Introspection on memory states guides decision-making, but little is known about how it emerges in childhood. Toddlers' behavioral responses to difficult memory decisions (e.g., information seeking) suggest early capacity to track uncertain situations, but it is unclear whether these behaviors relate to later emerging capacity to introspect on memory accuracy (i.e., metamemory monitoring). In a pre-registered longitudinal study, 176 25- to 34-month-olds encode images, then are asked to select the familiar image from arrays that also include a new image (Time 1). One year later (Time 2), 157 participants complete a similar memory task and report decision confidence. Higher gaze transitions between responses, indicative of evaluation processes, faster response latencies, and greater memory at Time 1 predict Time 2 metamemory monitoring (i.e., greater confidence for accurate than inaccurate decisions). At Time 2, gaze transitions are associated with lower overall confidence. Overall, this research reveals potential building blocks of emerging metamemory monitoring.

Accurate quantification of head acceleration event (HAE) exposure is critical for investigating brain injury risk in contact sports athletes. However, missing HAEs may be unavoidable in real-world data collection. This study introduces missing data imputation methods to estimate complete video- and sensor-based HAE exposure.

We captured and verified university men's ice hockey HAEs using video and instrumented mouthguards (iMGs) in one varsity season (nathletes = 27, ngames = 31). A statistical mapping technique was first introduced to impute missing video-based HAEs during away games with limited camera angles. We then applied multiple imputation to impute missing iMG-based HAEs using captured data, including the complete video-based HAE exposure. This enabled estimation of complete exposure data at a per-athlete level over all games of the season.

Among 591 athlete-games, 45% did not have any recorded iMG data. We find that data imputation increased the median values of per-athlete-season video- and iMG-based HAE counts by 10% and 69%, respectively. Consequently, common head kinematics- and brain deformation-based cumulative exposure metrics also increased substantially (median per-athlete-season cumulative peak linear acceleration by 95%, peak angular acceleration by 109%, and corpus callosum strain by 69%).

This study highlights the potential underestimation of exposure metrics due to missing HAEs and fills a critical gap in sports HAE exposure research. Future studies should incorporate missing data imputation methods for more accurate estimation of HAE exposure in investigating acute and long-term brain trauma risks.

Untargeted metabolomics often produce large datasets with missing values. These missing values are derived from biological or technical factors and can undermine statistical analyses and lead to biased biological interpretations. Imputation methods, such as k-Nearest Neighbors (kNN) and Random Forest (RF) regression, are commonly used, but their effects vary depending on the type of missing data, e.g., Missing Completely At Random (MCAR) and Missing Not At Random (MNAR). Here, we determined the impacts of degree and type of missing data on the accuracy of kNN and RF imputation using two datasets: a targeted metabolomic dataset with spiked-in standards and an untargeted metabolomic dataset. We also assessed the effect of compositional data approaches (CoDA), such as the centered log-ratio (CLR) transform, on data interpretation since these methods are increasingly being used in metabolomics. Overall, we found that kNN and RF performed more accurately when the proportion of missing data across samples for a metabolic feature was low. However, these imputations could not handle MNAR data and generated wildly inflated or imputed values where none should exist. Furthermore, we show that the proportion of missing values had a strong impact on the accuracy of imputation, which affected the interpretation of the results. Our results suggest imputation should be used with extreme caution even with modest levels of missing data and especially when the type of missingness is unknown.

Inflammation plays a role in the pathogenesis of cerebral infarction. Postoperative symptomatic cerebral infarction (SCI) is a complication after revascularization surgery in patients with moyamoya disease (MMD). We investigated the association between the systemic-immune-inflammation index (SII) and postoperative SCI during hospital stay in such patients.

Perioperative data were retrospectively obtained from 681 MMD patients who underwent revascularization surgery. SII cutoff values were identified as those where the sum of sensitivity and specificity associated with SCI were highest. Patients were divided into 4 subgroups according to the preoperative and immediate postoperative cutoff SII: HH (preoperative and postoperative SII high, n=22), LH (low preoperative and high postoperative SII, n=68), HL (high preoperative and low postoperative SII, n=125), and LL (preoperative and postoperative SII low, n=466).

Postoperative SCI occurred in 54 (7.6%) patients. The cutoff values for preoperative and immediate postoperative SII were 641.3 and 1925.4, respectively. Postoperative SCI during hospital stay was more frequent in the high postoperative SII group than in the low postoperative SII group (25.6% vs. 4.9%; P <0.001). Multivariate analysis revealed that a high immediate postoperative SII was a predictor of postoperative SCI (odds ratio, 11.61; 95% CI: 5.20-26.00; P <0.001). Postoperative SCI was lower in group LL than in group LH (3.6% vs. 23.5%, P <0.008) and was lower in group HL than in groups HH and LH (9.6% vs. 31.8% and 23.5%, both P <0.05).

A high immediate postoperative SII was associated with postoperative SCI during hospital stay in MMD patients who underwent revascularization surgery.

The relationship between fasting blood glucose and urea in non-diabetic individuals is still unclear. This study aimed to evaluate the association between fasting blood glucose and urea in a non-diabetic population.

Data from a cohort of non-diabetic individuals were collected from the 2009 China Health and Nutrition Survey dataset. We performed smooth curve and two piecewise linear regression analyses to assess the association between fasting blood glucose and urea in this non-diabetic population.

Data from a total of 7,596 adult participants without diabetes were included in this study; the mean age of the participants was 50.2 years, and 46.4% were male. There was an L-shaped relationship between fasting blood glucose and urea, and the inflection point of fasting blood glucose was 4.6 mmol/L. After adjusting for potential confounders, we found a negative correlation between fasting blood glucose and urea up to the inflection point (β = -0.3, 95% CI -0.5 to -0.2, P < 0.001), but beyond the inflection point, this relationship disappeared (β = 0.0, 95% CI -0.1 to 0.1 P = 0.848). In the group with lower fasting blood glucose (fasting blood glucose <4.6 mmol/L), smoking (interaction P = 0.037) and alcohol consumption (interaction P = 0.001) influenced the relationship between fasting blood glucose and urea.

The results suggest that lower fasting blood glucose was associated with higher urea in non-diabetic individuals with fasting blood glucose <4.6 mmol/L, revealing an L-shaped association between fasting blood glucose and urea.

Missing data in electronic health records are highly prevalent and result in analytical concerns such as heterogeneous sources of bias and loss of statistical power. One simple analytic method for addressing missing or unknown covariate values is to treat missingness for a particular variable as a category onto itself, which we refer to as the missing indicator method. For cross-sectional analyses, recent work suggested that there was minimal benefit to the missing indicator method; however, it is unclear how this approach performs in the setting of longitudinal data, in which correlation among clustered repeated measures may be leveraged for potentially improved model performance.

This study aims to conduct a simulation study to evaluate whether the missing indicator method improved model performance and imputation accuracy for longitudinal data mimicking an application of developing a clinical prediction model for falls in older adults based on electronic health record data.

