BACKGROUND: The mechanisms distinguishing metabolically healthy from unhealthy phenotypes within the same BMI categories remain unclear. This study aimed to investigate the associations between regional fat distribution and metabolically unhealthy phenotypes in Chinese adults across different BMI categories. METHODS: This cross-sectional study involving 11833 Chinese adults aged 20 years and older. Covariance analysis, adjusted for age, compared the percentage of regional fat (trunk, leg, or arm fat divided by whole-body fat) between metabolically healthy and unhealthy participants. Trends in regional fat percentage with the number of metabolic abnormalities were assessed by the Jonckheere-Terpstra test. Odds ratios (ORs) and their 95% confidence intervals (CIs) were estimated by logistic regression models. All analyses were performed separately by sex. RESULTS: In non-obese individuals, metabolically unhealthy participants exhibited higher percent trunk fat and lower percent leg fat compared to healthy participants. Additionally, percent trunk fat increased and percent leg fat decreased with the number of metabolic abnormalities. After adjustment for demographic and lifestyle factors, as well as BMI, higher percent trunk fat was associated with increased odds of being metabolically unhealthy [highest vs. lowest quartile: ORs (95%CI) of 1.64 (1.35, 2.00) for men and 2.00 (1.63, 2.46) for women]. Conversely, compared with the lowest quartile, the ORs (95%CI) of metabolically unhealthy phenotype in the highest quartile for percent arm and leg fat were 0.64 (0.53, 0.78) and 0.60 (0.49, 0.74) for men, and 0.72 (0.56, 0.93) and 0.46 (0.36, 0.59) for women, respectively. Significant interactions between BMI and percentage of trunk and leg fat were observed in both sexes, with stronger associations found in individuals with normal weight and overweight. CONCLUSIONS Trunk fat is associated with a higher risk of metabolically unhealthy phenotype, while leg and arm fat are protective factors. Regional fat distribution assessments are crucial for identifying metabolically unhealthy phenotypes, particularly in non-obese individuals.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Adipose Tissue ; Body Fat Distribution ; Body Mass Index ; China/epidemiology* ; Cross-Sectional Studies ; Health Surveys ; Phenotype

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.

OBJECTIVE：To study the anti- inflammatory effect and mechanism of Jingulian capsule on inflammatory model rats. METHODS ：Totally 48 rats were randomly divided into blank control group ，model group ，Jingulian capsule low-dose ， medium-dose and high-dose groups （0.66，1.32，2.64 g/kg），dexamethasone group （positive control ，0.945 mg/kg），with 8 rats in each group. Blank control group and model group were given constant volume of water intragastrically ，and other groups were given relevant medicine intragastrocally ，twice a day ，for consecutive 3 days. Thirty minutes after last administration ，model group and administration groups were given lipopolysaccharide （10 mg/kg）intraperitoneally to induce inflammatory model. Six hours after intraperitoneal injection ，the contents of TNF-α，IL-1β，IL-6，PGE2 in serum were detected by ELISA. The wet to dry weight （W/D）ratio of lung tissue were determined. HE staining was used to observe the pathological changes of lung tissue . RT-PCR was used to detect the mRNA expression of TNF-α，IL-6，PGE2 and IL- 1β in lung tissue. Western blot assay was used to detect the phosphorylation of NF-κB p65 protein and the expression of IκBα protein in lung tissue. RESULTS：Compared with blank ； control group ，the contents of TNF-α，IL-1β，IL-6 and PGE 2 in serum ，the W/D ratio of lung tissue ，the expression of TNF-α，IL-1β，IL-6 and PGE 2 mRNA and the phosphorylation level of NF-κB p65 protein in lung tissue of model group weresignificantly increased ，and the expression of IκBα proteinwas significantly decreased （P＜0.05 or P＜0.01）；a large number of alveolar atrophy and collapse ，alveolar wall thickening ，lung consolidation ，and a large number of inflammatory cell infiltration could be seen in lung tissue. Compared with model group ，the contents of TNF-α，IL-1β（except for low-dose group ）， IL-6 and PGE 2 in serum ，as well as the expression of TNF-α（except for high-dose group ），IL-1β，IL-6（except for low-dose ， high-dose groups ）and PGE 2 mRNA in lung tissue were decreased significantly in Jingulian capsule groups （P＜0.05 or P＜0.01）； the W/D ratio of lung tissue was decreased significantly in Jingulian high-dose group （P＜0.05 or P＜0.01）；the expression of phosphorylation level of NF-κB p65 protein in lung tissue of Jingulian medium-dose group were decreased significantly （P＜0.05 or P＜0.01），while the expression of IκBα protein was increased significantly（P＜0.05）；the alveolar structure was clear ，the alveolar wall was slightly thickened ， and a small amount of inflammatory cell infiltration was seen in lung tissue. CONCLUSIONS：Jingulian capsule has good anti-inflammatory effect on inflammatory model rats ，the mechanism of which may be related to the inhibition of NF-κB signal pathway.

