Neural Network-Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study

Effect of vitamin D supplementation on inflammatory markers and total antioxidant capacity in breast cancer women using a machine learning technique

Aim

This study aimed to establish a learning system using an artificial neural network (ANN) to predict the effects of vitamin D supplementation on the serum levels of vitamin D, inflammatory factors, and total antioxidant capacity (TAC) in women with breast cancer.

Methods

The data set of the current project was created from women with breast cancer who were referred to the Shafa State Hospital of Patients with Cancers in Ahvaz city, Iran. Modeling was implemented using the data set at the serum levels of vitamin D, tumor necrosis factor-α (TNF-α), transforming growth factor β (TGF-β), and TAC, before and after vitamin D3 supplement therapy. A prediction ANN model was designed to detect the effects of vitamin D3 supplementation on the serum level changes of vitamin D, inflammatory factors and TAC.

Results

The results showed that the ANN model could predict the effect of vitamin D3 supplementation on the serum level changes of vitamin D, TNF-α, TGF-β1, and TAC with an accuracy average of 85%, 40%, 89.5%, and 88.1%, respectively.

Conclusions

According to the findings of the study, the ANN method could accurately predict the effect of vitamin D3 supplementation on the serum levels of vitamin D, TNF-α, TGF-β1, and TAC. The results showed that the proposed ANN method can help specialists to improve the treatment process more confidently in terms of time and accuracy of predicting the influence of vitamin D supplementation on the factors affecting the progression of breast cancer (https://www.irct.ir/ identifier: IRCT2015090623924N1).

A qualitative and quantitative comparison of Crocus sativus and Nigella sativa immunomodulatory effects

The present article reviews and compares the immunomodulatory activities of Crocus sativus (C. sativus) and Nigella sativa (N. sativa) and their main bioactive compounds. Immunomodulatory effects of these plants, especially with respect to Th1 and Th2 cytokines, are discussed based on relevant articles, books, and conference papers published in English until the end of April 2020, that were retrieved from Web of Science, PubMed, Scopus and Google Scholar databases. C. sativus and its constituents increase immunoglobulin (Ig-)G, interleukin 2 (IL)-2, interferon gamma (IFN-γ), and IFN-γ/IL-4 ratio, but decreased IgM, IL-10 and IL-4 secretion. N. sativa extract and thymoquinone reduce the levels of IL-2, -4, -10, and -12, while enhance IFN-γ and serum IgG1 and 2a. The reviewed articles indicate that C. sativus and N. sativa and their constituents could be potentially considered promising treatments for disorders associated with immune-dysregulation such as asthma and cancer.

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.