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Muscles generate varying levels of force by recruiting different numbers of motor units (MUs), and as the force increases, the number of recruited MUs gradually rises. However, current decoding methods encounter difficulties in maintaining a stable and consistent growth trend in MU numbers with increasing force. In some instances, an unexpected reduction in the number of MUs can even be observed as force intensifies. To address this issue, in this study, we propose an enhanced decoding method that adaptively reutilizes MU filters. Specifically, in addition to the normal decoding process, we introduced an additional procedure where MU filters are reused to initialize the algorithm. The MU filters are iterated and adapted to the new signals, aiming to decode motor units that were actually activated but cannot be identified due to heavy superimposition. We tested our method on both simulated and experimental surface electromyogram (sEMG) signals. We simulated isometric signals (10%-70%) with known MU firing patterns using experimentally recorded MU action potentials from forearm muscles and compared the decomposition results to two baseline approaches: convolution kernel compensation (CKC) and fast independent component analysis (fastICA). Our method increased the decoded MU number by a rate of 135.4% ± 62.5 % and 63.6% ± 20.2 % for CKC and fastICA, respectively, across different signal-to-noise ratios. The sensitivity and precision for MUs decomposed using the enhanced method remained at the same accuracy level (p <0.001) as those of normally decoded MUs. For the experimental signals, eight healthy subjects performed hand movements at five different force levels (10%-90%), during which sEMG signals were recorded and decomposed. The results indicate that the enhanced process increased the number of decoded MUs by 21.8% ± 10.9 % across all subjects. We discussed the possibility of fully capturing all activated motor units by appropriately reusing previously decoded MU filters and improving the balance of activated motor unit numbers across varying excitation levels.

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Neural interfacing has played an essential role in advancing our understanding of fundamental movement neurophysiology and the development of human-machine interface. However, direct neural interfaces from brain and nerve recording are currently limited in clinical areas for their invasiveness and high selectivity. Here, we applied the surface electromyogram (EMG) in studying the neural control of movement and proposed a new non-invasive way of extracting neural drive to individual muscles. Sixteen subjects performed isometric contractions to complete six hand tasks. High-density surface EMG signals (256 channels in total) recorded from the forearm muscles were decomposed into motor unit firing trains. The location of each decomposed motor unit was represented by its center of gravity and was put into clustering for distinct muscle regions. All the motor units in the same cluster served as a muscle-specific motor pool from which individual muscle drive could be extracted directly. Moreover, we cross-validated the self-clustered muscle regions by magnetic resonance imaging (MRI) recorded from the subjects' forearms. All motor units that fall within the MRI region are considered correctly clustered. We achieved a clustering accuracy of 95.72% ± 4.01% for all subjects. We provided a new framework for collecting experimental muscle-specific drives and generalized the way of surface electrode placement without prior knowledge of the targeting muscle architecture.

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Motor unit spike trains (MUSTs) decomposed from surface electromyography (sEMG) have been an emerging solution for neural interfacing, especially for the control of upper limb prosthetics. Accurate and efficient decomposition techniques are essential and desirable. However, most decomposition methods are designed for motor units (MUs) with global maximum of single or large muscle, while in general forearm muscles are usually small and slender with low global energy. Thus, we propose a novel approach using local spatial information towards more accurate and efficient sEMG decomposition of forearm muscles. A fast spatial spike detection method is proposed to replace the time-consuming iteration process of blind source separation (BSS) methods. Here, spatial distribution characteristics of motor unit action potential are leveraged to pre-classify the candidate MUs, and further to create initial MU templates, aiming to avoid repeating convergence to high-energy MUs. The results of both simulated and experimental sEMG signals show that low-energy MUs from small muscles are more easily found compared with conventional BSS algorithm. Specifically, the proposed method can identify more 40% reliable MUs while only 30% consuming time are needed. The outcomes provide a novel solution for more efficient sEMG decomposition, potentially paving the way of MUST-based non-invasive neural interface.

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High-density surface electromyography (EMG) has been proposed to overcome the lower selectivity with respect to needle EMG and to provide information on a wide area over the considered muscle. Motor units decomposed from surface EMG signal of different depths differ in the distribution of action potentials detected in the skin surface. We propose a noninvasive model for estimating the depth of motor unit. We find that the depth of motor unit is linearly related to the Gaussian RMS width fitted by data points extracted from motor unit action potential. Simulated and experimental signals are used to evaluate the model performance. The correlation coefficient between reference depth and estimated depth is 0.92 ± 0.01 for simulated motor unit action potentials. Due to the symmetric nature of our model, no significant decrease is detected during the electrode selection procedure. We further checked the estimation results from decomposed motor units, the correlation coefficient between reference depth and estimated depth is 0.82 ± 0.07. For experimental signals, high discrimination of estimated depth vector is detected across gestures among trials. These results show the potential for a straightforward assessment of depth of motor units inside muscles. We discuss the potential of a non-invasive way for the location of decomposed motor units.

