Insight into the sorption and desorption pattern of pyrrolizidine alkaloids and their N-oxides in acidic tea (Camellia sinensis) plantation soils.

Pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs) are phytotoxins produced by various plant species and have been emerged as environmental pollutants. The sorption/desorption behaviors of PAs/PANOs in soil are crucial due to the horizontal transfer of these natural products from PA-producing plants to soil and subsequently absorbed by plant roots. This study firstly investigated the sorption/desorption behaviors of PAs/PANOs in tea plantation soils with distinct characteristics. Sorption amounts for seneciphylline (Sp) and seneciphylline-N-oxide (SpNO) in three acidic soils ranged from 2.9 to 5.9 µg/g and 1.7 to 2.8 µg/g, respectively. Desorption percentages for Sp and SpNO were from 22.2% to 30.5% and 36.1% to 43.9%. In the mixed PAs/PANOs systems, stronger sorption of PAs over PANOs was occurred in tested soils. Additionally, the Freundlich models more precisely described the sorption/desorption isotherms. Cation exchange capacity, sand content and total nitrogen were identified as major influencing factors by linear regression models. Overall, the soils exhibiting higher sorption capacities for compounds with greater hydrophobicity. PANOs were more likely to migrate within soils and be absorbed by tea plants. It contributes to the understanding of environmental fate of PAs/PANOs in tea plantations and provides basic data and clues for the development of PAs/PANOs reduction technology.

Development of an equation to predict delta bilirubin levels using machine learning.

OBJECTIVE: Delta bilirubin (albumin-covalently bound bilirubin) may provide important clinical utility in identifying impaired hepatic excretion of conjugated bilirubin, but it cannot be measured in real-time for diagnostic purposes in clinical laboratories. METHODS: A total of 210 samples were collected, and their delta bilirubin levels were measured four times using high-performance liquid chromatography. Data collected included age, sex, diagnosis code, delta bilirubin, total bilirubin, direct bilirubin, total protein, albumin, globulin, aspartate aminotransferase, alanine transaminase, alkaline phosphatase, gamma-glutamyl transferase, lactate dehydrogenase, hemoglobin, serum hemolysis value, hemolysis index, icterus value (Iv), icterus index (Ii), lipemia value (Lv), and lipemia index. To conduct feature selection and identify the optimal combination of variables, linear regression machine learning was performed 1,000 times. RESULTS: The selected variables were total bilirubin, direct bilirubin, total protein, albumin, hemoglobin, Iv, Ii, and Lv. The best predictive performance for high delta bilirubin concentrations was achieved with the combination of albumin-direct bilirubin-hemoglobin-Iv-Lv. The final equation composed of these variables was as follows: delta bilirubin = 0.35 × Iv + 0.05 × Lv - 0.23 × direct bilirubin - 0.05 × hemoglobin - 0.04 × albumin + 0.10. CONCLUSION: The equation established in this study is practical and can be easily applied in real-time in clinical laboratories.

Leveraging External Aggregated Information for the Marginal Accelerated Failure Time Model.

It is becoming increasingly common for researchers to consider leveraging information from external sources to enhance the analysis of small-scale studies. While much attention has focused on univariate survival data, correlated survival data are prevalent in epidemiological investigations. In this article, we propose a unified framework to improve the estimation of the marginal accelerated failure time model with correlated survival data by integrating additional information given in the form of covariate effects evaluated in a reduced accelerated failure time model. Such auxiliary information can be summarized by using valid estimating equations and hence can then be combined with the internal linear rank-estimating equations via the generalized method of moments. We investigate the asymptotic properties of the proposed estimator and show that it is more efficient than the conventional estimator using internal data only. When population heterogeneity exists, we revise the proposed estimation procedure and present a shrinkage estimator to protect against bias and loss of efficiency. Moreover, the proposed estimation procedure can be further refined to accommodate the non-negligible uncertainty in the auxiliary information, leading to more trustable inference conclusions. Simulation results demonstrate the finite sample performance of the proposed methods, and empirical application on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial substantiates its practical relevance.

Interaction between the oxidative balance score and serum per- and poly-fluoroalkyl substances (PFASs) on liver health: analysis of the NHANES 2007-2018 dataset.

