PLoS ONE最新文献

This study analyzed online public discourse on Twitter (later rebranded as X) during the COVID-19 pandemic to understand key factors associated with vaccine hesitancy by employing deep-learning techniques. Text classification analysis reveals a significant association between attitudes toward vaccination and the unique socio-economic characteristics of US states, such as education, race, income or voting behavior. However, our results indicate that attributing vaccine hesitancy solely to a single social factor is not appropriate. Furthermore, the topic modeling of online discourse identifies two distinct sets of justifications for vaccine hesitancy. The first set pertains to political concerns, including constitutional rights and conspiracy theories. The second pertains to medical concerns about vaccine safety and efficacy. However, vaccine-hesitant social media users pragmatically use broad categories of justification for their beliefs. This behavior may suggest that vaccine hesitancy is influenced by political beliefs, unconscious emotions, and gut-level instinct. Our findings have further implications for the critical role of trust in public institutions in shaping attitudes toward vaccination and the need for tailored communication strategies to restore faith in marginalized communities.

In response to the current challenge in genetic studies to make new associations, we advocate for a shift toward leveraging population fine-scale structure. Our exploration brings to light distinct fine-structure within populations having undergone a founder effect such as the Ashkenazi Jews and the population of the Quebec' province. We leverage the fine-scale population structure to explore its impact on the frequency of rare variants. Notably, we observed an 8-fold increase in frequency for a variant associated with the Usher syndrome in one Quebec subpopulation. Our study underscores that smaller cohorts with greater genetic similarity demonstrate an important increase in rare variant frequencies, offering a promising avenue for new genetic variants' discovery.

Reactive oxygen species (ROS) are physiological byproducts of neuronal metabolism. However, an imbalance between ROS generation and antioxidant capacity, often driven by dysregulated pro-oxidant enzymes like nicotinamide adenine dinucleotide phosphate oxidases (NOX), can result in deleterious oxidative stress. This oxidative stress is a critical factor in the pathogenesis of neurodegenerative diseases. While interventions with broad-spectrum antioxidants have demonstrated limited efficacy, the modulation of endogenous antioxidant mechanisms presents a promising therapeutic avenue. Here, we investigated the potential of the neuroprotective hormone prolactin to mitigate oxidative stress and subsequent neuronal cell death. Prolactin protected primary mouse hippocampal neurons from hydrogen peroxide (H2O2)-induced oxidative damage. Prolactin reduced ROS levels, lipid peroxidation, and apoptosis, and its effects were occluded by a specific prolactin receptor antagonist (G129R-hPRL). Mechanistically, prolactin suppressed H2O2-induced mRNA upregulation of pro-oxidative Nox4 and pro-apoptotic Bax. Moreover, prolactin induced nuclear factor kappa B (NF-κB) nuclear translocation, and the inhibition of the NF-κB signaling pathway abolished the neuroprotective and transcriptional effects of prolactin, indicating its central role in prolactin-mediated protection. Our findings indicate that prolactin exerts potent antioxidant and neuroprotective effects by modulating the expression of Nox4 and Bax, thereby reducing ROS generation and neuronal apoptosis. This study underscores the therapeutic potential of prolactin in attenuating oxidative stress and suggests a possible role in the treatment of neurodegenerative diseases.

There is convincing evidence that the learning environments digitalization of tools and equipment ultimately results in the speed and depth learning involvement of academia members, by raising attainment of each of the digital learning experiences. The majority of the research that was conducted on the topic of enhancing the digital skills of learners, which would ultimately lead to an increase in their active engagement, was conducted on students in primary and secondary education, leaving members of higher education outside of the scope of the study. Given the uninterrupted search for academic performance and innovation, the current research considers the technological changes that lead to the transformation of the traditional academic learning environments as previously known. The current paper considers the changes in the learners' engagement in the context of the dually digital transformation of the higher academic multi-institutional digitally-learning enhancements. An important factor to be considered regards the leadership evolution (in terms of teaching) that over time, led to a different speed contextual shift, according to its effectiveness, leading to higher or lower students learning (dis)engagement. The current manuscript aims to examine how the higher education digitalization levels could affect the student's learning engagement, under the close monitoring of the academia leadership styles practice. Data collection and analysis implied at first a qualitative approach by issuing an online-distributed survey that resulted in a number of 2272 valid responses. After performing structural equation modelling and proving a valid assessment tool, the analysis resulted into statistically proving the validity of two main hypotheses according to which students learning engagement has a positive effect on the practice of academic leadership. Additionally, results emphasized the fact that higher education digitalization has altogether a negative effect of students learning engagement. Consequently, the current study stresses on the importance of different peers' categories in the context of higher education institutions performance, with an emphasis on the different levels of students' engagement and the leadership styles evolution and practice, aspects uniformly developing within a continuously digitally transformation of the higher education environment.