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OBJECTIVE: To understand the current situation regarding the knowledge and demand for patients with prediabetes at different self-management levels, and to provide guide for improving their knowledge.
 Methods: A total of 312 prediabetes patients from 3 hospitals in Changsha were enrolled in this survey. The questionnaires covered diabetes self-management behavior scale and prediabetes knowledge status and demand questionnaire. Diabetes knowledge acquisition and demand were analyzed among patients with different levels of self-management.
 Results: The score of self-management behavior for patients with prediabetes was 39.1±13.9. The rate of knowledge acquisition was low and the rate of demand was high. The knowledge acquisition rate was high and the knowledge demand rate was low in patients with high levels of self-management. As for the contents of health education, the dietary collocation and method for glucose detection were highly needed by all self-management levels of patients.
 Conclusion: Prediabetes patients' self-management level are low. Health education to patients with prediabetes should be based on individualized demands.

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BACKGROUND: Involvement in road traffic accidents (RTAs) may put individuals at increased risk for a wide range of psychiatric disorders, among which posttraumatic stress disorder (PTSD) presents a public health issue. However, a great disparity was observed among studies exploring the prevalence of PTSD among RTA survivors. This meta-analysis aimed to explore the pooled prevalence of PTSD among RTA survivors. METHODS: Electronic databases of PubMed, Embase, Web of Science, PsycARTICLES, PsycINFO, and CINAHL were searched to identify relevant studies. Study selection and data extraction were conducted independently by 2 investigators, and a meta-analysis was performed to synthesize the data. Heterogeneity among studies was evaluated using the Cochran Q test and quantified using the I statistic. Subgroup analyses were performed to identify the source of the heterogeneity. The possibility of publication bias was assessed using Egger linear test. RESULTS: Fifteen eligible studies containing 6804 RTA survivors were identified in this meta-analysis, of which 1489 were identified with PTSD. The pooled prevalence of PTSD among RTA survivors was 22.25% (95% confidence interval: 16.71%-28.33%). A high degree of heterogeneity was observed across studies (I = 97.1%, P < .001), with reported PTSD prevalence ranging from 6.3% to 58.3%. Subgroup analyses found that the prevalence of PTSD among RTA survivors varied significantly across studies in relation to the instrument used to assess PTSD, country, race, gender, and education level (P < .05). CONCLUSION: The high pooled prevalence of PTSD among RTA survivors found in this study significantly underscores the need for providing timely and effective intervention strategies for RTA survivors.

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OBJECTIVE: To establish an interference mode of group counseling for diabetic patients with depression and to evaluate the effectiveness of this mode on depression, treatment compliance and blood sugar level in the patients.
 METHODS: One hundred diabetic patients with depression were randomly divided into a counseling group and a control group (n=50 per group). Self-Rating Depression Scale (SDS) was applied to all the patients. The interference mode of group counseling was established through literature review, expert consultation or interview. The counseling group received counseling for 8 times within 2 months.
 RESULTS: There was a significant difference in the SDS scores at 0, 3, 6 or 12 months after the intervention between the 2 groups (P<0.001). For the counseling group, there was a significant difference in the SDS scores between pre-intervention and 3, 6 or 12 months after intervention (P<0.001). However, there was no significant difference in the SDS scores between any two time points after the intervention (P>0.05). There was a significant difference in the compliance between any two time points after the intervention (P<0.05). Fasting blood glucose (FBG), 2 h postprandial blood glucose (2hPG) or glycosylated hemoglobin (HbA1c) was significantly different at any two time points after the intervention (P<0.05).
 CONCLUSION: Group counseling can improve depression, compliance and blood sugar control in the diabetic patients.

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We conducted a case-control study to evaluate the association of miR-146a rs2910164 (C>G), miR-149 rs2292832 (T>C), miR-196a2 rs11614913 (T>C) and miR-499 rs3746444 (T>C) polymorphisms with the risk of hepatocellular carcinoma. A total of 274 patients with HCC were collected between January 2013 and December 2014. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was taken to determine the polymorphism of miR-146a C>G, miR-149 T>C, miR-196a2 T>C and miR-499 T>C. By comparing with control groups, patients with HCC were more likely to be males (OR=2.01, 95% CI=1.38-2.95), have older age (OR=1.52, 95% CI=1.09-2.13), have a history of alcohol drinking (OR=2.09, 95% CI=1.49-2.93), and be infected with HBV (OR=32.98, 95% CI=19.70-55.46) and HCV (OR=56.26, 95% CI=23.28-152.98) infection. By conditional regression analysis, individuals carrying the TC and CC genotypes of miR-196a2 T>C were found to be associated with an elevated risk of HCC compared to the TT genotype, and the adjusted odds ratio were 1.50 (1.03-2.17) and 2.86 (1.60-5.16), respectively. Moreover, the TC+CC genotype was correlated with an increased risk of HCC (OR=1.69, 95% CI=1.19-2.41) compared to the wide-type genotype. In conclusion, our results suggested that miR-196a2 T>C polymorphism is associated with HCC risk in Chinese population.

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