Eur J Pharmacol. 2025 Mar 25:177537. doi: 10.1016/j.ejphar.2025.177537. Online ahead of print.

ABSTRACT

In severe COVID-19 patients, excessive inflammation can lead to multiorgan dysfunction. Current anti-inflammatory treatments like glucocorticoids partially improve the outcomes, while immune systems are compromised. We have identified that SARS-CoV-2-infected obese mice were a good model of the cytokine storm seen in COVID-19. Here, we revealed that iguratimod (IGU), an approved agent for rheumatoid arthritis, improved survival by attenuating inflammation with minimal immune suppression. In this study, C57BL/6 mice were fed a high-fat diet (HFD) or a normal-fat diet (NFD) for ten weeks before being infected with a mouse-adapted SARS-CoV-2. IGU significantly improved survival rates and reduced lung inflammation in HFD-fed mice, with minimal impact on interferon-induced genes and viral load. Meanwhile, dexamethasone (DEX) did not improve survival, while it suppressed various immune reactions with different mechanisms to IGU. Interestingly, IGU-treated mice had fewer SARS-CoV-2 positive cells in the lung, although viral replication was comparable to the control mice. Neither IGU nor DEX inhibited the SARS-CoV-2 infection in Vero-E6 cells, unlike the antiviral agent, remdesivir. Of note, IGU was effective prophylactically and therapeutically in HFD mice, and showed beneficial effects in NFD-fed mice with a lethal dose exposure of SARS-CoV-2. We demonstrated that IGU could be a promising treatment for severe COVID-19, especially in obese patients, by fine-tuning inflammation without compromising antiviral immunity. This study supports the possibility of drug repositioning for IGU COVID-19 beyond autoimmune diseases.

PMID:40147575 | DOI:10.1016/j.ejphar.2025.177537

Int J Infect Dis. 2025 Mar 25:107895. doi: 10.1016/j.ijid.2025.107895. Online ahead of print.

ABSTRACT

BACKGROUND: NAT2 polymorphisms affect isoniazid metabolism, but their effect on mortality among individuals with tuberculosis (TB) remains unclear.

METHODS: This study used data from two TB cohorts (2005-2011, 2014-2020) and death certificate records in Thailand. Newly diagnosed Thai individuals treated with isoniazid-containing regimens were included. NAT2 genotypes-rapid, intermediate, and slow acetylator (RA, IA, SA)-were classified via haplotype inference. The primary outcome was 1-year all-cause mortality, while secondary outcomes included TB-related mortality, TB+respiratory disease-related mortality recorded in the vital registration system, and death as a TB treatment outcome. Adjusted hazard ratios (aHRs) relative to the IA type were estimated using stratified Cox proportional hazards models. Subgroup analyses targeted individuals with isoniazid-resistant TB and HIV infection.

RESULTS: A total of 1,065 individuals (766 males; mean age=51 years) were analyzed. Individuals with RA had a 1.70-fold greater all-cause mortality risk (95% CI: 1.03-2.80) than IA. The aHRs for RA were 1.14 (0.43-3.03) for TB-related mortality, 1.59 (0.80-3.18) for TB+respiratory disease-related mortality, and 1.26 (0.67-2.14) for TB treatment outcome death. Among individuals with isoniazid-resistant TB, those with RA had a 4.68-fold (1.14-19.12) greater aHR for all-cause mortality.

CONCLUSION: The RA type is associated with increased 1-year all-cause mortality.

PMID:40147587 | DOI:10.1016/j.ijid.2025.107895

J Cyst Fibros. 2025 Mar 26:S1569-1993(25)00079-7. doi: 10.1016/j.jcf.2025.03.011. Online ahead of print.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) is a disease triggered by more than 2,100 variants in a single gene encoding for the CF Transmembrane Conductance Regulator (CFTR) protein, which is expressed in epithelial cells, where it functions an anion channel. A new era of high-throughput technologies ('omics') enabled the production and exploration of large CF-related datasets with unprecedented detail. However, this knowledge is scattered among different resources thus requiring a significant amount of time and training to collect and exploit. The objective of this work is to build a resource (CyFidb) that concentrates CF-related information in a single repository.

METHODS: This tool results from the intense manual curation of 407 scientific articles, including studies with CFTR variants in distinct conditions, drug treatments and cells/tissues.

RESULTS: CyFidb is divided into three levels of information: protein-protein interactions, gene expression and functional studies, from which it is possible to search and extract information.

