Proc Biol Sci. 2025 May;292(2046):20242233. doi: 10.1098/rspb.2024.2233. Epub 2025 May 7.

ABSTRACT

Traditional biomarkers, such as those obtained from blood tests, are essential for early disease detection, improving health outcomes and reducing healthcare costs. However, they often involve invasive procedures, specialized laboratory equipment or special handling of biospecimens. The retinal age gap (RAG) has emerged as a promising new biomarker that can overcome these limitations, making it particularly suitable for disease screening in low- and middle-income countries. This study aimed to evaluate the potential of the RAG as a biomarker for broad disease screening across a vast spectrum of diseases. Fundus images were collected from 86 522 UK Biobank participants aged 40-83 (mean age: 56.2 ± 8.3 years). A deep learning model was trained to predict retinal age using 17 791 images from healthy participants. The remaining images were categorized into disease/injury groups based on clinical codes. Additionally, 8524 participants from the Brazilian Multilabel Ophthalmological Dataset (BRSET) were used for external validation. Among the 159 disease/injury groups from the 2019 Global Burden of Disease Study, 56 groups (35.2%) exhibited RAG distributions significantly different from healthy controls. Notable examples included chronic kidney disease, cardiovascular disease, blindness, vision loss and diabetes. Overall, the RAG shows great promise as a cost-effective, non-invasive biomarker for early disease screening.

PMID:40328303 | DOI:10.1098/rspb.2024.2233

BMC Chem. 2025 May 6;19(1):119. doi: 10.1186/s13065-025-01468-4.

ABSTRACT

Trichinellosis represents great public health and economic problems worldwide. Moreover, the development of parasitic resistance against conventional anthelminthic treatment led to the urgent search for new therapeutic strategies, including drug repurposing. Bisphosphonates have been used to inhibit the growth of many parasites and have also emerged as promising candidates for the treatment of cryptosporidiosis and amoebic liver abscess. Alendronate is a second-generation bisphosphonate that is widely used for the treatment and prevention of osteoporosis. Till date, there is not enough data on the effect of this drug on Trichinella spiralis and it is unknown whether the regular use of this drug in osteoporotic patients may alter the course of the infection. ALN showed a significant lethal effect on both adult worms and juveniles, with severe tegumental damage in the form of fissures in the cuticle, widening of the hypodermal gland, and flattening of the cuticular annulation, ending with the appearance of multiple vesicles and large cauliflower masses. Molecular docking outcomes unveiled the potential inhibition of ALN against T. spiralis surface proteins (i.e., Ts-SP, Ts-PPase, Ts-MAPRC2, Ts-TS, Ts-MIF, etc.), with promising results confirmed its ability to defeat T. spiralis via targeting its surface proteins. Moreover, molecular dynamics simulation, through the analysis of RMSD, RMSF, RG, SASA and cluster analysis, proved the prolonged effective inhibition of ALN on T. spiralis inorganic pyrophosphatase, as an essential surface protein required for molting and developmental process of intestinal larval stages. Thus, ALN might be a valuable drug candidate for the treatment of trichinellosis and warrant further investigation in animal models of disease.

PMID:40329381 | DOI:10.1186/s13065-025-01468-4

Naunyn Schmiedebergs Arch Pharmacol. 2025 May 6. doi: 10.1007/s00210-025-04212-w. Online ahead of print.

