Category: Nevin Manimala

J Racial Ethn Health Disparities. 2023 Jul 14. doi: 10.1007/s40615-023-01717-z. Online ahead of print.

ABSTRACT

INTRODUCTION: Workplace experiences may place Black nurses at higher risk for poor sleep and adverse health outcomes. This study aimed to identify poor sleep prevalence and associations of workplace discrimination and workplace social capital with sleep.

METHODOLOGY: Descriptive statistics and multiple linear regression with exploratory analyses were conducted of cross sectional survey data from US Black nurses.

RESULTS: On average, 63 respondents reported sleeping 6.15 h, 45 min less daily than 6.9 h reported nationally for nurses. Ninety-percent of respondents reported poor sleep quality. While no direct significance was found, respondents reporting sleep quality changes had lower workplace social capital and higher workplace discrimination.

CONCLUSION: Black nurses may have higher prevalence of poor sleep than the larger nursing workforce. A potential relationship between decreased sleep quality and negative perceptions of the work environment may exist. Organizations should examine sleep and potential occupational health inequities among Black nurses when considering worker health.

PMID:37450253 | DOI:10.1007/s40615-023-01717-z

J Behav Med. 2023 Jul 14. doi: 10.1007/s10865-023-00435-5. Online ahead of print.

ABSTRACT

The premise of this study was to gain more insight into whether type 1 diabetes (T1D) can impact how youth perceive parents and peers. To address limitations of previous observational studies comparing youth with T1D to control youth, propensity weighting was used to mimic a randomized controlled trial. A total of 558 youth with T1D and 426 control youth (14-26y) completed questionnaires on parental responsiveness, psychological control, overprotection, friend support, extreme peer orientation, and a host of background and psychological functioning variables. The groups were statistically weighted to become as comparable as possible except for disease status. The analysis plan and hypotheses were preregistered on the open science framework. Youth with T1D perceived their mothers to be more overprotective, perceived fewer friend support, and were less extremely oriented toward peers than control youth. There were no group differences for paternal overprotection and paternal and maternal responsiveness and psychological control. Mothers of youth with T1D seem at risk to practice overprotective parenting and clinicians could play an important role in making mothers aware of this risk. However, the absence of group differences for the maladaptive parenting dimension of psychological control and adaptive dimension of responsiveness are reassuring and testify to the resilient nature of youth with T1D and their families. Additionally, there is accumulating evidence that T1D could interfere with engaging in supportive friendships.

PMID:37450207 | DOI:10.1007/s10865-023-00435-5

Biol Trace Elem Res. 2023 Jul 14. doi: 10.1007/s12011-023-03764-3. Online ahead of print.

ABSTRACT

Inorganic arsenic (iAs) and fluoride (iF) are ubiquitous elements whose coexistence is frequent in several regions of the world due to the natural contamination of water sources destined for human consumption. It has been reported that coexposure to these two elements in water can cause toxic effects on health, which are controversial since antagonistic and synergistic effects have been reported. However, there is little information on the possible toxicological interaction between concurrent exposure to iAs and iF on the iAs metabolism profile.The goal of this study was to determine the effect of iF exposure on iAs methylation patterns in the urine and the tissues of female mice of the C57BL/6 strain, which were divided into four groups and exposed daily for 10 days through drinking water as follows: purified water (control); arsenite 1 mg/L, fluoride 50 mg/L and arsenite & fluoride 1:50 mg/L.To characterize the iAs methylation pattern in concomitant iF exposure, iAs and its methylated metabolites (MAs and DMAs) were quantified in the tissues and the urine of mice was exposed to iAs alone or in combination. Our results showed a statistically significant decrease in the arsenic species concentrations and altered relative proportions of arsenic species in tissues and urine in the As-iF coexposure group compared to the iAs-exposed group. These findings show that iF exposure decreases arsenic disposition and alters methylation capacity.Nevertheless, additional studies are required to elucidate the mechanisms involved in the iAs-iF interaction through iF exposure affecting iAs disposition and metabolism.

PMID:37450204 | DOI:10.1007/s12011-023-03764-3

Ther Innov Regul Sci. 2023 Jul 14. doi: 10.1007/s43441-023-00550-0. Online ahead of print.

