Journal of Kerbala University

Magnetic resonance imaging (MRI) has been utilized as a non-invasive imaging technique to detect and diagnose central nervous system disorders, as well as to monitor their treatment course. Neurologists can more accurately detect abnormalities from brain imaging because of the three-dimensional images that MRI creates. The machine learning techniques such as K-Means, naive Bayesian, logistic, Decision tree, or random forest. Furthermore, deep learning used CNN to segment images into specific regions, such as “UNet”, “ResNet”, “GoogleNet”, etc. A computer-aided method for analyzing MRI images and precisely identifying abnormalities has been made possible by advancements in machine learning and rapid processing. Image segmentation has become more popular and a focal point of research in medical image analysis. The ability to rapidly classify the disease for early treatment is made possible by the computer-aided technique for identifying brain abnormalities. The research articles on brain tumor segmentation from MRI images are reviewed in this article. The comparison of segmentation methods in accuracy is in thresholding is about 0.75, in k-means clustering is about 0.8, in a U-Net is about 0.9, and in V-Net is about 0.92, respectively.

Interaction of nanoparticles with the blood coagulation is important prior to their using as the drug carriers or therapeutic agents. The main aim of this research is to study the primary effects of silver nanoparticles (AgNPs) on haemostasis in vitro. Blood samples were obtained from Iraqi individuals (n=90) (32 females, 58 males; smokers 45 and non-smokers 45) aged between 25 to 65 years. The effect of AgNPs on blood coagulation was investigated directly estimating the activation of Prothrombin Time (PT) and Activated Partial Thromboplastin Time (APTT) and to verify any possible effect of AgNPs on human platelets.
In addition, the influence of various factors including health status, gender, and smoking activity on the values of PT and APTT levels in blood samples was also investigated. The results show that the values of PT and APTT were significantly increased by using 100 μg/ml of AgNPs. In contrast, there is no significant effect of health status, gender, and smoking activity on the level of PT and APTT at P = 0.05.

Q-switching is a technique employed for control a laser system, enabling the generation of brief, high-energies laser pulses. The purpose of this study is to identify optimal values for certain rate equation parameters using an optimization technique. The Runge – Kutta – Fehlberg approach was employed to simulate the rate equations to find pulse characteristics of a Cr: LiSAF laser with Cr: YSO as a saturable absorber under a certain condition. The Rosenbrock optimization technique was used to find the optimal values of the decision variables (β, γa, γc) for different saturable absorber molecules number. The values of γa and γc are found to decrease with the increasing number of molecules due to the decrease in the absorption of photons by the material as a result of reaching the saturation state. For the β parameter, the increase in the saturable absorber molecules number leads to an increase in its value. According to the computer code, the calculated energy value was 4.19 mJ compared to the published 4.3 mJ, while the pulse duration is 35.1 nsec compared with 35 nsec.

Plasma diagnostics play a pivotal role in understanding plasma behavior and properties in various environments, which enables us to control the plasma parameters required for a suitable application, such as nanoparticle production. In this work, the underwater plasma was generated by exploding gold wires in a SiO₂ suspension. The process is controlled by an Arduino-based system that precisely regulates the circuit, ensuring accurate replication of each pulse. The optical emission spectroscopy (OES) is used to diagnose the plasma simultaneously.
Boltzmann plot was used to calculate the electron temperature, which ranged from 8.374 eV to 10.321 eV and the electron density is determined through Stark broadening, which increased from 0.166 ×1018 (cm-3) to 0.449×1018 (cm-3), indicating a direct proportionality between the applied current and the plasma parameters.
The X-ray diffraction (XRD) analysis showed that the nanostructures contained an FCC Au structure with crystallite sizes ranging from 12 to 34 nm.

Celiac disease is triggered by gluten -derived peptide (e.g., gliadin ) in genetically susceptible HLA DQ2 and DQ8 histocompatibility antigen, and enteropathy. This study aimed to investigate the relationship Anti-tissue Transglutaminase (Anti-tTG IgA),Anti-tissue Transglutaminase (Anti-tTG IgG) with membrane attack complex (MC5b-) and IL-17 and IL-21.From 1st August to 1st January, 2024, this study was carried out at Marjan Hospital in the Babylon Governorate. This study concluded that anti- IgG and IgA concentrations increased gradually with marsh (0,1,2,3) were (12.77, 26.60, 39.45, 60.56/ 9.17,11.38,41.96, 64.7) respectively, while (MC5b-9) and . (IL-17) and IL-21 were high significantly increased with marsh 3 only . (430.50) (77.20, 217.64) respectively. This study concluded increased serum anti-tTG IgG , IgA with all marsh gradually and IL-17, IL-21 and MC5b-9 concentrations with Marsh 3.

Within the domain of text mining and natural language processing, sentiment analysis is currently very relevant: it allows us to understand the opinions or feelings of folks as communicated in texts. Sentiment analysis, which is often directed at English and under-researched for Arabic (due to language complexity, large dialects, and low-quality annotated datasets), Most prior works. To tackle these problems, we propose the hybrid deep learning approach with Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) from Scenes to Encoder-Decoder Refinement using Bidirectional Encoder representations from Transformers (BERT) in this research. CNN-LSTM, for example, reaches the state-of-the-art accuracy of 95.5% on our dataset of 330k Arabic product reviews while BERT hits 92%. This research adds to it (1) the development of Arabic deep learning models for sentiment classification accuracy in social media, (2) proof of concept that hybrid deep learning is effective on Arabic text, and (3) helps alleviate the dearth of annotated datasets for this task on an enormous scale using extensive data. This research indicates that deep learning models can surmount several hurdles of Arabic sentiment analysis and serve as a stepping stone for more robust, yet scalable solutions.

