Search

Background: Blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease. However, an unresolved issue is whether gene set enrichment analysis of the signature transcripts alone is sufficient for prediction and differentiation or whether it is necessary to use the original model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data and missing details about the original trained model. To facilitate the utilization of these signatures in TB research, comparisons between gene set scoring methods cross-data validation of original model implementations are needed. Methods: We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both rrebuilt original models and gene set scoring methods. Existing gene set scoring methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, were used as alternative approaches to obtain the profile scores. The area under the ROC curve (AUC) value was computed to measure performance. Correlation analysis and Wilcoxon paired tests were used to compare the performance of enrichment methods with the original models. Results: For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original models. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies. Conclusion: Gene set enrichment scoring of existing gene sets can distinguish patients with active TB disease from other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement. [ABSTRACT FROM AUTHOR]

Background: Currently, there are more than two million people in prisons or jails, with nearly two-thirds meeting the criteria for a substance use disorder. Following these patterns, overdose is the leading cause of death following release from prison and the third leading cause of death during periods of incarceration in jails. Traditional quantitative methods analyzing the factors associated with overdose following incarceration may fail to capture structural and environmental factors present in specific communities. People with lived experiences in the criminal legal system and with substance use disorder hold unique perspectives and must be involved in the research process. Objective: To identify perceived factors that impact overdose following release from incarceration among people with direct criminal legal involvement and experience with substance use. Methods: Within a community-engaged approach to research, we used concept mapping to center the perspectives of people with personal experience with the carceral system. The following prompt guided our study: "What do you think are some of the main things that make people who have been in jail or prison more and less likely to overdose?" Individuals participated in three rounds of focus groups, which included brainstorming, sorting and rating, and community interpretation. We used the Concept Systems Inc. platform groupwisdom for our analyses and constructed cluster maps. Results: Eight individuals (ages 33 to 53) from four states participated. The brainstorming process resulted in 83 unique factors that impact overdose. The concept mapping process resulted in five clusters: (1) Community-Based Prevention, (2) Drug Use and Incarceration, (3) Resources for Treatment for Substance Use, (4) Carceral Factors, and (5) Stigma and Structural Barriers. Conclusions: Our study provides critical insight into community-identified factors associated with overdose following incarceration. These factors should be accounted for during resource planning and decision-making. [ABSTRACT FROM AUTHOR]

Occupational exposure to SARS-CoV-2 varies by profession, but "essential workers" are often considered in aggregate in COVID-19 models. This aggregation complicates efforts to understand risks to specific types of workers or industries and target interventions, specifically towards non-healthcare workers. We used census tract-resolution American Community Survey data to develop novel essential worker categories among the occupations designated as COVID-19 Essential Services in Massachusetts. Census tract-resolution COVID-19 cases and deaths were provided by the Massachusetts Department of Public Health. We evaluated the association between essential worker categories and cases and deaths over two phases of the pandemic from March 2020 to February 2021 using adjusted mixed-effects negative binomial regression, controlling for other sociodemographic risk factors. We observed elevated COVID-19 case incidence in census tracts in the highest tertile of workers in construction/transportation/buildings maintenance (Phase 1: IRR 1.32 [95% CI 1.22, 1.42]; Phase 2: IRR: 1.19 [1.13, 1.25]), production (Phase 1: IRR: 1.23 [1.15, 1.33]; Phase 2: 1.18 [1.12, 1.24]), and public-facing sales and services occupations (Phase 1: IRR: 1.14 [1.07, 1.21]; Phase 2: IRR: 1.10 [1.06, 1.15]). We found reduced case incidence associated with greater percentage of essential workers able to work from home (Phase 1: IRR: 0.85 [0.78, 0.94]; Phase 2: IRR: 0.83 [0.77, 0.88]). Similar trends exist in the associations between essential worker categories and deaths, though attenuated. Estimating industry-specific risk for essential workers is important in targeting interventions for COVID-19 and other diseases and our categories provide a reproducible and straightforward way to support such efforts. [ABSTRACT FROM AUTHOR]

