Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.08.23.457415

Time Evolution of the Stroke Symptom-Herb Networks Based on TCM Prescriptions

Fu, X.·Li, Q.·Yang, F.·Zhou, R.·Li, R.·Hou, J.·Tao, X.
View original source ↗Export metadata ↓

Abstract

Traditional Chinese Medicine (TCM) has its origins in distant antiquity and has piled up over a long time with much knowledge about diseases, especially stroke. Different combinations of symptom variables yield different combinations of herbs to form a myriad of prescriptions, and they have undergone repeated confirmation and are worthy objects of excavation and analysis. Herbal studies on stroke have developed from genomics to transcriptomics, proteomics and metabolomics, yet more thought is needed on putting time in a wider perspective of the symptoms and herbs for stroke in Chinese medicine. Due to this, we studied the dynamic structure of TCM prescriptions on stroke, using over 270 TCM prescription books containing 2231 prescriptions related to stroke recorded from 341 to 2000 CE. We labeled the functions of the prescriptions with the symptoms based on subject terms in MESH Neurologic Manifestations, then standardized the herbs in the prescriptions, and finally connected the co-occurring symptoms and herbs in the prescriptions to build an undirected complex network. The Stroke Symptom-Herb Networks (SSHNs) can be seen from its network characteristics that it is not a random network and has small-world characteristics. It has experienced two peaks in its nearly 1700-year history, during the Song dynasty, the Ming and Qing dynasties. From 600 years onwards, the core herb cluster has been initially formed. The comparison of sub-network similarities allowed us to identify several symptoms with similar herb clusters. We divided the community based on modularity, and by analyzing the community evolution, we found a more fixed historical evolutionary trend with Hemiplegia and Sialorrhea nodes and their associated symptom and herb nodes. In the time series analysis, we found many symptom-herb combinations that were consistently closely related to historic time depends on assessing the similarity between the symptoms and the herbs. The complex network provides a distinctive perspective for understanding the symptom-herb relationships embedded in TCM prescriptions in remote antiquity.

Explore related subjects

scientific communication and educationPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fu, X., Li, Q., Yang, F., Zhou, R., Li, R., Hou, J., Tao, X.. 2021-08-23. Time Evolution of the Stroke Symptom-Herb Networks Based on TCM Prescriptions. https://doi.org/10.1101/2021.08.23.457415

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗
bioRxiv2022

Welfare concerns for mounted load carrying by working donkeys in Pakistan

Working donkeys (Equus asinus) are vital to peoples livelihoods. They are essential for carrying goods, however globally, overloading is one of the primary welfare concerns of working donkeys. We studied mounted load carrying by donkeys and associated factors in Pakistan. A cross-sectional study of donkey owners (n = 332) was conducted, and interviews were undertaken based on a questionnaire. Owners estimated that the median weight of their donkeys was 110kg (interquartile range (IQR) 100-120kg), and that they carried a median mounted load of 81.5kg (IQR 63-99kg). We found that 87.4% of donkeys carried a load above 50% of their bodyweight ratio (BWR), the median BWR carried was 77.1% (IQR 54.5-90.7%), and 25.3% of donkeys carried above 90% BWR. Donkeys that were loaded at more than 50% BWR were more likely to sit, compared to donkeys loaded with less weight (p=0.01). Donkeys working in peri-urban and urban areas were more likely to carry a greater BWR than donkeys working in rural areas (P<0.001), as were those carrying construction materials or bricks, compared to agricultural materials (p=0.004). Age (p=0.03) and breed (p=0.01) were also associated with carrying a higher weight. Overloading based on current recommendations (50% BWR) was common, with the majority (87.4%) of donkeys reported to carry more than the recommended 50% limit. This survey provides evidence of on-the-ground working practices and factors associated with mounted load carrying, which is critical for developing evidence-based recommendations for loading, in order to improve the welfare of working donkeys.

scientific communication and education↗