Tag: pubmed

PLoS One. 2021 Mar 8;16(3):e0247943. doi: 10.1371/journal.pone.0247943. eCollection 2021.

ABSTRACT

BACKGROUND: Gender and health are two factors that shape the quality of life in old age. Previous available literature established an associaton between various demographic and socio-economic factors with the health and well-being of older adults in India; however, the influence of childless aged is neglected. Therefore, the study examined the gender differential in psychological health and subjective well-being among older adults, focusing on childless older adults.

METHODOLOGY: This study utilized data from Building a Knowledge Base on Population Aging in India (BKPAI). Psychological health and subjective well-being were examined for 9541 older adults aged 60 years & above. Descriptive statistics and bivariate analysis were used to find the preliminary results. Further, multivariate analysis has been done to fulfill the objective of the study.

RESULTS: Around one-fifth (21.2%) of the men reported low psychological health, whereas around one-fourth (25.5%) of the women reported low psychological health. Further, around 24 per cent of men and 29 per cent of women reported low subjective well-being. Results found that low psychological well-being (OR = 1.87, C.I. = 1.16-3.01), as well as low subjective well-being (OR = 1.78, C.I. = 1.15-2.76), was higher in childless older women than in childless older men. Higher education, community involvement, good self-rated health, richest wealth quintile, and residing in urban areas significantly decrease the odds of low subjective well-being and low psychological well-being among older adults.

CONCLUSION: There is a need to improve older adults’ psychological health and subjective well-being through expanded welfare provisions, especially for childless older adults. Moreover, there is an immediate requirement to cater to the needs of poor and uneducated older adults.

PMID:33684164 | DOI:10.1371/journal.pone.0247943

Int Health. 2021 Mar 3:ihab007. doi: 10.1093/inthealth/ihab007. Online ahead of print.

ABSTRACT

BACKGROUND: We sought to determine the prevalence and factors associated with the use of skilled assistance during delivery in Papua New Guinea.

METHODS: We analysed nationally representative data from 5210 women in Papua New Guinea using the 2016-2018 Demographic and Health survey. Both bivariate and multivariable analyses were performed. Statistical significance was set at p<0.05.

RESULTS: The prevalence of skilled assistance during delivery was 57.6%. The richest women (adjusted OR [AOR]=3.503, 95% CI 2.477 to 4.954), working women (AOR=1.221, 95% CI 1.037 to 1.439), women with primary (AOR=1.342, 95% CI 1.099 to 1.639), secondary or higher education (AOR=2.030, 95% CI 1.529 to 2.695), women whose partners had a secondary or higher level of education (AOR=1.712, 95% CI 1.343 to 2.181], women who indicated distance was not a big problem in terms of healthcare (AOR=1.424, 95% CI 1.181 to 1.718), women who had ≥4 antenatal care (ANC) visits (AOR=10.63, 95% CI 8.608 to 13.140), women from the Islands region (AOR=1.305, 95% CI 1.045 to 1.628), those who read newspapers or magazines (AOR=1.310, 95% CI 1.027 to 1.669) and women who watched television (AOR=1.477, 95% CI 1.054 to 2.069) less than once a week had higher odds of utilising skilled attendants during delivery. On the contrary, women in the Momase region (AOR=0.543, 95% CI 0.438 to 0.672), women in rural areas (AOR=0.409, 95% CI 0.306 to 0.546), as well as women with a parity of 3 (AOR=0.666, 95% CI 0.505 to 0.878) or ≥4 (AOR=0.645, 95% CI 0.490 to 0.850) had lower odds of utilising skilled attendance during delivery.

CONCLUSION: There is relatively low use of skilled delivery services in Papua New Guinea. Wealth, employment status, educational level, parity and number of ANC visits, as well as access to healthcare and place of residence, influence the utilisation of skilled delivery services.

PMID:33684205 | DOI:10.1093/inthealth/ihab007

PLoS One. 2021 Mar 8;16(3):e0247055. doi: 10.1371/journal.pone.0247055. eCollection 2021.

