Metabolic syndrome (MetS) has a high prevalence and is prone to many complications. However, current MetS diagnostic methods require blood tests that are not conducive to self-testing, so a user-friendly and accurate method for predicting MetS is needed to facilitate early detection and treatment. In this study, a MetS prediction model based on a simple, small number of Traditional Chinese Medicine (TCM) clinical indicators and biological indicators combined with machine learning algorithms is investigated. Electronic medical record data from 2040 patients who visited outpatient clinics at Guangdong Chinese medicine hospitals from 2020 to 2021 were used to investigate the fusion of Bayesian optimization (BO) and eXtreme gradient boosting (XGBoost) in order to create a BO-XGBoost model for screening nineteen key features in three categories: individual bio-information, TCM indicators, and TCM habits that influence MetS prediction. Subsequently, the predictive diagnostic model for MetS was developed. The experimental results revealed that the model proposed in this paper achieved values of 93.35 %, 90.67 %, 80.40 %, and 0.920 for the F1, sensitivity, FRS, and AUC metrics, respectively. These values outperformed those of the seven other tested machine learning models. Finally, this study developed an intelligent prediction application for MetS based on the proposed model, which can be utilized by ordinary users to perform self-diagnosis through a web-based questionnaire, thereby accomplishing the objective of early detection and intervention for MetS.

This review compares the efficacy of Uterine Artery Embolization (UAE) and Myomectomy (MYO) in managing symptomatic Uterine Fibroids (UFs) in women who do not want hysterectomy. A meta-analysis was performed on all available studies that evaluated the relative benefits and harms of MYO and UEA for the management of patients suffering from UFs. Outcomes evaluated reintervention, UFs scores for quality of life (QOL) and symptom severity, and other complications. To determine mean differences (MDs) or odds ratios (ORs) with 95% confidence intervals (CIs), a random or fixed-effects model was utilized. A meta-analysis of 13 studies (9 observational and 4 randomized controlled trials) was conducted. The results indicated that UAE had a higher reintervention rate (OR 1.84; 95% CI 1.62–2.10; P < 0.01; I² = 39%), hysterectomy rate (OR 4.04; 95% CI 3.45–4.72; P < 0.01; I² = 59%), and symptom-severity score (OR − 4.02; 95% CI 0.82, 7.22; P = 0.01; I² = 0%) compared to MYO at a four-year follow-up. However, UAE was associated with a lower rate of early complications (OR 0.44; 95% CI 0.20–0.95; P = 0.04; I² = 25%), and readmission rate (OR 1.16; 95% CI 1.01–1.33; P = 0.04; I² = 0%) compared to MYO. Furthermore, both procedures had comparable improvement in pregnancy rates and abnormal uterine bleeding. In conclusion, UAE and MYO are effective in treating symptomatic UFs but they have different outcomes. The decision on which procedure to choose should be made based on individual preferences and the physician’s expertise.

