Temporal associations of plasma levels of the secreted phospholipase A2 family and mortality in severe COVID-19.

Previous research suggests that group IIA-secreted phospholipase A2 (sPLA2-IIA) plays a role in and predicts lethal COVID-19 disease. The current study reanalyzed a longitudinal proteomic data set to determine the temporal relationship between levels of several members of a family of sPLA2 isoforms and the severity of COVID-19 in 214 ICU patients. The levels of six secreted PLA2 isoforms, sPLA2-IIA, sPLA2-V, sPLA2-X, sPLA2-IB, sPLA2-IIC, and sPLA2-XVI, increased over the first 7 ICU days in those who succumbed to the disease but attenuated over the same time period in survivors. In contrast, a reversed pattern in sPLA2-IID and sPLA2-XIIB levels over 7 days suggests a protective role of these two isoforms. Furthermore, decision tree models demonstrated that sPLA2-IIA outperformed top-ranked cytokines and chemokines as a predictor of patient outcome. Taken together, proteomic analysis revealed temporal sPLA2 patterns that reflect the critical roles of sPLA2 isoforms in severe COVID-19 disease.

Identification of an allele-specific transcription factor binding interaction that regulates PLA2G2A gene expression.

The secreted phospholipase A 2 (sPLA 2 ) isoform, sPLA 2 -IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA 2 -IIA. Through Genotype-Tissue Expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA 2 -IIA, PLA2G2A . SNP2TFBS ( https://ccg.epfl.ch/snp2tfbs/ ) was utilized to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified SNPs. Subsequently, ChIP-seq peaks highlighted the TF combinations that specifically bind to the SNP, rs11573156. SP1 emerged as a significant TF/SNP pair in liver cells, with rs11573156/SP1 interaction being most prominent in liver, prostate, ovary, and adipose tissues. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was affected by tissue SP1 protein levels. By leveraging an ordinary differential equation, structured upon Michaelis-Menten enzyme kinetics assumptions, we modeled the PLA2G2A transcription's dependence on SP1 protein levels, incorporating the SNP's influence. Collectively, these data strongly suggest that the binding affinity differences of SP1 for the different rs11573156 alleles can influence PLA2G2A expression. This, in turn, can modulate sPLA2-IIA levels, impacting a wide range of human diseases.

Temporal Associations of Plasma Levels of the Secreted Phospholipase A 2 Family and Mortality in Severe COVID-19.

Previous research suggests that group IIA secreted phospholipase A 2 (sPLA 2 -IIA) plays a role in and predicts severe COVID-19 disease. The current study reanalyzed a longitudinal proteomic data set to determine the temporal (days 0, 3 and 7) relationship between the levels of several members of a family of sPLA 2 isoforms and the severity of COVID-19 in 214 ICU patients. The levels of six secreted PLA 2 isoforms, sPLA 2 -IIA, sPLA 2 -V, sPLA 2 -X, sPLA 2 -IB, sPLA 2 -IIC, and sPLA 2 -XVI, increased over the first 7 ICU days in those who succumbed to the disease. sPLA 2 -IIA outperformed top ranked cytokines and chemokines as predictors of patient outcome. A decision tree corroborated these results with day 0 to day 3 kinetic changes of sPLA 2 -IIA that separated the death and severe categories from the mild category and increases from day 3 to day 7 significantly enriched the lethal category. In contrast, there was a time-dependent decrease in sPLA 2 -IID and sPLA 2 -XIIB in patients with severe or lethal disease, and these two isoforms were at higher levels in mild patients. Taken together, proteomic analysis revealed temporal sPLA 2 patterns that reflect the critical roles of sPLA 2 isoforms in severe COVID-19 disease.

Targeting Epigenetic Regulators for Endometrial Cancer Therapy: Its Molecular Biology and Potential Clinical Applications.

Endometrial cancer is one of the most frequently diagnosed gynecological malignancies worldwide. However, its prognosis in advanced stages is poor, and there are only few available treatment options when it recurs. Epigenetic changes in gene function, such as DNA methylation, histone modification, and non-coding RNA, have been studied for the last two decades. Epigenetic dysregulation is often reported in the development and progression of various cancers. Recently, epigenetic changes in endometrial cancer have also been discussed. In this review, we give the main points of the role of DNA methylation and histone modification in endometrial cancer, the diagnostic tools to determine these modifications, and inhibitors targeting epigenetic regulators that are currently in preclinical studies and clinical trials.

Application of deep learning to the classification of images from colposcopy.

The objective of the present study was to investigate whether deep learning could be applied successfully to the classification of images from colposcopy. For this purpose, a total of 158 patients who underwent conization were enrolled, and medical records and data from the gynecological oncology database were retrospectively reviewed. Deep learning was performed with the Keras neural network and TensorFlow libraries. Using preoperative images from colposcopy as the input data and deep learning technology, the patients were classified into three groups [severe dysplasia, carcinoma in situ (CIS) and invasive cancer (IC)]. A total of 485 images were obtained for the analysis, of which 142 images were of severe dysplasia (2.9 images/patient), 257 were of CIS (3.3 images/patient), and 86 were of IC (4.1 images/patient). Of these, 233 images were captured with a green filter, and the remaining 252 were captured without a green filter. Following the application of L2 regularization, L1 regularization, dropout and data augmentation, the accuracy of the validation dataset was ~50%. Although the present study is preliminary, the results indicated that deep learning may be applied to classify colposcopy images.