METHOD AND SYSTEM FOR DETECTION OF DISEASE AGENTS IN BLOOD

Abstract

The invention principally relates to a method of detecting a disease agent in blood, comprising: (i) creating a sample infra-red spectrum representative of the blood, with one or more spectral components, each having a wavenumber and absorbance value; (ii) providing a reference database of spectral models, each model having one or more database spectral components of a wavenumber and an absorbance value, wherein the database spectral components identify disease agents; (iii) determining whether one or more database spectral components corresponds to one or more sample spectral components; and (iv) compiling a list of corresponding database components identified.

Claims

1. A method of detecting a disease agent in a blood sample, the method comprising the steps of: (i) creating a sample infra-red spectrum representative of the blood sample, the sample spectrum having one or more spectral components, each component having a wavenumber and absorbance value. (ii) providing a reference database of spectral models, each model having one or more database spectral components of a wavenumber and an absorbance value, wherein the database spectral components identify disease agents, (iii) determining whether the reference database has one or more database spectral components corresponding to one or more sample spectral components, and (iv) compiling a list of corresponding database components identified.

2. A method according to claim 1, wherein step (ii) further includes selecting one or more spectral windows in which to undertake step (iii).

3. A method according to claim 1 which further comprises the step of: (iv) determining the number of sample components in each respective spectral model compiled and ranking said compiled spectral model.

4. A method according to claim 1 which further comprises the step of; (iv) determining the number of sample components in each respective spectral model compiled and classifying said compiled spectral model based on predetermined classification criteria.

5. A method according to claim 1 wherein the disease agent is chosen from the group comprising blood borne viral diseases.

6. A method according to claim 1 wherein the disease agent is chosen from the group comprising human immune deficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV), viruses of the family Arenaviridae (including Lassa fever, Junin and Machupo), viruses of the family Bunyaviridae (including Crimean-Congo haemorrhagic fever, Rift Valley Fever, Hantaan haemorrhagic fevers), viruses of the family Filoviridae (Ebola and Marburg) and viruses of the family Flaviviridae (yellow fever, dengue, Omsk haemorrhagic fever, Kyasanur forest disease, West Nile virus), viruses or vectors of the Alphaviridae, Babesia B. divergens, B. bigemina, B. equi, B. microfti, B. duncani, Leishmania Toxoplasma gondii, Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale curtisi, Plasmodium ovale wallikeri, Plasmodium malariae, Plasmodium knowlesi, Trypanosoma brucei and Trypanosoma cruzi.

7. A method according to claim 1 wherein the database spectral components identify a specific hepatitis virus chosen from hepatitis A, hepatitis B, hepatitis C, hepatitis D, hepatitis E, hepatitis G or combinations thereof.

8. A method according to claim 1 wherein the infra-red spectrum representative of the blood sample is created from a thick film of the blood sample.

9. A method according to claim 1 wherein the infra-red spectrum representative of the blood sample is created from a single droplet of blood.

10. A method according to claim 9 wherein the infra-red spectrum representative of the blood sample is created from a single droplet of blood of volume between 5 and 50 μl, more preferably between 5 and 25 μl.

11. A method of detecting malaria in a blood sample, the method comprising the steps of: (i) creating a sample infra-red spectrum representative of the blood sample, the sample spectrum having one or more spectral components, each component having a wavenumber and absorbance value. (ii) providing a reference database of spectral models, each model having one or more database spectral components of a wavenumber and an absorbance value, wherein the database spectral components identify malaria, (iii) determining whether the reference database has one or more database spectral components corresponding to one or more sample spectral components, and (iv) compiling a list of corresponding database components identified.

12. A method according to claim 11 wherein the infra-red spectrum representative of the blood sample is created from a thick film of the blood sample.

13. A method according to claim 11 wherein the infra-red spectrum representative of the blood sample is created from a single droplet of blood.

14. A method according to claim 13 wherein the infra-red spectrum representative of the blood sample is created from a single droplet of blood of volume between 5 and 50 μl, more preferably between 5 and 25 μl.

15. A method according to claim 11 wherein the database spectral components identify a specific phase of malaria.

16. A method according to claim 11 wherein the database spectral components identify one or more Plasmodium species.

17. A method according to claim 16 wherein the database spectral components identify one or more Plasmodium species chosen from the group comprising Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale curtisi, Plasmodium ovale wallikeri, Plasmodium malariae, Plasmodium knowlesi or combinations thereof.

18. A computer readable storage medium for storing in non-transient form an application for executing a method of detecting a disease agent in a blood sample, comprising the steps of: (i) recording an IR spectrum representative of the blood sample, (ii) comparing said spectrum to a reference database of spectral models to identify one or more spectral components of wavenumber and absorbance of the blood sample, wherein the spectral components identify disease agents, and (iii) compiling a list of sample components identified corresponding to a respective spectral model of the database, wherein steps (i) to (iii) are automated.

19. A system for detecting a disease agent in a blood sample, the system comprising a spectrometer for capture of an IR spectrum and a computer, wherein (i) the spectrometer creates an IR spectrum representative of the blood sample, (ii) the computer applies said spectrum to a reference database of spectral models to identify one or more spectral components of wavenumber and absorbance of the blood sample, wherein the spectral components identify disease agents, and (iii) the computer compiles a list of sample components identified corresponding to a respective spectral model of the database.

