Ann Am Thorac Soc. 2025 Aug 7. doi: 10.1513/AnnalsATS.202501-007RL. Online ahead of print.

NO ABSTRACT

PMID:40772887 | DOI:10.1513/AnnalsATS.202501-007RL

mSphere. 2025 Aug 7:e0082024. doi: 10.1128/msphere.00820-24. Online ahead of print.

ABSTRACT

The intestinal epithelium serves as a critical interface between the external environment and internal tissues, coordinating nutrient absorption, immune defense, and barrier integrity. Discerning the processes that maintain gut homeostasis has been challenging due to the complexity of the intestinal microenvironment and the difficulty in accessing human tissue. The advent of human intestinal organoid technology has transformed the field by providing relevant in vitro models that recapitulate the cellular diversity and function of the gut epithelium. A recent advance involves the integration of immune cells into organoid cultures, enabling the study of epithelial-immune cell interactions in both health and disease. Furthermore, the application of cutting-edge multi-omics approaches, including transcriptomics, proteomics, and metabolomics, has enabled a deeper understanding of intestinal cell signaling, niche factors, and host-microbe dynamics. These innovations have led to breakthroughs in translational research, particularly in the field of precision medicine. This minireview highlights how intestinal organoids derived from human tissue stem cells, coupled with high-resolution omics technologies, are advancing our knowledge of intestinal physiology, host responses, and disease mechanisms. It also describes the emergence of patient-derived organoids as tools to guide personalized therapeutic strategies for conditions such as inflammatory bowel disease and cystic fibrosis. As organoid models continue to evolve, the integration of additional tissue components-such as diverse immune cell lineages, stromal elements, vasculature, neural cells, and microbiota-will more accurately replicate the intricate nature of human physiology and broaden their translational potential.

PMID:40772719 | DOI:10.1128/msphere.00820-24

Microbiol Spectr. 2025 Aug 7:e0068225. doi: 10.1128/spectrum.00682-25. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa causes acute and chronic infections, such as those that occur in the lungs of people with cystic fibrosis (CF). In infection environments, oxygen (O2) concentrations are often low. The transcription factor Anr (anaerobic regulation of arginine deiminase and nitrate reduction) responds to low O2 by upregulating genes necessary for P. aeruginosa fitness in microoxic and anoxic conditions. Anr regulates Dnr (dissimilative nitrate respiration regulator), a gene encoding a transcriptional regulator that promotes the expression of genes required for using nitrate as an alternative electron acceptor during denitrification. In CF sputum, transcripts involved in denitrification are highly expressed. While Dnr is necessary for the anoxic growth of P. aeruginosa in CF sputum and artificial sputum media (ASMi), the contribution of denitrification to P. aeruginosa fitness in oxic conditions has not been well described. Here, we show that P. aeruginosa requires dnr for fitness in ASMi, and the requirement for dnr is abolished when nitrate is excluded from the media. Additionally, we show that P. aeruginosa consumes nitrate in lysogeny broth (LB) under microoxic conditions. Furthermore, strains without a functioning quorum sensing regulator LasR, which leads to elevated Anr activity, consume nitrate in LB even in normoxia. There was no growth advantage for P. aeruginosa when nitrate was present at concentrations from 100 to 1,600 µM. However, P. aeruginosa consumption of nitrate in oxic conditions created a requirement for Dnr and Dnr-regulated NorCB, likely due to the need to detoxify nitric oxide. These studies suggest that Anr- and Dnr-regulated processes may impact P. aeruginosa physiology in many common culture conditions.IMPORTANCEPseudomonas aeruginosa is an opportunistic pathogen commonly isolated from low-oxygen environments such as the lungs of people with cystic fibrosis. While the importance of P. aeruginosa energy generation by denitrification is clear in anoxic environments, the effects of denitrification in oxic cultures are not well understood. Here, we show that nitrate is consumed in microoxic environments and, in some strains, in normoxic environments. While nitrate does not appear to stimulate microoxic growth rate or yield, it does impact physiology. We show that the regulators Anr (anaerobic regulation of arginine deiminase and nitrate reduction) and Dnr (dissimilative nitrate respiration regulator), which are best known for their roles in anoxic conditions, contribute to P. aeruginosa fitness in common laboratory media in the presence of oxygen.

