Drug Test Anal. 2025 Apr 13. doi: 10.1002/dta.3887. Online ahead of print.

ABSTRACT

Pharmacological potential of Hypoxen, previously registered as Olifen is evaluated herein. Hypoxen is categorized as antihypoxic agent. The active substance is polydihydroxyphenylene thiosulfonate sodium. Human studies are limited and no clinical trials following international standards is available. There is however a developed body of knowledge emerging from original studies conducted by the Russian Military Medical Academy in 1980s and 1990s despite limited online access. Hypoxen is promoted to improve oxygen supply or reduce oxygen consumption under hypoxic conditions and physical load. It is thought to support faster recovery, and can be used in complex treatments of diseases accompanied by hypoxia like myocardial ischemia. From clinical perspective, it may enhance cellular respiration by improving coupling in the respiratory chain/accelerating oxidative phosphorylation, but also inhibit succinate dehydrogenase (SDH), and activate mitochondrial ATP-sensitive potassium channels (mitoKATP) in skeletal muscles and myocardium. In 2023, the World Anti-Doping Agency (WADA) added Hypoxen to the Monitoring Program as there had been documented evidence of its use by athletes. On in vitro experiments compared the influence of Hypoxen on oxidative phosphorylation with mitochondrial uncoupling agent 2,4-dinitrophenol (DNP) a unique metabolic modulator that strongly accelerates the metabolism rate, prohibited since 2024 by WADA. Most studies focus on exercise performance, and may provide some evidence that Hypoxen has the potential to enhance performance, the first criteria considered for addition of substance to the WADA Prohibited List. Pharmacodynamics and ergogenic effects of Hypoxen suggests potential as metabolic modulator.

PMID:40223246 | DOI:10.1002/dta.3887

Exp Clin Transplant. 2025 Mar;23(3):220-226. doi: 10.6002/ect.2024.0243.

ABSTRACT

OBJECTIVES: Lung transplant recipients are vulnerable to respiratory infections because of their compromised immune response. Limited research has been published on mental health as a result of the COVID-19 pandemic on lung transplant recipients, and uncertainty remains whether the COVID-19 vaccine affected mental health in lung transplant recipients.

MATERIALS AND METHODS: In this longitudinal, retrospective study, we assessed the psychosocial wellbeing of lung transplant recipients during the COVID-19 pandemic at 2 different time points (before and after COVID-19 vaccination). We measured wellbeing with the Hospital Anxiety and Depression Scale (cutoff of 11 points indicated anxiety and depression) and the Symptom Checklist consisting of 9 questions.

RESULTS: Our study included 83 patients (mean age 52.4 ± 14.5 years, 55.4% male). Among the patients, 3.8% and 4.8% of patients with cystic fibrosis had abnormal values for anxiety before and after the vaccine, respectively; abnormal values for depression were shown in 0% and 2.4% of patients with cystic fibrosis before and after the vaccine, respectively. Sex, age, level of education, time since transplant, and chronic allograft dysfunction were not significantly associated with psychosocial wellbeing. Vaccination against COVID-19 was not associated with a change in psychosocial wellbeing.

CONCLUSIONS: We found no evidence that the COVID-19 vaccine affected the psychosocial wellbeing of lung transplant recipients. However, it may be important to monitor wellbeing closely during a pandemic, especially in patients with cystic fibrosis.

PMID:40223386 | DOI:10.6002/ect.2024.0243

Eur J Ophthalmol. 2025 Apr 13:11206721251318384. doi: 10.1177/11206721251318384. Online ahead of print.

