U.S. patent application number 17/533510 was filed with the patent office on 2022-07-21 for methods and devices for improved signal detection from biological samples.
The applicant listed for this patent is Labrador Diagnostics LLC. Invention is credited to Toan Huynh.
Application Number | 20220228976 17/533510 |
Document ID | / |
Family ID | 1000006243753 |
Filed Date | 2022-07-21 |
United States Patent
Application |
20220228976 |
Kind Code |
A1 |
Huynh; Toan |
July 21, 2022 |
METHODS AND DEVICES FOR IMPROVED SIGNAL DETECTION FROM BIOLOGICAL
SAMPLES
Abstract
Methods and devices are provided to provide clean signals even
in the presence of spectral interference. At least some of these
methods can be applied for cases when interfering signals are to be
accounted for. These cases include, but are not limited to,
hemolysis detection, icterus detection, and assays. They can be
implemented in with data collected with spectrophotometers,
instruments that can collect absorbance values at the few
wavelengths of interest, and, in the case of the method based on
background subtraction, simple imaging setups with only two filters
(such as but not limited to narrow-band and wide-band) per
absorption peak of interest.
Inventors: |
Huynh; Toan; (Mountain View,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Labrador Diagnostics LLC |
Healdsburg |
CA |
US |
|
|
Family ID: |
1000006243753 |
Appl. No.: |
17/533510 |
Filed: |
November 23, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16527473 |
Jul 31, 2019 |
11187651 |
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17533510 |
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PCT/US18/16292 |
Jan 31, 2018 |
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16527473 |
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62519018 |
Jun 13, 2017 |
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62452949 |
Jan 31, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 21/33 20130101;
G01N 33/49 20130101 |
International
Class: |
G01N 21/33 20060101
G01N021/33; G01N 33/49 20060101 G01N033/49 |
Claims
1. A method for use with a biological sample, the method
comprising: using peak curvature to reduce interference in
detection of hemolysis in the biological sample.
2. (canceled)
3. A method for use with a biological sample, the method
comprising: using background subtraction in detection of hemolysis
and icterus in the biological sample.
4. (canceled)
5. A method for use with a biological sample, the method
comprising: acquiring background-subtracted signals using a
narrow-band optical filter and a wide-band optical filter; using
background-subtracted signals for quantification of hemolysis and
icterus in the biological sample.
6. The method of claim 5 further comprising: placing the
narrow-band optical filter and the wide-band optical filter in
regions of interest concurrently so that only one exposure is
needed to obtain the major signal, the background signal, and the
background-subtracted signal.
7. The method of claim 5 further comprising reducing signal
interference through calculation of background-subtracted
spectra.
8-14. (canceled)
Description
BACKGROUND
[0001] Hemolysis, icterus, and lipemia are indicated by different
features in the absorption spectra of some detection systems and
may interfere with one another in a negative manner. The
detection/quantification of each of these interference types alone
is straightforward, but is complicated when multiple types of
interference are present. For example, hemolysis is caused by the
lysis of blood cells before the separation to obtain plasma/serum,
and is indicated by the concentration of hemoglobin (Hb) from red
blood cells. The Hb concentration can be estimated by the
absorbance signals near peaks at 340-440 nm and 540-580 nm. Icterus
is the interference caused by the presence of bilirubin, which
absorbs light with a peak around 460 nm and does not significantly
interfere with hemolysis signals in the 540-580 nm range. Lipemia
is the interference by the presence of lipid particles, which
scatter light and lead to the apparent absorption across a wide
range of the UV-Vis spectrum (400-800+nm). Due to the close
proximity of their peak signal wavelengths, the hemolysis and
icterus peaks partially overlap with each other, and the lipemia
absorption increase affects the whole spectra. Example commercial
systems utilize absorbance values at wavelengths from 340 nm to 800
nm and complicated calibration procedures to account for this
issue.
[0002] Known techniques have various drawbacks and are overly
cumbersome and costly in their implementation.
INCORPORATION BY REFERENCE
[0003] All publications, patents, and patent applications mentioned
in this specification are herein incorporated by reference to the
same extent as if each individual publication, patent, or patent
application was specifically and individually indicated to be
incorporated by reference.
COPYRIGHT
[0004] This document contains material subject to copyright
protection. The copyright owner (Applicant herein) has no objection
to facsimile reproduction of the patent documents and disclosures,
as they appear in the US Patent and Trademark Office patent file or
records, but otherwise reserves all copyright rights whatsoever.
The following notice shall apply: Copyright 2016-2018 Theranos,
Inc.
SUMMARY
[0005] The disadvantages associated with the prior art are overcome
by embodiments described herein.
[0006] In one embodiment herein, it is desirable to detect/quantify
hemolysis, icterus, and lipemia (common types of interference) in
plasma/serum clinical samples. Some methods utilize absorbance
values of the samples at multiple wavelengths to account for the
cross signals. They use calibration sample sets that span possible
ranges of these interference types and instruments that can measure
absorbance values across the whole UV-vis wavelength range.
[0007] In one embodiment, new methods are described herein to
detect/quantify hemolysis and/or icterus (two out of the three
interference types) that are based on absorbance values at narrower
wavelength ranges, use fewer samples for calibration, and/or are
independent of the nature of the interference.
[0008] At least some embodiments of the new methods described here
aim to eliminate spectral interfering signals and obtain clean
final signals. With a first non-limiting example, the final signal
is the curvature at the peak of interest in the absorption
spectrum. With a second non-limiting example, the final signal,
termed background-subtracted signal, is the difference between the
raw (original/major) absorbance and the background absorbance in
the vicinity of the peak of interest.
[0009] In one embodiment, a method is provided for use with a
biological sample, the method comprising using peak curvature to
reduce interference in detection of hemolysis in the biological
sample. Optionally, the method comprises using peak curvature to
reduce interference in quantification of hemolysis in the
biological sample.
[0010] In another embodiment, a method is provided for use with a
biological sample, the method comprising using background
subtraction in the detection of hemolysis and icterus in the
biological sample. Optionally, the method comprises using
background subtraction in the quantification of hemolysis and
icterus in the biological sample.
[0011] In yet another embodiment, a method is provided for use with
a biological sample, the method comprising reducing signal
interference through calculation of background-subtracted
spectra.
[0012] In yet another embodiment, a method is provided for use with
a biological sample, the method comprising: acquiring
background-subtracted signals using a narrow-band optical filter
and a wide-band optical filter; and using background-subtracted
signals for quantification of hemolysis and icterus in the
biological sample. Optionally, the method comprises placing the
narrow-band optical filter and the wide-band optical filter in
regions of interest concurrently so that only one exposure is
needed to obtain the major signal, the background signal, and the
background-subtracted signal.
[0013] It should be understood that embodiments in this disclosure
may be adapted to have one or more of the features described below.
In one nonlimiting example, the sample may be plasma. Optionally,
in some embodiments, the sample may be serum. Optionally, in some
embodiments, any of the foregoing may be from capillary blood.
Optionally, in some embodiments, any of the foregoing may be from
venous whole blood. Optionally, in some embodiments, the sample may
be capillary blood. Optionally, in some embodiments, the sample may
be venous whole blood.
[0014] In one embodiment, the methods based on curvature
calculation and background subtraction can give clean signals even
in the presence of spectral interference. At least some of these
methods can be applied for cases when interfering signals are to be
accounted for. These cases include, but are not limited to,
hemolysis detection, icterus detection, and assays. They can be
implemented in with data collected with spectrophotometers,
instruments that can collect absorbance values at the few
wavelengths of interest, and, in the case of the method based on
background subtraction, simple imaging setups with only two filters
(such as but not limited to narrow-band and wide-band) per
absorption peak of interest.
[0015] By way of example and not limitation, the potential
applications of the methods described here include, but are not
limited to, the following:
[0016] Clinical or point-of-care use: hemolysis
detection/quantification, icterus detection/quantification, and/or
assays;
[0017] Research & development experiments: hemolysis
detection/quantification, icterus detection/quantification, and/or
assays;
[0018] At the sample collection sites: hemolysis
detection/quantification, icterus detection/quantification in
samples collected.
