Development of a PROMIS multidimensional cancer-related fatigue (mCRF) form using modern psychometric techniques

PURPOSE

To develop a PRO assessment of multidimensional cancer-related fatigue based on the PROMIS fatigue assessments.

METHOD

Cancer patients reporting fatigue were recruited from a comprehensive cancer care center and completed a survey including 39 items from the PROMIS Cancer Item Bank-Fatigue. Component and factor structures of the fatigue items were explored with Monte Carlo parallel factor and Mokken analyses, respectively. Psychometric properties were determined using item response theory, ensuring unidimensionality, scalability, and item independence.

RESULTS

Fatigue scores from a sample of 333 fatigued cancer patients (mean age = 59.50, SD = 11.62, 67% women) were used in all scale development analyses. Psychometric analyses yielded 3 dimensions: motivational fatigue (15 items), cognitive fatigue (9 items), and physical fatigue (9 items). The subscales showed strong unidimensionality, were scalable, and were free of differential item function. Confirmatory factor analyses in a new sample of 182 patients confirmed the findings.

CONCLUSION

The resulting 33-item PROMIS multidimensional cancer-related fatigue (mCRF) form provides a novel measure for the assessment of the different dimensions of cancer-related fatigue. It is the only multidimensional scale specific for cancer patients that has been developed using modern psychometric approaches. With its 3 dimensions (motivational, cognitive, and physical fatigue), this scale accurately captures the fatigue experienced by cancer patients, allowing clinicians to optimize fatigue management and improve patient care. The scale could also advance research on the nature and experience of cancer-related fatigue.

Technical note: Can photon-counting CT improve PET/CT's quantitative accuracy?

BACKGROUND

Linear attenuation coefficients (LACs) in positron emission tomography combined with computed tomography (PET/CT) are derived from CT scans that utilize energy-integrating detectors (EID-CT). These LACs are inaccurate when iodine contrast has been injected. Photon counting detector CT (PCD-CT) may be able to improve the accuracy.

PURPOSE

To investigate whether PCD-CT can improve PET/CT quantitative accuracy.

METHODS

Two experiments were performed: one with CT only and one that combined PET and CT. The first experiment used an electron density phantom whose inserts were imaged with EID-CT and PCD-CT. The inserts simulated normal human tissues, including bone and iodinated blood. In the case of PCD-CT, virtual-monoenergetic images at 190 keV were created. LACs were derived in each case and compared against known values. For inserts with iodine, more accurate LACs were expected with PCD-CT. The second experiment involved a custom PET phantom with various materials simulating human tissues (blood, iodinated blood, and bone) and 18F radioactivity. Data were first acquired with an EID-CT-based PET/CT system and then separately in a PCD-CT system without PET. PET images were reconstructed using LAC from EID-CT and PCD-CT. PET image values were compared against known activity values using recovery coefficients (RC).

RESULTS

In the first experiment, LAC based on EID-CT were in error by as much as 18%, whereas the corresponding PCD-CT based measurements were within 3%. In the second experiment, minimum, maximum, and mean RC were (96.1%, 115.4%, and 103.8%) for the EID-CT method, and (95.8%, 105.5%, and 101.0%) for the PCD-CT method. The consistency of PET images in body and head orientations was improved.

CONCLUSIONS

PCD-CT can acquire the information needed for accurate LAC for PET reconstruction in a single spiral acquisition.

Relationship between tooth loss and progression of frailty: Findings from the English longitudinal study of aging

OBJECTIVE

The objective of the present study was to investigate the relationship between indicators of oral health status (number of teeth; denture use) and the progression of frailty amongst adults in England.

METHODS

The subjects were participants of the English Longitudinal Study of Aging [ELSA] aged 50 years and older. We used panel data from three waves of the study (Waves 7-9). Indicators of oral health comprised the number of teeth (≥20; 10-19; 1-9; 0) and combination of removable denture usage and the number of teeth. Frailty was assessed by the 32-item Frailty Index (FI). Covariates were age, sex, education, marital status, smoking, alcohol, and physical activity. The longitudinal relationship between oral health indicators and change in FI were investigated using linear mixed-effect models considering frailty as a time-varying variable.

RESULTS

Among the 7,557 participants, compared to those people with 20 or more teeth, change in frailty over time was significantly higher among those with less than 20 teeth: 10-19 teeth (β: 0.249, 95 %CI: 0.116 to 0.382), and 1-9 teeth (β: 0.238, 95 %CI: 0.053 to 0.423) and being edentate (β: 0.286, 95 %CI: 0.106 to 0.465) when adjusting for co-variates. The rise in frailty over time was significantly higher among those with fewer teeth (<20 teeth), including those using dentures.

CONCLUSIONS AND IMPLICATIONS

This longitudinal analysis suggests that tooth loss is associated with accelerated progression of frailty and utilizing dentures did not reduce the trend in frailty. Thus, maintaining a functional natural dentition is important in healthy ageing.

Portal Venous and Hepatic Arterial Coefficients Predict Post-Hepatectomy Overall and Recurrence-Free Survival in Patients with Hepatocellular Carcinoma: A Retrospective Study

Background

The dominant artery blood supply is a characteristic of hepatocellular carcinoma (HCC). However, it is not known whether the blood supply can predict the post-hepatectomy prognosis of patients with HCC. This retrospective study investigated the prognostic value of the portal venous and arterial blood supply estimated on triphasic liver CT (as a portal venous coefficient, PVC, and hepatic arterial coefficient, HAC, respectively) in patients with HCC following hepatectomy.

Methods

HCC patients who were tested by triphasic liver CT 2 weeks before hepatectomy and received R0 hepatectomy at the Second Affiliated Hospital, Kunming Medical University between January 1, 2016 and December 31, 2020, were retrospectively screened. Their PVC and HAC, and other variables were analyzed for the prediction of overall survival (OS) and recurrence-free survival (RFS) using the least absolute shrinkage and selection operator and Cox proportional hazard regression models.

Results

Four hundred and nineteen patients (53.2 ± 10.6 years of age and 370 men) were evaluated. A shorter OS was independently associated with higher blood albumin and total bilirubin grade [hazard ratio (HR) 2.020, 95% confidence interval (CI) 1.534-2.660], higher Barcelona Clinic Liver Cancer (BCLC) stage (HR 1.514, 95% CI 1.290-1.777), PVC ≤ 0.386 (HR 1.628, 95% CI 1.149-2.305), and HAC > 0.029 (HR 1.969, 95% CI 1.380-2.809). A shorter RFS was independently associated with male (HR 1.652, 95% CI 1.005-2.716), higher serum α-fetoprotein ≥ 400 ng/mL (HR 1.672, 95% CI 1.236-2.263), higher BCLC stage (HR 1.516, 95% CI 1.300-1.768), tumor PVC ≤ 0.386 (HR 1.641, 95% CI 1.198-2.249), and tumor HAC > 0.029 (HR 1.455, 95% CI 1.060-1.997).

Conclusion

Tumor PVC or HAC before hepatectomy is valuable for independently predicting postoperative survival of HCC patients.

Co-analysis of methylation platforms for signatures of biological aging in the domestic dog reveals previously unexplored confounding factors

Chronological age reveals the number of years an individual has lived since birth. By contrast, biological age varies between individuals of the same chronological age at a rate reflective of physiological decline. Differing rates of physiological decline are related to longevity and result from genetics, environment, behavior, and disease. The creation of methylation biological age predictors is a long-standing challenge in aging research due to the lack of individual pre-mortem longevity data. The consistent differences in longevity between domestic dog breeds enable the construction of biological age estimators which can, in turn, be contrasted with methylation measurements to elucidate mechanisms of biological aging. We draw on three flagship methylation studies using distinct measurement platforms and tissues to assess the feasibility of creating biological age methylation clocks in the dog. We expand epigenetic clock building strategies to accommodate phylogenetic relationships between individuals, thus controlling for the use of breed standard metrics. We observe that biological age methylation clocks are affected by population stratification and require heavy parameterization to achieve effective predictions. Finally, we observe that methylation-related markers reflecting biological age signals are rare and do not colocalize between datasets.

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

Exploiting relationship directionality to enhance statistical modeling of peer-influence across social networks

Risky-prescribing is the excessive or inappropriate prescription of drugs that singly or in combination pose significant risks of adverse health outcomes. In the United States, prescribing of opioids and other "risky" drugs is a national public health concern. We use a novel data framework-a directed network connecting physicians who encounter the same patients in a sequence of visits-to investigate if risky-prescribing diffuses across physicians through a process of peer-influence. Using a shared-patient network of 10 661 Ohio-based physicians constructed from Medicare claims data over 2014-2015, we extract information on the order in which patients encountered physicians to derive a directed patient-sharing network. This enables the novel decomposition of peer-effects of a medical practice such as risky-prescribing into directional (outbound and inbound) and bidirectional (mutual) relationship components. Using this framework, we develop models of peer-effects for contagion in risky-prescribing behavior as well as spillover effects. The latter is measured in terms of adverse health events suspected to be related to risky-prescribing in patients of peer-physicians. Estimated peer-effects were strongest when the patient-sharing relationship was mutual as opposed to directional. Using simulations we confirmed that our modeling and estimation strategies allows simultaneous estimation of each type of peer-effect (mutual and directional) with accuracy and precision. We also show that failing to account for these distinct mechanisms (a form of model mis-specification) produces misleading results, demonstrating the importance of retaining directional information in the construction of physician shared-patient networks. These findings suggest network-based interventions for reducing risky-prescribing.

Enhancing Immunotherapy Response Prediction in Metastatic Lung Adenocarcinoma: Leveraging Shallow and Deep Learning with CT-Based Radiomics across Single and Multiple Tumor Sites

This study aimed to evaluate the potential of pre-treatment CT-based radiomics features (RFs) derived from single and multiple tumor sites, and state-of-the-art machine-learning survival algorithms, in predicting progression-free survival (PFS) for patients with metastatic lung adenocarcinoma (MLUAD) receiving first-line treatment including immune checkpoint inhibitors (CPIs). To do so, all adults with newly diagnosed MLUAD, pre-treatment contrast-enhanced CT scan, and performance status ≤ 2 who were treated at our cancer center with first-line CPI between November 2016 and November 2022 were included. RFs were extracted from all measurable lesions with a volume ≥ 1 cm3 on the CT scan. To capture intra- and inter-tumor heterogeneity, RFs from the largest tumor of each patient, as well as lowest, highest, and average RF values over all lesions per patient were collected. Intra-patient inter-tumor heterogeneity metrics were calculated to measure the similarity between each patient lesions. After filtering predictors with univariable Cox p < 0.100 and analyzing their correlations, five survival machine-learning algorithms (stepwise Cox regression [SCR], LASSO Cox regression, random survival forests, gradient boosted machine [GBM], and deep learning [Deepsurv]) were trained in 100-times repeated 5-fold cross-validation (rCV) to predict PFS on three inputs: (i) clinicopathological variables, (ii) all radiomics-based and clinicopathological (full input), and (iii) uncorrelated radiomics-based and clinicopathological variables (uncorrelated input). The Models' performances were evaluated using the concordance index (c-index). Overall, 140 patients were included (median age: 62.5 years, 36.4% women). In rCV, the highest c-index was reached with Deepsurv (c-index = 0.631, 95%CI = 0.625-0.647), followed by GBM (c-index = 0.603, 95%CI = 0.557-0.646), significantly outperforming standard SCR whatever its input (c-index range: 0.560-0.570, all p < 0.0001). Thus, single- and multi-site pre-treatment radiomics data provide valuable prognostic information for predicting PFS in MLUAD patients undergoing first-line CPI treatment when analyzed with advanced machine-learning survival algorithms.

Individual and interaction effects of monounsaturated fatty acids on their associations with hypertension in Chinese residents

Currently, associations between dietary intakes of individual monounsaturated fatty acids (MUFAs) and hypertension were not well disclosed, and the interaction effects of MUFAs on their associations with hypertension were unknown. Obesity was correlated with both MUFAs and hypertension, while if anthropometric obesity indices performed mediating roles in associations between MUFAs and hypertension remained underdetermined. In our study, 8509 Chinese adults investigated from 2004 to 2011 were included. Dietary information collection and physical examinations were performed at baseline and each timepoint of follow-up. As we found, inverse associations of MUFA17, MUFA18 and MUFA20 with hypertension were statistically significant after adjustments, hazard ratios (HRs) were 0.87, 0.90 and 0.91, respectively. MUFA15 was positively associated with hypertension, with an HR of 1.07 (95% confidence interval: 1.01, 1.12). By performing principal component analysis (PCA) to estimate the joint effects of MUFAs on hypertension, the PCA score of MUFAs was only inversely associated with blood pressure. No joint effect was observed in g-computation analyses. Both linear and nonlinear interactions of MUFAs on their associations with hypertension were estimated using restricted cubic spline analysis. The association between MUFA15 and hypertension was interacted by MUFA17, and the association between MUFA20 and hypertension was interacted by MUFA18. The mediation effects of body mass index and waist circumference were found on associations of hypertension with MUFA15, MUFA17 and MUFA20. Our findings suggested that associations with hypertension were different among individual MUFAs, and mutual interactions existed, implying that the utility of individual MUFAs might be recommended for estimating relationships between MUFAs and diseases. Moreover, fat accumulation might potentially underlie associations between MUFAs and hypertension.

Machine learning classification of new firearm injury encounters in the St Louis region: 2010-2020

OBJECTIVES

To improve firearm injury encounter classification (new vs follow-up) using machine learning (ML) and compare our ML model to other common approaches.

MATERIALS AND METHODS

This retrospective study used data from the St Louis region-wide hospital-based violence intervention program data repository (2010-2020). We randomly selected 500 patients with a firearm injury diagnosis for inclusion, with 808 total firearm injury encounters split (70/30) for training and testing. We trained a least absolute shrinkage and selection operator (LASSO) regression model with the following predictors: admission type, time between firearm injury visits, number of prior firearm injury emergency department (ED) visits, encounter type (ED or other), and diagnostic codes. Our gold standard for new firearm injury encounter classification was manual chart review. We then used our test data to compare the performance of our ML model to other commonly used approaches (proxy measures of ED visits and time between firearm injury encounters, and diagnostic code encounter type designation [initial vs subsequent or sequela]). Performance metrics included area under the curve (AUC), sensitivity, and specificity with 95% confidence intervals (CIs).

RESULTS

The ML model had excellent discrimination (0.92, 0.88-0.96) with high sensitivity (0.95, 0.90-0.98) and specificity (0.89, 0.81-0.95). AUC was significantly higher than time-based outcomes, sensitivity was slightly (but not significantly) lower than other approaches, and specificity was higher than all other methods.

DISCUSSION

ML successfully delineated new firearm injury encounters, outperforming other approaches in ruling out encounters for follow-up.

CONCLUSION

ML can be used to identify new firearm injury encounters and may be particularly useful in studies assessing re-injuries.

Predicting prognosis and immunotherapy response in colorectal cancer by pericytes insights from single-cell RNA sequencing

Immunotherapy has revolutionized the treatment of tumors, but there are still a large number of patients who do not benefit from immunotherapy. Pericytes play an important role in remodeling the immune microenvironment. However, how pericytes affect the prognosis and treatment resistance of tumors is still unknown. This study jointly analyzed single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data of multiple cancers to reveal pericyte function in the colorectal cancer microenvironment. Analyzing over 800 000 cells, it was found that colorectal cancer had more pericyte enrichment in tumor tissues than other cancers. We then combined the TCGA database with multiple public datasets and enrolled more than 1000 samples, finding that pericyte may be closely related to poor prognosis due to the higher epithelial-mesenchymal transition (EMT) and hypoxic characteristics. At the same time, patients with more pericytes have higher immune checkpoint molecule expressions and lower immune cell infiltration. Finally, the contributions of pericyte in poor treatment response have been demonstrated in multiple immunotherapy datasets (n = 453). All of these observations suggest that pericyte can be used as a potential biomarker to predict patient disease progression and immunotherapy response.

Children and adults produce distinct technology- and human-directed speech

This study compares how English-speaking adults and children from the United States adapt their speech when talking to a real person and a smart speaker (Amazon Alexa) in a psycholinguistic experiment. Overall, participants produced more effortful speech when talking to a device (longer duration and higher pitch). These differences also varied by age: children produced even higher pitch in device-directed speech, suggesting a stronger expectation to be misunderstood by the system. In support of this, we see that after a staged recognition error by the device, children increased pitch even more. Furthermore, both adults and children displayed the same degree of variation in their responses for whether "Alexa seems like a real person or not", further indicating that children's conceptualization of the system's competence shaped their register adjustments, rather than an increased anthropomorphism response. This work speaks to models on the mechanisms underlying speech production, and human-computer interaction frameworks, providing support for routinized theories of spoken interaction with technology.

Independent and joint associations of multiple metals exposure with vital capacity index: a cross-sectional study in Chinese children and adolescents

OBJECTIVE

The current study aimed to explore the relationships between urinary metals and vital capacity index (VCI) in 380 children and adolescents in Northeast China using a variety of statistical methods.

METHODS

A cross-sectional survey was conducted among 380 children and adolescents in Liaoning Province, China. To assess the relationships between urinary metals and VCI, Elastic-net (ENET) regression, multivariate linear regression, weighted quantile sum (WQS), bayesian kernel machine regression (BKMR) and quantile-based g computation (qgcomp) were adopted.

RESULTS

The ENET model selected magnesium (Mg), vanadium (V), manganese (Mn), arsenic (As), tin (Sn) and lead (Pb) as crucial elements. In multiple linear regression, we observed urinary Pb, Mn was negatively correlated with VCI individually in both total study population and adolescents (all p values < 0.05) in the adjustment model. The WQS indices were negatively related with VCI in total study population (β=-3.19, 95%CI: -6.07, -0.30) and adolescents (β=-3.46, 95%CI: -6.58, -0.35). The highest weight in total study population was Pb (38.80%), in adolescents was Mn (35.10%). In the qgcomp, Pb (31.90%), Mn (27.20%) were the major negative contributors to the association in the total population (β=-3.51, 95%CI: -6.29, -0.74). As (42.50%), Mn (39.90%) were the main negative contributors (β=-3.95, 95% CI: -6.68, -1.22) among adolescents. The results of BKMR were basically consistent with WQS and qgcomp analyses.

CONCLUSIONS

Our results indicated that Pb and Mn were priority toxic materials on VCI. The cumulative effect of metals was negatively related to VCI, and this relationship was more pronounced in adolescents.

Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA)

This study investigates the genetic factors contributing to the disparity in prostate cancer incidence and progression among African American men (AAM) compared to European American men (EAM). The research focuses on employing Weighted Gene Co-expression Network Analysis (WGCNA) on public microarray data obtained from prostate cancer patients. The study employed WGCNA to identify clusters of genes with correlated expression patterns, which were then analyzed for their connection to population backgrounds. Additionally, pathway enrichment analysis was conducted to understand the significance of the identified gene modules in prostate cancer pathways. The Least Absolute Shrinkage and Selection Operator (LASSO) and Correlation-based Feature Selection (CFS) methods were utilized for selection of biomarker genes. The results revealed 353 differentially expressed genes (DEGs) between AAM and EAM. Six significant gene expression modules were identified through WGCNA, showing varying degrees of correlation with prostate cancer. LASSO and CFS methods pinpointed critical genes, as well as six common genes between both approaches, which are indicative of their vital role in the disease. The XGBoost classifier validated these findings, achieving satisfactory prediction accuracy. Genes such as APRT, CCL2, BEX2, MGC26963, and PLAU were identified as key genes significantly associated with cancer progression. In conclusion, the research underlines the importance of incorporating AAM and EAM population diversity in genomic studies, particularly in cancer research. In addition, the study highlights the effectiveness of integrating machine learning techniques with gene expression analysis as a robust methodology for identifying critical genes in cancer research.

Combination of computational techniques and RNAi reveal targets in Anopheles gambiae for malaria vector control

Increasing reports of insecticide resistance continue to hamper the gains of vector control strategies in curbing malaria transmission. This makes identifying new insecticide targets or alternative vector control strategies necessary. CLassifier of Essentiality AcRoss EukaRyote (CLEARER), a leave-one-organism-out cross-validation machine learning classifier for essential genes, was used to predict essential genes in Anopheles gambiae and selected predicted genes experimentally validated. The CLEARER algorithm was trained on six model organisms: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and employed to identify essential genes in An. gambiae. Of the 10,426 genes in An. gambiae, 1,946 genes (18.7%) were predicted to be Cellular Essential Genes (CEGs), 1716 (16.5%) to be Organism Essential Genes (OEGs), and 852 genes (8.2%) to be essential as both OEGs and CEGs. RNA interference (RNAi) was used to validate the top three highly expressed non-ribosomal predictions as probable vector control targets, by determining the effect of these genes on the survival of An. gambiae G3 mosquitoes. In addition, the effect of knockdown of arginase (AGAP008783) on Plasmodium berghei infection in mosquitoes was evaluated, an enzyme we computationally inferred earlier to be essential based on chokepoint analysis. Arginase and the top three genes, AGAP007406 (Elongation factor 1-alpha, Elf1), AGAP002076 (Heat shock 70kDa protein 1/8, HSP), AGAP009441 (Elongation factor 2, Elf2), had knockdown efficiencies of 91%, 75%, 63%, and 61%, respectively. While knockdown of HSP or Elf2 significantly reduced longevity of the mosquitoes (p<0.0001) compared to control groups, Elf1 or arginase knockdown had no effect on survival. However, arginase knockdown significantly reduced P. berghei oocytes counts in the midgut of mosquitoes when compared to LacZ-injected controls. The study reveals HSP and Elf2 as important contributors to mosquito survival and arginase as important for parasite development, hence placing them as possible targets for vector control.

International External Validation of Risk Prediction Model of 90-Day Mortality after Gastrectomy for Cancer Using Machine Learning

BACKGROUND

Radical gastrectomy remains the main treatment for gastric cancer, despite its high mortality. A clinical predictive model of 90-day mortality (90DM) risk after gastric cancer surgery based on the Spanish EURECCA registry database was developed using a matching learning algorithm. We performed an external validation of this model based on data from an international multicenter cohort of patients.

METHODS

A cohort of patients from the European GASTRODATA database was selected. Demographic, clinical, and treatment variables in the original and validation cohorts were compared. The performance of the model was evaluated using the area under the curve (AUC) for a random forest model.

RESULTS

The validation cohort included 2546 patients from 24 European hospitals. The advanced clinical T- and N-category, neoadjuvant therapy, open procedures, total gastrectomy rates, and mean volume of the centers were significantly higher in the validation cohort. The 90DM rate was also higher in the validation cohort (5.6%) vs. the original cohort (3.7%). The AUC in the validation model was 0.716.

CONCLUSION

The externally validated model for predicting the 90DM risk in gastric cancer patients undergoing gastrectomy with curative intent continues to be as useful as the original model in clinical practice.

Mixture and Non-mixture Cure Models for Health Technology Assessment: What You Need to Know

There is increasing interest in the use of cure modelling to inform health technology assessment (HTA) due to the development of new treatments that appear to offer the potential for cure in some patients. However, cure models are often not included in evidence dossiers submitted to HTA agencies, and they are relatively rarely relied upon to inform decision-making. This is likely due to a lack of understanding of how cure models work, what they assume, and how reliable they are. In this tutorial we explain why and when cure models may be useful for HTA, describe the key characteristics of mixture and non-mixture cure models, and demonstrate their use in a range of scenarios, providing Stata code. We highlight key issues that must be taken into account by analysts when fitting these models and by reviewers and decision-makers when interpreting their predictions. In particular, we note that flexible parametric non-mixture cure models have not been used in HTA, but they offer advantages that make them well suited to an HTA context when a cure assumption is valid but follow-up is limited.

BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.

Extension of efficacy range for targeted malaria-elimination interventions due to spillover effects

Malaria-elimination interventions aim to extinguish hotspots and prevent transmission to nearby areas. Here, we re-analyzed a cluster-randomized trial of reactive, focal interventions (chemoprevention using artemether-lumefantrine and/or indoor residual spraying with pirimiphos-methyl) delivered within 500 m of confirmed malaria index cases in Namibia to measure direct effects (among intervention recipients within 500 m) and spillover effects (among non-intervention recipients within 3 km) on incidence, prevalence and seroprevalence. There was no or weak evidence of direct effects, but the sample size of intervention recipients was small, limiting statistical power. There was the strongest evidence of spillover effects of combined chemoprevention and indoor residual spraying. Among non-recipients within 1 km of index cases, the combined intervention reduced malaria incidence by 43% (95% confidence interval, 20-59%). In analyses among non-recipients within 3 km of interventions, the combined intervention reduced infection prevalence by 79% (6-95%) and seroprevalence, which captures recent infections and has higher statistical power, by 34% (20-45%). Accounting for spillover effects increased the cost-effectiveness of the combined intervention by 42%. Targeting hotspots with combined chemoprevention and vector-control interventions can indirectly benefit non-recipients up to 3 km away.

Peptides and primate personality: Central and peripheral oxytocin and vasopressin levels and social behavior in two baboon species (Papio hamadryas and Papio anubis)

The neurohormones oxytocin (OT) and arginine vasopressin (AVP) are involved in social behaviors and psychiatric conditions. However, more research on nonhuman primates with complex social behaviors is needed. We studied two closely-related primate species with divergent social and mating systems; hamadryas baboons (Papio hamadryas, n=38 individuals) and anubis baboons (Papio anubis, n=46). We measured OT in cerebrospinal fluid (CSF, n=75), plasma (n=81) and urine (n=77), and AVP in CSF (n=45), and we collected over 250 hours of focal behavioral observations. Using Bayesian multivariate models, we found no clear species difference in hormone levels; the strongest support was for hamadryas having higher CSF OT levels than anubis (posterior probability [PP] for females = 0.75, males = 0.84). Looking at nine specific behaviors, OT was associated with affiliative behaviors (approach, proximity, grooming, PP ∼ 0.85 - 1.00), albeit inconsistently across sources of measurement (CSF, plasma, and urine, which were uncorrelated with each other). Most behaviors had low repeatability (R ∼ 0 - 0.2), i.e. they did not exhibit stable between-individual differences (or "personality"), and different behaviors did not neatly coalesce into higher-order factors (or "behavioral syndromes"), which cautions against the use of aggregate behavioral measures and highlights the need to establish stable behavioral profiles when testing associations with baseline hormone levels. In sum, we found some associations between peptides and social behavior, but also many null results, OT levels from different sources were uncorrelated, and our behavioral measures did not indicate clear individual differences in sociability.

Construction and validation of a risk prediction model for postoperative ICU admission in patients with colorectal cancer: clinical prediction model study

BACKGROUND

Transfer to the ICU is common following non-cardiac surgeries, including radical colorectal cancer (CRC) resection. Understanding the judicious utilization of costly ICU medical resources and supportive postoperative care is crucial. This study aimed to construct and validate a nomogram for predicting the need for mandatory ICU admission immediately following radical CRC resection.

METHODS

Retrospective analysis was conducted on data from 1003 patients who underwent radical or palliative surgery for CRC at Ningxia Medical University General Hospital from August 2020 to April 2022. Patients were randomly assigned to training and validation cohorts in a 7:3 ratio. Independent predictors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression in the training cohort to construct the nomogram. An online prediction tool was developed for clinical use. The nomogram's calibration and discriminative performance were assessed in both cohorts, and its clinical utility was evaluated through decision curve analysis (DCA).

RESULTS

The final predictive model comprised age (P = 0.003, odds ratio [OR] 3.623, 95% confidence interval [CI] 1.535-8.551); nutritional risk screening 2002 (NRS2002) (P = 0.000, OR 6.129, 95% CI 2.920-12.863); serum albumin (ALB) (P = 0.013, OR 0.921, 95% CI 0.863-0.982); atrial fibrillation (P = 0.000, OR 20.017, 95% CI 4.191-95.609); chronic obstructive pulmonary disease (COPD) (P = 0.009, OR 8.151, 95% CI 1.674-39.676); forced expiratory volume in 1 s / Forced vital capacity (FEV1/FVC) (P = 0.040, OR 0.966, 95% CI 0.935-0.998); and surgical method (P = 0.024, OR 0.425, 95% CI 0.202-0.891). The area under the curve was 0.865, and the consistency index was 0.367. The Hosmer-Lemeshow test indicated excellent model fit (P = 0.367). The calibration curve closely approximated the ideal diagonal line. DCA showed a significant net benefit of the predictive model for postoperative ICU admission.

CONCLUSION

Predictors of ICU admission following radical CRC resection include age, preoperative serum albumin level, nutritional risk screening, atrial fibrillation, COPD, FEV1/FVC, and surgical route. The predictive nomogram and online tool support clinical decision-making for postoperative ICU admission in patients undergoing radical CRC surgery.

TRIAL REGISTRATION

Despite the retrospective nature of this study, we have proactively registered it with the Chinese Clinical Trial Registry. The registration number is ChiCTR2200062210, and the date of registration is 29/07/2022.

Genome-Wide Gene Birth-Death Dynamics Are Associated with Diet Breadth Variation in Lepidoptera

Comparative analyses of gene birth-death dynamics have the potential to reveal gene families that played an important role in the evolution of morphological, behavioral, or physiological variation. Here, we used whole genomes of 30 species of butterflies and moths to identify gene birth-death dynamics among the Lepidoptera that are associated with specialist or generalist feeding strategies. Our work advances this field using a uniform set of annotated proteins for all genomes, investigating associations while correcting for phylogeny, and assessing all gene families rather than a priori subsets. We discovered that the sizes of several important gene families (e.g. those associated with pesticide resistance, xenobiotic detoxification, and/or protein digestion) are significantly correlated with diet breadth. We also found 22 gene families showing significant shifts in gene birth-death dynamics at the butterfly (Papilionoidea) crown node, the most notable of which was a family of pheromone receptors that underwent a contraction potentially linked with a shift to visual-based mate recognition. Our findings highlight the importance of uniform annotations, phylogenetic corrections, and unbiased gene family analyses in generating a list of candidate genes that warrant further exploration.

Organ weights and length anthropometry measures at autopsy for sudden infant death syndrome cases and other infant deaths in the Chicago infant mortality study

Organ weights are a possible diagnostic or pathophysiological clue to distinguishing sudden infant death syndrome (SIDS) cases from other infant deaths but suffer from major confounding. Using autopsy data from the Chicago Infant Mortality Study, a majority African-American case-control study of deceased infants under 1 year conducted 1993-96, we assessed differences in the weights of brain, thymus, kidneys, lungs, liver, spleen, total body, and four length anthropometry measures in SIDS-diagnosed infants compared to controls. Using exact and coarsened matching, we ran Bayesian linear models with these anthropometry outcomes and repeated the analyses substituting the corresponding fitted allometrically-scaled organ weight indices to account for body size. After detailed analysis and adjustment for potential confounders, we found that matched SIDS infants were generally bigger than controls, with higher mean brain, liver, spleen, thymus, lung, and total body weights, and higher mean head and chest circumference, crown-heel, crown-rump lengths. SIDS infants also had higher mean thymus, liver, spleen, lung and total body weight indices. The association with thymus weight was proportionately greater in magnitude than any other outcome measure and independent of body size. The results of these more detailed analyses are consistent with recent findings from other studies with differing racial compositions, and substantially confirm the primary organ sites for more detailed mechanistic research into the biological dysregulation contributing to underlying pathophysiology of SIDS.

Cognitive neuropsychological and neuroanatomic predictors of naturalistic action performance in left hemisphere stroke: a retrospective analysis

Individuals who have experienced a left hemisphere cerebrovascular accident (LCVA) have been shown to make errors in naturalistic action tasks designed to assess the ability to perform everyday activities such as preparing a cup of coffee. Naturalistic action errors in this population are often attributed to limb apraxia, a common deficit in the representation and performance of object-related actions. However, naturalistic action impairments are also observed in right hemisphere stroke and traumatic brain injury, populations infrequently associated with apraxia, and errors across all these populations are influenced by overall severity. Based on these and other data, an alternative (though not mutually exclusive) account is that naturalistic action errors in LCVA are also a consequence of deficits in general attentional resource availability or allocation. In this study, we conducted a retrospective analysis of data from a large group of 51 individuals with LCVA who had completed a test of naturalistic action, along with a battery of tests assessing praxis, attention allocation and control, reasoning, and language abilities to determine which of these capacities contribute uniquely to naturalistic action impairments. Using a regularized regression method, we found that naturalistic action impairments are predicted by both praxis deficits (hand posture sequencing and gesture recognition), as well as attention allocation and control deficits (orienting and dividing attention), along with language comprehension ability and age. Using support vector regression-lesion symptom mapping (SVR-LSM), we also demonstrated that naturalistic action impairments are associated with lesions to posterior middle temporal gyrus and anterior inferior parietal lobule - regions known to be implicated in praxis; as well the middle frontal gyrus that has been implicated in both praxis and attention allocation and control. Taken together, these findings support the hypothesis that naturalistic action impairments in LCVA are a consequence of apraxia as well as deficits in attention allocation and control.

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

Large-Scale Proteomics in Early Pregnancy and Hypertensive Disorders of Pregnancy

Importance

There is no consensus regarding the best method for prediction of hypertensive disorders of pregnancy (HDP), including gestational hypertension and preeclampsia.

Objective

To determine predictive ability in early pregnancy of large-scale proteomics for prediction of HDP.

Design, Setting, and Participants

This was a nested case-control study, conducted in 2022 to 2023, using clinical data and plasma samples collected between 2010 and 2013 during the first trimester, with follow-up until pregnancy outcome. This multicenter observational study took place at 8 academic medical centers in the US. Nulliparous individuals during first-trimester clinical visits were included. Participants with HDP were selected as cases; controls were selected from those who delivered at or after 37 weeks without any HDP, preterm birth, or small-for-gestational-age infant. Age, self-reported race and ethnicity, body mass index, diabetes, health insurance, and fetal sex were available covariates.

