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The novel severe acute respiratory syndrome coronavirus (SARS CoV-2) was introduced as an epidemic in 2019 and had millions of deaths worldwide. Given the importance of this disease, the recommendation and design of new active compounds are crucial. 3-chymotrypsin-like protease (3 CLpro) inhibitors have been identified as potent compounds for treating SARS-CoV-2 disease. So, the design of new 3 CLpro inhibitors was proposed using a quantitative structure-activity relationship (QSAR) study. In this context, a powerful adaptive least absolute shrinkage and selection operator (ALASSO) penalized variable selection method with inherent advantages coupled with a nonlinear artificial neural network (ANN) modelling method were used to provide a QSAR model with high interpretability and predictability. After evaluating the accuracy and validity of the developed ALASSO-ANN model, new compounds were proposed using effective descriptors, and the biological activity of the new compounds was predicted. Ligand-receptor (LR) interactions were also performed to confirm the interaction strength of the compounds using molecular docking (MD) study. The pharmacokinetics properties and calculated Lipinski's rule of five were applied to all proposed compounds. Due to the ease of synthesis of these suggested new compounds, it is expected that they have acceptable pharmacological properties. [ABSTRACT FROM AUTHOR]

Aflatoxin B 1 (AFB 1 ) is one of the most hazardous mycotoxins for humans and livestock that mainly produced by members of the genus Aspergillus in a variety of food commodities. In this study, the effect of S. rosmarinus, T. fruticulosum, and T. caucasicum essential oils (EOs) was studied on fungal growth, AFB 1 production and aflR gene expression in toxigenic A. flavus IPI 247. The AFB 1 producer A. flavus strain was cultured in YES medium in presence of various two-fold concentrations of the plant EOs (62.5-500 μg/mL) for 4 days at 28 °C. EO composition of plants was analyzed by Gas Chromatography/Mass Spectrometry (GC/MS). The amount of fungal growth, ergosterol content of fungal mycelia and AFB 1 content of EO-treated and non-treated controls were measured. The expression of aflR gene was evaluated using Real-time PCR in the fungus exposed to minimum inhibitory concentration (MIC 50 ) of EOs. The main constituents of the oils analyzed by GC/MS analysis were elemicin (33.80 %) and 2,3-dihydro farnesol (33.19 %) in T. caucasicum, 1,8-cineole (17.87 %), trans-caryophyllene (11.14 %), α and ẞ-pinene (10.92 and 8.83 %) in S. rosmarinus, and camphor (17.65 %), bornyl acetate (15.08 %), borneol (12.48 %) and camphene (11.72 %) in T. fruticulosum. The results showed that plant EOs at the concentration of 500 μg/mL suppressed significantly the fungal growth by 35.24-71.70 %, while mycelial ergosterol content and AFB 1 production were inhibited meaningfully by 36.20-65.51 % and 20.61-89.16 %. T. caucasicum was the most effective plant, while T. fruticulosum showed the lowest effectiveness on fungal growth and AFB 1 production. The expression of aflR in T. caucasicum and S. rosmarinus -treated fungus was significantly down-regulated by 2.85 and 2.12 folds, respectively, while it did not change in T. fruticulosum-treated A. flavus compared to non-treated controls. Our findings on the inhibitory activity of T. caucasicum and S. rosmarinus EOs toward A. flavus growth and AFB 1 production could promise these plants as good candidates to control fungal contamination of agricultural crops and food commodities and subsequent contamination by AFB 1 . Down-regulation of aflR as the key regulatory gene in AF biosynthesis pathway warrants the use of these plants in AF control programs. Further studies to evaluate the inhibitory activity of studied plants EOs in food model systems are recommended., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The world's large lakes and their life-supporting services are rapidly threatened by eutrophication in the warming climate during the Anthropocene. Here, MODIS-Aqua level 3 chlorophyll- a data (2018-2021) were used to monitor trophic state in our planet's largest lake, that is, the Caspian Sea that accounts for approximately 40% of the total lacustrine waters on Earth. We also used the in situ measurements of chlorophyll- a data (2009-2019) to further verify the accuracy of the data derived from the MODIS-Aqua and to explore the deep chlorophyll- a maxima (DCMs) in the south Caspian Sea. Our findings show an acceptable agreement between the chlorophyll- a data derived from the MODIS-Aqua and those measured in situ in the coast of Iran (coefficient of determination = 0.71). The oligotrophic, mesotrophic, and eutrophic states cover 66%, 20%, and 13% of the sea surface area, respectively. The DCMs are dominantly regulated by water transparency and they generally observe at depths of less than 20 and 30 m during the cold (autumn and winter) and warm (spring and summer) seasons, respectively. Our results suggest an ever-increasing chlorophyll- a in the shallow zones (i.e., coasts) and even in deep regions of the sea, mainly due to nutrient inputs from the Volga river delta. Alarming increase of chlorophyll- a in this transboundary lake can amplify eutrophication under the lens of global warming and further threaten the lake ecosystem's health, where almost all legal agreements have not yet been implemented to protect the lake environment and its rich resources., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Background: Body Fat percentage (BFP) and body mass index (BMI) are used to measure obesity-related metabolic syndrome risk. The present study aimed to determine the values of percent body Fat and body mass index for predicting metabolic syndrome risk factors in diabetic patients of Yazd, Iran., Methods: A total of 1022 (499 males and 523 females) diabetic patients participated in this study. According to Asian BMI criteria, Overweight was diagnosed if a participant had a BMI ≥25 kg/m 2 (both male and female) or BFP ≥25% for male and ≥ 32% for female. Based on calculated BMI and BFP and after adjusting for age, height, weight and smoking habits, the participants were classified into group A (normal weight and Non-Fat), group B (overweight and Non-Fat), group C (normal weight and Fat), and group D (overweight and Fat)., Results: According to the results, the BMI of 23.4% were normal and BMI of 76.6% were overweight, respectively. Moreover, the BFP of 25.7 and 74.3% of the studied population were considered as Non-Fat and Fat, respectively. A strong relationship was found with respect to sex stratification; R 2  = 0.79. For men, BMI can be a better predictor of hypertension and hypertriglyceridemia than BFP. For women, BMI was a better predictor of hyperglycemia than BFP. Moreover, BFP can be regarded as a better predictor of hyperglycemia in male group, while it was a good predictor of hypertension and hypertriglyceridemia and hypo HDL than BMI, in female group., Conclusion: Significant differences were observed between BMI and BFP to predict metabolic syndrome risk factors in diabetic patients across different sexes in our study population. In conclusion, both BMI and BFP should be considered in screening steps., (© 2022. The Author(s).)

