New Phytol. 2025 May 29. doi: 10.1111/nph.70269. Online ahead of print.

NO ABSTRACT

PMID:40439021 | DOI:10.1111/nph.70269

Cancer Control. 2025 Jan-Dec;32:10732748251337362. doi: 10.1177/10732748251337362. Epub 2025 May 29.

ABSTRACT

IntroductionDrug-induced myelosuppression (DIM) is a serious side effect of several medications, particularly chemotherapy, immunosuppressants, and targeted therapies, which can lead to infections, anemia, and bleeding. While these drugs are effective, their adverse effects can disrupt treatment plans and reduce quality of life. However, early identification of DIM remains challenging, as many associated drugs do not explicitly list this risk, complicating clinical monitoring.MethodsThis study utilized the FDA Adverse Event Reporting System (FAERS) database to perform signal mining and assess the risks of DIM. Reports from the first quarter of 2004 to the third quarter of 2024 were analyzed using signal detection algorithms such as Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM). These methods helped identify drug signals related to DIM and explore risk factors and occurrence patterns.ResultsThe study analyzed 21 380 adverse event reports related to DIM, showing a significant increase in the number of reports since 2019, peaking at 3501 in 2021. Among patients, 50.2% were female, 35.5% were male, and the majority (44.42%) were aged between 18 and 65. Breast cancer patients had the highest DIM incidence (10.6%). Geographically, China reported the most cases (57.4%), followed by Japan (12.4%), and the United States (6.76%). The drugs most frequently linked to DIM included trastuzumab, bevacizumab, venetoclax, methotrexate, and pertuzumab. Additionally, 12 new drug signals were identified that were not labeled for DIM risk, including PERTUZUMAB, SODIUM CHLORIDE, and MESNA, which showed particularly strong or unexpected associations.ConclusionThis study identifies new DIM-related drug signals and emphasizes the need for early detection to improve clinical management and optimize treatment regimens. The findings provide valuable evidence for drug safety monitoring and can help reduce DIM-related risks in cancer treatment.

PMID:40439714 | DOI:10.1177/10732748251337362

Front Med (Lausanne). 2025 May 14;12:1576029. doi: 10.3389/fmed.2025.1576029. eCollection 2025.

ABSTRACT

BACKGROUND: Metabolic-associated fatty liver disease (MAFLD) is a prevalent chronic liver condition with significant health implications. Fermented Cordyceps Preparation (FCP) has shown promise in managing metabolic disorders, prompting interest in its potential for MAFLD prevention. There is, however, a lack of large-scale clinical evidence regarding its preventive efficacy and long-term safety.

AIM: We aimed to assess the preventive efficacy and safety of FCP, as regards combatting MAFLD.

METHODS: Propensity score matching was used to select 343 FCP users and 1372 non-users with metabolic syndrome, (MS) as recorded in EMR. These two groups were followed for 750 days, to track the incidence of MAFLD. The Kaplan Meier method was used to calculate the cumulative risk of MAFLD events in each subgroup. A Multiple linear regression model was used to compare the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST), as between the two groups.

RESULTS: Compared with non-users, FCP users were associated with a 26% decreased risk of MAFLD (hazard ratio 0.74, 95% confidence interval 0.56-0.97). During the follow-up, the changes in both ALT and AST, were insignificantly different between the two groups.

CONCLUSION: These findings highlight the potential of FCP in MAFLD prevention and offer insight into its safety profile, suggesting avenues for further clinical validation and drug repurposing efforts.

PMID:40438375 | PMC:PMC12116537 | DOI:10.3389/fmed.2025.1576029

RSC Med Chem. 2025 May 27. doi: 10.1039/d5md00203f. Online ahead of print.