Nonignorable technical variation is commonly observed across data from multiple experimental runs, platforms, or studies. These so-called batch effects can lead to difficulty in merging data from multiple sources, as they can severely bias the outcome of the analysis. Many groups have developed approaches for removing batch effects from data, usually by accommodating batch variables into the analysis (one-step correction) or by preprocessing the data prior to the formal or final analysis (two-step correction). One-step correction is often desirable due it its simplicity, but its flexibility is limited and it can be difficult to include batch variables uniformly when an analysis has multiple stages. Two-step correction allows for richer models of batch mean and variance. However, prior investigation has indicated that two-step correction can lead to incorrect statistical inference in downstream analysis. Generally speaking, two-step approaches introduce a correlation structure in the corrected data, which, if ignored, may lead to either exaggerated or diminished significance in downstream applications such as differential expression analysis. Here, we provide more intuitive and more formal evaluations of the impacts of two-step batch correction compared to existing literature. We demonstrate that the undesired impacts of two-step correction (exaggerated or diminished significance) depend on both the nature of the study design and the batch effects. We also provide strategies for overcoming these negative impacts in downstream analyses using the estimated correlation matrix of the corrected data. We compare the results of our proposed workflow with the results from other published one-step and two-step methods and show that our methods lead to more consistent false discovery controls and power of detection across a variety of batch effect scenarios. Software for our method is available through GitHub (https://github.com/jtleek/sva-devel) and will be available in future versions of the |$\texttt{sva}$| R package in the Bioconductor project (https://bioconductor.org/packages/release/bioc/html/sva.html). [ABSTRACT FROM AUTHOR]

Background Protein biomarkers may provide insight into kidney disease pathology but their use for the identification of phenotypically distinct kidney diseases has not been evaluated. Methods We used unsupervised hierarchical clustering on 225 plasma biomarkers in 541 individuals enrolled into the Boston Kidney Biopsy Cohort, a prospective cohort study of individuals undergoing kidney biopsy with adjudicated histopathology. Using principal component analysis, we studied biomarker levels by cluster and examined differences in clinicopathologic diagnoses and histopathologic lesions across clusters. Cox proportional hazards models tested associations of clusters with kidney failure and death. Results We identified three biomarker-derived clusters. The mean estimated glomerular filtration rate was 72.9 ± 28.7, 72.9 ± 33.4 and 39.9 ± 30.4 mL/min/1.73 m2 in Clusters 1, 2 and 3, respectively. The top-contributing biomarker in Cluster 1 was AXIN, a negative regulator of the Wnt signaling pathway. The top-contributing biomarker in Clusters 2 and 3 was Placental Growth Factor, a member of the vascular endothelial growth factor family. Compared with Cluster 1, individuals in Cluster 3 were more likely to have tubulointerstitial disease (P  < .001) and diabetic kidney disease (P  < .001) and had more severe mesangial expansion [odds ratio (OR) 2.44, 95% confidence interval (CI) 1.29, 4.64] and inflammation in the fibrosed interstitium (OR 2.49 95% CI 1.02, 6.10). After multivariable adjustment, Cluster 3 was associated with higher risks of kidney failure (hazard ratio 3.29, 95% CI 1.37, 7.90) compared with Cluster 1. Conclusion Plasma biomarkers may identify clusters of individuals with kidney disease that associate with different clinicopathologic diagnoses, histopathologic lesions and adverse outcomes, and may uncover biomarker candidates and relevant pathways for further study. [ABSTRACT FROM AUTHOR]

In the present study, data were collected on knowledge regarding management of Hospital Aquierd infections in children. The objective of the study is to identify the knowledge of B.Sc Nursing students regarding the management of Hospital acquired infections in children and to determine the association between post-test knowledge scores with selected demographic variables.The research method adopted for this study is the evaluative approach. The research design adopted for this study was a pre-experimental design with one group pretest and post-test design. A simple random technique was used to select the sample for the study. The sample consists of 60 B.Sc Nursing students of Nursing institutions. The instrument used for the data collection is a structured questionnaire. Collected data were analyzed by using descriptive and inferential statistics.The results of the study showed that the pretest overall knowledge score regarding various aspects of management of Hospital Aquierd infections in children was a mean percentage of 46.86% and SD was 3.51. During the post–the test overall knowledge score was 80.53% and SD was 2.98.Hence the difference between pretest and post-test over a knowledge score was 33.67. The results show that the difference between the pretest and post-test knowledge scores for the management of Hospital Aquierd infections in children is statistically significant and this difference is due to planned teaching program in the management of Hospital Aquierd infections in children. The conclusion was drawn on the basis of the following findings of the study. This study shows that there was a significant improvement in the knowledge of management of infections in children among B.Sc Nursing students as evidenced and post-test knowledge scores. [ABSTRACT FROM AUTHOR]

