medRxiv [Preprint]. 2025 Mar 6:2025.03.05.25323327. doi: 10.1101/2025.03.05.25323327.

ABSTRACT

Sweat parameters such as volume and chloride concentration may offer invaluable clinical insights for people with CF (PwCF). Pilocarpine-induced sweat collection for chloridometry measurement is the gold-standard for sweat chloride, but this technique is cumbersome and not suitable for remote settings. We have previously reported the utility of a skin-interfaced microfluidic device (CF Patch) in conjunction with a smartphone image processing platform that enables real-time measurement of sweating rates and sodium chloride loss in laboratory and remote settings. Here we conducted clinical studies characterizing the accuracy of the CF Patch compared to pilocarpine-induced sweat measurements using chloridometry and tested the feasibility of exercise-induced sweat chloride measurements in PwCF. The CF Patch demonstrated strong correlations compared to sweat chloride measured by chloridometry across clinic and remote settings and detected greater day-to-day sweat chloride variability in PwCF on CFTR modulators than healthy volunteers. These findings demonstrate that the CF Patch is suitable as a remote management device capable of measuring chloride concentrations and offers the potential of monitoring the efficacy of CF medication regimens.

PMID:40093258 | PMC:PMC11908303 | DOI:10.1101/2025.03.05.25323327

Turk Arch Pediatr. 2025 Mar 3;60(2):147-152. doi: 10.5152/TurkArchPediatr.2025.24199.

ABSTRACT

Objective: Patient-reported quality of life (QoL) measurement is crucial in making clinical decisions in unison with the patients. The current gold standard for cystic fibrosis (CF) is the Cystic Fibrosis Questionnaire-Revised (CFQ-R), which has different applications for different age groups and requires a computer program to be evaluated. There is a need for a straightforward way to evaluate QoL in both pediatric and adult patients with CF. The study aims to establish the validity and reliability of the Turkish version of the Alfred Wellness Score (AWESCORE) test that has been developed to evaluate QoL in patients with CF. Materials and Methods: This study is a methodological study. The AWESCORE form was translated into Turkish and was applied to patients above 10 years of age. It includes 10 questions. Each question was scored using a numerical rating scale of 0-10. Total scores ranged from 0 to 100. Test-retest reliability was assessed over 24 hours. To determine validity, comparisons were sought between stable subjects and those in pulmonary exacerbation, and between AWESCORE and CFQ-R. Results: A total of 99 patients were included, 29 of whom were during their acute exacerbation period (29%). All questions showed intraclass correlation coefficient (ICC) values above 0.9, indicating excellent reliability. Scores were higher during clinical stability compared to pulmonary exacerbation (mean ± SD): 79.35 ± 6.51 versus 41.93 ± 8.58 (P < .001). All questions were significantly worse in the acute exacerbation period, showing excellent validity with P values below .001 for each question. Conclusion: The Turkish version of the AWESCORE is valid and reliable in its ability to evaluate QoL in patients with CF.

PMID:40091631 | DOI:10.5152/TurkArchPediatr.2025.24199

Turk Arch Pediatr. 2025 Mar 3;60(2):117-125. doi: 10.5152/TurkArchPediatr.2025.24257.

ABSTRACT

Cystic fibrosis (CF) is a monogenic autosomal recessive disorder that primarily affects the respiratory and gastrointestinal systems. It results from variants in the CFTR gene, leading to dysfunctional chloride channels, thickened mucus secretion, and subsequent multisystem complications. Significant advances have been made in CF treatment, particularly with the development of CFTR modulators, which are unique to genotypes and have improved clinical outcomes in many people with CF. However, the benefits of these therapies are not universal, with a considerable portion of the CF population-especially those with rare mutations-still without access to effective treatment options. This review provides a comprehensive overview of the pathophysiology and genetic basis of CF, explores current and emerging treatments, and discusses the ongoing challenges in the field.

PMID:40091461 | DOI:10.5152/TurkArchPediatr.2025.24257

Plants (Basel). 2025 Mar 4;14(5):786. doi: 10.3390/plants14050786.

ABSTRACT

A novel eggplant disease detection method based on multimodal data fusion and attention mechanisms is proposed in this study, aimed at improving both the accuracy and robustness of disease detection. The method integrates image and sensor data, optimizing the fusion of multimodal features through an embedded attention mechanism, which enhances the model's ability to focus on disease-related features. Experimental results demonstrate that the proposed method excels across various evaluation metrics, achieving a precision of 0.94, recall of 0.90, accuracy of 0.92, and mAP@75 of 0.91, indicating excellent classification accuracy and object localization capability. Further experiments, through ablation studies, evaluated the impact of different attention mechanisms and loss functions on model performance, all of which showed superior performance for the proposed approach. The multimodal data fusion combined with the embedded attention mechanism effectively enhances the accuracy and robustness of the eggplant disease detection model, making it highly suitable for complex disease identification tasks and demonstrating significant potential for widespread application.

