Mathematical Modelling Using Predictive Biomarkers for the Outcome of Canine Leishmaniasis upon Chemotherapy
Abstract
1. Introduction
2. Materials and Methods
2.1. Ethics Approval
2.2. Animals and Sampling
2.3. Clinical Evaluation and Staging
2.4. Anti-Leishmania Chemotherapy
2.5. Hematological and Biochemical Parameters
2.6. Quantification of Total Globulin and Specific Anti-Leishmania Antibodies
2.7. Parasitic Load
2.8. Quantitative PCR
2.9. Statistical Analysis
3. Results
3.1. Responder Dogs Exhibit a Decrease in Splenic and Bone Marrow Parasite Loads and Improvement of Clinical Staging
3.2. Responder Dogs Display a Th1 Signature
3.3. Non-Responder Dogs Failed to Restore the Hematological Parameters to Normal Reference Values
3.4. Serum Biochemistry Parameters Are Predictive of Visceral Organ Injury in Non-Responder Dogs
3.5. Development of a Mathematical Model with Predictive Value for the Success of CanL Chemotherapy
4. Discussion
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
Abbreviations
AIC | Akaike information criterion |
ALT | Alanine aminotransferase |
AST | Aspartate transaminase |
Ca | Calcium |
CanL | Canine leishmaniasis |
CI | Confidence interval |
CK | Creatine kinase |
CK-MB | Creatine kinase MB |
DOAJ | Directory of open access journals |
GGT | Gamma-glutamyl transferase |
GNR | Group of non-responder dogs |
GR | Group of responder dogs |
IFNG/IFN-γ | Interferon-gamma |
IL1 | Interleukin-1 |
IL2 | Interleukin-2 |
IL4 | Interleukin-4 |
IL5 | Interleukin-5 |
IL10 | Interleukin-10 |
IL17 | Interleukin-17 |
IL22 | Interleukin-22 |
LD | Linear dichroism |
MDPI | Multidisciplinary Digital Publishing Institute |
P | Phosphorus |
q-PCR | Quantitative polymerase chain reaction |
RR | Relative risk |
Th1 | T helper 1 cell |
Th2 | T helper 2 cell |
TLA | Three letter acronym |
TNFA/TNF-α | Tumor necrosis factor alpha |
UPC | Urinary protein/creatinine ratio |
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Parameters | Responders (R) | p-Value (T0 vs T12) | Non Responders (NR) | p-Value (T0 vs. T12) | Normality Value | p-Value (T0 R vs. T0 NR) | ||
---|---|---|---|---|---|---|---|---|
Hematology | T0 | T12 | T0 | T12 | ||||
Erythrocyte (x106/μL) | 6.68 | 6.73 | 0.497 | 5.01 | 4.97 | 0.940 | 5.5–8.5 | 0.033 |
Hemoglobin (g/dL) | 15.03 | 14.75 | 0.771 | 10.21 | 10.54 | 0.885 | 12–18 | 0.0002 |
Hematocrit (%) | 41.82 | 45.12 | 0.689 | 31.2 | 32.78 | 0.641 | 37–55 | 0.0023 |
Leucocytes (x103/μL) | 9.6 | 13 | 0.120 | 8.9 | 8,8 | 0.935 | 6–17 | 0.345 |
Lymphocytes (x103/mm3) | 1.69 | 1.92 | 0.373 | 1.92 | 1.41 | 0.181 | 1–4.8 | 0.406 |
Monocytes (/mm3) | 493 | 424 | 0.606 | 457 | 429 | 0.972 | 150–1350 | 0.776 |
Reticulocytes (x103/mm3) | 90 | 34.6 | 0.144 | 51.6 | 46.1 | 0.678 | <60 | 0.303 |
Serum biochemistry | ||||||||
Total proteins (g/dL) | 8.6 | 7.7 | 0.102 | 9 | 8.6 | 0.254 | 5.4–7.1 | 0.426 |
Albumin (g/dL) | 2.79 | 3.04 | 0.167 | 2.21 | 2.54 | 0.260 | 2.6–3.3 | 0.017 |
Globulins (g/dL) | 5.7 | 4.3 | 0.032 | 6.7 | 6.2 | 0.186 | 2.7–4.4 | 0.045 |
Albumin/Globulin (A/G) ratio | 0,5 | 0,8 | 0.0682 | 0,4 | 0,5 | 0.242 | 0.5–1.7 | 0.081 |
Calcium (mg/dL) | 11.73 | 10.50 | 0.330 | 10.87 | 10.39 | 0.218 | 9–11.3 | 0.064 |
GGT (U/L) | 5.78 | 4.54 | 0.329 | 4.73 | 6.11 | 0.218 | 1–10 | 0.736 |
