PLoS Pathog. 2025 Feb 13;21(2):e1012922. doi: 10.1371/journal.ppat.1012922. eCollection 2025 Feb.

ABSTRACT

Persistent bacterial infections evade host immunity and resist antibiotic treatments through various mechanisms that are difficult to evaluate in a living host. Pseudomonas aeruginosa is a main cause of chronic infections in patients with cystic fibrosis (CF) and wounds. Here, by immersing wounded zebrafish embryos in a suspension of P. aeruginosa isolates from CF patients, we established a model of persistent infection that mimics a murine chronic skin infection model. Live and electron microscopy revealed persisting aggregated P. aeruginosa inside zebrafish cells, including macrophages, at unprecedented resolution. Persistent P. aeruginosa exhibited adaptive resistance to several antibiotics, host cell permeable drugs being the most efficient. Moreover, persistent bacteria could be partly re-sensitized to antibiotics upon addition of anti-biofilm molecules that dispersed the bacterial aggregates in vivo. Collectively, this study demonstrates that an intracellular location protects persistent P. aeruginosa in vivo in wounded zebrafish embryos from host innate immunity and antibiotics, and provides new insights into efficient treatments against chronic infections.

PMID:39946497 | DOI:10.1371/journal.ppat.1012922

NMR Biomed. 2025 Mar;38(3):e70001. doi: 10.1002/nbm.70001.

ABSTRACT

Due to the complex structure of the brain, variations in tumor shapes and sizes, and the resemblance between tumor and healthy tissues, the reliable and efficient identification of brain tumors through magnetic resonance imaging (MRI) presents a persistent challenge. Given that manual identification of tumors is often time-consuming and prone to errors, there is a clear need for advanced automated procedures to enhance detection accuracy and efficiency. Our study addresses the difficulty by creating an improved convolutional neural network (CNN) framework derived from DenseNet121 to augment the accuracy of brain tumor detection. The proposed model was comprehensively evaluated against 12 baseline CNN models and 5 state-of-the-art architectures, namely Vision Transformer (ViT), ConvNeXt, MobileNetV3, FastViT, and InternImage. The proposed model achieved exceptional accuracy rates of 98.4% and 99.3% on two separate datasets, outperforming all 17 models evaluated. Our improved model was integrated using Explainable AI (XAI) techniques, particularly Grad-CAM++, facilitating accurate diagnosis and localization of complex tumor instances, including small metastatic lesions and nonenhancing low-grade gliomas. The XAI framework distinctly highlights essential areas signifying tumor presence, hence enhancing the model's accuracy and interpretability. The results highlight the potential of our method as a reliable diagnostic instrument for healthcare practitioners' ability to comprehend and confirm artificial intelligence (AI)-driven predictions but also bring transparency to the model's decision-making process, ultimately improving patient outcomes. This advancement signifies a significant progression in the use of AI in neuro-oncology, enhancing diagnostic interpretability and precision.

PMID:39948696 | DOI:10.1002/nbm.70001

Bioelectron Med. 2025 Feb 14;11(1):4. doi: 10.1186/s42234-025-00166-9.

ABSTRACT

The octopus has many features that make it advantageous for revealing principles of motor circuits and control and predicting behavior. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100 ms after stimulation were predictive of the resultant movement response. Machine learning models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. For kinematic analysis, deep learning models and unsupervised dimensionality reduction identified a consistent set of features that could be used to distinguish different types of arm movements. The neural circuits and the computational models identified here generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit. This study demonstrates how real-time motor behaviors can be predicted and distinguished, contributing to the development of brain-machine interfaces. The ability to accurately model and predict complex movement patterns has broad implications for advancing technologies in robotics, neuroprosthetics, and artificial intelligence, paving the way for more sophisticated and adaptable systems.

PMID:39948616 | DOI:10.1186/s42234-025-00166-9

Alzheimers Res Ther. 2025 Feb 13;17(1):41. doi: 10.1186/s13195-025-01686-x.

ABSTRACT

BACKGROUND: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treatment modes. We aimed to develop a deep learning model to predict cognitive conversion to guide re-assignment decisions to more intensive therapies where needed.

