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The aim of the present study was to investigate the possible correlation between the percentage of daily energy intake from fat (PEF) with insulin resistance (IR) in women with polycystic ovary syndrome (PCOS). In this cross-sectional study, a total of 186 females with PCOS were screened. Daily dietary intake data were collected by a trained nutritionist using the 24-h dietary recall method over three consecutive days. A total of 111 subjects who had complete data were divided into two groups based on the percentage of daily energy intake from fat (PEF): the normal PEF (NPEF) group (PEF < 30%) and the high PEF (HPEF) group (PEF ≥ 30%). Pearson's correlation analysis and stepwise multivariate linear regression analysis were used to analyze the correlation of PEF with homeostasis model assessment of insulin resistance (HOMA-IR). The total prevalence rate of overweight/obesity was 80.2%. There were significant differences in waist circumference (WC), body mass index (BMI), fasting insulin, and HOMA-IR (P < 0.001) among the normal weight, the overweight, and the obese groups, but no significant differences were observed in total energy and dietary macronutrients intake in the three groups. The daily intake of fat and protein, fasting insulin, and HOMA-IR in the NPEF group were significantly higher than those in the HPEF group. Pearson's correlation analysis showed PEF in PCOS women was negatively correlated with BMI (r= -0.189, p=0.047) and HOMA-IR (log-transformed) (r= -0.217, p=0.022). Further, stepwise multivariate linear regression analysis showed PEF was negatively correlated with HOMA-IR (p<0.05). The percentage of daily energy intake from fat is negatively correlated with IR in women with PCOS.

To fully extract the frequency information and spatial topological information of multi-channel EEG signals,this paper introduces an EEG-based emotion recognition model utilizing a Capsule Network with a Convolutional Block Attention Module(CBAM-CapsNet). Firstly,EEG signals from different frequency bands are acquired to extract their differential entropy features. Secondly,these features are mapped into a three-dimensional compact feature matrix according to spatial lead distribution. Finally,the three-dimensional feature matrix is processed through the proposed CBAM-CapsNet for training and prediction. Experimental results indicate that the high frequency band has a greater impact on emotion recognition than the low frequency bands,and the use of four-frequency band three-dimensional matrix can significantly enhance the accuracy of emotion recognition. The proposed CBAM-CapsNet achieves binary classification accuracies of 95. 42% and 95. 52% on the Arousal and Valence dimensions of the DEAP dataset,respecti

Background Insulin resistance (IR) and adipose tissue amplify the metabolic and reproductive outcomes in women with polycystic ovary syndrome (PCOS). It has been widely discussed that body composition influences metabolic health. Still, limited studies were focused on the role of the fat-free mass index (FFMI) in assessing IR in PCOS women. Aims We aimed to explore the associations between FFMI/fat mass index (FMI) and IR in women with PCOS and assess the role of FFMI in predicting IR in women with PCOS. Methods In the current cross-sectional study, women with PCOS aged between 18 and 40 years were enrolled from October 2018 to July 2022. Baseline demographic information was obtained using standardized self-administered questionnaires. Anthropometric, biochemical, and hormonal information was measured and recorded by investigators. Pearson’s correlation and multivariable logistical regression were used to analyze the associations of FFMI/FMI and IR. In addition, receiver operating characteristic (ROC) curves were implied to measure the predictive role of FFMI/FMI for IR in women with PCOS. Results A total of 371 women with PCOS, reproductive age (27.58 ± 4.89) were enrolled. PCOS women with IR have higher levels of triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), homeostatic model assessment of insulin resistance (HOMA-IR), FMI, and FFMI than that without IR. FMI ( r  = 0.492, p  < 0.001) and FFMI ( r  = 0.527, p  < 0.001) were positively associated with IR. After adjusting for potential confounders, FMI and FFMI were significantly associated with IR in PCOS women, and the OR was 1.385 (95%CI: 1.212–1.583) and 2.306 (95%CI: 1.675–3.174), respectively. Additionally, the FFMI (0.847, 95%CI: 0.784–0.888) has a larger area of ROC (AUC) than the FMI (0.836, 95%CI: 0.799–0.896), while there is no difference in predicting IR (95%CI: −0.18–0.41, p  = 0.456). Conclusion These results indicated that FFMI and FMI could significantly increase the risk of IR, both of which could be feasible predictors of IR in PCOS women.