PMID:40772714 | DOI:10.1128/spectrum.00682-25

Discov Oncol. 2025 Aug 7;16(1):1492. doi: 10.1007/s12672-025-03303-7.

ABSTRACT

INTRODUCTION: Glioblastoma (GBM) is the most malignant primary brain cancer, associated with a median overall survival of 15 months. Traditional diagnosis and prognosis heavily rely on clinical examination and histological investigation, both of which are subjective and time-consuming. advances in machine learning (ML) and deep learning (DL) have largely accelerated the research of GBMs by enhancing tumour segmentation, molecular characterization and survival prediction.

METHODOLOGY: We refer to the PRISMA guidelines to report this systematic review and meta-analysis. A total of 44 studies published from 2021 to 2025 were analyzed. We thoroughly searched the following sources: PubMed, Scopus and Web of Science. Review-specific inclusion criteria included studies reporting on diagnostic, prognostic, or response-prediction tasks in GBM that used ML/DL models and reports on quantitative performance metrics. The independent random-effects model estimated the performance of each clinical task, and subgroup analysis determined the variables influencing model accuracy.

RESULTS: The performance of the machine and deep learning models was strong across different clinical tasks. For overall survival prognosis, the pooled C-index was 0.78 (95%CI 0.74-0.82, I2 = 68.5%). The tumor segmentation models had a high average Dice Similarity Coefficient value (0.91, 95% CI 0.87-0.94, I2 = 45.2%). Molecular tests were highly accurate for the prediction of IDH1 mutation (pooled accuracy = 90.5%, 95% CI 88.1% to 92.8%) and MGMT methylation status (pooled accuracy = 97.8%, 95% CI 96.4% to 99.1%). Transformer models excelled over CNN in segmentation, and radionics-based ML could improve non-invasive molecular assessment.

CONCLUSION: Although AI techniques have demonstrated encouraging results in GBM studies for various clinical tasks, substantial challenges still preclude efficient clinical applicability. These developments can potentially improve medical practice with improved diagnosis, personalized treatment and fewer invasive procedures. Nevertheless, variation in data, weak external validation, and missing prospective clinical studies warrant careful interpretation of these results.

PMID:40773129 | DOI:10.1007/s12672-025-03303-7

Curr Med Sci. 2025 Aug 7. doi: 10.1007/s11596-025-00103-6. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution. Currently, deep learning techniques, such as convolutional neural networks, enable intelligent information collection in fields such as tongue and pulse diagnosis owing to their robust feature-processing capabilities. Natural language processing models, including long short-term memory and transformers, have been applied to traditional Chinese medicine (TCM) for diagnosis, syndrome differentiation, and prescription generation. Traditional machine learning algorithms, such as neural networks, support vector machines, and random forests, are also widely used in TCM diagnosis and treatment because of their strong regression and classification performance on small structured datasets. Future research on AI in TCM diagnosis and treatment may emphasize building large-scale, high-quality TCM datasets with unified criteria based on syndrome elements; identifying algorithms suited to TCM theoretical data distributions; and leveraging AI multimodal fusion and ensemble learning techniques for diverse raw features, such as images, text, and manually processed structured data, to increase the clinical efficacy of TCM diagnosis and treatment.

PMID:40773005 | DOI:10.1007/s11596-025-00103-6

Radiol Med. 2025 Aug 7. doi: 10.1007/s11547-025-02051-6. Online ahead of print.

ABSTRACT

Fractures and their complications are recognized as major public health problems. Especially for occult fractures that are difficult to judge radiologically, timely and accurate diagnosis is particularly important for the treatment and prognosis of patients. In recent years, the successful application of radiomics and deep learning in medical diagnosis has shown great potential for providing more timely and accurate diagnostic methods for occult fractures. This review provides an introduction to radiomics and deep learning, summarizes their respective characteristics in detecting occult fractures, and subsequently conducts a detailed analysis on the potential value and future prospects of integrating these two techniques to develop an enhanced approach for prompt and precise detection of occult fractures.

