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Last update 23 Jun 2025
University of Mosul
Last update 23 Jun 2025
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ChiCTR2400093848
Management of acne scars by sequential combination of micro-needle radiofrequency followed by fractional CO2 laser versus each therapy alone
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Two Lactobacilli Strains as Adjuvant Therapy in the Management of Irritable Bowel Syndrome: A Randomized Clinical Trial
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University of Mosul, College of Physical Education and Sport Sciences & University of Sfax, High Institute of Sport and Physical Education at Sfax
100 Clinical Results associated with University of Mosul
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25 Dec 2025Oriental Journal of Chemistry
Synthesis and Insilco Study of Some New Derivatives Containing Barbiturate Moiety Compounds
Author: Dawood, Nagham M. Zaki ; Al-Sabawi, Ammar H. ; Younis, Dalal Th.
Computational chemistry has achieved great success in the field of drug design, as it has played an effective role in shortening the effort, money and time to discover compounds with good properties as a proposed drug. The research dealt with the preparation of nine new compounds derived from barbituric acid using simple methods, seven of which are azo compounds and three are ketones prepared from some pharmaceutical carboxylic acids (Indomethacin, Ibuprofen and Mefenamic acid). The chemical formulas were confirmed by physical methods (color change and melting point measurement) and some spectroscopic methods (FT-IR and 1HNMR spectra). Theoretical tests were conducted using the programs (Swiss ADME test) and (cardiotoxicity prediction), where the two programs predicted the unsuitability of some of the prepared compounds as drugs due to their poor kinetic properties in addition to containing toxic parts that affect some functions of the heart muscle. Accordingly, they must be excluded or their composition modified before completing biological and clinical studies.
01 Dec 2025FUNCTIONAL & INTEGRATIVE GENOMICS
New approaches of chimeric antigen receptor (CAR)-immune cell-based therapy in gastric cancer; highlight CAR-T and CAR-NK
Review
Author: Mustafa, Yasser Fakri ; Mohammed, Jaafaru Sani ; Eldesoqui, Mamdouh ; Zwamel, Ahmed Hussein ; Pallathadka, Harikumar ; Sivaprasad, G V ; Abdukarimovna, Rakhimova Khusnidakhon ; Pramanik, Atreyi ; Kumar, Ashwani ; Jasim, Saade Abdalkareem
One characteristic that makes gastric cancer (GC) against other cancers is the intricate immune system's reaction, particularly to tenacious inflammation. Consequently, the immunological function is essential to the growth of this malignancy. Tumor immunotherapy has yielded several encouraging outcomes, but despite this, different patients continue to not respond to treatment, and a far larger number become resistant to it. Also, activated CAR-T cells express a majority of immunological checkpoint factors, containing PD1, CTLA4, and LAG3, which counteracts the anti-tumor actions of CAR-T cells. Moreover, cytokine release syndrome is one of the possible adverse responses of CAR-T cell therapy. Therefore, producing universal allogeneic T lymphocytes with potent anti-tumor activity is essential. This study demonstrates current research on this cutting-edge technology, including the composition and mode of action of CAR-NK and CAR-T cells in GC. Also, in this study, we examined recent studies about various specific GC biomarkers that target CAR-T cells and CAR-NK cells.
01 Oct 2025INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
Evaluation of insulin sensitivity temporal prediction by using quantile regression combined with neural network model
Article
Author: Chase, J Geoffrey ; Alkhafaf, Omer S ; Benyó, Balázs
BACKGROUND:
Stress-induced hyperglycemia, a pathologically high blood glucose level, is a frequent complication in intensive care units. Blood glucose (BG) level control is crucial but challenging due to patient variability. The Stochastic TARgeted (STAR) protocol is clinically used for blood glucose control, which uses the current and predicted future patient insulin sensitivity (SI) parameter to assess BG outcomes of alternative treatment options.
OBJECTIVE:
Neural network (NN) models using quantile regression (QR) have enhanced SI prediction performance. However, remains a challenge in determining the optimal NN configuration to best predict SI. This study aims to find the NN configuration yielding the highest prediction accuracy to improve the STAR protocol and explores the behaviour of the QR method in predicting the percentiles of a non-Gaussian multi-mode distribution of a physiological parameter.
METHOD:
Alternative NN architectures combined with QR were implemented and trained on a large dataset comprising 1,897 patients collected between 2011 and 2023 using five-fold cross-validation ensuring model robustness. Prediction performance was evaluated among NN configurations and compared using case-specific metrics across the global SI domain as well as within subdomains to analyse the models' local performance.
RESULTS:
Outcomes indicate QR applied to simpler NN, consisting of one-hidden layer with four neurons, achieves a best prediction performance at a minimum network size. Using more complex NN did not improve the prediction performance significantly. However, at long prediction horizons, no compact network demonstrated improved outcomes. A more general methodological outcome of the study is that QR-based prediction does not need to be combined with complex NN to achieve the best prediction performance.
CONCLUSION:
The QR-based method was found to be appropriate for the SI prediction problem in short-term predictions which may improve the STAR protocol's clinical outcomes. Overall, the study provides a generalisable, empirical approach to network configuration optimisation for similar problems.
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