Addressing cost and time barriers in chronic disease management through telemedicine: An exploratory research in select low-and middle-income countries

Despite evidence supporting telehealth provision in developed countries, there is limited evidence regarding its economic benefits for patients living in areas where access and cost present major barriers to health care, particularly in low- and middle-income countries (LMICs). This study explores the economic benefits of telemedicine for patients, in terms of cost and times savings, and its potential role in improving chronic disease outcomes. This retrospective cross-sectional study compared telemedicine services with hypothetical in-person consultations, with a focus on patient travel time and travel cost savings. A database containing teleconsultation visits (N = 25,182) conducted at health facilities in remote regions of Afghanistan, Pakistan, Tajikistan, and the Kyrgyz Republic, was analyzed. A two-sample homoscedastic t test was used to determine differences between the two groups. A one-way sensitivity analysis was also conducted, presuming in-person teleconsultations at 90%, 75%, and 50%. The study extracted data from 25,182 teleconsultation visits (12,814 males; 12,368 females). The cumulative patient savings through the program amounted to USD 9,175,132, and 1,876,146 h, or 213.1 years. A significant difference was seen between the two groups in terms of mean time savings (p-valu

Exploring Carotenoid from Rhodococcus Kroppenstedtii as a Photosensitizer in a Dye Sensitised Solar Cell

A Dye-Sensitised Solar Cell (DSSC) is a low-cost thin film solar cell that works in diffused light and comes in a variety of colors. Most of the investigations on organic dye-based DSSC have used pigments from flowers and fruits as photosensitizers. With the majority of the world's economy reliant on agriculture to meet the food and feed demand, using agricultural resources for color extraction is not a realistic solution. Alternative dye resources, such as microorganisms, must thus be investigated in DSSCs to ensure a long-term future. The present study was a preliminary investigation to explore the potential of carotenoids derived from an actinobacteria Rhodococcus kroppenstedtii as a photosensitizer in a DSSC. The carotenoid extract from R. kroppenstedtii was subjected to stability analysis, to ascertain its potential as a photosensitizer. The extract was found to be stable at varying temperatures (0-80 °C), pH (3-11), and light conditions (dark, white light, sunlight), indicating its potential applicability as a photosensitizer. Further, increasing concentrations (12.5 mg/mL-100 mg/mL) of the extract was used for sensitization of TiO 2 in a DSSC assembly. The extract showed a linear rise in power output (078±0.0001 - 20.75±0.0003 mW), which proposes its scope as a stable and cheap photosensitizer in a DSSC

Smart Surveillance and Detection Framework Using YOLOv3 Algorithm

In this paper, we proposed a method for locating, identifying, and admitting the activities of intrigued, in nearly actual time, from outlines gotten by a ceaseless tide of video information from an observation camera. This article endorses the way to follow, distinguish, and take note of the exercises of captivated in about real-time from follows gotten by a nonstop stream of video information from a reconnaissance camera. The appearance takes input, follows an appeared time space and can provide an activity title based on a single format. We illustrate that YOLO is a viable strategy and comparatively quick for localization within the custom dataset. The findings and analysis of the model will be presented in the following sections. The demonstration collects input outlines after a foreordained interim and can dole out an activity name based on a single outline. We anticipated the activity name for the video stream by combining the discoveries over a period. Because of its benefits, this YOLO strategy is utilized to distinguish action. This method may be used in various settings to tackle real-world problems, such as shopping malls, ATMs, banks, offices, homes, and societies. We have developed a model that detects some ideal human actions

Numerical Investigation of Solidity Effect Based on Variable Diameter on Power Performance of H-type Darrieus Vertical Axis Wind Turbine (VAWT)

Renewable energy resources especially wind energy, have seen significant growth in the worldwide energy market as clean energy sources. This has brought attention to areas with low and moderate wind speeds. Small-scale Darrieus vertical axis wind turbine (VAWT) with omnidirectional capability captures potential energy in these areas at a cost-effective scale. Numerous studies have been conducted to optimise their design, hence improving the performance of these turbines. Turbine solidity, σ, representing the ratio of the overall area of the blades over the swept area of the turbine, is one of the influential geometrical factors that significantly affect wind turbine performance. Previous studies on solidity focused on the number of blades and blade length variations, while the study on turbine diameter is limited. Hence, this paper intends to numerically investigate the effect of solidity that corresponds to different turbine diameters. Power performance and flow characteristics are investigated closely according to different solidity, σ and tip speed ratios, λ using high-fidelity computational fluid dynamic (CFD) method, which solves the unsteady Reynolds-Averaged Navier-Stokes (RANS) equations. Solidity and tip speed ratios vary within a wide range of 0.3 – 0.7 and 0.5 – 4.5, respectively. The results show that decreasing the turbine solidity from 0.7 to 0.3 could significantly increase the maximum power coefficient, Cp, by 30%. However, turbine with high solidity (σ = 0.7) generate much higher instantaneous moment coefficient, Cm than the low solidity turbine (σ = 0.3), but at lower λ and a narrower range of λ. The difference in turbine's performance between high and low solidity turbine is attributed to stall experienced by the blade at low λ and the blockage effect experienced by the turbine at moderate to high λ that significantly influence the energy generation at downstream region

A machine learning algorithm for peripheral artery disease prognosis using biomarker data

Summary: Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management

Current Biomarkers for Carotid Artery Stenosis: A Comprehensive Review of the Literature

Carotid artery stenosis (CAS), an atherosclerotic disease of the carotid artery, is one of the leading causes of transient ischemic attacks (TIA) and cerebrovascular attacks (CVA). The atherogenic process of CAS affects a wide range of physiological processes, such as inflammation, endothelial cell function, smooth muscle cell migration and many more. The current gold-standard test for CAS is Doppler ultrasound; however, there is yet to be determined a strong, clinically validated biomarker in the blood that can diagnose patients with CAS and/or predict adverse outcomes in such patients. In this comprehensive literature review, we evaluated all of the current research on plasma and serum proteins that are current contenders for biomarkers for CAS. In this literature review, 36 proteins found as potential biomarkers for CAS were categorized in to the following nine categories based on protein function: (1) Inflammation and Immunity, (2) Lipid Metabolism, (3) Haemostasis, (4) Cardiovascular Markers, (5) Markers of Kidney Function, (6) Bone Health, (7) Cellular Structure, (8) Growth Factors, and (9) Hormones. This literature review is the most up-to-date and current comprehensive review of research on biomarkers of CAS, and the only review that demonstrated the several pathways that contribute to the initiation and progression of the disease. With this review, future studies can determine if any new markers, or a panel of the proteins explored in this study, may be contenders as diagnostic or prognostic markers for CAS