PLoS ONE最新文献

Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by oxidative stress, inflammation, and the degeneration of dopaminergic neurons. Current treatments focus more on symptom management rather than disease prevention. Acacia jacquemontii, rich in antioxidants, may offer a novel therapeutic approach for PD. This study aims to investigate the phytochemical composition, antioxidant capacity, anti-Parkinsonian efficacy, and in-silico validation of Acacia jacquemontii methanol extract (AJME) using liquid chromatography-mass spectrometry (LC-MS). Secondary metabolites were identified, and total alkaloid, phenolic, and flavonoid contents were quantified. LC-MS was used for detailed compound profiling. Antioxidant activity was evaluated using the DPPH assay. In vivo tests on Wistar rats modeled PD through haloperidol administration. AJME's anti-Parkinsonian effects were assessed via histological, biochemical, and behavioral analyses. In-silico techniques, including molecular docking, structural interaction fingerprinting, ADME prediction, DFT, MESP studies, and molecular dynamics (MD) simulations, were employed to understand AJME molecules' binding interactions and electronic properties. In vivo, AJME improved locomotor activity, memory, exploratory behavior, oxidative stress markers (SOD, CAT, GSH, MDA), and neurotransmitter levels (dopamine, noradrenaline, serotonin) in rats. In-silico validation identified CP21 as a potent ligand. MD simulations indicated stable AJME-AChE complexes, with enhanced binding affinity through hydrophobic and van der Waals interactions. A. jacquemontii exhibits significant phytochemical, antioxidant, and anti-Parkinsonian properties. The combined in vitro, in vivo, and in silico studies, supported by LC-MS analysis, suggest that AJME could provide a promising option for developing new therapeutic approaches for PD. However, clinical evaluation is necessary to establish its efficacy and safety in human subjects.

This cross-sectional study aimed to evaluate dentists' awareness, knowledge, and clinical practices regarding early-stage oral cancer lesions, with an emphasis on diagnostic behavior, patient education, and referral approaches. A total of 263 dentists from public and private institutions in Ankara, Türkiye, participated in the survey, which included a content-validated 36-item questionnaire covering demographic data, examination habits, knowledge of lesion features, biopsy practices, and educational experience. The data were analyzed via descriptive statistics, chi-square tests, and Monte Carlo simulations to explore associations between professional characteristics and clinical behaviors. While 78% of the participants acknowledged the dentist's role in reducing oral cancer mortality, only 17.5% reported performing routine oral cancer screenings. Biopsy practice was limited, with only 11% indicating that they had ever performed a biopsy, and 36.9% preferred to monitor lesions instead of referring them immediately. Experienced dentists were significantly more likely to examine their lymph nodes (p = 0.006) and conduct routine screenings (p < 0.001). Although tobacco and alcohol use are widely recognized as risk factors, only 27.7% of the participants routinely examined high-risk anatomical areas. Patient education was reported by 92.4% of the participants, but brochures and visual aids were rarely used. Fewer than one-third of the participants rated their undergraduate training on oral cancer as sufficient, and most supported mandatory continuing education. The findings reveal considerable gaps in dentists' preparedness and implementation of early detection strategies despite high awareness levels. Strengthening diagnostic education in undergraduate programs and promoting structured continuing education may improve early detection efforts and reduce oral cancer-related morbidity and mortality.

[This corrects the article DOI: 10.1371/journal.pone.0292860.].

Background: Patient engagement in cancer care is increasingly recognized as essential for improving clinical outcomes. Nurses play a crucial role in fostering patient engagement, yet there is a lack of validated instruments to assess which nursing behaviors are most effective in promoting engagement from the patient's perspective.

Objective: This study aimed to develop and test the psychometric characteristics of the Nurses' Behavioral Engagement Scale, a tool designed to measure nursing behaviors that support patient engagement in oncology settings, from patients' point of view.

Methods: The study followed a three-phase exploratory sequential mixed-methods design. In the first phase, 53 items were generated based on findings from a previous qualitative study and a systematic review of the literature. In the second phase, the items underwent content validation through a modified e-Delphi procedure with 19 experts in oncology nursing, patient engagement, tool development, and linguistics. Forty-eight items were deemed relevant and tested in the third phase with a sample of 250 cancer patients to evaluate construct validity, internal consistency, and convergent validity.

