Correlation of serum uric acid and proteinuria in patients with type 2 diabetes mellitus

Background: Diabetes mellitus is one of the most common and important metabolic diseases worldwide. Hperuricemia is associated with kidney damage manifested by glomerular hypertrophy and sclerosis. The aim of this study was evaluation of association between serum uric acid level and proteinuria in patients with type 2 diabetes mellitus. Methods: A total of 140 patients (48 men and 92 women) with type 2 diabetes mellitus were enrolled in the study. Demographic criteria as age, body mass index, serum uric acid level and 24 hours urine protein were measured in these patients. Finding: Mean of patients age was 59.8 ± 10 years. Uric acid level in patients with significant proteinuria (≥ 500 mg/24 h) and mild proteinuria (< 500 mg/24 h) were 6.70 ± 1.82 and 5.06 ± 1.46 mg/dl respectively (P < 0.001). Serum uric acid level had positive correlation with 24 hours urine protein, duration of diabetes and insulin treatment. Conclusion: In type 2 diabetes mellitus, serum uric acid level has positive correlation with proteinuria; so uric acid maybe has a role in progression of diabetic nephropathy

Application of MIA-QSAR in Designing New Protein P38 MAP Kinase Compounds Using a Genetic Algorithm

Multivariate image analysis quantitative structure-activity relationship (MIA-QSAR) study aims to obtain information from a descriptor set, which are image pixels of two-dimensional molecule structures. In the QSAR study of protein P38 mitogen-activated protein (MAP) kinase compounds, the genetic algorithm application for pixel selection and image processing is investigated. There is a quantitative relationship between the structure and the pIC50 based on the information obtained. (The pIC50 is the negative logarithm of the half-maximal inhibitory concentration ( IC50 ), so pIC50 = −log IC50 .) Protein P38 MAP kinase inhibitors are used in the treatment of malignant tumors. The development of a model to predict the pIC50 of these compounds was performed in this study. To accomplish this, the molecules were first plotted and fixed in the same coordinates in ChemSketch. Then, the images were processed in the MATLAB program. Partial least squares (PLS) model, orthogonal signal correction partial least squares (OSC-PLS) model, and genetic algorithm partial least squares (GA-PLS) model methods are used to generate quantitative models, and pIC50 prediction is performed. The GA-PLS model has the highest predictive power for a series of statistical parameters such as root mean square error of prediction (RMSEP) and relative standard errors of prediction (RSEP). Finally, the molecular junction (docking) was done for predicted molecules in quantitative structure activity relationship (QSAR) with an appropriate receptor and acceptable results were obtained. These results are good and proper for the prediction of compounds with better properties

A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models

Objective. Chemical named entity recognition (NER) models have the potential to impact a wide range of downstream tasks, from identifying adverse drug reactions to general pharmacoepidemiology. However, it is unknown whether these models work the same for everyone. Performance disparities can potentially cause harm rather than the intended good. Hence, in this paper, we measure gender-related performance disparities of chemical NER systems. Materials and Methods. We develop a framework to measure gender bias in chemical NER models using synthetic data and a newly annotated dataset of over 92,405 words with self-identified gender information from Reddit. We applied and evaluated state-of-the-art biomedical NER models. Results. Our findings indicate that chemical NER models are biased. The results of the bias tests on the synthetic dataset and the real-world data multiple fairness issues. For example, for synthetic data, we find that female-related names are generally classified as chemicals, particularly in datasets containing many brand names rather than standard ones. For both datasets, we find consistent fairness issues resulting in substantial performance disparities between female- and male-related data. Discussion. Our study highlights the issue of biases in chemical NER models. For example, we find that many systems cannot detect contraceptives (e.g., birth control). Conclusion. Chemical NER models are biased and can be harmful to female-related groups. Therefore, practitioners should carefully consider the potential biases of these models and take steps to mitigate them

Simultaneous Spectrophotometric Determination of FeII and FeIII in Pharmaceuticals by Partial Least Squares with Chromogenic Mixed Reagents

