Clinical practice guidelines represent the best recommendations for patient care. They are developed through systematically reviewing currently available clinical evidence and weighing the relative benefits and risks of various interventions. However, clinical practice guidelines have to go through a long translation cycle from development and revision to clinical promotion and application, facing problems such as scattered distribution, high duplication rate, and low actual utilization. At present, the clinical practice guideline information platform can directly or indirectly solve the problems related to the lengthy revision cycles, decentralized dissemination and limited application of clinical practice guidelines. Therefore, this paper systematically examines different types of clinical practice guideline information platforms and investigates their corresponding challenges and emerging trends in platform design, data integration, and practical implementation, with the aim of clarifying the current status of this field and providing valuable reference for future research on clinical practice guideline information platforms.

Objective: To explore the prospective association between multidimensional sleep indicators and the risk of newlyonset dental caries, providing a reference for childrens oral healthrelated sleep intervention. Methods: In October 2021, 1 417 students in grades 1 to 4 (aged 6 to 11) from two elementary schools in Bengbu, Anhui Province, were selected by cluster sampling method. Surveys and followup visits were conducted at baseline (T1), November 2022 (T2), May 2023 (T3), and November 2023 (T4), respectively, including parental questionnaires, oral health and physical examination. Bedtime, sleep duration, sleep midpoint, social jet lag, weekend catchup sleep, and sleep habits were collected and calculated. A multifactorial Cox proportional risk regression model was used to analyze the association between multidimensional sleep indicators and newlyonset caries in schoolaged children after 2 years. Results: The prevalence of dental caries in children was 65.1% at baseline, and the prevalence was 59.0% at the end of the 2year followup. Cox proportional risk regression model showed that for every 1point increase in the childrens bedtime resistance, nocturnal awakenings, parasomnias, and daytime sleepiness scores, the risk of newlyonset caries increased by 12% (HR=1.12, 95%CI=1.08-1.15), 22% (HR=1.22, 95%CI=1.15-1.29), 12% (HR=1.12, 95%CI=1.08-1.17), and 15% (HR=1.15, 95%CI=1.12-1.19), respectively; the risk of newlyonset caries increased by 23% for each 1 h increase in the length of weekend catchup sleep (HR=1.23, 95%CI=1.14 -1.33); compared with children who went to bed before 21:00 on school days, those who went to bed later than 22:00 had a 57% higher risk of newlyonset caries (HR=1.57, 95%CI=1.22-2.03). Compared to children who slept adequately (≥9 h/d), those with insufficient sleep had a 67% higher risk of new caries (HR=1.67, 95%CI=1.43-1.95) (P<0.01). Conclusions These findings suggest a significant association between sleep patterns/sleep disorders and the development of childhood dental caries. Incorporating sleep behavior optimization and sleep quality improvement into comprehensive caries prevention and oral health management protocols may represent a promising intervention strategy to enhance childrens oral health outcomes.

Objective: To explore the relationship of physical exercise behavior with mobile phone addiction and mental health of college students, so as to provide evidence for interventions to improve mobile phone addiction and mental health of college students. Methods: From October 8 to December 20 in 2024, 896 college students from 4 colleges in Beijing were selected using a combination of convenience sampling and stratified random cluster sampling method. Physical Activity Rating Scale (PARS-3), Mobile Phone Addiction Tendency Scale (MPATS), Adolescence Mental Health Diathesis Questionnaire (AMHDQ) and Symptom Checklist 90 (SCL-90) were used. The correlation of mobile phone addiction and mental health on physical exercise behavior of college students were analyzed by multivariable Logistic regression and linear regression. Results: Among the surveyed college students, 504 (56.25%) students had low exercise, 262 (29.24%) had moderate exercise, 130 (14.51%) had high exercise, and 392 (43.75%) had sufficient exercise. The total score of PARS-3 was 18.00 ( 15.00 , 33.00) points. Logistic regression analysis showed that the total score of MPATS ( OR=1.022, 95%CI =1.008-1.036), the total score of SCL-90 ( OR=1.010, 95%CI = 1.005 -1.015), the total AMHDQ score ( OR=0.995, 95%CI =0.992-0.998) were significantly associated with insufficient exercise among college students ( P <0.05). Linear regression analysis showed that the scores of MPATS, AMHDQS and SCL-90 were significantly correlated with physical exercise behavior ( B=-0.20, 0.04, -0.07, P <0.05). Conclusion The physical exercise behavior of college students is related to mobile phone addiction and mental health.