ABSTRACT

A good number of genome-wide association studies (GWAS), including meta-analyses, reported that single nucleotide polymorphisms (SNPs) of the IL-6 gene are significantly associated with various types of cancer risks, though some other studies reported insignificant association with cancers, in the literature. These contradictory results may be due to variations in sample sizes and/or deficiency of statistical modeling. Therefore, an attempt is made to provide a more comprehensive understanding of the association between the IL-6 gene SNPs (rs1800795, rs1800796, rs1800797) and different cancer risks, giving the weight on a large sample size, including different cancer types and appropriate statistical modeling with the meta-dataset. In order to attain a more reliable consensus decision about the association between the IL-6 gene polymorphisms and different cancer risks, in this study, we performed a multi-case statistical meta-analysis based on the collected information of 118 GWAS studies comprising of 50053 cases and 65204 control samples. Results from this Meta-analysis indicated a significant association (p-value < 0.05) of the IL-6 gene rs1800796 polymorphism with an overall increased cancer risk. The subgroup analysis data based on cancer types exhibited significant association (p-value < 0.05) of the rs1800795 polymorphism with an overall increased risk of cervical, liver and prostate cancers; the rs1800796 polymorphism with lung, prostate and stomach cancers; and the rs1800797 polymorphism with cervical cancer. The subgroup analysis of ethnicity data showed a significant association (p-value < 0.05) of an overall cancer risk with the rs1800795 polymorphism for the African and Asian populations, the rs1800796 polymorphism for the Asian only and the rs1800797 polymorphism in the African population. Comparative discussion showed that our multi-case meta-analyses received more support than any previously reported individual meta-analysis about the association between the IL-6 gene polymorphisms and cancer risks. Results from this study, more confidently showed that the IL-6 gene SNPs (rs1800795, rs1800796 and rs1800797) in humans are associated with increased cancer risks. Therefore, these three polymorphisms of the IL-6 gene have the potential to be evaluated as a population based rapid, low-cost PCR prognostic biomarkers for different types of cancers diagnosis and research.

PMID:33684135 | DOI:10.1371/journal.pone.0247055

PLoS Genet. 2021 Mar 8;17(3):e1009413. doi: 10.1371/journal.pgen.1009413. Online ahead of print.

ABSTRACT

Previous research on risk factors for obstructive heart defects (OHDs) focused on maternal and infant genetic variants, prenatal environmental exposures, and their potential interaction effects. Less is known about the role of paternal genetic variants or environmental exposures and risk of OHDs. We examined parent-of-origin effects in transmission of alleles in the folate, homocysteine, or transsulfuration pathway genes on OHD occurrence in offspring. We used data on 569 families of liveborn infants with OHDs born between October 1997 and August 2008 from the National Birth Defects Prevention Study to conduct a family-based case-only study. Maternal, paternal, and infant DNA were genotyped using an Illumina Golden Gate custom single nucleotide polymorphism (SNP) panel. Relative risks (RR), 95% confidence interval (CI), and likelihood ratio tests from log-linear models were used to estimate the parent-of-origin effect of 877 SNPs in 60 candidate genes in the folate, homocysteine, and transsulfuration pathways on the risk of OHDs. Bonferroni correction was applied for multiple testing. We identified 3 SNPs in the transsulfuration pathway and 1 SNP in the folate pathway that were statistically significant after Bonferroni correction. Among infants who inherited paternally-derived copies of the G allele for rs6812588 in the RFC1 gene, the G allele for rs1762430 in the MGMT gene, and the A allele for rs9296695 and rs4712023 in the GSTA3 gene, RRs for OHD were 0.11 (95% CI: 0.04, 0.29, P = 9.16×10-7), 0.30 (95% CI: 0.17, 0.53, P = 9.80×10-6), 0.34 (95% CI: 0.20, 0.57, P = 2.28×10-5), and 0.34 (95% CI: 0.20, 0.58, P = 3.77×10-5), respectively, compared to infants who inherited maternally-derived copies of the same alleles. We observed statistically significant decreased risk of OHDs among infants who inherited paternal gene variants involved in folate and transsulfuration pathways.