ABSTRACT

Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the gastrointestinal tract with a multifactorial etiology. Given the limitations and adverse effects of current therapies, there is a need for novel therapeutic approaches. Drug repurposing presents a promising opportunity to utilize medications with known safety and pharmacological profiles for alternative colitis treatment. Emerging evidence suggests the renin-angiotensin system (RAS) plays a significant role in the colitis pathophysiology. Angiotensin-converting enzyme (ACE) inhibitors may offer therapeutic potential by modulating pro-inflammatory cytokines and reducing oxidative stress. This study aims to evaluate the efficacy of lisinopril (LIS) in a 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis model in Wistar rats. Colitis was induced in Wistar rats via a single intracolonic TNBS dose (100 mg/kg). Treatment groups received oral interventions for 5 days: 5-aminosalicylic acid (5-ASA; 25.5 mg/kg), LIS (10 mg/kg), or LIS (20 mg/kg). Efficacy was evaluated using the disease activity score rate (DASR), colon/body weight ratio (CBWR), and colon length, diameter, and pH. Colonic tissue was analyzed macroscopically and histopathologically. Inflammatory biomarkers interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), oxidative stress markers glutathione (GSH), and malondialdehyde (MDA), as well as C-reactive protein (CRP) and complete blood count (CBC), were measured. LIS significantly reduced colitis severity, decreasing DASR and CBWR, while restoring colon dimensions and pH. LIS showed potent anti-colitic effects by suppressing TNF-α and IL-6 levels, reducing MDA, and increasing GSH. LIS restored RBC and WBC levels while normalizing CRP and hemoglobin levels. Histopathological and macroscopic analyses confirmed colonic protection with minimal detrimental effects on the stomach and liver. LIS, particularly at 20 mg/kg, exhibited dose-dependent anti-inflammatory, antioxidant, and tissue-protective effects, showing promise as a therapeutic agent for colitis treatment.

PMID:40328912 | DOI:10.1007/s00210-025-04212-w

Cell Death Differ. 2025 May 6. doi: 10.1038/s41418-025-01521-8. Online ahead of print.

ABSTRACT

Oxysterol-binding proteins (OSBPs), lipid transfer proteins functioning at intracellular membrane contact sites, are recently found to be dysregulated in cancer and promote cancer cell survival. However, their role as potential targets in cancer therapy remains largely unexplored. In this study, we found OSW-1, a natural compound and OSBP inhibitor, potently and selectively kills colon cancer cells by activating a previously unknown necroptosis pathway that is independent of receptor-interacting protein 1 (RIP1) and RIP3. OSW-1 stabilizes p53 and degrades OSBPs to promote endoplasmic reticulum (ER) stress and glycogen synthase kinase 3β (GSK3β)/Tip60-mediated p53 acetylation at Lysine 120, which selectively induces its target PUMA. PUMA-mediated mitochondrial calcium influx activates calcium/calmodulin-dependent protein kinase IIδ (CamKIIδ) to promote mixed lineage kinase domain-like (MLKL) phosphorylation and necroptotic cell death. Furthermore, OSW-1-induced necroptosis is highly immunogenic and sensitizes syngeneic colorectal tumors to anti-PD-1 immunotherapy. Together, our results identified a novel RIP1/RIP3-independent necroptosis pathway underlying the extremely potent anticancer activity of OSW-1, which can be harnessed to develop new anticancer therapies by selectively stimulating antitumor immunity.

PMID:40329104 | DOI:10.1038/s41418-025-01521-8

Sci Rep. 2025 May 6;15(1):15789. doi: 10.1038/s41598-025-94171-2.

ABSTRACT

To translate, cross-culturally adapt, and evaluate the psychometric properties of the Cystic Fibrosis (CF) Stigma Scale. This exploratory methodological study involved the translation and cross-cultural adaptation using the translation, back-translation, review by experts committee, and pre-test steps. The psychometric properties were analyzed by applying the adapted instrument to a sample of 52 Brazilian individuals with CF over 18 years old. Moreover, the participants responded to the Short-Form 12-Item Survey - version 2 (SF-12v2), General Anxiety Disorder 7-item scale (GAD-7) and Cystic Fibrosis Quality of Life Questionnaire - Revised (CFQ-R). The content validity, test-retest reliability, and convergent validity were also assessed. The translation and cross-cultural adaptation obtained Cohen's kappa coefficients > 0.61 in the experts committee step and ranged between 0.48 and 0.72 in the pre-test. The Brazilian version of the CF Stigma Scale showed excellent psychometric properties, observed by the internal consistency (α = 0.836), mean correlation between items (0.3) and test-retest reliability (r = 0.886; p < 0.0001), and convergent validity (positive correlation with the anxiety scale and negative correlation with scores of overall and specific quality of life for CF). The Brazilian version of the CF Stigma Scale was accurately translated and cross-culturally adapted, with favorable psychometric properties for future studies involving the stigma experience in Brazilian individuals with CF over 18 years.