BACKGROUND: Per- and poly-fluoroalkyl substances (PFASs) are pervasive synthetic compounds, prompting investigations into their intricate interactions with lifestyle factors and health indicators because of their enduring environmental presence and bioaccumulation. This study aimed to explore the effects of the oxidative balance score (OBS) and PFAS on liver-related indices. METHODS: Twenty dietary and lifestyle factors were used to calculate the OBS. The serum concentrations of PFASs were measured, and their sum was calculated for analysis. The levels of liver markers were also evaluated. Linear regression models and interaction analyses were used to assess the associations between OBS, PFAS concentrations, and liver indices. RESULTS: The results revealed an inverse association between high OBS and perfluorooctane sulfonic acid concentration, as well as the sum of PFAS concentrations. OBS was positively associated with liver markers. The PFAS concentrations were positively associated with total bilirubin, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) levels. Interaction analyses revealed significant interactions between OBS and specific PFASs for alkaline phosphatase (interaction P < 0.05). Possible interactions were also found between OBS and specific PFASs for ALT, and AST levels (interaction P < 0.10). CONCLUSIONS: This study clarified the association between total PFAS and OBS. This association was significant mainly for diet-related OBS. PFAS and OBS are associated with liver-related indicators in the blood.

Improving analysis of cognitive outcomes in cardiovascular trials using different statistical approaches.

BACKGROUND: The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) questionnaires are commonly used to measure global cognition in clinical trials. Because these scales are discrete and bounded with ceiling and floor effects and highly skewed, their analysis as continuous outcomes presents challenges. Normality assumptions of linear regression models are usually violated, which may result in failure to detect associations with variables of interest. METHODS: Alternative approaches to analyzing the results of these cognitive batteries include transformations (standardization, square root, or log transformation) of the scores in the multivariate linear regression (MLR) model, the use of nonlinear beta-binomial regression (which is not dependent on the assumption of normality), or Tobit regression, which adds a latent variable to account for bounded data. We aim to empirically compare the model performance of all proposed approaches using four large randomized controlled trials (ORIGIN, TRANSCEND, COMPASS, and NAVIGATE-ESUS), and using as metrics the Akaike information criterion (AIC). We also compared the treatment effects for the methods that have the same unit of measure (i.e., untransformed MLR, beta-binomial, and Tobit). RESULTS: The beta-binomial consistently demonstrated superior model performance, with the lowest AIC values among nearly all the approaches considered, followed by the MLR with square root and log transformations across all four studies. Notably, in ORIGIN, a substantial AIC reduction was observed when comparing the untransformed MLR to the beta-binomial, whereas other studies had relatively small AIC reductions. The beta-binomial model also resulted in a significant treatment effect in ORIGIN, while the untransformed MLR and Tobit regression showed no significance. The other three studies had similar and insignificant treatment effects among the three approaches. CONCLUSION: When analyzing discrete and bounded outcomes, such as cognitive scores, as continuous variables, a beta-binomial regression model improves model performance, avoids spurious significance, and allows for a direct interpretation of the actual cognitive measure. TRIALS REGISTRATION: ORIGIN (NCT00069784). Registered on October 1, 2003; TRANSCEND (NCT00153101). Registered on September 9, 2005; COMPASS (NCT01776424). Registered on January 24, 2013; NAVIGATE-ESUS (NCT02313909). Registered on December 8, 2014.

Study of SME employees' awareness level on lean manufacturing and ergonomics implementation in Malaysian and Indonesian production environments.

SMEs are small to medium-sized businesses with relatively fewer workers and lower revenues than large companies. However, SMEs also contribute to the local economic growth of a region, so the smooth production process needs to be considered to increase productivity. Applying lean manufacturing (LM) and Ergonomics concepts in the production process is critical because it can overcome smooth production and maintain the health and safety of SMEs workers. LM focuses on minimizing waste, while ergonomics focuses on humans as a source of energy in the smooth running of production activities. So, this study aims to measure the level of understanding of Malaysian and Indonesian SMEs workers on applying LM and Ergonomics concepts on the production floor and determine the effect of these two concepts using the SPSS and SmartPLS4 applications. SPSS serves to measure the validity, reliability and mean for the category of workers' understanding of LM and Ergonomics. SmartPLS4 helps us understand the influence of the two concepts. Based on the calculation of the mean for each variable of LM and ergonomics, it is found that Malaysian workers understand enough compared to Indonesian SMEs workers. As for the effect, Ergonomics has a significant influence on LM.