METHODS: Longitudinal data including five variable sets, i.e. demographics, medical history, neuropsychological outcomes, laboratory and neuroimaging results, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were analyzed. We first developed a deep learning model to predicted cognitive conversion using all five variable sets. We then gradually removed variable sets to obtained parsimonious models for four different years of forecasting after baseline within acceptable frames of reduction in overall model fit (AUC remaining > 0.8).

RESULTS: A total of 607 individuals were included at baseline, of whom 538 participants were followed up at 12 months, 482 at 24 months, 268 at 36 months and 280 at 48 months. Predictive performance was excellent with AUCs ranging from 0.87 to 0.92 when all variable sets were considered. Parsimonious prediction models that still had a good performance with AUC 0.80-0.84 were established, each only including two variable sets. Neuropsychological outcomes were included in all parsimonious models. In addition, biomarker was included at year 1 and year 2, imaging data at year 3 and demographics at year 4. Under our pre-set threshold, the rate of upgrade to more intensive therapies according to predicted cognitive conversion was always higher than according to actual cognitive conversion so as to decrease the false positive rate, indicating the proportion of patients who would have missed upgraded treatment based on prognostic models although they actually needed it.

CONCLUSIONS: Neurophysiological tests combined with other indicator sets that vary along the AD continuum can improve can provide aid for clinical treatment decisions leading to improved management of the disease.

TRAIL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier: NCT00106899 (Registration Date: 31 March 2005).

PMID:39948600 | DOI:10.1186/s13195-025-01686-x

BMC Cancer. 2025 Feb 13;25(1):250. doi: 10.1186/s12885-025-13622-1.

ABSTRACT

BACKGROUND: Androgen deprivation therapy (ADT) improves outcomes in men undergoing definitive radiotherapy for prostate cancer but carries significant toxicities. Clinical parameters alone are insufficient to accurately identify patients who will derive the most benefit, highlighting the need for improved patient selection tools to minimize unnecessary exposure to ADT's side effects while ensuring optimal oncological outcomes. The ArteraAI Prostate Test, incorporating a multimodal artificial intelligence (MMAI)-driven digital histopathology-based biomarker, offers prognostic and predictive information to aid in this selection. However, its clinical utility in real-world settings has yet to be measured prospectively.

METHODS: This multicentre implementation trial aims to collect real-world data on the use of the previously validated Artera MMAI-driven prognostic and predictive biomarkers in men with intermediate-risk prostate cancer undergoing curative radiotherapy. The prognostic biomarker estimates the 10-year risk of metastasis, while the predictive biomarker determines the likely benefit from short-term ADT (ST-ADT). A total of 800 participants considering ST-ADT in conjunction with curative radiotherapy will be recruited from multiple Australian centers. Eligible patients with intermediate-risk prostate cancer, as defined by the National Comprehensive Cancer Network, will be asked to participate. The primary endpoint is the percentage of patients for whom testing led to a change in the shared ST-ADT recommendation, analyzed using descriptive statistics and McNemar's test comparing recommendations before and after biomarker testing. Secondary endpoints include the impact on quality of life and 5-year disease control, assessed through linkage with the Prostate Cancer Outcomes Registry. The sample size will be re-evaluated at an interim analysis after 200 patients.

DISCUSSION: ASTuTE will determine the impact of a novel prognostic and predictive biomarker on shared decision-making in the short term, and both quality of life and disease control in the medium term. If the biomarker demonstrates a significant impact on treatment decisions, it could lead to more personalized treatment strategies for men with intermediate-risk prostate cancer, potentially reducing overtreatment and improving quality of life. A potential limitation is the variability in clinical practice across different centers inherent in real-world studies.

TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12623000713695p. Registered 5 July 2023.

PMID:39948585 | DOI:10.1186/s12885-025-13622-1

Plant Methods. 2025 Feb 13;21(1):18. doi: 10.1186/s13007-025-01332-5.

