An ELISA was set up using polyvinylchloride microtiter plates coated with rabbit anti-UK IgG's and affino-purified goat anti-UK IgG's as second antibody. Detection occurred with rabbit anti-goat IgG antibodies conjugated with alkaline phosphatase. The assay is specific for urokinase (UK) with a detection limit of 100 pg/ml sample. Tissue-type plasminogen activator, up to concentrations of 100 ng/ml, does not interfere. The assay measures the antigen of the inactive zymogen pro-UK, the active enzyme UK and the UK-inhibitor complex with equal efficiency and gives the total UK antigen present, irrespective of its molecular form. Culture media of fibroblasts, endothelial- and kidney cells showed, despite the absence of active UK, antigen levels of 1.2, 23 and 65 ng/ml, respectively. In human plasma the UK concentration was found to be 3.5 +/- 1.4 ng/ml (mean +/- SD, n = 54). The inter- and intra-assay variations were 20% and 6%, respectively.
Background: Digital health is well-positioned in low and middle-income countries (LMICs) to revolutionize health care due, in part, to increasing mobile phone access and internet connectivity. This paper evaluates the underlying factors that can potentially facilitate or hinder the progress of digital health in Pakistan. Objective: The objective of this study is to identify the current digital health projects and studies being carried out in Pakistan, as well as the key stakeholders involved in these initiatives. We aim to follow a mixed-methods strategy and to evaluate these projects and studies through a strengths, weaknesses, opportunities, and threats (SWOT) analysis to identify the internal and external factors that can potentially facilitate or hinder the progress of digital health in Pakistan. Methods: This study aims to evaluate digital health projects carried out in the last 5 years in Pakistan with mixed methods. The qualitative and quantitative data obtained from field surveys were categorized according to the World Health Organization’s (WHO) recommended building blocks for health systems research, and the data were analyzed using a SWOT analysis strategy. Results: Of the digital health projects carried out in the last 5 years in Pakistan, 51 are studied. Of these projects, 46% (23/51) used technology for conducting research, 30% (15/51) used technology for implementation, and 12% (6/51) used technology for app development. The health domains targeted were general health (23/51, 46%), immunization (13/51, 26%), and diagnostics (5/51, 10%). Smartphones and devices were used in 55% (28/51) of the interventions, and 59% (30/51) of projects included plans for scaling up. Artificial intelligence (AI) or machine learning (ML) was used in 31% (16/51) of projects, and 74% (38/51) of interventions were being evaluated. The barriers faced by developers during the implementation phase included the populations’ inability to use the technology or mobile phones in 21% (11/51) of projects, costs in 16% (8/51) of projects, and privacy concerns in 12% (6/51) of projects.
PURPOSE: The purpose of the study is to identify demographic, clinical, lifestyle-related, and social-cognitive correlates of physical activity (PA) intention and behavior in head and neck cancer (HNC) survivors using the theory of planned behavior (TPB).METHODS: Data from two cross-sectional studies on correlates of PA in HNC survivors were pooled. Both studies used self-reports to assess PA and social-cognitive correlates. Potential correlates were collected via self-report or medical records. Univariable and multivariable multilevel linear mixed-effects models were built to identify correlates of PA intention and PA behavior (Z scores). Structural equation model analyses were conducted to study the full TPB model in one analysis, taking into account relevant covariates.RESULTS: In total, 416 HNC survivors were surveyed. Their mean (SD) age was 66.6 (9.4) years; 64% were men, and 78% were diagnosed with laryngeal cancer. The structural equation model showed that PA intention was significantly higher in HNC survivors with a history of exercising, who had a more positive attitude, subjective norm, and perceived behavioral control. Patients with higher PA intention, higher PBC, a lower age, and without unintentional weight loss or comorbidities had higher PA behavior. The model explained 22.9% of the variance in PA intention and 16.1% of the variance in PA behavior.CONCLUSIONS: Despite significant pathways of the TPB model, the large proportion variance in PA intention and behavior remaining unexplained suggests the need for better PA behavior (change) models to guide the development of PA promotion programs, particularly for the elderly. Such programs should be tailored to comorbidities and nutritional status.
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