Creatinine (mg/dL) | 1.26 | 1.0 | 0.082 | 1.0 | 1.2 | 0.330 | 0.5–1.4 | 0.920 |
Urea (mg/dL) | 43.8 | 37.6 | 0.579 | 36.2 | 22.55 | 0.060 | 21–60 | 0.591 |
Cholesterol (mg/dL) | 22.9 | 33.5 | <0.0001 | 20.71 | 28.8 | 0.001 | 31–71 | 0.112 |
Magnesium (mg/dL) | 1.62 | 2.22 | 0.264 | 1.66 | 1.86 | 0.017 | 1.8–2.4 | 0.104 |
HDL (mg/dL) | 57.6 | 145.2 | <0.0001 | 45.5 | 112.9 | <0.0001 | 33–120 | 0.062 |
Triglycerides (mg/dL) | 75.72 | 73.42 | 0.853 | 72.7 | 100 | 0.029 | 20–112 | 0.333 |
ALT (UI/L) | 51.2 | 66.9 | 0.830 | 42.4 | 66.8 | 0.117 | 21–102 | 0.214 |
FA (UI/L) | 74.97 | 86.82 | 0.587 | 101 | 124.7 | 0.933 | 20–156 | 0.255 |
AST (UI/L) | 23.96 | 32.82 | 0.087 | 40.93 | 32.82 | 0.615 | 23–66 | 0.368 |
Glucose (mg/dL) | 93.7 | 90 | 0.472 | 90.8 | 87.5 | 0.617 | 65–118 | 0.497 |
Iron (μg/dL) | 280.07 | 85.29 | 0.094 | 360.4 | 142.2 | 0.075 | 30–180 | 0.149 |
Phosphorous (mg/dL) | 3.24 | 2.84 | 0.401 | 4.61 | 3.074 | 0.011 | 2.5–6.0 | 0.0085 |
Potassium (mmol/L) | 13.40 | 13.38 | 0.980 | 14.95 | 14.42 | 0.229 | 12.3–15.7 ** | 0.0023 |
CK (U/L) | 47.08 | 152.39 | <0.0001 | 37.35 | 87.79 | 0.106 | 100–200 ** | 0.504 |
CK-MB (U/L) | 145.71 | 24.84 | 0.310 | 82.07 | 38.07 | 0.121 | 31–38.8 | 0.432 |
Cholinesterase (U/L) | 4022.2 | 4200.5 | 0.737 | 3,198 | 2,807 | 0.911 | 1210–3020 | 0.057 |
UPC | 0.48 | 0.31 | 0.113 | 0.44 | 0,85 | 0.342 | 0.5 # | 0.833 |
Serologic | ||||||||
ELISA (OD)—Anti-Leishmania IgG | 0.42 | 0.13 | 0.032 | 1.27 | 0.81 | 0.155 | 0.17** | 0.324 |
Parasite Load | ||||||||
Spleen (Parasite Burden (log10)) | 3.225 | 1.398 | <0.0001 | 3.761 | 4.252 | 0.969 | 0.382 | |
Bone marrow (Parasite Burden (log10)) | 2.580 | 0.408 | <0.0001 | 2.879 | 2.579 | 0.431 | 0.748 |
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de Sousa Gonçalves, R.; Alves de Pinho, F.; Dinis-Oliveira, R.J.; Azevedo, R.; Gaifem, J.; Farias Larangeira, D.; Ramos-Sanchez, E.M.; Goto, H.; Silvestre, R.; Barrouin-Melo, S.M. Mathematical Modelling Using Predictive Biomarkers for the Outcome of Canine Leishmaniasis upon Chemotherapy. Microorganisms 2020, 8, 745. https://doi.org/10.3390/microorganisms8050745
de Sousa Gonçalves R, Alves de Pinho F, Dinis-Oliveira RJ, Azevedo R, Gaifem J, Farias Larangeira D, Ramos-Sanchez EM, Goto H, Silvestre R, Barrouin-Melo SM. Mathematical Modelling Using Predictive Biomarkers for the Outcome of Canine Leishmaniasis upon Chemotherapy. Microorganisms. 2020; 8(5):745. https://doi.org/10.3390/microorganisms8050745
Chicago/Turabian Stylede Sousa Gonçalves, Rafaela, Flaviane Alves de Pinho, Ricardo Jorge Dinis-Oliveira, Rui Azevedo, Joana Gaifem, Daniela Farias Larangeira, Eduardo Milton Ramos-Sanchez, Hiro Goto, Ricardo Silvestre, and Stella Maria Barrouin-Melo. 2020. "Mathematical Modelling Using Predictive Biomarkers for the Outcome of Canine Leishmaniasis upon Chemotherapy" Microorganisms 8, no. 5: 745. https://doi.org/10.3390/microorganisms8050745
APA Stylede Sousa Gonçalves, R., Alves de Pinho, F., Dinis-Oliveira, R. J., Azevedo, R., Gaifem, J., Farias Larangeira, D., Ramos-Sanchez, E. M., Goto, H., Silvestre, R., & Barrouin-Melo, S. M. (2020). Mathematical Modelling Using Predictive Biomarkers for the Outcome of Canine Leishmaniasis upon Chemotherapy. Microorganisms, 8(5), 745. https://doi.org/10.3390/microorganisms8050745