Jie Sun

Heart sound, as an important physiological signal representing the state of the cardiovascular system, is crucial for the diagnosis of cardiovascular disease (CVD). Traditional heart sound detection methods rely on doctors’ experience and specialized auscultation equipment, but these methods have limitations in terms of efficiency and environmental adaptability. To address these issues, this article proposes a mobile phone-based heart sound detection method. It uses the built-in microphone to record heart sounds and adopts a two-stage filtering method to improve the quality of heart sounds. Next, we design a multifeature fusion network named MBCF-Net to extract multidomain features, and effectively cross-domain fuse them while enhancing important channel information. We evaluate the proposed model on a self-collected smartphone heart sound dataset and two public heart sound datasets. The experimental results show that MBCF-Net achieves classification accuracies of 99.63%, 98.18%, and 99.88% on the smartphone dataset, the PhysioNet/CinC Challenge 2016 dataset, and Yaseen’s five-class dataset, respectively. Compared with previous works, MBCF-Net achieves higher diagnostic accuracy, demonstrating its strong performance and adaptability across different heart sound classification scenarios.

Background Accurate survival prediction for patients with bone metastatic cancer remains challenging. Existing prognostic models frequently show poor external validity, primarily due to small sample sizes, single-center designs, and insufficient inclusion of pathological and molecular variables. Moreover, few studies have concentrated on the prognostic heterogeneity of bone metastasis (BM) across different cancers using large, standardized datasets within a cancer-specific manner. This retrospective, multicenter, registry-based cohort study was conducted to evaluate the prognostic significance of BM across multiple cancer types and to identify cancer-specific clinical factors associated with survival. Methods Baseline demographic and clinical characteristics of 13,742 patients with AJCC stage IV or TNM stage M1 metastatic cancer diagnosis were collected across 42 clinical studies registered in the cBioPortal for Cancer Genomics database. Overall survival (OS) following metastatic diagnosis was set as the primary outcome. Univariate analyses were conducted to identify potential prognostic risk factors mainly using the Kaplan–Meier, log-rank test, and non-parametric tests. Variables with p < 0.20 were included in multivariate Cox proportional hazards models for further validation. Multiple imputation and bootstrap were applied for the missing value process and validation. Results BM was associated with favorable outcomes compared with other metastatic sites in osteotropic cancers such as breast, prostate, and thyroid cancer, whereas it indicated a worse prognosis in hepatobiliary, uterine sarcoma, and colorectal cancer with low affinity to skeletal tissue. Among prognostic variables, no single metastatic site served as a universal adverse prognostic factor across all cancers. Poorly differentiated or undifferentiated histology independently correlated with reduced survival (HR = 1.249, p < 0.001). Age above 60 years was also associated with inferior survival (univariate analysis, p < 0.001), while the primary cancer type remained the most influential prognostic determinant (HR = 1.422–1.758, p < 0.001). Conclusions BM demonstrates cancer-specific and heterogeneous influences on survival. Population for survival prediction in traditional studies could be expanded within a cancer-specific framework. Among the included prognostic variables, primary cancer type, pathological differentiation, and age stratify outcomes significantly, highlighting the demand for pathology-integrated, cancer-specific prognostic models. Incorporation of standardized treatment and molecular variables is essential for improving model precision and clinical applicability in the future.

Background and aims: The inclusion of gaming disorder as a new diagnosis in the 11th revision of the International Statistical Classification of Diseases (ICD-11) has caused ongoing debate. This review aimed to summarise the potential neural mechanisms of gaming disorder and provide additional evidence for this debate.Methods: We conducted a comprehensive literature review of gaming disorder, focusing on studies that investigated its clinical characteristics and neurobiological mechanisms. Based on this evidence, we further discuss gaming disorder as a psychiatric disorder.Results: The present review demonstrated that the brain regions involved in gaming disorder are related to executive functioning (e.g., anterior cingulate cortex and dorsolateral prefrontal cortex), reward systems (e.g., striatum and orbitofrontal cortex), and emotional regulation (e.g., insula and amygdala). Despite the inclusion of gaming disorder in the ICD-11, the debate remains on the benefits and harms of classifying it as a mental health disorder. Opponents argue that the current manifestations that support gaming disorder as a psychiatric disorder remain inadequate, it could cause moral panic among healthy gamers, and that the label of gaming disorder is stigmatising.Discussion: Evidence suggests that gaming disorder shares similar neurobiological alterations with other types of behavioural and substance-related addictions, which further supports gaming disorder as a behavioural addiction. Ongoing debates on whether gaming disorder is a psychiatric disorder push for further exploring the nature of gaming disorder and resolving this dilemma in the field.

