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Chest CT provides a way to quantify pulmonary airway and vascular tree measurements. In patients with COPD, CT airway measurement differences in females are concomitant with worse quality-of-life and other outcomes. CT total airway count (TAC), airway lumen area (LA), and wall thickness (WT) also differ in females with long-COVID. Our objective was to evaluate CT airway and pulmonary vascular and quality-of-life measurements in females with COPD as compared to ex-smokers and patients with long-COVID. Chest CT was acquired 3-months post-COVID-19 infection in females with long-COVID for comparison with the same inspiratory CT in female ex-smokers and COPD patients. TAC, LA, WT, and pulmonary vascular measurements were quantified. Linear regression models were adjusted for confounders including age, height, body-mass-index, lung volume, pack-years and asthma diagnosis. Twenty-one females (53 ± 14 years) with long-COVID, 17 female ex-smokers (69 ± 9 years) and 13 female COPD (67 ± 6 years) patients were evaluated. In the absence of differences in quality-of-life scores, females with long-COVID reported significantly different LA (p = 0.006) compared to ex-smokers but not COPD (p = 0.7); WT% was also different compared to COPD (p = 0.009) but not ex-smokers (p = 0.5). In addition, there was significantly greater pulmonary small vessel volume (BV5) in long-COVID as compared to female ex-smokers (p = 0.045) and COPD (p = 0.003) patients and different large (BV10) vessel volume as compared to COPD (p = 0.03). In females with long-COVID and highly abnormal quality-of-life scores, there was CT evidence of airway remodelling, similar to ex-smokers and patients with COPD, but there was no evidence of pulmonary vascular remodelling.Clinical Trial Registration: www.clinicaltrials.gov NCT05014516 and NCT02279329

Objective Theoretical frameworks are essential for understanding behaviour change, yet their current use is inadequate to capture the complexity of human behaviour such as physical activity. Real-time and big data analytics can assist in the development of more testable and dynamic models of current theories. To transform current behavioural theories into more dynamic models, it is recommended that researchers adopt principles such as control systems engineering. In this article, we aim to describe a control system model of capability-opportunity-motivation and behaviour (COM-B) framework for reducing sedentary behaviour (SB) and increasing physical activity (PA) in adults. Methods The COM-B model is explained in terms of control systems. Examples of effective behaviour change techniques (BCTs) (e.g. goal setting, problem-solving and social support) for reducing SB and increasing PA were mapped to the COM-B model for illustration. Result A fluid analogy of the COM-B system is presented. Conclusions The proposed integrated model will enable empirical testing of individual behaviour change components (i.e. BCTs) and contribute to the optimisation of digital behaviour change interventions.

Background:. Operating on temporal cutaneous tumors is challenging because of the intricate facial nerve system, particularly the frontal branches, and the possibility of brow dysfunction. Surgery for deep margin clearance is difficult because of the fragile and sensitive soft tissue in the temporal region. To address this research gap, this study assessed clearance margins, types of skin tumors, and nerve injuries in this critical anatomical position. This retrospective study assessed temporal skin cancer surgery, malignancy types, and clearance margins in patients with frontal-branch facial nerve injuries. Methods:. Forty-five patients with temporal skin carcinoma biopsies were analyzed. The deep and peripheral excision margins of skin malignancies were examined. Medical records were reviewed for clinically injured frontal nerve. Results:. Thirty-four patients were men (75%), and basal cell carcinoma was the most prevalent histological malignancy, followed by squamous cell carcinoma. The mean age of the patients was 77.8 years (39–107 years). Two patients experienced damage to the frontal branch nerve. Twenty-six percent of the skin malignancies had inadequate deep margin excision. Conclusions:. Removing temporal skin lesions is difficult. We discovered a 26% and 4% probability of inadequate deep margin excision and frontal branch facial nerve injury, respectively. Temporal skin lesions must be removed safely by surgeons to preserve the branches of the facial nerves. Insufficiently removed tumors require multidisciplinary teamwork and patient discussions regarding the advantages and risks to improve results.

