Objective To examine the distribution,volume and glucose-uptake activity of brown adipose tissue (BAT) in adults and investigate their correlations with metabolic indicators.Methods 18F-flurodeoxyglucose (FDG) PET/CT was used to analyze the distribution,volume and glucose-uptake activity of BAT.The clinical and metabolic differences between BAT positive group (n =121) and BAT negative group (n=257) were compared.The influences of metabolic indicators (fast blood glucose (FBG),triglyceride (TG),total cholesterol (TC),high density lipoprotein cholesterol (HDL-C),low density lipoprotein cholesterol (LDL-C),uric acid (UA)) on the distribution,volume and activity of BAT were investigated.Logistic regression analysis,two-sample t test,x2 test and multiple linear regression were used to analyze the data.Results The distribution,volume and glucose-uptake activity of BAT were found to be significantly higher in subjects being tested in colder seasons than those who were tested in warmer seasons:2.91％ (87/2 991) vs 1.68％(34/2018),(433±402) vs (329±298) ml,(212±183) vs (169±145) g (x2=7.66,t values:3.36 and 2.98,all P＜0.05).The female proportion was significantly higher in BAT positive group than that in BAT negative group:68.60％ (83/121) vs 31.91％ (82/257) (x2 =16.10,P＜0.01).The average levels of age,body mass index (BMI),FBG,TG,TC,LDL-C and UA in BAT positive group were significantly low-er than those in BAT negative group:(41.30±10.90) vs (48.70±9.60) years,(21.30±2.40) vs (24.50± 3.10) kg/m2,(4.56±0.74) vs (5.34±1.33) mmol/L,(0.94±0.36) vs (2.06±1.64) mmol/L,(4.42± 0.79) vs (4.88±0.87) mmol/L,(1.99±0.58) vs (3.10±0.77) mmol/L,(285.11±70.00) vs (347.70± 101.10) μmol/L (t values:from-6.25 to-2.94,all P＜0.01).Logistic regression analysis revealed that season,gender,age,BMI,FBG,TG and LDL-C levels were all independent influencing factors of BAT distribution in adults (odds ratios:5.36,2.06,0.95,0.79,0.49,0.23,0.02;P＜0.01 or P＜0.05).Among BAT positive adults,gender and FBG levels were found to be strongly affected by the volume and glucose-uptake activity of BAT (β values:0.28,-0.21,both P＜0.05).Conclusions The distribution,volume and glucose-uptake activity of BAT in adults are associated with multiple metabolic indicators including BMI,levels of glucose,lipid and UA.The distribution of BAT is affected by gender,age,season,BMI,blood glucose,and blood lipids.

Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

Objective To understand the awareness level of iodine deficiency (ID) impairments and the attitude on edible iodized salt,and its consumption among doctors and nurses in cities,in order to provide scientific evidence for health education on iodine deficient disorders (IDD) in the future.Methods The questionnaire was self-designed,and spread through the web page of Wenjuanxing,an online server company,from May 6 to June 6,2017.At the end of the survey,a total of 481 valid questionnaires were reclaimed,in which ratio of gender was female 63.8％ (307/481),male 36.2％ (174/481).The data were analyzed by SPSS 22.0,including logistic regression analysis with backward according to the statistical significant level of P ＜ 0.05.Results Among doctors and nurses,90.4％ (435/481) knew ID impairments;72.8％ (350/481) answered intelligent disability as the most serious problem of ID;55.5％ (267/481)misunderstood that the areas in which they lived were not ID areas,although all cities surveyed were ID areas;41.0％ (197/481) of doctors and nurses misunderstood that coastal residents did not need to consume iodized salt.About the evaluation of iodine nutrition status of current population,15.6％ (75/481) of respondents judged as iodine excess.About consuming edible salt,76.3％ (367/481) selected iodized,9.6％ (46/481)non-iodized,and 14.1％ (68/481) both iodized and non-iodized.The results of logistic regression analysis showed the factors that prevents health care workers from choosing iodized salt were:"living in coastal areas","think him or her as iodine adequate","know that iodine deficiency can affect the development of children but still adhere to the consumption of non-iodized salt";the factors that promoted the choice of iodized salt for medical staff were "insist on buying iodized salt,and do not choose non-iodized salt",and 75.8％ (238/314)of them knew that intelligent disability as the most serious problem of ID.Conclusions Most doctors and nurses have high level of knowledge on ID and its control.But lack of information,as well as misunderstanding of "coastal areas iodine adequate" and worry about "excess iodine causes thyroid diseases and cancer",which would hinder the active consumption of iodized salt.Health education for them should be conducted through professional ways,stressing on the threaten of ID environment,sharing the information about national and local progress on control of IDD and iodized salt safety,and clarifying the relationship between iodine salt or iodine and thyroid cancer and nodules.

OBJECTIVES: Primary tumor treatment through surgical resection and adjuvant therapy has been extensively studied, but there is a lack of effective strategies and drugs for the treatment of tumor metastases. Here, we describe a functional product based on a combination of compounds, which can be used as an adjuvant therapy and has well-known mechanisms for inhibiting cancer metastases, improving anti-cancer treatment, and enhancing immunity and antioxidant capacity. Our designed combination, named MVBL, consists of four inexpensive compounds: L-selenium-methylselenocysteine (MSC), D-‍α‍-tocopheryl succinic acid (VES), β‍-carotene (β‍-Ca), and L-lysine (Lys). METHODS: The effects of MVBL on cell viability, cell cycle, cell apoptosis, cell migration, cell invasion, reactive oxygen species (ROS), and paclitaxel (PTX)-combined treatment were studied in vitro. The inhibition of tumor metastasis, antioxidation, and immune enhancement capacity of MVBL were determined in vivo. RESULTS: MVBL exhibited higher toxicity to tumor cells than to normal cells. It did not significantly affect the cell cycle of cancer cells, but increased their apoptosis. Wound healing, adhesion, and transwell assays showed that MVBL significantly inhibited tumor cell migration, adhesion, and invasion. MVBL sensitized MDA-MB-231 breast cancer cells to PTX, indicating that it can be used as an adjuvant to enhance the therapeutic effect of chemotherapy drugs. In mice, experimental data showed that MVBL inhibited tumor metastasis, prolonged their survival time, and enhanced their antioxidant capacity and immune function. CONCLUSIONS This study revealed the roles of MVBL in improving immunity and antioxidation, preventing tumor growth, and inhibiting metastasis in vitro and in vivo. MVBL may be used as an adjuvant drug in cancer therapy for improving the survival and quality of life of cancer patients.
Mice ; Animals ; beta Carotene ; Lysine/pharmacology* ; Antioxidants/pharmacology* ; Quality of Life ; Paclitaxel/pharmacology* ; Apoptosis ; alpha-Tocopherol ; Succinates/pharmacology* ; Cell Line, Tumor ; Cell Proliferation ; Neoplasms