Post-appendectomy intra-abdominal abscess remains a common complication following pediatric appendicitis. Although drainage has traditionally been considered standard treatment, increasing evidence suggests selected patients may be successfully managed with antibiotics alone. A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420251075191). PubMed, Embase, and the Cochrane Central Register of Controlled Trials were searched through June 20, 2025. Eligible studies included pediatric patients (0–18 years) with post-appendectomy abscess managed with antibiotic vs. invasive drainage. The primary outcome was failure of first-line treatment requiring escalation. Secondary outcomes included recurrence and length of hospital stay. Ten observational studies involving 363 pediatric patients were included (152 drainage, 211 conservative). Treatment success was 80.9
Background: Cardiometabolic risk is increasingly observed in young adults, particularly during university years, and is not limited to individuals with elevated body mass index. Emerging evidence highlights the presence of normal weight obesity-characterized by excess adiposity and unfavorable body composition despite normal BMI-which may confer early metabolic vulnerability. Dietary diversity is often promoted as a marker of dietary adequacy; however, its relationship with adiposity, body composition, and muscular health remains inconsistent, particularly in Latin American populations. Moreover, few studies have directly contrasted dietary diversity indicators with empirically derived dietary patterns in relation to cardiometabolic and functional outcomes. Objective: To examine the associations between dietary diversity, dietary patterns, and indicators of adiposity, muscular strength, and relative muscle mass in Ecuadorian university students. Methods: A cross-sectional study was conducted among 349 undergraduate students aged 18-26 years enrolled in health sciences programs in Ecuador. Dietary intake was assessed using a validated food frequency questionnaire. Dietary diversity was quantified using the Food and Agriculture Organization's Individual Dietary Diversity Score, while dietary patterns were identified through principal component analysis followed by k-means clustering. Outcomes included excess body weight, relative muscle mass assessed by bioelectrical impedance analysis, and handgrip strength. Multivariable Poisson and linear regression models were fitted, adjusting for age, sex, academic program, physical activity level, and pre-existing conditions. Results: Despite their young age and low prevalence of diagnosed disease, approximately one-third of the participants exhibited markers of early cardiometabolic risk, including excess body weight and central adiposity. Higher dietary diversity was independently associated with a higher prevalence of excess body weight (adjusted prevalence ratio per one-unit increase in IDDS: 1.17; 95% CI: 1.06-1.30) and with greater relative muscle mass (adjusted β = 0.13; 95% CI: 0.05-0.22), whereas no association was observed with handgrip strength. In contrast, dietary patterns derived from multivariate analysis showed no significant associations with adiposity, muscular strength, or relative muscle mass after adjustment. Conclusions: In this young adult population, dietary diversity captured aspects of overall dietary exposure associated with both increased adiposity and greater lean mass, but not with muscular strength. Empirically derived dietary patterns demonstrated limited discriminatory capacity, likely reflecting dietary homogeneity within the cohort. These findings indicate that dietary diversity alone does not necessarily reflect diet quality and underscore the importance of interpreting diversity metrics alongside indicators of food quality, energy density, and body composition when evaluating early cardiometabolic risk in contemporary food environments.
The success of service robots in human-centric environments relies on their ability to navigate flexibly and robustly. However, the inherent stochasticity and dynamism of human behavior pose significant challenges for robots, particularly in crowded environments. To address this issue, we introduce a deep reinforcement learning approach (ARSA), equipped with memory-assisted capabilities . This framework incorporates bidirectional gated recurrent unit layers as a long-term memory component to capture and retain the ever-shifting environment. The proposed approach emphasizes human-robot interactions by encoding their significance in decision-making. Furthermore, the learned policy incorporates the concept of dynamic warning zones to prioritize human behaviors and proactive robot decisions. The proposed model enables the robot to make better-informed decisions by identifying and focusing on the most relevant human states. The simulation results demonstrate that the ARSA policy improves the success rate by 4%, maintains the collision rate below 4%, and reduces navigation time by approximately 14%, highlighting its superior efficiency and safety compared to state-of-the-art methods. , The performance was successfully validated through real-world experiments, demonstrating smooth, collision-free ARSA behavior in the presence of dynamic human movements.
Background: Survival rates of pediatric and childhood cancer are about 80% in 5 years, which suggests that side effects may appear a while after oncological treatment and can be associated with other health impairments. Early rehabilitation interventions, such as exercise-based physiotherapy, help reduce side effects and maintain an adequate physical condition, thereby improving daily capacity and health-related quality-of-life (HRQoL). The purpose of this systematic review with meta-analysis is to demonstrate which are the most common strategies performed in child and adolescent survivors of childhood cancer to improve their HRQoL and their physical condition. Methods: Two reviewers searched four databases to identify studies that evaluated the effects of physiotherapy and exercise interventions in child and adolescent survivors of childhood cancer. Results: Nine studies performing different exercise interventions were included. The most commonly evaluated outcomes were HRQoL, fatigue, and depression. Seven studies were included in the meta-analysis, with no significant results achieved. Conclusions: Aerobic interventions are the most common strategies performed in child and adolescent survivors of childhood cancer to improve their HRQoL. Depression and fatigue seem to improve with these interventions, but more research is needed to confirm these results. Our meta-analysis revealed inconsistent results supporting the use of exercise interventions in this population.
Introducción: El adenocarcinoma ductal de páncreas (PDAC) presenta una alta letalidad, siendo la resección quirúrgica con márgenes libres (R0) la principal opción curativa. La estadificación mediante imagen convencional presenta limitaciones, especialmente tras la terapia neoadyuvante. La radiología avanzada y la inteligencia artificial (IA) emergen como herramientas para optimizar la toma de decisiones. Objetivo: Analizar críticamente el impacto de las técnicas de imagen avanzada (TC de energía dual, RM multiparamétrica) y los modelos de IA (radiómica, deep learning) en la evaluación de resecabilidad y la planificación quirúrgica del PDAC. Método: Revisión narrativa estructurada bajo principios PRISMA 2020. Se exploraron las bases de datos PubMed, Embase y Cochrane utilizando términos MeSH, priorizando literatura de alto impacto de los últimos 5-10 años. Se evaluó el riesgo de sesgo y la calidad metodológica (QUADAS-2, RoB 2) y el reporte de IA (TRIPOD/CLAIM). Resultados: La TC de energía dual y la RM multiparamétrica mejoran la delimitación del tejido tumoral frente a los cambios peritumorales. Los modelos de deep learningalcanzan una sensibilidad del 89-93 % (IC 95 %: 85-96 %) en la detección del PDAC, aunque la validación externa sigue siendo escasa (presente en < 30 % de los estudios). Las firmas radiómicas han demostrado utilidad en la predicción de invasión vascular oculta (AUC 0,81-0,88) y en la respuesta a la neoadyuvancia, si bien existe un alto riesgo de sobreajuste. Conclusiones: La integración de la imagen avanzada y la IA en los comités multidisciplinares promete personalizar la selección de candidatos a cirugía. No obstante, su implementación clínica requiere de validación prospectiva multicéntrica y del desarrollo de algoritmos explicables.