ABSTRACT

Refractive error is among the leading causes of visual impairment globally. The diagnosis and management of refractive error has traditionally relied on comprehensive eye examinations by eye care professionals, but access to these specialized services has remained limited in many areas of the world. Given this, artificial intelligence (AI) has shown immense potential in transforming the diagnosis and management of refractive error. We review AI applications across various aspects of refractive error care - from axial length prediction using fundus images to risk stratification for myopia progression. AI algorithms can be trained to analyze clinical data to detect refractive error as well as predict associated risks of myopia progression. For treatments such as implantable collamer and orthokeratology lenses, AI models facilitate vault size prediction and optimal lens fitting with high accuracy. Furthermore, AI has demonstrated promise in optimizing surgical planning and outcomes for refractive procedures. Emerging digital technologies such as telehealth, smartphone applications, and virtual reality integrated with AI present novel avenues for refractive error screening. We discuss key challenges, including limited validation datasets, lack of data standardization, image quality issues, population heterogeneity, practical deployment, and ethical considerations regarding patient privacy that need to be addressed before widespread clinical implementation.

PMID:40223314 | DOI:10.1177/11206721251318384

Respirology. 2025 Apr 13. doi: 10.1111/resp.70043. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Idiopathic pulmonary fibrosis is a progressive interstitial lung disease characterised by excessive activation of myofibroblasts. However, currently available antifibrotic drugs exhibit limited efficacy. The dysregulation of redox processes plays a significant role in the pathogenesis of idiopathic pulmonary fibrosis. Dextromethorphan (DM) is used in the treatment of various inflammation-related diseases. This study aimed to investigate the effectiveness of the combination of DM and pirfenidone (PFD) in treating idiopathic pulmonary fibrosis in both animal models and humans.

METHODS: In a bleomycin-induced pulmonary fibrosis mouse model, the anti-fibrotic effects of DM and/or PFD were assessed by evaluating fibrotic area, hydroxyproline levels, and fibrotic markers. In a transforming growth factor-β1-induced cell model, proliferation, migration, fibrosis markers, and oxidative stress were analysed to elucidate the mechanisms underlying the anti-fibrotic actions of DM and/or PFD. Finally, the efficacy of DM combined with PFD in patients with pulmonary fibrosis was evaluated by comparing pulmonary imaging scores and pulmonary function before and after treatment in the PFD group and the PFD + DM group.

RESULTS: We observed that even ultralow doses of DM, either alone or in combination with PFD, demonstrated substantial protective effects in mice. Notably, administration of DM or combined drugs at 2 weeks after bleomycin modelling still showed anti-fibrotic effects. In vitro, DM monotherapy and combination therapy restored the redox balance by suppressing nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 4/reactive oxygen species production and upregulating superoxide dismutase, contributing to their anti-fibrotic mechanisms. In the clinical study, add-on DM improved PFD in mitigating pulmonary function decline and improving chest high-resolution computed tomography imaging scores.

CONCLUSIONS: Ultralow doses of dextromethorphan significantly alleviate pulmonary fibrosis in bleomycin-treated mice through restoring the redox balance. Add-on DM improves the effects of PFD in both bleomycin-treated mice and patients with pulmonary fibrosis.

TRIAL REGISTRATION: ChiCTR2000037602.

PMID:40223283 | DOI:10.1111/resp.70043

Clin Exp Med. 2025 Apr 13;25(1):117. doi: 10.1007/s10238-025-01631-0.

ABSTRACT

Glioblastoma (GBM) is an aggressive Grade IV brain tumor with a poor prognosis. It results from genetic mutations, epigenetic changes, and factors within the tumor microenvironment (TME). Traditional treatments like surgery, radiotherapy, and chemotherapy provide limited survival benefits due to the tumor's heterogeneity and resistance mechanisms. This review examines novel approaches for treating GBM, focusing on repurposing existing medications such as antipsychotics, antidepressants, and statins for their potential anti-GBM effects. Advances in molecular profiling, including next-generation sequencing, artificial intelligence (AI), and nanotechnology-based drug delivery, are transforming GBM diagnosis and treatment. The TME, particularly GBM stem cells and immune evasion, plays a key role in therapeutic resistance. Integrating multi-omics data and applying precision medicine show promise, especially in combination therapies and immunotherapies, to enhance clinical outcomes. Addressing challenges such as drug resistance, targeting GBM stem cells, and crossing the blood-brain barrier is essential for improving treatment efficacy. While current treatments offer limited benefits, emerging strategies such as immunotherapies, precision medicine, and drug repurposing show significant potential. Technologies like liquid biopsies, AI-powered diagnostics, and nanotechnology could help overcome obstacles like the blood-brain barrier and GBM stem cells. Ongoing research into combination therapies, targeted drug delivery, and personalized treatments is crucial. Collaborative efforts and robust clinical trials are necessary to translate these innovations into effective therapies, offering hope for improved survival and quality of life for GBM patients.