Background: Type 2 diabetes (T2D) is a progressive disease that should be managed with insulin in case of oral glucose lowering drugs (OGLDs) failure. If basal insulin is not sufficient, rapid acting insulin will be added before the largest meal. We assessed the impact of adding one prandial insulin to a basal based regimen and insulin glargine in patients with type 2 diabetes to measure the percentage of subjects achieving the HbA1c target by the end of 24 weeks of treatment in routine clinical practice. Methods: This study was a 24-week observational study of patients with T2D not adequately controlled with OGLDs and basal insulin, for whom the physician had decided to initiate prandial insulin. The study endpoint was assessed at visit 1 (baseline), visit 2 at week 12 (±1 week) and visit 3 at week 24 (±1 week). The percentage of patients who achieved HbA1c targets was assessed at week 24. Statistical analyses were performed using IBM SPSS for Windows v 19 (IBM, Armonk, New York, USA). Logistic regression analysis was used to detect predicting factors of achieving the HbA1c target by week 24. P<0.05 was considered as significant level. Results : Four hundred and eighteen patients with a mean±SD age of 56.24±9.85 years and a mean±SD duration of diabetes of 12.50±7.16 years were included. The median total daily dose of basal insulin was 24 units, while prandial insulin was started with 6 (4, 10) U/day, titrating up to 10 (8, 18) U/day at week 24. The daily dose of prandial insulin was the only factor that could significantly predict achieving targeted HbA1c by week 24 [OR: 1.04; 95% CI: 1.007,1.079; p-value: 0.019]. At week 24, 96 (22.9%) subjects achieved the HbA1c target with one prandial insulin. Conclusion : The results of our study suggest that "basal plus therapy" can lead to good glycemic control with a low risk of hypoglycemia and weight gain in patients with type 2 diabetes., (© 2021 Iran University of Medical Sciences.)

The novel severe acute respiratory syndrome coronavirus (SARS CoV-2) was introduced as an epidemic in 2019 and had millions of deaths worldwide. Given the importance of this disease, the recommendation and design of new active compounds are crucial. 3-chymotrypsin-like protease (3 CL pro ) inhibitors have been identified as potent compounds for treating SARS-CoV-2 disease. So, the design of new 3 CL pro inhibitors was proposed using a quantitative structure-activity relationship (QSAR) study. In this context, a powerful adaptive least absolute shrinkage and selection operator (ALASSO) penalized variable selection method with inherent advantages coupled with a nonlinear artificial neural network (ANN) modelling method were used to provide a QSAR model with high interpretability and predictability. After evaluating the accuracy and validity of the developed ALASSO-ANN model, new compounds were proposed using effective descriptors, and the biological activity of the new compounds was predicted. Ligand-receptor (LR) interactions were also performed to confirm the interaction strength of the compounds using molecular docking (MD) study. The pharmacokinetics properties and calculated Lipinski's rule of five were applied to all proposed compounds. Due to the ease of synthesis of these suggested new compounds, it is expected that they have acceptable pharmacological properties.

