Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Dec 25;41(6):1293-1300. doi: 10.7507/1001-5515.202404004.

ABSTRACT

Gastric tumors are neoplastic lesions that occur in the stomach, posing a great threat to human health. Gastric cancer represents the malignant form of gastric tumors, and early detection and treatment are crucial for patient recovery. Endoscopic examination is the primary method for diagnosing gastric tumors. Deep learning techniques can automatically extract features from endoscopic images and analyze them, significantly improving the detection rate of gastric cancer and serving as an important tool for auxiliary diagnosis. This paper reviews relevant literature in recent years, presenting the application of deep learning methods in the classification, object detection, and segmentation of gastric tumor endoscopic images. In addition, this paper also summarizes several computer-aided diagnosis (CAD) systems and multimodal algorithms related to gastric tumors, highlights the issues with current deep learning methods, and provides an outlook on future research directions, aiming to promote the clinical application of deep learning methods in the endoscopic diagnosis of gastric tumors.

PMID:40000222 | DOI:10.7507/1001-5515.202404004

Int J Pharm. 2025 Feb 23:125385. doi: 10.1016/j.ijpharm.2025.125385. Online ahead of print.

ABSTRACT

Psoriasis is a persistent inflammatory skin disorder characterized by hyper-proliferation and abnormal epidermal differentiation. Conventional treatments such as; topical therapies, phototherapy, systemic immune modulators, and biologics aim to relieve symptoms and improve patient quality of life. However, challenges like adverse effects, high costs, and individual response variability persist. Thus, the need for novel anti-psoriatic drugs has led to the exploration of drug repurposing, an approach that identifies new applications for existing drugs. This method is in its early stages but has gained popularity across both public and private sectors. Furthermore, artificial intelligence (AI) integration is revolutionizing the healthcare industry by enhancing efficiency, delivery, and personalization. Machine learning and deep learning algorithms have significantly impacted drug discovery, repurposing, and designing new molecules or drug delivery carriers. Nanotechnology, in addition to AI, plays a pivotal role in targeting repurposed drugs via the topical route with suitable nanocarriers. This method overcomes challenges associated with oral delivery, such as systemic toxicities, slow onset of action, first-pass effect, and poor bioavailability. This review addresses the practice of repurposing existing drugs for managing psoriasis, discussing the challenges of conventional therapy and how the incorporation of nanotechnology and AI can overcome these hurdles, facilitating the discovery of anti-psoriatic drugs and presenting promising strategies for novel therapeutics. Additionally, it discusses the general benefits of drug repurposing compared to de novo drug development and the potential drawbacks of drug repurposing.

PMID:39999900 | DOI:10.1016/j.ijpharm.2025.125385

Yonsei Med J. 2025 Mar;66(3):187-194. doi: 10.3349/ymj.2023.0628.

ABSTRACT

PURPOSE: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model.

MATERIALS AND METHODS: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital's electronic health record from South Korea; IQVIA's United Kingdom (UK) database for general practitioners; and IQVIA's United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea.

RESULTS: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%-62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34-2.07 (Korea), 0.13-0.30 (US); hypoparathyroidism, 0.40-1.20 (Korea), 0.59-1.01 (US), 0.00-1.78 (UK); and pheochromocytoma/paraganglioma, 0.95-1.67 (Korea), 0.35-0.77 (US), 0.00-0.49 (UK).

CONCLUSION: Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

PMID:39999994 | DOI:10.3349/ymj.2023.0628

BMC Nurs. 2025 Feb 25;24(1):210. doi: 10.1186/s12912-025-02847-x.

ABSTRACT

INTRODUCTION: Enhancing clinical outcomes and patient satisfaction can be achieved through interprofessional collaboration between physicians and nurses. Conversely, a lack of nurse-physician interprofessional collaboration compromises patient safety, care, and improvement, and creates moral discomfort for healthcare professionals. Studies indicate that failures in interprofessional collaboration between nurses and physicians lead to adverse medical events, including hospital-acquired infections, medication administration errors, and unnecessary health-related costs.

