Xiangshan County First People's Hospital

Hexapod robots are characterized by their static stability, broad application range, and ability to operate in complex environments. To leverage these characteristics and handle various challenging conditions, it is essential to maintain motion stability while enhancing flexibility and safety. This can be achieved either by exerting external flexible control over the robot’s motion or by enabling the robot to adaptively adjust to unknown ground conditions. This paper proposes a gait adaptive control method for hexapod robots based on environmental information. By using the motion velocity and angular velocity of the robot, the target position for the robot’s foot trajectory is calculated. The foot trajectory is then planned using polynomial functions to control and adjust the robot’s overall motion state. Additionally, a fuzzy control method is proposed to convert real-time data collected by the inertial measurement unit into gait control coefficients: the body height coefficient ( K h ), stride distance coefficient ( K I ), and leg lift height coefficient ( K f ). This approach relies solely on the inertial measurement unit, resulting in low cost and hardware requirements. The effectiveness of our control method is verified through experiments on sandy terrain. The results demonstrate that the hexapod robot using the proposed method exhibits improved motion stability compared to nonadaptive gait control. The gait adaptive control method based on environmental information offers an innovative solution for the adaptive motion of multilegged robots in complex environments.

BackgroundEpilepsy is a prevalent chronic neurological disorder. A substantial proportion of patients develop drug-resistant epilepsy. Existing antiepileptic drugs are associated with adverse effects and demonstrate limited efficacy in refractory cases. Oxidative stress plays a critical role in the pathogenesis of epilepsy. Antioxidants such as vitamin D, vitamin E, and melatonin, as well as dietary therapies like the ketogenic diet, have garnered attention as potential adjunctive treatments. However, existing studies exhibit significant controversy and heterogeneity.ObjectiveTo evaluate the efficacy and safety of antioxidants and dietary therapies in the treatment of epilepsy, and to provide evidence-based support for clinical decision-making.MethodsSystematic searches were conducted in PubMed, Web of Science, Embase, and the Cochrane Library. Eighteen eligible meta-analyses were selected for umbrella review based on predefined criteria. Pooled relative risks (RR) with 95% confidence intervals (CI) were calculated using random-effects or fixed-effects models. Heterogeneity was assessed using the I2 statistic, and evidence quality was evaluated with the AMSTAR 2 tool.ResultsDietary therapies—particularly the Low Glycemic Index Treatment (LGIT)—significantly increased the likelihood of achieving ≥50% (RR: 1.95; 95% CI: 1.58–2.33) and ≥90% (RR: 3.33; 95% CI: 1.51–5.14) seizure reduction. However, dietary therapies were ineffective in achieving seizure freedom (RR: 0.68; 95% CI: 0.00–1.36). In subgroup analyses of ≥50% seizure reduction, antioxidants did not demonstrate significant efficacy. Both antioxidants and dietary therapies significantly increased the overall incidence of adverse events (RR: 1.56; 95% CI: 1.19–1.92). Specifically, the ketogenic diet elevated the risks of dyslipidemia (RR: 3.56; 95% CI: 2.00–5.11), weight loss (RR: 4.80; 95% CI: 3.43–6.17), constipation (RR: 3.02; 95% CI: 1.55–4.48), and kidney stones (RR: 5.24; 95% CI: 3.73–6.74). Melatonin was ineffective in reducing seizure frequency (RR: 0.30; 95% CI: 0.00–0.63). Considerable heterogeneity was observed across studies.ConclusionDietary therapies—notably LGIT—demonstrate clear efficacy in reducing seizure frequency but do not facilitate seizure freedom. Current evidence does not support the use of antioxidants as effective adjunctive therapy for epilepsy. These interventions, particularly the ketogenic diet, are associated with increased risks of adverse effects, necessitating careful benefit–risk assessment in clinical application.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251166437, identifier: CRD420251166437.

