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PeerJ 2024Tungsten (W) is an emerging heavy metal pollutant, yet research remains scarce on the biomonitor and sensitive biomarkers for W contamination.
BACKGROUND
Tungsten (W) is an emerging heavy metal pollutant, yet research remains scarce on the biomonitor and sensitive biomarkers for W contamination.
METHODS
In this study, celery and pepper were chosen as study subjects and subjected to exposure cultivation in solutions with five different levels of W. The physiological and biochemical toxicities of W on these two plants were systematically analyzed. The feasibility of utilizing celery and pepper as biomonitor organisms for W contamination was explored and indicative biomarkers were screened.
RESULTS
The results indicated that W could inhibit plants' root length, shoot height, and fresh weight while concurrently promoting membrane lipid peroxidation. Additionally, W enhanced the activities of superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and total antioxidant capacity (TAOC) to counteract oxidative damage. From a physiological perspective, pepper exhibited potential as a biomonitor for W contamination. Biochemical indicators suggested that SOD could serve as a sensitive biomarker for W in celery, while TAOC and POD were more suitable for the roots and leaves of pepper. In conclusion, our study investigated the toxic effects of W on celery and pepper, contributing to the understanding of W's environmental toxicity. Furthermore, it provided insights for selecting biomonitor organisms and sensitive biomarkers for W contamination.
Topics: Apium; Capsicum; Tungsten; Lipid Peroxidation; Superoxide Dismutase; Antioxidants; Catalase; Biomarkers; Ecotoxicology; Plant Roots; Plant Leaves; Oxidative Stress
PubMed: 38938608
DOI: 10.7717/peerj.17601 -
PeerJ 2024Abiotic stress tolerance breeding programs present a spectrum of perspectives, yet definitive solutions remain elusive, with each approach carrying its own set of...
Abiotic stress tolerance breeding programs present a spectrum of perspectives, yet definitive solutions remain elusive, with each approach carrying its own set of advantages and disadvantages. This study systematically evaluates extant methodologies, comparing plant performance across varied genotypes and selection traits under optimal and stress conditions. The objective is to elucidate prevailing ambiguities. Ten homozygous lines (F8 generation) were assessed using a randomized block design alongside five control varieties, with four replicates cultivated under well-watered and deficit water conditions. It is noteworthy that six of the ten homozygous lines were cultivated exclusively under well-watered conditions (F3 to F7), while four lines experienced deficit water conditions (F3 to F7). All five control varieties underwent cultivation under both conditions. These findings underscore the necessity for tailored breeding programs attuned to specific environmental exigencies, recognizing that individual traits manifest divergent responses to varying conditions. It is evident that certain traits exhibit marked disparities under well-watered conditions, while others evince heightened differentiation under water deficit conditions. Significantly, our analysis reveals a pronounced interaction between irrigation regimes and selection traits, which serves to underscore the nuanced interplay between genotype and environmental stress.
Topics: Droughts; Gossypium; Plant Breeding; Stress, Physiological; Genotype; Selection, Genetic; Phenotype
PubMed: 38938605
DOI: 10.7717/peerj.17584 -
JACC. Advances Dec 2023As health care outcomes improve the priority for those living with adult congenital heart disease have changed to a more holistic focus on quality of life and... (Review)
Review
As health care outcomes improve the priority for those living with adult congenital heart disease have changed to a more holistic focus on quality of life and well-being. Although health care has embraced this, there are still areas where there is a deficit in advice, allyship, and advocacy. One of these deficits is in the area of sexual health and well-being. A healthy sexual life has a myriad of physical and psychosocial benefits. However, individuals with adult congenital heart disease may have significant barriers to achieving well-being in this aspect of their lives. These barriers and their potential solutions are outlined in this paper.
PubMed: 38938496
DOI: 10.1016/j.jacadv.2023.100716 -
JACC. Advances Dec 2023
PubMed: 38938490
DOI: 10.1016/j.jacadv.2023.100708 -
JACC. Advances Dec 2023
PubMed: 38938480
DOI: 10.1016/j.jacadv.2023.100712 -
JACC. Advances Oct 2023Mobile health (mHealth) interventions are increasingly being used for cardiovascular research and physical activity promotion.
BACKGROUND
Mobile health (mHealth) interventions are increasingly being used for cardiovascular research and physical activity promotion.
OBJECTIVES
As a result, the authors aimed to evaluate which features facilitate and impede routine engagement with mobile fitness applications.
METHODS
We distributed a pan-Canadian online questionnaire via the behavioral research platform Prolific.co to evaluate what features are associated with the use and routine engagement (ie, daily or weekly use) of mHealth fitness applications and attitudes about data sharing. Binary logistic regression was used to quantify the association between these endpoints and exploratory factors such as the perceived utility of various mHealth application features.
RESULTS
The survey received 694 responses. Most people were women (62%), the median age was 28 years (range: 18-78 years), and most people reported current use of an mHealth fitness application (48%). The perceived importance of personal health (OR: 2.40; 95% CI: 1.34-4.50) was the factor most associated with the current use of an mHealth fitness application. The feature most associated with routine engagement was the ability to track progress toward a goal (OR: 5.10; 95% CI: 2.73-9.61) while the most significant barrier was the absence of goal customization features (OR: 0.44; 95% CI: 0.25-0.81). The acceptance of sharing health data for research was high (56%), and privacy concerns did not significantly affect routine engagement (OR: 0.81; 95% CI: 0.40-1.77). Results were consistent across race and gender.