PMID:40772999 | DOI:10.1007/s11547-025-02051-6

Neuroinformatics. 2025 Aug 7;23(3):41. doi: 10.1007/s12021-025-09743-4.

ABSTRACT

The amygdala plays a central role in emotion, memory, and decision-making and comprises approximately 13 distinct nuclei with connectivity. Despite its functional importance, high-resolution subnuclear mapping is challenging. This study aimed to construct a 3D model of the anatomical location of the amygdala in the brain and a functional dynamic model of the amygdala, integrating deep learning and elastic shape metrics. We used multimodal datasets from the Julich-Brain Atlas, BigBrain Project, and FreeSurfer, which were aligned with the Montreal Neurological Institute (MNI) and Colin 27 spaces. Subnuclei segmentation was performed using a Bayesian Fully Convolutional Network (FCN), and geometric morphometrics were analyzed using elastic shape analysis on the unit sphere. Functional dynamics were simulated using a MATLAB-based model of the amygdala incorporating theta (4-8 Hz) and gamma (30-40 Hz) oscillations with spike-timing-dependent plasticity (STDP). The mean MNI coordinates of the left and right amygdalae were (-20, -4, -15) and (22, -2, -15), respectively, with an inter-amygdalar distance of 42.48 mm. The Dice Similarity Coefficients (DSCs) for FCN-based subnuclear segmentation were as follows: basolateral amygdala (BLA) nucleus = 0.89 ± 0.03, centromedial nucleus = 0.83 ± 0.04, and cortical nucleus = 0.81 ± 0.05. Principal component analysis of elastic shape metrics revealed post-traumatic stress disorder (PTSD)-related morphological deviations, with the first principal component (PC1) accounting for 38% of the variance (p < 0.01). Oscillatory simulations captured the BLA rhythm dynamics and STDP-induced synaptic changes. This study presents a comprehensive 3D model of the human amygdala that bridges anatomical accuracy with computational modeling. Unlike prior models that focus solely on structural or functional domains, our approach integrates subnuclear segmentation, morphometrics, and real-time functional simulation. This study introduces a fully integrated anatomical-functional 3D model of the human amygdala, providing a translational platform for neuromodulation targeting, psychiatric diagnostics, and computational neuroengineering applications.

PMID:40772991 | DOI:10.1007/s12021-025-09743-4

Rev Sci Instrum. 2025 Aug 1;96(8):084701. doi: 10.1063/5.0279493.

ABSTRACT

The accurate detection and evaluation of metal material defects is of great significance to the current production and life. When the metal material is damaged, its internal magnetic permeability will change locally. The electromagnetic tomography (EMT) technique can be used to reconstruct the combined permeability and conductivity distribution of metal materials. However, the ill-posed and ill-conditioned nature of the EMT inverse problem, coupled with the high permeability and conductivity of ferromagnetic materials, poses significant challenges for defect detection. To address this, we propose an improved deep learning model, P-LeNet, based on a convolutional neural network for EMT defect detection and image reconstruction. By establishing a nonlinear mapping between induced voltage measurements and the combined permeability and conductivity distribution, the model extracts multi-scale features to enhance reconstruction accuracy and robustness. The correlation coefficient and image error are used as indicators to evaluate the quality of image reconstruction. In order to visually demonstrate the imaging effect of the proposed model, numerical simulations are performed. The imaging results show that the proposed P-LeNet model is superior to traditional algorithms in imaging accuracy, artifact suppression, and overall performance. At the same time, Gaussian white noise is introduced to evaluate the anti-noise ability of the model, and the random sample is used to test the generalization ability of the model to fully demonstrate the superiority and application potential of the method. Furthermore, experiments with a nine-coil planar EMT sensor are conducted to verify the effectiveness and superiority of the proposed model.

