CLINICAL MASS SPECTROMETRY GROUP

FEATURED PUBLICATIONS

Selected papers representing our main research directions

A curated selection of publications highlighting our work in lipidomics, metabolomics, clinical mass spectrometry and translational laboratory medicine.

For the most up-to-date publication record, see our profiles: Google Scholar | PubMed | Web of Science

DATA ANALYSIS

A practical high-impact methodological paper providing open tools and recommendations for processing, statistical analysis and visualization of lipidomics and metabolomics data.

CLINICAL LIPIDOMICS

A landmark serum lipidomics study demonstrating the potential of lipid profiles for cancer detection and translational biomarker discovery.

CARDIOVASCULAR LIPIDOMICS

Shows how ceramide-based lipid scores may support laboratory-based cardiovascular risk stratification in clinically defined patient cohorts.

LABORATORY MEDICINE

Positions lipidomics as an emerging discipline within data-driven laboratory medicine and translational diagnostics.

INHERITED METABOLIC DISORDERS

Robust, semi-automated LC-MS/MS workflows for clinical diagnostics of inherited metabolic disorders.

METHOD DEVELOPMENT

A recent analytical study demonstrating broad metabolite coverage for diagnostic LC-MS/MS workflows in inherited metabolic disorders.

INFLAMMATION - GOUT

Demonstrates how lipidomics can distinguish clinically relevant disease phenotypes and treatment effects in inflammatory and metabolic disease.

NEURODEGENERATION - LIPID METABOLISM

A translational lipidomics study linking lipid metabolism to neurodegenerative disease progression and neurofibrillary pathology.

CLINICAL METABOLOMICS - LABORATORY MEDICINE

Connects the group with the international discussion on how metabolomics is entering clinical laboratory medicine.

LIST OF ALL PUBLICATIONS

2026

  1. A hybrid RNA-based reporter assay for robust quantification of cytidine deaminase activity. Ligasova et al.. Nucleic Acids Research. 2026;54.0(11):gkag588. https://doi.org/10.1093/nar/gkag588
  2. Derivatisation-free FIA-MS/MS assay for rapid simultaneous screening of atypical myopathy biomarkers and acylcarnitine profiles from dried blood spots. Kadlackova et al.. Analytical And Bioanalytical Chemistry. 2026. https://doi.org/10.1007/s00216-026-06566-3
  3. Long-Term Static Cultivation Alters Lipid Metabolism and Bioenergetic Capacity in A549 Cells. Durisova et al.. International Journal Of Molecular Sciences. 2026;27.0(8):3417. https://doi.org/10.3390/ijms27083417
  4. Aromatase Inhibitor Therapy Is Associated with Distinct Plasma Lipidomic Profiles in Postmenopausal Breast Cancer Patients. Arsic et al.. International Journal Of Molecular Sciences. 2026;27.0(4):1926. https://doi.org/10.3390/ijms27041926
  5. ABCA7 deficiency exacerbates glutamate excitotoxicity in Alzheimer's disease mice - A new pharmacological target for Glu-related neurotoxicity. Gorska et al.. Progress In Neurobiology. 2026;259.0:102891. https://doi.org/10.1016/j.pneurobio.2026.102891