CONCLUSIONS: CyFidb is an open-access resource (https://cyfidb.di.fc.ul.pt) designed to provide continuously updated, curated information on CFTR variants and their associated biological data.

PMID:40148144 | DOI:10.1016/j.jcf.2025.03.011

Respir Med. 2025 Mar 25:108056. doi: 10.1016/j.rmed.2025.108056. Online ahead of print.

ABSTRACT

BACKGROUND: People with cystic fibrosis-related diabetes (CFRD) are known to have reduced exercise capacity (EC), which in turn is related to increased morbidity and mortality. The aim of this study was to examine whether dysglycaemia may independently influence exercise capacity in people with CF (pwCF).

METHODS: Results from clinically conducted cardiopulmonary exercise tests were analysed retrospectively in 139 pwCF alongside routine clinical data. Subjects were grouped according to glycaemic status; normal glucose tolerance (NGT; n=43) and dysglycaemia; impaired glucose tolerance (IGT; n=17) and CFRD (n=79). Anthropometric data was assessed using chi-squared tests. Regression models were developed using analysis of co-variance (ANCOVA) to evaluate predictors of exercise capacity and correlations between variable were assessed using the Pearson method.

RESULTS: Maximal oxygen uptake (VO2max) was reduced in the CFRD group compared to NGT and IGT (p<0.01), however this was dependent on higher FEV1% in the NGT and IGT groups (p<0.001) and significant differences were no longer present when FEV1 was accounted for. A higher proportion of those with dysglycaemia were ventilatory limited (NGT;42%, IGT; 72% & CFRD; 65%, p<0.05). Age, gender, BMI, intravenous antibiotic days and FEV1% were significant predictors of VO2max across all patients (adjusted R2=0.528, p<0.001). HbA1c is a small but significant predictor of VO2max in patients with dysglycaemia (p<0.05).

CONCLUSIONS: Adults with CFRD have reduced VO2max compared to NGT or IGT which is mediated by poorer lung function and higher overall disease burden. In individuals with CFRD, better glycaemic control is associated with a greater EC.

PMID:40147571 | DOI:10.1016/j.rmed.2025.108056

Acad Radiol. 2025 Mar 26:S1076-6332(25)00263-6. doi: 10.1016/j.acra.2025.03.030. Online ahead of print.

NO ABSTRACT

PMID:40148166 | DOI:10.1016/j.acra.2025.03.030

Lancet Digit Health. 2025 Apr;7(4):e264-e274. doi: 10.1016/j.landig.2025.01.001.

ABSTRACT

BACKGROUND: Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of LVSD in the general adult population, it has yet to be applied comprehensively across congenital heart disease lesions.

METHODS: We trained a convolutional neural network on paired ECG-echocardiograms (≤2 days apart) across the lifespan of a wide range of congenital heart disease lesions to detect left ventricular ejection fraction (LVEF) of 40% or less. Model performance was evaluated on single ECG-echocardiogram pairs per patient at Boston Children's Hospital (Boston, MA, USA) and externally at the Children's Hospital of Philadelphia (Philadelphia, PA, USA) using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves.

FINDINGS: The training cohort comprised 124 265 ECG-echocardiogram pairs (49 158 patients; median age 10·5 years [IQR 3·5-16·8]; 3381 [2·7%] of 124 265 ECG-echocardiogram pairs with LVEF ≤40%). Test groups included internal testing (21 068 patients; median age 10·9 years [IQR 3·7-17·0]; 3381 [2·7%] of 124 265 ECG-echocardiogram pairs with LVEF ≤40%) and external validation (42 984 patients; median age 10·8 years [IQR 4·9-15·0]; 1313 [1·7%] of 76 400 ECG-echocardiogram pairs with LVEF ≤40%) cohorts. High model performance was achieved during internal testing (AUROC 0·95, AUPRC 0·33) and external validation (AUROC 0·96, AUPRC 0·25) for a wide range of congenital heart disease lesions. Patients with LVEF greater than 40% by echocardiogram who were deemed high risk by AI-ECG were more likely to have future dysfunction compared with low-risk patients (hazard ratio 12·1 [95% CI 8·4-17·3]; p<0·0001). High-risk patients by AI-ECG were at increased risk of mortality in the overall cohort and lesion-specific subgroups. Common salient features highlighted across congenital heart disaese lesions include precordial QRS complexes and T waves, with common high-risk ECG features including deep V2 S waves and lateral precordial T wave inversion. A case study on patients with ventricular pacing showed similar findings.