PMID:40223032 | DOI:10.1007/s10238-025-01631-0

Crit Rev Oncol Hematol. 2025 Apr 11:104720. doi: 10.1016/j.critrevonc.2025.104720. Online ahead of print.

ABSTRACT

Chemotherapy-induced toxicities remain challenging in pediatric oncology, affecting patient outcomes, hospital stays, and quality of life. Genetic variation can partly explain these toxicities, and pharmacogenomics could potentially optimize treatment. This review provides an overview of pharmacogenomic studies in relation to chemotherapy-induced toxicity in children with solid tumors. A systematic literature search was performed in PubMed, Embase, and Web of Science following PRISMA guidelines. Two independent reviewers assessed eligibility, risk of bias using ROBINS-I, and extracted data. Out of 9000 articles screened, 279 were deemed relevant, and 59 met the inclusion criteria by focusing on children with solid tumors and pharmacogenomics in relation to chemotherapy-induced toxicity. Following risk of bias assessment, 24 articles with low to moderate risk of bias were summarized. Identifying specific SNPs associated with toxicities proved challenging due to variability across studies. For methotrexate, the genes ABCC2, MTHFR, and SXR were associated with myelosuppression and hepatotoxicity. The genes ABCC3, COMT, ERCC2, GSTP1, GSTT1, LRP2, SLC22A2, and TPMT showed associations with ototoxicity due to platinum-based drugs. Anthracycline-induced cardiotoxicity was associated with CBR2, CELF4, GSTM1, HAS3, RARG, and SLC28A3, and further with HNMT and SLC22A2 in younger children, with ABCB4 in females, and with SULT2B1 in males. A dose-dependent effect of CELF4 on cardiotoxicity was noted with anthracycline doses over 300mg/m². This review highlights the complexity and variability of pharmacogenomic associations with chemotherapy-induced toxicities in pediatric oncology. While certain genetic variants show associations with specific toxicities, larger multinational/center studies are needed to strengthen the associations and improve clinical guidelines.

PMID:40222694 | DOI:10.1016/j.critrevonc.2025.104720

Brain Stimul. 2025 Apr 11:S1935-861X(25)00094-4. doi: 10.1016/j.brs.2025.04.013. Online ahead of print.

ABSTRACT

BACKGROUND: Reduced inhibitory control is associated with obesity and neuroimaging studies indicate that diminished prefrontal cortex activity influence eating behavior and metabolism. The hypothalamus regulates energy homeostasis and is functionally connected to cortical and subcortical regions especially the frontal areas.

OBJECTIVES: We tested network-targeted transcranial direct current stimulation (net-tDCS) to influence the excitability of brain regions involved in appetite control.

METHODS: In a randomized, double-blind parallel group design, 44 adults with overweight or obesity (BMI 30.6 kg/m2, 52.3 % female) received active (anodal or cathodal) or sham 12-channel net-tDCS on the hypothalamus appetite-control network for 25 minutes on three consecutive days while performing a Stop-Signal-Task to measure response inhibition. Before and after stimulation, state questionnaires assessed changes in desire to eat and food craving. Directly after stimulation, participants received a breakfast buffet to evaluate ad-libitum food intake. An oral glucose tolerance test was conducted at follow-up. Resting-state functional MRI was obtained at baseline and follow-up.

RESULTS: The Stop-Signal Reaction Time (SSRT) was shorter in both active groups versus sham, indicating improved response inhibition. Additionally, a stronger increase in hypothalamic functional connectivity was associated with shorter SSRT. Caloric intake of sweet food was lower in the anodal group versus sham, but no main effects between groups were observed on total and macronutrient intake, food craving ratings and desire to eat. At follow-up, no differences were observed between groups on peripheral metabolism.

