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Abstract Objective This scoping review aims to explore published literature testing Virtual Reality (VR) interventions for improving upper limb motor performance in children and adolescents with Developmental Coordination Disorder (DCD). Our primary focus was on the types of VR systems used and the measurement tools employed within the International Classification of Functioning, Disability and Health Children and Youth Version (ICF-CY) domains in these studies. Methods A comprehensive search of six electronic databases up to 11th January 2024 was conducted using predefined terms. Inclusion and exclusion criteria were applied to determine study eligibility, with two authors independently assessing titles, abstracts, and full-text articles. Results Out of 788 potential studies, 14 met the eligibility criteria. Studies predominantly utilized non-immersive VR (nVR) systems, for example, commercial platforms such as Nintendo Wii. Most interventions targeted general motor coordination or balance, with only four studies specifically focusing on upper limb motor performance. The Movement Assessment Battery for Children-2 was the predominant assessment tool. However, the use of game scores and trial durations raised concerns about the accuracy of assessments. The majority of studies reported no significant improvement in upper limb motor performance following VR interventions, though some noted improvements in specific tasks or overall outcomes. Conclusion The findings suggest that, while nVR interventions are being explored for paediatric motor rehabilitation, their impact on enhancing upper limb motor performance in children with DCD is unclear. The variability in intervention designs, outcome measures, and the predominant focus on general motor skills rather than specific upper limb improvements highlight the need for more targeted research in this area. Impact This review underscores the importance of developing precise and clinically relevant measurement tools in a broader range of VR technologies to optimize the use of VR in therapy for children with DCD. Future research should aim for more rigorous study designs and emerging immersive technologies to maximize therapeutic benefits.

Introduction: Climate change's severe impact on human health is becoming increasingly evident, particularly for vulnerable populations with serious illnesses. Climate-related extreme weather events are expected to increase demand for hospice and palliative care due to rising respiratory illnesses, heat-related issues, waterborne diseases, and aggravated chronic conditions. Our scoping review aimed to investigate the existing literature on climate change's impact on hospice and palliative medicine (HPM). Methods: We conducted a comprehensive literature search across various databases (e.g., Medline, EMbase, Web of Science, and Cochrane) using predefined climate change and HPM terms, resulting in 382 records. Following predetermined inclusion and exclusion criteria, 44 articles were selected for full-text review, and 20 were included for final analysis. In light of the limited literature on climate change's impact on HPM, we also sought narratives from HPM practitioners from across the world on their experiences in a changing climate. Results: Six major themes emerged: 1) The impact of climate change on HPM in low-income countries; 2) Descriptive pieces on climate change, climate disasters, and HPM; 3) Morbidity and mortality after climate disasters in the seriously ill population; 4) Discussion of euthanasia during climate disasters; 5) Recommendations and frameworks for disaster response in the field of HPM; 6) Carbon footprint of hospices. Additionally, narratives from HPM practitioners highlighted the disruptive effects of climate disasters on seriously ill patients and their caregivers as disasters caused care interruptions, reduced access to crucial health infrastructure, exacerbations of illness, accelerated disease progression, and increased morbidity and mortality. Conclusion: Existing research on climate change's impact on HPM is primarily anecdotal and descriptive, with a focus on climate-related disasters. Narratives from HPM practitioners worldwide underscore the disproportionate impact of climate disasters on seriously ill patients. Further research is necessary to comprehensively understand climate's intricate effects on HPM and to assess adaptable, mitigative, and resilient solutions against its adverse impacts.

Background: Research has shown that potential perpetrators and individuals high in psychopathic traits tend to body language cues to target a potential new victim. However, whether targeting occurs also by tending to vocal cues has not been examined. Thus, the role of voice in interpersonal violence merits investigation.Objective: In two studies, we examined whether perpetrators could differentiate female speakers with and without sexual and physical assault histories (presented as rating the degree of ‘vulnerability’ to victimization).Methods: Two samples of male listeners (sample one N = 105, sample two, N = 109) participated. Each sample rated 18 voices (9 survivors and 9 controls). Listener sample one heard spontaneous speech, and listener sample two heard the second sentence of a standardized passage. Listeners’ self-reported psychopathic traits and history of previous perpetration were measured.Results: Across both samples, history of perpetration (but not psychopathy) predicted accuracy in distinguishing survivors of assault.Conclusions: These findings highlight the potential role of voice in prevention and intervention. Gaining a further understanding of what voice cues are associated with accuracy in discerning survivors can also help us understand whether or not specialized voice training could have a role in self-defense practices.

