Tag: nevin manimala

Int J Nurs Knowl. 2023 Nov 21. doi: 10.1111/2047-3095.12451. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to perform specific causal validation of nursing diagnosis Risk for thrombosis (00291) of the NANDA International (NANDA-I) classification.

METHODS: This is a case-control study conducted in a university hospital from January to October 2020. A total of 516 adult patients were included-344 in the Case Group (with venous or arterial thrombosis evidenced by imaging) and 172 in the Control Group (without thrombosis). Statistical analysis was performed by univariate and multivariate logistic regression test, and odds ratios were calculated to measure the effect of exposure between groups. The study was approved by the Research Ethics Committee.

FINDINGS: The patients were predominantly female and aged 59 ± 16 years. In the univariate logistic analysis, five risk factors were significantly associated with thrombosis, two at-risk populations and 12 associated conditions. In the multivariate regression model, the following risk factors remained independently associated (p < 0.05): inadequate knowledge of modifiable factors (OR: 3.03; 95% CI: 1.25-8.56) and ineffective medication self-management (OR: 3.2; 95% CI:1.77-6.26); at-risk populations with history (OR: 2.16; 95% CI: 1.29-3.66) and family history of thrombosis (OR:2.60; 95% CI: 1.03-7.49); and the conditions associated with vascular diseases (OR:6.12; 95% CI:1.69-39.42), blood coagulation disorders (OR: 5.14; 95% CI:1.85-18.37), atherosclerosis (OR:2.07; 95% CI: 1.32-3.27), critical illness (OR: 2.28; 95% CI: 1.42-3.70), and immobility (OR: 2.09; 95% CI: 1.10-4.12).

CONCLUSIONS: The clinical validation allowed to establish strong evidence for the refinement of the diagnosis Risk for thrombosis and, consequently, to raise its level of evidence in the classification of NANDA-I.

IMPLICATIONS FOR NURSING PRACTICE: The evidence pointed out by this study favors the establishment of thrombosis diagnosis in an accurate way by nurses in clinical practice, directing preventive interventions to patients in this risk condition.

PMID:37990774 | DOI:10.1111/2047-3095.12451

Brain Behav. 2023 Nov 21:e3314. doi: 10.1002/brb3.3314. Online ahead of print.

ABSTRACT

BACKGROUND: The coronavirus disease of 2019 (COVID-19) pandemic is an unprecedented traumatic event that has severely impacted social, economic, and health well-being worldwide. The COvid Mental hEalth Trial was specifically designed to evaluate the impact of the COVID-19 pandemic and its containment measures on the mental health of the Italian general population in terms of COVID-19-related acute stress disorder (ASD) symptoms.

METHODS: The present cross-sectional study is based on an online survey carried out in the period March-May 2020. Italian general adult population was invited to compile an anonymous survey, which included the severity of acute stress symptoms scale/National Stressful Events Survey Short Scale to investigate the occurrence and severity of ASD symptoms.

RESULTS: The final sample consisted of 20,720 participants. During the lockdown, subjects with pre-existing mental health problems reported a statistically significant higher risk of acute post-traumatic symptoms compared to the general population (B: 2.57; 95% CI:2.04-3.09; p < .0001) and health care professionals (B: .37; 95% CI: .02-0.72; p < .05). According to multivariate regression models, the levels of acute post-traumatic symptoms (p < .0001) were higher in younger and female respondents. Social isolation and sleep disorder/insomnia represented positive predictors of acute stress (B = 3.32, 95% CI = 3.08-3.57).

CONCLUSIONS: Concerns about the risk of infection as well as social isolation caused a higher incidence of acute post-traumatic stress symptoms that may predict the subsequent development of post-traumatic stress disorder symptoms in the long term.

PMID:37990771 | DOI:10.1002/brb3.3314

Am J Hum Biol. 2023 Nov 22:e24009. doi: 10.1002/ajhb.24009. Online ahead of print.

ABSTRACT

OBJECTIVES: This study investigates the relationships between the COVID-19 pandemic, lifestyle factors, and their impact on bone mineral density in the radius forearm bone and the total bone mineral content in young adults from Slovakia.

METHODS: We assessed 773 Slovak young adults aged 18 to 30 years, divided into subgroups on their pandemic status. Bone mineral density (BMD) was analyzed by the QUS device (Sunlight MiniOmni™), and bone mineral content (BMC) and fat mass (FM) were measured by InBody 770 bioimpedance analyzer. Finally, linear regression analysis tested the associations.

