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AI in teaching English is reshaping language learning. While interest in AI-supported education is growing worldwide, research in this area is still emerging in Iraq. This review synthesizes empirical AI-based intervention studies to enhance English language learning in Iraqi higher education, and the perceptions of stakeholders regarding AI tools in language instruction. The reviewed intervention studies, comprising studies employed different AI platforms to support grammar instruction, speaking fluency, writing feedback, and pragmatic competence. These interventions yielded improvements in learners’ performance, motivation, and communicative confidence. In parallel, perception-focused studies revealed positive attitudes toward A

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

    The emergence of COVID-19 has resulted in an unprecedented escalation in different aspects of human activities, including medical education. Students and educators across academic institutions have confronted various challenges in following the guidelines of protection against the disease on one hand and accomplishing learning curricula on the other hand. In this short view, we presented our experience in implementing e-learning to the undergraduate nursing students during the present COVID-19 pandemic emphasizing the learning content, barriers, and feedback of students and educators. We hope that this view will trigger the preparedness of nursing faculties in Iraq to deal with this new modality of learning and improve it should t

Background: Smoking is considering a major risk factor for development and progression of periodontal disease. Investigations regarding the association between smoking and periodontal disease have consistently demonstrated negative periodontal effects and greater probabilities of established periodontal disease among smokers in comparison with non smokers. The purpose of this study was to evaluate the effects of smoking on periodontal health status and on the salivary levels of alkaline phosphatase (ALP), lactate dehydrogenase (LDH) and creatine kinase (CK), and to correlate the clinical parameters of periodontal health with the biochemical findings in smokers and non-smokers. Materials and methods: Unstimulated saliva sample was collected

The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a co

Background: Immune thrombocytopenia is an immune-related disorder that causes an impairment in platelet production and stimulates platelet destruction, causing variable bleeding symptoms. Objective: This study focuses on refractory immune thrombocytopenic purpura patients on romiplostim treatment and their level of illness perception related to treatment response. Method: A cross-sectional study was conducted from May 1st, 2025, to August 1st, 2025. Brief Illness Perception Questionnaires were administered to 84 patients with ITP to collect the data. The study took place at the Hematology and Bone Marrow Transplant Center, Medical City, Baghdad, Iraq. Results: The romiplostim response rate is 21 (25.0%), while the partial response rate is 4

Background: During the COVID-19 outbreak, the number of patients who have developed acute coronary syndromes (ACS) has soared rapidly, cardiovascular disease and mortality are influenced by the elevated inflammatory biomarkers. The aim of this study is to compare inflammatory markers between patients with ACS who hadn’t previously had COVID-19 and those who’d be infected within the preceding three months; as well as, evaluating the effect of statins on inflammatory biomarkers.

Methods: This is a comparative cross-sectional study of 42 patients who presented with ACS and had previously had COVID-19 and 48 patient who had never had CO

Background: The rising rate of cesarean deliveries has generated concern about whether all procedures are medically justified. Limited data exist on how well first-time mothers understand the indications for their cesarean section in Iraq. Objective: To assess maternal knowledge of the medical reasons for the first cesarean delivery and its relationship with sociodemographic, obstetric, and neonatal characteristics. Methods: A cross-sectional study was conducted from October to December 2023 among 158 Iraqi women who underwent their first cesarean delivery. Data were collected using a structured, self-administered online questionnaire that assessed demographic, obstetric, and knowledge-related factors. Results: Less than half of the