PMID:40772849 | DOI:10.1063/5.0279493

Expert Rev Proteomics. 2025 Aug 7. doi: 10.1080/14789450.2025.2545828. Online ahead of print.

ABSTRACT

INTRODUCTION: Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations.

AREAS COVERED: In this perspective, we critically examine the application of machine learning for biomarker discovery in clinical proteomics, emphasizing that algorithmic novelty alone cannot compensate for issues such as small sample sizes, batch effects, overfitting, data leakage, and poor model generalization.

EXPERT OPINION: We caution against the uncritical application of complex models, such as deep learning architectures, that often exacerbate these problems, offering limited interpretability and negligible performance gains in typical clinical proteomics datasets. Instead, we advocate for the realistic and responsible use of machine learning, grounded in rigorous study design, appropriate validation strategies, and transparent, reproducible modeling practices. Emphasizing simplicity, interpretability, and domain awareness over hype-driven complexity is essential if machine learning is to fulfill its translational potential in the clinic.

PMID:40772544 | DOI:10.1080/14789450.2025.2545828

Ann Med. 2025 Dec;57(1):2540596. doi: 10.1080/07853890.2025.2540596. Epub 2025 Aug 7.

ABSTRACT

BACKGROUND: The incidence of adrenal incidentalomas (AIs) is increasing yearly. The early discovery of AIs is helpful to better manage adrenal diseases, especially subclinical primary aldosteronism, Cushing's syndrome and pheochromocytoma.

METHODS: In this multicenter retrospective study, a total of 778 patients from three different medical centers were assessed. The two-stage cascade network consisted of a 3D Res-Unet network for adrenal gland segmentation and a classifier for determining the presence of AIs. The segmentation network was mainly evaluated by the Dice similarity coefficient (DSC), and the classifier was evaluated by the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity. The Delong test was used to compare the classification performance between the cascade network and manual segmentation.

RESULTS: A total of 443 patients were randomly assigned in a 7:3 ratio, stratified sampling, to train and valid sets of the model development cohort, and 335 patients from the three centers were included in the test cohort. In the validation set, the AUC of the model for identifying left AI was 88.15%, and the AUC of the model for identifying right AI was 87.90%. There was no significant difference between model performance and manual segmentation of AIs (p > 0.05). In the test cohort, the cascade network achieved AUC of more than 80% and accuracy of more than 75% for both left and right adrenal glands.

CONCLUSIONS: The two-stage cascade network based on a deep learning algorithm can be used for automatic recognition of AIs in nonenhanced CT from different centers.

PMID:40772430 | DOI:10.1080/07853890.2025.2540596

Proc Natl Acad Sci U S A. 2025 Aug 12;122(32):e2504578122. doi: 10.1073/pnas.2504578122. Epub 2025 Aug 7.

ABSTRACT

DNA-points accumulation for imaging in nanoscale topography (DNA-PAINT) enables nanoscale imaging with virtually unlimited multiplexing and molecular counting. Here, we address challenges, such as variable imaging performance and target accessibility, that can limit its broader applicability. Specifically, we enhance its capacity for robust single-protein imaging and molecular counting by optimizing the integration of total internal reflection fluorescence microscopy with physical sectioning, in particular, Tokuyasu cryosectioning. Our method, tomographic and kinetically enhanced DNA-PAINT (tkPAINT), achieves 3 nm localization precision across diverse samples, enhanced imager binding, and improved cellular integrity. tkPAINT can facilitate molecular counting with DNA-PAINT inside the nucleus, as demonstrated through its quantification of the in situ abundance of RNA Polymerase II in both HeLa cells as well as mouse tissues. Anticipating that tkPAINT could become a versatile tool for the exploration of biomolecular organization and interactions across cells and tissues, we also demonstrate its capacity to support multiplexing, multimodal targeting of proteins and nucleic acids, and three-dimensional (3D) imaging.

