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Although Item Response Theory (IRT) has been recommended for helping advance interprofessional education (IPE) research, its use remains limited. This may be partly explained by potential misconceptions regarding IRT`s "limitation" to cross-sectional data. The aim of this study is to demonstrate how Item Response Theory (IRT) can be applied effectively in before-and-after designs in IPE research. Specifically, a two-week before-after design with survey methodology using the Extended Professional Identity Scale (EPIS), an interprofessional identity measure, was conducted among n = 146 mixed health-science students. Results indicated that EPIS increased significantly before-after intervention by .74 standardised mean differences, t146 = 7.73, p < .05. The before-after IRT model also gave a test-retest reliability estimate of .60 which was considered acceptable. Comparison of the IRT model with a conventional paired-t-test indicated similar effect size estimates of Cohen's d = .56 and .54, respectively. We demonstrate IRT`s flexibility to before-after studies in IPE. Application of this model can yield accurate changes in target IPE constructs, and it is advantageous to classical test theory vis-à-vis baseline differences.

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Gamma-aminobutyric acid (GABA), the most important inhibitory neurotransmitter in the human brain, has long been considered essential in human behavior in general and learning in particular. GABA concentration can be quantified using magnetic resonance spectroscopy (MRS). Using this technique, numerous studies have reported associations between baseline GABA levels and various human behaviors. However, regional GABA concentration is not fixed and may exhibit rapid modulation as a function of environmental factors. Hence, quantification of GABA levels at several time points during the performance of tasks can provide insights into the dynamics of GABA levels in distinct brain regions. This review reports on findings from studies using repeated measures (n = 41) examining the dynamic modulation of GABA levels in humans in response to various interventions in the perceptual, motor, and cognitive domains to explore associations between GABA modulation and human behavior. GABA levels in a specific brain area may increase or decrease during task performance or as a function of learning, depending on its precise involvement in the process under investigation. Here, we summarize the available evidence and derive two overarching hypotheses regarding the role of GABA modulation in performance and learning. Firstly, training-induced increases in GABA levels appear to be associated with an improved ability to differentiate minor perceptual differences during perceptual learning. This observation gives rise to the 'GABA increase for better neural distinctiveness hypothesis'. Secondly, converging evidence suggests that reducing GABA levels may play a beneficial role in effectively filtering perceptual noise, enhancing motor learning, and improving performance in visuomotor tasks. Additionally, some studies suggest that the reduction of GABA levels is related to better working memory and successful reinforcement learning. These observations inspire the 'GABA decrease to boost learning hypothesis', which states that decreasing neural inhibition through a reduction of GABA in dedicated brain areas facilitates human learning. Additionally, modulation of GABA levels is also observed after short-term physical exercise. Future work should elucidate which specific circumstances induce robust GABA modulation to enhance neuroplasticity and boost performance.

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OBJECTIVES: This study identifies patterns of antidepressant prescribing and subsequent hospital admissions from 2010 to 2018 amongst older adults in Northern Ireland (NI). METHOD: Participants comprised all General Practitioner (GP)-registered adults aged fifty-five years and above on 01/01/2010 (n = 386,119). Administrative data linkage included demographic information; antidepressant prescribing data from the NI Enhanced Prescribing Database (EPD); and hospital patient admissions. Repeated measures latent class analysis (RMLCA) identified patterns of antidepressant prescribing (from 2010 to 2018). RESULTS: RMLCA identified four latent classes: decreasing antidepressant prescribing (5.9%); increasing antidepressant prescribing (8.0%); no-antidepressant prescribing (68.7%); and long-term antidepressant prescribing (17.5%). Compared with those in no-antidepressant prescribing class, persons in the remaining classes were more likely to be female and younger, and less likely to live in either rural areas or less-deprived areas. Compared with no-antidepressant prescribing, those with increasing antidepressant prescribing were 60% and 52% more likely to be admitted to hospital in 2019 and 2020, respectively, and their admission rate per year was 11% and 8% higher in 2019 and 2020, respectively. Similarly, those with long-term prescriptions were 70% and 67% more likely to be admitted to hospital in 2019 and 2020, respectively, and their admission rate per year was 14% and 9% higher in 2019 and 2020, respectively. CONCLUSION: Findings show that approximately 26% of the NI hospital admissions population were impacted by sustained or increasing antidepressant prescribing. Because of their increased likelihood of hospitalization, these individuals may benefit from psychosocial support and social prescribing alternatives to psychopharmacological treatment.

