Tag: nevin manimala

Rofo. 2024 May 28. doi: 10.1055/a-2301-3349. Online ahead of print.

ABSTRACT

Structured reporting (SR) not only offers advantages regarding report quality but, as an IT-based method, also the opportunity to aggregate and analyze large, highly structured datasets (data mining). In this study, a data mining algorithm was used to calculate epidemiological data and in-hospital prevalence statistics of pulmonary embolism (PE) by analyzing structured CT reports.All structured reports for PE CT scans from the last 5 years (n = 2790) were extracted from the SR database and analyzed. The prevalence of PE was calculated for the entire cohort and stratified by referral type and clinical referrer. Distributions of the manifestation of PEs (central, lobar, segmental, subsegmental, as well as left-sided, right-sided, bilateral) were calculated, and the occurrence of right heart strain was correlated with the manifestation.The prevalence of PE in the entire cohort was 24% (n = 678). The median age of PE patients was 71 years (IQR 58-80), and the sex distribution was 1.2/1 (M/F). Outpatients showed a lower prevalence of 23% compared to patients from regular wards (27%) and intensive care units (30%). Surgically referred patients had a higher prevalence than patients from internal medicine (34% vs. 22%). Patients with central and bilateral PEs had a significantly higher occurrence of right heart strain compared to patients with peripheral and unilateral embolisms.Data mining of structured reports is a simple method for obtaining prevalence statistics, epidemiological data, and the distribution of disease characteristics, as demonstrated by the PE use case. The generated data can be helpful for multiple purposes, such as for internal clinical quality assurance and scientific analyses. To benefit from this, consistent use of SR is required and is therefore recommended. · SR-based data mining allows simple epidemiologic analyses for PE.. · The prevalence of PE differs between outpatients and inpatients.. · Central and bilateral PEs have an increased risk of right heart strain.. · Jorg T, Halfmann MC, Graafen D et al. Structured reporting for efficient epidemiological and in-hospital prevalence analysis of pulmonary embolisms. Fortschr Röntgenstr 2024; DOI 10.1055/a-2301-3349.

PMID:38806150 | DOI:10.1055/a-2301-3349

Sci Total Environ. 2024 May 26:173557. doi: 10.1016/j.scitotenv.2024.173557. Online ahead of print.

ABSTRACT

Despite the success of the growing data-driven landslide susceptibility prediction, the model training heavily relies on the quality of the data (involving topography, geology, hydrology, land cover, climate, and human activity), the structure of the model, and the fine-tuning of the model parameters. Few data-driven methods have considered incorporating ‘landslide priors’, as in this article the prior knowledge or statistics related to landslide occurrence, to enhance the model’s perception in landslide mechanism. The main objective and contribution of this study is the coupling of landslide priors and a deep learning model to improve the model’s transferability and stability. This is accomplished by selecting non-landslide sample grounded on landslide statistics, disentangling input landslide features using a variational autoencoder, and crafting a loss function with physical constraints. This study utilizes the SHAP method to interpret the deep learning model, aiding in the acquisition of feature permutation results to identify underlying landslide causes. The interpretation result indicates that ‘slope’ is the most influential factor. Considering the extreme rainfall impact on landslide occurrences in Hong Kong, we combine this prior into the deep learning model and find feature ranking for ‘rainfall’ improved, in comparison to the ranking result interpreted from a pure MLP. Further, the potency of MT-InSAR is utilized to augment the landslide susceptibility map and promote efficient cross-validation. A comparison of InSAR results with historical images reveals that detectable movement before their occurrence is evident in only a minority of landslides. Most landslides occur spontaneously, exhibiting no precursor motion. Comparing with other data-driven methods, the proposed methods outperform in accuracy (by 2 %-5 %), precision (by 2 %-7 %), recall (by 1 %-3 %), F1-score (by 8 %-10 %), and AuROC (by 2 %-4 %). Especially, the Cohen Kappa performance surpasses nearly 20 %, indicating that the knowledge-aware methodology enhances model generalization and mitigates training bias induced by unbalanced positive and negative samples.

PMID:38806128 | DOI:10.1016/j.scitotenv.2024.173557

Sci Total Environ. 2024 May 26:173559. doi: 10.1016/j.scitotenv.2024.173559. Online ahead of print.

