Control of probability flow in Markov chain Monte Carlo-Nonreversibility and lifting

The Markov chain Monte Carlo (MCMC) method is widely used in various fields as a powerful numerical integration technique for systems with many degrees of freedom. In MCMC methods, probabilistic state transitions can be considered as a random walk in state space, and random walks allow for sampling from complex distributions. However, paradoxically, it is necessary to carefully suppress the randomness of the random walk to improve computational efficiency. By breaking detailed balance, we can create a probability flow in the state space and perform more efficient sampling along this flow. Motivated by this idea, practical and efficient nonreversible MCMC methods have been developed over the past ten years. In particular, the lifting technique, which introduces probability flows in an extended state space, has been applied to various systems and has proven more efficient than conventional reversible updates. We review and discuss several practical approaches to implementing nonreversible MCMC methods, including the shift method in the cumulative distribution and the directed-worm algorithm.

Deception Detection: Using Machine Learning to Analyze 911 Calls

This study examined the use of machine learning in detecting deception among 210 individuals reporting homicides or missing persons to 911. The sample included an equal number of false allegation callers (FAC) and true report callers (TRC) identified through case adjudication. Independent coders, unaware of callers' deception, analyzed each 911 call using 86 behavioral cues. Using the random forest model with k-fold cross-validation and repeated sampling, the study achieved an accuracy rate of 68.2% for all 911 calls, with sensitivity and specificity at 68.7% and 67.7%, respectively. For homicide reports, accuracy was higher at 71.2%, with a sensitivity of 77.3% but slightly lower specificity at 65.0%. In contrast, accuracy decreased to 61.4% for missing person reports, with a sensitivity of 49.1% and notably higher specificity at 73.6%. Beyond accuracy, key cues distinguishing FACs from TRCs were identified and included cues like "Blames others," "Is self-dramatizing," and "Is uncertain and insecure."

Leveraging calcium score CT radiomics for heart failure risk prediction

Studies have used extensive clinical information to predict time-to-heart failure (HF) in patients with and without diabetes mellitus (DM). We aimed to determine a screening method using only computed tomography calcium scoring (CTCS) to assess HF risk. We analyzed CTCS scans from 1,998 patients (336 with type 2 diabetes) from a no-charge coronary artery calcium score registry (CLARIFY Study, Clinicaltrials.gov NCT04075162). We used deep learning to segment epicardial adipose tissue (EAT) and engineered radiomic features of calcifications ("calcium-omics") and EAT ("fat-omics"). We developed models incorporating radiomics to predict risk of incident HF in patients with and without type 2 diabetes. At a median follow-up of 1.7 years, 5% had incident HF. In the overall cohort, fat-omics (C-index: 77.3) outperformed models using clinical factors, EAT volume, Agatston score, calcium-omics, and calcium-and-fat-omics to predict HF. For DM patients, the calcium-omics model (C-index: 81.8) outperformed other models. In conclusion, CTCS-based models combining calcium and fat-omics can predict incident HF, outperforming prediction scores based on clinical factors.Please check article title if captured correctly.YesPlease check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.Yes.

Navigating predictions at nanoscale: a comprehensive study of regression models in magnetic nanoparticle synthesis

The applicability of magnetic nanoparticles (MNP) highly depends on their physical properties, especially their size. Synthesizing MNP with a specific size is challenging due to the large number of interdepend parameters during the synthesis that control their properties. In general, synthesis control cannot be described by white box approaches (empirical, simulation or physics based). To handle synthesis control, this study presents machine learning based approaches for predicting the size of MNP during their synthesis. A dataset comprising 17 synthesis parameters and the corresponding MNP sizes were analyzed. Eight regression algorithms (ridge, lasso, elastic net, decision trees, random forest, gradient boosting, support vectors and multilayer perceptron) were evaluated. The model performance was assessed via root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and standard deviation of residuals. Support vector regression (SVR) exhibited the lowest RMSE values of 3.44 and a standard deviation for the residuals of 5.13. SVR demonstrated a favorable balance between accuracy and consistency among these methods. Qualitative factors like adaptability to online learning and robustness against outliers were additionally considered. Altogether, SVR emerged as the most suitable approach to predict MNP sizes due to its ability to continuously learn from new data and resilience to noise, making it well-suited for real-time applications with varying data quality. In this way, a feasible optimization framework for automated and self-regulated MNP synthesis was implemented. Key challenges included the limited dataset size, potential violations of modeling assumptions, and sensitivity to hyperparameters. Strategies like data regularization, correlation analysis, and grid search for model hyperparameters were employed to mitigate these issues.

Validating a cassava production spatial disaggregation model in sub-Saharan Africa

Cassava is a staple in the diet of millions of people in sub-Saharan Africa, as it can grow in poor soils with limited inputs and can withstand a wide range of environmental conditions, including drought. Previous studies have shown that the distribution of rural populations is an important predictor of cassava density in sub-Saharan Africa's landscape. Our aim is to explore relationships between the distribution of cassava from the cassava production disaggregation models (CassavaMap and MapSPAM) and rural population density, looking at potential differences between countries and regions. We analysed various properties of cassava cultivations collected from surveys at 69 locations in Côte d'Ivoire and 87 locations in Uganda conducted between February and March 2018. The relationships between the proportion of surveyed land under cassava cultivation and rural population and settlement data were examined using a set of generalized additive models within each country. Information on rural settlements was aggregated around the survey locations at 2, 5 and 10 km circular buffers. The analysis of the original survey data showed no significant correlation between rural population and cassava production in both MapSPAM and CassavaMap. However, as we aggregate settlement buffers around the survey locations using CassavaMap, we find that at a large scale this model does capture large-scale variations in cassava production. Moreover, through our analyses, we discovered country-specific spatial trends linked to areas of higher cassava production. These analyses are useful for validating disaggregation models of cassava production. As the certainty that existing cassava production maps increases, analyses that rely on the disaggregation maps, such as models of disease spread, nutrient availability from cassava with respect to population in a region, etc. can be performed with increased confidence. These benefit social and natural scientists, policymakers and the population in general by ensuring that cassava production estimates are increasingly reliable.

Quantum correlations and parameter estimation for two superconducting qubits interacting with a quantized field

In the present manuscript, we introduce a quantum system of two superconducting qubits (S-Qs) interacting with a quantized field under the influence of the Kerr nonlinear medium and Ising interaction. We formulate the Hamiltonian of the quantum model and determine the density operator of whole quantum system as well as quantum subsystems. We examine the dynamics of the quantumness measures for subsequent times including the S-Qs entanglement, S-Qs-field entanglement and quantum Fisher information in relation to the system parameters. Finally, we display the connection among the measures of quantumness during the time evolution.

Metabolite signatures of chronological age, aging, survival, and longevity

Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.

The persistent DDT footprint of ocean disposal, and ecological controls on bioaccumulation in fishes

Globally, ocean dumping of chemical waste is a common method of disposal and relies on the assumption that dilution, diffusion, and dispersion at ocean scales will mitigate human exposure and ecosystem impacts. In southern California, extensive dumping of agrochemical waste, particularly chlorinated hydrocarbon contaminants such as DDT, via sewage outfalls and permitted offshore barging occurred for most of the last century. This study compiled a database of existing sediment and fish DDT measurements to examine how this unique legacy of regional ocean disposal translates into the contemporary contamination of the coastal ocean. We used spatiotemporal modeling to derive continuous estimates of sediment DDT contamination and show that the spatial signature of disposal (i.e., high loadings near historic dumping sites) is highly conserved in sediments. Moreover, we demonstrate that the proximity of fish to areas of high sediment loadings explained over half of the variation in fish DDT concentrations. The relationship between sediment and fish contamination was mediated by ecological predictors (e.g., species, trophic ecology, habitat use), and the relative influence of each predictor was context-dependent, with habitat exhibiting greater importance in heavily contaminated areas. Thus, despite more than half a century since the cessation of industrial dumping in the region, local ecosystem contamination continues to mirror the spatial legacy of dumping, suggesting that sediment can serve as a robust predictor of fish contamination, and general ecological characteristics offer a predictive framework for unmeasured species or locations.

In silico exploration of phytochemicals as inhibitors for acute myeloid leukemia by targeting LIN28A gene: A cheminformatics study

BACKGROUND

Recent discoveries have illustrated that Lin28A is an oncogene in various cancers, particularly acute myeloid leukemia (AML). The upregulation of Lin28A can actively contribute to tumorigenesis and migration processes in multiple organs. Hence, the inhibition of Lin28A can be achieved by applying phytochemical herbals and targeting Lin28A protein using a computer-aided drug design (CAAD) approach.