Results: Exploratory Factor Analysis revealed a four-factor structure, collectively explaining 70.0% of the total variance: Factor 1 - Acknowledgment of Patient's Uniqueness, Factor 2 - Meeting Patient's Knowledge Expectations, Factor 3 - Fostering Patient's Motivation, and Factor 4 - Valuing Patient's Informal Caregivers. The final Nurses' Behavioral Engagement Scale comprises 29 items. Internal consistency was excellent, with Cronbach's alpha coefficients of 0.953, 0.890, 0.942, and 0.920 for the four factors, respectively. The Nurses' Behavioral Engagement Scale demonstrated significant and meaningful correlations with the Health Care Climate Questionnaire and items assessing satisfaction with nursing care, supporting its convergent validity.

Conclusion: The Nurses' Behavioral Engagement Scale provides a psychometrically sound measure of nursing behaviors that promote patient engagement in oncology care. This tool has the potential to inform targeted interventions and quality improvement initiatives, ultimately enhancing patient-centered care and improving health outcomes in cancer patients.

Objective: This study aimed to compared inpatients' intention to recommend hospitals between integrated nursing and caring service wards (INCSW) and general wards (GW), and examined between patients' experiences with nurses and recommendation intention.

Methods: This study analyzed 943 inpatients (INCSW = 223, GW = 720) using the Korea Medical Service Experience Survey (2020-2022). Intention to recommend and nurse experience were measured on 5-point Likert scales and treated as approximately continuous, a common approach that supports the use of multivariable linear regression. Ward differences were assessed using t-tests, and effect sizes were summarized using Cohen's d.

Results: Patients in INCSW reported higher recommendation intention than those in GW (Mean 4.11 vs 3.98), with a small effect size (Cohen's d = 0.27). However, ward type was not independently associated with recommendation intention in the fully adjusted model. All nurse experience domains were positively associated with recommendation intention, with courtesy showing the largest coefficient (β = 0.27, 95% CI 0.17-0.37).

Conclusion: Although recommendation intention was slightly higher among INCSW patients in unadjusted comparisons, ward type was not independently associated with willingness to recommend after adjustment. In contrast, all nurse experience domains were positively and significantly associated with patients' willingness to recommend the hospital. Strengthening nurse communication competencies, supported by ward-level monitoring and feedback-based training, may enhance patient experience and willingness to recommend.

While in theory people are expected to adhere to rational communicative principles, a growing body of work shows that people vary widely in their tendency to draw pragmatic inferences. It has been suggested that these differences may, in part, stem from depth of reasoning: Previous work has shown that individual participants' response patterns in a pragmatic reference game are predicted by three probabilistic pragmatic models of different reasoning depth. However, those models are agnostic to the cognitive traits which underlie those differences. In this study, we systematically investigate sources of individual variation in a pragmatic reference game, where participants are required to draw ad-hoc implicatures of various complexity. We relate the observed variability in reference game performance to cognitive traits, specifically logical reasoning ability, working memory and Theory of Mind, as well as to the strategies reported by participants. We find a positive effect of logical reasoning and Theory of Mind on pragmatic inference. We do not find evidence for an effect of working memory.

Gold mining activities are often suspected to increase mercury pollution-associated with human health and ecological risks in aquatic ecosystems. The objective of this study was to quantify total mercury (THg) concentrations in fish from four different sampling sectors along the Cayapas River watershed that varied according to different exposure levels to artisanal and small-scale gold mining (ASGM) activities. We analyzed 142 samples from eight freshwater fish species (Bryconamericus dahli, Brycon sp., Brycon dentex, Chaetostoma marginatum, Pimelodella modestus, Rhamdia quelen, Gobiomorus maculatus, and Mesoheros festae). Potential human exposure and health risks from fish consumption in three population groups (children, women, and men) was also evaluated to estimate intake rates and determine fish consumption with minimal risk to the population's health. We found different THg concentrations among feeding habits and sampling sectors. Carnivorous fish species (Pimelodella modestus, Rhamdia quelen, Gobiomorus maculatus, and Mesoheros festae) showed higher THg concentrations (0.063 ± 0.021 µg.g-1) and periphyton-feeder species (Chaetostoma marginatum) revealed the lowest levels (0.018 ± 0.007 µg.g-1). Downstream sites showed the highest levels of THg compared to the other upstream sites, despite some sites being directly impacted by ASGM activities. Regarding human exposure, no significant potential health risk was found for the exposed population over a lifetime. However, the THg of a Rhamdia quelen sample slightly exceeded the FDA-EPA Hg reference value in fish across all sites, representing a potential risk for children. Our results suggest that the THg concentrations in the studied fish species are independent of ASGM activities. The accumulation rates may be due to other parameters such as land uses, local hydrology, fishing pressure or natural habitats modification. Further ecological and physiological studies, including spatial and seasonal distribution of Hg in the surface sediment, water column and fish species, should be investigated to assess and modulate the impacts of the ASGM in the Santiago-Cayapas watershed compared with other land uses that contribute to the Hg inputs, bioaccumulation, and biomagnification in ichthyofauna.

