38 resultados para Naive Bayes classifier

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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Manual counting of bacterial colony forming units (CFUs) on agar plates is laborious and error-prone. We therefore implemented a colony counting system with a novel segmentation algorithm to discriminate bacterial colonies from blood and other agar plates.A colony counter hardware was designed and a novel segmentation algorithm was written in MATLAB. In brief, pre-processing with Top-Hat-filtering to obtain a uniform background was followed by the segmentation step, during which the colony images were extracted from the blood agar and individual colonies were separated. A Bayes classifier was then applied to count the final number of bacterial colonies as some of the colonies could still be concatenated to form larger groups. To assess accuracy and performance of the colony counter, we tested automated colony counting of different agar plates with known CFU numbers of S. pneumoniae, P. aeruginosa and M. catarrhalis and showed excellent performance.

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OBJECTIVE Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients. METHODS In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians. RESULTS Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946). CONCLUSIONS EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma. SIGNIFICANCE Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.

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To evaluate the prevalence of 25-hydroxyvitamin D [25(OH)D] deficiency in HIV-positive patients, a population at risk for osteoporosis.

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Patients with panic disorder (PD) have a bias to respond to normal stimuli in a fearful way. This may be due to the preactivation of fear-associated networks prior to stimulus perception. Based on EEG, we investigated the difference between patients with PD and normal controls in resting state activity using features of transiently stable brain states (microstates). EEGs from 18 drug-naive patients and 18 healthy controls were analyzed. Microstate analysis showed that one class of microstates (with a right-anterior to left-posterior orientation of the mapped field) displayed longer durations and covered more of the total time in the patients than controls. Another microstate class (with a symmetric, anterior-posterior orientation) was observed less frequently in the patients compared to controls. The observation that selected microstate classes differ between patients with PD and controls suggests that specific brain functions are altered already during resting condition. The altered resting state may be the starting point of the observed dysfunctional processing of phobic stimuli.

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Estimates of drug resistance incidence to modern first-line combination antiretroviral therapies against human immunodeficiency virus (HIV) type 1 are complicated by limited availability of genotypic drug resistance tests (GRTs) and uncertain timing of resistance emergence.

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OBJECTIVES: To assess the frequency of and risk factors for discordant responses at 6 months on highly active antiretroviral therapy (HAART) in previously treatment-naive HIV patients from resource-limited countries. METHODS: The Antiretroviral Therapy in Low-Income Countries Collaboration is a network of clinics providing care and treatment to HIV-infected patients in Africa, Latin America, and Asia. Patients who initiated therapy between 1996 and 2004, were aged 16 years or older, and had a baseline CD4 cell count were included in this analysis. Responses were defined based on plasma viral load (PVL) and CD4 cell count at 6 months as complete virologic and immunologic (VR(+)IR(+)), virologic only (VR(+)IR(-)), immunologic only (VR(-)IR(+)), and nonresponse (VR(-)IR(-)). Multinomial logistic regression was used to assess the association between therapy responses and clinical and demographic variables. RESULTS: Of the 3111 patients eligible for analysis, 1914 had available information at 6 months of therapy: 1074 (56.1%) were VR(+)IR(+), 364 (19.0%) were VR(+)IR(-), 283 (14.8%) were (VR(-)IR(+)), and 193 (10.1%) were VR(-)IR(-). OF THE 3111 patients eligible for analysis, 1914 had available information at 6 months of therapy: 1074 (56.1%) were VRIR, 364 (19.0%) were VRIR, 283 (14.8%) were (VRIR), and 193 (10.1%) were VRIR. Compared with complete responders, virologic-only responders were older, had a higher baseline CD4 cell count, had a lower baseline PVL, and were more likely to have received a nonstandard HAART regimen; immunologic-only responders were younger, had a lower baseline CD4 cell count, had a higher baseline PVL, and were more likely to have received a protease inhibitor-based regimen. CONCLUSIONS: The frequency of and risk factors for discordant responses were comparable to those observed in developed countries. Longer follow-up is needed to assess the long-term impact of discordant responses on mortality in these resource-limited settings.

