F1现场好多熟人

Approximately 3.8 billion people lack access to essential health services, and diagnostic interpretation remains a major bottleneck in remote and resource-constrained settings. Limited access to specialists and the complexity of biomedical signal interpretation (eg, electrocardiogram [ECG] and electroencephalogram) contribute to delays in recognizing cardiovascular and neurological conditions.

Based on ultrasound technology and clinical indicators, this study intends to develop multiple risk prediction models for diabetic peripheral neuropathy (DPN), conduct comparative analyses of these models, and further evaluate and validate the diagnostic efficacy of the optimal model for DPN as well as its potential in clinical application.

Host plant adaptability drives the ecological plasticity and global expansion of

Retinal hyperreflective foci (HRF) are associated with higher risk of progression to advanced age-related macular degeneration (AMD). To enable automated detection, we developed Foci-Net, a convolutional neural network for segmenting HRF in optical coherence tomography (OCT) volumes.

Precise control of the surface structure and acidity of γ-Al

The research underrepresentation of female athletes has left gaps in understanding factors influencing female athlete performance. We aimed to examine the impact of oral contraceptive (OC) use and low energy availability (LEA) risk on training loads and physical fitness changes over a competitive season.

Parental responses to an adolescent's pain impact pain-related variables, yet limited research has focused on parent-adolescent pain communication. Additionally, patterns have rarely been examined through comparisons of dyads where the adolescent has chronic pain versus those without.

This study aims to establish an interpretable disease classification model via machine learning and identify key features related to the disease to assist clinical disease diagnosis based on a multi-center CX9000 routine coagulation test.

To address the problems in medical electrical equipment risk management caused by the disconnection between unstructured medical electrical equipment standard documents and adverse event data, the lack of high-quality annotated data, and the reliance on manual combing for risk analysis.