PMID:40328878 | DOI:10.1038/s41598-025-94171-2

J Cyst Fibros. 2025 May 5:S1569-1993(25)01463-8. doi: 10.1016/j.jcf.2025.04.008. Online ahead of print.

ABSTRACT

The introduction of cystic fibrosis transmembrane conductance regulator (CFTR) modulators has changed the landscape of therapy for persons with cystic fibrosis. However, the steep cost of targeted therapy poses significant financial burden for individuals and health systems. We aimed to determine the trends in Medicare and Medicaid spending on CFTR modulators between the years 2015 and 2022 through retrospective analysis of the Medicare and Medicaid claims data. The outcome measures included total dosage units prescribed, number of claims, spending per claim, and total spending on CFTR modulators for Medicare and Medicaid between 2015 to 2022. Average annual percentage changes (AAPC) were calculated for all outcome measures. Our results show that from 2015 to 2022, Medicaid consistently had higher total dosage units prescribed, number of claims, spending per claim, and overall spending on CFTR modulators compared to Medicare. Total spending for both Medicaid [AAPC 38.9, 95 % confidence interval [CI] 27.2-51.6, p < 0.01] and Medicare [AAPC 39.2, 95 % CI 30.2-55.1, p < 0.01] increased significantly during this period. Increases in CFTR spending was accelerated beginning in 2019, when the triple combination CFTR modulator therapy, elexacaftor/tezacaftor/ivacaftor, was introduced to the US market. This increase has been accompanied by a reduction in spending and use of other CFTR agents. This study evaluated the increases in spending for CFTR modulators over the years and the main drivers behind them, which may help inform future negotiations between healthcare systems and pharmaceutical companies, as well as policy makers and stakeholders.

PMID:40328584 | DOI:10.1016/j.jcf.2025.04.008

Klin Padiatr. 2025 May 6. doi: 10.1055/a-2551-2560. Online ahead of print.

NO ABSTRACT

PMID:40328282 | DOI:10.1055/a-2551-2560

Biomed Eng Online. 2025 May 6;24(1):53. doi: 10.1186/s12938-025-01388-3.

ABSTRACT

BACKGROUND: Accurate measurement of anterior segment parameters is crucial for diagnosing and managing ophthalmic conditions, such as glaucoma, cataracts, and refractive errors. However, traditional clinical measurement methods are often time-consuming, labor-intensive, and susceptible to inaccuracies. With the growing potential of artificial intelligence in ophthalmic diagnostics, this study aims to develop and evaluate a deep learning model capable of automatically extracting key points and precisely measuring multiple clinically significant anterior segment parameters from ultrasound biomicroscopy (UBM) images. These parameters include central corneal thickness (CCT), anterior chamber depth (ACD), pupil diameter (PD), angle-to-angle distance (ATA), sulcus-to-sulcus distance (STS), lens thickness (LT), and crystalline lens rise (CLR).

METHODS: A data set of 716 UBM anterior segment images was collected from Tianjin Medical University Eye Hospital. YOLOv8 was utilized to segment four key anatomical structures: cornea-sclera, anterior chamber, pupil, and iris-ciliary body-thereby enhancing the accuracy of keypoint localization. Only images with intact posterior capsule lentis were selected to create an effective data set for parameter measurement. Ten keypoints were localized across the data set, allowing the calculation of seven essential parameters. Control experiments were conducted to evaluate the impact of segmentation on measurement accuracy, with model predictions compared against clinical gold standards.

