Consumer awareness and knowledge regarding use of non-steroidal anti-inflammatory drugs (NSAIDs) in a metropolitan area.

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) have well-known adverse effects, and numerous studies have shown inappropriate behaviors regarding their use. The primary aim of this study was to analyze the knowledge, attitudes, and behaviors regarding the use of NSAIDs simultaneously in one of the largest and most populated areas of Italy, Naples. Methods: From 2021 December 14th to 2022 January 4th, a cross-sectional survey study was conducted among community centers, working places, and universities using a snowball sampling method. For inclusion in the study, the participants were required to be at least 18 years old and residents in the metropolitan area of Naples. Three multiple linear regression analysis (MLRA) models were developed by including variables that could potentially be associated with the following outcomes of interest: knowledge (Model I), attitudes (Model II), and behavior (Model III) regarding the use of NSAIDs. Results: Data were acquired from 1,012 questionnaires administered to subjects evenly divided by gender with an average age of 36.8 years and revealed that only 7.9% of the participants self-admittedly did not take NSAIDs, while approximately half the participants (50%) admitted to occasionally using them. The results showed a statistically significant correlation between attitudes regarding the appropriate use of NSAIDs and less knowledge. The regression analyses indicated that behaviors regarding the appropriate use of NSAIDs were statistically significant in younger respondents, non-smokers, and those without children. These interesting results showed that behaviors regarding the appropriate use of NSAIDs were significantly higher among respondents with less knowledge and more positive attitudes. Conclusion: According to the collected data and statistical analysis results, it is possible to identify factors that can greatly affect inappropriate behaviors regarding the use of NSAIDs and establish targeted prevention programs.

[Meteorological Influences on Annual Variation and Trend of Autumn Surface Ozone in the Pearl River Delta].

Based on the ozone （O3） monitoring data of the Pearl River Delta （PRD） from 2015 to 2022 and the reanalysis of meteorological data, the impact of meteorological conditions on the annual variation and trends of the maximum daily 8-hour average O3 concentration （MDA8-O3） were quantified using multiple linear regression （MLR） and LMG methods. The results indicated that the MLR model constructed using meteorological parameters from individual months in autumn better simulated the variation in MDA8-O3 compared to that in the model built using meteorological parameters from the entire autumn season. The combined influence of total cloud cover, relative humidity, 2 m maximum temperature, and 850 hPa zonal wind led to a reduction of 34.1 µg·m-3 in MAD8-O3 in 2020 compared to that in 2019, with contributions of 31.3%, 45.2%, 15.8%, and 6.7%, respectively. The observed trends of MDA8-O3 in the PRD for September, October, November, and the autumn season during 2015-2022 were 7.3, 5.2, 4.8, and 5.8 µg·ï¼ˆm3·a）-1, respectively. Among these, the trends driven by meteorological factors were 3.6, 2.4, 2.4, and 3.1 µg·ï¼ˆm3·a）-1. Overall, meteorological conditions contributed 53.4% to the variations in autumn MDA8-O3 in the PRD from 2015 to 2022.

Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.

Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this procedure is invasive and time-consuming to the extent that physicians rather rely on non-invasive diagnostic tools. In this work, we assess the feasibility to develop a novel machine-learning (ML) approach to predict clinically relevant LVP indices. Synchronized invasive (pressure-volume tracings) and non-invasive signals (ECG, pulse oximetry, and cardiac sounds) were collected from anesthetized, closed-chest Göttingen minipigs. Animals were either healthy or had HF with reduced ejection fraction and circa 500 heartbeats were included in the analysis for each animal. The ML algorithm showed excellent prediction of LVP indices estimating, for instance, the end-diastolic pressure with a R2 of 0.955. This novel ML algorithm could assist clinicians in the care of HF patients.

Addressing the Gap: Racial Disparities and Public Health Strategies in the Epidemiology of Gastrointestinal Stromal Tumors.