IntroductionCirculating levels of the antiangiogenic protein vasoinhibin, a fragment of prolactin, are of interest in vasoproliferative retinopathies, preeclampsia, and peripartum cardiomyopathy; however, it is difficult to determine the circulating levels of vasoinhibin due to the lack of quantitative assays. MethodsThis study used human serum samples to assess the concentration and bioactivity of vasoinhibin using a novel enzyme-linked immunosorbent assay (ELISA) for human vasoinhibin, which employs an anti-vasoinhibin monoclonal antibody, a human umbilical vein endothelial cell (HUVEC) proliferation assay, and a chick chorioallantoic membrane (CAM) angiogenesis assay. ResultsSerum samples from 17 pregnant women without (one group) and with preeclampsia and pregnancy induced hypertension (another group) demonstrated endogenous vasoinhibin concentrations in the range of 5–340 ng/ml. Immunoactive vasoinhibin levels were significantly higher in preeclampsia serum compared to healthy pregnancy serum (mean 63.09 ± 22.15 SD vs. 19.67 ± 13.34 ng/ml, p = 0.0003), as was the bioactive vasoinhibin level as determined by the HUVEC proliferation assay (56.12 ± 19.83 vs. 13.38 ± 4.88 ng/ml, p < 0.0001). There was a correlation between the concentration of vasoinhibin measured by ELISA and the HUVEC proliferation assay (Pearson r = 0.95, p < 0.0001). Healthy serum demonstrated a proangiogenic effect in the CAM assay (p < 0.05, compared to control), while serum from preeclamptic patients demonstrated an antiangiogenic effect (p < 0.05 vs. control), as did recombinant human vasoinhibin and a synthetic circular retro-inverse vasoinhibin analogue (CRIVi45-51). The antiangiogenic effects in the CAM assay and the inhibition of HUVEC proliferation were abolished by addition of the ELISA anti-vasoinhibin monoclonal antibody, but not by mouse IgG. DiscussionThese results demonstrate the first quantitation of endogenous vasoinhibin in human sera and the elevation of it levels and antiangiogenic activity in sera from women with preeclampsia. The development and implementation of a quantitative assay for vasoinhibin overcomes a long-standing barrier and suggests the thorough clinical verification of vasoinhibin as a relevant biomarker.

Abstract The COVID-19 pandemic has posed clinical and public health challenges worldwide. The use of corticosteroids has become an evidence-based practice to reduce the hyperinflammatory process involved in severe COVID-19 disease. However, this can result in the reactivation of parasitic infestations, even with a short course. We report the case of a 64-year-old Cuban born patient who passed away from S. stercoralis hyperinfection syndrome following treatment with dexamethasone for severe COVID-19 disease on a background of prolonged immunosuppression for rheumatoid arthritis. Clinicians should be aware of the risk of strongyloidiasis as a complication of the treatment for severe COVID-19 and other immunosuppressive therapies. We recommend empiric Strongyloides treatment for those who are from, or who have accumulated risk by travelling to endemic areas, and are being treated with corticosteroids for severe COVID-19 disease.

Abstract Objective Full meal replacement (FMR) Intensive Lifestyle Interventions (ILI) have been used for weight management. However, predictors of efficacy with these programs are less clear. The primary objective was to assess weight loss predictors in a community‐based FMR ILI program. A secondary objective was to determine if weight loss was different between virtual and in person ILI. Methods This was a retrospective cohort study involving 234 patients who started the program between 1 January 2016 and 3 March 2021. In the 24‐week program, patients spent 12 weeks on FMR and then transitioned back to food for the remainder, with weekly follow up with a physician and group sessions with a dietitian. Visits were in person prior to March 2020 and virtual afterward. Results Patients' average age was 47.5 years (SD = 12.0) and 73.5% were female. Average weight loss was 14.3% (SD = 6.2%). There was no significant difference in weight loss between virtual and in person programs. Patients on a Glucagon‐like Peptide‐1 Receptor Agonist prior lost less weight. Other significant associations between groups were baseline Hemoglobin A1C, classes attended, as well as the age since peak weight. Conclusion Weight loss from virtual ILI was not significantly different from person ILI. More research is needed to determine how to best stratify care as virtual or in person using FMR programs.