RESULTS: The segmentation model achieved a mean IoU of 0.8836 and mPA of 0.9795. Following segmentation, the binary classification model attained an mAP of 0.9719, with a precision of 0.9260 and a recall of 0.9615. Keypoint localization exhibited a Euclidean distance error of 58.73 ± 63.04 μm, improving from the pre-segmentation error of 71.57 ± 67.36 μm. Localization mAP was 0.9826, with a precision of 0.9699, a recall of 0.9642 and an FPS of 32.64. In addition, parameter error analysis and Bland-Altman plots demonstrated improved agreement with clinical gold standards after segmentation.

CONCLUSIONS: This deep learning approach for UBM image segmentation, keypoint localization, and parameter measurement is feasible, enhancing clinical diagnostic efficiency for anterior segment parameters.

PMID:40329288 | DOI:10.1186/s12938-025-01388-3

BMC Cancer. 2025 May 6;25(1):830. doi: 10.1186/s12885-025-14113-z.

ABSTRACT

Non-coding RNAs (ncRNAs) play a crucial role in breast cancer progression, necessitating advanced computational approaches for precise disease classification. This study introduces a Deep Reinforcement Learning (DRL)-based framework for predicting ncRNA-disease associations in metaplastic breast cancer (MBC) using a multi-dimensional descriptor system (ncRNADS) integrating 550 sequence-based features and 1,150 target gene descriptors (miRDB score ≥ 90). The model achieved 96.20% accuracy, 96.48% precision, 96.10% recall, and a 96.29% F1-score, outperforming traditional classifiers such as support vector machines (SVM) and neural networks. Feature selection and optimization reduced dimensionality by 42.5% (4,430 to 2,545 features) while maintaining high accuracy, demonstrating computational efficiency. External validation confirmed model specificity to breast cancer subtypes (87-96.5% accuracy) and minimal cross-reactivity with unrelated diseases like Alzheimer's (8-9% accuracy), ensuring robustness. SHAP analysis identified key sequence motifs (e.g., "UUG") and structural free energy (ΔG = - 12.3 kcal/mol) as critical predictors, validated by PCA (82% variance) and t-SNE clustering. Survival analysis using TCGA data revealed prognostic significance for MALAT1, HOTAIR, and NEAT1 (associated with poor survival, HR = 1.76-2.71) and GAS5 (protective effect, HR = 0.60). The DRL model demonstrated rapid training (0.08 s/epoch) and cloud deployment compatibility, underscoring its scalability for large-scale applications. These findings establish ncRNA-driven classification as a cornerstone for precision oncology, enabling patient stratification, survival prediction, and therapeutic target identification in MBC.

PMID:40329245 | DOI:10.1186/s12885-025-14113-z

J Imaging Inform Med. 2025 May 6. doi: 10.1007/s10278-025-01528-0. Online ahead of print.

