A STUDY OF ILLNESS AND RESERVOIRS ASSOCIATED WITH GIARDIA LAMBLIA IN RURAL EGYPT

A longitudinal investigation of the health effects and reservoirs of Giardia lamblia was undertaken in forty households located in a rural Nile Delta region of Egypt. Stool specimens obtained once weekly for six months from two to four year old children were cyst or trophozoite-positive in 42 percent of the 724 examined. The mean duration of excretion in all but one Giardia-negative child was seven and one-half weeks with a range of one to 17 weeks. Clinical symptoms of illness were frequently observed within a month before or after Giardia excretion in stool of children, but a statistical inference of association was not demonstrated.^ Seventeen percent of 697 specimens obtained from their mothers was Giardia-positive for a mean duration of four weeks and a range of one to 18 weeks. Mothers were observed to excrete Giardia in stool less frequently during pregnancy than during lactation.^ Nine hundred sixty-two specimens were collected from 13 species of household livestock. Giardia was detected in a total of 22 specimens from cows, goats, sheep and one duck. Giardia cysts were detected in three of 899 samples of household drinking water.^ An ELISA technique of Giardia detection in human and animal stool was field tested under variable environmental conditions. The overall sensitivity of the assay of human specimens was 74 percent and specificity was 97 percent. These values for assay of animal specimens were 82 percent and 98 percent, respectively.^ Surface antigen studies reported from the NIH Laboratory of Parasitic Diseases show that antigens of three Egyptian human isolates are different from each other and from most other isolates against which they were tested.^ The ubiquity of human and animal fecal contamination combined with estimates of ill days per child per year in this setting are substantial arguments for the introduction of a suggested mass parasite control program to intervene in the cyclical transmission of agents of enteric disease. ^

Left, right and interval bivariate censored data: Evaluating screening mammography in the presence of lead -time bias, length bias and over-detection

Of the large clinical trials evaluating screening mammography efficacy, none included women ages 75 and older. Recommendations on an upper age limit at which to discontinue screening are based on indirect evidence and are not consistent. Screening mammography is evaluated using observational data from the SEER-Medicare linked database. Measuring the benefit of screening mammography is difficult due to the impact of lead-time bias, length bias and over-detection. The underlying conceptual model divides the disease into two stages: pre-clinical (T0) and symptomatic (T1) breast cancer. Treating the time in these phases as a pair of dependent bivariate observations, (t0,t1), estimates are derived to describe the distribution of this random vector. To quantify the effect of screening mammography, statistical inference is made about the mammography parameters that correspond to the marginal distribution of the symptomatic phase duration (T1). This shows the hazard ratio of death from breast cancer comparing women with screen-detected tumors to those detected at their symptom onset is 0.36 (0.30, 0.42), indicating a benefit among the screen-detected cases. ^

Statistical Methods for Differential Expressions of Genes Detected in Multiple-Condition Experiment of Microarray

Most studies of differential gene-expressions have been conducted between two given conditions. The two-condition experimental (TCE) approach is simple in that all genes detected display a common differential expression pattern responsive to a common two-condition difference. Therefore, the genes that are differentially expressed under the other conditions other than the given two conditions are undetectable with the TCE approach. In order to address the problem, we propose a new approach called multiple-condition experiment (MCE) without replication and develop corresponding statistical methods including inference of pairs of conditions for genes, new t-statistics, and a generalized multiple-testing method for any multiple-testing procedure via a control parameter C. We applied these statistical methods to analyze our real MCE data from breast cancer cell lines and found that 85 percent of gene-expression variations were caused by genotypic effects and genotype-ANAX1 overexpression interactions, which agrees well with our expected results. We also applied our methods to the adenoma dataset of Notterman et al. and identified 93 differentially expressed genes that could not be found in TCE. The MCE approach is a conceptual breakthrough in many aspects: (a) many conditions of interests can be conducted simultaneously; (b) study of association between differential expressions of genes and conditions becomes easy; (c) it can provide more precise information for molecular classification and diagnosis of tumors; (d) it can save lot of experimental resources and time for investigators.^