OBJECTIVE: This systematic review and meta-analysis aimed to investigate the pooled proportions of the interprofessional collaborations towards patient care and associated factors among nurses and physicians in Ethiopia, 2024.

METHODS: A comprehensive search was conducted to find articles on interprofessional collaboration towards patient care and associated factors among nurses and physicians in Ethiopia. The study included cross-sectional studies conducted in Ethiopia and published in English from inception up to August 20, 2024. Excluded were conference proceedings, qualitative research, commentaries, editorial letters, case reports, case series, and monthly and annual police reports. The search encompassed full-text publications written in English and databases such as PubMed/MEDLINE, African Journals Online (AJOL), Semantic Scholar, Google Scholar, and Google. A checklist from the Joanna Briggs Institute (JBI) was used to evaluate the quality of the studies. Two independent reviewers performed data extraction, critical appraisal, and article screening. Statistical analysis was performed using STATA-17 software. A random-effects model was employed to estimate pooled proportions, and effect sizes with 95% confidence intervals were used to analyze determinants of interprofessional collaboration in patient care among nurses and physicians. Funnel plots and Egger's test were used to examine the possibility of publication bias (p-value < 0.10), and the trim-and-fill method by Duval and Tweedie was applied to adjust for publication bias.

RESULTS: Five studies with a total of 1686 study participants that are conducted in three Ethiopian regions and meet the inclusion criteria were reviewed and pooled for this evaluation. The pooled proportions of the interprofessional collaboration towards patient care in Ethiopia is 52.73% (95% CI = 44.66, 60.79%, I2 = 91.5%). Factors such as attitude (favorable attitude towards collaboration) (OR = 1.13, 95% CI: 0.13, 9.89, I2 = 97.7%) and organizational support (satisfaction towards organizational support) (OR = 0.38, 95% CI: 0.07, 2.10, I2 = 97.5%) were not significantly associated with interprofessional collaboration towards patient care.

CONCLUSION: In summary, this systematic review and meta-analysis reveal that interprofessional collaboration between nurses and physicians in Ethiopia is moderately common, with a pooled proportion of 52.73%. This finding underscores the need for ongoing efforts to enhance collaborative practices to further improve patient care outcomes. Additionally, the review identified two potential contributors to interprofessional collaboration: satisfaction with organizational support and favorable attitudes towards collaboration. However, the pooled effects of these factors did not show a significant association with interprofessional collaboration. This highlights the necessity for further primary research to identify additional factors that may influence interprofessional collaboration and enhance patient care outcomes. Notable limitations of this study include significant variation among studies, a small number of studies, a focus solely on public hospitals, restriction to English-language publications, only observational studies, and limited access to databases such as EMBASE, CINAHL, and Web of Science.

REGISTRATION: This systematic review and meta-analysis was registered in Prospero with the registration ID and link as follows: CRD42024579370; https://www.crd.york.ac.uk/prospero/#recordDetails .

PMID:40001025 | DOI:10.1186/s12912-025-02847-x

NPJ Precis Oncol. 2025 Feb 25;9(1):53. doi: 10.1038/s41698-025-00837-5.

ABSTRACT

Preclinical models of breast cancer that better predict patient-specific drug responses are critical for expanding the clinical utility of targeted therapies, including for inhibitors of poly(ADP-ribose) polymerase (PARP). Reprogramming primary cancer cells into human induced pluripotent stem cells (hiPSCs) recently emerged as a powerful tool to model drug response phenotypes, but its use to date has been limited to hematopoietic malignancies. We designed an optimized reprogramming methodology to generate breast cancer-derived hiPSCs (BC-hiPSCs) from nine patients representing all major subtypes of breast cancer. BC-hiPSCs retain patient-specific oncogenic variants, including variants unique to individual tumor subclones. Additionally, we developed a protocol to differentiate BC-hiPSCs into mammary epithelial cells and mammary-like organoids for in vitro disease modeling, including drug response phenotyping. Using these tools, we demonstrated that BC-hiPSCs can be used to screen for differential sensitivity to PARP inhibitors and mechanistically investigated the causal genetic variant driving drug sensitivity in one patient.