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BACKGROUND: Taiwan became an aged society in March 2018, and it is expected to become a super-aged society by 2025. The trend of increasing proportions of older adults continuing to work is inevitable. However, few studies have been conducted to investigate the effects of employment on the mental health of older adults. Therefore, we longitudinally explored the relationship between employment status and depressive symptoms in Taiwanese older adults. METHODS: The study included 5,131 individuals aged 50 and above, of which 55.6% were men, who had participated in the national-wide Taiwan Longitudinal Study of Aging in 1996, 1999, 2003, and 2007. Of them, 1,091 older adults had completed all four surveys. Depressive symptoms were assessed using the Center for Epidemiological Studies of Depression scale; the total score on this scale ranges from 0 to 30. Employment status was assessed during each survey wave. Logistic regression was performed using a cross-sectional design. The effects of unemployment on depressive symptoms were analyzed using a generalized estimating equation model with a repeated measures design. RESULTS: In each survey wave, employed older adults exhibited better mental health than did unemployed ones. After adjustments for potential confounders, unemployment was found to exert a significant adverse effect on depressive symptoms. The repeated measures analysis revealed that employment protected against depressive symptoms, as noted in the subsequent surveys conducted after 3 to 4 years (aOR [95% CI] = 0.679 [0.465-0.989]). CONCLUSION: Employment may reduce the risk of depressive symptoms in community-dwelling older adults in Taiwan.

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BACKGROUND: The assessment of the effectiveness of teaching interventions in enhancing students' understanding of the Pharmaceutical Care Network Europe (PCNE) Classification System is crucial in pharmaceutical education. This is especially true in regions like China, where the integration of the PCNE system into undergraduate teaching is limited, despite its recognized benefits in addressing drug-related problems in clinical pharmacy practice. Therefore, this study aimed to evaluate the effectiveness of teaching interventions in improving students' understanding of the PCNE Classification System in pharmaceutical education. METHODS: Undergraduate pharmacy students participated in a series of sessions focused on the PCNE system, including lectures (t1), case analyses (t2), and practical implementation (t3). The levels of understanding were evaluated using time-course questionnaires. Initially, paired samples t-Tests were used to compare understanding levels between different time points. Subsequently, Repeated Measures Analysis (RMA) was employed. Pearson correlation analysis was conducted to examine the relationship between understanding levels and the usability and likelihood of using the PCNE system, as reported in the questionnaires. RESULTS: The paired samples t-Tests indicated insignificant differences between t2 and t3, suggesting limited improvement following the practical implementation of the PCNE system. However, RMA revealed significant time effects on understanding levels in effective respondents and the focused subgroup without prior experience (random intercept models: all p < 0.001; random slope models: all p < 0.001). These results confirmed the effectiveness of all three teaching interventions. Pearson correlation analysis demonstrated significant positive correlations between understanding levels and the usability and likelihood of using the PCNE system at all examined time points. This finding highlighted the reliability of the understanding levels reported in the questionnaires. The homework scores were used as external calibration standards, providing robust external validation of the questionnaire's validity. CONCLUSION: The implementation of RMA provided robust evidence of the positive impact of time on understanding levels. This affirmed the effectiveness of all teaching interventions in enhancing students' comprehension of the PCNE Classification System. By utilizing RMA, potential errors inherent in common statistical methods, such as t-Tests, were mitigated. This ensured a more comprehensive and accurate assessment of the effectiveness of the teaching interventions.