ABSTRACT

The problem of plant diseases is huge as it affects the crop quality and leads to reduced crop production. Crop-Convolutional neural network (CNN) depiction is that several scholars have used the approaches of machine learning (ML) and deep learning (DL) techniques and have configured their models to specific crops to diagnose plant diseases. In this logic, it is unjustifiable to apply crop-specific models as farmers are resource-poor and possess a low digital literacy level. This study presents a Slender-CNN model of plant disease detection in corn (C), rice (R) and wheat (W) crops. The designed architecture incorporates parallel convolution layers of different dimensions in order to localize the lesions with multiple scales accurately. The experimentation results show that the designed network achieves the accuracy of 88.54% as well as overcomes several benchmark CNN models: VGG19, EfficientNetb6, ResNeXt, DenseNet201, AlexNet, YOLOv5 and MobileNetV3. In addition, the validated model demonstrates its effectiveness as a multi-purpose device by correctly categorizing the healthy and the infected class of individual types of crops, providing 99.81%, 87.11%, and 98.45% accuracy for CRW crops, respectively. Furthermore, considering the best performance values achieved and compactness of the proposed model, it can be employed for on-farm agricultural diseased crops identification finding applications even in resource-limited settings.

PMID:39948565 | DOI:10.1186/s13007-025-01332-5

BMC Med Inform Decis Mak. 2025 Feb 13;25(1):77. doi: 10.1186/s12911-025-02870-7.

ABSTRACT

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) represents a significant global health challenge, placing considerable burdens on healthcare systems. The rise of digital health technologies (DHTs) and artificial intelligence (AI) algorithms offers new opportunities to improve COPD predictive capabilities, diagnostic accuracy, and patient management. This systematic review explores the types of data in COPD under DHTs, the AI algorithms employed for data analysis, and identifies key application areas reported in the literature.

METHODS: A systematic search was conducted in PubMed and Web of Science for studies published up to December 2024 that applied AI algorithms in digital health for COPD management. Inclusion criteria focused on original research utilizing AI algorithms and digital health technologies for COPD, while review articles were excluded. Two independent reviewers screened the studies, resolving discrepancies through consensus.

RESULTS: From an initial pool of 265 studies, 41 met the inclusion criteria. Analysis of these studies highlighted a diverse range of data types and modalities collected from DHTs in the COPD context, including clinical data, patient-reported outcomes, and environmental/lifestyle data. Machine learning (ML) algorithms were employed in 34 studies, and deep learning (DL) algorithms in 16. Support vector machines and boosting were the most frequently used ML models, while deep neural networks (DNN) and convolutional neural networks (CNN) were the most commonly used DL models. The review identified three key application domains for AI in COPD: screening and diagnosis (10 studies), exacerbation prediction (22 studies), and patient monitoring (9 studies). Disease progression prediction was a prevalent focus across three domains, with promising accuracy and performance metrics reported.

CONCLUSIONS: Digital health technologies and AI algorithms have a wide range of applications and promise for COPD management. ML models, in particularly, show great potential in improving digital health solutions for COPD. Future research should focus on enhancing global collaboration to explore the cost-effectiveness and data-sharing capabilities of DHTs, enhancing the interpretability of AI models, and validating these algorithms through clinical trials to facilitate their safe integration into the routine COPD management.

PMID:39948530 | DOI:10.1186/s12911-025-02870-7

Commun Biol. 2025 Feb 13;8(1):233. doi: 10.1038/s42003-025-07692-x.

ABSTRACT

The rapid advancement of single-cell technologies has created an urgent need for effective methods to integrate and harmonize single-cell data. Technical and biological variations across studies complicate data integration, while conventional tools often struggle with reliance on gene expression distribution assumptions and over-correction. Here, we present scCobra, a deep generative neural network designed to overcome these challenges through contrastive learning with domain adaptation. scCobra effectively mitigates batch effects, minimizes over-correction, and ensures biologically meaningful data integration without assuming specific gene expression distributions. It enables online label transfer across datasets with batch effects, allowing continuous integration of new data without retraining. Additionally, scCobra supports batch effect simulation, advanced multi-omic integration, and scalable processing of large datasets. By integrating and harmonizing datasets from similar studies, scCobra expands the available data for investigating specific biological problems, improving cross-study comparability, and revealing insights that may be obscured in isolated datasets.

PMID:39948393 | DOI:10.1038/s42003-025-07692-x

Nat Commun. 2025 Feb 13;16(1):1577. doi: 10.1038/s41467-025-56560-z.

