Complex Networks Approach for Analyzing the Correlation of Traditional Chinese Medicine Syndrome Evolvement and Cardiovascular Events in Patients with Stable Coronary Heart Disease

This is a multicenter prospective cohort study to analyze the correlation of traditional Chinese medicine (TCM) syndrome evolvement and cardiovascular events in patients with stable coronary heart disease (CHD). The impact of syndrome evolvement on cardiovascular events during the 6-month and 12-month follow-up was analyzed using complex networks approach. Results of verification using Chi-square test showed that the occurrence of cardiovascular events was positively correlated with syndrome evolvement when it evolved from toxic syndrome to Qi deficiency, blood stasis, or sustained toxic syndrome, when it evolved from Qi deficiency to blood stasis, toxic syndrome, or sustained Qi deficiency, and when it evolved from blood stasis to Qi deficiency. Blood stasis, Qi deficiency, and toxic syndrome are important syndrome factors for stable CHD. There are positive correlations between cardiovascular events and syndrome evolution from toxic syndrome to Qi deficiency or blood stasis, from Qi deficiency to blood stasis, or toxic syndrome and from blood stasis to Qi deficiency. These results indicate that stable CHD patients with pathogenesis of toxin consuming Qi, toxin leading to blood stasis, and mutual transformation of Qi deficiency and blood stasis are prone to recurrent cardiovascular events

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification

A Network-Based Approach to Investigate the Pattern of Syndrome in Depression

In Traditional Chinese Medicine theory, syndrome is essential to diagnose diseases and treat patients, and symptom is the foundation of syndrome differentiation. Thus the combination and interaction between symptoms represent the pattern of syndrome at phenotypic level, which can be modeled and analyzed using complex network. At first, we collected inquiry information of 364 depression patients from 2007 to 2009. Next, we learned classification models for 7 syndromes in depression using naïve Bayes, Bayes network, support vector machine (SVM), and C4.5. Among them, SVM achieves the highest accuracies larger than 0.9 except for Yin deficiency. Besides, Bayes network outperforms naïve Bayes for all 7 syndromes. Then key symptoms for each syndrome were selected using Fisher's score. Based on these key symptoms, symptom networks for 7 syndromes as well as a global network for depression were constructed through weighted mutual information. Finally, we employed permutation test to discover dynamic symptom interactions, in order to investigate the difference between syndromes from the perspective of symptom network. As a result, significant dynamic interactions were quite different for 7 syndromes. Therefore, symptom networks could facilitate our understanding of the pattern of syndrome and further the improvement of syndrome differentiation in depression

Predictive Learning from Real-World Medical Data: Overcoming Quality Challenges

Randomized controlled trials (RCTs) are pivotal in medical research, notably as the gold standard, but face challenges, especially with specific groups like pregnant women and newborns. Real-world data (RWD), from sources like electronic medical records and insurance claims, complements RCTs in areas like disease risk prediction and diagnosis. However, RWD's retrospective nature leads to issues such as missing values and data imbalance, requiring intensive data preprocessing. To enhance RWD's quality for predictive modeling, this thesis introduces a suite of algorithms developed to automatically resolve RWD's low-quality issues for predictive modeling. In this study, the AMI-Net method is first introduced, innovatively treating samples as bags with various feature-value pairs and unifying them in an embedding space using a multi-instance neural network. It excels in handling incomplete datasets, a frequent issue in real-world scenarios, and shows resilience to noise and class imbalances. AMI-Net's capability to discern informative instances minimizes the effects of low-quality data. The enhanced version, AMI-Net+, improves instance selection, boosting performance and generalization. However, AMI-Net series initially only processes binary input features, a constraint overcome by AMI-Net3, which supports binary, nominal, ordinal, and continuous features. Despite advancements, challenges like missing values, data inconsistencies, and labeling errors persist in real-world data. The AMI-Net series also shows promise for regression and multi-task learning, potentially mitigating low-quality data issues. Tested on various hospital datasets, these methods prove effective, though risks of overfitting and bias remain, necessitating further research. Overall, while promising for clinical studies and other applications, ensuring data quality and reliability is crucial for these methods' success