In order to improve the accuracy of human motion recognition, computer vision technology is selected as the research tool, and a representation method of human bone and joint is proposed. This method takes the adaptive human motion bone center architecture as the action recognition tool, constructs the human motion action recognition model by calculating the angular acceleration and angular velocity of the neck and hip joints, and uses the model to calculate the action data and generate the recognition results. The experimental results show that the motion recognition accuracy of this algorithm is high, and it can be used as a motion recognition tool.

During the pandemic era, quarantines might potentially have negative effects and disproportionately exacerbate health condition problems. We conducted this cross-sectional, national study to ascertain the prevalence of constant pain symptoms and how quarantines impacted the pain symptoms and identify the factors associated with constant pain to further guide reducing the prevalence of chronic pain for vulnerable people under the pandemic. The sociodemographic data, quarantine conditions, mental health situations and pain symptoms of the general population were collected. After adjusting for potential confounders, long-term quarantine (≥15 days) exposures were associated with an increased risk of constant pain complaints compared to those not under a quarantine (Odds Ratio (OR): 1.26; 95% Confidence Interval (CI): 1.03, 1.54; p = 0.026). Risk factors including unemployment (OR: 1.55), chronic disease history (OR: 2.38) and infection with COVID-19 (OR: 2.15), and any of mental health symptoms including depression, anxiety, insomnia and PTSD (OR: 5.44) were identified by a multivariable logistic regression. Additionally, mediation analysis revealed that the effects of the quarantine duration on pain symptoms were mediated by mental health symptoms (indirect effects: 0.075, p < 0.001). These results advocated that long-term quarantine measures were associated with an increased risk of experiencing pain, especially for vulnerable groups with COVID-19 infection and with mental health symptoms. The findings also suggest that reducing mental distress during the pandemic might contribute to reducing the burden of pain symptoms and prioritizing interventions for those experiencing a long-term quarantine.

The present study assessed the willingness of the general population to receive COVID-19 vaccines and identified factors that influence vaccine hesitancy and resistance. A national online survey was conducted from 29 January 2021 to 26 April 2021 in China. Multinomial logistic regression analyses were conducted to identify factors that influence vaccine hesitancy and resistance. Of the 34,041 participants surveyed, 18,810 (55.3%) were willing to get vaccinated, 13,736 (40.3%) were hesitant, and 1495 (4.4%) were resistant. Rates of vaccine acceptance increased over time, with geographical discrepancies in vaccine hesitancy and resistance between provinces in China. Vaccine safety was the greatest concern expressed by most participants (24,461 [71.9%]), and the major reason for participants’ refusing vaccination (974 [65.2%]). Government agencies (23,131 [68.0%]) and social media (20,967 [61.6%]) were the main sources of COVID-19 vaccine information. Compared with vaccination acceptance, female, young and middle-aged, high income, and perceived low-risk of infection were associated with vaccine hesitancy. Histories of allergic reactions to other vaccines and depression symptoms were related to vaccine resistance. Common factors that influenced vaccine hesitancy and resistance were residing in cities and perceiving less protection with vaccines than with other protective measures. The results indicate that the rate of vaccine resistance is relatively low, but vaccine hesitancy is common. Individuals who are female, young and middle-aged, with a high income, and residing in cities are more likely to be hesitant for vaccination and should be the target populations for vaccination campaigns. Specific vaccine messaging from the government and social media could alleviate public concerns about vaccine safety and efficacy.