Abstract Background While ethnic variations in gut microbiota are well-documented, their persistence in genetically distinct populations sharing the same geographic environment remains poorly understood. This study investigates the Han and Yi ethnic groups cohabiting rural Southwest China, where contrasting dietary traditions persist under identical environmental conditions. Methods We analyzed fecal samples from 100 healthy adults (50 Yi/50 Han) using 16S rRNA gene sequencing (V3-V4 region). Dietary intake was quantified via three 24-hour recalls validated with plasma biomarkers. Multivariate analyses (DESeq2, PERMANOVA) controlled for age, sex, and BMI covariates. Results Yi individuals exhibited significantly higher Prevotellaceae (log2FC=1.82, padj=0.028) and Succinivibrionaceae (log2FC=2.15, padj=0.013) abundances, corresponding to their buckwheat-rich diet (58.5 ± 12.3% vs Han 3.3±1.8%, p<0.001) Han microbiota showed enriched Bacteroidaceae (log2FC=1.24, padj=0.041), associated with higher animal fat intake (23.6% vs Yi 1.8%, p<0.001) Dietary factors (buckwheat/animal fat intake) explained greater microbiota variation than ethnic genetic ancestry (PERMANOVA R²=0.10 vs 0.07, p < 0.05) Conclusion Cohabiting Han and Yi populations maintain distinct gut microbiota compositions primarily driven by dietary differences. These findings highlight diet as a stronger determinant than genetic ancestry in shaping microbial communities under shared environments. Future studies should validate these observations with metagenomic sequencing.

Background: Population aging intensifies the public health challenge of malnutrition among the elderly, who face both undernutrition and overnutrition. The relationship between BMI and nutritional status in urban Chinese elderly remains underexplored. Objective: This study aimed to examine the association between Body Mass Index (BMI) and Mini-Nutritional Assessment Short-Form (MNA-SF) scores among community-dwelling elderly in Shanghai. Methods: A cross-sectional survey was conducted from January 2019 to January 2023, involving 106 elderly residents aged ≥60 years. Data on demographics, BMI, and MNA-SF scores were collected. Statistical analyses included descriptive statistics, Pearson correlation, and simple linear regression. Results: The mean age was 70.89 ± 6.56 years, with 75.5% females. Mean BMI was 22.85 ± 3.13 kg/m²; 30.2% were overweight and 7.5% underweight. The mean MNA-SF score was 12.23 ± 1.50, with 29.2% at risk of malnutrition. A significant positive correlation was found between BMI and nutritional score (r = 0.504, <i>p</i> < 0.001). Linear regression showed BMI positively predicted nutritional score (β = 0.243, <i>p</i> < 0.001), with the equation: Nutritional Score = 6.679 + 0.243 × BMI (R² = 0.254). Conclusion: BMI is positively associated with nutritional scores among Shanghai community-dwelling elderly. However, reliance solely on BMI may overlook hidden malnutrition risks in overweight individuals. Community health strategies should integrate comprehensive nutritional assessments for targeted interventions.

Abstract Abstract: Gridded population and flood hazard data are crucial for flood exposure assessments. However, current assessments incorporate uncertainties related to data selection, yet the mechanisms through which subjective data selection propagate uncertainties in exposure models remain poorly understood. To address this gap, this study conducted a comparative assessment of flood exposure in China using five population datasets and five flood hazard datasets. Furthermore, it explored the absolute and relative impacts of data uncertainties on 100-year return period flood exposure and discussed the underlying causes. Results exhibit substantial variations in flood exposure when different data combinations are employed. Specifically, there is a significant difference of 333 million individuals within the exposure range, with the highest estimate being 2.82 times the lowest one. Overall, the exposure variation was primarily from differences in flood hazards rather than population patterns, but their relative importance differed spatially depending on factors of slope, altitude, and artificial surface coverage. Despite the differences, all 25 data combinations revealed a disproportional larger share of population in floodplains, which was 2.28–3.49 times the share of floodplains. These findings are significant for understanding the uncertainties of flood exposure and can shield lights on informed policies for risk management.