METHODS

In this study, we comprehensively applied several bioinformatics tools, including gene ontologies, gene enrichment analysis, and protein-protein interactions (PPI), to determine the biological pathways, functional gene ontology, and biological pathway. Furthermore, we investigated a list of phytochemical herbs as a candidate drug by applying a computation technique involving molecular docking, density functional theory (DFT), molecular dynamics simulation (MDs), and pharmacokinetic and physiochemical properties by applying the SwissADME, pkCSM, and Molsoft LLC web-servers.

RESULTS

The Lin28A gene is related to two significant enrichment pathways, including proteoglycans in cancer and the pluripotency of stem cells through interactions with different genes such as MAPK12, MYC, MTOR, and PIK3CA. Interestingly, limonin, 18β Glycyrrhetic Acid, and baicalein have the highest binding energy scores of -8.4, -8.2, and -7.3 kcal/mol, respectively. The DFT study revealed that baicalein has a higher reactivity than limonin and 18β-Glycyrrhetic due to a small energy gap between LUMO and HUMO. Molecular dynamics simulation exhibited that baicalein complex with Lin28A protein is more stable than other complexes during simulation time due to low fluctuation with simulation periods as compared with other complexes, which indicated that baicalein was more fitting to docking and combining in the protein cave because of the largest number of H-bonds available for the docking simulation process. Furthermore, the drug-likeness and ADMET profiles revealed the activity of limonin, baicalein, and 18β-glycyrrhizic Acid, which possess significant inhibiting Lin28A proteins.

CONCLUSION

This study elucidated that baicalein, 18β-glycyrrhizic, and limonin may be applied as potential candidates for targeting Lin28A as an active oncogene for acute myeloid leukemia.

Construction and validation of a clinical prediction model for sepsis using peripheral perfusion index to predict in-hospital and 28-day mortality risk

Sepsis is a clinical syndrome caused by infection, leading to organ dysfunction due to a dysregulated host response. In recent years, its high mortality rate has made it a significant cause of death and disability worldwide. The pathophysiological process of sepsis is related to the body's dysregulated response to infection, with microcirculatory changes serving as early warning signals that guide clinical treatment. The Peripheral Perfusion Index (PI), as an indicator of peripheral microcirculation, can effectively evaluate patient prognosis. This study aims to develop two new prediction models using PI and other common clinical indicators to assess the mortality risk of sepsis patients during hospitalization and within 28 days post-ICU admission. This retrospective study analyzed data from sepsis patients treated in the Intensive Care Unit of Peking Union Medical College Hospital between December 2019 and June 2023, ultimately including 645 patients. LASSO regression and logistic regression analyses were used to select predictive factors from 35 clinical indicators, and two clinical prediction models were constructed to predict in-hospital mortality and 28-day mortality. The models' performance was then evaluated using ROC curve, calibration curve, and decision curve analyses. The two prediction models performed excellently in distinguishing patient mortality risk. The AUC for the in-hospital mortality prediction model was 0.82 in the training set and 0.73 in the validation set; for the 28-day mortality prediction model, the AUC was 0.79 in the training set and 0.73 in the validation set. The calibration curves closely aligned with the ideal line, indicating consistency between predicted and actual outcomes. Decision curve analysis also demonstrated high net benefits for the clinical utility of both models. The study shows that these two prediction models not only perform excellently statistically but also hold high practical value in clinical applications. The models can help physicians accurately assess the mortality risk of sepsis patients, providing a scientific basis for personalized treatment.

Cerebellar activity predicts vocalization in fruit bats

Echolocating bats exhibit remarkable auditory behaviors, enabled by adaptations both within and outside their auditory system. Yet research on echolocating bats has focused mostly on brain areas that belong to the classic ascending auditory pathway. This study provides direct evidence linking the cerebellum, an evolutionarily ancient and non-classic auditory structure, to vocalization and hearing. We report that in the fruit-eating bat Carollia perspicillata, external sounds can evoke cerebellar responses with latencies below 20 ms. Such fast responses are indicative of early inputs to the bat cerebellum. After establishing fruit-eating bats as a good model to study cerebellar auditory responses, we searched for a neural correlate of vocal production within the cerebellum. We investigated spike trains and field potentials occurring before and after vocalization and found that the type of sound produced (echolocation pulses or communication calls) can be decoded from pre-vocal and post-vocal neural signals, with prediction accuracies that reach above 85%. The latter provides a direct correlate of vocalization in an ancient motor-coordination structure that lies outside of the classic ascending auditory pathway. Taken together, our findings provide evidence of specializations for vocalization and hearing in the cerebellum of an auditory specialist.

Large-scale dependent multiple testing via higher-order hidden Markov models

Taking into account the local dependence structure in large-scale multiple testing is expected to improve both the efficiency of the testing procedure and the interpretability of scientific findings. The hidden Markov model (HMM), as an effective model to describe the sequential dependence, has been successfully applied to large-scale multiple testing with local correlations. However, in many applications, the first-order Markov chain is not flexible enough to capture the complexity of local correlations. To address this issue, this paper proposes a novel multiple testing procedure that uses a higher-order Markov chain to better characterize local correlations among tests. The proposed procedure is validated by theoretical results and simulation studies, which show that it outperforms its competitors in terms of power. Finally, a real data analysis is presented to demonstrate the favorable performance of the proposed procedure.

Identifying target populations to align with decision-makers' needs

Randomized trials estimate the average treatment effect within individuals who are eligible, invited, and agree to enroll. However, decision-makers often require evidence that extends beyond the trial's enrolled population to inform policy or actions for their specific target population. Each decision-maker has distinct target populations, the composition of which may not often align with that of the trial population. As researchers, we should identify a decision-maker for whom we aim to generate evidence early in the research process. We can then specify a target population of their interest and determine if a policy or action can be informed using results from a trial alone, or if additional complementary real-world data and analysis are required. In this commentary, we outline 5 key groupings of decision-makers: policymakers, payers, purchasers, providers, and patients. We then specify relevant target populations for decision-makers interested in the effectiveness of beta-blockers after a myocardial infarction with preserved ejection fraction. Finally, we summarize the scenarios in which results from a randomized trial may or may not apply to these target populations and suggest relevant analytic approaches that can generate evidence to better align with a decision-maker's needs. This article is part of a Special Collection on Pharmacoepidemiology.

Potential health risk assessment of cyanobacteria across global lakes

Cyanobacterial blooms pose environmental and health risks due to their production of toxic secondary metabolites. While current methods for assessing these risks have focused primarily on bloom frequency and intensity, the lack of comprehensive and comparable data on cyanotoxins makes it challenging to rigorously evaluate these health risks. In this study, we examined 750 metagenomic data sets collected from 103 lakes worldwide. Our analysis unveiled the diverse distributions of cyanobacterial communities and the genes responsible for cyanotoxin production across the globe. Our approach involved the integration of cyanobacterial biomass, the biosynthetic potential of cyanotoxin, and the potential effects of these toxins to establish potential cyanobacterial health risks. Our findings revealed that nearly half of the lakes assessed posed medium to high health risks associated with cyanobacteria. The regions of greatest concern were East Asia and South Asia, particularly in developing countries experiencing rapid industrialization and urbanization. Using machine learning techniques, we mapped potential cyanobacterial health risks in lakes worldwide. The model results revealed a positive correlation between potential cyanobacterial health risks and factors such as temperature, N2O emissions, and the human influence index. These findings underscore the influence of these variables on the proliferation of cyanobacterial blooms and associated risks. By introducing a novel quantitative method for monitoring potential cyanobacterial health risks on a global scale, our study contributes to the assessment and management of one of the most pressing threats to both aquatic ecosystems and human health.

IMPORTANCE

Our research introduces a novel and comprehensive approach to potential cyanobacterial health risk assessment, offering insights into risk from a toxicity perspective. The distinct geographical variations in cyanobacterial communities coupled with the intricate interplay of environmental factors underscore the complexity of managing cyanobacterial blooms at a global scale. Our systematic and targeted cyanobacterial surveillance enables a worldwide assessment of cyanobacteria-based potential health risks, providing an early warning system.

Non-breeding conditions induce carry-over effects on survival of migratory birds

Identifying the processes that limit populations is a foundational objective of ecology and an urgent need for conservation. For migratory animals, researchers must study individuals throughout their annual cycles to determine how environmental conditions limit demographic rates within each period of the annual cycle and also between periods through carry-over effects and seasonal interactions.1,2,3,4,5,6 Our poor understanding of the rates and causes of avian migration mortality7 hinders the identification of limiting factors and the reversal of widespread avian population declines.8,9 Here, we implement new methods to estimate apparent survival (hereafter survival) during migration directly from automated telemetry data10 in Kirtland's Warblers (Setophaga kirtlandii) and indirectly from mark-recapture data in Black-throated Blue Warblers (S. caerulescens). Previous experimental and observational studies of our focal species and other migratory songbirds have shown strong effects of Caribbean precipitation and habitat quality on food availability,11,12,13,14 body condition,12,13,14,15,16,17,18,19 migration timing,11,12,15,16,20,21,22,23 natal dispersal,24,25 range dynamics,26 reproductive success,20,22,27 and annual survival.18,19,20,23,28,29,30,31 Building on this research, we test the hypotheses that environmental conditions during the non-breeding period affect subsequent survival during spring migration and breeding. We found that reduced precipitation and environmental productivity in the non-breeding period strongly influenced survival in both species, primarily by reducing survival during spring migration. Our results indicate that climate-driven environmental conditions can carry over to affect survival in subsequent periods and thus likely play an important role in year-round population dynamics. These lethal carry-over effects may be widespread and are likely magnified by intensifying climate change.

Of goats and heat, the differential impact of summer temperature on habitat selection and activity patterns in mountain goats of different ecotypes

Climate change disproportionately affects northern and alpine environments, with faster rates of warming than the global average. Because alpine and northern species are particularly well adapted to cool temperatures, most species must modify their behavior when temperatures exceed a critical threshold. Evaluating how temperature increases affect species inhabiting northern and alpine environments is therefore essential to understand the effects of projected climate change on these ecosystems. We analyzed the influence of temperature on the activity patterns and habitat selection of four populations of a cold-adapted, mountain specialist, the mountain goat (Oreamnos americanus). We collected GPS location and activity sensor data during 2010-2019 from 223 mountain goats from two distinct ecotypes: coastal and continental. Using a resource selection modeling approach, we determined that mountain goats of both ecotypes decreased selection for alpine meadows when temperatures increased. Reduced selection for open, forage rich habitat was associated with increased selection for habitat dominated by snow/ice patches in coastal areas, and by forests in continental sites. Mountain goats in continental environments selected higher elevation habitats only when temperature increased, whereas goats in coastal environments selected higher elevation habitat at all temperatures. Mountain goats of both ecotypes reduced the proportion of time spent active when temperatures increased during the middle of the day. Our study reveals that mountain goats use diverse tactics to mitigate thermal stress, and that these tactics vary between ecotypes, highlighting the need for considering adaptation to specific environments within a species when assessing climate change impacts on populations.

Bayesian analysis of urban theft crime in 674 Chinese cities

Current academic research on fitting the volume of urban theft crimes at a macro size is limited, especially from the urban functionality perspective. Given this gap, this study utilizes a Bayesian model to conduct a fitting analysis of theft crime data from 674 cities in China from 2018 to 2020. This research aims to explore novel pathways for theft crime fitting. Results indicate that the size of urban functionality, particularly points of interest (POIs), exhibits excellent performance in fitting theft crimes, with POIs related to public services and commercial activities demonstrating the most significant fitting effects. This research successfully identifies effective indicators for crime fitting, thereby offering a new perspective and supplement to theft crime research. This study holds significant value for gaining a profound understanding of criminal phenomena and explaining the causes and mechanisms underlying the differences in theft crimes among various cities in China.

Latent cognitive phenotypes in juvenile myoclonic epilepsy: Clinical, sociodemographic, and neuroimaging associations

OBJECTIVE

Application of cluster analytic procedures has advanced understanding of the cognitive heterogeneity inherent in diverse epilepsy syndromes and the associated clinical and neuroimaging features. Application of this unsupervised machine learning approach to the neuropsychological performance of persons with juvenile myoclonic epilepsy (JME) has yet to be attempted, which is the intent of this investigation.

METHODS

A total of 77 JME participants, 19 unaffected siblings, and 44 unrelated controls, 12 to 25 years of age, were administered a comprehensive neuropsychological battery (intelligence, language, memory, executive function, and processing speed), which was subjected to factor analysis followed by K-means clustering of the resultant factor scores. Identified cognitive phenotypes were characterized and related to clinical, family, sociodemographic, and cortical and subcortical imaging features.

RESULTS

Factor analysis revealed three underlying cognitive dimensions (general ability, speed/response inhibition, and learning/memory), with JME participants performing worse than unrelated controls across all factor scores, and unaffected siblings performing worse than unrelated controls on the general mental ability and learning/memory factors, with no JME vs sibling differences. K-means clustering of the factor scores revealed three latent groups including above average (31.4% of participants), average (52.1%), and abnormal performance (16.4%). Participant groups differed in their distributions across the latent groups (p < 0.001), with 23% JME, 22% siblings, and 2% unrelated controls in the abnormal performance group; and 18% JME, 21% siblings, and 59% unrelated controls in the above average group. Clinical epilepsy variables were unassociated with cluster membership, whereas family factors (lower parental education) and abnormally increased thickness and/or volume in the frontal, parietal, and temporal-occipital regions were associated with the abnormal cognition group.

SIGNIFICANCE

Distinct cognitive phenotypes characterize the spectrum of neuropsychological performance of patients with JME for which there is familial (sibling) aggregation. Phenotypic membership was associated with parental (education) and imaging characteristics (increased cortical thickness and volume) but not basic clinical seizure features.

The artificial intelligence advantage: Supercharging exploratory data analysis

Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions. Tree-based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real-world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data-driven decision-making across various scientific domains. Level of Evidence: Level V - Expert opinion.

Exploring the potential of epigenetic clocks in aging research

The process of aging is a notable risk factor for numerous age-related illnesses. Hence, a reliable technique for evaluating biological age or the pace of aging is crucial for understanding the aging process and its influence on the progression of disease. Epigenetic alterations are recognized as a prominent biomarker of aging, and epigenetic clocks formulated on this basis have been shown to provide precise estimations of chronological age. Extensive research has validated the effectiveness of epigenetic clocks in determining aging rates, identifying risk factors for aging, evaluating the impact of anti-aging interventions, and predicting the emergence of age-related diseases. This review provides a detailed overview of the theoretical principles underlying the development of epigenetic clocks and their utility in aging research. Furthermore, it explores the existing obstacles and possibilities linked to epigenetic clocks and proposes potential avenues for future studies in this field.

Maritime traffic alters distribution of the harbour porpoise in the North Sea

The North Sea is one of the most industrialised marine regions globally. We integrated cetacean-dedicated aerial surveys (2015-2022) with environmental covariates and ship positions from the Automatic Identification System (AIS) to investigate the disturbance radius and duration on harbour porpoise distribution. This study is based on 81,511 km of line-transect survey effort, during which 6511 harbour porpoise groups (8597 individuals) were sighted. Several proxies for ship disturbance were compared, identifying those best explaining the observed distribution. Better model performance was achieved by integrating maritime traffic, with frequent traffic representing the most significant disturbance to harbour porpoise distribution. Porpoises avoided areas frequented by numerous vessels up to distances of 9 km. The number of ships and average approach distance over time improved model performance, while reasons for the lower performance of predicted ship sound levels remain unclear. This study demonstrates the short-term effects of maritime traffic on harbour porpoise distribution.

Marijuana Use and Hemoglobin Concentrations in NHANES 2009-2018: Implications for Subclinical Hypoxemia

Rationale: Cannabis use is rapidly growing in the United States, but its health implications are poorly understood, particularly when compared with cigarette smoking. Previous research conducted on animal models or nonrepresentative populations with small sample sizes has yielded mixed results on the impact of marijuana use on hemoglobin concentrations, which might reflect subclinical hypoxemia and/or carbon monoxide exposure. Objectives: We evaluated the association between marijuana use and hemoglobin concentrations in a nationally representative sample of U.S. adults. Methods: This cross-sectional study included 16,038 individuals 18-59 years of age enrolled in National Health and Nutrition Examination Survey (NHANES) from 2009 to 2018. We related current and former marijuana use with measured hemoglobin concentrations, with adjustment for demographics, education, housing, and cigarette smoking status in multivariable analyses that incorporated complex survey weights. As candidate positive and negative control exposures, we used similar methods to relate cigarette smoking and benzodiazepine use, respectively, with hemoglobin concentrations. Results: Current marijuana use was associated with significantly higher hemoglobin concentrations. After multivariable adjustment, compared with never use, current marijuana use was associated with a 0.111 (95% confidence interval, 0.021 to 0.201) g/dl higher hemoglobin concentration, whereas former use was associated with a 0.047 (95% confidence interval, -0.018 to 0.113) g/dl higher concentration (linear trend P = 0.01). As hypothesized, cigarette smoking was also associated with higher hemoglobin concentrations, whereas benzodiazepine use was not. Conclusions: Among American adults, current marijuana use was associated with higher hemoglobin concentrations, as is cigarette smoking but not benzodiazepine use. These results suggest the possibility that marijuana smoking induces subclinical hypoxemia stimulating hemoglobin production. Further confirmation of this observational finding is needed in light of the increasing medical and recreational use of smoked marijuana products.