Investigation of Rheological Properties of Molten Materials for Dripping Pills Based on Imaging Monitoring.

INTRODUCTION: Rheological properties, as critical material attributes (CMAs) of solid dispersion drugs such as dripping pills, affect the melting, dispersion, and solidification. Therefore, characterization and assessments of rheological properties in the pharmaceutical process are important in enhancing drug stability and bioavailability. OBJECTIVES: The study aimed to develop a method for analyzing the rheology of molten materials, assessing their consistency and how rheological properties affect the dripping process and pills quality. MATERIALS AND METHODS: The rheological behavior of molten materials composed of Ginkgo biloba leaf extract (GBE) and polyethylene glycol (PEG) 4000 was characterized. Batch consistency of molten materials was evaluated. Image monitoring technology was utilized to capture and process images of the droplet formation process. We established the relationship between the rheological properties of molten materials and various attributes. RESULTS: The quality consistency of molten materials was evaluated, with 12 batches showing similarity above 0.8. The MLR models showed strong correlations (R2 > 0.80) between rheological properties and evaluation attributes. The rheological properties, including consistency coefficient, flow index, and viscosity at 80°C, were identified as critical rheological properties of the molten materials. Rheological property differences of molten materials have an impact on the morphology of droplet and quality performance. CONCLUSION: A rheological method was established, enabling quality consistency evaluation of molten materials in dripping pills. This study revealed the influence of rheological properties on droplet formation process and dripping pills quality, providing a reference for researches on material attributes control of other traditional Chinese medicine dripping pills.

A novel mathematical model for modeling viscosity and temperature relationship for dead oils.

Viscosity is crucial in subsurface and surface transport, used in engineering domains like heat transfer and pipeline design. However, measurements are limited, necessitating predictive viscosity relationships. Existing models lack precision or pertain to limited fluids, and accurately forecasting dead oil viscosity remains challenging due to errors. The study presents a mathematical algorithm to accurately estimate viscosity values in hydrocarbon fluids. It uses a robust non-linear regression technique to establish a reliable relationship between fluid viscosity and temperature within a specific temperature range. The algorithm is applied to extra-heavy to light crude oil samples from Iranian oilfields, revealing viscosity values ranging from 0.29 cp to 5328.74 cp within a dataset of 243 viscosity data points. After modeling each of these five fluids, the highest values obtained for the maximum absolute error and relative error are related to the fluid with an API gravity of 12.92. The maximum absolute error and relative error for this fluid sample are 1.25 cp and 6.04%, respectively. The algorithm offers acceptable precision in outcome models, even with limited training data, demonstrating its effectiveness in training models with less than 30% of available data. Moreover, these models end up with a near-unity coefficient of determination in testing data, reaffirming their proficiency at reflecting empirical data with remarkable accuracy.

An assessment of the relationship between spring frost indicators and global crop yield losses.

Reports on the influences of spring frost on crop losses are not consistent, which may be because insufficient indicators of spring frost were included in the analysis. To bridge this gap, we analyzed global temperature datasets and production data for the three major crops of maize, winter wheat, and rice from 1981 to 2016. Five indicators of spring frost events: temperature fluctuation (Tv), temperature difference (Td), duration (Thour), occurrence date (Tdate), and frequency (Tnum) were considered to assess their relationship with yield losses. Linear regression was employed to analyze the change trends in five indicators and random forest was utilized to investigate the relationship between yield loss and indicators of spring frost. Our findings reveal that, despite a decline in the number of spring frost events during global warming, not all the five indicators declined over time. Tv is the most important indicator for yield losses in maize and winter wheat, which shows an increasing trend in their growing regions and provides an explanation for the increasing yield losses of maize and winter wheat over time. Td is the most important indicator of rice yield losses but it shows a decreasing trend in rice-growing areas, which explains why rice yield losses from spring frosts in recent years are not significant.

Relationship between COVID-19 Cases and Environmental Contaminants in Quito, Ecuador.

