What is claimed is:
1. A method for determining at least one of a pulmonary capillary blood flow and a cardiac output of a patient, comprising:
determining a plurality of data comprising carbon dioxide elimination data and data of an indicator of carbon dioxide content in blood of the patient;
identifying a geometric relationship between said carbon dioxide elimination data and said data of said indicator of carbon dioxide content; and
calculating the pulmonary capillary blood flow or the carbon dioxide content based at least partially on said geometric relationship.
2. The method of claim 1, wherein said identifying said geometric relationship comprises identifying a substantially linear relationship between said carbon dioxide elimination data and said data of said indicator of carbon dioxide content.
3. The method of claim 1, wherein said identifying comprises use of linear regression.
4. The method of claim 2, wherein said identifying said substantially linear relationship comprises determining a best-fit line through said plurality of data.
5. The method of claim 1, wherein said identifying includes determining a slope of at least a portion of said geometric relationship.
6. The method of claim 1, wherein said identifying is based on all of said carbon dioxide elimination data and said data of said indicator of carbon dioxide obtained during said determining.
7. The method of claim 1, wherein said determining comprises measuring a partial pressure of carbon dioxide in end tidal respiration of the patient.
8. The method of claim 1, further comprising determining an accuracy with which said geometric relationship corresponds to said plurality of data.
9. The method of claim 8, wherein said determining said accuracy comprises determining a correlation coefficient indicative of a correlation between said carbon dioxide elimination data and said data of said indicator of carbon dioxide content.
10. The method of claim 1, further comprising filtering at least one of said carbon dioxide elimination data and said data of said indicator of carbon dioxide content.
11. The method of claim 10, wherein said filtering comprises employing a low-pass filter, a high-pass filter, or a band pass filter.
12. The method of claim 10, wherein said filtering comprises employing a geometric or a nongeometric filter.
13. The method of claim 10, wherein said filtering comprises filtering said carbon dioxide elimination data.
14. The method of claim 1, further comprising selecting data points to use in said determining said geometric relationship.
15. The method of claim 14, wherein said selecting comprises clustering said carbon dioxide elimination data and said data of said indicator of carbon dioxide content.
16. The method of claim 15, wherein said clustering is effected before said determining said geometric relationship.
17. The method of claim 15, further comprising normalizing said carbon dioxide elimination data and said data of said indicator of carbon dioxide content.
18. The method of claim 17, wherein said normalizing is effected before said clustering.
19. The method of claim 17, wherein said normalizing comprises selecting substantially equal ranges for both said carbon dioxide elimination data and said data of said indicator of carbon dioxide content.
20. The method of claim 17, further comprising calculating an inverse of said normalizing following said clustering and before said identifying said geometric relationship.
21. The method of claim 20, wherein said calculating said inverse of said normalizing comprises setting ranges for said carbon dioxide elimination data and said data of said indicator of carbon dioxide content back to original values.
22. The method of claim 15, wherein said clustering comprises:
identifying a predetermined number of data points having values closest to an analyzed data point selected from one of said carbon dioxide elimination data and said data of said indicator of carbon dioxide content;
calculating differences between each of said predetermined number of data points and said analyzed data point;
summing said differences to obtain a sum;
comparing said sum to a threshold value; and
disregarding said analyzed data point if said sum exceeds said threshold value.
23. The method of claim 15, further comprising filtering at least one of said carbon dioxide elimination data and said data of said indicator of carbon dioxide content.
24. The method of claim 14, wherein said selecting comprises:
intersecting a first line representing a minimum expected pulmonary capillary blood flow and a second line representing a maximum pulmonary capillary blood flow at an analyzed data point of said data points;
determining a number of others of said data points located between said first line and said second line;
comparing said number to a threshold number; and
retaining said analyzed data point if said number is equal to or greater than said threshold number.
25. The method of claim 24, further comprising repeating at least said intersecting, said determining said number of others of said data points, and said comparing on at least one other data point of said data points.
26. The method of claim 24, further comprising repeating at least said intersecting, said determining said number of others of said data points, and said comparing on each of said data points.
27. The method of claim 24, further comprising filtering at least one of said carbon dioxide elimination data and said data of said indicator of carbon dioxide content.
28. The method of claim 24, further comprising clustering said carbon dioxide elimination data and said data of said indicator of carbon dioxide content.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for diagnosing renal cell carcinoma in a patient comprising steps of:
preparing a composition containing a binding molecule that specifically binds NNMT protein;
contacting the composition with a biological sample from a patient to measure expression level of NNMT protein, wherein the biological sample is a sample selected from the group consisting of a blood sample, a serum sample and a plasma sample; and
comparing the expression level of NNMT protein in the biological sample from the patient with the expression level of NNMT protein in a corresponding biological sample from a normal individual; and
classifying a patient with an elevated expression level of NNMT protein as having renal cell carcinoma.
2. The method according to claim 1, wherein the binding molecule is an antibody and the antibody is one or more selected from the group consisting of a monoclonal antibody, a polyclonal antibody, a multispecific antibody, a fragment of the antibody, a recombinant antibody, and a chemically modified antibody.
3. The method according to claim 2, wherein the antibody fragment is Fab, F(ab\u2032)2, scFv, Fv, Fabc, a digestion product of an antibody using a proteolytic enzyme, or an antibody prepared using a recombinant DNA technique.
4. The method according to claim 2, wherein the antibody is a monoclonal antibody or a polyclonal antibody.
5. The method according to claim 4, wherein the monoclonal antibody is prepared using a method of producing a monoclonal antibody comprising immunizing a mammal with the renal cell carcinoma marker and collecting antibody-producing cells, fusing the antibody-producing cells with myeloma cells to establish a hybridoma, and obtaining the monoclonal antibody from the hybridoma.
6. The method according to claim 5, wherein the antibody-producing cells are splenocytes (spleen cells), lymph node cells, or peripheral blood cells.
7. The method according to claim 5, wherein the mammal is a rat, a mouse, a rabbit, or a monkey.
8. The method according to claim 5, wherein the myeloma cells are derived from an animal of the same species as the mammal, have drug selectivity, and cannot survive in an HAT selection medium supplemented with hypoxanthine, aminopterin and thymidine in a state of being not fused with splenocytes, but can survive in a state of being fused with splenocytes.
9. The method according to claim 5, wherein the renal cell carcinoma marker is a fragment of a full-length protein.