ABSTRACT

Mapping cellular organization in the developing brain presents significant challenges due to the multidimensional nature of the data, characterized by complex spatial patterns that are difficult to interpret without high-throughput tools. Here, we present DeepCellMap, a deep-learning-assisted tool that integrates multi-scale image processing with advanced spatial and clustering statistics. This pipeline is designed to map microglial organization during normal and pathological brain development and has the potential to be adapted to any cell type. Using DeepCellMap, we capture the morphological diversity of microglia, identify strong coupling between proliferative and phagocytic phenotypes, and show that distinct spatial clusters rarely overlap as human brain development progresses. Additionally, we uncover an association between microglia and blood vessels in fetal brains exposed to maternal SARS-CoV-2. These findings offer insights into whether various microglial phenotypes form networks in the developing brain to occupy space, and in conditions involving haemorrhages, whether microglia respond to, or influence changes in blood vessel integrity. DeepCellMap is available as an open-source software and is a powerful tool for extracting spatial statistics and analyzing cellular organization in large tissue sections, accommodating various imaging modalities. This platform opens new avenues for studying brain development and related pathologies.

PMID:39948387 | DOI:10.1038/s41467-025-56560-z

Sci Rep. 2025 Feb 13;15(1):5408. doi: 10.1038/s41598-025-89823-2.

ABSTRACT

Hundreds of loci have been robustly associated with obesity-related traits, but functional characterization of candidate genes remains a bottleneck. Aiming to systematically characterize candidate genes for a role in accumulation of lipids in adipocytes and other cardiometabolic traits, we developed a pipeline using CRISPR/Cas9, non-invasive, semi-automated fluorescence imaging and deep learning-based image analysis in live zebrafish larvae. Results from a dietary intervention show that 5 days of overfeeding is sufficient to increase the odds of lipid accumulation in adipocytes by 10 days post-fertilization (dpf, n = 275). However, subsequent experiments show that across 12 to 16 established obesity genes, 10 dpf is too early to detect an effect of CRISPR/Cas9-induced mutations on lipid accumulation in adipocytes (n = 1014), and effects on food intake at 8 dpf (n = 1127) are inconsistent with earlier results from mammals. Despite this, we observe effects of CRISPR/Cas9-induced mutations on ectopic accumulation of lipids in the vasculature (sh2b1 and sim1b) and liver (bdnf); as well as on body size (pcsk1, pomca, irs1); whole-body LDLc and/or total cholesterol content (irs2b and sh2b1); and pancreatic beta cell traits and/or glucose content (pcsk1, pomca, and sim1a). Taken together, our results illustrate that CRISPR/Cas9- and image-based experiments in zebrafish larvae can highlight direct effects of obesity genes on cardiometabolic traits, unconfounded by their - not yet apparent - effect on excess adiposity.

PMID:39948378 | DOI:10.1038/s41598-025-89823-2

Sci Rep. 2025 Feb 13;15(1):5333. doi: 10.1038/s41598-024-83084-1.

ABSTRACT

Mining-induced subsidence is one of the major concerns of mining industry/mine owners, statutory bodies, and environmental organisations. Therefore, mine subsidence monitoring and prediction is of utmost importance for its effective management. In the present study, a modified LSTM model is developed to predict the InSAR deformation time series. The modified LSTM model may also be extended for prediction based on time-series data in general. Further, to check the developed model's performance, InSAR deformation time-series results obtained from 26 TSX/TDX datasets of Mine-A in Khetri Copper Belt, India, are used as an input. Further obtained results from mLSTM have been compared with the other two models, namely RNN and LSTM. Efficiency comparison results reveal that RNN, LSTM, and modified LSTM over the applied single reference PSI-derived deformation time-series result are 82.6%, 97.54%, and 98.57%, respectively. It also reveals that the RMS error of RNN, LSTM, and modified LSTM over the applied single reference PSI-derived deformation time-series result are 6.58 mm/year, 5.34 mm/year and 4.22 mm/year, respectively. In addition, the study reveals that the prediction of the mLSTM model, compared to RNN and LSTM, is quite close to the observed/measured deformation velocity values obtained from a single reference PSI-derived result. Furthermore, prediction for the next five years using mLSTM shows that the maximum value of the deformation is -20.87 mm/year and a minimum of 4.99 mm/year. Predictions for the next five years show that most of the area is stable, but points around the plant area have shown some deformation.

