Syst Rev. 2025 Mar 1;14(1):52. doi: 10.1186/s13643-025-02793-4.

ABSTRACT

INTRODUCTION: Empathy is a crucial skill that enhances the quality of patient care, reduces burnout among healthcare professionals, and fosters professionalism in medical students. Clinical practice and standardized patient-based education provide opportunities to enhance empathy, but a lack of consistency and reproducibility as well as significant dependency on resources are impediments. The COVID-19 pandemic has further restricted these opportunities, highlighting the need for alternative approaches. Virtual patients through standardized scenarios ensure consistency and reproducibility while offering safe, flexible, and repetitive learning opportunities unconstrained by time or location. Empathy education using virtual patients could serve as a temporary alternative during the COVID-19 pandemic and address the limitations of traditional face-to-face learning methods. This review aims to comprehensively map existing literature on the use of virtual patients in empathy education and identify research gaps.

METHODS: This scoping review will follow the Joanna Briggs Institute's guidelines and be reported according to PRISMA-P. The search strategy includes a comprehensive search across databases such as PubMed (MEDLINE), CINAHL, Web of Science, Scopus, ERIC, Google, Google Scholar, and Semantic Scholar, covering both published and gray literature without language restrictions. Both quantitative and qualitative studies will be included. Two independent researchers will screen all titles/abstracts and full texts for eligibility. Data will be extracted to summarize definitions of empathy, characteristics of virtual patient scenarios, and methods for measuring their impact on empathy development. Results will be presented in narrative and tabular formats to highlight key findings and research gaps.

DISCUSSION: As this review analyzes existing literature, ethical approval is not required. Findings will be actively disseminated through academic conferences and peer-reviewed publications, providing educators and researchers with valuable insights into the potential of virtual patients to enhance empathy in medical education. This study goes beyond the mere synthesis of academic knowledge by contributing to the advancement of medical education and clinical practice by clarifying virtual patient scenario design and evaluation methods in empathy education. The findings provide a critical foundation for our ongoing development of a medical education platform aimed at enhancing empathy through the use of virtual patients.

PMID:40025554 | DOI:10.1186/s13643-025-02793-4

Wiad Lek. 2024;78(1):90-99. doi: 10.36740/WLek/200331.

ABSTRACT

OBJECTIVE: Aim: The aims of this study are to detect the genetic polymorphisms of FSHR rs6166 (C> T) and rs6165 (C> T) gene particularly that associated with the response to FSH treatment and their effects on the pathogenesis of infertility in Iraqi women.

PATIENTS AND METHODS: Materials and Methods: 210 Iraqi women, aged 20 to 34, who had just been diagnosed with infertility were included in this prospective case control research, whereas the control group consisted of 50 clinically healthy women who were free of any disorders. Following the guidelines for inclusion and exclusion in the study, each of the participating women saw a gynecologist to confirm. The time frame for this From November 2021 to June 2022, the investigation was carried out.

RESULTS: Results: The findings of this study in infertile women, clearly indicates that multiple genotypes of FSHR gene particularly (rs6166) (C>T) and (rs6165) (C>T), that include the homozygous wild genotype (CC), homozygous mutant (TT) and heterozygous (CT) genotype. The T allele was significantly increased (P<0.05) in poor responder infertile women for both rs6166 and rs6165 in FSHR which associated significantly with poor response to FSH in Iraqi infertile women.

CONCLUSION: Conclusions: Polymorphisms in FSHR gene may be associated with decrease in response to FSH treatment and it was associated with pathogenesis of infertility in Iraqi women/ Kerbala province.

PMID:40023860 | DOI:10.36740/WLek/200331

Wiad Lek. 2024;78(1):71-81. doi: 10.36740/WLek/199949.

ABSTRACT

OBJECTIVE: Aim: To examine prevalence of genotypic distribution, particularly assessing how genetic polymorphisms in Insulin Receptor gene influence effectiveness of insulin therapy in a sample of Iraqi population.