PMID:40773232 | DOI:10.1073/pnas.2504578122

Proc Natl Acad Sci U S A. 2025 Aug 12;122(32):e2502160122. doi: 10.1073/pnas.2502160122. Epub 2025 Aug 7.

ABSTRACT

Understanding the molecular mechanisms behind plant response to stress can enhance breeding strategies and help us design crop varieties with improved stress tolerance, yield, and quality. To investigate resource redistribution from growth- to defense-related processes in an essential tuber crop, potato, here we generate a large-scale compartmentalized genome-scale metabolic model (GEM), potato-GEM. Apart from a large-scale reconstruction of primary metabolism, the model includes the full known potato secondary metabolism, spanning over 566 reactions that facilitate the biosynthesis of 182 distinct potato secondary metabolites. Constraint-based modeling identifies that the activation of the largest amount of secondary (defense) pathways occurs at a decrease of the relative growth rate of potato leaf, due to the costs incurred by defense. We then obtain transcriptomics data from experiments exposing potato leaves to two biotic stress scenarios, a herbivore and a viral pathogen, and apply them as constraints to produce condition-specific models. We show that these models recapitulate experimentally observed decreases in relative growth rates under treatment as well as changes in metabolite levels between treatments, enabling us to pinpoint the metabolic rewiring underlying growth-defense trade-offs. Potato-GEM thus presents a useful resource to study and broaden our understanding of potato and general plant defense responses under stress conditions.

PMID:40773226 | DOI:10.1073/pnas.2502160122

Development. 2025 Aug 7:dev.204663. doi: 10.1242/dev.204663. Online ahead of print.

ABSTRACT

Pericellular matrix-mediated cell-cell adhesion in Xenopus gastrula tissues is characterized by a spectrum of narrow and wide cell contacts that alternate with the non-adhesive surfaces of the interstitial space. Here we show, first, that knockdown of a pericellular matrix adhesion molecule, fibronectin, diminishes contact abundance and hence cell packing density, but without reducing adhesion strength. Second, we find that cell surfaces in gastrula tissues exhibit solid-like behavior in the form of buckling and crumpling, shape modifications which are typically seen in thin elastic films. We propose that both phenomena are explained by generic properties of the pericellular matrix: its compression and consequent stiffening by the interpenetration of matrix layers during adhesive contact formation. We argue that this renders part of the cell surface non-adhesive to form interstitial gaps, and gap surfaces as well as contacts prone to buckling and crumpling in step with cell contractility fluctuations. In this elasto-capillary model of tissue cohesion, the size of the interstitial space is determined by the abundance of pericellular matrix, not by adhesion strength.

PMID:40772725 | DOI:10.1242/dev.204663

Mol Ecol. 2025 Aug 7:e70061. doi: 10.1111/mec.70061. Online ahead of print.

ABSTRACT

Sex-specific genomic differentiation is a crucial yet frequently overlooked factor in population genetics. In this opinion piece, we leverage the substantial genomic resources available for the great tit (Parus major), including population-scale data sets from many European populations, to investigate genomic differentiation between males and females. Unlike in some other species, where high-quality genome assemblies exist but broad population sampling is lacking, the great tit offers a unique opportunity to study sex-based differentiation at both the genomic and population level. We identify significant differentiation at an autosomal locus on chromosome 5, which we hypothesise originates from sex-linked variation present on the sex chromosomes (Z and potentially W). By referencing genomic data from other songbirds with well-assembled sex chromosomes, we illustrate how autosomal loci may exhibit high sequence similarity to sex-linked regions. Our analyses demonstrate that uneven sex ratios in sampled populations can substantially bias differentiation metrics (e.g., FST), potentially resulting in false-positive interpretations of adaptive differentiation. To mitigate such issues, we stress the importance of sex-aware study designs, including balanced sex sampling and explicitly incorporating sex as a covariate. Furthermore, while optimal study designs would include high-quality reference genomes from both sexes, we recommend, as a pragmatic and cost-effective alternative for labs with limited resources, generating a reference genome from the heterogametic sex (females in birds) to ensure both sex chromosomes are represented in mapping and analysis. Finally, we emphasise the need for rigorous validation of candidate loci to ensure accurate and biologically meaningful outcomes in evolutionary genomic studies.