PMID:40000798 | DOI:10.1038/s41698-025-00837-5

Nutrition. 2025 Jan 27;133:112694. doi: 10.1016/j.nut.2025.112694. Online ahead of print.

ABSTRACT

OBJECTIVES: Having an optimal nutritional status and getting adequate energy and nutrients are important factors that affect the success of the treatment of cystic fibrosis (CF) and increase survival. The objective of this study was to determine the nutritional status, nutritional intake, and dietary quality among children aged 2 to 14 with CF. We aimed to assess the impact of a nutrition education intervention provided to mothers on these parameters and compare the results with a control group.

METHODS: Participants (n = 46) were divided into two groups, one group received nutrition education, the other group did not receive any intervention, and all participants were followed up in the 1st and 3rd months of the study. Each participant completed a questionnaire form prepared by the researcher including general information about the patient, anthropometric data, 3-day dietary intake, and Mediterranean Diet Quality Index.

RESULTS: While the children's Mediterranean Diet Quality Index scores did not change significantly during the study period, the proportion of children in the education group who had adequate nutrition according to body mass index percentile for age increased from 42.0% to 48.0%. In addition, energy (kcal), fat (g), and monounsaturated fatty acids (g) intake, vitamin D, E, K, B6, biotin, and iron intakes of the education group increased significantly during the study (P < 0.05).

CONCLUSIONS: This study contributes to the literature by showing that nutrition education given to mothers for CF children, improves the nutritional status of children and increases their energy and nutrient intakes.

PMID:39999653 | DOI:10.1016/j.nut.2025.112694

Breast Cancer Res. 2025 Feb 25;27(1):29. doi: 10.1186/s13058-025-01983-1.

ABSTRACT

BACKGROUND: Radiomics and AI have been widely used in breast cancer imaging, but a comprehensive systematic analysis is lacking. Therefore, this study aims to conduct a bibliometrics analysis in this field to discuss its research status and frontier hotspots and provide a reference for subsequent research.

METHODS: Publications related to AI, radiomics, and breast cancer imaging were searched in the Web of Science Core Collection. CiteSpace plotted the relevant co-occurrence network according to authors and keywords. VOSviewer and Pajek were used to draw relevant co-occurrence maps according to country and institution. In addition, R was used to conduct bibliometric analysis of relevant authors, countries/regions, journals, keywords, and annual publications and citations based on the collected information.

RESULTS: A total of 2,701 Web of Science Core Collection publications were retrieved, including 2,486 articles (92.04%) and 215 reviews (7.96%). The number of publications increased rapidly after 2018. The United States of America (n = 17,762) leads in citations, while China (n = 902) leads in the number of publications. Sun Yat-sen University (n = 75) had the largest number of publications. Bin Zheng (n = 28) was the most published author. Nico Karssemeijer (n = 72.1429) was the author with the highest average citations. "Frontiers in Oncology" was the journal with the most publications, and "Radiology" had the highest IF. The keywords with the most frequent occurrence were "breast cancer", "deep learning", and "classification". The topic trends in recent years were "explainable AI", "neoadjuvant chemotherapy", and "lymphovascular invasion".

CONCLUSION: The application of radiomics and AI in breast cancer imaging has received extensive attention. Future research hotspots may mainly focus on the progress of explainable AI in the technical field and the prediction of lymphovascular invasion and neoadjuvant chemotherapy efficacy in clinical application.

PMID:40001088 | DOI:10.1186/s13058-025-01983-1

Radiat Oncol. 2025 Feb 25;20(1):26. doi: 10.1186/s13014-025-02603-0.