[0019] In at least one embodiment, the processed signals correlated
well with concentrations of hemoglobin and bilirubin, indicators of
hemolysis and icterus, respectively. Through iterations of randomly
splitting the samples for calibration and testing, the two new
methods performed as well as those used on conventional analyzers.
By way of non-limiting example, it was demonstrated that the two of
the embodiments of the methods can each lessen the application
requirements of 1) prior knowledge of the absorption spectra of
individual interferents, 2) calibration over a wide concentration
range for each interferent, and 3) the need for full-range
spectrophotometers spanning most of the ultraviolet/visible
spectrum. We also proposed a hardware setup to detect and quantify
hemolysis or icterus with a camera and two optical filters.
[0020] It should be understood that traditional methods utilize
absorbance values at multiple wavelengths to account for
potentially interfering signals from other interferents. They
require calibrations that span possible concentration ranges of
these interferents and instruments that can measure absorbance
values across the ultraviolet/visible wavelength range. This paper
describes two new methods to quantify and detect hemolysis and
icterus (two of the three interferents) that have fewer
requirements in development and implementation.
[0021] By way of non-limiting example, at least some embodiments of
the method involve calculating either the background-subtracted
signals or curvatures from spectral data. The advantages of these
new methods are three-fold: 1) the elimination of the need to know
beforehand how other interferents affect the detection and
quantification of the interferent being investigated, 2) fewer
samples required for calibration, and 3) fewer constraints on
hardware design (thanks to the narrower ranges of required
wavelengths).
[0022] In one embodiment, a method for sample processing which may
include one or more the techniques as described herein for handling
sample that may contain interferents. Optionally, a method is
provided comprising at least one technical feature described
herein. Optionally, a method is provided comprising at least one
technical feature from any of the prior features. Optionally, the
method comprises at least any two technical features from any of
the prior features. Optionally, a device is provided comprising at
least one technical feature from any of the prior features.
Optionally, the device comprises at least any two technical
features from any of the prior features. Optionally, the system is
provided comprising at least one technical feature from any of the
prior features. Optionally, the system comprises at least any two
technical features from any of the prior features.
[0023] This Summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This Summary is not intended to identify
key features or essential features of the claimed subject matter,
nor is it intended to be used to limit the scope of the claimed
subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] FIG. 1 shows the use of peak curvatures to reduce spectral
interference according to at least one embodiment herein.
[0025] FIG. 1A shows spectra of samples with different
concentrations of hemoglobin at low/high permutations of bilirubin
and triglycerides as described herein.
[0026] FIG. 1B shows spectra of samples with different
concentrations of bilirubin at low/high permutations of hemoglobin
and triglycerides as described herein.
[0027] FIG. 2 shows examples of uniform kernels and Gaussian
kernels for use with the embodiment herein.
[0028] FIG. 3 shows the use of background-subtracted spectra to
reduce spectral interference according to at least one embodiment
herein.
[0029] FIGS. 3A-3B show embodiments of methods of background
subtraction as described herein.
[0030] FIG. 3C shows an embodiment of curvature calculation as
described herein.
[0031] FIG. 3D shows the use of background subtraction and
curvature removing interference signals for the
detection/quantification of hemolysis (A) and icterus (B) according
to at least one embodiment herein.
[0032] FIG. 4 shows charts of absorbance relative to wavelength
according to at least one embodiment herein.
[0033] FIG. 5 show background-subtracted spectra used for the
quantification of hemolysis according to at least some embodiments
as described herein.
[0034] FIG. 6 show background-subtracted spectra used for the
quantification of icterus according to at least some embodiments as
described herein.
[0035] FIGS. 7A-7B show example results of using curvature
calculation to quantify hemolysis according to at least some
embodiments as described herein.
[0036] FIGS. 8A-8B show example results of using curvature
calculation to quantify icterus according to at least some
embodiments as described herein.
[0037] FIG. 9A shows a method of using photography to acquire
background-subtracted signals with a narrow band pass optical
filter and a wide band optical filter according to at least one
embodiment as described herein.
[0038] FIG. 9B shows acquisition of background-subtracted signals
using a narrow-band optical filter and a wide-band optical filter
according to at least one embodiment herein.
[0039] FIG. 10 shows that curvatures can be used to avoid
interference from hemolysis and lipemia in icterus
detection/quantification according to at least one embodiment
herein.
[0040] FIG. 11 shows the use of background subtraction and
curvature calculation in removing interference signals according to
at least one embodiment herein.
[0041] FIGS. 12-16 show embodiments of devices that may be
configured to include hardware for implementing one or more
embodiments of techniques as described herein.
DESCRIPTION OF THE SPECIFIC EMBODIMENTS
[0042] It is to be understood that both the foregoing general
description and the following detailed description are exemplary
and explanatory only and are not restrictive of the invention, as
claimed. It may be noted that, as used in the specification and the
appended claims, the singular forms "a", "an" and "the" include
plural referents unless the context clearly dictates otherwise.
Thus, for example, reference to "a material" may include mixtures
of materials, reference to "a compound" may include multiple
compounds, and the like. References cited herein are hereby
incorporated by reference in their entirety, except to the extent
that they conflict with teachings explicitly set forth in this
specification.
[0043] In this specification and in the claims which follow,
reference will be made to a number of terms which shall be defined
to have the following meanings:
[0044] "Optional" or "optionally" means that the subsequently
described circumstance may or may not occur, so that the
description includes instances where the circumstance occurs and
instances where it does not. For example, if a device optionally
contains a feature for a sample collection unit, this means that
the sample collection unit may or may not be present, and, thus,
the description includes both structures wherein a device possesses
the sample collection unit and structures wherein sample collection
unit is not present.
[0045] As used herein, the terms "substantial" means more than a
minimal or insignificant amount; and "substantially" means more
than a minimally or insignificantly. Thus, for example, the phrase
"substantially different", as used herein, denotes a sufficiently
high degree of difference between two numeric values such that one
of skill in the art would consider the difference between the two
values to be of statistical significance within the context of the
characteristic measured by said values. Thus, the difference
between two values that are substantially different from each other
is typically greater than about 10%, and may be greater than about
20%, preferably greater than about 30%, preferably greater than
about 40%, preferably greater than about 50% as a function of the
reference value or comparator value.
[0046] As used herein, a "sample" may be but is not limited to a
blood sample, or a portion of a blood sample, may be of any
suitable size or volume, and is preferably of small size or volume.
In some embodiments of the assays and methods disclosed herein,
measurements may be made using a small volume blood sample, or no
more than a small volume portion of a blood sample, where a small
volume comprises no more than about 5 mL; or comprises no more than
about 3 mL; or comprises no more than about 2 mL; or comprises no
more than about 1 mL; or comprises no more than about 500 .mu.L; or
comprises no more than about 250 .mu.L; or comprises no more than
about 100 .mu.L; or comprises no more than about 75 .mu.L; or
comprises no more than about 50 .mu.L; or comprises no more than
about 35 .mu.L; or comprises no more than about 25 .mu.L; or
comprises no more than about 20 .mu.L; or comprises no more than
about 15 .mu.L; or comprises no more than about 10 .mu.L; or
comprises no more than about 8 .mu.L; or comprises no more than
about 6 .mu.L; or comprises no more than about 5 .mu.L; or
comprises no more than about 4 .mu.L; or comprises no more than
about 3 .mu.L; or comprises no more than about 2 .mu.L; or
comprises no more than about 1 .mu.L; or comprises no more than
about 0.8 .mu.L; or comprises no more than about 0.5 .mu.L; or
comprises no more than about 0.3 .mu.L; or comprises no more than
about 0.2 .mu.L; or comprises no more than about 0.1 .mu.L; or
comprises no more than about 0.05 .mu.L; or comprises no more than
about 0.01 .mu.L.
