Transl Cancer Res. 2025 Jul 30;14(7):4179-4193. doi: 10.21037/tcr-2024-2563. Epub 2025 Jul 23.

ABSTRACT

BACKGROUND: Neuroblastoma is a highly heterogeneous pediatric malignancy, with high-risk cases exhibiting poor clinical outcomes. Store-operated calcium entry (SOCE) channels have been implicated in cancer progression, yet their prognostic significance in neuroblastoma remains unclear. This study aimed to investigate the relevance of SOCE-related genes in predicting patient prognosis and guiding therapeutic strategies.

METHODS: We performed unsupervised clustering based on SOCE-related gene expression in multiple neuroblastoma RNA sequencing (RNA-seq) datasets. A prognostic scoring system, the SOCE_Score, was developed using machine learning algorithms. The model's predictive performance was validated across independent datasets. Immune characteristics were assessed using established deconvolution algorithms, and candidate therapeutic compounds were identified via the Connectivity Map (CMap) platform.

RESULTS: Two distinct molecular clusters were identified, differing significantly in survival outcomes, immune infiltration, and stemness signatures. The SOCE_Score stratified patients with high accuracy and outperformed conventional clinical predictors. Lower SOCE_Score groups were associated with favorable immune landscapes and greater responsiveness to immune checkpoint blockade. CMap analysis highlighted MS-275, a histone deacetylase (HDAC) inhibitor, as a promising compound targeting low SOCE_Score phenotypes.

CONCLUSIONS: SOCE-related transcriptional features serve as robust biomarkers for prognosis and immune activity in neuroblastoma. The SOCE_Score holds potential for guiding risk stratification, immunotherapeutic selection, and drug repurposing efforts. These findings underscore the clinical utility of integrating calcium signaling profiles into neuroblastoma management and warrant further experimental validation.

PMID:40792155 | PMC:PMC12335702 | DOI:10.21037/tcr-2024-2563

Biotechnol Rep (Amst). 2025 Jul 29;47:e00909. doi: 10.1016/j.btre.2025.e00909. eCollection 2025 Sep.

ABSTRACT

Alzheimer's disease affects 6.9 million Americans aged 65 and older, a number expected to double by 2060. Eight FDA-approved drugs target Alzheimer's, but no cure is available, and most treatments are symptomatic. Drug repurposing, the use of FDA-approved drugs for new indications, is a promising strategy to address this lack of effective therapies. However, despite prior safety approval, repurposable drugs may still trigger unexpected side-effects in new contexts. This study introduces a network-based approach to minimize side-effect risk in drug repositioning, focusing on QT interval prolongation, a cardiac side-effect observed in Alzheimer's patients treated with acetylcholinesterase inhibitors. The method integrates Mode-of-Action and Random Walk with Restart analyses to identify repositioning candidates while assessing QT-related risk. This strategy identified promising compounds including acamprosate, tolcapone, sitagliptin, and diazoxide, with potential to mitigate disease pathology. Gene set enrichment analysis was used to computationally assess the compounds' ability to reverse disease-related gene expression signatures.

PMID:40791766 | PMC:PMC12337645 | DOI:10.1016/j.btre.2025.e00909

BMC Med Inform Decis Mak. 2025 Aug 11;25(1):298. doi: 10.1186/s12911-025-03146-w.

ABSTRACT

BACKGROUND: Data quality (DQ) of electronic health record (EHR) is crucial for the advancement of health informatization, yet it remains a significant challenge. Scholars are showing a growing interest in leveraging semantic technologies to enhance EHR data quality. However, previous studies have focused predominantly on specific semantic technologies, scenarios, or objectives-such as interoperability-often overlooking the potential of a various semantic technologies across different scenarios.

OBJECTIVE: This systematic review aimed to explore the potential of employing a range of semantic technologies to improve EHR data quality in a broader spectrum of application scenarios.

METHODS: Our systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Three databases were searched, including PubMed, IEEE Xplore, and Web of Science Core Collection. The search terms used included "Semantic*", "Quality", "Electronic Health Record*", "EHR*", "Electronic Medical Record*", and "EMR*". These terms were combined via various Boolean operators to formulate multiple search queries.