ABSTRACT

Cystic fibrosis (CF) is a life-limiting genetic disease that affects multiple organ systems. It is caused by a mutation of the cystic fibrosis transmembrane conductance regulator (CFTR) gene, which results in the absence or damage of a relevant protein. If left untreated, it causes death in early childhood. The advent of more efficacious treatments has resulted in a notable increase in the life expectancy of CF patients. This has, in turn, led to an elevated risk of developing specific types of cancer. This review commences with an examination of CF from the standpoint of its etiology and therapeutic modalities. Subsequently, it presents a list of epidemiological studies that suggest an altered predisposition to certain cancers. A heightened risk is well documented, particularly in relation to the gastrointestinal tract. The following section addresses the role of CFTR in view of its potential involvement in the progression of various types of cancer. Several studies have indicated that the levels of the CFTR protein are reduced in many tumors and that this reduction is associated with the progression of the tumors. These decreased expressions are known to occur in the gastrointestinal tract, lungs, bladder, and/or prostate cancer. Conversely, ovarian, stomach, and cervical cancer are connected with its higher expression. The final section of the review focuses on the molecular mechanism of action of the CFTR protein in signaling pathways that affect cell proliferation and the process of carcinogenesis. This section attempts to explain the increased predisposition to cancer observed in patients with CF.

PMID:40438286 | PMC:PMC12107394 | DOI:10.1039/d5md00203f

Chem Sci. 2025 May 9. doi: 10.1039/d5sc02049b. Online ahead of print.

ABSTRACT

Burkholderia cepacia complex (Bcc) is a group of Gram-negative opportunistic pathogens highly responsible for chronic pulmonary infection in cystic fibrosis (CF). Current therapies involving double or triple antibiotic combinations can rarely eradicate the pathogen in chronically infected patients owing to its intrinsic resistance to a variety of antibiotics. Herein, we show that a bismuth drug (and related compounds) could inhibit the growth of clinically antibiotic-resistant Bcc strains, with MIC (ca. 25 μg mL-1) comparable to that for Helicobacter pylori, and the combination of a bismuth drug and antibiotics also demonstrated excellent activity against biofilm and persisters of Bcc. Importantly, the in vitro antimicrobial activity of a bismuth drug could be well translated into in vivo evidenced by about 50% survival rates in the Galleria mellonella infection model. Transcriptomics analysis shows the dynamic responses of Bcc to bismuth treatment. Using a homemade metalloproteomic approach, we could identify 26 BiIII-binding proteins (15 cytosolic proteins and 11 membrane proteins). Further mechanistic studies reveal that bismuth drugs initially target the TCA cycle through the binding and inactivation of a series of enzymes including malate dehydrogenase (MDH), malate synthase (AceB), and succinyl coenzyme A synthetase (SCS), then interfere oxidative phosphorylation through binding to terminal oxidases, i.e., CyoC and CydA, to disrupt electron transport chain, eventually, disrupt protein translation and ribosome via binding and down-regulation of key proteins. Our studies highlight the great potential of bismuth drugs and/or compounds to treat multidrug-resistant Bcc infections.

PMID:40438165 | PMC:PMC12107623 | DOI:10.1039/d5sc02049b

Biol Methods Protoc. 2025 Apr 28;10(1):bpaf033. doi: 10.1093/biomethods/bpaf033. eCollection 2025.

ABSTRACT

Cancer treatments often lose effectiveness as tumors develop resistance to single-agent therapies. Combination treatments can overcome this limitation, but the overwhelming combinatorial space of drug-dose interactions makes exhaustive experimental testing impractical. Data-driven methods, such as machine and deep learning, have emerged as promising tools to predict synergistic drug combinations. In this work, we systematically investigate the use of categorical embeddings within Deep Neural Networks to enhance drug synergy predictions. These learned and transferable encodings capture similarities between the elements of each category, demonstrating particular utility in scarce data scenarios.

PMID:40438791 | PMC:PMC12119136 | DOI:10.1093/biomethods/bpaf033

Biol Methods Protoc. 2025 Apr 26;10(1):bpaf032. doi: 10.1093/biomethods/bpaf032. eCollection 2025.