CONCLUSIONS
mHealth interventions have the potential to be scaled across populations. Optimizing applications to improve self-monitoring and personalization could increase routine engagement and, thus, user retention and intervention effectiveness.
PubMed: 38938369
DOI: 10.1016/j.jacadv.2023.100613 -
Clinical and Experimental Pediatrics Jun 2024This review examines the critical issues of declining total fertility rates (TFRs) and aging populations in East Asia with special focus on South Korea. It provides a...
This review examines the critical issues of declining total fertility rates (TFRs) and aging populations in East Asia with special focus on South Korea. It provides a comprehensive analysis of TFR trends, aging demographics, and the policy responses of these nations to the low-fertility crisis. This study highlights the intricate tapestry of the factors contributing to these demographic shifts, including economic, social, and cultural influences. It also examines the effectiveness of various prenatal policies implemented across these countries, offering insight into their successes and limitations. Furthermore, it explores the role of immigration as a potential solution to the structural challenges posed by low birth rates. This review underscores the importance of multifaceted strategies for addressing the complex demographic challenges faced by South Korea.
PubMed: 38938042
DOI: 10.3345/cep.2023.01599 -
BMC Medical Informatics and Decision... Jun 2024Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this...
BACKGROUND AND AIM
Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this disease has a crucial role in decreasing the mortality rate and improving the prognosis. So, this study.
MATERIALS AND METHODS
In this retrospective study, we used 654 alive and dead PC cases to establish the prediction model for PC. The six chosen machine learning algorithms and prognostic factors were utilized to build the prediction models. The importance of the predictive factors was assessed using the relative importance of a high-performing algorithm.
RESULTS
The XG-Boost with AU-ROC of 0.933 (95% CI= [0.906-0.958]) and AU-ROC of 0.836 (95% CI= [0.789-0.865] in internal and external validation modes were considered as the best-performing model for predicting the mortality risk of PC. The factors, including tumor size, smoking, and chemotherapy, were considered the most influential for prediction.
CONCLUSION
The XG-Boost gained more performance efficiency in predicting the mortality risk of PC patients, so this model can promote the clinical solutions that doctors can achieve in healthcare environments to decrease the mortality risk of these patients.
Topics: Humans; Pancreatic Neoplasms; Retrospective Studies; Male; Female; Middle Aged; Aged; Machine Learning; Risk Assessment; Prognosis; Models, Statistical; Adult; Algorithms
PubMed: 38937795
DOI: 10.1186/s12911-024-02590-4 -
BMC Medical Informatics and Decision... Jun 2024The analysis of extensive electronic health records (EHR) datasets often calls for automated solutions, with machine learning (ML) techniques, including deep learning...
The analysis of extensive electronic health records (EHR) datasets often calls for automated solutions, with machine learning (ML) techniques, including deep learning (DL), taking a lead role. One common task involves categorizing EHR data into predefined groups. However, the vulnerability of EHRs to noise and errors stemming from data collection processes, as well as potential human labeling errors, poses a significant risk. This risk is particularly prominent during the training of DL models, where the possibility of overfitting to noisy labels can have serious repercussions in healthcare. Despite the well-documented existence of label noise in EHR data, few studies have tackled this challenge within the EHR domain. Our work addresses this gap by adapting computer vision (CV) algorithms to mitigate the impact of label noise in DL models trained on EHR data. Notably, it remains uncertain whether CV methods, when applied to the EHR domain, will prove effective, given the substantial divergence between the two domains. We present empirical evidence demonstrating that these methods, whether used individually or in combination, can substantially enhance model performance when applied to EHR data, especially in the presence of noisy/incorrect labels. We validate our methods and underscore their practical utility in real-world EHR data, specifically in the context of COVID-19 diagnosis. Our study highlights the effectiveness of CV methods in the EHR domain, making a valuable contribution to the advancement of healthcare analytics and research.
Topics: Electronic Health Records; Humans; Deep Learning; COVID-19; Machine Learning
PubMed: 38937744
DOI: 10.1186/s12911-024-02581-5 -
Scientific Reports Jun 2024This research introduces a methodology for data-driven regression modeling of components exhibiting nonlinear characteristics, utilizing the sparse identification of...
This research introduces a methodology for data-driven regression modeling of components exhibiting nonlinear characteristics, utilizing the sparse identification of nonlinear dynamics (SINDy) method. The SINDy method is extended to formulate regression models for interconnecting components with nonlinear traits, yielding governing equations with physically interpretable solutions. The proposed methodology focuses on extracting a model that balances accuracy and sparsity among various regression models. In this process, a comprehensive model was generated using linear term weights and an error histogram. The applicability of the proposed approach is demonstrated through a case study involving a sponge gasket with nonlinear characteristics. By contrasting the predictive model with experimental responses, the reliability of the methodology is verified. The results highlight that the regression model, based on the proposed technique, can effectively establish an accurate dynamical system model, accounting for realistic conditions.
PubMed: 38937632
DOI: 10.1038/s41598-024-65680-3