bluuebunny/crossref_metadata_2025_split huggingface dataset:
StAC-DA: Structure aware cross-modality domain adaptation framework with image and feature-level adaptation for medical image segmentation
Authors: Baldeon-Calisto Maria , Lai-Yuen Susana K. , Puente-Mejia Bernardo 
Published: September 2024

Objective Convolutional neural networks (CNNs) have achieved state-of-the-art results in various medical image segmentation tasks. However, CNNs often assume that the source and target dataset follow the same probability distribution and when this assumption is not satisfied their performance degrades significantly. This poses a limitation in medical image analysis, where including information from different imaging modalities can bring large clinical benefits. In this work, we present an unsupervised Structure Aware Cross-modality Domain Adaptation (StAC-DA) framework for medical image segmentation. Methods StAC-DA implements an image- and feature-level adaptation in a sequential two-step approach. The first step performs an image-level alignment, where images from the source domain are translated to the target domain in pixel space by implementing a CycleGAN-based model. The latter model includes a structure-aware network that preserves the shape of the anatomical structure during translation. The second step consists of a feature-level alignment. A U-Net network with deep supervision is trained with the transformed source domain images and target domain images in an adversarial manner to produce probable segmentations for the target domain. Results The framework is evaluated on bidirectional cardiac substructure segmentation. StAC-DA outperforms leading unsupervised domain adaptation approaches, being ranked first in the segmentation of the ascending aorta when adapting from Magnetic Resonance Imaging (MRI) to Computed Tomography (CT) domain and from CT to MRI domain. Conclusions The presented framework overcomes the limitations posed by differing distributions in training and testing datasets. Moreover, the experimental results highlight its potential to improve the accuracy of medical image segmentation across diverse imaging modalities.
Reliability of Wearable Technology to Monitor Core Temperature Among Helicopter-Based EMS Crews
Authors: Callihan Michael , Cole Heather , Callihan Amanda , Penn Elizabeth , Peek Lauren , Barrow Mahalia , Lungu Claudiu , Odame Emmanuel Atuahene , Oh Jonghwa , Stokley Holly , Wickliffe Jeffrey , Winchester Lee 
Published: September 2024

Background: Excessive heat stress led to more than 400 deaths in the United States from 2011 to 2021. Common methods for heat injury prevention revolve around measurements of the environment and fail to account for the unique individual response to stressors. Methods: An observational approach was utilized with nine helicopter-based emergency medical services personnel during emergency flights to compare core temperature readings obtained from an ingestible temperature monitoring pill and the estimated core temperature reading of the Slate Safety Band V2 wearable device. Comparison of data was conducted within Microsoft Excel programming to determine the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean biased error (MBE), and Bland–Altman plot development. Findings: A significant bias ( t = 17.58, p < .001) toward the Slate Safety device reading higher with an average difference of −0.48°C (−0.86°F) was found, meaning the average temperature reading is 0.48°C (−0.86°F) higher with the Slate Safety device. A significant correlation of .26 ( p < .001) was noted between the ingestible pill and the wearable device with a 95% confidence interval of 0.23 to 0.29. Aggregate core temperature data demonstrated an MSE of 0.43, an RMSE of 0.65, an MAE of 0.54, and an MBE of −0.48. Conclusions/Application to Practice: The ability to monitor the physiological parameters of a worker remotely adds safety tools relative to the risks of heat stress. The slightly higher reading associated with the Slate Safety wearable device provides an added safety margin to protect our workers.
Comparison of Bond Strength of Soft Denture Liner on the Denture Base Materials Produced by Different Methods and Effect of Thermocycling
Authors: Tugut Faik , Koyu Tug˘çe , Turkyilmaz Orhun 
Published: September 2024

