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Mesothelial cells with reactive hyperplasia are difficult to distinguish from malignant mesothelioma cells based on cell morphology. This study aimed to identify and validate potential biomarkers that distinguish mesothelial cells from mesothelioma cells through machine learning combined with immunohistochemistry. It integrated the gene expression matrix from three Gene Expression Omnibus data sets (GSE2549, GSE12345, and GSE51024) to analyze the differently expressed genes between normal and mesothelioma tissues. Then, three machine learning algorithms, least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were used to screen and obtain four shared candidate markers, including ACADL, EMP2, GPD1L, and HMMR. The receiver operating characteristic curve analysis showed that the area under the curve for distinguishing normal mesothelial cells from mesothelioma was 0.976, 0.943, 0.962, and 0.956, respectively. The expression and diagnostic performance of these candidate genes were validated in two additional independent data sets (GSE42977 and GSE112154), indicating that the performances of ACADL, GPD1L, and HMMRwere consistent between the training and validation data sets. Finally, the optimal candidate marker ACADLwas verified by immunohistochemistry assay. Acyl-CoA dehydrogenase long chain (ACADL) was stained strongly in mesothelial cells, especially for reactive hyperplasic mesothelial cells, but was negative in malignant mesothelioma cells. Therefore, ACADLhas the potential to be used as a specific marker of reactive hyperplasic mesothelial cells in the differential diagnosis of mesothelioma.

Pregnancy is a physiological state that predisposes women to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, a disease that can cause adverse maternal and perinatal outcomes. The severity of coronavirus disease 2019 (COVID-19) disease is known to vary by viral strain; however, evidence for the effects of this virus in pregnant women has yet to be fully elucidated. In this review, we describe maternal and perinatal outcomes, vaccination, and vertical transmission, among pregnant women infected with the different SARS-CoV-2 variants identified to date. We also summarize existing evidence for maternal and perinatal outcomes in pregnant women with specific information relating to SARS-CoV-2 variants. Our analysis showed that Omicron infection was associated with fewer severe maternal and perinatal adverse outcomes while the Delta variant was associated with worse pregnancy outcomes. Maternal deaths arising from COVID-19 were found to be rare (<1.0%), irrespective of whether the virus was a wild-type strain or a variant. Severe maternal morbidity was more frequent for the Delta variant (10.3%), followed by the Alpha (4.7%), wild-type (4.5%), and Omicron (2.9%) variants. The rates of stillbirth were 0.8%, 4.1%, 3.1%, and 2.3%, respectively, in pregnancies infected with the wild-type strain, Alpha, Delta, and Omicron variants, respectively. Preterm birth and admission to neonatal intensive care units were more common for cases with the Delta infection (19.0% and 18.62%, respectively), while risks were similar for those infected with the wild-type (14.7% and 11.2%, respectively), Alpha (14.9% and 13.1%), and Omicron variants (13.2% and 13.8%, respectively). As COVID-19 remains a global pandemic, and new SARS-CoV-2 variants continue to emerge, research relating to the specific impact of new variants on pregnant women needs to be expanded.