Epistemological issues in the theory of Chinese medicine

Traditional Chinese Medicine (TCM) has been criticized for being unscientific because the theory on which it is based involves entities like qi and ’meridians’ that appear ambiguous and because the internal ‘organs’ like the kidney and the spleen are very different from those of modern anatomy and physiology. Even more so, TCM methods of therapy based on the yin-yang principle, the model of the five elements, and the classification of illnesses according to standard constellations of symptoms (TCM “syndromes”) are largely unproven by the protocols of modern evidence-based medicine. This dissertation attempts to reconstruct TCM theory by: (a) providing explanations of TCM entities as abstractions and constructs that relate to observable body functions and illness symptoms and (b) interpreting TCM theory as comprising heuristic models that were constructed from clinical experience to fit empirical observations of illnesses and their treatments with herbal medications and acupuncture. It suggests that scientists should be less concerned with the ontological status of TCM entities and the epistemic credentials of TCM models than with the ability of these concepts and models to guide physicians in therapy. More importantly, it makes the argument that these models are testable using the methods of evidence-based medicine. There are methodological difficulties associated with randomized controlled trials partly because TCM treatments tend to be individualized and syndromes are dynamic in nature; observational trials may be more appropriate in some situations. It is also possible that, for patients who are more culturally attuned to TCM, the placebo effect is strongly at play and may render the real effects of TCM treatments harder to tease out in clinical trials. The dissertation concludes that the main postulates of TCM should be put to rigorous test. The result may be a leaner but more robust theory, with parts that do not stand up to the test being rejected or modified, and a possible acceptance of its more modest therapeutic claims for a limited range of pathological conditions like pain and chronic illnesses

Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning

<p>Abstract</p> <p>Background</p> <p>Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.</p> <p>Methods</p> <p>Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.</p> <p>Results</p> <p>A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.</p> <p>Conclusions</p> <p>The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.</p

Prescriptions of Traditional Chinese Medicine Are Specific to Cancer Types and Adjustable to Temperature Changes

Targeted cancer therapies, with specific molecular targets, ameliorate the side effect issue of radiation and chemotherapy and also point to the development of personalized medicine. Combination of drugs targeting multiple pathways of carcinogenesis is potentially more fruitful. Traditional Chinese medicine (TCM) has been tailoring herbal mixtures for individualized healthcare for two thousand years. A systematic study of the patterns of TCM formulas and herbs prescribed to cancers is valuable. We analysed a total of 187,230 TCM prescriptions to 30 types of cancer in Taiwan in 2007, a year's worth of collection from the National Health Insurance reimbursement database (Taiwan). We found that a TCM cancer prescription consists on average of two formulas and four herbs. We show that the percentage weights of TCM formulas and herbs in a TCM prescription follow Zipf's law with an exponent around 0.6. TCM prescriptions to benign neoplasms have a larger Zipf's exponent than those to malignant cancers. Furthermore, we show that TCM prescriptions, via weighted combination of formulas and herbs, are specific to not only the malignancy of neoplasms but also the sites of origins of malignant cancers. From the effects of formulas and natures of herbs that were heavily prescribed to cancers, that cancers are a ‘warm and stagnant’ syndrome in TCM can be proposed, suggesting anti-inflammatory regimens for better prevention and treatment of cancers. We show that TCM incorporated relevant formulas to the prescriptions to cancer patients with a secondary morbidity. We compared TCM prescriptions made in different seasons and identified temperatures as the environmental factor that correlates with changes in TCM prescriptions in Taiwan. Lung cancer patients were among the patients whose prescriptions were adjusted when temperatures drop. The findings of our study provide insight to TCM cancer treatment, helping dialogue between modern western medicine and TCM for better cancer care

A comparative analysis of chronic obstructive pulmonary disease using machine learning, and deep learning

Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.