Background: The COVID‐19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community‐level risk factors that can change over time. Methods: Individual COVID‐19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: "Phase 1" (March–June 2020) and "Phase 2" (September 2020 to February 2021). Institutional cases associated with long‐term care facilities, prisons, or homeless shelters were identified using address data and modeled separately. Census tract sociodemographic and occupational predictors were drawn from the 2015–2019 American Community Survey. We used mixed‐effects negative binomial regression to estimate incidence rate ratios (IRRs), accounting for town‐level spatial autocorrelation. Results: Case incidence was elevated in census tracts with higher proportions of Black and Latinx residents, with larger associations in Phase 1 than Phase 2. Case incidence associated with proportion of essential workers was similarly elevated in both Phases. Mortality IRRs had differing patterns from case IRRs, decreasing less substantially between Phases for Black and Latinx populations and increasing between Phases for proportion of essential workers. Mortality models excluding institutional cases yielded stronger associations for age, race/ethnicity, and essential worker status. Conclusions: Geocoded home address data can allow for nuanced analyses of community disease patterns, identification of high‐risk subgroups, and exclusion of institutional cases to comprehensively reflect community risk. [ABSTRACT FROM AUTHOR]

Powdered xBaWO4/(1 − x)CaWO4 where (x = 0,0.25,0.5,0.75,1) nanocomposites were synthesized by the co-precipitation method at room temperature (RT). We are reporting DC and AC conductivity and dielectric properties of nanocomposites at room temperature (RT). Structural characterization and HRTEM image confirm interface formation of the nanocomposites. The current density–electric field (i.e., J–E) characteristics of the nanocomposites' studies reveal excellent varistor behavior. Dielectric properties were measured in the frequency 20 Hz–3 MHz at room temperature (RT). The dielectric constant was found to be higher for C2 (0.5BaWO4/0.5CaWO4) nanocomposites as compared to single phase and was maximum for atomic ratio (Ba/Ca) with optimal value of 'x'. DC conductivity (σdc), calculated from Jonscher's power law, of C2 (0.5BaWO4/0.5CaWO4) nanocomposites interfaced is higher than that of single phase, respectively. Enhanced AC conductivity is also observed for C2 (0.5BaWO4/0.5CaWO4) nanocomposites. These enhanced electrical properties of C2 (0.5BaWO4/0.5CaWO4) nanocomposites are due to the space charge layer (SCL) formation at the interface. In detail, conduction models for all the nanocomposites are explained. Thermopower investigations on the nanocomposites resulted in Seebeck coefficient (S) showing a transition from negative to positive values with confirmation n-type semiconductors at room temperature (RT). The enhanced power factor of C3 (0.75BaWO4/0.25CaWO4) nanocomposites is at higher temperature as compared to other nanocomposites. This C2 (0.5BaWO4/0.5CaWO4) nanocomposite can be considered as an efficient multifunctional nanocomposites due to its enhanced optical and electrical properties, and C3 (0.75BaWO4/ 0.25CaWO4) nanocomposite is very good efficient thermoelectric power devices at higher temperature application. [ABSTRACT FROM AUTHOR]

Background: Associations between community-level risk factors and COVID-19 incidence have been used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020.Methods: Using publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods from March to October 2020. We examined town-level demographic variables, including population proportions by race, ethnicity, and age, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM2.5), and institutional facilities. We calculated incidence rate ratios (IRR) associated with these predictors and compared these values across the multiple time periods to assess variability in the observed associations over time.Results: Associations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage of Black residents (IRR = 1.12 [95%CI: 1.12-1.13]) in early spring, IRR = 1.01 [95%CI: 1.00-1.01] in early fall) and COVID-19 incidence. The association with number of long-term care facility beds per capita also decreased over time (IRR = 1.28 [95%CI: 1.26-1.31] in spring, IRR = 1.07 [95%CI: 1.05-1.09] in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidences of COVID-19 throughout the pandemic (e.g., IRR = 1.30 [95%CI: 1.27-1.33] in spring, IRR = 1.20 [95%CI: 1.17-1.22] in fall). Towns with higher proportions of Latinx residents also had sustained elevated incidence over time (IRR = 1.19 [95%CI: 1.18-1.21] in spring, IRR = 1.14 [95%CI: 1.13-1.15] in fall).Conclusions: Town-level COVID-19 risk factors varied with time in this study. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence may have decreased across the first 8 months of the pandemic, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level. [ABSTRACT FROM AUTHOR]