ABSTRACT

BACKGROUND: Deformable registration plays an important role in the accurate delineation of tumors. Most of the existing deep learning methods ignored two issues that can lead to inaccurate registration, including the limited field of view in MR scans and the different scanning angles that can exist between multimodal images. The purpose of this study is to improve the registration accuracy between CT and MR for nasopharyngeal carcinoma cases.

METHODS: 269 cases were enrolled in the study, and 188 cases were designated for training, while a separate set of 81 cases was reserved for testing. Each case had a CT volume and a T1-MR volume. The treatment table was removed from their CT images. The CycleFCNs model was used for deformable registration, and two strategies including adaptive mask registration strategy and weight allocation strategy were adopted for training. Dice similarity coefficient, Hausdorff distance, precision, and recall were calculated for normal tissues of CT-MR image pairs, before and after the registration. Three deformable registration methods including RayStation, Elastix, and VoxelMorph were compared with the proposed method.

RESULTS: The registration results of RayStation and Elastix are essentially consistent. Upon employing the VoxelMorph model and the proposed method for registration, a clear trend of increased dice similarity coefficient and decreased hausdorff distance can be observed. It is noteworthy that for the temporomandibular joint, pituitary, optic nerve, and optic chiasma, the proposed method has improved the average dice similarity coefficient from 0.86 to 0.91, 0.87 to 0.93, 0.85 to 0.89, and 0.77 to 0.83, respectively, as compared to RayStation. Additionally, within the same anatomical structures, the average hausdorff distance has been decreased from 2.98 mm to 2.28 mm, 1.83 mm to 1.53 mm, 3.74 mm to 3.56 mm, and 5.94 mm to 5.87 mm. Compared to the original CycleFCNs model, the improved model has significantly enhanced the dice similarity coefficient of the brainstem, pituitary gland, and optic nerve (P < 0.001).

CONCLUSIONS: The proposed method significantly improved the registration accuracy for multi-modal medical images in NPC cases. These findings have important clinical implications, as increased registration accuracy can lead to more precise tumor segmentation, optimized treatment planning, and ultimately, improved patient outcomes.

PMID:40001040 | DOI:10.1186/s13014-025-02603-0

BMC Cancer. 2025 Feb 25;25(1):341. doi: 10.1186/s12885-025-13711-1.

ABSTRACT

OBJECTIVE: We aimed to develop a preoperative clinical radiomics survival prediction model based on the radiomics features via deep learning to provide a reference basis for preoperative assessment and treatment decisions for patients with gallbladder carcinoma (GBC).

METHODS: A total of 168 GBC patients who underwent preoperative upper abdominal enhanced CT from one high-volume medical center between January 2011 to December 2020 were retrospectively analyzed. The region of interest (ROI) was manually outlined by two physicians using 3D Slicer software to establish a nnU-Net model. The DeepSurv survival prediction model was developed by combining radiomics features and preoperative clinical variables.

RESULTS: A total of 1502 radiomics features were extracted from the ROI results based on the nnU-Net model and manual segmentation, and 13 radiomics features were obtained through the 4-step dimensionality reduction methods, respectively. The C-index and AUC of 1-, 2-, and 3-year survival prediction for the nnU-Net based clinical radiomics DeepSurv model was higher than clinical and nnU-Net based radiomics DeepSurv models in the training and testing sets, and close to manual based clinical radiomics DeepSurv model. Delong-test was performed on the AUC of 1-, 2-, and 3-year survival prediction for the two preoperative clinical radiomics DeepSurv prediction models in the testing set, and the results showed that the two models had the same prediction efficiency (all P > 0.05).

CONCLUSIONS: By using the DeepSurv model via nnU-Net segmentation, postoperative survival outcomes for individual gallbladder carcinoma patients could be assessed and stratified, which can provide references for preoperative diagnosis and treatment decisions.

PMID:40001024 | DOI:10.1186/s12885-025-13711-1

BMC Med Imaging. 2025 Feb 25;25(1):61. doi: 10.1186/s12880-025-01602-7.

