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Plastic pollution has become a global crisis, with microplastics contaminating every environment on the planet, including our food, water, and even our bodies. In response, there is a growing interest in developing plastics that biodegrade naturally, thus avoiding the creation of persistent microplastics. As a mechanism to increase the rate of polyester plastic degradation, we examined the potential of using the green microalga Chlamydomonas reinhardtii for the expression and secretion of PHL7, an enzyme that breaks down post-consumer polyethylene terephthalate (PET) plastics. We engineered C. reinhardtii to secrete active PHL7 enzyme and selected strains showing robust expression, by using agar plates containing a polyester polyurethane (PU) dispersion as an efficient screening tool. This method demonstrated the enzyme’s efficacy in degrading ester bond-containing plastics, such as PET and bio-based polyurethanes, and highlights the potential for microalgae to be implemented in environmental biotechnology. The effectiveness of algal-expressed PHL7 in degrading plastics was shown by incubating PET with the supernatant from engineered strains, resulting in substantial plastic degradation, confirmed by mass spectrometry analysis of terephthalic acid formation from PET. Our findings demonstrate the feasibility of polyester plastic recycling using microalgae to produce plastic-degrading enzymes. This eco-friendly approach can support global efforts toward eliminating plastic in our environment, and aligns with the pursuit of low-carbon materials, as these engineered algae can also produce plastic monomer precursors. Finally, this data demonstrates C. reinhardtii capabilities for recombinant enzyme production and secretion, offering a “green” alternative to traditional industrial enzyme production methods.

Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal, observational, and measurement data, within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days.

Background Individuals on the autism spectrum face significant disparities in health and physicians often report difficulties in providing care to autistic patients. In order to improve the quality of care autistic individuals receive, it is important to identify the barriers that physicians experience in providing care so that these may be addressed. This paper reports the initial development and preliminary evaluation of a physician-report ‘Barriers to Providing Healthcare’ measurement tool. Method An established taxonomy of healthcare barriers for autistic individuals informed the initial draft of a 22-item measurement tool. This measurement tool was distributed to physicians working in various healthcare specialties and settings. Exploratory factor analysis (EFA) was conducted to determine the construct validity of the tool; discriminant validity between, and internal consistency of, the resultant factors were assessed. Multiple regressions were used to explore variables potentially associated with barriers endorsed by physicians. Results A total of 203 physicians were included in the analyses. The EFA resulted in a 17-item tool with three distinct factors which explained 37.6% of the variance: 1) Patient-related barriers (Cronbach’s α = 0.83; e.g., the patient’s reactivity to the healthcare environment); 2) Healthcare provider (HCP)/family-related barriers (Cronbach’s α = 0.81; e.g., a lack of providers willing to work with autistic patients); and 3) System-related barriers (Cronbach’s α = 0.84; e.g., there is a lack of support for patients and families). Discriminant validity between the factors was adequate ( r  < .8). The barriers that were most frequently endorsed as occurring ‘often’ or ‘very often’ included a lack of support for patients and families (endorsed by 79.9% of physicians); communication difficulties (73.4%); and a lack of coordination between services (69.9%). The regression analyses identified no significant associated variables. Conclusion A preliminary version of a novel physician-report tool to assess barriers to providing care to autistic patients has been developed although further validation work is required. The use of this tool will help physicians to identify issues specific to different medical specialities and healthcare settings. This information may help identify the supports physicians require to recognise and implement the required accommodations. Future research which elucidates barriers to healthcare provision for autistic patients is required to support systemic change in healthcare so as to improve care experiences and health outcomes for people on the autism spectrum.

Background Low levels of success in performing lumbar puncture have been observed among paediatric trainees. This study assessed the efficacy of simulation-based education with frequency building and precision teaching for training lumbar puncture to behavioural fluency. Methods The intervention group was assessed at baseline, at the final training trial, in the presence of distraction, and a minimum of one month after the cessation of the intervention in order to ascertain whether behavioural fluency in lumbar puncture was obtained. Subsequently, the performance of this intervention group (10 paediatric senior house officers) was compared to the performance of a comparator group of 10 more senior colleagues (paediatric registrars) who had not received the intervention. Retrospective chart audit was utilised to examine performance in the clinical setting. Results Intervention group participants required a mean of 5 trials to achieve fluency. Performance accuracy was significantly higher in the intervention group than the comparator group. Learning was retained at follow-up and persisted during distraction. Retrospective chart audit revealed no significant difference between the performance of the intervention group and a comparator group, comprised of more senior physicians, in the clinical setting, although the interpretation of these analyses are limited by a low number of lumbar punctures performed in the clinical setting. Conclusions The programme of simulation-based education with frequency building and precision teaching delivered produced behavioural fluency in lumbar puncture among paediatric trainees. Following the intervention, the performance of these participants was equivalent to, or greater than, that of senior paediatricians. This study supports the need for further research exploring the effectiveness of simulation-based education with precision teaching to train procedural skills to fluency, and the consideration of how best to explore the impact of these on patient outcomes.