Background: There is a growing trend in the use of mobile health (mHealth) technologies in traditional Chinese medicine (TCM) and telemedicine, especially during the coronavirus disease (COVID-19) outbreak. Tongue diagnosis is an important component of TCM, but also plays a role in Western medicine, for example in dermatology. However, the procedure of obtaining tongue images has not been standardized and the reliability of tongue diagnosis by smartphone tongue images has yet to be evaluated. Objective: The first objective of this study was to develop an operating classification scheme for tongue coating diagnosis. The second and main objective of this study was to determine the intra-rater and inter-rater reliability of tongue coating diagnosis using the operating classification scheme. Methods: An operating classification scheme for tongue coating was developed using a stepwise approach and a quasi-Delphi method. First, tongue images (n=2023) were analyzed by 2 groups of assessors to develop the operating classification scheme for tongue coating diagnosis. Based on clinicians’ (n=17) own interpretations as well as their use of the operating classification scheme, the results of tongue diagnosis on a representative tongue image set (n=24) were compared. After gathering consensus for the operating classification scheme, the clinicians were instructed to use the scheme to assess tongue features of their patients under direct visual inspection. At the same time, the clinicians took tongue images of the patients with smartphones and assessed tongue features observed in the smartphone image using the same classification scheme. The intra-rater agreements of these two assessments were calculated to determine which features of tongue coating were better retained by the image. Using the finalized operating classification scheme, clinicians in the study group assessed representative tongue images (n=24) that they had taken, and the intra-rater and inter-rater reliability of their assessments was evaluated. Results: Intra-rater agreement between direct subject inspection and tongue image inspection was good to very good (Cohen κ range 0.69-1.0). Additionally, when comparing the assessment of tongue images on different days, intra-rater reliability was good to very good (κ range 0.7-1.0), except for the color of the tongue body (κ=0.22) and slippery tongue fur (κ=0.1). Inter-rater reliability was moderate for tongue coating (Gwet AC2 range 0.49-0.55), and fair for color and other features of the tongue body (Gwet AC2=0.34). Conclusions: Taken together, our study has shown that tongue images collected via smartphone contain some reliable features, including tongue coating, that can be used in mHealth analysis. Our findings thus support the use of smartphones in telemedicine for detecting changes in tongue coating.

Background and objectives: Metabolic disease has become a global health concern, and insulin resistance (IR) is a crucial underlying mechanism in various metabolic diseases. This study aims to compare the ability of seven anthropometric indicators in predicting IR in the Chinese population, and to find more sensitive and simple anthropometric indicator for early identification of IR.Methods: This prospective cross-sectional study obtained participants’ medical history, anthropometric indicators, and serum samples from three hospitals in China. Various anthropometric indicators were calculated, including body mass index (BMI), Waist-to-hip ratio (WHR), waist-to-height ratio (WtHR), conicity index (CI), A Body Shape Index (ABSI), body roundness index (BRI), abdominal volume index (AVI). The evaluation of IR is performed using the homeostasis model assessment-insulin resistance (HOMA-IR). Logistic regression analysis examined the relationship between indicators and HOMA-IR. The ability of the anthropometric indicators to predict IR was analyzed using the receiver operating characteristic (ROC) curve. Additionally, a stratified analysis was performed to evaluate the ability of the indicators in different age and gender groups.Results: The study included 1,592 adult subjects, with 531 in the non-IR group and 1,061 in the IR group. After adjusting for confounding factors, the anthropometric indicators showed a positive correlation with IR in the general population and across different genders and age groups (OR > 1, p < 0.05), except for ABSI. In the ROC curve analysis, WtHR and BRI had the highest AUC values of 0.711 for detecting IR. The optimal cut-off value for WtHR to diagnose IR was 0.53, while for BRI, it was 4.00. In the gender-stratified and age-stratified analysis, BMI, WtHR, BRI, and AVI all had AUC values >0.700 in females and individuals below 60.Conclusion: WtHR and BRI demonstrated a better ability to predict IR in the overall study population, making them preferred indicators for screening IR, and gender and age are important considerations. In the stratified analysis of different genders or age, BMI, WtHR, BRI, and AVI are also suitable for detecting IR in women or individuals under 60 years old in this study.Clinical trial registration: www.chictr.org.cn, ChiCTR2100054654.