2025

  1. Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data. Idkowiak et al.. Nature Communications. 2025;16.0(1):8714. https://doi.org/10.1038/s41467-025-63751-1
  2. Ceramide-based risk score: A novel laboratory tool for cardiovascular risk stratification in hyperuricemia and gout. Kvasnicka et al.. Vascular Pharmacology. 2025;159.0:107495. https://doi.org/10.1016/j.vph.2025.107495
  3. Cytidine and dCMP Deaminases-Current Methods of Activity Analysis. Ligasova et al.. International Journal Of Molecular Sciences. 2025;26.0(16):8045. https://doi.org/10.3390/ijms26168045
  4. Sex-dependent efficacy of sphingosine-1-phosphate receptor agonist FTY720 in mitigating Huntington's disease. Wu et al.. Pharmacological Research. 2025;211.0:107557. https://doi.org/10.1016/j.phrs.2024.107557
  5. Comparison of inflammatory biomarker levels in neurodegenerative proteinopathies: a case-control study. Cook et al.. Journal Of Neural Transmission. 2025;132.0(6):811–826. https://doi.org/10.1007/s00702-025-02902-6
  6. MInfer: Bridging MetaboAnalyst and Jacobian analysis for metabolomic networks. Schwarzerova et al.. Computer Methods And Programs In Biomedicine. 2025;263.0:108672. https://doi.org/10.1016/j.cmpb.2025.108672
  7. The kinetics of uracil-N-glycosylase distribution inside replication foci. Ligasova et al.. Scientific Reports. 2025;15.0(1):3026. https://doi.org/10.1038/s41598-024-84408-x
  8. Horse with myopathy caused by consumption of box elder tree seedlings in the Czech Republic. Jahn et al.. Equine Veterinary Education. 2025;37.0(5):e77–e84. https://doi.org/10.1111/eve.14081
  9. Serum Neopterin and Mortality in Lung Cancer Patients Treated With Immune Checkpoint Inhibitors. Spisarova et al.. Journal Of Thoracic Oncology. 2025;20.0(10).
  10. Multi-omics insights into human brown and white adipose tissue: identifying novel regulators of thermogenesis. Petriskova et al.. Diabetologia. 2025;68.0:S337–S338.
  11. Comprehensive metabolomic/lipidomic characterization of patients with mitochondrial ATP synthase, short-chain acyl-CoA dehydrogenase and combined variant deficiencies. Dobesova et al.. Heliyon. 2025;11.0(4):e42797. https://doi.org/10.1016/j.heliyon.2025.e42797
  12. Enhanced metabolomic predictions using concept drift analysis: identification and correction of confounding factors. Schwarzerova et al.. Bioinformatics Advances. 2025;5.0(1):vbaf073. https://doi.org/10.1093/bioadv/vbaf073

2024

  1. Changes in lipid metabolism track with the progression of neurofibrillary pathology in tauopathies. Olesova et al.. Journal Of Neuroinflammation. 2024;21.0(1):78. https://doi.org/10.1186/s12974-024-03060-4
  2. Patients with Neurodegenerative Proteinopathies Exhibit Altered Tryptophan Metabolism in the Serum and Cerebrospinal Fluid. Kaleta et al.. Acs Chemical Neuroscience. 2024;15.0(3):582–592. https://doi.org/10.1021/acschemneuro.3c00611
  3. Parallel Metabolomics and Lipidomics of a PSMA/GCPII Deficient Mouse Model Reveal Alteration of NAAG Levels and Brain Lipid Composition. Sedlak et al.. Acs Chemical Neuroscience. 2024;15.0(7):1342–1355. https://doi.org/10.1021/acschemneuro.3c00494
  4. A global perspective on the status of clinical metabolomics in laboratory medicine - a survey by the IFCC metabolomics working group. Fux et al.. Clinical Chemistry And Laboratory Medicine. 2024;62.0(10):1950–1961. https://doi.org/10.1515/cclm-2024-0550
  5. Assessing HCH isomer uptake in Alnus glutinosa: implications for phytoremediation and microbial response. Amirbekov et al.. Scientific Reports. 2024;14.0(1):4187. https://doi.org/10.1038/s41598-024-54235-1
  6. Wide metabolite coverage LC-MS/MS assay for the diagnosis of inherited metabolic disorders in urine. Ivanovova et al.. Talanta. 2024;271.0:125699. https://doi.org/10.1016/j.talanta.2024.125699
  7. Utilizing neurodegenerative markers for the diagnostic evaluation of amyotrophic lateral sclerosis. Klicova et al.. European Journal Of Medical Research. 2024;29.0(1):31. https://doi.org/10.1186/s40001-023-01596-4
  8. Long-chain polyunsaturated fatty acid-containing phosphatidylcholines predict survival rate in patients after heart failure. Kvasnicka et al.. Heliyon. 2024;10.0(21):e39979. https://doi.org/10.1016/j.heliyon.2024.e39979
  9. Adipokine Levels of RBP4, Resistin and Nesfatin-1 in Women Diagnosed With Gestational Diabetes. Kucerova et al.. Physiological Research. 2024;73.0(6):1037–1048. https://doi.org/10.33549/physiolres.935412
  10. Aminoacylase 1 deficiency: case report on three affected siblings. Smolka et al.. Ame Case Reports. 2024;8.0. https://doi.org/10.21037/acr-23-46
  11. CERT Score as a Potential Tool to Predict Cardiovascular Risk in Gout and Hyperuricemia. Stiburkova et al.. Arthritis & Rheumatology. 2024;76.0:2234–2235.

2023

  1. Alterations in lipidome profiles distinguish early-onset hyperuricemia, gout, and the effect of urate-lowering treatment. Kvasnicka et al.. Arthritis Research & Therapy. 2023;25.0(1):234. https://doi.org/10.1186/s13075-023-03204-6
  2. Clinical lipidomics in the era of the big data. Kvasnicka et al.. Clinical Chemistry And Laboratory Medicine. 2023;61.0(4):587–598. https://doi.org/10.1515/cclm-2022-1105
  3. From big data to better patient outcomes. Hulsen et al.. Clinical Chemistry And Laboratory Medicine. 2023;61.0(4):580–586. https://doi.org/10.1515/cclm-2022-1096
  4. Cerebrospinal fluid and blood serum biomarkers in neurodegenerative proteinopathies: A prospective, open, cross-correlation study. Konickova et al.. Journal Of Neurochemistry. 2023;167.0(2):168–182. https://doi.org/10.1111/jnc.15944
  5. A new technique for the analysis of metabolic pathways of cytidine analogues and cytidine deaminase activities in cells. Ligasova et al.. Scientific Reports. 2023;13.0(1):20530. https://doi.org/10.1038/s41598-023-47792-4
  6. Rapid and efficient LC-MS/MS diagnosis of inherited metabolic disorders: a semi-automated workflow for analysis of organic acids, acylglycines, and acylcarnitines in urine. Pisklakova et al.. Clinical Chemistry And Laboratory Medicine. 2023;61.0(11):2017–2027. https://doi.org/10.1515/cclm-2023-0084
  7. Principal balances of compositional data for regression and classification using partial least squares. Nesrstova et al.. Journal Of Chemometrics. 2023;37.0(12):e3518. https://doi.org/10.1002/cem.3518
  8. Multivariate Independent Component Analysis Identifies Patients in Newborn Screening Equally to Adjusted Reference Ranges. Kouril et al.. International Journal Of Neonatal Screening. 2023;9.0(4):60. https://doi.org/10.3390/ijns9040060
  9. Determining Thrombogenicity: Using a Modified Thrombin Generation Assay to Detect the Level of Thrombotic Event Risk in Lupus Anticoagulant-Positive Patients. Bradacova et al.. Biomedicines. 2023;11.0(12):3329. https://doi.org/10.3390/biomedicines11123329
  10. Metabolomic Profiling Confirms the Key Role of Oxidative Stress in the Pathophysiology of Diamond-Blackfan Anemia. Jahoda et al.. Blood. 2023;142.0. https://doi.org/10.1182/blood-2023-174840
  11. Selective pivot logratio coordinates for partial least squares discriminant analysis modelling with applications in metabolomics. Stefelova et al.. Stat. 2023;12.0(1):e592. https://doi.org/10.1002/sta4.592
  12. Systematic Comparison of Advanced Network Analysis and Visualization of Lipidomics Data. Schwarzerova et al.. Bioinformatics And Biomedical Engineering, Iwbbio 2023, Pt I. 2023;13919.0:378–389. https://doi.org/10.1007/978-3-031-34953-9_30