20. An application adapted to enable the detection of a disease agent in a blood sample, said application comprising a predetermined instruction set adapted to enable a method comprising the steps of: (i) creating a sample infra-red spectrum representative of the blood sample, the sample spectrum having one or more spectral components, each component having a wavenumber and absorbance value. (ii) providing a reference database of spectral models, each model having one or more database spectral components of a wavenumber and an absorbance value, wherein the database spectral components identify disease agents, (iii) determining whether the reference database has one or more database spectral components corresponding to one or more sample spectral components, and (iv) compiling a list of corresponding database components identified.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0148] Further disclosure, objects, advantages and aspects of preferred and other embodiments of the present application may be better understood by those skilled in the relevant art by reference to the following description of embodiments taken in conjunction with the accompanying drawings, which are given by way of illustration only, and thus are not limitative of the disclosure herein.

[0149] The figures relate to the following:

[0150] FIG. 1 displays are spectra obtained from different sets of samples; whole blood (1), lysed whole blood (3), plasma (5), RBC (7), RBC in methanol (9), and slurry of coagulated blood (11).

[0151] FIG. 2 is an ATR-FTIR spectrum for organic extracts of RBC (20), whole blood (21) and plasma (22) blood lipid extracts;

[0152] FIG. 3 illustrates typical IR bands associated with biological compounds and their assignments to various moieties in the compounds—vCH.sub.3, vCH.sub.2, vCH, (23); vCO (24); v(P—O), v(C═O) (25); δCH.sub.2 and δCH.sub.3 (26); amino acids (27) and amide I, amide II and amide III (28);

[0153] FIG. 4 is an ATR-FTIR spectrum of sera having different loads of Hepatitis B virus (30), Hepatitis C virus (31) and HIV (32), FIG. 4a showing the full spectrum from 3000 to 1000 cm.sup.−1, FIG. 4b showing an expansion of the region from 1750 to 900 cm.sup.−1; and FIG. 4c showing an further expansion of the region from 1250 to 950 cm.sup.−1 which is associated with nucleic acids. A visual inspection shows some bands in the spectra of samples infected by the HIV around 970 and 1160 cm.sup.−1. Those bands (black arrows), do not appear in the spectra obtained from Hepatitis infected patients, and can be related to the presence of RNA and DNA.

[0154] FIG. 5 is an ATR-FTIR spectrum of whole blood, including a control sample (35), and samples having different loads of HIV (36) and Hepatitis C virus (37). FIG. 5a shows the full spectrum from 3000 to 1000 cm.sup.−1, FIG. 5b shows an expansion of the region from 1750 to 900 cm.sup.−1, and FIG. 5c shows a further expansion of the region from 1250 to 950 cm.sup.−1 which is associated with nucleic acids. In this case the bands at the HIV spectra observed around 1140 and 1110 cm.sup.−1 are even more obvious.

[0155] FIG. 6 is an ATR-FTIR spectrum of samples having HIV loading including a control sample (40), whole blood (41) and serum (42). FIG. 6a shows the full spectrum from 3000 to 1000 cm.sup.−1, FIG. 6b shows an expansion of the region from 1750 to 900 cm.sup.−1, and FIG. 6c shows a further expansion of the region from 1250 to 950 cm.sup.−1 which is associated with nucleic acids. Again, the bands specifically assigned to nucleic are shown, being much more intense in the case of the WB. IT may be caused by the presence of lymphocytes in the WB. Those bands are not found in the control under study.

[0156] FIG. 7 is an ATR-FTIR spectrum of a control and serum sample loaded with hepatitis B virus. FIG. 7a shows the full spectrum from 3000 to 1000 cm.sup.−1, FIG. 7b shows an expansion of the region from 1750 to 900 cm.sup.−1, and FIG. 7c shows a further expansion of the region from 1250 to 950 cm.sup.−1 which is associated with nucleic acids. Naked eye cannot found differences between the spectra of the control (45) and the pathological samples of hepatitis B virus (46).

[0157] FIG. 8 is an ATR-FTIR spectrum of a control, whole blood and serum samples loaded with hepatitis C virus. FIG. 8a shows the full spectrum from 3000 to 1000 cm.sup.−1, FIG. 8b shows an expansion of the region from 1750 to 900 cm.sup.−1, and FIG. 8c shows a further expansion of the region from 1250 to 950 cm.sup.−1 which is associated with nucleic acids. Again, naked eye cannot found differences between the spectra of the control (50) and the pathological samples of hepatitis C in whole blood (51) and serum (52).

[0158] FIGS. 9a, 9b and 9c are plots depicting the result of a multivariate analysis showing samples/scores of HIV in WB (□), hepatitis C virus in WB(.box-tangle-solidup.), hepatitis B virus in serum (.Math.), Hepatitis C virus in serum (*) and HIV in serum (∘) against a control (⋄). Multivariate analysis is used to clarify differences or find hidden patterns. The method selected is principal components analysis (PCA), an unsupervised method (i.e., it does not take into account the classes, only uses the spectra) that projects the samples in new coordinates orientated in the directions of the variance. In the scores of the PCA performed over the whole set of data, the two first scores space show separation among the serum (56) and whole blood (57) and in particular, among HIV (58) and hepatitis (60) virus samples.

[0159] FIG. 10 is a multivariate analysis of the serum spectra in two different formats (FIG. 10a and FIG. 10b) for Hepatitis B (⋄), Hepatitis C (□) and HIV (.box-tangle-solidup.). In the case of serum data, HIV-HIV and hepatitis are clearly separated in the PC1 (62) vs PC2 space (63).

[0160] FIG. 11 is a multivariate analysis of the serum spectra (hepatitis only) in two different formats (FIG. 11a and FIG. 11b). Although the visual inspection of the spectra did not reveal differences, in the comparison between the Hepatitis B (⋄) and Hepatitis C (□) on the scores plot, there are different clusters for each illness.