Abstract The Checklist of the Vascular Plants of the Republic of Guinea (CVPRG) is a specimen-based, expert-validated knowledge product, which provides a concise synthesis and overview of current knowledge on 3901 vascular plant species documented from Guinea (Conakry), West Africa, including their accepted names and synonyms, as well as their distribution and status within Guinea (indigenous or introduced, endemic or not). The CVPRG is generated automatically from the Guinea Collections Database and the Guinea Names Backbone Database, both developed and maintained at the Royal Botanic Gardens, Kew, in collaboration with the staff of the National Herbarium of Guinea. A total of 3505 indigenous vascular plant species are reported of which 3328 are flowering plants (angiosperms); this represents a 26% increase in known indigenous angiosperms since the last floristic overview. Intended as a reference for scientists documenting the diversity and distribution of the Guinea flora, the CVPRG will also inform those seeking to safeguard the rich plant diversity of Guinea and the societal, ecological and economic benefits accruing from these biological resources.

Several competing neuro-computational theories of autism have emerged from predictive coding models of the brain. To disentangle their subtly different predictions about the nature of atypicalities in autistic perception, we performed computational modelling of two sensorimotor tasks: the predictive use of manual gripping forces during object lifting and anticipatory eye movements during a naturalistic interception task. In contrast to some accounts, we found no evidence of chronic atypicalities in the use of priors or weighting of sensory information during object lifting. Differences in prior beliefs, rates of belief updating, and the precision weighting of prediction errors were, however, observed for anticipatory eye movements. Most notably, we observed autism-related difficulties in flexibly adapting learning rates in response to environmental change (i.e., volatility). These findings suggest that atypical encoding of precision and context-sensitive adjustments provide a better explanation of autistic perception than generic attenuation of priors or persistently high precision prediction errors. Our results did not, however, support previous suggestions that autistic people perceive their environment to be persistently volatile.

The control of eye gaze is critical to the execution of many skills. The observation that task experts in many domains exhibit more efficient control of eye gaze than novices has led to the development of gaze training interventions that teach these behaviours. We aimed to extend this literature by i) examining the relative benefits of feed-forward (observing an expert’s eye movements) versus feed-back (observing your own eye movements) training, and ii) automating this training within virtual reality. Serving personnel from the British Army and Royal Navy were randomised to either feed-forward or feed-back training within a virtual reality simulation of a room search and clearance task. Eye movement metrics – including visual search, saccade direction, and entropy – were recorded to quantify the efficiency of visual search behaviours. Feed-forward and feed-back eye movement training produced distinct learning benefits, but both accelerated the development of efficient gaze behaviours. However, we found no evidence that these more efficient search behaviours transferred to better decision making in the room clearance task. Our results suggest integrating eye movement training principles within virtual reality training simulations may be effective, but further work is needed to understand the learning mechanisms.

Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However, incorrect mammography diagnoses are common and may harm patients through unnecessary treatments and operations (or a lack of treatment). Therefore, systems that can learn to detect breast cancer on their own could help reduce the number of incorrect interpretations and missed cases. Various deep learning techniques, which can be used to implement a system that learns how to detect instances of breast cancer in mammograms, are explored throughout this paper. Convolution Neural Networks (CNNs) are used as part of a pipeline based on deep learning techniques. A divide and conquer approach is followed to analyse the effects on performance and efficiency when utilising diverse deep learning techniques such as varying network architectures (VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2), class weights, input sizes, image ratios, pre-processing techniques, transfer learning, dropout rates, and types of mammogram projections. This approach serves as a starting point for model development of mammography classification tasks. Practitioners can benefit from this work by using the divide and conquer results to select the most suitable deep learning techniques for their case out-of-the-box, thus reducing the need for extensive exploratory experimentation. Multiple techniques are found to provide accuracy gains relative to a general baseline (VGG19 model using uncropped 512 × 512 pixels input images with a dropout rate of 0.2 and a learning rate of 1 × 10-3) on the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) dataset. These techniques involve transfer learning pre-trained ImagetNet weights to a MobileNetV2 architecture, with pre-trained weights from a binarised version of the mini Mammography Image Analysis Society (mini-MIAS) dataset applied to the fully connected layers of the model, coupled with using weights to alleviate class imbalance, and splitting CBIS-DDSM samples between images of masses and calcifications. Using these techniques, a 5.6% gain in accuracy over the baseline model was accomplished. Other deep learning techniques from the divide and conquer approach, such as larger image sizes, do not yield increased accuracies without the use of image pre-processing techniques such as Gaussian filtering, histogram equalisation and input cropping.

In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either "malignant", "other or benign" or "insufficient". An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with "malignant" and "other or benign" areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being "malignant" or "other or benign". Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either "malignant", "other or benign" or "insufficient". The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists' workload.

Abstract The integration of prior expectations, sensory information, and environmental volatility is proposed to be atypical in Autism Spectrum Disorder, yet few studies have tested these predictive processes in active movement tasks. To address this gap in the research, we used an immersive virtual-reality racquetball paradigm to explore how visual sampling behaviours and movement kinematics are adjusted in relation to unexpected, uncertain, and volatile changes in environmental statistics. We found that prior expectations concerning ball ‘bounciness’ affected sensorimotor control in both autistic and neurotypical participants, with all individuals using prediction-driven gaze strategies to track the virtual ball. However, autistic participants showed substantial differences in visuomotor behaviour when environmental conditions were more volatile. Specifically, uncertainty-related performance difficulties in these conditions were accompanied by atypical movement kinematics and visual sampling responses. Results support proposals that autistic people overestimate the volatility of sensory environments, and suggest that context-sensitive differences in active inference could explain a range of movement-related difficulties in autism.

For a method to be widely adopted in medical research or clinical practice, it needs to be reproducible so that clinicians and regulators can have confidence in its use. Machine learning and deep learning have a particular set of challenges around reproducibility. Small differences in the settings or the data used for training a model can lead to large differences in the outcomes of experiments. In this work, three top-performing algorithms from the Camelyon grand challenges are reproduced using only information presented in the associated papers and the results are then compared to those reported. Seemingly minor details were found to be critical to performance and yet their importance is difficult to appreciate until the actual reproduction is attempted. We observed that authors generally describe the key technical aspects of their models well but fail to maintain the same reporting standards when it comes to data preprocessing which is essential to reproducibility. As an important contribution of the present study and its findings, we introduce a reproducibility checklist that tabulates information that needs to be reported in histopathology ML-based work in order to make it reproducible.

We study an individual’s propensity for rational thinking; the avoidance of cognitive biases (unconscious errors generated by our mental simplification methods) using a novel augmented reality (AR) platform. Specifically, we developed an odd-one-out (OOO) game-like task in AR designed to try to induce and assess confirmatory biases. Forty students completed the AR task in the laboratory, and the short form of the comprehensive assessment of rational thinking (CART) online via the Qualtrics platform. We demonstrate that behavioural markers (based on eye, hand and head movements) can be associated (linear regression) with the short CART score – more rational thinkers have slower head and hand movements and faster gaze movements in the second more ambiguous round of the OOO task. Furthermore, short CART scores can be associated with the change in behaviour between two rounds of the OOO task (one less and one more ambiguous) – hand-eye-head coordination patterns of the more rational thinkers are more consistent in the two rounds. Overall, we demonstrate the benefits of augmenting eye-tracking recordings with additional data modalities when trying to understand complicated behaviours.

Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previously, we showed that, by extending BERT with a per-label attention mechanism, we can train a single model to perform automatic extraction of many labels in parallel. However, if we rely on pure data-driven learning, the model sometimes fails to learn critical features or learns the correct answer via simplistic heuristics (e.g., that “likely” indicates positivity), and thus fails to generalise to rarer cases which have not been learned or where the heuristics break down (e.g., “likely represents prominent VR space or lacunar infarct” which indicates uncertainty over two differential diagnoses). In this work, we propose template creation for data synthesis, which enables us to inject expert knowledge about unseen entities from medical ontologies, and to teach the model rules on how to label difficult cases, by producing relevant training examples. Using this technique alongside domain-specific pre-training for our underlying BERT architecture i.e., PubMedBERT, we improve F1 micro from 0.903 to 0.939 and F1 macro from 0.512 to 0.737 on an independent test set for 33 labels in head CT reports for stroke patients. Our methodology offers a practical way to combine domain knowledge with machine learning for text classification tasks.

Abstract Background Associations between kidney disease and periodontal disease are not well documented among Aboriginal people of Australia. The purpose of this investigation was to report and compare demographic, oral health, anthropometric and systemic health status of Aboriginal Australians with kidney disease and to compare against relevant Aboriginal Australians and Australian population estimates. This provides much needed evidence to inform dental health service provision policies for Aboriginal Australians with kidney disease. Methods Sample frequencies and means were assessed in adults represented in six datasets including: (1) 102 Aboriginal Australians with kidney disease residing in Central Australia who participated in a detailed oral health assessment; (2) 312 Aboriginal participants of the Northern Territory’s PerioCardio study; (3) weighted estimates from 4775 participants from Australia’s National Survey of Adult Oral Health (NSAOH); (4) Australian 2016 Census (all Australians); (5) National Health Survey 2017–2018 (all Australians) and; (6) Australian Health Survey: Biomedical Results for Chronic Diseases, 2011–2012 (all Australians). Oral health status was described by periodontal disease and experience of dental caries (tooth decay). Statistically significant differences were determined via non-overlapping 95% confidence intervals. Results Aboriginal Australians with kidney disease were significantly older, less likely to have a tertiary qualification or be employed compared with both PerioCardio study counterparts and NSAOH participants. Severe periodontitis was found in 54.3% of Aboriginal Australians with kidney disease, almost 20 times the 2.8% reported in NSAOH. A higher proportion of Aboriginal Australians with kidney disease had teeth with untreated caries and fewer dental restorations when compared to NSAOH participants. The extent of periodontal attachment loss and periodontal pocketing among Aboriginal Australians with kidney disease (51.0%, 21.4% respectively) was several magnitudes greater than PerioCardio study (22.0%, 12.3% respectively) and NSAOH (5.4%, 1.3% respectively) estimates. Conclusions Aboriginal Australians with kidney disease exhibited more indicators of poorer oral health than both the general Australian population and a general Aboriginal population from Australia’s Northern Territory. It is imperative that management of oral health among Aboriginal Australians with kidney disease be included as part of their ongoing medical care.

Objective: The Wada test is used to evaluate language lateralization and memory performance after inactivation of an isolated cerebral hemisphere. Methohexital a short-acting barbiturate has a history of use to induce interictal discharges during intraoperative corticography. We report a new finding of activation of lateralized periodic discharges (LPDs) after Methohexital injection. Methods: We retrospectively reviewed 174 consecutive adult patients who underwent Wada testing in preparation for epilepsy surgery (N = 129, 74%) or brain tumor resection (N = 45, 26%) at the University of Michigan to determine the frequency of induced periodic discharges by methohexital. Results: Four epilepsy patients (2.29%) had methohexital-induced LPDs within a median of 2 s (1–99 s) of the injection and lasting a median of 4 min (3–10 min) after a total of 7 injections. All LPDs occurred ipsilateral to the injection hemisphere in the known region of interictal epileptiform discharges. LPDs were not induced in brain tumor patients. In one patient, LPDs occurred during memory testing, and this patient's memory performance was below expectation based on pre-test neuropsychological testing. Conclusions: Methohexital can induce LPDs in ipsilateral hemisphere and that can potentially affect memory performance. Significance: This observation indicates that concurrent EEG monitoring during the Wada test is important and that induced discharges should be considered when interpreting Wada test results.

Abstract Objective Periodontal disease is associated with chronic kidney disease (CKD), with both conditions being highly prevalent among Australia’s Aboriginal population. This paper reflects on the lessons learned following implementation of a periodontal intervention in the Central Australian region of the Northern Territory among Aboriginal adults with CKD. Results Between Oct 2016 and May 2019, research staff recruited 102 eligible participants. This was far below the anticipated recruitment rate. The challenges faced, and lessons learned, were conceptualised into five specific domains. These included: (1) insufficient engagement with the Aboriginal community and Aboriginal community-controlled organisations; (2) an under-appreciation of the existing and competing patient commitments with respect to general health and wellbeing, and medical treatment to enable all study commitments; (3) most study staff employed from outside the region; (4) potential participants not having the required number of teeth; (5) invasive intervention that involved travel to, and time at, a dental clinic. A more feasible research model, which addresses the divergent needs of participants, communities and service partners is required. This type of approach, with sufficient time and resourcing to ensure ongoing engagement, partnership and collaboration in co-design throughout the conduct of research, challenges current models of competitive, national research funding.

The current paradigm of agricultural research and extension in support of rural development in Sub-Saharan Africa (SSA) is to disseminate improved technologies designed to increase the generally low crop yields per hectare on individual farms. Using data from a baseline survey (n = 7,539) from a large rural development programme implemented in five countries in SSA, we calculate the increases in yield per hectare required to significantly contribute to poverty alleviation for households managing such farms. We estimate the gap between current crop productivity and the productivity required to reach a poverty line of $1.90 per capita per day adjusted for Purchasing Power Parity (PPP). We find this gap to be very large, both in percentage and absolute terms. Median additional gross crop productivity required to reach this poverty threshold was: $324/ha/year (254% increase) in Mali; $1,359/ha/year (1,157% increase) in Niger; $4,989/ha/year (665% increase) in Ethiopia; $1,742/ha/year (818% increase) in Burkina Faso; $2,893/ha/year (1,297% increase) in Kenya. The required additional productivity taking account of production costs including the opportunity cost of family labor would need to be even higher. Given that (a) values of net productivity of improved rainfed crop technologies reported in the literature rarely exceed $1,000/ha/year; and (b) the majority of arable farms in SSA are two hectares or less with increasing trends toward land fragmentation, we argue that closing the yield gap among smallholder farmers in SSA will never—alone—be sufficient to meaningfully alleviate the high levels of poverty and deprivation many currently experience.

When ordinating plots of tropical rain forests using stand-level structural attributes such as biomass, basal area and the number of trees in different size classes, two patterns often emerge: a gradient from poorly to highly stocked plots and high positive correlations between biomass, basal area and the number of large trees. These patterns are inherited from the demographics (growth, mortality and recruitment) and size allometry of trees and tend to obscure other patterns, such as site differences among plots, that would be more informative for inferring ecological processes. Using data from 133 rain forest plots at nine sites for which site differences are known, we aimed to filter out these patterns in forest structural attributes to unveil a hidden pattern. Using a null model framework, we generated the anticipated pattern inherited from individual allometric patterns. We then evaluated deviations between the data (observations) and predictions of the null model. Ordination of the deviations revealed site differences that were not evident in the ordination of observations. These sites differences could be related to different histories of large-scale forest disturbance. By filtering out patterns inherited from individuals, our model analysis provides more information on ecological processes.

Effective communication within the patient–provider relationship is a key aspect of shared decision-making and associated with several positive patient outcomes. Although previous studies suggest that patients’ and providers’ conceptualization of what constitutes effective communication differ, there is no available literature discussing patient preferences for communication. The objective of this study was to determine the words and phrases pulmonary arterial hypertension patients prefer to hear when discussing their disease with their physician. A total of 227 pulmonary arterial hypertension patients completed a survey that included a 20-item questionnaire specifically designed to assess patient preference for words and phrases when discussing their disease and treatment; statistically significant differences were observed across all items of the questionnaire. Patients preferred their physician ask them how they have “been feeling” (63%) rather than how they have “been doing”. In addition, 96% of patients indicated that they wanted to hear that this is “… the best medicine for you ” rather than this is “the best medicine”. Considerably more patients (60%) indicated they want their physician to say, “We want you to have fewer symptoms” rather than “We want you to feel more normal.” They also indicated they wanted the “most effective” medicine (82%) rather than the “most aggressive” medicine (7%). The results of this study suggest that pulmonary arterial hypertension patients have strong preferences for the language their providers use when discussing their disease and treatment options. Given the role that effective communication has on important health outcomes, pulmonary arterial hypertension providers need to consider these findings when communicating with patients.

Creativity is intrinsic to Humanities and STEM disciplines. In the activities of artists and engineers, for example, an attempt is made to bring something new into the world through counterfactual thinking. However, creativity in these disciplines is distinguished by differences in motivations and constraints. For example, engineers typically direct their creativity toward building solutions to practical problems, whereas the outcomes of artistic creativity, which are largely useless to practical purposes, aspire to enrich the world aesthetically and conceptually. In this essay, an artist (DHS) and a roboticist (GS) engage in a cross-disciplinary conceptual analysis of the creative problem of artificial consciousness in a robot, expressing the counterfactual thinking necessitated by the problem, as well as disciplinary differences in motivations, constraints, and applications. We especially deal with the question of why one would build an artificial consciousness and we consider how an illusionist theory of consciousness alters prominent ethical debates on synthetic consciousness. We discuss theories of consciousness and their applicability to synthetic consciousness. We discuss practical approaches to implementing artificial consciousness in a robot and conclude by considering the role of creativity in the project of developing an artificial consciousness.

