Falls among the elderly comprise a major health problem. Daily activity monitoring and fall detection using wearable sensors provide an important healthcare system for elderly or frail individuals. We investigated the classification accuracy of daily activity and fall data based on surface electromyography (sEMG) and plantar pressure signals. sEMG and plantar pressure signals were collected, and their features were extracted. Suitable features were selected and combined for posture transition, gait, and fall using the Fisher class separability index. A feature-level fusion method, named as the global canonical correlation analysis of weighting genetic algorithm, was proposed to reduce dimensions. For the problem in which the number of daily activities is considerably more than the number of fall activities, Weighted Kernel Fisher Linear Discriminant Analysis (WKFDA) was proposed to classify gait and fall. Double Parameter Kernel Optimization based on Extreme Learning Machine (DPK-OMELM) was used to classify activities. Results showed that the classification accuracy of the posture transition is 100%, and the accuracy of gait and fall classified using WKFDA can reach 98%. For all types of posture transition, gait, and fall, sensitivity, specificity, and accuracy are over 96%.

Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system. Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model. Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals’ local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices. Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.

To develop and internally validate a nomogram for pulmonary embolism (PE) risk in hospitalized patients and to evaluate its performance in an independent h

The 2019 novel coronavirus (2019-nCov) has caused increasing number of infected cases globally. This study was performed to analyze information regarding the transmission route and presence of viral nucleic acids on several clinical samples. Confirmed 2019-nCov-infected cases were identified in Dongyang and were treated according to guidelines for the diagnosis of 2019-nCov infection released by the National Health Commission. Information regarding the contacts that the infected people had was collected to determine whether it caused clustered cases. A series of successive nucleic acid examination of feces, oropharyngeal swabs, and sputum was also performed, and the results were analyzed. A total of 19 confirmed cases of 2019-nCov infection were identified in Dongyang, Zhejiang Province, China. Five cases showed severe symptoms, and the remaining ones showed mild manifestations. Ten cases infected from two asymptomatic individuals were clustered into two groups. Among 14 cases with consecutive nucleic acid test results, four patients showed positive results in feces after their negative conversion in oropharyngeal swabs. Asymptomatic individuals with the virus could cause 2019-nCov clustered cases, and the clustered cases may differ from sporadic cases on age and length of hospitalization. In addition, nucleic acids in feces last longer than those in oropharyngeal swabs.

Purpose: Under China’s county medical consortium system, this study explores how level of care integration and other key attributes of medical institutions influence patients’ healthcare choices and examines differences in preferences among population subgroups. Patients and Methods: This study constructed a virtual medical institution choice set using a discrete choice experiment (DCE) with eight institutional attributes. A total of 676 respondents were recruited via an online convenience sample on the Credamo platform using a self-developed questionnaire. The conditional logit model and fixed effects model were adopted to analyze 10,816 valid observations. Results: Medical quality emerges as the primary factor influencing patients’ healthcare choices, followed by the level of care integration. Heterogeneity analysis reveals that rural residents and individuals with lower educational attainment prioritize practical factors such as convenience and medical costs. Individuals with higher education levels and female patients demonstrate greater concern for medical care quality. Conclusion: All findings reflect stated preferences in hypothetical scenarios rather than observed healthcare-seeking behavior. Within this stated-preference framework, higher levels of integrated care, streamlined referral procedures and higher levels of information sharing were consistently associated with stronger patient choice. Importantly, we make no causal claim that these preferences directly predict real-world care-seeking or system-level outcomes. To move from stated preference to observed behavior, future research combining offline representative sampling with real utilization data is needed to validate and extend these patterns.