ABSTRACT

BACKGROUND: To compare the influence of rectal susceptibility artifacts on the subjective evaluation and deep learning (DL) in prostate cancer (PCa) diagnosis.

METHODS: This retrospective two-center study included 1052 patients who underwent MRI and biopsy due to clinically suspected PCa between November 2019 and November 2023. The extent of rectal artifacts in these patients' images was evaluated using the Likert four-level method. The PCa diagnosis was performed by six radiologists and an automated PCa diagnosis DL method. The performance of DL and radiologists was evaluated using the area under the receiver operating characteristic curve (AUC) and the area under the multi-reader multi-case receiver operating characteristic curve, respectively.

RESULTS: Junior radiologists and DL demonstrated statistically significantly higher AUCs in patients without artifacts compared to those with artifacts (R1: 0.73 vs. 0.64; P = 0.01; R2: 0.74 vs. 0.67; P = 0.03; DL: 0.77 vs. 0.61; P < 0.001). In subgroup analysis, no statistically significant differences in the AUC were observed among different grades of rectal artifacts for both all radiologists (0.08 ≤ P ≤ 0.90) and DL models (0.12 ≤ P ≤ 0.96). The AUC for DL without artifacts significantly exceeded those with artifacts in both the peripheral zone (PZ) and transitional zone (TZ) (DLPZ: 0.78 vs. 0.61; P = 0.003; DLTZ: 0.73 vs. 0.59; P = 0.011). Conversely, there were no statistically significant differences in AUC with and without artifacts for all radiologists in PZ and TZ (0.08 ≤ P ≤ 0.98).

CONCLUSIONS: Rectal susceptibility artifacts have significant negative effects on subjective evaluation of junior radiologists and DL.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40000986 | DOI:10.1186/s12880-025-01602-7

Sci Rep. 2025 Feb 25;15(1):6820. doi: 10.1038/s41598-025-91553-4.

ABSTRACT

Monitoring the daily activity levels of black cattle is a crucial aspect of their well-being. The rapid advancements in artificial intelligence have transformed computer vision applications, including object detection, segmentation, and tracking. This has led to more effective and precise monitoring techniques for livestock. In modern cattle farms, video monitoring is essential for analyzing behavior, evaluating health, and predicting estrus events in precision farming. This paper introduces the novel Customized Multi-Camera Multi-Cattle Tracking (MCMCT) system. This unique approach uses four cameras to overcome the challenges of detecting and tracking black cattle in complex open ranch environments. The MCMCT system enhances a tracking-by-detection model with the YOLO v8 segmentation model as the detection backbone network to develop a precision black cattle monitoring system. Single-camera setups in real-world datasets of our open ranches, covering 23.3 m x 20 m with 55 cattle, have limitations in capturing all necessary details. Therefore, a multi-camera solution provides better coverage and more accurate behavior detection of cattle. The effectiveness of the MCMCT system is demonstrated through experimental results, with the YOLOv8-MCMCT system achieving an average Multi-Object Tracking Accuracy (MOTA) of 95.61% across 10 cases of 4 cameras at a processing speed of 30 frames per second. This high accuracy is a testament to the performance of the proposed MCMCT system. Additionally, integrating the Segment Anything Model (SAM) with YOLOv8 enhances the system's capability by automating cattle mask region extraction, reducing the need for manual labeling. Comparative analysis with state-of-the-art deep learning-based tracking methods, including Bot-sort, Byte-track, and OC-sort, further highlights the MCMCT's performance in multi-cattle tracking within complex natural scenes. The advanced algorithms and capabilities of the MCMCT system make it a valuable tool for non-contact automatic livestock monitoring in precision cattle farming. Its adaptability ensures effective performance across varied ranch environments without extensive retraining. This research significantly contributes to livestock monitoring, offering a robust solution for tracking black cattle and enhancing overall agricultural efficiency and management.