Genetics of Parkinson’s Disease: The Role of Copy Number Variations

Parkinson’s disease (PD), the second most common progressive neurodegenerative disorder, was long believed to be a non-genetic sporadic origin syndrome. The identification of distinct genetic loci responsible for rare Mendelian forms of PD has represented a revolutionary breakthrough, allowing to discover novel mechanisms underlying this debilitating still incurable condition. Along with single-nucleotide polymorphisms (SNPs), other kinds of DNA molecular defects have emerged as significant disease-causing mutations, including large chromosomic structural rearrangements and copy number variations (CNVs). Due to their size variability and to the different sensitivity and resolution of detection methodologies, CNVs constitute a particular challenge in genetic studies and the pathogenetic or susceptibility impact of specific CNVs on PD is currently under debate. In this chapter, we will review the current literature and bioinformatic data describing the involvement of CNVs on PD pathobiology. We will discuss the recently highlighted role of PARK2 heterozygous CNVs, the possible common founder effects of PD gene rearrangements and the importance to map genetic breakpoints. We will also add a summary about the current available molecular methods and bioinformatics web resources to detect and interpret CNVs. Assessing the global genome-wide burden of large CNVs and elucidating the role of de novo rare structural variants on PD may reveal new candidate genes and consequently ameliorate diagnosis and counselling of mutations carriers

Genetics and Genetic Testing in Congenital Heart Disease

Congenital heart defects (CHDs) are structural abnormalities of the heart and great vessels that are present from birth. The presence or absence of extra-cardiac anomalies has historically been used to identify patients with possible monogenic, chromosomal, or teratogenic CHD etiologies. These distinctions remain clinically relevant, particularly with regard to management; however, identification of genetic causes in patients with presumably non-syndromic CHD indicates that isolated CHD can also be genetic in origin. In recent years, the field of cardiac genetics has benefited from a growing understanding of the complex molecular mechanisms underpinning heart development, and the extreme genetic heterogeneity of CHD is increasingly appreciated. Progress has been largely supported by improvements in genetic testing technology derived from worldwide efforts to accurately and economically characterize the full breadth of human genomic variation. The last fifteen years in particular have witnessed emergence and refinement of novel cytogenetic and sequencing technologies, which have proven to be enormously effective tools for both diagnosis and identification of novel CHD-causing genes. These advancements have led to an increasing need for cardiac care providers to be well versed in the molecular genetic origins of CHD and to have working knowledge of the benefits and limitations of available testing methods. In this review, we provide a general overview of key morphologic, molecular, and signaling mechanisms relevant to heart development before summarizing overall progress in the molecular genetic analyses of CHDs and current recommendations for clinical application of genetic testing. Particular emphasis is placed on the utility and limitations of chromosomal microarray analyses (CMAs) and on emerging clinical roles for whole exome sequencing (WES) and other next-generation sequencing (NGS) technologies

Elucidation of the mechanisms underlying the anticholecystitis effect of the Tibetan medicine “Dida” using Network pharmacology

Purpose: To study the mechanism involved in the anti-cholecystitis effect the Tibetan medicine “Dida”, using network pharmacology-integrated molecular docking simulationsMethods: In this investigation, the bioactive compounds of Dida were collected, network pharmacology methods to predict their targets, and networks were constructed through GO and KEGG pathway analyses. The potential binding between the bioactive compounds and the targets were demonstrated using molecular docking simulations.Results: A total of 12 bioactive compounds and 50 key targets of Dida were identified. Two networks, namely, protein-protein interaction (PPI) network of cholecystitis targets, and compound-target-pathway network, were established. Network analysis showed that 10 targets (GAPDH, AKT1, CASP3, EGFR, TNF, MAPK3, MAPK1, HSP90AA1, STAT3, and BCL2L1) may be the therapeutic targets of Dida in cholecystitis. Analysis of the KEGG pathway indicated that the anti-cholecystitis effect of Dida may its regulation of a few crucial pathways, such as apoptosis, as well as toll-like&nbsp; receptor, T cell receptor, NOD-like receptor, and MAPK signaling pathways. Furthermore, molecular docking simulation revealed that CASP3, CAPDH, HSP90AA1, MAPK3, MAPK1, and STAT3 had well-characterized interactions with the corresponding compounds.Conclusion: The mechanism underlying the anti-cholecystitis effect of Dida was successfully predicted and verified using a combination of network pharmacology and molecular docking simulation. This provides a firm basis for the experimental verification of the use of Dida in the treatment of cholecystitis, and enhances its rational application in clinical medication. Keywords: Tibetan medicine, Dida, Cholecystitis, Mechanism of effect, Network pharmacology, Molecular docking simulatio