ABSTRACT

Although cycling has numerous health benefits, the increased breathing volume and lack of protection from exposure to the environment while cycling poses health risks that cannot be disregarded. Previous studies evaluating the exposure of cyclists to air pollution have typically focused on assessing exposure to a single pollutant or exposure concentrations on specific urban routes, and have not performed a comprehensive assessment considering the distribution of cyclists. The present study used bicycle-sharing big data to conduct a more comprehensive and refined real-time population weighted exposure risk assessment of pileless bike sharing riders in Beijing. We quantified the spatial distribution of high exposure areas at different times and found that the exposure risk during the evening peak period was significantly higher than that during the morning peak and early morning periods, particularly in the city center and its environs. By establishing stepwise regression models, we identified the significant impact of various urban points of interest (POIs) on exposure risk, with sports venues, public toilets, educational institutions, scenic spots, and financial entities particularly influential at different time periods. Medical institutions and shopping venues have a significant negative impact on the exposure levels of PM_2.5 and NO₂ among cyclists in most cases. These findings emphasize the need for targeted pollution control strategies. The aim of this study is to mitigate the impact of air pollution on cyclists and create a healthier cycling environment. The research results can provide new ideas for urban health planning and support scientific decision-making for sustainable urban development.

PMID:38806121 | DOI:10.1016/j.scitotenv.2024.173559

Sci Total Environ. 2024 May 26:173555. doi: 10.1016/j.scitotenv.2024.173555. Online ahead of print.

ABSTRACT

A sound evaluation of the cadmium (Cd) mass balance in agricultural soils needs accurate data of Cd leaching. Reported Cd concentrations from in situ studies are often one order of magnitude lower than predicted by empirical models, which were calibrated to pore water data from stored soils. It is hypothesized that this discrepancy is related to the preferential flow of water (non-equilibrium) and/or artefacts caused by drying and rewetting soils prior to pore water analysis. These hypotheses were tested on multiple soils (n = 27) with contrasting properties. Pore waters were collected by soil centrifugation from field fresh soil samples and also after incubating the same soils (28 days, 20 °C), following two drying-rewetting cycles, the idea being that chemical equilibrium in the soil is reached after incubation. Incubation increased pore water Cd by a factor 4, on average, and up to a factor 16. That increase was statistically related to the decrease of pore water pH and the increase of nitrate, both mainly related to incubation-induced nitrification. After correcting for both factors, the Cd rise was also highest at higher pore water Ca. This suggests that higher Ca in soil enlarges Cd concentration gradients among pore classes in field fresh soils because high Ca promotes soil aggregation and separation of mobile from immobile water. Several empirical models were used to predict pore water Cd. Predictions exceeded observations up to a factor 30 for the fresh pore waters but matched well with those of incubated soils; again, deviations from the 1:1 line in field fresh soils were largest in high Ca (>0.8 mM) soils, suggesting that local equilibrium conditions in field fresh soils are not found at higher Ca. Our results demonstrate that empirical models need recalibration with field fresh pore water data to make accurate soil Cd mass balances in risk assessments.

PMID:38806120 | DOI:10.1016/j.scitotenv.2024.173555

Toxicol In Vitro. 2024 May 26:105853. doi: 10.1016/j.tiv.2024.105853. Online ahead of print.

ABSTRACT

Pentachlorophenol (PCP) is a persistent organic compound that is widely present in the environment. The estimation of internal exposure levels for a given external exposure using toxicokinetic models is key to the human health risk assessment of PCP. The present study developed a physiologically based multicompartmental pharmacokinetic (PBTK) model to describe and predict the behavior of pentachlorophenol (PCP) in an organism. The model consists of stomach, intestines, adipose tissue, kidneys and fast- and poorly perfused tissues that are interconnected via blood circulation. We constructed a PBTK model of PCP in rats and extrapolated it to human dietary PCP exposure. The toxicokinetic data of PCP in human tissues and excreta were obtained from the published literature. Based on the collected PCP dietary survey and internal exposure data of pregnant women in Shanghai, Bayesian statistical analysis was performed for the model using Markov chain Monte Carlo (MCMC) simulation. The posterior distributions of the sensitive parameters were estimated, and the model was parameter optimized and validated using the pregnant women’s test dataset. The results showed that the root mean square error (RMSE) improved 37.3% compared to the original model, and a systematic literature search revealed that the optimized model achieved acceptable prediction results for other datasets in China. A PCP metabolism model based on the exposure characteristics of pregnant women in China was constructed in the present study. The model provides a theoretical basis for the study of PCP toxicity and risk assessment.

PMID:38806067 | DOI:10.1016/j.tiv.2024.105853

Nanotechnology. 2024 May 28. doi: 10.1088/1361-6528/ad50e4. Online ahead of print.

