Healthy lifestyles and good living habits are effective strategies and important approaches to prevent chronic non-communicable diseases. With the development of evidence-based medicine, the evidence translation system has made some achievements in clinical practice. There is, however, no comprehensive, professional and efficient system for translating lifestyle evidence globally. Therefore, the Health Lifestyle Evidence (HLSE) Group of Lanzhou University constructed the HLSE Evidence Translation System (https://hlse.cn/), with the aim of synthesizing, evaluating, translating, and applying lifestyle-related evidence resources. This paper aims to introduce the functional module design of the evidence translation system, the system development process and testing, introduce the interface design and function demonstration, and explain the significance of constructing the HLSE.

To systematically construct a guideline to provide a methodological guide for researchers to develop patient decision aids.

  Objective   To systematically assess the applicability of the clinical practice guidelines (CPGs) integrating Chinese and western medicine, thereby providing reference for enhancing their future usability.   Methods   PubMed, Web of Science, Embase, SinoMed, WanFang Data, and CNKI databases were searched for guidelines for the integration of Chinese and western medicine. Supplementary searches of Chinese Medical Association, Chinese Medicine Administration, MedSci websites, Website of China Association of Chinese Medicine, Medlive, Website of Dangdang, Chinese Association of Integrative Medicine and World Federation of Traditional Chinese Medicine Societies were conducted. The search time frame was from inception of the databases to December 31, 2022. Four reviewers independently evaluated the implementability of clinical practice guidelines by using "Clinical Practice Guideline (CPG) implementation evaluation tool".   Results   A total of 61 integrative Chinese and western medicine guidelines were included. Of the guidelines assessed, 9 guidelines(14.75%) exhibited strong implementability, 40(65.57%) demonstrated average implementability, and 12 (19.67%) demonstrated poor implementability. Among the 5 domains, the dimensions of "accessibility" and "implementability" were found to be of high quality, while those of "communication", "ease of identification" and "applicability" were relatively poor. Notably, compared to guidelines published between 2006—2016, those published between 2017—2022 showed improvements in the areas of "identification" and "application".   Conclusions   The implementabilityof the existing clinical practice guidelines of the integrative Chinese and western medicine is average and needs to be improved. In the future, emphasis should be placed on the integration of medical education and research, publicity of the guidelines, continuous medical education and training, a more concise form of recommendations, application of the guidelines by clinicians, and the implementability of the integrative Chinese and western medicine guideline.

Adaptive design, with advantages such as dynamically adjusting trial plans, reducing resource waste, and improving trial efficiency, has broken through the competitive situation of new drug development and gradually met the needs of clinical research. In recent years, the use of adaptive design in platform trials as an innovative research model has added impetus to new drug development. This article outlines the research progress, contents and characteristics, common design types, statistical analysis, and case interpretation of adaptive design, and introduces the concept, types, and applications of adaptive platform trials, with the hope of providing scientific reference for further exploration of clinical trials and new drug development.

Healthy lifestyles and good living habits are effective strategies and important approaches to prevent chronic non-communicable diseases. With the development of evidence-based medicine, the evidence translation system has made some achievements in clinical practice. There is, however, no comprehensive, professional and efficient system for translating lifestyle evidence globally. Therefore, the Health Lifestyle Evidence (HLSE) Group of Lanzhou University constructed the HLSE Evidence Translation System (https://hlse.cn/), with the aim of synthesizing, evaluating, translating, and applying lifestyle-related evidence resources. This paper aims to introduce the functional module design of the evidence translation system, the system development process and testing, introduce the interface design and function demonstration, and explain the significance of constructing the HLSE.

To systematically construct a guideline to provide a methodological guide for researchers to develop patient decision aids.

Artificial intelligence(AI) empowers the development of the medical industry, providing precise and intelligent assistance for clinical diagnosis, treatment, and rehabilitation.AI has the potential to facilitate shared decision making (SDM), but AI interventions used for SDM are currently in their infancy, presenting both challenges and opportunities. This paper aims to describe the application of AI in SDM, explore the problems and challenges of AI-based decision aid used for SDM, and propose possible solutions, aiming to provide a guide for the development and implementation of AI-based decision aid.

BACKGROUND: Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. METHODS: In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. RESULTS: A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05). CONCLUSIONS The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.
Humans ; Retrospective Studies ; Pneumonia/diagnostic imaging* ; Analysis of Variance ; Tomography, X-Ray Computed ; Lymphoma/diagnostic imaging*

