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- Bjerner, Johan, et al.
(författare)
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Non-parametric estimation of reference intervals in small non-Gaussian sample sets
- 2009
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Ingår i: ACCREDITATION AND QUALITY ASSURANCE. - : Springer Science and Business Media LLC. - 0949-1775 .- 1432-0517. ; 14:4, s. 185-192
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Tidskriftsartikel (refereegranskat)abstract
- This study aimed at validating common bootstrap algorithms for reference interval calculation.We simulated 1500 random sets of 50-120 results originating from eight different statistical distributions. In total, 97.5 percentile reference limits were estimated from bootstrapping 5000 replicates, with confidence limits obtained by: (a) normal, (b) from standard error, (c) bootstrap percentile (as in RefVal) (d) BCa, (e) basic, or (f) student methods. Reference interval estimates obtained with ordinary bootstrapping and confidence intervals by percentile method were accurate for distributions close to normality and devoid of outliers, but not for log-normal distributions with outliers. Outlier removal and transformation to normality improved reference interval estimation, and the basic method was superior in such cases. In conclusions, if the neighborhood of the relevant percentile contains non-normally distributed results, bootstrapping fails. The distribution of bootstrap estimates should be plotted, and a non-normal distribution should warrant transformation or outlier removal.
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| 2. |
- Lindberg, Johan, 1977-
(författare)
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Transcriptional patterns in inflammatory disease
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the studies this thesis is based upon, microarrays were applied to profilemRNA populations in biological samples to gain insights into transcriptionalpatterns and their relation to inflammatory disease.Rheumatoid arthritis (RA) is a chronic inflammatory disease, which leads todegradation of cartilage and bone. RA is characterized by synovial inflammationwith varying levels of tissue heterogeneity. This was confirmed by microarrayanalyses of multiple biopsies from the joints of 13 patients, which showed interindividualvariation in transcript populations to be higher than intra‐individualvariationTherapeutic antibodies targeting TNF‐α have revolutionized treatment of RA,although some patients do not respond well. Identification of non‐responders isimportant, not only because anti‐TNF treatment elevates the risk of infections,but also because of the cost of treatment. A proof‐of‐concept study to investigatetranscriptional effects of anti‐TNF treatment demonstrated that differencesbetween response groups could be identified and that these differences revealedbiological themes related to inflammatory disease.A subsequent study was therefore initiated with a larger cohort of 62 patients toinvestigate gene expression patterns in the synovium prior to anti‐TNFtreatment. Here, the heterogeneity was even more pronounced, thetranscriptional patterns were confounded by the presence of synovial aggregatesand only a weak therapy‐correlated signature was detected. The presence oflymphocyte aggregates was found to correlate to response to therapy, which isconsistent with previous findings indicating a higher level of inflammation ingood responding patients.Periodontitis is an inflammatory disease with many similarities to RA. Both areincurable chronic auto‐immune diseases, characterized by tissue destructionwith common genetic associations. Individuals with RA are at higher risk ofaccumulating significant periodontal problems than the general population. PGE2(prostaglandin E2) is known to stimulate inflammation and bone resorption inperiodontitis. In further studies, microarrays were applied in a time seriesdesign on human gingival fibroblats to explore the signal transduction pathwayscontrolling TNF‐α induced PGE2 synthesis in order to identify novel therapeutictargets. The JNK and NF‐kb pathways were identified as being differentiallyaffected by TNF‐a treatment. The transcriptional patterns were further verifiedusing antibodies against phosphorylated JNK/NF‐kb molecules and specificinhibitors of the JNK and NF‐kb signaling cascades.
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