RESULTS: Statistically significant lower speed of sound (SOS) along the length of the forearm radius bone and Z-score values was determined in participants during the COVID-19 pandemic than before it, and statistically significant lower BMC values were observed in the male group during COVID-19 than beforehand. Regression analysis confirmed the negative pandemic effect in the following indices: SOS (p < .001 for women and p = .035 for men), Z-score (p < .001 for women and p = .003 for men), and BMC (p = .024 for men). Vitamin D was a further significant SOS predictor in women at p = .029, but this association was not detected in men. In contrast, the significant male BMC predictors were pandemic presence (p = .028), physical activity (p = .028), and fat mass percentage (p = .001).

CONCLUSIONS: Significant COVID-19 pandemic effects on bone tissue were determined on bone mass density in the radius forearm bone and the total bone mineral content. These effects establish that the pandemic had a negative impact on both their bone quality and health.

PMID:37990761 | DOI:10.1002/ajhb.24009

Sci Rep. 2023 Nov 21;13(1):20347. doi: 10.1038/s41598-023-47043-6.

ABSTRACT

A large proportion of the world’s population has some form of immunity against SARS-CoV-2, through either infection (‘natural’), vaccination or both (‘hybrid’). This retrospective cohort study used data on SARS-CoV-2, vaccination, and hospitalization from national health system from February 2020 to June 2022 and Cox regression modelling to compare those with natural immunity to those with no (Cohort1, n = 94,982), hybrid (Cohort2, n = 47,342), and vaccine (Cohort3, n = 254,920) immunity. In Cohort 1, those with natural immunity were at lower risk for infection during the Delta (aHR 0.17, 95%CI 0.15-0.18) and higher risk (aHR 1.24, 95%CI 1.18-1.32) during the Omicron period than those with no immunity. Natural immunity conferred substantial protection against COVID-19-hospitalization. Cohort 2-in comparison to natural immunity hybrid immunity offered strong protection during the Delta (aHR 0.61, 95%CI 0.46-0.80) but not the Omicron (aHR 1.05, 95%CI 0.93-1.1) period. COVID-19-hospitalization was extremely rare among individuals with hybrid immunity. In Cohort 3, individuals with vaccine-induced immunity were at higher risk than those with natural immunity for infection (Delta aHR 4.90, 95%CI 4.48-5.36; Omicron 1.13, 95%CI 1.06-1.21) and hospitalization (Delta aHR 7.19, 95%CI 4.02-12.84). These results show that risk of infection and severe COVID-19 are driven by personal immunity history and the variant of SARS-CoV-2 causing infection.

PMID:37989858 | DOI:10.1038/s41598-023-47043-6

Sci Rep. 2023 Nov 21;13(1):20372. doi: 10.1038/s41598-023-47651-2.

ABSTRACT

Additive manufacturing of as-build metal materials with laser powder bed fusion typically leads to the formations of various chemical phases and their corresponding microstructure types. Such microstructures have very complex shape and size anisotropic distributions due to the history of the laser heat gradients and scanning patterns. With higher complexity compared to the post-heat-treated materials, the synthetic volume reconstruction of as-build materials for accurate modelling of their mechanical properties is a serious challenge. Here, we present an example of complete workflow pipeline for such nontrivial task. It takes into account the statistical distributions of microstructures: object sizes for each phase, several shape parameters for each microstructure type, and their morphological and crystallographic orientations. In principle, each step in the pipeline, including the parameters in the crystal plasticity model, can be fine-tuned to achieve suitable correspondence between experimental and synthetic microstructures as well as between experimental stress-strain curves and simulated results. To our best knowledge, this work represents an example of the most challenging synthetic volume reconstruction for as-build additive manufacturing materials to date.

PMID:37989841 | DOI:10.1038/s41598-023-47651-2

Cell Mol Life Sci. 2023 Nov 21;80(12):370. doi: 10.1007/s00018-023-05009-x.

ABSTRACT

Individuals with Down syndrome (DS) have a higher prevalence of obesity compared to the general population. Conventionally, this has been attributed to endocrine issues and lack of exercise. However, deficits in neural reward responses and dopaminergic disturbances in DS may be contributing factors. To investigate this, we focused on a mouse model (Ts65Dn) bearing some triplicated genes homologous to trisomy 21. Through detailed meal pattern analysis in male Ts65Dn mice, we observed an increased preference for energy-dense food, pointing towards a potential “hedonic” overeating behavior. Moreover, trisomic mice exhibited higher scores in compulsivity and inflexibility tests when limited access to energy-dense food and quinine hydrochloride adulteration were introduced, compared to euploid controls. Interestingly, when we activated prelimbic-to-nucleus accumbens projections in Ts65Dn male mice using a chemogenetic approach, impulsive and compulsive behaviors significantly decreased, shedding light on a promising intervention avenue. Our findings uncover a novel mechanism behind the vulnerability to overeating and offer potential new pathways for tackling obesity through innovative interventions.

