AIM To prepare the borneol-menthol eutectic mixture-loaded nanoemulsion gel of tetramethylpyrazine.METHODS The equilibrium solubilities of tetramethylpyrazine in different oil phases,emulsifiers and co-emulsifiers were determined,after which compatibility experiment was performed,and pseudo-ternary phase diagram was drawn.With Km value,oil phase proportion and water phase consumption as influencing factors,particle size,PDI and saturated drug loading as evaluation indices,the formulation was optimized by central composite design-response surface method.The drug-loaded nanoemulsion was dispersed into carbomer 940 gel matrix to prepare nanoemulsion gel,then the physicochemical properties,in vitro drug release and transdermal absorption properties were investigated.RESULTS The optimal formulation was determined to be 3.31∶6.16∶1.56∶88.97 for eutectic mixture-EL-40-1,2-propylene glycol-water ratio,the particle size,PDI and saturated drug loading were 37.85 nm,23.04 and 5.82 mg/g,respectively.The obtained white,semi-solid nanoemulsion gel demonstrated the average pH value and viscosity of 6.68±0.07 and(289.69±1.06)mPa·s,respectively,whose in vitro drug release accorded with Higuchi equation,and the accumulative permeability per unit area was(2 048.23±55.6)μg/cm2 within 24 h,which were 3.72 and 1.21 times higher than those of hydrogel and aqueous solution,respectively.CONCLUSION The borneol-menthol eutectic mixture-loaded nanoemulsion gel of tetramethylpyrazine meets preparation requirements,thus can achieve the effective transdermal delivery of raw medicine.

Objective:To develop a multi-parameter diagnostic model for pediatric McCune-Albright syndrome(MAS) using machine learning techniques based on laboratory data from MAS patients, with the goal of providing a rapid and reliable auxiliary diagnostic tool for clinical practice.Methods:In this retrospective study, 232 children diagnosed with MAS at the Department of Pediatrics, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from March 2023 to November 2024 were enrolled as the positive group. After removing duplicate or missing data, 119 cases were finally selected for statistical analysis as the positive group. Meanwhile, 113 children with normal physical examinations during the same period were selected as the control group. The clinical manifestations of the classic " triad" in the positive group were documented. Fasting serum samples were obtained from both groups at 8: 00 AM for laboratory testing, including bone metabolism-related and hormone-related indicators, which served as candidate features. Baseline descriptive analysis was conducted on the hormone-related indicators. For the bone metabolism indicators, six machine learning models—support vector machine(SVM), XGBoost, decision tree, random forest, Logistic regression, and K-nearest neighbor(KNN)—were constructed using R software. XGBoost subgroup analysis was performed based on the triad symptoms. The contribution of individual features to model predictions was visualized using SHAP diagrams. Results:SHAP visualization indicated that age, serum phosphorus, osteocalcin, and β-C-terminal cross-linked telopeptide of type Ⅰ collagen had the greatest average impact on model predictions. Among the six models, the SVM model achieved the highest diagnostic performance, with a sensitivity of 0.742 9, a specificity of 0.909 1, and an area under the curve (AUC) of 0.917.Conclusion:This study demonstrates that machine learning models, based on data from the positive patients and normal controls, can effectively distinguish MAS patients from healthy controls. The diagnostic model developed offers clinicians a valuable tool for early detection of MAS in children, contributing to earlier diagnosis, timely intervention, and improved clinical management.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

AIM To prepare the borneol-menthol eutectic mixture-loaded nanoemulsion gel of tetramethylpyrazine.METHODS The equilibrium solubilities of tetramethylpyrazine in different oil phases,emulsifiers and co-emulsifiers were determined,after which compatibility experiment was performed,and pseudo-ternary phase diagram was drawn.With Km value,oil phase proportion and water phase consumption as influencing factors,particle size,PDI and saturated drug loading as evaluation indices,the formulation was optimized by central composite design-response surface method.The drug-loaded nanoemulsion was dispersed into carbomer 940 gel matrix to prepare nanoemulsion gel,then the physicochemical properties,in vitro drug release and transdermal absorption properties were investigated.RESULTS The optimal formulation was determined to be 3.31∶6.16∶1.56∶88.97 for eutectic mixture-EL-40-1,2-propylene glycol-water ratio,the particle size,PDI and saturated drug loading were 37.85 nm,23.04 and 5.82 mg/g,respectively.The obtained white,semi-solid nanoemulsion gel demonstrated the average pH value and viscosity of 6.68±0.07 and(289.69±1.06)mPa·s,respectively,whose in vitro drug release accorded with Higuchi equation,and the accumulative permeability per unit area was(2 048.23±55.6)μg/cm2 within 24 h,which were 3.72 and 1.21 times higher than those of hydrogel and aqueous solution,respectively.CONCLUSION The borneol-menthol eutectic mixture-loaded nanoemulsion gel of tetramethylpyrazine meets preparation requirements,thus can achieve the effective transdermal delivery of raw medicine.