PMID:40000894 | DOI:10.1038/s41598-025-91553-4

Commun Med (Lond). 2025 Feb 25;5(1):50. doi: 10.1038/s43856-025-00770-5.

ABSTRACT

BACKGROUND: Huntington's disease, a neurodegenerative disorder, impairs both upper and lower limb function, typically assessed in clinical settings. However, wearable sensors offer the opportunity to monitor real-world data that complements clinical assessments, providing a more comprehensive understanding of disease symptoms.

METHODS: In this study, we monitor upper limb function in individuals with Huntington's disease (HD, n = 16), prodromal HD (pHD, n = 7), and controls (CTR, n = 16) using a wrist-worn wearable sensor over a 7-day period. Goal-directed hand movements are detected through a deep learning model, and kinematic features of each movement are analyzed. The collected data is used to predict disease groups and clinical scores using statistical and machine learning models.

RESULTS: Here we show that significant differences in goal-directed movement features exist between the groups. Additionally, several of these features strongly correlate with clinical scores. Classification models accurately distinguish between HD, pHD, and CTR individuals, achieving a balanced accuracy of 67% and a recall of 0.72 for the HD group. Regression models effectively predict clinical scores.

CONCLUSIONS: This study demonstrates the potential of wearable sensors and machine learning to monitor upper limb function in Huntington's disease, offering a tool for early detection, remote monitoring, and assessing treatment efficacy in clinical trials.

PMID:40000872 | DOI:10.1038/s43856-025-00770-5

Sci Rep. 2025 Feb 25;15(1):6702. doi: 10.1038/s41598-025-91203-9.

ABSTRACT

Generally, music genres have not new established framework, since they are often determined by the composer's background by cultural or historical impact and geographical origin. In this work, a new methodology is presented based on deep learning and metaheuristic algorithms to enhance the performance in music style categorization. The model consists of two main parts: a pre-trained model, a ZFNet, through which high level features are extracted from audio signals and a ResNeXt model for classification. A fractional-order-based variant of the Grey Lag Goose Optimization (FGLGO) algorithm is used to optimize the parameters of ResNeXt to boost the performance of the model. A dual-path recurrent network is employed for real-time music generation and evaluate the model on two benchmark datasets, ISMIR2004 and extended Ballroom, compared to the state-of-the-art models included CNN, PRCNN, BiLSTM and BiRNN. Experimental results show that with accuracy rates of 0.918 on the extended Ballroom dataset and 0.954 on the ISMIR2004 dataset, the proposed model improves accuracy and efficiency incrementally over existing models.

PMID:40000796 | DOI:10.1038/s41598-025-91203-9

Sci Rep. 2025 Feb 25;15(1):6798. doi: 10.1038/s41598-025-91329-w.

ABSTRACT

Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization and urbanization, Liaocheng has experienced increasing of ozone concentration over several years. Therefore, ozone has become a major environmental problem in Liaocheng City. Long short-term memory (LSTM) and artificial neural network (ANN) models are established to predict ozone concentrations in Liaocheng City from 2014 to 2023. The results show a general improvement in the accuracy of the LSTM model compared to the ANN model. Compared to the ANN, the LSTM has an increase in determination coefficient (R2), value from 0.6779 to 0.6939, a decrease in root mean square error (RMSE) value from 27.9895 μg/m3 to 27.2140 μg/m3 and a decrease in mean absolute error (MAE) value from 21.6919 μg/m3 to 20.8825 μg/m3. The prediction accuracy of the LSTM is superior to the ANN in terms of R, RMSE, and MAE. In summary, LSTM is a promising technique for predicting ozone concentrations. Moreover, by leveraging historical data and LSTM enables accurate predictions of future ozone concentrations on a global scale. This model will open up new avenues for controlling and mitigating ozone pollution.

PMID:40000767 | DOI:10.1038/s41598-025-91329-w

Sci Rep. 2025 Feb 25;15(1):6800. doi: 10.1038/s41598-025-91362-9.

