Using deep learning-derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data.

Objectives: Siamese neural networks (SNN) were used to classify the presence of radiopaque beads as part of a colonic transit time study (CTS). The SNN output was then used as a feature in a time series model to predict progression through a CTS.

Methods: This retrospective study included all patients undergoing a CTS in a single institution from 2010 to 2020.

Early detection of subclinical mastitis in lactating dairy cows using cow-level features.

Subclinical mastitis in cows affects their health, well-being, longevity, and performance, leading to reduced productivity and profit. Early prediction of subclinical mastitis can enable dairy farmers to perform interventions to mitigate its effect. The present study investigated how well predictive models built using machine learning techniques can detect subclinical mastitis up to 7 d before its occurrence.

Surrogate explanations for role discovery on graphs.

Role discovery is the task of dividing the set of nodes on a graph into classes of structurally similar roles. Modern strategies for role discovery typically rely on graph embedding techniques, which are capable of recognising complex graph structures when reducing nodes to dense vector representations. However, when working with large, real-world networks, it is difficult to interpret or validate a set of roles identified according to these methods.

Surface Potential Modulation in Boronate-Functionalized Magnetic Nanoparticles Reveals Binding Interactions: Toward Magnetophoretic Capture/Quantitation of Sugars from Extracellular Matrix.

Phenylboronic acids (BAs) are important synthetic receptors that bind reversibly to cis-diols enabling their use in molecular sensing. When conjugated to magnetic iron oxide nanoparticles, BAs have potential for application in separations and enrichment. Realizing this will require a new understanding of their inherent binding modes and measurement of their binding capacity and their stability in/extractability from complex environments.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions.

Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts).

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation.

Background: Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures.

Summary: In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools.

Integration of Clinical Information and Imputed Aneuploidy Scores to Enhance Relapse Prediction in Early Stage Lung Cancer Patients.

Early-stage lung cancer is crucial clinically due to its insidious nature and rapid progression. Most of the prediction models designed to predict tumour recurrence in the early stage of lung cancer rely on the clinical or medical history of the patient. However, their performance could likely be improved if the input patient data contained genomic information.

How to apply evidence-based practice to the use of artificial intelligence in radiology (EBRAI) using the data algorithm training output (DATO) method.

Objective: As the number of radiology artificial intelligence (AI) papers increases, there are new challenges for reviewing the AI literature as well as differences to be aware of, for those familiar with the clinical radiology literature. We aim to introduce a tool to aid in this process.

Methods: In evidence-based practise (EBP), you must Ask, Search, Appraise, Apply and Evaluate to come to an evidence-based decision.

Cutting uncertain stock and vehicle routing in a sustainability forestry harvesting problem.

Sustainable forest management is concerned with the management of forests according to the principles of sustainable development. As a contribution to the field, this paper combines the Vehicle Routing Problem (VRP) (in which the vehicles are harvesters) with the Multiple Stock Size Cutting Stock Problem under uncertainty (in which the stock is logs). We present an Integer Linear Program that dynamically combines the cutting of the uncertain stock with vehicle routing, and uses it to address real-life problems.

Assessing epidemic curves for evidence of superspreading.

The expected number of secondary infections arising from each index case, referred to as the reproduction or number, is a vital summary statistic for understanding and managing epidemic diseases. There are many methods for estimating ; however, few explicitly model heterogeneous disease reproduction, which gives rise to superspreading within the population. We propose a parsimonious discrete-time branching process model for epidemic curves that incorporates heterogeneous individual reproduction numbers.

Using web log analysis to evaluate healthcare students' engagement behaviours with multimedia lectures on YouTube.

The objective of this study was to utilise web log analysis to evaluate the relationship between University students' engagement (e.g., watch time) and the characteristics of a catalogue of multimedia lectures, including their duration, the speaking rate of the narrator and the extent to which they implemented certain principles from Mayer's Cognitive Theory of Multimedia Learning (CTML).

Hollow silicon microneedles, fabricated using combined wet and dry etching techniques, for transdermal delivery and diagnostics.

