A Prospective Study of Prevalence of Carotid Artery Disease in Patients with Coronary Artery Disease and its Correlation with Traditional Atherosclerotic Risk Factors in Central India

Introduction: Early atherosclerosis mainly involves carotid artery, which leads to increased carotid artery intima media thickness (CIMT).The potential value of CIMT improving the predictive capacity of traditional risk factors of CAD is an understudied and underutilized issue. Because of increasing availability of highly sensitive ultrasonography probes and for a noninvasive procedures, we can predict coronary artery disease (CAD) more precisely in patients having multiple traditional risk factors so it may reduce morbidity and mortality due to CAD and elevated CIMT can be used as surrogate marker of underlying CAD.Methods: This study enrolled 250 admitted patients as a case of CAD. The patients were assessed by detailed history taking, thorough clinical examination, measurement of CIMT, blood sugar and lipid level.Results: Carotid artery disease was present in 88 (35%) of 250 CAD patients. All modifiable cardiovascular risk factors were statistically significantly high in patients of CAD with carotid artery disease. In obese, diabetic, hypertensive, dyslipidemia and smoker patients, carotid artery disease was present in 55% (P = 0.00), 41% (P = 0.00), 43% (P = 0.007), 47% (P = 0.002) and 43% (P = 0.003) respectively. CAD patients who had 1 risk factor, 29% were associated with carotid artery disease. Comparison of single risk factor with patients who had no risk factor, there was non-significant correlation for carotid artery disease. CAD patients who had 2, 3, 4 and 5 risk factors, carotid artery disease was present 24 (32%) (p = 0.02), 15 (55%) (P = 0.0003), 17 (61%) (P = 0.00006) and 6 (67%) (P = 0.0008).Conclusion: elevated CIMT can be used as one of the important risk factor for early diagnosis of CAD and to reduce morbidity and mortality due to CAD

In-silico designing of an inhibitor against mTOR FRB domain: Therapeutic implications against breast cancer

Worldwide breast cancer causes significant fatalities in women. The effective therapeutic solution for treating the disease is using new and probable antagonistic biologically available ligands as anticancer drugs. To identify a successful therapeutic approach, the scientific community is now interested in creating novel ligands that in the future may be used as anticancer drugs. The mechanistic target of rapamycin (mTOR) is a protein kinase connected to several processes governing immunity, metabolism, cell development, and survival. The proliferation and metastasis of tumors have both been linked to the activation of the mTOR pathway. Female breast cancer represents about 15.3% of all new cancer cases in the U.S. alone and is frequently diagnosed among women aged 55 to 69 years. Given that the P13K/AKT/mTOR pathway is one of the most often activated in cancer, much attention has been paid to its resistance as a novel oncological treatment approach. mTOR/FRB Domain’s recruitment cleft as, well as substrate recruitment mechanism, was targeted using a structural-based approach. A series of selective inhibitory small molecules have been designed and screened for the best inhibiting target binding triad of the FRB Domain with better ADME and no detectable toxic effects

Silver triflate catalyzed synthesis of 3-aminoalkylated indoles and evaluation of their antibacterial activities

An efficient, one-pot synthesis was developed for 3-aminoalkylated indoles by three-component coupling reaction of aldehydes, N-methylanilines, and indoles using AgOTf as a catalyst. A series of twenty 3-aminoalkylated indoles was evaluated for their antibacterial activities against both Gram negative and Gram positive bacteria. Compounds 4b and 4r showed good antibacterial activity against both Gram positive and Gram negative strains. However, inversing the property of substituent (from 4r to 4q) resulted in the significant fall in the magnitude of antibacterial activity against Escherichia coli