ABSTRACT

This study was conducted to develop and validate a novel deep reinforcement learning (DRL) algorithm incorporating the segment anything model (SAM) to enhance the accuracy of automatic contouring organs at risk during radiotherapy for cervical cancer patients. CT images were collected from 150 cervical cancer patients treated at our hospital between 2021 and 2023. Among these images, 122 CT images were used as a training set for the algorithm training of the DRL model based on the SAM model, and 28 CT images were used for the test set. The model's performance was evaluated by comparing its segmentation results with the ground truth (manual contouring) obtained through manual contouring by expert clinicians. The test results were compared with the contouring results of commercial automatic contouring software based on the deep learning (DL) algorithm model. The Dice similarity coefficient (DSC), 95th percentile Hausdorff distance, average symmetric surface distance (ASSD), and relative absolute volume difference (RAVD) were used to quantitatively assess the contouring accuracy from different perspectives, enabling the contouring results to be comprehensively and objectively evaluated. The DRL model outperformed the DL model across all evaluated metrics. DRL achieved higher median DSC values, such as 0.97 versus 0.96 for the left kidney (P < 0.001), and demonstrated better boundary accuracy with lower HD95 values, e.g., 14.30 mm versus 17.24 mm for the rectum (P < 0.001). Moreover, DRL exhibited superior spatial agreement (median ASSD: 1.55 mm vs. 1.80 mm for the rectum, P < 0.001) and volume prediction accuracy (median RAVD: 10.25 vs. 10.64 for the duodenum, P < 0.001). These findings indicate that integrating SAM with RL (reinforcement learning) enhances segmentation accuracy and consistency compared to conventional DL methods. The proposed approach introduces a novel training strategy that improves performance without increasing model complexity, demonstrating its potential applicability in clinical practice.

PMID:40000766 | DOI:10.1038/s41598-025-91362-9

Sci Rep. 2025 Feb 25;15(1):6739. doi: 10.1038/s41598-025-91275-7.

ABSTRACT

The rapid growth of e-commerce has made product recommendation systems essential for enhancing customer experience and driving business success. This research proposes an advanced recommendation framework that integrates sentiment analysis (SA) and collaborative filtering (CF) to improve recommendation accuracy and user satisfaction. The methodology involves feature-level sentiment analysis with a multi-step pipeline: data preprocessing, feature extraction using a log-term frequency-based modified inverse class frequency (LFMI) algorithm, and sentiment classification using a Multi-Layer Attention-based Encoder-Decoder Temporal Convolution Neural Network (MLA-EDTCNet). To address class imbalance issues, a Modified Conditional Generative Adversarial Network (MCGAN) generates balanced oversamples. Furthermore, the Ocotillo Optimization Algorithm (OcOA) fine-tunes the model parameters to ensure optimal performance by balancing exploration and exploitation during training. The integrated system predicts sentiment polarity-positive, negative, or neutral-and combines these insights with CF to provide personalized product recommendations. Extensive experiments conducted on an Amazon product dataset demonstrate that the proposed approach outperforms state-of-the-art models in accuracy, precision, recall, F1-score, and AUC. By leveraging SA and CF, the framework delivers recommendations tailored to user preferences while enhancing engagement and satisfaction. This research highlights the potential of hybrid deep learning techniques to address critical challenges in recommendation systems, including class imbalance and feature extraction, offering a robust solution for modern e-commerce platforms.