PATIENTS AND METHODS: Materials and Methods: Effect of Single Nucleotide Polymorphisms rs2229429 G>A have been investigated in 99 T1DM individuals, with a mean age of 12.3 years. These patients were managed with exogenous insulin through a basal-bolus monotherapy regimen. Genotyping was performed using an allele-specific polymerase chain reaction technique, and the data were statistically analyzed.

RESULTS: Results: The prevalence of the minor allele frequency is 12% in a sample of Iraqi population. Homozygous mutant carriers of rs2229429 G>A were 10.479 times at higher risk for developing poor glycemic control (HbA1c >86 mmol/mol) compared to wild genotype in type 1 diabetes mellitus, p=0.008. Ultimately poor responders to exogenous insulin, demonstrating significantly higher plasma insulin receptors levels p<0.001.

CONCLUSION: Conclusions: The investigated Single Nucleotide Polymorphisms is significantly associated with hyperglycemia in type 1 diabetes mellitus and contributes to the development of double diabetes.

PMID:40023858 | DOI:10.36740/WLek/199949

Eur J Clin Nutr. 2025 Mar 1. doi: 10.1038/s41430-025-01591-4. Online ahead of print.

ABSTRACT

OBJECTIVES: Malnutrition is prevalent among children with cystic fibrosis (CF), often resulting from frequent pulmonary exacerbations and intestinal malabsorption. In addition to providing sufficient calorie intake through enteral formulas, appetite stimulants may help address nutritional deficiencies and improve overall prognosis.

METHODS: This retrospective study included children who received cyproheptadine (CH) as an appetite stimulant for at least three consecutive months. Data on CH-related adverse effects, z-scores for weight, height, body mass index (BMI), and percentage of forced expiratory volume in 1 s (FEV1%) were collected from medical records. Z-scores of growth parameters were calculated at baseline (CH initiation), three months before baseline, and three and six months after treatment.

RESULTS: The study included 45 children with a mean age of 11 years. One patient was on modulator therapy, one was pancreatic sufficient, and another one had diabetes. Only one patient was using enteral supplementation simultaneously with CH. Significant improvements in weight and BMI z-scores were observed from baseline to three months of CH therapy (p = 0.004 and p = 0.006, respectively), with no significant changes noted in the three months before treatment. A modest increase in weight and BMI z-scores was seen from three to six months of therapy. Additionally, FEV1 z-scores significantly increased from baseline to three months of therapy, with no further improvement observed in the subsequent three months.

CONCLUSION: Six months of CH therapy was associated with significant improvements in weight and BMI z-scores, particularly within the first three months. No adverse effects were reported. Given the deceleration in the rate of increase in anthropometric z-scores from the third to sixth month, a three-month duration of CH therapy appears to be optimal and sufficient for children with CF.

PMID:40025246 | DOI:10.1038/s41430-025-01591-4

Int Forum Allergy Rhinol. 2025 Mar 2:e23555. doi: 10.1002/alr.23555. Online ahead of print.

ABSTRACT

BACKGROUND: Severe chronic rhinosinusitis (CRS) is a near universal manifestation of cystic fibrosis. Elexacaftor/tezacaftor/ivacaftor (ETI) is an oral, small molecule, highly effective Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) corrector-potentiator drug. In people with cystic fibrosis age > 12 years, ETI improves sinonasal symptoms, endoscopy findings, polyp size, and radiologic findings. This study evaluates changes in CRS in children ages 6-12 years newly started on ETI.

METHODS: This was a prospective, three center, pre-post study of 11 children age 6-11 years newly started on ETI. Study endpoints included the SN-5 sinonasal health survey, Sniffin' Kids olfaction test, a sinus computerized tomography (CT) scan, and nasal endoscopy with mucus sampling for full-length 16S rRNA sequencing microbiome analysis. Study visits were conducted before ETI and at a median of 9 months after treatment initiation.