PMID:40772596 | DOI:10.1111/mec.70061

Sci Rep. 2025 Aug 6;15(1):28742. doi: 10.1038/s41598-025-14503-0.

ABSTRACT

Discovering new drug candidates for complex diseases like cancer is a significant challenge in modern drug discovery. Drug repurposing provides a cost-effective and time-efficient strategy to identify existing drugs for novel therapeutic targets. Here, we exploited an integrated in-silico approach to identify repurposed drugs that could inhibit programmed death-ligand 1 (PD-L1). PD-L1 is a crucial protein that plays a pivotal role in immune checkpoint regulation, making it a potential target for cancer treatment. Using a drug repurposing approach, we combined molecular docking and molecular dynamics (MD) simulations to study the binding efficiency of FDA-approved drug molecules targeting PD-L1. From the binding affinities and interaction analysis of the first screening, several molecules emerged as PD-L1 binders. Two of them, Lumacaftor and Vedaprofen, showed appropriate drug profiles and biological activities and stood out as highly potent binding partners of the PD-L1. MD simulation was performed for 500 ns to assess the conformational and stability changes of PD-L1-Lumacaftor and PD-L1-Vedaprofen complexes. The simulations revealed sustained structural integrity and stable binding of both complexes throughout the 500 ns trajectories, supporting their potential as PD-L1 inhibitors. While the findings are promising, they remain computational and require experimental validation to confirm biological efficacy and specificity. This study also emphasizes the role of bioinformatics approaches in drug repurposing that can help in the identification of novel anticancer agents.

PMID:40770405 | DOI:10.1038/s41598-025-14503-0

Cureus. 2025 Jul 7;17(7):e87436. doi: 10.7759/cureus.87436. eCollection 2025 Jul.

ABSTRACT

Osteoporosis is a chronic skeletal disorder marked by reduced bone mineral density (BMD) and increased fracture risk, posing a substantial global health burden. Traditionally considered multifactorial, growing evidence highlights a significant genetic contribution across both early-onset monogenic and adult-onset polygenic forms. Understanding the molecular and genetic architecture of osteoporosis is crucial for guiding targeted diagnostics and developing personalised therapeutic strategies. This review aimed to: (1) identify and summarise genetic mutations and polymorphisms associated with osteoporosis, classifying them into monogenic and multifactorial causes; (2) distinguish between syndromic and non-syndromic forms of genetically influenced osteoporosis; (3) evaluate how specific genetic variations influence the risk, onset, and severity of osteoporosis, particularly in postmenopausal populations; (4) examine current anti-resorptive and anabolic treatments in the context of genetic backgrounds; and (5) identify gaps in knowledge to guide future research into genetics-based screening and individualised treatment. A comprehensive literature search was conducted across PubMed, Embase, CINAHL, and the Cochrane Library for studies published between 2000 and 2025. Medical Subject Headings (MeSH) and free-text keywords were used to retrieve peer-reviewed articles, clinical trials, genetic association studies, and systematic reviews. Eligible studies explored genetic variants, bone signalling pathways (e.g., WNT/β-catenin, Notch), or pharmacological therapies in relation to BMD, fracture incidence, or osteogenesis. Data were extracted and thematically analysed under the following three core domains: genetic and molecular mechanisms, osteogenesis and bone remodelling, and treatment responses linked to genetic profiles. The review identified a wide spectrum of genetic contributors to osteoporosis. Monogenic forms, often syndromic, were linked to mutations in genes such as COL1A1, COL1A2, and WNT1, whereas multifactorial osteoporosis, particularly postmenopausal, was associated with variants in LRP5, SOST, VDR, and other GWAS-identified loci. The interplay between these variants and osteogenic signalling cascades was found to influence bone homeostasis. Treatments were categorised as anti-resorptive (e.g., bisphosphonates, denosumab) or anabolic (e.g., parathyroid hormone analogues, romosozumab), with genetic factors influencing efficacy. The evidence suggests a future need for personalised therapeutic strategies based on genetic profiling. There remains a need for further large-scale studies to validate genotype-phenotype correlations and treatment responses across diverse populations. Further exploration into pharmacogenomics, microRNA regulation, and gene-targeted interventions is required. Advancing osteoporosis care will depend on integrating genetic insights into clinical practice to enable earlier diagnosis, individualised treatment, and improved patient outcomes.

