How Accurate Are Blood (or Breath) Tests for Identifying Self-Reported Heavy Drinking Among People with Alcohol Dependence?

AIMS: Managing patients with alcohol dependence includes assessment for heavy drinking, typically by asking patients. Some recommend biomarkers to detect heavy drinking but evidence of accuracy is limited. METHODS: Among people with dependence, we assessed the performance of disialo-carbohydrate-deficient transferrin (%dCDT, ≥1.7%), gamma-glutamyltransferase (GGT, ≥66 U/l), either %dCDT or GGT positive, and breath alcohol (> 0) for identifying 3 self-reported heavy drinking levels: any heavy drinking (≥4 drinks/day or >7 drinks/week for women, ≥5 drinks/day or >14 drinks/week for men), recurrent (≥5 drinks/day on ≥5 days) and persistent heavy drinking (≥5 drinks/day on ≥7 consecutive days). Subjects (n = 402) with dependence and current heavy drinking were referred to primary care and assessed 6 months later with biomarkers and validated self-reported calendar method assessment of past 30-day alcohol use. RESULTS: The self-reported prevalence of any, recurrent and persistent heavy drinking was 54, 34 and 17%. Sensitivity of %dCDT for detecting any, recurrent and persistent self-reported heavy drinking was 41, 53 and 66%. Specificity was 96, 90 and 84%, respectively. %dCDT had higher sensitivity than GGT and breath test for each alcohol use level but was not adequately sensitive to detect heavy drinking (missing 34-59% of the cases). Either %dCDT or GGT positive improved sensitivity but not to satisfactory levels, and specificity decreased. Neither a breath test nor GGT was sufficiently sensitive (both tests missed 70-80% of cases). CONCLUSIONS: Although biomarkers may provide some useful information, their sensitivity is low the incremental value over self-report in clinical settings is questionable.

Impact of orthologous melan-A peptide immunizations on the anti-self melan-A/HLA-A2 T cell cross-reactivity.

In HLA-A2 individuals, the CD8 T cell response against the differentiation Ag Melan-A is mainly directed toward the peptide Melan-A26-35. The murine Melan-A24-33 sequence encodes a peptide that is identical with the human Melan-A26-35 decamer, except for a Thr-to-Ile substitution at the penultimate position. Here, we show that the murine Melan-A24-33 is naturally processed and presented by HLA-A2 molecules. Based on these findings, we compared the CD8 T cell response to human and murine Melan-A peptide by immunizing HLA-A2 transgenic mice. Even though the magnitude of the CTL response elicited by the murine Melan-A peptide was lower than the one elicited by the human Melan-A peptide, both populations of CTL recognized the corresponding immunizing peptide with the same functional avidity. Interestingly, CTL specific for the murine Melan-A peptide were completely cross-reactive against the orthologous human peptide, whereas anti-human Melan-A CTL recognized the murine Melan-A peptide with lower avidity. Structurally, this discrepancy could be explained by the fact that Ile32 of murine Melan-A24-33 created a larger TCR contact area than Thr34 of human Melan-A26-35. These data indicate that, even if immunizations with orthologous peptides can induce strong specific T cell responses, the quality of this response against syngeneic targets might be suboptimal due to the structure of the peptide-TCR contact surface.

Mobile robot localization using sonar ranging and wlan intensity maps

 Main goal of this thesis was to implement a localization system which uses sonars and WLAN intensity maps to localize an indoor mobile robot. A probabilistic localization method, Monte Carlo Localization is used in localization. Also the theory behind probabilistic localization is explained. Two main problems in mobile robotics, path tracking and global localization, are solved in this thesis. Implemented system can achieve acceptable performance in path tracking. Global localization using WLAN received signal strength information is shown to provide good results, which can be used to localize the robot accurately, but also some bad results, which are no use when trying to localize the robot to the correct place. Main goal of solving ambiguity in office like environment is achieved in many test cases.