Objective To evaluate the clinical efficacy of a novel sleep-aid decoction in treating elderly insomnia patients with different traditional Chinese medicine (TCM) constitutional types, and its effects on neurotransmitter and inflammatory factor levels. Methods A total of 200 patients with four different TCM constitutions-peaceful, Qi-deficient, Yin-deficient, and Yang-deficient-were recruited. Peripheral blood neurotransmitter and inflammatory factor levels were measured for variations among insomnia patients across different constitutions. These patients were treated using the novel sleep-aid decoction, the effects of which were evaluated based on changes in neurotransmitters and inflammatory factors. Results Compared to the peaceful constitution group, insomnia patients with Qi-deficient, Yin-deficient, and Yang-deficient constitutions exhibited significantly elevated baseline levels of neurotransmitters (5-HT, GABA) and inflammatory factors (IL-6, TNF-α, IL-1β, CRP). Following the treatment, the Qi-deficient and Yin-deficient groups showed a marked increase in 5-HT levels, restored balance of Glu, GABA, and melatonin, and significant reductions in IL-6 and TNF-α levels. The overall effective rate was 83.5%, with optimal efficacy observed in the Qi-deficient (97.72%) and Yin-deficient (95.34%) groups. Conclusion The novel sleep-aid decoction is effective in treating insomnia in elderly patients, with the best results observed in the Qi-deficient and Yin-deficient constitution groups.
Humans ; Sleep Initiation and Maintenance Disorders/blood* ; Aged ; Male ; Female ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Middle Aged ; Tumor Necrosis Factor-alpha/blood* ; Sleep Aids, Pharmaceutical/therapeutic use* ; Anti-Inflammatory Agents/therapeutic use* ; Interleukin-6/blood* ; Interleukin-1beta/blood* ; Neurotransmitter Agents/blood* ; Aged, 80 and over ; C-Reactive Protein/metabolism*

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To develop a multi-parameter diagnostic model for pediatric McCune-Albright syndrome(MAS) using machine learning techniques based on laboratory data from MAS patients, with the goal of providing a rapid and reliable auxiliary diagnostic tool for clinical practice.Methods:In this retrospective study, 232 children diagnosed with MAS at the Department of Pediatrics, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from March 2023 to November 2024 were enrolled as the positive group. After removing duplicate or missing data, 119 cases were finally selected for statistical analysis as the positive group. Meanwhile, 113 children with normal physical examinations during the same period were selected as the control group. The clinical manifestations of the classic " triad" in the positive group were documented. Fasting serum samples were obtained from both groups at 8: 00 AM for laboratory testing, including bone metabolism-related and hormone-related indicators, which served as candidate features. Baseline descriptive analysis was conducted on the hormone-related indicators. For the bone metabolism indicators, six machine learning models—support vector machine(SVM), XGBoost, decision tree, random forest, Logistic regression, and K-nearest neighbor(KNN)—were constructed using R software. XGBoost subgroup analysis was performed based on the triad symptoms. The contribution of individual features to model predictions was visualized using SHAP diagrams. Results:SHAP visualization indicated that age, serum phosphorus, osteocalcin, and β-C-terminal cross-linked telopeptide of type Ⅰ collagen had the greatest average impact on model predictions. Among the six models, the SVM model achieved the highest diagnostic performance, with a sensitivity of 0.742 9, a specificity of 0.909 1, and an area under the curve (AUC) of 0.917.Conclusion:This study demonstrates that machine learning models, based on data from the positive patients and normal controls, can effectively distinguish MAS patients from healthy controls. The diagnostic model developed offers clinicians a valuable tool for early detection of MAS in children, contributing to earlier diagnosis, timely intervention, and improved clinical management.