[0047] As used herein, the term "point of service location" may
include locations where a subject may receive a service (e.g.
testing, monitoring, treatment, diagnosis, guidance, sample
collection, ID verification, medical services, non-medical
services, etc.), and may include, without limitation, a subject's
home, a subject's business, the location of a healthcare provider
(e.g., doctor), hospitals, emergency rooms, operating rooms,
clinics, health care professionals' offices, laboratories,
retailers [e.g. pharmacies (e.g., retail pharmacy, clinical
pharmacy, hospital pharmacy), drugstores, supermarkets, grocers,
etc.], transportation vehicles (e.g. car, boat, truck, bus,
airplane, motorcycle, ambulance, mobile unit, fire engine/truck,
emergency vehicle, law enforcement vehicle, police car, or other
vehicle configured to transport a subject from one point to
another, etc.), traveling medical care units, mobile units,
schools, day-care centers, security screening locations, combat
locations, health assisted living residences, government offices,
office buildings, tents, bodily fluid sample acquisition sites
(e.g. blood collection centers), sites at or near an entrance to a
location that a subject may wish to access, sites on or near a
device that a subject may wish to access (e.g., the location of a
computer if the subject wishes to access the computer), a location
where a sample processing device receives a sample, or any other
point of service location described elsewhere herein.
Using Peak Curvature to Avoid Interference in the
Detection/Quantification of Hemolysis
[0048] For at least one non-limiting example, it should be
understood that the shape of a specific absorption peak (e.g. the
415-nm hemoglobin peak) does not change if there is interference by
a nearby peak (e.g. the 460-nm bilirubin peak) or by an increase in
absorption across a wide range of wavelengths (e.g. in the case of
lipemia). To demonstrate this, spectra of multiple samples at
different levels hemolysis, icterus, and lipemia were acquired.
With each spectrum, at the 415-nm peak, the circle center {right
arrow over (M)} and the corresponding radius of curvature R were
determined by fitting to 5 data points at 405, 410, 415, 420, and
425 nm ({right arrow over (X)}.sub.i) using the cost function
specified in Equation 1. Other numbers of points (.gtoreq.3) can be
used for the fitting; and if only 3 data points (e.g. those at 410,
415, and 240 nm) are used, the center {right arrow over (M)} and
the radius R can be calculated exactly. The curvature of the
hemolysis peak at 415 nm, K, is the inverse of the radius of the
curvature, and is insensitive to icterus and lipemia. Therefore,
the curvature K is a quantity that can be independently and
reliably used for the detection/quantification of hemolysis in the
presence of icterus/lipemia. Other methods of calculating the
curvatures can also be used.
Cost=.SIGMA..sub.i=1.sup.i=5(.parallel.{right arrow over
(M)}-{right arrow over (X)}.sub.i.parallel.-R).sup.2 Equation 1
[0049] Referring now to FIG. 1, peak curvatures are used to reduce
spectral interference from icterus and lipemia in hemolysis
detection/quantification. The plots show absorption spectra near
the major hemoglobin peak (415 nm, dashed-line) of samples at
different hemolysis levels ([Hb] of 0-1000 mg/dL) at combinations
of low and high icterus and lipemia (total bilirubin=0.24 or 39.53
mg/dL; total glycerides=58 or 928 mg/dL). The blue shaded areas
indicate circles fitted to the sets of 5 points centered at the
415-nm peak using Equation 1 (see above). The circles appear to be
ellipses because of the different in scales of the absorbance and
the wavelength. Each curvature value is the inverse of the
corresponding radius.
[0050] Hemolysis, icterus, and lipemia are indicated by different
features in the absorption spectra that may interfere with one
another. Hemolysis is caused by the lysis of blood cells before the
cell/supernatant separation to obtain plasma or serum, and
hemolysis is quantified by the concentration of hemoglobin, which
absorbs at 340 mm to 440 nm and 540 nm to 580 nm (FIG. 1A). Icterus
is the interference caused by bilirubin, which absorbs light with a
broad peak around 460 nm that strongly interferes with the major
hemoglobin peak at 415 nm (FIG. 1B). Lipemia is the interference
caused by lipid particles, which scatter light and lead to an
apparent absorption across a wide range of the ultraviolet/visible
spectrum (400 nm to 800+nm). Due to their proximity, the hemoglobin
and bilirubin peaks partially overlap with each other, and the
apparent absorption by triglycerides affects the whole spectrum
(FIGS. 1-2). Many commercial clinical analyzers utilize absorbance
values at wavelengths from 340 nm to 800 nm and complicated
calibration procedures to account for this issue.
Background Subtraction in the Detection/Quantification of Hemolysis
and Icterus:
[0051] Calculation of Background-Subtracted Spectra
[0052] For at least one non-limiting example herein to avoid
interfering signals, true signals near or at spectral peaks of
interest can be obtained by way of background subtraction. In each
measurement of this non-limiting example, the background spectrum
is calculated by convoluting the raw spectrum (also known as
original spectrum and major spectrum) with a kernel, and the
background-subtracted spectrum is the difference between the raw
spectrum and the background. The types of kernels include, but are
not limited to, uniform distributions, Gaussian distributions, or
other suitable distributions. Other kernels can be adapted for use
with the embodiments herein.
[0053] In this embodiment, the size of a uniform kernel is defined
as the width of the range of positive density, while the size of a
Gaussian kernel is defined as the corresponding standard deviation
divided by {square root over ( 1/12)}, so that kernels of different
types but of the same size have the same standard deviation. The
convolution using a uniform kernel is equivalent to the application
of a mean filter; and the convolution using a Gaussian kernel is
equivalent to the application of a Gaussian filter (for example, a
low-pass filter). Other filters that are used for smoothing may be
used to calculate the background as well.
[0054] FIG. 2 shows examples of uniform kernels and Gaussian
kernels for use with the embodiment herein. The plots show the
probability density functions of the kernels of the specified types
(Gaussian or uniform) and sizes (2-20). The functions are centered
at 0. For each uniform kernel, the width of the non-zero range is
defined as the size. For each Gaussian kernel, the standard
deviation is defined as the size multiplied by {square root over (
1/12)} (to match the standard deviation of a uniform kernel of the
same size). Note that only values at integer x-positions are
plotted because the kernels are discretized to be convoluted with
discretized signals.
[0055] Background-Subtraction for Hemolysis
Detection/Quantification
[0056] In another non-limiting example, background-subtracted
spectra can be utilized for the quantification/detection of
hemolysis. This idea is demonstrated by the analysis of samples at
different levels of hemolysis ([Hb] of 0, 20, 60, 80, 180, 340, and
1000 mg/dL) at different combinations of low and high levels of
icterus (0.24 or 39.53 mg/dL total bilirubin) and lipemia (58 or
928 mg/dL total glycerides). Without the background subtraction
step, the raw absorption spectra contains unexpected signals from
icterus and lipemia, and the peaks at 415 nm are elevated
unexpectedly as seen in FIG. 3. Background-subtracted spectra with
different levels of icterus and lipemia are similar in the region
of interest. For example, with the uniform kernel of size 14 (1
unit=5 nm), the background-subtracted absorbance values at 410 nm
at different hemolysis levels are similar in all interference
conditions tested, and are not dependent on the levels of icterus
and/or lipemia.
[0057] As seen in FIG. 3, an example is shown using
background-subtracted spectra used for the detection/quantification
of hemolysis (via [Hb]). The plots show raw, background, and
background-subtracted (raw-minus-background) spectra of samples at
different hemolysis levels ([Hb] of 0, 20, 60, 80, 180, 340, and
1000 mg/dL) at combinations of low and high icterus and lipemia
(total bilirubin=0.24 or 39.53 mg/dL; total glycerides=58 or 928
mg/dL). The dashed line at 410 nm indicates the wavelength where
the background-subtracted signals are independent of icterus and
lipemia levels, and can be used to quantify [Hb]. The raw spectra
were collected at wavelengths that are multiples of 5 nm. The
background spectra were calculated by convoluting the raw spectra
with a uniform kernel of size 14 (1 unit=5 nm) (FIG. 2).
