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202 lines
6.8 KiB
Markdown
202 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] I I R I R S S S I S
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mean_amr_distance(sir)
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#> [1] -0.4743416 -0.4743416 1.8973666 -0.4743416 1.8973666 -0.4743416
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#> [7] -0.4743416 -0.4743416 -0.4743416 -0.4743416
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mic <- random_mic(10)
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mic
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#> Class 'mic'
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#> [1] 0.004 2 0.002 0.0001 0.004 0.002 >=4 0.0002 0.032 0.004
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mean_amr_distance(mic)
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#> [1] -0.2047915 1.5799751 -0.4038557 -1.2641969 -0.2047915 -0.4038557
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#> [7] 1.7790393 -1.0651327 0.3924011 -0.2047915
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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] -0.2047915 1.5799751 -0.4038557 -1.2641969 -0.2047915 -0.4038557
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#> [7] 1.7790393 -1.0651327 0.3924011 -0.2047915
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disk <- random_disk(10)
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disk
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#> Class 'disk'
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#> [1] 43 12 28 32 22 31 35 25 43 35
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mean_amr_distance(disk)
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#> [1] 1.30998909 -1.96498364 -0.27467513 0.14790199 -0.90854082 0.04225771
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#> [7] 0.46483484 -0.59160798 1.30998909 0.46483484
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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 S 31 2 >=16
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#> 2 B S 27 <=1 8
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#> 3 C R 25 2 4
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#> 4 D R 25 <=1 2
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#> 5 E I 31 <=1 2
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#> 6 F S 32 <=1 8
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#> 7 G I 29 2 2
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#> 8 H S 18 <=1 4
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#> 9 I S 28 <=1 4
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#> 10 J R 17 <=1 2
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mean_amr_distance(y)
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#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
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#> and "tobr"
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#> [1] 0.90606144 -0.03989270 0.66241774 -0.09230226 -0.32300020 0.19914999
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#> [7] 0.09893189 -0.70734036 -0.22925499 -0.47477055
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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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#> 4 D R 25 <=1 2 -0.7848712
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#> 5 E I 31 <=1 2 -0.7848712
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#> 10 J R 17 <=1 2 -0.7848712
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#> 8 H S 18 <=1 4 -0.3105295
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#> 9 I S 28 <=1 4 -0.3105295
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#> 2 B S 27 <=1 8 0.1638121
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#> 6 F S 32 <=1 8 0.1638121
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#> 7 G I 29 2 2 0.2502272
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#> 3 C R 25 2 4 0.7245688
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#> 1 A S 31 2 >=16 1.6732521
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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",
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#> and "tobr"
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#> id amox cipr gent tobr amr_distance check_id_C
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#> 1 C R 25 2 4 0.66241774 0.0000000
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#> 2 A S 31 2 >=16 0.90606144 0.2436437
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#> 3 F S 32 <=1 8 0.19914999 0.4632678
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#> 4 G I 29 2 2 0.09893189 0.5634858
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#> 5 B S 27 <=1 8 -0.03989270 0.7023104
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#> 6 D R 25 <=1 2 -0.09230226 0.7547200
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#> 7 I S 28 <=1 4 -0.22925499 0.8916727
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#> 8 E I 31 <=1 2 -0.32300020 0.9854179
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#> 9 J R 17 <=1 2 -0.47477055 1.1371883
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#> 10 H S 18 <=1 4 -0.70734036 1.3697581
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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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