Undertreatment of human immunodeficiency virus in psychiatric inpatients: a cross-sectional study of seroprevalence and associated factors

Background: The aims of this study were to evaluate the prevalence of HIV and its associated demographic and clinical factors among psychiatric inpatients of a general hospital. Methods: This was a single-center, observational, cross-sectional study that included patients consecutively admitted to our unit aged 16 years or older and with no relevant cognitive problems. The patients were evaluated using a semistructured interview and an appropriate test for HIV infection. Results: Of the 637 patients who were screened, 546 (86%) who consented to participate were included in the analyses. Twenty-five (4.6%, 95% confidence interval [CI] 3.0-6.8) patients were HIV-positive. The prevalence was higher among patients with substance misuse (17.4%, 95% CI 9.7-28.8). All except one of the 25 patients knew of their seropositive condition prior to participation in the study. Only 14 (56%) of the 25 seropositive patients had previously received pharmacological treatment for their infection. According to the multiple logistic regression analysis, the likelihood of HIV infection was lower in patients with higher levels of education and higher among patients who were single, had history of intravenous drug use, and had an HIV-positive partner, particularly if they did not use condoms. Among the patients with HIV infection, 18 (72%) had a history of suicide attempts compared with 181 (34.7%) of the patients without HIV infection (relative risk 2.1, 95% CI 1.6-2.7; P<0.001). Conclusion: HIV infection is highly prevalent in patients admitted to a psychiatric unit, especially those with a diagnosis of substance misuse. Seropositive patients show very poor treatment adherence. The risk of suicide seems to be very high in this population. Implementing interventions to reduce the suicide risk and improve adherence to antiretroviral therapy and psychotropic medications seems crucial.

MW, Medical Watches

My project is a business plan about the set up of a company and the development of a new and innovative product aimed for the elders. I decide do this project when I discover that one of the more important needs that have the elders is to remember the medicines that they have to take. I thought that a good way could be through a smart watch. My watch have an only function, is a cheap device, easy to use, easy to understand and easy to set up, because the elders usually do not know to use complex electronics devices. There are other similar smart watches and other devices but do not have the necessary characteristics to be a good reminder for elders. My watch is centred to improve the life of the elders, but my product could also be useful for ill people who have to take many medicines during the day. After realizing this business plan, I have proved that my company is viable in the environment and profitable in the market.

Towards Application of One-Class Classification Methods to Medical Data

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques-Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description-using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.