Piezo-Tribo Dual Effect Hybrid Nanogenerators for Health Monitoring

Over the years, nanogenerators for health monitoring have become more and more attractive as they provide a cost-effective and continuous way to successfully measure vital signs, physiological status, and environmental changes in/around a person. Using such sensors can positively affect the way healthcare workers diagnose and prevent life-threatening conditions. Recently, the dual piezo-tribological effect of hybrid nanogenerators (HBNGs) have become a subject of investigation, as they can provide a substantial amount of data, which is significant for healthcare. However, real-life exploitation of these HBNGs in health monitoring is still marginal. This review covers piezo-tribo dual-effect HBNGs that are used as sensors to measure the different movements and changes in the human body such as blood circulation, respiration, and muscle contractions. Piezo-Tribo dual-effect HBNGs are applicable within various healthcare settings as a means of powering noninvasive sensors, providing the capability of constant patient monitoring without interfering with the range of motion or comfort of the user. This review also intends to suggest future improvements in HBNGs. These include incorporating surface modification techniques, utilizing nanowires, nanoparticle technologies, and other means of chemical surface modifications. These improvements can contribute significantly in terms of the electrical output of the HBNGs and can enhance their prospects of applications in the field of health monitoring, as well as various in vitro/in vivo biomedical applications. While a promising option, improved HBNGs are still lacking. This review also discusses the technical issue which has prevented so far, the real use of these sensors

Diagnostic performance of 128-slice computed tomography angiography in patients with suspected coronary artery disease

الملخص: أهداف البحث: لتحديد الأداء التشخيصي والعوامل المؤثرة في التصوير الوعائي المقطعي المحوسب للشريان التاجي المكون من 128 شريحة مقارنة بتصوير الأوعية التاجية المجتاحة في مرضى الشريان التاجي. طريقة البحث: سجلت دراسة تحليلية مقطعية 139 مريضا يشتبه في إصابتهم بمرض الشريان التاجي والذين خضعوا لـ 128 شريحة للتصوير المقطعي المحوسب للأوعية وكذلك تصوير الأوعية التاجية المجتاحة. النتائج: أظهر النموذج المعتمد على المريض حساسية عالية بنسبة 93.2٪ وقيمة تنبؤية إيجابية بنسبة 95.3٪ للتضيق ≥ 50٪. ومع ذلك ، كانت هذه القيم أقل عند تحليلها من خلال النماذج المستندة إلى الشرايين (حساسية عالية بنسبة 85.6٪ وقيمة تنبؤية إيجابية بنسبة 81.1٪) والقطاعات (حساسية عالية بنسبة 73.9٪ وقيمة تنبؤية إيجابية بنسبة 66.6٪). كانت الخصوصية والقيمة التنبؤية السلبية هي الأعلى في النموذج المعتمد على القطعة ، وانخفضت في النماذج المستندة إلى الأوعية والمرضى بنسبة 96.4٪ و 95.4٪ ؛ 90.5٪ و 90.0٪ ؛ و 36.4٪ و 42.1٪ على التوالي (للتضيق ≥ 70٪). انخفضت جميع القيم التشخيصية عندما كانت درجة الكالسيوم 400 وحدة أغاتستون. الاستنتاجات: يظهر التصوير الوعائي المقطعي المحوسب للشريان التاجي المكون من 128 شريحة الطريقة المثلى وذات الحد الأدنى من الاجتياح وعالي الأداء لتشخيص تضيق وتشكل آفات الشريان التاجي. كان الأداء التشخيصي لتصوير الأوعية المقطعي المحوسب للشريان التاجي المكون من 128 شريحة مرتفعا جدا. لم يؤثر معدل ضربات القلب ومؤشر كتلة الجسم على دقة التشخيص، بينما تم العثور على درجة الكالسيوم البالغة 400 وحدة أغاتستون كعامل يتسبب في انخفاض الأداء التشخيصي. Abstract: Objectives: To determine the diagnostic performance and influencing factors of 128-slice coronary computed tomography angiography (CCTA) compared with invasive coronary angiography (ICA) in patients with suspected coronary artery disease (CAD). Methods: A cross-sectional analysis study enrolled 139 patients suspected of having CAD, who underwent and received a 128-slice CCTA and ICA. Results: The patient-based model showed high sensitivity and a positive predictive value of 93.2% and 95.3%, respectively (for stenosis ≥50%). However, these values were lower when analyzed using vessel-based (85.6% and 81.1%) and segment-based (73.9% and 66.6%) models. Specificity and negative predictive value were highest in the segment-based model, decreasing in vessel- and patient-based models at 96.4% and 95.4%, 90.5% and 90.0%, and 36.4% and 42.1%, respectively (for stenosis ≥70%). All diagnostic values were reduced when the calcium score was ≥400 Agatston units. Conclusion: 128-slice CCTA is an optimal, minimally invasive, and high-performance method to diagnose the stenosis and morphology of coronary artery lesions. The diagnostic performance of 128-slice CCTA is very high. Heart rate and body mass index do not affect diagnostic accuracy, whereas a calcium score ≥400 Agatston units is a factor that causes a decrease in diagnostic performance