We simulated a longitudinal binary outcome using mixed effects logistic regression that emulated a falls assessment at annual follow-up visits. Using multivariate imputation by chained equations, we simulated time-invariant predictors such as sex and medical history, as well as dynamic predictors such as physical function, BMI, and medication use. We induced missing data in predictors under scenarios that had both random (missing at random) and dependent missingness (missing not at random). We evaluated aggregate performance using the area under the receiver operating characteristic curve (AUROC) for models with and with no missing indicators as predictors, as well as complete case analysis, across simulation replicates. We evaluated imputation quality using normalized root-mean-square error for continuous variables and percent falsely classified for categorical variables.

Independent of the mechanism used to simulate missing data (missing at random or missing not at random), overall model performance via AUROC was similar regardless of whether missing indicators were included in the model. The root-mean-square error and percent falsely classified measures were similar for models including missing indicators versus those with no missing indicators. Model performance and imputation quality were similar regardless of whether the outcome was related to missingness. Imputation with or with no missing indicators had similar mean values of AUROC compared with complete case analysis, although complete case analysis had the largest range of values.

The results of this study suggest that the inclusion of missing indicators in longitudinal data modeling neither improves nor worsens overall performance or imputation accuracy. Future research is needed to address whether the inclusion of missing indicators is useful in prediction modeling with longitudinal data in different settings, such as high dimensional data analysis.

Developing and validating a simple and clinically useful dynamic nomogram for predicting early acute kidney injury (AKI) in patients with acute heart failure (AHF) admitted to the intensive care unit (ICU).

Clinical data from patients with AHF were obtained from the Medical Information Mart for Intensive Care IV database. The patients with AHF were randomly allocated into derivation and validation sets. The independent predictors for AKI development in AHF patients were identified through least absolute shrinkage and selection operator and multivariate logistic regression analyses. A nomogram was developed based on the results of the multivariable logistic regression to predict early AKI onset in AHF patients, which was subsequently implemented as a web-based calculator for clinical application. An evaluation of the nomogram was conducted using discrimination, calibration curves, and decision curve analyses (DCA).

After strict screening, 1,338 patients with AHF were included in the derivation set, and 3,129 in the validation set. Sepsis, use of human albumin, age, mechanical ventilation, aminoglycoside administration, and serum creatinine levels were identified as predictive factors for AKI in patients with AHF. The discrimination of the nomogram in both the derivation and validation sets was 0.81 (95% confidence interval: 0.78-0.83) and 0.79 (95% confidence interval: 0.76-0.83). Additionally, the calibration curve demonstrated that the predicted outcomes aligned well with the actual observations. Ultimately, the DCA curves indicated that the nomogram exhibited favorable clinical applicability.

The nomogram that integrates clinical risk factors and enables the personalized prediction of AKI in patients with AHF upon admission to the ICU, which has the potential to assist in identifying AHF patients who would derive the greatest benefit from interventions aimed at preventing and treating AKI.

Optimizing school physical education (PE) schedules is crucial for enhancing public health outcomes, particularly among school-aged children.

Therefore, in this study, a weighted fitness function is developed to evaluate health fitness scores. This function integrates multiple health metrics such as BMI reduction, fitness improvement, calories burned, and heart rate reduction. Six optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), Differential Evolution (DE), and Artificial Bee Colony (ABC) optimization algorithms are utilized to optimize PE schedules based on the designed weighted fitness function. Using a dataset of 1,360 student entries, the study incorporates health metrics such as BMI reduction, fitness score improvement, caloric expenditure, and heart rate reduction into a weighted fitness function for optimization.

The results show that ACO achieved the highest allocation of PE time (9.91 h/week), the most significant caloric expenditure (370 kcal/session), and the greatest reduction in heart rate (8.5 bpm). GA excelled in the reduction of BMI, achieving a decrease of 10.63 units.

These analyses reveal the transformative potential of optimized PE schedules in reducing the burden of lifestyle-related diseases, promoting equitable health outcomes, and supporting cognitive and mental well-being. Finally, recommendations are provided for policymakers and stakeholders to implement data-driven PE programs that maximize long-term public health benefits.

Accurate time series forecasts are crucial for various applications, such as traffic management, electricity consumption, and healthcare. However, limitations in models and data quality can significantly impact forecasts' accuracy. One common issue with data quality is the absence of data points, referred to as missing data values. It is often caused by sensor malfunctions, equipment failures, or human errors. This paper proposes Hinge-FM2I, a novel method for handling missing data values in univariate time series data. Hinge-FM2I builds upon the strengths of the Forecasting Method by Image Inpainting (FM2I). FM2I has proven effective, but selecting the most accurate forecasts remains a challenge. To overcome this issue, we proposed a selection algorithm. Inspired by door hinges, Hinge-FM2I drops a data point either before or after the gap (left/right-hinge), then uses FM2I for imputation. In fact, it selects the imputed gap based on the lowest error of the dropped data point. Hinge-FM2I was evaluated on a comprehensive sample composed of 1356 time series. These latter are extracted from the M3 competition benchmark dataset, with missing value rates ranging from 3.57 to 28.57%. Experimental results demonstrate that Hinge-FM2I significantly outperforms established methods such as linear/spline interpolation, K-Nearest Neighbors, and ARIMA. Notably, Hinge-FM2I achieves an average Symmetric Mean Absolute Percentage Error score of 5.6% for small gaps and up to 10% for larger ones. These findings highlight the effectiveness of Hinge-FM2I as a promising new method for addressing missing values in univariate time series data.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder, and critically ill patients with T2DM in intensive care unit (ICU) have an increased risk of mortality. In this study, we investigated the relationship between nine inflammatory indicators and prognosis in critically ill patients with T2DM to provide a clinical reference for assessing the prognosis of patients admitted to the ICU. Critically ill patients with T2DM were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided into training and testing sets (7:3 ratio). An external validation cohort was collected from a single center in China using identical criteria. Logistic and Cox regression analyses were used to evaluate the relationship between nine inflammatory indicators and ICU, 30-day, and 90-day mortality rates. Significant predictive variables were chosen using least absolute shrinkage selection operator (LASSO) regression from logistic regression results, and a prognostic prediction model was built with multivariate logistic regression. The model was validated in both test and external validation sets. A total of 4,783 patients were included for model development and testing; an additional 204 served as the external validation set. The levels of eight inflammatory indicators were significantly correlated with short-term prognosis in critically ill patients with T2DM (P < 0.05 for all). The prediction model showed excellent discrimination performance, with AUC values of 0.825 (95% CI, 0.785-0.864) in the test set and 0.741 (95% CI, 0.630-0.851) in the external validation set. Calibration curves demonstrated strong consistency in both sets. In addition, decision curve analysis showed a net clinical benefit within 1-60% threshold probability in the test set and 10-41% threshold probability in the external validation set. Eight inflammatory indicators were identified as independent risk factors for prognosis in critically ill patients with T2DM. The prediction model showed promising performance in both internal and external validation cohorts, highlighting its potential as a valuable tool for early risk stratification and prediction of the outcomes of personalized treatment strategies in ICU settings.

