ABSTRACT
PURPOSE: Firefighting is known to be carcinogenic to humans. However, current lung cancer screening guidelines do not account for occupational exposure. We hypothesize that firefighting is an independent risk factor associated with the development of high-risk lung nodules on low-dose CT (LDCT). METHODS: Members of a firefighter's union underwent LDCT at a single institution between April 2022 and June 2023 within a lung cancer screening program. Results were interpreted by designated chest radiologists and reported using the Lung-RADS scoring system. Demographic and radiographic data were recorded, and summary statistics are reported. RESULTS: 1347 individuals underwent lung cancer screening, with a median age of 51 years (IQR 42-58), including 56 (4.2%) females. Overall, 899 (66.7%) were never smokers, 345 (25.6%) were former smokers, and 103 (7.7%) were current smokers. There were 41 firefighters (3.0%) who had high-risk (Lung-RADS 3 or 4) nodules requiring intervention or surveillance, of which 21 (1.5%) were Lung-RADS 3 and 20 (1.5%) that were Lung-RADS 4. Of the firefighters with high-risk nodules, only 6 (14.6%) were eligible for LDCT based on current screening guidelines. There were 7 high-risk nodules (0.5%) that required procedural intervention, 6 (85.7%) of which were from the non-screening eligible cohort. There were also 20 never-smoking firefighters (57.1%) with high-risk nodules that were non-screening eligible. CONCLUSION: Firefighting, even in the absence of smoking history, may be associated with the development of high-risk lung nodules on LDCT. Carefully selected occupational exposures should be considered in the development of future lung cancer screening guidelines.
ABSTRACT
Engagement plays a key role in improving the cognitive and motor development of children with autism spectrum disorder (ASD). Sensing and recognizing their engagement is crucial before sustaining and improving the engagement. Engaging technologies involving interactive and multi-sensory stimuli have improved engagement and alleviated hyperactive and stereotyped behaviors. However, due to the scarcity of data on engagement recognition for children with ASD, limited access to and small pools of participants, and the prohibitive application requirements such as robots, high cost, and expertise, implementation in real world is challenging. However, serious games have the potential to overcome those drawbacks and are suitable for practical use in the field. This study proposes Engagnition, a dataset for engagement recognition of children with ASD (N = 57) using a serious game, "Defeat the Monster," based on enhancing recognition and classification skills. The dataset consists of physiological and behavioral responses, annotated by experts. For technical validation, we report the distributions of engagement and intervention, and the signal-to-noise ratio of physiological signals.
Subject(s)
Autism Spectrum Disorder , Child , Humans , Autism Spectrum Disorder/psychologyABSTRACT
Advancements in artificial intelligence (AI) for point-of-care ultrasound (POCUS) have ushered in new possibilities for medical diagnostics in low-resource settings. This review explores the current landscape of AI applications in POCUS across these environments, analyzing studies sourced from three databases-SCOPUS, PUBMED, and Google Scholars. Initially, 1196 records were identified, of which 1167 articles were excluded after a two-stage screening, leaving 29 unique studies for review. The majority of studies focused on deep learning algorithms to facilitate POCUS operations and interpretation in resource-constrained settings. Various types of low-resource settings were targeted, with a significant emphasis on low- and middle-income countries (LMICs), rural/remote areas, and emergency contexts. Notable limitations identified include challenges in generalizability, dataset availability, regional disparities in research, patient compliance, and ethical considerations. Additionally, the lack of standardization in POCUS devices, protocols, and algorithms emerged as a significant barrier to AI implementation. The diversity of POCUS AI applications in different domains (e.g., lung, hip, heart, etc.) illustrates the challenges of having to tailor to the specific needs of each application. By separating out the analysis by application area, researchers will better understand the distinct impacts and limitations of AI, aligning research and development efforts with the unique characteristics of each clinical condition. Despite these challenges, POCUS AI systems show promise in bridging gaps in healthcare delivery by aiding clinicians in low-resource settings. Future research endeavors should prioritize addressing the gaps identified in this review to enhance the feasibility and effectiveness of POCUS AI applications to improve healthcare outcomes in resource-constrained environments.