WENDY: Covariance dynamics based gene regulatory network inference

Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.

Score-driven exponential random graphs: A new class of time-varying parameter models for temporal networks

Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the exponential random graph models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on dynamic conditional score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics.

Multimodality in systems driven by Ornstein-Uhlenbeck noise

The presence of noise in nonlinear dynamical systems can significantly change their properties. Here, we study the properties of a noise perturbed motion in a single-well potential of |x|n (n>0) type. We explore under what conditions the action of the Ornstein-Uhlenbeck noise induces bimodality of stationary states in static, single-well, power-law potentials. In particular, we inspect the transition from unimodality (n⩽2) to bimodality (n>2). Results of numerical simulations are compared with estimates obtained from the unified colored-noise approximation. Furthermore, we explore the role of a harmonic addition to the general single-well power-law potentials showing its constructive or destructive role.

Persistent Racial Disparities in Morbidity Following Major Elective Operations

Introduction: Despite considerable national attention, racial disparities in surgical outcomes persist. We sought to consider whether race-based inequities in outcomes following major elective surgery have improved in the contemporary era. Methods: All adult hospitalization records for elective coronary artery bypass grafting, abdominal aortic aneurysm repair, colectomy, and hip replacement were tabulated from the 2016-2020 National Inpatient Sample. Patients were stratified by Black or White race. To consider the evolution in outcomes, we included an interaction term between race and year. We designated centers in the top quartile of annual procedural volume as high-volume hospitals (HVH). Results: Of ∼2,838,485 patients, 245,405 (8.6%) were of Black race. Following risk-adjustment, Black race was linked with similar odds of in-hospital mortality, but increased likelihood of major complications (Adjusted Odds Ratio [AOR] 1.41, 95%Confidence Interval [CI] 1.36-1.47). From 2016-2020, overall risk-adjusted rates of major complications declined (patients of White race: 9.2% to 8.4%; patients of Black race 11.8% to 10.8%, both P < .001). Yet, the delta in risk of adverse outcomes between patients of White and Black race did not significantly change. Of the cohort, 158,060 (8.4%) were treated at HVH. Following adjustment, Black race remained associated with greater odds of morbidity (AOR 1.37, CI 1.23-1.52; Ref:White). The race-based difference in risk of complications at HVH did not significantly change from 2016 to 2020. Conclusion: While overall rates of complications following major elective procedures declined from 2016 to 2020, patients of Black race faced persistently greater risk of adverse outcomes. Novel interventions are needed to address persistent racial disparities and ensure acceptable outcomes for all patients.

Use of transportability methods for real-world evidence generation: a review of current applications

Aim: To evaluate how transportability methods are currently used for real-world evidence (RWE) generation to inform good practices and support adoption and acceptance of these methods in the RWE context. Methods: We conducted a targeted literature review to identify studies that transported an effect estimate of the clinical effectiveness or safety of a biomedical exposure to a target real-world population. Records were identified from PubMed-indexed articles published any time before 25 July 2023 (inclusive). Two reviewers screened abstracts/titles and reviewed the full text of candidate studies to identify the final set of articles. Data on the therapeutic area, exposure(s), outcome(s), original and target populations and details of the transportability analysis (e.g., analytic method used, estimate transported, stated assumptions) were abstracted from each article. Results: Of 458 unique records identified, six were retained in the final review. Articles were published during 2021-2023, focused on the US/Canada context, and covered a range of therapeutic areas. Four studies transported an RCT effect estimate, while two transported effect estimates derived from real-world data. Almost all articles used weighting methods to transport estimates. Two studies discussed all transportability assumptions, and one evaluated the likelihood of meeting all assumptions and the impact of potential violations. Conclusion: The use of transportability methods for RWE generation is an emerging and promising area of research to address evidence gaps in settings with limited data and infrastructure. More transparent and rigorous reporting of methods, assumptions and limitations may increase the use and acceptability of transportability for producing robust evidence on treatment effectiveness and safety.

Overall side effect assessment of oxaliplatin toxicity in rectal cancer patients in NRG oncology/NSABP R04

PURPOSE

Regulatory guidance suggests capturing patient-reported overall side effect impact in cancer trials. We examined whether the Functional Assessment of Cancer Therapy (FACT) GP5 item ("I am bothered by side effects of treatment") post-neoadjuvant chemotherapy/radiotherapy differed between oxaliplatin vs. non- oxaliplatin arms in the National Surgical Adjuvant Breast and Bowel Project (NSABP) R-04 trial of stage II-III rectal cancer patients.

METHODS

The R-04 neoadjuvant trial compared local-regional tumor control between patients randomized to receive 5-fluorouracil or capecitabine with radiation, with or without oxaliplatin (4 treatment arms). Participants completed surveys at baseline and immediately after chemoradiotherapy. GP5 has a 5-point response scale: "Not at all" (0), "A little bit" (1), "Somewhat" (2), "Quite a bit" (3), and "Very much" (4). Logistic regression compared the odds of reporting moderate-high side effect impact (GP5 2-4) between patients receiving oxaliplatin or not after chemoradiotherapy, controlling for relevant patient characteristics. We examined associations between GP5 and other patient-reported outcomes reflecting side effects.

RESULTS

Analyses were performed among 1132 study participants. Participants receiving oxaliplatin were 1.58 times (95% CI: 1.22-2.05) more likely to report moderate-high side effect bother at post-chemotherapy/radiation. In both arms, worse overall side effect impact was associated with patient-reported diarrhea, nausea, vomiting, and peripheral sensory neuropathy (p < 0.01 for all).

CONCLUSION

This secondary analysis of R-04 found that GP5 distinguished between patients receiving oxaliplatin or not as part of their post-neoadjuvant chemoradiotherapy, adding patient-centric evidence on the reduced tolerability of oxaliplatin and demonstrating that GP5 is sensitive to known toxicity differences between treatments.

CLINICALTRIALS

GOV: NCT00058474.

Conversion practice recall and mental health symptoms in sexual and gender minority adults in the USA: a cross-sectional study

BACKGROUND

Conversion practices are associated with psychological morbidity, yet few studies have evaluated differences between efforts to change gender identity, sexual orientation, or both. We aimed to examine the individual and joint association of conversion practice recall targeted at gender identity or sexual orientation, or both, with current mental health symptoms among sexual and gender minority people.

METHODS

This cross-sectional study used data from The PRIDE Study, a US-based, online, prospective cohort study of sexual and gender minority adults who were recruited through social media, digital advertisements, and sexual and gender minority community-based events and organisations. For this analysis, we included participants who completed a lifetime questionnaire in 2019-20 and a subsequent annual questionnaire in 2020-21 without missing outcome data. All questionnaires were in English. The exposure was lifetime recall of conversion practice targeting gender identity alone, sexual orientation alone, or both (versus no conversion practice). Mental health outcomes were continuous measures: Generalized Anxiety Disorder 7-item scale, Patient Health Questionnaire 9-item (depression) scale, Post-Traumatic Stress Disorder Checklist 6-item scale, and Suicide Behaviors Questionnaire-Revised scale. We used linear regression to analyse the associations of conversion practice recall and mental health symptoms, controlling for demographic and childhood factors and stratified between cisgender and transgender and gender diverse groups. Sensitivity analyses evaluated the potential impact of unmeasured confounding. Analyses were conducted in R. We included people with related lived experience in the design and implementation of this study.

