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Nevin Manimala Statistics

Effects of 6 weeks treatment with dapagliflozin, a sodium-glucose co-transporter 2 inhibitor, on myocardial function and metabolism in patients with type 2 diabetes: a randomized placebo-controlled exploratory study

Diabetes Obes Metab. 2021 Feb 24. doi: 10.1111/dom.14363. Online ahead of print.

ABSTRACT

AIMS: To explore early effects of dapagliflozin on myocardial function and metabolism in patients with type 2 diabetes without heart failure.

MATERIALS AND METHODS: Patients with type 2 diabetes on metformin treatment were randomized to double-blind 6-week placebo or dapagliflozin 10 mg daily treatment. Investigations included cardiac function and structure with myocardial resonance imaging (MRI); cardiac oxygen consumption, perfusion and efficiency with [11 C]-acetate positron emission tomography (PET); and cardiac and hepatic fatty acid uptake with [18 F]-FTHA PET, analyzed by ANCOVA as least square means with 95% confidence intervals.

RESULTS: Evaluable patients (placebo: n = 24, dapagliflozin: n = 25; 53% males) had mean age 64.4 years, BMI 30.2 kg/m2 , and HbA1c 6.7%. Body weight and HbA1c were significantly decreased by dapagliflozin vs placebo. Dapagliflozin had no effect on myocardial efficiency, but external left ventricular (LV) work -0.095 (-0.145, -0.043) J/g/min, and LV oxygen consumption were significantly reduced -0.30 (-0.49, -0.12) J/g/min by dapagliflozin, but changes were not statistically significant vs changes in placebo group. Change in left atrial maximal volume with dapagliflozin vs placebo was -3.19 (-6.32, -0.07) mL/m2 , p = 0.056. Peak global radial strain decreased with dapagliflozin vs placebo, -3.92 (-7.57, -0.28) %, p = 0.035; peak global longitudinal and circumferential strains were unchanged. Hepatic fatty acid uptake was increased by dapagliflozin vs placebo, 0.024 (0.004, 0.044) μmol/g/min, p = 0.018, while cardiac uptake was unchanged.

CONCLUSIONS: This exploratory study indicates reduced heart work but limited effects on myocardial function, efficiency, and cardiac fatty acid uptake, while hepatic fatty acid uptake increased, after 6 weeks treatment with dapagliflozin.

CLINICAL TRIAL REGISTRATION: NCT03387683. This article is protected by copyright. All rights reserved.

PMID:33625777 | DOI:10.1111/dom.14363

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Nevin Manimala Statistics

Molecular dynamics-guided receptor-dependent 4D-QSAR studies of HDACs inhibitors

Mol Divers. 2021 Feb 24. doi: 10.1007/s11030-021-10181-y. Online ahead of print.

ABSTRACT

Histone deacetylases (HDACs) were highlighted as a novel category of anticancer targets. Several HDACs inhibitors were approved for therapeutic use in cancer treatment. Comparatively, receptor-dependent 4D-QSAR, LQTA-QSAR, is a new approach which generates conformational ensemble profiles of compounds by molecular dynamics simulations at binding site of enzyme. This work describes a receptor-dependent 4D-QSAR studies on hydroxamate-based HDACs inhibitors. The 4D-QSAR model was generated by multiple linear regression method of QSARINS. Leave-N-out cross-validation (LNO) and Y-randomization were performed to analysis of the independent test set and to verify the robustness of the model. Best 4D-QSAR model showed the following statistics: R2 = 0.8117, Q2LOO = 0.6881, Q2LNO = 0.6830, R2Pred = 0.884. The results may be used for further virtual screening and design for novel HDACs inhibitors. The receptor dependent 4D-QSAR model was developed for the hydroxamate derivatives as HDAC inhibitors by making use of molecular dynamics simulation to obtain conformational ensemble profile for each compound. The multiple linear regression method was used to generate 4D-QSAR model with the suitable predictive ability and the excellent statistical parameters.

PMID:33625673 | DOI:10.1007/s11030-021-10181-y

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Nevin Manimala Statistics

A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation

Environ Sci Pollut Res Int. 2021 Feb 24. doi: 10.1007/s11356-021-12792-2. Online ahead of print.

ABSTRACT

Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources.

