Journal Staff

There are fewer longitudinal studies from China on symptoms as described for the sick building syndrome (SBS). Here, we performed a two-year prospective study and investigated associations between environmental parameters such as room temperature, relative air humidity (RH), carbon dioxide (CO2), nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O-3), particulate matter (PM10), and health outcomes including prevalence, incidence and remission of SBS symptoms in junior high schools in Taiyuan, China. Totally 2134 pupils participated at baseline, and 1325 stayed in the same classrooms during the study period (2010-2012). The prevalence of mucosal symptoms, general symptoms and symptoms improved when away from school (school-related symptoms) was 22.7%, 20.4% and 39.2%, respectively, at baseline, and the prevalence increased during follow-up (P<0.001). At baseline, both indoor and outdoor SO2 were found positively associated with prevalence of school-related symptoms. Indoor O-3 was shown to be positively associated with prevalence of skin symptoms. At follow-up, indoor PM10 was found to be positively associated with new onset of skin, mucosal and general symptoms. CO2 and RH were positively associated with new onset of mucosal, general and school-related symptoms. Outdoor SO2 was positively associated with new onset of skin symptoms, while outdoor NO2 was positively associated with new onset of skin, general and mucosal symptoms. Outdoor PM10 was found to be positively associated with new onset of skin, general and mucosal symptoms as well as school-related symptoms. In conclusion, symptoms as described for SBS were commonly found in school children in Taiyuan City, China, and increased during the two-year follow-up period. Environmental pollution, including PM10, SO2 and NO2, could increase the prevalence and incidence of SBS and decrease the remission rate. Moreover, parental asthma and allergy (heredity) and pollen or pet allergy (atopy) can be risk factors for SBS

Prevalence of internet addiction disorder in Chinese university students: A comprehensive meta-analysis of observational studies

Background and aims: Internet addiction disorder (IAD) is common in university students. A number of studies have examined the prevalence of IAD in Chinese university students, but the results have been inconsistent. This is a meta-analysis of the prevalence of IAD and its associated factors in Chinese university students. Methods: Both English (PubMed, PsycINFO, and Embase) and Chinese (Wan Fang Database and Chinese National Knowledge Infrastructure) databases were systematically and independently searched from their inception until January 16, 2017. Results: Altogether 70 studies covering 122,454 university students were included in the meta-analysis. Using the random-effects model, the pooled overall prevalence of IAD was 11.3% (95% CI: 10.1%–12.5%). When using the 8-item Young Diagnostic Questionnaire, the 10-item modified Young Diagnostic Questionnaire, the 20-item Internet Addiction Test, and the 26-item Chen Internet Addiction Scale, the pooled prevalence of IAD was 8.4% (95% CI: 6.7%–10.4%), 9.3% (95% CI: 7.6%–11.4%), 11.2% (95% CI: 8.8%–14.3%), and 14.0% (95% CI: 10.6%–18.4%), respectively. Subgroup analyses revealed that the pooled prevalence of IAD was significantly associated with the measurement instrument (Q = 9.41, p = .024). Male gender, higher grade, and urban abode were also significantly associated with IAD. The prevalence of IAD was also higher in eastern and central of China than in its northern and western regions (10.7% vs. 8.1%, Q = 4.90, p = .027). Conclusions: IAD is common among Chinese university students. Appropriate strategies for the prevention and treatment of IAD in this population need greater attention

Thoughts on Intervention in HIV/AIDS with Traditional Chinese Medicine

AbstractsHIV/AIDS has become a worldwide pandemic and highly active antiretroviral therapy (HAART) is the only generally recognized effective therapy at present. However, various unresolvable problems appear with the widespread use of HAART. Traditional Chinese Medicine shows good efficacy for intervention in HIV/AIDS and could become an effective treatment option

Determination and pharmacokinetic study of catechin in rat plasma by HPLC

AbstractA high performance liquid chromatographic method was developed and validated for the quantitative determination of catechin in rat plasma and its pharmacokinetic study after intragastric administration of Catechu and Xiongdanjiangre Wan into SD rats. Plasma samples were prepared by protein precipitation using methanol–5% aqueous zinc sulfate (70:30, v/v) as precipitant. Chromatographic separation was achieved on Hypersil C18 column (250mm×4.6mm, 10μm) with acetonitrile–water–triethylamine (6:94:0.3, v/v/v, pH 4.0±0.1, adjusted with phosphoric acid) as mobile phase, followed by a UV detection at 207nm. Good linearity was obtained over the range of 0.143–7.15mg/L of catechin, with correlation coefficient of 0.9992. The method was simple, sensitive, accurate and reproducible and has been successfully applied to the pharmacokinetic study of catechin in rat plasma