RESULTS: Thirty-seven papers that met the inclusion criteria between 2008 and 2024 were analyzed. Six semantic techniques were identified as instrumental in improving EHR DQ: EHR standardization, controlled vocabulary, ontology, semantic web, knowledge graph, and natural language processing (NLP). These technologies were further mapped to 16 core data quality indicators and the FAIR principles (Findable, Accessible, Interoperable, and Reusable), highlighting their contributions across both technical and governance dimensions.

CONCLUSIONS: The six identified semantic technologies can be categorized into three levels: foundational, general, and advanced. These technologies show significant potential in enhancing EHR DQ, particularly in the areas of conformance, portability, usability, and applicability, and they are suitable for a variety of contexs beyond interoperability, aligning with FAIR-aligned best practices in data management and reuse.

PMID:40790196 | DOI:10.1186/s12911-025-03146-w

Front Cardiovasc Med. 2025 Jul 28;12:1546493. doi: 10.3389/fcvm.2025.1546493. eCollection 2025.

ABSTRACT

BACKGROUND: Phenotypic heterogeneity is apparent among individuals with putative monogenic disease, such as familial hypertrophic cardiomyopathy. Genome sequencing (GS) allows interrogation of the full spectrum of inborn genetic variation in an individual and RNA profiling provides a snapshot of the cardiac-specific pathogenic effects on gene expression.

OBJECTIVES: Identify candidate genetic modifiers of hypertrophic cardiomyopathy phenotype.

METHODS: We performed GS of 48 individuals with variants in MYH7, the gene encoding beta myosin heavy chain, and a personal or family history of cardiomyopathy. The genome sequences were annotated with a custom pipeline optimized for cardiovascular gene variant detection. We utilized multiple lines of evidence to prioritize genes together with rare variant gene-based association testing to identify candidate genetic modifiers.

RESULTS: GS identified the MYH7 variant in all 48 cases. Several variants were reclassified based on best available data. We identified known disease-associated genes (MYBPC3, FHOD3), a priori candidate modifiers (ATP1A2, RYR2), and novel candidate modifiers of cardiomyopathy including PACSIN3 and SORBS2. We identified regulatory variants and intergenic regions associated with the phenotypes. Using RNA profiling, we show that several genes identified through gene-based association testing are differentially regulated in human hypertrophic cardiomyopathy, and in models of disease.

CONCLUSION: Evaluation of the whole genome, even in the case of alleged monogenic disease, leads to important new insights. The identified variants, regions, and genes are candidates to modify disease presentation in cardiomyopathy.

PMID:40791945 | PMC:PMC12336239 | DOI:10.3389/fcvm.2025.1546493

BMC Med Inform Decis Mak. 2025 Aug 11;25(1):301. doi: 10.1186/s12911-025-03139-9.

ABSTRACT

BACKGROUND: Understanding early predictors of treatment outcomes allows better outcome prediction and resource allocation for efficient tuberculosis (TB) management.

OBJECTIVES: This study aimed to predict treatment outcomes of TB patients from a real-world population-wide health record dataset with a significant rate of incomplete observations. In addition, potential risk factors associated with death during TB treatment were investigated.

METHODS: We exploited the upweighting approach and multiple imputation analysis (MIA) to address the extreme imbalance in responses and missing data. Three algorithms were employed for TB treatment outcome prediction, including logistic regression (LOGIT), random forest, and stochastic gradient boosting. The three models exhibited similar performance in predicting the treatment outcomes. Moreover, an interpretation of LOGIT was conducted, adjusted odds ratios (aORs) were computed, and the interpretation results were compared between MIA and complete case analysis (CCA).

RESULTS: MIA was an appropriate method for coping with missing data. In addition, compared to CCA, the interpretation results of the MIA-derived LOGIT showed more statistically significant covariates associated with TB treatment outcomes. In MIA, factors such as TB clinical form involving both pulmonary TB and extrapulmonary TB [aOR = 3.077, 95% confidence interval (CI) = 2.994-3.163], retreatment after abandonment (aOR = 2.272, 95% CI = 2.209-2.338), and the absence of isoniazid (aOR = 2.072, 95% CI = 1.892-2.269) or rifampicin (aOR = 1.968, 95% CI = 1.746-2.218) in the treatment regimen were associated with increased odds of death.