ABSTRACT
We demonstrate an understanding of different physicochemical properties of copolymers induced by systematic changes in their structural parameters, i.e., the chemical structure of the comonomer unit, composition, molecular weight, and dispersity. The terpolymers were designed to be implemented in a chemically amplified resist (CAR) to form negative-tone patterns. With two basic repeating units of 4-hydroxystyrene and 2-ethyl-2-methacryloxyadamantane as monomers for conventional CARs, the pendant group of the third methacrylate comonomer was varied from aromatic, aliphatic lactone to lactone rings to modulate the interaction capability of the copolymer chains with n-butyl acetate, which is a negative-tone developer. Along with these structures, the monomer composition, molecular weight, and dispersity were also controlled. Physicochemical properties of the synthesized copolymers having controlled structures, i.e., dissolution behaviors and quantified Hansen solubility parameters, surface wetting characteristics, and surface roughness, which can be important properties affecting patterning capability in high-resolution lithography, were explored. Furthermore, the feasibility to use experimentally determined Hansen solubility parameters of the copolymers for the prediction of pattern formation using a coarse-grained model was assessed. Our comprehensive studies on the correlation of the structural parameters of the copolymers with final properties offer fundamental avenues to attain effective designs of the complex CAR system toward the lithographic process to achieve a sub-10 nm dimension, which is close to a single-chain dimension.
ABSTRACT
The sports industry is witnessing an increasing trend of utilizing multiple synchronized sensors for player data collection, enabling personalized training systems with multi-perspective real-time feedback. Badminton could benefit from these various sensors, but there is a scarcity of comprehensive badminton action datasets for analysis and training feedback. Addressing this gap, this paper introduces a multi-sensor badminton dataset for forehand clear and backhand drive strokes, based on interviews with coaches for optimal usability. The dataset covers various skill levels, including beginners, intermediates, and experts, providing resources for understanding biomechanics across skill levels. It encompasses 7,763 badminton swing data from 25 players, featuring sensor data on eye tracking, body tracking, muscle signals, and foot pressure. The dataset also includes video recordings, detailed annotations on stroke type, skill level, sound, ball landing, and hitting location, as well as survey and interview data. We validated our dataset by applying a proof-of-concept machine learning model to all annotation data, demonstrating its comprehensive applicability in advanced badminton training and research.
Subject(s)
Athletic Performance , Racquet Sports , Wearable Electronic Devices , Biomechanical Phenomena , Lower Extremity , Racquet Sports/physiology , HumansABSTRACT
INTRODUCTION: Increasing engagement in HIV care among people living with HIV, especially those from Black/African American and Hispanic/Latinx communities, is an urgent need. Mobility data that measure individuals' movements over time in combination with sociostructural data (eg, crime, census) can potentially identify barriers and facilitators to HIV care engagement and can enhance public health surveillance and inform interventions. METHODS AND ANALYSIS: The proposed work is a longitudinal observational cohort study aiming to enrol 400 Black/African American and Hispanic/Latinx individuals living with HIV in areas of the USA with high prevalence rates of HIV. Each participant will be asked to share at least 14 consecutive days of mobility data per month through the study app for 1 year and complete surveys at five time points (baseline, 3, 6, 9 and 12 months). The study app will collect Global Positioning System (GPS) data. These GPS data will be merged with other data sets containing information related to HIV care facilities, other healthcare, business and service locations, and sociostructural data. Machine learning and deep learning models will be used for data analysis to identify contextual predictors of HIV care engagement. The study includes interviews with stakeholders to evaluate the implementation and ethical concerns of using mobility data to increase engagement in HIV care. We seek to study the relationship between mobility patterns and HIV care engagement. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Institutional Review Board of the University of California, Irvine (#20205923). Collected data will be deidentified and securely stored. Dissemination of findings will be done through presentations, posters and research papers while collaborating with other research teams.