Figure 5 - Recovery From Footshock

Published Image

Figure 5. CFA-priming enhances mechanical sensitivity induced by electrical footshock in the previously injured and the contralateral hind paw in males (and not females). (A) Timeline of experimental testing (B) Footshock was simultaneously delivered to the site of the previous injury and the contralateral hindpaw. CFA-primed male mice exhibited enhanced mechanical sensitivity after footshock in both the left (C, D) and the right (E, F) hind paws relative to mice that had undergone fear conditioning but had not been subjected to a previous injury. There was no effect of CFA-priming on shock-induced mechanical sensitivity among female mice (G-J). Data expressed as mean +/- SEM. \(***\) Indicates a between-group difference where p < 0.05 and # indicates a within-subject difference from baseline where p < 0.05.

Statistics

Left Paws

L_Male$Sex <- "Male"
L_Female$Sex <- "Female"

# Select the left paws
left_paws <- rbind(L_Male,L_Female)

# Switch to long form
a <- left_paws %>%
  melt(id.vars=c("ID","CFA","Sex"))

# Run 3-way ANOVA: Sex X CFA X Day of testing (VF)
b <- anova_test(data=a,dv=value,between=c(Sex,CFA),within=variable,wid=ID)
knitr::kable(get_anova_table(b))

Effect	DFn	DFd	F	p	p<.05	ges
Sex	1	27	0.251	0.6200000		0.002
CFA	1	27	22.366	0.0000631	*	0.177
variable	4	108	36.017	0.0000000	*	0.497
Sex:CFA	1	27	29.188	0.0000103	*	0.219
Sex:variable	4	108	12.032	0.0000000	*	0.248
CFA:variable	4	108	9.054	0.0000024	*	0.199
Sex:CFA:variable	4	108	5.894	0.0002500	*	0.139

# Run both sets of follow ups: 

## Effect of CFA on each day of testing split by sex
b <- a %>%
  group_by(Sex,variable) %>%
  pairwise_t_test(value~CFA)

tt(b)

Sex	variable	.y.	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif
Female	Baseline	value	Naive	CFA	8	8	0.96400000	ns	0.96400000	ns
Female	3 hr	value	Naive	CFA	8	8	0.82300000	ns	0.82300000	ns
Female	24 hr	value	Naive	CFA	8	8	0.32200000	ns	0.32200000	ns
Female	3 days	value	Naive	CFA	8	8	0.14700000	ns	0.14700000	ns
Female	7 days	value	Naive	CFA	8	8	0.25400000	ns	0.25400000	ns
Male	Baseline	value	Naive	CFA	7	8	0.82100000	ns	0.82100000	ns
Male	3 hr	value	Naive	CFA	7	8	0.00000518	****	0.00000518	****
Male	24 hr	value	Naive	CFA	7	8	0.00002210	****	0.00002210	****
Male	3 days	value	Naive	CFA	7	8	0.01170000	*	0.01170000	*
Male	7 days	value	Naive	CFA	7	8	0.98500000	ns	0.98500000	ns

## Effect of Sex on each day of testing split by CFA history
c <- a %>%
  group_by(CFA,variable) %>%
  pairwise_t_test(value~Sex)

tt(c)

CFA	variable	.y.	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif
Naive	Baseline	value	Female	Male	8	7	0.1470000	ns	0.1470000	ns
Naive	3 hr	value	Female	Male	8	7	0.2300000	ns	0.2300000	ns
Naive	24 hr	value	Female	Male	8	7	0.9360000	ns	0.9360000	ns
Naive	3 days	value	Female	Male	8	7	0.0004790	***	0.0004790	***
Naive	7 days	value	Female	Male	8	7	0.3520000	ns	0.3520000	ns
CFA	Baseline	value	Female	Male	8	8	0.2360000	ns	0.2360000	ns
CFA	3 hr	value	Female	Male	8	8	0.0000190	****	0.0000190	****
CFA	24 hr	value	Female	Male	8	8	0.0000155	****	0.0000155	****
CFA	3 days	value	Female	Male	8	8	0.9980000	ns	0.9980000	ns
CFA	7 days	value	Female	Male	8	8	0.9330000	ns	0.9330000	ns