2022

  1. Lipidomic profiling of human serum enables detection of pancreatic cancer. Wolrab et al.. Nature Communications. 2022;13.0(1):124. https://doi.org/10.1038/s41467-021-27765-9
  2. Biomarkers of Neurodegenerative Diseases: Biology, Taxonomy, Clinical Relevance, and Current Research Status. Konickova et al.. Biomedicines. 2022;10.0(7):1760. https://doi.org/10.3390/biomedicines10071760
  3. Specific gut bacterial and fungal microbiota pattern in the first half of pregnancy is linked to the development of gestational diabetes mellitus in the cohort including obese women. Vavreckova et al.. Frontiers In Endocrinology. 2022;13.0:970825. https://doi.org/10.3389/fendo.2022.970825
  4. Lipidomic and metabolomic analysis reveals changes in biochemical pathways for non-small cell lung cancer tissues. Cifkova et al.. Biochimica Et Biophysica Acta-Molecular And Cell Biology Of Lipids. 2022;1867.0(2):159082. https://doi.org/10.1016/j.bbalip.2021.159082
  5. Metabolomic, Lipidomic and Proteomic Characterisation of Lipopolysaccharide-induced Inflammation Mouse Model. Puris et al.. Neuroscience. 2022;496.0:165–178. https://doi.org/10.1016/j.neuroscience.2022.05.030
  6. Altered Plasma, Urine, and Tissue Profiles of Sulfatides and Sphingomyelins in Patients with Renal Cell Carcinoma. Jirasko et al.. Cancers. 2022;14.0(19):4622. https://doi.org/10.3390/cancers14194622
  7. Dynamics of acylcarnitines, hypoglycin A, methylenecyclopropylglycine and their metabolites in a Kladruber stallion with atypical myopathy. Jahn et al.. Veterinary Quarterly. 2022;42.0(1):183–191. https://doi.org/10.1080/01652176.2022.2126537
  8. Determination of Thrombogenicity Levels of Various Antiphospholipid Antibodies by a Modified Thrombin Generation Assay in Patients with Suspected Antiphospholipid Syndrome. Bradacova et al.. International Journal Of Molecular Sciences. 2022;23.0(16):8973. https://doi.org/10.3390/ijms23168973
  9. Combined Targeted and Untargeted Profiling of HeLa Cells Deficient in Purine De Novo Synthesis. Madrova et al.. Metabolites. 2022;12.0(3):241. https://doi.org/10.3390/metabo12030241
  10. Targeted Plasma Lipidomics Distinguishes Patients with Gout and Hyperuricemia from Controls. Stiburkova et al.. Arthritis & Rheumatology. 2022;74.0:3598–3599.
  11. Metabolomic analysis of HeLa cells deficient in purine de novo synthesis. Madrova et al.. Clinica Chimica Acta. 2022;530.0:S404–S405. https://doi.org/10.1016/j.cca.2022.04.426

2021

  1. Impact of Newborn Screening and Early Dietary Management on Clinical Outcome of Patients with Long Chain 3-Hydroxyacyl-CoA Dehydrogenase Deficiency and Medium Chain Acyl-CoA Dehydrogenase Deficiency-A Retrospective Nationwide Study. Rucklova et al.. Nutrients. 2021;13.0(9):2925. https://doi.org/10.3390/nu13092925
  2. Metabolomic and lipidomic changes triggered by lipopolysaccharide-induced systemic inflammation in transgenic APdE9 mice. Puris et al.. Scientific Reports. 2021;11.0(1):13076. https://doi.org/10.1038/s41598-021-92602-4
  3. GM3 Ganglioside Linked to Neurofibrillary Pathology in a Transgenic Rat Model for Tauopathy. Olesova et al.. International Journal Of Molecular Sciences. 2021;22.0(22):12581. https://doi.org/10.3390/ijms222212581
  4. SLIDE-Novel Approach to Apocrine Sweat Sampling for Lipid Profiling in Healthy Individuals. Kvasnicka et al.. International Journal Of Molecular Sciences. 2021;22.0(15):8054. https://doi.org/10.3390/ijms22158054
  5. Novel LC-MS tools for diagnosing inborn errors of metabolism. Ivanovova et al.. Microchemical Journal. 2021;170.0:106654. https://doi.org/10.1016/j.microc.2021.106654
  6. Evaluation of the Determination of Dabigatran, Rivaroxaban, and Apixaban in Lupus Anticoagulant-Positive Patients. Ulehlova et al.. Diagnostics. 2021;11.0(11):2027. https://doi.org/10.3390/diagnostics11112027
  7. Plasma Short-Chain Fatty Acids and Their Derivatives in Women with Gestational Diabetes Mellitus. Ivanovova et al.. Separations. 2021;8.0(10):188. https://doi.org/10.3390/separations8100188

2020

  1. CROP: correlation-based reduction of feature multiplicities in untargeted metabolomic data. Kouril et al.. Bioinformatics. 2020;36.0(9):2941–2942. https://doi.org/10.1093/bioinformatics/btaa012
  2. Cellwise outlier detection and biomarker identification in metabolomics based on pairwise log ratios. Walach et al.. Journal Of Chemometrics. 2020;34.0(1):e3182. https://doi.org/10.1002/cem.3182
  3. Bayesian multiple hypotheses testing in compositional analysis of untargeted metabolomic data. de Sousa et al.. Analytica Chimica Acta. 2020;1097.0:49–61. https://doi.org/10.1016/j.aca.2019.11.006
  4. Global DNA Methylation in Rats' Liver Is Not Affected by Hypercholesterolemic Diet. Jurcikova-Novotna et al.. Physiological Research. 2020;69.0(2):347–352. https://doi.org/10.33549/physiolres.934313