PMID:40094753 | DOI:10.3390/plants14050786

Plants (Basel). 2025 Feb 21;14(5):671. doi: 10.3390/plants14050671.

ABSTRACT

Soybean is a vital crop globally and a key source of food, feed, and biofuel. With advancements in high-throughput technologies, soybeans have become a key target for genetic improvement. This comprehensive review explores advances in multi-omics, artificial intelligence, and economic sustainability to enhance soybean resilience and productivity. Genomics revolution, including marker-assisted selection (MAS), genomic selection (GS), genome-wide association studies (GWAS), QTL mapping, GBS, and CRISPR-Cas9, metagenomics, and metabolomics have boosted the growth and development by creating stress-resilient soybean varieties. The artificial intelligence (AI) and machine learning approaches are improving genetic trait discovery associated with nutritional quality, stresses, and adaptation of soybeans. Additionally, AI-driven technologies like IoT-based disease detection and deep learning are revolutionizing soybean monitoring, early disease identification, yield prediction, disease prevention, and precision farming. Additionally, the economic viability and environmental sustainability of soybean-derived biofuels are critically evaluated, focusing on trade-offs and policy implications. Finally, the potential impact of climate change on soybean growth and productivity is explored through predictive modeling and adaptive strategies. Thus, this study highlights the transformative potential of multidisciplinary approaches in advancing soybean resilience and global utility.

PMID:40094561 | DOI:10.3390/plants14050671

Plants (Basel). 2025 Feb 22;14(5):675. doi: 10.3390/plants14050675.

ABSTRACT

Accurate detection of soybean diseases is a critical component in achieving intelligent agricultural management. However, traditional methods often underperform in complex field scenarios. This paper proposes a diffusion-based object detection model that integrates the endogenous diffusion sub-network and the endogenous diffusion loss function to progressively optimize feature distributions, significantly enhancing detection performance for complex backgrounds and diverse disease regions. Experimental results demonstrate that the proposed method outperforms multiple baseline models, achieving a precision of 94%, recall of 90%, accuracy of 92%, and mAP@50 and mAP@75 of 92% and 91%, respectively, surpassing RetinaNet, DETR, YOLOv10, and DETR v2. In fine-grained disease detection, the model performs best on rust detection, with a precision of 96% and a recall of 93%. For more complex diseases such as bacterial blight and Fusarium head blight, precision and mAP exceed 90%. Compared to self-attention and CBAM, the proposed endogenous diffusion attention mechanism further improves feature extraction accuracy and robustness. This method demonstrates significant advantages in both theoretical innovation and practical application, providing critical technological support for intelligent soybean disease detection.

PMID:40094551 | DOI:10.3390/plants14050675

Mol Omics. 2025 Mar 17. doi: 10.1039/d4mo00241e. Online ahead of print.

ABSTRACT

Genome architecture in eukaryotes exhibits a high degree of complexity. Amidst the numerous intricacies, the existence of genes as non-continuous stretches composed of exons and introns has garnered significant attention and curiosity among researchers. Accurate identification of exon-intron (EI) boundaries is crucial to decipher the molecular biology governing gene expression and regulation. This includes understanding both normal and aberrant splicing, with aberrant splicing referring to the abnormal processing of pre-mRNA that leads to improper inclusion or exclusion of exons or introns. Such splicing events can result in dysfunctional or non-functional proteins, which are often associated with various diseases. The currently employed frameworks for genomic signals, which aim to identify exons and introns within a genomic segment, need to be revised primarily due to the lack of a robust consensus sequence and the limitations posed by the training on available experimental datasets. To tackle these challenges and capitalize on the understanding that DNA exhibits function-dependent local physicochemical variations, we present ChemEXIN, an innovative novel method for predicting EI boundaries. The method utilizes a deep-learning (DL) architecture alongside tri- and tetra-nucleotide-based structural and energy features. ChemEXIN outperforms existing methods with notable accuracy and precision. It achieves an accuracy of 92.5% for humans, 79.9% for mice, and 92.0% for worms, along with precision values of 92.0%, 79.6%, and 91.8% for the same organisms, respectively. These results represent a significant advancement in EI boundary annotations, with potential implications for understanding gene expression, regulation, and cellular functions.