ABSTRACT

Nanotechnology has gained immense popularity and observed rapid development due to the remarkable physio-chemical properties of nanoparticles (NPs) and related nanomaterials. The green production of nanoparticles has many benefits over traditional techniques because the current procedures are expensive, time-consuming, and involve harmful substances that limit their applicability. This study aimed to use a novel green source, the Salsola imbricata (SI) plant, which is commonly found in Central Asia and known for its medicinal properties as a reducing and stabilizing agent for the synthesis of AgNPs. The current study also utilized efficient statistical design, the Plackett-Burman Design of Experiment (DOE) method to synthesize the nanoparticles. The characterization of nanoparticles was carried out using UV-Vis spectroscopy, Fourier Transform Infrared Spectroscopy, and scanning electron microscopy (SEM). The Plackett-Burman design results showed that only two out of four factors i.e., AgNO3 concentration and incubation time, were significant for the synthesis of AgNPs. While remaining factors, incubation temperature and plant extract: AgNO3 ratio were non-significant. The SEM analysis result showed that SI-AgNPs had a size of 20-50nm. The SI-AgNPs demonstrated strong antimicrobial activity against oral pathogens such as Streptococcus mutans and Lactobacillus acidophilus, with the highest efficacy observed at a concentration of 2 mg/ml. The addition of SI-AgNPs in glass ionomer cement significantly increased the antimicrobial efficacy of GIC at different concentration (p≤0.000 for 0.2% and 0.1%, p≤0.01 for 0.05% and p≤0.05 for 0.025% respectively). Based on the results of the current study, the plant based AgNPs can be further evaluated in detail as alternate antimicrobial agent either alone or in combination with other antimicrobial agents for different dental applications.

PMID:38806018 | DOI:10.1088/1361-6528/ad50e4

J Occup Health. 2024 May 28:uiae028. doi: 10.1093/joccuh/uiae028. Online ahead of print.

ABSTRACT

INTRODUCTION: Participatory organizational interventions to improve psychosocial working conditions are important for a safe and healthy work environment. However, there are few systematic reviews or meta-analyses investigating the effects of these interventions on workers’ mental health and work-related outcomes. We apply the protocol for systematic review and meta-analysis to examine the effect of participatory organizational intervention on mental health and work performance.

METHODS AND ANALYSIS: The participants, interventions, comparisons, and outcomes (PICO) of the studies in this systematic review and meta-analysis were defined as follows: (P) inclusion of all workers, (I) participatory organizational intervention, (C) treatment as usual or no intervention (including waitlist control), and (O) mental health and work performance. Published studies will be searched using the following electronic databases: PubMed, EMBASE, PsycINFO, PsycARTICLES, and Japan Medical Abstracts Society. Studies that (1) included participatory organizational intervention, (2) included participants who were working as of the baseline survey period, (3) assessed mental health or work performance outcomes, (4) used a cluster randomized controlled trials design, (5) were published in English or Japanese, and (6) were published in peer-reviewed journals (including advanced online publication) will be included. Study selection and the risk of bias assessment will be performed independently by two reviewers. A meta-analysis will be performed to statistically synthesize the included studies. Publication bias will be assessed for meta-bias using Egger’s test as well as visually on a funnel plot. We will assess the heterogeneity by using the Q statistic.

PMID:38805736 | DOI:10.1093/joccuh/uiae028

J Med Internet Res. 2024 May 28;26:e53321. doi: 10.2196/53321.

ABSTRACT

BACKGROUND: The pandemic has accelerated digital work transformation, yet little is known about individuals’ willingness to sustain such digital modes and its associated factors. A better understanding of this willingness and its drivers is crucial for guiding the development of future digital work infrastructure, training programs, and strategies to monitor and prevent related health issues.

OBJECTIVE: This study aims to quantify the general population’s willingness to sustain pandemic-induced digital work, identify its associated factors, and examine how screen time moderates these relationships.

METHODS: A cross-sectional study was conducted targeting Hong Kong residents aged ≥18 years who have increased engagement in digital work since the pandemic. Data were collected through self-reported, web-based surveys. Descriptive statistics determined prevalence rates, while structured multiphase logistic regression identified associated factors and explored the moderating effects of screen hour levels.