CONCLUSION: In conclusion, our results shed light on the potential risk factors for death during TB treatment and suggest the use of simple yet interpretable LOGIT for the prediction of TB treatment outcomes.

PMID:40790736 | DOI:10.1186/s12911-025-03139-9

Mater Today Bio. 2025 Jul 28;34:102148. doi: 10.1016/j.mtbio.2025.102148. eCollection 2025 Oct.

ABSTRACT

Cystic fibrosis (CF) is an autosomal recessive disorder resulting from impaired anion transport in the epithelium of multiple organs, thereby affecting various physiological functions throughout the body. The heterogeneity of CF complicates drug development, highlighting the growing importance of individualized therapies. CF patient-derived organoid models and organ-on-a-chip (OOC) platforms are promising in vitro models for recapitulating CF pathology, owing to their high simulation fidelity, individualized therapeutic capabilities, cost-effectiveness, and high-throughput screening potential. This review systematically summarizes the technological development pathways of patient-derived organoids and OOC platforms for CF, along with recent advances in their applications to CF-related basic research, and particularly focuses on exploratory studies using organoid-on-a-chip (OrgOC) systems to elucidate CF pathogenesis and assess therapeutic approaches.

PMID:40791795 | PMC:PMC12336816 | DOI:10.1016/j.mtbio.2025.102148

Front Immunol. 2025 Jul 28;16:1595994. doi: 10.3389/fimmu.2025.1595994. eCollection 2025.

ABSTRACT

Cystic fibrosis (CF) lung disease manifests through abnormally thick mucus, persistent bacterial infections and a dysregulated innate immune system that involves significant neutrophilic inflammation. Neutrophils, immune cells essential to fight infections, accumulate in large numbers in CF airways and release neutrophil extracellular traps (NETs) into the airway lumen that deliver extracellular DNA, granule content and cytokines including IL-1β. Interleukin-1β, a powerful, proinflammatory cytokine, represents another, significant component of the innate immune system that is dysregulated in CF. Both defense mechanisms become problematic as NETs and IL-1β are present at elevated levels in CF airways, potentially creating a destructive cycle that exacerbates lung damage rather than protects against infections. Therefore, understanding the interplay between IL-1β and NETs is crucial for addressing CF lung disease progression. This review examines the general mechanisms of IL-1β release and NET formation, with particular focus on their role in CF lung disease, and proposes that a self-perpetuating, positive feedback loop between these two innate immune processes represents a major driving force in disease progression. This understanding suggests potential therapeutic targets for interrupting the cycle of inflammation and tissue damage in CF airways.

PMID:40791588 | PMC:PMC12337492 | DOI:10.3389/fimmu.2025.1595994

Front Cell Infect Microbiol. 2025 Jul 28;15:1602883. doi: 10.3389/fcimb.2025.1602883. eCollection 2025.

ABSTRACT

BACKGROUND: Non-puerperal mastitis (NPM) arises from heterogeneous factors ranging from autoimmune dysregulation to occult infections. To establish a diagnosis, biopsy is reliable but invasive. Imaging exhibits a limited specificity and may cause diagnostic delays, patient discomfort, and suboptimal management. Inspired by non-invasive tongue diagnosis in traditional Chinese medicine, this study integrated tongue-coating microbiota profiling and AI-quantified tongue image phenotyping to establish an objective, non-invasive diagnostic framework for NPM.

METHODS: A total of 100 NPM patients from the Breast Surgery Department of Longhua Hospital and 100 healthy volunteers were included. Their clinical characteristics, tongue images, and tongue-coating microbiota data were collected. Features of tongue images (detection, segmentation, and classification) were quantitated and extracted via deep learning. The microbiota composition was assessed using 16S rRNA gene sequencing (V3-V4 region) and bioinformatic pipelines (QIIME2, DADA2). Based on clinical, imaging, and microbial features, three machine learning models-logistic regression (LR), support vector machine (SVM), and gradient boosting decision tree (GBDT)-were trained to distinguish NPM.