RESULTS: ETI lead to improvement in symptoms, endoscopy scores and radiologic findings of CRS. Olfaction was below normal at baseline and did not improve. The sinonasal microbiome was dominated by typically commensal organisms before and after treatment for most participants. Additionally, Staphylococcus aureus was found in five participants at baseline and six participants on treatment.

CONCLUSIONS: ETI improves sinonasal symptoms and endoscopy findings in children 6-11 years of age. Olfaction did not improve with ETI treatment in this age group, suggesting that olfactory dysfunction associated with CF is established early in life. This younger cohort of pediatric patients presented with abundant Staphylococcus aureus and only very rare Pseudomonas aeruginosa at baseline or after treatment.

PMID:40024881 | DOI:10.1002/alr.23555

Microb Pathog. 2025 Feb 28:107378. doi: 10.1016/j.micpath.2025.107378. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa, a formidable opportunistic pathogen, is notorious for its ability to form biofilms and produce virulence factors that favor chronic infections, especially in cystic fibrosis patients. The misuse of disinfectants, combined with environmental leakage and biodegradation, has led to widespread exposure of microorganisms to sub-lethal concentrations of disinfectants, particularly quaternary ammonium compounds (QACs). This study investigates the interaction between QACs, specifically ethylbenzalkyl dimethyl ammonium chloride (EBAC), and the quorum sensing (QS) mechanisms governing P. aeruginosa behavior. The results demonstrate that exposure to sub-minimum inhibitory concentrations (sub-MICs) of EBAC not only enhances the biofilm-forming capability of P. aeruginosa isolates but also modulates the expression of crucial QS-regulated genes. Notably, the bacteria exhibit increased production of biofilm-associated virulence factors such as pyocyanin and elastase, and altered antibiotic susceptibility profiles, indicating a shift towards persistent infection phenotypes. These findings reveal that QAC exposure can significantly increase resistance to antibiotics and external stressors like hydrogen peroxide. These results emphasize the need to reassess the efficacy of QACs in clinical disinfection settings, particularly against P. aeruginosa infections, and highlight the potential for unintended consequences of their use regarding bacterial behavior and virulence. This study provides novel insights into the role of QACs in modulating QS-mediated virulence and antibiotic resistance, offering a new perspective on the risks associated with sub-lethal disinfectant exposure.

PMID:40024542 | DOI:10.1016/j.micpath.2025.107378

J Pediatr Adolesc Gynecol. 2025 Feb 28:S1083-3188(25)00226-8. doi: 10.1016/j.jpag.2025.02.009. Online ahead of print.

ABSTRACT

This case report highlights a rare occurrence of urethral prolapse (UP) in a 7-year-old girl diagnosed with cystic fibrosis (CF). The patient displayed a protruding, swollen, and bleeding mass at the urinary orifice, accompanied by chronic constipation and a recent respiratory syncytial virus (RSV) infection. Cystovaginoscopy and circumferential excision were performed, leading to a successful outcome. Since the operation, the patient has been pain-free. The association between CF and UP underscores the need for heightened awareness among clinicians. The report focuses on recognizing and managing UP in pediatric CF patients to provide the best possible care.

PMID:40024430 | DOI:10.1016/j.jpag.2025.02.009

Biomed Eng Online. 2025 Mar 2;24(1):27. doi: 10.1186/s12938-025-01355-y.

ABSTRACT

BACKGROUND: To develop and validate a model that integrates clinical data, deep learning radiomics, and radiomic features to predict high-risk patients for cage subsidence (CS) after lumbar fusion.

METHODS: This study analyzed preoperative CT and MRI data from 305 patients undergoing lumbar fusion surgery from three centers. Using a deep learning model based on 3D vision transformations, the data were divided the dataset into training (n = 214), validation (n = 61), and test (n = 30) groups. Feature selection was performed using LASSO regression, followed by the development of a logistic regression model. The predictive ability of the model was assessed using various machine learning algorithms, and a combined clinical model was also established.