[0058] Kernel Optimization
[0059] Overall, the technique of background subtraction can be used
to detect/quantify hemolysis or icterus in the presence of
different levels of the other types of interference. It should be
understood in some embodiments that the relevant wavelengths to
obtain background-subtracted signals do not necessarily have to be
at the peak of the substances of interest (e.g. 415 nm for
hemoglobin or 460 nm for bilirubin), and the background-subtracted
signals are not necessarily non-negative. The kernels used to
demonstrate the idea (size-14 uniform kernel for hemolysis, and
size-18 uniform kernel for icterus) were chosen from different
options (FIG. 2) to minimize the p-value of the t test to
distinguish 2 lowest levels of the interference of interest in the
presence of all combinations of other types of interference (Table
1).
[0060] It should be understood that, by way of non-limiting
example, at least two new methods described herein (FIG. 3A) can be
intended to eliminate interfering signals and obtain clean spectral
signals that enable the quantification of one interferent
(hemolysis or icterus) in the presence of various amounts of
others. While they are both based on the geometry of the spectra,
the details are different.
[0061] By way of non-limiting example, explanation of the methods
of background subtraction (FIGS. 3A, 3B) and curvature calculation
(FIG. 3C). For at least one embodiment herein, FIG. 3A shows plots
of possible kernels used for the convolution to calculate the
background spectra, which are probability density functions of
uniform distributions and Gaussian distributions. The kernel size
of a certain distribution function is defined as its standard
deviation multiplied by the square root of 12, which is the range
of the distribution if it is uniform. An optimization step may be
used to choose an appropriate method specified by the kernel and
the wavelength used to obtain the final values. B) Plots showing
example "raw," "background," and "raw-background" spectra of a
sample. The background spectrum was calculated with a uniform
kernel of size 14 (14 points*5 nm/points=70 nm). C) Plots
illustrating curvature calculation. At a certain wavelength (at or
near a peak), a certain number of points are used for fitting a
circle going through them by minimizing a cost function (Eq. 1).
The resulting curvature is the inverse of the radius of such
circle. To display the circles with clarity, the wavelength and the
absorbance were normalized in these plots, while the actual
calculations were done with the original values. An optimization
step may be done to choose a curvature calculation method specified
by a certain wavelength and number of points used for fitting.
[0062] By way of non-limiting example, the first method takes
inspiration from background subtraction techniques used in image
processing and is more general than those previously employed. The
processed signal is called the background-subtracted signal. It is
obtained by convoluting the raw signal with a blurring kernel to
calculate the background (FIG. 3A), and subsequently, subtracting
the background from the raw signal (FIG. 3B). Optionally, the
second method is derived from the empirical observation that the
shapes of the peaks generally do not depend on the background. In
this case, we took the processed signal as the spectral curvature
near or at the peak of interest, which is the inverse of the radius
of the circle fitted through spectral points (FIG. 3C).
Derivatives, which also provide shape information, have previously
been employed to detect and measure hemolysis and icterus. Herein,
the curvature, which can be calculated using the first and second
derivatives, provides a direct description of the shape of the
curve near or at a particular peak.
[0063] The methods were evaluated with 510 samples containing
permutations of levels of hemolysis, icterus, and lipemia, as
specified by the concentrations of hemoglobin, bilirubin, and
triglycerides, respectively (Table 1). We used this sample set to
explain the two new methods described herein and demonstrate their
performance in comparison to traditional methods.
TABLE-US-00001 TABLE 1 Icterus: Lipemia: Level hemoglobin bilirubin
triglycerides Hemolysis: (mg/dL) (mg/dL) (mg/dL) 0 0 0.18 76 1 30
2.76 127 2 50 4.77 175 3 70 9.62 215 4 180 14.63 275 5 370 19.20
462 6 760 29.67 740 7 1190 39.65 984
[0064] Performance in Many Samples
[0065] The methods based on curvature calculation and background
subtraction were applied to hundreds of samples, which include
those with different combinations of hemolysis/icterus/lipemia
combinations and those collected less systematically as seen in
FIG. 3D. The source of hemolysis interference was natural in all
samples because the hemolysate solutions were obtained from blood
cells lysed by freezing/thawing. Naturally occurring icterus and
lipemia were of lower levels; therefore, bilirubin and lipid from
artificial sources were added into the samples to achieve higher
levels of icterus and lipemia. For hemolysis
detection/quantification, the raw absorbance values correlate
weakly to the expected [Hb], while both the background-subtracted
signals and the curvatures correlated very well with the expected
[Hb]. For icterus, the background subtraction method performs well,
but the curvature method does not. Therefore, the method based on
background subtraction is suitable for the detection/quantification
of both hemolysis and icterus, while the method based on curvature
calculation is only suitable for the detection/quantification of
hemolysis.
[0066] Referring still to FIG. 3D, the performance is shown of the
methods based on background subtraction and curvature calculation
in removing interference signals for the detection/quantification
of hemolysis (A) and icterus (B). The sample sizes for hemolysis
plots and icterus plots are 352 and 392, respectively. The
background subtractions for the hemolysis and icterus signals were
done using uniform kernels of sizes 14 and 18 (unit=5 nm),
respectively (FIG. 2). The curvatures for hemolysis
detection/quantification were calculated using absorbance values at
405, 410, 415, 420, and 425 nm. The curvatures for icterus
detection/quantification were calculated using absorbance values at
450, 455, 460, 465 and 475 nm. Artificial sources of icterus and
lipemia were conjugated bilirubin and Intralipid.RTM.,
respectively.
[0067] FIG. 4 shows charts of absorbance relative to wavelength
according to at least one embodiment herein.
Advantage(s)
[0068] For at least some embodiments described herein, they have at
least some advantages in comparison to interference correction
methods that are based on absorbance values at multiple wavelengths
across the UV-Vis range, such as the ones used for hemolysis and
icterus detection on traditional instruments.
[0069] Firstly, only a short range of wavelengths around the major
wavelength of interest is used in most embodiments. This advantage
enables simplifications/improvements of the detection instrument,
such as but not limited to a smaller required wavelength range of
the light source, a smaller required width of the array detector
(in a prism/grating-based spectrophotometer), and a higher
wavelength resolution (the same array detector real estate can be
used for a smaller wavelength range in a prism/grating-based
spectrophotometer).
[0070] Secondly, a smaller range of samples used for the
calibration of a method is required. In at least one embodiment,
the processed signals (curvatures or background-subtracted
absorbance values) do not depend on the levels of interference
types (FIG. 5), so the calibration process does not need
information from samples with wide ranges of interference levels.
For example, if it is hard to find samples at high icterus levels,
only normally icteric samples can be used for the calibration.
[0071] Thirdly, embodiments of the methods described here are less
susceptible to the presence of unknown interference types. The
choice of wavelengths in traditional methods depends on absorption
wavelengths of known interference types, while both curvature
calculation and background subtraction are mostly agnostic of the
interference types and only depend on the absorption of the
substance of interest.
Data Processing and Analysis (Signal Calculation)
[0072] As part of the calculation of background-subtracted signals,
the convolution of the original signals with blurring kernels was
performed to obtain the background signals. A specific kernel is a
probability density function (PDF) (mean, .mu.=0; standard
deviation, .sigma.). The size of the kernel is defined as .sigma.
12 (e.g., the range, in the case of a uniform distribution).
Because the spectra were sampled at discrete wavelengths, the
kernels were represented as discrete points along the continuous
curves (the PDFs) in the calculation. In particular, for each
Gaussian kernel, only points in the [-3.sigma., 3.sigma.] (rounded)
range were used.
[0073] The curvature at a specific region of a spectrum is defined
as, where is the radius of the circle fitted through the data
points in that region. The fitting was done by minimizing the cost
function (see Eq. 1 below), where is the number of points used for
fitting, is the coordinate (wavelength, absorbance) of point i, and
is the coordinate of the center.
