. doi: 10.1093/jxb/eraf236. Online ahead of print.

ABSTRACT

Already in the 19th Century it was proposed that ecophysiology could be best studied in regions with extreme climatic conditions. In the present perspective, we argue that perhaps this is timelier than ever. The main reason is the need to improve crops to be simultaneously more productive - due to increased population - and more stress-tolerant - due to climate change. Climate change induces plants to face not just the harsh but also the 'unexpected' (unpredictable) climatic conditions. In this sense, we hypothesize that 'sherplants', i.e. plants living in the extremes of plant life (e.g. hot deserts, Arctic and Antarctica, or high elevations) can provide cues on how to break the trade-off between productivity and stress tolerance, as they need to produce fast due to the very short growing period while being stress tolerant due to the harsh and unpredictable climate endured during most of the year. We present glimpses of results from three consecutive projects developed for the last 10 years, in which hundreds of species from different regions of the world have been studied. In particular, we propose a path for developing 'shercrops' learning from 'sherplants', debate whether some of the already studied species may have really broken the aforementioned trade-off, and present a number of interesting 'side' findings achieved when studying plants from extreme climates.

PMID:40439080 | DOI:10.1093/jxb/eraf236