RESULTS: Ultimately, 11 traditional radiomic features, 5 deep learning radiomic features, and 1 clinical feature were selected. The combined model demonstrated strong predictive performance, with area under the curve (AUC) values of 0.941, 0.832, and 0.935 for the training, validation, and test groups, respectively. Notably, our model outperformed predictions made by two experienced surgeons.

CONCLUSIONS: This study developed a robust predictive model that integrates clinical features and imaging data to identify high-risk patients for CS following lumbar fusion. This model has the potential to improve clinical decision-making and reduce the need for revision surgeries, easing the burden on healthcare systems.

PMID:40025592 | DOI:10.1186/s12938-025-01355-y

J Cheminform. 2025 Mar 2;17(1):27. doi: 10.1186/s13321-025-00975-9.

ABSTRACT

Molecular optimization is a crucial step in drug development, involving structural modifications to improve the desired properties of drug candidates. Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. To address this issue, we first developed a general pipeline capable of constructing functional reaction template library specific to any property where a predictive model can be built. Based on these functional templates, we introduced Syn-MolOpt, a synthesis planning-oriented molecular optimization method. During optimization, functional reaction templates steer the process towards specific properties by effectively transforming relevant structural fragments. In four diverse tasks, including two toxicity-related (GSK3β-Mutagenicity and GSK3β-hERG) and two metabolism-related (GSK3β-CYP3A4 and GSK3β-CYP2C19) multi-property molecular optimizations, Syn-MolOpt outperformed three benchmark models (Modof, HierG2G, and SynNet), highlighting its efficacy and adaptability. Additionally, visualization of the synthetic routes for molecules optimized by Syn-MolOpt confirms the effectiveness of functional reaction templates in molecular optimization. Notably, Syn-MolOpt's robust performance in scenarios with limited scoring accuracy demonstrates its potential for real-world molecular optimization applications. By considering both optimization and synthesizability, Syn-MolOpt promises to be a valuable tool in molecular optimization.Scientific contribution Syn-MolOpt takes into account both molecular optimization and synthesis, allowing for the design of property-specific functional reaction template libraries for the properties to be optimized, and providing reference synthesis routes for the optimized compounds while optimizing the targeted properties. Syn-MolOpt's universal workflow makes it suitable for various types of molecular optimization tasks.

PMID:40025591 | DOI:10.1186/s13321-025-00975-9

J Cheminform. 2025 Mar 2;17(1):28. doi: 10.1186/s13321-025-00973-x.

ABSTRACT

Computer-aided drug design has the potential to significantly reduce the astronomical costs of drug development, and molecular docking plays a prominent role in this process. Molecular docking is an in silico technique that predicts the bound 3D conformations of two molecules, a necessary step for other structure-based methods. Here, we describe version 1.3 of the open-source molecular docking software GNINA. This release updates the underlying deep learning framework to PyTorch, resulting in more computationally efficient docking and paving the way for seamless integration of other deep learning methods into the docking pipeline. We retrained our CNN scoring functions on the updated CrossDocked2020 v1.3 dataset and introduce knowledge-distilled CNN scoring functions to facilitate high-throughput virtual screening with GNINA. Furthermore, we add functionality for covalent docking, where an atom of the ligand is covalently bound to an atom of the receptor. This update expands the scope of docking with GNINA and further positions GNINA as a user-friendly, open-source molecular docking framework. GNINA is available at https://github.com/gnina/gnina .Scientific contributions: GNINA 1.3 is an open source a molecular docking tool with enhanced support for covalent docking and updated deep learning models for more effective docking and screening.

PMID:40025560 | DOI:10.1186/s13321-025-00973-x

BMC Oral Health. 2025 Mar 1;25(1):329. doi: 10.1186/s12903-025-05677-0.