ABSTRACT

Monitoring of clinical trials is a fundamental process required by regulatory agencies. It assures the compliance of a center to the required regulations and the trial protocol. Traditionally, monitoring teams relied on extensive on-site visits and source data verification. However, this is costly, and the outcome is limited. Thus, central statistical monitoring (CSM) is an additional approach recently embraced by the International Council for Harmonisation (ICH) to detect problematic or erroneous data by using visualizations and statistical control measures. Existing implementations have been primarily focused on detecting inlier and outlier data. Other approaches include principal component analysis and distribution of the data. Here we focus on the utilization of comparisons of centers to the Grand mean for different model types and assumptions for common data types, such as binomial, ordinal, and continuous response variables. We implement the usage of multiple comparisons of single centers to the Grand mean of all centers. This approach is also available for various non-normal data types that are abundant in clinical trials. Further, using confidence intervals, an assessment of equivalence to the Grand mean can be applied. In a Monte Carlo simulation study, the applied statistical approaches have been investigated for their ability to control type I error and the assessment of their respective power for balanced and unbalanced designs which are common in registry data and clinical trials. Data from the German Multiple Sclerosis Registry (GMSR) including proportions of missing data, adverse events and disease severity scores were used to verify the results on Real-World-Data (RWD).

PMID:37450198 | DOI:10.1007/s43441-023-00550-0

Diabetes Ther. 2023 Jul 14. doi: 10.1007/s13300-023-01444-y. Online ahead of print.

ABSTRACT

INTRODUCTION: This study utilized continuous glucose monitoring data to analyze the effects of switching to treatment with fast-acting insulin aspart (faster aspart) in adults with type 1 diabetes (T1D) in clinical practice.

METHODS: A noninterventional database review was conducted in Sweden among adults with T1D using multiple daily injection (MDI) regimens who had switched to treatment with faster aspart as part of basal-bolus treatment. Glycemic data were retrospectively collected during the 26 weeks before switching (baseline) and up to 32 weeks after switching (follow-up) to assess changes in time in glycemic range (TIR; 70-180 mg/dL), mean sensor glucose, glycated hemoglobin (HbA1c) levels, coefficient of variation, time in hyperglycemia (level 1, > 180 to ≤ 250 mg/dL; level 2, > 250 mg/dL), and time in hypoglycemia (level 1, ≥ 54 to < 70 mg/dL; level 2, < 54 mg/dL) (ClinicalTrials.gov Identifier NCT03895515).

RESULTS: Overall, 178 participants were included in the study cohort. The analysis population included 82 individuals (mean age 48.5 years) with adequate glucose sensor data. From baseline to follow-up, statistically significant improvements were reported for TIR (mean increase 3.3%-points [approximately 48 min/day]; p = 0.006) with clinically relevant improvement (≥ 5%) in 43% of participants. Statistically significant improvements from baseline were observed for mean sensor glucose levels, HbA1c levels, and time in hyperglycemia (levels 1 and 2), with no statistically significant changes in time spent in hypoglycemia.

CONCLUSIONS: Switching to faster aspart was associated with improvements in glycemic control without increasing hypoglycemia in adults with T1D using MDI in this real-world setting.

PMID:37450196 | DOI:10.1007/s13300-023-01444-y

Environ Sci Pollut Res Int. 2023 Jul 14. doi: 10.1007/s11356-023-28481-1. Online ahead of print.

ABSTRACT

The environment has become a growing concern for many countries, as pollution and other environmental degradation can harm human health, economic growth, and overall well-being. This paper probes into the asymmetrical implications of economic complexity and freedom on ecological quality in four South Asian countries from 1995 to 2019. Using Nonlinear Autoregressive Distributed Lag methodology approach, our findings indicate that carbon dioxide (CO₂) emissions are intensified by economic freedom both in the long and short term, while negative and positive shocks to economic complexity increase CO₂ emissions in the long term. However, a negative economic complexity shock increases CO₂ emissions, whereas a positive shock has the opposite effect in the short run. Moreover, our results confirm the validity of the environmental Kuznets curve (EKC) hypothesis in the long run. Furthermore, we find that renewable energy usage and the interaction of FDI and renewable energy usage can help reduce environmental damage in both the short and long run. The findings suggest that countries should focus on attracting foreign direct investment that promotes the use of renewable energy. Additionally, policies aimed at encouraging renewable energy use should be implemented. It is important to note that as economic freedom and complexity increase, there is a corresponding increase in CO₂ emissions. Therefore, South Asian policy makers are advised to prioritize the reduction in fossil fuels, the promotion of energy-saving technologies and efficient production, and trade that encourages the transition of renewable energy sources to reduce CO₂ emissions.