Linear Regression
[0074] In at least one embodiment described herein, linear
regression models were used to evaluate different methods of
hemolysis and icterus measurements. In such a model, Y, the
quantity of interest (e.g., hemoglobin or bilirubin), is a linear
combination of signals from the samples (see Equation 2 below).
Y=a.sub.0+a.sub.1 Signal.sub.1++a.sub.2 Signal.sub.2+ . . .
++a.sub.n Signal.sub.n
[0075] The model can be trained (calibrated) using a set of samples
with known true Ys. In such a process, the coefficients (a.sub.i's)
are varied to minimize the sum of the squared differences between
the true Ys and the calculated Ys. The optimized coefficients can
then be used to calculate the Ys of the test samples. The signals
may be absorbance values at specific wavelengths, differences of
absorbance values at two specific wavelengths, the
background-subtracted signals, or the curvatures. Models with the
newly derived signals were compared with models used in commercial
analyzers as described in the literature.
Results (Example Results with Background Subtraction)
[0076] In at least one embodiment, it was first tested whether
background-subtracted spectra can be utilized for the
quantification of hemolysis. The accuracy of this approach was
demonstrated by the analysis of samples at various levels of
hemolysis (hemoglobin=0, 30, 50, 70, 180, 370, 760, and 1,190
mg/dL) while in the presence of low and high levels of icterus
(bilirubin=0.18 and 39.65 mg/dL) and lipemia (triglycerides=76 and
984 mg/dL) (FIG. 5). Without the background subtraction step, the
raw absorption spectra contained unwanted signals from icterus and
lipemia, and the peaks at 415 nm were elevated.
Background-subtracted spectra with different levels of icterus and
lipemia were similar in the region of interest. For example, with
the uniform kernel of size 14 (1 unit=5 nm), the
background-subtracted absorbance values at 420 nm at different
hemolysis levels were similar in all interference conditions tested
and were not dependent on the levels of icterus and/or lipemia.
[0077] By way of non-limiting example, FIG. 5 shows an example
background-subtracted spectra used for the quantification of
hemolysis. The plots show raw, background, and
background-subtracted spectra of samples at different hemolysis
levels (hemoglobin [hemo]=0, 30, 50, 70, 180, 370, 760 and 1190
mg/dL) at permutations of low and high icterus and lipemia
(bilirubin [bili]=0.18 and 39.65 mg/dL; triglycerides [trig]=76 and
984 mg/dL). The raw spectra were acquired at 5-nm intervals. The
background spectra were calculated by convoluting the raw spectra
with a uniform kernel of size 14 (or (14-1)*5=65 nm; FIG. 3A).
[0078] Similarly, background-subtracted spectra can be utilized for
the quantification of icterus (FIG. 6). The bilirubin
concentrations were 0.18, 2.76, 4.77, 9.62, 14.63, 19.20, 29.67,
and 39.65 mg/dL; the hemoglobin concentrations were 0 and 1,190
mg/dL; and the triglyceride concentrations were 76 and 984 mg/dL.
While the raw spectra were significantly affected by hemolysis or
lipemia, the background-subtracted spectra were similar in all
interference permutations tested. In particular, with the uniform
kernel of size 17 (1 unit=5 nm), the background-subtracted
absorbance values at 525 nm were not affected by the levels of
hemolysis and/or lipemia.
[0079] FIG. 6 shows example background-subtracted spectra used for
the quantification of icterus. The plots show raw, background, and
background-subtracted (raw-minus-background) spectra of samples at
different icterus levels (bilirubin [bili]=0.18, 2.76, 4.77, 9.62,
14.63, 19.20, 29.67, and 39.65 mg/dL) at permutations of low and
high hemolysis and lipemia (hemoglobin [hemo]=0 and 1,190 mg/dL;
triglycerides [trig]=76 and 984 mg/dL). The raw spectra were
acquired at 5-nm intervals. The background spectra were calculated
by convoluting the raw spectra with a uniform kernel of size 17 (or
(17-1)*5=80 nm; FIG. 3A).
Example Results with Curvature Calculation
[0080] Referring now to FIGS. 7A-7B, in at least one embodiment
herein, it was observed that the shape of a specific absorption
peak (e.g., the 415-nm hemoglobin peak) did not change if there was
interference by a nearby peak (e.g., the 460-nm bilirubin peak) or
by an increase in absorption across a wide range of wavelengths
(e.g., in the case of lipemia). This was demonstrated with samples
having different hemolysis levels (hemoglobin=0, 180, or 1,190
mg/dL) and different permutations of low/high levels of icterus
(bilirubin=0.18 and 39.65 mg/dL) and lipemia (triglycerides=76 and
984 mg/dL). At each hemolysis level, samples with different icterus
and lipemia levels were found to have markedly different absorbance
values (FIG. 7A). Circles were fitted to points near the peaks (at
415, 420, 425, and 430 nm) using least-squares regression with the
cost function defined in Eq. 1 (FIG. 7A). The resulting curvatures
(the inverses of the radii), which were used as metrics to
quantitatively describe the shapes of the peaks, were similar for
each group of samples at each hemolysis level, regardless of
icterus and lipemia levels (FIG. 7B). This result demonstrates the
possibility of using the curvatures to quantify and detect
hemolysis.
[0081] FIGS. 7A-7B show example results of using curvature
calculation to quantify hemolysis. FIG. 7A shows plots showing
spectra of samples with three different hemoglobin concentrations
(0, 180, 1,190 mg/dL) in separate sub-panels. Each sub-panel shows
spectra of samples at low/high permutations of icterus (bilirubin
[bili]=0.18 and 39.65 mg/dL) and lipemia (triglycerides [trig]=76
and 984 mg/dL). The dashed lines indicate 420 nm. The shaded
circular sectors indicate fitted results using four data points
around 420 nm (415, 420, 425, and 430 nm). FIG. 7B shows bar charts
showing curvatures calculated from results shown in FIG. 7A.
[0082] This method was also applied to icterus detection and
quantification. Similar to the case of hemolysis (FIG. 7A-7B),
samples at three different levels of icterus (bilirubin=0.18, 4.77,
and 14.63 mg/dL) and different low/high permutations of hemolysis
(hemoglobin=0 and 1,190 mg/dL) and lipemia (triglycerides=76 and
984 mg/dL) were used to demonstrate feasibility (FIGS. 8A-8B). At
each icterus level, samples with different hemolysis and lipemia
levels were found to have markedly different absorbance values
(FIG. 8A). Circles were fitted to data points near the peaks (at
465, 470, 475, and 480 nm) using least-squares regression with the
cost function defined in Eq. 1 (FIG. 8A). The resulting curvatures
(the inverses of the radii), which we used as metrics to
quantitatively describe the shapes of the peaks, turned out to be
similar for each group of samples at each hemolysis level,
regardless of icterus and lipemia levels (FIG. 8B).
[0083] By way of non-limiting example, FIGS. 8A-8B show example
results of using curvature calculation to quantify icterus. FIG. 8A
shows plots showing spectra of samples with three different
bilirubin (bili) concentrations (0.18, 4.77, 14.63 mg/dL) in
separate sub-panels. Each sub-panel shows spectra of samples at
low/high permutations of hemolysis (hemoglobin [hemo]=0 and 1190
mg/dL) and lipemia (triglycerides [trig]=76 and 984 mg/dL). The
dashed lines indicate 470 nm. The part-circles indicate fitted
results using four data points around 470 nm (465, 470, 475, and
480 nm). FIG. 8B shows bar charts showing curvatures calculated
from results shown in FIG. 8A.
[0084] Optimization of Parameters
[0085] There are multiple options to consider when applying the new
methods described herein. The background calculation step can be
done with different kernels of different types and sizes and
requires specification of the wavelength of interest to obtain the
processed signal. The curvature calculation can be done with
different choices for the center wavelength and a different number
of points around each chosen center wavelength. In addition, there
are multiple metrics to evaluate different implementations, and we
considered two metrics herein.