CONCLUSION: Our study suggests that modulating hypothalamic functional network connectivity patterns via net-tDCS may improve food choice and inhibitory control.

PMID:40222666 | DOI:10.1016/j.brs.2025.04.013

Biochimie. 2025 Apr 11:S0300-9084(25)00067-7. doi: 10.1016/j.biochi.2025.04.003. Online ahead of print.

ABSTRACT

Prostanoids are lipid mediators of human body that involved in the inflammation and platelet aggregation. Prostacyclin is a vasodilator and inhibitor of platelet aggregation, and a product of the enzymatic reaction catalyzed by prostacyclin synthase (PTGIS). Thromboxane is a vasoconstrictor and synthesized by thromboxane synthase (TBXAS1). An imbalance of prostanoids can accompany cardio-/ cerebrovascular diseases and cancers. PTGIS and TBXAS1 are clinically relevant membrane-bound enzymes of the multigene family of cytochromes P450 (CYPs), also known as CYP8A1 and CYP5A1, respectively. Particular studies of these functional antagonists will contribute to elucidation of pathogenic mechanisms. The purpose of this work was to analyze the literature landscape over a period of 2020-2024 in the field of biological, pharmacogenomic, and pharmacological features of PTGIS and TBXAS1 as well as to explore the potential of their regulation at the post-transcriptional and post-translational levels using systems biological analysis. The review discusses recent findings on the novel features of both synthases established in gene knockout and overexpression experiments, aspects of current preclinical pharmacology, and potential ways of gene expression regulation. Identification of protein-protein interactions and post-translational modifications appear to be the main options for modulating of PTGIS and TBXAS1 activity. The microsomal CYPs used to form complexes with each other and direct interactions of CYP2E1 with both synthases can probably lead to modulation of their activity. A progress in the preclinical development of low molecular weight compounds as inhibitors of TBXAS1 is more prospective than PTGIS that is applied as a gene therapy biological for in vivo production of prostacyclin due to its noticeable anticancer and vasodilator effects.

PMID:40222477 | DOI:10.1016/j.biochi.2025.04.003

Dig Liver Dis. 2025 Apr 12:S1590-8658(25)00299-3. doi: 10.1016/j.dld.2025.03.021. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: The SCOPE (Sclerosing Cholangitis Outcomes in Pediatrics) index was developed to provide the first pediatric prognostic score for primary sclerosing cholangitis (PSC), but its efficacy has yet to be confirmed. We aimed to assess its ability to predict liver transplantation (LT) over a 5-year follow-up.

METHODS: We retrospectively included PSC-diagnosed patients under 18 years of age from two European tertiary-care centers. The SCOPE index was calculated at diagnosis and at 1, 3, and 5 years post-diagnosis. The ability of the SCOPE index to predict LT was assessed using multivariate Cox regression and ROC curve analysis.

RESULTS: Sixty patients were included. In transplanted patients, the mean SCOPE index at diagnosis was similar to non-transplanted patients, but significantly higher at 1, 3, and 5 years post-diagnosis (p < 0.001, p = 0.009, p = 0.006, respectively). Patients with overlapping autoimmune hepatitis (AIH) had higher SCOPE at diagnosis (p = 0.005), but this difference diminished over time. The SCOPE index was a significant predictor of LT at various time points (HRs: 1.32 to 3.44) and showed good-to-excellent discriminative power (AUC 0.87 at diagnosis; 0.97 at 1 year).

CONCLUSIONS: The SCOPE index effectively predicts LT in pediatric PSC, with strong reliability over time. Coexisting AIH may affect score accuracy at diagnosis due to inflammation.

PMID:40222859 | DOI:10.1016/j.dld.2025.03.021

J Cyst Fibros. 2025 Apr 12:S1569-1993(25)00765-9. doi: 10.1016/j.jcf.2025.03.669. Online ahead of print.