Objective: To evaluate the efficacy and safety of fecal microbiota transplantation (FMT) in the treatment of autism spectrum disorder (ASD). Methods: A longitudinal study was conducted. Clinical data from ASD patients with gastrointestinal symptoms and who underwent FMT in the Tenth People's Hospital affiliated to Tongji University or Jinling Hospital between May 2012 to May 2021 were retrospectively collected. Scores derived from the autism behavior checklist (ABC), the childhood autism rating scale (CARS), the Bristol stool form scale (BSFS), and the gastrointestinal symptom rating scale (GSRS) were analyzed at baseline and at the 1st, 3rd, 6th, 12th, 24th, 36th, 48th and 60th month after FMT. Records of any adverse reactions were collected. Generalized estimating equations were used for analysis of data on time points before and after FMT. Results: A total of 328 patients met the inclusion criteria for this study. Their mean age was 6.1±3.4 years old. The cohort included 271 boys and 57 girls. The percentage of patients remaining in the study for post-treatment follow-up at the 1st, 3rd, 12th, 24th, 36th, 48th and 60th month were as follows: 303 (92.4%), 284 (86.7%), 213 (64.9%), 190 (57.9%), 143 (43.6%), 79 (24.1%), 46 (14.0%), 31 (9.5%). After FMT, the average ABC score was significantly improved in the first 36 months and remained improved at the 48th month. However, the average score was not significantly different from baseline by the 60th month (1st-36th month, P<0.001; 48th month, P=0.008; 60th month, P=0.108). The average CARS score improved significantly during the first 48 months and remained improved at the 60th month (1st-48th month, P<0.001; 60th month, P=0.010). The average BSFS score was also significantly improved in the first 36 months (with an accompanying stool morphology that resembled type 4). This improvement was maintained at the 48th month. However, the average score was similar to baseline at the 60th month (1st-36th month, P<0.001; 48th month, P=0.008; 60th month, P=0.109). The average GSRS score was significantly improved during the first 24 months, but not afterwards (1st-24th month, P<0.001; 36th month, P=0.209; 48th month, P=0.996; 60th month, P=0.668). The adverse events recorded during treatment included abdominal distension in 21 cases (6.4%), nausea in 14 cases (4.3%), vomiting in 9 cases (2.7%), abdominal pain in 15 cases (4.6%), diarrhea in 18 cases (5.5%), fever in 13 cases (4.0%), and excitement in 24 cases (7.3%). All adverse reactions were mild to moderate and improved immediately after suspension of FMT or on treatment of symptoms. No serious adverse reactions occurred. Conclusion: FMT has satisfactory long-term efficacy and safety for the treatment of ASD with gastrointestinal symptoms.
Autism Spectrum Disorder/therapy* ; Child ; Child, Preschool ; Fecal Microbiota Transplantation/adverse effects* ; Feces ; Female ; Gastrointestinal Diseases ; Humans ; Longitudinal Studies ; Male ; Retrospective Studies

Objective:To establish a prediction model using the random forest (RF) and support vector machine (SVM) algorithms to achieve the numerical and classification predictions of the gamma passing rate (GPR) for volumetric arc intensity modulation (VMAT) validation.Methods:A total of 258 patients who received VMAT radiotherapy in the 1 st Affiliated Hospital of Wenzhou Medical University from April 2019 to August 2020 were retrospectively selected for patient-specific QA measurements, including 38 patients who received VMAT radiotherapy for head and neck, and 220 patients who received VMAT radiotherapy for chest and abdomen. Thirteen complexity parameters were extracted from the patient′s VMAT plans and the GPRs for VMAT validation under the analysis criteria of 3%/3 mm and 2%/2 mm were collected. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%) , and the complexity parameters for the numerical and classification predictions were screened using the RF and minimum redundancy maximum correlation (mRMR) method, respectively. Complexity models and mixed models were established using PTV volume, subfield width, and smoothness factors based on the RF and SVM algorithms individually. The prediction performance of the established models was analyzed and compared. Results:For the validation cohort, the GPR numerical prediction errors of the complexity models based on RF and SVM under the two analysis criteria are as follows. The root-mean-square errors (RMSEs) under the analysis criterion of 3%/3 mm were 1.788% and 1.753%, respectively; the RMSEs under the analysis criterion of 2%/2 mm were 5.895% and 5.444%, respectively; the mean absolute errors (MAEs) under the analysis criterion of 3%/3 mm were 1.415% and 1.334%, respectively, and the MAEs under the analysis criteria of 2%/2 mm were 4.644% and 4.255%, respectively. For the validation cohort, the GPR numerical prediction errors of the mixed models based on RF and SVM under the two analysis criteria were as follows. The RMSEs under the analysis criterion of 3%/3 mm were 1.760% and 1.815%, respectively; the RMSEs under the analysis criterion of 2%/2 mm were 5.693% and 5.590%, respectively; the MAEs under the analysis criterion of 3%/3 mm were 1.386% and 1.319%, respectively, and the MAEs under the analysis criteria of 2%/2 mm were 4.523% and 4.310, respectively. For the validation cohort, the AUC result of the GPR classification prediction of the complexity models based on RF and SVM were 0.790 and 0.793, respectively under the analysis criterion of 3%/3 mm and were 0.763 and 0.754, respectively under the analysis criterion of 2%/2 mm. For the validation cohort, the AUC result of the GPR classification prediction of the mixed models based on RF and SVM were 0.806 and 0.859, respectively under the analysis criterion of 3%/3 mm and were 0.796 and 0.796, respectively under the analysis criterion of 2%/2 mm cohort.Conclusions:Complexity models and mixed models were developed based on the RF and SVM method. Both types of models allow for the numerical and classification predictions of the GPRs of VMAT radiotherapy plans under analysis criteria of 3%/3 mm and 2%/2 mm. The mixed models have higher prediction accuracy than the complexity models.