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203 lines
6.8 KiB
Markdown
203 lines
6.8 KiB
Markdown
# Calculate the Mean AMR Distance
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Calculates a normalised mean for antimicrobial resistance between
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multiple observations, to help to identify similar isolates without
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comparing antibiograms by hand.
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## Usage
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``` r
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mean_amr_distance(x, ...)
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# S3 method for class 'sir'
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mean_amr_distance(x, ..., combine_SI = TRUE)
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# S3 method for class 'data.frame'
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mean_amr_distance(x, ..., combine_SI = TRUE)
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amr_distance_from_row(amr_distance, row)
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```
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## Arguments
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- x:
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A vector of class [sir](https://amr-for-r.org/reference/as.sir.md),
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[mic](https://amr-for-r.org/reference/as.mic.md) or
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[disk](https://amr-for-r.org/reference/as.disk.md), or a
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[data.frame](https://rdrr.io/r/base/data.frame.html) containing
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columns of any of these classes.
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- ...:
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Variables to select. Supports [tidyselect
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language](https://tidyselect.r-lib.org/reference/starts_with.html)
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such as `where(is.mic)`, `starts_with(...)`, or `column1:column4`, and
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can thus also be [antimicrobial
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selectors](https://amr-for-r.org/reference/antimicrobial_selectors.md).
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- combine_SI:
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A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
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all values of S, SDD, and I must be merged into one, so the input only
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consists of S+I vs. R (susceptible vs. resistant) - the default is
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`TRUE`.
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- amr_distance:
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The outcome of `mean_amr_distance()`.
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- row:
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An index, such as a row number.
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## Details
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The mean AMR distance is effectively [the
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Z-score](https://en.wikipedia.org/wiki/Standard_score); a normalised
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numeric value to compare AMR test results which can help to identify
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similar isolates, without comparing antibiograms by hand.
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MIC values (see [`as.mic()`](https://amr-for-r.org/reference/as.mic.md))
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are transformed with [`log2()`](https://rdrr.io/r/base/Log.html) first;
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their distance is thus calculated as
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`(log2(x) - mean(log2(x))) / sd(log2(x))`.
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SIR values (see [`as.sir()`](https://amr-for-r.org/reference/as.sir.md))
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are transformed using `"S"` = 1, `"I"` = 2, and `"R"` = 3. If
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`combine_SI` is `TRUE` (default), the `"I"` will be considered to be 1.
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For data sets, the mean AMR distance will be calculated per column,
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after which the mean per row will be returned, see *Examples*.
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Use `amr_distance_from_row()` to subtract distances from the distance of
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one row, see *Examples*.
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## Interpretation
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Isolates with distances less than 0.01 difference from each other should
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be considered similar. Differences lower than 0.025 should be considered
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suspicious.
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## Examples
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``` r
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sir <- random_sir(10)
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sir
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#> Class <sir>
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#> [1] S R I R R R R I I I
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mean_amr_distance(sir)
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#> [1] -0.9486833 0.9486833 -0.9486833 0.9486833 0.9486833 0.9486833
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#> [7] 0.9486833 -0.9486833 -0.9486833 -0.9486833
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mic <- random_mic(10)
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mic
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#> Class <mic>
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#> [1] 0.032 0.064 0.125 0.5 0.016 0.008 <=0.0005 0.032
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#> [9] 0.5 1
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mean_amr_distance(mic)
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#> [1] -0.20876566 0.09541835 0.38919449 0.99756251 -0.51294967 -0.81713368
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#> [7] -2.03386972 -0.20876566 0.99756251 1.30174652
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# equal to the Z-score of their log2:
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(log2(mic) - mean(log2(mic))) / sd(log2(mic))
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#> [1] -0.20876566 0.09541835 0.38919449 0.99756251 -0.51294967 -0.81713368
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#> [7] -2.03386972 -0.20876566 0.99756251 1.30174652
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disk <- random_disk(10)
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disk
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#> Class <disk>
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#> [1] 48 45 48 40 44 9 39 49 39 29
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mean_amr_distance(disk)