Microneedle-based technologies are the subject of intense research and commercial interest for applications in transdermal delivery and diagnostics, primarily because of their minimally invasive and painless nature, which in turn could lead to increased patient compliance and self-administration. In this paper, a process for the fabrication of arrays of hollow silicon microneedles is described. This method uses just two bulk silicon etches - a front-side wet etch to define the 500 μm tall octagonal needle structure itself, and a rear-side dry etch to create a 50 μm diameter bore through the needle.

Physical Match Demands of International Women's Rugby Union: A Three-Year Longitudinal Analysis of a Team Competing in The Women's Six Nations Championship.

There is a paucity of studies describing the physical match demands of elite international women's rugby union, which limits coaches' ability to effectively prepare players for the physical demands required to compete at the elite level. Global positioning system technologies were used to measure the physical match demands of 53 international female rugby union players during three consecutive Women's Six Nations Championships (2020-2022), resulting in 260 individual match performances. Mixed-linear modelling was used to investigate differences in physical match demands between positions.

Role of digital health in pulmonary rehabilitation and beyond: shaping the future.

Pulmonary rehabilitation (PR) is a cost-effective intervention with well-known benefits to exercise capacity, symptoms and quality of life in patients with chronic respiratory diseases. Despite the compelling evidence of its benefits, PR implementation is still suboptimal, and maintenance of PR benefits is challenging. To overcome these pitfalls, there has been a growing interest in developing novel models for PR delivery.

DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy.

Objectives: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion.

Statistical techniques used in analysing simultaneous continuous glucose monitoring and ambulatory electrocardiography in patients with diabetes: A systematic review.

Objectives: There has been a steady increase in the number of studies of the complex relationship between glucose and electrical cardiac activity which use simultaneous continuous glucose monitors (CGM) and continuous electrocardiogram (ECG). However, data collected on the same individual tend to be similar (yielding correlated or dependent data) and require analyses that take into account that correlation. Many opt for simplified techniques such as calculating one measure from the data collected and analyse one observation per subject.

Learning a novel rhythmic stepping task in children with probable developmental coordination disorder.

Background: Developmental coordination disorder affects approximately 6% of children, interfering with participation in physical activity and can persist through adulthood. However, no studies have investigated the neuromotor mechanisms of learning of a novel task with rhythmic cueing.

Methods: Movement Assessment Battery for Children-2nd edition was used to identify 48 children with probable developmental coordination disorder (13.

Comprehensive assessment and classification of upper and lower limb pain in athletes: a scoping review.

Background: Upper and lower limb (peripheral) pain is prevalent in athletes. Contemporary research prioritises multidimensional pain assessment and classification. This study aims to review comprehensive athlete pain assessment practices against the reference standard (International Olympic Committee, IOC Athlete Pain framework), identifying trends and highlighting gaps.

Acceptability of wearable devices for measuring mobility remotely: Observations from the Mobilise-D technical validation study.

Background: This study aimed to explore the acceptability of a wearable device for remotely measuring mobility in the Mobilise-D technical validation study (TVS), and to explore the acceptability of using digital tools to monitor health.

Methods: Participants ( = 106) in the TVS wore a waist-worn device (McRoberts Dynaport MM + ) for one week. Following this, acceptability of the device was measured using two questionnaires: The Comfort Rating Scale (CRS) and a previously validated questionnaire.

Listening to the patients' voice: a conceptual framework of the walking experience.

Background: walking is crucial for an active and healthy ageing, but the perspectives of individuals living with walking impairment are still poorly understood.

Objectives: to identify and synthesise evidence describing walking as experienced by adults living with mobility-impairing health conditions and to propose an empirical conceptual framework of walking experience.

Methods: we performed a systematic review and meta-ethnography of qualitative evidence, searching seven electronic databases for records that explored personal experiences of walking in individuals living with conditions of diverse aetiology.

Moving well-being well: Using machine learning to explore the relationship between physical literacy and well-being in children.

Physical literacy provides a foundation for lifelong engagement in physical activity, resulting in positive health outcomes. Direct pathways between physical literacy and health have not yet been investigated thoroughly. Associations between physical literacy and well-being in children (n = 1073, mean age 10.