ABSTRACT

Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and validated on a dataset of 1001 patients (510 ECE-positive, 491 ECE-negative cases). ProSliceFinder achieved an area under the ROC curve (AUC) of 0.92 (95% confidence interval [CI]: 0.89-0.94) for slice classification, while ExCapNet demonstrated robust performance with an AUC of 0.88 (95% CI: 0.83-0.92) for patient-level ECE detection. Additionally, AutoRadAI's modular design ensures scalability and adaptability for applications beyond ECE detection. Validated through a user-friendly web-based interface for seamless clinical integration, AutoRadAI highlights the potential of AI-driven solutions in precision oncology. This framework improves diagnostic accuracy and streamlines preoperative staging, offering transformative applications in PCa management and beyond.

PMID:40438790 | PMC:PMC12119131 | DOI:10.1093/biomethods/bpaf032

Front Plant Sci. 2025 May 14;16:1576756. doi: 10.3389/fpls.2025.1576756. eCollection 2025.

ABSTRACT

INTRODUCTION: Precision agriculture relies on advanced technologies to optimize crop protection and resource utilization, ensuring sustainable and efficient farming practices. Anomaly detection plays a critical role in identifying and addressing irregularities, such as pest outbreaks, disease spread, or nutrient deficiencies, that can negatively impact yield. Traditional methods struggle with the complexity and variability of agricultural data collected from diverse sources.

METHODS: To address these challenges, we propose a novel framework that integrates the Integrated Multi-Modal Smart Farming Network (IMSFNet) with the Adaptive Resource Optimization Strategy (AROS). IMSFNet employs multimodal data fusion and spatiotemporal modeling to provide accurate predictions of crop health and yield anomalies by leveraging data from UAVs, satellites, ground sensors, and weather stations. AROS dynamically optimizes resource allocation based on real-time environmental feedback and multi-objective optimization, balancing yield maximization, cost efficiency, and environmental sustainability.

RESULTS: Experimental evaluations demonstrate the effectiveness of our approach in detecting anomalies and improving decision-making in precision agriculture.

DISCUSSION: This framework sets a new standard for sustainable and data-driven crop protection strategies.

PMID:40438741 | PMC:PMC12116526 | DOI:10.3389/fpls.2025.1576756

Front Plant Sci. 2025 May 14;16:1546756. doi: 10.3389/fpls.2025.1546756. eCollection 2025.

ABSTRACT

The need for large amounts of annotated data is a major obstacle to adopting deep learning in agricultural applications, where annotation is typically time-consuming and requires expert knowledge. To address this issue, methods have been developed to select data for manual annotation that represents the existing variability in the dataset, thereby avoiding redundant information. Coreset selection methods aim to choose a small subset of data samples that best represents the entire dataset. These methods can therefore be used to select a reduced set of samples for annotation, optimizing the training of a deep learning model for the best possible performance. In this work, we propose a simple yet effective coreset selection method that combines the recent foundation model DINOv2 as a powerful feature selector with the well-known K-Means clustering method. Samples are selected from each calculated cluster to form the final coreset. The proposed method is validated by comparing the performance metrics of a multiclass classification model trained on datasets reduced randomly and using the proposed method. This validation is conducted on two different datasets, and in both cases, the proposed method achieves better results, with improvements of up to 0.15 in the F1 score for significant reductions in the training datasets. Additionally, the importance of using DINOv2 as a feature extractor to achieve these good results is studied.

PMID:40438735 | PMC:PMC12116677 | DOI:10.3389/fpls.2025.1546756

Front Oncol. 2025 May 14;15:1603672. doi: 10.3389/fonc.2025.1603672. eCollection 2025.

ABSTRACT

OBJECTIVE: Vertebral compression fractures (VCFs) represent a prevalent clinical problem, yet distinguishing acute benign variants from malignant pathological fractures constitutes a persistent diagnostic dilemma. To develop and validate a MRI-based nomogram combining clinical and deep learning radiomics (DLR) signatures for the differentiation of benign versus malignant vertebral compression fractures (VCFs).