INTERPRETATION: Our externally validated algorithm shows promise in prediction of current and future LVSD in patients with congenital heart disease, providing a clinically impactful, inexpensive, and convenient cardiovascular health tool in this population.

FUNDING: Kostin Innovation Fund, Thrasher Research Fund Early Career Award, Boston Children's Hospital Electrophysiology Research Education Fund, National Institutes of Health, National Institute of Childhood Diseases and Human Development, and National Library of Medicine.

PMID:40148010 | DOI:10.1016/j.landig.2025.01.001

J Microbiol Biotechnol. 2025 Mar 26;35:e2411010. doi: 10.4014/jmb.2411.11010.

ABSTRACT

Group A Streptococcus (GAS) is a major pathogen with diverse clinical outcomes linked to its genetic variability, making accurate phenotype prediction essential. While previous studies have identified many GAS-associated genetic factors, translating these findings into predictive models remains challenging due to data complexity. The current study aimed to integrate deep learning models with genome-wide association study-derived genetic variants to predict pathogenic phenotypes in GAS. We evaluated the performance of several deep neural network models, including CNN, ResNet18, LSTM, and their ensemble approach in predicting GAS phenotypes. It was found that the ensemble model consistently achieved the highest prediction accuracy across phenotypes. Models trained on the full 4722-genotype set outperformed those trained on a reduced 175-genotype set, underscoring the importance of comprehensive variant data in capturing complex genotype-phenotype interactions. Performance changes in the reduced 175-genotype set compared to the full-set genotype scenarios revealed the impact of data dimensionality on model effectiveness, with CNN remaining robust, while ResNet18 and LSTM underperformed. Our findings emphasized the potential of deep learning in phenotype prediction and the critical role of data-model compatibility.

PMID:40147921 | DOI:10.4014/jmb.2411.11010

Zhonghua Kou Qiang Yi Xue Za Zhi. 2025 Mar 28;60(4):326-331. doi: 10.3760/cma.j.cn112144-20250116-00021. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) has significantly enhanced the precision and efficiency of dental restoration design, smile analysis, and personalized treatment in the field of esthetic dentistry through technologies such as deep learning. This advancement provides patients with more accurate and efficient esthetic treatment solutions. However, its application still faces challenges such as technical limitations, ethical concerns, and insufficient data diversity. In the future, with the integration of high-quality data, optimization of real-time learning technologies, and the advancement of multidisciplinary collaboration, AI is expected to further promote the intelligent and human-centered development of esthetic dentistry, bringing profound and positive impacts to patients and clinical practice.

PMID:40147889 | DOI:10.3760/cma.j.cn112144-20250116-00021

AJNR Am J Neuroradiol. 2025 Mar 27. doi: 10.3174/ajnr.A8567. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Deep learning (DL)-based reconstruction enables improving the quality of MR images acquired with a short scan time. We aimed to prospectively compare the image quality and diagnostic performance in evaluating cervical degenerative spine diseases and myelopathy between conventional cervical MRI and accelerated cervical MRI with a commercially available vendor-neutral DL-based reconstruction.

MATERIALS AND METHODS: Fifty patients with degenerative cervical spine disease or myelopathy underwent both conventional cervical MRI and accelerated cervical MRI by using a DL-based reconstruction operating within the DICOM domain. The images were evaluated both quantitatively, based on SNR and contrast-to-noise ratio (CNR), and qualitatively, by using a 5-point scoring system for the overall image quality and clarity of anatomic structures on sagittal T1WI, sagittal contrast-enhanced (CE) T1WI, and axial/sagittal T2WI. Four radiologists assessed the sensitivity and specificity of the 2 protocols for detecting degenerative diseases and myelopathy.

RESULTS: The DL-based protocol reduced MRI acquisition time by 47%-48% compared with the conventional protocol. DL-reconstructed images demonstrated a higher SNR on sagittal T1WI (P = .046) and a higher CNR on sagittal T2WI (P = .03) than conventional images. The SNR on sagittal T2WI and the CNR on sagittal T1WI did not significantly differ (P > .05). DL-reconstructed images had better overall image quality on sagittal T1WI (P < .001), sagittal T2WI (Dixon in-phase or TSE) (P < .001), and sagittal T2WI (Dixon water-only) (P = .013) and similar image quality on axial T2WI and sagittal CE T1WI (P > .05). DL-reconstructed images had better clarity of anatomic structures (P values were < .001 for all structures, except for the neural foramen [P = .024]). DL-reconstructed images had a higher sensitivity for detecting neural foraminal stenosis (P = .005) and similar sensitivities for diagnosing other degenerative spinal diseases and myelopathy (P > .05). The specificities for diagnosing degenerative spinal diseases and myelopathy did not differ between the 2 images (P > .05).

