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.

NMSDR: Drug repurposing approach based on transcriptome data and network module similarity.

Computational drug repurposing aims to discover new treatment regimens by analyzing approved drugs on the market. This study proposes previously approved compounds that can change the expression profile of disease-causing proteins by developing a network theory-based drug repurposing approach. The novelty of the proposed approach is an exploration of module similarity between a disease-causing network and a compound-specific interaction network; thus, such an association leads to more realistic modeling of molecular cell responses at a system biology level.

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.

Computational drug repurposing study of the RNA binding domain of SARS-CoV-2 nucleocapsid protein with antiviral agents.

The recent outbreak of coronavirus disease (COVID-19) in China caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to worldwide human infections and deaths. The nucleocapsid (N) protein of coronaviruses (CoVs) is a multifunctional RNA binding protein necessary for viral RNA replication and transcription. Therefore, it is a potential antiviral drug target, serving multiple critical functions during the viral life cycle.

Comparison of enhancement methods for mammograms with performance measures.

Mammograms are generally contaminated by noise which assures the need for image enhancement to aid interpretation. The enhancement of mammograms is a very important problem for easy extraction of suspicious regions known as regions of interest (ROIs). This paper introduces comparison of various hybrid enhancement algorithms based on mathematical morphology, contrast stretching, wavelet transform, anisotropic diffusion filter and contrast limited adaptive histogram equalization (CLAHE).

A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding.

Mass detection is a very important process for breast cancer diagnosis and computer aided systems. It can be very complex when the mass is small or invisible because of dense breast tissue. Therefore, the extraction of suspicious mass region can be very challenging.

Does instructor evaluation by students using a WEB-based questionnaire impact instructor performance?

Background: Student feedback is a valuable method to evaluate the quality of education. Using a WEB-based questionnaire, the objective of this study was to evaluate the factors that may affect the ratings given by the students and the impact of those ratings on the instructor's teaching performance.

Methods: The questionnaire was organized into four areas: containment, presentation skills, measurement and assessment, and communication skills.