METHODS: A retrospective cohort study was conducted involving 234 VCF patients, randomly allocated to training and testing sets at a 7:3 ratio. Radiomics (Rad) features were extracted using traditional Rad techniques, while 2.5-dimensional (2.5D) deep learning (DL) features were obtained using the ResNet50 model. These features were combined through feature fusion to construct deep learning radiomics (DLR) models. Through a feature fusion strategy, this study integrated eight machine learning architectures to construct a predictive framework, ultimately establishing a visualized risk assessment scale based on multimodal data (including clinical indicators and Rad features).The performance of the various models was evaluated using the receiver operating characteristic (ROC) curve.

RESULTS: The standalone Rad model using ExtraTrees achieved AUC=0.801 (95%CI:0.693-0.909) in testing, while the DL model an AUC value of 0.805 (95% CI: 0.690-0.921) in the testing cohort. Compared with the Rad model and DL model, the performance superiority of the DLR model was demonstrated. Among all these models, the DLR model that employed ExtraTrees algorithm performed the best, with area under the curve (AUC) values of 0.971 (95% CI: 0.948-0.995) in the training dataset and 0.828 (95% CI: 0.727-0.929) in the testing dataset. The performance of this model was further improved when combined with clinical and MRI features to form the DLR nomogram (DLRN), achieving AUC values of 0.981 (95% CI: 0.964-0.998) in the training dataset and 0.871 (95% CI: 0.786-0.957) in the testing dataset.

CONCLUSION: Our study integrates handcrafted radiomics, 2.5D deep learning features, and clinical data into a nomogram (DLRN). This approach not only enhances diagnostic accuracy but also provides superior clinical utility. The novel 2.5D DL framework and comprehensive feature fusion strategy represent significant advancements in the field, offering a robust tool for radiologists to differentiate benign from malignant VCFs.

PMID:40438697 | PMC:PMC12116352 | DOI:10.3389/fonc.2025.1603672

Front Oncol. 2025 May 14;15:1586715. doi: 10.3389/fonc.2025.1586715. eCollection 2025.

ABSTRACT

BACKGROUND: Peritumoral characteristics demonstrate significant predictive value for neoadjuvant chemotherapy (NAC) response in breast cancer (BC) through tumor-stromal interactions. Radiomics analysis of peritumoral regions has shown robust capability in predicting treatment outcomes; however, the optimal peritumoral thickness for maximizing predictive accuracy remains undefined.

OBJECTIVE: To establish a clinically implementable framework for early identification of NAC non-responders through standardized prediction modeling. This study aims to determine the optimal peritumoral thickness for NAC response prediction by training and systematically comparing artificial intelligence (AI)-driven radiomics models across multiple peritumoral zones using Automated Breast Volume Scanning (ABVS).

METHODS: A total of 402 BC patients who received NAC were retrospectively analyzed. Pre-treatment ABVS images were processed to extract radiomic features from five regions of interest (ROIs): the intratumoral region (R0) and four consecutive peritumoral zones (R2-R8) extending outward at 2-mm intervals. The study cohort was divided into training and testing cohorts. ROI-specific TabNet models were developed using the training cohort data. Comparative analysis was performed in the testing cohort through comprehensive performance evaluation, including discrimination, calibration, clinical utility assessment, and classification metrics, to identify the optimal peritumoral zone. The radiomics features of the best-performing model were ranked by importance, with subsequent ablation studies validating the predictive contribution of high-ranking features.

RESULTS: Among the study population, 138 patients (34.3%) were classified as NAC non-responders. Model evaluation demonstrated progressively improved predictive performance from R0 to R6, with area under the ROC curves increasing from 0.681 to 0.845. The R6 model demonstrated optimal performance with accuracy of 0.810 and precision of 0.765. The combined model integrating R0 and R6 features enhanced predictive capability, achieving accuracy of 0.909, precision of 0.841, and recall of 0.902. Feature importance analysis identified textural heterogeneity and volumetric characteristics as the most influential variables, with the top features derived predominantly from the 6-mm peritumoral region.

CONCLUSION: The 6-mm peritumoral zone demonstrated optimal predictive value for NAC response, with the AI-driven combined intratumoral-peritumoral model achieving superior performance. This standardized ABVS-based radiomics approach enables early identification of potential NAC non-responders, facilitating timely therapeutic modifications.

