Chemical Analysis and in Vitro Antiviral Effects of Northeast Türkiye Propolis Samples against HSV-1.

Propolis is one of the mixtures with the widest biological activity among natural products used in complementary medicine. HSV-1 is a highly contagious and endemic virus. Available drugs are insufficient for recurrent HSV-1 infections. Therefore, new approaches to treat HSV-1 infections are still being developed. In this study, it was aimed to investigate the inhibition effect of ethanolic Anatolian propolis extracts obtained from the Eastern Black Sea Region (Pazar, Ardahan, and Uzungöl) on HSV-1. In addition to the total phenolic (TPC) and the total flavonoid content (TFC), the phenolic profiles of the extracts were analyzed by HPLC-UV. The antiviral activity of the extracts were tested by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), quantitative Real Time Polymerase Chain Reaction (qRT-PCR), and plaque reduction tests, and the results were evaluated statistically. It was determined that the total amount of phenolic substances varied between 44.12 and 166.91 mg GAE/g, and the total flavonoid content of the samples varied between 12.50 and 41.58 (mg QUE/g). It was shown that all propolis samples used in the current study were effective against HSV-1, but the higher phenolic compounds contained in the samples showed the higher activity. The results show that ethanolic propolis extracts are promising candidates for HSV-1 treatment.

Finding underlying genetic mechanisms of two patients with autism spectrum disorder carrying familial apparently balanced chromosomal translocations.

BACKGROUND: Genetic etiologies of autism spectrum disorders (ASD) are complex, and the genetic factors identified so far are very diverse. In complex genetic diseases such as ASD, de novo or inherited chromosomal abnormalities are valuable findings for researchers with respect to identifying the underlying genetic risk factors. With gene mapping studies on these chromosomal abnormalities, dozens of genes have been associated with ASD and other neurodevelopmental genetic diseases. In the present study, we aimed to idenitfy the causative genetic factors in patients with ASD who have an apparently balanced chromosomal translocation in their karyotypes. METHODS: For mapping the broken genes as a result of chromosomal translocations, we performed whole genome DNA sequencing. Chromosomal breakpoints and large DNA copy number variations (CNV) were determined after genome alignment. Identified CNVs and single nucleotide variations (SNV) were evaluated with VCF-BED intersect and Gemini tools, respectively. A targeted resequencing approach was performed on the JMJD1C gene in all of the ASD cohorts (220 patients). For molecular modeling, we used a homology modeling approach via the SWISS-MODEL. RESULTS: We found that there was no contribution of the broken genes or regulator DNA sequences to ASD, whereas the SNVs on the JMJD1C, CNKSR2 and DDX11 genes were the most convincing genetic risk factors for underlying ASD phenotypes. CONCLUSIONS: Genetic etiologies of ASD should be analyzed comprehensively by taking into account of the all chromosomal structural abnormalities and de novo or inherited CNV/SNVs with all possible inheritance patterns.

A prospective study for gestational diabetes mellitus: Analysis of risk factors in Turkish women for early prediction.

OBJECTIVE: To define risk factors for the early prediction of gestational diabetes mellitus (GDM) because the risk of pre-eclampsia and preterm birth increases in mothers who are diagnosed with GDM. MATERIALS AND METHODS: A prospective study was designed and the data were collected by physicians prospectively from the patients who came to the clinic between the years 2019 and 2021; informed consent was obtained from the women. The prospective data comprised 489 patient records with 72 variables and the risk factors for early prediction of GDM were determined using logistic regression and random forest (RF), which is an advanced analysis method. RESULTS: The obtained sensitivity and specificity values are 90% and 75% for logistic regression and 71% and 90% for the RF, respectively. CONCLUSION: In this prospective study of GDM in Turkish women; age, body mass index, level of hemoglobin A1c, level of fasting blood sugar, physical activity time in first trimester, gravidity, triglycerides, and high-density lipoprotein cholesterol were confirmed to be risk factors in analysis results.

Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques.

The study aimed to develop a clinical diagnosis system to identify patients in the GD risk group and reduce unnecessary oral glucose tolerance test (OGTT) applications for pregnant women who are not in the GD risk group using deep learning algorithms. With this aim, a prospective study was designed and the data was taken from 489 patients between the years 2019 and 2021, and informed consent was obtained. The clinical decision support system for the diagnosis of GD was developed using the generated dataset with deep learning algorithms and Bayesian optimization. As a result, a novel successful decision support model was developed using RNN-LSTM with Bayesian optimization that gave 95% sensitivity and 99% specificity on the dataset for the diagnosis of patients in the GD risk group by obtaining 98% AUC (95% CI (0.95-1.00) and p < 0.001). Thus, with the clinical diagnosis system developed to assist physicians, it is planned to save both cost and time, and reduce possible adverse effects by preventing unnecessary OGTT for patients who are not in the GD risk group.