Given the rising incidence of polypharmacy, it is more crucial than ever to take medication interactions into account. A drug-drug interaction (DDI) occurs when one drug alters the pharmacokinetics or pharmacodynamics of another, affecting its function. Drugs and other compounds found in foods, supplements, herbs, and tobacco products can also interact. Drug–disease and drug–laboratory test interactions are examples of further drug interactions. Drug-drug interactions (DDIs), which occur 20–40% of the time, are among the most common causes of prescription mistakes in developed countries, particularly for elderly patients. who are receiving multiple medications. Specifically, poly-therapy makes therapeutic management more complicated and raises the possibility of clinically significant DDIs, which can either decrease clinical effectiveness or cause adverse medication responses. Substance-substance Specifically, by complicating therapeutic management. Pharmacokinetics and pharmacodynamics are the two primary categories into which drug-drug interactions fall. When one medication changes the pace or degree of another's absorption, distribution, metabolism, or excretion, this is known as a pharmacokinetic drug interaction. With a focus on their clinical implications, This review aims to provide an overview of clinically important pharmacokinetic drug excretion interactions and their implications that are clinically important.

This study evaluated the efficacy of various treatments against Sarcoptes scabiei var. cuniculi in 30 local breed rabbits (1–2 years old, 1–2 kg body weight). Infected animals showing mange lesions were randomly assigned to six groups (n=5). Group I received 1% ivermectin subcutaneously; Group II was treated with pyrethroid dipping; Group III with a topical 10% lindane-sulfur ointment; Group IV with 25% benzyl benzoate lotion; Group V with 20% Sibahbah fruit ointment; and Group VI with 20% Cotoneaster fruit ointment. Clinical signs, mite and ova counts, and hematological changes were monitored.Ivermectin was the most effective treatment, resulting in complete clinical recovery and disappearance of pruritus and lesions by day 16 post-treatment. One rabbit in this group died on day 8 due to emaciation despite improved skin lesions. Pyrethroid treatment showed limited efficacy, with continued itching and lesions in some animals; two rabbits died by day 10. Lindane-sulfur ointment was ineffective, as mite and ova counts remained high and all animals died. Benzyl benzoate reduced lesions slightly but failed to eliminate mites or their ova. The Sibahbah ointment significantly reduced mite and ova counts (from 170 to 60), showing partial efficacy. Cotoneaster ointment had no observable effect. In all ineffective treatments, total leukocyte counts remained elevated and did not return to normal levels.These results indicate that ivermectin is the most effective treatment against rabbit mange, while some herbal preparations like Sibahbah may have supportive roles in integrated mange control.

Stem cells are a category of cells that facilitate the developing of organs and systems inside the body. When given particular stimuli, they may differentiate into different types of cells and have the unusual capacity to continuously self-renew. In the fields of tissue repair and regenerative medicine, stem cells are being thoroughly researched. They have enormous potential for use in congenital malformations, age-related illnesses, traumas, and neurodegenerative diseases. Furthermore, because cancer stem cells are linked to malignancies, studying stem cells is crucial for cancer research. The capacity of stem cells to differentiate into distinct cell subsets, or potency, can be used to categorize them. The aim of this research is to give an overview of adult and embryonic biology. For medical professionals working in this area, this provides an overview of stem cells and their potential therapeutic uses. Treatments based on stem cells are now a practical means of repairing damaged tissue, producing more insulin, and reducing the effects of diabetes. With an emphasis on the various types of stem cells used in diabetes treatment and the fundamental processes through which they mediate their therapeutic effects, this review explores the development of stem cell research. This study aims to give a comprehensive knowledge of stem cells' function in diabetes therapy and their potential to enhance patient outcomes in the future.

An experiment was carried out in plastic pots in the College of Agriculture , University of Kerbala, to investigate the effect of foliar spraying with Nano nitrogen and potassium on some phenotypic characters of Calendula officinalis L. under the water stress condition in sandy loam soil in winter growing season (2023-2024). Factorial experiment in a CRD with 3 replications was used for the study. The experiment consisted of three factors with three replicates. The first factor consisted of two levels of irrigation water levels (50% and 100% of soil field capacity) denoted as S1 and S2 (Table 1). The second factor was foliar application using three levels of liquid Nano nitrogen fertilizer (0, 2 and 4 ml) L-1, denoted as N0, N1, and N2 respectively. The third factor was foliar application of the complex Nano-potassium fertilizer at three concentrations, 0, 2, and 4 g L-1, designated K0, K1, and K2, respectively. These levels were randomly assigned to all experimental units. The design involved 54 experimental units. Data were subjected to statistical analysis as per the design adopted and means were compared at 5% level of probability. The results indicated a significant impact of the factors examined in this study, as well as their interactions, on various phenotypic traits, including plant height, stem diameter, number of leaves, and number of flowers. The average values of these traits decreased under conditions of water stress when irrigation was applied at 50% of the soil field capacity. Additionally, the values further declined with lower concentrations of foliar spray containing Nano nitrogen and potassium.