PMID:40438687 | PMC:PMC12116539 | DOI:10.3389/fonc.2025.1586715

Front Aging Neurosci. 2025 May 14;17:1586282. doi: 10.3389/fnagi.2025.1586282. eCollection 2025.

ABSTRACT

BACKGROUND: As the incidence of neurodegenerative diseases increases, the related AI research is getting more and more advanced. In this study, we analyze the literature in this field over the last decade through bibliometric and visualization methods with the aim of mining the prominent journals, institutions, authors, and countries in this field and analyzing the keywords in order to speculate on possible future research trends.

METHODS: Our study extracted 1,921 relevant publications spanning 2015-2025 from the Web of Science Core Collection database. We conducted comprehensive bibliometric analyses and knowledge mapping visualizations using established scientometric tools: CiteSpace and Bibliometrix.

RESULTS: A total of 1921 documents were included in the study, the number of publications in this field showed an overall increasing trend, and the average number of citations showed a downward trend since 2019. Among the journals, Scientific Reports had the highest number of publications. In addition, we identified 22 core journals. Institution wise, University of London has the highest participation. Among the authors, the highest number of publications is Benzinger, Tammie. The highest number of citations is Fingere Elizabeth. At the national level, the United States is number one in the world in terms of influence in this field, and China is ranked number two, both of which are well ahead of other countries and are major contributors to this field. The analysis of keywords showed the centrality of Alzheimer disease, machine learning, Parkinsons disease, and deep learning. All the studies were clustered based on keywords to get seven clusters: 0. immune infiltration; 1. Parkinsons disease; 2. multiple sclerosis; 3. mild cognitive impairment; 4. deep learning; 5. machine learning; 6. freesurfer; 7. scale. In addition, we also found the continuation of the trending topics, which are Parkinsons disease, deep learning, and machine learning.

CONCLUSION: Based on the relationship between keywords and time, we speculate that there are four possible research trends: 1. Precision diagnosis with multimodal data fusion. 2. Pathological mechanism analysis and target discovery. 3. Interpretable AI and clinical translation. 4. Technology differentiation for subdivided diseases.

PMID:40438502 | PMC:PMC12116524 | DOI:10.3389/fnagi.2025.1586282

EPMA J. 2025 Mar 5;16(2):387-400. doi: 10.1007/s13167-025-00401-y. eCollection 2025 Jun.

ABSTRACT

BACKGROUND/AIMS: Early recognition of eyelid morphological abnormalities was crucial, as untreated conditions could lead to blinding complications. An eyelid screening system that could provide both anatomical and pathological information was essential for formulating personalized treatment strategies. This study aimed to develop a clinically concerns-based framework capable of identifying common eyelid diseases requiring further intervention by evaluating individual anatomical and pathological changes. This approach would enhance individualized and efficient prevention, while supporting targeted treatment strategies.

METHODS: The eyelid disorder screening system, Eyetome, was developed based on a morphological atlas and comprised four modules designed to identify 14 common eyelid disorders and pathological changes. A total of 6180 eye patches were analyzed to extract anatomical and pathological features. The performance of Eyetome was evaluated using average accuracy (aACC) and F1 score, with comparisons made against traditional models and ophthalmologists. To assess the system's expandability, an additional test was conducted in a multimorbidity scenario.

RESULTS: Eyetome demonstrated high performance in recognizing single diseases, achieving an aACC of 98.83% and an F1 score of 0.93. The system outperformed classic models, with an aACC of 98.83% compared to 96.72% for Desnet101 and 97.59% for Vit. Additionally, Eyetome's aACC exceeded that of a junior ophthalmologist (JO) (97.11%) and was comparable to a senior ophthalmologist (SO) (98.69%). In the extended multimorbidity dataset, Eyetome maintained robust performance with an accuracy of 97.97%, surpassing JO (95.47%) and closely matching SO (97.81%).