PMID:40094442 | DOI:10.1039/d4mo00241e

Diagn Interv Radiol. 2025 Mar 17. doi: 10.4274/dir.2025.242999. Online ahead of print.

ABSTRACT

PURPOSE: Established methods for bone age assessment (BAA), such as the Greulich and Pyle atlas, suffer from variability due to population differences and observer discrepancies. Although automated BAA offers speed and consistency, limited research exists on its performance across different populations using deep learning. This study examines deep learning algorithms on the Turkish population to enhance bone age models by understanding demographic influences.

METHODS: We analyzed reports from Bağcılar Hospital's Health Information Management System between April 2012 and September 2023 using "bone age" as a keyword. Patient images were re-evaluated by an experienced radiologist and anonymized. A total of 2,730 hand radiographs from Bağcılar Hospital (Turkish population), 12,572 from the Radiological Society of North America (RSNA), and 6,185 from the Radiological Hand Pose Estimation (RHPE) public datasets were collected, along with corresponding bone ages and gender information. A random set of 546 radiographs (273 from Bağcılar, 273 from public datasets) was initially randomly split for an internal test set with bone age stratification; the remaining data were used for training and validation. BAAs were generated using a modified InceptionV3 model on 500 × 500-pixel images, selecting the model with the lowest mean absolute error (MAE) on the validation set.

RESULTS: Three models were trained and tested based on dataset origin: Bağcılar (Turkish), public (RSNA-RHPE), and a Combined model. Internal test set predictions of the Combined model estimated bone age within less than 6, 12, 18, and 24 months at rates of 44%, 73%, 87%, and 94%, respectively. The MAE was 9.2 months in the overall internal test set, 7 months on the public test set, and 11.5 months on the Bağcılar internal test data. The Bağcılar-only model had an MAE of 12.7 months on the Bağcılar internal test data. Despite less training data, there was no significant difference between the combined and Bağcılar models on the Bağcılar dataset (P > 0.05). The public model showed an MAE of 16.5 months on the Bağcılar dataset, significantly worse than the other models (P < 0.05).

CONCLUSION: We developed an automatic BAA model including the Turkish population, one of the few such studies using deep learning. Despite challenges from population differences and data heterogeneity, these models can be effectively used in various clinical settings. Model accuracy can improve over time with cumulative data, and publicly available datasets may further refine them. Our approach enables more accurate and efficient BAAs, supporting healthcare professionals where traditional methods are time-consuming and variable.

CLINICAL SIGNIFICANCE: The developed automated BAA model for the Turkish population offers a reliable and efficient alternative to traditional methods. By utilizing deep learning with diverse datasets from Bağcılar Hospital and publicly available sources, the model minimizes assessment time and reduces variability. This advancement enhances clinical decision-making, supports standardized BAA practices, and improves patient care in various healthcare settings.

PMID:40094318 | DOI:10.4274/dir.2025.242999

EClinicalMedicine. 2025 Feb 26;81:103128. doi: 10.1016/j.eclinm.2025.103128. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: Misdiagnosis of hemorrhage secondary to cerebral venous sinus thrombosis (CVST-ICH) as arterial-origin spontaneous intracerebral hemorrhage (sICH) can lead to inappropriate treatment and the potential for severe adverse outcomes. The current practice for identifying CVST-ICH involves venography, which, despite being increasingly utilized in many centers, is not typically used as the initial imaging modality for ICH patients. The study aimed to develop an explainable deep learning model to quickly identify ICH caused by CVST based on non-contrast computed tomography (NCCT).

METHODS: The study population included patients diagnosed with CVST-ICH and other spontaneous ICH from January 2016 to March 2023 at the Second Affiliated Hospital of Zhejiang University, Taizhou First People's Hospital, Taizhou Hospital, Quzhou Second People's Hospital, and Longyan First People's Hospital. A transfer learning-based 3D U-Net with segmentation and classification was proposed and developed only on admission plain CT. Model performance was assessed using the area under the curve (AUC), sensitivity, and specificity metrics. For further evaluation, the average diagnostic performance of nine doctors on plain CT was compared with model assistance. Interpretability methods, including Grad-CAM++, SHAP, IG, and occlusion, were employed to understand the model's attention.

