Objective: The objective of this study was to appraise the interrelation between overweight/obesity and the safety and efficacy of COVID-19 vaccination by synthesizing the currently available evidence. Methods: A systematic review of published studies on the safety and efficacy of the COVID-19 vaccine in people who were overweight or obese was conducted. Databases including Embase, Medline Epub (Ovid), PsychInfo (Ovid), Web of Science, PubMed, CINAHL, and Google Scholar were searched to identify relevant studies. The databases of the Centers for Disease Control (CDC) and World Health Organization (WHO) were also searched for relevant unpublished and gray literature. Results: Fifteen studies were included in the review. All the included studies used observational study designs; there were ten cohort studies and five cross-sectional studies. The sample size of these studies ranged from 21 to 9,171,524. Thirteen studies reported using BNT162b2 (Pfizer-BioNTech, USA), four reported using ChAdOx-nCov19 (AstraZeneca, U.K), two were reported using CoronaVac (Sinovac, China), and two were reported using mRNA1273 (Moderna, USA). The efficacy and safety of COVID-19 vaccines have been extensively studied in individuals with overweight/obesity. Most studies have shown that the humoral response decreases with increasing BMI. The available evidence does not conclusively indicate that these vaccines are generally safe in this population. Conclusion: While the efficacy of the COVID-19 vaccine may be less than ideal in people who are overweight or obese, it does not mean that obese people should not be vaccinated, as the vaccine can still provide some protection. There is a lack of evidence for conclusions to be drawn about the safety of the vaccine in the population. This study calls on health professionals, policymakers, caregivers, and all other stakeholders to focus on monitoring the possible adverse effects of injections in overweight/obese people.

Pain management is a crucial concern in medicine, particularly in the case of children who may struggle to effectively communicate their pain. Despite the longstanding reliance on various assessment scales by medical professionals, these tools have shown limitations and subjectivity. In this paper, we present a pain assessment scheme based on skin potential signals, aiming to convert subjective pain into objective indicators for pain identification using machine learning methods. We have designed and implemented a portable non-invasive measurement device to measure skin potential signals and conducted experiments involving 623 subjects. From the experimental data, we selected 358 valid records, which were then divided into 218 silent samples and 262 pain samples. A total of 38 features were extracted from each sample, with seven features displaying superior performance in pain identification. Employing three classification algorithms, we found that the random forest algorithm achieved the highest accuracy, reaching 70.63%. While this identification rate shows promise for clinical applications, it is important to note that our results differ from state-of-the-art research, which achieved a recognition rate of 81.5%. This discrepancy arises from the fact that our pain stimuli were induced by clinical operations, making it challenging to precisely control the stimulus intensity when compared to electrical or thermal stimuli. Despite this limitation, our pain assessment scheme demonstrates significant potential in providing objective pain identification in clinical settings. Further research and refinement of the proposed approach may lead to even more accurate and reliable pain management techniques in the future.

Children's clinical pain phenotypes are complex, and there is a lack of objective biological diagnostic markers and cognitive patterns. Detecting physiological signals through wearable devices simplifies disease diagnosis and holds the potential for remote medical applications. This research established a pain recognition model based on AI skin potential (SP) signal analysis. A total of 237 subjects participated in this study, comprising 152 boys and 85 girls, ranging in age from 2 to 16 years old. Initially, we preprocessed SP signals and built datasets for pain and non-pain conditions, including 195 pain and 97 non-pain samples. Then, we applied wavelet transform (WT) to capture the time-frequency characteristics of the signals and extract energy features and created a feature set comprising 30 features and selected 10 most relevant ones using the "SelectKBest" function.We compared six algorithms, optimized their parameters, and evaluated the stability and fitting performance of each algorithm. The random forest (RF) algorithm emerged as the best, demonstrating significant performance in pain recognition with an accuracy of 80.3 % and a sensitivity of 92 %. The SP signals generated by children of different genders, ages, and needling positions during indwelling needle puncture were accurately recognized. We developed a comprehensive SP recognition model, innovatively employing WT for SP signal analysis. This time-frequency analysis method, by preserving low-frequency features, is particularly suitable for SP signals. By combining pain monitoring with SP signals and ML, subjective pain experiences are transformed into quantifiable data, achieving high accuracy and real-time measurement capabilities. These advantages provide valuable technical support for clinical pediatric pain management.

Background: Child Life Services (CLS) provide developmentally appropriate psychosocial support to reduce children’s fear, pain, and distress during medical care and to enhance family-centered pediatric care. Although CLS have been gradually introduced in China, their implementation remains uneven, and multi-institutional evidence on service models, staffing, organizational support, and barriers to development is limited. Objectives: This study aimed to describe the development and current status of CLS among participating medical institutions in China and to provide evidence to inform service management and policy planning. Methods: In December 2025, a cross-sectional survey was conducted using convenience sampling at 36 medical institutions participating in the CLS Collaborative Innovation Network, which is coordinated by the National Clinical Research Center for Children and Adolescents' Health and Diseases. Data were collected using a purpose-designed questionnaire covering organizational support, service content and formats, staffing patterns, perceived effects, barriers, and future development plans. Results: Among all participating institutions (n = 36), 19.4% had established systematic CLS programs, 63.9% had implemented non-systematic CLS, and 16.7% had not implemented CLS. Among institutions that had implemented any CLS (n = 30), services were mainly affiliated with nursing or pediatric departments; 83.3% employed fewer than 2 full-time staff members, and only 36.7% had a regular training system. Emotional support, disease education, and psychological preparation were the most commonly reported service components. More than 90% of respondents from institutions with CLS reported improvements in children’s pain, emotional status, and parental satisfaction. Conclusions: This study provides a multi-institutional overview of CLS development among participating institutions in China. CLS implementation remains variable across these institutions. Strengthening policy support, workforce development, and sustainable funding mechanisms is essential to facilitate the integration of CLS into routine pediatric care.