[0161] FIG. 12 is a multivariate analysis of the serum spectra (hepatitis B only)(⋄). Labels indicate the log of the virus load. The sample with the highest load is clearly separated from the others.

[0162] FIG. 13 is a multivariate analysis of the whole blood spectra in two different formats (FIG. 13a and FIG. 13b). In the case of whole blood, the scores PC2 (70) and PC3 (71) clearly separate among the HIV (□), hepatitis C (.box-tangle-solidup.) and the control (⋄).

[0163] FIG. 14 is an ATR-FTIR plot that allows comparison of spectra from infected WB (73, 74) and control (75) using dry WB samples. FIG. 14a shows the full spectrum from 3000 to 1000 cm.sup.−1, FIG. 14b shows an expansion of the region from 1750 to 900 cm.sup.−1; and FIG. 14c shows a further expansion of the region from 1250 to 950 cm.sup.−1 which is associated with nucleic acids.

[0164] FIG. 15 is an ATR-FTIR plot that allows comparison of spectra from infected WB (77) and controls (78) using wet lysed blood samples. FIG. 15a shows the full spectrum from 3000 to 1000 cm.sup.−1, FIG. 15b shows an expansion of the region from 1750 to 900 cm.sup.−1.

[0165] FIG. 16 is an ATR-FTIR plot that allows comparison of spectra from infected WB (80) and controls (81) using dry lysed blood samples. FIG. 16a shows the full spectrum from 3000 to 1000 cm.sup.−1, FIG. 16b shows an expansion of the region from 1750 to 900 cm.sup.−1.

[0166] FIG. 17 includes ATR-FTIR plots of methanol extracts of WB spiked with different loadings of malaria (control (90); 0.0077% (91); 0.031% (92); 0.25% (93); 0.49% (94); 1.96% (95). FIG. 17 shows an expansion of the region from 1700 to 1728 cm.sup.−1. A shift of the carbonyl band can be clearly seen, reflecting the spiking level.

[0167] FIG. 18 is a partial least squares regression plot of predicted vs actual level of parasite in the WB samples. The regression analysis enables discrimination between high and low parasite loadings. Note that the method is linear, and covers several orders of magnitude. A permutation test indicated that the regression is significant at the 95% confidence level.

[0168] FIG. 19 is an ATR-FTIR spectrum of whole blood samples having a high viral load of HIV after subtraction of a control spectra.

[0169] FIG. 20 is a flow chart illustrating the steps according to one embodiment of the method of the present invention.

[0170] FIG. 21 is a flow chart illustrating in more detail the steps of FIG. 20, including an indication of the preferred data manipulation.

[0171] FIG. 22 is a flow chart illustrating the method of the present invention when used to create a model for classifying blood samples as positive or negative for hepatitis virus (Hep) or HIV.

[0172] FIG. 23 is a further flow chart depicting the creation of a suitable mode for classifying blood samples according to the present invention.

[0173] FIG. 24 is a plot of Y CV predicted against a linear index for methanol fixed RBC blood samples infected with malaria (□) and control (⋄) samples with Discrim Y 1 marked in broken line.

[0174] FIG. 25 is an IR absorption plot for the spectral window from 3000-2820 cm.sup.−1.

[0175] FIG. 26 is a plot of Y CV predicted against a linear index for methanol fixed RBC blood samples infected with malaria (□) and control (0) samples.

[0176] FIG. 27 is an IR absorption plot for the spectral window from 3000-2820 cm.sup.−1 and 1400-900 cm.sup.−1.

[0177] FIG. 28 is an IR absorption plot for the spectral window from 4000-0 cm.sup.−1 for controls (100), and blood samples loaded with malaria paraseitemia (rings and trophocytes—RP (101), RNP (102) and TP (103)).

[0178] FIGS. 29(a) and 29(b) are plots in two different formats depicting the result of a multivariate analysis of the results depicted in FIG. 28, showing samples/scores of multiple SPC files for the samples in the region from 3116.19 to 2768.71 and 1828.46 to 852.91 cm.sup.−1. (Control (⋄), RP (□), RNP (.box-tangle-solidup.), TP (.Math.) with the broken oval line indicating 95% confidence level across x and y axes, also marked in broken lines. PC1 (70.17%)(110) and PC2 (15.20%)(111) are included in FIG. 29b with depicts variables/loading for multiple SPC files.

[0179] FIG. 30 is a partial least squares regression plot of predicted vs actual level of parasitemia in the samples depicted in FIG. 46. (Control (⋄), RP (□), RNP (.box-tangle-solidup.), TP (.Math.)).

[0180] FIG. 31 illustrates visible images of cells that are stained (FIG. 31a) and IR images of untreated cells (FIG. 31b) based on mean spectra in the region 2500 to 4000 cm.sup.−1. FIG. 31c is a visual close up of two cells (marked A and B, circled in FIG. 31a) with corresponding IR images (FIG. 31d).

[0181] FIG. 32 illustrates a PCA corresponding to FIG. 31 for the plasmodium infected area (⋄), the first uninfected are (.box-tangle-solidup.) and second uninfected area (□).

[0182] FIG. 33 is a variables/loadings plot.

[0183] FIG. 34 illustrates a supervised model PLSDA (without derivative) was performed showing the LV 1 component (unbroken line) and Reg Vector for Y1 (broken line).

[0184] FIG. 35 is an IR spectrum for plasma samples bearing different types of hepatitis (Hepatitis B (120); Hepatitis C (125)).