PMID:37450190 | DOI:10.1007/s11356-023-28481-1

Environ Sci Pollut Res Int. 2023 Jul 14. doi: 10.1007/s11356-023-28731-2. Online ahead of print.

ABSTRACT

We propose a climate change attention (CCA) index based on Google search volume index (GSVI) from 2004 to 2021 and show that it is an economically and statistically significant negative predictor for next month’s energy stock returns. The index is extracted using principal component analysis (PCA), but the results are similar by using the equal-weighted average method. Compared with 14 traditional macroeconomic predictors, CCA performs the best and provides complementary information when added into bivariate and multivariate macro predictive models. When further considering the effect of CCA’s forecasting power over different periods, strong evidence is shown that this outperformance is especially prominent in economic depressions and down market conditions. From the asset allocation perspective, CCA can provide a mean-variance investor with significant economic gains under alternative risk aversions. Our empirical results prove that investors’ attention to climate change contains predictive information for excess returns of global traditional energy stock index.

PMID:37450186 | DOI:10.1007/s11356-023-28731-2

Environ Sci Pollut Res Int. 2023 Jul 14. doi: 10.1007/s11356-023-28635-1. Online ahead of print.

ABSTRACT

Cutinase, a multifunctional enzyme, has shown great potential in environmental applications such as degradation of plastics and some commonly used insecticides. To overcome these environmental threatening problems, attempts should be made to enhance enzyme production. In the present study, a cutinolytic fungus was isolated from the soil. Based on 18 s rDNA sequencing, it was found that isolate AR08 belongs to the genus Fusarium and clades with Fusarium verticillioides. Optimization of medium composition for enhancement in cutinase production was done using. classical and statistical methods. Firstly, key factors were selected by one variable at a time (OVAT) method, then by Plackett- Burman design. Concentration of these important factors was optimized by Central Composite design. A total of 30 experiments were conducted and the optimized concentration of sodium nitrate, dipotassium hydrogen phosphate, flaxseed oil and zinc sulphate were found to be 0.455%, 0.305%, 2% and 0.0355% respectively. The result of ANOVA (analysis of variance) test revealed that p value was significant for the model. Interaction between flaxseed oil and sodium nitrate was found to have a positive effect on cutinase production. A 14.57 fold increase in enzyme activity was found under optimized conditions with the maximum cutinase activity of 626.6 IUml^-1.

PMID:37450178 | DOI:10.1007/s11356-023-28635-1

Methods Mol Biol. 2023;2690:335-354. doi: 10.1007/978-1-0716-3327-4_27.

ABSTRACT

Proteins are rapidly and dynamically post-transcriptionally modified as cells respond to changes in their environment. For example, protein phosphorylation is mediated by kinases while dephosphorylation is mediated by phosphatases. Quantifying and predicting interactions between kinases, phosphatases, and target proteins over time will aid the study of signaling cascades under a variety of environmental conditions. Here, we describe methods to statistically analyze label-free phosphoproteomic data and infer posttranscriptional regulatory networks over time. We provide an R-based method that can be used to normalize and analyze label-free phosphoproteomic data using variance stabilizing normalization and a linear mixed model across multiple time points and conditions. We also provide a method to infer regulator-target interactions over time using a discretization scheme followed by dynamic Bayesian modeling computations to validate our conclusions. Overall, this pipeline is designed to perform functional analyses and predictions of phosphoproteomic signaling cascades.

PMID:37450158 | DOI:10.1007/978-1-0716-3327-4_27

Methods Mol Biol. 2023;2690:299-310. doi: 10.1007/978-1-0716-3327-4_25.

ABSTRACT

Affinity purification coupled to mass spectrometry (AP-MS) is a powerful method to analyze protein-protein interactions (PPIs). The AP-MS approach provides an unbiased analysis of the entire protein complex and is useful to identify indirect interactors. However, reliable protein identification from the complex AP-MS experiments requires appropriate control of false identifications and rigorous statistical analysis. Another challenge that can arise from AP-MS analysis is to distinguish bona fide interacting proteins from the non-specifically bound endogenous proteins or the “background contaminants” that co-purified by the bait experiments. In this chapter, we will first describe the protocol for performing in-solution trypsinization for the samples from the AP experiment followed by LC-MS/MS analysis. We will then detail the MaxQuant workflow for protein identification and quantification for the PPI data derived from the AP-MS experiment. Finally, we describe the CRAPome interface to process the data by filtering against contaminant lists, score the interactions and visualize the protein interaction networks.

PMID:37450156 | DOI:10.1007/978-1-0716-3327-4_25