Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.

Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.

A critical review of different factors governing the fate of pesticides in soil under biochar application.

Pesticides are extensively used in the modern agricultural system. The inefficient and extensive use of pesticides during the last 5 to 6 decades inadvertently led to serious deterioration of environmental quality with health risk to living organisms, including humans. It is important to use some environmentally-friendly and sustainable approaches to remediate, restore and maintain soil quality. Biochar has gained considerable attention globally as a promising soil amendment because it has the ability to adsorb and as such minimize the bioavailability of pesticides in soils. This review emphasizes the recent trends and implications of biochar in pesticide-contaminated soils, as well as highlights need of the pesticides use and associated environmental issues in context of the biochar application. The overarching aim of this review is to signify the role of biochar on primary processes such as effect of biochar on the persistence, mineralization, leaching and efficacy of pesticides in soil. Notably, the effects of biochar on pesticide adsorption-desorption, degradation and bioavailability under various operating/production conditions are critically discussed. This review delineates the indirect impact of biochar on pesticides persistence in soils and proposes key recommendations for future research which are essential for the remediation and restoration of pesticides-impacted soils.

The preparation of an infectious full-length cDNA clone of Saffold virus.

The pathogenicity of Saffold virus (SAFV) among humans still remains unclear, although it was identified as a novel human cardiovirus in 2007. In order to encourage the molecular pathogenetic studies of SAFV, we generated an infectious cDNA clone of SAFV type 3 (SAFV-3). The present study demonstrated that the synthesis of the full-length infectious RNA by T7 RNA polymerase was terminated by a homologous sequence motif with the human preproparathyroid hormone (PTH) signal in the SAFV-3 genome. To obtain the infectious RNA using T7 promoter, a variant of T7 RNA polymerase, which fails to recognize the PTH signal, was useful. This study will provide a valuable technical insight into the reverse genetics of SAFV.