Background Gastrointestinal stromal tumors (GISTs) represent the most common mesenchymal neoplasms of the gastrointestinal tract, arising from the interstitial cells of Cajal. These tumors bridge the nervous system and muscular layers of the gastrointestinal tract, playing a crucial role in the digestive process. The incidence of GISTs demonstrates notable variations across different racial and ethnic groups, underscoring the need for in-depth analysis to understand the interplay of genetic, environmental, and socioeconomic factors behind these disparities. Linear regression analysis is a pivotal statistical tool in such epidemiological studies, offering insights into the temporal dynamics of disease incidence and the impact of public health interventions. Methodology This investigation employed a detailed dataset from 2009 to 2020, documenting GIST incidences across Asian, African American, Hispanic, and White populations. A meticulous preprocessing routine prepared the dataset for analysis, which involved data cleaning, normalization of racial terminologies, and aggregation by year and race. Linear regression models and Pearson correlation coefficients were applied to analyze trends and correlations in GIST incidences across the different racial groups, emphasizing an understanding of temporal patterns and racial disparities in disease incidence. Results The study analyzed GIST cases among four racial groups, revealing a male predominance (53.19%) and an even distribution of cases across racial categories: Whites (27.66%), Hispanics (25.53%), African Americans (24.47%), and Asians (22.34%). Hypertension was the most common comorbidity (32.98%), followed by heart failure (28.72%). The linear regression analysis for Asians showed a decreasing trend in GIST incidences with a slope of -0.576, an R-squared value of 0.717, and a non-significant p-value of 0.153. A significant increasing trend was observed for Whites, with a slope of 0.581, an R-squared value of 0.971, and a p-value of 0.002. African Americans exhibited a moderate positive slope of 0.277 with an R-squared value of 0.470 and a p-value of 0.201, indicating a non-significant increase. Hispanics showed negligible change over time with a slope of -0.095, an R-squared value of 0.009, and a p-value of 0.879, suggesting no significant trend. Conclusions This study examines GIST incidences across racial groups, revealing significant disparities. Whites show an increasing trend (p = 0.002), while Asians display a decreasing trend (p = 0.153), with stable rates in African Americans and Hispanics. Such disparities suggest a complex interplay of genetics, environment, and socioeconomic factors, highlighting the need for targeted research and interventions that address these differences and the systemic inequalities influencing GIST outcomes.

Remediation of phenanthrene contaminated soil by persulphate coupled with Pseudomonas aeruginosa GZ7 based on oxidation prediction model.

Chemical oxidation coupled with microbial remediation has attracted widespread attention for the removal of polycyclic aromatic hydrocarbons (PAHs). Among them, the precise evaluation of the feasible oxidant concentration of PAH-contaminated soil is the key to achieving the goal of soil functional ecological remediation. In this study, phenanthrene (PHE) was used as the target pollutant, and Fe2+-activated persulphate (PS) was used to remediate four types of soils. Linear regression analysis identified the following important factors influencing remediation: PS dosage and soil PHE content for PHE degradation, Fe2+ dosage, hydrolysable nitrogen (HN), and available phosphorus for PS decomposition. A comprehensive model of "soil characteristics-oxidation conditions-remediation effect" with a high predictive accuracy was constructed. Based on model identification, Pseudomonas aeruginosa GZ7, which had high PAHs degrading ability after domestication, was further applied to coupling repair remediation. The results showed that the optimal PS dose was 0.75% (w/w). The response relationship between soil physical, chemical, and biological indicators at the intermediate interface and oxidation conditions was analysed. Coupled remediation effects were clarified using microbial diversity sequencing. The introduction of Pseudomonas aeruginosa GZ7 stimulated the relative abundance of Cohnella, Enterobacter, Paenibacillus, and Bacillus, which can promote material metabolism and energy transformation during remediation.

The use of the self-organizing map (SOM) methodology to study the dissolved organic matter (DOM) in different water body types.

This study investigated the characteristics of dissolved organic matter (DOM) in two distinct water bodies, through the utilization of three-dimensional fluorescence spectroscopy coupled with self-organizing map (SOM) methodology. Specifically, this analysis concentrated on neurons 3, 14, and 17 within the SOM model, identifying notable differences in the DOM compositions of a coal subsidence water body (TX) and the MaChang Reservoir (MC). The humic substance content of DOM TX exceeded that of MC. The origin of DOM in TX was primarily linked to agricultural inputs and rainfall runoff, whereas the DOM in MC was associated with human activities, displaying distinctive autochthonous features and heightened biological activity. Principal component analysis revealed that humic substances dominated the DOM in TX, while the natural DOM in MC was primarily autochthonous. Furthermore, a multiple linear regression model (MLR) determined that external pollution was responsible for 99.11% of variation in the humification index (HIX) of water bodies.

