bluuebunny/crossref_metadata_2025_split huggingface dataset:
One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study
Authors: Barrigon Maria Luisa , Romero-Medrano Lorena , Moreno-Muñoz Pablo , Porras-Segovia Alejandro , Lopez-Castroman Jorge , Courtet Philippe , Artés-Rodríguez Antonio , Baca-Garcia Enrique 
Published: June 2023

Background
Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach.

Objective
We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation.

Methods
We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested.

Results
During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy.

Conclusions
We describe an innovative method to identify mental health crises based on passively collected information from patients’ smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.
Digital Application of Clinical Staging to Support Stratification in Youth Mental Health Services: Validity and Reliability Study
Authors: Chong Min K , Hickie Ian B , Cross Shane P , McKenna Sarah , Varidel Mathew , Capon William , Davenport Tracey A , LaMonica Haley M , Sawrikar Vilas , Guastella Adam , Naismith Sharon L , Scott Elizabeth M , Iorfino Frank 
Published: June 2023

Background
As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals’ risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services.

Objective
The aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders.

Methods
We conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm’s allocation based on a multidimensional self-report questionnaire.

Results
Of the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts’ ratings, with a substantial interrater agreement (κ=0.67; P<.001). The algorithm demonstrated an accuracy of 91% (95% CI 86%-95%; P=.03), a sensitivity of 80%, a specificity of 93%, and an F1-score of 73%. Of the concordant ratings, 16 young people were allocated to stage 1a, while 103 were assigned to stage 1b+. Among the 12 discordant cases, the digital algorithm allocated a lower stage (stage 1a) to 8 participants compared to the experts. These individuals had significantly milder symptoms of depression (P<.001) and anxiety (P<.001) compared to those with concordant stage 1b+ ratings.

Conclusions
This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in the early stages of common anxiety and depressive disorders. Between 11% and 27% of young people seeking care may benefit from low-intensity, self-directed, or brief interventions. Findings from this study suggest the possibility of redirecting clinical capacity to focus on individuals in stage 1b+ for further assessment and intervention.
Design and Early Use of the Nationally Implemented Healthier You National Health Service Digital Diabetes Prevention Programme: Mixed Methods Study
Authors: Ross Jamie , Hawkes Rhiannon E , Miles Lisa M , Cotterill Sarah , Bower Peter , Murray Elizabeth 
Published: June 2023

Background
The Healthier You National Health Service Digital Diabetes Prevention Programme (NHS-digital-DPP) is a 9-month digital behavior change intervention delivered by 4 independent providers that is implemented nationally across England. No studies have explored the design features included by service providers of digital diabetes prevention programs to promote engagement, and little is known about how participants of nationally implemented digital diabetes prevention programs such as this one make use of them.

Objective
This study aimed to understand engagement with the NHS-digital-DPP. The specific objectives were to describe how engagement with the NHS-digital-DPP is promoted via design features and strategies and describe participants’ early engagement with the NHS-digital-DPP apps.

Methods
Mixed methods were used. The qualitative study was a secondary analysis of documents detailing the NHS-digital-DPP intervention design and interviews with program developers (n=6). Data were deductively coded according to an established framework of engagement with digital health interventions. For the quantitative study, anonymous use data collected over 9 months for each provider representing participants’ first 30 days of use of the apps were obtained for participants enrolled in the NHS-digital-DPP. Use data fields were categorized into 4 intervention features (Track, Learn, Coach Interactions, and Peer Support). The amount of engagement with the intervention features was calculated for the entire cohort, and the differences between providers were explored statistically.

Results
Data were available for 12,857 participants who enrolled in the NHS-digital-DPP during the data collection phase. Overall, 94.37% (12,133/12,857) of those enrolled engaged with the apps in the first 30 days. The median (IQR) number of days of use was 11 (2-25). Track features were engaged with the most (number of tracking events: median 46, IQR 3-22), and Peer Support features were the least engaged with, a median value of 0 (IQR 0-0). Differences in engagement with features were observed across providers. Qualitative findings offer explanations for the variations, including suggesting the importance of health coaches, reminders, and regular content updates to facilitate early engagement.

