Correction: Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review.

[This corrects the article DOI: 10.2196/44406.].

Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review.

BACKGROUND: In the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap. OBJECTIVE: With the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic. METHODS: In this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. RESULTS: Mobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)-based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19. CONCLUSIONS: Mobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients.

Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis.

BACKGROUND: Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task. METHODS AND MATERIALS: A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. RESULTS: We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively. CONCLUSION: MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.

Brain MRI findings in neurologically symptomatic COVID-19 patients: a systematic review and meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has been associated with nervous system involvement, with more than one-third of COVID-19 patients experiencing neurological manifestations. Utilizing a systematic review, this study aims to summarize brain MRI findings in COVID-19 patients presenting with neurological symptoms. METHODS: Systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) checklist. The electronic databases of PubMed/MEDLINE, Embase, Scopus, and Web of Science were systematically searched for literature addressing brain MRI findings in COVID-19 patients with neurological symptoms. RESULTS: 25 publications containing a total number of 3118 COVID-19 patients with neurological symptoms who underwent MRI were included. The most common MRI findings and the respective pooled incidences in decreasing order were acute/subacute infarct (22%), olfactory bulb abnormalities (22%), white matter abnormalities (20%), cerebral microbleeds (17%), grey matter abnormalities (12%), leptomeningeal enhancement (10%), ADEM (Acute Disseminated Encephalomyelitis) or ADEM-like lesions (10%), non-traumatic ICH (10%), cranial neuropathy (8%), cortical gray matter signal changes compatible with encephalitis (8%), basal ganglia abnormalities (5%), PRES (Posterior Reversible Encephalopathy Syndrome) (3%), hypoxic-ischemic lesions (4%), venous thrombosis (2%), and cytotoxic lesions of the corpus callosum (2%). CONCLUSION: The present study revealed that a considerable proportion of patients with COVID-19 might harbor neurological abnormalities detectable by MRI. Among various findings, the most common MRI alterations are acute/subacute infarction, olfactory bulb abnormalities, white matter abnormalities, and cerebral microbleeds.

Immunologic aspects of migraine: A review of literature.

Migraine headaches are highly prevalent, affecting 15% of the population. However, despite many studies to determine this disease's mechanism and efficient management, its pathophysiology has not been fully elucidated. There are suggested hypotheses about the possible mediating role of mast cells, immunoglobulin E, histamine, and cytokines in this disease. A higher incidence of this disease in allergic and asthma patients, reported by several studies, indicates the possible role of brain mast cells located around the brain vessels in this disease. The mast cells are more specifically within the dura and can affect the trigeminal nerve and cervical or sphenopalatine ganglion, triggering the secretion of substances that cause migraine. Neuropeptides such as calcitonin gene-related peptide (CGRP), neurokinin-A, neurotensin (NT), pituitary adenylate-cyclase-activating peptide (PACAP), and substance P (SP) trigger mast cells, and in response, they secrete pro-inflammatory and vasodilatory molecules such as interleukin-6 (IL-6) and vascular endothelial growth factor (VEGF) as a selective result of corticotropin-releasing hormone (CRH) secretion. This stress hormone contributes to migraine or intensifies it. Blocking these pathways using immunologic agents such as CGRP antibody, anti-CGRP receptor antibody, and interleukin-1 beta (IL-1ß)/interleukin 1 receptor type 1 (IL-1R1) axis-related agents may be promising as potential prophylactic migraine treatments. This review is going to summarize the immunological aspects of migraine.

Estimating the Dissolution of Anticancer Drugs in Supercritical Carbon Dioxide with a Stacked Machine Learning Model.

Synthesizing micro-/nano-sized pharmaceutical compounds with an appropriate size distribution is a method often followed to enhance drug delivery and reduce side effects. Supercritical CO2 (carbon dioxide) is a well-known solvent utilized in the pharmaceutical synthesis process. Reliable knowledge of a drug's solubility in supercritical CO2 is necessary for feasible study, modeling, design, optimization, and control of such a process. Therefore, the current study constructs a stacked/ensemble model by combining three up-to-date machine learning tools (i.e., extra tree, gradient boosting, and random forest) to predict the solubility of twelve anticancer drugs in supercritical CO2. An experimental databank comprising 311 phase equilibrium samples was gathered from the literature and applied to design the proposed stacked model. This model estimates the solubility of anticancer drugs in supercritical CO2 as a function of solute and solvent properties and operating conditions. Several statistical indices, including average absolute relative deviation (AARD = 8.62%), mean absolute error (MAE = 2.86 × 10-6), relative absolute error (RAE = 2.42%), mean squared error (MSE = 1.26 × 10-10), and regression coefficient (R2 = 0.99809) were used to validate the performance of the constructed model. The statistical, sensitivity, and trend analyses confirmed that the suggested stacked model demonstrates excellent performance for correlating and predicting the solubility of anticancer drugs in supercritical CO2.

Neuroprotective Potential of Aromatic Herbs: Rosemary, Sage, and Lavender.

Hundreds of millions of people around the world suffer from neurological disorders or have experienced them intermittently, which has significantly reduced their quality of life. The common treatments for neurological disorders are relatively expensive and may lead to a wide variety of side effects including sleep attacks, gastrointestinal side effects, blood pressure changes, etc. On the other hand, several herbal medications have attracted colossal popularity worldwide in the recent years due to their availability, affordable prices, and few side effects. Aromatic plants, sage (Salvia officinalis), lavender (Lavandula angustifolia), and rosemary (Salvia Rosmarinus) have already shown anxiolytics, anti-inflammatory, antioxidant, and neuroprotective effects. They have also shown potential in treating common neurological disorders, including Alzheimer's disease, Parkinson's disease, migraine, and cognitive disorders. This review summarizes the data on the neuroprotective potential of aromatic herbs, sage, lavender, and rosemary.