Alzheimer's disease (AD) is a widespread neurodegenerative disease, often accompanied by multiple comorbidities, significantly increasing the risk of death for patients. The age adjusted Charlson Comorbidity Index (aCCI) is an important clinical tool for measuring the burden of comorbidities in patients, closely related to mortality and prognosis. This study aims to use the MIMIC-V database and various regression and machine learning models to screen and validate features closely related to aCCI, providing a theoretical basis for personalized management of AD patients.

The research data is sourced from the MIMIC-V database, which contains detailed clinical information of AD patients. Multiple logistic regression, LASSO regression, random forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) models were used to screen for feature factors significantly correlated with aCCI. By comparing model performance, evaluating the classification ability and prediction accuracy of each method, and ultimately selecting the best model to construct a regression model and a nomogram. The model performance is evaluated through classification accuracy, net benefit, and robustness. The feature selection results were validated by regression analysis.

Multiple models have performed well in classifying aCCI patients, among which the model constructed using LASSO regression screening feature factors has the best performance, with the highest classification accuracy and net benefit. LASSO regression identified the following 11 features closely related to aCCI: age, respiratory rate, base excess, glucose, red blood cell distribution width (RDW), alkaline phosphatase (ALP), whole blood potassium, hematocrit (HCT), phosphate, creatinine, and mean corpuscular hemoglobin (MCH). The column chart constructed based on these feature factors enables intuitive prediction of patients with high aCCI probability, providing a convenient clinical tool.

The results of this study indicate that the features screened by LASSO regression have the best predictive performance and can significantly improve the predictive ability of aCCI related comorbidities in AD patients. The column chart constructed based on this feature factor provides theoretical guidance for personalized management and precise treatment of AD patients.

This study aims to determine whether modified Hospital Frailty Risk Score (mHFRS) can identify frail hospitalised older adults by comparing mHFRS to HFRS and Clinical Frailty Scale (CFS).

A retrospective review was undertaken in patients =>65 years hospitalised following an Emergency Department attendance between 1st July 2022 and 31st March 2023. Predictive models were evaluated with correlation and measure of agreement between frailty risk scores, CFS and HFRS, CFS and modified HFRS (mHFRS) using the Spearman's rank correlation and Cohen's kappa (κ).

Of 3042 patients, CFS categorised 1635 (53.7%) patients as non-frail (CFS 1-4) and 1407 (46.3%) as frail (CFS 5-9,p<0.001). Frail patients were more likely to be female (55.9%), older (81.8 years, SD 8.41 vs 75.3 years, SD 7.20, p<0.001), with longer LOS (52.5% % vs 31.5%, p<0.001), higher 30-day emergency re-admission (18.5% vs 9.9%, p<0.001) and higher mortality at all time points. We could compute mHFRS for 1623 (53.4%) patients, of whom, 37.5% were low risk, 40.5% intermediate risk and 22.1% high frailty risk. mHFRS showed significant correlation with CFS (p<0.001) and HFRS (p<0.001), respectively and achieved comparable association with clinical outcomes.

mHFRS was better at identifying non-frail patients and provides a novel, standardised and comparable frailty risk stratification tool for screening older hospitalised patients.

Ambient fine particulate matter (PM2.5) is a risk factor for atherosclerosis disease. We aimed to assess whether nitric oxide stable metabolites (NOx) and l-arginine mediate the association between PM2.5 and carotid intima media thickness (cIMT) increase.

We selected 251 participants from the control group of GEA (Genetics of Atheroslerosis Disease Mexican) study (2008-2013) in Mexico City. Mediation models were carried out using pathway analyses, a special case of structural equation models.

The median concentration of PM2.5 area under the curve (auc) was 25.2 µg/m3 (interquartile range: 24.2-26.4 µg/m3). Employing participants with observed values for both biomarkers (n = 117), the total effect of PM2.5auc on mean cIMT at bilateral, right, and left was 19.27 µm (95% confidence interval [CI]: 5.77, 32.78; P value = 0.005), 12.69 µm (95% CI: 0.67, 24.71; P value = 0.039), and 25.86 µm (95% CI: 3.18, 48.53; P value = 0.025) per each 1 µg/m3 increase of PM2.5auc. The direct effect of PM2.5auc (per 1 µg/m3 increase) was 18.89 µm (95% CI: 5.37, 32.41; P value = 0.006) for bilateral, 13.65 µm (95% CI: 0.76, 26.55; P value = 0.038) for right, and 24.13 µm (95% CI: 3.22, 45.03; P value = 0.024) for left. The indirect effects of NOx and l-arginine were not statistically significant showing that endothelial function biomarkers did not mediate PM2.5 and cIMT associations. Although l-arginine was not a mediator in the PM2.5 and cIMT pathway, a decrease in l-arginine was significantly associated with PM2.5auc.

In this study of adults from Mexico City, we found that PM2.5 was associated with an increase in cIMT at bilateral, left, and right, and these associations were not mediated by endothelial function biomarkers (l-arginine and NOx).

Extreme temperatures and rainfall influence crop yields, soil health, and natural ecosystems. This study examined the extent of extreme agro-climatic factors in Northwestern Ethiopia, with a focus on identifying vulnerability hotspots. Rainfall and temperature data from 1982 to 2022 were collected from eight meteorological stations of the Ethiopian Meteorological Institute, and missing values and outliers were corrected using imputation and Z-scores. ClimPact2 software extracted agro-climatic indicators, and trend analyses were performed using the Mann-Kendall test and Sen's slope. Consecutive dry days (CDD) ranged from 27 in Fincha'a to 57 in Obora, with Obora showing an annual increase of 2.033 days. Consecutive wet days (CWD) varied from 12 in Obora to 138 in Fincha'a. A positive trend in the warmest maximum temperatures (TXx) and a negative trend in the cold night index (TN10P) were observed. The Amuru District recorded the highest vulnerability index at 61, with most districts ranging from 42 to 60. These variations may significantly affect agriculture and water management in the region, necessitating the adoption of heat-tolerant crops and improved irrigation practices to enhance climate resilience.