Retention in HIV pre-exposure prophylaxis (PrEP) care is critical for effective PrEP implementation. Few studies have reported long-term lost to follow-up (LTFU) and re-engagement in PrEP care in the United States. Medical record data for all cisgender patients presenting to the major Rhode Island PrEP clinic from 2013 to 2019 were included. LTFU was defined as no PrEP follow-up appointment within 98 days. Re-engagement in care was defined as individuals who were ever LTFU and later attended a follow-up appointment. Recurrent event survival analysis was performed to explore factors associated with PrEP retention over time. Of 654 PrEP patients, the median age was 31 years old [interquartile range (IQR): 25, 43]. The majority were male (96%), White (64%), non-Hispanic (82%), and insured (97%). Overall, 72% patients were ever LTFU and 27% of those ever LTFU re-engaged in care. Female patients were 1.37 times [crude hazard ratio (cHR): 1.37; 95% confidence interval (CI): 0.86–2.18] more likely to be LTFU than male patients, and a 1-year increase in age was associated with a 1% lower hazard of being LTFU (cHR: 0.99; CI: 0.98–0.99). Being either heterosexual (aHR: 2.25, 95% (CI): 1.70–2.99] or bisexual (aHR: 2.35, 95% CI: 1.15–4.82) was associated with a higher hazard of loss to follow-up compared with having same-sex partners only. The majority of PrEP users were LTFU, especially at the first 6 months of PrEP initiation. Although a significant number were re-engaged in care, targeted interventions are needed to improve retention in PrEP care. This study characterized the natural projection of loss to follow-up and re-engagement in HIV PrEP care using a longitudinal clinic cohort data and explored associated factors for guiding future interventions to improve retention in PrEP care. [ABSTRACT FROM AUTHOR]

Motivation Genomic data are often produced in batches due to practical restrictions, which may lead to unwanted variation in data caused by discrepancies across batches. Such 'batch effects' often have negative impact on downstream biological analysis and need careful consideration. In practice, batch effects are usually addressed by specifically designed software, which merge the data from different batches, then estimate batch effects and remove them from the data. Here, we focus on classification and prediction problems, and propose a different strategy based on ensemble learning. We first develop prediction models within each batch, then integrate them through ensemble weighting methods. Results We provide a systematic comparison between these two strategies using studies targeting diverse populations infected with tuberculosis. In one study, we simulated increasing levels of heterogeneity across random subsets of the study, which we treat as simulated batches. We then use the two methods to develop a genomic classifier for the binary indicator of disease status. We evaluate the accuracy of prediction in another independent study targeting a different population cohort. We observed that in independent validation, while merging followed by batch adjustment provides better discrimination at low level of heterogeneity, our ensemble learning strategy achieves more robust performance, especially at high severity of batch effects. These observations provide practical guidelines for handling batch effects in the development and evaluation of genomic classifiers. Availability and implementation The data underlying this article are available in the article and in its online supplementary material. Processed data is available in the Github repository with implementation code, at https://github.com/zhangyuqing/bea%5fensemble. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: Postoperative gastrointestinal leak and venous thromboembolism (VTE) are devastating complications of bariatric surgery. The performance of currently available predictive models for these complications remains wanting, while machine learning has shown promise to improve on traditional modeling approaches. The purpose of this study was to compare the ability of two machine learning strategies, artificial neural networks (ANNs), and gradient boosting machines (XGBs) to conventional models using logistic regression (LR) in predicting leak and VTE after bariatric surgery. Methods: ANN, XGB, and LR prediction models for leak and VTE among adults undergoing initial elective weight loss surgery were trained and validated using preoperative data from 2015 to 2017 from Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program database. Data were randomly split into training, validation, and testing populations. Model performance was measured by the area under the receiver operating characteristic curve (AUC) on the testing data for each model. Results: The study cohort contained 436,807 patients. The incidences of leak and VTE were 0.70% and 0.46%. ANN (AUC 0.75, 95% CI 0.73–0.78) was the best-performing model for predicting leak, followed by XGB (AUC 0.70, 95% CI 0.68–0.72) and then LR (AUC 0.63, 95% CI 0.61–0.65, p < 0.001 for all comparisons). In detecting VTE, ANN, and XGB, LR achieved similar AUCs of 0.65 (95% CI 0.63–0.68), 0.67 (95% CI 0.64–0.70), and 0.64 (95% CI 0.61–0.66), respectively; the performance difference between XGB and LR was statistically significant (p = 0.001). Conclusions: ANN and XGB outperformed traditional LR in predicting leak. These results suggest that ML has the potential to improve risk stratification for bariatric surgery, especially as techniques to extract more granular data from medical records improve. Further studies investigating the merits of machine learning to improve patient selection and risk management in bariatric surgery are warranted. [ABSTRACT FROM AUTHOR]