CD8+ T cell epitope-enriched HIV-1-Gag antigens with preserved structure and function

Control of disease progression in certain HIV-1 infected individuals is often associated with CD8+ T cell responses directed towards Gag-derived epitopes presented on HLA class I molecules. This indicates that such responses play a crucial role in combating virus replication. However, both the large variability of HIV-1 and the diversity of HLA alleles impose a challenge on the elicitation of protective CD8+ T cell responses by vaccination. To address this problem, an algorithm was conceived to generate Gag antigens enriched with patient-derived CD8+ T cell epitopes. Since the function of Gag to produce virus-like particles (VLPs) was deemed important for priming of an adequate CD8+ T cell response, the program excluded all epitopes with budding-deleterious properties. To achieve this, all amino acid substitutions (AAS) that had been identified in the epitope set through mapping them to a Gag reference sequence, were assessed using a trained classifier that considers structural-energy- and sequence-conservation-based features to predict whether each AAS is compatible with budding. These predictions were validated experimentally for over 100 variants, showing a precision of 100% regarding classification of budding competence. Next, epitopes that contain only budding-retaining AAS were assigned a score that considers various customizable epitope-specific properties, like frequencies of HLA class I molecules presenting the epitope in a given population, subtype affiliation, and conservation status. Using a genetic algorithm, as many compatible epitopes as possible were combined into a novel Gag antigen sequence, aiming to maximize their cumulative score. After each round of antigen generation, all previously integrated epitopes were eliminated from the input data set. Thus, in subsequent rounds only the remaining epitopes were used, which resulted in a set of complementary antigens. To evaluate the performance of the algorithm, a trivalent set of globally applicable CD8+ T cell epitope-enriched Gag antigens (teeGags1-3) was generated and computationally validated in this thesis. It could be shown that the teeGags are superior to any known, naturally found or in silico generated Gag sequence from previously published work regarding the number and quality of epitopes, as well as the population coverage, defined as the average number of epitopes presented per person. The shape and size of teeGag VLPs were examined biochemically and wildtype-like characteristics were observed for teeGag1 and teeGag3. teeGag2, however, exhibited some aberrant, tubular structures and slightly larger particles, probably due to a set of mutations within the p2 region of Gag. To characterize the increased immunological breadth of the teeGags, a method to directly identify HLA-class-I-presented epitopes was conceived. For this, the conditioned supernatant from cells that produce soluble forms of HLA (sHLA) was used for HLA-affinity chromatography. Peptides from the isolated sHLA complexes were further purified and employed for sequencing through LC-MS/MS analysis. It was shown in this thesis that this method can be used to identify sHLA-restricted peptides. However, the sensitivity has to be further increased to allow examination of the immunological breadth of antigens. In conclusion, with the in silico validated enhanced immunological breadth and the biochemically verified structural conservation, the presented designer teeGags qualify as next-generation vaccine antigens that potentially elicit superior CD8+ T cell responses

Routledge Handbook of Chinese Medicine

The Routledge Handbook of Chinese Medicine is an extensive, interdisciplinary guide to the nature of traditional medicine and healing in the Chinese cultural region, and its plural epistemologies. Established experts and the next generation of scholars interpret the ways in which Chinese medicine has been understood and portrayed from the beginning of the empire (third century BCE) to the globalisation of Chinese products and practices in the present day, taking in subjects from ancient medical writings to therapeutic movement, to talismans for healing and traditional medicines that have inspired global solutions to contemporary epidemics. The volume is divided into seven parts: Longue Durée and Formation of Institutions and Traditions Sickness and Healing Food and Sex Spiritual and Orthodox Religious Practices The World of Sinographic Medicine Wider Diasporas Negotiating Modernity This handbook therefore introduces the broad range of ideas and techniques that comprise pre-modern medicine in China, and the historiographical and ethnographic approaches that have illuminated them. It will prove a useful resource to students and scholars of Chinese studies, and the history of medicine and anthropology. It will also be of interest to practitioners, patients and specialists wishing to refresh their knowledge with the latest developments in the field. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 licens

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute