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COVID-19 vaccination uptake and risk of severe COVID-19 disease among those in, and released from, prison care in Scotland: a national cohort and case-control study
No full textObjectives Given the potential higher risk of COVID-19 infection and disease for those incarcerated, we examined uptake of, and factors associated with vaccination, and the risks of severe disease for those in, and released from, prison in Scotland. Methods During follow-up (01/03/2020 to 13/04/2022), vaccine uptake among ∼15,000 individuals in prison, and following release, was compared with general population matched controls. Conditional logistic regression was used to compare prison status of ∼72,000 individuals admitted or died due to COVID-19 during follow-up to matched controls. Results By the end of follow-up, similar vaccine uptake was observed among those in prison (dose 1: 73.4 %, dose 2: 64.2 % and dose 3: 43.4 %) compared to matched controls (72.9 %, 67.9 % and 48.7 %). Individuals released (with <14 days incarcerated) were less likely to receive a first dose (aOR: 0.57, CI: 0.52, 0.66) than those who remained in prison. Following first and second doses, those released during the subsequent 12 weeks were less likely to receive their subsequent dose compared to those continuously incarcerated (aORs: 0.48, CI: 0.43, 0.54; 0.35, CI: 0.31, 0.40, respectively). Compared to the wider community outside prison, those incarcerated and recently released were more likely to be admitted or die from COVID-19 (aORs: 3.08, CI: 2.58, 3.69; and 4.53, CI: 3.37, 6.09, respectively). Conclusions Our findings highlight the important role of prisons in facilitating rapid high coverage of vaccination, involving accelerated schedules where appropriate, to help mitigate the raised risk of severe disease outcomes among both those incarcerated and released into the community.</p
Energy-aware hierarchical fractional-order terminal sliding mode control with hybrid learning observer for enhanced gait comfort in active prosthetics
No full textRecent advances in biomedical engineering have transformed prosthetic devices from simple mechanical constructs into intelligent active systems capable of complex movements such as stair climbing and slope walking. However, the highly nonlinear dynamics of lower-limb prostheses pose significant challenges in protecting residual limb tissues from impact-induced vibrations, particularly during the critical ∼150ms neurological delay following heel-strike. This paper proposes an energy-aware hierarchical learning-based disturbance observer fractional-order terminal sliding mode control (EAH-DO-FDTSMC) framework featuring a novel three-layer sliding surface structure to mitigate impact vibrations from the prosthetic foot and pylon. The method introduces a novel integration of adaptive fractional-order dynamics with hierarchical sliding surfaces for prosthetic control, integrating bio-inspired constraints from healthy gait patterns and dynamically optimizing power consumption using an energy-aware control allocation. Furthermore, a hybrid disturbance observer with progressive learning capabilities compensates for actuator non-idealities, including hysteresis, dead-zone, and temperature-dependent variations, demonstrating substantial reductions in estimation error and significant improvements in hysteresis compensation over multiple gait cycles. Rigorous Lyapunov-based stability analysis confirms finite-time convergence and robustness through adaptive fractional calculus. Extensive simulation results demonstrate exceptional improvements over advanced control methods, achieving substantial reductions in peak tissue displacement, significant chattering amplitude suppression, enhanced battery life extension, and superior robustness under parameter variations. The obtained results validate the practical potential of the proposed approach for improving comfort, efficiency, and mobility in active prosthetic applications.</p
The adaptive physical activity study in stroke (TAPAS): a feasibility sequential multiple assignment randomized trial
Full text linkPhysical inactivity post-stroke increases risk of recurrent stroke. Adaptive physical activity (PA) interventions are recommended, and alternative designs, such as sequential multiple assignment randomized trials (SMARTs) can be used. This SMART investigates the feasibility of a mobile health (mHealth) PA intervention post-stroke. People post-stroke are randomized to 12-week online exercise (EX) or lifestyle PA (LPA). Six-week daily step count data are used to classify participants as responders or nonresponses. Nonresponders are re-randomized to switch or augment their mHealth intervention, responders continue unchanged. Primary outcomes include recruitment, retention and adherence rates. Secondary outcomes include PA, sedentary