Conclusions
Almost all participants in the NHS-digital-DPP started using the apps. Differences across providers identified by the mixed methods analysis provide the opportunity to identify features that are important for engagement with digital health interventions and could inform the design of other digital behavior change interventions.
Pro-DOBIE: A Protocol for Domain and Outcome Bias Exploration in Healthcare Machine Learning Models (Preprint)
Authors: Davis Matthew , Alekseyenko Alexander V. 
Published: June 2023

UNSTRUCTURED

Understanding bias, auditing and quality control are key challenges for developing machine learning methods in healthcare. Models that have been trained on biased data, have the potential to automate decisions that are unfair and inequitable. We propose a protocol for domain and outcome bias exploration (Pro-DOBIE) as a standard framework and reference set of techniques to evaluate performance equitably across subgroups and whether the training data are representative of novel data expected in the model application. The framework consists of three dimensions that address: (i) subgroup performance equitability; (ii) application and training data similarity; and (iii) training data grouping variable congruence.­­ We applied Pro-DOBIE to models developed on two publicly available datasets and a novel data set from MUSC describing mammogram screening uptake. These application examples demonstrate how to use Pro-DOBIE as a standard operating procedure to investigate whether and why the model, the data, or both may be biased.
The Influence of Joe Wicks on Physical Activity During the COVID-19 Pandemic: Thematic, Location, and Social Network Analysis of X Data (Preprint)
Authors: Ahmed Wasim , Aiyenitaju Opeoluwa , Chadwick Simon , Hardey Mariann , Fenton Alex 
Published: June 2023

BACKGROUND

Social media (SM) was essential in promoting physical activity during the COVID-19 pandemic, especially among people confined to their homes. Joe Wicks, a fitness coach, became particularly popular on SM during this time, posting daily workouts that millions of people worldwide followed.

OBJECTIVE

This study aims to investigate the influence of Joe Wicks on SM and the impact of his content on physical activity levels among the public.

METHODS

We used NodeXL Pro (Social Media Research Foundation) to collect data from X (formerly Twitter) over 54 days (March 23, 2020, to May 15, 2020), corresponding to the strictest lockdowns in the United Kingdom. We collected 290,649 posts, which we analyzed using social network analysis, thematic analysis, time-series analysis, and location analysis.

RESULTS

We found that there was significant engagement with content generated by Wicks, including reposts, likes, and comments. The most common types of posts were those that contained images, videos, and text of young people (school-aged children) undertaking physical activity by watching content created by Joe Wicks and posts from schools encouraging pupils to engage with the content. Other shared posts included those that encouraged others to join the fitness classes run by Wicks and those that contained general commentary. We also found that Wicks’ network of influence was extensive and complex. It contained numerous subcommunities and resembled a broadcast network shape. Other influencers added to engagement with Wicks via their networks. Our results show that influencers can create networks of influence that are exhibited in distinctive ways.

CONCLUSIONS

Our study found that Joe Wicks was a highly influential figure on SM during the COVID-19 pandemic and that his content positively impacted physical activity levels among the public. Our findings suggest that influencers can play an important role in promoting public health and that government officials should consider working with influencers to communicate health messages and promote healthy behaviors. Our study has broader implications beyond the status of fitness influencers. Recognizing the critical role of individuals such as Joe Wicks in terms of health capital should be a critical area of inquiry for governments, public health authorities, and policy makers and mirrors the growing interest in health capital as part of embodied and digital experiences in everyday life.
Caracterización fisicoquímica y compuestos bioactivos en los frutos de pitaya (Stenocereus thurberi) de cuatro colores
Authors: Hinojosa-Gómez Jeny , Muy-Rangel María D. 
Published: June 2023

En los frutos de pitaya silvestre (Stenocereus thurberi) se distinguen cuatro tipos diferentes de coloración (rojo, tinto, amarillo y anaranjado); el estudio se realizó en su estado de maduración y con base en los que presentaron estos colores, para a través de un análisis químico determinar su color, composición mineral, calidad química y nutracéutica. Los frutos tuvieron en promedio 83 % de humedad, 0.8 % de cenizas y 0.1 % de grasa. Los mayores contenidos de fibra total fueron de (38 g kg-1), SST (13.5 °Brix), glucosa (12.6 g 100 g-1), fructosa (6.1 g 100g-1), fenoles totales (1.9 mg EAG g-1) y capacidad antioxidante (ORAC, ABTS+) para el color anaranjado. Los valores altos de betacianinas (125.4 mg 100 g-1) y betaxantinas (275.1 mg 100 g-1) son valores reportados para el color tinto que también contiene un número mayor de minerales (K, Ca, Mg, Fe y Mn), seguidos de los frutos rojos con (N, Na y Cu), anaranjados (P, Zn) y amarillos (N, K). Los valores del ángulo de matiz para la pulpa en los cuatro colores de la pitaya se situaron en la región amarillenta-rojiza, revelando el color visual para cada fruto con el uso del software ColorHexa. La variabilidad en las características evaluadas estuvo influenciada por el color del fruto de pitaya.
Physician Practices in the Management of Myocardial Injury after Non-Cardiac Surgery
Authors: Selznick Asher , Wang Michael Ke , Borges Flavia , Conen David , Blum Steffen , Devereaux PJ , Marcucci Maura 
Published: June 2023

Objective: To describe how physicians manage patients with myocardial injury (i.e., a troponin elevation of presumed ischemic origin) after non-cardiac surgery (MINS).
Methods: Web-based survey to physicians distributed between December 2020 and September 2021, including a case scenario of asymptomatic MINS.
Results: Of 103 respondents, 94% were practicing in Canada and 65% were general internists. 97% of respondents would order an ECG; following a normal ECG, 46% of would order an echocardiogram; following a normal echocardiogram, 42% would order myocardial perfusion imaging. Of the respondents, 91% and 90% would initiate ASA and a statin, respectively; 24%, 21%, and 7% would initiate an ACE inhibitor, a beta-blocker, and dabigatran, respectively. Most participants indicated that outpatient follow-up with a medicine specialist within 1–2 months (90%) and 1 year (68%) was appropriate.
Conclusion: Respondents generally agreed that ASA and statins should be prescribed for MINS, and that post-discharge specialist follow-up is warranted. However, opinions regarding the role of cardiac imaging varied.
RésuméObjectif: Décrire la manière dont les médecins prennent en charge les patients atteints d’une lésion myo-cardique (c’est-à-dire une élévation de la troponine d’origine ischémique présumée) à la suite d’une intervention chirurgicale non cardiaque (MINS pour myocardial injury after non-cardiac surgery).
Méthodologie: Enquête en ligne menée auprès de médecins et distribuée entre décembre 2020 et septembre 2021 et comprenant un scénario de cas de MINS asymptomatique.
Résultats: Sur les 103 répondants au sondage, 94% pratiquent au Canada et 65% sont des internistes généralistes. Une proportion de 97% des répondants demanderaient un ECG; si l’ECG s’avère normal, 46% demanderaient un échocardiogramme; s’il s’avère normal, 42% demanderaient une imagerie de perfusion myocardique. Une proportion de 90 à 91% des répondants prescriraient un traitement par l’acide acétylsalicylique (ASA) ou une statine; 24% un traitement par un inhibiteur de l’enzyme de conversion de l’angioten-sine (IECA), 21% un traitement par un bêtabloquant et 7% un traitement par le dabigatran. La plupart des participants indiquent qu’il est approprié d’assurer un suivi en consultation externe par un spécialiste dans le mois ou les deux mois (90%) et un an (68%) suivant l’intervention.
Conclusion: Les répondants au sondage sont généralement d’avis que l’ASA et les statines devraient être prescrits pour la MINS et qu’il est justifié d’assurer un suivi par un spécialiste après la sortie de l’hôpital. Les avis concernant le rôle de l’imagerie cardiaque varient.
Lenalidomide-Associated Progressive Multifocal Leukoencephalopathy
Authors: Wang Michael , Jevtic Stefan , Rebello Ryan , Komorowski Adam 
Published: June 2023

