Investigating the Performance of Frequentist and Bayesian Techniques in Genomic Evaluation

The genomic evaluation process relies on the assumption of linkage disequilibrium between dense single-nucleotide polymorphism (SNP) markers at the genome level and quantitative trait loci (QTL). The present study was conducted with the aim of evaluating four frequentist methods including Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, and Genomic Best Linear Unbiased Prediction (GBLUP) and five Bayesian methods including Bayes Ridge Regression (BRR), Bayes A, Bayesian LASSO, Bayes C, and Bayes B, in genomic selection using simulation data. The difference between prediction accuracy was assessed in pairs based on statistical significance (p-value) (i.e., t test and Mann-Whitney U test) and practical significance (Cohen's d effect size) For this purpose, the data were simulated based on two scenarios in different marker densities (4000 and 8000, in the whole genome). The simulated data included a genome with four chromosomes, 1 Morgan each, on which 100 randomly distributed QTL and two different densities of evenly distributed SNPs (1000 and 2000), at the heritability level of 0.4, was considered. For the frequentist methods except for GBLUP, the regularization parameter λ was calculated using a five-fold cross-validation approach. For both scenarios, among the frequentist methods, the highest prediction accuracy was observed by Ridge Regression and GBLUP. The lowest and the highest bias were shown by Ridge Regression and GBLUP, respectively. Also, among the Bayesian methods, Bayes B and BRR showed the highest and lowest prediction accuracy, respectively. The lowest bias in both scenarios was registered by Bayesian LASSO and the highest bias in the first and the second scenario were shown by BRR and Bayes B, respectively. Across all the studied methods in both scenarios, the highest and the lowest accuracy were shown by Bayes B and LASSO and Elastic Net, respectively. As expected, the greatest similarity in performance was observed between GBLUP and BRR ( d = 0.007 , in the first scenario and d = 0.003 , in the second scenario). The results obtained from parametric t and non-parametric Mann-Whitney U tests were similar. In the first and second scenario, out of 36 t test between the performance of the studied methods in each scenario, 14 ( P < . 001 ) and 2 ( P < . 05 ) comparisons were significant, respectively, which indicates that with the increase in the number of predictors, the difference in the performance of different methods decreases. This was proven based on the Cohen's d effect size, so that with the increase in the complexity of the model, the effect size was not seen as very large. The regularization parameters in frequentist methods should be optimized by cross-validation approach before using these methods in genomic evaluation.

A Novel Pancreatic Cancer Hypoxia Status Related Gene Signature for Prognosis and Therapeutic Responses

Pancreatic cancer (PAC) is a highly fatal and aggressive type of cancer. Hypoxia is a common feature of PAC. The aim of this study was to develop a hypoxia status-related prognostic model for predicting the survival outcomes in PAC. The data sets of PAC from The Cancer Genome Atlas and the International Cancer Genome Consortium were used to construct and validate the signature. A 6 hypoxia status-related differential expression genes prognostic model for predicting the survival outcomes was established. The Kaplan-Meier analysis and Received operating characteristic curve indicated the good performance of the signature at predicting overall survival. Univariate and Multivariate Cox regression revealed that the signature was an independent prognostic factor in PAC. Weighted Gene Co-expression Network Analysis and immune infiltration analysis indicated that Immune-related pathways and immune cell infiltration was mostly enriched in the low-risk group, which presented a better prognosis. We also evaluated the predictive of the signature for immunotherapy and chemoradiotherapy. Risk gene LY6D may be a potential prognostic predictor of PAC. This model can be used as an independent prognostic factor for predicting clinical outcomes and a possible classifier for response to chemotherapy.

A scalable approach for continuous time Markov models with covariates

Existing methods for fitting continuous time Markov models (CTMM) in the presence of covariates suffer from scalability issues due to high computational cost of matrix exponentials calculated for each observation. In this article, we propose an optimization technique for CTMM which uses a stochastic gradient descent algorithm combined with differentiation of the matrix exponential using a Padé approximation. This approach makes fitting large scale data feasible. We present two methods for computing standard errors, one novel approach using the Padé expansion and the other using power series expansion of the matrix exponential. Through simulations, we find improved performance relative to existing CTMM methods, and we demonstrate the method on the large-scale multiple sclerosis NO.MS data set.

Development and Evaluation of an Enhanced Recovery Protocol to Reduce Length of Stay following Elective Endovascular Aneurysm Repair

BACKGROUND

Elective endovascular abdominal aortic aneurysm repair (EVAR) can be performed safely with a short postoperative length of stay (LOS). We aimed to develop and assess the impact of an enhanced recovery protocol (ERP) on LOS after elective EVAR.

METHODS

Pre-ERP development single center retrospective review of elective EVAR procedures from January 2012 to December 2019. ERP was developed by targeting factors associated with prolonged LOS (>2 days) elucidated from semistructured interviews and Bayesian additive regression tree analysis. Post-ERP development, a subsequent retrospective review of elective EVAR performed from January 2018 to June 2021 was performed to evaluate LOS before and after ERP. Primary outcome was LOS.

RESULTS

Two hundred sixteen patients underwent elective infrarenal EVAR from 2012 to 2019. Periprocedural factors identified as associated with LOS >2 days included noncommercial insurance (43.6% vs. 26.5%; P = 0.01), preoperative anemia (hemoglobin 12.56 g/dL vs. 13.57 g/dL; P = 0.001), worse renal function (creatinine 1.31 mg/dL vs. 1.01/dL; P = 0.004), open femoral access (74.4% vs. 26.5%; P < 0.001), intensive care unit (ICU) stay (2.7 days vs. 0.9 days; P < 0.001), postoperative anemia (9.8 g/dL vs. 11.9 g/dL; P < 0.001), postoperative creatinine (1.55 mg/dL vs. 0.97 mg/dL; P < 0.001), and beta blocker need on discharge (45.5% vs. 25%; P = 0.003) as significant between patients with short and prolonged LOS groups. Semistructured interviews revealed postoperative day 1 complete blood count/chemistry, postoperative physical therapy evaluation, ICU admission, urinary retention, patient expectations, and unavailability of transportation home as modifiable factors that delayed early discharge. A 14-component ERP was created to target the factors identified from combined qualitative and quantitative results. Post-ERP development, 74 elective EVAR patients were reviewed from 2018 to 2021 (37 pre-ERP and 37 post-ERP). Following ERP development, the mean LOS was reduced from 2.6 (standard deviation: 1.9) to 1.3 days (standard deviation: 1.3); P < 0.01. There were no significant differences in 30-day readmission, postoperative complications, emergency room visits, or 90-day mortality before and after the ERP was used.

CONCLUSIONS

Practice and procedural factors can be modified through an informed and safe process to reduce LOS after elective EVAR. LOS following elective EVAR was safely reduced following the use of a systematically developed ERP.

Distinct genetic liability profiles define clinically relevant patient strata across common diseases

Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

Using Power Analysis to Choose the Unit of Randomization, Outcome, and Approach for Subgroup Analysis for a Multilevel Randomized Controlled Clinical Trial to Reduce Disparities in Cardiovascular Health

We give examples of three features in the design of randomized controlled clinical trials which can increase power and thus decrease sample size and costs. We consider an example multilevel trial with several levels of clustering. For a fixed number of independent sampling units, we show that power can vary widely with the choice of the level of randomization. We demonstrate that power and interpretability can improve by testing a multivariate outcome rather than an unweighted composite outcome. Finally, we show that using a pooled analytic approach, which analyzes data for all subgroups in a single model, improves power for testing the intervention effect compared to a stratified analysis, which analyzes data for each subgroup in a separate model. The power results are computed for a proposed prevention research study. The trial plans to randomize adults to either telehealth (intervention) or in-person treatment (control) to reduce cardiovascular risk factors. The trial outcomes will be measures of the Essential Eight, a set of scores for cardiovascular health developed by the American Heart Association which can be combined into a single composite score. The proposed trial is a multilevel study, with outcomes measured on participants, participants treated by the same provider, providers nested within clinics, and clinics nested within hospitals. Investigators suspect that the intervention effect will be greater in rural participants, who live farther from clinics than urban participants. The results use published, exact analytic methods for power calculations with continuous outcomes. We provide example code for power analyses using validated software.