RESULTS: The GBDT model achieved a superior diagnostic performance (AUROC = 0.98, accuracy = 0.95, and specificity = 0.95), outperforming the LR (AUROC = 0.98, accuracy = 0.95, and specificity = 0.90) and SVM models (AUROC = 0.87, accuracy = 0.80, and specificity = 0.75). Integration of clinical characteristics, tongue image features, and bacterial profiles (at the genus/family level) yielded the highest accuracy, whereas models using a single class of features showed a lower discriminatory ability (AUROC = 0.90-0.91). Key predictors included Campylobacter (12%), waist-hip ratio (11%), and Alloprevotella (6%).

CONCLUSIONS: Integrating clinical characteristics, tongue image features, and tongue-coating microbiota profiles, the multimodal GBDT model demonstrates a high diagnostic accuracy, supporting its utility for early screening and diagnosis of NPM.

PMID:40792098 | PMC:PMC12336138 | DOI:10.3389/fcimb.2025.1602883

Ophthalmol Sci. 2025 Jun 3;5(6):100837. doi: 10.1016/j.xops.2025.100837. eCollection 2025 Nov-Dec.

ABSTRACT

PURPOSE: Age-related cataract is the leading cause of vision impairment. Researchers have utilized various imaging modalities, including slit beam, diffuse anterior segment, and retinal imaging, to develop deep learning (DL) algorithms for automated cataract analysis. However, the comparative performance of these algorithms across different ocular imaging modalities remains unevaluated, mainly due to the absence of standardized test sets across studies.

DESIGN: Retrospective study.

PARTICIPANTS: Across all the models, the Singapore Malay Eye Study data set was used for training (N = 7093 eyes) and internal testing (N = 1649 eyes). The Singapore Indian Eye Study (SINDI; N = 5579 eyes) and the Singapore Chinese Eye Study (SCES; N = 5658 eyes) were used for external testing. A community study data set of nonmydriatic retinal photos (N = 310 eyes) was used for external testing of the retinal model.

METHODS: We developed 3 single-modality DL models (retinal, slit beam, and diffuse anterior segment photos) and 4 ensemble models (4 different combinations of the 3 single-modality models) to detect visually significant cataract (VSC). We defined eyes with VSC as having significant cataract (based on the modified Wisconsin cataract grading system) with a best-corrected visual acuity of <20/60.

MAIN OUTCOME MEASURES: Area under receiver operating characteristic curve (AUC).

RESULTS: In the internal test, the retinal model had the highest AUC value (97.0%; 95% confidence interval [CI], 95.9-98.2), compared with the slit beam model (AUC, 93.4%; 95% CI, 90.1-96.7; P diff = .029) and diffuse anterior segment model (AUC, 94.4; 95% CI, 92.3-96.4; P diff = .002). There was no significant difference in AUC when comparing the retinal model with the ensemble models (all P diff ≥ .07). These trends were consistently observed in the external test sets. In nonmydriatic eyes, the retinal model showed reasonable performance (AUC, 89.8%; 95% CI, 89.6-89.9).

CONCLUSIONS: Our findings highlight the retinal model as a promising tool for detecting VSC, outperforming slit beam and diffuse anterior segment models. Because retinal photography is routine in diabetic retinopathy screening, this approach could enable opportunistic cataract screening with minimal add-on cost.

FINANCIAL DISCLOSURE: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PMID:40792062 | PMC:PMC12336814 | DOI:10.1016/j.xops.2025.100837

iScience. 2025 Jul 18;28(8):113158. doi: 10.1016/j.isci.2025.113158. eCollection 2025 Aug 15.

ABSTRACT

Detecting tumor-specific DNA methylation in circulating tumor DNA (ctDNA) offers a non-invasive method for tumor detection. The primary challenge lies in identifying the extremely low abundance of ctDNA in cell-free blood plasma (cfDNA). In this study, we present Oncoder, an interpretable deep learning-based tool for economical and accurate non-invasive tumor monitoring and diagnosis. Unlike other methods, Oncoder learns scientifically sound reference methylation atlases from patient blood to provide additional diagnostic insights, fostering trust among clinicians and patients, and continuously improves its accuracy through iterative learning. In simulations, Oncoder reduced prediction errors of tumor signals in blood by at least 30% compared to existing methods and showed the highest prediction correlation, indicating more accurate tumor progression monitoring. We also evaluated Oncoder's performance in various real-world applications. Oncoder sensitively detected changes in ctDNA levels during tumor development and treatment and exhibited superior diagnostic potential even in the earliest stages of cancer.