ABSTRACT

Bacteriophages (phages) are viruses that selectively infect bacteria and have been utilized to treat Mycobacterium abscessus (Mab) with varying success. The POSTSTAMP study is an ongoing, multi-site phage therapy protocol for treatment-refractory pulmonary Mab disease in people with cystic fibrosis (pwCF). Participants (n = 10) are prospectively assessed while utilizing FDA investigational new drug (IND) approval for compassionate use. Participants are >6 years old, able to produce sputum, have been treated with guideline-based antibiotic therapy (GBT) for >12 months without culture conversion, and are currently receiving GBT with at least 3 and ≥ 80 % positive Mab cultures in the prior year. At enrollment, an isolate is assessed for the availability of lytic phage(s). Open-label phage therapy consists of 1 or 2 phages administered intravenously twice daily for 52 weeks. Participants without a phage match will be followed on GBT as a comparison group. Follow-up visits will occur monthly, with one follow-up visit at completion and intermittent visits for a year after phage therapy. Efficacy will be assessed by culture, standard clinical measures and a patient-reported quality-of-life instrument. Frequency of Mab detection 12 months prior to treatment will be compared with the 12-month period beginning 6 months after treatment initiation. Individual-level tests of difference in percent positive cultures within subjects will be used to identify "responders". Collectively and including all persons, a mixed-effect model will be used to test for a difference in frequency of Mab detection following treatment or without treatment. The trial will also test for markers of treatment failure and pathogen adaptation in participants who did not achieve microbiological response, and will monitor for safety and tolerance.

PMID:40222858 | DOI:10.1016/j.jcf.2025.03.669

Sci Rep. 2025 Apr 13;15(1):12742. doi: 10.1038/s41598-025-93766-z.

ABSTRACT

Efficient image augmentation for hyperspectral satellite images requires design of multiband processing models that can assist in improving classification performance for different application scenarios. Existing models either work on dynamic band fusions, or use deep learning techniques for identification of application-specific augmentation operations. Moreover, these models use static augmentations, and do not take into consideration image-specific parameters which limits their efficiency levels. To overcome these limitations, this text proposes design of a novel multimodal object-detection based augmentation model for satellite image sets. The proposed model initially applies a customized YOLO (You Only Look Once) based object detection technique on each of the hyperspectral image bands. This is followed by a context-specific classification layer that assists in identification of detected object types. The identified objects are analyzed via a cascaded dual Generative Adversarial Network (cdGAN), which estimates an object-level importance metric, which is used to evaluate its importance probability levels. Based on these probability levels, an Elephant Herding Optimization (EHO) based hyperspectral band-selection model is used, which assists in identification of high priority image bands for classification purposes. Augmentations on these image bands is controlled via a Firefly Optimizer (FFO) which assists in identification of object-level augmentations for efficient classification of satellite images. The augmented image sets are updated via an Incremental Learning (IL) layer that assists in continuous improvement of accuracy levels for different application scenarios. Due to these optimizations, the proposed model is able to improve classification accuracy by 8.5%, precision by 4.3%, recall by 6.5%, while reducing classification delay by 2.9% when compared with existing augmentation-based classification techniques.

PMID:40223115 | DOI:10.1038/s41598-025-93766-z

Sci Rep. 2025 Apr 13;15(1):12754. doi: 10.1038/s41598-025-97610-2.

ABSTRACT

Climate change poses a significant threat to flood-prone areas by altering precipitation patterns and the water cycle. Here, we analyzed the impact of climate change on future flood trends. We trained a Long Short-Term Memory (LSTM) model to estimate long term discharge at 638 river sites over contiguous United States (CONUS) based on inputs from the gridMET meteorological datasets, and downscaled and bias-corrected Coupled Model Intercomparison Project 5 (CMIP5) projections. Our results indicate that the LSTM model can replicate observed discharge with reliable accuracy. The projected changes in flood magnitude for the 10-year and 100-year return periods reveal consistent geographical patterns robust across climate models, with increasing trends of approximately + 10 to + 40% in the East and West coastal regions and decreasing trends of about - 10 to - 30% in the Southwestern areas. The regions exhibiting an increasing flood trend are likely driven by an increase in total seasonal extreme precipitation and changes in the timing and amount of peak flow. In contrast, the decreasing flood trends result from a significant reduction in snowpack. To support adaptation planning, we developed an interactive map providing the historical and projected flood changes for 10- and 100-year floods across the 638 selected basins over CONUS.