Balance Control is Sequentially Correlated with Proprioception, Joint Range of Motion, Strength, Pain, and Plantar Tactile Sensation Among Older Adults with Knee Osteoarthritis.

BACKGROUND: Patients with knee osteoarthritis (KOA) are at high risk for falls, which is attributed to their impaired balance control. Identifying factors associated with balance control facilitates the development of precise KOA rehabilitation programs. This study was to investigate the correlations of balance control with proprioception, plantar tactile sensation (PTS), pain, joint range of motion (ROM), and strength among older adults with and without KOA, as well as the magnitudes and sequence of correlation of these factors to balance control. METHODS: A total of 240 older adults with (n = 124, female: 84, age: 68.8 ± 4.0 years) and without (n = 116, female: 64, age: 67.9 ± 3.5 years) KOA were recruited and assigned to the KOA and control groups. Their proprioception, PTS, pain, ROM, and strength were measured. Pearson or Spearman correlations were used to test whether they were significantly related to their Berg Balance Scale (BBS), and factor analysis and multivariate linear regression were used to determine the degrees of correlation between each factor and the BBS. RESULTS: Compared to the control group, the KOA group had lower BBS score, larger proprioception and PTS thresholds, smaller ROM, and less strength (p: 0.008, < 0.001-0.016, < 0.001-0.005, < 0.001-0.014, and < 0.001-0.002, respectively). In the KOA group, the BBS was weakly to moderately correlated with proprioception, PTS, pain, ROM, and strength (r: 0.332-0.501, 0.197-0.291, 0.340, 0.212-0.508, and 0.236-0.336, respectively). While in the control group, the BBS was correlated with proprioception and strength (r: 0.207-0.379, and 0.212-0.410). In the KOA group, BBS = 54.41+ (0.668*strength) - (0.579*PTS) - (1.141*proprioception) + (1.054* ROM) - (0.339*pain). While in the control group, BBS = 53.85+ (0.441*strength) - (0.677*proprioception). CONCLUSION: Worse proprioception and PTS, smaller ROM, and less strength were detected among older adults with KOA, and their proprioception, PTS, pain, ROM, and strength were all related to balance control. Proprioception had the strongest correlations, followed by ROM, strength, pain, and PTS. Precise KOA rehabilitation programs may be proposed following the sequence of improving the five factors.

QSPR/QSAR study of antiviral drugs modeled as multigraphs by using TI's and MLR method to treat COVID-19 disease.

The ongoing COVID-19 pandemic continues to pose significant challenges worldwide, despite widespread vaccination. Researchers are actively exploring antiviral treatments to assess their efficacy against emerging virus variants. The aim of the study is to employ M-polynomial, neighborhood M-polynomial approach and QSPR/QSAR analysis to evaluate specific antiviral drugs including Lopinavir, Ritonavir, Arbidol, Thalidomide, Chloroquine, Hydroxychloroquine, Theaflavin and Remdesivir. Utilizing degree-based and neighborhood degree sum-based topological indices on molecular multigraphs reveals insights into the physicochemical properties of these drugs, such as polar surface area, polarizability, surface tension, boiling point, enthalpy of vaporization, flash point, molar refraction and molar volume are crucial in predicting their efficacy against viruses. These properties influence the solubility, permeability, and bio availability of the drugs, which in turn affect their ability to interact with viral targets and inhibit viral replication. In QSPR analysis, molecular multigraphs yield notable correlation coefficients exceeding those from simple graphs: molar refraction (MR) (0.9860), polarizability (P) (0.9861), surface tension (ST) (0.6086), molar volume (MV) (0.9353) using degree-based indices, and flash point (FP) (0.9781), surface tension (ST) (0.7841) using neighborhood degree sum-based indices. QSAR models, constructed through multiple linear regressions (MLR) with a backward elimination approach at a significance level of 0.05, exhibit promising predictive capabilities highlighting the significance of the biological activity I C 50 (Half maximal inhibitory concentration). Notably, the alignment of predicted and observed values for Remdesivir's with obs p I C 50 = 6.01 ,pred p I C 50 = 6.01 ( p I C 50 represents the negative logarithm of I C 50 ) underscores the accuracy of multigraph-based QSAR analysis. The primary objective is to showcase the valuable contribution of multigraphs to QSPR and QSAR analyses, offering crucial insights into molecular structures and antiviral properties. The integration of physicochemical applications enhances our understanding of factors influencing antiviral drug efficacy, essential for combating emerging viral strains effectively.