Soil plays a critical role in nutrient availability, microbial activity, and fertility in agriculture. However, the effects of agroclimatic conditions on soil pH are not well understood, particularly in the Horro Guduru Zone of Ethiopia. This study aimed to investigate the soil pH under extremely wet and dry conditions across 3 shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Baseline agroclimatic data (1981-2010) and future projections (2041-2070) were obtained from the European Commission Climate Change Services. Soil pH data at a 250 m resolution were extracted from the FAO-UNESCO global soil map. Missing values, multicollinearity, and outliers were addressed before modeling. Predictive models, including neural networks, generalized regression, and bootstrap forests, were validated, with the generalized regression model showing the best performance. The results indicate that soil pH decreases under consecutive dry‒wet conditions and increases with increasing maximum day temperatures across all scenarios. Soil pH is significantly influenced by the number of consecutive dry days, consecutive wet days, and maximum day temperature. The SSP1-2.6 and SSP2-4.5 scenarios resulted in improved pH levels, whereas SSP5-8.5 led to a decrease in soil pH, averaging 5.79 and decreasing to 5.54. These findings suggest that under SSP5-8.5, soil health and farming productivity may be compromised. This study emphasizes the need to adjust soil management practices based on prevailing climatic conditions to ensure soil health and agricultural sustainability.

The objective of this study is to develop and validate an effective prognostic nomogram for predicting the short-term survival rate of patients with acute heart failure (AHF) complicated by acute kidney injury (AKI) who are admitted to the intensive care unit (ICU).

We conducted an analysis of data from patients of AHF with AKI spanning the period from 2008 to 2019, utilizing the MIMIC-IV database. Patients were randomly divided into training and validation sets. The training set employed the least absolute shrinkage and selection operator regression model to identify predictors of AKI. Subsequently, a dynamic nomogram was constructed using multivariate Cox regression analysis within the training set and was subsequently validated using the validation set. The nomogram's predictive accuracy, calibration, and clinical utility were evaluated through the concordance index (C-index), calibration plots, and decision curve analysis (DCA).

A total of 978 AHF patients with AKI were analyzed. Multivariate analysis identified serum creatinine, race, age, use of human albumin, use of vasoactive drug, and hemoglobin as independent predictors significantly influencing the short-term prognosis of AHF patients with AKI upon ICU admission. The C-index for the training and validation sets were 0.81 (95%CI: 0.74-0.87) and 0.80 (95 % CI: 0.67-0.92), respectively. The calibration plot of the nomogram demonstrated a close alignment between predicted and observed probabilities. Furthermore, the DCA confirmed the clinical utility of the nomogram.

This study presents a dynamic nomogram that incorporates clinical risk factors and can be conveniently utilized to predict short-term prognosis for AHF patients with AKI upon ICU admission.

Primary-secondary school transitions are critical transitions for children that can be emotionally demanding longitudinal experiences, which can positively and negatively impact future emotional wellbeing and mental health. However, interventions that have been developed to reduce the negative outcomes children commonly experience are limited in number, sustainability, and reach and rely on a cross-sectional approach, as opposed to longitudinal evaluations. The current study evaluates Transitions 5-7, a universal, class-based 9-week intervention to develop children's awareness and ability to cope with the multiple changes experienced over primary-secondary school transitions.

The evaluation utilized a mixed-methods approach, combining both quantitative outcome and qualitative process intervention evaluation. For the outcome evaluation, a quasi-experimental research design was used, and children of the intervention and comparison groups completed a questionnaire in Year 5 (n = 185), Year 6 (n = 217), and Year 7 (n = 162), which assessed their self-reported perception of Transitions Worries, Transitions Excitement, Emotional Wellbeing, Parental Support, and Coping Efficacy. To understand the implementation of Transitions 5-7, three focus groups were conducted with Year 6 children, 3 interviews with teachers, and 1 interview with the Transitions Manager of the local government education authority during the project, who developed Transitions 5-7.

The outcome evaluation found that children participating in the intervention showed a decrease in Transitions Worries and an increase in Transitions Excitement and Coping Efficacy compared with the comparison group, resulting in a lowered impact on Emotional Wellbeing over time. The need for a more systemic approach to primary-secondary school support provision, which is gradual, has a distinct delivery and follows a skills-based curriculum, was discussed in the process evaluation. Meta-inferences drawn demonstrate the importance of gradual emotional centered transitions provision embedded within Years 5, 6, and 7.

The present study makes a unique empirical contribution in demonstrating the need and viability to take a preventative as opposed to a curative approach to primary-secondary school transitions support provision and begin early in Year 5. Conceptual and methodological implications for future research and implications for educational policy and practice are discussed.

People who inject drugs (PWID) experience high rates of violence, especially in early childhood, increasing their likelihood of engaging in risky substance use behavior in adulthood. Additionally, complex trauma has been reported among PWID due to witnessing and experiencing an overdose, further highlighting the need to examine the role of multiple experiences of trauma on their vulnerability to substance misuse.

Our study of 350 PWID from rural Kentucky examined differences in polysubstance use between participants who experienced violence earlier (≤15 years old) versus later (≥16 years old) in their childhood.

Findings highlighted a direct association between experiences of early childhood violence and polysubstance use in adulthood. Additionally, our sample of PWID who experienced violence early in their childhood reported higher rates of severe substance use disorder and mental health distress.

Tailored approaches that incorporate trauma-informed care may help to address the disproportionate rates of substance use and related adverse effects among PWID.

Polycyclic aromatic hydrocarbons (PAHs) represent a class of ubiquitous pollutants recognized as established human carcinogens and endocrine-disrupting chemicals. PAHs have seldom been modeled at the population-level in epidemiological studies. Fluoranthene is a prevalent PAH in urban settings and correlates with the occurrence of other PAHs. The purpose of this study was to evaluate associations between long-term residential exposure to ambient PAHs and breast cancer risk, both pre- and post-menopausal, in Canada.

Using the National Enhanced Cancer Surveillance System (NECSS), a national-scale Canadian population-based case-control study, annual fluoranthene exposures were estimated using the GEM-MACH-PAH chemical transport model on the basis of geocoded residential histories throughout a 20-year exposure window. Odds ratios (ORs) and 95% confidence intervals (CIs) controlling for potential confounders were estimated using logistic regression. Separate analyses were conducted for Ontario and national samples given a finer-resolution exposure surface and additional risk factor information available for Ontario.

Positive associations were observed between fluoranthene exposure and premenopausal breast cancer, with inconsistent findings for postmenopausal breast cancer. For premenopausal breast cancer, adjusted ORs of 2.48 (95% CI: 1.29, 4.77) and 1.59 (95% CI: 1.11, 2.29) were observed when comparing the second highest category of exposure to the lowest, among the Ontario and national samples, respectively. For postmenopausal breast cancer, adjusted ORs were 1.10 (95% CI: 0.67, 1.80) and 1.33 (95% CI: 1.02, 1.73). Associations for the highest level of exposure, across both samples and menopausal strata, were non-significant.

This study provides support for the hypothesis that ambient PAH exposures increase the risk of premenopausal breast cancer.

In Brazil, social service employees are exposed to poor work conditions, mental strain, and there is a lack of workforce, which contributes for the accumulation of tasks and to work overload.

To describe the prevalence of socioeconomic and occupational characteristics, working conditions and self-reported health of social service employees; and verify the relationship between working conditions and the workers self-reported health.