[0185] FIG. 36 is a partial least squares regression plot of predicted vs actual level of hepatitis in the samples (Hepatitis B (⋄); Hepatitis C (□)).

[0186] FIG. 37 is an FTIR spectrum for blood samples loaded with a range of concentrations of glucose and urea and dried on a glass fibre (100 mg/dl (130); 297 mg/dl (132); 490 mg/dl (134); 679 mg/dl (136); 865 mg/dl (138); neat glucose] (140)).

[0187] FIG. 38 is a partial least squares plot of the results for glucose as shown in FIG. 33. The unbroken line illustrates the best fit, and the broken line illustrates the 1:1 correlation between predicted and spiked correlations.

[0188] FIG. 39 is a standard IR spectrum for glucose.

[0189] FIG. 40 is a partial least squares plot of the results for urea as shown in FIG. 33. The unbroken line illustrates the best fit, and the broken line illustrates the 1:1 correlation between predicted and spiked correlations.

[0190] FIG. 41 is a standard IR spectrum of loading value against wavenumber.

[0191] FIG. 42 is an example of a typical graphical user interface for spectral quality control that would be displayed to a user.

DETAILED DESCRIPTION

[0192] The present invention will be further described with reference to the following examples of protocols suitable for obtaining samples suitable for ATR-IR analysis.

1. General Procedure for Crystal Cleaning

[0193] In general, the ATR crystal is cleaned using the following steps: [0194] a) Humidified Soft cellulose is employed for eliminating the sample. [0195] b) The ATR Crystal is cleaned using soft cellulose and water and/or organic solvents. [0196] c) A spectrum of the empty crystal is obtained in order to discard any memory effect. [0197] d) If proteins are difficult to remove, it is recommended the use of PBS, detergents or micellar water.

2. General Procedures for Sample Preparation

[0198] 2.1 Whole blood (WB) Sampling Method

[0199] Whole blood is extracted from the patient in EDTA tubes or directly with a lancet.

2.1.1 Wet WB

[0200] A wet whole blood sample is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum similar to those shown in FIG. 1 using the following steps: [0201] a) A background is obtained using the empty clean ATR crystal. [0202] b) A blank of water (W) is obtained by measuring the spectrum of 10 microliters of water. [0203] c) 10 microliters of the EB are taken with a micropipette and deposited on the surface of the ATR crystal. [0204] d) Raw spectrum of whole blood (RWB) is immediately acquired. [0205] e) The final spectrum (WB.sub.w) of the whole blood is obtained by subtracting the intensity of the i wavenumber of W from the i wavenumber of RWB.

WB.sub.w(i)=RWB(i)−W(i) [0206] f) Crystal is cleaned according to the General Procedure.

2.1.2 Dry WB

[0207] A dry whole blood sample is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum shown in FIG. 1 using the following steps: [0208] a) A background is obtained using the empty clean ATR crystal. [0209] b) Between 1 and 5 microlitres (the amount depending on the application) are taken with a micropipette and deposited on the surface of the ATR crystal. [0210] c) Sample is dried through different methods (Allowing to dry/using a drier or heat lamp) [0211] d) After drying, spectrum (WB.sub.d) of the whole blood is acquired. [0212] e) Crystal is cleaned according to the General Procedure set out above.

2.2 Lysed WB Sample Method

[0213] WB samples are obtained in the same procedure as described above and are lysed by mixing whole blood with distillated water in a ratio 1:1 (v/v) or with a 7% (w/v) sodium dodecyl sulfate (SDS) solution at a ratio 8:1 (v/v).

2.2.1 Wet Lysed WB

[0214] A wet lysed whole blood sample is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum shown in FIG. 1 using the following steps: [0215] a) A background is obtained using the empty clean ATR crystal. [0216] b) A blank of water (W) is obtained by measuring the spectrum of 10 microliters of distillated water or a blank of 7% (w/v) SDS solution, mixed with distillated water at a ratio 8:1 (v/v), depending on the method used on the lysis. [0217] c) 10 microliters of lysed WB are taken with a micropipette and deposited on the surface of the ATR crystal. Raw spectrum of plasma (RL) is immediately obtained. [0218] d) The final spectrum (L.sub.w) of the lysed whole blood is obtained subtracting the intensity of the i wavenumber of W to the i wavenumber of RL.

L.sub.w(i)=RL(i)−W(i) [0219] e) Crystal is cleaned according to the General Procedure set out above.

2.2.2 Dry Lysed WB

[0220] A dry lysed whole blood sample is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum shown in FIG. 1 using the following steps: [0221] a) A background is obtained using the empty clean ATR crystal. [0222] b) 1-5 (Depending on the application) microliters of lysed WB are taken with a micropipette and deposited on the surface of the ATR crystal. [0223] c) Sample is dried through different methods (Allowing to dry/using a drier or heat lamp) [0224] d) After drying, spectrum (La) of the plasma is acquired. [0225] e) Crystal is cleaned according to the General Procedure set out above.

2.3 Plasma (P) Sample Method.

[0226] Patient plasma samples are typically prepared by first extracting whole blood from the patient in ethylene diamine tetra acetic acid (EDTA) containing tubes or (or serum tubes if serum is required) directly with a lancet. WB samples are centrifuged at 1600 g during 10 minutes. Plasma is obtained from the upper phase with a Pasteur pipette.