FINDINGS: An internal dataset was constructed using propensity score matching based on age, initially including 102 CVST-ICH patients (median age: 44 [29, 61] years) and 683 sICH patients (median age: 65 [52, 73] years). After matching, 102 CVST-ICH patients and 306 sICH patients (median age: 50 [40, 62] years) were selected. An external dataset consisted of 38 CVST-ICH and 119 sICH patients from four other hospitals. Validation showed AUC 0·94, sensitivity 0·96, and specificity 0·8 for the internal testing subset; AUC 0·85, sensitivity 0·87, and specificity 0·82 for the external dataset, respectively. The discrimination performance of nine doctors interpreting CT images significantly improved with the assistance of the proposed model (accuracy 0·79 vs 0·71, sensitivity 0·88 vs 0·81, specificity 0·75 vs 0·68, p < 0·05). Interpretability methods highlighted the attention of model to the features of hemorrhage edge appearance.

INTERPRETATION: The present model demonstrated high-performing and robust results on discrimination between CVST-ICH and spontaneous ICH, and aided doctors' diagnosis in clinical practice as well. Prospective validation with larger-sample size is required.

FUNDING: The work was funded by the National Key R&D Program of China (2023YFE0118900), National Natural Science Foundation of China (No.81971155 and No.81471168), the Science and Technology Department of Zhejiang Province (LGJ22H180004), Medical and Health Science and Technology Project of Zhejiang Province (No.2022KY174), the 'Pioneer' R&D Program of Zhejiang (No. 2024C03006 and No. 2023C03026) and the MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University.

PMID:40093990 | PMC:PMC11909457 | DOI:10.1016/j.eclinm.2025.103128

EClinicalMedicine. 2025 Feb 26;81:103125. doi: 10.1016/j.eclinm.2025.103125. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: Accurate prediction of early recurrence is essential for disease management of patients with non-muscle-invasive bladder cancer (NMIBC). We aimed to develop and validate a deep learning-based early recurrence predictive model (ERPM) and a treatment response predictive model (TRPM) on whole slide images to assist clinical decision making.

METHODS: In this retrospective, multicentre study, we included consecutive patients with pathology-confirmed NMIBC who underwent transurethral resection of bladder tumour from five centres. Patients from one hospital (Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China) were assigned to training and internal validation cohorts, and patients from four other hospitals (the Third Affiliated Hospital of Sun Yat-sen University, and Zhujiang Hospital of Southern Medical University, Guangzhou, China; the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China; Shenshan Medical Centre, Shanwei, China) were assigned to four independent external validation cohorts. Based on multi-instance and ensemble learning, the ERPM was developed to make predictions on haematoxylin and eosin (H&E) staining and immunohistochemistry staining slides. Sharing the same architecture of the ERPM, the TRPM was trained and evaluated by cross validation on patients who received Bacillus Calmette-Guérin (BCG). The performance of the ERPM was mainly evaluated and compared with the clinical model, H&E-based model, and integrated model through the area under the curve. Survival analysis was performed to assess the prognostic capability of the ERPM.

FINDINGS: Between January 1, 2017, and September 30, 2023, 4395 whole slide images of 1275 patients were included to train and validate the models. The ERPM was superior to the clinical and H&E-based model in predicting early recurrence in both internal validation cohort (area under the curve: 0.837 vs 0.645 vs 0.737) and external validation cohorts (area under the curve: 0.761-0.802 vs 0.626-0.682 vs 0.694-0.723) and was on par with the integrated model. It also stratified recurrence-free survival significantly (p < 0.0001) with a hazard ratio of 4.50 (95% CI 3.10-6.53). The TRPM performed well in predicting BCG-unresponsive NMIBC (accuracy 84.1%).

INTERPRETATION: The ERPM showed promising performance in predicting early recurrence and recurrence-free survival of patients with NMIBC after surgery and with further validation and in combination with TRPM could be used to guide the management of NMIBC.

FUNDING: National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, the Guangdong Provincial Clinical Research Centre for Urological Diseases, and the Science and Technology Projects in Guangzhou.

PMID:40093987 | PMC:PMC11909458 | DOI:10.1016/j.eclinm.2025.103125

Research (Wash D C). 2025 Mar 14;8:0603. doi: 10.34133/research.0603. eCollection 2025.

ABSTRACT

Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road. Despite recent studies highlighting the importance of drivers' emotions in takeover safety, the lack of emotion-aware takeover datasets hinders further investigation, thereby constraining potential applications in this field. To this end, we introduce ViE-Take, the first Vision-driven (Vision is used since it constitutes the most cost-effective and user-friendly solution for commercial driver monitor systems) dataset for exploring the Emotional landscape in Takeovers of autonomous driving. ViE-Take enables a comprehensive exploration of the impact of emotions on drivers' takeover performance through 3 key attributes: multi-source emotion elicitation, multi-modal driver data collection, and multi-dimensional emotion annotations. To aid the use of ViE-Take, we provide 4 deep models (corresponding to 4 prevalent learning strategies) for predicting 3 different aspects of drivers' takeover performance (readiness, reaction time, and quality). These models offer benefits for various downstream tasks, such as driver emotion recognition and regulation for automobile manufacturers. Initial analysis and experiments conducted on ViE-Take indicate that (a) emotions have diverse impacts on takeover performance, some of which are counterintuitive; (b) highly expressive social media clips, despite their brevity, prove effective in eliciting emotions (a foundation for emotion regulation); and (c) predicting takeover performance solely through deep learning on vision data not only is feasible but also holds great potential.