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#> [1] 0.74202711 0.49468474 0.74202711 0.08244746 0.41223728 -2.47342369
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#> [7] 0.00000000 0.82447456 0.00000000 -0.82447456
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y <- data.frame(
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id = LETTERS[1:10],
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amox = random_sir(10, ab = "amox", mo = "Escherichia coli"),
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cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
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gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
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tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
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)
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y
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#> id amox cipr gent tobr
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#> 1 A R 26 4 4
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#> 2 B R 28 8 1
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#> 3 C R 31 8 1
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#> 4 D R 30 4 2
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#> 5 E I 32 8 2
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#> 6 F S 28 32 2
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#> 7 G S 33 <=2 >=8
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#> 8 H I 24 <=2 4
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#> 9 I S 20 <=2 1
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#> 10 J R 19 16 2
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mean_amr_distance(y)
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#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent", and
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#> "tobr"
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#> [1] 0.31430391 0.09942875 0.25436185 0.26948082 0.08306514 0.24576214
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#> [7] 0.26823618 -0.44796371 -1.15734233 0.07066724
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y$amr_distance <- mean_amr_distance(y, is.mic(y))
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#> ℹ Calculating mean AMR distance based on columns "gent" and "tobr"
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y[order(y$amr_distance), ]
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#> id amox cipr gent tobr amr_distance
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#> 9 I S 20 <=2 1 -1.1069930
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#> 2 B R 28 8 1 -0.3684440
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#> 3 C R 31 8 1 -0.3684440
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#> 4 D R 30 4 2 -0.2349174
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#> 8 H I 24 <=2 4 -0.1013907
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#> 5 E I 32 8 2 0.1343571
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#> 1 A R 26 4 4 0.2678838
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#> 7 G S 33 <=2 >=8 0.4014105
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#> 10 J R 19 16 2 0.5036316
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#> 6 F S 28 32 2 0.8729061
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if (require("dplyr")) {
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y %>%
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mutate(
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amr_distance = mean_amr_distance(y),
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check_id_C = amr_distance_from_row(amr_distance, id == "C")
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) %>%
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arrange(check_id_C)
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}
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#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent", and
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#> "tobr"
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#> id amox cipr gent tobr amr_distance check_id_C
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#> 1 C R 31 8 1 0.25436185 0.000000000
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#> 2 F S 28 32 2 0.24576214 0.008599711
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#> 3 G S 33 <=2 >=8 0.26823618 0.013874329
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#> 4 D R 30 4 2 0.26948082 0.015118966
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#> 5 A R 26 4 4 0.31430391 0.059942062
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#> 6 B R 28 8 1 0.09942875 0.154933106
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#> 7 E I 32 8 2 0.08306514 0.171296709
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#> 8 J R 19 16 2 0.07066724 0.183694618
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#> 9 H I 24 <=2 4 -0.44796371 0.702325561
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#> 10 I S 20 <=2 1 -1.15734233 1.411704178
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if (require("dplyr")) {
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# support for groups
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example_isolates %>%
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filter(mo_genus() == "Enterococcus" & mo_species() != "") %>%
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select(mo, TCY, carbapenems()) %>%
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group_by(mo) %>%
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mutate(dist = mean_amr_distance(.)) %>%
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arrange(mo, dist)
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}
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#> ℹ Using column mo as input for `mo_genus()`
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#> ℹ Using column mo as input for `mo_species()`
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#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
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#> ℹ Calculating mean AMR distance based on columns "TCY", "IPM", and "MEM"
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#> # A tibble: 63 × 5
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#> # Groups: mo [4]
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#> mo TCY IPM MEM dist
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#> <mo> <sir> <sir> <sir> <dbl>
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#> 1 B_ENTRC_AVIM S S NA 0
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#> 2 B_ENTRC_AVIM S S NA 0
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#> 3 B_ENTRC_CSSL NA S NA NA
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#> 4 B_ENTRC_FACM S S NA -2.66
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#> 5 B_ENTRC_FACM S R R -0.423
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#> 6 B_ENTRC_FACM S R R -0.423
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#> 7 B_ENTRC_FACM NA R R 0.224
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#> 8 B_ENTRC_FACM NA R R 0.224
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#> 9 B_ENTRC_FACM NA R R 0.224
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#> 10 B_ENTRC_FACM NA R R 0.224
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#> # ℹ 53 more rows
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```
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