2.3.1 Wet Plasma

[0227] A wet plasma sample is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum shown in FIG. 1 using the following steps: [0228] a) A background is obtained from the empty clean ATR crystal. [0229] b) A blank of water (W) is obtained by measuring the spectrum of 10 microliters of water. [0230] c) 10 microliters of plasma are taken with a micropipette and deposited on the surface of the ATR crystal. Raw spectrum of plasma (RP) is immediately obtained. [0231] d) The final spectrum (P.sub.w) of the plasma is obtained subtracting the intensity of the i wavenumber of W to the i wavenumber of RP.

P.sub.w(i)=RP(i)−W(i) [0232] e) Crystal is cleaned according to the steps set out above.

2.3.2 Dry Plasma

[0233] A dry plasma sample is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum shown in FIG. 1 using the following steps: [0234] a) A background is obtained using the empty clean ATR crystal. [0235] b) 1 to 5 microlitres (the amount depending on the application) of plasma are taken with a micropipette and deposited on the surface of the ATR crystal. [0236] c) Sample is dried through different methods (Allowing to dry/using a drier or heat lamp) [0237] d) After drying, spectrum (P.sub.d) of the plasma is acquired. [0238] e) Crystal is cleaned according to the General Procedure set out above.

2.4 Red Blood Cells (RBCs) Sample Method

[0239] A sample of patient RBCs are obtained by extracting whole blood from the patient in EDTA tubes (or serum tubes if serum is required) or directly with a lancet. WB samples are centrifuged at 1600 g during 10 minutes. RBCs are obtained from the lower phase with a Pasteur pipette.

2.4.1 Wet RBC

[0240] A wet RBC sample is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum shown in FIG. 1 using the following steps: [0241] a) A background is obtained using the empty clean ATR crystal. [0242] b) A blank of water (W) is obtained by measuring the spectrum of 10 microliters of water. [0243] c) 10 microliters of RBCs are taken with a micropipette and deposited on the surface of the ATR crystal. Raw spectrum of RBCs (RRBCs) is immediately obtained. [0244] d) The final spectrum (RBCs.sub.w) of the plasma is obtained subtracting the intensity of the i wavenumber of W to the i wavenumber of RP.

RBCs.sub.w(i)=RRBCs(i)−W(i) [0245] e) Crystal is cleaned according to the General Procedure set out above.

2.4.2 Dry RBC

[0246] A dry RBC sample is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum shown in FIG. 1 using the following steps: [0247] a) A background is obtained using the empty clean ATR crystal. [0248] b) 1-5 (Depending on the application) microliters of RBCs are taken with a micropipette and deposited on the surface of the ATR crystal. [0249] c) Sample is dried through different methods (Allowing to dry/using a drier or heat lamp) [0250] d) After drying, spectrum (RBCs.sub.d) of the plasma is obtained. [0251] e) Crystal is cleaned according to the General Procedure set out above.

2.5 RBC Packed in Solvent

[0252] An RBC sample in solvent, such as methanol (MeOH) is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum shown in FIG. 1 using the following steps: [0253] a) RBC obtained are washed with PBS (phosphate buffered saline), to remove the plasma/serum components and then mixed with 1 mL of cold methanol 0.4:1 (v/v). [0254] b) 1-5 (Depending on the application) microliters of the RBCs packed in methanol are taken with a micropipette and deposited on the surface of the ATR crystal. [0255] c) Sample is dried through different methods (Allowing to dry/using a drier or heat lamp) [0256] d) After drying, spectrum (RBCsm) of the RBC packed in methanol is obtained. [0257] e) Crystal is cleaned according to the General Procedure set out above.

2.5 Slurry of Coagulated Whole Blood in a Solvent

[0258] A slurry of coagulated whole blood in a solvent, such as methanol (MeOH) is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum shown in FIG. 1 using the following steps: [0259] a) WB is extracted using the same procedure as in section 1.1. [0260] b) 1-5 microliters of WB are deposited on the ATR crystal with a micropipette. [0261] c) The same amount of methanol is deposited on the previous drop of WB, creating a slurry of coagulated blood. [0262] d) Sample is dried through different methods (Allowing to dry/using a drier or heat lamp) [0263] e) After drying, spectrum of the dry slurry is acquired. [0264] f) Cleaning of the crystal according to the General Procedure set out above

2.6 Serum/Plasma/Blood Lipid Extracts

[0265] Lipid extracts from serum, or plasma or blood or a combination thereof in a solvent, such as methanol (MeOH) is typically processed according to the method of the present invention to generate the ATR-FTIR spectrum shown in FIG. 2 using the following steps: [0266] a) WB/Plasma/Serum is extracted using the same procedure as in section 1.1. [0267] b) WB/Plasma/Serum is mixed with an organic solvent. If emulsion is formed, sample should be centrifuged. [0268] c) 1-5 microliters of the extracting phase are taken with a micropipette and deposited on the ATR crystal. [0269] d) Sample is dried through different methods (Allowing to dry/using a drier or heat lamp) [0270] e) After drying, spectrum of the dry film is acquired. [0271] f) Cleaning of the crystal according to the General Procedure above.

Malaria as the Disease Agent

[0272] Malaria is caused by different species of Plasmodium. The different species of plasmodium have a different molecular phenotype and corresponding infrared spectra. Different species of Malaria causative agent are included in the Malaria reference database. To speciate or identify the different species of plasmodium typically one would use the following method first to identify that the person has malaria such as the following.