FINDINGS

Of 6601 participants who completed the lifetime questionnaire in 2019-20, 4440 completed the subsequent annual questionnaire in 2020 or 2021, and 4426 did not have missing outcome data. Of the 4426 included participants, 4073 (92·0%) identified as White (either alone or in combination with other ethnoracial options), 460 (10·4%) identified with multiple ethnoracial identities, and 1923 (43·4%) were transgender and gender diverse. Participants' age ranged from 18 years to 84 years (median 31·7 years, IQR 25·5-44·1). 149 (3·4%) participants reported sexual orientation-related conversion practice alone, 43 (1·0%) reported gender identity-related conversion practice alone, and 42 (1·0%) reported both. Recalling both forms of conversion practice was most strongly associated with greater post-traumatic stress disorder (PTSD; β 2·84, 95% CI 0·94-4·74) and suicidality (2·14, 0·95-3·32) symptoms. Recall of only sexual orientation-related conversion practice was associated with greater symptoms of PTSD (1·10, 0·22-1·98). Recall of gender identity-related conversion practice alone was most strongly associated with greater depressive symptoms (3·24, 1·03-5·46). Only associations for suicidality differed between cisgender and transgender and gender diverse participants, although the latter showed higher mental health symptoms overall. Findings were moderately robust to potential sources of unmeasured confounding in sensitivity analysis.

INTERPRETATION

Recall of conversion practice exposure was associated with a range of mental health symptoms among sexual and gender minority people. These findings support calls to ban conversion practices because of their effects as a structural determinant of mental health.

FUNDING

Gill Foundation, Dona Rockstad, and Patient-Centered Outcomes Research Institute.

Estimating Test-Retest Reliability in the Presence of Self-Selection Bias and Learning/Practice Effects

Test-retest reliability is often estimated using naturally occurring data from test repeaters. In settings such as admissions testing, test takers choose if and when to retake an assessment. This self-selection can bias estimates of test-retest reliability because individuals who choose to retest are typically unrepresentative of the broader testing population and because differences among test takers in learning or practice effects may increase with time between test administrations. We develop a set of methods for estimating test-retest reliability from observational data that can mitigate these sources of bias, which include sample weighting, polynomial regression, and Bayesian model averaging. We demonstrate the value of using these methods for reducing bias and improving precision of estimated reliability using empirical and simulated data, both of which are based on more than 40,000 repeaters of a high-stakes English language proficiency test. Finally, these methods generalize to settings in which only a single, error-prone measurement is taken repeatedly over time and where self-selection and/or changes to the underlying construct may be at play.

Spatial Multiomics Reveals Intratumoral Immune Heterogeneity with Distinct Cytokine Networks in Lung Cancer Brain Metastases

The tumor microenvironment of brain metastases has become a focus in the development of immunotherapeutic drugs. However, countless patients with brain metastasis have not experienced clinical benefit. Thus, understanding the immune cell composition within brain metastases and how immune cells interact with each other and other microenvironmental cell types may be critical for optimizing immunotherapy. We applied spatial whole-transcriptomic profiling with extensive multiregional sampling (19-30 regions per sample) and multiplex IHC on formalin-fixed, paraffin-embedded lung cancer brain metastasis samples. We performed deconvolution of gene expression data to infer the abundances of immune cell populations and inferred spatial relationships from the multiplex IHC data. We also described cytokine networks between immune and tumor cells and used a protein language model to predict drug-target interactions. Finally, we performed deconvolution of bulk RNA data to assess the prognostic significance of immune-metastatic tumor cellular networks. We show that immune cell infiltration has a negative prognostic role in lung cancer brain metastases. Our in-depth multiomics analyses further reveal recurring intratumoral immune heterogeneity and the segregation of myeloid and lymphoid cells into distinct compartments that may be influenced by distinct cytokine networks. By using computational modeling, we identify drugs that may target genes expressed in both tumor core and regions bordering immune infiltrates. Finally, we illustrate the potential negative prognostic role of our immune-metastatic tumor cell networks. Our findings advocate for a paradigm shift from focusing on individual genes or cell types toward targeting networks of immune and tumor cells.

SIGNIFICANCE

Immune cell signatures are conserved across lung cancer brain metastases, and immune-metastatic tumor cell networks have a prognostic effect, implying that targeting cytokine networks between immune and metastatic tumor cells may generate more precise immunotherapeutic approaches.

Estimating the Relationship Between Nurse Staffing and Medication Pass Workload Using National Barcode Data

BACKGROUND

Measuring and assessing the relationship between inpatient nurse staffing and workload across a national health system is difficult because of challenges in systematically observing inpatient workload at the unit level.

OBJECTIVE

The objective of this study was to apply a novel measure of inpatient nurse workload to estimate the relationship between inpatient nurse staffing and nurse workload at the unit level during a key nursing activity: the peak-time medication pass.

METHODS

A retrospective observational study was conducted in the Veterans Health Administration, the largest employer of nurses in the United States. The sample included all patients ( n = 1,578,399 patient days) admitted to 311 non-intensive care unit inpatient acute care units in 112 hospitals in 2019 (104,588 unit days). Staffing was measured as the unit-level, nurse-to-patient ratio, and workload was measured using average time (duration) for RNs to complete the peak-time medication pass.

RESULTS

We found a negative relationship between the RN-to-patient ratio and average peak-time medication pass duration after adjusting for unit-level patient volume and average patient severity of illness and other unit-level factors. This relationship was nonlinear: The marginal effect of staffing on workload decreased as staffing increased.

DISCUSSION

As unit-level nurse staffing increased, average RN workload decreased. This result suggests that interventions to improve nurse staffing may have larger nonlinear effects for units with lower staffing levels. Understanding the effect of differing staffing decisions on variations in nursing workload is critical for adopting models of care that effectively use scarce staffing resources and contribute to retaining nurses in the inpatient workforce. This work provides evidence that peak-time medication pass duration is a valid process-based measure of workload and highlights the potential diminishing returns to increasing staffing.

Deep survival analysis using pseudo values and its application to predict the recurrence of stage IV colorectal cancer after tumor resection

An improved DeepSurv model is proposed for predicting the prognosis of colorectal cancer patients at stage IV. Our model, called as PseudoDeepSurv, is optimized by a novel loss function, which is the combination of the average negative log partial likelihood and the mean-squared error derived from the pseudo-observations approach. The public BioStudies dataset including 999 patients was utilized for performance evaluation. Our PseudoDeepSurv model produced a C-index of 0.684 and 0.633 on the training and testing dataset, respectively. While for the original DeepSurv model, the corresponding values are 0.671 and 0.618, respectively.

Deep network embedding with dimension selection

Network embedding is a general-purpose machine learning technique that converts network data from non-Euclidean space to Euclidean space, facilitating downstream analyses for the networks. However, existing embedding methods are often optimization-based, with the embedding dimension determined in a heuristic or ad hoc way, which can cause potential bias in downstream statistical inference. Additionally, existing deep embedding methods can suffer from a nonidentifiability issue due to the universal approximation power of deep neural networks. We address these issues within a rigorous statistical framework. We treat the embedding vectors as missing data, reconstruct the network features using a sparse decoder, and simultaneously impute the embedding vectors and train the sparse decoder using an adaptive stochastic gradient Markov chain Monte Carlo (MCMC) algorithm. Under mild conditions, we show that the sparse decoder provides a parsimonious mapping from the embedding space to network features, enabling effective selection of the embedding dimension and overcoming the nonidentifiability issue encountered by existing deep embedding methods. Furthermore, we show that the embedding vectors converge weakly to a desired posterior distribution in the 2-Wasserstein distance, addressing the potential bias issue experienced by existing embedding methods. This work lays down the first theoretical foundation for network embedding within the framework of missing data imputation.

An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions

BACKGROUND

Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop an approach that combines clinical judgment with ML to generate appropriate comparative effectiveness evidence for informing decision making.

METHODS

We motivate this approach in evaluating the effectiveness of nonemergency surgery (NES) strategies, such as antibiotic therapy, for people with acute appendicitis who have multiple long-term conditions (MLTCs) compared with emergency surgery (ES). Our 4-stage approach 1) draws on clinical judgment about which patient characteristics and morbidities modify the relative effectiveness of NES; 2) selects additional covariates from a high-dimensional covariate space (P > 500) by applying an ML approach, least absolute shrinkage and selection operator (LASSO), to large-scale administrative data (N = 24,312); 3) generates estimates of comparative effectiveness for relevant subgroups; and 4) presents evidence in a suitable form for decision making.

