Search

The China Surgery and Anaesthesia Cohort (CSAC) study was launched in July 2020 and is an ongoing prospective cohort study recruiting patients aged 40–65 years who underwent elective surgeries with general anaesthesia across four medical centres in China. The general objective of the CSAC study is to improve our understanding of the complex interaction between environmental and genetic components as well as to determine their effects on a wide range of interested surgery/anaesthesia-related outcomes. To achieve this goal, we collected enriched phenotypic data, e.g., sociodemographic characteristics, lifestyle factors, perioperative neuropsychological changes, anaesthesia- and surgery-related complications, and medical conditions, at recruitment, as well as through both active (at 1, 3, 7 days and 1, 3, 6, 12 months after surgery) and passive (for more than 1 year after surgery) follow-up assessments. We also obtained omics data from blood samples. In addition, COVID-19-related information was collected from all participants since January 2023, immediately after COVID-19 restrictions were eased in China. As of July 18, 2023, 12,766 participants (mean age = 52.40 years, 57.93% were female) completed baseline data collection (response rate = 94.68%), among which approximately 70% donated blood and hair samples. The follow-up rates within 12 months after surgery were > 92%. Our initial analyses have demonstrated the incidence of and risk factors for chronic postsurgical pain (CPSP) and postoperative cognitive dysfunction (POCD) among middle-aged Chinese individuals, which may prompt further mechanistic exploration and facilitate the development of effective interventions for preventing those conditions. Additional studies, such as genome-wide association analyses for identifying the genetic determinants of CPSP and POCD, are ongoing, and their findings will be released in the future. [ABSTRACT FROM AUTHOR]

Background: China has one of the highest hepatitis B virus (HBV) disease burdens worldwide and tracking progress toward the 2030 HBV elimination targets is essential. This study aimed to assess the impact of biomedical interventions (i.e., adult vaccination, screening and treatment) on the adult HBV epidemic, estimate the time for HBV elimination, and evaluate the cost-effectiveness of the interventions in China. Methods: A deterministic compartmental model was developed to project the HBV epidemic from 2022 to 2050 and estimate the time to meet elimination targets under four intervention scenarios. Cost-effectiveness was calculated using incremental cost per quality-adjusted life year (QALY) gained, i.e., average cost-effectiveness ratio (CER). Results: Under the status quo, there will be 42.09–45.42 million adults living with HBV in 2050 and 11.04–14.36 million HBV-related deaths cumulatively from 2022 to 2050. Universal vaccination would cumulatively avert 3.44–3.95 million new cases at a cost of US$1027–1261/QALY gained. The comprehensive strategy would cumulatively avert 4.67–5.24 million new chronic cases and 1.39–1.85 million deaths, expediting the realization of the elimination targets forward to 2049. This strategy was also cost-effective with an average CER of US$20,796–26,685/QALY and a saved healthcare cost of US$16.10–26.84 per person. Conclusion: China is not on track to meet the elimination targets but comprehensive biomedical interventions can accelerate the realization of the targets. A comprehensive strategy is cost-effective and cost-saving, which should be promoted in primary care infrastructures. Universal adult vaccination may be appropriate in the near future considering practical feasibility. [ABSTRACT FROM AUTHOR]

Background: To combat/control the COVID-19 pandemic, a complete lockdown was implemented in China for almost 6 months during 2020. Purpose: To determine the impact of a long-term lockdown on the academic performance of first-year nursing students via mandatory online learning, and to determine the benefits of online teaching. Methods: The recruitment and academic performance of 1st-year nursing students were assessed between 2019 [prior to COVID-19, n = 195, (146 women)] and 2020 [during COVID-19, n = 180 (142 women)]. The independent sample t test or Mann-Whitney test was applied for a comparison between these two groups. Results: There was no significant difference in student recruitment between 2019 and 2020. The overall performance of the first-year students improved in the Biochemistry, Immunopathology, Traditional Chinese Medicine Nursing and Combined Nursing courses via mandatory online teaching in 2020 compared with traditional teaching in 2019. Conclusion: Suspension of in-class learning but continuing education virtually online has occurred without negatively impacting academic performance, thus academic goals are more than achievable in a complete lockdown situation. This study offers firm evidence to forge a path for developments in teaching methods to better incorporate virtual learning and technology in order to adapt to fast-changing environments. However, the psychological/psychiatric and physical impact of the COVID-19 lockdown and the lack of face-to-face interaction on these students remains to be explored. [ABSTRACT FROM AUTHOR]

Purpose: Through this nationwide survey on ACR BI-RADS including ultrasound images of 10 selected breast lesions, we aimed to learn about consistency in feature interpretation and assessment categories and to identify factors that might contribute to inconsistencies, thereby promoting the application of BI-RADS in China. Materials and Methods: The survey was delivered through a self-developed website about blinded image interpretation and was released to the public through online platforms and social media. A total of 10 representative lesions were selected by an experienced radiologist to gather information about the general practice of BI-RADS lexicons and categories. The Kappa statistic, the chi-squared test, and descriptive statistics were used for data analysis. Results: Nine hundred ultrasound workers completed the questionnaire, coming from all provinces and major cities in China. They had different positions, grades of work organization, and seniority. The interrater agreement of BI-RADS features was fair to substantial (kappa value: 0.37– 0.66). For BI-RADS categories, the highest agreement was observed in the typical benign group (average constituent rate = 74.78%), and generally lower agreement was observed in the typical malignant (average constituent rate = 36.03%) and suspicious groups (average constituent rate = 39.02%). Conclusion: We found inconsistencies in BI-RADS applications, providing direction for image feature research using big data. Therefore, we call for more efforts to improve the consistency of BI-RADS application and provide an evidence-based basis for identifying benign and malignant lesions by sonographic features. [ABSTRACT FROM AUTHOR]

Supply chains are complex systems that can be conceptualized as networks. Several studies have examined the structural properties of economic networks (i.e., given in units of financial value) and virtual water supply chain networks (i.e., where economic connections are reported in terms of their embodied water). In both cases, connections both within and between economic sectors are important to fully represent structural network properties, highlighting the importance of multilayer network analyses. The similarities and differences between economic and virtual water multilayer networks are yet to be well understood. The goal of this study is to consistently compare the economic and virtual water multilayer networks within China. Environmentally extended multi-regional input–output (EE-MRIO) data is used to build multilayer networks between 31 provinces and 41 economic sectors (nodes are province-sectors) for commodities and services in the year 2017, using both economic [¥] and virtual water [m 3 ] weights. This study shows that virtual water multilayer networks are relevant to address questions of water sustainability, scarcity, and hazards in supply chains, but that examining virtual water networks in isolation may not identify the economically important core nodes. Consideration of both economic and embedded resource networks could enhance our understanding of complex supply chains. • Economic and virtual water multilayer networks are highly connected and heterogeneous. • Agriculture is the most central sector in China's virtual water multilayer. • The central nodes in virtual water and economic multiplex networks show little overlap. [ABSTRACT FROM AUTHOR]

• As far as we know, we are the first to develop a machine learning (ML)-based diagnostic system, based on electronic medical records (EMR) data, to mimic the clinical reasoning of human physicians to differentiate MDD and BD (especially BD depressive episodes) patients. • The diagnostic system accurately identified patients with BD in various clinical scenarios. The underlying causal reasoning for the machine decisions is explainable, enabling population-level and individual-level interventions. • Differences in patterns of peripheral markers between BD and MDD could enrich our understanding of potential underlying pathophysiological mechanisms of them. Caused by shared genetic risk factors and similar neuropsychological symptoms, bipolar disorder (BD) and major depressive disorder (MDD) are at high risk of misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. We aimed to develop a machine learning (ML)-based diagnostic system, based on electronic medical records (EMR) data, to mimic the clinical reasoning of human physicians to differentiate MDD and BD (especially BD depressive episodes) patients about to be admitted to a hospital and, hence, reduce the misdiagnosis of BD as MDD on admission. In addition, we examined to what extent our ML model could be made interpretable by quantifying and visualizing the features that drive the predictions. By identifying 16,311 patients admitted to a hospital located in western China between 2009 and 2018 with a recorded main diagnosis of MDD or BD, we established three sub-cohorts with different combinations of features for both the MDD-BD cohort and the MDD-BD depressive episodes cohort, respectively. Four different ML algorithms (logistic regression, extreme gradient boosting (XGBoost), random forest, and support vector machine) and four train-test splits were used to train and validate diagnostic models, and explainable methods (SHAP and Break Down) were utilized to analyze the contribution of each of the features at both population-level and individual-level, including feature importance, feature interaction, and feature effect on prediction decision for a specific subject. The XGBoost algorithm provided the best test performance (AUC: 0.838 (0.810–0.867), PPV: 0.810 and NPV: 0.834) for separating patients with BD from those with MDD. Core predictors included symptoms (mood-up, exciting, bad sleep, loss of interest, talking, mood-down, provoke), along with age, job, myocardial enzyme markers (creatine kinase, hydroxybutyrate dehydrogenase), diabetes-associated marker (glucose), bone function marker (alkaline phosphatase), non-enzymatic antioxidant (uric acid), markers of immune/inflammation (white blood cell count, lymphocyte count, basophil percentage, monocyte count), cardiovascular function marker (low density lipoprotein), renal marker (total protein), liver biochemistry marker (indirect bilirubin), and vital signs like pulse. For separating patients with BD depressive episodes from those with MDD, the test AUC was 0.777 (0.732–0.822), with PPV 0.576 and NPV 0.899. Additional validation in models built with self-reported symptoms removed from the feature set, showed test AUC of 0.701 (0.666–0.736) for differentiating BD and MDD, and AUC of 0.564 (0.515–0.614) for detecting patients in BD depressive episodes from MDD patients. Validation in the datasets without removing the patients with comorbidity showed an AUC of 0.826 (0.806–0.846). The diagnostic system accurately identified patients with BD in various clinical scenarios, and differences in patterns of peripheral markers between BD and MDD could enrich our understanding of potential underlying pathophysiological mechanisms of them. [ABSTRACT FROM AUTHOR]

The rapid advancement of modern technology has significantly driven progress in various IT-related activities, resulting in a substantial increase in internet penetration rates, particularly among college students. The utilization of the internet has become one of the most essential tools in our modern society. However, internet addiction (IA) has emerged as a serious concern, particularly among college students, adversely affecting academic performance and having significant psychological and psychiatric implications. The aim of the current study was to determine the impact of physical exercise, gender and academic year on IA among college students. In the present study, we investigated internet usage, engagement in sports activities, and academic performance among college students from Western, Middle, and Eastern regions of Chinese universities. It's noteworthy that most of the respondents were freshmen. Our findings indicate that freshmen students were more susceptible to experiencing IA. Approximately 75% of students engaged in leisure sports activities, revealing an inverse correlation between sports activity and IA. This correlation aligns with the level of sports involvement, emphasizing the potential benefits of physical activity in mitigating IA. However, our study did not uncover any correlation between geographic location and the occurrence of IA, nor did it find differences between medical and non-medical students. Furthermore, our study revealed no significant variations in IA among students from different ethnic backgrounds. The underlying mechanism of IA is being currently determined. Our data suggest that physical exercise, gender, and academic year have a significant impact on IA among college students., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Cai, Wang, Ye, Feng, Yang, Huang, Chen, Hambly, Bao and Liang.)

Methane is a primary greenhouse gas which is responsible for global warming. The sulfate-dependent anaerobic methane oxidation (S-AOM) process catalyzed by an aerobic me thanotrophic (ANME) archaea and sulfate-reducing bacteria (SRB) is a vital link connecting the global carbon and sulfur cycles, and it is considered to be the overriding methane sink in marine ecosystem. However, there have been few studies regarding the role of S-AOM process and the distribution of ANME archaea in intertidal ecosystem. The intertidal zone is a buffer zone between sea and land and plays an important role in global geochemical cycle. In the present study, the abundance, potential methane oxidation rate, and community structure of ANME archaea in the intertidal zone were studied by quantitative PCR, stable isotope tracing method and high-throughput sequencing. The results showed that the potential S-AOM activity ranged from 0 to 0.77 nmol 13 CO 2 g -1 (dry sediment) day -1 The copy number of 16S rRNA gene of ANME archaea reached 10 6 ∼ 10 7 copies g -1 (dry sediment). The average contribution of S-AOM to total anaerobic methane oxidation was up to 34.5%, while denitrifying anaerobic methane oxidation accounted for the rest, which implied that S-AOM process was an essential methane sink that cannot be overlooked in intertidal ecosystem. The simulated column experiments also indicated that ANME archaea were sensitive to oxygen and preferred anaerobic environmental conditions. This study will help us gain a better understanding of the global carbon-sulfur cycle and greenhouse gas emission reduction and introduce a new perspective into the enrichment of ANME archaea. IMPORTANCE The sulfate-dependent anaerobic methane oxidation (S-AOM) process catalyzed by an aerobic me thanotrophic (ANME) archaea and sulfate-reducing bacteria (SRB) is a vital link connecting the global carbon and sulfur cycles. We conducted a research into the spatial-temporal pattern of S-AOM process and the distribution of ANME archaea in coastal sediments collected from the intertidal zone. The results implied that S-AOM process was a methane sink that cannot be overlooked in the intertidal ecosystem. We also found that ANME archaea were sensitive to oxygen and preferred anaerobic environmental conditions. This study will help us gain a better understanding of the global carbon-sulfur cycle and greenhouse gas emission reduction and introduce a new perspective into the enrichment of ANME archaea., (Copyright © 2019 American Society for Microbiology.)