Eclampsia is a severe complication of preeclampsia involving blood-brain barrier (BBB) disruption. While small extracellular vesicles (sEVs) contribute to endothelial dysfunction in preeclampsia, their role in eclampsia remains unclear. We examined the effects of plasma and plasma-derived sEVs from women with eclampsia on BBB integrity. Plasma and plasma-sEVs were isolated from women with normotensive pregnancies (n=18), preeclampsia (n=19), preeclampsia with organ complications (n=17), and eclampsia (n=20). An in vitro BBB model based on the culture of human brain endothelial cells was used to evaluate electrical resistance (TEER) and Dextran 70 kDa permeability in the presence of women's plasmas or plasma-sEVs. sEVs cargo of relevant proteins involved in BBB regulation, eNOS, and TNF-α, were analyzed. Plasma from women with eclampsia disrupted the BBB, with marked reductions in TEER and increased permeability compared to normotensive controls, preeclampsia, and preeclampsia with organ complications. Moreover, plasma-sEVs of women with eclampsia caused a drop in TEERs but less BBB permeability than plasma-sEVs from normotensive controls or preeclampsia. Lower levels of eNOS and TNF-α in eclampsia-derived sEVs compared to normotensive controls were found. We report the critical role of circulating plasma factors in the disruption of the BBB during eclampsia. Although plasma-derived sEVs induce some alterations in barrier properties, our findings suggest they are not the main drivers of the BBB impairment observed in eclampsia, likely due to altered cargo composition.

The aim of the present pilot study was to compare the effects of low load blood flow restriction (LL-BFR) with high load resistance training (HL) in advanced level climbers. Twenty-two climbers were randomly allocated to LL-BFR or HL performing training twice per week for five weeks. Before and after the intervention the participants were tested in isometric pull-up (peak- and average force), maximal voluntary contraction (MVC) in a finger flexor exercise, finger endurance, forearm circumference, and climbing performance. There were no group differences in any of the tests (p=0.346-0.891), however, both groups increased their average force in the pull-up (LL-BFR; 52 N, p=0.012, HL; 56 N, p=0.024), MVC (LL-BFR; 15 kg, p=0.008, HL; 17 kg, p=0.002), forearm circumference (LL-BFR; 0.8 cm, p=0.012, HL; 0.6, p=0.038) and climbing performance (LL-BFR; 13.5 moves, p=0.012, HL; 10 moves, p=0.003). No pre-post differences were observed for the peak force in the pull-up (p=0.132-0.376) or the endurance test (p=0.752-1.000). In conclusion, resistance training of the finger flexors with HL or LL-BFR resulted in no between-group differences, with both interventions improving maximal strength, hypertrophy, and climbing performance, but not endurance.

Explainable Artificial Intelligence (XAI) holds the promise to compensate for the "black-box" nature of deep learning which impedes transcriptome-based cancer survival prediction. However, there is a lack of systematic benchmarking XAI frameworks tailored for high-dimensional survival data. To bridge this gap, we systematically evaluated six representative XAI methods in three main categories: gradient-based, propagation-based, and perturbation-based approaches by using a Self-Normalizing Neural Network (SNN) as the baseline survival model. 6,248 samples across 15 cancer types from The Cancer Genome Atlas (TCGA) was analysed in this evaluation with a unified framework we developed. The evaluation metrics encompassed three key dimensions: prognostic factor enrichment (univariate Cox regression significance), biological consistency (supported by four authoritative databases, including OpenTargets), and explanation stability (Kuncheva Index). Among the six XAI methods, we find that DeepSHAP achieved the best overall performance, identifying the highest number of statistically significant prognostic factors while maintaining superior explanation stability; LRP (Layer-wise Relevance Propagation) showed slightly lower prognostic specificity but the highest consensus with biological databases in capturing general cancer genes, making it suitable for validating biological plausibility. In contrast, the perturbation-based method, PFI (Permutation Feature Importance) exhibited systematic failure and extremely low stability due to its inability to handle feature collinearity in high-dimensional transcriptomic data. Furthermore, we identified explanation stability as a robust proxy for the biological validity of the XAI. Collectively, this study establishes an empirical framework for selecting trustworthy AI explanation tools for precision medicine.

[This corrects the article DOI: 10.3389/fphys.2026.1778735.].

Glucagon is a key hormone regulating gluconeogenesis and glucose homeostasis in mammals, yet its regulatory mechanisms in glucose metabolism in carnivorous fish remain incompletely understood. To systematically investigate glucagon-mediated glucose metabolism in Japanese flounder (Paralichthys olivaceus), liver samples were collected before glucagon injection (0h) and at 1h and 6h post-injection for transcriptome sequencing (RNA-seq). Transcriptome analysis identified numerous differentially expressed genes involved in glucose and energy metabolism. In total, 507, 1458, and 709 differentially expressed genes were detected in the comparisons of 0h vs 1h, 1h vs 6h, and 0h vs 6h, respectively. KEGG enrichment analysis showed that glucagon activated pathways related to glucagon signaling, insulin resistance, FoxO signaling, and energy metabolism, including AMPK and PPAR pathways, suggesting that glucagon rapidly stimulates gluconeogenesis. At 6h post-injection, genes involved in glycolysis and glucose transport were upregulated, whereas key gluconeogenic genes were downregulated, indicating attenuation of glucagon-induced metabolic responses. Further analysis showed that glucagon suppressed the insulin-mediated PI3K/AKT signaling pathway. Among the candidate genes, SOCS3 and TRIB3 were upregulated and may serve as key regulators linking glucagon and insulin signaling. Functional experiments further showed that knockdown of TRIB3 reduced glucose levels in hepatocyte culture medium and increased the expression of insulin signaling-related genes. Overall, glucagon regulates glucose metabolism in Japanese flounder by promoting gluconeogenesis while suppressing insulin signaling, providing transcriptomic insights into endocrine regulation in carnivorous fish.

