Non-Diabetic Kidney Disease in Type 2 Diabetes Mellitus: A Study of 82 Patients and Review of the Literatures

Background: Worldwide, diabetic nephropathy-DN is the leading cause of end-stage kidney disease-ESKD, DN is a common cause of renal failure with a reported frequency of 10% - 15% in type-2-diabetes-mellitus-T2DM patients, however there is a great discrepancy between countries. The aim of the pre-sent study is to evaluate the findings of kidney biopsies performed on diabetic patients. Materials and Methods: We studied native kidney histopathological findings in the period from January 2016 till end of December 2018 done for patients with T2DM with chronic kidney diseases-CKD. Results: A total of 82 DM-patients, 50 males (61%) and 32 females (39%) with age mean (95% CI) of 50.8 (47.1 - 55.2) years for all patients, ranged between 15 to 65 years. Histological findings showed that 57.3% of patients had DN. While focal-segmental-glomerulosclerosis-FSGS was present in 20.7%—primary in 8.6% and secondary in 12.1%. IgA represented 4.9%, while Lupus nephritis, Membranous and drug induced interstitial nephritis were each present in 3.7%. MCD was present in 2.4%. Lastly diffuse proliferative GN, ANCA associated glomerulonephritis, and hypertensive nephrosclerosis accounted for 1.2%. Conclusion: The prevalence of NDKD is remarkably frequent in DM patients who underwent kidney biopsy and FSGS was the most frequent diagnosis. To get a proper histopathological diagnosis, an adequate tissue biopsy is needed with an adequate number of glomeruli. There is a great need for more consideration to biopsy diabetic patients, as the finding of NDKD requires a different therapeutic approach. This, hopefully, will help to manage these patients better and therefore, ameliorate the progression to ESKD over time and therefore delay the need for RRT.

Genetic association study of P2x7 A1513C(rs 3751143) polymorphism and susceptibility to pulmonary tuberculosis: A meta-analysis based on the findings of 11 case-control studies

Objective:To summarize the precise association between pulmonary tuberculosis(PTB) and P2x7 A1513 C gene polymorphism.Methods:PubMed and Google Scholar web-databases were searched for the studies reporting the association of P2x7 A1513 C polymorphism and PTB risk.A meta-analysis was performed for the selected case-control studies and pooled odds ratios(ORs) and 95%confidence intervals(95%CIs) were calculated for all the genetic models.Results:Eleven studies comprising 2 678 controls and 2 113 PTB cases were included in this meta-analysis.We observed overall no significant risk in all the five genetic models.When stratified population by the ethnicity,Caucasian population failed to show any risk of PTB in all the genetics models.In Asian ethnicity,variant allele(C vs.A:P=0.001;QR=1.375,95%CI=1.159-1.632) and heterozygous genotype(AC vs.AA:P=0.001;OR=1.570,95%CI=1.269-1.944) demonstrated significant increased risk of PTB.Likewise,recessive genetic model(CC+AC vs.AA:P=0.001;OR=1.540,95%CI= 1.255-1.890) also demonstrated increased risk of PTB in Asians.Conclusions:Our meta-analysis did not suggest the association of P2x7 A1513 C polymorphism with PTB risk in overall or separately in Caucasian population.However,it plays a significant risk factor for predisposing PTB in Asians.Future larger sample and expression studies are needed to validate this association.

Identification of chemical compositions from“featureless”optical absorption spectra:Machine learning predictions and experimental validations

Rapid and accurate chemical composition identification is critically important in chemistry.While it can be achieved with optical absorption spectrometry by comparing the experimental spectra with the reference data when the chemical compositions are simple,such application is limited in more complicated scenarios especially in nano-scale research.This is due to the difficulties in identifying optical absorption peaks(i.e.,from“featureless”spectra)arose from the complexity.In this work,using the ultraviolet-visible(UV-Vis)absorption spectra of metal nanoclusters(NCs)as a demonstration,we develop a machine-learningbased method to unravel the compositions of metal NCs behind the“featureless”spectra.By implementing a one-dimensional convolutional neural network,good matches between prediction results and experimental results and low mean absolute error values are achieved on these optical absorption spectra that human cannot interpret.This work opens a door for the identification of nanomaterials at molecular precision from their optical properties,paving the way to rapid and high-throughput characterizations.

Role of salivary matrix metalloproteinase-8 （MMP-8） in chronic periodontitis diagnosis

Periodontitis is an inflammatory disease of the periodontium. Any imbalance between the matrix metalloproteinases （MMPs） secreted by neutrophils and tissue inhibitors initiates the destruction of collagen in gum tissue, leading to chronic periodontitis. This study aimed to correlate salivary levels of MMP-8 and periodontal parameters of chronic periodontitis to establish MMP-8 as a noninvasive marker for the early diagnosis of chronic periodontitis. The study involved 40 subjects visiting the periodontic OPD of Dr. Ziauddin Ahmad Dental College and Hospital, located in Aligarh, U.P., India, from 2011 to 2012. The subjects were divided into two groups： group I consisted of 20 periodontally healthy subjects （controls） while group II consisted of 20 patients with chronic periodontitis. Chronic periodontitis was assessed on the basis of several periodontal parameters, including pocket probing depth （PPD）, clinical attachment level （CAL）, gingival index （GI）, and plaque index （PI）. Around 3 ml of unstimulated and whole expectorated saliva was collected for MMP-8 estimation by ELISA using Quantikine human total MMP-8 immunoassay kits. Data were analyzed using STATISTICA （Windows version 6） software. Salivary MMP-8 levels of groups I and II were 190.91 ± 143.89 ng/ml and 348.26± 202.1 ng/ml, respectively. The MMP-8 levels and periodontal status （PPD, CAL, GI, and PI） of groups I and II showed positive and significant correlations （for PPD, r = 0.63, P 〈 0.001; for CAL, r = 0.54, P 〈 0.001; for GI, r = 0.49, P 〈 0.001; and for PI, r = 0.63, P 〈 0.001）. The results of this study demonstrate elevated concentrations of MMP-8 in individuals with chronic periodontitis.

Two-step machine learning enables optimized nanoparticle synthesis

In materials science,the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming.In this study,we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum.Combining a Gaussian process-based Bayesian optimization(BO)with a deep neural network(DNN),the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions.Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum,the DNN is used to predict the colour palette accessible with the reaction synthesis.While remaining interpretable by humans,the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties,such as the role of each reactant on the shape and amplitude of the absorbance spectrum.