bluuebunny/crossref_metadata_2025_split huggingface dataset:
A multimodal characterization of cardiopulmonary resuscitation-associated lung edema
Authors: Magliocca Aurora , Zani Davide , De Zani Donatella , Castagna Valentina , Merigo Giulia , De Giorgio Daria , Fumagalli Francesca , Zambelli Vanessa , Boccardo Antonio , Pravettoni Davide , Bellani Giacomo , Richard Jean Christophe , Grasselli Giacomo , Rezoagli Emanuele , Ristagno Giuseppe 
Published: October 2024

Abstract
Background
Cardiopulmonary resuscitation-associated lung edema (CRALE) is a phenomenon that has been recently reported in both experimental and out-of-hospital cardiac arrest patients.
We aimed to explore the respiratory and cardiovascular pathophysiology of CRALE in an experimental model of cardiac arrest undergoing prolonged manual and mechanical chest compression (CC). Oxygen delivery achieved during mechanical or manual CC were also investigated as a secondary aim, to describe CRALE evolution under different hemodynamic supports generated during CPR.

Methods
Ventricular fibrillation (VF) was induced and left untreated for 5 min prior to begin cardiopulmonary resuscitation (CPR), including CC, ventilation with oxygen, epinephrine administration and defibrillation. Continuous mechanical and manual CC was performed alternating one of the two strategies every 5 min for a total of 25 min. Unsynchronized mechanical ventilation was resumed simultaneously to CC. A lung computed tomography (CT) was performed at baseline and 1 h after return of spontaneous circulation (ROSC) in surviving animals. Partitioned respiratory mechanics, gas exchange, hemodynamics, and oxygen delivery were evaluated during the experimental study at different timepoints. Lung histopathology was performed.

Results
After 25 min of CPR, a marked decrease of the respiratory system compliance with reduced oxygenation and CO2 elimination were observed in all animals. The worsening of the respiratory system compliance was driven by a significant decrease in lung compliance. The presence of CRALE was confirmed by an increased lung weight and a reduced lung aeration at the lung CT, together with a high lung wet-to-dry ratio and reduced airspace at histology. The average change in esophageal pressure during the 25-min CPR highly correlated with the severity of CRALE, i.e., lung weight increase.

Conclusions
In this porcine model of cardiac arrest followed by a 25-min interval of CPR with mechanical and manual CC, CRALE was consistently present and was characterized by lung inhomogeneity with alveolar tissue and hemorrhage replacing alveolar airspace. Despite mechanical CPR is associated with a more severe CRALE, the higher cardiac output generated by the mechanical compression ultimately accounted for a greater oxygen delivery. Whether specific ventilation strategies might prevent CRALE while preserving hemodynamics remains to be proved.
Forecasting migration movements using prediction markets
Authors: Morgenstern Sandra , Strijbis Oliver 
Published: October 2024

AbstractMigration forecasts are crucial for proactive immigration and integration management. While the demand for accurate migration forecasts continues to grow, the current state of migration forecasting is still unsatisfactory, because they tend to lack precision. We introduce an alternative method to forecast migration movements: prediction markets. While prediction markets are mainly unknown in migration studies, they are established in the political economy of forecasting election outcomes. For its application to a complex phenomenon in a more constrained information environment such as migration movements, we argue that prediction markets allow to balance complementarities of current qualitative and quantitative approaches if they provide solutions to avoid thin trading and integrate expert knowledge into the market. We apply the prediction market to forecast immigration in four West European countries in 2020 and find encouraging results. We discuss the strengths and limitations of prediction markets to migration forecasting, including ethical considerations, and guide its future application.
Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy
Authors: Edwards Lindsay A. , Yang Christina , Sharma Surbhi , Chen Zih-Hua , Gorantla Lahari , Joshi Sanika A. , Longhi Nicolas J. , Worku Nahom , Yang Jamie S. , Martinez Di Pietro Brandy , Armenian Saro , Bhat Aarti , Border William , Buddhe Sujatha , Blythe Nancy , Stratton Kayla , Leger Kasey J. , Leisenring Wendy M. , Meacham Lillian R. , Nathan Paul C. , Narasimhan Shanti , Sachdeva Ritu , Sadak Karim , Chow Eric J. , Boyle Patrick M. 
Published: October 2024

Abstract
Background
Despite routine echocardiographic surveillance for childhood cancer survivors, the ability to predict cardiomyopathy risk in individual patients is limited. We explored the feasibility and optimal processes for machine learning-enhanced cardiomyopathy prediction in survivors using serial echocardiograms from five centers.