Exposures

Proteomics using an aptamer-based assay that included 6481 unique human proteins was performed on stored plasma. Covariates were used in predictive models.

Main Outcomes and Measures

Prediction models were developed using the elastic net, and analyses were performed on a randomly partitioned training dataset comprising 80% of study participants, with the remaining 20% used as an independent testing dataset. Primary measure of predictive performance was area under the receiver operating characteristic curve (AUC).

Results

This study included 753 HDP cases and 1097 controls with a mean (SD) age of 26.9 (5.5) years. Maternal race and ethnicity were 51 Asian (2.8%), 275 non-Hispanic Black (14.9%), 275 Hispanic (14.9%), 1161 non-Hispanic White (62.8% ), and 88 recorded as other (4.8%), which included those who did not identify according to these designations. The elastic net model, allowing for forced inclusion of prespecified covariates, was used to adjust protein-based models for clinical and demographic variables. Under this approach, no proteins were selected to augment the clinical and demographic covariates. The predictive performance of the resulting model was modest, with a training set AUC of 0.64 (95% CI, 0.61-0.67) and a test set AUC of 0.62 (95% CI, 0.56-0.68). Further adjustment for study site yielded only minimal changes in AUCs.

Conclusions and Relevance

In this case-control study with detailed clinical data and stored plasma samples available in the first trimester, an aptamer-based proteomics panel did not meaningfully add to predictive utility over and above clinical and demographic factors that are routinely available.

Development and validation of a nomogram model of depression and sleep disorders and the risk of disease progression in patients with breast cancer

BACKGROUND

In this study, we investigated the relationship between the risk of postoperative progressive disease (PD) in breast cancer and depression and sleep disorders in order to develop and validate a suitable risk prevention model.

METHODS

A total of 750 postoperative patients with breast cancer were selected from the First People's Hospital of LianYunGang, and the indices of two groups (an event group and a non-event group) were compared to develop and validate a risk prediction model. The relationship between depression, sleep disorders, and PD events was investigated using the follow-up data of the 750 patients.

RESULTS

SAS, SDS, and AIS scores differed in the group of patients who experienced postoperative disease progression versus those who did not; the differences were statistically significant and the ability to differentiate prognosis was high. The area under the receiver operating characteristic (ROC) curves (AUC) were: 0.8049 (0.7685-0.8613), 0.768 (0.727-0.809), and 0.7661 (0.724--0.808), with cut-off values of 43.5, 48.5, and 4.5, respectively. Significant variables were screened by single-factor analysis and multi-factor analysis to create model 1, by lasso regression and cross-lasso regression analysis to create model 2, by random forest calculation method to create model 3, by stepwise regression method (backward method) to create model 4, and by including all variables for Cox regression to include significant variables to create model 5. The AUC of model 2 was 0.883 (0.848-0.918) and 0.937 (0.893-0.981) in the training set and validation set, respectively. The clinical efficacy of the model was evaluated using decision curve analysis and clinical impact curve, and then the model 2 variables were transformed into scores, which were validated in two datasets, the training and validation sets, with AUCs of 0.884 (0.848-0.919) and 0.885 (0.818-0.951), respectively.

CONCLUSION

We established and verified a model including SAS, SDS and AIS to predict the prognosis of breast cancer patients, and simplified it by scoring, making it convenient for clinical use, providing a theoretical basis for precise intervention in these patients. However, further research is needed to verify the generalization ability of our model.

Using Runs of Homozygosity and Machine Learning to Disentangle Sources of Inbreeding and Infer Self-Fertilization Rates

Runs of homozygosity (ROHs) are indicative of elevated homozygosity and inbreeding due to mating of closely related individuals. Self-fertilization can be a major source of inbreeding which elevates genome-wide homozygosity and thus should also create long ROHs. While ROHs are frequently used to understand inbreeding in the context of conservation and selective breeding, as well as for consanguinity of populations and their demographic history, it remains unclear how ROH characteristics are altered by selfing and if this confounds expected signatures of inbreeding due to demographic change. Using simulations, we study the impact of the mode of reproduction and demographic history on ROHs. We apply random forests to identify unique characteristics of ROHs, indicative of different sources of inbreeding. We pinpoint distinct features of ROHs that can be used to better characterize the type of inbreeding the population was subjected to and to predict outcrossing rates and complex demographic histories. Using additional simulations and four empirical datasets, two from highly selfing species and two from mixed-maters, we predict the selfing rate and validate our estimations. We find that self-fertilization rates are successfully identified even with complex demography. Population genetic summary statistics improve algorithm accuracy particularly in the presence of additional inbreeding, e.g. from population bottlenecks. Our findings highlight the importance of ROHs in disentangling confounding factors related to various sources of inbreeding and demonstrate situations where such sources cannot be differentiated. Additionally, our random forest models provide a novel tool to the community for inferring selfing rates using genomic data.

Predicting subacute ruminal acidosis from milk mid-infrared estimated fatty acids and machine learning on Canadian commercial dairy herds

Our objective was to validate the possibility of detecting SARA from milk Fourier transform mid-infrared spectroscopy estimated fatty acids (FA) and machine learning. Subacute ruminal acidosis is a common condition in modern commercial dairy herds for which the diagnostic remains challenging due to its symptoms often being subtle, nonexclusive, and not immediately apparent. This observational study aimed at evaluating the possibility of predicting SARA by developing machine learning models to be applied to farm data and to provide an estimated portrait of SARA prevalence in commercial dairy herds. A first data set composed of 488 milk samples of 67 cows (initial DIM = 8.5 ± 6.18; mean ± SD) from 7 commercial dairy farms and their corresponding SARA classification (SARA+ if rumen pH <6.0 for 300 min, else SARA-) was used for the development of machine learning models. Three sets of predictive variables: i) milk major components (MMC), ii) milk FA (MFA), and iii) MMC combined with MFA (MMCFA) were submitted to 3 different algorithms, namely Elastic net (EN), Extreme gradient boosting (XGB), and Partial least squares (PLS), and evaluated using 3 different scenarios of cross-validation. Accuracy, sensitivity, and specificity of the resulting 27 models were analyzed using a linear mixed model. Model performance was not significantly affected by the choice of algorithm. Model performance was improved by including fatty acids estimations (MFA and MMCFA as opposed to MMC alone). Based on these results, one model was selected (algorithm: EN; predictive variables: MMCFA; 60.4, 65.4, and 55.3% of accuracy, sensitivity, and specificity, respectively) and applied to a large data set comprising the first test-day record (milk major components and FA within the first 70 DIM of 211,972 Holstein cows (219,503 samples) collected from 3001 commercial dairy herds. Based on this analysis, the within-herd SARA prevalence of commercial farms was estimated at 6.6 ± 5.29% ranging from 0 to 38.3%. A subsequent linear mixed model was built to investigate the herd-level factors associated to higher within-herd SARA prevalence. Milking system, proportion of primiparous cows, herd size and seasons were all herd-level factors affecting SARA prevalence. Furthermore, milk production was positively, and milk fat yield negatively associated with SARA prevalence. Due to their moderate levels of accuracy, the SARA prediction models developed in our study, using data from continuous pH measurements on commercial farms, are not suitable for diagnostic purpose. However, these models can provide valuable information at the herd level.

State-Dependent tACS Effects Reveal the Potential Causal Role of Prestimulus Alpha Traveling Waves in Visual Contrast Detection

The intricate relationship between prestimulus alpha oscillations and visual contrast detection variability has been the focus of numerous studies. However, the causal impact of prestimulus alpha traveling waves on visual contrast detection remains largely unexplored. In our research, we sought to discern the causal link between prestimulus alpha traveling waves and visual contrast detection across different levels of mental fatigue. Using electroencephalography alongside a visual detection task with 30 healthy adults (13 females; 17 males), we identified a robust negative correlation between prestimulus alpha forward traveling waves (FTWs) and visual contrast threshold (VCT). Inspired by this correlation, we utilized 45/-45° phase-shifted transcranial alternating current stimulation (tACS) in a sham-controlled, double-blind, within-subject experiment with 33 healthy adults (23 females; 10 males) to directly modulate these alpha traveling waves. After the application of 45° phase-shifted tACS, we observed a substantial decrease in FTW and an increase in backward traveling waves, along with a concurrent increase in VCT, compared with the sham condition. These changes were particularly pronounced under a low fatigue state. The findings of state-dependent tACS effects reveal the potential causal role of prestimulus alpha traveling waves in visual contrast detection. Moreover, our study highlights the potential of 45/-45° phase-shifted tACS in cognitive modulation and therapeutic applications.

