Evaluating the impact of soy compounds on breast cancer using the data mining approach

A Comprehensive Review on Harnessing Soy Proteins in the Manufacture of Healthy Foods through Extrusion

The global development of livestock production systems, accelerated by the growing demand for animal products, has greatly contributed to land-use change, greenhouse gas emissions, and pollution of the local environment. Further, excessive consumption of animal products has been linked with cardiovascular diseases, digestive system diseases, diabetes, and cancer. On the other hand, snacks, pasta, and bread available on the market are made from wheat, fat, salt, and sugar, which contribute to the risk of cardiovascular diseases. To counter these issues, a range of plant protein-based food products have been developed using different processing techniques, such as extrusion. Given the easy scalability, low cost of extrusion technology, and health benefits of soy proteins, this review focuses on the extrusion of soy protein and the potential application of soy protein-based extrudates in the manufacture of healthy, nutritious, and sustainable meat analogs, snacks, pasta products, and breakfast cereals. This review discusses the addition of soy protein to reformulate hypercaloric foods through extrusion technology. It also explores physical and chemical changes of soy proteins/soy protein blends during low and high moisture extrusion. Hydrogen bonds, disulfide bonds, and hydrophobic interactions influence the properties of the extrudates. Adding soy protein to snacks, pasta, breakfast cereals, and meat analogs affects their nutritional value, physicochemical properties, and sensory characteristics. The use of soy proteins in the production of low-calorie food could be an excellent opportunity for the future development of the soybean processing industry.

Glyceollins from soybean: Their pharmacological effects and biosynthetic pathways

Flavonoids are a highly abundant class of secondary metabolites present in plants. Isoflavonoids, in particular, are primarily synthesized in leguminous plants within the subfamily Papilionoideae. Numerous reports have established the favorable role of isoflavonoids in preventing a range of human diseases. Among the isoflavonoid components, glyceollins are synthesized specifically in soybean plants and have displayed promising effects in mitigating the occurrence and progression of breast and ovarian cancers as well as other diseases. Consequently, glyceollins have become a sought-after natural component for promoting women's health. In recent years, extensive research has focused on investigating the molecular mechanism underlying the preventative properties of glyceollins against various diseases. Substantial progress has also been made toward elucidating the biosynthetic pathway of glyceollins and exploring potential regulatory factors. Herein, we provide a review of the research conducted on glyceollins since their discovery five decades ago (1972-2023). We summarize their pharmacological effects, biosynthetic pathways, and advancements in chemical synthesis to enhance our understanding of the molecular mechanisms of their function and the genes involved in their biosynthetic pathway. Such knowledge may facilitate improved glyceollin synthesis and the creation of health products based on glyceollins.

Prediction of Breast Cancer using Machine Learning Approaches

BACKGROUND

Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data.

OBJECTIVE

This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data.

MATERIAL AND METHODS

In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer.

RESULTS

RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network.

CONCLUSION

Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.

Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development

In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes ('NEURAL-NETWORKS', 'CANCER', 'ELETRONIC-HEALTH-RECORDS', 'DIABETES-MELLITUS', 'ALZHEIMER'S-DISEASE', 'BREAST-CANCER', 'DEPRESSION', and 'RANDOM-FOREST') are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field's evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.