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

Int Immunopharmacol. 2025 Apr 12;155:114653. doi: 10.1016/j.intimp.2025.114653. Online ahead of print.

ABSTRACT

Idiopathic Pulmonary Fibrosis (IPF) is a progressive interstitial lung disease marked by increasing dyspnea and respiratory failure. The underlying mechanisms remain poorly understood, given the complexity of its pathogenesis. This review investigates the microenvironment of IPF to identify novel mechanisms and therapeutic avenues. Studies have revealed that various cell types, including alveolar epithelial cells, fibroblasts, myofibroblasts, and immune cells, are integral to disease progression, engaging in cellular stress responses and inflammatory regulation via signaling pathways such as TGF-β, Wnt, mTOR, and ROS. Non-coding RNAs, particularly miRNAs, are critical in IPF and may serve as diagnostic and prognostic biomarkers. Regarding treatment, mesenchymal stem cells (MSCs) and their extracellular vesicles (EVs) or non-vesicular derivatives offer promise by modulating immune responses, enhancing tissue repair, and inhibiting fibrosis. Additionally, alterations in the lung microbiota are increasingly recognized as a contributing factor to IPF progression, offering fresh insights into potential treatments. Despite the encouraging results of MSC-based therapies, the precise mechanisms and clinical applications remain subjects of ongoing research. This review emphasizes the significance of the IPF microenvironment and highlights the need for further exploration to develop effective therapies that could enhance patient outcomes.

PMID:40222273 | DOI:10.1016/j.intimp.2025.114653

Neuropsychopharmacology. 2025 Apr 13. doi: 10.1038/s41386-025-02092-5. Online ahead of print.

ABSTRACT

Refining previous learning when environmental contingencies change is a critical adaptive function. Studies have shown that systemic noradrenaline (NA) manipulations, as well as optogenetic manipulations of the locus coeruleus (LC), the primary source of forebrain NA, can improve long-term retention of appetitive extinction. To determine whether the contribution of NA is specific to extinction or extends to other forms of learning where reward is less than expected, we suppressed LC activity with clonidine, an α2A-adrenergic receptor agonist, in two tasks: compound extinction, where two previously rewarded cues are presented together and no longer rewarded, and overexpectation, where animals are presented with two previously rewarded cues but receive a single reward rather than the expected two. In compound extinction, we found no differences between groups in training, extinction, or a spontaneous recovery test. However, animals that received clonidine reacquired responding to the previously extinguished cue significantly faster than saline-treated animals, suggesting weakened extinction learning. In overexpectation testing, the saline group responded significantly less to a stimulus that had undergone overexpectation relative to a control stimulus, indicating that they had recalibrated their estimation of reward magnitude following training where reward was less than expected. In contrast, clonidine-treated animals did not differ in responding to the overexpectation versus control stimuli, suggesting that clonidine impaired learning resulting from overexpectation. These results demonstrate that activity of the LC is important for learning to reduce responding in both extinction and overexpectation paradigms.

PMID:40223132 | DOI:10.1038/s41386-025-02092-5

Cell Res. 2025 Apr 14. doi: 10.1038/s41422-025-01115-6. Online ahead of print.

NO ABSTRACT

PMID:40223018 | DOI:10.1038/s41422-025-01115-6

Biotechnol Adv. 2025 Apr 11:108579. doi: 10.1016/j.biotechadv.2025.108579. Online ahead of print.

ABSTRACT

Synthetic biology offers transformative opportunities to optimise Fusarium species as efficient platforms for the sustainable production of diverse bioproducts. Advanced engineering techniques, including CRISPR/Cas9, RNA interference and synthetic promoters, have enhanced the manipulation of metabolic pathways, enabling higher yields of industrially relevant compounds. Recent insights from next-generation sequencing and omics technologies have significantly expanded our understanding of Fusarium's metabolic networks, leading to more precise strain engineering. Despite these advances, challenges such as metabolic bottlenecks, regulatory complexities and strain stability remain significant barriers to industrial-scale applications. The development of efficient genetic tools, together with the expansion of our knowledge of Fusarium physiology and genetics thanks to systems biology approaches, holds promise to unlock Fusarium's full potential as a sustainable cell factory. This review focuses on the genetic and metabolic tools available to enhance Fusarium's capacity to produce biofuels, pharmaceuticals, enzymes and other valuable compounds. It also highlights key innovations and discusses future directions for leveraging Fusarium as an environmentally friendly bioproduction system.

PMID:40222460 | DOI:10.1016/j.biotechadv.2025.108579

Reprod Biol. 2025 Apr 12;25(2):101017. doi: 10.1016/j.repbio.2025.101017. Online ahead of print.

ABSTRACT

PCOS is an endocrine disorder that affects women of reproductive age. The root of PCOS is ovarian dysfunction, which presents as hormonal disturbances affecting normal ovarian function to cause the symptoms and complications of the disease. This dysfunction causes symptoms like impaired maturation of follicles and disorders of various origins with multiple treatment regimens that are not always clear. Therefore, the present review mainly concentrates on the genetic level of ovarian dysfunction of PCOS. The articles were identified through a vigorous literature search where search engines such as PubMed, Google Scholar, databases, and Science Direct were used, and the articles published from 2015 to 2025 were referred. We identified that the key genes involved in the ovarian dysfunctions in PCOS include CYP11A1, CYP17A1, CYP19A1, AR, FSHR, LHCGR, AMH, INSR, SHBG, IRS1, GATA4, ADIPOQ, YAP1, TCF7L2, and DENND1A, which play a role in gonadotropin action, steroidogenesis, and folliculogenesis. Furthermore, epigenetic factors and miRNAs miR-93, 222, 155, 146a, 132, 320, 27a, 483, 21, 378, 17-92 Cluster, and 375, 221 are also involved in it. Abnormal expression of these genes is known to play a critical role in the etiology and pathogenesis of PCOS. Present treatment includes the use of oral contraceptives, anti-androgen agents, insulin-sensitizing agents, and ovulation-inducing agents, and future treatment may consist of miRNA therapy, drug repositioning, and genetic markers that might be used for early identification and better management of ovarian dysfunction. Thus, the current review discusses ovarian dysfunction in PCOS, the involvement of potential genes and epigenetic factors, and miRNAs concerning ovulation and its therapeutic implications.

PMID:40222066 | DOI:10.1016/j.repbio.2025.101017