[0273] Accordingly, in a further embodiment of the method of detecting malaria in a blood sample according to the present invention, the method comprises the steps of: [0274] (i) creating a sample infra-red spectrum representative of the blood sample, the sample spectrum having one or more spectral components, each component having a wavenumber and absorbance value. [0275] (ii) providing a reference database of spectral models, each model having one or more database spectral components of a wavenumber and an absorbance value, wherein the database spectral components identify malaria, [0276] (iii) determining whether the reference database has one or more database spectral components corresponding to one or more sample spectral components, and [0277] (iv) compiling a list of corresponding database components identified.

[0278] In a further embodiment of the method of the present invention, to speciate and determine the causative agent of malaria into the various Plasmodium species, the method comprises the steps of: [0279] (i) creating a sample infra-red spectrum representative of the blood sample, the sample spectrum having one or more spectral components, each component having a wavenumber and absorbance value. [0280] (ii) providing a reference database of spectral models, each model having one or more database spectral components of a wavenumber and an absorbance value, wherein the database spectral components identify the different Plasmodium species such (as and not limited to) Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale curtisi, Plasmodium ovale wallikeri, Plasmodium malariae, Plasmodium knowlesi or combinations thereof, [0281] (iii) determining whether the reference database has one or more database spectral components corresponding to one or more sample spectral components, and [0282] (iv) compiling a list of corresponding database components identified.

Experimental Results—Malaria

[0283] Experimental test carried out using the above methods have shown a correlation between the spectra and malaria parasite concentration in blood. Red blood cells (RBC) and whole plasma samples (WB) loaded with different concentrations of parasitemia (rings and trophocytes) were dried in glass fibre paper. The loading regime is summarized in Table 4:

TABLE-US-00004 TABLE 4 Type of Type of Parasitemia Blood Sample Parasitemia (level of loading) RBC CONTROL 0 RBC CONTROL 0 RBC CONTROL 0 RBC CONTROL 0 RBC CONTROL 0 WB CONTROL 0 WB CONTROL 0 WB CONTROL 0 WB CONTROL 0 WB CONTROL 0 GF TR GF TR 0 GF UNT GF UNT 0 RBC RING 0.078125 RBC RING 0.15625 RBC RING 0.3125 RBC RING 0.625 RBC RING 1.25 RBC RING 10 RBC RING 10 RBC RING 2.5 RBC RING 5 RBC RING 5 WB RING 0.078125 WB RING 0.15625 WB RING 0.3125 WB RING 10 WB RING 2.5 WB RING 5 RBC TROPHOCYTE 0.078125 RBC TROPHOCYTE 0.15625 RBC TROPHOCYTE 0.3125 RBC TROPHOCYTE 0.625 RBC TROPHOCYTE 1.25 RBC TROPHOCYTE 2.5 RBC TROPHOCYTE 5 WB TROPHOCYTE 0.078125 WB TROPHOCYTE 0.15625 WB TROPHOCYTE 1.25 WB TROPHOCYTE 5

[0284] FIG. 28 is an IR absorption spectrum of the control, RP, RNP and TP samples loaded with malaria as listed in Table 4. The naked eye cannot readily distinguish differences between the spectra of the control and the pathological samples.

[0285] FIGS. 29a and 29b are plots in two different formats depicting the result of a multivariate analysis showing samples/scores of multiple SPC files for the samples in the region from 3116.19 to 2768.71 and 1828.46 to 852.91 cm.sup.−1. Multivariate analysis is used to clarify differences or find hidden patterns. The method selected is principal components analysis (PCA), an unsupervised method (i.e., it does not take into account the classes, only uses the spectra) that projects the samples in new coordinates orientated in the directions of the variance.

[0286] FIG. 30 is a partial least squares regression plot of predicted vs actual level of parasitemia in the samples. The regression analysis enables discrimination between high and low parasite loadings. Note that the method is linear, and covers several orders of magnitude.

[0287] Further experimental testing was carried out to see whether the IR signature of the malarial trophocyte on the RBC was maintained when dried in the paper. Ten RBC samples were loaded with 5% paraseitemia (trophocytes) and dried on normal filter paper. Twelve normal RBC samples were also created as controls.

[0288] The method of the present invention has also been used for detection in respect of samples known to contain plasmodium falciparum and/or plasmodium vivax by microscopy and PCR. The results using FTR demonstrated that the method was suitable for detection of infection by both malarial species and mixed infection.

Experimental Results—Detection of Malaria Using Images Obtained from Thin Smears of RBC in Glass

[0289] Experimental investigations were undertaken to investigate the efficacy of the method of the present invention with respect to distinguishing between RBCs infected with 5% malarial trophozoites, an uninfected blood cells.

[0290] In this case Focal Plane Array was used, that is, FP spectroscopic imaging of thin blood smears on glass. After image acquisition the samples were stained with Giemsa stain for the visual detection of the trophozoites.

[0291] FIG. 31 shows visible images of cells that are stained (FIG. 31a) and IR images of untreated cells (FIG. 31b) based on mean spectra in the region 2500 to 4000 cm.sup.−1. FIG. 31c is a visual close up of two cells (marked A and B, circled in FIG. 31a) with corresponding IR images (FIG. 31d). It is clear from these images that the density of the spectra is greater when the parasite is not present. PCA analysis reveals three areas—uninfected RBC areas (112), plasmodium for the trophozoites (113) and a second uninfected area for the part of the infected RBC without the trophozoite (114).

[0292] The corresponding PCA is recorded in FIG. 32 for the plasmodium infected area (⋄), the first uninfected are (.box-tangle-solidup.) and second uninfected area (□). The 95% confidence level is marked in at −4 and 4.