Current methods of measuring heart rate (HR) and oxygen levels (SPO2) require physical contact, are individualised, and for accurate oxygen levels may also require a blood test. No-touch or non-invasive technologies are not currently commercially available for use in healthcare settings. To date, there has been no assessment of a system that measures HR and SPO2 using commercial off-the-shelf camera technology that utilises R, G, B, and IR data. Moreover, no formal remote photoplethysmography studies have been performed in real-life scenarios with participants at home with different demographic characteristics. This novel study addresses all these objectives by developing, optimising, and evaluating a system that measures the HR and SPO2 of 40 participants. HR and SPO2 are determined by measuring the frequencies from different wavelength band regions using FFT and radiometric measurements after pre-processing face regions of interest (forehead, lips, and cheeks) from colour, IR, and depth data. Detrending, interpolating, hamming, and normalising the signal with FastICA produced the lowest RMSE of 7.8 for HR with the r-correlation value of 0.85 and RMSE 2.3 for SPO2. This novel system could be used in several critical care settings, including in care homes and in hospitals and prompt clinical intervention as required.

Introduction An effective workforce is essential for optimal care of all forms of chronic diseases. The objective of this study was to assess workforce capacity for kidney failure (KF) care across world countries and regions.Methods Data were collected from published online sources and a survey was administered online to key stakeholders. All country-level data were analysed by International Society of Nephrology region and World Bank income classification.Results The general healthcare workforce varies by income level: high-income countries have more healthcare workers per 10 000 population (physicians: 30.3; nursing personnel: 79.2; pharmacists: 7.2; surgeons: 3.5) than low-income countries (physicians: 0.9; nursing personnel: 5.0; pharmacists: 0.1; surgeons: 0.03). A total of 160 countries responded to survey questions pertaining to the workforce for the management of patients with KF. The physicians primarily responsible for providing care to patients with KF are nephrologists in 92% of countries. Global nephrologist density is 10.0 per million population (pmp) and nephrology trainee density is 1.4 pmp. High-income countries reported the highest densities of nephrologists and nephrology trainees (23.2 pmp and 3.8 pmp, respectively), whereas low-income countries reported the lowest densities (0.2 pmp and 0.1 pmp, respectively). Low-income countries were most likely to report shortages of all types of healthcare providers, including nephrologists, surgeons, radiologists and nurses.Conclusions Results from this global survey demonstrate critical shortages in workforce capacity to care for patients with KF across world countries and regions. National and international policies will be required to build a workforce capacity that can effectively address the growing burden of KF and deliver optimal care.

Obesity has reached epidemic proportions and is often accompanied by elevated levels of pro-inflammatory cytokines that promote many chronic diseases, including cancer. However, not all obese people develop these diseases and it would be very helpful to identify those at high risk early on so that preventative measures can be instituted. We performed an extensive evaluation of the effects of obesity on inflammatory markers, on innate and adaptive immune responses, and on blood cell composition to identify markers that might be useful in distinguishing those at elevated risk of cancer. Plasma samples from 42 volunteers with a BMI>35 had significantly higher CRP, PGE2, IL-1RA, IL-6 and IL-17 levels than 34 volunteers with normal BMIs. Of the cytokines and chemokines tested, only IL-17 was significantly higher in men with a BMI>35 than women with a BMI>35. As well, only IL-17 was significantly higher in those with a BMI>35 that had type 2 diabetes versus those without type 2 diabetes. Whole blood samples from participants with a BMI>35, when challenged with E. coli, produced significantly higher levels of IL-1RA while HSV-1 challenge resulted in significantly elevated IL-1RA and VEGF, and a non-significant increase in G-CSF and IL-8 levels. T cell activation of PBMCs, via anti-CD3 plus anti-CD28, resulted in significantly higher IFNγ production from volunteers with a BMI>35. In terms of blood cells, red blood cell distribution width (RDW), monocytes, granulocytes, CD4+T cells and Tregs were all significantly higher while, natural killer (NK) and CD8+ T cells were all significantly lower in the BMI>35 cohort, suggesting that obesity may reduce the ability to kill nascent tumor cells. Importantly, however, there was considerable person-to-person variation amongst participants with a BMI>35, with some volunteers showing markedly different values from controls and others showing normal levels of many parameters measured. These person-to-person variations may prove useful in identifying those at high risk of developing cancer.

