RESUMO
Background and Aims: This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis. Methods: On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements. Results: After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms. Conclusion: The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.
RESUMO
Background and Aims: This systematic review examined healthcare students' attitudes, knowledge, and skill in Artificial Intelligence (AI). Methods: On August 3, 2022, studies were retrieved from the PubMed, Embase, Scopus, and Web of Science databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations were followed. We included cross-sectional studies that examined healthcare students' knowledge, attitudes, skills, and perceptions of AI in this review. Using the eligibility requirements as a guide, titles and abstracts were screened. Complete texts were then retrieved and independently reviewed per the eligibility requirements. To collect data, a standardized form was used. Results: Of the 38 included studies, 29 (76%) of healthcare students had a positive and promising attitude towards AI in the clinical profession and its use in he future; however, in nine of the studies (24%), students considered AI a threat to healthcare fields and had a negative attitude towards it. Furthermore, 26 studies evaluated the knowledge of healthcare students about AI. Among these, 18 studies evaluated the level of student knowledge as low (50%). On the other hand, in six of the studies, students' high knowledge of AI was reported, and two of the studies reported average student general knowledge (almost 50%). Of the six studies, four (67%) of the students had very low skills, so they stated that they had never worked with AI. Conclusion: Evidence from this review shows that healthcare students had a positive and promising attitude towards AI in medicine; however, most students had low knowledge and limited skills in working with AI. Face-to-face instruction, training manuals, and detailed instructions are therefore crucial for implementing and comprehending how AI technology works and raising students' knowledge of the advantages of AI.
RESUMO
Neurodegenerative diseases are devastating and incurable disorders characterized by neuronal dysfunction. The major focus of experimental and clinical studies are conducted on the effects of natural products and their active components on neurodegenerative diseases. This review will discuss an herbal constituent known as cinnamaldehyde (CA) with the neuroprotective potential to treat neurodegenerative disorders, such as Alzheimer's disease (AD) and Parkinson's disease (PD). Accumulating evidence supports the notion that CA displays neuroprotective effects in AD and PD animal models by modulating neuroinflammation, suppressing oxidative stress, and improving the synaptic connection. CA exerts these effects through its action on multiple signaling pathways, including TLR4/NF-κB, NLRP3, ERK1/2-MEK, NO, and Nrf2 pathways. To summarize, CA and its derivatives have been shown to improve pathological changes in AD and PD animal models, which may provide a new therapeutic option for neurodegenerative interventions. To this end, further experimental and clinical studies are required to prove the neuroprotective effects of CA and its derivatives.