PMID:33625698 | DOI:10.1007/s11356-021-12792-2

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Nevin Manimala Statistics

Performance analysis and optimization of solar-powered E-rickshaw for environmental sustainability in rural transportation

Environ Sci Pollut Res Int. 2021 Feb 24. doi: 10.1007/s11356-021-12894-x. Online ahead of print.

ABSTRACT

The last mile connectivity through public transport is a challenging task in India. However, according to the Society of Indian Automotive Manufacturers (SIAM) statistics, three-wheelers sales increased by 10.27% in the year financial year of 2018-2019 compared to the previous financial year. In this growth, it is recorded that the passenger carrier three-wheeler sales increased by 10.62% and goods carrier three-wheeler grew by 8.75% in the year 2019 compared to the previous year. The existing design consideration in the three-wheeler development shows poor performance in the real-world scenario because the three-wheeler’s open drive compartment creates more aerodynamic drag to the vehicle. This increases the amount of energy consumption to achieve the same amount of range (km/L). Three-wheeler’s extra energy consumption will directly increase the amount of exhaust emission in internal combustion engines and electrical energy consumption in electric vehicles. The present paper attempts in designing a solar-powered electrical auto-rickshaw for rural transportation. The paper aims to obtain an optimal solar module placement angle and analyzes the solar-powered electrical auto-rickshaw performance by incorporating the National Advisory Committee for Aeronautics (NACA) aerodynamic design principles. The optimal solar module placement angle is identified by analyzing the various configurations like front alone tilt at 16°-degree, rear alone tilt at the 5°-degree and combined front at 16°-degree, and rear at 5°-degree to reduce the aerodynamic drag effect. The paper also aims to identify the effect of the optimal angle on vehicle speed, and solar power generation to enhance the performance and energy efficiency for achieving environmentally sustainable transportation.

PMID:33625701 | DOI:10.1007/s11356-021-12894-x

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Nevin Manimala Statistics

Testing the long-run effects of economic growth, financial development and energy consumption on CO2 emissions in Turkey: new evidence from RALS cointegration test

Environ Sci Pollut Res Int. 2021 Feb 24. doi: 10.1007/s11356-021-12661-y. Online ahead of print.

ABSTRACT

This study analyses the long-run effects of economic growth, energy consumption and financial development on carbon dioxide (CO2) emissions in Turkey using annual time series data for the period 1965-2018. This research investigates the relationship between the variables using a RALS-EG (residual augmented least squares-Engle and Granger) cointegration test procedure developed by Lee et al. Stud Nonlinear Dyn Econ 19:397-413, (2015). In addition, this study uses a bootstrap causality analysis developed by Hacker and Hatemi-J J Econ Stud 39:144-160, (2012) to specify the causal relationship between the series. RALS cointegration test results show a long-run relationship between CO2 emissions and economic growth, energy consumption and financial development. According to a dynamic ordinary least squares estimation, economic growth has a negative and statistically significant effect on CO2 emissions, whereas energy consumption and financial development have positive and statistically significant effects on CO2 emissions in the long run. In particular, energy consumption is the most effective parameter of environmental pollution in Turkey. However, the causality test results indicate a unidirectional causal relationship from financial development to CO2 emissions, economic growth and energy consumption. Increasing the investment in renewable energy sources will be an effective policy tool to improve the environmental quality in Turkey.

PMID:33625708 | DOI:10.1007/s11356-021-12661-y

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Nevin Manimala Statistics

Does Famotidine Reduce the Risk of Progression to Severe Disease, Death, and Intubation for COVID-19 Patients? A Systemic Review and Meta-Analysis

Dig Dis Sci. 2021 Feb 24. doi: 10.1007/s10620-021-06872-z. Online ahead of print.

ABSTRACT

BACKGROUND: Famotidine was reported to potentially provide benefits to Coronavirus Disease 2019 (COVID-19) patients. However, it remains controversial whether it is effective in treating COVID-19.

AIMS: This study aimed to explore whether famotidine use is associated with reduced risk of the severity, death, and intubation for COVID-19 patients.