[0086] The first metric is the R2 obtained from fitting the
reference concentrations with the calculated signals. The second is
the p-value of the Welch's t-test performed on two groups of
samples of the lowest and second lowest levels, which is motivated
by the possible need for very sensitive detection in some
applications. For practicality, -log 10(p) values were used instead
of pvalues. Note that the Welch's t-test was chosen over the
student's t-test because the variances at different levels are not
expected to be the same.
[0087] In at least one embodiment, an optimization step was
performed to determine the optimal parameters for each method
(background subtraction or curvature calculation) for each
interferent (hemolysis or icterus). In particular, the center
wavelength was varied from 350 nm to 650 nm (with steps of 5 nm).
For background calculation, the kernel types were 1) Gaussian and
2) uniform, while the size was varied from 2 to 20 units (1 unit=5
nm). For the curvature calculation, the number of points was varied
from three to 16. At a certain center wavelength, if the number of
points was even, more points were chosen on the side of larger
wavelengths. Metrics of R2 and -log 10(p) for all cases were
calculated. Only points with good overall performance
(R.gtoreq.0.95 and p.ltoreq.0.05.revreaction.-log 10(p).gtoreq.1.3)
were plotted (FIG. 9). The metric-log 10(p) was chosen because of
its usefulness; if a method has a high-log 10(p), it also has a
high R2, while the converse is not true.
[0088] Comparison:
[0089] By way of non-limiting example, the performance was compared
of each of the optimized methods (Table 2) to those of traditional
methods currently used in conventional chemical analyzers [1, 3,
20] using linear regression models. Each model (Table 3, Eq. 2) was
evaluated by 10 iterations at each training fraction. In each
iteration, the sample set (Table 1) was randomly split into
training and testing sets, with the training fraction specifying
the ratio of the number of samples in the training set versus the
total number of samples. The metric calculated from each iteration
was the same as the metric used to optimize the new methods (FIG.
9), which is the -log 10(p) value obtained from the Welch's t-test
performed on two groups of samples of the lowest and second lowest
interferent levels. The -log 10(p) values were averaged over the
iterations. The results for both hemolysis and icterus showed that
the background-subtracted signals and the curvatures performed
better than most other models that involve multiple absorbance
values (Table 3). In particular, models that use either
background-subtracted signals or curvatures were in the top three
for both hemolysis and icterus detection.
[0090] Table 2 shows parameter search results for hemolysis and
icterus detection using background subtraction and curvature
calculation, in comparison to the raw signals (raw absorbance
values). The selected parameters are those that provided the
highest -log 10(p).
TABLE-US-00002 TABLE 2 Hemolysis Icterus Raw signal 415 nm (raw
absorbance 460 nm (raw absorbance values) values) Background- 410
nm size-14 uniform 525 nm size-17 uniform subtracted signal kernel
kernel Curvature 4 points about 420 nm 4 points about 470 nm
Advantages of the New Methods
[0091] The two methods described herein have three major practical
advantages versus interference correction methods that are based on
absorbance values at multiple wavelengths across the
ultraviolet/visible range, such as those used for hemolysis and
icterus detection on many commercial analyzers. First, the methods
described herein are less susceptible to the presence of unknown
interferents. The choice of wavelengths in traditional methods
depends on absorption wavelengths of known interferents, while both
curvature calculation and background subtraction are mostly
agnostic of the interferents and only depend on the absorption of
the substance of interest. Even though the optimized hemolysis and
icterus signals slightly deviated from the peaks (415 nm for
hemolysis and 460 nm for icterus) (Table 2), the signals at the
peaks would still provide good performance, with p-values
distinguishing the two lowest interference levels (of hemolysis or
icterus) much lower than 0.05 (i.e., -log 10(p) values much larger
than 1.3).
[0092] Second, traditional methods require calibration using
samples with wide ranges of interference levels, while the methods
using background-subtracted signals or curvatures do not. We
performed an example analysis to demonstrate this notion. Using
only samples with a maximum interference level of 1 (Table 1) to
calibrate regression models, it was calculated the corresponding
hemoglobin and bilirubin values for all 510 samples. In the eight
samples used for these calibration steps, the highest hemoglobin
(30 mg/dL), bilirubin (2.76 mg/dL), and triglyceride (127 mg/dL)
levels were practically low. Traditional methods gave large biases
and poor correlations, while those using background-subtracted
signals and curvatures gave good agreement. As expected, with -log
10(p) as the metric, the maximum level of interference used for
calibration had to be increased for the performance of traditional
methods to improve. In contrast, the methods using
background-subtracted signals or curvatures performed very well,
even when a maximum level of 1 is used for calibration. The
independence of the methods involving background-subtracted signals
and curvatures allows the calibration to be done even with samples
of limited interference levels (e.g., those naturally collected
instead of those made via a comprehensive procedure like the
samples used for this work).
[0093] Background-subtracted signals require ranges spanning 65 nm
for hemolysis and 80 nm for icterus, which are ranked in the middle
in both cases. This advantage enables possible simplifications and
improvements of the detection instrument, such as a smaller
wavelength range of the light source, a smaller required width of
the array detector (in a prism/grating based spectrophotometer),
and a higher wavelength resolution (due to a smaller wavelength
range for the same detector size). While typical commercial
analyzers have full-scale spectrophotometers, point-of-care devices
may benefit from this advantage.
[0094] Discussion
[0095] By way of non-limiting example, one approach of the two new
methods described herein (those involving background-subtracted
signals and curvatures) is to obtain clean spectral signals even
with interference. Therefore, their application can be extended to
other spectral measurements. For example, many clinical assays with
optical readouts based on absorption spectra employ
multiple-wavelength readings to subtract out interfering signals
and require the knowledge of possible interferents. The two new
methods described can be readily applied to those assays, with the
practical advantages described above.
[0096] It is worth noting that the background-subtracted signals
may be negative at certain points in the parameter space (as
specified by the wavelength, kernel type, and kernel size) (FIGS.
5-6). A negative signal may occur at a wavelength next to a peak or
at a peak near another one that is much higher. At first glance, it
may not seem intuitive to use such negative signals, but they do
contain information about the peaks of interest. Indeed, the
optimal background-subtracted signal for icterus measurements
(Table 3) was negative (FIG. 6) but still performed well.
TABLE-US-00003 TABLE 3 Signals (absorbance values Model name
Interferent or other types) CLSI 1 Hemolysis 405/700 CLSI 1 Icterus
452/700 CLSI 2 Hemolysis 571/596 CLSI 2 Icterus 478/505 CLSI 3
Hemolysis 572/604 628/660 CLSI 3 Icterus 500/524 572/604 628/660
CLSI 4 Hemolysis 522/750 CLSI 4 Icterus 507/776 CLSI 5 Hemolysis
410/480 600/800 CLSI 5 Icterus 480/570 600/800 CLSI 6 Hemolysis 340
410 470 600 670 CLSI 6 Icterus 340 410 470 600 670 CLSI 7 Hemolysis
570/600 CLSI 7 Icterus 480/505 Farrell2016_5 Hemolysis 583/629
Farrell2016_5 Icterus 480/512 Bg-sub signal (Table 2) Hemolysis 410
nm size-14 uniform kernel Bg-sub signal (Table 2) Icterus 525 nm
size-17 uniform kernel Curvature (Table 2) Hemolysis 4 points about
420 nm Curvature (Table 2) Icterus 4 points about 470 nm Raw
Hemolysis 415 Raw Icterus 460
[0097] In some settings, such as when hemolysis detection is
required at the collection site, the use of an inexpensive and
simple device to collect spectral data is desirable. The background
subtraction method described herein can potentially enable such
data acquisition to be done with a simple camera. The novelty is
that the background subtraction method only requires two optical
filters, one with a narrow band to obtain the major signal, and the
other one with a wider band to obtain the background signal (FIG.