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Children with low executive functions (EFs) are described as having lower levels of playfulness, the quality of children's play, compared to children with EFs within the normal range. However, how playfulness in children with low EFs develops over time remains unclear. Additionally, little is known about how parental playfulness and parental playtime with their child affect these developmental trajectories in children with low EFs. To address these research gaps, we measured playfulness in 62 children with low EFs and 62 children with EFs within the normal range aged 3 to 6 years at three time points over 2 years. We used the Children's Playfulness Scale, which captures multi-informant perspectives from parents and teachers. Moreover, the parents of children with low EFs reported their own playfulness and their playtime with their children at T1. Repeated-measures hierarchical linear models indicated significantly lower levels of playfulness in the children with low EFs than in the controls, with no significant changes observed over 2 years in either group. In the children with low EFs, we found a significant positive relationship between parental playfulness at T1 and children's playfulness 2 years later but a significant negative relationship between parental playtime at T1 and children's playfulness 2 years later. These results prompt a broad discussion on potential implications for the enhancement of playfulness in children with low EFs within the family environment.

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The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires the monitoring of patients and collection of primary outcome data following termination of randomised treatment. However, when patients withdraw from a study early before completion this creates true missing data complicating the analysis. One possible way forward uses multiple imputation to replace the missing data based on a model for outcome on- and off-treatment prior to study withdrawal, often referred to as retrieved dropout multiple imputation. This article introduces a novel approach to parameterising this imputation model so that those parameters which may be difficult to estimate have mildly informative Bayesian priors applied during the imputation stage. A core reference-based model is combined with a retrieved dropout compliance model, using both on- and off-treatment data, to form an extended model for the purposes of imputation. This alleviates the problem of specifying a complex set of analysis rules to accommodate situations where parameters which influence the estimated value are not estimable, or are poorly estimated leading to unrealistically large standard errors in the resulting analysis. We refer to this new approach as retrieved dropout reference-base centred multiple imputation.

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BACKGROUND: Widespread use of e-cigarette (EC) or vaping products causes respiratory disorders including the nationwide outbreak of e-cigarette or vaping product use-associated lung injury (EVALI) in 2019. Chronic adverse health effects are now being reported as well. To address this important public health issue, an innovative approach of epidemic control and epidemiologic study is required. We aimed to assess the association between short-term and long-term use of EC products and respiratory health in adults using smartphone app data. METHODS: A population-based, repeated measures, longitudinal smartphone app study that performed 8-day survey participation over 60 days for each participant from August 2020 to March 2021, including 306 participants aged 21 years and older in the US. The participants were asked to complete the respiratory health questionnaire daily, weekly, and monthly on their smartphone app. We analyzed the association between vaping habits and respiratory health using generalized linear mixed models (GLMMs). RESULTS: EC use in the previous 7 days was associated with frequent cough (OR: 5.15, 95% CI: 2.18, 12.21), chronic cough (OR: 3.92, 95% CI: 1.62, 9.45), frequent phlegm (OR: 3.99, 95% CI: 1.44, 11.10), chronic phlegm (OR: 3.55, 95% CI: 1.41, 8.96), episodes of cough and phlegm (OR: 4.68, 95% CI: 1.94, 11.28), mMRC grade 3-4 dyspnea (OR: 3.32, 95% CI: 1.35 to 8.13), chest cold (OR: 3.07, 95% CI: 1.29, 7.33), eye irritation (OR: 2.94, 95% CI: 1.34, 6.47) and nose irritation (OR : 2.02, 95% CI: 0.95, 4.30). Relatively long-term effects of the past 90 days EC use was associated with an increased risk of wheeze (OR: 3.04, 95% CI: 1.31, 7.03), wheeze attack (OR: 2.78, 95% CI: 1.07, 7.24), mMRC grade 3-4 dyspnea (OR: 2.54, 9% CI: 1.05 to 6.18), eye irritation (OR: 3.16, 95% CI: 1.49, 6.68), and eye irritation during the past month (OR: 3.50, 95% CI: 1.52, 8.04). CONCLUSIONS: In this smartphone app-based repeated measures study, short-term and relatively long-term use of EC increased the risk of respiratory symptoms.