Methods
We designed a series of deep convolutional neural networks (DCNNs) for prediction of cardiomyopathy (shortening fraction ≤ 28% or ejection fraction ≤ 50% on two occasions) for at-risk survivors ≥ 1-year post initial cancer therapy. We built DCNNs with four subsets of echocardiographic data differing in timing relative to case (survivor who developed cardiomyopathy) index diagnosis and two input formats (montages) with differing image selections. We used holdout subsets in a 10-fold cross-validation framework and standard metrics to assess model performance (e.g., F1-score, area under the precision-recall curve [AUPRC]). Performance of the input formats was compared using a combined 5 × 2 cross-validation F-test.

Results
The dataset included 542 pairs of montages: 171 montage pairs from 45 cases at time of cardiomyopathy diagnosis or pre-diagnosis and 371 pairs from 70 at-risk survivors who didn’t develop cardiomyopathy during follow-up (non-case). The DCNN trained to distinguish between non-case and time of cardiomyopathy diagnosis or pre-diagnosis case montages achieved an AUROC of 0.89 ± 0.02, AUPRC 0.83 ± 0.03, and F1-score: 0.76 ± 0.04. When limited to smaller subsets of case data (e.g., ≥ 1 or 2 years pre-diagnosis), performance worsened. Model input format did not impact performance accuracy across models.

Conclusions
This methodology is a promising first step toward development of a DCNN capable of accurately differentiating pre-diagnosis versus non-case echocardiograms to predict survivors more likely to develop cardiomyopathy.

Graphical Abstract
Generalized transport costs in intermodal shipping: the context of the Northeast Passage
Authors: Kovalenko Alina , Mathisen Terje Andreas , Pruyn Jeroen 
Published: October 2024

AbstractIntermodality is regarded as one way of achieving more sustainable transport solutions. To make intermodal transport the preferred solution among shippers, it must be attractive according to the concept of generalized transport costs. An extended model for generalized transport costs is developed which includes elements important for comparing maritime transport solutions in the Artic. This framework forms the basis for a principal discussion on the conditions that make one transport solution preferable to another within the context of maritime logistics. This model is then applied to the context of the Northeast Passage to discuss the necessary requirements for making an intermodal transport solution attractive relative to the current main route from Northeast Asia to Northwest Europe through the Suez Canal. Even though intermodality could be preferable in principle, current solutions cannot compete with either Arctic routes using unimodal solutions with high ice-class vessels or with the Suez route. Due to uncertainty and limited empirical evidence, a sensitivity analysis is conducted, focusing on the variables with the greatest impact on the result. Policymakers and stakeholders can consider the findings to improve transport competitiveness via the Northeast Passage.
Insecticide resistance status and mechanisms in Aedes aegypti and Aedes albopictus from different dengue endemic regions of Panama
Authors: Carrera Lorenzo Cáceres , Piedra Luis , Torres-Cosme Rolando , Castillo Anakena M. , Bruno Antonio , Ramírez José Luis , Martínez Dan , Rodríguez María Magdalena , Bisset Juan A. 
Published: October 2024

Abstract
Background
Dengue is a serious public health problem worldwide, including Panama. During the last years, the number of dengue cases has increased. This may be due to the presence of mosquito populations resistant to insecticides. The aim of this study was to characterize the resistance status, its enzymatic mechanisms and Kdr mutations in wild populations of Aedes aegypti and Aedes albopictus.

Methods
Standard WHO bioassays were performed using insecticide-treated filter papers to determine resistance in populations Ae. aegypti and Ae. albopictus to pyrethroids insecticides, organophosphates, to the carbamate propoxur and to the organochlorine DDT. Biochemical assays were conducted to detect metabolic resistance mechanisms and real-time PCR was performed to determine the frequencies of the Kdr mutations Val1016IIe and F1534C.