PMID:40792040 | PMC:PMC12337691 | DOI:10.1016/j.isci.2025.113158

Front Neurorobot. 2025 Jul 28;19:1604453. doi: 10.3389/fnbot.2025.1604453. eCollection 2025.

ABSTRACT

Myoelectric control systems translate electromyographic signals (EMG) from muscles into movement intentions, allowing control over various interfaces, such as prosthetics, wearable devices, and robotics. However, a major challenge lies in enhancing the system's ability to generalize, personalize, and adapt to the high variability of EMG signals. Artificial intelligence, particularly neural networks, has shown promising decoding performance when applied to large datasets. However, highly parameterized deep neural networks usually require extensive user-specific data with ground truth labels to learn individual unique EMG patterns. However, the characteristics of the EMG signal can change significantly over time, even for the same user, leading to performance degradation during extended use. In this work, we propose an innovative three-stage neural network training scheme designed to progressively develop an adaptive workflow, improving and maintaining the network performance on 28 subjects over 2 days. Experiments demonstrate the importance and necessity of each stage in the proposed framework.

PMID:40791943 | PMC:PMC12336220 | DOI:10.3389/fnbot.2025.1604453

Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025. doi: 10.1109/isbi60581.2025.10981108. Epub 2025 May 12.

ABSTRACT

With the rapid development of deep learning, CNN-based U-shaped networks have succeeded in medical image segmentation and are widely applied for various tasks. However, their limitations in capturing global features hinder their performance in complex segmentation tasks. The rise of Vision Transformer (ViT) has effectively compensated for this deficiency of CNNs and promoted the application of ViT-based U-networks in medical image segmentation. However, the high computational demands of ViT make it unsuitable for many medical devices and mobile platforms with limited resources, restricting its deployment on resource-constrained and edge devices. To address this, we propose EViT-UNet, an efficient ViT-based segmentation network that reduces computational complexity while maintaining accuracy, making it ideal for resource-constrained medical devices. EViT-UNet is built on a U-shaped architecture, comprising an encoder, decoder, bottleneck layer, and skip connections, combining convolutional operations with self-attention mechanisms to optimize efficiency. Experimental results demonstrate that EViT-UNet achieves high accuracy in medical image segmentation while significantly reducing computational complexity. The code is available at https://github.com/Retinal-Research/EVIT-UNET.

PMID:40791942 | PMC:PMC12337706 | DOI:10.1109/isbi60581.2025.10981108

Front Neurol. 2025 Jul 28;16:1615523. doi: 10.3389/fneur.2025.1615523. eCollection 2025.

ABSTRACT

Brain diseases pose a significant global health challenge due to their complexity and the limitations of traditional medical strategies. Recent advancements in artificial intelligence (AI), especially deep learning models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), offer powerful new tools for analysis. These neural networks are effective at extracting complex patterns from high-dimensional data. By integrating diverse data sources-such as neuroimaging, multi-omics, and clinical information-multimodal AI provides the comprehensive view needed to understand intricate disease mechanisms. This review outlines how these technologies enhance precision drug development and enable closed-loop treatment systems for brain disorders. Key applications include improving diagnostic accuracy, identifying novel biomarkers, accelerating drug discovery through target identification and virtual screening, and predicting patient-specific treatment responses. These AI-driven methods have the potential to shift medicine from a one-size-fits-all model to a personalized approach, with diagnostics and therapies tailored to individual profiles. However, realizing this potential requires addressing significant challenges related to data access, model interpretability, clinical validation, and practical integration.

PMID:40791911 | PMC:PMC12336123 | DOI:10.3389/fneur.2025.1615523

medRxiv [Preprint]. 2025 Jul 14:2025.07.11.25331386. doi: 10.1101/2025.07.11.25331386.

ABSTRACT

BACKGROUND: Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks, and reliance on specialized infrastructure limit accessibility and scalability of standard-of-care right heart catheterization (RHC). Seismocardiography (SCG), a non-invasive technique which records subtle chest wall vibrations generated by cardiac mechanical activity, may offer a promising alternative for CO determination.