Simultaneous determination of FeII and FeIII mixtures by spectrophotometric methods is a difficult problem in analytical chemistry because of spectral interferences. By multivariate calibration methods, such as partial least squares (PLS), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. The method is based on developing the reaction between the analytes and 1,10 phenanthroline and 5-sulfosalicylic acid as the chromogenic reagent at pH = 4.5. Experimental conditions were established so as to reduce interferences, decrease system complexity and produce a robust procedure that could be used for routine analysis. Spectra should be recorded from 5 to 10 minutes after mixing the reagents. In this study, the calibration model is based on absorption spectra in the 400–600 nm range for 34 different mixtures of FeII and FeIII. Calibration matrices contained 0.1–7.0 and 0.5–14.0 mg cm–3 of FeII and FeIII, respectively. Detection limits were 0.045 and 0.158 mg cm–3 for FeII and FeIII, respectively. RMSEP for FeII and FeIII was 0.1559 and 0.2067, respectively. The procedure was confirmed by FeII and FeIII analyses in pharmaceutical products, and good reliability of the determination was proven

Corporate Governance And Performance Of Peer Firms: A Cross-Lagged Analysis Of An Emerging Economy

In this study, we examine the effects of corporate governance practices on financial performance of Pakistani listed firms. On the bases of agency theory, MM theorem, and theory of firm, we suggest that corporate governance effects firm performance directly as well as indirectly via mediation of capital structure and dividend policy. The model was tested using a cross-lagged analysis of 100 non-financial firms with the structural equation modeling (SEM). The study concludes that corporate governance improves financial performance by exploiting capital structure and dividend policy. The findings of this study, highlights the importance of corporate governance practices for peer firms to restructure their debt and dividend policies for the enhancement of their financial performance.

IN VITRO ANTIDIABETIC EFFECTS OF FERULA ASSA-FOETIDA EXTRACTS THROUGH DIPEPTIDYL PEPTIDASE IV AND α-GLUCOSIDASE INHIBITORY ACTIVITY

Objective: Diabetes mellitus (DM) causes hyperglycemia, which is one of the most common diseases in the world. One of the strategies for the treatment of diabetes is maintaining postprandial glucose level through inhibition of dipeptidyl peptidase IV (DPP-IV) and α-glucosidase enzymes. The aim of this study was to determine in vitro antidiabetic potential of Ferula assa-foetida via DPP-IV and α-glucosidase inhibitory activities.Methods: F. assa-foetida seeds were extracted in methanol, ethanol, ethanol-methanol, and water. Inhibitory activity on DPP-IV and α-glucosidase wasperformed in vitro and measured spectrophotometrically at λ=405 nm.Results: The result showed that the F. assa-foetida seed extract is effective against both enzymes. All fractions had DPP-IV inhibitory activity, but the ethanolic fraction had the highest inhibitory activity on DPP-IV enzyme and significantly decreased DPP-IV activity (24.5%). With respect to α-glucosidase inhibitory activity, the aqueous extract has the highest inhibitory activity (28%).Conclusion: According to the results of this study, F. assa-foetida contains DPP-IV and α-glucosidase inhibitors and could be a potential source for the discovery of active constituents as α-glucosidase and DPP-IV inhibitors to treat Type 2 DM.Â

Review of mismatch mitigation techniques for PV modules

The installation of photovoltaic (PV) systems is continuously increasing in both standalone and grid-connected applications. The energy conversion from solar PV modules is not very efficient, but it is clean and green, which makes it valuable. The energy output from the PV modules is highly affected by the operating conditions. Varying operating conditions may lead to faults in PV modules, e.g. the mismatch faults, which may occur due to shadows over the modules. Consequently, the entire PV system performance in terms of energy production and lifetime is degraded. To address this issue, mismatch mitigation techniques have been developed in the literature. In this context, this study provides a review of the state-of-the-art mismatch mitigation techniques, and operational principles of both passive and active techniques are briefed for better understanding. A comparison is presented among all the techniques in terms of component count, complexity, efficiency, cost, control, functional reliability, and appearance of local maximums. Selected techniques are also benchmarked through simulations. This review serves as a guide to select suitable techniques according to the corresponding requirements and applications. More importantly, it is expected to spark new ideas to develop advanced mismatch mitigation techniques.</p