PMID:40093973 | PMC:PMC11908832 | DOI:10.34133/research.0603

Theranostics. 2025 Feb 18;15(8):3223-3233. doi: 10.7150/thno.100786. eCollection 2025.

ABSTRACT

Retinal images provide a non-invasive and accessible means to directly visualize human blood vessels and nerve fibers. Growing studies have investigated the intricate microvascular and neural circuitry within the retina, its interactions with other systemic vascular and nervous systems, and the link between retinal biomarkers and various systemic diseases. Using the eye to study systemic health, based on these connections, has been given a term as oculomics. Advancements in artificial intelligence (AI) technologies, particularly deep learning, have further increased the potential impact of this study. Leveraging these technologies, retinal analysis has demonstrated potentials in detecting numerous diseases, including cardiovascular diseases, central nervous system diseases, chronic kidney diseases, metabolic diseases, endocrine disorders, and hepatobiliary diseases. AI-based retinal imaging, which incorporates established modalities such as digital color fundus photographs, optical coherence tomography (OCT) and OCT angiography, as well as emerging technologies like ultra-wide field imaging, shows great promises in predicting systemic diseases. This provides a valuable opportunity for systemic diseases screening, early detection, prediction, risk stratification, and personalized prognostication. As the AI and big data research field grows, with the mission of transforming healthcare, they also face numerous challenges and limitations both in data and technology. The application of natural language processing framework, large language model, and other generative AI techniques presents both opportunities and concerns that require careful consideration. In this review, we not only summarize key studies on AI-enhanced retinal imaging for predicting systemic diseases but also underscore the significance of these advancements in transforming healthcare. By highlighting the remarkable progress made thus far, we provide a comprehensive overview of state-of-the-art techniques and explore the opportunities and challenges in this rapidly evolving field. This review aims to serve as a valuable resource for researchers and clinicians, guiding future studies and fostering the integration of AI in clinical practice.

PMID:40093903 | PMC:PMC11905132 | DOI:10.7150/thno.100786

Eur J Radiol Open. 2025 Feb 24;14:100639. doi: 10.1016/j.ejro.2025.100639. eCollection 2025 Jun.

ABSTRACT

OBJECTIVES: This study aimed to develop a predictive model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients by deep learning radiomic (DLRad) and clinical features.

METHODS: This study included 271 thyroid lobes from 228 PTC patients who underwent preoperative neck non-contrast CT at Center 1 (May 2021-April 2024). LNM status was confirmed via postoperative pathology, with each thyroid lobe labeled accordingly. The cohort was divided into training (n = 189) and validation (n = 82) cohorts, with additional temporal (n = 59 lobes, Center 1, May-August 2024) and external (n = 66 lobes, Center 2) test cohorts. Thyroid lobes were manually segmented from the isthmus midline, ensuring interobserver consistency (ICC ≥ 0.8). Deep learning and radiomics features were selected using LASSO algorithms to compute DLRad scores. Logistic regression identified independent predictors, forming DLRad, clinical, and combined models. Model performance was evaluated using AUC, calibration, decision curves, and the DeLong test, compared against radiologists' assessments.

RESULTS: Independent predictors of LNM included age, gender, multiple nodules, tumor size group, and DLRad. The combined model demonstrated superior diagnostic performance with AUCs of 0.830 (training), 0.799 (validation), 0.819 (temporal test), and 0.756 (external test), outperforming the DLRad model (AUCs: 0.786, 0.730, 0.753, 0.642), clinical model (AUCs: 0.723, 0.745, 0.671, 0.660), and radiologist evaluations (AUCs: 0.529, 0.606, 0.620, 0.503). It also achieved the lowest Brier scores (0.167, 0.184, 0.175, 0.201) and the highest net benefit in decision-curve analysis at threshold probabilities > 20 %.

CONCLUSIONS: The combined model integrating DLRad and clinical features exhibits good performance in predicting LNM in PTC patients.