A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Medical Images

Self-supervised pre-training has become the priory choice to establish reliable models for automated recognition of massive medical images, which are routinely annotation-free, without semantics, and without guarantee of quality. Note that this paradigm is still at its infancy and limited by closely related open issues: 1) how to learn robust representations in an unsupervised manner from unlabelled medical images of low diversity in samples? and 2) how to obtain the most significant representations demanded by a high-quality segmentation? Aiming at these issues, this study proposes a knowledge-based learning framework towards enhanced recognition of medical images, which works in three phases by synergizing contrastive learning and generative learning models: 1) Sample Space Diversification: Reconstructive proxy tasks have been enabled to embed a priori knowledge with context highlighted to diversify the expanded sample space; 2) Enhanced Representation Learning: Informative noise-contrastive estimation loss regularizes the encoder to enhance representation learning of annotation-free images; 3) Correlated Optimization: Optimization operations in pre-training the encoder and the decoder have been correlated via image restoration from proxy tasks, targeting the need for semantic segmentation. Extensive experiments have been performed on various public medical image datasets (e.g., CheXpert and DRIVE) against the state-of-the-art counterparts (e.g., SimCLR and MoCo), and results demonstrate that: The proposed framework statistically excels in self-supervised benchmarks, achieving 2.08, 1.23, 1.12, 0.76 and 1.38 percentage points improvements over SimCLR in AUC/Dice. The proposed framework achieves label-efficient semi-supervised learning, e.g., reducing the annotation cost by up to 99% in pathological classification.Comment: 10 pages, 9 figures, 3 tables, submitted to IEEE-TM

Introducing a new concept: Psychological capital of older people and its positive effect on mental health

ObjectiveThis study aimed to explore the structure of psychological capital (PsyCap) and its positive effects on mental health among older people.MethodsStudy 1 used grounded theory to analyze the semi-structured interviewing data of 17 Chinese older people (60–96 years old) to develop a primary PsyCap questionnaire for older people. Study 2, respectively, applied exploratory factor analysis (EFA) with 198 Chinese older people (M = 69.2; SD = 6.685) and confirmatory factor analysis (CFA) with 370 Chinese older people (M = 73.84; SD = 9.416) to test a seven-factor structure for PsyCap. Study 3 used 328 participants (M = 79.73; SD = 9.073) to examine the correlation between PsyCap and mental health.ResultsStudy 1 identified that PsyCap of older people contains ‘resilience,’ ‘self-efficacy,’ ‘optimism,’ ‘ease and content,’ ‘gratitude and dedication, ‘wisdom,’ and ‘meaning in life’ and generated a primary seven-factor questionnaire. Study 2 proved the overall and internal structure reliability of PsyCap were good (Cronbach’s alphas ranged 0.809 ~ 0.935), and the seven-factor measurement model fitted the data well (χ2/df = 2.07, RMSEA = 0.05, RMR = 0.05, CFI = 0.95, IFI = 0.95, TLI = 0.94, NFI = 0.91). The PsyCap scale was also proved to an excellent convergent validity, discriminant validity, calibration validity, and measurement invariance across different groups. Study 3 found that PsyCap and its seven factors significantly correlated with depression (r = −0.419 ~ −0.163, p < 0.01) after controlling the demographic variables.ConclusionThese findings provide a reliable and valid assessment for quantitative empirical research of PsyCap among older people and show significant impacts on mental health among older people, which offers new insight into improving mental health from the perspective of positive psychology

Industrial Multi-Energy and Production Management Scheme in Cyber-Physical Environments: A Case Study in a Battery Manufacturing Plant

Among the various electricity consumer sectors, the consumption level of the industrial sector is often considered as the largest portion of electricity consumption, highlighting the urgent need to implement demand response (DR) energy management. However, implementation of DR for the industrial sector requires a more sophisticated and different scheme compared to the residential and commercial sector. This study explores all the elastic segments of plant multi-energy production, conversion, and consumption. We then construct a real-time industrial facilities management problem as an optimal dispatch model to enclose these elastic segments and production constraints in cyber-physical environments. Moreover, a model predictive-based centralised dispatch scheme is proposed to address the uncertainties of real-time price and renewable energy forecasting while considering the sequence of the production process. Numerical results demonstrate that the proposed scheme can enhance energy efficiency and economics of lithium battery manufacturing plant through responding to the real-time price whilst ensuring the completion of production tasks