The impact of external auditors with forensic accounting competencies on auditee firm performance.

The purpose of this study is to examine how the presence of auditors with forensic accounting skills impacts the financial performance of audited companies. Using a quantitative approach, this research employs linear regression analysis and examines a sample of 74 companies from the industrial and service sectors listed on the Amman Stock Exchange between 2012 and 2021. The findings reveal that external auditors with forensic accounting competencies have a positive impact on the financial performance of audited companies. The insights presented in this study could serve as valuable tools for improving the financial performance of companies in the service and industry sectors, highlighting the importance of supporting and promoting the skills and competencies of forensic accountants.

Biometric factors associated with the postoperative visual performance of a multifocal intraocular lens.

Purpose: To identify the biometric factors associated with postoperative visual performance after uneventful phacoemulsification with multifocal intraocular lens (MIOL) implantation. Methods: In this retrospective cohort study, 72 eyes of 72 patients implanted with the HumanOptics Diff-aAY MIOL were included. Preoperative examination data including the white-to-white distance (WTW), anterior chamber depth (ACD), axial length and corneal astigmatism were gathered through the electronic medical records. One month postoperatively, the pupil parameters, corneal aberrations, corneal astigmatism, IOL tilts and IOL decentrations were measured using an OPD-Scan III aberrometer. Postoperative visual performance parameters were recorded as the visual acuity, depth of focus, modulation transfer function (MTF) and point spread function (PSF) values, area under log contrast sensitivity function (AULCSF), retinal straylight and visual function questionnaire scores. Univariate and multivariate linear regression analyses were then performed to evaluate the associations between the potential biometric factors and postoperative visual outcomes. Results: Younger age predicted greater MTF and PSF values, better AULCSF and better retinal straylight (P < 0.05). A lower corneal trefoil predicted better MTF and PSF values (P < 0.05). Smaller IOL decentration predicted better distance-corrected near visual acuity, greater AULCSF and better retinal straylight (P < 0.05). A less negative spherical equivalent (SE) predicted better MTF values (P = 0.017), while a more negative SE predicted better Visual Function Index-14 (VF-14) questionnaire scores and satisfaction scores (P < 0.05). A higher IOL power predicted better best corrected distance visual acuity (P = 0.005). Lower preoperative corneal astigmatism predicted greater MTF values (P = 0.020). Lower postoperative corneal astigmatism, smaller corneal high-order aberrations (HOAs), smaller photopic pupil size, larger WTW and deeper ACD predicted a better AULCSF (P < 0.05). Conclusions: IOL decentration, IOL power, age, preoperative and postoperative corneal astigmatism, SE, photopic pupil size, corneal trefoil, WTW, ACD and corneal HOAs were significantly associated with postoperative visual performance. These findings might aid in patient selection prior to MIOL implantation.

Perception matters: How air pollution influences life satisfaction in China.

Academic studies on environmental pollution have convincingly acknowledged the salient relevance of ambient pollutant emissions on individual life satisfaction. However, an understanding of how the different dimensions of air pollution influence public self-assessment of their living condition is required. This research investigates whether objective pollutant emissions and subjective evaluation influence individual life satisfaction. The findings were based on data from the China Environment Yearbook and China Social Survey in 2019. The multi-level linear regression model found that air pollutants emissions, including particulate matter (PM) and sulfur dioxide (SO2), failed to explain the variations in public life satisfaction because of the lag effect of public perception. A significant nexus between perceived air pollution and public life satisfaction was observed at a significance level of 0.01. Specially, as the perceived air pollution by the public increased by one-point, life satisfaction decreased by 0.22 on a scale of 1-10, on average. Heterogeneous analysis based on income further suggested the salient negative effect of PM emissions on life satisfaction only occurred in the high-income group. The findings were robust after various methodological analyses. This study has theoretical implications for understanding the effects of air pollution on public subjective perception and provides guidance for how the government can manage the relationship between environmental governance and life satisfaction.