PMID:40000752 | DOI:10.1038/s41598-025-91275-7

Acta Pharmacol Sin. 2025 Feb 25. doi: 10.1038/s41401-025-01488-9. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive lethal disease. Profibrotic fibroblast polarization during wound healing is one of the main causes of IPF, and the molecular mechanisms involved have yet to be fully determined. LIM domain-only protein 7 (LMO7), which acts as an E3 ubiquitin ligase, is highly expressed in the lung, brain and heart and plays important roles in embryonic development, cancer progression, inflammatory bowel disease and Dreifuss muscular dystrophy (EDMD). In this study, we investigated the role of LMO7 in pulmonary fibrosis. Bleomycin (BLM)-induced lung fibrosis was established in mice. For AAV-mediated gene therapy, AAV-Lmo7 shRNA (AAV-Lmo7 shRNA) was intratracheally administered 6 days before BLM injection. Through transcriptome analysis, we found that the expression of LMO7 was significantly upregulated in the fibroblasts of IPF patients and BLM-induced mice. Knockdown of LMO7 impaired the profibrotic phenotype of fibroblasts in BLM-treated mice and in primary lung fibroblasts stimulated with TGF-β in vitro. We observed that LMO7 binds to SMAD7, mediating its degradation by polyubiquitination of lysine 70 and increasing the stability of TGF-β receptor 1 (TGFβR1). Finally, intratracheal administration of adeno-associated virus (AAV)-mediated Lmo7 shRNA significantly ameliorated the progression of BLM-induced lung fibrosis. Our results suggest that LMO7 is a promising target for blocking profibrotic fibroblast polarization for the treatment of fibrotic lung disease. A model for the role of LMO7 in TGF-β/SMAD signaling during pulmonary fibrosis. During pulmonary fibrosis, ubiquitin E3 ligase LMO7 is up-regulated, and binds with. SMAD7. LMO7 mediates the ubiquitination of SMAD7 on Lysine 70, leading to its degradation, and further enhances the stability of transforming growth factor-beta receptor 1 (TGFβR1).

PMID:40000880 | DOI:10.1038/s41401-025-01488-9

Nat Commun. 2025 Feb 25;16(1):1962. doi: 10.1038/s41467-025-57237-3.

ABSTRACT

Naïve CD4 T cells are traditionally viewed as a quiescent, homogeneous, resting population, but emerging evidence reveals their heterogeneity, which can be crucial for understanding disease contexts and therapeutic outcomes. In this study, we identify distinct subpopulations within both murine and human naïve CD4 T cells by single cell-RNA-sequencing (scRNA-seq), particularly focusing on a subpopulation that expresses super-high levels of interleukin-7 receptor (IL-7Rsup-hi), along with CD97, IL-18R, and Ly6C. This subpopulation, absent in the thymus and peripherally induced, exhibits type 1 helper T cell (Th1)-poised characteristics and contributes to the inhibition of cancer progression in B16F10 tumor-bearing mice. In humans, this IL-7Rsup-hi subpopulation expressing CD97 correlates with the responsiveness to anti-PD-1 therapy in cancer patients and the disease state of multiple sclerosis. By elucidating the heterogeneity of naive CD4 T cells and identifying a Th1-poised subpopulation capable of robust type 1 responses, we highlight the importance of this heterogeneity in inflammatory conditions for defining the disease states and predicting drug responsiveness.

PMID:40000667 | DOI:10.1038/s41467-025-57237-3

Cell Mol Gastroenterol Hepatol. 2025 Feb 22:101476. doi: 10.1016/j.jcmgh.2025.101476. Online ahead of print.

NO ABSTRACT

PMID:39999952 | DOI:10.1016/j.jcmgh.2025.101476

Curr Opin Plant Biol. 2025 Feb 24;84:102697. doi: 10.1016/j.pbi.2025.102697. Online ahead of print.

ABSTRACT

Biomolecular condensates, often formed through liquid-liquid phase separation (LLPS), are increasingly recognized as a critical mechanism for cellular compartmentalization across diverse biological systems. Although traditionally considered membrane-less entities, recent discoveries highlight their dynamic interactions with membranes, where they regulate various processes, including signal transduction. Signaling lipids are observed in condensates. Despite these advancements, our understanding of such condensates in plant biology remains limited. This review highlights recent studies involving membrane-associated condensates in plants, focusing particularly on their interactions with the plasma membrane (PM) and their potential roles in PM-based signaling.

PMID:39999604 | DOI:10.1016/j.pbi.2025.102697