OBJECTIVES: To develop and evaluate a deep learning model for estimating CO directly from SCG, electrocardiogram (ECG), and body mass index (BMI) in heart failure patients undergoing RHC.

METHODS: We trained a deep convolutional neural network for CO estimation using an open-access dataset comprising 73 heart failure patients with simultaneous RHC, SCG, and ECG recordings. Model performance was evaluated using a rotating leave-pair-out cross-validation strategy.

RESULTS: When estimating CO, the deep learning model achieved a mean bias of -0.35 L/min with limits of agreement (LoA) from -2.21 to 1.51 L/min. When predicting cardiac index in patients with a reference index < 2.2 L/min/m 2 , the model yielded a mean bias of 0.07 L/min/m 2 with LoA from -0.35 to 0.48 L/min/m 2 .

CONCLUSIONS: This study demonstrates the feasibility of using deep learning in combination with wearable SCG sensors to non-invasively estimate CO. Model performance was particularly strong in low-output states. These findings highlight the potential of SCG-based monitoring to augment clinical decision-making in settings where invasive measurements are impractical or unavailable. Prospective multicenter validation is needed to confirm generalizability and assess clinical impact.

SOURCES OF SUPPORT: This work was supported by NIH grants T32 HL129964 (N.J.K.), K08 ES037420 (N.J.K.), R01 HL124021 (S.Y.C.), R01 HL122596 (S.Y.C.); R01 HL151228 (S.Y.C.); the McKamish Family Foundation, the Hemophilia Center of Western Pennsylvania, and the Institute for Transfusion Medicine (N.J.K.), United Therapeutics Jenesis Innovative Research Award (N.J.K.), and the Pulmonary Hypertension Association (N.J.K.).

DISCLOSURES: S.Y.C. has served as a consultant for Merck, Janssen, and United Therapeutics; S.Y.C. is a director, officer, and shareholder in Synhale Therapeutics and Amlysion Therapeutics; S.Y.C. and N.J.K. hold research grants from United Therapeutics; S.Y.C. holds research grants from Bayer and the WoodNext Foundation. S.Y.C. has filed patent applications regarding the targeting of metabolism in pulmonary hypertension. Other authors: none.

TWITTER SUMMARY: We developed a deep learning model to non-invasively estimate cardiac output from wearable seismocardiogram (SCG) signals in patients undergoing right heart catheterization. This is the largest study to date using SCG for noninvasive cardiac output monitoring. #HeartFailure, #WearableTech, #AIInCardiology.

PMID:40791697 | PMC:PMC12338910 | DOI:10.1101/2025.07.11.25331386

medRxiv [Preprint]. 2025 Jul 17:2025.07.16.25331662. doi: 10.1101/2025.07.16.25331662.

ABSTRACT

Biomedical data are inherently multimodal, capturing complementary aspects of a patient condition. Deep learning (DL) algorithms that integrate multiple biomedical modalities can significantly improve clinical decisionmaking, especially in domains where collecting data is not simple and data are highly heterogeneous. However, developing effective and reliable multimodal DL methods remains challenging, requiring large training datasets with paired samples from modalities of interest. An increasing number of de-identifed biomedical datasets are publicly accessible, though they still tend to be unimodal. For example, several publicly available skin lesion datasets aid automated dermatology clinical decision-making. Still, they lack annotated reports paired with the images, thereby limiting the advance and use of multimodal DL algorithms. This work presents a strategy exploiting real and synthesized data in a multimodal architecture that encodes finegrained text representations within image embeddings to create a robust representation of skin lesion data. Large language models (LLMs) are used to synthesize textual descriptions from image metadata that are subsequently paired with the original skin lesion images and used for model development. The architecture is evaluated on the classification of skin lesion images, considering nine internal and external data sources. The proposed multimodal representation outperforms the unimodal one on the classification of skin lesion images, achieving superior performance in every tested dataset.

PMID:40791679 | PMC:PMC12338939 | DOI:10.1101/2025.07.16.25331662

Data Brief. 2025 Jul 20;62:111912. doi: 10.1016/j.dib.2025.111912. eCollection 2025 Oct.