BackgroundThis study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants. ObjectiveThe primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL. MethodsA total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. ResultsOverall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=–0.3% to 0.9%), 1.19% for standing (LoA=–1.5% to 3.42%), and –4.71% for walking (LoA=–9.26% to –0.16%). The mean biases between SORD and activPAL were –3.45% for sitting and reclining (LoA=–11.59% to 4.68%), 7.45% for standing (LoA=–5.04% to 19.95%), and –5.40% for walking (LoA=–11.44% to 0.64%). ConclusionsResults suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

BackgroundReducing sedentary behavior and increasing physical activity in people with type 2 diabetes (T2D) are associated with various positive health benefits. Just-in-time adaptive interventions offer the potential to target both of these behaviors through more contextually aware, tailored, and personalized support. We have developed a just-in-time adaptive intervention to promote sitting less and moving more in people with T2D. ObjectiveThis paper presents the study protocol for a microrandomized trial to investigate whether motivational messages are effective in reducing time spent sitting in people with T2D and to determine what behavior change techniques are effective and in which context (eg, location, etc). MethodsWe will use a 6-week microrandomized trial design. A total of 22 adults with T2D will be recruited. The intervention aims to reduce sitting time and increase time spent standing and walking and comprises a mobile app (iMove), a bespoke activity sensor called Sedentary Behavior Detector (SORD), a messaging system, and a secured database. Depending on the randomization sequence, participants will potentially receive motivational messages 5 times a day. ResultsRecruitment was initiated in October 2022. As of now, 6 participants (2 female and 4 male) have consented and enrolled in the study. Their baseline measurements have been completed, and they have started using iMove. The mean age of 6 participants is 56.8 years, and they were diagnosed with T2D for 9.4 years on average. ConclusionsThis study will inform the optimization of digital behavior change interventions to support people with T2D Sit Less and Move More to increase daily physical activity. This study will generate new evidence about the immediate effectiveness of sedentary behavior interventions, their active ingredients, and associated factors. Trial RegistrationAustralian New Zealand Clinical Trial Registry ACTRN12622000426785; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=383664 International Registered Report Identifier (IRRID)DERR1-10.2196/41502

Mobile health (mHealth) applications rely on personal data visualisations to promote user adoption and engagement. To ensure the success of these applications, it is crucial to understand user requirements when utilising these visualisations. This study investigates the needs and challenges of mHealth app users with a focus on data visualisations. This paper presents the results of a user survey with 56 participants, which found curiosity to be the top motivation behind using health-tracking apps. 51% of the participants had to use more than one mHealth app to achieve their health tracking goals, which usually come with inconsistency and duplication of data entry. Bar and pie charts were the most preferred forms for viewing health data. Users do not want to use complex charts. Furthermore, users preferred a combination of text summaries and charts to make sure they correctly interpret the charts. On the other hand, users face challenges related to improper information presentation, inaccurate touch interface, and an overwhelming amount of data. Customisable and readable charts encouraged frequent app usage. The implications of these findings confirms the gap we identified in existing data visualisation design and development guidelines. We have developed a set of data visualisation guidelines to address these challenges, and currently conducting evaluations on designers and end users.

Software exploitation detection remains unresolved problem. Software exploits that target known and unknown vulnerabilities are constantly used in attacks. Signature-based detection techniques are limited to known exploits and susceptible to circumvention. Current research on the use of Machine Learning (ML) for software exploitation detection is limited in quantity and use cases. Existing research lacks the use of public datasets, discussions of feature importance, and elaboration of parameters that affect data preparation and subsequently model performance. This paper presents ML models based on different ensemble algorithms to detect software exploitation using runtime traces. We focus on buffer overflow vulnerabilities in user-space applications within Windows Operating Systems (OS), given the prevalence of the type of vulnerability and the OS. We utilized a publicly available raw dataset of 11 Windows applications under exploitation. Multiple distinct models (based on Random Forest and XGBoost) are created and tested. Testing was performed several times using various aggregation parameters and different testing applications. Our results demonstrate that we can achieve up to 100% recall with 0% false positive rate. We report on the different parameters that must be addressed to curate runtime traces and demonstrate their impact on the performance of the ML models. We demonstrate that the proper training of models on a subset of exploitation techniques enables the model to detect techniques never seen before, such as return-oriented programming. Finally, we conclude with a discussion of the important features that had the highest impact on each of the models, along with the key takeaways.