Aim: This study set out to determine the tensile bond strength between denture bases (produced by 3D printing technology, conventional technique, and computer-aided design and computer-aided manufacturing [CAD/CAM] milled) and silicone-based soft lining material. The consequence of thermocycling on the bonding strength was also investigated. Materials and Methods: The bonding between denture foundation materials produced through three distinct techniques (conventional, CAD/CAM milled, and 3D printed) and silicone-based soft lining material was examined. Before tensile testing, half of the samples underwent thermocycling (5–55°C, 5,000 cycles) in 37°C distilled water for 48 hours. A universal testing apparatus employed a crosshead speed of 5 mm/min. The failure type was identified visually, and the maximum tensile strength was noted. The Shapiro-Wilk test and analysis of variances ( P = .05) were used to assess the statistics. Results: CAD/CAM milled denture base material (1.56 ± 0.62/1.36 ± 0.16 MPa) showed higher bond strength values than the other denture bases in the tensile test conducted before and after thermocycling ( P < .001). The denture base material made conventionally had the lowest bond strength (1.02 ± 0.24/0.77 ± 0.1 MPa) ( P < .001). The tensile bond strength values of the conventional and 3D printing groups showed a statistically significant drop before and after thermocycling ( P = .001). Regardless of thermocycling, adhesive failure was primarily seen in all groups (76.6%). Conclusion: Compared to conventionally produced denture bases, the bond strength of soft relining materials to CAD/CAM milled and 3D printed denture base is different. In denture base materials that are conventionally, CAD/CAM and 3D printed, the thermocycling method reduced bonding strength values.
Evaluating the impact of urban traffic patterns on air pollution emissions in Dublin: a regression model using google project air view data and traffic data
Authors: Tafidis Pavlos , Gholamnia Mehdi , Sajadi Payam , Krishnan Vijayakrishnan Sruthi , Pilla Francesco 
Published: September 2024

AbstractAir pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.
Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique
Authors: Yang Dengfa , Yang Yang , Zhao MinYi , Ji Hongli , Niu Zhongfeng , Hong Bo , Shi Hengfeng , He Linyang , Shao Meihua , Wang Jian 
Published: September 2024

Abstract
Objective
To intelligently evaluate the invasiveness of pure ground-glass nodules with multiple classifications using deep learning.

Methods
pGGNs in 1136 patients were pathologically confirmed as lung precursor lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Four different models [EfficientNet-b0 2D, dual-head ResNet_3D, a 3D model combining three features (3D_3F), and a 3D model combining 19 features (3D_19F)] were constructed to evaluate the invasiveness of pGGNs using the EfficientNet and ResNet networks. The Obuchowski index was used to evaluate the differences in diagnostic efficiency among the four models.

Results
The patients with pGGNs (360 men, 776 women; mean age, 54.63 ± 12.36 years) included 235 cases of AAH + AIS, 332 cases of MIA, and 569 cases of IAC. In the validation group, the areas under the curve in detecting the invasiveness of pGGNs as a three-category classification (AAH + AIS, MIA, IAC) were 0.8008, 0.8090, 0.8165, and 0.8158 for EfficientNet-b0 2D, dual-head ResNet_3D, 3D_3F, and 3D_19F, respectively, whereas the accuracies were 0.6422, 0.6158, 0.651, and 0.6364, respectively. The Obuchowski index revealed no significant differences in the diagnostic performance of the four models.

Conclusions
The dual-head ResNet_3D_3F model had the highest diagnostic efficiency for evaluating the invasiveness of pGGNs in the four models.
Implementation of health-promoting retail initiatives in the Healthier Choices in Supermarkets Study—qualitative perspectives from a feasibility study
Authors: Duus Katrine Sidenius , Tjørnhøj-Thomsen Tine , Krølner Rikke Fredenslund 
Published: September 2024

Abstract
Background
Improving food environments like supermarkets has the potential to affect customers’ health positively. Scholars suggest researchers and retailers collaborate closely on implementing and testing such health-promoting interventions, but knowledge of the implementation of such interventions is limited. We explore the implementation of four health-promoting food retail initiatives selected and developed by a partnership between a research institution, a large retail group, and a non-governmental organisation.