METHODS: This study was registered on International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42020213536). A comprehensive search was performed to identify relevant studies up to October 2020. I-squared statistic and Q-test were utilized to assess the heterogeneity. Pooled risk ratios (RR) and 95% confidence intervals (CI) were calculated through the random effects or fixed effects model according to the heterogeneity. Subgroup analyses, sensitivity analysis, and publication bias assessment were also conducted.

RESULTS: Five studies including 36,635 subjects were included. We found that famotidine use was associated with a statistically non-significant reduced risk of progression to severe disease, death, and intubation for Coronavirus Disease 2019 (COVID-19) patients (pooled RR was 0.82, 95% CI = 0.52-1.30, P = 0.40).

CONCLUSION: Famotidine has no significant protective effect in reducing the risk of developing serious illness, death, and intubation for COVID-19 patients. More original studies are needed to further clarify whether it is associated with reduced risk of the severity, death, and intubation for COVID-19 patients.

PMID:33625613 | DOI:10.1007/s10620-021-06872-z

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Nevin Manimala Statistics

Intraoral radiograph anatomical region classification using neural networks

Int J Comput Assist Radiol Surg. 2021 Feb 24. doi: 10.1007/s11548-021-02321-4. Online ahead of print.

ABSTRACT

PURPOSE: Dental radiography represents 13% of all radiological diagnostic imaging. Eliminating the need for manual classification of digital intraoral radiographs could be especially impactful in terms of time savings and metadata quality. However, automating the task can be challenging due to the limited variation and possible overlap of the depicted anatomy. This study attempted to use neural networks to automate the classification of anatomical regions in intraoral radiographs among 22 unique anatomical classes.

METHODS: Thirty-six literature-based neural network models were systematically developed and trained with full supervision and three different data augmentation strategies. Only libre software and limited computational resources were utilized. The training and validation datasets consisted of 15,254 intraoral periapical and bite-wing radiographs, previously obtained for diagnostic purposes. All models were then comparatively evaluated on a separate dataset as regards their classification performance. Top-1 accuracy, area-under-the-curve and F1-score were used as performance metrics. Pairwise comparisons were performed among all models with Mc Nemar’s test.

RESULTS: Cochran’s Q test indicated a statistically significant difference in classification performance across all models (p < 0.001). Post hoc analysis showed that while most models performed adequately on the task, advanced architectures used in deep learning such as VGG16, MobilenetV2 and InceptionResnetV2 were more robust to image distortions than those in the baseline group (MLPs, 3-block convolutional models). Advanced models exhibited classification accuracy ranging from 81 to 89%, F1-score between 0.71 and 0.86 and AUC of 0.86 to 0.94.

CONCLUSIONS: According to our findings, automated classification of anatomical classes in digital intraoral radiographs is feasible with an expected top-1 classification accuracy of almost 90%, even for images with significant distortions or overlapping anatomy. Model architecture, data augmentation strategies, the use of pooling and normalization layers as well as model capacity were identified as the factors most contributing to classification performance.

PMID:33625664 | DOI:10.1007/s11548-021-02321-4

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Nevin Manimala Statistics

Yoga Therapy as an Adjuvant in Management of Asthma

Indian J Pediatr. 2021 Feb 24. doi: 10.1007/s12098-021-03675-y. Online ahead of print.

ABSTRACT

OBJECTIVE: To assess the effect of yoga on control of asthma in children with bronchial asthma.

METHODS: This hospital-based interventional randomized controlled trial conducted in the Department of Pediatrics at a tertiary care center of North India from November 2017 to October 2018 enrolled 140 newly diagnosed cases of asthma of age 10-16 y who were randomly divided into two groups. Seventy children in the case group practiced yoga under supervision for a period of 3 mo in addition to pharmacological treatment. Seventy controls received only pharmacological treatment. Pulmonary-function tests were done at baseline, 6 wk, and 12 wk along with quality of life (QOL) assessment by Pediatric Asthma Quality of Life Questionnaire (PAQLQ). The outcome measures assessed were forced vital capacity (FVC), forced expiratory volume in one second (FEV1), FEV1/FVC and peak expiratory flow rate (PEFR). QOL evaluation was done in 3 domains: activity limitation, symptoms, and emotional function.

RESULTS: The asthmatic children practicing yoga have shown significant improvement in FVC, FEV1, FEV1/FVC and PEFR which was better as compared to controls. Improvement was also noted in mean-PAQLQ score in cases which was statistically significantly better as compared to controls.

