Longitudinal study of multidimensional factors influencing maternal and offspring health outcomes: a study protocol.

BACKGROUND: Reducing preventable adverse maternal and offspring outcomes is a global priority. The causes of adverse maternal and fetal outcomes are complex with multidimensional influencing factors. In addition, the Covid-19 epidemic has had a significant psychological and physical impact on people. China is now stepping into the post-epidemic era. We are curious about the psychological and physical situation of maternity in China at this stage. Therefore, we plan to initiate a prospective longitudinal study to investigate the multidimensional influences and mechanisms that affect maternal and offspring health. METHOD: We will recruit eligible pregnant women at Renmin Hospital of Hubei Province, China. The expected sample size is 1490. We will assess socio-demographics, Covid-19 related information, social capital, sleep, mental health and medical records, including clinical examination and biochemical tests. Eligible pregnant women will be enrolled in the study with less than 14 weeks of gestation. Participants will receive a total of nine follow-up visits between mid-pregnancy and one year postpartum. The offspring will be followed up at birth, 6 weeks, 3 months, 6 months and one year. In addition, a qualitative study will be conducted to understand the underlying causes that affect maternal and offspring health outcomes. DISCUSSION: This is the first longitudinal study of maternity in Wuhan, Hubei Province which integrates physical, psychological and social capital dimensions. Wuhan is the first city to be affected by Covid-19 in China. As China moves into the post-epidemic era, this study will provide us with a better understanding of the long-term impact of the epidemic on maternal and offspring health outcomes. We will implement a range of rigorous measures to enhance participants' retention rate and ensure the quality of data. The study will provide empirical results for maternal health in the post-epidemic era.

Exploring the Relationship Between Hospital Service Quality, Patient Trust, and Loyalty From a Service Encounter Perspective in Elderly With Chronic Diseases.

Based on the service encounter perspective, this study combines theoretical foundations for such factors as service quality and the characteristics of the hospital service industry to develop a research model scale to investigate whether the quality of hospital services affects patients' perceptions of health service encounters, trust, and loyalty. Nowadays, with the advancement of medical technology, patients pay more attention to the quality of medical services and good service encounters provided by healthcare professionals in order to establish positive patient relationships; hospitals need to improve their own service quality and establish good patient trust relationships so that doctor-patient satisfaction and loyalty can be improved. In a review of related literature, this study found that most past studies focused on issues related of quality of medical services and patient satisfaction, but ignored those related to the relationship between medical service encounters and patient trust and loyalty, as well as the lack of scientific measurement markers for service encounters in the Chinese medical service industry. Therefore, this study uses the Service Encounter Perspective and Service Quality Theory Development Research Scale to collect and analyze data for a typical case of a Chinese tertiary hospital. Finally, this study explores the relationship between the four variables of service quality, service encounter, trust, and loyalty by means of a questionnaire and statistical analysis of the data. Finally, it is concluded that the higher the service quality of the hospital, the higher the customer trust, the higher the service encounter, and in the greater the doctor-patient loyalty.

Noise-assisted Multivariate Empirical Mode Decomposition based Causal Decomposition for Detecting Upper Limb Movement in EEG-EMG Hybrid Brain Computer Interface.

EEG-EMG based hybrid Brain Computer Interface (hBCI) utilizes the brain-muscle physiological system to interpret and identify motor behaviors, and transmit human intelligence to automated machines in AI applications such as neurorehabilitations and brain-like intelligence. The study introduces a hBCI method for motor behaviors, where multiple time series of the brain neuromuscular network are introduced to indicate brain-muscle causal interactions, and features are extracted based on Relative Causal Strengths (RCSs) derived by Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) based Causal Decomposition. The complex process in brain neuromuscular interactions is specifically investigated towards a monitoring task of upper limb movement, whose 63-channel EEGs and 2-channel EMGs are composed of data inputs. The energy and frequency factors counted from RCSs were extracted as Core Features (CFs). Results showed accuracies of 91.4% and 81.4% with CFs for identifying cascaded (No Movement and Movement Execution) and 3-class (No Movement, Right Movement, and Left Movement) using Naive Bayes classifier, respectively. Moreover, those reached 100% and 94.3% when employing CFs combined with eigenvalues processed by Common Spatial Pattern (CSP). This initial work implies a novel causality inference based hBCI solution for the detection of human upper limb movement.