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BACKGROUND: Epidemiological studies on heavy metal exposure and liver injury are predominantly cross-sectional, lacking longitudinal data and exploration of potential mechanisms. METHOD: We conducted a repeated-measures study in Northeast China from 2016 to 2019, involving 322 participants. Linear mixed models (LMM) and Bayesian kernel machine regression (BKMR) were employed to explore the associations between individual and mixed blood metal concentrations [chromium (Cr), cadmium (Cd), vanadium (V), manganese (Mn), lead (Pb)] and liver function biomarkers [alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), globulin (GLB), total protein (TP)]. Mediation and enrichment analyses were used to determine whether the inflammatory response is a critical pathway for heavy metal-induced liver damage. RESULT: We obtained a total of 958 observations. The results from LMM and BKMR indicated significant associations between individual and mixed heavy metals and liver function biomarkers. Longitudinal analysis revealed associations between Cd and the annual increase rate of ALT (ß = 2.61; 95% CI: 0.97, 4.26), the annual decrease rate of ALB (ß = -0.21; 95% CI: -0.39, -0.03), Mn and the annual increase rate of GLB (ß = 0.38; 95% CI: 0.05, 0.72), and V and the annual decrease rate of ALB/GLB (ß = -1.15; 95% CI: -2.00, -0.31). Mediation analysis showed that high-sensitivity C-reactive protein (hsCRP) mediated the associations between Cd and AST, TP, with mediation effects of 27.7% and 13.4%, respectively. Additionally, results from Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses supported the role of inflammatory response pathways. CONCLUSION: Our findings indicate that heavy metal exposure leads to liver damage, with the inflammatory response potentially serving as a crucial pathway in this process. This study offers a novel perspective on understanding heavy metal-induced liver injury and provides insights for preventive measures against the health damage caused by heavy metals.

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INTRODUCTION: Ongoing improvement to residential treatment for substance use disorders is critical as it typically targets people with the highest need. Assessing multiple recovery indicators, such as cravings and mental health, at intake and following discharge is important in evaluating treatment effectiveness. To refine services, research should explore whether there are subgroups of individuals with different patterns of recovery following treatment. METHODS: Participants (n = 554) were attending Australian Salvation Army residential treatment services for substance use issues. Data were collected by surveys at intake and 3-month post-discharge ('early recovery'). Recovery indicators were cravings, confidence to resist substance use and the Depression, Anxiety and Stress Scale. Subgroups of individuals based on these recovery indicators ('profiles') were identified using repeated measures latent profile analysis. RESULTS: Five profiles were identified, three profiles improved over time (81.4%) and two (18.6%) deteriorated across all indicators. These two profiles had the poorest mental health and addiction scores at intake and reported shorter time in treatment compared to the three profiles showing improvement. There were no demographic or substance type differences between profiles. DISCUSSION AND CONCLUSIONS: By considering initial severity and multiple recovery indicators at early recovery, this study suggests that individuals at-risk of poor early recovery can be identified at intake. This opens opportunities for tailored treatment approaches to address both mental health and substance use, thereby potentially improving treatment outcomes and reducing the risk of relapse.

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INTRODUCTION: Estimating treatment effects as time savings in disease progression may be more easily interpretable than assessing the absolute difference or a percentage reduction. In this study, we investigate the statistical considerations of the existing method for estimating time savings and propose alternative complementary methods. METHODS: We propose five alternative methods to estimate the time savings from different perspectives. These methods are applied to simulated clinical trial data that mimic or modify the Clinical Dementia Rating Sum of Boxes progression trajectories observed in the Clarity AD lecanemab trial. RESULTS: Our study demonstrates that the proposed methods can generate more precise estimates by considering two crucial factors: (1) the absolute difference between treatment arms, and (2) the observed progression rate in the treatment arm. DISCUSSION: Quantifying treatment effects as time savings in disease progression offers distinct advantages. To provide comprehensive estimations, it is important to use various methods. HIGHLIGHTS: We explore the statistical considerations of the current method for estimating time savings. We proposed alternative methods that provide time savings estimations based on the observed absolute differences. By using various methods, a more comprehensive estimation of time savings can be achieved.