CONCLUSIONS: The accelerated cervical MRI reconstructed with a vendor-neutral DL-based reconstruction algorithm did not compromise image quality and had higher or similar diagnostic performance for diagnosing cervical degenerative spine diseases and myelopathy compared with the conventional protocol.

PMID:40147833 | DOI:10.3174/ajnr.A8567

Semin Cancer Biol. 2025 Mar 25:S1044-579X(25)00052-5. doi: 10.1016/j.semcancer.2025.03.004. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therapeutic options. Risk stratification has traditionally been performed using epidemiological studies and genetic analyses, through which key risk factors, including smoking, diabetes, chronic pancreatitis, and inherited predispositions, have been identified. However, the multifactorial nature of PDAC has often been insufficiently addressed by these methods, leading to limited precision in individualized risk assessments. Advances in artificial intelligence (AI) have been proposed as a transformative approach, allowing the integration of diverse datasets-spanning genetic, clinical, lifestyle, and imaging data into dynamic models capable of uncovering novel interactions and risk profiles. In this review, the evolution of PDAC risk stratification is explored, with classical epidemiological frameworks compared to AI-driven methodologies. Genetic insights, including genome-wide association studies and polygenic risk scores, are discussed, alongside AI models such as machine learning, radiomics, and deep learning. Strengths and limitations of these approaches are evaluated, with challenges in clinical translation, such as data scarcity, model interpretability, and external validation, addressed. Finally, future directions are proposed for combining classical and AI-driven methodologies to develop scalable, personalized predictive tools for PDAC, with the goal of improving early detection and patient outcomes.

PMID:40147701 | DOI:10.1016/j.semcancer.2025.03.004

Neuroimage. 2025 Mar 25:121151. doi: 10.1016/j.neuroimage.2025.121151. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is a primary degenerative brain disorder commonly found in the elderly, Mild cognitive impairment (MCI) can be considered a transitional stage from normal aging to Alzheimer's disease. Therefore, distinguishing between normal aging and disease-induced neurofunctional impairments is crucial in clinical treatment. Although deep learning methods have been widely applied in Alzheimer's diagnosis, the varying data formats used by different methods limited their clinical applicability. In this study, based on the ADNI dataset and previous clinical diagnostic experience, we propose a method using oblique coronal MRI to assist in diagnosis. We developed an algorithm to extract oblique coronal slices from 3D MRI data and used these slices to train classification networks. To achieve subject-wise classification based on 2D slices, rather than image-wise classification, we employed ensemble learning methods. This approach fused classification results from different modality images or different positions of the same modality images, constructing a more reliable ensemble classification model. The experiments introduced various decision fusion and feature fusion schemes, demonstrating the potential of oblique coronal MRI slices in assisting diagnosis. Notably, the weighted voting from decision fusion strategy trained on oblique coronal slices achieved accuracy rates of 97.5% for CN vs. AD, 100% for CN vs. MCI, and 94.83% for MCI vs. AD across the three classification tasks.

PMID:40147601 | DOI:10.1016/j.neuroimage.2025.121151

Cell. 2025 Mar 24:S0092-8674(25)00213-2. doi: 10.1016/j.cell.2025.02.025. Online ahead of print.

ABSTRACT

Efforts to use genome-wide assays or brain scans to diagnose autism have seen diminishing returns. Yet the clinical intuition of healthcare professionals, based on longstanding first-hand experience, remains the gold standard for diagnosis of autism. We leveraged deep learning to deconstruct and interrogate the logic of expert clinician intuition from clinical reports to inform our understanding of autism. After pre-training on hundreds of millions of general sentences, we finessed large language models (LLMs) on >4,000 free-form health records from healthcare professionals to distinguish confirmed versus suspected autism cases. By introducing an explainability strategy, our extended language model architecture could pin down the most salient single sentences in what drives clinical thinking toward correct diagnoses. Our framework flagged the most autism-critical DSM-5 criteria to be stereotyped repetitive behaviors, special interests, and perception-based behaviors, which challenges today's focus on deficits in social interplay, suggesting necessary revision of long-trusted diagnostic criteria in gold-standard instruments.