CONCLUSIONS: This study developed a clinical concerns-based system for screening and monitoring eyelid disorders, aimed at supporting predictive diagnosis, preventing diseases progression, and facilitating more effective, patient-centered treatment of common eyelid disorders, aligning with the principles of predictive, preventive, and personalized medicine (PPPM/3PM). The system's interpretability, scalability, and user-friendly data acquisition design could further enhance its acceptance among both doctors and patients, facilitating the shift from reactive medicine to proactive precision medicine.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-025-00401-y.

PMID:40438500 | PMC:PMC12106165 | DOI:10.1007/s13167-025-00401-y

EPMA J. 2025 May 20;16(2):519-533. doi: 10.1007/s13167-025-00412-9. eCollection 2025 Jun.

ABSTRACT

OBJECTIVE: Diabetes and hypertension pose significant health risks, especially when poorly managed. Retinal evaluation though fundus photography can provide non-invasive assessment of these diseases, yet prior studies focused on disease presence, overlooking control statuses. This study evaluated vision transformer (ViT)-based models for assessing the presence and control statuses of diabetes and hypertension from retinal images.

METHODS: ViT-based models with ResNet-50 for patch projection were trained on images from the UK Biobank (n = 113,713) and Singapore Epidemiology of Eye Diseases study (n = 17,783), and externally validated on the Singapore Prospective Study Programme (n = 7,793) and the Beijing Eye Study (n = 6064). Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) for multiple tasks: detecting disease, identifying poorly controlled and well-controlled cases, distinguishing between poorly and well-controlled cases, and detecting pre-diabetes or pre-hypertension.

RESULTS: The models demonstrated strong performance in detecting disease presence, with AUROC values of 0.820 for diabetes and 0.781 for hypertension in internal testing. External validation showed AUROCs ranging from 0.635 to 0.755 for diabetes, and 0.727 to 0.832 for hypertension. For identifying poorly controlled cases, the performance remained high with AUROCs of 0.871 (internal) and 0.655-0.851 (external) for diabetes, and 0.853 (internal) and 0.792-0.915 (external) for hypertension. Detection of well-controlled cases also yielded promising results for diabetes (0.802 [internal]; 0.675-0.838 [external]), and hypertension (0.740 [internal] and 0.675-0.807 [external]). In distinguishing between poorly and well-controlled disease, AUROCs were more modest with 0.630 (internal) and 0.512-0.547 (external) for diabetes, and 0.651 (internal) and 0.639-0.683 (external) for hypertension. For pre-disease detection, the models achieved AUROCs of 0.746 (internal) and 0.523-0.590 (external) for pre-diabetes, and 0.669 (internal) and 0.645-0.679 (external) for pre-hypertension.

CONCLUSION: ViT-based models show promise in classifying the presence and control statuses of diabetes and hypertension from retinal images. These findings support the potential of retinal imaging as a tool in primary care for opportunistic detection of diabetes and hypertension, risk stratification, and individualised treatment planning. Further validation in diverse clinical settings is warranted to confirm practical utility.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-025-00412-9.

PMID:40438493 | PMC:PMC12106178 | DOI:10.1007/s13167-025-00412-9

Front Robot AI. 2025 May 14;12:1620551. doi: 10.3389/frobt.2025.1620551. eCollection 2025.