For the first time xBaWO4/(1 − x)CaWO4 where (x = 0, 0.25, 0.5, 0.75, 1) nanocomposites powder samples were prepared by co-precipitation method and sintered at 200 °C and 400 °C. They were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and high-resolution transmission electron microscopy (HRTEM). UV–Visible reflectance, photoluminescence (PL) and PL lifetime studies methods. The intrinsic blue emission of CaWO4 at 420 nm was found to be maximum in xBaWO4/(1 − x)CaWO4 nanocomposite sample for the optimized value of 'x' and sintering temperature. The lifetime of this emission was also enhanced for the same sample. The mechanism for the enhanced PL emission intensity and lifetime are also discussed elaborately. [ABSTRACT FROM AUTHOR]

This article considers replicability of the performance of predictors across studies. We suggest a general approach to investigating this issue, based on ensembles of prediction models trained on different studies. We quantify how the common practice of training on a single study accounts in part for the observed challenges in replicability of prediction performance. We also investigate whether ensembles of predictors trained on multiple studies can be combined, using unique criteria, to design robust ensemble learners trained upfront to incorporate replicability into different contexts and populations. [ABSTRACT FROM AUTHOR]

A series of novel 18-crown-6 ether functionalized bis-benzimidazole derivatives ( 3a- g) have been synthesized by reacting various substituted ortho-phenylene diamines and pyridine diamines with 4,4′-diformyl dibenzo-18-crown-6 ether ( 1). The required starting material 4,4′-diformyl dibenzo-18-crown-6 ether ( 1) was prepared by Duff formylation method. All synthesized novel molecules have been identified on the basis of infrared (IR), H nuclear magnetic resonance (NMR), C NMR, mass spectroscopy (MS), and elemental analyses. The title compounds ( 3a- g) were tested for antifungal activity against Aspergillus niger, Trichothecium sp., Rhizopus sp., and Sclerotium rolfsii fungi species using agar well diffusion method, showing mild to good results. Graphical abstract: [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Currently, much attention is being paid for improving the texture of food by screening the new exopolysaccharides (EPS) producing strains. The aim of the present work was to isolate EPS producing Lactic acid bacteria (LAB) strains from raw milk and milk products samples. Total of thirty eight dahi, lassi and raw milk samples were collected from different villages and towns of Karnal and Delhi District. The samples were plated on milk agar and colonies showing ropy polysaccharides production were subjected to biochemical test. After molecular identification 2 were found as S. thermophilus, 2 were Lb. rhamnosus and 2 were confirmed as Lb. fermentum. Two S. thermophilus strains (PD7 and PD11) and Lb. fermentum strains (AL6 and AD3) showed better curdling pattern, acidity, exopolysaccharides production, and sensory properties. These cultures can be used for manufacture of indigenous fermented milk products. [ABSTRACT FROM AUTHOR]

