Esophageal cancer is a prevalent malignant tumor of the digestive tract in China, and radical surgery remains the cornerstone of its comprehensive treatment. However, multifactorial challenges such as postoperative gastrointestinal tract reconstruction, traumatic stress, and tumor-related metabolic disturbances render esophageal cancer patients highly susceptible to malnutrition. Perioperative nutritional support therapy plays a crucial role in enhancing surgical safety, improving clinical outcomes, and elevating patients' quality of life by regulating metabolic homeostasis, preserving organ function, and optimizing the immune microenvironment. This article reviews the mechanisms underlying malnutrition in esophageal cancer, methods for nutritional status assessment, and precision intervention pathways based on multi-omics evaluations. The aim is to strengthen clinicians' awareness of standardized perioperative nutritional management for esophageal cancer patients and promote its clinical implementation, thereby facilitating postoperative recovery and improving long-term quality of life.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

OBJECTIVE: Although dietary preferences influence chronic diseases, few studies have linked dietary preferences to mortality risk, particularly in large cohorts. To investigate the relationship between dietary preferences and mortality risk (all-cause, cancer, and cardiovascular disease [CVD]) in a large adult cohort. METHODS: A cohort of 1,160,312 adults (mean age 62.48 ± 9.55) from the Shenzhen Healthcare Big Data Cohort (SHBDC) was analyzed. Hazard ratios ( HRs) for mortality were estimated using the Cox proportional hazards model. RESULTS: The study identified 12,308 all-cause deaths, of which 3,865 (31.4%) were cancer-related and 3,576 (29.1%) were attributed to CVD. Compared with a mixed diet of meat and vegetables, a mainly meat-based diet (hazard ratio [ HR] = 1.13; 95% confidence interval [ CI]: 1.02, 1.27) associated with a higher risk of all-cause mortality, while mainly vegetarian ( HR = 0.87; 95% CI: 0.78, 0.97) was linked to a reduced risk. Furthermore, there was a stronger correlation between mortality risk and dietary preference in the > 65 age range. CONCLUSION A meat-based diet was associated with an increased risk of all-cause mortality, whereas a mainly vegetarian diet was linked to a reduced risk.
Humans ; China/epidemiology* ; Middle Aged ; Male ; Female ; Prospective Studies ; Aged ; Cardiovascular Diseases/mortality* ; Diet/statistics & numerical data* ; Neoplasms/mortality* ; Adult ; Cause of Death ; Food Preferences ; Proportional Hazards Models ; Mortality ; Cohort Studies

Abstract In recent years, the prevalence of overweight and obesity among children and adolescents has risen rapidly, posing a serious threat to their physical and mental health. To provide scientific, systematic, and standardized weight management guidance for overweight and obese children and adolescents, the study focuses on the core concept of healthy lifestyle intervention, integrates multidisciplinary expert opinions and research findings,and proposes a comprehensive multidisciplinary intervention framework covering scientific exercise intervention, precise nutrition and diet, optimized sleep management, and standardized psychological support. It calls for the establishment of a multi agent collaborative management mechanism led by the government, implemented by families, fostered by schools, initiated by individuals, optimized by communities, reinforced by healthcare, and coordinated by multiple stakeholders. Emphasizing a child and adolescent centered approach, the consensus advocates for comprehensive, multi level, and personalized guidance strategies to promote the internalization and maintenance of a healthy lifestyle. It serves as a reference and provides recommendations for the effective prevention and control of overweight and obesity, and enhancing the health level of children and adolescents.

Objective:To analyze the prevalence trends and epidemiological characteristics of cardiometabolic multimorbidity(CMM) in Beijing from 2005 to 2022.Methods:A series of representative cross-sectional surveys were conducted in Beijing between 2005 and 2022 using a stratified multistage cluster random sampling method. A total of 110 496 permanent residents aged 18-79 years participated in face-to-face interviews, physical examinations, and laboratory testing. Complex sampling logistic regression models were employed to identify factors associated with CMM, and Joinpoint regression was used to assess temporal trends in prevalence. Results:The prevalence of CMM was 22.3% in 2005 and 24.3% in 2022, with an average annual percent change of 0.1%(95% CI -1.3%-1.3%, P>0.05). In rural areas, the prevalence increased by 1.3% per year(95% CI 0.2%-2.6%, P<0.05), while among obese individuals, it decreased by 1.0% annually( P<0.05). The most common CMM patterns were hypertension combined with dyslipidemia(13.2%), hypertension combined with diabetes(7.0%), and diabetes combined with dyslipidemia(5.8%). The prevalence of hypertension and dyslipidemia comorbidity showed a long-term decline among females, those aged 60-79 and obese individuals( P<0.05). In contrast, the prevalence of hypertension and diabetes comorbidity increased over time in rural residents and individuals with normal body weight( P<0.05). Furthermore, diabetes and dyslipidemia comorbidity rates increased significantly among males, adults aged 18-59 years, those with a college education or above, rural residents and individuals with normal body weight( P<0.05). Multivariable logistic regression indicated that male, older age, overweight, obese, and lower education level were independently associated with a higher risk of CMM( P<0.05). Conclusion:From 2005 to 2022, the prevalence of CMM remained high among adults in Beijing. While prevalence decreased among obese individuals, it increased significantly in rural areas. Hypertension combined with dyslipidemia was the most common multimorbidity pattern throughout the study period.