RESULTS: This unfunded study enrolled 1014 participants from May 2 to June 24, 2022, and completed data analysis within 3 months after data collection. A total of 391 (38.6%; 95% CI 35.6%-41.6%) participants expressed willingness to sustain digital work. Positive factors associated with this willingness included being an employee (odds ratio [OR] 3.12, 95% CI 1.59-6.45; P=.001), being health professionals (OR 3.32, 95% CI 1.49-7.82; P=.004), longer screen hours (OR 1.09, 95% CI 1.03-1.15; P=.002), and higher depression levels (OR 1.20, 95% CI 1.01-1.44; P=.04). Conversely, negatively associated factors included older age (OR 0.87, 95% CI 0.81-0.94; P=.001), extroversion (OR 0.66, 95% CI 0.51-0.86; P=.002), higher eHealth literacy (OR 0.96, 95% CI 0.93-0.98; P<.001), perceived greater susceptibility to COVID-19 (OR 0.84, 95% CI 0.74-0.96; P=.009), residence in a high-severity COVID-19 community (OR 0.73, 95% CI 0.63-0.84; P<.001), having infected individuals in the immediate social circle (OR 0.64, 95% CI 0.46-0.88; P=.006), higher BMI (OR 0.94, 95% CI 0.90-0.99; P=.02), feelings of being out of control (OR 0.96, 95% CI 0.93-0.98; P=.002), and higher fear of COVID-19 (OR 0.96, 95% CI 0.94-0.98; P=.001). In addition, a moderating effect of screen hour level (high: >8 h/d; low: ≤8 h/d) influenced the association among 10 factors related to willingness to sustain pandemic-induced digital work, including age, education level, household size, needs for regular medical care, BMI, frequency of both vigorous and moderate physical activities, perceived COVID-19 severity, immediate social circle COVID-19 presence, and fear of COVID-19 (all P values for interaction <.05).

CONCLUSIONS: The substantial willingness of the general population to sustain digital work after the pandemic highlights the need for robust telework infrastructure, thorough monitoring of adverse health outcomes, and the potential to expand telehealth services among this group. The identification of factors influencing this willingness and the moderating role of screen hours inform the development of personalized strategies to enhance digital work acceptance where needed.

PMID:38805704 | DOI:10.2196/53321

J Med Internet Res. 2024 May 28;26:e50853. doi: 10.2196/50853.

ABSTRACT

BACKGROUND: Clinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of these technologies by patients remains unclear.

OBJECTIVE: We aimed to investigate whether patient-physician trust is affected when medical decision-making is supported by a CDSS.

METHODS: We conducted a vignette study among the patient panel (N=860) of the University Medical Center Utrecht, the Netherlands. Patients were randomly assigned into 4 groups-either the intervention or control groups of the high-risk or low-risk cases. In both the high-risk and low-risk case groups, a physician made a treatment decision with (intervention groups) or without (control groups) the support of a CDSS. Using a questionnaire with a 7-point Likert scale, with 1 indicating “strongly disagree” and 7 indicating “strongly agree,” we collected data on patient-physician trust in 3 dimensions: competence, integrity, and benevolence. We assessed differences in patient-physician trust between the control and intervention groups per case using Mann-Whitney U tests and potential effect modification by the participant’s sex, age, education level, general trust in health care, and general trust in technology using multivariate analyses of (co)variance.

RESULTS: In total, 398 patients participated. In the high-risk case, median perceived competence and integrity were lower in the intervention group compared to the control group but not statistically significant (5.8 vs 5.6; P=.16 and 6.3 vs 6.0; P=.06, respectively). However, the effect of a CDSS application on the perceived competence of the physician depended on the participant’s sex (P=.03). Although no between-group differences were found in men, in women, the perception of the physician’s competence and integrity was significantly lower in the intervention compared to the control group (P=.009 and P=.01, respectively). In the low-risk case, no differences in trust between the groups were found. However, increased trust in technology positively influenced the perceived benevolence and integrity in the low-risk case (P=.009 and P=.04, respectively).

CONCLUSIONS: We found that, in general, patient-physician trust was high. However, our findings indicate a potentially negative effect of AI applications on the patient-physician relationship, especially among women and in high-risk situations. Trust in technology, in general, might increase the likelihood of embracing the use of CDSSs by treating professionals.

PMID:38805702 | DOI:10.2196/50853

G3 (Bethesda). 2024 May 28:jkae114. doi: 10.1093/g3journal/jkae114. Online ahead of print.

ABSTRACT

In the wake of increasing demand for renewable energy sources, plant-based sources including alternative oilseeds have come to the forefront of interest. Hydroxy fatty acids (HFAs), produced in a few oilseed species, are important chemical feedstocks for industrial applications. An integrated approach was taken to assemble the first draft genome of the alternative HFA producer Physaria fendleri (n = 6), an outcrossing species with high heterozygosity. Both de novo transcriptome assemblies and genome assemblies were produced with public and generated sequencing reads. Resulting intermediate assemblies were then scaffolded and patched with multiple data sources, followed by super-scaffolding onto a masked genome of Camelina laxa (n = 6). Despite a current lack of available resources for the physical mapping of genomic scaffolds of Physaria fendleri, topography of the genome with respect to repeat and gene content was preserved at the scaffold level and not significantly lost via super-scaffolding. Read representation, gene and genome completion statistics, and annotation results illustrated the creation of a functional draft genome and a tool for future research on alternative oil species.

PMID:38805698 | DOI:10.1093/g3journal/jkae114