PMID:39948371 | DOI:10.1038/s41598-024-83084-1

Fungal Biol Biotechnol. 2025 Feb 13;12(1):1. doi: 10.1186/s40694-024-00192-3.

ABSTRACT

BACKGROUND: Fungal-based composites have emerged as renewable, high-performance biomaterials that are produced on lignocellulosic residual streams from forestry and agriculture. Production at an industrial scale promises to revolutionize the world humans inhabit by generating sustainable, low emission, non-toxic and biodegradable construction, packaging, textile, and other materials. The polypore Fomes fomentarius is one of the basidiomycete species used for biomaterial production, yet nothing is known about the transcriptional basis of substrate decomposition, nutrient uptake, or fungal growth during composite formation. Co-expression network analysis based on RNA-Seq profiling has enabled remarkable insights into a range of fungi, and we thus aimed to develop such resources for F. fomentarius.

RESULTS: We analysed gene expression from a wide range of laboratory cultures (n = 9) or biomaterial formation (n = 18) to determine the transcriptional landscape of F. fomentarius during substrate decomposition and to identify genes important for (i) the enzymatic degradation of lignocellulose and other plant-based substrates, (ii) the uptake of their carbon monomers, and (iii) genes guiding mycelium formation through hyphal growth and cell wall biosynthesis. Simple scripts for co-expression network construction were generated and tested, and harnessed to identify a fungal-specific transcription factor named CacA strongly co-expressed with multiple chitin and glucan biosynthetic genes or Rho GTPase encoding genes, suggesting this protein is a high-priority target for engineering adhesion and branching during composite growth. We then updated carbohydrate activated enzymes (CAZymes) encoding gene annotation, used phylogenetics to assign putative uptake systems, and applied network analysis to predict repressing/activating transcription factors for lignocellulose degradation. Finally, we identified entirely new types of co-expressed contiguous clusters not previously described in fungi, including genes predicted to encode CAZymes, hydrophobins, kinases, lipases, F-box domains, chitin synthases, amongst others.

CONCLUSION: The systems biology data generated in this study will enable us to understand the genetic basis of F. fomentarius biomaterial formation in unprecedented detail. We provided proof-of-principle for accurate network-derived predictions of gene function in F. fomentarius and generated the necessary data and scripts for analysis by any end user. Entirely new classes of contiguous co-expressed gene clusters were discovered, and multiple transcription factor encoding genes which are high-priority targets for genetic engineering were identified.

PMID:39948638 | DOI:10.1186/s40694-024-00192-3

Nat Genet. 2025 Feb 13. doi: 10.1038/s41588-025-02081-w. Online ahead of print.

ABSTRACT

Cell fate plasticity enables development, yet unlocked plasticity is a cancer hallmark. While transcription master regulators induce lineage-specific genes to restrict plasticity, it remains unclear whether plasticity is actively suppressed by lineage-specific repressors. Here we computationally predict so-called safeguard repressors for 18 cell types that block phenotypic plasticity lifelong. We validated hepatocyte-specific candidates using reprogramming, revealing that prospero homeobox protein 1 (PROX1) enhanced hepatocyte identity by direct repression of alternative fate master regulators. In mice, Prox1 was required for efficient hepatocyte regeneration after injury and was sufficient to prevent liver tumorigenesis. In line with patient data, Prox1 depletion caused hepatocyte fate loss in vivo and enabled the transition of hepatocellular carcinoma to cholangiocarcinoma. Conversely, overexpression promoted cholangiocarcinoma to hepatocellular carcinoma transdifferentiation. Our findings provide evidence for PROX1 as a hepatocyte-specific safeguard and support a model where cell-type-specific repressors actively suppress plasticity throughout life to safeguard lineage identity and thus prevent disease.

PMID:39948437 | DOI:10.1038/s41588-025-02081-w

EMBO J. 2025 Feb 13. doi: 10.1038/s44318-024-00330-y. Online ahead of print.

