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.

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.

Sequence analysis of lysozyme C from the scorpion mesobuthus eupeus venom glands using semi-nested rt-PCR

BACKGROUND

Lysozyme is an antimicrobial protein widely distributed among eukaryotes and prokaryotes and take part in protecting microbial infection. Here, we amplified cDNA of MesoLys-C, a c-type lysozyme from the most common scorpion in Khuzestan Province, Southern Iran.

METHODS

Scorpions of Mesobuthus eupeus were collected from the Khuzestan Province. Using RNXTM solution, the total RNA was extracted from the twenty separated venom glands. cDNA was synthesized with extracted total RNA as template and modified oligo(dT) as primer. In order to amplify cDNA encoding a lysozyme C, semi-nested RT-PCR was done with the specific primers. Follow amplification, the fragment was sequenced.

RESULTS

Sequence determination of amplified fragment revealed that MesoLys-C cDNA had 438 bp, encoding for 144 aa residues peptide with molecular weight of 16.702 kDa and theoretical pI of 7.54. A putative 22-aminoacids signal peptide was identified. MesoLys-C protein was composed of one domain belonged to c-type lysosyme/ alphalactalbumin.

CONCLUSION

Multiple alignment of MesoLys-C protein with the related cDNA sequences from various organisms by ClustalW program revealed that some of the conserved residues of other c-type lysosymes were also seen in MesoLys-C. However, the comparison suggested that Mesobuthus eupeus of Khuzestan and east Mediterranean Mesobuthus eupeus belonged to different subspecies.