The relationship between COVID-19 infections and environmental contaminants provides insight into how environmental factors can influence the spread of infectious diseases. By integrating epidemiological and environmental variables into a mathematical framework, the interaction between virus spread and the environment can be determined. The aim of this study was to evaluate the impact of atmospheric contaminants on the increase in COVID-19 infections in the city of Quito through the application of statistical tests. The data on infections and deaths allowed to identify the periods of greatest contagion and their relationship with the contaminants O3, SO2, CO, PM2.5, and PM10. A validated database was used, and statistical analysis was applied through five models based on simple linear regression. The models showed a significant relationship between SO2 and the increase in infections. In addition, a moderate correlation was shown with PM2.5, O3, and CO, and a low relationship was shown for PM10. These findings highlight the importance of having policies that guarantee air quality as a key factor in maintaining people's health and preventing the proliferation of viral and infectious diseases.

Sources of perceived social support and cognitive function among older adults: a longitudinal study in rural China.

Background: Studies have shown the positive impact of perceived social support on cognitive function among older adults in rural areas. However, existing studies often overlook the impact of different support sources. This study aimed to explore the relationship between the diverse sources of perceived social support and cognitive function. Methods: Participants were drawn from the Guizhou Rural Older Adults' Health Study (HSRO) in China. We included 791 participants who participated in a baseline survey in 2019 and a 3-year follow-up survey. Perceived social support was investigated from the six main sources (friend, relative, children, spouse, sibling, and neighbor). Hierarchical linear regression models were used to observe the effects of diverse sources of perceived social support and their combinations on cognitive function. Results: Cognitive function was positively associated with perceived support from children, friends, and neighbors. A positive association was found between cognitive function and increases in each additional source [ß = 0.75 (95%CI: 0.51, 0.98), p < 0.001]. Older adults who perceived support from both children and friends showed better cognitive function [ß = 2.53 (95%CI: 1.35, 3.72), p < 0.001]. The perception of support from spouse, siblings, and relatives did not show a statistically significant association with cognitive function among older adults in rural areas. Conclusion: This study found that the association between different sources of perceived social support and cognitive function was varied. This study provides scientific evidence that personalized support strategies may benefit in promoting cognitive health in rural older adults.

Associations between APOE genotypes, urine 8-isoprostane and blood trace elements in middle-aged mothers (CROME study).

BACKGROUND: There is almost no data on the combined associations between apolipoprotein E gene (APOE) genotypes, trace elements (TEs), and lipid peroxidation in vivo. The aim of our study was to evaluate the association between APOE genotypes and TE levels in blood (B-TEs) and erythrocytes (E-TEs), and 8-isoprostane in urine (U-8-isoprostane) in women with low exposure to potentially toxic TEs and with adequate supply of essential TEs. METHODS: B-TEs, E-TEs and U-8-isoprostane were determined in 172 healthy women of childbearing age (30.1-51.4 years) using ICP-MS and ELISA competitive assay, respectively. All women were divided into three APOE genotype groups according to the presence of the ɛ4 allele, ɛ2 allele or ɛ3 homozygotic allele. The associations between B-TEs, E-TE, U-8-isoprostane, and the APOE genotype groups were estimated by multiple variable linear regression models with relevant explanatory variables (e.g., age, BMI, and seafood). RESULTS: All TE and U-8-isoprostane levels were inside the reference ranges for the healthy population. In the multiple variable linear regression models, our results showed that urine 8-isoprostane levels increased by up to 43.3% in the APOE4 group compared to the APOE3 group and a negligible negative modifying effect for essential TEs. However, the APOE genotype groups were associated also with some TEs. A clear positive association was found between the APOE2 and APOE4 groups (vs. APOE3) with B-molybdenum. CONCLUSIONS: Our study suggests that the APOE4 genotype played an important role in 8-isoprostane variability in a population with an adequate supply of essential and with low exposure to potentially toxic TEs. Adequate copper, zinc and selenium status seemed to be protective against, while the levels of nonessential TEs were probably too low to play a decisive role in 8-isoprostane formation. The observed impact of the APOE2 and APOE4 groups on increased B-molybdenum opens a new research topic.

Automated Approach for Enhancing Fetal Head Station Assessment in Labor with Transperineal Ultrasound.