ABSTRACT

Although inflammation has been widely associated with cancer development, how it affects the outcomes of immunotherapy and chemotherapy remains incompletely understood. Here, we show that CKLF-like MARVEL transmembrane domain-containing member 4 (CMTM4) is highly expressed in multiple human and murine cancer types including Lewis lung carcinoma, triple-negative mammary cancer and melanoma. In lung carcinoma, loss of CMTM4 significantly reduces tumor growth and impairs NF-κB, mTOR, and PI3K/Akt pathway activation. Furthermore, we demonstrate that CMTM4 can regulate epidermal growth factor (EGF) signaling post-translationally by promoting EGFR recycling and preventing its Rab-dependent degradation. Consequently, CMTM4 knockout sensitizes human lung tumor cells to EGFR inhibitors. In addition, CMTM4 knockout tumors stimulated with EGF show a decreased ability to produce inflammatory cytokines including granulocyte colony-stimulating factor (G-CSF), leading to decreased recruitment of polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and therefore establishing a less suppressive tumor immune environment in both lung and mammary cancers. We also present evidence indicating that CMTM4-targeting siRNA-loaded liposomes reduce lung tumor growth in vivo and prolong animal survival. Knockout of CMTM4 enhances immune checkpoint blockade or chemotherapy to further reduce lung tumor growth. These data suggest that CMTM4 represents a novel target for the inhibition of tumor inflammation, and improvement of the immune response and tumor drug sensitivity.

PMID:39948411 | DOI:10.1038/s44318-024-00330-y

J Dairy Sci. 2025 Feb 11:S0022-0302(25)00065-7. doi: 10.3168/jds.2024-25425. Online ahead of print.

ABSTRACT

Body condition score and BW yield insights into body tissue reserves and diet, and serum biochemical measures reflect the metabolic status of cows. Associations between body composition measures and biochemistry are unclear and investigation may reveal important information on the metabolic and physiological status of cattle with varying levels of labile tissue reserves. Cohorts of 739 nonlactating, late-pregnancy, dry cows (26.9 d prepartum, SD = 12.4) and 690 peak-milk cows (58.0 DIM, SD = 14.5) were selected by stratified (parity: 1, 2, 3, >3) random sampling from 30 farms (15 pasture, 15 TMR) in this cross-sectional study. A single serum, BCS (1-5 scale), BW, and milk-production datum was collected per cow, per cohort between November 2022 and July 2023. Eleven analytes were collected, analyzed, and standardized within group (cohort/breed/farm). Mixed linear models for BCS and BW were specified, with the random effect of group. A 6-point, unordered, categorical body-group classification that combined BCS (greater, equal to, or less than group median; as high, median or low BCS) and BW (greater or less than group median; as high or low BW) was analyzed by polytomous logistic regression. Effect sizes are listed for a 1 SD increase in the specified analyte, keeping other covariables at their mean value. Dry BCS was positively associated with albumin (0.075 BCS ± 0.014 SE), urea (0.038 BCS ± 0.014 SE) and glucose (0.052 BCS ± 0.014 SE), and negatively with the interaction between cholesterol and days precalving. Dry BW positively associated with albumin (11.03 kg ± 2.48 SE) and negatively with cholesterol (-8.47 kg ± 2.57 SE). Peak-milk BCS was positively associated with albumin (0.47 BCS ± 0.015 SE), BHB (0.048 BCS ± 0.015 SE) and glucose (0.051 BCS ± 0.015 SE). Peak-milk BW was positively associated with albumin (6.94 kg ± 2.35 SE) and negatively with Ca (-7.02 kg ± 2.33 SE). Increasing BW and decreasing BCS was associated with increasing parity, except in dry second-parity cows that had low BCS. The dry polytomous model associated a 1 SD increase in albumin with a 4.89% ± 1.56 SE decreased risk of being low BCS and low BW and 5.87% ± 1.46 SE increased risk of high BCS and high BW. Risk change associated with 1 SD of glucose was -5.61% ± 1.58 SE for low BCS and high BW and 3.17% ± 1.58 SE for high BCS and high BW. For the peak-milk cohort, change in risk was associated with albumin for low BCS and low BW -3.67% ± 1.56 SE, low BCS and high BW -3.22% ± 1.53 SE. Risk change with 1 SD of BHB was -3.36% ± 1.47 SE for median BCS and low BW, 2.86% ± 1.44 SE for high BCS and low BW, and 2.69% ± 1.37 SE for high BCS and high BW. Risk of low BCS and low BW was greatest in second-parity cows, and high BCS and high BW was greatest in dry cows with greater than third parity and third-parity cows in peak milk. There were no interactions between parity and analytes. Albumin was consistently associated with BCS and BW, potentially reflecting innate differences in protein metabolism of cows.