Dietary patterns and the risk of diabetes in Korean adults: A cross-sectional and prospective cohort study.

OBJECTIVE: The aim of this study was to identify dietary patterns associated with diabetes in Korean adults and to investigate their association with diabetes risk in both a cross-sectional and prospective study. METHODS: Predefined food groups collected by the Korea National Health and Nutrition Examination Survey (KNHANES 2015-2018, n = 19 721) were entered in a reduced rank regression (RRR) model, followed by stepwise linear regression analyses to identify the most predictive dietary patterns. We evaluated the construct validity of dietary patterns in two independent samples from KNHANES 2019 to 2021 (n = 14 223) and the Health Examinees (HEXA) cohort study (n = 30 013). Associations between dietary patterns and diabetes risk were examined using multivariable regression and multivariable-adjusted Cox proportional hazard models, respectively. RESULTS: A dietary pattern was identified with high positive loadings for refined white rice, kimchi and salted vegetables, wheat flour and bread, and seasonings, and high negative loadings for whole grains, legumes with tofu and soymilk, poultry, eggs, and plant oils. The higher pattern scores were significantly associated with diabetes risk in KNHANES 2015 to 2018 (male: odds ratio [OR]: 1.59; 95% confidence interval [CI]: 1.35, 1.88; female: OR: 1.37; 95% CI: 1.18, 1.52), KNHANES 2019 to 2021 (male: OR: 1.47; 95% CI: 1.01, 1.69; female: OR: 1.37; 95% CI: 1.18, 1.54), and HEXA study (male: hazard ratio [HR]: 1.10; 95% CI: 1.01, 1.34; female: HR: 1.24; 95% CI: 1.02, 1.52). CONCLUSIONS: Dietary patterns derived by RRR followed by stepwise linear regression analyses were associated with increased risks of diabetes among Korean adults.

Chaotic neural network algorithm with competitive learning integrated with partial Least Square models for the prediction of the toxicity of fragrances in sanitizers and disinfectants.

This study addresses the need for accurate structural data regarding the toxicity of fragrances in sanitizers and disinfectants. We compare the predictive and descriptive (model stability) potential of multiple linear regression (MLR) and partial least squares (PLS) models optimized through variable selection (VS). A novel hybrid chaotic neural network algorithm with competitive learning (CCLNNA)-PLS modeling strategy can offer specific optimization with satisfactory results, even for a limited dataset. While also exploring the preliminary comparative analysis, the goal is to introduce an adapted novel CCLNNA optimization strategy for VS, inspired by neural networks, along with exploring the influence of the percentage of significant descriptors in the optimization function to enhance the final model's capabilities. We analyzed an available dataset of 24 molecules, incorporating ADMET and PaDEL descriptors as predictor variables, to explore the relationship between the response/target variable (pLC50) and the meticulously optimized set of descriptors. The suitability of the selected PLS models (cross- and external-validated accuracy combined with percentage of significant descriptors at a level equal to or >80 %) underscores the importance of expanding the dataset to amplify the validation protocols, thus enhancing future model reliability and environmental impact.

Corrigendum: A preliminary exploration of the regression equation for performance in amateur half-marathon runners: a perspective based on respiratory muscle function.

[This corrects the article DOI: 10.3389/fphys.2024.1340513.].

Yoke-Type Elasto-Magnetic Sensor-Based Tension Force Monitoring Method for Enhancement of Field Applicability.

Tension members are key members that maintain stability and improve the strength of structures such as cable-stayed bridges, PSC structures, and slopes. Their application has recently been expanded to new fields such as mooring lines in subsea structures and aerospace fields. However, the tensile strength of the tension members can be abnormal owing to various risk factors that may lead to the collapse of the entire structure. Therefore, continuous tension monitoring is necessary to ensure structural safety. In this study, an improved elasto-magnetic (E/M) sensor was used to monitor tension force using a nondestructive method. General E/M sensors have limitations that make it difficult to apply them to operating tension members owing to their solenoid structure, which requires field winding. To overcome this problem, the magnetization part of the E/M sensor was improved to a yoke-type sensor, which was used in this study. For the development of the sensors, the numerical design and magnetization performance verification of the sensor were performed through eddy current solution-type simulations using ANSYS Maxwell. Using the manufactured yoke-type E/M sensor, the induced voltage signals according to the tension force of the specimen increasing from 0 to 10 tons at 1-ton intervals were repeatedly measured using DAQ with wireless communication. The measured signals were indexed using peak-to-peak value of induced voltages and used to analyze the signal change patterns as the tension increased. Finally, the analyzed results were compared with those of a solenoid-type E/M sensor to confirm the same pattern. Therefore, it was confirmed that the tension force of a tension member can be estimated using the proposed yoke-type E/M sensor. This is expected to become an effective tension monitoring technology through performance optimization and usability verification studies for each target tension member in the future.

