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Table 2 Results of statistical analysis of resistance contributed by different sets

From: Characterization of two HIV-1 infectors during initial antiretroviral treatment, and the emergence of phenotypic resistance in reverse transcriptase-associated mutation patterns

Name of drug

Background mutations factora

P (Single seatb)

P (Interaction(181*221)c)

F value

P

181

221

T215Y/V179E

T215Y/K103N

EFV

93.10

<0.0001

0.0002 (F = 43.20)d

0.0598 (F = 4.80)d

0.3052 (F = 1.20)d

0.0003 (38.12)e

0.7404 (F = 0.12)e

0.0736 (F = 4.24)e

AZT

1.79

0.1968

<.0001 (F = 25.84)

0.018 (F = 6.69)

0.2270 (F = 1.56)f

3TC

7.19

0.0148

0.0010 (F = 14.94)

0.5356 (F = 0.40)

0.2513 (F = 1.40)

d4T

4.99

0.0376

0.0719 (F = 4.99)

0.4400 (F = 0.62)

0.2269 (F = 1.56)

  1. aAnalyze if it is statistical significance between the impact of T215Y/V179E and T215Y/K103N on the IC50 of different drugs. Here, T215Y/V179E and T215Y/K103N are the background mutations factors
  2. bAnalyze the impact of single mutations (Y181C or H221Y) on the IC50 of different drugs
  3. cAnalysis of the interaction between 181 and 221 on the different background mutations factors. Due to the difference is significant of impact of T215Y/V179E and T215Y/K103N on EFV (F = 93.10, P < 0.0001)
  4. dand eanalyze the interaction between 181 and 221 on the background of T215Y/V179E and T215Y/K103N respectively. f is the P of the interaction between 181 and 221 ignoring the background mutations on different drugs
  5. P ≤ 0.01 was considered to be statistically significant