Background: Stress outside of the couple relationship (extradyadic stress) can spill over into the couple relationship, increasing stress between the partners (intradyadic stress). Extra- and intradyadic stress are furthermore associated with depressive symptoms. Due to the interdependence of romantic partners, this study aimed to investigate the influence of the person's own and their partner's extra- and intradyadic stress on the person's depressive symptoms in parents of toddlers. The second aim was to evaluate whether intradyadic stress mediates the within-person and between-partner association between extradyadic stress and depressive symptoms.

Methods: Longitudinal data of a community sample of 878 opposite-sex couples, participating in the prospective cohort study DREAM, were collected two and three years after birth. Extra- and intradyadic stress were assessed by the Multidimensional Stress Questionnaire for Couples and depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale. An actor-partner interdependence mediation model was applied to the data, while controlling for the confounder academic degree.

Results: The person's own extradyadic stress predicted their depressive symptoms one year later, partially mediated by their intradyadic stress. The partner's extradyadic stress and the person's own depressive symptoms one year later were only indirectly associated through the person's own intradyadic stress. In a sensitivity analysis, between-partner effects were no longer significant after including autoregressive pathways.

Conclusion: Our findings highlight the importance of the extradyadic-intradyadic stress spillover for the mental health of women and men with young children. Early targeted interventions could help to prevent later depressive symptoms by reducing stress inside the couple relationship that results from both partners' stress from outside the couple relationship.

Tennis, a widely enjoyed sport, motivates athletes and coaches to optimize training for competitive success. This retrospective predictive study examines anthropometric features and statistics of 1990 tennis players in the 2022 season, using 20,040 data points retrospectively obtained from the ATP official source after the end of the season. These data were cross-verified with information from other sources before categorisation to address any discrepancies. Employing various analytical methods, the results emphasize the strategic importance of tournament participation and gameplay for financial gains and higher rankings. Prize money analysis reveals a significant disparity favoring top players. Multivariate Analysis of Variance highlights the need to consider multiple variables for understanding ATP rankings. Multinomial Logistic Regression identifies age, height, and specific service-related metrics as key determinants, with older and taller players more likely to secure top positions. Neural Network models exhibit potential in predicting ATP Rank outcomes, particularly for ATP Rank (500). Our results argue for the use of Artificial Intelligence (AI), specifically Neural Networks, in handling complex interactions and emphasize that AI is a supportive tool in decision-making, requiring careful consideration by experienced individuals. In summary, this study enhances our understanding of ATP ranking factors, providing actionable insights for coaches, players, and stakeholders in the tennis community.

Aims: To compare the likelihood of being prescribed an antidepressant in depressed individuals with and without type 2 diabetes.

Methods: We performed a matched cohort study using primary care record data from the UK Clinical Practice Research Datalink. We used multivariable logistic regression to compare antidepressant prescribing during the first five years of starting oral antidiabetic medication to a comparison group without type 2 diabetes, matched based on GP practice, age and sex. We performed subgroup analyses stratified by sex, age and ethnicity.

Results: People with type 2 diabetes and depression were 75% less likely to be prescribed an antidepressant compared to people with depression alone (odds ratio (OR) 0.25, 95% confidence interval (CI) 0.25 to 0.26). This difference was greater in males (OR 0.23, 95% CI, 0.22 to 0.24), people older than 56 years (OR 0.23, 95% CI, 0.22 to 0.24), or from a minoritised ethnic background (Asian OR 0.14, 95% CI 0.12-0.14; Black OR 0.12, 95% CI 0.09-0.14).