PMID:40093877 | PMC:PMC11908562 | DOI:10.1016/j.ejro.2025.100639

J Inflamm Res. 2025 Mar 11;18:3449-3468. doi: 10.2147/JIR.S497734. eCollection 2025.

ABSTRACT

BACKGROUND: Rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF) share a common pathogenic mechanism, but the underlying mechanisms remain ambiguous. Our study aims at exploring the genetic-level pathogenic mechanism of these two diseases.

METHODS: We carried out bioinformatics analysis on the GSE55235 and GSE213001 datasets. Machine learning was employed to identify candidate genes, which were further verified using the GSE92592 and GSE89408 datasets, as well as quantitative real-time PCR (qRT-PCR). The expression levels of TOP2A in RA and IPF in vitro models were confirmed using Western blotting and qRT-PCR. Furthermore, we explored the influence of TOP2A on the occurrence and development of RA and IPF by using the selective inhibitor PluriSIn #2 in an in vitro model. Finally, an in vivo model of RA and IPF was constructed to assess TOP2A expression levels via immunohistochemistry.

RESULTS: Our bioinformatics analysis suggests a potential intersection in the pathogenic mechanisms of RA and IPF. We have identified 7 candidate genes: CXCL13, TOP2A, MMP13, MMP1, LY9, TENM4, and SEMA3E. Our findings reveal that the expression level of TOP2A is significantly elevated in both in vivo and in vitro models of RA and IPF. Additionally, our research indicates that PluriSIn #2 can effectively restrain inflammatory factors, extracellular matrix deposition, migration, invasion, the expression and nuclear uptake of p-smad2/3 protein in RA and IPF in vitro models.

CONCLUSION: There is a certain correlation between RA and IPF at the genetic level, and the molecular mechanisms of their pathogenesis overlap, which might be the reason for the progression of RA. Among the candidate genes we identified, TOP2A may influence the occurrence and development of RA and IPF through the TGF-β/Smad signal pathway. This could be beneficial to the study of the pathogenesis and treatment of RA and IPF.

PMID:40093950 | PMC:PMC11910056 | DOI:10.2147/JIR.S497734

Front Pharmacol. 2025 Feb 28;16:1486357. doi: 10.3389/fphar.2025.1486357. eCollection 2025.

ABSTRACT

BACKGROUND: Glycolysis plays a crucial role in fibrosis, but the specific genes involved in glycolysis in idiopathic pulmonary fibrosis (IPF) are not well understood.

METHODS: Three IPF gene expression datasets were obtained from the Gene Expression Omnibus (GEO), while glycolysis-related genes were retrieved from the Molecular Signatures Database (MsigDB). Differentially expressed glycolysis-related genes (DEGRGs) were identified using the "limma" R package. Diagnostic glycolysis-related genes (GRGs) were selected through least absolute shrinkage and selection operator (LASSO) regression regression and support vector machine-recursive feature elimination (SVM-RFE). A prognostic signature was developed using LASSO regression, and time-dependent receiver operating characteristic (ROC) curves were generated to evaluate predictive performance. Single-cell RNA sequencing (scRNA-seq) data were analyzed to examine GRG expression across various cell types. Immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were performed to elucidate potential molecular mechanisms. A bleomycin (BLM)-induced pulmonary fibrosis mouse model was used for experimental validation via reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

RESULTS: 14 GRGs (VCAN, MERTK, FBP2, TPBG, SDC1, AURKA, ARTN, PGP, PLOD2, PKLR, PFKM, DEPDC1, AGRN, CXCR4) were identified as diagnostic markers for IPF, with seven (ARTN, AURKA, DEPDC1, FBP2, MERTK, PFKM, SDC1) forming a prognostic model demonstrating predictive power (AUC: 0.831-0.793). scRNA-seq revealed cell-type-specific GRG expression, particularly in macrophages and fibroblasts. Immune infiltration analysis linked GRGs to imbalanced immune responses. Experimental validation in a bleomycin-induced fibrosis model confirmed the upregulation of GRGs (such as AURKA, CXCR4). Drug prediction identified inhibitors (such as Tozasertib for AURKA, Plerixafor for CXCR4) as potential therapeutic agents.

CONCLUSION: This study identifies GRGs as potential prognostic biomarkers for IPF and highlights their role in modulating immune responses within the fibrotic lung microenvironment. Notably, AURKA, MERTK, and CXCR4 were associated with pathways linked to fibrosis progression and represent potential therapeutic targets. Our findings provide insights into metabolic reprogramming in IPF and suggest that targeting glycolysis-related pathways may offer novel pharmacological strategies for antifibrotic therapy.