Background: Barriers to accessing healthcare have significantly contributed to the high rates of maternal and child mortality and morbidity in developing regions, particularly in Sub-Saharan Africa. Access to healthcare is influenced by multiple factors, including financial constraints, geographic location, the availability of services, and the quality of care provided. This study aimed to assess the magnitude of these barriers and to identify the factors influencing healthcare access among women of reproductive age in Sub-Saharan Africa.

Methods: This study utilized secondary data from the most recent Demographic and Health Surveys conducted between 2019 and 2023 across Sub-Saharan Africa. A weighted sample of 134,470 women of reproductive age was included in the analysis. Data were analyzed using Stata version 14, employing a multilevel mixed-effects logistic regression model. Factors associated with barriers to healthcare access were considered statistically significant at P-values less than 0.05. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were used to interpret the results.

Results: In this study, the prevalence of barriers to healthcare access among women found to be 55.84% (95% CI:55.57, 56.10). Husband educational status (AOR=1.23, 95% CI: 1.15, 1.30), media exposure (AOR=1.17, 95% CI: 1.11, 1.24), antenatal care visit (AOR=1.31,95% CI: 1.10, 1.55), delivery place (AOR=1.33, 95% CI:1.24, 1.43), health insurance (AOR=1.18, 95% CI: 1.11, 1.26), wealth index(AOR=2.29, 95% CI 2.14, 2.45), (AOR=1.57, 95CI: 1.46, 1.68) and visiting health facility within the past 12 months (AOR=0.91,95% CI:0.87, 0.97) were individual level factors associated with outcome variable. In addition, place of residence (AOR=1.30, 95% CI:1.22, 1.39), community literacy level (AOR=1.13, 95% CI:1.03, 1.24), community poverty level (AOR=1.12, 95% CI:1.02, 1.22), and Region were community level factors associated with barriers to healthcare access.

Conclusion: In this study, over half of women in Sub-Saharan Africa face significant barriers to healthcare access. Educational status, media exposure, place of residence, antenatal care attendance, place of delivery, health insurance coverage, wealth index, recent visits to health facilities, community literacy levels, community wealth status, and regional differences were determinant factors. These findings highlight the need for Sub-Saharan African governments to prioritize these determinants when formulating policies and strategies aimed at reducing preventable barriers to healthcare access in the region.

Earthquake prediction is an extremely challenging problem, one that has been in the past (and sometimes still is) claimed to be impossible. Given this undisputed high level of difficulty, work that reports a high level of prediction success might reasonably be regarded with a degree of caution. We will discuss here how these results may in many cases be due to data leakage. However, a recent paper co-authored by one of us has shown a promising level of predictive ability even when its methodology strictly controls for possible overfitting and data leakage. We here build on that prior work by asking if the demonstrated predictive value of the seismic statistical features used there is due to their being able to capture domain-specific knowledge. Specifically, we compare the value of the same set of 60 seismic statistical features used in the aforementioned previous work to the value of a set of 428 generic time series features from the tsfresh package. We train an XGBoost model to predict if there will be an earthquake of magnitude M ≥ 5 in the following 15 days, and find models using the seismic statistical features can attain AUCs of up to 0.87, while models using the tsfresh features alone cannot obtain results substantially better than random. It therefore does appear that seismic-specific catalogue features are able to capture valuable information about subsurface conditions prior to an impending earthquake. We do not attempt to carry out operational earthquake prediction, considering it premature at this time. However, the demonstrated seismic-specific origin of the predictive power of our features gives hope that by augmenting and enhancing them such prediction may become feasible, and we conclude by discussing some novel directions for future work.

The rapid advancement of artificial intelligence (AI) technologies has not only driven convergence with diverse technological domains but also swiftly spread across various industrial sectors. As a knowledge-intensive field, AI is particularly shaped by the flow of knowledge from scientific research to technological development, yet remains insufficiently examined in a systematic and structural way. This study addresses this gap by investigating science-to-technology knowledge flow that underpins AI's technological evolution. We propose a semantic science-technology exploration framework specifically designed for the AI domain, consisting of the two stages: technology classification and semantic topic exploration. First, AI patents are classified into four categories using centrality measures derived from a CPC co-occurrence network. Then, we extract abstracts from both patents and their cited scientific publications to apply BERTopic modelling and generate topic labels using generative AI. Analyzing AI-related patents filed from 2002 to 2021, we trace key technological trends and elucidate the structural pathways of knowledge flow science to technology. The findings offer practical implications for corporate R&D strategies and innovation policy design in the era of AI.