ABSTRACT

Quantitative cardiovascular PET/CT imaging is useful in the diagnosis of multiple cardiac perfusion and motion pathologies. The common approach for cardiac segmentation consists in using co-registered CT images, exploiting publicly available deep learning (DL)-based segmentation models. However, the mismatch between structural CT images and PET uptake limits the usefulness of these approaches. Besides, the performance of DL models is not consistent over low-dose or ultra-low-dose CT images commonly used in clinical PET/CT imaging. In this work, we developed a DL-based methodology to tackle this issue by segmenting directly cardiac PET images. This study included 406 cardiac PET images from 146 patients (43 18F-FDG, 329 13N-NH3, and 37 82Rb images). Using previously trained DL nnU-Net models in our group, we segmented the whole heart and the three main cardiac components, namely the left myocardium (LM), left ventricle cavity (LV), and right ventricle (RV) on co-registered CT images. The segmentation was resampled to PET resolution and edited through a combination of automated image processing and manual correction. The corrected segmentation masks and SUV PET images were fed to a nnU-Net V2 pipeline to be trained in fivefold data split strategy by defining two tasks: task #1 for whole cardiac segmentation and task #2 for segmentation of three cardiac components. Fifteen cardiac images were used as external validation set. The DL delineated masks were compared with standard of reference masks using Dice coefficient, Jaccard distance, mean surface distance, and segment volume relative error (%). Task #1 average Dice coefficient in internal validation fivefold was 0.932 ± 0.033. The average Dice on the 15 external cases were comparable with the fivefold Dice reaching an average of 0.941 ± 0.018. Task #2 average Dice in fivefold validation was 0.88 ± 0.063, 0.828 ± 0.091, and 0.876 ± 0.062 for LM, LV, and RV, respectively. There was no statistically significant difference among the Dice coefficients, neither between images acquired by three radiotracers nor between the different folds (P-values > > 0.05). The overall average volume prediction error in cardiac components segmentation was less than 2%. We developed an automated DL-based segmentation pipeline to segment the whole heart and cardiac components with acceptable accuracy and robust performance in the external test set and over three radiotracers used in nuclear cardiovascular imaging. The proposed methodology can overcome unreliable segmentations performed on CT images.

PMID:40329157 | DOI:10.1007/s10278-025-01528-0

J Imaging Inform Med. 2025 May 6. doi: 10.1007/s10278-025-01527-1. Online ahead of print.

ABSTRACT

Deep learning techniques have demonstrated potential in various fields, including segmentation, and have recently been applied to medical image processing. This study aims to develop and evaluate computer-based diagnostic software designed to assess the segmentation of the mandibular condyle in ultrasound images. A total of 668 retrospective ultrasound images of anonymous adult mandibular condyles were analyzed. The CranioCatch labeling program (CranioCatch, Eskişehir, Turkey) was utilized to annotate the mandibular condyle using a polygonal labeling method. These annotations were subsequently reviewed and validated by experts in oral and maxillofacial radiology. In this study, all test images were detected and segmented using the YOLOv8 deep learning artificial intelligence (AI) model. When evaluating the model's performance in image estimation, it achieved an F1 score of 0.93, a sensitivity of 0.90, and a precision of 0.96. The automatic segmentation of the mandibular condyle from ultrasound images presents a promising application of artificial intelligence. This approach can help surgeons, radiologists, and other specialists save time in the diagnostic process.

PMID:40329156 | DOI:10.1007/s10278-025-01527-1

J Imaging Inform Med. 2025 May 6. doi: 10.1007/s10278-025-01525-3. Online ahead of print.

ABSTRACT

The purpose of this study is to assess the ability of deep learning models to classify different subtypes of solid renal parenchymal tumors using contrast-enhanced ultrasound (CEUS) images and to compare their classification performance. A retrospective study was conducted using CEUS images of 237 kidney tumors, including 46 angiomyolipomas (AML), 118 clear cell renal cell carcinomas (ccRCC), 48 papillary RCCs (pRCC), and 25 chromophobe RCCs (chRCC), collected from January 2017 to December 2019. Two deep learning models, based on the ResNet-18 and RepVGG architectures, were trained and validated to distinguish between these subtypes. The models' performance was assessed using sensitivity, specificity, positive predictive value, negative predictive value, F1 score, Matthews correlation coefficient, accuracy, area under the receiver operating characteristic curve (AUC), and confusion matrix analysis. Class activation mapping (CAM) was applied to visualize the specific regions that contributed to the models' predictions. The ResNet-18 and RepVGG-A0 models achieved an overall accuracy of 76.7% and 84.5% across all four subtypes. The AUCs for AML, ccRCC, pRCC, and chRCC were 0.832, 0.829, 0.806, and 0.795 for the ResNet-18 model, compared to 0.906, 0.911, 0.840, and 0.827 for the RepVGG-A0 model, respectively. The deep learning models could reliably differentiate between various histological subtypes of renal tumors using CEUS images in an objective and non-invasive manner.