9A). If the two optical filters are placed in the region of
interest side-by-side in the same field of view, only one image is
needed to obtain the background-subtracted signal for each sample.
Such a design would be compatible with resource-limited settings.
For example, to detect hemolysis at the collection site without a
spectrophotometer, one could photograph the plasma fractions of
centrifuged collection devices using a simple point-and-shoot
camera (or a cell phone camera) with the light path that includes a
hybrid filter, which is composed of a narrow-band pass region and a
wide-band pass region (e.g., 5-nm and 65-nm wide filters for
hemolysis measurements). There have been efforts to develop
technologies to detect hemolysis at the collection site such as one
involving the use of color intensities of images taken with a
camera. The method proposed herein would have a much smaller and
simpler sample set for calibration and provide performance similar
to that of a spectrophotometer, thus avoiding the need to deal with
the complex, nontrivial conversion of the absorption/scattering
spectra to recorded colors.
[0098] By way of non-limiting example, The new methods described in
this paper, which are based on background-subtraction and curvature
calculation, provide the ability to quantify and detect hemolysis
and icterus with several practical advantages: 1) better robustness
in terms of eliminating signals from unwanted substances, some of
which may not be known beforehand, 2) smaller sets of samples used
for calibration with few levels of interference, and 3) simpler
instruments (spectrophotometers with smaller detectors/short
wavelength ranges or cameras equipped with pairs of filters). These
new methods do not have advantages over traditional methods with
respect to the number of discrete wavelengths required. A
camera-based implementation would require further hardware
engineering, and the implementation of these new methods, in
general, may involve other methods of performing
background-subtraction (e.g., those with other blurring methods) or
curvature calculation (e.g., those using methods other than circle
fitting). Such implementation could benefit cases of sample
collection in resource-limited settings. For example, in remote
sites where samples are collected and sent to centralized
laboratories, the ability to detect interference at the point of
collection would allow for immediate re-drawing. Furthermore, if
the hardware is adapted to work with small-volume samples (e.g.,
those collected by fingersticks), it would be possible to integrate
the methods described herein with point-of-care diagnostic
instruments and contribute to the effort of bringing diagnostics to
developing countries or other under-served settings. Overall, these
new data analysis methods can enable new practical possibilities in
the development of interference screening methods.
OTHER EMBODIMENTS
[0099] Applications in Other Spectral Measurements
[0100] For at least one non-limiting example, the utility of the
two methods described above is to obtain clean signals in the
presence of interference in spectra. Optionally, their application
can be extended to other spectral measurements. For example,
currently, the background subtraction step using single background
wavelengths is part of some clinical chemistry assays, such as
glucose, total iron, and total iron binding capacity. The
curvatures and the background subtracted signals may allow the
assays to be even less sensitive to spectral interference (e.g.
hemolysis, icterus, and lipemia).
[0101] Applying the Background Subtraction Method in Imaging
[0102] For at least one non-limiting example, getting meaningful
spectral data from simple imaging (e.g. photography with a simple
color camera) is challenging because signals from the whole spectra
are combined into a small number of channels (e.g. red, green, and
blue in RGB images). One way to obtain spectral data is to use
optical filters that allow light to pass through only at specified
wavelength ranges. However, it is impractical to set up many
optical filters when absorbance values at multiple wavelengths are
needed, especially in resource-limited settings. An example of such
case is when a hemolysis detection step is required at the
collection site (where no spectrophotometer is available) and the
detection method requires absorbance values at 5 wavelengths to
account for icterus and lipemia.
[0103] Optionally, the background subtraction method can be used in
place of methods that require absorbance values at multiple
wavelengths to account for spectral interference, such as in the
cases of hemolysis and icterus detection/quantification. In one
non-limiting example, the novelty is that the background
subtraction method only requires 2 optical filters, one with a
narrow band to obtain the major signal, and the other one with a
wider band to obtain the background signal (FIG. 9B). If the two
optical filters are placed in the region of interest at the same
time, only one image is used to obtain the background subtracted
signal for each sample (FIG. 9B). Such design is compatible with
resource-limited settings. For example, to detect hemolysis at the
collection site without a spectrophotometer, one can image the
plasma fractions of centrifuged collection devices using a simple
point-and-shoot camera (or a cell phone camera or other image
capture device) with the light path including a hybrid filter with
two sides that pass a narrow band and a wide band of
wavelengths.
[0104] In this non-limiting example, FIG. 9B shows acquisition of
background-subtracted signals using a narrow-band optical filter
and a wide-band optical filter. If the two filters can be placed in
the regions of interest concurrently, only one exposure is needed
to obtain the major signal, the background signal, and the
background-subtracted signal. The bands are centered near the
absorption peak of interest but do not necessary have to be
centered at the peak of interest.
[0105] Icterus Detection/Quantification with Curvatures
[0106] Referring now to FIG. 10, curvatures can be used to avoid
interference from hemolysis and lipemia in icterus
detection/quantification. The plots show absorption spectra near
the bilirubin peak of samples at specified icterus levels (0.24,
4.97, 9.89, 19.91, and 39.53 mg/dL), at combinations of low and
high hemolysis and lipemia ([Hb]=0 or 1000 mg/dL; total
glycerides=58 or 928 mg/dL). The dashed lines indicate the
wavelength of 470 nm (just off the 460-nm peak). The shaded areas
indicate circles fitted to the sets of 5 points centered around 470
nm (at 460, 465, 470, 475, and 480 nm). The circles appear to be
ellipses because of the different in scales of the absorbance and
the wavelength. Each curvature value is the inverse of the
corresponding radius.
[0107] For at least one non-limiting example, the curvature of the
absorption peak at 460 nm was found to be unsuitable for the
quantification/detection of icterus. An optimization step revealed
that curvatures at 465 nm and 470 nm (with 3, 5, or 7 points used
for the calculation) can be used to quantify icterus levels (via
bilirubin concentrations). Very high hemolysis levels (e.g. 1000
mg/dL of [Hb]) can lead to significant interference on the left
side of the 460-nm peak (wavelength.ltoreq.460 nm) but not the
right side, so the shape of the absorption curve just off the peak
on the right side is not affected by hemolysis (FIG. 10). Results
with almost 400 samples show a good correlation between the
calculated curvatures and the total bilirubin assay results (FIG.
11). Therefore, the method with curvature calculation can also be
used for icterus quantification/detection. Also, in general, the
use of this method is not limited to absorption peaks and can be
expanded wavelengths near the peaks.
[0108] Referring now to FIG. 11, methods can be used based on
background subtraction and curvature calculation in removing
interference signals for the detection/quantification of icterus
(392 samples). The background subtractions were performed using
uniform kernels of size 18 (unit=5 nm). The curvatures were
calculated using absorbance values at 460, 465, 470, 475, and 480
nm. Artificial sources of icterus and lipemia were conjugated
bilirubin and Intralipid.RTM., respectively.
[0109] FIG. 12 shows that there may be an assay station receiving
location 10110 configured to receive a cartridge. In one
non-limiting example, the assay station receiving location 10110
may be a tray that is movable as indicated by arrow 10112 by the
use of motor 10114 and gear tracks 10116 to move the tray outside
of the module to facilitate user placement of one or more
cartridges into the module.
[0110] Once a cartridge is in the system, individual elements of
the cartridge such as but not limited to cuvettes, pipette tips,
vessels, other physical items, regent(s), fluids, or the like may
be moved from the cartridge. FIG. 12 also shows that there may be a
variety of components in the module 10100 such as but not limited
to a centrifuge 10120, a high sensitivity optical detector 10122
such as but not limited to a PMT, a multi-array optical detector
10124 such as but not limited to a spectrophotometer, and a nucleic
acid amplification module 10126. Each of these components may have
its own sample vessel receiving location such as but not limited to
locations 10130, 10132, 10134, and 10136. In one non-limiting
example, the locations 10130, 10132, 10134, and 10136 may be sized
to be different shapes, sized to receive different types of
vessels, and in the case of the centrifuge, may have a variable
location depending on where the centrifuge finishes spinning. A
controller of the system is configured to direct sample vessels to
the desired locations and be able to accurately place them in the
appropriate receiving locations for each of the components.