Comparison of 3 Different Perioperative Care Models for Patients With Hip Fractures Within 1 Health Service

Introduction: Orthogeriatric care models have been introduced within many health-care facilities to improve outcomes for hip fracture patients. This study aims to evaluate differences in care between 3 models, an orthopedic model, a geriatric model, and a comanaged model. Materials and Methods: A retrospective analysis was conducted for hip fracture patients treated at Western Health between November 2012 and March 2014. All patients aged 65 years or older were included in the analysis. Results: There were 183 patients in the orthopedic model, 137 in the geriatric model, and 126 in the comanaged model. Demographics and clinical characteristics were similar across the 3 models. Length of stay, mortality, and discharge destination were also consistent across the 3 groups. However, groups involving geriatricians were more likely to receive preoperative medical assessments, have greater recognition of postoperative medical problems, and have implementation of long-term osteoporosis management. Conclusion: The involvement of geriatricians in perioperative care models resulted in more comprehensive medical care without impacting length of stay, mortality, or discharge destination

Factors Underlying Patient and Surgeon Willingness to Participate in a Placebo Surgery Controlled trial

Objective:. To investigate the factors underlying willingness to participate in a hypothetical trial among patients and surgeons, to inform the design of future placebo surgery controlled trials. Background:. Placebo surgery controlled trials are the gold standard for testing the efficacy of surgical procedures. However, these trials commonly fail to meet the target sample size and terminate underpowered. Methods:. From October 2019 to July 2020, eligible patients were identified from the orthopedic waiting list at a single tertiary hospital and surgeons were identified from orthopedic clinics at three tertiary hospitals in Australia. Qualitative interviews explored factors underlying willingness to participate in a hypothetical trial, including understanding of trial concepts; attitudes; and trial design preferences. Data collection and analysis were conducted in parallel. Recruitment ceased when no new concepts emerged. Interview data were analyzed using reflexive thematic analysis. Results:. The majority of surgeons and only a few patients indicated a willingness to participate in a placebo surgery controlled trial. Factors underlying willingness were captured in four themes: (1) Understanding and attitudes toward placebo; (2) Attitudes towards randomization/perception of equipoise; (3) Perception of risk; and (4) Ethical concerns. Conclusions:. To optimize recruitment in the future, trialists may consider embedding strategies into the recruitment process that validate patients’ symptoms, encourage an altruistic mindset, address surgeon biases, and involve surgeons in explaining trial concepts to patients. Trialists may also consider designing three arm trials that meet surgeons’ preferences for a “low” and “high” fidelity placebo

Ensemble machine learning models based on Reduced Error Pruning Tree for prediction of rainfall-induced landslides

In this paper, we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D), and Random Subspace (RSS) ensemble learning techniques for spatial prediction of rainfall-induced landslides in the Uttarkashi district, located in the Himalayan range, India. To do so, a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation datasets. Root Mean Square Error (RMSE) and Area Under the receiver operating characteristic Curve (AUC) were used to evaluate the training and validation performances of the models. The results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of landslides. The D-REPT model with RMSE = 0.351 and AUC = 0.907 was identified as the most accurate model, followed by RSS-REPT (RMSE = 0.353 and AUC = 0.898), B-REPT (RMSE = 0.396 and AUC = 0.876), and the single REPT model (RMSE = 0.398 and AUC = 0.836), respectively. The prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world