Mobile health (mHealth) applications have become ubiquitous and have enabled self-monitoring to help provide better health outcomes. However, the wide availability of mHealth apps introduces new challenges when users need to download and use several apps. While past app evaluations have highlighted many issues, the surrounding work is limited. This study aims to analyse the current user challenges and expectations from future mHealth apps. This information is important to inform and guide the design of better and more attractive mHealth platforms of the future. For our empirical investigation of user feedback, we designed an anonymous online survey using key dimensions from the Mobile Application Rating Scale (MARS), the Technology Acceptance Model (TAM) and the Value Proposition Canvas. Our survey was distributed via online channels such as Twitter and LinkedIn, and we received 70 valid responses that indicated challenges such as functional overlaps between different apps, unnecessary features, and poor customizability. Similarly, most respondents expressed their preference for a single platform to manage their health. These challenges suggest the need to design more capable unified mHealth platforms that can be tailored to a user’s needs. While the development of such platforms raise valid questions around the increase in software complexity and privacy concerns around user data, an open design can address these concerns and offer a better experience. Overall, these findings indicate the need for more research into mHealth app design strategies where the regular use of more than one app must be considered to create better, more engaging mHealth apps.

Abstract Aim of study To evaluate the role of diffuse tensor imaging (DTI) in detecting structural alterations following TBI with persistent symptoms without findings on conventional structural imaging. Methods This was a prospective study that enrolled a total of 30 individuals (12 females and 18 males), 6 control with no history of trauma and 24 with previous history of trauma. All subjects underwent conventional brain MRI and DTI. The eigenvalues of the genue and splenium of corpus callosum (CC), superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), corticospinal tract (CS), arcuate fasciculus (AF) were measured bilaterally in all cases using suitable region of interest and the fraction anisotropy (FA), mean diffusivity (MD), were calculated. FA and MD values were compared between patients and control subjects. Diagnostic accuracy was calculated for FA, MD and combined. Sensitivity and Specificity were determined from the curve and PPV, NPV and accuracy underwent calculation through cross-tabulation. Result As compared with controls, FA was significantly decreased and MD was significantly increased in genue and splenium of CC and CS. For the Combined FA & MD in differentiating cases with trauma there was statistically significant difference in SLF, AF, genue, splenium, ILF and CS as p value

The influence of tailwater depth on earth dam failure due to overtopping is investigated experimentally in this work. Four different values of tailwater depths are examined. For each experiment, the evolution of the dam bathymetry profile, the duration of failure, the eroded volume, and the outflow hydrograph are observed and recorded. The results reveal that increasing the tailwater depth reduces the time of failure by up to 57% and decreases the relative eroded crest height by up to 77.6%. In addition, for relative tailwater depths equal to 3, 4, and 5, the reduction in the cumulative eroded volume is 23, 36.5, and 75%, while the reduction in peak discharge is 7, 14, and 17.35%, respectively. The experimental results are utilized to evaluate the performance of the Flow 3D software in replicating the erosion process. The numerical model successfully simulates the erosion process of non-cohesive earth dams.

Chronic myeloid leukemia (CML) is a hematologic malignancy that has significant improvement in its prognosis after the introduction of tyrosine kinase inhibitors. Transformation to accelerated phase or blast phase can happen. Myeloid sarcoma or chloroma is an uncommon extramedullary disease. It is very unusual for patients with CML to develop myeloid sarcoma. We report a young man with CML in the chronic phase who developed myeloid sarcoma. There were many difficulties in the diagnosis of myeloid sarcoma due to the simulation of other more common conditions like infections and other malignancies. In addition, there are treatment challenges because of lack of standardized treatment. The case shed light on this rare complication, the challenging diagnosis, and its implication in patients with CML.

