Validation of evidence-based questionnaire for TCM syndrome differentiation of heart failure and evaluation of expert consensus

Analysis of clinical evidence on traditional Chinese medicine for the treatment of diabetic nephropathy: a comprehensive review with evidence mapping

Objective

This study aims to map evidence from Randomized Controlled Trials (RCTs) and systematic reviews/Meta-analyses concerning the treatment of Diabetic Nephropathy (DN) with Traditional Chinese Medicine (TCM), understand the distribution of evidence in this field, and summarize the efficacy and existing problems of TCM in treating DN. The intention is to provide evidence-based data for TCM in preventing and treating DN and to offer a reference for defining future research directions.

Methods

Comprehensive searches of major databases were performed, spanning from January 2016 to May 2023, to include clinical RCTs and systematic reviews/Meta-analyses of TCM in treating DN. The analysis encompasses the publishing trend of clinical studies, the staging of research subjects, TCM syndrome differentiation, study scale, intervention plans, and outcome indicators. Methodological quality of systematic reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist, and evidence distribution characteristics were analyzed using a combination of text and charts.

Results

A total of 1926 RCTs and 110 systematic reviews/Meta-analyses were included. The majority of studies focused on stage III DN, with Qi-Yin deficiency being the predominant syndrome type, and sample sizes most commonly ranging from 60 to 100. The TCM intervention durations were primarily between 12-24 weeks. Therapeutic measures mainly consisted of Chinese herbal decoctions and patented Chinese medicines, with a substantial focus on clinical efficacy rate, TCM symptomatology, and renal function indicators, while attention to quality of life, dosage of Western medicine, and disease progression was inadequate. Systematic reviews mostly scored between 5 and 8 on the AMSTAR scale, and evidence from 94 studies indicated potential positive effects.

Conclusion

DN represents a significant health challenge, particularly for the elderly, with TCM showing promise in symptom alleviation and renal protection. Yet, the field is marred by research inconsistencies and methodological shortcomings. Future investigations should prioritize the development of standardized outcome sets tailored to DN, carefully select evaluation indicators that reflect TCM's unique intervention strategies, and aim to improve the robustness of clinical evidence. Emphasizing TCM's foundational theories while incorporating advanced scientific technologies will be essential for innovating research methodologies and uncovering the mechanisms underlying TCM's efficacy in DN management.

Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning

Background

With the development of traditional Chinese medicine (TCM) syndrome knowledge accumulation and artificial intelligence (AI), this study proposes a holistic TCM syndrome differentiation model for the classification prediction of multiple TCM syndromes based on deep learning and accelerates the construction of modern foundational TCM equipment.

Methods

We searched publicly available TCM guidelines and textbooks for expert knowledge and validated these sources using ten-fold cross-validation. Based on the BERT and CNN models, with the classification constraints from TCM holistic syndrome differentiation, the TCM-BERT-CNN model was constructed, which completes the end-to-end TCM holistic syndrome text classification task through symptom input and syndrome output. We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics.

Results

The TCM-BERT-CNN model had a higher precision (0.926), recall (0.9238), and F1 score (0.9247) than the BERT, TextCNN, LSTM RNN, and LSTM ATTENTION models and achieved superior results in model performance and predictive classification of most TCM syndromes. Symptom feature visualization demonstrated that the TCM-BERT-CNN model can effectively identify the correlation and characteristics of symptoms in different syndromes with a strong correlation, which conforms to the diagnostic characteristics of TCM syndromes.

Conclusions

The TCM-BERT-CNN model proposed in this study is in accordance with the TCM diagnostic characteristics of holistic syndrome differentiation and can effectively complete diagnostic prediction tasks for various TCM syndromes. The results of this study provide new insights into the development of deep learning models for holistic syndrome differentiation in TCM.

Effects of Shenmai injection against chronic heart failure: a meta-analysis and systematic review of preclinical and clinical studies

Objective: This study aims to evaluate the clinical and preclinical efficacy of SMI in treating CHF, and to summarize the relevant mechanisms of action in order to provide evidence for its role in CHF treatment. Methods: A systematic computerized search of eight databases and three registry systems was performed, with the time frame spanning from the inception of the databases to 30 June 2023. Strict procedures were used for data extraction, quality assessment, and data analysis. The methodological quality of the included studies was assessed using RoB-2 and SYRCLE tools. Statistical analysis was performed using Rev Man 5.4 software, using either fixed-effects or random-effects models. Results: A total of 25 clinical trials (including test group 1,367 patients, control group 1,338 patients) and 11 animal studies (including 201 animals) were included in this review. The meta-analysis of clinical studies showed that SMI can improve cardiac function indicators (LVEF, LVFS, LVEDV, LVESV, LVEDD, LVESD) (p < 0.00001), reduce BNP/NT-proBNP levels (p < 0.01), and improve inflammatory markers (hs-CRP, TNF-α, IL-6) (p < 0.00001) and endothelin (ET) levels (p < 0.0001). In animal studies, SMI demonstrated improved cardiac function (LVEF, LVFS) (p < 0.05), and improved heart failure markers (NT-proBNP, p < 0.05) when compared to control groups. Conclusion: This study represents the first meta-analysis which includes both preclinical and clinical studies on SMI. Clinical and animal studies have shown that SMI can improve cardiac function in CHF patients through its anti-apoptotic effects, antioxidant activities, anti-inflammatory effects, and improvement of myocardial metabolism. This study has certain limitations in terms of literature quality, quantity, and follow-up time. Therefore, the conclusions drawn from this study may require further validation through larger-scale, high-quality RCT trials.