ABSTRACT

BACKGROUND: Several commercial programs incorporate artificial intelligence in diagnosis, but very few dental professionals have been surveyed regarding its acceptability and usability. Furthermore, few have explored how these advances might be incorporated into routine practice.

METHODS: Our team developed and implemented a deep learning (DL) model employing semantic segmentation neural networks and object detection networks to precisely identify alveolar bone crestal levels (ABCLs) and cemento-enamel junctions (CEJs) to measure change in alveolar crestal height (ACH). The model was trained and validated using a 550 bitewing radiograph dataset curated by an oral radiologist, setting a gold standard for ACH measurements. A twenty-question survey was created to compare the accuracy and efficiency of manual X-ray examination versus the application and to assess the acceptability and usability of the application.

RESULTS: In total, 56 different dental professionals classified severe (ACH > 5 mm) vs. non-severe (ACH ≤ 5 mm) periodontal bone loss on 35 calculable ACH measures. Dental professionals accurately identified between 35-87% of teeth with severe periodontal disease, whereas the artificial intelligence (AI) application achieved an 82-87% accuracy rate. Among the 65 participants who completed the acceptability and usability survey, more than half the participants (52%) were from an academic setting. Only 21% of participants reported that they already used automated or AI-based software in their practice to assist in reading of X-rays. The majority, 57%, stated that they only approximate when measuring bone levels and only 9% stated that they measure with a ruler. The survey indicated that 84% of participants agreed or strongly agreed with the AI application measurement of ACH. Furthermore, 56% of participants agreed that AI would be helpful in their professional setting.

CONCLUSION: Overall, the study demonstrates that an AI application for detecting alveolar bone has high acceptability among dental professionals and may provide benefits in time saving and increased clinical accuracy.

PMID:40025477 | DOI:10.1186/s12903-025-05677-0

BMC Oral Health. 2025 Mar 1;25(1):328. doi: 10.1186/s12903-024-05231-4.

ABSTRACT

BACKGROUND: The aim of our study was to develop and evaluate a deep learning model (BiStageNet) for automatic detection of dens evaginatus (DE) premolars on orthodontic intraoral photographs. Additionally, based on the training results, we developed a DE detection platform for orthodontic clinical applications.

METHODS: We manually selected the premolar areas for automatic premolar recognition training using a dataset of 1,400 high-quality intraoral photographs. Next, we labeled each premolar for DE detection training using a dataset of 2,128 images. We introduced the Dice coefficient, accuracy, sensitivity, specificity, F1-score, ROC curve as well as areas under the ROC curve to evaluate the learning results of our model. Finally, we constructed an automatic DE detection platform based on our trained model (BiStageNet) using Pytorch.

RESULTS: Our DE detection platform achieved a mean Dice coefficient of 0.961 in premolar recognition, with a diagnostic accuracy of 85.0%, sensitivity of 88.0%, specificity of 82.0%, F1 Score of 0.854, and AUC of 0.93. Experimental results revealed that dental interns, when manually identifying DE, showed low specificity. With the tool's assistance, specificity significantly improved for all interns, effectively reducing false positives without sacrificing sensitivity. This led to enhanced diagnostic precision, evidenced by improved PPV, NPV, and F1-Scores.

CONCLUSION: Our BiStageNet was capable of recognizing premolars and detecting DE with high accuracy on intraoral photographs. On top of that, our self-developed DE detection platform was promising for clinical application and promotion.

PMID:40025464 | DOI:10.1186/s12903-024-05231-4

Commun Med (Lond). 2025 Mar 1;5(1):55. doi: 10.1038/s43856-025-00767-0.

ABSTRACT

BACKGROUND: Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events.

METHODS: We conducted a retrospective analysis of 357 IDH-wildtype glioblastomas with pre-operative multiparametric MRI and targeted genetic sequencing data. Radiogenomic signatures and spatial distribution maps were generated for key mutations in genes such as EGFR, PTEN, TP53, and NF1 and their corresponding pathways. Machine and deep learning models were used to identify imaging biomarkers and stratify tumors based on their genetic profiles and molecular heterogeneity.