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Blueberry (Vaccinium spp.) is among the most-consumed soft fruit and has been recognized as an important source of health-promoting compounds. Highly perishable and susceptible to rapid spoilage due to fruit softening and decay during postharvest storage, modern breeding programs are looking to maximize the quality and extend the market life of fresh blueberries. However, it is uncertain how genetically controlled postharvest quality traits are in blueberries. This study aimed to investigate the prediction ability and the genetic basis of the main fruit quality traits affected during blueberry postharvest to create breeding strategies for developing cultivars with an extended shelf life. To achieve this goal, we carried out target genotyping in a breeding population of 588 individuals and evaluated several fruit quality traits after 1 day, 1 week, 3 weeks, and 7 weeks of postharvest storage at 1°C. Using longitudinal genome-based methods, we estimated genetic parameters and predicted unobserved phenotypes. Our results showed large diversity, moderate heritability, and consistent predictive accuracies along the postharvest storage for most of the traits. Regarding the fruit quality, firmness showed the largest variation during postharvest storage, with a surprising number of genotypes maintaining or increasing their firmness, even after 7 weeks of cold storage. Our results suggest that we can effectively improve the blueberry postharvest quality through breeding and use genomic prediction to maximize the genetic gains in the long term. We also emphasize the potential of using longitudinal genomic prediction models to predict the fruit quality at extended postharvest periods by integrating known phenotypic data from harvest.

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Purpose: The primary aim of this study was to explore whether intravoxel incoherent motion (IVIM) can offer a contrast-agent-free alternative to dynamic contrast-enhanced (DCE)-MRI for measuring breast tumor perfusion. The secondary aim was to investigate the relationship between tissue diffusion measures from DWI and DCE-MRI measures of the tissue interstitial and extracellular volume fractions. Materials and methods: A total of 108 paired DWI and DCE-MRI scans were acquired at 1.5 T from 40 patients with primary breast cancer (median age: 44.5 years) before and during neoadjuvant chemotherapy (NACT). DWI parameters included apparent diffusion coefficient (ADC), tissue diffusion (Dt), pseudo-diffusion coefficient (Dp), perfused fraction (f), and the product f×Dp (microvascular blood flow). DCE-MRI parameters included blood flow (Fb), blood volume fraction (vb), interstitial volume fraction (ve) and extracellular volume fraction (vd). All were extracted from three tumor regions of interest (whole-tumor, ADC cold-spot, and DCE-MRI hot-spot) at three MRI visits: pre-treatment, after one, and three cycles of NACT. Spearman's rank correlation was used for assessing between-subject correlations (r), while repeated measures correlation was employed to assess within-subject correlations (rrm) across visits between DWI and DCE-MRI parameters in each region. Results: No statistically significant between-subject or within-subject correlation was found between the perfusion parameters estimated by IVIM and DCE-MRI (f versus vb and f×Dp versus Fb; P=0.07-0.81). Significant moderate positive between-subject and within-subject correlations were observed between ADC and ve (r=0.461, rrm=0.597) and between Dt and ve (r=0.405, rrm=0.514) as well as moderate positive within-subject correlations between ADC and vd and between Dt and vd (rrm=0.619 and 0.564, respectively) in the whole-tumor region. Conclusion: No correlations were observed between the perfusion parameters estimated by IVIM and DCE-MRI. This may be attributed to imprecise estimates of fxDp and vb, or an underlying difference in what IVIM and DCE-MRI measure. Care should be taken when interpreting the IVIM parameters (f and f×Dp) as surrogates for those measured using DCE-MRI. However, the moderate positive correlations found between ADC and Dt and the DCE-MRI parameters ve and vd confirms the expectation that as the interstitial and extracellular volume fractions increase, water diffusion increases.

