4 research outputs found

    HLA-B∗57 and Gender Influence the Occurrence of Tuberculosis in HIV Infected People of South India

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    Background. Substantial evidence exists for HLA and other host genetic factors being determinants of susceptibility or resistance to infectious diseases. However, very little information is available on the role of host genetic factors in HIV-TB coinfection. Hence, a longitudinal study was undertaken to investigate HLA associations in a cohort of HIV seropositive individuals with and without TB in Bangalore, South India. Methods. A cohort of 238 HIV seropositive subjects were typed for HLA-A, B, and DR by PCR-SSP and followed up for 5 years or till manifestation of Tuberculosis. HLA data of 682 HIV Negative healthy renal donors was used as control. Results. The ratio of males and females in HIV cohort was comparable (50.4% and 49.6%). But the incidence of TB was markedly lower in females (12.6%,) than males (25.6%). Further, HLA-B*57 frequency in HIV cohort was significantly higher among females without TB (21.6%, 19/88) than males (1.7%, 1/59); P = 0.0046; OR = 38. CD4 counts also were higher among females in this cohort. Conclusion. This study suggests that HIV positive women with HLA-B*57 have less occurrence of TB as compared to males

    HLA-B * 57 and Gender Influence the Occurrence of Tuberculosis in HIV Infected People of South India

    Get PDF
    Background. Substantial evidence exists for HLA and other host genetic factors being determinants of susceptibility or resistance to infectious diseases. However, very little information is available on the role of host genetic factors in HIV-TB coinfection. Hence, a longitudinal study was undertaken to investigate HLA associations in a cohort of HIV seropositive individuals with and without TB in Bangalore, South India. Methods. A cohort of 238 HIV seropositive subjects were typed for HLA-A, B, and DR by PCR-SSP and followed up for 5 years or till manifestation of Tuberculosis. HLA data of 682 HIV Negative healthy renal donors was used as control. Results. The ratio of males and females in HIV cohort was comparable (50.4% and 49.6%). But the incidence of TB was markedly lower in females (12.6%,) than males (25.6%). Further, HLA-B * 57 frequency in HIV cohort was significantly higher among females without TB (21.6%, 19/88) than males (1.7%, 1/59); P = 0.0046; OR = 38. CD4 counts also were higher among females in this cohort. Conclusion. This study suggests that HIV positive women with HLA-B * 57 have less occurrence of TB as compared to males

    Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

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    Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals
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