RESULTS: Here, we show that glioblastoma mutations produce distinctive imaging signatures, which are more pronounced in tumors with less molecular heterogeneity. These signatures provide insights into how mutations affect tumor characteristics such as neovascularization, cell density, invasion, and vascular leakage. We also found that tumor location and spatial distribution correlate with genetic profiles, revealing associations between tumor regions and specific oncogenic drivers. Additionally, imaging features reflect the cross-sectionally inferred evolutionary trajectories of glioblastomas.

CONCLUSIONS: This study establishes clinically accessible imaging biomarkers that capture the molecular composition and oncogenic drivers of glioblastoma. These findings have potential implications for noninvasive tumor profiling, personalized therapies, and improved patient stratification in clinical trials.

PMID:40025245 | DOI:10.1038/s43856-025-00767-0

Sci Rep. 2025 Mar 1;15(1):7301. doi: 10.1038/s41598-025-91823-1.

ABSTRACT

The classification of tongue shapes is essential for objective tongue diagnoses. However, the accuracy of classification is influenced by numerous factors. First, considerable differences exist between individuals with the same tongue shape. Second, the lips interfere with tongue shape classification. Additionally, small datasets make it difficult to conduct network training. To address these issues, this study builds a two-level nested tongue segmentation and tongue image classification network named IF-RCNet based on feature fusion and mixed input methods. In IF-RCNet, RCA-UNet is used to segment the tongue body, and RCA-Net is used to classify the tongue shape. The feature fusion strategy can enhance the network's ability to extract tongue features, and the mixed input can expand the data input of RCA-Net. The experimental results show that tongue shape classification based on IF-RCNet outperforms many other classification networks (VGG 16, ResNet 18, AlexNet, ViT and MobileNetv4). The method can accurately classify tongues despite the negative effects of differences between homogeneous tongue shapes and the misclassification of normal versus bulgy tongues due to lip interference. The method exhibited better performance on a small dataset of tongues, thereby enhancing the accuracy of tongue shape classification and providing a new approach for tongue shape classification.

PMID:40025207 | DOI:10.1038/s41598-025-91823-1

Sci Rep. 2025 Mar 1;15(1):7337. doi: 10.1038/s41598-025-92105-6.

ABSTRACT

Machine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson's disease. These approaches rely on different types of data with voice recordings being the most used due to the convenient and non-invasive nature of data acquisition. Our group has successfully developed a novel approach that uses convolutional neural network with transfer learning to analyze spectrogram images of the sustained vowel /a/ to identify people with Parkinson's disease. We tested this approach by collecting a dataset of voice recordings via analog telephone lines, which support limited bandwidth. The convolutional neural network with transfer learning approach showed superior performance against conventional machine learning methods that collapse measurements across time to generate feature vectors. This study builds upon our prior results and presents two novel contributions: First, we tested the performance of our approach on a larger voice dataset recorded using smartphones with wide bandwidth. Our results show comparable performance between two datasets generated using different recording platforms despite the differences in most important features resulting from the limited bandwidth of analog telephonic lines. Second, we compared the classification performance achieved using linear-scale and mel-scale spectrogram images and showed a small but statistically significant gain using mel-scale spectrograms.

PMID:40025201 | DOI:10.1038/s41598-025-92105-6

NPJ Digit Med. 2025 Mar 1;8(1):133. doi: 10.1038/s41746-025-01527-z.