RESULTS

This approach provides useful evidence for clinically relevant subgroups. We found that overall NES strategies led to increases in the mean number of days alive and out-of-hospital compared with ES, but estimates differed across subgroups, ranging from 21.2 (95% confidence interval: 1.8 to 40.5) for patients with chronic heart failure and chronic kidney disease to -10.4 (-29.8 to 9.1) for patients with cancer and hypertension. Our interactive tool for visualizing ML output allows for findings to be customized according to the specific needs of the clinical decision maker.

CONCLUSIONS

This principled approach of combining clinical judgment with an ML approach can improve trust, relevance, and usefulness of the evidence generated for clinical decision making.

HIGHLIGHTS

Machine learning (ML) methods have many potential applications in medical decision making, but the lack of model interpretability and usability constitutes an important barrier for the wider adoption of ML evidence in practice.We develop a 4-stage approach for integrating clinical judgment into the way an ML approach is used to estimate and report comparative effectiveness.We illustrate the approach in undertaking an evaluation of nonemergency surgery (NES) strategies for acute appendicitis in patients with multiple long-term conditions and find that NES strategies lead to better outcomes compared with emergency surgery and that the effects differ across subgroups.We develop an interactive tool for visualizing the results of this study that allows findings to be customized according to the user's preferences.

Sensitivity-Aware Density Estimation in Multiple Dimensions

We formulate an optimization problem to estimate probability densities in the context of multidimensional problems that are sampled with uneven probability. It considers detector sensitivity as an heterogeneous density and takes advantage of the computational speed and flexible boundary conditions offered by splines on a grid. We choose to regularize the Hessian of the spline via the nuclear norm to promote sparsity. As a result, the method is spatially adaptive and stable against the choice of the regularization parameter, which plays the role of the bandwidth. We test our computational pipeline on standard densities and provide software. We also present a new approach to PET rebinning as an application of our framework.

Predicting State-Level Firearm Suicide Rates: A Machine Learning Approach Using Public Policy Data

INTRODUCTION

Over 40,000 people die by suicide annually in the U.S., and firearms are the most lethal suicide method. There is limited evidence on the effectiveness of many state-level policies on reducing firearm suicide. The objective of this study was to identify public policies that best predict state-level firearm suicide rates.

METHODS

Data from the Centers for Disease Control and Prevention's WONDER system and the State Firearm Law Database, a longitudinal catalog of 134 firearm safety laws, were analyzed. The analysis included 1,450 observations from 50 states spanning 1991-2019. An ElasticNet regression technique was used to analyze the relationship between the policy variables and firearm suicide rates. Nested cross-validation was performed to tune the model hyperparameters. The study data were collected and analyzed in 2023 and 2024.

RESULTS

The optimized ElasticNet approach had a mean squared error of 2.07, which was superior to nonregularized and dummy regressor models. The most influential policies for predicting the firearm suicide rate on average included laws requiring firearm dealers that sell handguns to have a state license and laws requiring individuals to obtain a permit to purchase a firearm through an approval process that includes law enforcement, among others.

CONCLUSIONS

On average, firearm suicide rates were lower in state-years that had each influential policy active. Notably, these analyses were ecological and noncausal. However, this study was able to use a supervised machine learning approach with inherent feature selection and many policy types to make predictions using unseen data (i.e., balancing Lasso and Ridge regularization penalties).

Comparison of outcomes of percutaneous deep venous arterialization in multiple practice settings

OBJECTIVE

We compared the efficacy of percutaneous deep venous arterialization (pDVA) in patients with no-option chronic limb-threatening ischemia in the hospital vs in office-based laboratory (OBL) settings.

METHODS

A retrospective chart review was performed of all patients who underwent pDVA using off-the-shelf devices from January 2018 to March 2023 in a hospital and an OBL. We identified 73 eligible patients, 41 from a hospital setting (59% male; median age, 72 years; interquartile range, 18 years) and 32 from an OBL setting (59% males; 67 years; interquartile range, 16 years). All eligible patients were deemed to have no-option critical limb ischemia, had at least one patent proximal tibial artery available for the creation of an arteriovenous anastomosis, and were classified as having Rutherford classification IV or higher peripheral arterial disease. Patients were ineligible if classified as Rutherford classification III or lower, had active infection, did not have at least one appropriate venous target, and/or had rapidly progressing wounds requiring immediate major amputation. The primary outcome was major amputation-free survival (AFS). Secondary outcomes included technical success, limb salvage, survival, primary patency, reintervention rate, adverse events, and partial and complete wound healing. Outcomes were evaluated using Kaplan-Meier method, log-rank, and two-stage procedure tests.

RESULTS

Technical success was achieved in 70 patients (96%) with 1 hospital (2.4%) and 2 OBL (6.3%) patients lost to follow-up. Major AFS estimates at 6 months, 1 year, and 2 years were 51.4%, 40.4%, and 30.2% in the hospital group and 69.4%, 54.0%, and 49.5% in the OBL group, respectively. Partial wound healing estimates at 6 months, 1 year, and 2 years were 27.5%, 71.7%, and 81.2% in the hospital group and 62.7% at all time points in the OBL group. Complete wound healing estimates at 6 months, 1 year, and 2 years were 6.7%, 33.3%, and 33.3% in the hospital group and 5.3%, 37.7%, and 41.6% in the OBL group, respectively. There was no significant difference in major AFS (P = .13), limb salvage (P = .07), survival (P = .69), primary patency (P = .53), partial (P = .08), or complete wound healing (P = .79) between groups. Reintervention was performed in 8 hospital (20.5%) and 14 OBL (45.2%) patients.

CONCLUSIONS

pDVA is a feasible and safe procedure for no-option critical limb ischemia in the hospital and OBL setting without significant differences in outcomes at ≤2 years.

Bring Out Your Dead: A Review of the Cost Minimisation Approach in Health Technology Assessment Submissions to the Australian Pharmaceutical Benefits Advisory Committee

OBJECTIVES

Published literature has levied criticism against the cost-minimisation analysis (CMA) approach to economic evaluation over the past two decades, with multiple papers declaring its 'death'. However, since introducing the requirements for economic evaluations as part of health technology (HTA) decision-making in 1992, the cost-minimisation analysis (CMA) approach has been widely used to inform recommendations about the public subsidy of medicines in Australia. This research aimed to highlight the breadth of use of CMA in Australia and assess the influence of preconditions for the approach on subsidy recommendations METHODS: Relevant information was extracted from Public Summary Documents of Pharmaceutical Benefits Advisory Committee (PBAC) meetings in Australia considering submissions for the subsidy of medicines that included a CMA and were assessed between July 2005 and December 2022. A generalised linear model was used to explore the relationship between whether medicines were recommended and variables that reflected the primary preconditions for using CMA set out in the published PBAC Methodology Guidelines. Other control variables were selected through the Bolasso Method. Subgroup analysis was undertaken which replicated this modelling process.

RESULTS

While the potential for inferior safety or efficacy reduced the likelihood of recommendation (p < 0.01), the effect sizes suggest that the requirements for CMA were not requisite for recommendation.

CONCLUSION

The Australian practice of CMA does not strictly align with the PBAC Methodology Guidelines and the theoretically appropriate application of CMA. However, within the confines of a deliberative HTA decision-making process that balances values and judgement with available evidence, this may be considered acceptable, particularly if stakeholders consider the current approach delivers sufficient clarity of process and enables patients to access medicines at an affordable cost.

Chronic stress increases adaptive immune response over six weeks in the house sparrow, Passer domesticus

The vertebrate stress response enables an organism to shift energy towards activities that promote immediate survival when facing a threat to homeostasis, but it can also have detrimental effects on organismal health. Acute and chronic stressors generally have contrasting effects on immune responses, but the timeline of this transition between acute and chronic stressors and their effects on immune responses remains unclear. In this study, we investigate changes in immune markers in captive house sparrows (Passer domesticus) after exposure to normal laboratory conditions, an acute stressor, and chronic stressors for 42 days. Specifically, we examined changes in baseline and stress-induced corticosterone concentrations, body condition, heterophil/lymphocyte (H:L) ratio, hemolysis-hemagglutination, and wound healing. We found that individuals exposed to a single acute stressor had significantly higher stress-induced corticosterone concentrations 24 h after stressor exposure, however this effect was reversed after 48 h. Chronic stressor exposure resulted in generally stronger adaptive immune responses, demonstrated by higher baseline and stress-induced lysis, higher baseline hemagglutination, and slower wound healing. Within-trait correlations also increased with chronic stressor exposure, suggesting limitations on phenotypic plasticity. Most of the effects of chronic stressor exposure on immune markers strengthened over the 42 days of the experiment and differences between captivity-only and treatment groups were not apparent until approximately 20 days of chronic stressor exposure. These results highlight the importance of stressor duration in understanding the effects of chronic stressor exposure on immune responses.