Methods
The four initiatives included downsizing of bags for pick’n’ mix sweets and soda bottles at the check-out registers, shelf tags promoting healthier breakfast cereal options, and replacing a complimentary bun with a banana offered to children. The initiatives were implemented for 6 weeks (or longer if the store manager allowed it) in one store in Copenhagen, Denmark. Data were collected through observations, informal interviews with customers, and semi-structured interviews with retailers. We conducted a thematic analysis of transcripts and field notes inspired by process evaluation concepts and included quantitative summaries of selected data.

Results
Two out of four initiatives were not implemented as intended. The implementation was delayed due to delivery issues, which also resulted in soda bottles not being downsized as intended. The maintenance of the shelf tags decreased over time. Retailers expressed different levels of acceptability towards the initiatives, with a preference for the complimentary banana for children. This was also the only initiative noticed by customers with both positive and negative responses. Barriers and facilitators of implementation fell into three themes: Health is not the number one priority, general capacity of retailers, and influence of customers and other stakeholders on store operation.

Conclusions
The retailers’ interests, priorities, and general capacity influenced the initiative implementation. Retailers’ acceptability of the initiatives was mixed despite their involvement in the pre-intervention phase. Our study also suggests that customer responses towards health-promoting initiatives, as well as cooperation with suppliers and manufacturers in the development phase, may be determining to successful implementation. Future studies should explore strategies to facilitate implementation, which can be applied prior to and during the intervention.
Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions
Authors: Chen Shanquan , Yu Jiazhou , Chamouni Sarah , Wang Yuqi , Li Yunfei 
Published: September 2024

AbstractThe integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology offers remarkable opportunities to advance our understanding of the complex interplay between biological, social, and environmental factors that shape health trajectories across the lifespan. This perspective summarizes the current applications, discusses future potential and challenges, and provides recommendations for harnessing ML and AI technologies to develop innovative public health solutions. ML and AI have been increasingly applied in epidemiological studies, demonstrating their ability to handle large, complex datasets, identify intricate patterns and associations, integrate multiple and multimodal data types, improve predictive accuracy, and enhance causal inference methods. In life-course epidemiology, these techniques can help identify sensitive periods and critical windows for intervention, model complex interactions between risk factors, predict individual and population-level disease risk trajectories, and strengthen causal inference in observational studies. By leveraging the five principles of life-course research proposed by Elder and Shanahan—lifespan development, agency, time and place, timing, and linked lives—we discuss a framework for applying ML and AI to uncover novel insights and inform targeted interventions. However, the successful integration of these technologies faces challenges related to data quality, model interpretability, bias, privacy, and equity. To fully realize the potential of ML and AI in life-course epidemiology, fostering interdisciplinary collaborations, developing standardized guidelines, advocating for their integration in public health decision-making, prioritizing fairness, and investing in training and capacity building are essential. By responsibly harnessing the power of ML and AI, we can take significant steps towards creating healthier and more equitable futures across the life course.
Cruciferous vegetables lower blood pressure in adults with mildly elevated blood pressure in a randomized, controlled, crossover trial: the VEgetableS for vaScular hEaLth (VESSEL) study
Authors: Connolly Emma L. , Liu Alex H. , Radavelli-Bagatini Simone , Shafaei Armaghan , Boyce Mary C. , Wood Lisa G. , McCahon Lyn , Koch Henrietta , Sim Marc , Hill Caroline R. , Parmenter Benjamin H. , Bondonno Nicola P. , Devine Amanda , Croft Kevin D. , Mithen Richard , Gan Seng Khee , Schultz Carl J. , Woodman Richard J. , Bondonno Catherine P. , Lewis Joshua R. , Hodgson Jonathan M. , Blekkenhorst Lauren C. 
Published: September 2024

Abstract
Background
Higher cruciferous vegetable intake is associated with lower cardiovascular disease risk in observational studies. The pathways involved remain uncertain. We aimed to determine whether cruciferous vegetable intake (active) lowers 24-h brachial systolic blood pressure (SBP; primary outcome) compared to root and squash vegetables (control) in Australian adults with mildly elevated BP (SBP 120–160 mmHg inclusive).