[0293] FIG. 33 is a variables/loadings plot. In order to more closely examine the differences, a supervised model PLSDA (without derivative) was performed and is illustrated at FIG. 34. The LV 1 component (unbroken line) and Reg Vector for Y1 (broken line) are shown. Although the regression vector is quite noisy, there is a shift at the 3300 cm.sup.−1 band which is able to discriminate between infected and non-infected pixels.

[0294] Based on the aforementioned results the following methodology for the identification of paraseitemia in untreated RBC thin films on glass can be proposed: [0295] (i) create a thin blood film, [0296] (ii) carry out microscopic visual analysis and create a visual image, [0297] (iii) create an FTIR image, [0298] (iv)(a) model each pixel of the image in order to classify them as parasite or RBC, [0299] (iv)(b) extract RBC means spectra, averaging the pixels of each RBC and investigate whether each RBC is infected or not.

Experimental Results—Hepatitis

[0300] Experimental tests carried out using the above methods have shown that it is possible to distinguish between plasma samples bearing different types of hepatitis.

[0301] For each sample, approximately 3 microliters of plasma bearing Hepatitis B (HB) and Hepatitis C (HC) was placed onto pre-cut glass filter paper and air-dried for 20 minutes. The glass paper with the dried plasma sample was then placed onto the crystal of a diamond ATR-FTIR window and a spectrum recorded at 8 cm.sup.−1 with 50 scans co-added and ratioed against a background spectrum of air. The resulting spectrum is illustrated in FIG. 35.

[0302] FIG. 36 is a partial least squares regression plot of predicted vs actual level of hepatitis in the samples. The data analysis was carried out for the samples in the region from 1583.18 to 1492.13 cm.sup.−1 and 1304.45 to 1120.49 cm.sup.−1. The regression analysis enables discrimination between high and low parasite loadings and between HB and HC infection.

Experimental Results—Glucose & Urea

[0303] The previous experimental results illustrated spectral effects relating to IR energy absorbed directly by a disease agent in the form of parasitemia present in the blood. Experimental tests carried out using the method of the present invention have also shown that it is possible to detect a disease agent indirectly, via the energy absorbed by other biological entities caused by the disease agent. For example, the disease agent may cause rises in glucose, urea or both.

[0304] Blood samples were loaded with a wide range of concentrations of glucose and urea and dried on glass fibre. FIG. 37 illustrates the FTIR spectrum for the samples and illustrates the absorbance of glucose is proportional to the concentration of glucose in the sample.

[0305] FIG. 38 provides a partial least squares plot of the results. A regression vector was then correlated with the glucose standard spectrum as shown in FIG. 39.

[0306] A similar approach was taken with urea. FIG. 40 provides a partial least squares plot of the results. A regression vector was then correlated with the glucose standard spectrum as shown in FIG. 41.

Experimental Results—Quality Controls

[0307] Validation of the spectra can be carried out prior to inclusion into one of the aforementioned models. This ensures that an acquired spectrum has features similar to the features included in the model. It also ensures that technical issues are not going to interfere in the extraction of information from the model. For example, the following two methods of quality control were developed.

Quality Control—Model Independent

[0308] The first relies on quality control independent of the model that is, depending only on the database. The quality control focuses on trying to monitor excesses (or defects) of the different of components and interferences pertaining to the sample. The component relative concentration is calculated using an algorithm, and this concentration is compared with a threshold value. For example, a distribution of relative concentration values of the component can be created on the database. Then the portions of the distribution that tail off at the upper and lower ends can be used for defining the threshold. If the relative concentration of the component is outside the threshold, the spectrum does not pass the quality control.

[0309] Typically, the following three components are considered sequentially in this quality control method: [0310] (i) Atmospheric interferences: Fluctuation of IR active atmospheric vapours between the background and sample measurements can cause negative and positive bands which are detected by using a positive and negative thresholds; [0311] (ii) Solvent: The solvent (Water, MeOH) has not been properly eliminated; and [0312] (iii) Sample: There is not enough sample on the crystal, for example, due to bad contact.

Quality Control—Model Dependent

[0313] The second quality control method is associated with the model and relies on measurement of the distance between the sample and the calibration samples in terms of the modelling. A typical example is the use of the T.sup.2 and SQ residuals on a PLSDA and a 95% confidence interval.

[0314] For example, the quality control for a spectrum recorded could be carried out in the sequence (i) atmospheric interference (water), (ii) solvent (methanol), (iii) sample, and finally (iv) distance to the model. Typically this would correlate with results such as those in Table 5:

TABLE-US-00005 TABLE 5 Calculation of relative QC Pre-processing concentration Thresholds H.sub.2O (g) Normalization Abs at 3846 cm.sup.−1- <1.5 SD Abs at 3852 cm.sup.−1 >1.5 SD MeOH Derivative Abs at 1029 cm.sup.−1- >1.5 SD Abs at 1033 cm.sup.−1 Sample none Absorbance at 1650 cm.sup.−1 <1.5 SD

[0315] An example of a typical graphical user interface that would be displayed to the user is depicted in FIG. 42.

[0316] While this invention has been described in connection with specific embodiments thereof, it will be understood that it is capable of further modification(s). This application is intended to cover any variations uses or adaptations of the invention following in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains and as may be applied to the essential features hereinbefore set forth.

[0317] As the present invention may be embodied in several forms without departing from the spirit of the essential characteristics of the invention, it should be understood that the above described embodiments are not to limit the present invention unless otherwise specified, but rather should be construed broadly within the spirit and scope of the invention as defined in the appended claims. The described embodiments are to be considered in all respects as illustrative only and not restrictive.