PMID:40222992 | DOI:10.1038/s41598-025-97610-2

Sci Rep. 2025 Apr 13;15(1):12748. doi: 10.1038/s41598-025-97927-y.

ABSTRACT

In order to gain a more accurate understanding and enhance the relationship between the fitness ecological environment and artificial intelligence (AI)-driven sports public services, this study combines a Convolutional Neural Network (CNN) approach based on residual modules and attention mechanisms with the SERVQUAL evaluation model. The method employed involves the analysis of big data collected from questionnaire surveys, literature reviews, and interviews. This study critically examines the impact of advanced AI technologies on residents' satisfaction with the fitness ecological environment in sports public services and conducts theoretical analysis of the obtained data. The results show that the quality of sports public services empowered by AI significantly influences residents' satisfaction with the fitness ecological environment, such as running, swimming, ball games and other sports with high requirements for sports service quality and ecological environment. Only the good public sports service quality matching with them can meet the needs of the ecological environment for fitness, and stimulate the enthusiasm of the people for fitness. The study also shows that swimming, running and all kinds of ball games account for the largest proportion of all sports. To sum up, the satisfaction of residents' fitness ecological environment is greatly affected by the quality of public sports services, which is mainly reflected in the good and perfect sports environment and facilities that can provide residents with a wealth of fitness options, greatly improving the sports ecological environment. This study is helpful to realize the relationship between sports public service and sports ecological environment. It contributes to understanding the role of AI and deep learning in enhancing the correlation between sports public service and the ecological environment of sports.

PMID:40222989 | DOI:10.1038/s41598-025-97927-y

Nat Commun. 2025 Apr 13;16(1):3506. doi: 10.1038/s41467-025-58883-3.

ABSTRACT

Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. The DL model achieves robust performance across nine hospitals. In a multi-reader, multi-case study, it outperforms cytopathologists' sensitivity by 9%. Reading time significantly decreases with DL assistance (218s vs 30s; p < 0.0001). In community-based organized screening, the DL model's sensitivity matches that of senior cytopathologists (0.878 vs 0.854; p > 0.999), yet it has reduced specificity (0.831 vs 0.901; p < 0.0001). Notably, hospital-based opportunistic screening shows that junior cytopathologists with DL assistance significantly improve both their sensitivity and specificity (0.857 vs 0.657, 0.840 vs 0.737; both p < 0.0001). When triaging human papillomavirus-positive cases, DL assistance exhibits better performance than junior cytopathologists alone. These findings support using the DL model as an assistance tool in cervical screening and case triage.

PMID:40222978 | DOI:10.1038/s41467-025-58883-3

Sci Rep. 2025 Apr 13;15(1):12727. doi: 10.1038/s41598-025-94677-9.

ABSTRACT

In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate Dementia (MOD), Non-Demented (ND), and Very Mild Dementia (VMD). The proposed DSR-GAN is implemented using a PyTorch library and uses a dataset of 6,400 MRI images. A super-resolution (SR) technique is applied to enhance the clarity and detail of the images, allowing the DSR-GAN to refine particular image features. The CNN model undergoes hyperparameter optimization and incorporates data augmentation strategies to maximize its efficiency. The normalized error matrix and area under ROC curve are used experimentally to evaluate the CNN's performance which achieved a testing accuracy of 99.22%, an area under the ROC curve of 100%, and an error rate of 0.0516. Also, the performance of the DSR-GAN is assessed using three different metrics: structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and multi-scale structural similarity index measure (MS-SSIM). The achieved SSIM score of 0.847, while the PSNR and MS-SSIM percentage are 29.30 dB and 96.39%, respectively. The combination of the DSR-GAN and CNN models provides a rapid and precise method to distinguish between various stages of Alzheimer's disease, potentially aiding professionals in the screening of AD cases.

