Hanan Hassan Fouad

Osteoarthritis is a substantial burden for patients with the disease. The known medications for the disease target the mitigation of the disease's symptoms. So, drug development for the management of osteoarthritis represents an important challenge in the medical field. This work is based on the development of a new benzofuran-pyrazole-pyridine-based compound with potential anti-inflammatory and anti-osteoarthritis properties. Microanalytical and spectral data confirmed the chemical structure of compound . The biological assays indicated that compound produces multifunctional activity as an anti-osteoarthritic candidate via inhibition of pro-inflammatory mediators, including RANTES, CRP, COMP, CK, and LPO in OA rats. Histopathological and pharmacokinetic studies confirmed the safety profile of the latter molecule. Accordingly, compound is considered a promising anti-osteoarthritis agent and deserves deeper investigation in future trials.

A prognostic scoring system that can differentiate β-thalassemia patients based on mortality risk is lacking. We analysed data from 3145 β-thalassemia patients followed through a retrospective cohort design for the outcome of death. An a priori list of prognostic variables was collected. β Coefficients from a multivariate cox regression model were used from a development dataset (n = 2516) to construct a formula for a Thalassemia International Prognostic Scoring System (TIPSS) which was subsequently applied to a validation dataset (n = 629). The median duration of observation was 10.0 years. The TIPSS score formula was constructed as exp (1.4 × heart disease + 0.9 × liver disease + 0.9 × diabetes + 0.9 × sepsis + 0.6 × alanine aminotransferase ≥42 IU/L + 0.6 × hemoglobin ≤9 g/dL + 0.4 × serum ferritin ≥1850 ng/mL). TIPSS score thresholds of greatest differentiation were assigned as <2.0 (low-risk), 2.0 to <5.0 (intermediate-risk), and ≥5.0 (high-risk). The TIPSS score was a good predictor for the outcome of death in the validation dataset (AUC: 0.722, 95%CI: 0.641-0.804) and survival was significantly different between patients in the three risk categories (P < 0.001). Compared to low-risk patients, the hazard ratio for death was 2.778 (95%CI: 1.335-5.780) in patients with intermediate-risk and 6.431 (95%CI: 3.151-13.128) in patients with high-risk. This study provides a novel tool to support mortality risk categorization for patients with β-thalassemia that could help management and research decisions.

BACKGROUND: Eye lesions, occur in nearly half of patients with Behçet's Disease (BD), can lead to irreversible damage and vision loss; however, limited studies are available on identifying risk factors for the development of vision-threatening BD (VTBD). Using an Egyptian college of rheumatology (ECR)-BD, a national cohort of BD patients, we examined the performance of machine-learning (ML) models in predicting VTBD compared to logistic regression (LR) analysis. We identified the risk factors for the development of VTBD.

METHODS: Patients with complete ocular data were included. VTBD was determined by the presence of any retinal disease, optic nerve involvement, or occurrence of blindness. Various ML-models were developed and examined for VTBD prediction. The Shapley additive explanation value was used for the interpretability of the predictors.

RESULTS: A total of 1094 BD patients [71.5% were men, mean ± SD age 36.1 ± 10 years] were included. 549 (50.2%) individuals had VTBD. Extreme Gradient Boosting was the best-performing ML model (AUROC 0.85, 95% CI 0.81, 0.90) compared with logistic regression (AUROC 0.64, 95%CI 0.58, 0.71). Higher disease activity, thrombocytosis, ever smoking, and daily steroid dose were the top factors associated with VTBD.

CONCLUSIONS: Using information obtained in the clinical settings, the Extreme Gradient Boosting identified patients at higher risk of VTBD better than the conventional statistical method. Further longitudinal studies to evaluate the clinical utility of the proposed prediction model are needed.

As part of our ongoing endeavour to identify novel inhibitors of cancer-associated CA isoforms IX and XII as possible anticancer candidates, here we describe the design and synthesis of small library of 2-aryl-quinazolin-4-yl aminobenzoic acid derivatives (, , and ) as new non-classical CA inhibitors. On account of its significance in the anticancer drug discovery and in the development of effective CAIs, the 4-anilinoquinazoline privileged scaffold was exploited in this study. Thereafter, the free carboxylic acid functionality was appended in the (), (), or -positon () of the anilino motif to furnish the target inhibitors. All compounds were assessed for their inhibitory activities against the hCA I, II (cytosolic), IX, and XII (trans-membrane, tumour-associated) isoforms. Moreover, six quinazolines (, , and ) were chosen by the NCI-USA for anti-proliferative activity evaluation against 59 human cancer cell lines representing nine tumour subpanels.