Phrenic nerve stimulation (PNS) may preserve diaphragm activation and mitigate multiorgan injury during mechanical ventilation (MV); however, a minimal invasive rat model integrating PNS with MV is lacking. We established an omohyoid muscle-based PNS rat model combined with MV. Bilateral nerves were exposed within 20 ± 2 min by transection at the intermediate tendon of omohyoid muscle, minimizing trauma and bleeding. Threshold stimulation (0.6 ± 0.2 mA) correlated with body weight. Ventilator-synchronized stimulation increased compound muscle action potentials by ~30%, whereas histology confirmed intact nerve. Physiological parameters remained stable throughout ventilation. This model provides a safe and scalable platform for mechanistic and preclinical studies on PNS-mediated protection against MV-induced organ injury.

Diabetes has become a major public health challenge worldwide, especially in low- and middle-income countries (LMICs). Uncontrolled hyperglycemia, hypertension, and dyslipidemia major risk factors for all-cause mortality and cardiovascular disease (CVD) are common in patients with diabetes in China. We propose to compare the effectiveness of team-based care plus a clinical decision support system (CDSS) with team-based care alone on glycemic, blood pressure (BP), and lipid control, and clinical CVD reduction among patients with type-2 diabetes and at high risk for CVD. The Diabetes Complication Control in Community Clinics (D4C) study is a cluster-randomized trial conducted among 38 community health centers in Xiamen City, China. Nineteen clinics have been randomly assigned to team-based care plus CDSS and 19 to team-based care alone. Team-based care includes primary care providers, health coaches, and diabetes specialists working collaboratively with patients to achieve shared treatment goals for CVD risk factor reduction. The CDSS integrates guideline-based treatment algorithms for glycemic, BP, and lipid control, along with a patient's medical history and insurance policy, to recommend treatment and follow-up plans. In phase 1, the co-primary outcomes are mean reduction in glycated hemoglobin (HbA1c), systolic BP (SBP), and low-density lipoprotein (LDL)-cholesterol over 18 months, and the proportion of patients with controlled HbA1c, SBP, and LDL-cholesterol at 18 months’ between the 2 comparison groups. In phase 2, the primary outcome is the difference in major CVD incidence (non–fatal stroke, non–fatal myocardial infarction, hospitalized heart failure, and CVD mortality) between the 2 comparison groups. Mean reduction in HbA1c, SBP, and LDL-cholesterol levels will be simultaneously modeled for a single overall treatment effect. The D4C trial will generate evidence on whether a CDSS will further reduce the CVD burden among patients with diabetes beyond team-based care at community clinics. If proven effective, this implementation strategy could be scaled up within primary care settings in China and other LMICs to reduce CVD incidence and mortality among patients with diabetes.

Objective: The aim of the present study was to investigate the possible correlation between the percentage of daily energy intake from fat (PEF) with insulin resistance (IR) in women with polycystic ovary syndrome (PCOS). Methods: In this cross-sectional study, a total of 186 females with PCOS were screened. Daily dietary intake data were collected by a trained nutritionist using the 24-h dietary recall method over three consecutive days. A total of 111 subjects who had complete data were divided into two groups based on the percentage of daily energy intake from fat (PEF): the normal PEF (NPEF) group (PEF < 30%) and the high PEF (HPEF) group (PEF [greater than or equal to] 30%). Pearson's correlation analysis and stepwise multivariate linear regression analysis were used to analyze the correlation of PEF with homeostasis model assessment of insulin resistance (HOMA-IR). Results: The total prevalence rate of overweight/obesity was 80.2%. There were significant differences in waist circumference (WC), body mass index (BMI), fasting insulin, and HOMA-IR (P < 0.001) among the normal weight, the overweight, and the obese groups, but no significant differences were observed in total energy and dietary macronutrients intake in the three groups. The daily intake of fat and protein, fasting insulin, and HOMA-IR in the NPEF group were significantly higher than those in the HPEF group. Pearson's correlation analysis showed PEF in PCOS women was negatively correlated with BMI (r= -0.189, p=0.047) and HOMA-IR (log-transformed) (r= -0.217, p=0.022). Further, stepwise multivariate linear regression analysis showed PEF was negatively correlated with HOMA-IR (p<0.05). Conclusion: The percentage of daily energy intake from fat is negatively correlated with IR in women with PCOS. Keywords: the percentage of daily energy intake from fat, polycystic ovary syndrome, insulin resistance, daily dietary intake, obesity

The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18–30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts. In addition to micro-environmental measurements, this study also collects hundreds of quantitative macro-environmental measurements from remote sensing and national survey databases based on the locations of each participant from birth to present, which will facilitate discoveries of new environmental factors associated with neuroimaging phenotypes. With lifespan environmental measurements, this study can also provide insights on the macro-environmental exposures that affect the human brain as well as their timing and mechanisms of action.