Mitotic catastrophe heterogeneity: implications for prognosis and immunotherapy in hepatocellular carcinoma

Background and aims

The mitotic catastrophe (MC) pathway plays an important role in hepatocellular carcinoma (HCC) progression and tumor microenvironment (TME) regulation. However, the mechanisms linking MC heterogeneity to immune evasion and treatment response remain unclear.

Methods

Based on 94 previously published highly correlated genes for MC, HCC patients' data from the Cancer Genome Atlas (TCGA) and changes in immune signatures and prognostic stratification were studied. Time and spatial-specific differences for MCGs were assessed by single-cell RNA sequencing and spatial transcriptome (ST) analysis. Multiple external databases (GEO, ICGC) were employed to construct an MC-related riskscore model.

Results

Identification of two MC-related subtypes in HCC patients from TCGA, with clear differences in immune signatures and prognostic risk stratification. Spatial mapping further associates low MC tumor regions with significant immune escape-related signaling. Nomogram combining MC riskscore and traditional indicators was validated great effect for early prediction of HCC patient outcomes.

Conclusion

MC heterogeneity enables immune escape and therapy resistance in HCC. The MC gene signature serves as a reliable prognostic indicator for liver cancer. By revealing clear immune and spatial heterogeneity of HCC, our integrated approach provides contextual therapeutic strategies for optimal clinical decision-making.

How to assess the long-term recovery outcomes of patients with cauda equina syndrome before surgery: a retrospective cohort study

BACKGROUND

Factors influencing recovery after decompression surgery for cauda equina syndrome (CES) are not completely identified. The authors aimed to investigate the most valuable predictors (MVPs) of poor postoperative recovery (PPR) in patients with CES and construct a nomogram for discerning those who will experience PPR.

METHODS

Three hundred fifty-six patients with CES secondary to lumbar degenerative diseases treated at Xijing Hospital were randomly divided into training ( N =238) and validation ( N =118) cohorts at a 2:1 ratio. Moreover, 92 patients from the 970 th Hospital composed the testing cohort. Least Absolute Shrinkage and Selection Operator regression (LASSO) was used for selecting MVPs. The nomogram was developed by integrating coefficients of MVPs in the logistic regression, and its discrimination, calibration, and clinical utility were validated in all three cohorts.

RESULTS

After 3 to 5 years of follow-up, the residual rates of bladder dysfunction, bowel dysfunction, sexual dysfunction, and saddle anesthesia were 41.9, 44.1, 63.7, and 29.0%, respectively. MVPs included stress urinary incontinence, overactive bladder, low stream, difficult defecation, fecal incontinence, and saddle anesthesia in order. The discriminatory ability of the nomogram was up to 0.896, 0.919, and 0.848 in the training, validation, and testing cohorts, respectively. Besides, the nomogram showed good calibration and clinical utility in all cohorts. Furthermore, the optimal cutoff value of the nomogram score for distinguishing those who will experience PPR was 148.02, above which postoperative outcomes tend to be poor.

CONCLUSION

The first pretreatment nomogram for discerning CES patients who will experience PPR was developed and validated, which will aid clinicians in clinical decision-making.

COVID-19 Research in Communication Journals: A Structural Topic Modeling-Assisted Bibliometric Analysis

This article presents a bibliometric analysis of research on COVID-19 health communication. We reviewed and analyzed 1,851 articles published in 170 peer-reviewed communication journals between January 2020 and November 2022, to identify key bibliometric information and major research topics in this rapidly expanding field of research. The distribution of countries indicates that the United States is the most productive country, and researchers from Spain, China and the United Kingdom also play an important role. Health Communication is the most influential journal in terms of research productivity and impact. The analysis of highly cited references demonstrates the interdisciplinary nature of this research field. The topics generated by structural topic modeling show that scholars have responded to a variety of issues in COVID-19 communication, encompassing different levels of health communication, the effects of information dissemination, the impact on the general public as well as vulnerable populations, health preventive behaviors and communication technologies. This study aims to enhance researchers' understanding of the current state of this research field and provide insights for future studies.

Constructing a screening model to obtain the functional herbs for the treatment of active ulcerative colitis based on herb-compound-target network and immuno-infiltration analysis

The therapeutic effect of most traditional Chinese medicines (TCM) on ulcerative colitis is unclear, The objective of this study was to develop a core herbal screening model aimed at facilitating the transition from active ulcerative colitis (UC) to inactive. We obtained the gene expression dataset GSE75214 for UC from the GEO database and analysed the differentially expressed genes (DEGs) between active and inactive groups. Gene modules associated with the active group were screened using WGCNA, and immune-related genes (IRGs) were obtained from the ImmPort database. The TCMSP database was utilized to acquire the herb-molecule-target network and identify the herb-related targets (HRT). We performed intersection operations on HRTs, DEGs, IRGs, and module genes to identify candidate genes and conducted enrichment analyses. Subsequently, three machine learning algorithms (SVM-REF analysis, Random Forest analysis, and LASSO regression analysis) were employed to refine the hubgene from the candidate genes. Based on the hub genes identified in this study, we conducted compound and herb matching and further screened herbs related to abdominal pain and blood in stool using the Symmap database.Besides, the stability between molecules and targets were assessed using molecular docking and molecular dynamic simulation methods. An intersection operation was performed on HRT, DEGs, IRGs, and module genes, leading to the identification of 23 candidate genes. Utilizing three algorithms (RandomForest, SVM-REF, and LASSO) for analyzing the candidate genes and identifying the intersection, we identified five core targets (CXCL2, DUOX2, LYZ, MMP9, and AGT) and 243 associated herbs. Hedysarum Multijugum Maxim. (Huangqi), Sophorae Flavescentis Radix (Kushen), Cotyledon Fimbriata Turcz. (Wasong), and Granati Pericarpium (Shiliupi) were found to be capable of relieving abdominal pain and hematochezia during active UC. Molecular docking demonstrated that the compounds of the four aforementioned herbs showed positive docking activity with their core targets. The results of molecular dynamic simulations indicated that well-docked active molecules had a more stable structure when bound to their target complexes. The study has shed light on the potential of TCMs in treating active UC from an immunomodulatory perspective, consequently, 5 core targets and 4 key herbs has been identified. These findings can provide a theoretical basis for subsequent management and treatment of active UC with TCM, as well as offer original ideas for further research and development of innovative drugs for alleviating UC.

Forest harvest causes rapid changes of maternal investment strategies in ground beetles

Species recovery following anthropogenic disturbances will depend on adaptations in survivorship and fecundity. Life-history theory predicts increased environmental stress will result in (1) shifts in resource allocation from fecundity to body growth/maintenance and (2) increased provisioning among offspring at the cost of reproductive output. For remnant populations that persist after forest harvesting, selection mediated through anthropogenic disturbances may affect resilience to additional stressors such as climate change. We tested how rapid changes in environmental conditions affected maternal investment strategies in two ground beetle species, Pterostichus pensylvanicus and Pterostichus coracinus, by comparing fecundity and survivorship in populations from recently clear-cut and uncut habitats. Using parents drawn from clear-cut or uncut stands, we reared progeny in both common garden and reciprocal transplant experiments. In P. pensylvanicus, we found that neither lineage nor rearing habitat affected the number of eggs laid per female or survivorship of offspring. However, eggs laid by females from clear-cuts were more likely to hatch and offspring reached maturity more quickly, suggesting increased provisioning per offspring. In P. coracinus, females from clear-cuts laid more eggs, and their eggs hatched more rapidly and had greater hatching success, suggesting increased investment in overall reproductive output and increased offspring provisioning. In the reciprocal transplant, we observed significant habitat by lineage interactions on survival in P. coracinus, with survivorship increasing when progeny were reared in novel habitats. In both species, increased maternal investment among offspring was not associated with a reduction in overall reproductive output, as anticipated. However, maternal investment among offspring declined with increasing female size, implying trade-offs between increased metabolic demand and fecundity. Taken together, our work suggests that females from more stressful, clear-cut habitats increased investment in fecundity, compared to females from uncut habitats, and may compensate for larval mortality. These changes were driven by smaller individuals, suggesting that increased environmental stress can influence the relationship between female size and maternal investment strategy. Additionally, reciprocal increases in offspring survivorship in habitats other than the parents suggest that adjacent areas between unharvested and clear-cut habitat may be useful in maintaining biodiversity under future climate stressors.

A comprehensive review and shiny application on the matching-adjusted indirect comparison

Population-adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head-to-head trials are unavailable. Three commonly used PAIC methods include matching-adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta-regression (ML-NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user-friendly R Shiny application Shiny-MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny-MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny-MAIC application, enabling a user-friendly approach conducting MAIC for healthcare decision-making. The Shiny-MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/.

Chemoinformatic regression methods and their applicability domain

The growing interest in chemoinformatic model uncertainty calls for a summary of the most widely used regression techniques and how to estimate their reliability. Regression models learn a mapping from the space of explanatory variables to the space of continuous output values. Among other limitations, the predictive performance of the model is restricted by the training data used for model fitting. Identification of unusual objects by outlier detection methods can improve model performance. Additionally, proper model evaluation necessitates defining the limitations of the model, often called the applicability domain. Comparable to certain classifiers, some regression techniques come with built-in methods or augmentations to quantify their (un)certainty, while others rely on generic procedures. The theoretical background of their working principles and how to deduce specific and general definitions for their domain of applicability shall be explained.

Association between multipollutant exposure and thyroid hormones in elderly people: A cross-sectional study in China

Environmental chemicals have been indicated to cause disruption of thyroid homeostasis in human populations. However, previous studies mostly focused on single group of chemicals. Herein, we investigate the independent and combined effects of multiple pollutants on thyroid homeostasis, including thyroid-stimulating hormone (TSH), total and free thyroxine (tT4 and fT4) and total and free triiodothyronine (tT3 and fT3) in elderly people. These environmental pollutants (n = 144) are from ten categories, including phenols, parabens, perfluoroalkyl substances (PFASs), polychlorinated biphenyls (PCBs), phthalate esters (PAEs), polycyclic aromatic hydrocarbons (PAHs), organochlorine pesticides (OCPs), organophosphate pesticides (OPPs), synthetic pyrethroids (SPs), herbicides, and metals. Few studies have evaluated the health risks of these 144 chemicals, especially their joint effects. In single-pollutant evaluations, multiple linear regression (MLR) models were used to estimate the independent associations between multiple exposures and thyroid biomarkers. In multi-pollutant evaluations, elastic net regression and Bayesian kernel machine regression (BKMR) models were used to estimate the combined associations. The MLR models showed that 41 chemicals were significantly related to THs levels. BKMR models revealed the most important chemical groups: metals for TSH, PAHs, SPs and PCBs for tT4, herbicides and SPs for tT3. This study will contribute to the understanding of multipollutant exposure and help prioritize specific chemical groups related to thyroid hormone disruption.

A novel nomogram for the prediction of subsyndromal delirium in patients in intensive care units: A prospective, nested case-controlled study

BACKGROUND

Subsyndromal delirium is a dynamic, recognizable condition commonly observed in intensive care unit (ICU) patients that can lead to poor patient prognosis, and its prompt recognition and management can prevent disease progression. However, no evidence-based predictive tool has been developed specifically to assess the occurrence of subsyndromal delirium in the ICU.

OBJECTIVE

To develop and validate a novel, simple and effective tool for estimating the risk of subsyndromal delirium among ICU patients.

DESIGN

A prospective, nested case-controlled study.

DATA SOURCES

A total of 731 patients were recruited from the central ICU of a tertiary hospital in southwestern China from August 2021 to November 2022.

METHODS

The least absolute shrinkage and selection operator was applied to screen potential features for univariate and multivariate logistic regression. A nomogram was constructed using the selected variables. The performance of the nomogram was evaluated by combining the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA).

RESULTS

The prevalence of subsyndromal delirium among ICU patients was 23.06 %. Multiple logistic regression analysis revealed that the independent predictive factors for subsyndromal delirium among ICU patients were vision impairment, a history of falls, the use of restraint, blood transfusion, the use of antibiotics, surgery, the Caprini score, and the Braden score, all of which were used to construct the nomogram. The AUCs for the model were 0.710 (95 % CI, 0.654-0.766, P < 0.001) and 0.825 (95 % CI, 0.732-0.917, P < 0.001) in the training and validation cohorts, respectively, indicating that the model had high accuracy in distinguishing patients with and without subsyndromal delirium. The calibration curve of the nomogram showed good consistency between the predicted and actual probabilities. The DCA indicated that the nomogram has clinical application for patients in the ICU.

CONCLUSIONS

We developed an easy-to-use nomogram for identifying subsyndromal delirium in ICU patients with satisfactory predictive ability based on simple and easily accessible clinical features. The nomogram can identify ICU patients at high-risk for subsyndromal delirium and may be a useful subsyndromal delirium tool for current ICU physicians.

Emotional State of Older Adults During the COVID-19 Pandemic: Insights from the Cognitive and Social Well-Being (CoSoWELL) Corpus

OBJECTIVES

In view of the fallout of the COVID-19 pandemic, psychologists face a challenge to document the pandemic-related change in emotional well-being of individuals and groups and evaluate the emotional response to this fallout over time.

METHODSP

We contribute to this goal by analyzing the new CoSoWELL corpus (version 2.0), an 1.8 million-word collection of narratives written by over 1,300 older adults (55+ y.o.) in eight sessions before, during and after the global lockdown. In the narratives, we examined a range of linguistic variables traditionally associated with emotional well-being and observed signs of distress, i.e., lower positivity and heightened levels of fear, anger, and disgust.

RESULTS

In most variables, we observed a characteristic timeline of change, i.e., a delayed (by 4 months) and abrupt drop in optimism and increase in negative emotions that reached its peak about 7 months after the lockdown and returned to pre-pandemic levels one year after. Our examination of risk factors showed that higher levels of self-reported loneliness came with elevated levels of negative emotions but did not change the timeline of emotional response to the pandemic.

CONCLUSIONS

We discuss implications of the findings for theories of emotion regulation.

Intelligence and cortical morphometry: caveats in brain-behavior associations

It is well-established that brain size is associated with intelligence. But the relationship between cortical morphometric measures and intelligence is unclear. Studies have produced conflicting results or no significant relations between intelligence and cortical morphometric measures such as cortical thickness and peri-cortical contrast. This discrepancy may be due to multicollinearity amongst the independent variables in a multivariate regression analysis, or a failure to fully account for the relationship between brain size and intelligence in some other way. Our study shows that neither cortical thickness nor peri-cortical contrast reliably improves IQ prediction accuracy beyond what is achieved with brain volume alone. We show this in multiple datasets, with child data, developmental data, and with adult data; we show this with data acquired either at multiple sites, or at a single site; we show this with data acquired with different MRI scanner manufacturers, or with all data acquired on a single scanner; and we show this with fluid intelligence, full-scale IQ, performance IQ, and verbal IQ. But our point is not really even about IQ; rather we proffer a methodological caveat and potential explanation of the discrepancies in previous results, and which applies broadly.

Psychometric properties of the Kidney Disease Quality of Life short form 36 (KDQOL-36) scale for the assessment of quality of life in Colombian patients with chronic kidney disease on dialysis

PURPOSE

Considering the importance of incorporating quality of life (QoL) construct during the health care of patients with stage 5 chronic kidney disease (CKD) on dialysis, it is necessary to have evidence on the clinimetric properties of the instruments used for its measurement. This study aimed to establish the clinimetric properties of the Kidney Disease Quality of Life Short Form 36 (KDQOL-36) scale in patients with stage 5 CKD on dialysis in Colombia.

METHODS

A scale validation study was conducted using the classical test theory methodology. The statistical analysis included exploratory factor analysis (EFA) and confirmatory (CFA) techniques performed on two independent subsamples; concurrent criterion validity assessments; internal consistency using four different coefficients; test-retest reliability; and sensitivity to change using mixed model for repeated measures.

RESULTS

The KDQOL-36 scale was applied to 506 patients with a diagnosis of stage 5 CKD on dialysis, attended in five renal units in Colombia. The EFA endorsed the three-factor structure of the scale, and the CFA showed an adequate fit of both the original and empirical models. Spearman's correlation coefficient values ≥0.50 were found between the domains of the CKD-specific core of the KDQOL-36 scale and the KDQ. Cronbach's alpha, McDonald's omega, Greatest lower bound (GLB), and Guttman's lambda coefficients were ≥0.89, indicating a high degree of consistency. A high level of concordance correlation was found between the two moments of application of the instrument, with values for Lin's concordance correlation coefficient ≥0.7. The application of the instrument after experiencing an event that could modify the quality of life showed statistically significant differences in the scores obtained.

CONCLUSION

The KDQOL-36 scale is an adequate instrument for measuring QoL in Colombian patients with stage 5 CKD on dialysis.

Diverse immunological dysregulation, chronic inflammation, and impaired erythropoiesis in long COVID patients with chronic fatigue syndrome

A substantial number of patients recovering from acute SARS-CoV-2 infection present serious lingering symptoms, often referred to as long COVID (LC). However, a subset of these patients exhibits the most debilitating symptoms characterized by ongoing myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS). We specifically identified and studied ME/CFS patients from two independent LC cohorts, at least 12 months post the onset of acute disease, and compared them to the recovered group (R). ME/CFS patients had relatively increased neutrophils and monocytes but reduced lymphocytes. Selective T cell exhaustion with reduced naïve but increased terminal effector T cells was observed in these patients. LC was associated with elevated levels of plasma pro-inflammatory cytokines, chemokines, Galectin-9 (Gal-9), and artemin (ARTN). A defined threshold of Gal-9 and ARTN concentrations had a strong association with LC. The expansion of immunosuppressive CD71+ erythroid cells (CECs) was noted. These cells may modulate the immune response and contribute to increased ARTN concentration, which correlated with pain and cognitive impairment. Serology revealed an elevation in a variety of autoantibodies in LC. Intriguingly, we found that the frequency of 2B4+CD160+ and TIM3+CD160+ CD8+ T cells completely separated LC patients from the R group. Our further analyses using a multiple regression model revealed that the elevated frequency/levels of CD4 terminal effector, ARTN, CEC, Gal-9, CD8 terminal effector, and MCP1 but lower frequency/levels of TGF-β and MAIT cells can distinguish LC from the R group. Our findings provide a new paradigm in the pathogenesis of ME/CFS to identify strategies for its prevention and treatment.

Exploratory subgroup identification in the heterogeneous Cox model: A relatively simple procedure

For survival analysis applications we propose a novel procedure for identifying subgroups with large treatment effects, with focus on subgroups where treatment is potentially detrimental. The approach, termed forest search, is relatively simple and flexible. All-possible subgroups are screened and selected based on hazard ratio thresholds indicative of harm with assessment according to the standard Cox model. By reversing the role of treatment one can seek to identify substantial benefit. We apply a splitting consistency criteria to identify a subgroup considered "maximally consistent with harm." The type-1 error and power for subgroup identification can be quickly approximated by numerical integration. To aid inference we describe a bootstrap bias-corrected Cox model estimator with variance estimated by a Jacknife approximation. We provide a detailed evaluation of operating characteristics in simulations and compare to virtual twins and generalized random forests where we find the proposal to have favorable performance. In particular, in our simulation setting, we find the proposed approach favorably controls the type-1 error for falsely identifying heterogeneity with higher power and classification accuracy for substantial heterogeneous effects. Two real data applications are provided for publicly available datasets from a clinical trial in oncology, and HIV.

Aging atlas reveals cell-type-specific effects of pro-longevity strategies

Organismal aging involves functional declines in both somatic and reproductive tissues. Multiple strategies have been discovered to extend lifespan across species. However, how age-related molecular changes differ among various tissues and how those lifespan-extending strategies slow tissue aging in distinct manners remain unclear. Here we generated the transcriptomic Cell Atlas of Worm Aging (CAWA, http://mengwanglab.org/atlas ) of wild-type and long-lived strains. We discovered cell-specific, age-related molecular and functional signatures across all somatic and germ cell types. We developed transcriptomic aging clocks for different tissues and quantitatively determined how three different pro-longevity strategies slow tissue aging distinctively. Furthermore, through genome-wide profiling of alternative polyadenylation (APA) events in different tissues, we discovered cell-type-specific APA changes during aging and revealed how these changes are differentially affected by the pro-longevity strategies. Together, this study offers fundamental molecular insights into both somatic and reproductive aging and provides a valuable resource for in-depth understanding of the diversity of pro-longevity mechanisms.

Global climate change-driven impacts on the Asian distribution of Limassolla leafhoppers, with implications for biological and environmental conservation

Knowing the impacts of global climate change on the habitat suitability distribution of Limassolla leafhoppers contributes to understanding the feedback of organisms on climate change from a macroecological perspective, and provides important scientific basis for protecting the ecological environment and biodiversity. However, there is limited knowledge on this aspect. Thus, our study aimed to address this gap by analyzing Asian habitat suitability and centroid shifts of Limassolla based on 19 bioclimatic variables and occurrence records. Selecting five ecological niche models with the outstanding predictive performance (Maxlike, generalized linear model, generalized additive model, random forest, and maximum entropy) along with their ensemble model from 12 models, the current habitat suitability of Limassolla and its future habitat suitability under two Shared Socio-economic Pathways (SSP1-2.6 and SSP5-8.5) in the 2050s and 2090s were predicted. The results showed that the prediction results of the five models are generally consistent. Based on ensemble model, 11 potential biodiversity hotspots with high suitability were identified. With climate change, the suitable range of Limassolla will experience both expansion and contraction. In SSP5-8.52050s, the expansion area is 118.56 × 104 km2, while the contraction area is 25.40 × 104 km2; in SSP1-2.62090s, the expansion area is 91.71 × 104 km2, and the contraction area is 26.54 × 104 km2. Furthermore, the distribution core of Limassolla will shift toward higher latitudes in the northeast direction, and the precipitation of warmest quarter was found to have the greatest impact on the distribution of Limassolla. Our research results supported our four hypotheses. Finally, this research suggests establishing ecological reserves in identified contraction to prevent habitat loss, enhancing the protection of biodiversity hotspots, and pursuing a sustainable development path with reduced emissions.

A statistical approach to assess interspecific consumptive competition and functional redundancy in ephemeral resource uses using camera traps

Camera traps have been widely used in wildlife research, offering significant potential for monitoring species interactions at ephemeral resources. However, raw data obtained from camera traps often face limitations due to observation censoring, where resource consumption by dominant animals may obscure potential resource use by less dominant animals. We extended time-to-detection occupancy modeling to quantify interspecific consumptive competition and redundancy of ecosystem functions through consumption between two species, while accounting for observation censoring. By treating resource use by rival species as censored data, we estimated the proportion of resources potentially used in the absence of rival species and calculated the loss caused by the rival species, which is defined as "Competition Intensity Index." We also defined the Unique Functional Contribution, which represents the net functional loss when a species is removed, calculated by excluding the contribution potentially substituted by the other species. We also considered resource degradation and computed the quantity of resources acquired by each species. This established framework was applied to predation data on bird nests by alien squirrels and other predators (Case 1) as well as scavenging on mammalian carcasses by two carnivores (Case 2). In Case 1, the introduction of squirrels significantly affected the breeding success of birds. Although nests were being preyed upon by native crows also, our model estimated that Unique Functional Contribution by the squirrels was 0.47. This means that, by eradicating the squirrels, the reproductive success of the birds could potentially increase by as much as 47%. In Case 2, the Competition Intensity Index for foxes was 0.17, whereas that for raccoon dogs was 0.46, suggesting an asymmetric effect of resource competition between the two species. The frequency distribution of wet mass available to the two species differed significantly. This approach will enable a more robust construction of resource-consumer interaction networks.

Who develops long COVID? Longitudinal pre-pandemic predictors of long COVID and symptom clusters in a representative Dutch population

OBJECTIVES

Prior studies show that long COVID has a heterogeneous presentation. Whether specific risk factors are related to subclusters of long COVID remains unknown. This study aimed to determine pre-pandemic predictors of long COVID and symptom clustering.

METHODS

A total of 3,022 participants of a panel representative of the Dutch population completed an online survey about long COVID symptoms. Data was merged into 2018/2019 panel data covering sociodemographic, medical, and psychosocial predictors. A total of 415 participants were classified as having long COVID. K-means clustering was used to identify patient clusters. Multivariate and lasso regression was used to identify relevant predictors compared to a COVID-19 positive control group.

RESULTS

Predictors of long-term COVID included older age, Western ethnicity, BMI, chronic disease, COVID-19 reinfections, severity, and symptoms, lower self-esteem, and higher positive affect (AUC = 0.79, 95%CI 0.73-0.86). Four clusters were identified: a low and a high symptom severity cluster, a smell-taste and respiratory symptoms cluster, and a neuro-cognitive, psychosocial, and inflammatory symptom cluster. Predictors for the different clusters included regular health complaints, healthcare use, fear of COVID-19, anxiety, depressive symptoms, and neuroticism.

CONCLUSIONS

A combination of sociodemographic, medical, and psychosocial factors predicted long COVID. Heterogenous symptom clusters suggest that there are different phenotypes of long COVID-19 presentation.

Decoupling the Conflicting Evaluative Meanings in Automatically Activated Race-Based Associations

Implicit measures of attitudes have classically focused on the association between a social group and generalized valence, but debate exists surrounding how these associations arise and what they can tell us about beliefs and attitudes. Here, we suggest that representations of oppression, which relate positively to implicitly measured prejudice but negatively to explicitly measured prejudice, can serve to decrease the predictive validity of implicit measures through statistical suppression. We had participants complete a Black-White implicit association test (IAT) and an IAT measuring representations of oppression, and find that oppression-related representations statistically suppress the relation between IAT scores and explicit attitudes, such that accounting for these representations increases the total amount of variance explained by implicit measures. We discuss the implications of this work both for practical matters around use of the IAT and for theoretical debates on the conceptualization of valence in implicit attitudes.

Resting state networks in patients with acute disorders of consciousness after severe traumatic brain injury

OBJECTIVES

This study aims to describe resting state networks (RSN) in patients with disorders of consciousness (DOC)s after acute severe traumatic brain injury (TBI).

METHODS

Adult patients with TBI with a GCS score <8 who remained in a coma, minimally conscious state (MCS), or unresponsive wakefulness syndrome (UWS), between 2017 and 2020 were included. Blood-oxygen-level dependent imaging was performed to compare their RSN with 10 healthy volunteers.

RESULTS

Of a total of 293 patients evaluated, only 13 patients were included according to inclusion criteria: 7 in coma (54%), 2 in MCS (15%), and 4 (31%) had an UWS. RSN analysis showed that the default mode network (DMN) was present and symmetric in 6 patients (46%), absent in 1 (8%), and asymmetric in 6 (46%). The executive control network (ECN) was present in all patients but was asymmetric in 3 (23%). The right ECN was absent in 2 patients (15%) and the left ECN in 1 (7%). The medial visual network was present in 11 (85%) patients. Finally, the cerebellar network was symmetric in 8 patients (62%), asymmetric in 1 (8%), and absent in 4 (30%).

CONCLUSIONS

A substantial impairment in activation of RSN is demonstrated in patients with DOC after severe TBI in comparison with healthy subjects. Three patterns of activation were found: normal/complete activation, 2) asymmetric activation or partially absent, and 3) absent activation.

Molecular subtype identification and prognosis stratification by a immunogenic cell death-related gene expression signature in colorectal cancer

OBJECTIVES

This study intended to develop a new immunogenic cell death (ICD)-related prognostic signature for colorectal cancer (CRC) patients.

RESEARCH DESIGN AND METHODS

The Non-Negative Matrix Factorization (NMF) algorithm was adopted to cluster tumor samples based on ICD gene expression to obtain ICD-related subtypes. Survival analysis and immune microenvironment analysis were conducted among different subtypes. Regression analysis was used to construct the model. Based on riskscore median, cancer patients were classified into high and low risk groups, and independent prognostic ability of the model was analyzed. The CIBERSORT algorithm was adopted to determine the immune infiltration level of both groups.

RESULTS

We analyzed the differential genes between cluster 4 and cluster 1-3 and obtained 12 genes with the best prognostic features finally (NLGN1, SLC30A3, C3orf20, ADAD2, ATOH1, ATP6V1B1, KCNQ2, MUCL3, RGCC, CLEC17A, COL6A5, and INSL4). In addition, patients with lower risk had higher levels of infiltration of most immune cells, lower Tumor Immune Dysfunction and Exclusion (TIDE) level and higher immunophenscore (IPS) level than those with higher risk.

CONCLUSIONS

This study constructed and validated the ICD feature signature predicting CRC prognosis and provide a reference criteria for guiding the prognosis and immunotherapy of CRC cancer patients.

Following Regulation, Imidacloprid Persists and Flupyradifurone Increases in Nontarget Wildlife

After regulation of pesticides, determination of their persistence in the environment is an important indicator of effectiveness of these measures. We quantified concentrations of two types of systemic insecticides, neonicotinoids (imidacloprid, acetamiprid, clothianidin, thiacloprid, and thiamethoxam) and butenolides (flupyradifurone), in off-crop nontarget media of hummingbird cloacal fluid, honey bee (Apis mellifera) nectar and honey, and wildflowers before and after regulation of imidacloprid on highbush blueberries in Canada in April 2021. We found that mean total pesticide load increased in hummingbird cloacal fluid, nectar, and flower samples following imidacloprid regulation. On average, we did not find evidence of a decrease in imidacloprid concentrations after regulation. However, there were some decreases, some increases, and other cases with no changes in imidacloprid levels depending on the specific media, time point of sampling, and site type. At the same time, we found an overall increase in flupyradifurone, acetamiprid, thiamethoxam, and thiacloprid but no change in clothianidin concentrations. In particular, flupyradifurone concentrations observed in biota sampled near agricultural areas increased twofold in honey bee nectar, sevenfold in hummingbird cloacal fluid, and eightfold in flowers after the 2021 imidacloprid regulation. The highest residue detected was flupyradifurone at 665 ng/mL (parts per billion [ppb]) in honey bee nectar. Mean total pesticide loads were highest in honey samples (84 ± 10 ppb), followed by nectar (56 ± 7 ppb), then hummingbird cloacal fluid (1.8 ± 0.5 ppb), and least, flowers (0.51 ± 0.06 ppb). Our results highlight that limited regulation of imidacloprid does not immediately reduce residue concentrations, while other systemic insecticides, possibly replacement compounds, concurrently increase in wildlife. Environ Toxicol Chem 2024;43:1497-1508. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records

PURPOSE

Patients with epithelial ovarian cancer (EOC) have an elevated risk for venous thromboembolism (VTE). To assess the risk of VTE, models were developed by statistical or machine learning algorithms. However, few models have accommodated deep learning (DL) algorithms in realistic clinical settings. We aimed to develop a predictive DL model, exploiting rich information from electronic health records (EHRs), including dynamic clinical features and the presence of competing risks.

METHODS

We extracted EHRs of 1,268 patients diagnosed with EOC from January 2007 through December 2017 at the National Cancer Center, Korea. DL survival networks using fully connected layers, temporal attention, and recurrent neural networks were adopted and compared with multi-perceptron-based classification models. Prediction accuracy was independently validated in the data set of 423 patients newly diagnosed with EOC from January 2018 to December 2019. Personalized risk plots displaying the individual interval risk were developed.

RESULTS

DL-based survival networks achieved a superior area under the receiver operating characteristic curve (AUROC) between 0.95 and 0.98 while the AUROC of classification models was between 0.85 and 0.90. As clinical information benefits the prediction accuracy, the proposed dynamic survival network outperformed other survival networks for the test and validation data set with the highest time-dependent concordance index (0.974, 0.975) and lowest Brier score (0.051, 0.049) at 6 months after a cancer diagnosis. Our visualization showed that the interval risk fluctuating along with the changes in longitudinal clinical features.

CONCLUSION

Adaption of dynamic patient clinical features and accounting for competing risks from EHRs into the DL algorithms demonstrated VTE risk prediction with high accuracy. Our results show that this novel dynamic survival network can provide personalized risk prediction with the potential to assist risk-based clinical intervention to prevent VTE among patients with EOC.

Cerebrospinal fluid proteomic profile of frailty: Results from the PROLIPHYC cohort

Frailty is a clinical state reflecting a decrease in physiological reserve capacities, known to affect numerous biological pathways and is associated with health issues, including neurodegenerative diseases. However, how global protein expression is affected in the central nervous system in frail subject remains underexplored. In this post hoc cross-sectional biomarker analysis, we included 90 adults (52-85 years) suspected of normal pressure hydrocephalus (NPH) and presenting with markers of neurodegenerative diseases. We investigated the human proteomic profile of cerebrospinal fluid associated with frailty defined by an established cumulated frailty index (FI, average = 0.32), not enriched for neurology clinical features. Using a label-free quantitative proteomic approach, we identified and quantified 999 proteins of which 13 were positively associated with frailty. Pathway analysis with the top positively frailty-associated proteins revealed enrichment for proteins related to inflammation and immune response. Among the 60 proteins negatively associated with frailty, functional pathways enriched included neurogenesis, synaptogenesis and neuronal guidance. We constructed a frailty prediction model using ridge regression with 932 standardized proteins. Our results showed that the "proteomic model" could become an equivalent predictor of FI in order to study chronological age. This study represents the first comprehensive exploration of the proteomic profile of frailty within cerebrospinal fluid. It sheds light on the physiopathology of frailty, particularly highlighting processes of neuroinflammation and inhibition of neurogenesis. Our findings unveil a range of biological mechanisms that are dysregulated in frailty, in NPH subjects at risk of neurodegenerative impairment, offering new perspectives on frailty phenotyping and prediction.

Identification of risk factors on rabies vaccine efficacy from censored data: Pre-travel tests for dogs and cats from Yaoundé (2005-2015)

Little research is available on acquired immunity to rabies in dogs and cats from Central Africa, particularly regarding the legal movements of pets. Movement of domestic animals from rabies-endemic countries like Cameroon to rabies free areas poses one of the main risks for rabies introduction into rabies-free areas. Thus, the aim of this study was to assess the effect of various risk factors on rabies vaccine efficacy in Cameroonian. Since the dependent variable, rabies neutralizing titres, were censored from above (right-censoring), Generalized Additive Model for Location, Scale and Shape (GAMLSS) was used in the analysis. Overall, 85.7% of dogs and 100% of cats had titres greater than or equal to 0.5 IU/mL, which is considered protective. Additionally, compared to cats, the value of the rabies-neutralizing serum titres in dogs was on average smaller by 2.3 IU/mL. For each additional year of age, the value of the rabies-neutralizing serum titre, on average, increased by approximately 0.14 IU/mL. Finally, for each 30 additional days between the date of the last rabies vaccination and the date of the sampling, the value the rabies neutralizing titre, on average, decreased by approximately 0.10 IU/mL, given the species and age at sampling were equivalent. These results are useful for assessing risk and improving surveillance to prevent the introduction of rabies into a country via the international movement of animals.

Exploring the diagnostic value of endothelial cell and angiogenesis-related genes in Hashimoto's thyroiditis based on transcriptomics and single cell RNA sequencing

(1) BACKGROUND: Hashimoto's thyroiditis (HT) can cause angiogenesis in the thyroid gland. However, the molecular mechanism of endothelial cells and angiogenesis related genes (ARGs) has not been extensively studied in HT. (2) METHODS: The HRA001684, GSE29315 and GSE163203 datasets were included in this study. Using single-cell analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, machine learning algorithms and expression analysis for exploration. And receiver operator characteristic (ROC) curves was draw. Gene set enrichment analysis (GSEA) was utilized to investigate the biological function of the biomarkers. Meanwhile, we investigated into the relationship between biomarkers and different types of immune cells. Additionally, the expression of biomarkers in the TCGA-TC dataset was examined and the mRNA-drug interaction network was constructed. (3) RESULTS: We found 14 cell subtypes were obtained in HT samples after single-cell analysis. A total of 5 biomarkers (CD52, CD74, CD79A, HLA-B and RGS1) were derived, and they had excellent diagnostic performance. Then, 27 drugs targeting biomarkers were predicted. The expression analysis showed that CD74 and HLA-B were significantly up-regulated in HT samples. (4) CONCLUSION: In this study, 5 biomarkers (CD52, CD74, CD79A, HLA-B and RGS1) were screened and their expressions in endothelial cells was compared to offer a new reference for the recognition and management of HT.

Difficulty-as-Improvement: The Courage to Keep Going in the Face of Life's Difficulties

When a task or goal is hard to think about or do, people can infer that it is a waste of their time (difficulty-as-impossibility) or valuable to them (difficulty-as-importance). Separate from chosen tasks and goals, life can present unchosen difficulties. Building on identity-based motivation theory, people can see these as opportunities for self-betterment (difficulty-as-improvement). People use this language when they recall or communicate about difficulties (autobiographical memories, Study 1; "Common Crawl" corpus, Study 2). Our difficulty mindset measures are culture-general (Australia, Canada, China, India, Iran, New Zealand, Turkey, the United States, Studies 3-15, N = 3,532). People in Western, Educated, Industrialized, Rich, Democratic (WEIRD)-er countries slightly agree with difficulty-as-improvement. Religious, spiritual, conservative people, believers in karma and a just world, and people from less-WEIRD countries score higher. People who endorse difficulty-as-importance see themselves as conscientious, virtuous, and leading lives of purpose. So do endorsers of difficulty-as-improvement-who also see themselves as optimists (all scores lower for difficulty-as-impossibility endorsers).

Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study

BACKGROUND

Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes.

METHODS

We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins.

FINDINGS

Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures.

INTERPRETATION

We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings.

FUNDING

Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.

Distinguishing vulnerability and resilience to posttraumatic stress disorder evaluating traumatic experiences, genetic risk and electronic health records

What distinguishes vulnerability and resilience to posttraumatic stress disorder (PTSD) remains unclear. Levering traumatic experiences reporting, genetic data, and electronic health records (EHR), we investigated and predicted the clinical comorbidities (co-phenome) of PTSD vulnerability and resilience in the UK Biobank (UKB) and All of Us Research Program (AoU), respectively. In 60,354 trauma-exposed UKB participants, we defined PTSD vulnerability and resilience considering PTSD symptoms, trauma burden, and polygenic risk scores. EHR-based phenome-wide association studies (PheWAS) were conducted to dissect the co-phenomes of PTSD vulnerability and resilience. Significant diagnostic endpoints were applied as weights, yielding a phenotypic risk score (PheRS) to conduct PheWAS of PTSD vulnerability and resilience PheRS in up to 95,761 AoU participants. EHR-based PheWAS revealed three significant phenotypes positively associated with PTSD vulnerability (top association "Sleep disorders") and five outcomes inversely associated with PTSD resilience (top association "Irritable Bowel Syndrome"). In the AoU cohort, PheRS analysis showed a partial inverse relationship between vulnerability and resilience with distinct comorbid associations. While PheRSvulnerability associations were linked to multiple phenotypes, PheRSresilience showed inverse relationships with eye conditions. Our study unveils phenotypic differences in PTSD vulnerability and resilience, highlighting that these concepts are not simply the absence and presence of PTSD.

