Bioorganic and Medicinal Chemistry Reports
A scientific open access journal in the field of Bioorganic and Medicinal ChemistryLATEST ARTICLES
Impact of COVID-19 on the thyroid gland in Iraqi females
Thyroid dysfunction has been observed in COVID-19 patients, prompting investigations by endocrinologists. The management of thyroid disease may be affected by pandemic-related restrictions and healthcare reorganization. In this study, we explored the association between COVID-19 and thyroid problems in Iraqi patients, with a particular focus on potential gender-specific effects and the impact of COVID-19 treatment on thyroid function. Our results revealed that SARS-CoV-2 can lead to reversible thyroid dysfunction, including subclinical and atypical thyroiditis. Significantly, our study demonstrated that COVID-19 patients had lower levels of triiodothyronine (T3) and tetraiodothyronine (T4) compared to a healthy control group. Additionally, we found that TSH levels in COVID-19 patients were higher than in non-COVID-19 patient group. whereas the CRP and Il-6 levels were increased in COVID-19 patients in comparison to the control group (p<0.05). These findings highlight the heightened susceptibility of Iraqi women to develop thyroid-related conditions and associated issues in the context of COVID-19. Our study aimed to investigate the correlation of TSH, T4, T3 among COVID-19 survivors (150 Individuals; 75 Healthy and 75 who had COVID-19 virus). Our results suggest that COVID-19 virus may have an important impact on thyroid health and the hypothalamus-pituitary-thyroid (HPT) axis in women which might be aggravated by the severity and chronicity of the disease.
DOI http://doi.org/10.25135/bmcr.31.23.01.2775 Keywords COVID-19 SARS-CoV-2 thyroiditis chronic lymphocytic hypothyroidism hyperthyroidism DETAILS PDF OF ARTICLE © 2023 ACG Publications. All rights reserved.Fluorinated benzimidazole derivatives: In vitro antimicrobial activity
Given the pharmacological significance of fluorine-containing heterocycles, in the present paper, a series of benzimidazole having fluoro-benzene moiety within a single molecular framework were screened for their in vitro antimicrobial activity against some Gram-positive and Gram-negative bacteria and fungal strains. The results of antimicrobial activity demonstrated that fluoro-substituted compounds (13-15 and 17-19) have good antibacterial and antifungal properties as compared to unsubstituted parent compounds (12 and 16). Compound 18 with meta-fluoro substitution group on the phenyl ring of the side chain displayed high activity against Gram-negative bacteria with a MIC value of 31.25 μg/mL. Similarly, 2-(m-fluorophenyl)-benzimidazole derivatives 14 and 18 showed good anti-B. subtilis with a MIC value of 7.81 μg/mL. SAR studies suggested that the presence of methyl substitution group at position 5 of benzimidazole is recommended for significant antifungal activity against C. parapsilosis. The high potency suggested that compound 18 could be a starting point for further optimization to develop novel antimicrobial agents.
DOI http://doi.org/10.25135/bmcr.30.23.01.2692 Keywords Benzimidazole fluorine MIC antibacterial antifungal DETAILS PDF OF ARTICLE © 2023 ACG Publications. All rights reserved.Comparing machine learning models for acetylcholine esterase inhibitors
Acetylcholinesterase is the main neurotransmitter in the cholinergic system. Impairment of the cholinergic system can be a reason for Alzheimer's and multiple sclerosis. Alzheimer's disease and multiple sclerosis affect patients and their relatives' daily lives enormously. New therapies with more benefits than current therapies for these diseases would facilitate patients' lives. In this respect, discovering novel acetylcholine esterase inhibitors with more effective and fewer side effects is highly important. Machine learning algorithms are very useful to predict the activity of molecules for a biological target. In this study, our classification models were built with Deep Neural Networks (DNN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost) to predict molecules as active or inactive for acetylcholinesterase inhibitors. These models were evaluated with various metrics. As a result, The DNN model showed a better ability to classify (accuracy=0.93, F1 score=0.88, MCC=0.8, Roc-Auc=0.89 in the test set) molecules than the other models.
DOI http://doi.org/10.25135/bmcr.29.22.06.2483 Keywords Alzheimer’s disease machine learning deep learning acetylcholinesterase inhibitors multiple sclerosis DETAILS PDF OF ARTICLE © 2022 ACG Publications. All rights reserved.Determination of Lidocaine HCl in commercially cream and injection forms by GC-FID method
In the present study, GC-FID method for the determination of lidocaine HCl in commercially creams and injection forms was developed and validated. The linearity of method was observed in the concentration range of 0.1-5.0 µg/mL. The accuracy (RE%) and precision (RSD%) values of the within-day and between-day of GC-FID method are less than 10.0% and 3.0%, respectively, and also limit of detection (LOD) and the limit of quantitation (LOQ) values were observed as 0.03 and 0.11 µg/mL, respectively. The analytical recovery value of lidocaine HCl was determined as 99.47% on average. As a result, it was concluded that the developed and validated GC-FID method can be easily used in routine analyzes in quality control laboratories.
DOI http://doi.org/10.25135/bmcr.27.2204.2418 Keywords Lidocaine HCl local anesthesia cream injection GC-FID Method DETAILS PDF OF ARTICLE © 2022 ACG Publications. All rights reserved.