ABSTRACT

Street vending is a prominent component of the informal economy, yet its prevalence remains poorly quantified due to the limitations of traditional survey methods, which are costly, invasive, and labor-intensive. To enable scalable, image-based assessments of this activity, we present the StreetVendor-SLI dataset, specifically designed for detecting vendors in urban environments. The dataset comprises 2794 high-resolution images (2416×1359 px), obtained from video footage recorded with a user grade camera mounted on a motorcycle. The original dataset contains 1397 images, with an average size of 5 MB per image, resulting in a total dataset size of 4.63 GB. Privacy compliance with GDPR guidelines was achieved by anonymizing pedestrian faces and vehicle license plates using an open-source YOLO object detection pipeline. Every image is annotated utilizing the YOLO format, with vendors enclosed in bounding boxes and classified into three categories: fixed-stall vendor (1774 labels), semi-fixed vendor (459 labels), and itinerant vendor (124 labels). To address class imbalance and enhance model generalization, data augmentation techniques-including geometric transformations (rotation, flipping, scaling, shearing) and spectral adjustments (brightness, contrast, hue)-were applied. The Steet-level Imagery dataset thus provides an openly available option for the detection of street vendors, offering a valuable resource for researchers studying informal economic activities and urban policies.

PMID:40791665 | PMC:PMC12337018 | DOI:10.1016/j.dib.2025.111912

Data Brief. 2025 Jul 21;62:111908. doi: 10.1016/j.dib.2025.111908. eCollection 2025 Oct.

ABSTRACT

The mango image dataset presented in this article contains clear and detailed images of the fifteen most common and popular mango (Mangifera indica) varieties in Bangladesh: Amrapali, Ashshina Classic, Ashshina Zhinuk, Banana Mango, Bari-4, Bari-11, Fazli Classic, Fazli Shurmai, Gourmoti, Harivanga, Himsagor, Katimon, Langra, Rupali, and Shada. The mango specimens were sourced from various fruit markets across six districts of Bangladesh, namely Rajshahi, Chapai Nawabganj, Satkhira, Panchagarh, Rangpur, and Dhaka, which are famous for popular mango cultivation and availability to ensure a wide geographic representation. To maintain the quality and uniformity of images across the dataset, the images were captured using a high-definition smartphone camera under a standardized and controlled environment. Overall, the full dataset contains a total of 28,515 images, where 5703 images are original (raw) and 5703 images are processed with a blend of both real and virtual backgrounds. The processed images were further augmented resulting in a total of 17,109 augmented images. This is done to enhance their utility for training machine learning and deep learning models, particularly for performing computer vision tasks such as object detection, classification, and segmentation. This augmentation includes transformations such as flipping, rotation, shearing, blurring, variation of brightness and exposure, and introduction of noise to simulate diverse real-world scenarios and improve model robustness. This dataset holds strong reuse potential across computer vision, agriculture, food processing, and biodiversity research. It supports automated mango variety identification, sorting, grading, and quality assessment in precision agriculture. It can also aid in breeding climate-resilient, high-yield mango varieties, enhancing food security and sustainable farming. Additionally, it facilitates studies on phenotypic diversity, genetic correlations, and regional trait comparisons. The dataset can help ensure traceability, authenticity, and quality assurance, improving supply chains and export potential. From a biodiversity standpoint, it contributes to documenting and conserving unique mango varieties.