Background: MRI has an essential role in breast imaging, along with mammography and ultrasonography. DCE- MRI is not 100% accurate in discriminating benign from malignant lesions. In attempt to improve the specificity of DCE-MRI, multiparametric MRI (mp-MRI) with additional functional parameters had been introduced. Objectives: To assess the added diagnostic value of MR spectroscopy in evaluation of suspicious breast lesions. Patients and methods: This prospective study included 60 females, their mean age was 42.1 ± 12 SD with 75 breast lesions categorized as BI-RADS 3 or BI-RADS 4 based on sonomamography. They were subjected to multiparametric breast MRI (T2WI, dynamic contrast-enhanced MR imaging, diffusion-weighted images, and MR spectroscopy). ROC-analysis was employed for comparison between the diagnostic accuracy of mp-MRI and mp-MRI combined with MRS to predict the malignant lesions using the histopathological results as a standard of reference. Results: 33 (55 %) patients had histopathologically diagnosed 43 (58%) benign breast lesions and 27 (45%) patients had 32 (42%) malignant lesions. Using mp-MRI (dynamic contrast-enhanced MR imaging, T2-weighted images and diffusion-weighted images) had 98% accuracy of with 97.8%, sensitivity and 98.6%, specificity in differentiating malignant from benign lesions. A higher diagnostic 99.2% accuracy was obtained from combined mp-MRI and MRS with 99.5% sensitivity, 98.6% specificity. Conclusion: Despite of its limitations, MRS is a promising functional MRI technique thus improves the diagnostic accuracy of MRI for characterization of suspicious breast lesions when combined to mp-MRI to avoid unnecessary biopsy.

BackgroundExisting research has demonstrated the potential of mHealth apps in improving the caregiving outcomes of stroke. Since most of the apps were published in commercially available app stores without explaining their design and evaluation processes, it is necessary to identify the user experience issues to promote long-term adherence and usage.ObjectiveThe purpose of this study was to utilize published user reviews of commercially available apps to determine the user experience issues to guide future app development in stroke caregiving.MethodsUser reviews were extracted from the previously identified 46 apps that support stroke caregiving needs using a python-scraper. The reviews were pre-processed and filtered using python scripts to consider English reviews that described issues faced by the user. The final corpus was categorized based on TF-IDF vectorization and k-means clustering technique, and the issues extracted from the various topics were classified based on the seven dimensions of user experience to highlight factors that may affect the usage of the app.ResultsA total of 117,364 were extracted from the two app stores. After filtration, 13,368 reviews were included and classified based on the user experience dimensions. Findings highlight critical issues that affect the usability, usefulness, desirability, findability, accessibility, credibility, and value of the app that contribute to decreased satisfaction and increased frustration.ConclusionThe study identified several user experience issues due to the inability of the app developers to understand the needs of the user. Further, the study describes the inclusion of a participatory design approach to promote an improved understanding of user needs; therefore, limiting any issues and ensuring continued use.

