Aims/Introduction: To explore the association between lactation and type 2 diabetes incidence in women with prior gestational diabetes.Materials and Methods: We searched PubMed, Embase and the Cochrane Library for cohort studies published through 12 June 2017 that evaluated the effect of lactation on the development of type 2 diabetes in women with prior gestational diabetes. A random effects model was used to estimate relative risks (RRs) with 95% confidence intervals (CIs).Results: A total of 13 cohort studies were included in the meta‐analysis. The pooled result suggested that compared with no lactation, lactation was significantly associated with a lower risk of type 2 diabetes (RR 0.66, 95% CI 0.48–0.90, I² = 72.8%, P < 0.001). This relationship was prominent in a study carried out in the USA (RR 0.66, 95% CI 0.43–0.99), regardless of study design (prospective design RR 0.56, 95% CI 0.41–0.76; retrospective design RR 0.63, 95% CI 0.40–0.99), smaller sample size (RR 0.52, 95% CI 0.30–0.92, P = 0.024) and follow‐up duration >1 years (RR 0.75, 95% CI 0.56–1.00), and the study used adjusted data (RR 0.69, 95% CI 0.50–0.94). Finally, by pooling data from three studies, we failed to show that compared with no lactation, long‐term lactation (>1 to 3 months postpartum) was associated with the type 2 diabetes risk (RR 0.69, 95% CI 0.41–1.17).Conclusions: The present meta‐analysis showed that lactation was associated with a lower risk of type 2 diabetes in women with prior gestational diabetes. Furthermore, no significant relationship between long‐term lactation and type 2 diabetes risk was detected. The impact of long‐term lactation and the risk of type 2 diabetes should be verified in further large‐scale studies.

Aim: To investigate the characteristics of workplace violence at primary hospitals in Southeast China and identify associated risk factors.Design: A cross‐sectional survey design was used for this work.Methods: We distributed a workplace violence questionnaire among medical staff at primary hospitals in Southeast Zhejiang Province, China. The data were collected between December 2016 and December 2017. We analysed the categorical data by using the chi‐square test and expressed it as frequencies. The risk factors were analysed by using multiple logistic regression analysis.Results: Among the 2,560 questionnaires, 1,842 (71.9%) medical staff indicated that they had experienced workplace violence. Verbal assault was the most common type, followed by physical and sexual assault. Furthermore, gender, age, marital status, education, technical position and number of hospital beds' numbers were independent risk factors.

Thyroid cancer is the most common endocrine cancer, and its prevalence has been increasing for decades. Approx. 95% of differentiated thyroid carcinomas are treated using ¹³¹iodine (¹³¹I), a radionuclide with a half-life of 8 days, to achieve optimal thyroid residual ablation following thyroidectomy. However, while ¹³¹I is highly enriched in eliminating thyroid tissue, it can also retain and damage other body parts (salivary glands, liver, etc.) without selectivity, and even trigger salivary gland dysfunction, secondary cancer, and other side effects. A significant amount of data suggests that the primary mechanism for these side effects is the excessive production of reactive oxygen species, causing a severe imbalance of oxidant/antioxidant in the cellular components, resulting in secondary DNA damage and abnormal vascular permeability. Antioxidants are substances that are capable of binding free radicals and reducing or preventing the oxidation of the substrate in a significant way. These compounds can help prevent damage caused by free radicals, which can attack lipids, protein amino acids, polyunsaturated fatty acids, and double bonds of DNA bases. Based on this, the rational utilization of the free radical scavenging function of antioxidants to maximize a reduction in ¹³¹I side effects is a promising medical strategy. This review provides an overview of the side effects of ¹³¹I, the mechanisms by which ¹³¹I causes oxidative stress-mediated damage, and the potential of natural and synthetic antioxidants in ameliorating the side effects of ¹³¹I. Finally, the disadvantages of the clinical application of antioxidants and their improving strategies are prospected. Clinicians and nursing staff can use this information to alleviate ¹³¹I side effects in the future, both effectively and reasonably.