PMID:40147442 | DOI:10.1016/j.cell.2025.02.025

Comput Med Imaging Graph. 2025 Mar 23;123:102527. doi: 10.1016/j.compmedimag.2025.102527. Online ahead of print.

ABSTRACT

Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurate diagnosis and treatment planning. Despite significant progress in deep learning-based registration methods, current approaches still face considerable challenges, such as insufficient capture of local details, difficulty in effectively modeling global contextual information, and limited robustness in handling complex deformations. These limitations hinder the precision of high-resolution registration, particularly when dealing with medical images with intricate structures. To address these issues, this paper presents a novel registration network (TCDE-Net), an unsupervised medical image registration method based on a dual-encoder architecture. The dual encoders complement each other in feature extraction, enabling the model to effectively handle large-scale nonlinear deformations and capture intricate local details, thereby enhancing registration accuracy. Additionally, the detail-enhancement attention module aids in restoring fine-grained features, improving the network's capability to address complex deformations such as those at gray-white matter boundaries. Experimental results on the OASIS, IXI, and Hammers-n30r95 3D brain MR dataset demonstrate that this method outperforms commonly used registration techniques across multiple evaluation metrics, achieving superior performance and robustness. Our code is available at https://github.com/muzidongxue/TCDE-Net.

PMID:40147215 | DOI:10.1016/j.compmedimag.2025.102527

Comput Biol Med. 2025 Mar 26;190:110074. doi: 10.1016/j.compbiomed.2025.110074. Online ahead of print.

ABSTRACT

Numerical simulations of fluid dynamics in tube-like structures are important to biomedical research to model flow in blood vessels and airways. It is further useful to some clinical applications, such as predicting arterial fractional flow reserves, and assessing vascular flow wall shear stresses to predict atherosclerosis disease progression. Traditionally, they are conducted via computational fluid dynamics (CFD) simulations, which, despite optimization, still take substantial time, limiting clinical adoption. To improve efficiency, we investigate the use of the multi-case Neural Network (NN) to enable real-time predictions of fluid dynamics (both steady and pulsatile flows) in a 3D curved tube (with a narrowing in the middle mimicking a stenosis) of any shape within a geometric range, using only geometric parameters and boundary conditions. We compare the unsupervised approach guided by physics governing equations (physics informed neural network or PINN) to the supervised approach of using mass CFD simulations to train the network (supervised network or SN). We find that multi-case PINN can generate accurate velocity, pressure and wall shear stress (WSS) results under steady flow (spatially maximum error < 2-5 %), but this requires a specific enhancement strategies: (1) estimating the curvilinear coordinate parameters via a secondary NN to use as inputs into PINN, (2) imposing no-slip wall boundary condition as a hard constraint, and (3) advanced strategy to better spatially propagate effects of boundary conditions. However, we cannot achieve reasonable accuracy for pulsatile flow with PINN. Conversely, SN provides very accurate velocity, pressure, and WSS predictions under both steady and pulsatile flow scenarios (spatially and/or temporally maximum error averaged over all geometries <1 %), and is much less computationally expensive to train. To achieve this, strategies (1) and (2) above and a spectral encoding strategy for pulsatile flow are necessary. Thus, interestingly, the use of physics constraints is not effective in our application.

PMID:40147188 | DOI:10.1016/j.compbiomed.2025.110074

Am J Hosp Palliat Care. 2025 Mar 27:10499091251329920. doi: 10.1177/10499091251329920. Online ahead of print.

ABSTRACT

BackgroundBreathlessness is a distressing and prevalent symptom in fibrotic interstitial lung disease. Dyspnea management requires systematic assessment including patients' lived experiences; however, most dyspnea tools are point-in-time numerical severity scales. The Edmonton Dyspnea Inventory was developed to assess severity at rest, during activities of daily living and self-reported activities. It enables documentation of crisis dyspnea episodes and triggers clinicians to guide action plans and dyspnea management. This study is part of a larger project to validate the tool. The purpose was to describe patient perceptions of assessment of breathlessness of patient use of the tool.MethodsPatients with fibrotic interstitial lung disease were invited to share their perceptions and experiences of breathlessness and the tool. Focus groups were led on Zoom©, with patient-participants in their homes. Data were analysed with inductive content analysis for development of themes.ResultsThirteen patients participated in 2 focus groups. There were 4 major themes, each with minor themes: physicians need to explicitly ask about breathlessness; the tool conveys breathlessness and disease progression; the tool increases self-awareness of breathlessness and complexity; and the tool helps prevent crises and manage breathlessness. Patient-participants perceived the tool provided the needed language and means to focus and relay their breathlessness to others.ConclusionPatient-participants reported the tool was easy to understand and integrate in daily living. They recommended its use for general and specialized practitioners. Developed to assess breathlessness, the tool may provide a framework to promote patient self-awareness, describe individual progression, and tailor breathlessness self-management.