PMID:40222973 | DOI:10.1038/s41598-025-94677-9

Neuroimage. 2025 Apr 11:121212. doi: 10.1016/j.neuroimage.2025.121212. Online ahead of print.

ABSTRACT

Short-range association fibers located in the superficial white matter play an important role in mediating higher-order cognitive function in humans. Detailed morphological characterization of short-range association fibers at the microscopic level promises to yield important insights into the axonal features driving cortico-cortical connectivity in the human brain yet has been difficult to achieve to date due to the challenges of imaging at nanometer-scale resolution over large tissue volumes. This work presents results from multi-beam scanning electron microscopy (EM) data acquired at 4 × 4 × 33 nm3 resolution in a volume of human superficial white matter measuring 200 × 200 × 112 μm (Braitenberg and Schüz, 2013), leveraging automated analysis methods. Myelin and myelinated axons were automatically segmented using deep convolutional neural networks (CNNs), assisted by transfer learning and dropout regularization techniques. A total of 128,285 myelinated axons were segmented, of which 70,321 and 2,102 were longer than 10 and 100 μm, respectively. Marked local variations in diameter (i.e., beading) and direction (i.e., undulation) were observed along the length of individual axons. Myelinated axons longer than 10 μm had inner diameters around 0.5 µm, outer diameters around 1 µm, and g-ratios around 0.5. This work fills a gap in knowledge of axonal morphometry in the superficial white matter and provides a large 3D human EM dataset and accurate segmentation results for a variety of future studies in different fields.

PMID:40222502 | DOI:10.1016/j.neuroimage.2025.121212

Neuroimage. 2025 Apr 11:121210. doi: 10.1016/j.neuroimage.2025.121210. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its early detection and precise monitoring. Within this realm, PET/MR imaging stands out as a potent dual-modality approach that transforms sensor data into detailed perceptual mappings, thereby enriching our grasp of brain pathophysiology. To capitalize on the strengths of PET/MR imaging in diagnosing AD, we have introduced a novel deep learning framework named "IT", which is inspired by the Transformer architecture. This innovative model adeptly captures both local and global characteristics within the imaging data, refining these features through advanced feature engineering techniques to achieve a synergistic integration. The efficiency of our model is underscored by robust experimental validation, wherein it delivers superior performance on a host of evaluative benchmarks, all while maintaining low demands on computational resources. Furthermore, the features we extracted resonate with established medical theories regarding feature distribution and usage efficiency, enhancing the clinical relevance of our findings. These insights significantly bolster the arsenal of tools available for AD diagnostics and contribute to the broader narrative of deciphering brain functionality through state-of-the-art imaging modalities.

PMID:40222500 | DOI:10.1016/j.neuroimage.2025.121210

Comput Methods Programs Biomed. 2025 Apr 8;266:108754. doi: 10.1016/j.cmpb.2025.108754. Online ahead of print.

ABSTRACT

OBJECTIVE: Early fluid resuscitation is crucial in the treatment of sepsis, yet the optimal dosage remains debated. This study aims to determine the optimal multi-stage fluid resuscitation dosage for sepsis patients.

METHODS: We propose a reinforcement learning algorithm with neural networks (RL-NN), utilizing the flexibility of deep learning architectures to mitigate model misspecification. We use cross-validation and random search for hyperparameter tuning to further enhance model robustness and generalization.

RESULTS: Simulation results demonstrate that our method outperforms existing methods in terms of both the percentage of correctly classified optimal treatments and the predicted counterfactual mean outcome. Applying this method to the sepsis cohort from the Medical Information Mart for Intensive Care III (MIMIC-III), we recommend that all sepsis patients receive adequate fluid resuscitation (≥ 30 mL/kg) within the first 3 h of admission to the MICU. Our approach is expected to significantly reduce the mean SOFA score by 23.71%, enhancing patient outcomes.

CONCLUSION: Our RL-NN method offers an accurate, real-time approach to optimizing sepsis treatment and aligns with the 'Surviving Sepsis Campaign' guidelines. It also has the potential to be integrated with existing electronic health record (EHR) systems, guiding clinical decision-making and thereby improving patient prognosis.

