Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called ‘COVID-19’, as a ‘public health emergency of international concern’. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

Jojoba is considered a promising oil crop and is cultivated for diverse purposes in many countries. The jojoba seed produces unique high-quality oil with a wide range of applications such as medical and industrial-related products. The plant also has potential value in combatting desertification and land degradation in dry and semi-dry areas. Although the plant is known for its high-temperature and high-salinity tolerance growth ability, issues such as its male-biased ratio, relatively late flowering and seed production time hamper the cultivation of this plant. The development of efficient biotechnological platforms for better cultivation and an improved production cycle is a necessity for farmers cultivating the plant. In the last 20 years, many efforts have been made for in vitro cultivation of jojoba by applying different molecular biology techniques. However, there is a lot of work to be done in order to reach satisfactory results that help to overcome cultivation problems. This review presents a historical overview, the medical and industrial importance of the jojoba plant, agronomy aspects and nutrient requirements for the plant’s cultivation, and the role of recent biotechnology and molecular biology findings in jojoba research.

Fungi, especially edible mushrooms, are considered as high-quality food with nutritive and functional values. They are of considerable interest and have been used in the synthesis of nutraceutical supplements due to their medicinal properties and economic significance. Specific fungal groups, including predominantly filamentous endophytic fungi from Ascomycete phylum and several Basidiomycetes, produce secondary metabolites (SMs) with bioactive properties that are involved in the antimicrobial and antioxidant activities. These beneficial fungi, while high in protein and important fat contents, are also a great source of several minerals and vitamins, in particular B vitamins that play important roles in carbohydrate and fat metabolism and the maintenance of the nervous system. This review article will summarize and discuss the abilities of fungi to produce antioxidant, anticancer, antiobesity, and antidiabetic molecules while also reviewing the evidence from the last decade on the importance of research in fungi related products with direct and indirect impact on human health.