PMID:40147029 | DOI:10.1177/10499091251329920

Physiol Plant. 2025 Mar-Apr;177(2):e70181. doi: 10.1111/ppl.70181.

ABSTRACT

Oats (Avena sativa L) is a temperate cereal and an important healthy cereal cultivated for food and feed. Therefore, understanding drought responses in oats could significantly impact oat production under harsh climatic conditions. In particular, drought during anthesis (flowering) affects grain filling, quality and yield. Here, we characterised metabolite responses of two Mediterranean oat (Avena sativa L.) cultivars, Flega and Patones, during drought stress at anthesis. In the more drought-tolerant Patones, the developing grains from the top (older) and bottom (younger) spikelets of primary panicle were found to be larger in size in response to drought, suggesting accelerated grain development. Flega showed a more rapid transition to flowering and grain development under drought. The metabolomes of source (sheath, flag leaf, rachis) and sink (developing grains) tissues from Patones showed differential accumulation in fatty acids levels, including α-linolenic acid, sugars and amino acids with drought. Flega showed enhanced energy metabolism in both source and sink tissues. Lower levels of glutathione in source tissues and the accumulation of ophthalmic acid in the grains of Flega were indicators of oxidative stress. Our study revealed two distinct metabolite regulatory patterns in these cultivars during drought at anthesis. In Patones, α-linolenic acid-associated processes may accelerate grain-filling, while in Flega oxidative stress appears to influence traits such as flowering time. Overall, this work provides a first insight into the metabolite regulation in oat's source and sink tissues during anthesis under drought stress.

PMID:40148256 | DOI:10.1111/ppl.70181

J Sci Med Sport. 2025 Mar 5:S1440-2440(25)00062-3. doi: 10.1016/j.jsams.2025.02.009. Online ahead of print.

ABSTRACT

OBJECTIVES: Durability is emerging as a key performance determinant in cycling, but scarce evidence exists on the durability of female cyclists, and particularly on whether there are sex differences. We therefore aimed to determine potential sex differences in durability.

DESIGN: Observational field-based study.

METHODS: Power output data from training and competitions were registered in female and male professional cyclists (n = 42 each) during 1-5 seasons. Participants' highest power output values achieved for different effort durations (10 s, 1 min, 5 min, and 20 min) (or 'record power profile') were determined under non-fatigued conditions (0 kJ/kg) and after varying levels of accumulated work (10, 20 and 30 kJ/kg).

RESULTS: A significant reduction in the record power profile compared with non-fatigued conditions was observed after > 10 kJ/kg in both female and male cyclists (p < 0.001), with no significant impairment observed below this level of accumulated work (p > 0.05 for all). A similar relative decay (% decline compared with the fresh condition) was observed between sexes for 10-s efforts (p > 0.05). However, a significantly higher relative decay was observed in female cyclists after 20 kJ/kg for 1-min, 5-min, and 20-min efforts (4 %, 4 % and 2 %, respectively; p < 0.05), with these differences enlarging after 30 kJ/kg (8 %, 6 % and 7 %; p < 0.001).

CONCLUSIONS: Professional female cyclists show a greater relative decay in the record power profile after a given accumulated work compared to male cyclists, which might reflect a lower durability.

PMID:40148210 | DOI:10.1016/j.jsams.2025.02.009

J Cyst Fibros. 2025 Mar 26:S1569-1993(25)00079-7. doi: 10.1016/j.jcf.2025.03.011. Online ahead of print.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) is a disease triggered by more than 2,100 variants in a single gene encoding for the CF Transmembrane Conductance Regulator (CFTR) protein, which is expressed in epithelial cells, where it functions an anion channel. A new era of high-throughput technologies ('omics') enabled the production and exploration of large CF-related datasets with unprecedented detail. However, this knowledge is scattered among different resources thus requiring a significant amount of time and training to collect and exploit. The objective of this work is to build a resource (CyFidb) that concentrates CF-related information in a single repository.

