Immunity against COVID-19: Potential role of Ayurveda: A Review

The Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is an infectious disease caused by coronavirus disease 2019 (COVID-19) and has affected people's lives globally, since first case was detected in Wuhan, China in December 2019. The coronavirus pandemic has turned the world’s attention to the immune system, the body’s defense mechanism against disease. Concept of Ojas is well explained in all ayurvedic classics, in modern perspective it is considered as immunity (Vyadhikshamatava). Ojas is necessary for well-being of the body, and mind. In Ayurveda textbook, the epidemics and along with their management are discussed under the term of Janapadodhvansa. The preventive and curative treatments for communicable diseases of the Janapadodhvansa (epidemics) are Panchkarma (five bio-purification therapies), Rasayana Chikitsa (rejuvenation treatment), Achara Rasayana (good conducts), and migrate to the place, free from communicable diseases. The intake of all types Rasayanas leads to increase of Ojas and reduce all psychological (mainly stress and emotional) disorder, thereby causes increase immunity responses and help to fight against covid-19. Key Words: Ayurveda, Covid-19, Immunity, Janapadodhvansa, Ojas, Rasayana Chikitsa.

Family Dynamics and Grandparents' Anxiety and Depression in Intergenerational Rearing Families: A Correlational Study

Background: In China, intergenerational rearing is a ubiquitous phenomenon based on unique national conditions. This study aimed to explore family dynamics in intergenerational rearing families as well as their correlation with older household members' anxiety and depression.Methods: The elderly from intergenerational (n = 141) and non-intergenerational rearing families (n = 266) were investigated using the following scales: the general information questionnaire, Self-Rating Scale of Systemic Family Dynamics, Geriatric Depression Scale, and Self-Rating Anxiety Scale.Results: Scores from the four dimensions (family atmosphere, system logic, individuation, and the concept of disease) of the structure of family dynamics were computed. The comparison of these dimensions scores and the total scores of grandparents' anxiety and depression for the two groups were not statistically significant (p > 0.05). In Pearson's correlation analysis, no significant correlation between the family atmosphere dimension and the total score of the grandparents' depression and anxiety scales was observed. The system logic aspect was negatively correlated with depression and anxiety scale scores. The individual dimension was positively correlated with the anxiety scale scores. The disease concept dimension was positively correlated with depression and anxiety scale scores. Hence, the results were statistically significant.Conclusion: There were no significant differences in terms of family dynamics and risk of anxiety and depression among grandparents between the two family types. The system logic, individuation, and disease concept dimensions were correlated with their anxiety and depression.

THE DISEASE CONCEPT THROUGH THE LANGUAGE MIRROR

The article analyzes language means verbalizing the concept of DESEASE. The paper suggests multi-vecrored interpretations of the concept names' etymology and highlights a number of typological parallels thus targeting certain universalia that pertain to biological systems' disorders and dusfunctions. The implemented methodology exercises the eco-centric focus of interpretations, considers irrational premises of cognition and rationalization of reality, fuzzy nature of objects and phenomena involved in multidimensional interactions, non-linear causative correlations of diverse phenomena and respective generic systems, quantum peculiarities of verbally conveyed informational clusters, enigmatic nature of systems' development at bifurcation points as well as systems' inverse fluctuations. The synthetic-analytical interpretations of the concept names' etymology allowed reconstructing the inchoative "nano-myth" that impacts the trajectories of further conceptualization and designation of disease-related phenomena. This "nano-myth' contains the idea of structural deformation (distortion or uncontrolled expansion) which triggers the systems' entropy rise, energy loss, dysfunctionality, discomfort and painful symptoms, as well as negative assessment (as either auto-diagnostics or external assessment). Semantics of respective stems suggests an explanation of traditional asspciating disease and mythic creatures. The paper introduces a folmal logical model of DISEASE as scenario / "eventive" concept. The article discusses functional-semantic roles of language signs verbalizing DISEASE in different texts in Germanic languages of diverse historic periods. The paper provides quantitative analysis of respective designation units within a customary corpus encompassing English lyrics authored by the present-day rock musicians. The units from the corpus are subjected to semantic and linguo-cognitive analysis which traces the way the DISEASE is projected onto a variety of social and cultural spheres of human existence.

Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study

Background The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. Because EHRs can serve as a reference for this limited information, doctors’ treatment capabilities can be enhanced. Natural language processing and deep learning methods can help organize and translate EHR information into medical knowledge and experience. Objective In this study, we aimed to create a model to extract concept embeddings from EHRs for disease pattern retrieval and further classification tasks. Methods We collected 1,040,989 emergency department visits from the National Taiwan University Hospital Integrated Medical Database and 305,897 samples from the National Hospital and Ambulatory Medical Care Survey Emergency Department data. After data cleansing and preprocessing, the data sets were divided into training, validation, and test sets. We proposed a Transformer-based model to embed EHRs and used Bidirectional Encoder Representations from Transformers (BERT) to extract features from free text and concatenate features with structural data as input to our proposed model. Then, Deep InfoMax (DIM) and Simple Contrastive Learning of Visual Representations (SimCLR) were used for the unsupervised embedding of the disease concept. The pretrained disease concept-embedding model, named EDisease, was further finetuned to adapt to the critical care outcome prediction task. We evaluated the performance of embedding using t-distributed stochastic neighbor embedding (t-SNE) to perform dimension reduction for visualization. The performance of the finetuned predictive model was evaluated against published models using the area under the receiver operating characteristic (AUROC). Results The performance of our model on the outcome prediction had the highest AUROC of 0.876. In the ablation study, the use of a smaller data set or fewer unsupervised methods for pretraining deteriorated the prediction performance. The AUROCs were 0.857, 0.870, and 0.868 for the model without pretraining, the model pretrained by only SimCLR, and the model pretrained by only DIM, respectively. On the smaller finetuning set, the AUROC was 0.815 for the proposed model. Conclusions Through contrastive learning methods, disease concepts can be embedded meaningfully. Moreover, these methods can be used for disease retrieval tasks to enhance clinical practice capabilities. The disease concept model is also suitable as a pretrained model for subsequent prediction tasks.

The concept of disease in the time of COVID-19

Philosophers of medicine have formulated different accounts of the concept of disease. Which concept of disease one assumes has implications for what conditions count as diseases and, by extension, who may be regarded as having a disease (disease judgements) and for who may be accorded the social privileges and personal responsibilities associated with being sick (sickness judgements). In this article, we consider an ideal diagnostic test for coronavirus disease 2019 (COVID-19) infection with respect to four groups of people—positive and asymptomatic; positive and symptomatic; negative; and untested—and show how different concepts of disease impact on the disease and sickness judgements for these groups. The suggestion is that sickness judgements and social measures akin to those experienced during the current COVID-19 outbreak presuppose a concept of disease containing social (risk of) harm as a component. We indicate the problems that arise when adopting this kind of disease concept beyond a state of emergency.

Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study (Preprint)

BACKGROUND The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. Because EHRs can serve as a reference for this limited information, doctors’ treatment capabilities can be enhanced. Natural language processing and deep learning methods can help organize and translate EHR information into medical knowledge and experience. OBJECTIVE In this study, we aimed to create a model to extract concept embeddings from EHRs for disease pattern retrieval and further classification tasks. METHODS We collected 1,040,989 emergency department visits from the National Taiwan University Hospital Integrated Medical Database and 305,897 samples from the National Hospital and Ambulatory Medical Care Survey Emergency Department data. After data cleansing and preprocessing, the data sets were divided into training, validation, and test sets. We proposed a Transformer-based model to embed EHRs and used Bidirectional Encoder Representations from Transformers (BERT) to extract features from free text and concatenate features with structural data as input to our proposed model. Then, Deep InfoMax (DIM) and Simple Contrastive Learning of Visual Representations (SimCLR) were used for the unsupervised embedding of the disease concept. The pretrained disease concept-embedding model, named EDisease, was further finetuned to adapt to the critical care outcome prediction task. We evaluated the performance of embedding using t-distributed stochastic neighbor embedding (t-SNE) to perform dimension reduction for visualization. The performance of the finetuned predictive model was evaluated against published models using the area under the receiver operating characteristic (AUROC). RESULTS The performance of our model on the outcome prediction had the highest AUROC of 0.876. In the ablation study, the use of a smaller data set or fewer unsupervised methods for pretraining deteriorated the prediction performance. The AUROCs were 0.857, 0.870, and 0.868 for the model without pretraining, the model pretrained by only SimCLR, and the model pretrained by only DIM, respectively. On the smaller finetuning set, the AUROC was 0.815 for the proposed model. CONCLUSIONS Through contrastive learning methods, disease concepts can be embedded meaningfully. Moreover, these methods can be used for disease retrieval tasks to enhance clinical practice capabilities. The disease concept model is also suitable as a pretrained model for subsequent prediction tasks.