The cross-sectional study evaluated sociodemographic and occupational characteristics, lifestyle and health information of social service employees; and used the Working Environment Assessment Protocol to assess their working conditions. The data was analyzed descriptively and by the chi-square test.

A total of 41 employees participated in this study, which corresponds to 60.3% of the total number of social service employees in the city. The majority was female (65.9%), aged 40.72 (standard deviation = 14.83), more than 8 years of schooling (82.9%), and occupying higher level functions (psychologists and social workers). Regarding health characteristics, 56.1% of participants practiced physical activities; 70.7% evaluated their own health as good or very good; and 43.9% related musculoskeletal pain. Not having a dining room; and temperature, ventilation, equipment, material resources and furniture were the most reported working conditions as inadequate. There was also an association between episodes of aggression and insecurity with self-perception of health.

The results suggest that there could be a relationship between precarious working conditions and health. Specifically, this study indicated an association between poor working conditions and negative self-perception of health, insecurity and episodes of violence at work.

In this study, we aimed to investigate the risk factors associated with in-hospital mortality in patients with cirrhosis and sepsis, establish and validate the nomogram. This retrospective study included patients diagnosed with liver cirrhosis and sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV). Models were compared by the area under the curve (AUC), integrated discriminant improvement (IDI), net reclassification index (NRI) and decision curve analysis (DCA). A total of 1,696 patients with cirrhosis and sepsis were included in the final cohort. Our final model included the following 9 variables: age, heartrate, total bilirubin (TBIL), glucose, sodium, anion gap (AG), fungal infections, mechanical ventilation, and vasopressin. The nomogram were constructed based on these variables. The AUC values of the nomograms were 0.805 (95% CI 0.776-0.833), which provided significantly higher discrimination compared to that of SOFA score [0.684 (95% CI 0.647-0.720)], MELD-Na [0.672 (95% CI 0.636-0.709)] and ABIC [0.674(95% CI 0.638-0.710)]. We established the first nomogram for predicting in-hospital mortality in patients with liver cirrhosis and sepsis based on these factors. This nomogram can performs well and facilitates clinicians to identify people at high risk of in-hospital mortality.

Acute pancreatitis (AP) has heterogeneous clinical features, and identifying clinically relevant sub-phenotypes is useful. We aimed to identify novel sub-phenotypes in hospitalized AP patients using longitudinal total serum calcium (TSC) trajectories.

AP patients had at least two TSC measurements during the first 24 h of hospitalization in the US-based critical care database (Medical Information Mart for Intensive Care-III (MIMIC-III) and MIMIC-IV were included. Group-based trajectory modeling was used to identify calcium trajectory phenotypes, and patient characteristics and treatment outcomes were compared between the phenotypes.

A total of 4518 admissions were included in the analysis. Four TSC trajectory groups were identified: "Very low TSC, slow resolvers" (n = 65; 1.4% of the cohort); "Moderately low TSC" (n = 559; 12.4%); "Stable normal-calcium" (n = 3875; 85.8%); and "Fluctuating high TSC" (n = 19; 0.4%). The "Very low TSC, slow resolvers" had the lowest initial, maximum, minimum, and mean TSC, and highest SOFA score, creatinine and glucose level. In contrast, the "Stable normal-calcium" had the fewest ICU admission, antibiotic use, intubation and renal replace treatment. In adjusted analysis, significantly higher in-hospital mortality was noted among "Very low TSC, slow resolvers" (odds ratio [OR], 7.2; 95% CI, 3.7 to 14.0), "moderately low TSC" (OR, 5.0; 95% CI, 3.8 to 6.7), and "Fluctuating high TSC" (OR, 5.6; 95% CI, 1.5 to 20.6) compared with the "Stable normal-calcium" group.

We identified four novel sub-phenotypes of patients with AP, with significant variability in clinical outcomes. Not only the absolute TSC levels but also their trajectories were significantly associated with in-hospital mortality.

Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy problem by considering five available heart disease datasets and their combined form. All potential training and testing mode combinations are systematically executed to assess discrepancies before and after applying the proposed methods. Imbalance data handling using SMOTE-Tomek, feature selection using random forest (RF), and feature extraction using principle component analysis (PCA) with a long preprocessing pipeline are used to mitigate the inter-dataset discrepancy problem. The preprocessing pipeline builds on missing value handling using RF regression, log transformation, outlier removal, normalization, and data balancing that convert the datasets to more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic regression, and multilayer perceptron are used as classifiers. Experimental results show that feature selection and classification using RF produce better results than other combination strategies in both single- and inter-dataset setups. In certain configurations of individual datasets, RF demonstrates 100% accuracy and 96% accuracy during the feature selection phase in an inter-dataset setup, exhibiting commendable precision, recall, F1 score, specificity, and AUC score. The results indicate that an effective preprocessing technique has the potential to improve the performance of the ML model without necessitating the development of intricate prediction models. Addressing inter-dataset discrepancies introduces a novel research avenue, enabling the amalgamation of identical features from various datasets to construct a comprehensive global dataset within a specific domain.

Alzheimer's disease (AD) is the most common type of dementia with millions of affected patients worldwide. Currently, there is still no cure and AD is often diagnosed long time after onset because there is no clear diagnosis. Thus, it is essential to study the physiology and pathogenesis of AD, investigating the risk factors that could be strongly connected to the disease onset. Despite AD, like other complex diseases, is the result of the combination of several factors, there is emerging agreement that environmental pollution should play a pivotal role in the causes of disease. In this work, we implemented an Artificial Intelligence model to predict AD mortality, expressed as Standardized Mortality Ratio, at Italian provincial level over 5 years. We employed a set of publicly available variables concerning pollution, health, society and economy to feed a Random Forest algorithm. Using methods based on eXplainable Artificial Intelligence (XAI) we found that air pollution (mainly O 3 and N O 2 ) contribute the most to AD mortality prediction. These results could help to shed light on the etiology of Alzheimer's disease and to confirm the urgent need to further investigate the relationship between the environment and the disease.

Frailty results from age-associated declines in physiological reserve and function and is prevalent in older people. Our aim is to examine the association of the Hospital Frailty Risk Score (HFRS) with adverse events in older patients hospitalized with community-acquired pneumonia (CAP) and hypothesise that frailty is a comparable predictor of outcomes in CAP versus traditional severity indices such as CURB-65.

Retrospective review of electronic medical records in patients ≥65 years with CAP admitted to a tertiary hospital from 1 January to 30 April 2021. Patients were identified using ICD codes for CAP and categorized as high risk (>15), intermediate risk (5-15) and low risk (<5) of frailty using the HFRS.