METHODS: This tool results from the intense manual curation of 407 scientific articles, including studies with CFTR variants in distinct conditions, drug treatments and cells/tissues.

RESULTS: CyFidb is divided into three levels of information: protein-protein interactions, gene expression and functional studies, from which it is possible to search and extract information.

CONCLUSIONS: CyFidb is an open-access resource (https://cyfidb.di.fc.ul.pt) designed to provide continuously updated, curated information on CFTR variants and their associated biological data.

PMID:40148144 | DOI:10.1016/j.jcf.2025.03.011

Mol Cell Proteomics. 2025 Mar 25:100956. doi: 10.1016/j.mcpro.2025.100956. Online ahead of print.

ABSTRACT

Quantitative mass spectrometry-based proteomics of extracellular vesicles (EVs) provides systems-level exploration for the analysis of snakebite envenoming (SBE) as the venom progresses, causing injuries such as hemorrhage, trauma, and death. Predicting EV biomarkers has become an essential aspect of this process, offering an avenue to explore the specific pathophysiological changes that occur after envenoming. As new omics approaches emerge to advance our understanding of SBE, further bioinformatics analyses are warranted to incorporate the use of antivenom or other therapeutics to observe their global impact on various biological processes. Herein, we used an in vivo BALB/c mouse model and proteomics approach to analyze the physiological impacts of SBE and antivenom neutralization in intact animals; this was followed by bioinformatics methods to predict potential EV biomarkers. Groups of mice (n=5) were intramuscularly injected with Saline or Crotalus atrox venom. After 30 minutes, the mice received saline or antivenom (ANTIVIPMYN®) by intravenous injection. After 24 hours, blood was collected to extract the plasma to analyze the EV content and determine the exposome of C. atrox venom as well as the neutralizing capabilities of the antivenom. The predicted biomarkers consistently and significantly sensitive to antivenom treatment are Slc25a4, Rps8, Akr1c6, Naa10, Sult1d1, Hadha, Mbl2, Zc3hav, Tgfb1, Prxl2a, Coro1c, Tnni1, Ryr3, C8b, Mycbp, and Cfhr4. These biomarkers pointed towards specific physiological alterations, causing significant metabolic changes in mitochondrial homeostasis, lipid metabolism, immunity, and cytolysis, indicating hallmarks of traumatic injury. Here, we present a more comprehensive view of murine plasma EV proteome and further identify significant changes in abundance for potential biomarkers associated with antivenom treatment. The predicted biomarkers have the potential to enhance current diagnostic tools for snakebite management, thereby contributing significantly to the evolution of treatment strategies in the diagnosis and prognosis of SBE.

PMID:40147718 | DOI:10.1016/j.mcpro.2025.100956

Cell Rep Med. 2025 Mar 24:102029. doi: 10.1016/j.xcrm.2025.102029. Online ahead of print.

ABSTRACT

Peripheral T cell lymphomas (PTCLs) comprise heterogeneous malignancies with limited therapeutic options. To uncover targetable vulnerabilities, we generate a collection of PTCL patient-derived tumor xenografts (PDXs) retaining histomorphology and molecular donor-tumor features over serial xenografting. PDX demonstrates remarkable heterogeneity, complex intratumor architecture, and stepwise trajectories mimicking primary evolutions. Combining functional transcriptional stratification and multiparametric imaging, we identify four distinct PTCL microenvironment subtypes with prognostic value. Mechanistically, we discover a subset of PTCLs expressing Epstein-Barr virus-specific T cell receptors and uncover the capacity of cancer-associated fibroblasts of counteracting treatments. PDXs' pre-clinical testing captures individual vulnerabilities, mirrors donor patients' clinical responses, and defines effective patient-tailored treatments. Ultimately, we assess the efficacy of CD5KO- and CD30- Chimeric Antigen Receptor T Cells (CD5KO-CART and CD30_CART, respectively), demonstrating their therapeutic potential and the synergistic role of immune checkpoint inhibitors for PTCL treatment. This repository represents a resource for discovering and validating intrinsic and extrinsic factors and improving the selection of drugs/combinations and immune-based therapies.

PMID:40147445 | DOI:10.1016/j.xcrm.2025.102029