[0111] FIG. 13 shows a side view of the various components in the
module 10100.
[0112] It should also be understood that thermal control of
conditions within the module 10100 can be regulated so that thermal
conditioning by way of controlled temperature air flow through the
system is accomplished so that temperature sensor(s) in the module
detect that ambient air in the system is within a desired range.
Optionally, the thermal regulation is by way of a combination of
controlled air temperature and controlled support structure
temperature. This can be of particular use when the support
structure comprises of a thermally conductive material.
[0113] Referring now to FIG. 14, one embodiment of a system 10200
with linked convective flow between modules will now be described.
As seen in FIG. 14, the common flow between modules 10100 is shown
by arrow 10202. Inlet air flow into each of the modules 10100 is
indicated by arrow 10204. Optionally, the thermal conditioning of
adjacent modules can be used to condition the underside or other
surfaces of adjacent modules. In this manner, combined module
thermal conditioning can create a more stable thermal state for all
of the modules sharing a common mounting. This convective air flow
within a module is indicated by arrow 10208. Optionally, a
convective flow unit 10220 which may provide thermally conditioned
(heated, cooled, or neutral) airflow can be used to maintain a
desired air temperature range within the substantially light tight
confines of the modules 10100. One more temperature sensors 10230
may be included in the modules 10100 to provide feedback to a
controller to adjust flow rate and/or air temperature coming from
device 10220. A fully or at least partially enclosed pathway 10232
may be used to direct exhaust air flow to a filtered outlet 10234
that may have an exhaust fan therein. Optionally, flow can be
reversed on the exhaust fan such that it can also function as an
inlet if the fan is operated in reversed.
[0114] Referring now to FIGS. 15 and 16, optionally, some
embodiments may have a bilayer module configuration wherein certain
hardware elements are mounted on a first plane while second
elements that may have a different height are mounted on a second
plane such that the features on the first plane and second plane
have sample vessel loading areas in zones or planes accessible by a
common pipette system mounted on an XY gantry. By way of
non-limiting example, FIG. 15 shows that an optical detector
component 10250 may have an upper surface 10252 that is located
within the range of motion of the sample handling system with
gantry 10102. In one non-limiting example, the upper surface 10252
is above the first support layer 10260 while the device 10250 is
mounted on the second support layer 10262. The surface 10252 may be
sized to receive one cuvette or multiple cuvettes. FIG. 15 also
shows that the pipette 10104 in a second location, or optionally,
some systems may use second pipette and gantry system with the
module 10100. It should be understood that the housing 10270 may be
a light-tight housing. Some embodiments may align a plurality of
the bilayer modules in a stack (similar to FIG. 108) and/or
horizontal combination wherein all of the resources are contained
in each of the bilayer modules. Some may not use any additional
transport devices between bilayer modules, but such transport
devices are not excluded in alternative embodiments. Optionally,
some embodiments may include a module 10251 for detecting and/or
correcting for the conditions described herein regarding samples
that are lipemic or contain other interferents that are to
adjusted, detected, and/or accounted for.
[0115] While the invention has been described and illustrated with
reference to certain particular embodiments thereof, those skilled
in the art will appreciate that various adaptations, changes,
modifications, substitutions, deletions, or additions of procedures
and protocols may be made without departing from the spirit and
scope of the invention. For example, with any of the above
embodiments, it should be understood that the signal processing
techniques described herein are not limited to those assays
mentioned in the text, but may also be applied to other assays not
expressly listed or described herein. It should be understood that
the detection and/or correction techniques (or associated hardware)
can be integrated into stand-alone device. Optionally, the
detection and/or correction techniques (or associated hardware) may
be integrated into automated sample analyzers or sample processors.
Optionally, they may be part of a sample preparation and/or sample
pre-processing stage of the above devices. Optionally, they may be
used during various steps/processes of assay processing or
analysis.
[0116] Additionally, concentrations, amounts, and other numerical
data may be presented herein in a range format. It is to be
understood that such range format is used merely for convenience
and brevity and should be interpreted flexibly to include not only
the numerical values explicitly recited as the limits of the range,
but also to include all the individual numerical values or
sub-ranges encompassed within that range as if each numerical value
and sub-range is explicitly recited. For example, a size range of
about 1 nm to about 200 nm should be interpreted to include not
only the explicitly recited limits of about 1 nm and about 200 nm,
but also to include individual sizes such as 2 nm, 3 nm, 4 nm, and
sub-ranges such as 10 nm to 50 nm, 20 nm to 100 nm, etc . . . .
[0117] The publications discussed or cited herein are provided
solely for their disclosure prior to the filing date of the present
application. Nothing herein is to be construed as an admission that
the present invention is not entitled to antedate such publication
by virtue of prior invention. Further, the dates of publication
provided may be different from the actual publication dates which
may need to be independently confirmed. All publications mentioned
herein are incorporated herein by reference to disclose and
describe the structures and/or methods in connection with which the
publications are cited. By way of non-limiting example, the
following applications are fully incorporated herein by reference
for all purposes: 62/452,949 entitled "METHODS AND DEVICES FOR
IMPROVED SIGNAL DETECTION FROM BIOLOGICAL SAMPLES" filed Jan. 31,
2017, U.S. Patent Application No. 62/519,018 entitled "Spectral
analysis methods based on background subtraction and curvature
calculation used in the detection or quantification of hemolysis
and icterus in blood-derived clinical samples" filed Jun. 13, 2017,
U.S. Pat. Nos. 7,888,125, 8,007,999, 8,088,593 and U.S. Publication
No., US20120309636, PCT Application No. PCT US2012/057155, U.S.
patent application Ser. No. 13/244,952, and PCT Application No.
PCT/US2011/53188, filed Sep. 25, 2011. PCT Application No.
PCT/US2011/53188, filed Sep. 25, 2011, U.S. patent application Ser.
No. 13/244,946, filed Sep. 26, 2011, PCT Application No.
PCT/US11/53189, filed Sep. 25, 2011, Patent Cooperation Treaty
Application No. PCT/US2011/53188; Patent Cooperation Treaty
Application No. PCT/US2012/57155; U.S. patent application Ser. No.
13/244,947; U.S. patent application Ser. No. 13/244,949; U.S.
patent application Ser. No. 13/244,950; U.S. patent application
Ser. No. 13/244,951; U.S. patent application Ser. No. 13/244,952;
U.S. patent application Ser. No. 13/244,953; U.S. patent
application Ser. No. 13/244,954; U.S. patent application Ser. No.
13/244,956; and U.S. application Ser. No. 15/595,489.
[0118] While preferred embodiments of the present invention have
been shown and described herein, it will be obvious to those
skilled in the art that such embodiments are provided by way of
example only. Numerous variations, changes, and substitutions will
now occur to those skilled in the art without departing from the
invention. It should be understood that various alternatives to the
embodiments of the invention described herein may be employed in
practicing the invention. Any feature, whether preferred or not,
may be combined with any other feature, whether preferred or not.
The appended claims are not to be interpreted as including
means-plus-function limitations, unless such a limitation is
explicitly recited in a given claim using the phrase "means for."
It should be understood that as used in the description herein and
throughout the claims that follow, the meaning of "a," "an," and
"the" includes plural reference unless the context clearly dictates
otherwise. For example, a reference to "an assay" may refer to a
single assay or multiple assays. Also, as used in the description
herein and throughout the claims that follow, the meaning of "in"
includes "in" and "on" unless the context clearly dictates
otherwise. Finally, as used in the description herein and
throughout the claims that follow, the meaning of "or" includes
both the conjunctive and disjunctive unless the context expressly
dictates otherwise. Thus, the term "or" includes "and/or" unless
the context expressly dictates otherwise.
* * * * *