Of 429 patients with CAP, 53.8% male, mean age of 82.9 years, older patients (85 vs. 79.7 years, P < 0.001) were at higher risk of frailty. Using the HFRS, 47.6% were deemed at high risk, 35.9% at intermediate risk, and 16.6% at low risk of frailty. Multivariate logistic regression shows that HFRS was more strongly associated (≥7 days, OR 1.042, CI 1.017-1.069) than CURB-65 (OR 0.995, CI 0.810-1.222) with long hospital length of stay (LOS), while CURB-65 (Confusion, Urea >7mmol/L, Respiratory rate >30, Blood pressure, age => 65 years old) was more strongly associated with mortality at 30, 90 and 365 days, compared with the HFRS. Comparing the values for the area under the receiver operator characteristic curve, the HFRS was found to be a better predictor of long LOS, while CURB-65 remains a better predictor of mortality.

Patients with high risk of frailty have higher healthcare utilization and HFRS is a better predictor of long LOS than CURB-65 but CURB-65 was a better predictor of mortality. Geriatr Gerontol Int 2024; 24: 135-141.

Our objective was to explore the potential link between systemic inflammation response index (SIRI) and chronic kidney disease (CKD).

The data used in this study came from the National Health and Nutrition Examination Survey (NHANES), which gathers data between 1999 and 2020. CKD was diagnosed based on the low estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m2 or albuminuria (urinary albumin-to-creatinine ratio (ACR) of more than 30 mg/g). Using generalized additive models and weighted multivariable logistic regression, the independent relationships between SIRI and other inflammatory biomarkers (systemic immune-inflammation index (SII), monocyte/high-density lipoprotein ratio (MHR), neutrophil/high-density lipoprotein ratio (NHR), platelet/high-density lipoprotein ratio (PHR), and lymphocyte/high-density lipoprotein ratio (LHR)) with CKD, albuminuria, and low-eGFR were examined.

Among the recruited 41,089 participants, males accounted for 49.77% of the total. Low-eGFR, albuminuria, and CKD were prevalent in 8.30%, 12.16%, and 17.68% of people, respectively. SIRI and CKD were shown to be positively correlated in the study (OR = 1.24; 95% CI: 1.19, 1.30). Furthermore, a nonlinear correlation was discovered between SIRI and CKD. SIRI and CKD are both positively correlated on the two sides of the breakpoint (SIRI = 2.04). Moreover, increased SIRI levels were associated with greater prevalences of low-eGFR and albuminuria (albuminuria: OR = 1.27; 95% CI: 1.21, 1.32; low-eGFR: OR = 1.11; 95% CI: 1.05, 1.18). ROC analysis demonstrated that, compared to other inflammatory indices (SII, NHR, LHR, MHR, and PHR), SIRI exhibited superior discriminative ability and accuracy in predicting CKD, albuminuria, and low-eGFR.

When predicting CKD, albuminuria, and low-eGFR, SIRI may show up as a superior inflammatory biomarker when compared to other inflammatory biomarkers (SII, NHR, LHR, MHR, and PHR). American adults with elevated levels of SIRI, SII, NHR, MHR, and PHR should be attentive to the potential risks to their kidney health.

Introduction: There is conflicting evidence for the association between antihypertensive medications and colorectal cancer risk, possibly reflecting methodological limitations of previously conducted studies. Here, we aimed to clarify associations between commonly prescribed antihypertensive medication classes and colorectal cancer risk in a large, retrospective, cohort study. Methods: Using linked administrative data between 1996 and 2017 from British Columbia, we identified a cohort of 1,693,297 men and women who were 50 years of age or older, initially cancer-free and nonusers of antihypertensive medications. Medication use was parameterized as ever use, cumulative duration, and cumulative dose. Cox proportional hazard models were used to estimate hazard ratios (HRs) and associated 95% confidence intervals (95% CIs) for associations of time-varying medication use [angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), beta-blockers (BBs), calcium channel blockers (CCBs), and diuretics] with colorectal cancer risk. Results: There were 28,460 incident cases of colorectal cancer identified over the follow-up period (mean = 12.9 years). When medication use was assessed as ever/never, diuretics were associated with increased risk of colorectal cancer (HR 1.08, 95% CI 1.04-1.12). However, no similar association was observed with cumulative duration or cumulative dose of diuretics. No significant associations between the other four classes of medications and colorectal cancer risk were observed. Conclusion: No compelling evidence of associations between antihypertensive medications and colorectal cancer were observed.

This study was conducted to investigate the relationship between atrial fibrillation and the clinical prognosis of patients with sepsis in intensive care unit. A total of 21,538 sepsis patients were enrolled in the study based on the Medical Information Mart for Intensive Care IV database, of whom 6,759 had AF. Propensity score matching was used to compare the clinical characteristics and outcomes of patients with and without AF. Besides, the inverse probability of treatment weighting, univariate and multivariate Cox regression analyzes were performed. Of the 21,538 patients, 31.4% had AF. The prevalence of AF increased in a step-by-step manner with growing age. Patients with AF were older than those without AF. After PSM, 11,180 patients remained, comprising 5,790 matched pairs in both groups. In IPTW, AF was not associated with 28-day mortality [hazard ratio (HR), 1.07; 95% confidence interval (CI), 0.99-1.15]. In Kaplan-Meier analysis, it was not observed difference of 28-day mortality between patients with and without AF. AF could be associated with increased ICU LOS, hospital LOS and need for mechanical ventilation; however, it does not remain an independent short-term predictor of 28-day mortality among patients with sepsis after PSM with IPTW and multivariate analysis.

Methotrexate (MTX) is subject to therapeutic drug monitoring because of its high pharmacokinetic variability and safety risk outside the therapeutic window. This study aimed to develop a population pharmacokinetic model (popPK) of MTX for Brazilian pediatric acute lymphoblastic leukemia (ALL) patients who attended the Hospital de Clínicas de Porto Alegre, Brazil.

The model was developed using NONMEM 7.4 (Icon®), ADVAN3 TRANS4, and FOCE-I. To explain inter-individual variability, we evaluated covariates from demographic, biochemical, and genetic data (single nucleotide polymorphisms [SNPs] related to the transport and metabolism of drugs).

A two-compartment model was built using 483 data points from 45 patients (0.33-17.83 years of age) treated with MTX (0.25-5 g/m2) in different cycles. Serum creatinine (SCR), height (HT), blood urea nitrogen (BUN) and a low BMI stratification (according to the z-score defined by the World Health Organization [LowBMI]) were added as clearance covariates. The final model described MTX clearance as [Formula: see text]. In the two-compartment structural model, the central and peripheral compartment volumes were 26.8 L and 8.47 L, respectively, and the inter-compartmental clearance was 0.218 L/h. External validation of the model was performed through a visual predictive test and metrics using data from 15 other pediatric ALL patients.

The first popPK model of MTX was developed for Brazilian pediatric ALL patients, which showed that inter-individual variability was explained by renal function and factors related to body size.

The impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods.

We conducted an empirical (simulation) study to quantify the bias and power loss in estimating treatment effects in CER using EHR data. We considered various missing scenarios and used the propensity scores to control for confounding. We compared the performance of the multiple imputation and spline smoothing methods to handle missing data.