Milk has long been acknowledged as a source of macro- and micro nutrients. Presently, several identified biologically active substances from milk and their derivatives has attracted much attention from the scientific community. These bioactive compounds confer many health benefits that might support disease prevention. Worldwide, there is an increasing interest in the therapeutic potential of bioactive peptides which collectively present a cornucopia of bioactivities for utilization in humans. Bioactive peptides are hydrolysates with specific amino acid sequences that exert a positive physiological effect on the body. Most of the biological activities are encrypted within the primary sequence of the native protein and can be released during digestion by proteolytic enzymes in the gastrointestinal tract or during fermentation and food processing. Milk protein is an important source of bioactive peptides which may contribute to regulate the nervous, gastrointestinal, and cardiovascular systems as well as the immune system. Milk protein derived bioactive peptides are shown to have antihypertensive, antimicrobial, immunomodulatory, antioxidative and mineral-binding properties. Bioactive peptides derived from milk proteins are of particular interest to the food industry due to the potential functional and physiological roles that they exhibit. [ABSTRACT FROM AUTHOR]

The alternate synthesis of indeno-pyridine and indeno-pyran derivatives have been described by an unprecedented, green, of aromatic aldehydes, malononitrile, and indane 1,3-dione. The formation of either indeno-pyridine or indeno-pyran derivatives based on a precise change in the of indane 1,3-dione or malononitrile on Knoevenagel product during the progress of reaction has been investigated. The reaction completed up to 30 min of time using CuO nanoparticles in water at an ambient temperature renders the protocol novel and environment friendly. All the synthesized compounds have been confirmed on the basis of their spectroscopic data. The CuO nanoparticles have been prepared by coprecipitation method and characterized by SEM, PXRD spectroscopy, and UV spectroscopy. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes (T2D), one of the major common human health problems, is growing at an alarming rate around the globe. Alpha-glucosidase and dipeptidyl peptidase IV (DPP-IV) enzymes play a significant role in development of T2D. Hence, reduction or inhibition of their activity can be one of the important strategies in management of T2D. Studies in the field of bioactive peptides have shown that dietary proteins could be natural source of alpha-glucosidase and DPP-IV inhibitory peptides. Purpose: The purpose of this review is to provide an overview of food protein-derived peptides as potential inhibitors of alpha-glucosidase and DPP-IV with major focus on milk proteins. Methods: Efforts have been made to review the available information in literature on the relationship between food protein-derived peptides and T2D. This review summarizes the current data on alpha-glucosidase and dipeptidyl peptidase IV inhibitory bioactive peptides derived from proteins and examines the potential value of these peptides in the treatment and prevention of T2D. In addition, the proposed modes of inhibition of peptide inhibitors are also discussed. Results: Studies revealed that milk and other food proteins-derived bioactive peptides play a vital role in controlling T2D through several mechanisms, such as the satiety response, regulation of incretin hormones, insulinemia levels, and reducing the activity of carbohydrate degrading digestive enzymes. Conclusions: The bioactive peptides could be used in prevention and management of T2D through functional foods or nutraceutical supplements. Further clinical trials are necessary to validate the findings of in vitro studies and to confirm the efficiency of these peptides for applications. [ABSTRACT FROM AUTHOR]

An innovatively designed novel crown ether functionalized imidazolium-based reusable acidic ionic liquid [crown ether MIm] [HSO] has been efficiently implemented for the synthesis of pyrazole derivatives using various substituted enaminones, hydrazine hydrate and phenyl hydrazine under solvent-free conditions. Structural novelty and task efficiency of the catalyst, high yields of desired products, greener approach attributing high atom economy and solvent-free conditions render this protocol suitable to cope with the current demand in contemporary organic chemistry. The inventive idea of utilizing crown ether functionalized ionic liquid as a catalyst was for the first time demonstrated in this protocol. Graphical Abstract: [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

A novel series of benzo crown ether (dibenzo 18-crown-6 ether, benzo 18-crown-6 ether, and benzo 15-crown-5 ether) functionalized enamines derivatives from amino benzo crown ether (4-amino dibenzo 18-crown-6 ether, 4-amino benzo 18-crown-6 ether, 4-amino benzo 15-crown-5 ether) and substituted 3-(dimethylamino)-1-phenylprop-2-en-1-one compounds have been synthesized. All the synthesized compounds were characterized by infrared,1H NMR,13C NMR, distortionless enhancement polarization transfer, and mass and elemental analysis techniques. The cation recognition property for benzo crown ether enamine8awas studied by absorption and fluorescence spectroscopy. [ABSTRACT FROM PUBLISHER]