PMID:40093327 | PMC:PMC11906445 | DOI:10.3389/fphar.2025.1486357

Open Vet J. 2025 Jan;15(1):118-125. doi: 10.5455/OVJ.2024.v15.i1.11. Epub 2025 Jan 31.

ABSTRACT

BACKGROUND: Pulmonary fibrosis represents the most prevalent form of idiopathic interstitial pneumonia. The pathogenesis of pulmonary fibrosis using a bleomycin-induced mice model has indicated an imbalanced immune response such as an early massive inflammatory response, followed by fibrosis development. Therapy focused on restraining inflammation is one of the ways to inhibit fibrosis development. Elephantopus scaber ethanolic extract (ESEE) is known to have many beneficial compounds that were proven to possess anti-inflammatory activities, but its prospect in inhibiting pulmonary fibrosis development needs to be investigated.

AIM: This study aimed to evaluate the potency of ESEE treatment in inhibiting fibrosis development in the bleomycin-induced pulmonary fibrosis mice model.

METHODS: Healthy male BALB/c mice were divided into seven experimental groups (n = 8): healthy mice (N), vehicle mice (VC), pulmonary fibrosis mice (C-), pulmonary fibrosis received dexamethasone (C+), and pulmonary fibrosis mice received ESEE at a 0.0504 mg/kg body weight (BW) (D1), 0.1008 mg/kg BW (D2), and 0.2016 mg/kg BW (D3). Mice were given ESEE orally and intraperitoneal bleomycin injection daily for 14 days. Mice were then sacrificed on days 7 and 14 and spleens were isolated to determine the production of IL-10, TNF-α, and IFN-γ using flow cytometry.

RESULTS: The results revealed that a remarkable increase of TNF-α was found in the macrophage of pulmonary fibrosis mice model from day 7 to 14. An increase in IFN-γ production was also observed on day 7 and then decreased on day 14. The production of IL-10 was reduced in the fibrosis group at day 7 and continued to increase at day 14. Interestingly, ESEE treatment for 14 days could effectively reduce TNF-α and increase IFN-γ production. ESEE treatment could also maintain a stable production of IL-10 at each time point. ESEE at 0.1004 mg/kg BW (D2) showed the most effective activity in reducing pro-fibrotic cytokine than the dexamethasone group.

CONCLUSION: Ethanolic extract of ESEE has demonstrated its beneficial prospect in regulating pro-inflammatory and pro-fibrotic cytokine to inhibit fibrosis development.

PMID:40092211 | PMC:PMC11910286 | DOI:10.5455/OVJ.2024.v15.i1.11

J Transl Med. 2025 Mar 16;23(1):337. doi: 10.1186/s12967-025-06368-8.

ABSTRACT

OBJECTIVE: To identify potential therapeutic targets and evaluate the safety profiles for Idiopathic Pulmonary Fibrosis (IPF) using a comprehensive multi-omics approach.

METHOD: We integrated genomic and transcriptomic data to identify therapeutic targets for IPF. First, we conducted a transcriptome-wide association study (TWAS) using the Omnibus Transcriptome Test using Expression Reference Summary data (OTTERS) framework, combining plasma expression quantitative trait loci (eQTL) data with IPF Genome-Wide Association Studies (GWAS) summary statistics from the Global Biobank (discovery) and Finngen (duplication). We then applied Mendelian randomization (MR) to explore causal relationships. RNA-seq co-expression analysis (bulk, single-cell and spatial transcriptomics) was used to identify critical genes, followed by molecular docking to evaluate their druggability. Finally, phenome-wide MR (PheW-MR) using GWAS data from 679 diseases in the UK Biobank assessed the potential adverse effects of the identified genes.

RESULT: We identified 696 genes associated with IPF in the discovery dataset and 986 genes in the duplication dataset, with 126 overlapping genes through TWAS. MR analysis revealed 29 causal genes in the discovery dataset, with 13 linked to increased and 16 to decreased IPF risk. Summary data-based MR (SMR) confirmed six essential genes: ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1. Bulk RNA-seq showed FAM13A upregulation and SFR1 and EZH1 downregulation in IPF. Single-cell RNA-seq revealed gene expression changes across cell types. Molecular docking identified binding solid affinities for essential genes with respiratory drugs, and PheW-MR highlighted potential side effects.

CONCLUSION: We identified six key genes-ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1-as potential drug targets for IPF. Molecular docking revealed strong drug affinities, while PheW-MR analysis highlighted therapeutic potential and associated risks. These findings offer new insights for IPF treatment and further investigation of potential side effects.