Anticancer treatment for acute lymphocytic leukemia is based on drugs such as methotrexate, 6-mercaptopurine, vincristine, and asparaginase. Asparaginase-related pancreatitis is known to have an incidence of up to 18%, and is a major cause of discontinuation of anticancer treatment for leukemia due to acute onset and chronic complications. There were various cases of treatment of peripancreatic fluid retention caused by anticancer drugs in leukemia patients. Use of lumen-apposing metal stents (LAMS) for walled-off necrosis (WON) drainage has recently increased. The electrocautery-enhanced delivery system allowed simpler and faster stent placement, streamlining the overall procedure and potentially reducing procedure time. Therefore, favorable outcomes have been reported with the use of LAMS for endoscopic drainage of various conditions. In this paper, we discuss a case in which hot-system LAMS was performed to treat L-asparaginase-induced acute pancreatitis and pancreatic pseudocyst in an adult patient with acute lymphoblastic leukemia.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Bioassay-guided fractionation of the methanolic extract of Artemisia iwayomogi Kitamura led to the isolation of 12 known compounds (1‒12). Notably, this study marks the first report of 3-epimeridinol (1) being isolated and structurally characterized from a natural source. Additionally, compounds 3, 4, and 7 were isolated from the Asteraceae family for the first time. The structural elucidation of the isolated compound was achieved through analysis of 1D, 2D NMR, and MS data. Upon evaluation of their inhibitory effects against lipopolysaccharideinduced nitric oxide production, compound 12 demonstrated significant inhibitory activity with greater potency than the reference compound quercetin. These results established A. iwayomogi as a promising source of antiinflammatory agents.

The roots of Astragalus membranaceus Bunge have long been used in herbal medicine for their diversebiological activities. Notably, its potential anti-diabetic properties have been extensively studied, highlighting promising therapeutic prospects. In this study, we conducted a comprehensive investigation focusing on flavonoid components from the roots of A. membranaceus and their PTP1B inhibitory activity. As a result, we isolated a total of 24 flavonoids, among which formonentin (1), pratensein (3), and vesticarpan (19) emerged as the most potent inhibitors against PTP1B with IC50 value of 10.9 ± 1.09 μM, 10.0 ± 1.71 μM, and 10.3 ± 1.31 μM, respectively.Additionally, through the enzyme kinetic analysis, the inhibition mode of compound 19 was determined as a competitive inhibitor, with Ki value of 7.6 ± 1.17 μM. Furthermore, the molecular docking simulation elucidated the binding mechanism of compound 19 with PTP1B, mainly through van der Waals forces and hydrogen bonds.This study highlights the PTP1B inhibitory potential of the flavonoid constituents derived from the roots of A. membranaceus. Moreover, discovering vesticarpan (19) as a novel PTP1B inhibitor provides a significant foundation for further investigations to develop innovative therapeutic strategies for diabetes treatment.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Objective: Previous studies have reported an association between cancer-related symptoms and perceived social support (PSS). The objective of this study was to analyze whether Chemotherapy-Induced Peripheral Neuropathy (CIPN), a prevalent side effect of chemotherapy, varies according to PSS level using a validated tool for CIPN at prospective follow-up. Methods: A total of 39 breast cancer patients were evaluated for PSS using the Multidimensional Scale of Perceived Social Support (MSPSS) prior to chemotherapy and were subsequently grouped into one of two categories for each subscale: low-to-moderate PSS and high PSS. CIPN was prospectively evaluated using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Chemotherapy-Induced Peripheral Neuropathy 20 (CIPN20) at five time points. A linear mixed-effects model with square root transformation was employed to investigate whether the CIPN20 scales varied by PSS level and time point. Results: Statistical analysis of the MSPSS total scale and subscales revealed a significant effect of the friends subscale group and time point on the CIPN20 sensory scale. The sensory scale score of CIPN20 was found to be lower in participants with high PSS from friends in comparison to those with low-to-moderate PSS at 1 month post-chemotherapy (p=0.010). Conclusion This is the first study to prospectively follow the long-term effect of pre-treatment PSS from friends on CIPN. Further studies based on larger samples are required to analyze the effects of PSS on the pathophysiology of CIPN.