Results
The strains Ae. aegypti El Coco showed confirmed resistance to deltamethrin (78.5% mortality) and lambda-cyhalothrin (81%), Aguadulce to deltamethrin (79.3%), David to deltamethrin (74.8%) and lambda-cyhalothrin (87.5%) and Puerto Armuelles to permethrin (83%). Aedes aegypti El Empalme showed confirmed resistance to pirimiphos-methyl (62.3% mortality), chlorpyrifos-methyl (55.5%) and propoxur (85.3%). All strains of Ae. albopictus showed possible resistance to PYs and five strains to DDT. Only Ae. albopictus Canto del Llano showed confirmed resistance to pirimiphos-methyl (70% mortality) and malathion (62%). Esterase activity was variable across sites with the most frequent expression of α-EST compared to β-EST in Ae. aegypti populations. In Ae. Albopictus, the expressed enzymes were β-EST and MFOs. Through ANOVA, significant differences were established in the levels of enzymatic activity of α- and β-EST, MFOs and GST, with p < 0.001 in the Ae. aegypti and Ae. albopictus. The Kdr Val1016IIe mutation was detected in Ae. aegypti Aguadulce, El Coco and David. The odds ratio for the Val1016Ile mutation ranged from 0.8 to 20.8 in resistant mosquitoes, indicating the association between pyrethroid phenotypic resistance and the kdr mutation.

Conclusion
The presence of a varied and generalized resistance, enzymatic mechanisms and the Val1016IIe mutation may be associated with the intensive use and possibly misuse of the different insecticides applied to control Aedes populations. These results highlight the need to develop a program for resistance management. Also, alternative approaches to mosquito control that do not involve insecticides should be explored.
Impact of gist intervention on automated system interpretability and user decision making
Authors: Gleaves Lydia P. , Broniatowski David A. 
Published: October 2024

AbstractAs they become more common, automated systems are also becoming increasingly opaque, challenging their users’ abilities to explain and interpret their outputs. In this study, we test the predictions of fuzzy-trace theory—a leading theory of how people interpret quantitative information—on user decision making after interacting with an online decision aid. We recruited a sample of 205 online crowdworkers and asked them to use a system that was designed to detect URLs that were part of coordinated misinformation campaigns. We examined how user endorsements of system interpretability covaried with performance on this coordinated misinformation detection task and found that subjects who endorsed system interpretability displayed enhanced discernment. This interpretability was, in turn, associated with both objective mathematical ability and mathematical self-confidence. Beyond these individual differences, we evaluated the impact of a theoretically motivated intervention that was designed to promote sensemaking of system output. Participants provided with a “gist” version of system output, expressing the bottom-line meaning of that output, were better able to identify URLs that might have been part of a coordinated misinformation campaign, compared to users given the same information presented as verbatim quantitative metrics. This work highlights the importance of enabling users to grasp the essential, gist meaning of the information they receive from automated systems, which benefits users regardless of individual differences.
Interpreting the shifts in forest structure, plant community composition, diversity, and functional identity by using remote sensing-derived wildfire severity
Authors: Trotta Giacomo , Cadez Luca , Boscutti Francesco , Vuerich Marco , Asquini Edoardo , Boscarol Giacomo , Cingano Paolo , Azzani Giacomo , Pischedda Sara , Tomao Antonio , Alberti Giorgio 
Published: October 2024

Abstract
Background
Wildfires are increasingly impacting ecosystems worldwide especially in temperate dry habitats, often interplaying with other global changes (e.g., alien plant invasions). Understanding the ecological consequences of wildfires is crucial for effective conservation and management strategies. The aim of this study was to investigate the impacts of wildfire severity on plant community (both the canopy trees and herbaceous layer) and alien plant invasion, combining field observations and remotely sensed data.
We conducted an observational study in the Karst forests (North-East Italy) 1 year after the large wildfire which affected the area in 2022. We assessed the impact through 35 field plots (200 m2 each) distributed among different fire severity (i.e., the loss of organic matter) classes assessed using the differenced normalized burn ratio (dNBR) calculated from satellite images. In each plot, tree species, diameter, vitality, resprouting capacity, and seedling density were measured. In addition, herb species richness (taxonomical diversity) was quantified, and plant cover was visually estimated. Functional diversity was also assessed considering six functional traits retrieved from databases.