[0318] Various modifications and equivalent arrangements are intended to be included within the spirit and scope of the invention and appended claims. Therefore, the specific embodiments are to be understood to be illustrative of the many ways in which the principles of the present invention may be practiced. In the following claims, means-plus-function clauses are intended to cover structures as performing the defined function and not only structural equivalents, but also equivalent structures.

[0319] It should be noted that where the terms “server”, “secure server” or similar terms are used herein, a communication device is described that may be used in a communication system, unless the context otherwise requires, and should not be construed to limit the present invention to any particular communication device type. Thus, a communication device may include, without limitation, a bridge, router, bridge-router (router), switch, node, or other communication device, which may or may not be secure.

[0320] It should also be noted that where a flowchart is used herein to demonstrate various aspects of the invention, it should not be construed to limit the present invention to any particular logic flow or logic implementation. The described logic may be partitioned into different logic blocks (e.g., programs, modules, functions, or subroutines) without changing the overall results or otherwise departing from the true scope of the invention. Often, logic elements may be added, modified, omitted, performed in a different order, or implemented using different logic constructs (e.g., logic gates, looping primitives, conditional logic, and other logic constructs) without changing the overall results or otherwise departing from the true scope of the invention.

[0321] Various embodiments of the invention may be embodied in many different forms, including computer program logic for use with a processor (e.g., a microprocessor, microcontroller, digital signal processor, or general purpose computer and for that matter, any commercial processor may be used to implement the embodiments of the invention either as a single processor, serial or parallel set of processors in the system and, as such, examples of commercial processors include, but are not limited to Merced™, Pentium™, Pentium II™, Xeon™, Celeron™, Pentium Pro™, Efficeon™, Athlon™, AMD™ and the like), programmable logic for use with a programmable logic device (e.g., a Field Programmable Gate Array (FPGA) or other PLD), discrete components, integrated circuitry (e.g., an Application Specific Integrated Circuit (ASIC)), or any other means including any combination thereof. In an exemplary embodiment of the present invention, predominantly all of the communication between users and the server is implemented as a set of computer program instructions that is converted into a computer executable form, stored as such in a computer readable medium, and executed by a microprocessor under the control of an operating system.

[0322] Computer program logic implementing all or part of the functionality where described herein may be embodied in various forms, including a source code form, a computer executable form, and various intermediate forms (e.g., forms generated by an assembler, compiler, linker, or locator). Source code may include a series of computer program instructions implemented in any of various programming languages (e.g., an object code, an assembly language, or a high-level language such as Fortran, C, C++, JAVA, or HTML. Moreover, there are hundreds of available computer languages that may be used to implement embodiments of the invention, among the more common being Ada; Algol; APL; awk; Basic; C; C++; Conol; Delphi; Eiffel; Euphoria; Forth; Fortran; HTML, Icon; Java; Javascript; Lisp; Logo; Mathematica; MatLab; Miranda; Modula-2; Oberon; Pascal; Perl; PL/I, Prolog; Python; Rexx, SAS; Scheme; sed; Simula; Smalltalk; Snobol; SQL; Visual Basic; Visual C++; Linux and XML.) for use with various operating systems or operating environments. The source code may define and use various data structures and communication messages. The source code may be in a computer executable form (e.g., via an interpreter), or the source code may be converted (e.g., via a translator, assembler, or compiler) into a computer executable form.

[0323] The computer program may be fixed in any form (e.g., source code form, computer executable form, or an intermediate form) either permanently or transitorily in a tangible storage medium, such as a semiconductor memory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memory device (e.g., a diskette or fixed disk), an optical memory device (e.g., a CD-ROM or DVD-ROM), a PC card (e.g., PCMCIA card), or other memory device. The computer program may be fixed in any form in a signal that is transmittable to a computer using any of various communication technologies, including, but in no way limited to, analog technologies, digital technologies, optical technologies, wireless technologies (e.g., Bluetooth), networking technologies, and inter-networking technologies. The computer program may be distributed in any form as a removable storage medium with accompanying printed or electronic documentation (e.g., shrink wrapped software), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the communication system (e.g., the Internet or World Wide Web).

[0324] Hardware logic (including programmable logic for use with a programmable logic device) implementing all or part of the functionality where described herein may be designed using traditional manual methods, or may be designed, captured, simulated, or documented electronically using various tools, such as Computer Aided Design (CAD), a hardware description language (e.g., VHDL or AHDL), or a PLD programming language (e.g., PALASM, ABEL, or CUPL). Hardware logic may also be incorporated into display screens for implementing embodiments of the invention and which may be segmented display screens, analogue display screens, digital display screens, CRTs, LED screens, Plasma screens, liquid crystal diode screen, and the like.

[0325] Programmable logic may be fixed either permanently or transitorily in a tangible storage medium, such as a semiconductor memory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memory device (e.g., a diskette or fixed disk), an optical memory device (e.g., a CD-ROM or DVD-ROM), or other memory device. The programmable logic may be fixed in a signal that is transmittable to a computer using any of various communication technologies, including, but in no way limited to, analog technologies, digital technologies, optical technologies, wireless technologies (e.g., Bluetooth), networking technologies, and internetworking technologies. The programmable logic may be distributed as a removable storage medium with accompanying printed or electronic documentation (e.g., shrink wrapped software), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the communication system (e.g., the Internet or World Wide Web).

[0326] “Comprises/comprising” and “includes/including” when used in this specification is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps, components or groups thereof. Thus, unless the context clearly requires otherwise, throughout the description and the claims, the words ‘comprise’, ‘comprising’, ‘includes’, ‘including’ and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to”.