To reduce child poverty and income instability, and eliminate extreme poverty among families with children in the United States, we propose converting the Child Tax Credit and child tax exemption into a universal, monthly child allowance. Our proposal is based on principles we argue should undergird the design of such policies: universality, accessibility, adequate payment levels, and more generous support for young children. Whether benefits should decline with additional children to reflect economies of scale is a question policymakers should consider. Analyzing 2015 Current Population Survey data, we estimate our proposed child allowance would reduce child poverty by about 40 percent, deep child poverty by nearly half, and would effectively eliminate extreme child poverty. Annual net cost estimates range from $66 billion to $105 billion.

In older adults, hearing loss is independently associated with an increased rate of cognitive decline, and has been identified to be a modifiable risk factor for dementia. The mechanism underlying the cognitive decline associated with hearing loss is not understood, but it is known that the greater the hearing loss, the faster the rate of decline. It is unknown whether remediation of hearing loss with hearing devices can delay cognitive decline. This 5-year international longitudinal study is investigating the impact of cochlear implants on cognitive function in older people with severe-profound hearing loss, and whether remediation of hearing loss could delay the onset of cognitive impairment. This is the first study to examine the major primary risk factors associated with dementia in the same cohort. Participants were assessed before cochlear implantation and 18 months later using an identical battery including a visually presented cognitive assessment tool (Cogstate battery) that is highly sensitive to small changes in cognition and suitable for use with people with hearing loss. Hearing and speech perception ability were assessed in sound-treated conditions by an audiologist, and a range of questionnaire tools was administered to assess self-perceived ease of listening, quality of life, physical activity, diet, social and emotional loneliness, isolation, anxiety, and depression. A detailed medical health history was taken. Pre-operatively, despite the small initial sample size (n = 59), increased hearing loss and age predicted significantly poorer executive function and visual attention, while tertiary education predicted better executive function. Better self-reported quality of life was correlated with better visual learning performance, and engaging in frequent vigorous physical activity was correlated with poorer visual learning performance. At 18 months, for the first 20 participants, significant benefits of cochlear implants were seen in terms of speech perception, communication ability, and quality of life. Multiple linear regression modeling showed executive function improved significantly for non-tertiary educated males, while cognitive function remained stable for other participants. Further follow-up at 18 month intervals with a larger sample will reveal the effects of cochlear implant intervention on all outcomes, and whether this can delay cognitive decline.

Background and Aim. Lipedema is a common painful SAT disorder characterized by enlargement of fat primarily in the legs of women. Case reports of lipedema tissue samples demonstrate fluid and fibrosis in the interstitial matrix, increased macrophages, and adipocyte hypertrophy. The aims of this project are to investigate blood vasculature, immune cells, and structure of lipedema tissue in a cohort of women. Methods. Forty-nine participants, 19 controls and 30 with lipedema, were divided into groups based on body mass index (BMI): Non-Obese (BMI 20 to

The transient receptor potential (TrpA—ankyrin) receptor has been linked to pathological conditions in cardiac function in mammals. To better understand the function of the TrpA1 in regulation of the heart, a Drosophila melanogaster model was used to express TrpA1 in heart and body wall muscles. Heartbeat of in intact larvae as well as hearts in situ, devoid of hormonal and neural input, indicate that strong over-expression of TrpA1 in larvae at 30 or 37 °C stopped the heart from beating, but in a diastolic state. Cardiac function recovered upon cooling after short exposure to high temperature. Parental control larvae (UAS-TrpA1) increased heart rate transiently at 30 and 37 °C but slowed at 37 °C within 3 min for in-situ preparations, while in-vivo larvae maintained a constant heart rate. The in-situ preparations maintained an elevated rate at 30 °C. The heartbeat in the TrpA1-expressing strains could not be revived at 37 °C with serotonin. Thus, TrpA1 activation may have allowed enough Ca2+ influx to activate K(Ca) channels into a form of diastolic stasis. TrpA1 activation in body wall muscle confirmed a depolarization of membrane. In contrast, blowfly Lucilia sericata (syn Phaenicia sericata) larvae increased heartbeat at 30 and 37 °C, demonstrating greater cardiac thermotolerance.