PMID:40772209 | PMC:PMC12327379 | DOI:10.7759/cureus.87436

Expert Rev Clin Pharmacol. 2025 Aug 7. doi: 10.1080/17512433.2025.2545403. Online ahead of print.

ABSTRACT

INTRODUCTION: Drug-resistant epilepsy (DRE) affects 30% of epilepsy patients and represents a major therapeutic challenge. Understanding its genetic determinants is crucial for the development of effective precision medicine strategies.

AREAS COVERED: This review comprehensively evaluates genetic factors in DRE, including polymorphisms in pharmacokinetic (e.g. ABCB1) and pharmacodynamic (e.g. SCN1A), findings from genome-wide association studies (GWAS) that recently identified a significant locus at 1q42.11-q42.12 (CNIH3/WDR26) for focal DRE, the critical role of rare variants (e.g. in SCN1A, KCNQ2) and copy number variations (CNVs) in severe epileptic encephalopathies, and the emerging fields of epigenetics and polygenic risk scores (PRS).

EXPERT OPINION: Methodological limitations, including modest sample sizes and phenotypic heterogeneity, hamper genetic research on DRE. While common variants show little impact, rare variants, including CNVs, and epigenetic alterations offer promising opportunities. Future priorities include functional studies to clarify the impact of gene variants, the integration of multi-omics data and the development of advanced analytical techniques, such as machine learning and network approaches, to translate genetic discoveries into clinically actionable precision medicine and ultimately improve outcomes for DRE patients.

PMID:40771158 | DOI:10.1080/17512433.2025.2545403

Cephalalgia. 2025 Aug;45(8):3331024251353421. doi: 10.1177/03331024251353421. Epub 2025 Aug 6.

ABSTRACT

BackgroundThe present prospective, real-world study aims to assess anti-calcitonin gene-related peptide (CGRP) monoclonal antibodies (mAbs) effectiveness across three consecutive one-year treatment cycles by means of a conservative intention-to-treat (ITT) analysis.MethodsWe enrolled 179 subjects (75.4% females, 51.3 years 95% confidence interval [49.2-53.4] years), 87.2% with chronic migraine and medication overuse) who started mAbs between 2018 and 2020. We recorded clinical data supported by a prospectively filled headache diary up to three one-year treatment cycles. The ITT analysis was performed with a multivariate linear mixed model considering the entire population.ResultsWe observed a marked and consistent reduction in monthly migraine days (MMDs) across the three one-year cycles of treatment: -12.7 )[-11.4 - -14.1] at end of the first year of treatment (C1), -12.4 [-11.0 - -13.8] at the end of the second year (C2) and -12.9 [-11.4 - -14.3] at the end of the third year (C3). Baseline and residual MMDs progressively decreased across the three cycles (p = 0.008): from 21.1 [19.8-22.4] to 9.6 [8.3-11.0] in C1, from 19.0 [17.4-20.5] to 9.6 [8.1-11.1] in C2, and from 15.9 [14.3-17.5] to 8.5 [6.9-10.1] in C3. At the end of C3, the 50% response rate was 38.5% (69/179).ConclusionsIn our cohort, mAbs induced a meaningful and sustained reduction in MMDs across three consecutive one-year cycles of treatment. The ITT analysis revealed a remaining high burden of disease. While confirming mAbs effectiveness in migraine prevention, these findings underscore the need for more treatment approaches and for exploring other non-CGRP dependent pathways.