When missing data depended on the stochastic progression of disease and medical practice patterns, the spline smoothing method produced results that were close to those obtained when there were no missing data. Compared to multiple imputation, the spline smoothing generally performed similarly or better, with smaller estimation bias and less power loss. The multiple imputation can still reduce study bias and power loss in some restrictive scenarios, eg, when missing data did not depend on the stochastic process of disease progression.

Missing data in EHRs could lead to biased estimates of treatment effects and false negative findings in CER even after missing data were imputed. It is important to leverage the temporal information of disease trajectory to impute missing values when using EHRs as a data resource for CER and to consider the missing rate and the effect size when choosing an imputation method.

The purpose of this study was to describe the longitudinal dynamic hemoglobin trajectories in patients undergoing cardiac surgery and to explore whether they provide a broader perspective in predicting AKI compared to traditional threshold values. Additionally, the interaction of red blood cell transfusion was also investigated.

The MIMIC-IV database was searched to identify patients undergoing cardiac surgery with cardiopulmonary bypass. Group-based trajectory modeling (GBTM) was used to determine the hemoglobin trajectories in the first 72 h after ICU admission. The correlation between hemoglobin trajectories and AKI was evaluated using multivariable logistic regression and inverse probability of treatment weighting. Receiver operating characteristic (ROC) curves were created in the dataset to further validate previously reported thresholds.

A total of 4,478 eligible patients were included in this study. Three hemoglobin trajectories were identified by GBTM, which were significantly different in the initial hemoglobin level and evolution pattern. Compared to the "the lowest, rising, and then declining" trajectory, patients in the "the highest, declining" and "medium, declining" trajectory groups had significantly lower AKI risk (OR 0.56; 95% CI 0.48, 0.67) and (OR 0.70; 95% CI 0.55, 0.90), respectively. ROC analysis yielded a disappointing result, with an AUC of 0.552, sensitivity of 0.25, and specificity of 0.86 when the hemoglobin threshold was set at 8 g/dl in the entire cohort. In the subgroup analysis of red blood cell transfusion, hemoglobin levels above 10 g/dl predicted higher AKI risk, and there was no correlation between hemoglobin trajectories and AKI in the non-red blood cell transfusion subgroup.

This study identified a hemoglobin trajectory that is associated with an increased risk of AKI after cardiac surgery. It is noteworthy that fixed hemoglobin thresholds should not be applied to all patient types. In patients receiving red blood cell transfusion, maintaining hemoglobin levels above 10 g/dl through transfusion was associated with an increased risk of AKI.

Acute kidney injury (AKI) is one of the most common and deadly complications among cirrhotic patients at intensive care unit (ICU) admission. We aimed to develop and validate a simple and clinically useful dynamic nomogram for predicting AKI in cirrhotic patients upon ICU admission.

We analyzed the admission data of 4,375 patients with liver cirrhosis in ICU from 2008 to 2019 in the intensive care unit IV (MIMIC-IV) database. The eligible cirrhotic patients were non-randomly divided into derivation (n = 2,188) and validation (n = 2,187) cohorts at a ratio of 1:1, according to the order of admission. The least absolute shrinkage and selection operator regression model was used to identify independent predictors of AKI in the derivation cohort. A dynamic online nomogram was built using multivariate logistic regression analysis in the derivation cohort and then validated in the validation cohort. The C-index, calibration curve, and decision curve analysis were used to assess the nomogram's discrimination, calibration, and clinical usefulness, respectively.

The incidence of AKI in 4,375 patients was 71.3%. Ascites, chronic kidney disease, shock, sepsis, diuretic drugs, hepatic encephalopathy, bacterial infections, vasoactive drugs, admission age, total bilirubin, and blood urea nitrogen were identified using the multivariate logistic regression analysis as significant predictors of AKI upon ICU admission. In the derivation cohort, the model showed good discrimination (C-index, 0.786; 95% CI, 0.765-0.806) and good calibration. The model in the validation cohort yielded good discrimination (C-index, 0.774; 95% CI, 0.753-0.795) and good calibration. Decision curve analysis demonstrated that the dynamic online nomogram was clinically useful.

Our study presents a dynamic online nomogram that incorporates clinical predictors and can be conveniently used to facilitate the individualized prediction of AKI in cirrhotic patients upon ICU admission.

Several studies have been conducted to classify various real life events but few are in medical fields; particularly about breast recurrence under statistical techniques. To our knowledge, there is no reported comparison of statistical classification accuracy and classifiers' discriminative ability on breast cancer recurrence in presence of imputed missing data. Therefore, this article aims to fill this analysis gap by comparing the performance of binary classifiers (logistic regression, linear and quadratic discriminant analysis) using several datasets resulted from imputation process using various simulation conditions. Our study aids the knowledge about how classifiers' accuracy and discriminative ability in classifying a binary outcome variable are affected by the presence of imputed numerical missing data. We simulated incomplete datasets with 15, 30, 45 and 60% of missingness under Missing At Random (MAR) and Missing Completely At Random (MCAR) mechanisms. Mean imputation, hot deck, k-nearest neighbour, multiple imputations via chained equation, expected-maximisation, and predictive mean matching were used to impute incomplete datasets. For each classifier, correct classification accuracy and area under the Receiver Operating Characteristic (ROC) curves under MAR and MCAR mechanisms were compared. The linear discriminant classifier attained the highest classification accuracy (73.9%) based on mean-imputed data at 45% of missing data under MCAR mechanism. As a classifier, the logistic regression based on predictive mean matching imputed-data yields the greatest areas under ROC curves (0.6418) at 30% missingness while k-nearest neighbour tops the value (0.6428) at 60% of missing data under MCAR mechanism.

Anemia is a risk factor for acute kidney injury (AKI) following cardiopulmonary bypass (CPB). Whether red blood cell (RBC) transfusion-enhanced hemoglobin levels contribute to low AKI rates remains unclear. We investigated the interaction between hemoglobin, RBC transfusion, and AKI after CPB. Hemoglobin trajectories within 72 h were analyzed using group-based trajectory analysis. Multivariable logistic analysis and inverse probability-weighted regression were adopted to evaluate the associations between hemoglobin and AKI in RBC and non-RBC transfusion subgroups. We analyzed 6226 patients' data. In the transfusion subgroup, three hemoglobin trajectories were identified. The AKI incidence was lowest in the trajectory with the lowest hemoglobin level (trajectory 1, less transfusion), and it was comparable in trajectories 2 and 3 (20.7% vs. 32.7% vs. 29.4%, p < 0.001, respectively). In four logistic models, the odds ratio for AKI with trajectory 1 as the reference ranged from 1.44 to 1.85 for trajectory 2 (p < 0.001) and 1.45 to 1.66 for trajectory 3 (p < 0.050). The average treatment effect on AKI was 5.6% (p = 0.009) for trajectory 2 and 7.5% (p = 0.041) for trajectory 3, with trajectory 1 as the reference. In the non-RBC transfusion subgroup, three approximately linear hemoglobin trajectories (9, 10, and 12 g/dL) were observed; however, both the crude and adjusted AKI incidence were similar within the three trajectories. In patients undergoing CPB, hemoglobin level >9 g/dL was not associated with decreased AKI incidence in the subgroup without RBC transfusion. However, in patients with RBC transfusion, maintaining hemoglobin level >9 g/dL by RBC transfusion was associated with increased AKI incidence.