The hospitalisation risk of chronic circulatory and respiratory diseases associated with coal mining in the general population in Queensland, Australia

Queensland is the main coal mining state in Australia where populations in coal mining areas have been historically exposed to coal mining emissions. Although a higher risk of chronic circulatory and respiratory diseases has been associated with coal mining globally, few studies have investigated these associations in the Queensland general population. This study estimates the association of coal production with hospitalisations for chronic circulatory and respiratory diseases in Queensland considering spatial and temporal variations during 1997-2014. An ecological analysis used a Bayesian hierarchical spatiotemporal model to estimate the association of coal production with standardised rates of each, chronic circulatory and respiratory diseases, adjusting for sociodemographic factors and considering the spatial structure of Queensland's statistical areas (SA2) in the 18-year period. Two specifications; with and without a space-time interaction effect were compared using the integrated nested Laplace approximation -INLA approach. The posterior mean of the best fit model was used to map the spatial, temporal and spatiotemporal trends of risk. The analysis considered 2,831,121 hospitalisation records. Coal mining was associated with a 4 % (2.4-5.5) higher risk of hospitalisation for chronic respiratory diseases in the model with a space-time interaction effect which had the best fit. An emerging higher risk of either chronic circulatory and respiratory diseases was identified in eastern areas and some coal-mining areas in central and southeast Queensland. There were important disparities in the spatiotemporal trend of risk between coal -and non-coal mining areas for each, chronic circulatory and respiratory diseases. Coal mining is associated with an increased risk of chronic respiratory diseases in the Queensland general population. Bayesian spatiotemporal analyses are robust methods to identify environmental determinants of morbidity in exposed populations. This methodology helps identifying at-risk populations which can be useful to support decision-making in health. Future research is required to investigate the causality links between coal mining and these diseases.

The heterogeneity of breast cancer metastasis: a bioinformatics analysis utilizing single-cell RNA sequencing data

PURPOSE

Breast cancer is a common malignancy in women, and its metastasis is a leading cause of cancer-related deaths. Single-cell RNA sequencing (scRNA-seq) can distinguish the molecular characteristics of metastasis and identify predictor genes for patient prognosis. This article explores gene expression in primary breast cancer tumor tissue against metastatic cells in the lymph node and liver using scRNA-seq.

METHODS

Breast cancer scRNA-seq data from the Gene Expression Omnibus were used. The data were processed using R and the Seurat package. The cells were clustered and identified using Metascape. InferCNV is used to analyze the variation in copy number. Differential expression analysis was conducted for the cancer cells using Seurat and was enriched using Metascape.

RESULTS

We identified 18 distinct cell clusters, 6 of which were epithelial. CNV analysis identified significant alterations with duplication of chromosomes 1, 8, and 19. Differential gene analysis resulted in 17 upregulated and 171 downregulated genes for the primary tumor in the primary tumor vs. liver metastasis comparison and 43 upregulated and 4 downregulated genes in the primary tumor in the primary tumor vs lymph node metastasis comparison. Several enriched terms include Ribosome biogenesis, NTP synthesis, Epithelial dedifferentiation, Autophagy, and genes associated with epithelial-to-mesenchymal transitions.

CONCLUSION

No single gene or pathway can clearly explain the mechanisms behind tumor metastasis. Several mechanisms contribute to lymph node and liver metastasis, such as the loss of differentiation, epithelial-to-mesenchymal transition, and autophagy. These findings necessitate further study of metastatic tissue for effective drug development.

Brief mindfulness-based meditation enhances the speed of learning following positive prediction errors

Recent research has demonstrated that mindfulness-based meditation facilitates basic aspects of cognition, including memory and attention. Further developing this line of inquiry, here we considered the possibility that similar effects may extend to another core psychological process-instrumental learning. To explore this matter, in combination with a probabilistic selection task, computational modelling (i.e., reinforcement drift diffusion model analysis) was adopted to establish whether and how brief mindfulness-based meditation influences learning under conditions of uncertainty (i.e., choices based on the perceived likelihood of positive and negative outcomes). Three effects were observed. Compared with performance in the control condition (i.e., no meditation), mindfulness-based meditation (1) accelerated the rate of learning following positive prediction errors; (2) elicited a preference for the exploration (vs. exploitation) of choice selections; and (3) increased response caution. Collectively, these findings elucidate the pathways through which brief meditative experiences impact learning and decision-making, with implications for interventions designed to debias aspects of social-cognitive functioning using mindfulness-based meditation.

Contamination bias in the estimation of child maltreatment causal effects on adolescent internalizing and externalizing behavior problems

BACKGROUND

When unaddressed, contamination in child maltreatment research, in which some proportion of children recruited for a nonmaltreated comparison group are exposed to maltreatment, downwardly biases the significance and magnitude of effect size estimates. This study extends previous contamination research by investigating how a dual-measurement strategy of detecting and controlling contamination impacts causal effect size estimates of child behavior problems.

METHODS

This study included 634 children from the LONGSCAN study with 63 cases of confirmed child maltreatment after age 8 and 571 cases without confirmed child maltreatment. Confirmed child maltreatment and internalizing and externalizing behaviors were recorded every 2 years between ages 4 and 16. Contamination in the nonmaltreated comparison group was identified and controlled by either a prospective self-report assessment at ages 12, 14, and 16 or by a one-time retrospective self-report assessment at age 18. Synthetic control methods were used to establish causal effects and quantify the impact of contamination when it was not controlled, when it was controlled for by prospective self-reports, and when it was controlled for by retrospective self-reports.

RESULTS

Rates of contamination ranged from 62% to 67%. Without controlling for contamination, causal effect size estimates for internalizing behaviors were not statistically significant. Causal effects only became statistically significant after controlling contamination identified from either prospective or retrospective reports and effect sizes increased by between 17% and 54%. Controlling contamination had a smaller impact on effect size increases for externalizing behaviors but did produce a statistically significant overall effect, relative to the model ignoring contamination, when prospective methods were used.

CONCLUSIONS

The presence of contamination in a nonmaltreated comparison group can underestimate the magnitude and statistical significance of causal effect size estimates, especially when investigating internalizing behavior problems. Addressing contamination can facilitate the replication of results across studies.

Semi-automated title-abstract screening using natural language processing and machine learning

BACKGROUND

Title-abstract screening in the preparation of a systematic review is a time-consuming task. Modern techniques of natural language processing and machine learning might allow partly automatization of title-abstract screening. In particular, clear guidance on how to proceed with these techniques in practice is of high relevance.

METHODS

This paper presents an entire pipeline how to use natural language processing techniques to make the titles and abstracts usable for machine learning and how to apply machine learning algorithms to adequately predict whether or not a publication should be forwarded to full text screening. Guidance for the practical use of the methodology is given.

RESULTS

The appealing performance of the approach is demonstrated by means of two real-world systematic reviews with meta analysis.

CONCLUSIONS

Natural language processing and machine learning can help to semi-automatize title-abstract screening. Different project-specific considerations have to be made for applying them in practice.

Anticoagulation Use and Endovascular Thrombectomy in Patients with Large Core Stroke: A Secondary Analysis of the SELECT2 Trial

Endovascular thrombectomy (EVT) safety and efficacy in patients with large core infarcts receiving oral anticoagulants (OAC) are unknown. In the SELECT2 trial (NCT03876457), 29 of 180 (16%; vitamin K antagonists 15, direct OACs 14) EVT, and 18 of 172 (10%; vitamin K antagonists 3, direct OACs 15) medical management (MM) patients reported OAC use at baseline. EVT was not associated with better clinical outcomes in the OAC group (EVT 6 [4-6] vs MM 5 [4-6], adjusted generalized odds ratio 0.89 [0.53-1.50]), but demonstrated significantly better outcomes in patients without OAC (EVT 4 [3-6] vs MM 5 [4-6], adjusted generalized odds ratio 1.87 [1.45-2.40], p = 0.02). The OAC group had higher comorbidities, including atrial fibrillation (70% vs 17%), congestive heart failure (28% vs 10%), and hypertension (87% vs 72%), suggesting increased frailty. However, the results were consistent after adjustment for these comorbidities, and was similar regardless of the type of OACs used. Whereas any hemorrhage rates were higher in the OAC group receiving EVT (86% in OAC vs 70% in no OAC), no parenchymal hemorrhage or symptomatic intracranial hemorrhage were observed with OAC use in both the EVT and MM arms. Although we did not find evidence that the effect was due to excess hemorrhage or confounded by underlying cardiac disease or older age, OAC use alone should not exclude patients from receiving EVT. Baseline comorbidities and ischemic injury extent should be considered while making individualized treatment decisions. ANN NEUROL 2024;96:887-894.