A man in his 80s with IgG kappa and free light chain multiple myeloma was admitted to hospital with several weeks’ history of falls and cognitive decline. Magnetic resonance imaging of the brain demonstrated bilateral lesions in the corona radiata, centrum semiovale, and corpus callosum. The suspicion for progressive multifocal leukoencephalopathy was raised due to his ongoing use of lenalidomide for multiple myeloma. A diagnostic lumbar puncture was performed, and polymerase chain reaction testing of his cerebral spinal fluid was positive for John Cunningham Virus. The patient was diagnosed with progressive multifocal leukoencephalopathy, and lenalidomide was stopped in consultation with his hematologist. The patient died three weeks after the diagnosis was established. Physicians should consider the diagnosis of progressive multifocal leukoencephalopathy in patients presenting with rapid cognitive decline and chronic use of immunosuppressive medications such as lenalidomide.
Résumé
Un octogénaire atteint d’un myélome multiple à chaînes légères libres et à IgG kappa est hospitalisé pour cause de chutes et d’un déclin cognitif observés depuis plusieurs semaines. Une IRM cérébrale révèle des lésions bilatérales dans la couronne rayonnante, le centre ovale de Vieussens et le corps calleux. On soupçonne la présence d’une leucoencéphalopathie multifocale progressive en raison de la prise continue de lénalidomide pour traiter le myélome multiple. Une ponction lombaire diagnostique est alors réalisée, et l’analyse par amplification en chaîne par polymérase (PCR) du liquide céphalorachidien révèle la présence du virus John Cunningham. Une leucoencéphalopathie multifocale progressive est donc diagnostiquée chez le patient, et la prise de lénalidomide est arrêtée en consultation avec son hématologue. Le patient est décédé trois semaines après l’établissement du diagnostic. Les médecins devraient envisager le diagnostic de leucoencéphalopathie multifocale progressive chez les patients présentant un déclin cognitif rapide et qui prennent des immunosuppresseurs, comme le lénalidomide, de façon chronique.
Experimental study of gasification rate of fast-growing willow biomass
Authors: Jasinskas Algirdas , Kukharets Savelii , Sukmaniuk Olena , Kovalchuk Vladyslav 
Published: June 2023

The biomass of fast-growing hybrid plants can be an effective source for energy conversion. The biomass of energy plants: hybrid poplar, hybrid willow etc., is a potential fuel for obtaining thermal and electrical energy. It should be noted that a small size downdraft gasifier, which is economically available and the most reliable equipment, is the most suitable for the use in small or medium production conditions for energy conversion of fast-growing willow biomass. The experimental confirmation of the mathematical model of fast-growing willow biomass gasification rate is proposed to carry out in the paper. According to the model, the rate of biomass gasification is proportional to the amount of biomass remaining ungasified. The gasification rate coefficients of four fractions of fast-growing willow biomass pieces and pellets were determined by the research results. During the research, the supply of oxidizer (air) to the working area (zone) of the downdraft gasifier was changed. When the air supply into a gasifier is minimal, the gasification rate coefficient is actually the same for all biomass fractions of fast-growing willow Salix Viminalis, L. and is 4.1·10-5 ± 0.1·10-5 s-1. With an increase in air supply the gasification rate coefficient increases and reaches its maximum value at an air supply of 9·10-3 m3·s-1, further increase in the air volume does not lead to an increase in the gasification rate coefficient. As the size of fuel pieces decreases, the rate coefficient also increases and is 9.476·10-5 s-1 for the fraction with the smallest pieces.
Shape optimization of ventilation elements for protective clothing by using metamodeling approach
Authors: Janushevskis Alexander , Vejanand Sanjay-Rajni , Gulevskis Agris 
Published: June 2023

There are different types of protective clothing available to protect human body from different external conditions such as rain, dust, direct sun radiation, insect access and their bites. The problem of overheating of the body may arise when such proactive clothing is required to wear in warm environment or heavy workload conditions. This is because the outer layer of cloth lacks sufficient air permeability, which causes the accumulation of warm and moist air at the body and causes discomfort. To enhance air exchange, various closable vents and open spaces of clothing have been developed. However, this only results in a partial improvement in air exchange, and reducing mechanical strength of the clothing. The mechanical strength of the clothing can be increased by attaching appropriate ventilation elements at the inner side of ventilation holes, which may permit proper air exchange as well as restrict direct access of insects to the body. The design of elements involving fluid flows usually is based on time-consuming Computational Fluid Dynamics (CFD) simulations. In this paper metamodeling approach different order polynomial local and global as well as kriging approximations are compared for shape optimization purposes of ventilation elements. The main goal is to identify the geometrical shape of the element that causes the least amount of flow energy losses along the cell flow channel, which can also be known from pressure difference. For this a multistep procedure was realized to achieve the best results. 1) Planning the position of control points of Non-Uniform Rational B-Splines (NURBS) for obtaining elements with a smooth shape. 2) Building geometrical models using Computer Aided Design (CAD) software SolidWorks in conformity with the design of the experiment. 3) Calculation of responses for a complete model using Computer Aided Engineering (CAE) software SolidWorks Flow Simulation. 4) Building metamodels for responses based on the computer experiment. 5) Using metamodels for shape optimization. 6) Validating the optimal design using CAE software for the complete model.