The amount of prediction models for disability in older adults is increasing but the prediction performance of different models varies greatly, and the quality of prediction models is still unclear. To systematically review and critically appraise the studies on risk prediction models for disability in older adults. A systematic literature search was conducted on PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and Wanfang Database, published up until June 30, 2023. Data were extracted according to the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the included studies. In addition, all included studies were evaluated for clinical value. A total of 5722 articles were initially retrieved from databases, 16 studies and 17 prediction models were finally included after screening. The sample sizes of studies ranged from 420 to 90,889. Model development methods mainly included logistic regression analysis, Cox proportional hazards regression, and machine learning methods. The C statistic or area under the curve (AUC) of models ranged from 0.650 to 0.853, and nine models had C statistic/AUC higher than 0.75. Age, chronic disease, gender, self-rated health, body mass index (BMI), drinking, smoking and education level were the most common predictors. According to the PROBAST, all included studies were at high risk of bias, and 10 studies were at high concerns for applicability. Only two studies reported following the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. After evaluation, only two models reached the standard of clinical value. Although most of the included prediction models had acceptable discrimination, the overall quality and clinical value of the current studies were poor. In the future, researchers should follow the TRIPOD statement and PROBAST checklist to develop prediction models with larger sample sizes, more reasonable study designs, and more scientific analysis methods, to improve the predictive performance and application value. The review protocol was registered in PROSPERO (registration ID: CRD42023446657).

Background: Diabetic foot (DF) is one of the most common and serious complications of diabetes. Effective self-management by patients can delay disease progression and improve quality of life. Digital intelligent technologies have emerged as advantageous in assisting patients with chronic diseases in self-management. However, the impact of digital intelligent technologies on self-management of patients with DF remains unclear. Objective: This systematic review aimed to determine the effects of digital intelligent interventions on self-management in patients with DF. Methods: A systematic literature search was conducted across PubMed, Web of Science, Embase, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Cochrane Central Register of Controlled Trial, ProQuest, China National Knowledge Internet, WanFang, China Science and Technology Journal Database, and SinoMed up to February 6, 2025, to identify eligible articles. Randomized controlled trials (RCTs) that assessed the effects of digital intelligent interventions on self-management of patients with DF were included. In total, 2 researchers independently conducted literature screening, quality assessment, and data extraction. The Cochrane Risk of Bias 2.0 tool (revised version 2019) for RCTs was used to assess the quality of the studies. A qualitative synthesis was conducted on the extracted data. Results: In total, 1079 articles were retrieved, and 18 RCTs were included. All studies were rated as having a high risk of bias. The digital intelligent interventions in the included studies varied in forms, components, and durations. The intervention forms included WeChat (Tencent Holdings Limited; 7/18, 39%), apps (4/18, 22%), electronic platforms (3/18, 17%), mixed interventions (3/18, 17%), and smartphone thermography (1/18, 6%). The intervention components included self-management education (17/18, 94%), blood glucose and foot condition monitoring (8/18, 44%), self-management supervision and follow-up (6/18, 33%), and other components like foot risk assessment, foot care reminders, visit reminders, and remote consultations. Intervention durations ranged from 5 weeks to 12 months, with the majority (10/18, 56%) lasting 6 months. Among the 18 included studies, 17 studies (17/18, 94%) indicated that, compared with routine care, digital intelligent interventions significantly improved the self-management behaviors of patients with DF, including diabetes control, foot care behaviors, and blood glucose monitoring. Only 1 study (1/18, 6%) showed that the effects of digital intelligent interventions were not significantly different from those of routine care. Conclusions: In this systematic review, evidence suggests that digital intelligent interventions can improve self-management behaviors and capabilities in patients with DF. However, due to the overall low quality of the included studies, current evidence should be interpreted and applied with caution. This field is still in the exploratory stage, with significant heterogeneity among different studies and a lack of consensus on intervention strategies, necessitating further exploration tailored to different populations. Future RCTs with large sample sizes and rigorous design are needed to develop high-quality evidence. Trial Registration: PROSPERO CRD42024524473; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024524473