RATIONALE AND OBJECTIVES: Accurate assessment of fetal head station (FHS) is crucial during labor management to reduce the risk of complications and plan the mode of delivery. Although digital vaginal examination (DVE) has been associated with inaccuracies in FHS assessment, ultrasound (US) evaluation remains dependent on sonographer expertise. This study aimed at investigating the reliability and accuracy of an automatic approach to assess the FHS during labor with transperineal US (TPU). MATERIALS AND METHODS: In this prospective observational study, 27 pregnant women in the second stage of labor, with fetuses in cephalic presentation, underwent conventional labor management with additional TPU examination. A total of 45 2D B-mode TPU acquisitions were performed at different FHS, before performing DVE. The FHS was assessed by the algorithm (FHSaut) on TPU images and by DVE (FHSdig). The sonographic assessment of FHS by expert sonographer (FHSexp) on the same TPU acquisition used for the automatic measurement served as gold standard. The performance and accuracy were assessed through Spearman's ρ, the coefficient of determination (R2), root mean square error (RMSE), and Bland-Altman analysis. RESULTS: A strong correlation between FHSaut and FHSexp (ρ = 0.97, p < 0.001) and a high coefficient of determination (R2 = 0.95) were found. A lower correlation with FHSexp (ρ = 0.66, p < 0.001) and coefficient of determination (R2 = 0.52) was found for DVE. Moreover, the RMSE reported higher accuracy of FHSaut (RMSE = 0.32 cm) compared to FHSdig (RMSE = 0.97 cm). Bland-Altman analysis showed that the algorithm performed with smaller bias and narrower limits of agreement compared to DVE. CONCLUSION: The proposed algorithm can evaluate FHS with high accuracy and low RMSE. This approach could facilitate the use of US in labor, supporting the clinical staff in labor management.

A new strategy for systematically classifying HLA alleles into serological specificities: Update and refinement.

HLA antigens were historically defined according to the unique reactivity pattern of cells expressing HLA molecules with distinctive clusters of allo-antisera and/or monoclonal antibodies. Subsequently, amino acid residues determining epitopes (DEP) in the HLA molecule were correlated with reactivity patterns. In current clinical practice, the presence of allo-antibodies is assessed using Luminex-based solid phase single antigen bead (SAB) assays for transplantation. Recently, novel antigens were proposed for HLA molecules with DEP patterns that do not match any serologically defined antigens recognised by the WHO Nomenclature Committee. To validate the antigens, mean fluorescence intensity values of SABs tested on >13,000 patients' sera were extracted from clinical databases and analysed by scatter plots using a linear regression model. We found that when two proteins were considered as the same antigen in the original study, for example, HLA-A*02:01 and -A*02:06, their correlation ranked among the highest values at each locus. In contrast, discrete asymmetric outliers were observed when there were different antigens, for example, HLA-A*30:01 and -A*30:02, allowing validation and confirmation of 20 novel antigens for HLA-A, -B, -C and -DR. The outliers were confirmed to be true or false by flow cytometric crossmatches. In addition to the previously defined residues for antigen assignments, findings suggest that further distinction should be made for common antigens by including the substitutions at residue 67 of HLA-B, 67 and 74 of -DR. These serologic analyses can be applied systematically to identify and confirm novel antigens. These developments will lead to designing optimal SAB panels and further improving virtual donor-specific antibodies assessment.

Prediction of Whole Liver Graft Weight Based on Biometric Variables in Paediatric and Adult Liver Donors.

BACKGROUND/OBJECTIVES: In paediatric liver transplantation, donor-recipient compatibility depends on graft size. We explored whether the graft weight can be predicted using the donor's biometric parameters. METHODS: We used seven easily available biometric variables in 142 anonymised paediatric and adult donors, with data collected between 2016 and 2022. The whole or partial liver was transplanted in our hospital from these donors. We identified the variables that had the strongest correlation to our response variable: whole liver graft weight. RESULTS: In child donors, we determined two linear models: using donor weight and height on the one hand and using donor weight and right liver span on the other hand. Both models had a coefficient of determination R2 = 0.86 and p-value < 10-5. We also determined two models in adult donors using donor weight and height (R2 = 0.33, p < 10-4) and donor weight and sternal height (R2 = 0.38, p < 10-4). The models proved valid based on our external dataset of 245 patients from two institutions. CONCLUSIONS: In clinical practise, our models could provide rapidly accessible estimates to determine whole graft dimension compatibility in liver transplantation in children and adults. Determining similar models predicting the left lobe and lateral segment weight could prove invaluable in paediatric transplantation.