PMID:40329155 | DOI:10.1007/s10278-025-01525-3

J Imaging Inform Med. 2025 May 6. doi: 10.1007/s10278-025-01465-y. Online ahead of print.

ABSTRACT

Breast cancer has remained one of the most frequent and life-threatening cancers in females globally, putting emphasis on better diagnostics in its early stages to solve the problem of therapy effectiveness and survival. This work enhances the assessment of breast cancer by employing progressive residual networks (PRN) and ResNet-50 within the framework of Progressive Residual Multi-Class Support Vector Machine-Net. Built on concepts of deep learning, this creative integration optimizes feature extraction and raises the bar for classification effectiveness, earning an almost perfect 99.63% on our tests. These findings indicate that PRMS-Net can serve as an efficient and reliable diagnostic tool for early breast cancer detection, aiding radiologists in improving diagnostic accuracy and reducing false positives. The separation of the data into different segments is possible to determine the architecture's reliability using the fivefold cross-validation approach. The total variability of precision, recall, and F1 scores clearly depicted in the box plot also endorse the competency of the model for marking proper sensitivity and specificity-highly required for combating false positive and false negative cases in real clinical practice. The evaluation of error distribution strengthens the model's rationale by giving validation of practical application in medical contexts of image processing. The high levels of feature extraction sensitivity together with highly sophisticated classification methods make PRMS-Net a powerful tool that can be used in improving the early detection of breast cancer and subsequent patient prognosis.

PMID:40329154 | DOI:10.1007/s10278-025-01465-y

Sci Rep. 2025 May 6;15(1):15773. doi: 10.1038/s41598-025-99694-2.

ABSTRACT

Passive localization is necessary for Internet of Things (IoT) applications to observe and follow people without requiring them to carry massive equipment. This is crucial in private settings like security and medical monitoring, where individuals are reluctant to wear tracking equipment. Localizing and tracking objects in these spaces are vital since wall loss causes GPS signals to perform poorly in indoor environments. Therefore, passive localization using Radio Tomography Images (RTI) has gained significant importance in present life. Because there are flaws in the RSSI data that models might exploit, previous problems with RTI sparked innovation and resulted in the development of more complex systems, such as a passive localization system that leverages deep learning. This paper employs a set of ESP32 nodes for a mesh network and utilizes a radio frequency sensor network with ESP32 modules to collect RSSI values. We have developed and thoroughly examined the working of radio tomography generation algorithms and present a deep learning approach using a convolutional neural network (CNN) to address the inverse problem. Two CNN models are developed to reconstruct static tomographic images, improve the quality of these images, and localize targeted objects. The targeted object localization accuracy is above 92% by using the proposed system. The results of the proposed system are also compared with previously developed approaches, and it is clearly shown that the proposed system outperforms the previously developed approaches.

PMID:40328896 | DOI:10.1038/s41598-025-99694-2

Sci Rep. 2025 May 6;15(1):15820. doi: 10.1038/s41598-025-96577-4.

ABSTRACT

Accurate high-resolution runoff predictions are essential for effective flood mitigation and water planning. In hydrology, conceptual models are preferred for their simplicity, despite their limited capacity for accurate predictions. Deep-learning applications have recently shown promise for runoff predictions; however, they usually require longer input data sequences, especially for high-temporal resolution simulations, thus leading to increased model complexity. To address these challenges, this study evaluates the robustness of two novel approaches using Long Short-Term Memory (LSTM) models. The first model integrates the outputs of a simple conceptual model with LSTM capabilities, while the second model is a stand-alone model that combines coarse and fine temporal inputs to capture both long and short dependencies. To ensure accuracy and reliability, we utilized a century-long meteorological dataset generated from a sophisticated physics-based model, eliminating any influence of measurement errors. The training phase employed multiple sub-periods ranging from 7- to 50-year, with a separate 50-year subset for validation. Our findings highlight the consistent improvement of both LSTM models with increasing training dataset lengths, while conceptual models show no notable enhancement beyond 15 years of training data. Both LSTM models demonstrate superior performance in capturing the reference flow duration curve, offering a promising pathway for more computationally efficient models for runoff predictions.