NO ABSTRACT

PMID:40438458 | PMC:PMC12117187 | DOI:10.3389/frobt.2025.1620551

Stem Cells Int. 2025 May 21;2025:6324980. doi: 10.1155/sci/6324980. eCollection 2025.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a long-term, diffuse pulmonary parenchyma lesion that primarily affects middle-aged and older adults. It is characterized by pulmonary interstitial fibrosis of unknown cause. The death rate upon diagnosis is higher than that of many other cancer types. Mesenchymal stem cell (MSC) treatment of organ fibrosis is a hot topic in preclinical and clinical research because it effectively treats IPF. In recent years, decorin (DCN) has been regarded as a critical mediator for its anti-inflammatory and antifibrotic effects. The purpose of this study was to generate human umbilical cord MSCs (HUC-MSCs) that overexpress DCN and to investigate the safety, mechanism, and effectiveness of using these cells to cure pulmonary fibrosis caused by bleomycin (BLM). First, lentiviral (LV) particles carrying the therapeutic DCN gene (LV-DCN) and control LV particles were created and transfected using the plasmid vector GV208 to create a viral solution for infecting HUC-MSCs. These solutions were used to create a DCN overexpression cell line and an MSC-Con. cell line infected with the control lentivirus. Intratracheal injection of BLM was used to establish a rat model of pulmonary fibrosis. On the second day following modeling, different treatments were administered, and the body weight and survival status of the rats were noted. The relevant tests were performed on days 15 and 29 following modeling. The results demonstrated that the overexpression of DCN did not affect the properties of HUC-MSCs and that these cells were effective in treating IPF. MSC-Con. and MSC-DCN reduced systemic inflammation by reducing serum interleukin (IL) 1β. Both cell types successfully treated pulmonary fibrosis in rats, as demonstrated by hematoxylin and eosin (HE) and Masson staining. MSC-DCN showed better efficacy due to lower mortality, higher weight gain, less alveolar inflammation, and less fibrosis. The safety of venous transplantation with MSCs was established by HE staining of the heart, liver, spleen, and kidney, as well as serum lactate dehydrogenase (LDH), creatinine (CRE), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) levels. Immunohistochemical (IHC) staining of CD68 and CD206 in lung tissue and in vitro experiments on THP-1-induced M2 macrophage polarization and transforming growth factor-beta 1 (TGF-β1)-induced MRC-5 fibrosis indicated that MSC-DCN may mitigate lung inflammation by altering macrophage recruitment and polarization and inhibiting TGF-β1 expression to reduce fibrous hyperplasia and collagen deposition, thereby improving the treatment of BLM-induced IPF.

PMID:40438789 | PMC:PMC12119169 | DOI:10.1155/sci/6324980

Front Pharmacol. 2025 May 14;16:1524654. doi: 10.3389/fphar.2025.1524654. eCollection 2025.

ABSTRACT

Idiopathic Pulmonary Fibrosis (IPF) is a chronic fibrotic interstitial lung disease (ILD) of unknown etiology, characterized by increasing incidence and intricate pathogenesis. Current FDA-approved drugs suffer from significant side effects and limited efficacy, highlighting the urgent need for innovative therapeutic agents for IPF. Natural products (NPs), with their multi-target and multifaceted properties, present promising candidates for new drug development. This review delineates the anti-fibrotic pathways and targets of various natural products based on the established pathological mechanisms of IPF. It encompasses over 20 compounds, including flavonoids, saponins, polyphenols, terpenoids, natural polysaccharides, cyclic peptides, deep-sea fungal alkaloids, and algal proteins, sourced from both terrestrial and marine environments. The review explores their potential roles in mitigating pulmonary fibrosis, such as inhibiting inflammatory responses, protecting against lipid peroxidation damage, suppressing mesenchymal cell activation and proliferation, inhibiting fibroblast migration, influencing the synthesis and secretion of pro-fibrotic factors, and regulating extracellular matrix (ECM) synthesis and degradation. Additionally, it covers various in vivo and in vitro disease models, methodologies for analyzing marker expression and signaling pathways, and identifies potential new therapeutic targets informed by the latest research on IPF pathogenesis, as well as challenges in bioavailability and clinical translation. This review aims to provide essential theoretical and technical insights for the advancement of novel anti-pulmonary fibrosis drugs.

PMID:40438605 | PMC:PMC12116445 | DOI:10.3389/fphar.2025.1524654

Ther Adv Drug Saf. 2025 May 27;16:20420986251341645. doi: 10.1177/20420986251341645. eCollection 2025.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, life-threatening lung disease with a global incidence of 0.09-1.30 per 10,000 individuals. Pirfenidone and nintedanib are the approved treatments for IPF.

OBJECTIVES: This study evaluated the real-world safety and tolerability profiles of pirfenidone and nintedanib in IPF patients treated at the Mediterranean Institute for Transplantation and Advanced Specialized Therapies (IRCCS ISMETT). A comparative analysis was conducted based on the number, types, and severity of adverse drug reactions (ADRs) and to identify potential predictors of treatment discontinuation or ADR onset based on patient characteristics.