BackgroundSeveral research studies have demonstrated the potential of mobile health apps in supporting health management. However, the design and development process of these apps are rarely presented.ObjectiveWe present the design and development of a smartphone-based lifestyle app integrating a wearable device for hypertension management.MethodsWe used an intervention mapping approach for the development of theory- and evidence-based intervention in hypertension management. This consisted of six fundamental steps: needs assessment, matrices, theoretical methods and practical strategies, program design, adoption and implementation plan, and evaluation plan. To design the contents of the intervention, we performed a literature review to determine the preferences of people with hypertension (Step 1) and necessary objectives toward the promotion of self-management behaviors (Step 2). Based on these findings, we implemented theoretical and practical strategies in consultation with stakeholders and researchers (Steps 3), which was used to identify the functionality and develop an mHealth app (Step 4). The adoption (Step 5) and evaluation (Step 6) of the mHealth app will be conducted in a future study.ResultsThrough the needs analysis, we identified that people with hypertension preferred having education, medication or treatment adherence, lifestyle modification, alcohol and smoking cessation and blood pressure monitoring support. We utilized MoSCoW analysis to consider four key elements, i.e., education, medication or treatment adherence, lifestyle modification and blood pressure support based on past experiences, and its potential benefits in hypertension management. Theoretical models such as (i) the information, motivation, and behavior skills model, and (ii) the patient health engagement model was implemented in the intervention development to ensure positive engagement and health behavior. Our app provides health education to people with hypertension related to their condition, while utilizing wearable devices to promote lifestyle modification and blood pressure management. The app also contains a clinician portal with rules and medication lists titrated by the clinician to ensure treatment adherence, with regular push notifications to prompt behavioral change. In addition, the app data can be reviewed by patients and clinicians as needed.ConclusionsThis is the first study describing the design and development of an app that integrates a wearable blood pressure device and provides lifestyle support and hypertension management. Our theory-driven intervention for hypertension management is founded on the critical needs of people with hypertension to ensure treatment adherence and supports medication review and titration by clinicians. The intervention will be clinically evaluated in future studies to determine its effectiveness and usability.

BackgroundGlobally, stroke is a leading cause of death and disability, with most care undertaken by caregivers who are generally family and friends without prior experience of care. The lack of experience or unpreparedness results in feelings of uncertainty, burnout, anxiety, burden, etc. Hence, it is necessary to identify the needs of caregivers to better support them in their caregiving journey and improve the quality of care delivered.MethodsThe study employed a grounded theory methodology that utilizes information gathered from literature reviews and social media to represent the needs and create a storyline visually. The storyline is further refined and evaluated using an online survey of 72 participants recruited through online stroke caregiving communities.ResultsThe study identified four core categories of needs: (i) Information: sufficient information delivered in layman's terms based on the individual situation of the caregiver and survivor through oral and hands-on demonstrations, (ii) Involvement: inclusion in the decision-making processes at different stages of recovery through face-to-face communication at the hospital, (iii) Self-care: ability to engage in work and leisure activities, (iv) Support: receive support in the form of resources, services and finances from different other stakeholders.ConclusionsThere is a need to create a caregiver-centered approach in stroke recovery to ensure limited obstruction to care and reduced uncertainty in stroke recovery. Moreover, through the inclusion of caregivers in stroke recovery, it may be possible to reduce the burden of care to the caregiver and ensure the satisfaction of the healthcare system throughout stroke recovery.

Abstract Background Idiopathic pulmonary fibrosis (IPF) is characterized by a poor prognosis, with a progressive decline in lung function and considerable variability in the disease’s natural history. Besides lung transplantation (LTx), the only available treatments are anti-fibrosing drugs, which have shown to slow down the disease course. Therefore, predicting the prognosis is of pivotal importance to avoid treatment delays, which may be fatal for patients with a high risk of progression. Previous studies showed that a multi-dimensional approach is practical and effective in the development of a reliable prognostic score for IPF. In the RIsk Stratification scorE (RISE), physiological parameters, an objective measure of patient-reported dyspnea and exercise capacity are combined to capture different domains of the complex pathophysiology of IPF. Methods This is an observational, multi-centre, prospective cohort study, designed to reflect common clinical practice in IPF. A development cohort and a validation cohort will be included. Patients newly diagnosed with IPF based on the ATS/ERS criteria and multi-disciplinary discussion will be included in the study. A panel of chest radiologists and lung pathologists will further assess eligibility. At the first visit (time of diagnosis), and every 4-months, MRC dyspnea score, pulmonary function tests (FEV1, FVC and DLCO), and 6-min walking distance will be recorded. Patients will be prospectively followed for 3 years. Comorbidities will be considered. The radiographic extent of fibrosis on HRCT will be recalculated at a 2-year interval. RISE, Gender-Age-Physiology, CPI and Mortality Risk Scoring System will be calculated at 4-month intervals. Longitudinal changes of each variable considered will be assessed. The primary endpoint is 3-year LTx-free survival from the time of diagnosis. Secondary endpoints include several, clinically-relevant information to ensure reproducibility of results across a wide range of disease severity and in concomitance of associated pulmonary hypertension or emphysema. Discussion The objective of this study is to validate RISE as a simple, straightforward, inexpensive and reproducible tool to guide clinical decision making in IPF, and potentially as an endpoint for future clinical trials. Trial registration: U.S National Library of Medicine Clinicaltrials.gov, trial n. NCT02632123 “Validation of the risk stratification score in idiopathic pulmonary fibrosis”. Date of registration: December 16th, 2015.