PMID:40770917 | DOI:10.1177/03331024251353421

Front Genet. 2025 Jul 23;16:1614222. doi: 10.3389/fgene.2025.1614222. eCollection 2025.

ABSTRACT

INTRODUCTION: Enhancer-promoter interactions (EPIs) play a vital role in the regulation of gene expression. Although traditional wet-lab methods provide valuable insights into EPIs, they are often constrained by high costs and limited scalability. As a result, the development of efficient computational models has become essential. However, many current deep learning and machine learning approaches utilize simplistic feature fusion strategies, such as direct averaging or concatenation, which fail to effectively model complex relationships and dynamic importance across features. This often results in suboptimal performance in challenging biological contexts.

METHODS: To address these limitations, we propose a deep learning model named EPI-DynFusion. This model begins by encoding DNA sequences using pre-trained DNA embeddings and extracting local features through convolutional neural networks (CNNs). It then integrates a Transformer and Bidirectional Gated Recurrent Unit (BiGRU) architecture with a Dynamic Feature Fusion mechanism to adaptively learn deep dependencies among features. Furthermore, we incorporate the Convolutional Block Attention Module (CBAM) to enhance the model's ability to focus on informative regions. Based on this core architecture, we develop two variants: EPI-DynFusion-gen, a general model, and EPI-DynFusion-best, a fine-tuned version for cell line-specific data.

RESULTS: We evaluated the performance of our models across six benchmark cell lines. The average area under the receiver operating characteristic curve (AUROC) scores achieved by the specific, generic, and best models were 94.8%, 95.0%, and 96.2%, respectively. The average area under the precision-recall curve (AUPR) scores were 81.2%, 71.1%, and 83.3%, respectively, demonstrating the superior performance of the fine-tuned model in the precision-recall space. These results confirm that the proposed fusion strategies and attention mechanisms contribute to significant improvements in performance.

DISCUSSION: In conclusion, EPI-DynFusion presents a robust and scalable framework for predicting enhancer-promoter interactions solely based on DNA sequence information. By addressing the limitations of conventional fusion techniques and incorporating attention mechanisms alongside sequence modeling, our method achieves state-of-the-art performance while enhancing the interpretability and generalizability of enhancer-promoter interaction prediction tasks.

PMID:40772277 | PMC:PMC12325019 | DOI:10.3389/fgene.2025.1614222

Front Neurosci. 2025 Jul 23;19:1592070. doi: 10.3389/fnins.2025.1592070. eCollection 2025.

ABSTRACT

Emotion recognition based on electroencephalogram (EEG) faces substantial challenges. The variability of neural signals among different subjects and the scarcity of labeled data pose obstacles to the generalization ability of traditional domain adaptation (DA) methods. Existing approaches, especially those relying on the maximum mean discrepancy (MMD) technique, are often highly sensitive to domain mean shifts induced by noise. To overcome these limitations, a novel framework named Domain Adaptive Deep Possibilistic clustering (DADPc) is proposed. This framework integrates deep domain-invariant feature learning with possibilistic clustering, reformulating Maximum Mean Discrepancy (MMD) as a one-centroid clustering task under a fuzzy entropy-regularized framework. Moreover, the DADPc incorporates adaptive weighted loss and memory bank strategies to enhance the reliability of pseudo-labels and cross-domain alignment. The proposed framework effectively mitigates noise-induced domain shifts while maintaining feature discriminability, offering a robust solution for EEG-based emotion recognition in practical applications. Extensive experiments conducted on three benchmark datasets (SEED, SEED-IV, and DEAP) demonstrate the superior performance of DADPc in emotion recognition tasks. The results show significant improvements in recognition accuracy and generalization capability across different experimental protocols, including cross-subject and cross-session scenarios. This research contributes to the field by providing a comprehensive approach that combines deep learning with possibilistic clustering, advancing the state-of-the-art in cross-domain EEG analysis.

PMID:40772260 | PMC:PMC12326482 | DOI:10.3389/fnins.2025.1592070