ABSTRACT

This systematic review evaluated natural language processing (NLP) approaches for detecting cognitive impairment in electronic health record clinical notes. Following PRISMA guidelines, we analyzed 18 studies (n = 1,064,530) that employed rule-based algorithms (67%), traditional machine learning (28%), and deep learning (17%). NLP models demonstrated robust performance in identifying cognitive decline, with median sensitivity 0.88 (IQR 0.74-0.91) and specificity 0.96 (IQR 0.81-0.99). Deep learning architectures achieved superior results, with area under the receiver operating characteristic curves up to 0.997. Major implementation challenges included incomplete electronic health record data capture, inconsistent clinical documentation practices, and limited external validation. While NLP demonstrates promise, successful clinical translation requires establishing standardized approaches, improving access to annotated datasets, and developing equitable deployment frameworks.

PMID:40025194 | DOI:10.1038/s41746-025-01527-z

Sci Rep. 2025 Mar 1;15(1):7327. doi: 10.1038/s41598-025-91839-7.

ABSTRACT

As the water economy advances and the concepts of water ecology protection and sustainable development take root in people's minds, underwater imaging equipment has made remarkable progress. However, due to various factors, underwater images still suffer from low quality. How to enhance the quality of underwater images so that people can understand them quickly has become a crucial issue. Therefore, aiming at the degradation problems such as detail blurring, color imbalance, and noise interference in low-quality underwater images, this paper proposes an optimized UNet framework with a joint loss function (OUNet-JL). Firstly, to alleviate the problem of detail blurring, we construct a multi-residual module (MRM) to enhance the ability to represent detail features by using serially stacked convolutional blocks and residual connections. Secondly, we build a spatial multi-scale feature extraction module fused with channel attention (SMFM) to address the color imbalance issue through multi-scale dilated convolution and channel attention. Thirdly, to improve the signal-to-noise ratio of the enhanced image and solve the problem of blurring distortion, a strengthen-operate-subtract feature reconstruction module (SOSFM) is presented. Fourthly, to guide the network to perform training more efficiently and help it converge rapidly, a joint loss function is designed by integrating four different loss functions. Extensive experiments conducted on the well-known UIEB and UFO-120 datasets have shown the superiority of our OUNet-JL compared with several state-of-the-art algorithms. Moreover, ablation studies have also verified the effectiveness of the proposed modules. Our source code is publicly available at https://github.com/WangXin81/OUNet_JL .

PMID:40025128 | DOI:10.1038/s41598-025-91839-7

SLAS Discov. 2025 Feb 28:100223. doi: 10.1016/j.slasd.2025.100223. Online ahead of print.

ABSTRACT

Senescent cells contribute to the pathogenesis of idiopathic pulmonary fibrosis (IPF), a disease with significant unmet need and therefore, there is an interest in discovering new drug targets that regulate this process. We design and perform a phenotypic screen with a secreted protein library in primary human lung fibroblasts to identify modulators of cell senescence. We identify FGF9 as a suppressor of several senescence phenotypes reducing stimulated p21 expression, enlarged morphology, DNA damage and SASP secretion, which is consistent with both DNA-damage and ROS induced senescence. We also show that FGF9 reduces fibroblast activation in both healthy and IPF fibroblasts shown by a reduction in pro-fibrotic markers such as α-smooth muscle actin and COL1A1 mRNA. Our findings identify FGF9 as a suppressor of both senescence and fibrotic features in lung fibroblasts and therefore could be targeted as a new therapeutic strategy for respiratory diseases such as IPF.

PMID:40024445 | DOI:10.1016/j.slasd.2025.100223

Int J Biol Macromol. 2025 Feb 28:141602. doi: 10.1016/j.ijbiomac.2025.141602. Online ahead of print.

ABSTRACT

BACKGROUND: SARS-CoV-2 disrupts lung vascular endothelial integrity, contributing to severe COVID-19 complications. However, the molecular mechanisms driving endothelial dysfunction remain underexplored, and targeted therapeutic strategies are lacking.

OBJECTIVE: This study investigates Naringenin-7-O-glucoside (N7G) as a multi-target therapeutic candidate for modulating vascular integrity and immune response by inhibiting MMP1, MMP7, and SERPINE1-key regulators of extracellular matrix (ECM) remodeling and inflammation.