Methods
In this randomized, controlled, crossover trial, participants completed two 2-week dietary interventions separated by a 2-week washout. Cruciferous vegetables were compared to root and squash vegetables (~ 300 g/day) consumed with lunch and dinner meals. Participants were blinded to which interventions were the active and control. Adherence was assessed using food diaries and biomarkers (S-methyl cysteine sulfoxide (SMCSO, active) and carotenoids (control)). Twenty-four-hour brachial ambulatory SBP and secondary outcomes were assessed pre- and post each intervention. Differences were tested using linear mixed effects regression.

Results
Eighteen participants were recruited (median (IQR) age: 68 (66–70); female: n = 16/18; mean ± SD clinic SBP: 135.9 ± 10.0 mmHg). For both interventions, 72% participants had 100% adherence (IQR: 96.4–100%). SMCSO and carotenoids were significantly different between interventions (mean difference active vs. control SMCSO: 22.93 mg/mL, 95%CI 15.62, 30.23, P < 0.0001; carotenoids: − 0.974 mg/mL, 95%CI − 1.525, − 0.423, P = 0.001). Twenty-four-hour brachial SBP was significantly reduced following the active vs. control (mean difference − 2.5 mmHg, 95%CI − 4.2, − 0.9, P = 0.002; active pre: 126.8 ± 12.6 mmHg, post: 124.4 ± 11.8 mmHg; control pre: 125.5 ± 12.1 mmHg, post: 124.8 ± 13.1 mmHg, n = 17), driven by daytime SBP (mean difference − 3.6 mmHg, 95%CI − 5.4, − 1.7, P < 0.001). Serum triglycerides were significantly lower following the active vs. control (mean difference − 0.2 mmol/L, 95%CI − 0.4, − 0.0, P = 0.047).

Conclusions
Increased intake of cruciferous vegetables resulted in reduced SBP compared to root and squash vegetables. Future research is needed to determine whether targeted recommendations for increasing cruciferous vegetable intake benefits population health.

Trial registration
Clinical trial registry ACTRN12619001294145. https://www.anzctr.org.au
Efficacy and safety of cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy in patients with pancreatic cancer peritoneal metastasis
Authors: Yan Guojun , Zhang Kai , Yan Lijun , Zhang Yanbin 
Published: September 2024

Abstract
Objectives
Pancreatic cancer with peritoneal metastasis presents a challenging prognosis, with limited effective treatment options available. This study aims to evaluate the efficacy and safety of combining cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) as a treatment strategy for this patient group.

Methods
A retrospective analysis was conducted on patients with peritoneal metastasis of pancreatic cancer who underwent CRS + HIPEC treatment at Beijing Shijitan Hospital from March 2017 to December 2023. The study focused on assessing clinical features, the incidence of sever adverse events (SAEs), and overall survival (OS).

Results
A total of 10 patients were enrolled in this study. The median OS was 24.2 months, suggesting an improvement over traditional therapies. While SAEs were noted, including two cases of severe complications necessitating additional surgical interventions, no perioperative fatalities were recorded. The overall survival time for patients with CC0/1 was not significantly different from that of patients with CC2/3, and no prognostic predictors were identified.

Conclusions
The combination of CRS and HIPEC appears to be a viable and promising treatment modality for patients with peritoneal metastasis of pancreatic cancer, offering an improved survival rate with manageable safety concerns. Further research is needed to refine patient selection criteria and to explore the long-term benefits of this approach.
Developmental trends in young children’s device-measured physical activity and sedentary behaviour
Authors: Christian Hayley E. , Adams Emma K. , Moore Hannah L. , Nathan Andrea , Murray Kevin , Schipperijn Jasper , Trost Stewart G. 
Published: September 2024

Abstract
Background
Knowledge of developmental trends in meeting age-specific 24-hour movement behaviour guidelines is lacking. This study describes developmental trends in device-measured physical activity and sedentary time over a three-year period among Western Australian children aged two to seven years, including differences between boys and girls. The proportion of children meeting age-specific physical activity guidelines before and after they transition to full-time school was also examined.