Use of the linear regression method to evaluate population accuracy of predictions from non-linear models.

Background: To address the limitations of commonly used cross-validation methods, the linear regression method (LR) was proposed to estimate population accuracy of predictions based on the implicit assumption that the fitted model is correct. This method also provides two statistics to determine the adequacy of the fitted model. The validity and behavior of the LR method have been provided and studied for linear predictions but not for nonlinear predictions. The objectives of this study were to 1) provide a mathematical proof for the validity of the LR method when predictions are based on conditional means, regardless of whether the predictions are linear or non-linear 2) investigate the ability of the LR method to detect whether the fitted model is adequate or inadequate, and 3) provide guidelines on how to appropriately partition the data into training and validation such that the LR method can identify an inadequate model. Results: We present a mathematical proof for the validity of the LR method to estimate population accuracy and to determine whether the fitted model is adequate or inadequate when the predictor is the conditional mean, which may be a non-linear function of the phenotype. Using three partitioning scenarios of simulated data, we show that the one of the LR statistics can detect an inadequate model only when the data are partitioned such that the values of relevant predictor variables differ between the training and validation sets. In contrast, we observed that the other LR statistic was able to detect an inadequate model for all three scenarios. Conclusion: The LR method has been proposed to address some limitations of the traditional approach of cross-validation in genetic evaluation. In this paper, we showed that the LR method is valid when the model is adequate and the conditional mean is the predictor, even when it is a non-linear function of the phenotype. We found one of the two LR statistics is superior because it was able to detect an inadequate model for all three partitioning scenarios (i.e., between animals, by age within animals, and between animals and by age) that were studied.

Powerful t-tests in the presence of nonclassical measurement error.

This article proposes a powerful alternative to the t-test of the null hypothesis that a coefficient in a linear regression is equal to zero when a regressor is mismeasured. We assume there are two contaminated measurements of the regressor of interest. We allow the two measurement errors to be nonclassical in the sense that they may both be correlated with the true regressor, they may be correlated with each other, and we do not require any location normalizations on the measurement errors. We propose a new maximal t-statistic that is formed from the regression of the outcome onto a maximally weighted linear combination of the two measurements. The critical values of the test are easily computed via a multiplier bootstrap. In simulations, we show that this new test can be significantly more powerful than t-statistics based on OLS or IV estimates. Finally, we apply the proposed test to a study of returns to education based on twin data from the UK. With our maximal t-test, we can discover statistically significant returns to education when standard t-tests do not.

Prediction of arsenic retention in vadose zone based on empirical relationship between soil properties and segmented retardation factors.

Arsenic (As) is a widespread environmental contaminant that poses a significant threat to ecosystems and human health. Although previous studies have qualitatively revealed the effects of individual soil properties on the transport and fate of As in the vadose zone, their integrated impacts remain obscure. Moreover, studies investigating the retardation factor therein, which is a key parameter for comprehending As transport in the vadose zone, are extremely limited. In this study, we investigated the interplay of soil properties with As transport and retention within the vadose zone, while focusing on the retardation factor of As. We employed steady-state unsaturated water-flow soil column experiments coupled with a mobile-immobile model and multiple linear regression analysis to elucidate the dependence of As retardation factors on the soil properties. In the mobile water zone, iron and organic matter contents emerged as the two most influential properties that impedes As mobility. Whereas, in the immobile water zone, the coefficient of uniformity and bulk density were the most influential factors that enhanced As retention. Finally, we derived an empirical equation for calculating the As retardation factors in each zone, offering a valuable tool for describing and predicting As behavior to protect the groundwater resources underneath.