PMID:40222267 | DOI:10.1016/j.cmpb.2025.108754

Eur Neuropsychopharmacol. 2025 Apr 12;95:40-48. doi: 10.1016/j.euroneuro.2025.03.013. Online ahead of print.

ABSTRACT

It is unclear if lithium and quetiapine have neuroprotective effects, especially in early stages of bipolar and schizoaffective disorders. Here, an age-related multivariate brain structural measure (i.e., brain-PAD) at baseline and changes in response to treatment after a first-episode mania (FEM) were examined. FEM participants were randomized to lithium (n=21) or quetiapine (n=18) monotherapy. T1-weighted scans were acquired at baseline, 3-months (FEM participants only) and 12-months. Brain age predictions for healthy controls (n=29) and young people with bipolar or schizoaffective disorder (15-25 years) were derived using a deep learning model trained on one of the largest datasets (N=53,542) to date. Notably, a higher brain-PAD value (predicted brain age - age) signifies an older-appearing brain. Baseline brain-PAD was higher in young people with FEM compared to controls (+1.70 year, p=0.04; Cohen's d=0.53 [SE=0.25], CI 95% [0.04 to 1.01]). However, no significant effects of time or treatment group, nor an interaction between the two, were observed throughout the course of the study. Baseline brain-PAD did not predict any change in symptomatic, quality of life or functional outcome scores over 12 months. In young individuals with FEM, baseline findings show their brains appeared older than controls. However, brain-PAD remained stable over time across treatment groups and neither baseline values nor treatment predicted 12-month outcomes. A longer follow-up with a larger sample is warranted to determine if treatment effects emerge later in bipolar and schizoaffective disorders. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry - ACTRN12607000639426.

PMID:40222151 | DOI:10.1016/j.euroneuro.2025.03.013

Br J Pharmacol. 2025 Apr 13. doi: 10.1111/bph.70043. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Activation of the sphingosine-1-phosphate receptor-2 (S1P2 receptor) promotes idiopathic pulmonary fibrosis (IPF). However, the mechanisms associated with IPF development via S1P2 receptor signalling are poorly understood and no S1P2 receptor antagonists have been approved for clinical use.

EXPERIMENTAL APPROACH: Western blotting and immunohistochemical assays analysed inflammatory factors and epithelial-mesenchymal transition (EMT) markers. Co-immunoprecipitation and immunofluorescence analysed the binding of S1P2 receptor to dapper1 (Dpr1) and cyclic AMP response-binding protein 1 (CREB1). X-ray-based computed tomography diagnosed IPF in bleomycin (BLM)-treated mice. Barometric whole-body plethysmography tested pulmonary function of mice. Masson's trichrome and Sirius red staining analysed extracellular matrix deposition. Enzyme-linked immunosorbent assays analysed inflammatory factors and hydroxyproline.

KEY RESULTS: Activation of S1P2 receptors promoted IPF through the binding of S1P2 receptor to Dpr1, decreasing dishevelled (Dvl) degradation to accumulate β-catenin. The β-catenin accumulated in the nucleus, upregulating its target genes by binding to T-cell factor/lymphoid enhancer factor. The binding of S1P2 receptor to Dpr1 also led to S1P2 receptor translocation to the nucleus, where it promoted EMT by activating CREB1. BLM-induced IPF in mice was characterised by activated-S1P2 receptor signalling. Inhibition of S1P2 receptor prevented the binding of S1P2 receptor to Dpr1, resulting in decreased β-catenin accumulation and blocking nuclear translocation of S1P2 receptor. The S1P2 receptor antagonist S118 was more effective than pirfenidone in attenuating IPF through anti-inflammatory, anti-fibrosis, and anti-EMT effects.

CONCLUSIONS AND IMPLICATIONS: Activation of S1P2 receptors promotes IPF through the binding of S1P2 receptor to Dpr1 and the nuclear translocation of S1P2 receptor to activate CREB1. Thus, the S1P2 receptor antagonist S118 has potential clinical application in attenuating IPF.

PMID:40222913 | DOI:10.1111/bph.70043