The effect of consecutive ambient air pollution on the hospital admission from chronic obstructive pulmonary disease in the Chengdu region, China.

Hospitalisation risks for chronic obstructive pulmonary disease (COPD) have been attributed to ambient air pollution worldwide. However, a rise in COPD hospitalisations may indicate a considerable increase in fatality rate in public health. The current study focuses on the association between consecutive ambient air pollution (CAAP) and COPD hospitalisation to offer predictable early guidance towards estimates of COPD hospital admissions in the event of consecutive exposure to air pollution. Big data analytics were collected from 3-year time series recordings (from 2015 to 2017) of both air data and COPD hospitalisation data in the Chengdu region in China. Based on the combined effects of CAAP and unit increase in air pollutant concentrations, a quasi-Poisson regression model was established, which revealed the association between CAAP and estimated COPD admissions. The results show the dynamics and outbreaks in the variations in COPD admissions in response to CAAP. Cross-validation and mean squared error (MSE) are applied to validate the goodness of fit. In both short-term and long-term air pollution exposures, Z test outcomes show that the COPD hospitalisation risk is greater for men than for women; similarly, the occurrence of COPD hospital admissions in the group of elderly people (> 65 years old) is significantly larger than that in lower age groups. The time lag between the air quality and COPD hospitalisation is also investigated, and a peak of COPD hospitalisation risk is found to lag 2 days for air quality index (AQI) and PM10, and 1 day for PM2.5. The big data-based predictive paradigm would be a measure for the early detection of a public health event in post-COVID-19. The study findings can also provide guidance for COPD admissions in the event of consecutive exposure to air pollution in the Chengdu region.

Efficacy and safety of dapagliflozin as monotherapy in patients with type 2 diabetes mellitus: A meta-analysis of randomized controlled trials.

BACKGROUND: Dapagliflozin, a novel inhibitor of sodium-glucose cotransporter-2 (SGLT-2), lowers blood glucose level by specifically inhibiting the activity of SGLT-2. Previous studies showed efficacy and safety of dapagliflozin combined with other antihyperglycemic agents in type 2 diabetes (T2DM), however, there are few studies for dapagliflozin as monotherapy. The aim of this study was to assess the efficacy and safety of dapagliflozin as a monotherapy in T2DM and provide theoretical basis for clinical rational use of drugs. METHODS: We did a systematic review and meta-analysis of randomized, placbo-controlled clinical studies in patients with type 2 diabetes. We searched PubMed, Embase, Cochrane Library, CNKI, Wanfang, and VIP database through October 2018, we also manually screened list of references to the previous meta-analysis of dapagliflozin in the treatment of type 2 diabetes. Data search and extraction were completed with a standardized data form and any discrepancies were resolved by consensus. A meta-analysis was conducted by using RevMan 5.3 software. RESULTS: Six randomized controlled trials (RCTs) including 2033 patients were analyzed. Compared with placebo, dapagliflozin monotherapy was associated with a reduction in glycosylated hemoglobin A1c (HbA1c) (weighted mean difference [WMD]: -0.60%; 95% confidence interval [CI]: -0.67%, -0.52%; P < .00001), fasting plasam glucose (FPG) (WMD: -1.30 mmol/L; 95% CI: -1.52, -1.08; P < .00001), and body weight (WMD: -1.50 kg; 95% CI: -1.67, -1.32; P < .00001). Dapagliflozin was associated with an increased risk of urinary tract infections (relative risk [RR]: 1.74; 95% CI: 1.21, 2.49; P = .003) and genital tract infections (RR: 3.52; 95% CI: 2.06, 6.03; P < .00001). CONCLUSIONS: Dapagliflozin monotherapy was well tolerated and effective in reducing the level of HbA1c, FPG, and body weight in patients with T2DM without increasing hypoglycaemia, although it may increase the risk of urinary tract infections and genital tract infections. This meta-analysis provides an evidence for the treatment in patients with T2DM. However, more randomized clinical evidences are still needed to verify the results.