Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

BackgroundUnequal housing access resulted in more than 150 million homeless people worldwide, with millions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research.MethodsWe used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research.ResultsOur findings indicated a geographic clustering of case study locations– 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters.ConclusionMost studies were restricted in analyzing and visualizing the patterns and disease clusters; however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).     

A review of approaches to 18F radiolabelling affinity peptides and proteins

Affinity peptide and protein‐ (APP) based radiotracers are an increasingly popular class of radiotracer in positron emission tomography (PET), which was once dominated by the use of small molecule radiotracers. Radiolabelled monoclonal antibodies (mAbs) are important examples of APPs, yet a preference for smaller APPs, which exhibit fast pharmacokinetics and permit rapid PET aided diagnosis, has become apparent. ¹⁸F exhibits favourable physical characteristics for APP radiolabelling and has been described as an ideal PET radionuclide. Notwithstanding, ¹⁸F radiolabelling of APP is challenging, and this is echoed in the literature where a number of diverse approaches have been adopted. This review seeks to assess and compare the approaches taken to ¹⁸F APP radiolabelling with the intention of highlighting trends within this expanding field. Generic themes have emerged in the literature, namely the use of mild radiolabelling conditions, a preference of site‐specific methodologies with an impetus for short, automated procedures which produce high‐yielding [¹⁸F]APPs.     

Severe periodontitis is associated with the serum levels of hypersensitive C reactive protein and lipoprotein-associated phospholipase A2 in the patients of acute ischemic stroke

BackgroundPeriodontitis is associated with the pathogenesis of atherosclerotic plaque, and hypersensitive C reactive protein (hs-CRP) and lipoprotein-associated phospholipase A2 (Lp-PLA2) are the serum biomarkers of the stability of atherosclerotic plaque. Whether periodontitis is associated with the serum level of hs-CRP and Lp-PLA2 of acute ischemic stroke remains unclear.Material and MethodsWe recruited 103 cases with acute ischemic stroke within 7 days after stroke onset. Pocket depth and clinical attachment loss were assessed by oral examination to define the severe periodontitis. Demographic information including gender, age and body weight index, income level, education level, past medical history include smoking history, drinking history, ischemic stroke history, coronary heart disease, hypertension, diabetes and hyperlipidemia were collected, and serum biomarkers including white blood cell (WBC), fibrinogen, total cholesterol (TC), triglyceride (TG), lower density lipoprotein (LDL-C), high density lipoprotein (HDL-C), hs-CRP, HemoglobinA1c (HbAlc), Homocysteine (HCY) and Lp-PLA2 were tested.Results65 (63.1%) cases were diagnosed as severe periodontitis. Severe periodontitis group showed more male, age, drinking history, higher levels of hs-CRP and Lp-PLA2. Multivariate logistic regression showed that severe periodontitis was were significantly associated with hs-CRP (OR = 2.367, 95%CI: 1.182–4.738; P = .015) and Lp-PLA2 (OR = 2.577, 95% CI: 1.010–6.574; P = .048).ConclusionsSevere periodontitis is independently associated with the serum Level of hs-CRP and Lp-PLA2 in patients with acute ischemic stroke. Whether the improvement of periodontitis could decrease the occurrence and re-occurrence of ischemic stroke by stablizating atherosclerotic plaque need be further studied in future.