It is becoming a public health issue to predict which expectant women will develop gestational diabetes mellitus (GDM). The goal of this case control research is to investigate the role of maternal oxidative stress levels in the first, second, and third trimesters, as well as other factors, in the development of gestational diabetes mellitus (GDM). Methods Between October and December 2021, 142 women participated in this research. The 101 GDM patients were split into three groups based on their gestation (T1, T2, and T3), and 41 healthy pregnant women were chosen as the comparison group. TAS and TOS levels of oxidative stress and XO were calculated using a Spectrophotometer for colorimetric techniques; fasting and random sugar levels, as

Natural settings make it challenging to identify facial expressions since head position, illumination level, and ‎‎occlusion vary. Thus, developing a more generic model without front-facing images alone is quite crucial. This ‎research proposes a facial expression ‎recognition model based on pre-trained deep convolutional neural networks ‎with transfer learning. The model was trained ‎on several cases to classify face expressions into seven ‎classifications efficiently. The proposed system used the EfficientNetB0 model ‎that has one dense dropout layer. The model first rescales and norms the input dataset in the input ‎layer that takes images of a larger resolution to get better results. After entering 7 blocks sequential

Background Parkinson’s disease (PD) is currently the fastest-growing neurological disorder in the world. Patients with PD face numerous challenges in managing their chronic condition, particularly in countries with scarce healthcare infrastructure. Objective This qualitative study aimed to delve into neurologists’ perspectives on challenges and gaps in the Iraqi healthcare system that influence the management of PD, as well as strategies to mitigate these obstacles. Method Semi-structured interviews were conducted with neurologists from five different Iraqi provinces, working in both hospitals and private neurology clinics, between November 2024 and January 2025. A thematic analysis approach was employed to identify the main challenge

Medication safety is an important part of the comprehensive patient safety term. Medication safety is gaining more attention as the World Health Organization set the goal of decreasing medication harm by (50%) for the next 5 years when launching the third global challenge. Studying medication safety in the risk groups such as young ages, children are crucial to learn more about the effect of medicines in this risk group since they are not included in the clinical trials. Adverse drug reaction is defined as any harm resulted from the drug itself during medical process journey, while medication errors are any harm resulted from the treatment process rather than the drug or it is the result of the failure in a step of the treatment process

  The research aims to know The Effect Of Flexible Grouping Strategy  and Three Step Interview strategy on achievement of the history material among student of the first literary class, The Researcher used the experimental design of the two experimental groups and the control group and with post test, researcher group (a) represent the experimental group taught according the Flexible Grouping Strategy  , and Division (c) to represent the second experimental group which studied according Three Step Interview strategy and Division (b ) to represent the control group taught in the traditional method, the number of students (99) students of (33) female students in each division. T

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

In the present study, nanoporous material type MCM-41 was prepared by the sol-gel technique and was used as a carrier for prednisolone (PRD) drug delivery. The structural properties of mesoporous were fully characterized by X-ray diffraction (XRD), N₂ adsorption /desorption and Fourier-transform infrared (FTIR). The mass transfer in term of adsorption process (loading) and desorption process (releasing) properties were investigated. The maximum drug loading efficiency was equal to 38% and 47.5% at different concentrations. The PRD released was prudently studied in water media of pH 6.8 simulated body fluid (SBF) in according to "United State Pharmacopeia (USP38)". The results proved that the release of prednisolone from MCM-41

The main objective of this study is to understand the work of the pile caps made of lightweight aerated foam concrete and study the many factors affecting the ability and the capacity of the shear. The study was done by analyzing previous practical and theoretical experiences on the reinforced concrete pile caps. The previous practical results indicated that all specimens failed by shear diagonal compression or tension modes except one specimen that failed flexural-shear mode. Based on test specimens' practical results and behavior, some theoretical methods for estimating the ultimate strength of reinforced concrete pile caps have been recommended, some of which evolved into the design documents available on the subject.