Exploring free amino acid profiles in Crimean-Congo hemorrhagic fever patients: Implications for disease progression.

This study investigated the intricate interplay between Crimean-Congo hemorrhagic fever virus infection and alterations in amino acid metabolism. The primary aim is to elucidate the impact of Crimean-Congo hemorrhagic fever (CCHF) on specific amino acid concentrations and identify potential metabolic markers associated with viral infection. One hundred ninety individuals participated in this study, comprising 115 CCHF patients, 30 CCHF negative patients, and 45 healthy controls. Liquid chromatography-tandem mass spectrometry techniques were employed to quantify amino acid concentrations. The amino acid metabolic profiles in CCHF patients exhibit substantial distinctions from those in the control group. Patients highlight distinct metabolic reprogramming, notably characterized by arginine, histidine, taurine, glutamic acid, and glutamine metabolism shifts. These changes have been associated with the underlying molecular mechanisms of the disease. Exploring novel therapeutic and diagnostic strategies addressing specific amino acids may offer potential means to mitigate the severity of the disease.

Exploring free amino acid profiles in CCHF patients: Implications for disease progression.

This study investigated the intricate interplay between Crimean-Congo hemorrhagic fever virus (CCHFV) infection and alterations in amino acid metabolism. Our primary aim is to elucidate the impact of Crimean-Congo hemorrhagic fever (CCHF) on specific amino acid concentrations and identify potential metabolic markers associated with viral infection. One hundred ninety individuals participated in this study, comprising 115 CCHF patients, 30 CCHF negative patients, and 45 healthy controls. Liquid chromatography-tandem mass spectrometry techniques were employed to quantify amino acid concentrations. The amino acid metabolic profiles in CCHF patients exhibit substantial distinctions from those in the control group. Patients highlight distinct metabolic reprogramming, notably characterized by arginine, histidine, taurine, glutamic acid, and glutamine metabolism shifts. These changes have been associated with the underlying molecular mechanisms of the disease. Exploring novel therapeutic and diagnostic strategies addressing specific amino acids may offer potential means to mitigate the severity of the disease.

Concordance of LDL-C Estimating Equations with Direct Enzymatic Measurement in Diabetic and Prediabetic Subjects.

Low-density lipoprotein cholesterol (LDL-C) is a well-established biomarker in the management of dyslipidemia. Therefore, we aimed to evaluate the concordance of LDL-C-estimating equations with direct enzymatic measurement in diabetic and prediabetic populations. The data of 31,031 subjects included in the study were divided into prediabetic, diabetic, and control groups according to HbA1c values. LDL-C was measured by direct homogenous enzymatic assay and calculated by Martin-Hopkins, Martin-Hopkins extended, Friedewald, and Sampson equations. The concordance statistics between the direct measurements and estimations obtained by the equations were evaluated. All equations evaluated in the study had lower concordance with direct enzymatic measurement in diabetic and prediabetic groups compared to the non-diabetic group. Even so, the Martin-Hopkins extended approach demonstrated the highest concordance statistic in diabetic and prediabetic patients. Further, Martin-Hopkins extended was found to have the highest correlation with direct measurement compared with other equations. Over the 190 mg/dL LDL-C concentrations, the equation with the highest concordance was again Martin-Hopkins extended. In most scenarios, the Martin-Hopkins extended performed best in prediabetic and diabetic groups. Additionally, direct assay methods can be used at low values of the non-HDL-C/TG ratio (<2.4), as the performance of the equations in LDL-C estimation decreases as non-HDL-C/TG decreases.

Validation of low-density lipoprotein cholesterol equations in pediatric population.