Results
Some woody species (e.g., Quercus pubescens) showed a higher resistance to the fire (i.e., lower mortality rate), while others showed a higher resilience (i.e., recovery after fire through resprouting or seedlings, e.g., Cotinus coggygria). The transition to a shrub-dominated community (i.e., Cotinus coggygria) where fire severity was the highest underlines the dynamic nature of the post-fire succession. We detected a significant variation in the herbaceous plant community composition, diversity, and functional identity (i.e., community-weighted mean of trait) along the fire severity gradient. In particular, high-fire severity areas exhibited higher species richness compared to low-severity or unburned areas. Total alien plant cover increased with fire severity, while native cover remained constant. We also found shifts in species that enhance traits related to germination potential and growth strategy.

Conclusions
Our results highlight the vulnerability of the forest stands to an increase in wildfire severity, resulting in significant mortality and changes in tree community structure. This study contributes to the understanding of ecological processes after wildfires using a novel remote sensing approach in a temperate forest, emphasizing the need for conservation strategies aimed at mitigating high severity wildfires.
Prevalence of attention-deficit hyperactivity disorder (ADHD): systematic review and meta-analysis
Authors: Popit Sara , Serod Klara , Locatelli Igor , Stuhec Matej 
Published: October 2024

Abstract

Background
The estimates of attention-deficit hyperactivity disorder (ADHD) prevalence across various studies are significantly variable, contributing to uncertainty in ADHD prevalence estimation. Previous systematic reviews and meta-analyses have attributed this variability primarily to the methodological characteristics of the studies, including the diagnostic criteria, source of information, and impairment requirement for the diagnosis.

Methods
Review identified studies reporting ADHD prevalence in representative samples of children and adults in Europe and worldwide. Studies that were conducted in the general population were included. We focused on studies that report ADHD prevalence based on clinical diagnosis (clinical diagnostic criteria based on the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases criteria, other diagnostic tools, such as various scales or interviews based on clinical diagnostic criteria). PubMed/Medline was searched to identify relevant articles published until 2024/2/01. The study was registered in PROSPERO (CRD42020200220) and followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines for systematic review and meta-analysis.

Results
In total, 117 studies were subjected to full evaluation. In the meta-analysis, 103 studies representing 159 independent datapoints were included. The overall prevalence of ADHD in register studies was 1.6%, 95% CI [0.9; 3.0], in survey studies 5.0%, 95% CI [2.9; 8.6], in one-stage clinical studies 4.2%, 95% CI [2.9; 6.0], and in two-stage clinical studies 4.8%, 95% CI [4.0; 5.8].

Conclusions
Exact comparisons among studies with different diagnostic criteria and types of sampling can impact prevalence estimates. When comparing data from methodologically different studies, these factors need to be considered.
Bias in Prediction Models to Identify Patients With Colorectal Cancer at High Risk for Readmission After Resection
Authors: Lucas Mary M. , Schootman Mario , Laryea Jonathan A. , Orcutt Sonia T. , Li Chenghui , Ying Jun , Rumpel Jennifer A. , Yang Christopher C. 
Published: October 2024

PURPOSE
Machine learning (ML) algorithms are used for predictive modeling in medicine, but studies often do not evaluate or report on the potential biases of the models. Our purpose was to develop clinical prediction models for readmission after surgery in patients with colorectal cancer (CRC) and to examine their potential for racial bias.

METHODS
We used the 2012-2020 American College of Surgeons' National Surgical Quality Improvement Program Participant Use File and Targeted Colectomy File. Patients were categorized into four race groups—White, Black or African American, other, and unknown/not reported. Potential predictive features were identified from studies of risk factors of 30-day readmission in patients with CRC. We compared four ML-based methods—logistic regression, multilayer perceptron, random forest, and XGBoost (XGB). Model bias was assessed using false-negative rate (FNR) difference, false-positive rate (FPR) difference, and disparate impact (DI).

RESULTS
In all, 112,077 patients were included, 67.2% of whom were White, 9.2% Black, 5.6% other race, and 18% with race not recorded. There were significant differences in the area under the receiver operating characteristics curve, FPR, and FNR between race groups across all models. Notably, patients in the other race category had higher FNR compared with Black patients in all but the XGB model, while Black patients had higher FPR than White patients in some models. Patients in the other category consistently had the lowest FPR. Applying the 80% rule for DI, the models consistently met the threshold for unfairness for the other race category.