A Broad-Scale Look at Nutrient Limitation and a Shift toward Co-limitation in United States Lakes

There is a longstanding debate about the role of nitrogen (N) vs phosphorus (P) in limiting primary production in lakes and whether co-nutrient limitation should be considered for managing eutrophication. We evaluated nutrient limitation and eutrophication at a subcontinental scale. Using U.S. Environmental Protection Agency National Lakes Assessment data, we assessed broad-scale patterns in nutrient limitation and compared samples of all surveyed lakes and lakes resurveyed in multiple surveys. We found that N correlated more strongly with productivity in the western U.S., while P correlated more strongly in the eastern U.S. The aggregated subcontinental effect suggests the importance of factors like N-deposition, terrestrial vegetation, underlying geology, and land use for understanding drivers of nutrient dynamics in lakes. Our study showed how patterns can aggregate across subcontinental scales yet still demonstrate considerable variation when more deeply examined on an individual lake level. Overall, we found that nutrient limitation is dynamic over space and time, with most lakes being co-limited. The prevalence of co-limitation also increased from 2007 to 2017. Trophic states within each limitation category varied substantially. Our findings underscore the need for combined N and P reductions to mitigate accelerated eutrophication.

Whole blood transcriptomic signature of Cushing's syndrome

OBJECTIVE

Cushing's syndrome is characterized by high morbidity and mortality with high interindividual variability. Easily measurable biomarkers, in addition to the hormone assays currently used for diagnosis, could reflect the individual biological impact of glucocorticoids. The aim of this study is to identify such biomarkers through the analysis of whole blood transcriptome.

DESIGN

Whole blood transcriptome was evaluated in 57 samples from patients with overt Cushing's syndrome, mild Cushing's syndrome, eucortisolism, and adrenal insufficiency. Samples were randomly split into a training cohort to set up a Cushing's transcriptomic signature and a validation cohort to assess this signature.

METHODS

Total RNA was obtained from whole blood samples and sequenced on a NovaSeq 6000 System (Illumina). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore the transcriptome profile. Ridge regression was used to build a Cushing's transcriptome predictor.

RESULTS

The transcriptomic profile discriminated samples with overt Cushing's syndrome. Genes mostly associated with overt Cushing's syndrome were enriched in pathways related to immunity, particularly neutrophil activation. A prediction model of 1500 genes built on the training cohort demonstrated its discriminating value in the validation cohort (accuracy .82) and remained significant in a multivariate model including the neutrophil proportion (P = .002). Expression of FKBP5, a single gene both overexpressed in Cushing's syndrome and implied in the glucocorticoid receptor signaling, could also predict Cushing's syndrome (accuracy .76).

CONCLUSIONS

Whole blood transcriptome reflects the circulating levels of glucocorticoids. FKBP5 expression could be a nonhormonal marker of Cushing's syndrome.

Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology

It is common for people working on linguistic geography, language contact and typology to make use of some type of distance metric between lects. However, most work so far has either used Euclidean distances, or geodesic distance, both of which do not represent the real separation between communities very accurately. This paper presents two datasets: one on walking distances and one on topographic distances between over 8700 lects across all macro-areas. We calculated walking distances using Open Street Maps data, and topographic distances using digital elevation data. We evaluate these distance metrics on three case studies and show that from the four distances, the topographic and geodesic distances showed the most consistent performance across datasets, and would be likely to be reasonable first choices. At the same time, in most cases, the Euclidean distances were not much worse than the other distances, and might be a good enough approximation in cases for which performance is critical, or the dataset cover very large areas, and the point-location information is not very precise.

Where was this thing again? Evaluating methods to indicate remembered object positions in virtual reality

A current focus in sensorimotor research is the study of human perception and action in increasingly naturalistic tasks and visual environments. This is further enabled by the recent commercial success of virtual reality (VR) technology, which allows for highly realistic but well-controlled three-dimensional (3D) scenes. VR enables a multitude of different ways to interact with virtual objects, but only rarely are such interaction techniques evaluated and compared before being selected for a sensorimotor experiment. Here, we compare different response techniques for a memory-guided action task, in which participants indicated the position of a previously seen 3D object in a VR scene: pointing, using a virtual laser pointer of short or unlimited length, and placing, either the target object itself or a generic reference cube. Response techniques differed in availability of 3D object cues and requirement to physically move to the remembered object position by walking. Object placement was the most accurate but slowest due to repeated repositioning. When placing objects, participants tended to match the original object's orientation. In contrast, the laser pointer was fastest but least accurate, with the short pointer showing a good speed-accuracy compromise. Our findings can help researchers in selecting appropriate methods when studying naturalistic visuomotor behavior in virtual environments.

An expedited screening platform for the discovery of anti-ageing compounds in vitro and in vivo

BACKGROUND

Restraining or slowing ageing hallmarks at the cellular level have been proposed as a route to increased organismal lifespan and healthspan. Consequently, there is great interest in anti-ageing drug discovery. However, this currently requires laborious and lengthy longevity analysis. Here, we present a novel screening readout for the expedited discovery of compounds that restrain ageing of cell populations in vitro and enable extension of in vivo lifespan.

METHODS

Using Illumina methylation arrays, we monitored DNA methylation changes accompanying long-term passaging of adult primary human cells in culture. This enabled us to develop, test, and validate the CellPopAge Clock, an epigenetic clock with underlying algorithm, unique among existing epigenetic clocks for its design to detect anti-ageing compounds in vitro. Additionally, we measured markers of senescence and performed longevity experiments in vivo in Drosophila, to further validate our approach to discover novel anti-ageing compounds. Finally, we bench mark our epigenetic clock with other available epigenetic clocks to consolidate its usefulness and specialisation for primary cells in culture.

RESULTS

We developed a novel epigenetic clock, the CellPopAge Clock, to accurately monitor the age of a population of adult human primary cells. We find that the CellPopAge Clock can detect decelerated passage-based ageing of human primary cells treated with rapamycin or trametinib, well-established longevity drugs. We then utilise the CellPopAge Clock as a screening tool for the identification of compounds which decelerate ageing of cell populations, uncovering novel anti-ageing drugs, torin2 and dactolisib (BEZ-235). We demonstrate that delayed epigenetic ageing in human primary cells treated with anti-ageing compounds is accompanied by a reduction in senescence and ageing biomarkers. Finally, we extend our screening platform in vivo by taking advantage of a specially formulated holidic medium for increased drug bioavailability in Drosophila. We show that the novel anti-ageing drugs, torin2 and dactolisib (BEZ-235), increase longevity in vivo.

CONCLUSIONS

Our method expands the scope of CpG methylation profiling to accurately and rapidly detecting anti-ageing potential of drugs using human cells in vitro, and in vivo, providing a novel accelerated discovery platform to test sought after anti-ageing compounds and geroprotectors.

GSDME-mediated pyroptosis promotes anti-tumor immunity of neoadjuvant chemotherapy in breast cancer

Paclitaxel and anthracycline-based chemotherapy is one of the standard treatment options for breast cancer. However, only about 6-30% of breast cancer patients achieved a pathological complete response (pCR), and the mechanism responsible for the difference is still unclear. In this study, random forest algorithm was used to screen feature genes, and artificial neural network (ANN) algorithm was used to construct an ANN model for predicting the efficacy of neoadjuvant chemotherapy for breast cancer. Furthermore, digital pathology, cytology, and molecular biology experiments were used to verify the relationship between the efficacy of neoadjuvant chemotherapy and immune ecology. It was found that paclitaxel and doxorubicin, an anthracycline, could induce typical pyroptosis and bubbling in breast cancer cells, accompanied by gasdermin E (GSDME) cleavage. Paclitaxel with LDH release and Annexin V/PI doubule positive cell populations, and accompanied by the increased release of damage-associated molecular patterns, HMGB1 and ATP. Cell coculture experiments also demonstrated enhanced phagocytosis of macrophages and increased the levels of IFN-γ and IL-2 secretion after paclitaxel treatment. Mechanistically, GSDME may mediate paclitaxel and doxorubicin-induced pyroptosis in breast cancer cells through the caspase-9/caspase-3 pathway, activate anti-tumor immunity, and promote the efficacy of paclitaxel and anthracycline-based neoadjuvant chemotherapy. This study has practical guiding significance for the precision treatment of breast cancer, and can also provide ideas for understanding molecular mechanisms related to the chemotherapy sensitivity.