behavior, fatigue, quality of life, psychological distress, and activities of daily living. General linear models estimate trends regarding first-stage interventions, nonresponse strategies, and adaptive interventions are examined using weighted and replicated regressions. Fifty participants are included. Recruitment, retention, and adherence rates are 85%, 84%, and 82%. Positive trends are seen for nonresponse strategies, switching interventions, on step count, fatigue, and quality of life. Starting with EX and switching to LPA show potential benefits for fatigue, quality of life and return to normal living. Potential benefits of these interventions are preliminary and require validation in a full-scale trial. This SMART offers novel evidence supporting the design of adaptive mHealth PA interventions post-stroke, confirming the feasibility of a definitive SMART
Time for consensus: establishing core outcomes meaningful to survivors of out-of-hospital cardiac arrest
No full textMore people are surviving out-of-hospital cardiac arrest (OHCA) than ever before, yet meaningful functional recovery and reintegration into daily life remain uncertain for many survivors.1,2 Current clinical guidelines, consensus statements, and quality standards all emphasize the importance of systematically measuring quality of life, functional impairments, cognitive deficits and psychopathology. Survivors’ outcome measures are primarily used in three key areas: firstly, in clinical practice, where outcome measures enable identification and monitoring of survivors’ individual problems; secondly, in clinical registries and observational research studies, to better understand the epidemiology of cardiac arrest survivorship; and thirdly, in interventional research studies to determine and compare the effect of survivor aftercare intervention
Comparative performance evaluation of triple quadrupole tandem mass spectrometry and orbitrap high-resolution mass spectrometry for analysis of antibiotics in creek water impacted by CETP discharge
No full textThe widespread detection of antibiotics in aquatic environments, particularly in effluent-receiving surface waters, poses significant ecological and public health concerns due to their role in promoting antimicrobial resistance. Accurate trace-level antibiotic measurement is essential for environmental risk assessment and for improving wastewater treatment strategies. This study presents the development, optimization, and validation of two complementary liquid chromatography-mass spectrometry (LC-MS) workflows for the simultaneous quantification of nine antibiotics across five therapeutic classes in creek water impacted by a Common Effluent Treatment Plant (CETP). The performance of a triple quadrupole LC-MS/MS system (LC-QqQ-MS) was compared to that of a high-resolution Orbitrap mass spectrometer (LC-Orbitrap-HRMS). Both instruments demonstrated excellent linearity (R 2 > 0.99) and satisfactory recoveries (70-90%) across a wide concentration range. The method detection limits ranged from 0.11 to 0.23 ng L −1 for LC-QqQ-MS and from 0.02 to 0.13 ng L −1 for LC-Orbitrap-HRMS, confirming the superior sensitivity of the high-resolution system approach. Application to real-world creek water samples revealed the ubiquitous presence of multiple antibiotics, with azithromycin and enrofloxacin dominating the detected concentrations, particularly near the CETP discharge point and a nearby waste dumping site. A three-way ANOVA confirmed that antibiotic concentrations were significantly affected by instrument type, sampling site, and antibiotic class along with their interactions. Additionally, non-target screening performed using LC-Orbitrap-HRMS enabled the detection of additional antibiotics belonging to quinolones, sulfonamides and aminoglycosides, further demonstrating the broader analytical scope of high-resolution mass spectrometry. The study highlights the necessity of using advanced analytical tools for the accurate quantification of antibiotics in complex matrices and underscores the environmental risks posed by pharmaceutical pollution in industrial discharge-impacted water bodies.</p
Contextualizing the interplay of community resilience and digital technologies to create sustainable destinations: two complementary cases