Abstract Background COVID-19 is a disease of varying presentation and neurological sequelae of the disease are being studied. Following a cluster of paediatric facial nerve palsy (FNP) cases in an area of South Wales with a high prevalence of COVID-19, we conducted an opportunistic study to determine whether there has been an increase of incidence of FNP and if there is an association between the FNP and COVID-19 in children. Methods We performed a retrospective review of the incidence of FNP between 2015 and 2020 across two hospitals within the health board. The incidence was compared with that in 2020 including a cluster of six children in 14 weeks, presenting to Royal Glamorgan Hospital between June and October. Results There were 48 cases of children with FNP across both hospital within the study years. Seven (7) cases in 2020. The incidence was not statistically different in comparison to other years. Five out of six of these children in 2020 had antibody testing for COVID-19. All serology testing (100%) returned negative for SARS-CoV- 2 antibodies. Conclusions In high prevalence area for COVID-19, cases of children with FNP have not shown a commensurate increase. we have found no causal link between COVID-19 and FNP in children. While this is a small study, larger cohort studies are needed to support this finding. As new strains of COVID-19 are being reported in UK, South Africa and Brazil, physicians need to continue to be vigilant for consistent pattern of signs and symptoms, especially in children.

Origin of the COVID-19 virus (SARS-CoV-2) has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that bats or pangolins might be the hosts for SARS-CoV-2 based on comparative studies using its genomic sequences. This paper investigates the SARS-CoV-2 origin by using artificial intelligence (AI)-based unsupervised learning algorithms and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined SARS-CoV-2 genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidence strongly supporting scientific hypotheses that bats and pangolins are probable hosts for SARS-CoV-2. At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.

This paper proposes a hierarchical multi-stage approach based on a distributed level phasor measurement unit (µPMU) at local controllers and a phasor data concentrator (PDC) at the central control unit to restore system voltage when it exceeds the limits recommended by the IEEE 1547-2018 standard. The proposed algorithm does not need an accurate system model or employ optimization solutions. Therefore, it has less implementation complexity and would be popular among distribution network service providers (DNSPs) and distribution network operators (DNOs) as it does not suffer from cost and computational complexity limitations. A PMU-PDC-based communication platform has been developed as a more efficient alternative to the supervisory control and data acquisition (SCADA) system, and provides superior characteristics, including a higher sample rate, higher data resolutions, and faster communication. The proposed coordinated algorithm aims to postpone power generation curtailment in distributed energy resources (DERs) with overvoltage problems (local DERs) by incorporating all the DERs that are not subjected to voltage violation (remote DERs) by absorbing their maximum reactive power capacity. If the system voltage has not recovered after absorbing all of the reactive power capacity of all the DERs, a reduced active power curtailment proposed by the algorithm is then applied to the system to control the voltage. The proposed strategy has been simulated in MATLAB and applied to IEEE 13-bus and IEEE 33-bus radial distribution benchmark systems to validate the performance of the system, in terms of its ability to coordinate voltage control and the accuracy of the PMU-PDC-based communication interface.