Objective To analyze the relationship between the visceral adiposity index(VAI)and nocturia in the US adult population.Methods A cross-sectional study was performed.Data from subjects aged≥20 years in the National Health and Nutrition Examination Survey(NHANES)database from 2007 to 2020 were collected,including waist circumference,triglyceride,body mass index(BMI),high-density lipoprotein,age,gender,race,poverty income ratio,education level,marital status,smoking,alcohol consumption,sleep disorders,depression,occupation,hypertension,diabetes,congestive heart failure,cancer,and nocturnal urination frequency.Weighted analysis,multivariate logistic regression,generalized additive model(GAM),and curve fitting were employed to evaluate the association between VAI and nocturia,adjusting for age,gender,race,poverty income ratio,education level,marital status,smoking,alcohol consumption,sleep disorders,depression,occupation,hypertension,diabetes,congestive heart failure,and cancer.Subgroup analyses were conducted based on age,gender,race,hypertension and diabetes to further evaluate the relationship between VAI and the risk of nocturia.Results A total of 29,196 American adults were included.All subjects were divided into 4 groups based on VAI quartiles:Q1 group(0.32≤VAI<1.01),Q2 group(1.01≤VAI<1.70),Q3 group(1.70≤VAI<2.95),and Q4 group(2.95≤VAI<13.59),with nocturia prevalence rates of 28.5%,31.4%,33.3%,and 34.9%,respectively.In subgroup analyses,the risk of nocturia significantly increased with higher VAI in the 20-40 age group,females and other Hispanics(OR=1.04,95%CI 1.01-1.08,P=0.006;OR=1.02,95%CI 1.00-1.04,P=0.035;OR=1.05,95%CI 1.01-1.09,P=0.026).GAM analysis results showed a nonlinear relationship between VAI and nocturia.Conclusion VAI is positively associated with the risk of nocturia,and may be an effective indicator for predicting the risk of nocturia occurrence.

Objective:To investigate the differences in clinical outcomes of septic children with varying serum zinc levels,and to analyze the relationship between reduced serum zinc levels and organ dysfunction as well as 28-day mortality in septic children.Methods:This study conducted a retrospective analysis of clinical data from pediatric patients diagnosed with sepsis or septic shock in the Department of critical care medicine of the children's Hospital of Soochow University between January 2017 and December 2022.Clinical characteristics,organ dysfunction,and prognosis were compared between two groups:children with low serum zinc levels and those with normal zinc levels.Results:The serum zinc level of septic children within 24 hours of admission was 9.60(5.52,13.80)μmol/L,with 50.54%(94/186)of the children exhibiting low serum zinc levels(<10.07 μmol/L).Compared to the normal serum zinc group,the low serum zinc group had a significantly lower Pediatric Critical Illness Score(PCIS)[(78.71±9.35)vs.(85.12±8.51),P=0.005]and higher 28-day mortality(46.80%vs.14.13%,P<0.001).The low serum zinc group also had a higher proportion of invasive mechanical ventilation(64.89%vs.47.82%,P=0.019),renal replacement therapy(15.59%vs.3.26%,P=0.003),and use of vasoactive drugs(56.38%vs.30.43%,P<0.001).The rate of underlying conditions in the low serum zinc group was significantly higher than that in the normal serum zinc group(57.44%vs.36.95%,P=0.005).Additionally,the low serum zinc group had a higher incidence of disseminated intravascular coagulation(DIC),respiratory failure,acute kidney injury,shock,and multiple organ dysfunction syndrome(MODS)compared to the normal serum zinc group(P<0.05).Serum zinc levels had predictive value for 28-day mortality in septic children(AUC=0.813;95%CI:0.725～0.902;P<0.001).A serum zinc level of less than 6.950 μmol/L predicted the death of septic children with a sensitivity of 0.618 and a specificity of 0.902.Conclusion:Sepsis in children is commonly associated with low serum zinc levels,especially in those with underlying conditions such as hematologic and oncologic disorders.Sepsis patients hypozincemia with a higher incidence of DIC,respiratory failure,acute kidney injury,shock,and MODS.A serum zinc level below 6.95 μmol/L serves as a significant predictor of 28-day mortality in children with severe sepsis.