No full textWe live in times where natural disasters, inter-state conflicts and health scares, among others, pose significant challenges to the safety and security of tourism destinations. The rapid variability of climate also continues to be a growing threat for disruption to tourism. Understanding how to enhance community resilience in tourism destinations becomes critical in such a context. Intricately woven into the social fabric of societies, digital technologies (DTs) are becoming a new paradigm of community resilience, aiming to contribute to sustainable tourism destinations, and calling for community resilience centered destination offerings, operations and governance models. Although community resilience and DTs are two emerging topics, the interplay of these two has remained under-researched., This chapter demonstrates how digital tools function as catalysts for driving tourism communities toward enhanced resilience, further serving as a long-term enabler of sustainable tourism development. While the first case study relates to an action research intervention that focuses on the economic aspect of community resilience through DTs aimed inclusion and legitimacy of a local fishing community in a Maltese coastal village, the second case study relates to a regional tourism project that centers on the social aspect of community resilience in the Irish and Welsh coastal uplands, and demonstrates how a different tourism governance model toward regenerative tourism can be developed through utilizing DTs to prioritize the needs of the local communities
Analyzing and predicting the risk factors of cervical cancer using machine learning techniques
No full textCervical cancer is one of the most prevalent gynecological cancers world-wide, and early screening plays a crucial role in mitigating its global burden. This disease is largely preventable, yet disadvantaged groups often lack access to regular screenings due to limited knowledge, restricted medical facility access, and high treatment costs, particularly in developing countries. Addressing this challenge, our study introduces an innovative ensemble machine learning approach to accurately predict cervical cancer risk. This novel method is distinct in its integration of multiple advanced algorithms, including decision tree, random forest, support vector machine, and k-nearest neighbor, offering a comprehensive analysis, unlike previous singular model approaches. Applying these techniques to a dataset of 858 patients from the University of California, Irvine (UCI) machine learning repository, collected at the “Hospital Universitario de Caracas” in Venezuela, we encompass a wide range of data including demographic information, routines, medical records, and 36 distinct features. A key step in our methodology was the preprocessing of this data, where missing values were judiciously replaced with mean values to preserve data integrity. The findings are groundbreaking, with the random forest model outshining others by achieving an accuracy of 97%. This level of precision in forecasting cervical cancer threat is unmatched and holds substantial promise for healthcare professionals. By utilizing a confusion matrix, we have thoroughly evaluated each design’s efficiency. This research not only demonstrates the effectiveness of machine learning in boosting healthcare but additionally highlights its potential to boost the quality of life of patients through early discovery and targeted care of those at enhanced risk of cervical cancer
Commentary on NICE guidance 249 - falls: assessment and prevention in older people and in people 50 and over at higher risk
No full textThe new National Institute for Health and Care Excellence (NICE) Falls Guideline (NG249) updates CG161 (2013). NG249 aims to reduce the risk and incidence of falls, associated clinical consequences and loss of confidence or independence. The scope has expanded to include people aged 50–64 at higher risk of falls, identified by their medical condition and people in hospital or residential care. The intended audience includes health, social care and local authority commissioners and practitioners, care home providers and people at risk of falls. The Quality Standards (QS86) have been updated and simplified. The 39 recommendations on identifying people at risk, comprehensive falls risks assessment, interventions, maximising participation, information and education, are intended to be feasible and cost-effective in the UK national health and care system. Risk assessment tools are not recommended in any setting; however, a welcome change is tiered intervention responses dependent on initial risk assessment, which roughly aligns to the World Falls Guidelines. Will NG249 generate new actions to reduce population falls rates? Two aspects suggest perhaps not. The general description of recommended exercise is not strengthened with details on dose and lacks a clear statement discouraging low intensity/untargeted exercise; such programmes are likely more prevalent than those based on level one evidence. Linked to this, the guidance statement that most recommendations, including exercise, have no cost implications as they reflect current practice is highly contestable. With no reliable audit data and much anecdotal evidence to refute it, the danger is that commissioners and funders will see no case for review or reinvestment.</p
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