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The aim of this study was to search for differences in the EEG of first-episode, drug-naive patients having a schizophrenic syndrome which presented different time courses in response to antipsychotic treatment. Thirteen patients who fulfilled DSM-IV diagnosis for schizophrenia or schizophreniform disorder participated in this study. Before beginning antipsychotic treatment, the EEG was recorded. On the same day psychopathological ratings were assessed using the ADMDP system, and again after 7 and 28 days of treatment. The resting EEG (19 leads) was subject to spectral analysis involving power values for six frequency bands. The score for the schizophrenic syndrome was used to divide the patients into two groups: those who displayed a clinically meaningful improvement of this syndrome (reduction of more than 30%) after 7 days of treatment (early responders, ER) and those who showed this improvement after 28 days (late responders. LR). Analysis of variance for repeated measures between ER, LR and their matched controls with the 19 EEG leads yielded highly significant differences for the factor group in the alpha2 and beta2 frequency band. No difference was found between the slow-wave frequency bands. Compared to controls the LR group showed significantly higher alpha2 and beta2 power and, in comparison to the ER group, significantly higher alpha2 power. There were no significant differences between the ER and the control group. These findings point to differences in brain physiology between ER and LR. The implications for diagnosis and treatment are discussed.

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BACKGROUND: Blood lipid abnormalities in patients on highly active antiretroviral therapy (HAART) have been associated with exposure to protease inhibitors (PIs), particularly ritonavir. First therapy with a non-nucleoside reverse transcriptase inhibitor (NNRTI) leads to relatively favourable lipid profiles. We report on medium-term lipid profiles (up to 5 years) for antiretroviral-naive patients starting NNRTI- and PI-based HAART in the Swiss HIV Cohort Study. METHODS: Since April 2000, blood samples taken at visits scheduled every 6 months have been analysed for cholesterol and triglyceride concentrations. For 1065 antiretroviral-naive patients starting HAART after April 2000, we estimated changes in concentration over time using multivariate linear regression with adjustment for baseline covariates, use of lipid-lowering drugs and whether the sample was taken in a fasting state. RESULTS: Non-high density lipoprotein (HDL) cholesterol levels increase with increasing exposure to either PI- or NNRTI-based therapy, HDL cholesterol levels increase and triglyceride levels decrease with increasing exposure to NNRTI-based therapy, whereas triglyceride levels increase with increasing exposure to PI-based therapy. Between NNRTI-based therapies, there is a slight difference in triglyceride levels, which tend to increase with increasing exposure to efavirenz and to decrease with increasing exposure to nevirapine. Of the three common PI-based therapies, nelfinavir appears to have a relatively favourable lipid profile, with little change with increasing exposure. Of the other two PI therapies, lopinavir with ritonavir has a more favourable profile than indinavir with ritonavir, with smaller increases in both non-HDL cholesterol and triglycerides and an increase in HDL cholesterol. Increasing exposure to abacavir is associated with a decrease in the level of triglycerides. CONCLUSION: In general, NNRTI-based therapy is associated with a more favourable lipid profile than PI-based therapy, but different PI-based therapies are associated with very different lipid profiles. Nelfinavir appears to have a relatively favourable lipid profile. Of the two boosted PI therapies, lopinavir appears to have a more favourable lipid profile than indinavir.

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BACKGROUND: Body fat changes are common in patients with HIV. For patients on protease inhibitor (PI)-based highly active antiretroviral therapy (HAART), these changes have been associated with increasing exposure to therapy in general and to stavudine in particular. Our objective is to show whether such associations are more or less likely for patients on non-nucleoside reverse transcriptase inhibitor (NNRTI)-based HAART. METHODS: We included all antiretroviral-naive patients in the Swiss HIV Cohort Study starting HAART after April 2000 who had had body weight, CD4 cell count and plasma HIV RNA measured between 6 months before and 3 months after starting HAART, and at least one assessment of body fat changes after starting HAART. At visits scheduled every 6 months, fat loss or fat gain is reported by agreement between patient and physician. We estimate the association between reported body fat changes and both time on therapy and time on stavudine, using conditional logistical regression. RESULTS: Body fat changes were reported for 85 (9%) out of 925 patients at their first assessment; a further 165 had only one assessment. Of the remaining 675 patients, body fat changes were reported for 156 patients at a rate of 13.2 changes per 100 patient-years. Body fat changes are more likely with increasing age [odds ratio (OR) 1.18 (1.00-1.38) per 10 years], with increasing BMI [OR 1.06 (1.01-1.11)] and in those with a lower baseline CD4 cell count [OR 0.91 (0.83-1.01) per 100 cells/microl]. There is only weak evidence that body fat changes are more likely with increasing time on HAART [OR 1.16 (0.93-1.46)]. After adjusting for time on HAART, fat loss is more likely with increasing stavudine use [OR 1.70 (1.34-2.15)]. There is no evidence of an association between reported fat changes and time on NNRTI therapy relative to PI therapy in those patients who used either one therapy or the other [OR 0.98 (0.56-1.63)]. CONCLUSION: Fat loss is more likely to be reported with increasing exposure to stavudine. We find no evidence of major differences between PI and NNRTI therapy in the risk of reported body fat changes.