Bayesian semiparametric Markov renewal mixed models for vocalization syntax

Speech and language play an important role in human vocal communication. Studies have shown that vocal disorders can result from genetic factors. In the absence of high-quality data on humans, mouse vocalization experiments in laboratory settings have been proven useful in providing valuable insights into mammalian vocal development, including especially the impact of certain genetic mutations. Such data sets usually consist of categorical syllable sequences along with continuous intersyllable interval (ISI) times for mice of different genotypes vocalizing under different contexts. ISIs are of particular importance as increased ISIs can be an indication of possible vocal impairment. Statistical methods for properly analyzing ISIs along with the transition probabilities have however been lacking. In this article, we propose a class of novel Markov renewal mixed models that capture the stochastic dynamics of both state transitions and ISI lengths. Specifically, we model the transition dynamics and the ISIs using Dirichlet and gamma mixtures, respectively, allowing the mixture probabilities in both cases to vary flexibly with fixed covariate effects as well as random individual-specific effects. We apply our model to analyze the impact of a mutation in the Foxp2 gene on mouse vocal behavior. We find that genotypes and social contexts significantly affect the length of ISIs but, compared to previous analyses, the influences of genotype and social context on the syllable transition dynamics are weaker.

Blood protein assessment of leading incident diseases and mortality in the UK Biobank

The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c-a clinical marker used to monitor and diagnose type 2 diabetes. The performance of scores using metabolomic and proteomic features is also compared. These data characterize early proteomic contributions to major age-related diseases, demonstrating the value of the plasma proteome for risk stratification.

Prioritising quality: investigating the influence of image quality on forensic facial comparison

Morphological analysis in forensic facial comparison was recently validated for judicial use. However, no image quality assurance systems exist for this purpose, despite image triage being considered the best practice approach. Hence, this study aimed at testing a semi-quantitative scoring method to assess image quality and investigated facial image resolution and lighting quality quantitatively in a context of forensic facial comparison. For this purpose, 400 facial comparison photographic and CCTV image pools developed from the Wits Face Database were used. These facial images were analysed in prior studies that investigated the validity of morphological analysis. A semi-quantitative image quality scoring system was adapted and tested on the above sample and compared across correct and incorrect matches obtained as part of previous studies using a logistic regression model. In addition, facial images were cropped to the closest pixel comprising the face, head and neck areas; then, a face-to-image pixel proportion was calculated as an estimator of resolution quality; and pixel exposure qualities were obtained to be compared to facial comparison outcomes. Ideal and high image quality scores were related to correctness of matches, while low-quality scores were related to incorrect matches. High pixel proportions were related to true matches and low exposure was related to false positives, while high exposure was related to false negatives. These results suggest that an easy method for image triage could be employed by scoring image quality. Quantitative measures should be investigated further for thresholding quality suitability for confidence of facial comparisons.

The mediating effect of sleep quality on exposure to community violence and posttraumatic stress symptoms in the United States

Objectives

The role of sleep quality is not yet fully understood in the context of posttraumatic stress disorder (PTSD) following exposure to community violence. Thus, the primary aim of this study is to examine the mediating effect of sleep quality in the relationship between community violence exposure and posttraumatic stress symptoms.

Methods

Utilizing a cross-sectional survey administered to an online opt-in panel of adults in the United States in 2023 (age ≥ 18 years) (N = 342), respondents reported on their exposure to community violence, sleep quality, and posttraumatic stress symptoms. Covariate-adjusted regressions were used to test these relationships.

Results

Directly experiencing community violence was associated with poorer sleep quality (β = 0.11, 95 % CI [0.02, 0.20], p = 0.022) and posttraumatic stress symptoms (β = 0.33, 95 % CI [0.17, 0.48], p = < 0.001), and poorer sleep quality predicted greater posttraumatic stress symptoms (β = 0.74, 95 % CI [0.58, 0.91], p = 0<.001). Further, sleep quality was a partial mediator (β = 0.24, 95 % CI [0.04, 0.50], p = 0.028), accounting for 24 % of the relationship.

Conclusions

Findings from this study help deepen understanding of the processes that contribute to the development of PTSD and provide insights into possible interventions, including treatment for sleep problems in the aftermath of violence exposure as a means for lessening the mental health burdens of community violence.

Epigenetic clocks, sex markers and age-class diagnostics in three harvested large mammals

The development of epigenetic clocks, or the DNA methylation-based inference of age, is an emerging tool for ageing in free ranging populations. In this study, we developed epigenetic clocks for three species of large mammals that are the focus of extensive management throughout their range in North America: white-tailed deer, black bear and mountain goat. We quantified differential DNA methylation patterns at over 30,000 cytosine-guanine sites (CpGs) from tissue samples of all three species (black bear n = 49; white-tailed deer n = 47; mountain goat n = 45). We used a penalized regression model (elastic net) to build explanatory (black bear r = .95; white-tailed deer r = .99; mountain goat r = .97) and robust (black bear Median Absolute Error or MAE = 1.33; white-tailed deer MAE = 0.29; mountain goat MAE = 0.61) models of age or clocks. We also characterized individual CpG sites within each species that demonstrated clear differences in methylation levels between age classes and sex, which can be used to develop a suite of accessible diagnostic markers. This tool has the potential to contribute to wildlife monitoring by providing easily obtainable representations of age structure in managed populations.

Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits

OBJECTIVE

Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD.

METHODS

Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks.

RESULTS

Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications.

CONCLUSIONS

This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.

Prevalence of Bacterial Codetection and Outcomes for Infants Intubated for Respiratory Infections

OBJECTIVES

To determine the prevalence of respiratory bacterial codetection in children younger than 2 years intubated for acute lower respiratory tract infection (LRTI), primarily viral bronchiolitis, and identify the association of codetection with mechanical ventilation duration.

DESIGN

Prospective observational study evaluating the prevalence of bacterial codetection (moderate/heavy growth of pathogenic bacterial plus moderate/many polymorphonuclear neutrophils) and the impact of codetection on invasive mechanical ventilation (IMV) duration.

SETTING

PICUs in 12 high and low/middle-income countries.

PATIENTS

Children younger than 2 years old requiring intubation and ICU admission for LRTI and who had a lower respiratory tract culture obtained at the time of intubation between December 1, 2019, and November 30, 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of the 472 analyzed patients (median age 4.5 mo), 55% had a positive respiratory culture and 29% ( n = 138) had codetection. 90% received early antibiotics starting at a median of 0.36 hours after respiratory culture. Median (interquartile range) IMV duration was 151 hours (88, 226), and there were 28 deaths (5.3%). Codetection was more common with younger age, a positive respiratory syncytial virus test, and an admission diagnosis of bronchiolitis; it was less common with an admission diagnosis of pneumonia, with admission to a low-/middle-income site, and in those receiving vasopressors. When adjusted for confounders, codetection was not associated with longer IMV duration (adjusted relative risk 0.854 [95% CI 0.684-1.065]). We could not exclude the possibility that codetection might be associated with a 30-hour shorter IMV duration compared with no codetection, although the CI includes the null value.

CONCLUSIONS

Bacterial codetection was present in almost a third of children younger than 2 years requiring intubation and ICU admission for LRTI, but this was not associated with prolonged IMV. Further large studies are needed to evaluate if codetection is associated with shorter IMV duration.

Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

Subjective evidence evaluation survey for many-analysts studies

Many-analysts studies explore how well an empirical claim withstands plausible alternative analyses of the same dataset by multiple, independent analysis teams. Conclusions from these studies typically rely on a single outcome metric (e.g. effect size) provided by each analysis team. Although informative about the range of plausible effects in a dataset, a single effect size from each team does not provide a complete, nuanced understanding of how analysis choices are related to the outcome. We used the Delphi consensus technique with input from 37 experts to develop an 18-item subjective evidence evaluation survey (SEES) to evaluate how each analysis team views the methodological appropriateness of the research design and the strength of evidence for the hypothesis. We illustrate the usefulness of the SEES in providing richer evidence assessment with pilot data from a previous many-analysts study.