The growing ageing population and prevalence of comorbidities pose significant healthcare challenges, from increasing hospitalisations to dementia risk. Healthcare systems primarily treat single conditions, overlooking the complex interplay of chronic diseases. Advances in wearable technology and remote healthcare monitoring technologies offer opportunities to enhance management of comorbidities and early intervention to improve healthcare outcomes. This study presents the RESILIENT dataset, a collection of physiological, sleep, and mental health assessment data conducted as part of an ageing-related comorbidities and dementia study. The RESILIENT study has developed a digital platform to integrate data from wearable devices and in-home monitoring technologies to track physiological, sleep, and cognitive patterns. The validation analysis using the Resilient data highlights correlations between cognitive function, mental health, physical activity, and sleep, aligning with existing literature. By leveraging this dataset, researchers can develop predictive models for early detection and personalised interventions aimed at reducing unplanned hospital admissions and improving health outcomes. The study provides technical foundations and pilot validation for constructing virtual wards to support and complement healthcare services.

Urinary Tract Infections (UTIs) are one of the most prevalent bacterial infections in older adults and a significant contributor to unplanned hospital admissions in People Living with Dementia (PLWD), with early detection being crucial due to the predicament of reporting symptoms and limited help-seeking behaviour. The most common diagnostic tool is urine sample analysis, which can be time-consuming and is only employed where UTI clinical suspicion exists. In this method development and proof-of-concept study, participants living with dementia were monitored via low-cost devices in the home that passively measure activity, sleep, and nocturnal physiology. Using 27828 person-days of remote monitoring data (from 117 participants), we engineered features representing symptoms used for diagnosing a UTI. We then evaluate explainable machine learning techniques in passively calculating UTI risk and perform stratification on scores to support clinical translation and allow control over the balance between alert rate and sensitivity and specificity. The proposed UTI algorithm achieves a sensitivity of 65.3% (95% Confidence Interval (CI) = 64.3–66.2) and specificity of 70.9% (68.6–73.1) when predicting UTIs on unseen participants and after risk stratification, a sensitivity of 74.7% (67.9–81.5) and specificity of 87.9% (85.0–90.9). In addition, feature importance methods reveal that the largest contributions to the predictions were bathroom visit statistics, night-time respiratory rate, and the number of previous UTI events, aligning with the literature. Our machine learning method alerts clinicians of UTI risk in subjects, enabling earlier detection and enhanced screening when considering treatment.

Background The design of medical devices impacts upon the performance of healthcare professionals and patient safety. However, multiple devices serving the same function are often available. The purpose of this study was to use simulation as a means of examining the impact of differences in device design on (1) learning of, or attainment of behavioral fluency in, peripheral intravenous cannulation (PIVC); and (2) the generalization, or transfer, of learning on one device to performance of PIVC using an untrained device. Methods A total of 25 final cycle medical students participated in this study which used a randomized two-group design. Participants were randomly assigned to learn PIVC using either a closed PIVC device (a single device which consists of an intravenous cannula with a pre-attached extension tube; n  = 14) or an open PIVC device (a two-piece device made up of an intravenous cannula and a separate extension tube which is attached following insertion of the cannula; n  = 11). Task analyses were developed for the performance of PIVC using each device. Subsequently, simulation-based fluency training was delivered to both groups using their assigned PIVC device, and continued for each participant until the fluency criterion was achieved. Following achievement of fluency, participants were asked to perform PIVC using the untrained device (i.e., the PIVC device that they had not been trained on). Results All participants in both groups met the fluency criterion, and no significant differences were observed in the number of trials or total training required by groups to achieve fluency. Participants in both groups improved significantly from baseline ( M  = 11.69) to final training trial ( M  = 100). However, a significant decrement in performance ( M  = 81.5) was observed when participants were required to perform PIVC using the untrained device. Conclusions Participants achieved fluency in PIVC regardless of the device used. However, significant decrements in performance were observed when participants were required to perform PIVC using a novel device. This finding supports the need for careful consideration of devices purchased and supplied in the clinical setting, and the need for training prior to the introduction of novel devices or for new staff members.

