Endoscopic retrograde appendicitis therapy (ERAT) is a novel and minimally invasive technological alternative for the management of acute or chronic appendicitis. Through endoscopic appendiceal intubation, obstructions such as appendiceal feces and parasites within the appendiceal lumen can be effectively eliminated, leading to patient recovery. Additionally, in cases where the orifices are swollen or complicated appendicitis is present, a stent may be inserted following appendiceal flushing. Due to the utilization of endoscopy for accessing the orifices of the appendix in order to alleviate appendiceal obstruction, patients were able to avoid undergoing appendectomy and experienced a reduced likelihood of recurrence when compared to antibiotic therapy. Additionally, the ERAT provided alternative options for individuals with appendicitis and comorbidities. Recent advancements in techniques, such as the “mother–baby” endoscopic system and the use of microbubble contrast agents, have expanded the range of indications and the eligible patient populations. The objective of this review is to present a comprehensive overview of the development, procedural aspects, therapeutic principles, treatment efficacy, therapeutic applications, and potential complications associated with ERAT.

Objective: Despite triglyceride glucose-body mass index (TyG-BMI) being a validated marker of insulin resistance, its prospective association with diabetes risk remains unclear; we therefore examined the impact of TyG-BMI on incident diabetes. Methods: This was a retrospective cohort study based on 48,288 adults without diabetes at baseline, identified from a publicly available dataset on the DRYAD platform originally derived from 11 cities and 32 locations in China.Using Cox proportional risk regression modeling combined with cubic spline functions and smoothed curve fitting, we assessed the relationship between the baseline TyG-BMI and the risk of developing DM and explored its nonlinear association. We performed subgroup analyses to assess the consistency of the association across different subgroups. Results: Among 48,288 initially diabetes mellitus (DM)-free participants, 1,230 (2.54%) developed diabetes at follow-up.Adjusted for covariates, TyG-BMI levels(per 10-unit) were positively associated with the risk of diabetes onset (HR = 1.25,95% CI: 1.20-1.29, P < 0.001). The risk of incident diabetes increased progressively across quartiles of TyG-BMI levels (Q1 to Q4), with a significantly higher risk in Q4 compared to Q1 (adjusted HR = 11.24,95% CI = 5.05-24.99).Furthermore, a threshold effect of TyG-BMI on DM risk was found, with a threshold of 163.38. The HR to the right of the inflection point was 1.025 (95% CI: 1.020-1.029). When TyG-BMI was <163.38, the association was not statistically significant(HR = 0.928; 95% CI: 0.843–1.022), suggesting that the relationship may be absent or even slightly inverse. Conclusion: In Chinese adults, TyG-BMI exhibits a threshold effect on incident diabetes. Individuals above the threshold have a significantly increased risk of developing diabetes. However, the identified threshold (163.38) requires prospective validation in contemporary cohorts. If validated, it may help identify high-risk individuals for targeted prevention in the future.

Background: Valproate, a first-line anti-seizure medication, has a narrow therapeutic range of 50–100 μg/mL. Many children are prescribed insufficient doses of valproate, resulting in inadequate seizure control or potential toxicity. Currently, no predictive algorithms are available to customize treatment according to the specific needs of children. Our objective was to develop a nomogram that predicts the likelihood of suboptimal valproate concentrations in pediatric patients with epilepsy. Methods: We conducted a single-center retrospective cohort study of pediatric patients with epilepsy aged 2–18 years who were receiving valproate and had steady-state trough concentrations. The primary outcome was the identification of suboptimal valproate concentrations, defined as levels below 50 μg/mL or above 100 μg/mL. The Boruta algorithm was implemented to identify relevant characteristics from demographic, clinical, and pharmacological variables. Significant predictors identified through this process were incorporated into a multivariable logistic regression model, which was subsequently presented as a nomogram. We assessed the model’s performance regarding discrimination using the area under the curve (AUC) and concordance index (C-index), calibration through a calibration plot and the Hosmer-Lemeshow test, and clinical value via decision curve analysis to guarantee robustness. Bootstrap resampling was performed for internal validation. Results: Among the 121 included patients,38 (31.4%) patients presented with suboptimal concentrations. The Boruta algorithm and multivariate regression analysis identified four predictors: daily valproate dose (mg/kg/d), acute liver injury (ALI), acute kidney injury (AKI), and the concurrent use of meropenem. The model showed excellent discrimination with an AUC of 0.911 (95% CI 0.849–0.974) and an optimism-corrected C-index of 0.902, alongside good calibration. Decision curves showed a clinical net benefit over a broad probability threshold range (3%–99%). AKI (odds ratio [OR] 16.5), meropenem use (OR 17.39), and ALI (OR 10.86) were significantly associated with suboptimal concentrations. Conclusion: We developed and internally validated a predictive nomogram that integrates dose, AKI, ALI, and meropenem use to assess the risk of suboptimal concentrations of valproate in pediatric epilepsy. This tool can aid in the early identification of high-risk patients, enabling targeted therapeutic drug monitoring.