PMID:40328848 | DOI:10.1038/s41598-025-96577-4

Sci Rep. 2025 May 6;15(1):15845. doi: 10.1038/s41598-025-00592-4.

ABSTRACT

This study focuses on the application of virtual reality (VR) technology in English language teaching (ELT), and discusses the effect of combining VR with machine translation (MT) technology. VR technology is introduced into the study to provide innovative teaching methods for English teachers and create an immersive learning environment for students. Based on the latest development of deep learning (DL), a new MT model is proposed in this study, and it is successfully integrated with VR technology to optimize the quality of ELT. The experimental results show that the translation accuracy of the MT model designed in this study reaches 98.5%, the F1 score is stable at around 93%, and the semantic information recall rate is as high as 92%, all of which are better than the traditional model. In the preliminary test, the comparative experiment of 40 English majors further verified the effectiveness of the model in improving translation efficiency and quality. This study shows the great potential of the integration of VR and MT technology in ELT, and also proves its advantages by using experimental data, which provides technical support for ELT and provides reference for future practice.

PMID:40328816 | DOI:10.1038/s41598-025-00592-4

Nat Commun. 2025 May 6;16(1):4212. doi: 10.1038/s41467-025-59516-5.

ABSTRACT

Efficient and accurate road distress detection is crucial for infrastructure maintenance and transportation safety. Traditional manual inspections are labor-intensive and time-consuming, while increasingly popular automated systems often rely on computationally intensive devices, limiting widespread adoption. To address these challenges, this study introduces MobiLiteNet, a lightweight deep learning approach designed for mobile deployment on smartphones and mixed reality systems. Utilizing a diverse dataset collected from Europe and Asia, MobiLiteNet incorporates Efficient Channel Attention to boost model performance, followed by structural refinement, sparse knowledge distillation, structured pruning, and quantization to significantly increase the computational efficiency while preserving high detection accuracy. To validate its effectiveness, MobiLiteNet improves the existing MobileNet model. Test results show that the improved MobileNet outperforms baseline models on mobile devices. With significantly reduced computational costs, this approach enables real-time, scalable, and accurate road distress detection, contributing to more efficient road infrastructure management and intelligent transportation systems.

PMID:40328808 | DOI:10.1038/s41467-025-59516-5

Exp Cell Res. 2025 May 4:114584. doi: 10.1016/j.yexcr.2025.114584. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis is a chronic fibrotic lung disease with limited treatment options, making the development of new therapies crucial. We previously demonstrated that γδ T cells, a subset of immune effector cells, exhibit antifibrotic properties. We have also shown that natural killer (NK) cells, another class of immune effectors, can be efficiently expanded in culture using interleukin-2 (IL-2) and interleukin-18 (IL-18). This study examined the effects of interleukin-12 (IL-12)-stimulated NK cells, expanded with IL-2 and IL-18, on type I collagen and α-smooth muscle actin (αSMA) expression in pulmonary fibroblasts. IL-12-stimulated human NK cells exhibited reduced cytotoxicity toward pulmonary fibroblasts while retaining their proliferative capacity. Co-culture of IL-12-stimulated NK cells with fibroblasts significantly suppressed type I collagen and αSMA expression, even without direct cell contact, indicating the involvement of soluble factors. Supernatants from IL-12-stimulated NK cells partially inhibited the expression of these antifibrotic factors, suggesting a dual mechanism: direct cell-cell interaction and soluble factor secretion. Interferon-γ (IFN-γ) in the supernatant significantly increased, and neutralizing anti-IFN-γ monoclonal antibody partially reversed type I collagen and αSMA suppression. Similarly, IL-12-stimulated murine NK cells suppressed type I collagen in mouse pulmonary fibroblasts. These findings suggest that IL-12-stimulated NK cells inhibit the expression of fibrosis-associated molecules via contact-dependent and -independent mechanisms, supporting their potential for adoptive cell therapy in pulmonary fibrosis.