Several studies have shown a high prevalence of dyslipidemia in children. Since childhood lipid concentrations continue into adulthood, recognition of lipid abnormalities in the early period is crucial to prevent the development of future coronary heart disease (CHD). Low density lipoprotein cholesterol (LDL-C) is one of the most used parameters in the initiation and follow-up of treatment in patients with dyslipidemia. It is a well known fact that LDL-C lowering therapy reduces the risk of future CHD. Therefore, accurate determination of the LDL-C levels is so important for the management of lipid abnormalities. This study aimed to validate different LDL-C estimating equations in the Turkish population, composed of children and adolescents. A total of 3,908 children below 18 years old at Sivas Cumhuriyet University Hospital (Sivas, Turkey) were included in this study. LDL-C was directly measured by direct homogeneous assays, i.e., Roche, Beckman, Siemens and estimated by Friedewald's, Martin/Hopkins', extended Martin-Hopkins' and Sampson's formulas. The concordances between the estimations obtained by the formulas and the direct measurements were evaluated both overall and separately for the LDL-C, triglycerides (TG) and non-high-density lipoprotein cholesterol (non-HDL-C) sublevels. Linear regression analysis was performed and residual error plots were generated between each estimation and direct measurement method. Coefficient of determination (R 2) and mean absolute deviations were also evaluated. The overall concordance of Friedewald, Sampson, Martin-Hopkins and the extended Martin-Hopkins formula were 64.6%, 69.9%, 69.4%, and 84.3% for the Roche direct assay, 69.8%, 71.6%, 73.6% and 80.4% for the Siemens direct assay, 66.5%, 68.8%, 68.9% and 82.1% for the Beckman direct assay, respectively. The extended Martin-Hopkins formula had the highest concordance coefficient in both overall and all sublevels of LDL-C, non-HDL-C, and TG. When estimating the LDL-C categories, the highest underestimation degrees were obtained with the Friedewald formula. Our analysis, conducted in a large pediatric population, showed that the extended Martin-Hopkins equation gives more reliable results in estimation of LDL-C compared to other equations.

Classification of colon cancer patients into consensus molecular subtypes using support vector machines.

Background/aim: The molecular heterogeneity of colon cancer has made classification of tumors a requirement for effective treatment. One of the approaches for molecular subtyping of colon cancer patients is the consensus molecular subtypes (CMS), developed by the Colorectal Cancer Subtyping Consortium. CMS-specific RNA-Seq-dependent classification approaches are recent, with relatively low sensitivity and specificity. In this study, we aimed to classify patients into CMS groups using their RNA-seq profiles. Materials and methods: We first identified subtype-specific and survival-associated genes using the Fuzzy C-Means algorithm and log-rank test. We then classified patients using support vector machines with backward elimination methodology. Results: We optimized RNA-seq-based classification using 25 genes with a minimum classification error rate. In this study, we reported the classification performance using precision, sensitivity, specificity, false discovery rate, and balanced accuracy metrics. Conclusion: We present a gene list for colon cancer classification with minimum classification error rates and observed the lowest sensitivity but the highest specificity with CMS3-associated genes, which significantly differed due to the low number of patients in the clinic for this group.

StemnesScoRe: an R package to estimate the stemness of glioma cancer cells at single-cell resolution.

Background/aim: Glioblastoma is the most heterogeneous and the most difficult-to-treat type of brain tumor and one of the deadliest among all cancers. The high plasticity of glioma cancer stem cells and the resistance they develop against multiple modalities of therapy, along with their high heterogeneity, are the main challenges faced during treatment of glioblastoma. Therefore, a better understanding of the stemness characteristics of glioblastoma cells is needed. With the development of various single-cell technologies and increasing applications of machine learning, indices based on transcriptomic and/or epigenomic data have been developed to quantitatively measure cellular states and stemness. In this study, we aimed to develop a glioma-specific stemness score model using scATAC-seq data for the first time. Materials and methods: We first applied three powerful machine-learning algorithms, i.e. random forest, gradient boosting, and extreme gradient boosting, to glioblastoma scRNA-seq data to discover the most important genes associated with cellular states. We then identified promoter and enhancer regions associated with these genes. After downloading the scATAC-seq peaks and their read counts for each patient, we identified the overlapping regions between the single-cell peaks and the peaks of genes obtained through machine-learning algorithms. Then we calculated read counts that were mapped to these overlapping regions. We finally developed a model capable of estimating the stemness score for each glioma cell using overlapping regions and the importance of genes predictive of glioblastoma cellular states. We also created an R package, accessible to all researchers regardless of their coding proficiency. Results: Our results showed that mesenchymal-like stem cells display higher stemness scores compared to neural-progenitor-, oligodendrocyte-progenitor-, and astrocyte-like cells. Conclusion: scATAC-seq can be used to assess heterogeneity in glioblastoma and identify cells with high stemness characteristics. The package is publicly available at https://github.com/Necla/StemnesScoRe and includes documentation with implementation of a real-data experiment.