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 investigate the prognostic value of aldosterone synthase (CYP11B2) immunohistochemical expression in adrenal specimens for surgical outcomes of primary aldosteronism (PA).Methods:The clinical data of 99 patients who underwent total unilateral adrenalectomy from June 2022 to January 2023 at Beijing Anzhen Hospital was retrospectively analysed. The clinical data of 99 patients who underwent unilateral total adrenalectomy at Beijing Anzhen Hospital from June 2022 to January 2023 were retrospectively analyzed.There were 59 patients in the PA group, age (53.02±10.56) years, body mass index (BMI) (26.28±4.33) kg/m 2, preoperative aldosterone 29.0(15.9, 61.5)ng/dl, plasma renin 1.3(0.6, 2.8)μIU/ml, aldosterone renin ratio (ARR) 19.3(9.1, 59.2) μg/μIU, preoperative potassium (3.60±0.69) mmol/L, and systolic blood pressure (156.54±21.39) mmHg (1 mmHg=0.133 kPa).There were 40 cases in the nonfunctioning adenoma (NFA) group, age (57.23±9.39) years, BMI (27.07±3.46) kg/m 2, preoperative aldosterone 9.0(7.2, 14.1) ng/dl, plasma renin 18.0(5.2, 47.6)μIU/ml, ARR 0.6(0.2, 1.4) μg/μIU, preoperative potassium (4.17±0.41) mmol/L, and systolic blood pressure (157.97±26.87) mmHg. The differences between the two groups were statistically significant for potassium ( P<0.01), aldosterone ( P=0.012), renin ( P<0.01), and ARR ( P<0.01).Surgical outcomes were assessed using the Consensus on the Outcome of Surgery for Primary Aldosteronism (PASO) (complete/partial/no success for clinical and biochemical outcomes). CYP11B2 expression was evaluated by immunohistochemistry using the 2022 World Health Organization's histopathology of primary aldosteronism (HISTALDO) criteria. The correlation between the expression of CYP11B2 and surgical outcomes was assessed. Results:The mean follow-up of 99 patients was (11.73±4.92) months. Of these, 36 out of 59 PA patients had positive CYP11B2 expression in their adrenal specimens, while 23 were negative; all 40 NFA patients were negative for CYP11B2. Among the 36 CYP11B2-positive PA patients, there were 19 cases of aldosterone-producing adenomas, 3 aldosterone-producing nodules, 4 aldosterone-producing micronodules, 8 multiple aldosterone-producing micronodules, and 2 aldosterone-producing diffuse hyperplasia. 36 cases of CYP11B2-positive PA patients had complete clinical success in 15 cases, partial success in 20 cases, and no success in 1 case, and complete biochemical success in 24 cases, partial success in 11 cases, and no success in 1 case; 23 CYP11B2-negative PA patients had complete clinical success in 4 cases, partial success in 15 cases, and no success in 4 cases, and complete biochemical success in 6 cases, partial success in 15 cases, and no success in 2 cases. Adrenal specimens from CYP11B2-positive PA patients had significantly better clinical ( P=0.038) and biochemical ( P=0.008) success rates than CYP11B2-negative PA patients. Patients with aldosterone-producing adenomas had complete clinical success in 8 cases, partial success in 11 cases, and no success in 0 cases, and biochemical success was completely achieved in 16 cases, partially achieved in 2 cases, and not successful in 1 case. They also had significantly higher clinical ( P=0.028) and biochemical ( P<0.01) success rates compared to CYP11B2-negative PA patients. Conclusions:Patients with PA who had immunohistochemical staining for CYP11B2 positivity and high expression in adrenal specimens had a better postoperative clinical and biochemical prognosis. Patients with aldosterone-producing adenomas had the greatest postoperative outcome of all pathological subtypes of PA.