Methods
Data from waves 1 and 2 of the Play Spaces and Environments for Children’s Physical Activity (PLAYCE) cohort study were used (analysis n = 1217). Physical activity and sedentary time were measured by accelerometry at ages two to five (preschool, wave 1) and ages five to seven (commenced full-time school, wave 2). Accelerometer data were processed using a validated machine-learning physical activity classification model. Daily time spent in sedentary behaviour, energetic play (moderate-to-vigorous physical activity (MVPA)), total physical activity, and meeting physical activity guidelines were analysed using linear and generalised linear mixed-effects models with age by sex interaction terms.

Results
All movement behaviours changed significantly with increasing age, and trends were similar in boys and girls. Total daily physical activity increased from age two to five then declined to age seven. Mean daily total physical activity exceeded 180 min/day from ages two to five. Daily energetic play increased significantly from age two to seven, however, was below 60 min/day at all ages except for seven-year-old boys. Daily sedentary time decreased to age five then increased to age seven but remained lower than at age two. All two-year-olds met their age-specific physical activity guideline, decreasing to 5% of girls and 6% of boys at age four. At age seven, 46% of boys and 35% of girls met their age-specific physical activity guideline.

Conclusions
Young children’s energetic play and total physical activity increased with age, but few children aged three to seven met the energetic play (MVPA) guideline. Interventions should focus on increasing children’s energetic play in early childhood. Clearer guidance and strategies are needed to support young children as they change developmentally and as they transition from one age-specific movement guideline to the next.
Tilorone mitigates the propagation of α-synucleinopathy in a midbrain-like organoid model
Authors: Zhang Qi , Liu Meng , Xu Yue , Lee Juhyung , Jones Brothely , Li Bing , Huang Wenwei , Ye Yihong , Zheng Wei 
Published: September 2024

Abstract
Background
Parkinson’s disease (PD) is a neurodegenerative condition characterized by the loss of dopaminergic neurons and the accumulation of Lewy-body protein aggregates containing misfolded α-synuclein (α-syn) in a phosphorylated form. The lack of effective models for drug screens has hindered drug development studies for PD. However, the recent development of in vitro brain-like organoids provides a new opportunity for evaluating therapeutic agents to slow the progression of this chronic disease.

Methods
In this study, we used a 3D brain-like organoid model to investigate the potential of repurposing Tilorone, an anti-viral drug, for impeding the propagation of α-synucleinopathy. We assessed the effect of Tilorone on the uptake of fluorescently labeled α-syn preformed fibrils (sPFF) and sPFF-induced apoptosis using confocal microscopy. We also examined Tilorone’s impact on the phosphorylation of endogenous α-syn induced by pathogenic sPFF by immunoblotting midbrain-like organoid extracts. Additionally, quantitative RT-PCR and proteomic profiling of sPFF-treated organoids were conducted to evaluate the global impact of Tilorone treatment on tissue homeostasis in the 3D organoid model.

Results
Tilorone inhibits the uptake of sPFF in both mouse primary neurons and human midbrain-like organoids. Tilorone also reduces the phosphorylation of endogenous α-syn induced by pathogenic α-syn fibrils and mitigates α-syn fibril-induced apoptosis in midbrain-like organoids. Proteomic profiling of fibril-treated organoids reveals substantial alterations in lipid homeostasis by α-syn fibrils, which are reversed by Tilorone treatment. Given its safety profile in clinics, Tilorone may be further developed as a therapeutic intervention to alleviate the propagation of synucleinopathy in PD patients.