Validation of Friedewald, Martin-Hopkins and Sampson low-density lipoprotein cholesterol equations.

BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) is an important biomarker for determining cardiovascular risk and regulating lipid lowering therapy. Therefore, the accurate estimation of LDL-C concentration is essential in cardiovascular disease diagnosis and prognosis. Sampson recently proposed a new formula for the estimation of LDL-C. However, little is known regarding the validation of this formula. OBJECTIVES: This study aimed to validate this new formula with other well-known formulas in Turkish population, composed of adults. METHODS: A total of 88,943 participants above 18 years old at Sivas Cumhuriyet University Hospital (Sivas, Turkey) were included to this study. LDL-C was directly measured by homogeneous assays, i.e., Roche, Beckman and Siemens and estimated by Friedewald's, Martin-Hopkins', extended Martin-Hopkins' and Sampson's formulas. The concordances between the estimations obtained by the formulas and the direct measurements were evaluated both in general and separately for the LDL-C, TG and non-HDL-C sublevels. Linear regression analysis was applied and residual error plots were generated between each estimation and direct measurement method. Coefficient of determination (R2) and mean absolute deviations were also calculated. RESULTS: The results showed that the extended Martin-Hopkins approach provided the most concordant results with the direct assays for LDL-C estimation. The results also showed that the highest concordances were obtained between the direct assays with the extended Martin-Hopkins formula calculated with the median statistics obtained from our own population. On the other hand, it was observed that the results of the methods may differ in different assays. The extended Martin-Hopkins approach, calculated from the median statistics of our population, gave the most concordant results in patients with "low LDL-C level (LDL-C levels < 70 mg/dL) or hypertriglyceridemia (TG levels ≥ 400 mg/dL)". CONCLUSIONS: Although the results of the formulas in different assays may vary, the extended Martin-Hopkins approach was the best one with the highest overall concordances. The validity of the Martin Hopkins' and Sampson's formulas has to be further investigated in different populations.

Different selection dynamics of S and RdRp between SARS-CoV-2 genomes with and without the dominant mutations.

SARS-CoV-2 is a betacoronavirus responsible for the COVID-19 pandemic that has affected millions of people worldwide. Pharmaceutical research against COVID-19 and the most frequently used tests for SARS-CoV-2 both depend on the genomic and peptide sequences of the virus for their robustness. Therefore, understanding the mutation rates and content of the virus is critical. Two key proteins for SARS-CoV-2 infection and replication are the S protein, responsible for viral entry into the cells, and RdRp, the RNA polymerase responsible for replicating the viral genome. Due to their roles in the viral cycle, these proteins are crucial for the fitness and infectiousness of the virus. Our previous findings had shown that the two most frequently observed mutations in the SARS-CoV-2 genome, 14408C>T in the RdRp coding region, and 23403A>G in the S gene, are correlated with higher mutation density over time. In this study, we further detail the selection dynamics and the mutation rates of SARS-CoV-2 genes, comparing them between isolates carrying both mutations, and isolates carrying neither. We find that the S gene and the RdRp coding region show the highest variance between the genotypes, and their selection dynamics contrast each other over time. The S gene displays higher tolerance for positive selection in mutant isolates early during the appearance of the double mutant genotype, and undergoes increasing negative selection over time, whereas the RdRp region in the mutant isolates shows strong negative selection throughout the pandemic.

qtQDA: quantile transformed quadratic discriminant analysis for high-dimensional RNA-seq data.

Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine. We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes. The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian quadratic discriminant analysis (QDA) using regularized covariance matrix estimates. We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches. An R package implementing the method is also available on https://github.com/goknurginer/qtQDA.