Development of a multivariate model predictive of post-adrenalectomy renal function decline in patients with primary aldosteronism: a large-cohort single-center study.

PURPOSE: To develop a multivariate liniear model for predicting long-term (> 3 months) post-adrenalectomy renal function decline in patients with primary aldosteronism (PA). The model aims to help identify patients who may experience a significant decline in renal function after surgery. METHODS: We retrospectively analyzed the clinical data of 357 patients who were diagnosed with PA and underwent adrenalectomy between September 2012 and February 2023. LASSO and multivariate linear regression analyses were used to identify significant risk factors for model construction. The models were further internally validated using bootstrap method. RESULTS: Age (P < 0.001), plasma aldosterone concentration (PAC) measured in the upright-position (PACU, P = 0.066), PAC measured after saline infusion (PACafterNS, P = 0.010), preoperative blood adrenocorticotropic-hormone level (ACTH, P = 0.048), preoperative estimated glomerular filtration rate (eGFR, P < 0.001) and immediate postoperative eGFR (P < 0.001) were finally included in a multivariate model predictive of post-adrenalectomy renal function decline and the coefficients were adjusted by internal validation. The final model is: predicted postoperative long-term (> 3 months) eGFR decline =-70.010 + 0.416*age + 6.343*lg PACU+4.802*lg ACTH + 7.424*lg PACafterNS+0.637*preoperative eGFR-0.438*immediate postoperative eGFR. The predicted values are highly related to the observed values (adjusted R = 0.63). CONCLUSION: The linear model incorporating perioperative clinical variables can accurately predict long-term (> 3 months) post-adrenalectomy renal function decline.

Enhancing the estimation of PaCO2 from etCO2 during ventilation through non-invasive parameters in the ovine model.

BACKGROUND: In mechanically ventilated neonates, the arterial partial pressure of CO 2 ( PaCO 2 ) is an important indicator for the adequacy of ventilation settings. Determining the PaCO 2 is commonly done using invasive blood gas analyses, which constitute risks for neonates and are typically only available infrequently. An accurate, reliable, and continuous estimation of PaCO 2 is of high interest for medical staff, giving the possibility of a closer monitoring and faster reactions to changes. We aim to present a non-invasive estimation method for PaCO 2 in neonates on the basis of end-tidal CO 2 ( etCO 2 ) with inclusion of different physiological and ventilation parameters. The estimation method should be more accurate than an estimation by unaltered etCO 2 measurements with regard to the mean absolute error and the standard deviation. METHODS: Secondary data from 51 preterm lambs are used, due to its high comparability to preterm human data. We utilize robust linear regression on 863 PaCO 2 measurements below or equal to 75 mmHg from the first day of life. etCO 2 along with a set of ventilation settings and measurements as well as vital parameters are included in the regression. Included independent variables are chosen iteratively by highest Pearson correlation to the remaining estimation deviation. RESULTS: The evaluation is carried out on 12 additional neonatal lambs with 246 PaCO 2 measurements below or equal to 75 mmHg from the first two days of life. The estimation method shows a mean absolute error of 3.80 mmHg with a 4.92 mmHg standard deviation of differences and a standard error of 0.31 mmHg in comparison to measured PaCO 2 by blood gas analysis. CONCLUSIONS: The estimation of PaCO 2 by the proposed equation is less biased than unaltered etCO 2 . The usage of this method in clinical practice or in applications like the automation of ventilation needs further investigation.

Effects of temperature, relative humidity and soil organic carbon content on soil-air partitioning coefficients of volatile PFAS.

Soil-air partitioning coefficient (KSA) values are often used to assess the environmental fate of organic contaminants in soil. Till now, sufficient KSA values have not yet been measured for many compounds of interest, including some emerging pollutants such as volatile PFAS. Moreover, the effects of environmental factors such as temperature, relative humidity and soil organic carbon content on KSA of volatile PFAS are also unclear. In this study, the KSA values of target volatile PFAS were measured under various temperature (20-40 °C), relative humidity (30-100 %) and soil organic carbon content (2.1 %-8.0 %) using a modified solid-phase fugacity meter. The results showed that higher temperatures, higher relative humidity and lower organic carbon content in soil may accelerate the diffusion of target volatile PFAS. Furthermore, the KSA measurements were used to derive a multiple linear regression model to depict the relationship between logKSA and temperature, relative humidity, soil organic carbon content and PFAS-specific logKOA. When compared with the predictions obtained from semi-empirical model, we argued that the multiple linear regression model is more robust and easier to implement for target volatile PFAS or other emerging volatile PFAS than the semi-empirical approach to help depict the diffusion process at target volatile PFAS contaminated sites.