Purpose: Both private and public funding cover outpatient physiotherapy (PT) in Canada. Knowledge is lacking in who does and does not access PT services, which limits the ability to identify health/access inequities created by current financing structures. This study characterizes the individuals accessing private PT in Winnipeg to better understand whether inequities exist, given the very limited publicly financed PT. Methods: Patients attending PT in 32 private businesses, sampled for geographic variation, completed a survey online or on paper. We compared the sample's demographic characteristics with Winnipeg population data using chi-square goodness-of-fit tests. Results: In total, 665 adults accessing PT participated. Respondents were older and had higher levels of income and education compared to Winnipeg census data (p < 0.001). Our sample included higher proportions of female and White individuals, and lower proportions of Indigenous persons, newcomers, and people from visible minorities (p < 0.001). Conclusions: There are signs that inequities exist in access to PT in Winnipeg; the cohort who access private PT services does not reflect the wider population, which suggests that some segments of the population are not receiving care.

For the analysis of clinical effects, multiple imputation (MI) of missing data were shown to be unnecessary when using longitudinal linear mixed-models (LLM). It remains unclear whether this also applies to trial-based economic evaluations. Therefore, this study aimed to assess whether MI is required prior to LLM when analyzing longitudinal cost and effect data.

Two-thousand complete datasets were simulated containing five time points. Incomplete datasets were generated with 10, 25, and 50% missing data in follow-up costs and effects, assuming a Missing At Random (MAR) mechanism. Six different strategies were compared using empirical bias (EB), root-mean-squared error (RMSE), and coverage rate (CR). These strategies were: LLM alone (LLM) and MI with LLM (MI-LLM), and, as reference strategies, mean imputation with LLM (M-LLM), seemingly unrelated regression alone (SUR-CCA), MI with SUR (MI-SUR), and mean imputation with SUR (M-SUR).

For costs and effects, LLM, MI-LLM, and MI-SUR performed better than M-LLM, SUR-CCA, and M-SUR, with smaller EBs and RMSEs as well as CRs closers to nominal levels. However, even though LLM, MI-LLM and MI-SUR performed equally well for effects, MI-LLM and MI-SUR were found to perform better than LLM for costs at 10 and 25% missing data. At 50% missing data, all strategies resulted in relatively high EBs and RMSEs for costs.

LLM should be combined with MI when analyzing trial-based economic evaluation data. MI-SUR is more efficient and can also be used, but then an average intervention effect over time cannot be estimated.

In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)-powered informational view that integrates the relevant scientific fields and explores new territories.

Here, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers.

The performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.

Inflammatory bowel diseases (IBD) have been related to high-sugar dietary patterns, but the associations of different types of beverages with IBD risk are largely unknown.

To examine any associations between intake of sugar-sweetened beverages, artificially sweetened beverages and natural juices and IBD risk METHODS: This cohort study included 121,490 participants in the UK Biobank who were free of IBD at recruitment. Intake of beverages was obtained from repeated 24-h diet recalls in 2009-2012. Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for associations of beverage intake with IBD risk.

During a mean (standard deviation) follow-up of 10.2 (1.5) years, we documented 510 incident IBD cases, (143 Crohn's disease (CD) and 367 ulcerative colitis (UC)). Compared to non-consumers, participants consuming >1 unit per day of sugar-sweetened beverages were at significantly higher risk of IBD (HR 1.51, 95% CI 1.11-2.05), but the trend was non-significant (p-trend = 0.170). This association was significant for CD (HR 2.05, 95% CI 1.22-3.46), but not for UC (HR 1.31, 95% CI 0.89-1.92). We did not observe significant associations for the consumption of artificially sweetened beverages or natural juices.

Our findings suggest an association between consumption of sugar-sweetened beverages, rather than artificially sweetened beverages or natural juices, and IBD risk.

Parental stress is related to child mental health problems, with numerous evidence indicating that it is an important predictor of parenting and parent-child relationship. New approaches to psychopathology could be particularly informative for clinical research, however, there is limited research that employs network analysis with parents. Network analysis could contribute to a better understanding of the relationship between child mental health problems and parental stress by highlighting the most central nodes and how the two constructs influence each other. The scope of the study was to identify potential new intervention targets to reduce the mental health problems of children and prevent contagion between parent stress and child psychopathology. Furthermore, we also sought to test whether the dynamic between parental stress and child psychopathology differs across the level of parent stress and child total difficulties. In this endeavor, we had three main directions. First, we estimated a network at the level of child mental health problems and identified the most central nodes. Second, we mapped the main paths through which parent stress and child mental health problems communicate with each other. Third, we investigated the network invariance across the level of parent stress (high vs moderate) and child mental health problems (low vs. high total difficulties). Participants (N = 1014) were parents of children with ages between four and 17 years old. The analyses were conducted in RStudio. Results indicated that perceived coping was a central node, bridging the two constructs. The global strength of the network was higher for parents who reported high levels of stress compared to those who reported only moderate levels of stress. In contrast, we found that the global strength of the network was lower for children with high levels of total difficulties compared to those with low levels of total difficulties. In conclusion, we argue the importance of focusing on the targeting nodes with high bridge centrality, such as perceived coping, for designing prevention and intervention programs. Future research should use temporal dynamics between parental stress and child mental health problems and explore mechanisms between the two constructs.

The online version contains supplementary material available at 10.1007/s12144-022-03520-1.

In this study, we assess the association between academics' research agendas and their preferences for basic research, applied research, or experimental development. Using a sample of Mexican academics working in some of the country's most research-oriented universities, we identify three clusters. The largest is composed of applied research-oriented academics, the second largest is composed of basic research-oriented academics, and the smallest is composed of academics who engage in both basic and applied research, and experimental development. The strategic research agendas of the three clusters are distinguished from each other along four main dimensions: Divergence, Discovery, Mentor Influence, and Social Orientation. These findings show that strategic research agendas are associated with preferences for basic research, applied research, or experimental development, but only to some extent. We also extend the Multi-Dimensional Research Agendas Inventory - Revised, a widely used instrument for measuring strategic research agendas, by adding a new dimension, "Government," and validating the instrument in a new context. We also make the scale available in Spanish for use by academics, practitioners, managers, and administrators in Spanish-speaking countries.

The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients' baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest accuracy, specificity, and precision. This study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Three ML models with adequate performance and sensitivity scores suggest a potential use for patient screenings. Future studies include external validation and improving the models with additional predictor variables.

Gas chromatography-coupled mass spectrometry (GC-MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC-MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and p-values.