Designing a Bayesian adaptive clinical trial to evaluate novel mechanical ventilation strategies in acute respiratory failure using integrated nested Laplace approximations

BACKGROUND

Adaptive trials usually require simulations to determine values for design parameters, demonstrate error rates, and establish the sample size. We designed a Bayesian adaptive trial comparing ventilation strategies for patients with acute hypoxemic respiratory failure using simulations. The complexity of the analysis would usually require computationally expensive Markov Chain Monte Carlo methods but this barrier to simulation was overcome using the Integrated Nested Laplace Approximations (INLA) algorithm to provide fast, approximate Bayesian inference.

METHODS

We simulated two-arm Bayesian adaptive trials with equal randomization that stratified participants into two disease severity states. The analysis used a proportional odds model, fit using INLA. Trials were stopped based on pre-specified posterior probability thresholds for superiority or futility, separately for each state. We calculated the type I error and power across 64 scenarios that varied the probability thresholds and the initial minimum sample size before commencing adaptive analyses. Two designs that maintained a type I error below 5%, a power above 80%, and a feasible mean sample size were evaluated further to determine the optimal design.

RESULTS

Power generally increased as the initial sample size and the futility threshold increased. The chosen design had an initial recruitment of 500 and a superiority threshold of 0.9925, and futility threshold of 0.95. It maintained high power and was likely to reach a conclusion before exceeding a feasible sample size.

CONCLUSIONS

We designed a Bayesian adaptive trial to evaluate novel strategies for ventilation using the INLA algorithm to efficiently evaluate a wide range of designs through simulation.

Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI

Synthesizing 7T Susceptibility Weighted Imaging (SWI) from 3T SWI could offer significant clinical benefits by combining the high sensitivity of 7T SWI for neurological disorders with the widespread availability of 3T SWI in diagnostic routines. Although methods exist for synthesizing 7T Magnetic Resonance Imaging (MRI), they primarily focus on traditional MRI modalities like T1-weighted imaging, rather than SWI. SWI poses unique challenges, including limited data availability and the invisibility of certain tissues in individual 3T SWI slices. To address these challenges, we propose a Self-supervised Anatomical Continuity Enhancement (SACE) network to synthesize 7T SWI from 3T SWI using plentiful 3T SWI data and limited 3T-7T paired data. The SACE employs two specifically designed pretext tasks to utilize low-level representations from abundant 3T SWI data for assisting 7T SWI synthesis in a downstream task with limited paired data. One pretext task emphasizes input-specific morphology by balancing the elimination of redundant patterns with the preservation of essential morphology, preventing the blurring of synthetic 7T SWI images. The other task improves the synthesis of tissues that are invisible in a single 3T SWI slice by aligning adjacent slices with the current slice and predicting their difference fields. The downstream task innovatively combines clinical knowledge with brain substructure diagrams to selectively enhance clinically relevant features. When evaluated on a dataset comprising 97 cases (5495 slices), the proposed method achieved a Peak Signal-to-Noise Ratio (PSNR) of 23.05 dB and a Structural Similarity Index (SSIM) of 0.688. Due to the absence of specific methods for 7T SWI, our method was compared with existing enhancement techniques for general 7T MRI synthesis, outperforming these techniques in the context of 7T SWI synthesis. Clinical evaluations have shown that our synthetic 7T SWI is clinically effective, demonstrating its potential as a clinical tool.

Understanding heterogeneity, comorbidity, and variability in depression: Idiographic models and depression outcomes

This study uses time-intensive, item-level assessment to examine individual depressive and co-occurring symptom dynamics. Participants experiencing moderate-severe depression (N = 31) completed ecological momentary assessment (EMA) four times per day for 20 days (total observations = 2480). We estimated idiographic networks using MDD, anxiety, and ED items. ED items were most frequently included in individual networks relative to depression and anxiety items. We built ridge and logistic regression ensembles to explore how idiographic network centrality metrics performed at predicting between-subject depression outcomes (PHQ-9 change score and clinical deterioration, respectively) at 6-months follow-up. For predicting PHQ-9 change score, R2 ranged between 0.13 and 0.28. Models predicting clinical deterioration ranged from no better than chance to 80 % accuracy. This pilot study shows how co-occurring anxiety and ED symptoms may contribute to the maintenance of depressive symptoms. Future work should assess the predictive utility of psychological networks to develop understanding of how idiographic models may inform clinical decisions.

A case study: Assessing the efficacy of the revised dosage regimen via prediction model for recurrent event rate using biomarker data

In recently conducted phase III trials in a rare disease area, patients received monthly treatment at a high dose of the drug, which targets to lower a specific biomarker level, closely associated with the efficacy endpoint, to around 10% across patients. Although this high dose demonstrated strong efficacy, treatments were withheld due to the reports of serious adverse events. Dosing in these studies were later resumed at a reduced dosage which targets to lower the biomarker level to 15%-35% across patients. Two questions arose after this disruption. The first is whether the efficacy of this revised regimen as measured by the reduction in annualized event rate is adequate to support the continuation of the development and the second is whether the potential bias due to the loss of patients during this dosing gap process can be gauged. To address these questions, we built a prediction model that quantitatively characterizes biomarker vs. endpoint relationship and predicts efficacy at the 15%-35% range of the biomarker level using the available data from the original high dose. This model predicts favorable event rate in the target biomarker level and shows that the bias due to the loss of patients is limited. These results support the continued development of the revised regimen, however, given the limitation of the data available, this prediction is planned to be validated further when data under the revised regimen become available.

Identifying effective moderators of cognitive behavioural trauma treatment with caregiver involvement for youth with PTSD: a meta-analysis

Children can develop post-traumatic stress disorder (PTSD) and mental health symptoms after traumatic events. This meta-analysis evaluated the influence of moderators of cognitive behavioural trauma treatment (CBTT) with caregiver involvement in traumatized children. A total of 28 studies were included, with 23 independent samples and 332 effect sizes, representing the data of 1931 children (M age = 11.10 years, SD = 2.36). Results showed a significant medium overall effect (d = 0.55, t = 2.478, p = 0.014), indicating CBTT with caregiver involvement was effective in treating PTSD (d = 0.70), with somewhat smaller effect sizes for internalizing, externalizing, social, cognitive and total problems (0.35 < d > 0.48). The positive treatment effect was robust; we found somewhat smaller effect sizes at follow-up (d = 0.49) compared to post-test (d = 0.57) assessments. Furthermore, several sample (i.e. child's age, gender, and trauma event), programme (i.e. the duration of treatment, number of sessions), study (i.e. control condition, type of instrument, informant, type of sample), and publication (i.e. publication year and impact factor) characteristics moderated the treatment outcomes of the child. In sum, the results of our meta-analysis might help to improve the effectiveness of cognitive behavioural trauma treatment for youth with PTSD, and guide the development of innovative trauma interventions that involve caregivers. Implications for theory and practice are discussed.

Weather-Based Logistic Regression Models for Predicting Wheat Head Blast Epidemics

Wheat head blast is a major disease of wheat in the Brazilian Cerrado. Empirical models for predicting epidemics were developed using data from field trials conducted in Patos de Minas (2013 to 2019) and trials conducted across 10 other sites (2012 to 2020) in Brazil, resulting in 143 epidemics, with each being classified as either outbreak (≥20% head blast incidence) or nonoutbreak. Daily weather variables were collected from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER) website and summarized for each epidemic. Wheat heading date (WHD) served to define four time windows, with each comprising two 7-day intervals (before and after WHD), which combined with weather-based variables resulted in 36 predictors (nine weather variables × four windows). Logistic regression models were fitted to binary data, with variable selection using least absolute shrinkage and selection operator (LASSO) and sequentially best subset analyses. The models were validated using the leave-one-out cross-validation (LOOCV) technique, and their statistical performance was compared. One model was selected, implemented in a 24-year series, and assessed by experts and literature. Models with two to five predictors showed accuracies between 0.80 and 0.85, sensitivities from 0.80 to 0.91, specificities from 0.72 to 0.86, and area under the curve (AUC) from 0.89 to 0.91. The accuracy of LOOCV ranged from 0.76 to 0.81. The model applied to a historical series included temperature and relative humidity in preheading date, as well as postheading precipitation. The model accurately predicted the occurrence of outbreaks, aligning closely with real-world observations, specifically tailored for locations with tropical and subtropical climates.