[Pollution Characteristics, Risk Assessment, and Source Analysis of Heavy Metals in Soil from a Typical Abandoned Antimony Smelting Factory].

Considering the surface soil （0-20 cm） from a typical abandoned antimony smelting factory area in Dachang Town, Qinglong County, Guizhou Province, as a case study, a total of 14 soil samples were systematically collected from both within and outside the smelting factory area. The analysis focused on the pollution status, distribution characteristics, and potential ecological risks of heavy metals such as Sb, As, Cd, Cr, Pb, Cu, Zn, Ni, and V in the soil. Additionally, an evaluation and analysis of pollution sources were conducted. The results showed that the mean concentrations of heavy metals including ω（Sb）, ω（As）, ω（Cd）, ω（Cr）, ω（Pb）, ω（Cu）, ω（Zn）, ω（Ni）, and ω（V） in the surface soil of the abandoned antimony smelting factory ranged from 4.58 to 15 049.33 mg·kg-1. With the exception of Cr and Ni, all values exceeded the background values of soils in Guizhou province. The single factor pollution indices of Sb and As were 83.61 and 7.01, respectively, indicating severe contamination. In contrast, Pb fell within the non-polluted to slightly polluted range. The comprehensive potential ecological risk of soil heavy metals was characterized by severe potential ecological risk levels for Sb, As, and Cd, while the remaining heavy metals fell within a range of moderate to substantial potential ecological risk levels. The assessment of the geoaccumulation index revealed that the soil in the study area was primarily contaminated by Sb and As, predominantly exhibiting contamination levels ranging from moderate to severe. The results from the RAC method suggested that Sb was the dominant focus for remediation in this abandoned smelting factory. The two primary pollutants, Sb and As, exhibited elevated levels in leachate toxicity, acid-soluble fraction, available fraction, gastric phase, and intestinal phase in terms of bioavailable content, indicating a certain potential hazard. Further, correlation analysis indicated a certain correlation between the total amount of heavy metals and leachate toxicity, available fraction, acid-soluble fraction, reducible fraction, oxidizable fraction, gastric phase extractable fraction, and intestinal phase extractable fraction. The APCS-MLR model indicated that the sources of Sb, As, Zn, Cu, and Cd were primarily industrial, while the sources of Cr and V were mainly natural, and Pb originated mainly from mixed sources.

Colorectal Cancer Screening: A Multicomponent Intervention to Increase Uptake in Patients Aged 45-49.

PURPOSE: Colorectal cancer (CRC) screening is recommended starting at age 45, but there has been little research on strategies to promote screening among patients younger than 50. This study assessed the effect of a multicomponent intervention on screening completion in this age group. METHODS: The intervention consisted of outreach to patients aged 45 to 49 (n = 3,873) via mailed fecal immunochemical test (FIT) (sent to 46%), text (84%), e-mail (53%), and the extension to this age group of an existing standing order protocol allowing primary care nurses and medical assistants to order FIT at primary care clinics in an urban safety-net system. We used segmented linear regression to assess changes in CRC screening completion trends. Patients aged 51 to 55 were included as a comparison group (n = 3,943). Data were extracted from the EHR. RESULTS: The percentage of patients aged 45 to 49 who were up-to-date with CRC screening (colonoscopy in 10 years or FIT in last year) increased an average of 0.4% (95% CI 0.3, 0.6)) every 30 days before intervention rollout and 2.8% (95% CI 2.5, 3.1) after (slope difference 2.3% [95% CI 2.0, 2.7]). This difference persisted after accounting for small changes in the outcome observed in the comparison group (slope difference 1.7% [95% CI 1.2, 2.2]). CONCLUSIONS: These results suggest that the intervention increased CRC screening completion among patients 45 to 49. Health care systems seeking to improve CRC screening participation among patients aged 45 to 49 should consider implementing similar interventions.