BackgroundDespite significant advancements in early reperfusion therapy and pharmacological treatment, which have reduced mortality rates after myocardial infarction in recent decades, the in-hospital mortality rate remains high due to factors such as rapid disease progression, comorbid conditions, and potential complications. We aimed to develop and validate a predictive model for in-hospital mortality in myocardial infarction patients.MethodsLASSO regression analysis, univariate analysis, and multivariate logistic analysis were used to construct the nomogram in the training set, followed by model comparison, internal validation, and sensitivity analysis.ResultsThe analysis comprised 4688 patients in total. The population of patients was randomly assigned to the training set (n = 3512) and validation set (n = 1176). According to the results of LASSO regression analysis and other results, our nomogram contained a total of 10 independent variables related to patient death, including age, respiratory rate, blood glucose, lactate, PTT, BUN, cerebrovascular disease, chronic lung disease, mild liver disease, and metastatic solid cancer. Moreover, the web calculator and nomogram performed exceptionally well at predicting in-hospital death in myocardial infarction patients. The AUC for the training and validation sets’ respective prediction models was 0.869 (95% CI: 0.849–0.889) and 0.846 (95% CI: 0.807–0.875) (p<0.01). Compared to the Sequential Organ Failure Assessment (SOFA), the nomogram showed greater discrimination in the training and validation sets, and the calibration plots demonstrated an adequate fit for the nomogram in predicting the risk of in-hospital mortality in both groups. The decision curve analysis (DCA) of the nomogram demonstrated a higher net benefit in the training and validation sets and in terms of clinical usefulness than the SOFA.ConclusionWe developed a useful nomogram model and developed a nomogram-based web calculator to predict in-hospital mortality in myocardial infarction patients, which will support doctors in patient counseling and logical diagnosis and therapy.

This study aims to identify biomarkers for reliably predicting diabetic kidney disease (DKD), systematically characterize serum metabolites and gut microbiota in DKD patients, and investigate the correlation between differential serum metabolites and gut microbiota. From September 2021 to January 2023, 90 subjects were recruited: 30 with DKD, 30 with type 2 diabetes mellitus (T2DM), and 30 normal controls (NCs). Serum metabolites, including 180 different metabolites, were analyzed using untargeted metabolomics UPLC-MS/MS, and gut microbiota were assessed via 16S rRNA sequencing. Differential metabolites were identified through univariate (t-test or Mann–Whitney U-test, P < 0.05) and multivariate analyses (OPLS-DA model, VIP > 1, P < 0.05), followed by selection using the least absolute shrinkage and selection operator (LASSO). The selected overlapping serum metabolites, along with DKD-associated differential gut microbiota, were used to develop a logistic regression prediction model for DKD based on six markers. In the DKD group compared to the DM and NC groups, 39 and 60 differential serum metabolites were identified, respectively (VIP > 1, P < 0.01). Among these, 36 serum metabolites, including alpha-Hydroxyisobutyric acid, were significantly elevated in DKD patients compared to those with DM. Of these, 28 metabolites showed a negative correlation with estimated glomerular filtration rate (eGFR), while 29 showed a positive correlation with urine albumin creatinine ratio (UACR). Patients with DKD were further categorized into subgroups (DKD middle and DKD early) based on eGFR (eGFR < 90 ml/min/1.73m2, eGFR ≥ 90 ml/min/1.73m2), revealing 23 differential metabolites. Dysbiosis of the gut microbiota was evident in DKD patients, with lower relative abundances of g_Prevotella and g_Faecalibacterium compared to the DM and NC groups. Subgroup analysis indicated decreased relative abundances of g_Prevotella and g_Faecalibacterium in the DKD middle group, along with a decrease in g_Klebsiella compared to the DKD early group, which correlated positively with DKD patients’ eGFR. There were 11 common metabolites among the three groups of differential metabolites. Among these, three serum metabolites—imidazolepropionic acid, adipoylcarnitine, and 1-methylhistidine—were identified as predictive serum metabolic markers. Disease prediction models (logistic regression models) were constructed based on these three metabolites combined with three genera of bacteria. These models demonstrated strong discriminatory power for diagnosing patients with DKD compared to patients with DM (area under the receiver operating characteristic curve (AUROC) = 0.939 and precision-recall curve (AUPR) = 0.940). The models also effectively discriminated between patients with DKD and NCs (0.976, 0.973). This study revealed distinctive serum metabolites and gut microbiota in patients with DKD. It demonstrated the potential utility of three specific serum metabolites and three genera of bacteria in diagnosing patients with DKD and assessing their renal dysfunction.