CONCLUSION: Yoga appears to have significant positive effect on control of asthma measured by pulmonary-function test and QOL. Therefore yoga therapy can be recommended as an adjuvant in management of asthma along with standard pharmacological management.

PMID:33625666 | DOI:10.1007/s12098-021-03675-y

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Nevin Manimala Statistics

Hypoxia-mediated down-regulation of miRNAs’ biogenesis promotes tumor immune escape in bladder cancer

Clin Transl Oncol. 2021 Feb 24. doi: 10.1007/s12094-021-02569-x. Online ahead of print.

ABSTRACT

BACKGROUND: The study examines the function of hypoxia-mediated down-regulation of microRNAs (miRNAs) (mir-30c, mir-135a, and mir-27a) in the process of bladder cancer immune escape.

METHODS: Quantitative Real-time PCR (qRT-PCR) was carried out to determine gene expression levels of Drosha and Dicer under hypoxia treatment, while western blotting and flow cytometry were used to determine protein expression. Seven reported miRNAs were identified via qRT-PCR assay. Flow cytometry detection of CD3/CD4/CD8-positive expression and statistics. Enzyme-linked immunosorbent assay (ELISA) detected cellular immune factors content. Cell apoptosis was checked via flow cytometry assay. Luciferase report assay and western blot assays were both used to verify the relationship between miRNAs and Casitas B-lineage lymphoma proto-oncogene b (Cbl-b). The animal model was established and Hematoxylin-eosin (HE) staining, TdT-mediated dUTP Nick-End Labeling (TUNEL) staining, and immunohistochemistry (IHC) assays were separately used to verify the conclusions.

RESULTS: The CD3 + /CD4 + expression was increased in the hypoxia group, while CD3 + /CD8 + expression, the cellular immune factors content Interleukin-2 (IL-2) and Tumor Necrosis Factor-α (TNFα) along with the cell apoptosis were suppressed. The protein expression of Cbl-b was found to be up-regulated in the hypoxia group. After constructing the overexpression/ knockdown of Cbl-b in peripheral blood mononuclear cell (PBMC), Cbl-b has been found to promote tumor immune escape in bladder cancer. Furthermore, Cbl-b had been identified as the co-targets of mir-30c, mir-135a, and mir-27a and down-regulation of miRNA biogenesis promotes Cbl-b expression and deactivating T cells in vitro/in vivo.

CONCLUSION: Hypoxia-mediated down-regulation of miRNAs’ biogenesis promotes tumor immune escape in bladder cancer, which could bring much more advance to the medical research on tumors.

PMID:33625672 | DOI:10.1007/s12094-021-02569-x

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Nevin Manimala Statistics

An investigation into the impact of nine catchment characteristics on the accuracy of two phosphorus load apportionment models

Environ Monit Assess. 2021 Feb 24;193(3):142. doi: 10.1007/s10661-021-08875-9.

ABSTRACT

Phosphorus (P) load apportionment models (LAMs), requiring only spatially and temporally paired P and flow (Q) measurements, provide outputs of variable accuracy using long-term monthly datasets. Using a novel approach to investigate the impact of catchment characteristics on accuracy variation, 91 watercourses’ Q-P datasets were applied to two LAMs, BM and GM, and bootstrapped to ascertain standard errors (SEs). Random forest and regression analysis on data pertaining to catchments’ land use, steepness, size, base flow and sinuosity were used to identify the individual relative importance of a variable on SE. For BM, increasing urban cover was influential on raising SEs, accounting for c.19% of observed variation, whilst analysis for GM found no individually important catchment characteristic. Assessment of model fit evidenced BM consistently outperformed GM, modelling P values to ±10% of actual P values in 85.7% of datasets, as opposed to 17.6% by GM. Further catchment characteristics are needed to account for SE variation within both models, whilst interaction between variables may also be present. Future research should focus on quantifying these possible interactions and should expand catchment characteristics included within the random forest. Both LAMs must also be tested on a wide range of high temporal resolution datasets to ascertain if they can adequately model storm events in catchments with diverse characteristics.

PMID:33625605 | DOI:10.1007/s10661-021-08875-9