PMID:33684136 | DOI:10.1371/journal.pgen.1009413

PLoS One. 2021 Mar 8;16(3):e0248159. doi: 10.1371/journal.pone.0248159. eCollection 2021.

ABSTRACT

A novel assay technique that involves quantification of lysozyme (Lys) through machine learning is put forward here. This article reports the tendency of the well- documented Ellington group anti-Lys aptamer, to produce aggregates when exposed to Lys. This property of apta-aggregation has been exploited here to develop an assay that quantifies the Lys using texture and area parameters from a photograph of the elliptical aggregate mass through machine learning. Two assay sets were made for the experimental procedure: one with high Lys concentration between 25-100 mM and another with low concentration between 1-20 mM. The high concentration set had a sample volume of 10 μl while the low concentration set had a higher sample volume of 100 μl, in order to obtain the statistical texture values reliably from the aggregate mass. The platform exhibited an experimental limit of detection of 1 mM and a response time of less than 10 seconds. Further, two potential operating modes for the aptamer were hypothesized for this aggregation property and the more accurate mode among the two was ascertained through bioinformatics studies.

PMID:33684138 | DOI:10.1371/journal.pone.0248159

PLoS One. 2021 Mar 8;16(3):e0248220. doi: 10.1371/journal.pone.0248220. eCollection 2021.

ABSTRACT

BACKGROUND: The current standard of care (SOC) for whole breast radiotherapy (WBRT) in the US is conventional tangential photon fields. Advanced WBRT techniques may provide similar tumor control and better normal tissue sparing, but it is controversial whether the medical benefits of an advanced technology are significant enough to justify its higher cost.

OBJECTIVE: To analyze the cost-effectiveness of six advanced WBRT techniques compared with SOC.

METHODS: We developed a Markov model to simulate health states for one cohort of women (65-year-old) with early-stage breast cancer over 15 years after WBRT. The cost effectiveness analyses of field-in-field (FIF), hybrid intensity modulated radiotherapy (IMRT), full IMRT, standard volumetric modulated arc therapy (STD-VMAT), multiple arc VMAT (MA-VMAT), non-coplanar VMAT (NC-VMAT) compared with SOC were performed with both tumor control and radiogenic side effects considered. Transition probabilities and utilities for each health state were obtained from literature. Costs incurred by payers were adopted from literature and Medicare data. Quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER) were calculated. One-way sensitivity analyses and probabilistic sensitivity analyses (PSA) were performed to evaluate the impact of uncertainties on the final results.

RESULTS: FIF has the lowest ICER value of 1,511 $/QALY. The one-way analyses show that the cost-effectiveness of advanced WBRT techniques is most sensitive to the probability of developing contralateral breast cancer. PSAs show that SOC is more cost effective than almost all advanced WBRT techniques at a willingness-to-pay (WTP) threshold of 50,000 $/QALY, while FIF, hybrid IMRT and MA-VMAT are more cost-effective than SOC with a probability of 59.2%, 72.3% and 72.6% at a WTP threshold of 100,000 $/QALY, respectively.

CONCLUSIONS: FIF might be the most cost-effective option for WBRT patients at a WTP threshold of 50,000 $/QALY, while hybrid IMRT and MA-VMAT might be the most cost-effective options at a WTP threshold of 100,000 $/QALY.

PMID:33684139 | DOI:10.1371/journal.pone.0248220

PLoS One. 2021 Mar 8;16(3):e0243517. doi: 10.1371/journal.pone.0243517. eCollection 2021.

