WHO Trial Registration Data Set (TRDS) extension for traditional Chinese medicine 2020: recommendations, explanation, and elaboration.

BACKGROUND: Although the WHO Trial Registration Data Set (TRDS) has been published for many years, the quality of clinical trial registrations with traditional Chinese medicine (TCM) is still not satisfactory, especially about the inadequate reporting on TCM interventions. The development of the WHO TRDS for TCM Extension 2020 (WHO TRDS-TCM 2020) aims to address this inadequacy. METHODS: A group of clinical experts, methodologists, epidemiologists, and editors has developed this WHO TRDS-TCM 2020 through a comprehensive process, including the baseline survey, draft of the initial items, three-round of Delphi survey, solicitation of comments, revision, and finalization. RESULTS: The WHO TRDS-TCM 2020 statement extends the latest version (V.1.3.1) of TRDS published in November 2017. The checklist includes 11 extended items (including subitems), namely Source(s) of Monetary or Material Support (Item 4), Scientific Title (Item 10a and 10b), Countries of Recruitment (Item 11), Health Condition(s) or Problem(s) Studied (Item 12), Intervention(s) (Item 13a, 13b and 13c), Key Inclusion and Exclusion Criteria (Item 14), Primary and Key Secondary Outcomes (Item 19 to 20), and Lay Summary (Item B1). For Item 13 (Interventions), three common TCM interventions--i.e., Chinese herbal medicine formulas, acupuncture and moxibustion-are elaborated. CONCLUSIONS: The group hopes that the WHO TRDS-TCM 2020 can improve the reporting quality and transparency of TCM trial registrations, assist registries in assessing the registration quality of TCM trials, and help readers understand TCM trial design.

Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods.

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease with an increasing incidence and prevalence worldwide. The diagnosis for UC mainly relies on clinical symptoms and laboratory examinations. As some previous studies have revealed that there is an association between gene expression signature and disease severity, we thereby aim to assess whether genes can help to diagnose UC and predict its correlation with immune regulation. A total of ten eligible microarrays (including 387 UC patients and 139 healthy subjects) were included in this study, specifically with six microarrays (GSE48634, GSE6731, GSE114527, GSE13367, GSE36807, and GSE3629) in the training group and four microarrays (GSE53306, GSE87473, GSE74265, and GSE96665) in the testing group. After the data processing, we found 87 differently expressed genes. Furthermore, a total of six machine learning methods, including support vector machine, least absolute shrinkage and selection operator, random forest, gradient boosting machine, principal component analysis, and neural network were adopted to identify potentially useful genes. The synthetic minority oversampling (SMOTE) was used to adjust the imbalanced sample size for two groups (if any). Consequently, six genes were selected for model establishment. According to the receiver operating characteristic, two genes of OLFM4 and C4BPB were finally identified. The average values of area under curve for these two genes are higher than 0.8, either in the original datasets or SMOTE-adjusted datasets. Besides, these two genes also significantly correlated to six immune cells, namely Macrophages M1, Macrophages M2, Mast cells activated, Mast cells resting, Monocytes, and NK cells activated (P  <  0.05). OLFM4 and C4BPB may be conducive to identifying patients with UC. Further verification studies could be conducted.

Chinese herbal medicines in the treatment of ulcerative colitis: a review.

OBJECTIVE: To investigate how the ulcerative colitis (UC) be treated with Chinese herbal medicines (CHM), using Chinese medicine (CM) pattern (zheng) identification, in the current clinical practice. METHODS: A total of 7 electronic databases were systematically searched for UC clinical studies with CHM interventions (including single herbs and CHM formulas) published in English and Chinese from the date of their inception to November 25, 2020. Descriptive statistics were adopted to demonstrate the characteristics of study design, and to collate the commonly CM patterns of UC and frequently used CHM herbs and formulas. Further, IBM SPSS Modeler 18.0 and Cytoscape 3.7.1 software were used to analyze and visualize the associations between different categories of CHM and their zheng indications. RESULTS: A total of 2311 articles were included in this study, of which most (> 90%) were RCTs with CHM formulas. The most common zheng of UC was Large intestine dampness-heat, while the basic type of CM patten was Spleen deficiency. The most frequently used classical formula was Bai-Tou-Weng-Tang, followed by Shen-Ling-Bai-Zhu-San, and the commonly used proprietary CHM was Xi-Lei-San (enema). Sulfasalazine and Mesalazine are commonly used as concomitant western medicines. The most frequently used single medicinals were Huang Lian and Bai Zhu, which also identified as the core herbs for different CM patterns. CONCLUSION: This study examined the application of CHM interventions for UC and summarized their characteristics in clinical practice. These data indicated there were limited information about the safety assessment of CHM formulas and further RCTs including CM pattern(s) with strict design are necessary.

Temporal matrix completion with locally linear latent factors for medical applications.

Regular medical records are useful for medical practitioners to analyze and monitor patient's health status especially for those with chronic disease. However, such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based models have been developed for missing data imputation in recent papers. This approach makes use of the low-rank tensor assumption for highly correlated data in a short-time interval. Nevertheless, when the time intervals are long, data correlation may not be high between consecutive time stamps so that such assumption is not valid. To address this problem, we propose to decompose matrices with missing data over time into their latent factors. Then, the locally linear constraint is imposed on the latent factors for temporal matrix completion. By using three publicly available medical datasets and two medical datasets collected from Prince of Wales Hospital in Hong Kong, experimental results show that the proposed algorithm achieves the best performance compared with state-of-the-art methods.