PMID:40328414 | DOI:10.1016/j.yexcr.2025.114584

Microbiome. 2025 May 6;13(1):115. doi: 10.1186/s40168-025-02107-9.

ABSTRACT

BACKGROUND: The human microbiome is transmissible between individuals, including pathogens and commensals with metabolic and immune-modulating effects, which could influence susceptibility, severity, and outcomes of both infection and non-infection diseases. However, limited studies of respiratory microbiome transmission within populations have been conducted. Herein, we performed species- and strain-level metagenomic analyses on oropharyngeal (OP) swabs from 1046 healthy urban dwellers across 13 districts, including 111 households with at least two cohabitants, to elucidate the transmission dynamics of the respiratory microbiome within households and communities.

RESULTS: We found that geographic districts accounted for the greatest variation in the OP microbiome, with unrelated individuals from the same district showing greater microbiome similarity and higher strain-sharing rates than those from different districts. Cohabitants, especially spouses and siblings, exhibited similar microbial abundances and shared more strains, with 16.7% (IQR 0.0-33.3%) of strains shared among cohabitants, compared to 0.0% (IQR 0.0-11.1%) in non-cohabiting pairs (p < 0.05). Both respiratory commensals and opportunistic pathogens were shared among cohabitants. In contrast, no evidence of vertical transmission was detected between mother-offspring pairs. Additionally, the OP microbiome contained diverse antibiotic resistance genes (ARGs), with 15.0% linked to mobile genetic elements (MGEs) or plasmids; the flanking sequences of these ARGs were more conserved across species than those of non-MGE-associated ARGs, suggesting horizontal transfer of ARGs among respiratory microorganisms.

CONCLUSIONS: In summary, we characterized the transmissible nature of the OP microbiome and the risk of ARG dissemination among respiratory microorganisms. These findings underscore the role of respiratory microbes and ARGs exchange in shaping the microbiome of healthy populations and emphasize their relevance to public health strategies for respiratory health management. Video Abstract.

PMID:40329426 | DOI:10.1186/s40168-025-02107-9

Mol Syst Biol. 2025 May 6. doi: 10.1038/s44320-025-00107-3. Online ahead of print.

ABSTRACT

Genetic variation and 3D chromatin structure have major roles in gene regulation. Due to challenges in mapping chromatin conformation with haplotype-specific resolution, the effects of genetic sequence variation on 3D genome structure and gene expression imbalance remain understudied. Here, we applied Genome Architecture Mapping (GAM) to a hybrid mouse embryonic stem cell (mESC) line with high density of single-nucleotide polymorphisms (SNPs). GAM resolved haplotype-specific 3D genome structures with high sensitivity, revealing extensive allelic differences in chromatin compartments, topologically associating domains (TADs), long-range enhancer-promoter contacts, and CTCF loops. Architectural differences often coincide with allele-specific differences in gene expression, and with Polycomb occupancy. We show that histone genes are expressed with allelic imbalance in mESCs, and are involved in haplotype-specific chromatin contacts marked by H3K27me3. Conditional knockouts of Polycomb enzymatic subunits, Ezh2 or Ring1, show that one-third of ASE genes, including histone genes, is regulated through Polycomb repression. Our work reveals highly distinct 3D folding structures between homologous chromosomes, and highlights their intricate connections with allelic gene expression.

PMID:40329044 | DOI:10.1038/s44320-025-00107-3