ABSTRACT

Deuterium (D), the second most abundant isotope of hydrogen is present in natural waters at an approximate concentration of 145-155 ppm (ca. 1.5E-4 atom/atom). D is known to influence various biological processes due to its physical and chemical properties, which significantly differ from those of hydrogen. For example, increasing D-concentration to >1000-fold above its natural abundance has been shown to increase the frequency of genetic mutations in several species. An interesting deterministic hypothesis, formulated with the intent of explaining the mechanism of D-mutagenicity is based on the calculation that the theoretical probability of base pairs to comprise two adjacent D-bridges instead of H-bridges is 2.3E-8, which is equal to the mutation rate of certain species. To experimentally challenge this hypothesis, and to infer the mutagenicity of D present at natural concentrations, we investigated the effect of a nearly 100-fold reduction of D concentration on the bacterial mutation rate. Using fluctuation tests, we measured the mutation rate of three Escherichia coli genes (cycA, ackA and galK) in media containing D at either <2 ppm or 150 ppm concentrations. Out of 15 pair-wise fluctuation analyses, nine indicated a significant decrease, while three marked the significant increase of the mutation/culture value upon D-depletion. Overall, growth in D-depleted minimal medium led to a geometric mean of 0.663-fold (95% confidence interval: 0.483-0.911) change in the mutation rate. This falls nowhere near the expected 10,000-fold reduction, indicating that in our bacterial systems, the effect of D abundance on the formation of point mutations is not deterministic. In addition, the combined results did not display a statistically significant change in the mutation/culture value, the mutation rate or the mutant frequency upon D-depletion. The potential mutagenic effect of D present at natural concentrations on E. coli is therefore below the limit of detection using the indicated methods.

PMID:33684107 | DOI:10.1371/journal.pone.0243517

PLoS Comput Biol. 2021 Mar 8;17(3):e1008822. doi: 10.1371/journal.pcbi.1008822. Online ahead of print.

ABSTRACT

An intriguing fact long defying explanation is the observation of a universal exponential distribution of beneficial mutations in fitness effect for different microorganisms. To explain this effect, we use a population model including mutation, directional selection, linkage, and genetic drift. The multiple-mutation regime of adaptation at large population sizes (traveling wave regime) is considered. We demonstrate analytically and by simulation that, regardless of the inherent distribution of mutation fitness effect across genomic sites, an exponential distribution of fitness effects emerges in the long term. This result follows from the exponential statistics of the frequency of the less-fit alleles, f, that we predict to evolve, in the long term, for both polymorphic and monomorphic sites. We map the logarithmic slope of the distribution onto the previously derived fixation probability and demonstrate that it increases linearly in time. Our results demonstrate a striking difference between the distribution of fitness effects observed experimentally for naturally occurring mutations, and the “inherent” distribution obtained in a directed-mutagenesis experiment, which can have any shape depending on the organism. Based on these results, we develop a new method to measure the fitness effect of mutations for each variable residue using DNA sequences sampled from adapting populations. This new method is not sensitive to linkage effects and does not require the one-site model assumptions.

PMID:33684109 | DOI:10.1371/journal.pcbi.1008822

PLoS One. 2021 Mar 8;16(3):e0247582. doi: 10.1371/journal.pone.0247582. eCollection 2021.

ABSTRACT

In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.

PMID:33684120 | DOI:10.1371/journal.pone.0247582

J Med Internet Res. 2021 Mar 5. doi: 10.2196/26718. Online ahead of print.

ABSTRACT

This article will provide a perspective on data sharing practices in the context of the novel coronavirus disease (COVID-19) pandemic. The scientific community has made several important inroads in the fight against COVID-19 and there are over 2,500 clinical trials registered globally. Within the rapidly changing pandemic, we are seeing a large number of trials conducted without results made available. It is likely that a plethora of trials have stopped early, not for statistical reasons, but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with total sample size and even small reductions in patient numbers or events can have a substantial impact. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of substantial numbers of false positive and negative trials emerging with the increasing number of trials, adding to public perceptions of uncertainty. Complicating this is the evolving nature of the pandemic, where baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than in well-documented diseases. The standard answer to these challenges during non-pandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.

PMID:33684053 | DOI:10.2196/26718