CONCLUSION
Predictive models for 30-day readmission after colorectal surgery may perform unequally for different race groups, potentially propagating to inequalities in delivery of care and patient outcomes if the predictions from these models are used to direct care.
Impaired 11β-Hydroxysteroid Dehydrogenase Type 2 Activity in Kidney Disease Disrupts 11-Oxygenated Androgen Biosynthesis
Authors: Tomkins Maria , McDonnell Tara , Cussen Leanne , Sagmeister Michael S , Oestlund Imken , Shaheen Fozia , Harper Lorraine , Hardy Rowan S , Taylor Angela E , Gilligan Lorna C , Arlt Wiebke , McIlroy Marie , de Freitas Declan , Conlon Peter , Magee Colm , Denton Mark , O’Seaghdha Conall , Snoep Jacky L , Storbeck Karl-Heinz , Sherlock Mark , O’Reilly Michael W 
Published: October 2024

Abstract

Context
11-Oxygenated androgens are a group of adrenal-derived steroids that require peripheral activation. In vitro data highlight a putative role for 11β-hydroxysteroid dehydrogenase type 2 (HSD11B2) in 11-oxygenated androgen biosynthesis, converting 11β-hydroxyandrostenedione to 11-ketoandrostenedione (11KA4), the direct precursor of the potent androgen 11-ketotestosterone (11KT). As the kidney is the major site of HSD11B2 expression, we hypothesized that patients with chronic kidney disease (CKD) would have reduced 11-oxygenated androgen biosynthesis due to impaired HSD11B2 activity.

Objective
To determine the role of HSD11B2 in 11-oxygenated androgen biosynthesis using a human CKD cohort alongside complementary cell culture and computational modeling approaches.

Methods
Cross-sectional observational study of patients with CKD (n = 85) and healthy controls (n = 46) measuring serum and urinary concentrations of glucocorticoids, and classic and 11-oxygenated androgens by liquid chromatography tandem mass spectrometry. A computational model of peripheral 11-oxygenated androgen biosynthesis was fitted to the serum data to calculate relative HSD11B2 expression levels for each participant.

Results
HSD11B2 activity declined with estimated glomerular filtration rate (eGFR), evidenced by higher cortisol/cortisone (E) ratios in patients with CKD than in controls (P &lt; .0001). Serum concentrations of E, 11KA4, 11KT, and 11β-hydroxytestosterone were lower in patients with CKD than in controls (P &lt; .0001 for each). A computational model based on enzyme kinetic parameters of HSD11B2, 11β-hydroxysteroid dehydrogenase type 1, 17β-hydroxysteroid dehydrogenase type 2, and aldo-keto reductase 1C3 confirmed HSD11B2 as the key enzyme responsible for reduced 11-oxygenated androgen biosynthesis in CKD. Predicted HSD11B2 expression correlated with eGFR.

Conclusion
This is the first in vivo study to confirm a central role for renal HSD11B2 in 11-oxygenated androgen biosynthesis. Determining the clinical implications of this observation for patients with CKD requires further research.
A role for organ level dynamics in morphogenesis of the C. elegans hermaphrodite distal tip cell
Authors: Tolkin Theadora , Burnett Julia , Hubbard E. Jane Albert 
Published: October 2024

ABSTRACT
The morphology of cells in vivo can arise from a variety of mechanisms. In the Caenorhabditis elegans hermaphrodite gonad, the distal tip cell (DTC) elaborates into a complex plexus over a relatively short developmental time period, but the mechanisms underlying this change in cell morphology are not well defined. We correlated the time of DTC elaboration with the L4-to-adult molt, but ruled out a relevant heterochronic pathway as a cue for DTC elaboration. Instead, we found that the timing of gonad elongation and aspects of underlying germline flux influence DTC elaboration. We propose a ‘hitch and tow’ aspect of organ-level dynamics that contributes to cellular morphogenesis, whereby germline flux drags the flexible DTC cell cortex away from its stationary cell body. More broadly, we speculate that this mechanism may contribute to cell shape changes in other contexts with implications for development and disease.