Multiple evolutionary pressures shape identical consonant avoidance in the world's languages

Languages disfavor word forms containing sequences of similar or identical consonants, due to the biomechanical and cognitive difficulties posed by patterns of this sort. However, the specific evolutionary processes responsible for this phenomenon are not fully understood. Words containing sequences of identical consonants may be more likely to arise than those without; processes of word form mutation may be more likely to remove than create sequences of identical consonants in word forms; finally, words containing identical consonants may die out more frequently than those without. Phylogenetic analyses of the evolution of homologous word forms indicate that words with identical consonants arise less frequently than those without. However, words with identical consonants do not die out more frequently than those without. Further analyses reveal that forms with identical consonants are replaced in basic meaning functions more frequently than words without. Taken together, results suggest that the underrepresentation of sequences of identical consonants is overwhelmingly a by-product of constraints on word form coinage, though processes related to word usage also serve to ensure that such patterns are infrequent in more salient vocabulary items. These findings clarify aspects of processes of lexical evolution and competition that take place during language change, optimizing communicative systems.

Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study

BACKGROUND

Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy could be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized controlled trials on psychological interventions for common mental health problems.

METHODS

This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203).

RESULTS

Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU.

LIMITATIONS

Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables.

CONCLUSIONS

Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.

Maternal early life and prenatal stress in relation to birth outcomes in Argentinian mothers

Environmental influences before and during pregnancy significantly impact offspring development. This study investigates open research questions regarding the associations between maternal early life stress (ELS), prenatal psychosocial stress, prenatal hair cortisol (HC), and birth outcomes in Argentinian women. Data on ELS, prenatal life events, HC (two samples representing first and second half of pregnancy), and birth outcomes were collected from middle-class Argentinian women (N = 69) upon delivery. Linear mixed models indicated that HC increased from the first half to the second half of pregnancy with considerable variability in the starting values and slopes between individuals. Mothers who experienced more ELS, were taller, or more educated, tended to show lower increases in HC. Older age was positively related to HC increases. Our data did not suggest an interaction between ELS and prenatal life events in relation to HC. We found that the change in HC was most likely negatively associated with birth weight. Our data are most compatible with either a weak or the absence of an association between ELS or prenatal life events and absolute values of HC. Mothers with stronger increases in hair cortisol tended to have newborns with slightly lower birth weight. Hence, ELS and birthweight may either have been related to changes in cortisol exposure during pregnancy or to factors that influence accumulation or retention of cortisol in hair.

The natural anthraquinone dye emodin: Eco/genotoxicological characterization for aquatic organisms

Emodin is an anthraquinone secondary metabolite produced by several species of plants and fungi. Emodin is known for its pharmacological versatility, and, in the textile industry, for its good dyeing properties. However, its use in the textile industry can result in the formation and disposal of large volumes of wastewater. Emodin mutagenicity has been shown in bacteria and in human cells, but little is known about its possible toxic, genotoxic, or mutagenic effects in aquatic organisms. We have evaluated the eco/genotoxicity of emodin to aquatic organisms. Emodin was toxic to Daphnia similis (EC50 = 130 μg L-1) and zebrafish embryos (LC50 = 25 μg L-1). No toxicity was observed for Raphidocelis subcapitata, Ceriodaphnia dubia, or Parhyale hawaiensis. Additional biochemistry/molecular studies are needed to elucidate the toxic/mutagenic pathways of emodin in aquatic organisms. The PNEC value for emodin was 0.025 μg L-1. In addition to mutagenicity in the Salmonella/microsome assay, emodin was mutagenic in the micronucleus assay in the amphipod P. hawaiensis. Among the anthraquinone dyes tested to date, natural or synthetic, emodin was the most toxic to aquatic species.

Bayesian Meta-Analysis of Health State Utility Values: A Tutorial with a Practical Application in Heart Failure

Researchers incorporate health state utility values as inputs to inform economic models. However, for a particular health state or condition, multiple utility values derived from different studies typically exist and a single study is often insufficient to represent the best available source of utility needed to inform policy decisions. The purpose of this paper is to provide an introductory guidance for conducting Bayesian meta-analysis of health state utility values to generate a single parameter input for economic evaluation, using R. The tutorial is illustrated using data from a systematic review of health state utilities of patients with heart failure, with 21 studies that reported utilities measured using the EuroQol-5D (EQ-5D). Explanations, key considerations and suggested readings are provided for each step of the tutorial, adhering to a clear workflow for conducting Bayesian meta-analysis: (1) setting-up the data; (2) employing methods to impute missing standard deviations; (3) defining the priors; (4) fitting the model; (5) diagnosing model convergence; (6) interpreting the results; and (7) performing sensitivity analyses. The posterior distributions for the pooled effect size (i.e. mean health state utility) and between-study heterogeneity are discussed and interpreted in light of the data, priors and models used. We hope that this tutorial will foster interest in Bayesian methods and their applications in the meta-analysis of utilities.

Screening and Analysis of Core Genes for Osteoporosis Based on Bioinformatics Analysis and Machine Learning Algorithms

Objective

This study aimed to identify osteoporosis-related core genes using bioinformatics analysis and machine learning algorithms.

Methods

mRNA expression profiles of osteoporosis patients were obtained from the Gene Expression Profiles (GEO) database, with GEO35958 and GEO84500 used as training sets, and GEO35957 and GSE56116 as validation sets. Differential gene expression analysis was performed using the R software "limma" package. A weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and modular genes of osteoporosis. Kyoto Gene and Genome Encyclopedia (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were performed on the differentially expressed genes. LASSO, SVM-RFE, and RF machine learning algorithms were used to screen for core genes, which were subsequently validated in the validation set. Predicted microRNAs (miRNAs) from the core genes were also analyzed, and differential miRNAs were validated using quantitative real-time PCR (qPCR) experiments.

Results

A total of 1280 differentially expressed genes were identified. A disease key module and 215 module key genes were identified by WGCNA. Three core genes (ADAMTS5, COL10A1, KIAA0040) were screened by machine learning algorithms, and COL10A1 had high diagnostic value for osteoporosis. Four core miRNAs (has-miR-148a-3p, has-miR-195-3p, has-miR-148b-3p, has-miR-4531) were found by intersecting predicted miRNAs with differential miRNAs from the dataset (GSE64433, GSE74209). The qPCR experiments validated that the expression of has-miR-195-3p, has-miR-148b-3p, and has-miR-4531 was significantly increased in osteoporosis patients.

Conclusion

This study demonstrated the utility of bioinformatics analysis and machine learning algorithms in identifying core genes associated with osteoporosis.

A single-cell transcriptome atlas of human euploid and aneuploid blastocysts

Aneuploidy is frequently detected in early human embryos as a major cause of early pregnancy failure. However, how aneuploidy affects cellular function remains elusive. Here, we profiled the transcriptomes of 14,908 single cells from 203 human euploid and aneuploid blastocysts involving autosomal and sex chromosomes. Nearly all of the blastocysts contained four lineages. In aneuploid chromosomes, 19.5% ± 1.2% of the expressed genes showed a dosage effect, and 90 dosage-sensitive domains were identified. Aneuploidy leads to prevalent genome-wide transcriptome alterations. Common effects, including apoptosis, were identified, especially in monosomies, partially explaining the lower cell numbers in autosomal monosomies. We further identified lineage-specific effects causing unstable epiblast development in aneuploidies, which was accompanied by the downregulation of TGF-β and FGF signaling, which resulted in insufficient trophectoderm maturation. Our work provides crucial insights into the molecular basis of human aneuploid blastocysts and may shed light on the cellular interaction during blastocyst development.

The Health Impact of Waiting for Elective Procedures in the NHS in England: A Modeling Framework Applied to Coronary Artery Bypass Graft and Total Hip Replacement

INTRODUCTION

The aim of this study is to demonstrate a practical framework that can be applied to estimate the health impact of changes in waiting times across a range of elective procedures in the National Health Service (NHS) in England. We apply this framework by modeling 2 procedures: coronary artery bypass graft (CABG) and total hip replacement (THR).

METHODS

We built a Markov model capturing health pre- and postprocedure, including the possibility of exiting preprocedure to acute NHS care or self-funded private care. We estimate the change in quality-adjusted life-years (QALYs) over a lifetime horizon for 10 subgroups defined by sex and Index of Multiple Deprivation quintile groups and for 7 alternative scenarios. We include 18 wk as a baseline waiting time consistent with current NHS policy. The model was populated with data from routinely collected data sets where possible (Hospital Episode Statistics, Patient-Reported Outcome Measures, and Office for National Statistics Mortality records), supplemented by the academic literature.