DESIGN: A retrospective observational study was conducted on 531 IPF patients treated at IRCCS ISMETT with either pirfenidone or nintedanib.

METHODS: Eligible patients were selected based on the logged monthly dispensations provided by the pharmacy service for both therapies. Covariates were extracted from electronic medical records (age, sex, body mass index, smoking history, comorbidities, forced vital capacity (FVC) %, diffusing capacity of the lung for carbon monoxide (DLCO) %, 6-minute walk test (6-MWT), polytherapy, oxygen therapy, drug switch, etc.). ADRs were categorized by severity and follow-up status, and further classified according to the Medical Dictionary for Regulatory Activities, specifying the Preferred Terms and the related System Organ Classes. Chi-square or Fisher's exact test was used for categorical variables, and univariate and multiple logistic regression identified potential risk factors for ADR onset. Backward Stepwise logistic regression (BSLR) was used to determine independent variables associated with ADR occurrence.

RESULTS: The nintedanib group had more frequent ADRs related to gastrointestinal and hepatobiliary disorders, with nausea, diarrhea, anorexia, and weight loss as the most common. The pirfenidone group had more ADRs related to skin, nervous system, and vascular disorders, such as rash, nausea, dizziness, and blood pressure imbalances. Significant baseline differences between groups included age, smoking status, FVC (%), DLCO (%), and 6-MWT, with the nintedanib cohort showing worse baseline characteristics. A total of 450 ADRs were reported: 59.6% for nintedanib and 40.4% for pirfenidone. Independent variables that significantly increased the likelihood of experiencing ADR were drug change, treatment type, gender, and age.

CONCLUSION: Identifying ADR predictors is essential for personalizing treatment strategies. Both pirfenidone and nintedanib are crucial in managing IPF, highlighting the need for further research to optimize personalized therapies and patient outcomes.

PMID:40438276 | PMC:PMC12117236 | DOI:10.1177/20420986251341645

Microb Genom. 2025 May;11(5). doi: 10.1099/mgen.0.001414.

ABSTRACT

Micro-organisms can survive and thrive in unusual and extreme environments. Here, we present a metagenomic analysis of living bacteria found in highly pure, deleterious heavy water (>99% D2O), stored in sealed plastic containers for more than 30 years, without any external supply. Deep DNA sequencing analyses have revealed that the most abundant genetic signatures were primarily associated with Pseudomonadota and Bacteroidota. These bacteria exhibited shorter gene lengths and depletion of polar and metabolically costly amino acids compared to the related species from light water environments. Genes related to DNA transposition, repair and modification were notably abundant, particularly mutant forms of the IS3 transposable elements family. We also explore potential carbon and energy sources and discuss the evolutionary implications of bacteria capable of surviving in such an extreme human-made environment.

PMID:40438915 | DOI:10.1099/mgen.0.001414

Front Plant Sci. 2025 May 14;16:1538566. doi: 10.3389/fpls.2025.1538566. eCollection 2025.

ABSTRACT

Climate change has significantly impacted the distribution patterns of medicinal plants, highlighting the need for accurate models to predict future habitat shifts. In this study, the Maximum Entropy model to analyze the habitat distribution of Pulsatilla chinensis (Bunge) Regel under current conditions and two future climate scenarios (SSP245 and SSP585). Based on 105 occurrence records and 12 environmental variables, precipitation of the wettest quarter, isothermality, average November temperature, and the standard deviation of temperature seasonality were identified as key factors influencing the habitat suitability for P. chinensis. The reliability of the model was supported by a mean area under the curve (AUC) value of 0.916 and a True Skill Statistic (TSS) value of 0.608. The results indicated that although the total suitable habitat for P. chinensis expanded under both scenarios, the highly suitable area contracted significantly under SSP585 compared to SSP245. This suggests the importance of incorporating climate change considerations into P. chinensis management strategies to address potential challenges arising from future ecosystem dynamics.

PMID:40438736 | PMC:PMC12116669 | DOI:10.3389/fpls.2025.1538566