The studies on infectiousness of person infected with SARS-CoV-2 at different stages of illness are an important basis for making effective prevention and control measures such as investigating the infectious source, determining the scope of close contacts and the timing of case isolation. This review discusses the infectiousness of cases infected with SARS-CoV-2 in the incubation period, symptomatic period and convalescent period by reviewing national and international literatures, technical and professional guidelines. Existing researches suggest that the infectious viruses could be isolated at the end of the incubation period as well as since illness onset, and viral load in upper respiratory tract swabs reached the peak on day 4-6 after illness onset and thereafter began to decline, implying the infectiousness was relatively strong at the end of incubation period and within one week after illness onset. Although there were a few cases who tested positive for SARS-CoV-2 after recovery, no evidence was found to indicate these cases can cause the transmission.

The Chinese space station will be built around 2020. As a national space laboratory, it will offer unique opportunities for studying the physiological effects of weightlessness and the efficacy of the countermeasures against such effects. In this paper, we described the development of countermeasure systems in the Chinese space program. To emphasize the need of the Chinese space program to implement its own program for developing countermeasures, we reviewed the literature on the negative physiological effects of weightlessness, the challenges of completing missions, the development of countermeasure devices, the establishment of countermeasure programs, and the efficacy of the countermeasure techniques in American and Russian manned spaceflights. In addition, a brief overview was provided on the Chinese research and development on countermeasures to discuss the current status and goals of the development of countermeasures against physiological problems associated with weightlessness.
China ; Humans ; Program Evaluation ; Space Flight ; Weightlessness ; Weightlessness Simulation

Objective:To investigate the effects of IRF 3 shRNA adenovirus on dynamic changes of early cytokines in LPS-stimulated primary Kupffer cells ( KCs ).Methods: Rat KCs were isolated and purified by means of in situ perfusion.After being infected with adenovirus carrying IRF 3 shRNA for 48 h, KCs were stimulated with LPS.Cell culture supernatants were collected respectively at 0,2,4 and 6 h after LPS stimulation as well as cells at 6 h.Supernatant cytokine secretion levels were detected by enzyme-linked immunosorbent assay ( ELISA).Intracellular gene expressions were tested by RT-PCR and Westeron blot.Results:IRF3 mRNA and protein were induced by LPS ,but suppressed by IRF 3 shRNA adenovirus in LPS-stimulated or non-stimulated KCs.IFN-βsecretions rose in the very early stage ( at 2 h) ,reached the peak at 4 h,and began to reduce but still remained high levels at 6 h after LPS stimulation in KCs.Interference adenovirus pretreatment suppressed IFN-βsecretions ( especially the secretion peak ) at each time point after LPS stimulation.IFN-βsecretions reached normal levels at 6 h after the stimulation in adenovirus-pretreated cells;TNF-αse-cretions rapidly increased in the very early stage and reached the peak at 2 h,then began to decrease gradually ,but remained high levels at 6 h after LPS stimulation in KCs.Interference adenovirus pretreatment inhibited LPS-induced TNF-αsecretions, especially the secretion peak;IL-1βsecretions did not increase untill 4 h, but reached a higher level at 6 h after LPS stimulation.Interference adenovirus suppressed IL-1βsecretions in the early stage of LPS stimulation;IL-10 secretions began to rise in the very early stage ,and gradually increased over time after LPS stimulation in KCs.Pretreatment of adenovirus with IRF 3 shRNA promoted upregulations of IL-10 secretions at each time point of the early of LPS stimulation.Conclusion:IRF3 gene expression can be silenced by IRF 3 shRNA ad-enovirus.IRF3 can promote its downstream signaling molecule IFN-βand pro-inflammatory cytokines including TNF-αand IL-1β,and block anti-inflammation cytokine IL-10 secretions in LPS-stimulated primary KCs.Therefore,IRF3 may play a central role in immune inflammatory injury of liver tissues.