Background Growing evidence suggests sleep disturbance may promote Alzheimer’s disease (AD) pathology. However, the relationship between sleep and AD progression remains poorly defined due to lack of longitudinal sleep monitoring methods. We previously presented a novel approach for contactless sleep monitoring in people living with dementia (PLWD)1. Here, we examine the association between the Dementia Research Institute Sleep Index (DRI‐SI) ‐ an interpretable measure of sleep disturbances derived from contactless sensor data ‐ and clinical disease progression. Method Withing’s Sleep Analyser was used to monitor 120PLWD taking part in a home‐monitoring study between 2019‐2023 (46k total nights). Participants were assessed 6‐monthly with ADAS‐Cog and Pittsburgh Sleep Quality Index (PSQI), and 3‐monthly with Neuropsychiatric Inventory (NPI) and Bristol Activities of Daily Living Scale (BADL). We used mixed linear models to examine the association between each assessment and the DRI‐SI score and variability. Additionally, a predictive model using an explainable boosting regressor was constructed to assess the DRI‐SI’s ability to predict ADAS‐Cog, PSQI, NPI and BADL scores. Result We included 107 participants (69 AD, 38 non‐AD dementia) with sufficient contemporaneous questionnaire and sleep‐mat data. Minute‐to‐minute timeseries were extracted from the sleep‐mats to calculate nightly DRI‐SI scores (Figure 1). Longitudinally, there was a significant gradual decline in ADAS‐Cog and BADL scores over the 18‐month follow‐up, while the NPI and PSQI showed little change (Figure 2). The DRI‐SI score and variability were significantly associated with BADL (Coef.=2.0±0.6, Z=3.3, P=0.001; Coef.=3.9±1.7, Z=2.3, P=0.02). ADAS‐Cog and NPI were also significantly associated with the DRI‐SI score, but not the DRI‐SI variability (Coef.=3.4±1.1, Z=2.95, P=0.003; Coef.=3.1±0.9, Z=3.3, P=0.001). Interestingly, no association was seen between the DRI‐SI and PSQI. The predictive model demonstrated significant predictive power for ADAS‐Cog and BADL scores, indicating the potential of DRI‐SI as a digital biomarker for cognitive and daily function in dementia (Figure 3c,d). Conclusion Our results suggest that DRI‐SI, a novel digital biomarker of sleep disturbance and night‐time behaviour in dementia derived from contactless remote sleep‐monitoring, associates with measures of cognition and function in dementia. As such, DR‐SI shows great promise for better predicting dementia progression, personalizing treatments, and augmenting outcomes in clinical trials.

Autistic individuals report barriers to accessing and receiving healthcare, and experience increased morbidity and mortality. This systematic review synthesizes 31 research studies evaluating interventions implemented to improve the healthcare experiences and/or access of autistic persons. Interventions were most commonly patient-focused (58.1%), focused on supporting the autistic individual to engage with, tolerate, or anticipate medical procedures, care, or settings. Fewer studies were provider-focused (48.4%) or organization-focused (6.5%). Interventions were typically evaluated using measures of reactions (45.2%) or behavior (48.4%), and outcomes were predominantly positive (80.6%). Further research is imperative and should look to how providers and organizations must change. Future research must be inclusive of the autistic community, must measure what matters, and must offer complete detail on interventions implemented.

Women are substantially underrepresented in senior and leadership positions in medicine and experience gendered challenges in their work settings. This systematic review aimed to synthesise research that has evaluated interventions for improving gender equity in medicine. English language electronic searches were conducted across MEDLINE, CINAHL, Academic Search Complete, PsycINFO and Web of Science. Reference list screening was also undertaken. Peer-reviewed studies published between 2000 and March 2020 that evaluated interventions to improve gender equity, or the experiences of women, in academic or clinical medicine were reviewed. Dual reviewer data extraction on setting, participants, type of intervention, measurement and outcomes was completed. Methodological rigour and strength of findings were evaluated. In total, 34 studies were included. Interventions were typically focused on equipping the woman (82.4%), that is, delivering professional development activities for women. Fewer focused on changing cultures (20.6%), ensuring equal opportunities (23.5%) or increasing the visibility or valuing of women (23.5%). Outcomes were largely positive (87.3%) but measurement typically relied on subjective, self-report data (69.1%). Few interventions were implemented in clinical settings (17.6%). Weak methodological rigour and a low strength of findings was observed. There has been a focus to-date on interventions which Equip the Woman. Interventions addressing systems and culture change require further research consideration. However, institutions cannot wait on high quality research evidence to emerge to take action on gender equity. Data collated suggest a number of recommendations pertaining to research on, and the implementation of, interventions to improve gender equity in academic and clinical settings.