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In case of random effects models for balanced designs, the analysis is simple and no problem is encountered in testing the variance components since the sums of squares are independent, sums of squares are chi-square variates, ratio of variance components follow standard F-distribution and hence exact testing is possible. When a random effects model is considered in unbalanced designs, analysis of variance technique rarely produce exact tests for testing the hypothesis. Under the conventional normality assumptions, except for the error component, the analysis of variance fails to decompose the total sums of squares into independently distributed sums of squares. Also, sums of squares are neither chi-square variates nor multiple of chi-square variate. The sums of squares are not independent either. Another standardized measure that quantifies the difference between means and relationship between independent and the dependent variable is effect-size measure. Two generally used statistics for computing effect-size are eta and omega squared statistics. But, these statistics do not yield correct estimate of effect-size that are comparable across different designs [Bakeman (2005)]. In that scenario, generalized eta and omega statistics given by Olejnik and Algina (2003) can be used. There was a conversation on two-way factorial ANOVA with mixed effects and interactions [Nelder (1977, 1982, 1994, 2008)]. The major assessments about the two-way factorial ANOVA model is no substantial rationale for the imposed constraints on random interactions and a lack of clear interpretation of its variance components, especially for the main random effects in respect of the response [Nelder (1977), Wolfinger and Stroup (2000), Lencina et al. (2007)]. As a result, the usual model is more widely used nowadays. The unbalanced mixed ANOVA models are often analyzed under the linear mixed models (LMM) framework using the restricted maximum likelihood (REML) or generalized least squares approaches [Littell (2002), Stroup (2013), Jiang (2017)]. Kaur and Garg (2020) attempted for Computer aided construction of rectangular PBIB designs. Gupta and Sharma (2020) constructed a set of balanced incomplete block designs (BIBD) against the loss of two blocks where loss of some observations lie in between at most two common treatments. Gupta (2021) worked on nested partially balanced incomplete block designs and its analysis. Singh et al. (2021) presented mixture designs generated using orthogonal arrays. In this study, the one way random effects model for unbalanced nested design in which we have given the model, hypothesis to be tested, sums of squares and testing procedure for the hypothesis along with analysis of variance table. In the next section, we have explained model, hypothesis testing, sums of squares, hypothesis testing procedure and analysis of variance table for two way unbalanced nested design. Since in two way unbalanced case the means squares are generally not independent and are not distributed as chi-square variates, exact testing is not available for the main class variance component. We have obtained the expected size of approximate tests and the actual size for both conventional and approximate tests. Then with the help of a simulated data we found out the numerical for actual size of the conventional test and the actual and expected size of the approximate tests for some assumed values of the variance components.Under unbalanced design, testing of variance ratios are generally neither independent nor distributed as chi- square variates and does not follow standard F-distribution. In this case, exact testing of variance ratios is not available in the literature. Procedure for unbalanced data (generally not independent and are not distributed as chi-square variates) has been developed for testing the variance components in one way and two way unbalanced nested designs.Not Availabl

Role of big data in Agriculture-A Statistical Prospective

Not AvailableData are playing an important role making good planning and policies for agricultural growth and development. Population growth and climate change are worldwide trends that are increasing the importance of using big data science to improve agriculture. Add to that land degradation increasing marginal land and loss of biodiversity are better deals with study of big data science. Crop data can be break down into bits and bytes it will give better study about the crop development by using advance data analytics tools for betterment of agriculture. Here, talk about some important tools and techniques to handle and study the big data

Ameliorations in dyslipidemia and atherosclerotic plaque by the inhibition of HMG-CoA reductase and antioxidant potential of phytoconstituents of an aqueous seed extract of Acacia senegal (L.) Willd in rabbits