Recent research suggests that the emerging neutrophil to high-density lipoprotein ratio (NHR) has a significant correlation with the survival outcomes across a range of tumors. However, its prognostic value in nasopharyngeal carcinoma (NPC) remained to be elucidated. This study aimed to evaluate the relationship between NHR and overall survival (OS) in patients with NPC, as well as to develop a corresponding prognostic model. We conducted a retrospective analysis of 834 NPC patients who received platinum-based concurrent chemoradiotherapy (CCRT) between January 2010 and December 2014. The optimal NHR cutoff was determined using maximally selected rank statistics. Survival analyses were performed using Kaplan-Meier methods and Cox regression models. A prognostic nomogram incorporating NHR with conventional risk factors was developed and validated. The NHR score successfully segregated NPC patients into two categories with significantly different OS (HR = 0.672, 95% CI: 0.481-0.939, P = 0.019). Multivariate analysis confirmed NHR as an independent prognostic factor, along with age, T stage, and N stage. Considering EBV-DNA is a well-established prognostic factor for NPC in accordance with clinical practice and previous studies, we incorporated it together with the above factors to construct the prognostic nomogram. The established nomogram showed modestly higher predictive accuracy (C-index = 0.696, 95% CI: 0.654-0.738) compared to conventional staging parameters (C-index = 0.648, 95% CI: 0.583-0.713). Pre-treatment NHR serves as an independent prognostic indicator for survival in NPC patients undergoing CCRT. This readily available, cost-effective biomarker, when integrated with traditional prognostic factors, provides a comprehensive tool for risk stratification and treatment planning. External validation through multicenter studies is warranted to confirm these findings.

Ovarian tissue cryopreservation (OTC) is essential for fertility preservation in cancer survivors, prepubertal girls, and individuals at high risk of premature ovarian insufficiency (POI). Yet conventional evaluation methods, such as histology and follicle counting, provide limited insight into tissue viability, microenvironmental integrity, molecular injury, and oncologic safety. Recent advances in multi-omics, including transcriptomics, single-cell sequencing, proteomics, and spatial transcriptomics, enable high-resolution characterization of follicular heterogeneity, stromal status, and potential malignant contamination. Concurrently, artificial intelligence (AI) offers automated follicle detection, quantitative tissue assessment, and multimodal prediction models that can support individualized clinical decisions. This review summarizes emerging applications of multi-omics and AI in ovarian tissue quality assessment and highlights their potential to transform fertility preservation strategies. Integrating molecular profiling with AI-based prediction may establish a more precise and intelligent framework for tissue selection, transplantation planning, and reproductive outcome prediction.

The aim of this systematic review was to evaluate the current evidence regarding resting metabolic rate fluctuations across menstrual cycle phases in healthy, naturally menstruating females. A systematic search was conducted in the PubMed and Web of Science databases for articles published from January 2020 to September 2025. Of the 331 records identified, seven studies met the eligibility criteria and were included. Five studies reported a trend toward an elevated metabolic rate during the luteal phase, with four reporting statistically significant increases. The estimated increase ranged from approximately 30 to 120 kcal/day. However, the magnitude of this physiological shift appears to be small (approximately 3-5%) and may overlap with typical day-to-day biological variability, methodological limitations, or measurement error, potentially contributing to inconsistent findings across studies. Future research should prioritise longitudinal, repeated-measures designs with confirmed ovulation to accurately quantify menstrual cycle-related metabolic fluctuations.

Multiple point and nonpoint sources add complex mixtures of pollutants that may pose detrimental impacts on freshwater fish. These pollutants include metallic and nonmetallic inorganic ions and an array of organic compounds. Climate-related scenarios and a mixture of contaminants entering riverine ecosystems have impacted many endemic freshwater fish species in Sri Lanka. The present research aims to identify morphological, physiological, and behavioral changes upon exposure to xenobiotics and to predict the influence of climate on these fishes. We discussed the biomarker responses of feral fish, combined with their physicochemical characterization. Moreover, the discussion emphasized empirical evidence from controlled laboratory experiments. Together, these elements were used to interpret the possible future impacts of climate change on the fish in lotic ecosystems. The effects of nanoparticles, microplastics, pharmaceuticals, and endocrine disruptors, and their interplay with climate-related physicochemical variation, have been identified as a research gap. The primary research directions for the future include establishing multi-stressor experimental frameworks that integrate a mixture of xenobiotic exposures of indigenous fishes. The development of standardized biological monitoring protocols that simulate real-world conditions in lotic ecosystems is crucial. Introduction of scientific, evidence-based, robust, and urgent legislative reforms to regulate cumulative pollution may provide a strong legal framework to prevent devastating impacts on freshwater fishes in lotic ecosystems in Sri Lanka.