METHODS & RESULTS: RNA-set analysis of COVID-19 lung tissues identified 17 upregulated N7G targets, including MMP1, MMP7, and SERPINE1, with the latter exhibiting the highest expression. PPI network analysis linked these targets to ECM degradation, IL-17, HIF-1, and AGE-RAGE signaling pathways, and endothelial dysfunction. Disease enrichment associated these genes with idiopathic pulmonary fibrosis and asthma. Molecular docking, 200 ns MD simulations (triplicate), and MMGBSA calculations confirmed N7G's stable binding affinity to MMP1, MMP7, and SERPINE1. Immune profiling revealed increased neutrophils and activated CD4+ T cells, alongside reduced mast cells, NK cells, and naïve B cells, indicating immune dysregulation. Correlation analysis linked MMP1, MMP7, and SERPINE1 to distinct immune cell populations, supporting N7G's immunomodulatory role.

CONCLUSION: These findings suggest that N7G exhibits multi-target therapeutic potential by modulating vascular integrity, ECM remodeling, and immune dysregulation, positioning it as a promising candidate for mitigating COVID-19-associated endothelial dysfunction.

PMID:40024412 | DOI:10.1016/j.ijbiomac.2025.141602

Phytomedicine. 2025 Mar 1;140:156545. doi: 10.1016/j.phymed.2025.156545. Online ahead of print.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an irreversible lung disease for which there is a lack of effective and safe therapeutic drugs. 13-Methylpalmatine (13-Me-PLT) is an active compound from Coptis chinensis, and no study has yet been reported on its pharmacological effects in pulmonary fibrotic diseases. The group has previously demonstrated the antimyocardial fibrosis efficacy of 13-Me-PLT but its effect on pulmonary fibrosis and its potential mechanism has not yet been investigated.

PURPOSE: The present research is designed to clarify the therapeutic potential and mechanism of action of 13-Me-PLT in IPF using a bleomycin (BLM)-induced mouse model of IPF.

METHODS: In vivo, mice were administrated with BLM to establish the IPF model, and IPF mice were treated with 13-Me-PLT (5, 10, and 20 mg/kg) and pirfenidone (PFD, 300 mg/kg) by gavage. In vitro, we employed TGF-β1 (10 ng/ml)-induced MRC5 cells, which were then treated with 13-Me-PLT (5, 10, 20 μM) and PFD (500 μM). High-throughput transcriptome sequencing, molecular dynamics simulations, molecular docking and Surface plasmon resonance (SPR) were employed to elucidate the underlying mechanisms of 13-Me-PLT in mitigating IPF.

RESULT: In vivo experiments showed that 13-Me-PLT significantly ameliorated BLM-induced lung fibrosis in mice. In vitro studies, 13-Me-PLT showed good antifibrotic potential by inhibiting fibroblast differentiation. Transcriptomic analysis of mouse lung tissues identified ITGA5 and TGF-β/Smad signaling pathways as key targets for the antifibrotic effects of 13-Me-PLT. Molecular docking and kinetic analyses further supported these findings. Functional studies involving ITGA5 silencing and overexpression confirmed that 13-Me-PLT down-regulated ITGA5 expression and inhibited the activation of the TGF-β/Smad signaling pathway, confirming its mechanism of action.

CONCLUSION: To our best knowledge, these results provide the first insight that 13-Me-PLT is protective against BLM-induced IPF in mice. Unlike existing antifibrotic drugs, 13-Me-PLT specifically targets the ITGA5/TGF-β/Smad signaling pathway, offering a novel and potentially more effective therapeutic approach. This study not only validates the antifibrotic efficacy of 13-Me-PLT but also elucidates its unique mechanism of action, these findings may provide an opportunity to develop new drugs to treat IPF.

PMID:40023972 | DOI:10.1016/j.phymed.2025.156545