The assigned work was aimed to examine the capability of phytoconstituents of an aqueous seed extract of Acacia senegal (L.) Willd to inhibit HMG-CoA reductase and regression of the atherosclerotic plaque. The chemical fingerprinting of the test extract was assessed by LC-MS/MS. Consequently, the analyses of in-vitro, in-vivo, and in-silico were executed by using the standard protocols. The in-vitro assessment of the test extract revealed 74.1% inhibition of HMG-CoA reductase. In-vivo assessments of the test extract indicated that treated hypercholesterolemic rabbits exhibited a significant (P≤0.001) amelioration in the biomarker indices of the dyslipidaemia i.e., atherogenic index, Castelli risk index(I&II), atherogenic coefficient along with lipid profile. Subsequently, significant reductions were observed in the atherosclerotic plaque and antioxidant levels. The in-silico study of molecular docking shown interactions capabilities of the leading phytoconstituents of the test extract i.e., eicosanoic acid, linoleic acid, and flavan-3-ol with target protein of HMG-CoA reductase. The values of RSMF and potential energy of top docked complexes were show significant interactions. Accordingly, the free energy of solvation, interaction angle, radius of gyration and SASA were shown significant stabilities of top docked complex. The cumulative data of results indicate phytoconstituents of an aqueous seed extract of Acacia senegal have capabilities to inhibit the HMG-CoA reductase and improve the levels of antioxidants

Forecasting of growth rates of wheat yield of Uttar Pradesh through non-linear growth models

Wheat production in India is about 70 million tonnes per year which counts for approximately 12 per cent of world’s production. Being the second largest in population, it is also the second largest in wheat consumption after China, with a huge and growing wheat demand. Major wheat growing states in India are Uttar Pradesh, Punjab, Haryana, Rajasthan, Madhya Pradesh, Gujarat and Bihar. All of north is replenished with wheat cultivation. Uttar Pradesh, the largest wheat growing region of the country, produces around 28 million tonnes of wheat and Bihar produces around 5 million tonnes. The usual parametric approach for growth rate analysis is to assume multiplicative error in the underlying nonlinear geometric model and then fit the linearized model by ‘method of least squares'. This paper deals with a critical study of wheat yield of Uttar Pradesh with a non-linear approach. The available data of rice during different years is taken into consideration and different statistical models are fitted for that. The time series data on annual yield of wheat in UP from 1970-2010 were collected from various sources. Growth rates are computed through non-linear models, viz. Logistic, Gompertz and Monomolecular models. Different nonlinear procedures such as Gauss-Newton Method, Steepest-Descent Method, Levenberg-Merquadt Technique and Do Not Use Derivative (DUD) Method were used in this study to estimate the nonlinear growth rates. The results showed that logistic model performed better followed by Gompertz and monomolecular

Interpreting genotype by environment interaction using weather covariates

Understanding genotype by environment interaction (G*E) has a lways been a challenge to statisticians and plant breeders. Recen tly site regression analysis has emerged as a powerful analysis tool to understand G*E, speci fic and general adaptability of genotypes and grouping of environments into mega-environments . This paper attempts to enhance power of site regression by using environmental co variates in tandem to explain G*E better. In this present study, performances o f eighteen genotypes were investigated across five environments during the year 2008 rainy se ason. Three traits, namely grain yield, harvest index and dry fodder yield were us ed for analysis purpose. Biplot analysis identified two major groups of environments , first group of environments included Karad and Coimbatore and second group consisted Udaipur, Palem and Surat. SPH 1615 and SPH 1609 were identified as winning genotypes for firs t mega- environment whereas SPH 1596, SPH 1611 and CSH 16 were winners fo r second mega- environment for grain yield. High yielding genotypes, SPH 1606, SP H 1616 and CSH 23 performed consistently well across all environments and sh ould be considered for general adaptability. Genotype SPH 1596 was identified for both specif ic and general adaptability. By superimposing GGE biplots for different trai ts, genotypes SPH 1596 and CSH 23 were identified as stable for all three traits. C limatic data on average maximum temperature and minimum temperature at early (June-July) a nd late phase (August) of plant growth was incorporated to study G*E by using factorial regression. Average maximum temperature and minimum temperature at early phas e and average minimum temperature during late phase were found significantly affec ting genotype performance