Smartphone-Based Survey and Message Compliance in Adults Initially Unready to Quit Smoking: Secondary Analysis of a Randomized Controlled Trial.

BACKGROUND: Efficacy of smartphone-based interventions depends on intervention content quality and level of exposure to that content. Smartphone-based survey completion rates tend to decline over time; however, few studies have identified variables that predict this decline over longer-term interventions (eg, 26 weeks). OBJECTIVE: This study aims to identify predictors of survey completion and message viewing over time within a 26-week smoking cessation trial. METHODS: This study examined data from a 3-group pilot randomized controlled trial of adults who smoke (N=152) and were not ready to quit smoking within the next 30 days. For 182 days, two intervention groups received smartphone-based morning and evening messages based on current readiness to quit smoking. The control group received 2 daily messages unrelated to smoking. All participants were prompted to complete 26 weekly smartphone-based surveys that assessed smoking behavior, quit attempts, and readiness to quit. Compliance was operationalized as percentages of weekly surveys completed and daily messages viewed. Linear regression and mixed-effects models were used to identify predictors (eg, intervention group, age, and sex) of weekly survey completion and daily message viewing and decline in compliance over time. RESULTS: The sample (mean age 50, SD 12.5, range 19-75 years; mean years of education 13.3, SD 1.6, range 10-20 years) was 67.8% (n=103) female, 74.3% (n=113) White, 77% (n=117) urban, and 52.6% (n=80) unemployed, and 61.2% (n=93) had mental health diagnoses. On average, participants completed 18.3 (71.8%) out of 25.5 prompted weekly surveys and viewed 207.3 (60.6%) out of 345.1 presented messages (31,503/52,460 total). Age was positively associated with overall weekly survey completion (P=.003) and daily message viewing (P=.02). Mixed-effects models indicated a decline in survey completion from 77% (114/148) in the first week of the intervention to 56% (84/150) in the last week of the intervention (P<.001), which was significantly moderated by age, sex, ethnicity, municipality (ie, rural/urban), and employment status. Similarly, message viewing declined from 72.3% (1533/2120) in the first week of the intervention to 44.6% (868/1946) in the last week of the intervention (P<.001). This decline in message viewing was significantly moderated by age, sex, municipality, employment status, and education. CONCLUSIONS: This study demonstrated the feasibility of a 26-week smartphone-based smoking cessation intervention. Study results identified subgroups that displayed accelerated rates in the decline of survey completion and message viewing. Future research should identify ways to maintain high levels of interaction with mobile health interventions that span long intervention periods, especially among subgroups that have demonstrated declining rates of intervention engagement over time. TRIAL REGISTRATION: ClinicalTrials.gov NCT03405129; https://clinicaltrials.gov/ct2/show/NCT03405129.

Exploring factors influencing delayed first antibiotic treatment for suspected early-onset sepsis in preterm newborns: a study before quality improvement initiative.

BACKGROUND: Early-onset sepsis (EOS) is a serious illness that affects preterm newborns, and delayed antibiotic initiation may increase the risk of adverse outcomes. PURPOSE: The objective of this study was to examine the present time of antibiotic administration in preterm infants with suspected EOS and the factors that contribute to delayed antibiotic initiation. METHODS: In this retrospective study in China, a total of 82 early preterm infants with suspected EOS between December 2021 and March 2023 were included. The study utilized a linear regression analytical approach to identify independent factors that contribute to delayed antibiotic administration. RESULTS: The mean gestational age and birth weight of the study population were 29.1 ± 1.4 weeks and 1265.7 ± 176.8 g, respectively. The median time of initial antibiotic administration was 3.8 (3.1-5.0) hours. Linear regression revealed that severe respiratory distress syndrome (RDS) (ß = 0.07, P = 0.013), penicillin skin test (PST) timing (ß = 0.06, P < 0.001) and medical order timing (ß = 0.04, P = 0.017) were significantly associated with the initial timing of antibiotic administration. CONCLUSIONS: There is an evident delay in antibiotic administration in preterm infants with suspected EOS in our unit. Severe RDS, PST postponement and delayed medical orders were found to be associated with the delayed use of antibiotics, which will be helpful for quality improvement efforts in the neonatal intensive care unit (NICU).