Conclusions: There may be inequalities in access to antidepressant treatment for people with type 2 diabetes, particularly those who are male, older or from minoritised ethnic backgrounds.

Background: Successful aging is associated with an increase in life expectancy. For a better understanding of the aging process, recognize the relationship between telomere length and nutritional status is a novel approach in geriatric science. Telomers shortening coincides with a decrease in life expectancy, and an increased risk of malnutrition-related diseases.

Goals: The goal of this study was to investigate whether a shorter telomere length is associated with a greater likelihood of malnutrition in community-dwelling older adults.

Methods: A cross-sectional study with a probabilistic sample of 448 older people aged 60 years old or over, and living in the urban area of an inland Brazilian municipality was conducted. The information was gathered in two stages: a) a personal interview was conducted to obtain sociodemographic, cognitive, and functional autonomy data. The Mini Nutritional Assessment was used to assess the risk of malnutrition. b) a blood sample was taken to proceed with the relative quantitative study of telomere length using real-time qPCR method. The differences between the groups were estimated using Pearson's v2 and Fisher's exact tests. In the data analysis, descriptive statistics and multiple logistic regression were applied.

Results: In 34.15% of the total sample, malnutrition was recognized as a risk factor. Older people with the shortest telomere length had more chances of getting malnutrition (OR = 1.63; IC:95% = 1.04-2.55) compared to those with longer telomeres, independent of age groups, family income, multimorbidity, cognitive decline, and depressive symptoms.

Conclusion: The creation of clinical trials and the implementation of therapies to reduce the risk of malnutrition will be aided using the telomere length as an aging innovative biomarker, connected with nutritional status.

Genome-Wide Non-Invasive Prenatal Testing (GW-NIPT) can provide positive results not only for common autosomal aneuploidies but also for rare autosomal trisomies (RATs) and structural chromosomal abnormalities (StrCAs). Due to their rarity, there is currently insufficient information on positive predictive value PPV of RAT and StrCA-positive cases in the literature. In this study, the screening accuracy and pregnancy outcomes of cases positive for rare chromosomal abnormalities were examined based on publications in which GW-NIPT testing was performed. True positive cases were determined using two different methodologies. One was a confirmed methodology, where only cases validated by genetic testing were considered true positives with a definite diagnosis, and the other was an extended methodology, where, in addition to cases confirmed by genetic testing, intrauterine fetal death and termination of pregnancy due to an abnormality confirmed by ultrasound examination were also considered true positives, where no diagnosis had been made but the fetus was probably affected. Seventeen studies were analyzed, with a total GW-NIPT population of 740,076. Of these, 1,738 were RAT positive. Using the confirmed method, we found the highest rates of true positives in T16, followed by T22, and T2, using the extended method, the highest rate of true positives in T15, T16 and T22. This is the first meta-analysis to determine the frequency of rare chromosomal abnormalities, test-positive rates, and the PPV of each chromosomal abnormality with high precision. Our results could aid pre- and post-test genetic counselling and help patients and clinicians in their decision-making.

Bay-scale empirical evaluations of how bivalve aquaculture alters plankton composition, and subsequently ecological functioning and higher trophic levels, are lacking. Temporal, inter- and within-bay variation in hydrodynamic, environmental, and aquaculture pressure complicate plankton monitoring design to detect bay-scale changes and inform aquaculture ecosystem interactions. Here, we used flow cytometry to investigate spatio-temporal variations in bacteria and phytoplankton (< 20 μm) composition in four bivalve aquaculture embayments. We observed higher abundances of bacteria and phytoplankton in shallow embayments that experienced greater freshwater and nutrient inputs. Depleted nutrient conditions may have led to the dominance of picophytoplankton cells, which showed strong within-bay variation as a function of riverine vs marine influence and nutrient availability. Although environmental forcings appeared to be a strong driver of spatio-temporal trends, results showed that bivalve aquaculture may reduce near-lease phytoplankton abundance and favor bacterial growth. We discuss confounding environmental factors that must be accounted for when interpreting aquaculture effects such as grazing, benthic-pelagic coupling processes, and microbial biogeochemical cycling. Conclusions provide guidance on sampling considerations using flow cytometry in aquaculture sites based on embayment geomorphology and hydrodynamics.