## Effect of DAY within each group
d <- a %>%
  group_by(CFA,Sex) %>%
  pairwise_t_test(value~variable,p.adjust.method = "bonferroni")

tt(d)

CFA	Sex	.y.	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif
Naive	Female	value	Baseline	3 hr	8	8	0.00468000000000	**	0.0468000000000	*
Naive	Female	value	Baseline	24 hr	8	8	0.05740000000000	ns	0.5740000000000	ns
Naive	Female	value	3 hr	24 hr	8	8	0.29800000000000	ns	1.0000000000000	ns
Naive	Female	value	Baseline	3 days	8	8	0.00001600000000	****	0.0001600000000	***
Naive	Female	value	3 hr	3 days	8	8	0.05580000000000	ns	0.5580000000000	ns
Naive	Female	value	24 hr	3 days	8	8	0.00452000000000	**	0.0452000000000	*
Naive	Female	value	Baseline	7 days	8	8	0.42600000000000	ns	1.0000000000000	ns
Naive	Female	value	3 hr	7 days	8	8	0.03320000000000	*	0.3320000000000	ns
Naive	Female	value	24 hr	7 days	8	8	0.25400000000000	ns	1.0000000000000	ns
Naive	Female	value	3 days	7 days	8	8	0.00017700000000	***	0.0017700000000	**
Naive	Male	value	Baseline	3 hr	7	7	0.02860000000000	*	0.2860000000000	ns
Naive	Male	value	Baseline	24 hr	7	7	0.00662000000000	**	0.0662000000000	ns
Naive	Male	value	3 hr	24 hr	7	7	0.54100000000000	ns	1.0000000000000	ns
Naive	Male	value	Baseline	3 days	7	7	0.44400000000000	ns	1.0000000000000	ns
Naive	Male	value	3 hr	3 days	7	7	0.13800000000000	ns	1.0000000000000	ns
Naive	Male	value	24 hr	3 days	7	7	0.04040000000000	*	0.4040000000000	ns
Naive	Male	value	Baseline	7 days	7	7	0.17500000000000	ns	1.0000000000000	ns
Naive	Male	value	3 hr	7 days	7	7	0.37000000000000	ns	1.0000000000000	ns
Naive	Male	value	24 hr	7 days	7	7	0.13700000000000	ns	1.0000000000000	ns
Naive	Male	value	3 days	7 days	7	7	0.54400000000000	ns	1.0000000000000	ns
CFA	Female	value	Baseline	3 hr	8	8	0.00645000000000	**	0.0645000000000	ns
CFA	Female	value	Baseline	24 hr	8	8	0.01930000000000	*	0.1930000000000	ns
CFA	Female	value	3 hr	24 hr	8	8	0.65900000000000	ns	1.0000000000000	ns
CFA	Female	value	Baseline	3 days	8	8	0.00735000000000	**	0.0735000000000	ns
CFA	Female	value	3 hr	3 days	8	8	0.95900000000000	ns	1.0000000000000	ns
CFA	Female	value	24 hr	3 days	8	8	0.69600000000000	ns	1.0000000000000	ns
CFA	Female	value	Baseline	7 days	8	8	0.90100000000000	ns	1.0000000000000	ns
CFA	Female	value	3 hr	7 days	8	8	0.00467000000000	**	0.0467000000000	*
CFA	Female	value	24 hr	7 days	8	8	0.01430000000000	*	0.1430000000000	ns
CFA	Female	value	3 days	7 days	8	8	0.00534000000000	**	0.0534000000000	ns
CFA	Male	value	Baseline	3 hr	8	8	0.00000000000326	****	0.0000000000326	****
CFA	Male	value	Baseline	24 hr	8	8	0.00000000010800	****	0.0000000010800	****
CFA	Male	value	3 hr	24 hr	8	8	0.19600000000000	ns	1.0000000000000	ns
CFA	Male	value	Baseline	3 days	8	8	0.00027300000000	***	0.0027300000000	**
CFA	Male	value	3 hr	3 days	8	8	0.00000029100000	****	0.0000029100000	****
CFA	Male	value	24 hr	3 days	8	8	0.00001580000000	****	0.0001580000000	***
CFA	Male	value	Baseline	7 days	8	8	0.19600000000000	ns	1.0000000000000	ns
CFA	Male	value	3 hr	7 days	8	8	0.00000000010800	****	0.0000000010800	****
CFA	Male	value	24 hr	7 days	8	8	0.00000000444000	****	0.0000000444000	****
CFA	Male	value	3 days	7 days	8	8	0.00989000000000	**	0.0989000000000	ns

Right Paws

R_Male$Sex <- "Male"
R_Female$Sex <- "Female"

# Select the right paws
right_paws <- rbind(R_Male,R_Female)

# Switch to long form
a <- right_paws %>%
  melt(id.vars=c("ID","CFA","Sex"))

# Run 3-way ANOVA: Sex X CFA X Day of testing (VF)
b <- anova_test(data=a,dv=value,between=c(Sex,CFA),within=variable,wid=ID)
knitr::kable(get_anova_table(b))

Effect	DFn	DFd	F	p	p<.05	ges
Sex	1	27	1.484	0.2340000		0.014
CFA	1	27	28.665	0.0000118	*	0.213
variable	4	108	24.583	0.0000000	*	0.404
Sex:CFA	1	27	32.973	0.0000042	*	0.238
Sex:variable	4	108	8.449	0.0000057	*	0.189
CFA:variable	4	108	6.520	0.0000972	*	0.152
Sex:CFA:variable	4	108	7.949	0.0000118	*	0.180

# Run both sets of follow ups: 

## Effect of CFA on each day of testing split by sex
b <- a %>%
  group_by(Sex,variable) %>%
  pairwise_t_test(value~CFA)

tt(b)

Sex	variable	.y.	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif
Female	Baseline	value	Naive	CFA	8	8	0.54900000	ns	0.54900000	ns
Female	3 hr	value	Naive	CFA	8	8	0.88500000	ns	0.88500000	ns
Female	24 hr	value	Naive	CFA	8	8	0.39700000	ns	0.39700000	ns
Female	3 days	value	Naive	CFA	8	8	0.36000000	ns	0.36000000	ns
Female	7 days	value	Naive	CFA	8	8	0.74000000	ns	0.74000000	ns
Male	Baseline	value	Naive	CFA	7	8	0.96300000	ns	0.96300000	ns
Male	3 hr	value	Naive	CFA	7	8	0.00000193	****	0.00000193	****
Male	24 hr	value	Naive	CFA	7	8	0.00000781	****	0.00000781	****
Male	3 days	value	Naive	CFA	7	8	0.02970000	*	0.02970000	*
Male	7 days	value	Naive	CFA	7	8	0.40400000	ns	0.40400000	ns

## Effect of Sex on each day of testing split by CFA history
c <- a %>%
  group_by(CFA,variable) %>%
  pairwise_t_test(value~Sex)

tt(c)

CFA	variable	.y.	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif
Naive	Baseline	value	Female	Male	8	7	0.8090000000	ns	0.8090000000	ns
Naive	3 hr	value	Female	Male	8	7	0.2030000000	ns	0.2030000000	ns
Naive	24 hr	value	Female	Male	8	7	0.1420000000	ns	0.1420000000	ns
Naive	3 days	value	Female	Male	8	7	0.0031500000	**	0.0031500000	**
Naive	7 days	value	Female	Male	8	7	0.2580000000	ns	0.2580000000	ns
CFA	Baseline	value	Female	Male	8	8	0.3350000000	ns	0.3350000000	ns
CFA	3 hr	value	Female	Male	8	8	0.0000000132	****	0.0000000132	****
CFA	24 hr	value	Female	Male	8	8	0.0000001120	****	0.0000001120	****
CFA	3 days	value	Female	Male	8	8	0.9250000000	ns	0.9250000000	ns
CFA	7 days	value	Female	Male	8	8	0.7800000000	ns	0.7800000000	ns

## Effect of DAY within each group
d <- a %>%
  group_by(CFA,Sex) %>%
  pairwise_t_test(value~variable,p.adjust.method = "bonferroni")

tt(d)