PMID:39947600 | DOI:10.3168/jds.2024-25425

Lancet Infect Dis. 2025 Feb 10:S1473-3099(24)00769-2. doi: 10.1016/S1473-3099(24)00769-2. Online ahead of print.

ABSTRACT

There are no approved treatments for post-COVID-19 condition (also known as long COVID), a debilitating disease state following SARS-CoV-2 infection that is estimated to affect tens of millions of people. A growing body of evidence shows that SARS-CoV-2 can persist for months or years following COVID-19 in a subset of individuals, with this reservoir potentially driving long-COVID symptoms or sequelae. There is, therefore, an urgent need for clinical trials targeting persistent SARS-CoV-2, and several trials of antivirals or monoclonal antibodies for long COVID are underway. However, because mechanisms of SARS-CoV-2 persistence are not yet fully understood, such studies require important considerations related to the mechanism of action of candidate therapeutics, participant selection, duration of treatment, standardisation of reservoir-associated biomarkers and measurables, optimal outcome assessments, and potential combination approaches. In addition, patient subgroups might respond to some interventions or combinations of interventions, making post-hoc analyses crucial. Here, we outline these and other key considerations, with the goal of informing the design, implementation, and interpretation of trials in this rapidly growing field. Our recommendations are informed by knowledge gained from trials targeting the HIV reservoir, hepatitis C, and other RNA viruses, as well as precision oncology, which share many of the same hurdles facing long-COVID trials.

PMID:39947217 | DOI:10.1016/S1473-3099(24)00769-2

Int Immunopharmacol. 2025 Feb 12;150:114197. doi: 10.1016/j.intimp.2025.114197. Online ahead of print.

ABSTRACT

INTRODUCTION: Sulfur mustard (SM) is a substance that causes blisters and has been repeatedly used by Iraq in chemical warfare against more than 100,000 Iranians. The main issue for these people is various pulmonary problems similar to chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS: Our study analyzed the total RNA profile extracted using the RNA-seq technique from peripheral blood mononuclear cells (PBMCs) isolated from Mustard Lung (ML) patients of all three groups (Severe, Moderate, and Mild) in terms of disease in healthy control (HC) subjects on the BGISEQ platform (Paired-end, 7 GB data, and rRNA depletion). However, given the severe group's importance in clinical problems, we prioritized studying this group. Differentially expressed genes (DEGs) of the severe group versus HC were obtained using the limma package. DEGs were analyzed through bioinformatics tools, and their gene ontology (GO) and enrichment analysis (EA) were evaluated. Then, String-db and Cytoscape tools were used to search for the most important functional genes.

RESULTS: We identified SERPINA1, MAPK3, MMP9, FOXO3, SLC4A1, FCGR3B, CXCR2, PTGS2, HBA2, GPX1, IL1RN, IFNG, RPS29, CXCL1, FPR1, and RPS9 genes using hub and bottleneck criteria. Based on the analysis of important genes, several biological pathways were identified, including innate immunity, inflammatory response, and activation of neutrophils, cellular response to cytokines, and cellular response to oxidative stress, lipoxygenase pathway, and macrophage differentiation.

CONCLUSION: Innate immunity and neutrophils play a crucial role in the pathogenesis of these individuals. The signaling pathways of interleukins 4, 10, and 13 stimulate the differentiation of lung macrophages (MQs) into M2, essential for repair, remodeling, and inflammation. Additionally, reactive oxygen species (ROS) activate Protein kinase B (PKB), also known as AKT, through Phosphoinositide 3-kinases (PI3K) and increase the activity of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), which results in decreased histone deacetylase 2 (HDAC2) being one of the important pathways of pathophysiology in these patients.