PMID:37989807 | DOI:10.1007/s00018-023-05009-x

Environ Monit Assess. 2023 Nov 22;195(12):1509. doi: 10.1007/s10661-023-12046-3.

ABSTRACT

Although existing studies mainly focused on the air quality status in Bangladesh, quantifying the natural and manmade effects, the frequency of high pollution levels, and the associated health risks remained beyond detailed investigation. Air quality and meteorological data from the Department of Environment for 2012-2019 were analyzed, attempting to answer those questions. Cluster analysis of PM_2.5, PM₁₀, and gaseous pollutants implied that Dhaka and neighboring cities, Narayangonj and Gazipur, are from similar sources compared to the other major cities in the country. Apart from the transboundary sources, land use types and climate parameters unevenly affected local pollution loadings across city domains. The particulate concentrations persistently remained above the national standard for almost half the year, with the peaks during the dry months. Even though nitrogen oxides remained high in all three cities, other gaseous pollutants, such as CO and O₃, except SO₂, showed elevated concentrations solely in Dhaka city. Concentrations of gaseous pollutants in Dhaka vary spatially, but no statistical differences could be discerned between the working days and holidays. Frequency analysis results and hazard quotients revealed the likelihood of adverse health outcomes in Narayangonj ensuing from particulate exposures surpasses the other cities for different age, gender, and occupation groups. Nonetheless, school-aged children and construction workers were most at risk from chronic exposure to gaseous pollutants mostly in Dhaka. One limitation of this study was that the routine air quality monitoring happens just from five sites, making the evidence-based study concerning health outcomes quite challenging.

PMID:37989796 | DOI:10.1007/s10661-023-12046-3

Nat Protoc. 2023 Nov 21. doi: 10.1038/s41596-023-00892-x. Online ahead of print.

ABSTRACT

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user’s desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

PMID:37989764 | DOI:10.1038/s41596-023-00892-x

Sci Rep. 2023 Nov 21;13(1):20367. doi: 10.1038/s41598-023-45973-9.

ABSTRACT

The emergence of antibacterial resistance (ABR) is an urgent and complex public health challenge worldwide. Antibiotic resistant genes (ARGs) are considered as a new pollutant by the WHO because of their wide distribution and emerging prevalence. The role of environmental factors in developing ARGs in bacterial populations is still poorly understood. Therefore, the relationship between environmental factors and bacteria should be explored to combat ABR and propose more tailored solutions in a specific region. Here, we collected and analyzed surface water samples from Yangtze Delta, China during 2021, and assessed the nonlinear association of environmental factors with ARGs through a sigmoid model. A high abundance of ARGs was detected. Amoxicillin, phosphorus (P), chromium (Cr), manganese (Mn), calcium (Ca), and strontium (Sr) were found to be strongly associated with ARGs and identified as potential key contributors to ARG detection. Our findings suggest that the suppression of ARGs may be achieved by decreasing the concentration of phosphorus in surface water. Additionally, Group 2A light metals (e.g., magnesium and calcium) may be candidates for the development of eco-friendly reagents for controlling antibiotic resistance in the future.

PMID:37989759 | DOI:10.1038/s41598-023-45973-9

Nat Commun. 2023 Nov 21;14(1):7573. doi: 10.1038/s41467-023-43250-x.

ABSTRACT

The value of the environment determines animals’ motivational states and sets expectations for error-based learning^1-3. How are values computed? Reinforcement learning systems can store or cache values of states or actions that are learned from experience, or they can compute values using a model of the environment to simulate possible futures³. These value computations have distinct trade-offs, and a central question is how neural systems decide which computations to use or whether/how to combine them^4-8. Here we show that rats use distinct value computations for sequential decisions within single trials. We used high-throughput training to collect statistically powerful datasets from 291 rats performing a temporal wagering task with hidden reward states. Rats adjusted how quickly they initiated trials and how long they waited for rewards across states, balancing effort and time costs against expected rewards. Statistical modeling revealed that animals computed the value of the environment differently when initiating trials versus when deciding how long to wait for rewards, even though these decisions were only seconds apart. Moreover, value estimates interacted via a dynamic learning rate. Our results reveal how distinct value computations interact on rapid timescales, and demonstrate the power of using high-throughput training to understand rich, cognitive behaviors.

PMID:37989741 | DOI:10.1038/s41467-023-43250-x