PMID:40791664 | PMC:PMC12337025 | DOI:10.1016/j.dib.2025.111908

bioRxiv [Preprint]. 2025 Jul 14:2025.07.09.663937. doi: 10.1101/2025.07.09.663937.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a devastating chronic lung disorder with limited treatment options. Macropinocytosis is one of the key cellular processes involved in nutrient consumption from the extracellular environment under stress conditions. Here, we studied the role of macropinocytosis in lung fibroblast activation and experimental pulmonary fibrosis. We found that macropinocytosis is increased in human lung fibroblasts (HLFs) derived from IPF patients. The inhibition of macropinocytosis with 5-(n-ethyl-n-isopropyl)-amiloride (EIPA) significantly inhibited profibrotic responses in IPF-derived and TGF-β1-stimulated HLFs. EIPA exerted antifibrotic effects by regulating amino acid (AA) uptake, mammalian target of rapamycin complex 1 (mTORC1) activation and mesenchyme homeobox1 (MEOX1) expression in activated HLFs. Both genetic and pharmacological inhibition of macropinocytosis significantly ameliorated pulmonary fibrosis in bleomycin (Bleo)-injured mice. Using IPF-derived precision cut lung slices (PCLS), we observed robust repression of profibrotic gene expression programs in EIPA-treated PCLS across different fibroblast subpopulations. Finally, we found that imipramine (Imi), a tricyclic antidepressant approved by the Food and Drug Administration (FDA), effectively inhibited macropinocytosis and ameliorated profibrotic responses in lung fibroblasts, Bleo-injured mice and IPF-derived PCLS. Taken together, our results suggest macropinocytosis inhibition as a potential therapeutic strategy to treat pulmonary fibrosis.

PMID:40791420 | PMC:PMC12338576 | DOI:10.1101/2025.07.09.663937

bioRxiv [Preprint]. 2025 Jul 18:2025.07.16.665213. doi: 10.1101/2025.07.16.665213.

ABSTRACT

YAP/TAZ signaling is required for initiation of lung alveolar repair, yet previous studies in idiopathic pulmonary fibrosis (IPF) predicted increased YAP/TAZ signaling in alveolar epithelial cells (AECs). We investigated whether persistent YAP/TAZ AEC signaling contributes to failed epithelial repair and persistent fibrotic remodeling. In IPF lungs, we identified increased YAP + /TAZ + AECs and increased expression of YAP/TAZ transcriptional targets compared to donor control lungs. In human lung organoids, pharmacological YAP/TAZ activation resulted in phenotype shifts of AECs into aberrant transitional states. In mice with Yap/Taz activation (YT active ) resulting from deletion of Hippo-kinases Stk3/4 in alveolar-type 2 (AT2) cells, resulted in persistent fibrotic remodeling at 28- and 56-days post-bleomycin injury. Gene promoter activity associated with transitional cell markers ( Krt19, Hopx, and Runx2 ) was increased in YT active AT2 cells. Immunofluorescent staining showed a loss of AT2 associated Cebpa and increased Krt19 in YT active lineage traced AT2 cells 28 days post-injury. Inhibition of Yap/Taz using Verteporfin resulted in improved lung repair in YT active mouse lungs, including increased Cebpa and decreased Krt19 + transitional cells. These findings demonstrate sustained Yap/Taz activation drives abnormal alveolar repair and persistent fibrotic remodeling. Blocking aberrant persistent Yap/Taz activity promotes adaptive repair and has potential as a therapeutic strategy for PF.

PMID:40791334 | PMC:PMC12338630 | DOI:10.1101/2025.07.16.665213

Front Microbiol. 2025 Jul 28;16:1618327. doi: 10.3389/fmicb.2025.1618327. eCollection 2025.

ABSTRACT

Tomato spotted wilt orthotospovirus (TSWV) is a major plant pathogen causing significant economic losses in tomato production worldwide. Understanding its genetic diversity and evolutionary mechanisms is crucial for effective disease management. This study analyzed TSWV isolates from symptomatic tomato plants collected across Croatia, Montenegro and Slovenia between 2020 and 2024. High-throughput sequencing (HTS) was employed to obtain whole-genome sequences, followed by phylogenetic analyses to assess genetic variability and relationships among isolates from these three countries and other isolates of worldwide geographic origin. Phylogenetic analyses placed all studied isolates within the L1-M3-S3 genotype, commonly associated with solanaceous crops in Europe. While Croatian and Slovenian isolates exhibited high genetic similarity, Montenegrin isolates clustered in a distinct subgroup, showing closer relationships to Asian and Mediterranean accessions. Despite the severe disease symptoms observed, no substitutions in the NSm protein associated with resistance-breaking (RB) phenotypes were detected. These findings suggest that additional virome components, environmental factors or so far unknown mechanism(s) may contribute to infection and disease severity in tomato and strongly support the need of continuous surveillance of TSWV genetic diversity in order to inform breeding programs and develop sustainable management strategies to mitigate future outbreaks.

PMID:40792267 | PMC:PMC12336143 | DOI:10.3389/fmicb.2025.1618327