PMID:40091050 | DOI:10.1186/s12967-025-06368-8

Plants (Basel). 2025 Mar 1;14(5):761. doi: 10.3390/plants14050761.

ABSTRACT

The regulatory mechanisms underlying embryogenic callus (EC) formation in polyploid bananas remain unexplored, posing challenges for genetic transformation and biotechnological applications. Here, we conducted transcriptome sequencing on cultured explants, non-embryogenic callus, EC, and browning callus in the ABB cultivar 'MJ' (Musa spp. cv. Bengal). Our analysis of differentially expressed genes (DEGs) revealed significant enrichment in plant hormones, MAPK, and zeatin biosynthesis pathways. Notably, most genes in the MJ variety exhibited balanced expression of the A and B alleles, but A-specific allele expression was dominant in the key signaling pathways, whereas B-specific allele expression was very rare during EC induction. In the auxin signaling pathway, six A-specific MJARF genes were markedly downregulated, underscoring their critical roles in the negative regulation of callus formation. Additionally, six A-specific MJEIN3 alleles were found to play negative regulatory roles in ethylene signaling during EC development. We also identified phenylpropanoids responsible for enzymatic browning. Furthermore, the expression patterns of transcription factors in bananas exhibited specific expression modes, highlighting the unique mechanisms of callus formation. This study enhanced our understanding of the regulatory roles of these alleles in EC induction and offers new insights into the utilization of alleles to improve the efficiency of somatic embryogenesis in bananas.

PMID:40094726 | DOI:10.3390/plants14050761

Plants (Basel). 2025 Feb 24;14(5):692. doi: 10.3390/plants14050692.

ABSTRACT

Lanthanum (La) is often used in industry and agriculture, leading to its accumulation in natural environments and potential ecological risks. The objective of this study was to examine the effects on the growth, metabolism, and nutrient composition of Brassica rapa exposed to at low (1 µM), medium (1 mM), and high (10 mM) La concentrations. We used chemical analytical, molecular, and metabolomic methods and found that high La exposure induced a hormetic effect, triggering both stimulatory and inhibitory responses. La reduced aluminum (Al), cobalt (Co), nickel (Ni), and chromium (Cr) levels at all concentrations, while medium and high doses also decreased phosphorus (P) and iron (Fe). La accumulation in B. rapa increased with La levels, affecting metabolic processes by modulating reactive oxygen species (ROS), increasing proline, and reducing total polyphenol content. Flavonoid levels were altered, chlorophyll and carotenoids declined, and non-photochemical quenching increased. Gene expressions related to flavonoid, carotenoid, and chlorophyll metabolism, as well as ion transport, exhibited a dose-dependent modulation. On the contrary, fatty acid composition remained unaffected. Our results indicate that La accumulates in in B. rapa and disrupts the plant metabolism. Despite an evident effect on plant productivity, our results also raise concerns about the potential health risks of consuming La-enriched B. rapa plants.

PMID:40094588 | DOI:10.3390/plants14050692

Semin Ophthalmol. 2025 Mar 17:1-11. doi: 10.1080/08820538.2025.2471443. Online ahead of print.

ABSTRACT

PURPOSE: Many fundamental cellular and molecular changes are known to occur in biological systems during spaceflight, including oxidative stress, DNA damage, mitochondrial damage, epigenetic factors, telomere lengthening, and microbial shifts. We can apply the consequences of these molecular changes in ocular cells, such as the retinal ganglion cells and corneal epithelium, to identify ophthalmologic risks during spaceflight. This review aims to discuss the potential molecular changes in greater detail and apply the principles to ocular cells and ophthalmic disease risk in astronauts.

METHODS: A targeted, relevant search of the literature on the topic and related topics of ocular surface and spaceflight was conducted with scholarly databases PubMed, Web of Science, and Embase from inception to July2024 with search terms "oxidative stress"; "DNA damage"; "Mitochondrial Dysfunction"; "Epigenetics"; "Telomeres"; "Microbiome"; "ocular cells"; "spaceflight"; "microgravity"; "radiation."

RESULTS: A total of 115 articles were included following screening and eligibility assessment. Key findings include molecular changes and their contributions to ophthalmic diseases like cataracts, spaceflight-associated neuro-ocular syndrome, and dry eye syndrome.

CONCLUSION: This review provides a comprehensive overview of risks to vision associated with long-duration spaceflight missions beyond low Earth orbit (LEO). Further investigation into targeted countermeasures is imperative to mitigate vision-threatening sequelae in astronauts undertaking deep-space exploration.

PMID:40094398 | DOI:10.1080/08820538.2025.2471443