PMID:39946765 | DOI:10.1016/j.intimp.2025.114197

Sci Rep. 2025 Feb 13;15(1):5322. doi: 10.1038/s41598-025-89632-7.

ABSTRACT

To explore and analyze the potential adverse event (AE) signals of trazodone, with reference to the safe clinical use of drugs. Based on the FDA Adverse Event Reporting System (FAERS), the AE data of trazodone were extracted from the first quarter of 2004 to the second quarter of 2024, and the extracted data were statistically analyzed using the method, to identify valid AE signals that met our judgment, compare them with those recorded in the authorized information for trazodone thereby identifying unexpected potential adverse reactions. A total of 5199 AE reports with trazodone as the main suspect were extracted, with a higher reported proportion of females (52.68%) than males (38.83%). Many reports (31.47%) did not provide age information, although for those reports with identifiable age data, the 50-60 years age group was the most common (14.20%), and the country of reporting was predominantly the United States (82.58%). A total of 179 significant AE signals were unearthed, with suicide, formulation toxicity, abnormal penile erection, insomnia, and cardiac and respiratory arrest reported with high frequency, which was not entirely consistent with the specification record. The study unearthed 156 new potential AEs on the basis of trazodone drug inserts and suggested precautions for overdosing and dose adjustment, which is conducive to safeguarding the safety of patients' medication.

PMID:39948419 | DOI:10.1038/s41598-025-89632-7

Int J Mol Sci. 2025 Feb 2;26(3):1287. doi: 10.3390/ijms26031287.

ABSTRACT

Space exploration has progressed from contemporary discoveries to current endeavors, such as space tourism and Mars missions. As human activity in space accelerates, understanding the physiological effects of microgravity on the human body is becoming increasingly critical. This study analyzes transcriptomic data from human cell lines exposed to microgravity, investigates its effects on gene expression, and identifies potential therapeutic interventions for health challenges posed by spaceflight. Our analysis identified five under-expressed genes (DNPH1, EXOSC5, L3MBTL2, LGALS3BP, SPRYD4) and six over-expressed genes (CSGALNACT2, CSNK2A2, HIPK1, MBNL2, PHF21A, RAP1A), all of which exhibited distinct expression patterns in response to microgravity. Enrichment analysis highlighted significant biological functions influenced by these conditions, while in silico drug repurposing identified potential modulators that could counteract these changes. This study introduces a novel approach to addressing health challenges during space missions by repurposing existing drugs and identifies specific genes and pathways as potential biomarkers for microgravity effects on human health. Our findings represent the first systematic effort to repurpose drugs for spaceflight, establishing a foundation for the development of targeted therapies for astronauts. Future research should aim to validate these findings in authentic space environments and explore broader biological impacts.

PMID:39941055 | DOI:10.3390/ijms26031287

Int J Mol Sci. 2025 Jan 31;26(3):1244. doi: 10.3390/ijms26031244.

ABSTRACT

Rare diseases (RDs) often have a genetic basis, yet conventional diagnostic techniques fail to identify causative genetic variations in up to 50% of cases. Structural variants (SVs), including balanced rearrangements, frequently evade detection by karyotyping, microarray, and exome sequencing. The present study utilized optical genome mapping (OGM) to investigate two patients with RDs whose genetic etiology remained unresolved despite prior genomic analyses. Patient 1 exhibited a balanced reciprocal translocation disrupting the BCL11A gene, associated with Dias-Logan syndrome. Patient 2 had a mosaic 682 kb deletion near the IHH gene, causing ectopic enhancer-promoter interactions and polydactyly, mirroring phenotypes observed in mouse models and similar human cases. These findings highlight OGM's efficacy in identifying complex SVs and underline novel pathogenic mechanisms in rare genetic disorders. Consequently, the incorporation of OGM into routine diagnostic procedures will enhance genetic diagnosis, discover new syndromes of currently unknown cause, and eventually improve the clinical management of numerous patients with rare diseases.

PMID:39941010 | DOI:10.3390/ijms26031244