Bachelor's

Sep 1, 2011 - Jul 1, 2019

Master's

Sep 1, 2020 - Nov 14, 2022

Al Noor University / Department of Dental Technology [lecture]

Feb 19, 2023 - Sep 1, 2024

Assistant lecturer/lecture in the scientific departments of the university's colleges, such as the Department of Medical Laboratory Technical, Dentistry, Pharmacy and Anesthesia.

Spine Surgery Uses of Artificial Learning and Machine Learning: A LDH Treatment

Oct 13, 2023

Journal Published in: 2023 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

publisher IEEE

DOI 10.1109/DISCOVER58830.2023.10316719

The study evaluates the efficacy of various conventional techniques and ML (machine learning) models in predicting patients' 1-year follow-up outcomes based on preoperative factors. The study used the DaneSpine to identify sufferers who met the inclusion criteria and underwent (LDH) Lumbar Disc Herniation surgery. The model's initial training consisted of 16 distinct features, such as presurgical and demographic measures based on patient self-reports. The criteria for inclusion in this study encompassed sufferers who underwent LDH (Lumbar Disc Herniation) surgical treatment, recognized through the DaneSpine (Danish national registry for spine surgery). The patients were divided into groups based on whether they achieved the least clinically significant variation for EuroQol, VAS Back, Oswestry Disability Index (ODI), VAS (Visual Analog Scale) Leg, and their capacity to resume work duties after a one-year follow-up period. A random splitting method was used to create three subsets from the data, comprising testing, validation, and training sets with a ratio of 15%, 35%, and 50%, respectively. To compare the performance of various models like decision trees, deep learning, random forest, support vector machines, and boosted tree models were trained, while LR and MARS models were employed. Model fitness was evaluated by examining the performance and AUC for the duration of validation. The study generated seven models, with classification errors ranging from a minimum of 1% to a maximum of 4% standard deviation over the validation folds. Both deep learning and MARS (Multivariate Adaptive Regression Splines) models consistently performed well. The study developed two conventional and five ML (Machine Learning) predictive models to predict improvement in patients with LDH (Lumbar Disc Herniation) at the 1-year follow-up. The results indicate that building an ensemble of models requires minimal effort and is an initial basis for additional model selection and optimization.

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Prevalence of ovine theileriosis in Mosul city, Iraq

Jan 1, 2023

Journal Iraqi Journal of Veterinary Sciences

publisher Iraqi Journal of Veterinary Sciences

DOI DOI: 10.33899/ijvs.2022.134478.2370

Issue 1

Volume 37

The present study aimed to determine the prevalence of ovine theileriosis (OT) in sheep in Mosul city, Iraq using microscopic examination (ME) of the blood smears stained with MGG- Quick stain and conventional polymerase chain reaction technique (c-PCR) to compare between c-PCR technique and ME as techniques for the diagnosis of disease, and to investigate the pattern and type of infections based on multiplex polymerase chain reaction technique (m-PCR). From October 2021 to May 2022, one-handed eighty-five Blood samples were drawn randomly from sheep in various regions of Mosul city. The overall prevalence of OT was 42% (22.7 out of 185) and 52.4% (97 out of 185) using microscopic examination and c-PCR technique, respectively. A slight agreement was observed between ME of blood smears and c-PCR technique according to Kappa value 0.190, with low sensitivity, specificity, and accuracy of ME method was 30%, 88.6%, 58.4%, respectively, compared with c-PCR technique. The prevalence of mixed infection 22.7% and single infection with T. lestoquardi 20% were significantly higher (P<0.05) than single infection with T. ovis 9.7%. This study concludes that OT is widespread in Mosul city, Iraq, and the c-PCR technique is more reliable and suitable for detecting Theileria infection in sheep than the ME method.

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Phylogenetic study of theileria ovis and theileria lestoquardi in infected sheep and it is associated ticks in mosul city, Iraq

May 9, 2022

Journal International journal of health sciences

publisher Iraqi Journal of Veterinary Sciences

DOI https://doi.org/10.53730/ijhs.v6nS7.12510

Issue 7

Volume 6

This is a first molecular report investigate the phylogenic analysis of Theileria spp. in sheep and it's infested ticks in Mosul city-Iraq. A total of 185 blood samples were collected from sheep in different areas of Mosul city. A sixty five Ixodid ticks were also collected from different parts of infected animals. The overall prevalence of Theileria spp. was 52.4% (97 out of 185), for Theileria ovis it was 9.7%, Theileria lestoquardi it was 20% and mixed infection it was 22.7% in sheep in Mosul city, based on conventional-PCR and multiplex-PCR techniques. the infestation rat of Ixodid ticks on sheep was 11.8% (22 out of 185) and three species of Ixodid ticks (n=65) were identified and classified: Hyalomma anatolicum anatolicum 37 (56.9%), Rhipicephalus sanguineus 16 (24.6%), Rh. turanicus 12 (18.4%) based on microscopic examination. BLASTn individual sequencing analysis of six sequences of 18S rRNA gene, composing sequences of T. ovis (n=3) (One extracted from sheep blood and two extracted from engorged female ticks), and sequences of T. lestoquardi (n=3) (One extracted from sheep blood and two extracted from engorged female ticks).

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