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In 203 (mean age = 38.04 years, SD=12.05) participants, we tested the association between valenced news and affect using a 14-day, smartphone-based ecological momentary assessment protocol consisting of two components: 1) a once-per-day experimental protocol in which participants were exposed to good news and bad news stories and 2) a four-times-per-day protocol capturing ecological fluctuations in news consumption. Across both protocols, we replicate findings that consumption of positively valenced news is associated with increased positive affect and decreased negative affect while consumption of negatively valenced news is associated with increased negative affect and decreased positive affect. By integrating the ecological momentary assessment data with network science methodologies, news selection and news effects were modeled simultaneously, uncovering selection processes whereby current positive affect, but not negative affect, predicted future valenced news consumption. Altogether, findings indicate that everyday news consumption influences positive and negative affect and may serve mood management functions for positive but not negative affect.

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Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches-linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.

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Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed-effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure.

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Rationale and objectives: Postoperative pelvic radiographs remain a vital tool for assessing cup orientation after total hip arthroplasty (THA), with the accuracy influenced by various factors. The objective of this study is to investigate the accuracy of cup anteversion measurement in developmental dysplasia of the hip (DDH) patients and others based on postoperative pelvic radiographs conducted under the current heavy workload conditions. Materials and methods: Patients who underwent THA at our hospital with both postoperative X-ray and CT images from January 2020 to December 2022 were included in this retrospective cohort study. Virtual X-ray films were generated using digitally reconstructed radiographs (DRR) technology from CT images, with pelvic position perfectly controlled. Radiographic anteversion (RA) was measured on 3D-CT, virtual X-rays, and actual postoperative X-rays, abbreviated as RA_3D, RA_DRR, and RA_Xray, respectively. A repeated-measures analysis of covariance (ANCOVA) was utilized to evaluate the variations in RA within and between different groups across three methods. The Bland-Altman plot analysis showed the variations among methods in DDH and non-DDH patients, setting a clinically acceptable limits of agreement (LOA) at ±5°. Results: This study included 154 hip cases, with 63 DDH and 91 other diseases. Repeated-measures ANCOVA revealed a descending trend in RA across three methods, with differences of 2.64° (DDH) vs. 2.74° (others) from 3D to DRR, and 4.89° (DDH) vs. 1.07° (others) from DRR to X-ray. The group by methods interaction effect were significant (p = 0.002). Significant statistical differences in RA_Xray (P = 0.035) were observed between DDH and non-DDH patients, but not in RA_3D and RA_DRR. Bland-Altman plots showed 71.4 % of DDH patients exceeded the clinically acceptable LOA, compared to 36.3 % of other patients. Conclusion: Our study indicated that under the current intense workload, the reliability of assessing cup anteversion using postoperative pelvic radiographs is challenged, especially in patients with DDH.

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BACKGROUND: Epidemiological studies have reported associations between heavy metals and renal function. However, longitudinal studies are required to further validate these associations and explore the interactive effects of heavy metals on renal function and their directional influence. METHOD: This study, conducted in Northeast China from 2016 to 2021, included a four-time repeated measures design involving 384 participants (1536 observations). Urinary concentrations of chromium (Cr), cadmium (Cd), manganese (Mn), and lead (Pb) were measured, along with renal biomarkers including urinary microalbumin (umAlb), urinary albumin-to-creatinine ratio (UACR), N-acetyl-ß-D-glucosaminidase (NAG), and ß2-microglobulin (ß2-MG) levels. Estimated glomerular filtration rate (eGFR) was calculated. A Linear Mixed Effects Model (LME) examined the association between individual metal exposure and renal biomarkers. Subsequently, Quantile g-computation and Bayesian Kernel Machine Regression (BKMR) models assessed the overall effects of heavy metal mixtures. Marginal Effect models examined the directional impact of metal interactions in the BKMR on renal function. RESULT: Results indicate significant impacts of individual and combined exposures of Cr, Cd, Pb, and Mn on renal biomarkers. Metal interactions in the BKMR model were observed, with synergistic effects of Cd-Cr on NAG, umAlb, UACR; Cd-Pb on NAG, UACR; Pb-Cr on umAlb, UACR, eGFR-MDRD, eGFR-EPI; and an antagonistic effect of Mn-Pb-Cr on UACR. CONCLUSION: Both individual and combined exposures to heavy metals are associated with renal biomarkers, with significant synergistic interactions leading to renal damage. Our findings elucidate potential interactions among these metals, offering valuable insights into the mechanisms linking multiple metal exposures to renal injury.

