Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review.

OBJECTIVE: The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS). METHODS: Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains. RESULTS: This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms. CONCLUSION: Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.

Medical magnetic resonance imaging publications in Arab countries: A 25-year bibliometric analysis.

Magnetic Resonance Imaging (MRI) is increasingly becoming a cornerstone in modern diagnostic healthcare, offering unparalleled capabilities in stroke, dementia, and cancer screening. Therefore, this study aims to map medical MRI literature affiliated with Arab countries, focusing on publication trends, top journals, author affiliations, study countries, and authors' collaboration, and keyword analysis. The scientific database used is the Scopus database. Microsoft Excel, VOSviewer software, and Biblioshiny for the Bibliometrix R package are the bibliometric tools used in this analysis. A total of 2592 publications were published between 1988 and 2022, with total citations of 22,115. Most of them were original articles (91,7%) and 89.9% were published in traditional journals. The number of total publications exhibited a steady increase over time, whereas total citations showed fluctuations, peaking in 2015 with 1571 citations for publications from that year. The most cited article was authored by Yaseen M. Arabi, receiving 286 citations. Saudi Arabia was the top active country. In addition, the most prolific author was Maha S Zaki, and the most prolific source was the "Egyptian Journal of Radiology and Nuclear Medicine". The most prolific affiliation was Cairo University. The "multiple sclerosis" and "case report" were the most trending keywords. The analysis revealed a significant growth in MRI research inside Arab countries, as shown by an increase in the total number of publications and international collaborations. Despite these developments, the results of this study suggest that there is still room for MRI research in the Arab region to advance. This can be achieved through increasing international collaboration and multidisciplinary work.

The Status Quo of Pharmacogenomics of Tyrosine Kinase Inhibitors in Precision Oncology: A Bibliometric Analysis of the Literature.

Precision oncology and pharmacogenomics (PGx) intersect in their overarching goal to institute the right treatment for the right patient. However, the translation of these innovations into clinical practice is still lagging behind. Therefore, this study aimed to analyze the current state of research and to predict the future directions of applied PGx in the field of precision oncology as represented by the targeted therapy class of tyrosine kinase inhibitors (TKIs). Advanced bibliometric and scientometric analyses of the literature were performed. The Scopus database was used for the search, and articles published between 2001 and 2023 were extracted. Information about productivity, citations, cluster analysis, keyword co-occurrence, trend topics, and thematic evolution were generated. A total of 448 research articles were included in this analysis. A burst of scholarly activity in the field was noted by the year 2005, peaking in 2017, followed by a remarkable decline to date. Research in the field was hallmarked by consistent and impactful international collaboration, with the US leading in terms of most prolific country, institutions, and total link strength. Thematic evolution in the field points in the direction of more specialized studies on applied pharmacokinetics of available and novel TKIs, particularly for the treatment of lung and breast cancers. Our results delineate a significant advancement in the field of PGx in precision oncology. Notwithstanding the practical challenges to these applications at the point of care, further research, standardization, infrastructure development, and informed policymaking are urgently needed to ensure widespread adoption of PGx.

L-Carnitine prevents memory impairment induced by post-traumatic stress disorder.

BACKGROUND: Post-traumatic stress disorder (PTSD) is a genuine obstructing mental disorder. As indicated by the name, it is related to the patients' stress augmented by life-threatening conditions or accidents. The PTSD has linked to oxidative stress that can result in neurodegeneration. L-carnitine (L-CAR) is known for its antioxidant properties, which can protect against neuronal damage. OBJECTIVE: In the current study, we investigated the beneficial effects of L-CAR on the memory impairment induced by PTSD using a rat model. METHODS: A model of single-prolonged stress (a cycle of restraining, forced swimming, rest, and finally diethyl ether exposure for 2 h, 20 min, 15 min, and 1-2 min, respectively) was used to induce PTSD-like behavior. Intraperitoneal L-CAR treatment (300 mg/kg/day) was introduced for four weeks. Both memory and special learning were evaluated utilizing the radial arm water maze (RAWM). Moreover, the levels of glutathione peroxidase (GPx), glutathione reduced (GSH), and glutathione oxidized (GSSG) were assessed as biomarkers oxidative stress in the hippocampus. RESULTS: The results demonstrated that both the short and long-term memories were impaired by PTSD/SPS model (P < 0.05), while L-CAR treatment prevented this memory impairment in PTSD rats. Besides, L-CAR prevented the reduction in GPx activity and increase in GSSG, which were altered in the hippocampus of the PTSD/SPS rats (P < 0.05). Levels of GSH were not changed in PTSD and/or L-CAR rats. CONCLUSIONS: L-CAR administration prevented short- and long-term memories' impairments induced in the PTSD/SPS rat model. This is probably related to its antioxidant effects in the hippocampus.