RESULTS

Compared with 18 wk, increasing the wait time to 36 wk resulted in a mean discounted QALY loss in the range of 0.034 to 0.043 for CABG and 0.193 to 0.291 for THR. The QALY impact of longer NHS waits was greater for those living in more deprived areas, partly as fewer patients switch to private care.

DISCUSSION/CONCLUSION

The proposed framework was applied to 2 different procedures and patient populations. If applied to an expanded group of procedures, it could provide decision makers with information to inform prioritization of waiting lists. There are a number of limitations in routine data on waiting for elective procedures, primarily the lack of information on people still waiting.

HIGHLIGHTS

We present a modeling framework that allows for an estimation of the health impact (measured in quality-adjusted life-years) of waiting for elective procedures in the NHS in England.We apply our model to waiting for coronary artery bypass graft (CABG) and total hip replacement (THR). Increasing the wait for THR results in a larger health loss than an equivalent increase in wait for CABG.This model could potentially be used to estimate the impact across an expanded group of procedures to inform prioritization of activities to reduce waiting times.

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

Diffusion tensor imaging analysis along the perivascular space in the UK biobank

BACKGROUND

The recently discovered glymphatic system may support the removal of neurotoxic proteins, mainly during sleep, that are associated with neurodegenerative diseases such as Alzheimer's and Parkinson's Disease. Diffusion tensor image analysis along the perivascular space (DTI-ALPS) has been suggested as a method to index the health of glymphatic system (with higher values indicating a more intact glymphatic system). Indeed, in small-scale studies the DTI-ALPS index has been shown to correlate with age, cognitive health, and sleep, and is higher in females than males.

OBJECTIVE

To determine whether these relationships are stable we replicated previous findings associating the DTI-ALPS index with demographic, sleep-related, and cognitive markers in a large sample of participants from the UK Biobank.

METHODS

We calculated the DTI-ALPS index in UK Biobank participants (n = 17723). Using Bayesian and Frequentist analysis approaches, we replicate previously reported relationships between the DTI-ALPS index.

RESULTS

We found the predicted associations between the DTI-ALPS index and age, longest uninterrupted sleep window (LUSWT) on a typical night, cognitive performance, and sex. However, these effects were substantially smaller than those found in previous studies. Parameter estimates from this study may be used as priors in subsequent studies using a Bayesian approach. These results suggest that the DTI-ALPS index is consistently, and therefore predictably, associated with demographics, LUWST, and cognition.

CONCLUSION

We propose that the metric, calculated for the first time in a large-scale, population-based cohort, is a stable measure, but one for which stronger links to glymphatic system function are needed before it can be used to understand the relationships between glymphatic system function and health outcomes reported in the UK Biobank.

Gestational exposure to organochlorine compounds and metals and infant birth weight: effect modification by maternal hardships

BACKGROUND

Gestational exposure to toxic environmental chemicals and maternal social hardships are individually associated with impaired fetal growth, but it is unclear whether the effects of environmental chemical exposure on infant birth weight are modified by maternal hardships.

METHODS

We used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian cohort of 1982 pregnant females enrolled between 2008 and 2011. We quantified eleven environmental chemical concentrations from two chemical classes - six organochlorine compounds (OCs) and five metals - that were detected in ≥ 70% of blood samples collected during the first trimester. We examined fetal growth using birth weight adjusted for gestational age and assessed nine maternal hardships by questionnaire. Each maternal hardship variable was dichotomized to indicate whether the females experienced the hardship. In our analysis, we used elastic net to select the environmental chemicals, maternal hardships, and 2-way interactions between maternal hardships and environmental chemicals that were most predictive of birth weight. Next, we obtained effect estimates using multiple linear regression, and plotted the relationships by hardship status for visual interpretation.

RESULTS

Elastic net selected trans-nonachlor, lead, low educational status, racially minoritized background, and low supplemental folic acid intake. All were inversely associated with birth weight. Elastic net also selected interaction terms. Among those with increasing environmental chemical exposures and reported hardships, we observed stronger negative associations and a few positive associations. For example, every two-fold increase in lead concentrations was more strongly associated with reduced infant birth weight among participants with low educational status (β = -100 g (g); 95% confidence interval (CI): -215, 16), than those with higher educational status (β = -34 g; 95% CI: -63, -3). In contrast, every two-fold increase in mercury concentrations was associated with slightly higher birth weight among participants with low educational status (β = 23 g; 95% CI: -25, 71) compared to those with higher educational status (β = -9 g; 95% CI: -24, 6).

CONCLUSIONS

Our findings suggest that maternal hardships can modify the associations of gestational exposure to some OCs and metals with infant birth weight.

Bayesian estimation in veterinary pharmacology: A conceptual and practical introduction

Sophisticated mathematical and computational tools have become widespread and important in veterinary pharmacology. Although the theoretical basis and practical applications of these have been widely explored in the literature, statistical inference in the context of these models has received less attention. Optimization methods, often with frequentist statistical inference, have been predominant. In contrast, Bayesian statistics have not been widely applied, but offer both practical utility and arguably greater interpretability. Veterinary pharmacology applications are generally well supported by relevant prior information, from either existing substantive knowledge, or an understanding of study and model design. This facilitates practical implementation of Bayesian analyses that can take advantage of this knowledge. This essay will explore the specification of Bayesian models relevant to veterinary pharmacology, including demonstration of prior selection, and illustrate the capability of these models to generate practically useful statistics, including uncertainty statements, that are difficult or impossible to obtain otherwise. Case studies using simulated data will describe applications in clinical trials, pharmacodynamics, and pharmacokinetics, all including multilevel modeling. This content may serve as a suitable starting point for researchers in veterinary pharmacology and related disciplines considering Bayesian estimation for their applied work.

Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes

Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the weights have minimal dispersion and thus produce a more stable estimator. A recent, increasingly common approach directly optimizes the weights toward these two objectives. However, this approach has not yet been feasible in large-scale datasets when investigators wish to flexibly balance general basis functions in an extended feature space. To address this practical problem, we describe a scalable and flexible approach to weighting that integrates a basis expansion in a reproducing kernel Hilbert space with state-of-the-art convex optimization techniques. Specifically, we use the rank-restricted Nyström method to efficiently compute a kernel basis for balancing in nearly linear time and space, and then use the specialized first-order alternating direction method of multipliers to rapidly find the optimal weights. In an extensive simulation study, we provide new insights into the performance of weighting estimators in large datasets, showing that the proposed approach substantially outperforms others in terms of accuracy and speed. Finally, we use this weighting approach to conduct a national study of the relationship between hospital profit status and heart attack outcomes in a comprehensive dataset of 1.27 million patients. We find that for-profit hospitals use interventional cardiology to treat heart attacks at similar rates as other hospitals but have higher mortality and readmission rates.

The influence of neighborhood income on healthcare utilization in pediatric liver transplant

OBJECTIVES

Neighborhood contextual factors are associated with liver transplant outcomes. We analyzed associations between neighborhood-level socioeconomic status and healthcare utilization for pediatric liver transplant recipients.

METHODS

We merged the Pediatric Health Information System and Scientific Registry of Transplant Recipients databases and included liver transplant recipients ≤21 years hospitalized between January 2004 and May 2022. Outcomes were annual inpatient bed-days, risk of hospitalizations, and risk of liver biopsies. The primary exposure was zip code-based neighborhood income at transplant. We applied causal inference for variable selection in multivariable analysis. We modeled annual inpatient bed-days with mixed-effect zero-inflated Poisson regression, and rates of hospitalization and liver biopsy with a Cox-type proportional rate model.

RESULTS

We included 1006 participants from 29 institutions. Children from low-income neighborhoods were more likely to be publicly insured (67% vs. 46%), Black (20% vs. 12%), Hispanic (30% vs. 17%), and have higher model for end-stage liver disease/pediatric end-stage liver disease model scores at transplant (17 vs. 13) than the remaining cohort. We found no differences in inpatient bed-days or rates of hospitalization across neighborhood groups. In univariable analysis, low-income neighborhoods were associated with increased rates of liver biopsy (rate ratio [RR]: 1.57, 95% confidence interval [CI]: 1.04-2.34, p = 0.03). These findings persisted after adjusting for insurance, race, and ethnicity (RR: 1.86, 95% CI: 1.23-2.83, p < 0.01).

CONCLUSIONS

Children from low-income neighborhoods undergo more liver biopsies than other children. These procedures are invasive and potentially preventable. In addition to improving outcomes, interventions to mitigate health inequities among liver transplant recipients may reduce resource utilization.