CFA	Sex	.y.	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif
Naive	Female	value	Baseline	3 hr	8	8	0.0181000000	*	0.181000000	ns
Naive	Female	value	Baseline	24 hr	8	8	0.2670000000	ns	1.000000000	ns
Naive	Female	value	3 hr	24 hr	8	8	0.1850000000	ns	1.000000000	ns
Naive	Female	value	Baseline	3 days	8	8	0.0040400000	**	0.040400000	*
Naive	Female	value	3 hr	3 days	8	8	0.5540000000	ns	1.000000000	ns
Naive	Female	value	24 hr	3 days	8	8	0.0592000000	ns	0.592000000	ns
Naive	Female	value	Baseline	7 days	8	8	0.8510000000	ns	1.000000000	ns
Naive	Female	value	3 hr	7 days	8	8	0.0115000000	*	0.115000000	ns
Naive	Female	value	24 hr	7 days	8	8	0.1970000000	ns	1.000000000	ns
Naive	Female	value	3 days	7 days	8	8	0.0024400000	**	0.024400000	*
Naive	Male	value	Baseline	3 hr	7	7	0.2450000000	ns	1.000000000	ns
Naive	Male	value	Baseline	24 hr	7	7	0.8400000000	ns	1.000000000	ns
Naive	Male	value	3 hr	24 hr	7	7	0.1750000000	ns	1.000000000	ns
Naive	Male	value	Baseline	3 days	7	7	0.7780000000	ns	1.000000000	ns
Naive	Male	value	3 hr	3 days	7	7	0.1520000000	ns	1.000000000	ns
Naive	Male	value	24 hr	3 days	7	7	0.9360000000	ns	1.000000000	ns
Naive	Male	value	Baseline	7 days	7	7	0.3020000000	ns	1.000000000	ns
Naive	Male	value	3 hr	7 days	7	7	0.0330000000	*	0.330000000	ns
Naive	Male	value	24 hr	7 days	7	7	0.4040000000	ns	1.000000000	ns
Naive	Male	value	3 days	7 days	7	7	0.4500000000	ns	1.000000000	ns
CFA	Female	value	Baseline	3 hr	8	8	0.0076700000	**	0.076700000	ns
CFA	Female	value	Baseline	24 hr	8	8	0.9270000000	ns	1.000000000	ns
CFA	Female	value	3 hr	24 hr	8	8	0.0060600000	**	0.060600000	ns
CFA	Female	value	Baseline	3 days	8	8	0.0146000000	*	0.146000000	ns
CFA	Female	value	3 hr	3 days	8	8	0.7970000000	ns	1.000000000	ns
CFA	Female	value	24 hr	3 days	8	8	0.0116000000	*	0.116000000	ns
CFA	Female	value	Baseline	7 days	8	8	0.0696000000	ns	0.696000000	ns
CFA	Female	value	3 hr	7 days	8	8	0.0000394000	****	0.000394000	***
CFA	Female	value	24 hr	7 days	8	8	0.0838000000	ns	0.838000000	ns
CFA	Female	value	3 days	7 days	8	8	0.0000854000	****	0.000854000	***
CFA	Male	value	Baseline	3 hr	8	8	0.0000000112	****	0.000000112	****
CFA	Male	value	Baseline	24 hr	8	8	0.0000001390	****	0.000001390	****
CFA	Male	value	3 hr	24 hr	8	8	0.4010000000	ns	1.000000000	ns
CFA	Male	value	Baseline	3 days	8	8	0.0445000000	*	0.445000000	ns
CFA	Male	value	3 hr	3 days	8	8	0.0000058600	****	0.000058600	****
CFA	Male	value	24 hr	3 days	8	8	0.0000757000	****	0.000757000	***
CFA	Male	value	Baseline	7 days	8	8	0.9120000000	ns	1.000000000	ns
CFA	Male	value	3 hr	7 days	8	8	0.0000000155	****	0.000000155	****
CFA	Male	value	24 hr	7 days	8	8	0.0000001950	****	0.000001950	****
CFA	Male	value	3 days	7 days	8	8	0.0565000000	ns	0.565000000	ns