Motivation: Prior to applying genomic predictors to clinical samples, the genomic data must be properly normalized to ensure that the test set data are comparable to the data upon which the predictor was trained. The most effective normalization methods depend on data from multiple patients. From a biomedical perspective, this implies that predictions for a single patient may change depending on which other patient samples they are normalized with. This test set bias will occur when any cross-sample normalization is used before clinical prediction. Results: We demonstrate that results from existing gene signatures which rely on normalizing test data may be irreproducible when the patient population changes composition or size using a set of curated, publicly available breast cancer microarray experiments. As an alternative, we examine the use of gene signatures that rely on ranks from the data and show why signatures using rank-based features can avoid test set bias whilemaintaining highly accurate classification, even across platforms. [ABSTRACT FROM AUTHOR]

Polystyrene-supported sulfonic acid has been found to be an efficient catalyst for the one-pot, three-component synthesis of 2 H-indazolo[2,1- b]phthalazinetriones in greener solvent glycerol. Use of greener solvent glycerol along with a polymer-supported, low toxic, and inexpensive catalyst renders the method eco-friendly. High yields, simple product isolation procedure, and fluorescent and electroluminescent natures of the products are the noteworthy aspects of this protocol. [ABSTRACT FROM AUTHOR]

(±)-Camphor-10-sulfonic acid is used for the synthesis of triazolo[1,2-a]indazole-triones and spiro triazolo[1,2-a]indazole-tetraones by condensation reaction of dimedone, urazole and aromatic aldehydes or isatin. The noteworthy aspects of this new method are high yields, high atom efficiency, use of non-toxic solvent, mild conditions, simple workup and synthesis of complex molecules using an eco-friendly, inexpensive catalyst. Graphical Abstract: [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Abstracts: A novel, highly efficient and recyclable Brønsted acidic ionic liquid ([CMIM][CFCOO]) has been successfully implemented for the synthesis of acridinediones in aqueous media. Recyclability of novel catalyst, high yields, use of environmentally benign aqueous media as solvent, simple product isolation, high atom economy and sidestep to column chromatography are the noteworthy features of this protocol. This protocol is competent for producing wide library of acridinediones in good to excellent yields. Furthermore, molecular structure and relative stereochemistry of 4c and 4s derivatives were confirmed by single-crystal X-ray diffraction. Graphical Abstract: [InlineMediaObject not available: see fulltext.] [ABSTRACT FROM AUTHOR]

The article focuses on the use of cloud computing services of Amazon Web Services LLC (AWS) in biomedical research. It states that AWS provides computing environment in storage, random acess memory (RAM), and networking for biomedical research. Moreover, information regarding cloud computing security, scalable computing development, and a case study on genome mapping is also discussed.

Background: Large comparative genomics studies and tools are becoming increasingly more compute-expensive as the number of available genome sequences continues to rise. The capacity and cost of local computing infrastructures are likely to become prohibitive with the increase, especially as the breadth of questions continues to rise. Alternative computing architectures, in particular cloud computing environments, may help alleviate this increasing pressure and enable fast, large-scale, and cost-effective comparative genomics strategies going forward. To test this, we redesigned a typical comparative genomics algorithm, the reciprocal smallest distance algorithm (RSD), to run within Amazon's Elastic Computing Cloud (EC2). We then employed the RSD-cloud for ortholog calculations across a wide selection of fully sequenced genomes. Results: We ran more than 300,000 RSD-cloud processes within the EC2. These jobs were farmed simultaneously to 100 high capacity compute nodes using the Amazon Web Service Elastic Map Reduce and included a wide mix of large and small genomes. The total computation time took just under 70 hours and cost a total of $6,302 USD. Conclusions: The effort to transform existing comparative genomics algorithms from local compute infrastructures is not trivial. However, the speed and flexibility of cloud computing environments provides a substantial boost with manageable cost. The procedure designed to transform the RSD algorithm into a cloud-ready application is readily adaptable to similar comparative genomics problems. [ABSTRACT FROM AUTHOR]

The authors offer a discussion on an article about exploratory analysis of statistical models. Topics discussed include boundaries of a decision rule in data analysis, use of model visualization for checking and updating model in data analysis, and visualizing collections of data models. Also they mention the statistical model visualization approach to understanding data analyst behavior.