Browse Articles

Incidence of recurrence and development of polypoidal lesions following half-dose and two-thirds dose photodynamic therapy for pachychoroid neovasculopathy

Evaluating performance of large language models for atrial fibrillation management using different prompting strategies and languages

Deep learning based motion correction in ultrasound microvessel imaging approach improves thyroid nodule classification

Author Correction: A lignan from Alnus japonica inhibits glioblastoma tumorspheres by suppression of FOXM1

Finite element analysis of pedicle screw fixation biomechanics and adjacent segment degeneration in varied bone conditions

A novel war strategy optimization algorithm based maximum power point tracking method for PV systems under partial shading conditions

Soil nitrogen-hydrolyzing enzyme activities respond differently to the freeze-thaw temperature and number of cycles

Assessing the performance of domain-specific models for plant leaf disease classification: a comprehensive benchmark of transfer-learning on open datasets

A bearing fault diagnosis method for hydrodynamic transmissions integrating few-shot learning and transfer learning

Abstract To address the insufficient generalization capability of bearing fault diagnosis models caused by scarce vibration data from high-power hydrodynamic transmission testbeds, this study proposes a diagnostic method integrating deep few-shot learning with transfer learning. First, a Siamese Wide Convolutional Neural Network (Siamese-WDCNN) is constructed based on public bearing datasets to extract essential features of vibration signals through few-shot contrastive learning. Second, we introduce a transfer learning strategy to address cross-condition generalization challenges. This approach adapts pre-trained model parameters from the CWRU dataset to real industrial hydrodynamic transmission data. We then fine-tune the model using limited target-domain samples to optimize performance. Experiments evaluating the generalization capability under variable operating conditions compare diagnostic performance across SVM, WDCNN, WDCNN + TL, FSL + TL, and FSL + TL + AM methods. Results demonstrate that FSL + TL achieves an accuracy of 85.30% under mixed operating conditions. Further optimization by incorporating an attention mechanism (FSL + TL + AM) elevates accuracy to 88.75%, effectively enhancing the generalization capability of the bearing fault diagnosis model. This validates the engineering practicality of the proposed method and explores a viable pathway for industrial equipment health monitoring.

The magnitude of sagittal head posture displacement and patient demographics predict rehabilitation outcomes in patients with chronic nonspecific neck pain

Inflammatory factors released by ACL reconstruction tendon grafts and their potential for macrophage polarization

Exploring intracellular anti-mycobacterium activity of lactoferricin-loaded niosomes: proteomics insights into Immunomodulation

Deep convolutional fuzzy neural networks with stork optimization on chronic cardiovascular disease monitoring for pervasive healthcare services

Detection of moderate to high antiphospholipid antibodies by chemiluminescence meets 2023 APS classification criteria

Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning

Association between nitrogen dioxide and incident breast cancer in Alberta’s tomorrow project

Abstract Breast cancer is a significant global health concern, with millions of cases diagnosed annually. Risk factors for breast cancer include obesity, physical inactivity, poor diet, and alcohol use. Urbanization introduces exposures such as traffic-related air pollution (TRAP), which may impact breast cancer risk. To assess the impact of TRAP, measured in this study using nitrogen dioxide (NO2), on the risk of post-menopausal breast cancer we conducted a secondary analysis of baseline and follow-up data from the Alberta Tomorrow Project (ATP) cohort, linked to geographic data at the postal code level from the Canadian Urban Environmental Health Research Consortium (CANUE). Cox proportional hazard regression models were built to assess the association between breast cancer risk and NO2. Direct acyclic graphs (DAG) were used to identify and select relevant confounders for adjustment. The study included 15,536 post-menopausal women and 523 incident breast cancer cases over 12.6 years of follow-up. The hazard ratio (HR) for the initial model, which included all potential confounders (age, education, number of births, fruits and vegetables consumption, income, physical activity, BMI, and smoking) was 1.01 per 10-ppb increase in NO2. The adjusted HR for a 10-ppb increase in NO2 measured at baseline (the year participants completed the health and behaviours survey) was 1.10 (95% CI = 0.90, 1.34). Our study found no significant association between NO2 exposure and post-menopausal breast cancer risk. The variation in reported hazard ratios (HRs) — ranging from 1.01 to 1.10 — reflects different models adjusting for different variables. Our findings suggest a risk magnitude similar to other studies, yet the lack of significant association may be due to sample size limitations and lower NO2 levels in Alberta compared to other regions globally.

Research on the occurrence mechanism of mine earthquake in Longwanggou Coal Mine and its damage assessment to surface buildings

Contamination of fabric surface by the particles woven from the cotton waste open end rotor yarn

Shikonin inhibits NLRP3 inflammasome activation and controls inflammatory disease

Understanding of the characteristics of fibroblasts in ischemic cardiomyopathy using single-nucleus RNA sequencing

Abstract Fibroblasts play a crucial role in the development and progression of heart failure in many heart-related diseases. However, the contributions of fibroblast and its subtypes to the pathogenesis of ischemic cardiomyopathy (ICM) remain unclear. In this study, we analyzed single-nucleus RNA sequencing data from cardiac tissues of seven ICM patients with heart failure and eight control subjects. Our findings indicate that, compared to controls, fibroblasts from ICM patients exhibit dysregulation of genes associated with cell-substrate adhesion and matrix or collagen fibril organization. We further identified five fibroblast subtypes, designated as FB1 through FB5, with FB3 exhibiting the highest frequency in ICM. Pseudotime trajectory analysis further revealed an evolutionary progression of fibroblasts from FB1/FB2 toward FB3. Differential gene expression analysis of FB3 highlighted several subtype-specific genes, such as NTM, THBS4, NRK, NAV2, and PLA2G5. Furthermore, intercellular interaction analysis revealed that FB3 exhibits weakened interactions with other cells under ICM. Specifically, signaling interactions involving ANGPTL and COLLAGEN are reduced, whereas pathways mediated by THBS, NRXN, and APP are strengthened. These results provide valuable insights into the fibroblast microenvironment and elucidate the pathological mechanisms underlying ICM, laying a theoretical foundation for the identification of novel therapeutic targets.