A new approach is introduced for the construction of a predictive quantitative structure-activity relationship model in which only ligand-receptor (LR) interaction features are used as relevant descriptors. This approach combines the benefit of the random forest (RF) as a new variable selection method with the intrinsic capability of the artificial neural network (ANN). The interaction information of the ligand-receptor (LR) complex was used as molecular docking descriptors. The most relevant descriptors were selected using the RF technique and used as inputs of ANN. The proposed RF ANN (RF-LM-ANN) method was optimized and then evaluated by the prediction of pEC 50 for some of the azine derivatives as non-nucleoside reverse transcriptase inhibitors. RF-LM-ANN model under the optimal conditions was evaluated using internal (validation) and external test sets. The determination coefficients of the external test and validation sets were 0.88 and 0.89, respectively. The mean square deviation (MSE) values for the prediction of biological activities in the external test and validation sets were found to be 0.10 and 0.11, respectively. The results obtained demonstrated the good prediction ability and high generalizability of the proposed RF-LM-ANN model based on the MMDs alone., (© 2020 John Wiley & Sons Ltd.)

Objective: Peripheral neuropathy is a common complication of diabetes mellitus. This study was set to assess the effect of vitamin D supplementation on peripheral neuropathy in patients with type 2 diabetes (T2DM)., Materials and Methods: This study was a quasi-experimental trial in Yazd diabetic research center. Sixty T2DM subjects (30-65 years old) with painful diabetic neuropathy enrolled in this study from March 2017 till April 2018. Patients received weekly 50000 IU of vitamin D3 for 12 weeks orally. Evaluation of diabetic neuropathy was performed by using Michigan Neuropathy Screening Instrument (MNSI) before and after trial. Also fasting plasma glucose, HbA1c, calcium and vitamin D checked before and after the trial. SPSS version 20 software was used for statistical analysis. P ≤ 0.05 was considered to be statistically significant., Results: Among 60 T2DM patients, 58 completed the study. Most of them (53.4%) were male. At the end of study, HbA1c, vitamin D, MNSI (both questionnaire and physical examination) improved that is statistically significant (p-value: <0.001)., Conclusion: Oral supplementation of vitamin D 3 (50,000 IU) once weekly for 12 weeks was associated with improvement in the serum level of vitamin D and significant decrease in the symptoms and sign of diabetic neuropathy. So serum vitamin D level should be checked in persons with diabetic neuropathy and low levels of it should be corrected in order to reducing neuropathy severity., (Copyright © 2018 Diabetes India. Published by Elsevier Ltd. All rights reserved.)

Reaction of sulfonium ylides (Me)(2)SCHC(O)C(6)H(4)R (R = H; m-NO(2); p-NO(2); p-OMe; p-Me and p-Br) with AgNO(3) in dichloromethane leads to various compounds. Single crystal X-ray diffraction analysis reveals that the adducts take 3 forms: (i) two-dimensional polymer, [AgNO(3)(Me(2)SCHC(O)C(6)H(5))](n) (1), with nitrate bridges in which each nitrate coordinates to three silver atoms through two oxygen atoms and two Me(2)SCHC(O)C(6)H(5) ligands coordinate to silver centers through carbon atoms; (ii) cationic binuclear, [Ag(Me(2)SCHC(O)C(6)H(4)-m-NO(2))(2)](2)(NO(3))(2)·2H(2)O (2), in which Me(2)SCHC(O)C(6)H(4)-m-NO(2) ligands simultaneously coordinate through both carbon and oxygen atoms with nitrate as a counter ion, and (iii) cationic mononuclear and anionic binuclear, [Ag(Me(2)SCHC(O)C(6)H(4)-p-NO(2))(2)](2)[{AgNO(3)(μ-NO(3)) (Me(2)SCHC(O)C(6)H(4)-p-NO(2))}(2)]·2CH(3)OH (3), in which nitrate groups act as bridging as well as terminal ligands, and Me(2)SCHC(O)C(6)H(4)-p-NO(2) ligands display C-coordination. Characterization of the obtained compounds was also performed by infrared, (1)H- and (13)C-NMR spectroscopy and analytical data indicated a 1 : 2 stoichiometry between the silver(I) nitrate and ylide p-OMe (4) and 1 : 1 for ylides p-Me (5) and p-Br (6). In addition, the antibacterial effects of DMSO-solutions of complexes 1-6 were evaluated by the agar disc diffusion method against three Gram positive and three Gram negative bacteria. All complexes displayed antibacterial activity against these bacteria, with high levels of inhibitory potency exhibited against the Gram negative species.