Fast estimation of generalized linear latent variable models for performance and process data with ordinal, continuous, and count observed variables

Different data types often occur in psychological and educational measurement such as computer-based assessments that record performance and process data (e.g., response times and the number of actions). Modelling such data requires specific models for each data type and accommodating complex dependencies between multiple variables. Generalized linear latent variable models are suitable for modelling mixed data simultaneously, but estimation can be computationally demanding. A fast solution is to use Laplace approximations, but existing implementations of joint modelling of mixed data types are limited to ordinal and continuous data. To address this limitation, we derive an efficient estimation method that uses first- or second-order Laplace approximations to simultaneously model ordinal data, continuous data, and count data. We illustrate the approach with an example and conduct simulations to evaluate the performance of the method in terms of estimation efficiency, convergence, and parameter recovery. The results suggest that the second-order Laplace approximation achieves a higher convergence rate and produces accurate yet fast parameter estimates compared to the first-order Laplace approximation, while the time cost increases with higher model complexity. Additionally, models that consider the dependence of variables from the same stimulus fit the empirical data substantially better than models that disregarded the dependence.

Short term air pollution exposure during pregnancy and associations with maternal immune markers

BACKGROUND

Air pollution exposure during pregnancy has been associated with numerous adverse pregnancy, birth, and child health outcomes. One proposed mechanism underlying these associations is maternal immune activation and dysregulation. We examined associations between PM2.5 and NO2 exposure during pregnancy and immune markers within immune function groups (TH1, TH2, TH17, Innate/Early Activation, Regulatory, Homeostatic, and Proinflammatory), and examined whether those associations changed across pregnancy.

METHODS

In a pregnancy cohort study (n = 290) in Rochester, New York, we measured immune markers (using Luminex) in maternal plasma up to 3 times during pregnancy. We estimated ambient PM2.5 and NO2 concentrations at participants' home addresses using a spatial-temporal model. Using mixed effects models, we estimated changes in immune marker concentrations associated with interquartile range increases in PM2.5 (2.88 μg/m3) and NO2 (7.82 ppb) 0-6 days before blood collection, and assessed whether associations were different in early, mid, and late pregnancy.

RESULTS

Increased NO2 concentrations were associated with higher maternal immune markers, with associations observed across TH1, TH2, TH17, Regulatory, and Homeostatic groups of immune markers. Furthermore, the largest increases in immune markers associated with each 7.82 ppb increase in NO2 concentration were in late pregnancy (e.g., IL-23 = 0.26 pg/ml, 95% CI = 0.07, 0.46) compared to early pregnancy (e.g., IL-23 = 0.08 pg/ml, 95% CI = -0.11, 0.26).

CONCLUSIONS

Results were suggestive of NO2-related immune activation. Increases in effect sizes from early to mid to late pregnancy may be due to changes in immune function over the course of pregnancy. These findings provide a basis for immune activation as a mechanism for previously observed associations between air pollution exposure during pregnancy and reduced birthweight, fetal growth restriction, and pregnancy complications.

Data-driven subclassification of ANCA-associated vasculitis: model-based clustering of a federated international cohort

BACKGROUND

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is a heterogenous autoimmune disease. While traditionally stratified into two conditions, granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), the subclassification of ANCA-associated vasculitis is subject to continued debate. Here we aim to identify phenotypically distinct subgroups and develop a data-driven subclassification of ANCA-associated vasculitis, using a large real-world dataset.

METHODS

In the collaborative data reuse project FAIRVASC (Findable, Accessible, Interoperable, Reusable, Vasculitis), registry records of patients with ANCA-associated vasculitis were retrieved from six European vasculitis registries: the Czech Registry of ANCA-associated vasculitis (Czech Republic), the French Vasculitis Study Group Registry (FVSG; France), the Joint Vasculitis Registry in German-speaking Countries (GeVas; Germany), the Polish Vasculitis Registry (POLVAS; Poland), the Irish Rare Kidney Disease Registry (RKD; Ireland), and the Skåne Vasculitis Cohort (Sweden). We performed model-based clustering of 17 mixed-type clinical variables using a parsimonious mixture of two latent Gaussian variable models. Clinical validation of the optimal cluster solution was made through summary statistics of the clusters' demography, phenotypic and serological characteristics, and outcome. The predictive value of models featuring the cluster affiliations were compared with classifications based on clinical diagnosis and ANCA specificity. People with lived experience were involved throughout the FAIRVASVC project.

FINDINGS

A total of 3868 patients diagnosed with ANCA-associated vasculitis between Nov 1, 1966, and March 1, 2023, were included in the study across the six registries (Czech Registry n=371, FVSG n=1780, GeVas n=135, POLVAS n=792, RKD n=439, and Skåne Vasculitis Cohort n=351). There were 2434 (62·9%) patients with GPA and 1434 (37·1%) with MPA. Mean age at diagnosis was 57·2 years (SD 16·4); 2006 (51·9%) of 3867 patients were men and 1861 (48·1%) were women. We identified five clusters, with distinct phenotype, biochemical presentation, and disease outcome. Three clusters were characterised by kidney involvement: one severe kidney cluster (555 [14·3%] of 3868 patients) with high C-reactive protein (CRP) and serum creatinine concentrations, and variable ANCA specificity (SK cluster); one myeloperoxidase (MPO)-ANCA-positive kidney involvement cluster (782 [20·2%]) with limited extrarenal disease (MPO-K cluster); and one proteinase 3 (PR3)-ANCA-positive kidney involvement cluster (683 [17·7%]) with widespread extrarenal disease (PR3-K cluster). Two clusters were characterised by relative absence of kidney involvement: one was a predominantly PR3-ANCA-positive cluster (1202 [31·1%]) with inflammatory multisystem disease (IMS cluster), and one was a cluster (646 [16·7%]) with predominantly ear-nose-throat involvement and low CRP, with mainly younger patients (YR cluster). Compared with models fitted with clinical diagnosis or ANCA status, cluster-assigned models demonstrated improved predictive power with respect to both patient and kidney survival.

INTERPRETATION

Our study reinforces the view that ANCA-associated vasculitis is not merely a binary construct. Data-driven subclassification of ANCA-associated vasculitis exhibits higher predictive value than current approaches for key outcomes.

FUNDING

European Union's Horizon 2020 research and innovation programme under the European Joint Programme on Rare Diseases.

Predicting Prenatal Depression and Assessing Model Bias Using Machine Learning Models

Background

Perinatal depression is one of the most common medical complications during pregnancy and postpartum period, affecting 10% to 20% of pregnant individuals, with higher rates among Black and Latina women who are also less likely to be diagnosed and treated. Machine learning (ML) models based on electronic medical records (EMRs) have effectively predicted postpartum depression in middle-class White women but have rarely included sufficient proportions of racial/ethnic minorities, which has contributed to biases in ML models. Our goal is to determine whether ML models could predict depression in early pregnancy in racial/ethnic minority women by leveraging EMR data.

Methods

We extracted EMRs from a large U.S. urban hospital serving mostly low-income Black and Hispanic women (n = 5875). Depressive symptom severity was assessed using the Patient Health Questionnaire-9 self-report questionnaire. We investigated multiple ML classifiers using Shapley additive explanations for model interpretation and determined prediction bias with 4 metrics: disparate impact, equal opportunity difference, and equalized odds (standard deviations of true positives and false positives).

Results

Although the best-performing ML model's (elastic net) performance was low (area under the receiver operating characteristic curve = 0.61), we identified known perinatal depression risk factors such as unplanned pregnancy and being single and underexplored factors such as self-reported pain, lower prenatal vitamin intake, asthma, carrying a male fetus, and lower platelet levels. Despite the sample comprising mostly low-income minority women (54% Black, 27% Latina), the model performed worse for these communities (area under the receiver operating characteristic curve: 57% Black, 59% Latina women vs. 64% White women).

Conclusions

EMR-based ML models could moderately predict early pregnancy depression but exhibited biased performance against low-income minority women.