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Example sentences for: inter-patient
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Epidemiologically, HLA genotype partially accounts for inter-patient variation in disease severity or progression rates for such long-term viral infections as HIV, human T-cell lymphotropic virus, type 1 (HTLV-1), and hepatitis B and C [ 5 6 7 8 9 ] . Such associations can lead to mechanistic studies to test hypotheses about the immunologic basis of these associations [ 10 11 ] and could, in principle, be used to help predict individual prognoses [ 12 13 ] . At least one clinical study has shown that HLA genotype affects the immune response to a candidate HIV vaccine [ 14 ] . Vaccine designers have begun to take account of the HLA genotypes of potential recipients in choosing antigens for inclusion in a vaccine [ 15 ] , and it has been suggested that HLA genotype be considered in designing samples for inclusion in epidemiologic studies of anti-HIV immune response [ 12 ] . In evolutionary biology, the possibility that HLA heterozygotes are more resistant to infectious diseases is the basis for a leading hypothesis to explain the unparalleled diversity of HLA genes (and MHC genes in other vertebrates) and the maintenance of this diversity over long periods of evolutionary time [ 16 ] .
We have shown that mixing of patient samples effectively normalizes much of the intra- and inter-patient noise, while still identifying the majority of the most significant gene expression changes that would have been detected by larger numbers of individual patient profiles.
To analyze the impact of intra-patient variability (tissue heterogeneity), inter-patient variability (polymorphic noise in outbred populations), and the effect of sample mixing on the sensitivity of detection of gene expression differences between patient groups, we conducted a series of individual and mixed profiling (Table 1).
We have recently reported generation of expression profiling results using mixed patient samples [ 23 ] . Our hypothesis was that mixing of RNA samples from multiple regions of muscle biopsies, and from multiple patients matched for most variables (disease, age, sex), would effectively normalize both intra-patient variability (tissue heterogeneity), and inter-patient variability (SNP noise; e.g. normal human polymorphic variation unrelated to the primary defect).
Specifically, we studied the correlation coefficients of profiles considering the following variables: 1. variation due to probe production (same RNA); 2. variation due to the microarray itself (same cRNA on different GeneChips); 3. tissue heterogeneity (different regions of the same muscle biopsy); 4. inter-patient variability (SNP noise); 5. diagnosis (underlying pathological variable); and 6. patient age.
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