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Background  The effectiveness of interventions based on applied behavior analysis (ABA) for individuals with autism has been well documented in numerous meta-analyses, systematic reviews, and cost-benefit analyses. However, an observed 'efficacy-effectiveness gap' exists, which can be attributed to various factors. This third replication study, therefore, has significant implications for the field. By assessing the impact of ABA treatment, specifically involving discrete trial training and mass trials, within a naturalistic environment, the study provides valuable insights that can inform and improve the delivery of ABA treatments in real-world settings. Methods  The study was conducted using a repeated measures research design. Retrospective chart review data were collected from 62 individuals with autism, age (M=8.65, SD=4.53), all of whom were level two autistic and required moderate support in communication, socialization, and daily life. These individuals received ABA treatment over five months. The study measured cumulative target behaviors using a repeated measures design, which allowed for the identification of statistically significant differences across 12 time points. This robust methodology ensures the validity and reliability of the study's findings. Results  Mixed repeated measures analysis of variance (ANOVA) indicated statistical significance (sphericity assumed), F(11,495) = 55.432, p < 0.001 (time). Multiple comparisons using bootstrapped paired t-tests showed p < 0.05 on time points 1-8 and non-significance (p > 0.05) on time points 9-12. There was a significant interaction effect (sphericity assumed) with time x (age category), F(44,495) = 2.338, p < 0.001. Interaction contrasts indicated statistically significant differences over time, mainly within the one-year to four-year-old, five to eight-year-old, and most in the nine to 12-year-old age groups. There was some significance within the 13- to 16-year-old age group and no significance within the 17- to 26-year-old age group. Conclusions  Over five months, individuals with autism who underwent ABA treatments demonstrated a statistically significant enhancement in general target behaviors. This finding is crucial as it underscores the effectiveness of ABA treatments in a naturalistic environment. Moreover, the study's discovery of a significant interaction between time and age in these behaviors provides valuable insights into the impact of age on treatment outcomes. Extensive large-N studies of general ABA broad effectiveness and repeated measures designs are lacking and can lead to further research to improve quality and outcomes. These findings contribute to the body of empirical evidence and emphasize the importance of replicative efficacy studies in ensuring the reliability of research findings.

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BACKGROUND: Intensive care unit (ICU) patients are at risk of suffering from posttraumatic stress symptoms (PTSS) after ICU survival. OBJECTIVES: To describe the prevalence of high levels of PTSS the first year after ICU admission. Further, to identify specific combinations of patient characteristics (latent classes based on pre-ICU data, demographics, and clinical characteristics), and to investigate possible associations among these classes and PTSS at 3, 6, and 12 months after ICU admission. METHODS: Self-reported PTSS were measured with Impact of Event Scale-Revised (IES-R). PTSS and possible predictive factors (pre-ICU data, demographics, and clinical characteristics) were analyzed using descriptive statistics, latent class analysis, and linear mixed model for repeated measures. RESULTS: High PTSS levels (IES-R ≥ 33) were reported by 14.9 % (95 % confidence interval [CI] [10.0; 21.1]), 16.7 % (95 % CI [11.5; 23.1]), and 18.4 % (95 % CI [12.9; 25.0]) of patients (sample 1, n = 174) at 3, 6, and 12 months, respectively. Three latent classes were identified (sample 2, n = 417). PTSS were significantly associated with class 2 (male with longer hospital stay) at 6 months and class 3 (age≥70, lower level of education, higher Simplified Acute Physiology Score, being mechanically ventilated) at all three measurement times. CONCLUSIONS: The prevalence of high levels of PTSS is the greatest 12 months after ICU admission. Health professionals can use this information to be aware of specific groups of ICU patients reporting PTSS during the first year and follow up on these.

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