Background We assessed the efficacy and safety of the Xiaoketongbi Formula (XF) vs. pregabalin in patients with painful diabetic neuropathy (PDN). Methods Patients with PDN (n = 68) were included in a single-center, randomized, single-blind, double-dummy, parallel controlled clinical trial. The primary outcome was the change in the Brief Pain Inventory for Diabetic Peripheral Neuropathy (BPI-DPN). Secondary outcomes evaluated included the reduction of BPI-DPN >50%, changes in the numeric rating scale-11 (NRS-11) score for pain, Daily Sleep Interference Diary (DSID), Patient Global Impression of Change (PGIC), nerve conduction velocity (NCV), and adverse events. Results After 10 weeks of treatment, the BPI-DPN score reduced from 42.44 +/- 17.56 to 26.47 +/- 22.22 and from 52.03 +/- 14.30 to 37.85 +/- 17.23 in the XF and pregabalin group (P-s < 0.001), respectively. The difference in the absolute change in BPI-DPN score between both groups was -1.79 (95% CI: -9.09, 5.50; p = 0.625). In the XF and pregabalin groups, 44.1% (15/34) and 20.6% (7/34) of patients reported a BPI-DPN reduction >50% (p = 0.038), respectively. There were no significant differences between groups in NRS-11 and DSID (P-s > 0.05). A significantly greater number of patients in the XF group felt "significantly improved" or "improved" than in the pregabalin group (35.3% (12/34) vs. 11.8% (4/34), p = 0.045). The absolute change in motor nerve conduction velocity of the right median nerve was significantly different between both groups (XF group 0.7 +/- 2.3 vs. pregabalin group -2.2 +/- 4.1, p = 0.004). No serious adverse events were reported in either group. Conclusions XF is equivalent to pregabalin in reducing pain symptoms and improves the quality of life in patients with PDN. In addition, XF has the potential to improve nerve function by increasing NCV.

Introduction: Vitamin D is a crucial lipid-soluble hormone that has been demonstrated to be closely associated with systemic chronic inflammation and various diseases, including metabolic disorders, cardiovascular conditions, autoimmune diseases, cancer, and aging. The pathological underpinnings of these diseases are intricately linked to systemic chronic inflammation. The systemic immune-inflammation index (SII), an emerging biomarker, offers a more comprehensive reflection of the state of systemic inflammation and the immune response by integrating interactions among diverse immune cell types. This study aimed to evaluate the association between serum 25-hydroxyvitamin D (25(OH)D) concentrations and the SII while further exploring the association between vitamin D levels and systemic chronic inflammation in the Chinese population. Additionally, it analyses how lifestyle choices and dietary habits influence vitamin D levels.Methodology: This investigation employed a cross-sectional research design involving 1,177 participants aged 18–90 years who were selected from Zhong Hospital in Guangdong Province, China, through screening procedures. Ultimately, data from 726 participants were analysed following the screening and inclusion criteria. The participants were categorized into three groups on the basis of their serum vitamin D concentrations: the deficient group (SVD < 20 ng/mL), suboptimal group (SVD ≥ 20 but<30 ng/mL), and optimal group (SVD ≥ 30 ng/mL). Physiological indicators, medical history, lifestyle and dietary habits were collected; the SII was calculated via the following formulas: SII=platelet count×neutrophil count/lymphocyte count. Statistical comparisons of the intergroup differences were subsequently conducted, followed by logistic regression and correlation analyses. Subsequently, intergroup differences were assessed, and logistic regression and correlation analysis were performed.Results: The findings indicated that the SII value in the vitamin D-deficient group was significantly higher than that in the optimal group (p < 0.05). Furthermore, individuals in this deficient category presented elevated levels of metabolic markers such as total cholesterol, low-density lipoprotein cholesterol (LDL-C), fasting blood glucose, glycated hemoglobin A1c (HbA1c), and uric acid alongside unhealthy lifestyle practices characterized by frequent consumption of cold foods and sugary beverages coupled with high work-related stressors and prolonged air conditioning use (p < 0.05). Conversely, high-density lipoprotein cholesterol (HDL-C) was positively correlated with vitamin D status (p < 0.05).Conclusion: These results substantiate the association between vitamin D levels and the SII while suggesting that interventions targeting lifestyle modifications may positively impact vitamin D status, thereby ameliorating systemic chronic inflammation. Although this study provides preliminary evidence regarding the interplay between vitamin D deficiency and systemic inflammatory processes, further investigations are warranted to elucidate the underlying biological mechanisms and explore potential strategies for regulating vitamin D concentrations through improved lifestyles and dietary choices.