Objective To investigate the impacts of adenovirus on the transfection efficiency and proliferative activity of primary Kupffer cells (KCs). Methods Rat liver KCs were separated and purified by density gradient centrifugation , and was then transfected with adenovirus carrying green fluorescence protein (GFP) gene at different multiplicity of infection (MOI). After 24 h, the transfection efficiency was evaluated by fluorescence microscope and flow cytometry. The proliferative activity of KCs was assessed by colorimetric method. Results The positive percentages of GFP staining cells were statistically different among different doses of adenovirus (MOI 0, 100, 300, 500, 700 and 900) under fluorescence microscopy or by flow cytometry (P <0.05 for all comparisons). The cell proliferative activity had significant differences among MOI 300, 500, 700 and 900(P < 0.05 for all comparisons), but had no differences among MOI 0, 100 and 300 (P > 0.05 for all comparisons) by CCK8 assay. Conclusions KCs can effectively be transfected by GFP adenovirus; and with an increase in virus MOI, the transfection efficiency rises gradually. A higher dose of adenovirus may have a negative effect on cell proliferative.

Objective To assess the altered regional homogeneity (ReHo) of local intrinsic cerebral activity within sensorimotor network(SMN) in patients with cervical spondylotic myelopathy (CSM) before or after spinal cord decompression using functional MRI (fMRI). Methods Twenty-one CSM patients who would decompress spinal canal, and 21 healthy volunteers (age, gender and level of education matched) were enrolled from June 2013 to April 2014. All the patients underwent rs-fMRI examination before and 3 months after spinal cord decompression. ReHo measurement was performed statistically within a SMN mask. A second-level random-effect 2-tailed Student's t test was applied to compare the ReHo results between pre-and post-operation CSM patients and healthy volunteers. A second-level paired 2-tailed Student's t test was applied to compare the ReHo results between pre-and post-operation CSM patients. Pearson correlation analysis was performed to assess the correlations between the altered ReHo and clinical evaluation. Results Compared with healthy volunteers, pre-operation patients showed significantly lower ReHo in the left postcentral gyrus/precentral gyrus, together with enhanced ReHo in the right superior parietal lobule (GRF correction, P<0.05). Post-operation CSM patients showed significantly lower ReHo in the right superior parietal lobule comparing with healthy volunteers, as well as enhanced ReHo in the left postcentral gyrus/precentral gyrus comparing with pre-operation (GRF correction, P<0.05). Abnormal ReHo areas in CSM patients demonstrated no significant correlation with clinical measurements (P>0.05) between pre-operation and post-operation. Conclusions Myelopathy in cervical cord may affect intrinsic cerebral activity, as patients with CSM show disrupted regional homogeneity within sensorimotor network. The change of ReHo following decompression suggests that central plasticity may influence functional recovery.