2010 - 2019

  1. Evaluation of the DOAC-Stop Procedure by LC-MS/MS Assays for Determining the Residual Activity of Dabigatran, Rivaroxaban, and Apixaban. Slavik et al.. Clinical And Applied Thrombosis-Hemostasis. 2019;25.0:1076029619872556. https://doi.org/10.1177/1076029619872556
  2. EPIDEMIOLOGY OF RARE DISEASES DETECTED BY NEWBORN SCREENING IN THE CZECH REPUBLIC. David et al.. Central European Journal Of Public Health. 2019;27.0(2):153–159. https://doi.org/10.21101/cejph.a5441
  3. Impact of sample dimensionality on orthogonality metrics in comprehensive two-dimensional separations. Jacova et al.. Analytica Chimica Acta. 2019;1064.0:138–149. https://doi.org/10.1016/j.aca.2019.03.018
  4. Urease-immobilized magnetic microparticles in urine sample preparation for metabolomic analysis by gas chromatography-mass spectrometry. Jacova et al.. Journal Of Chromatography A. 2019;1605.0:360355. https://doi.org/10.1016/j.chroma.2019.07.009
  5. White Adipose Tissue Response of Obese Mice to Ambient Oxygen Restriction at Thermoneutrality: Response Markers Identified, but no WAT Inflammation. Hoevenaars et al.. Genes. 2019;10.0(5):359. https://doi.org/10.3390/genes10050359
  6. Mass spectrometric analysis of purine de novo biosynthesis intermediates. Madrova et al.. Plos One. 2018;13.0(12):e0208947. https://doi.org/10.1371/journal.pone.0208947
  7. Structural elucidation of novel biomarkers of known metabolic disorders based on multistage fragmentation mass spectra. Vaclavik et al.. Journal Of Inherited Metabolic Disease. 2018;41.0(3):407–414. https://doi.org/10.1007/s10545-017-0109-4
  8. Human White Adipose Tissue Metabolome: Current Perspective. Kucera et al.. Obesity. 2018;26.0(12):1870–1878. https://doi.org/10.1002/oby.22336
  9. Newborn foal with atypical myopathy. Karlikova et al.. Journal Of Veterinary Internal Medicine. 2018;32.0(5):1768–1772. https://doi.org/10.1111/jvim.15236
  10. Multianalyte Determination of NOACs Using LC-MS/MS and Comparison with Functional Coagulation Assays. Slavik et al.. Clinical Laboratory. 2018;64.0(10):1611–1621. https://doi.org/10.7754/Clin.Lab.2018.180335
  11. Radio-sensitizing effects of VE-821 and beyond: Distinct phosphoproteomic and metabolomic changes after ATR inhibition in irradiated MOLT-4 cells. Salovska et al.. Plos One. 2018;13.0(7):e0199349. https://doi.org/10.1371/journal.pone.0199349
  12. Toll-Like Receptor 7/8 Ligand, S28463, Suppresses Ascaris suum-induced Allergic Asthma in Nonhuman Primates. Camateros et al.. American Journal Of Respiratory Cell And Molecular Biology. 2018;58.0(1):55–65. https://doi.org/10.1165/rcmb.2017-0184OC
  13. Metabolic Response of Visceral White Adipose Tissue of Obese Mice Exposed for 5 Days to Human Room Temperature Compared to Mouse Thermoneutrality. van der Stelt et al.. Frontiers In Physiology. 2017;8.0:179. https://doi.org/10.3389/fphys.2017.00179
  14. Metabolic status of CSF distinguishes rats with tauopathy from controls. Karlikova et al.. Alzheimers Research & Therapy. 2017;9.0:78. https://doi.org/10.1186/s13195-017-0303-5
  15. Novel sulphur-containing imatinib metabolites found by untargeted LC-HRMS analysis. Vrobel et al.. European Journal Of Pharmaceutical Sciences. 2017;104.0:335–343. https://doi.org/10.1016/j.ejps.2017.04.014
  16. Sample-independent approach to normalize two-dimensional data for orthogonality evaluation using whole separation space scaling. Jacova et al.. Journal Of Chromatography A. 2017;1511.0:1–8. https://doi.org/10.1016/j.chroma.2017.06.076
  17. Metabolite Profiling of the Plasma and Leukocytes of Chronic Myeloid Leukemia Patients. Karlikova et al.. Journal Of Proteome Research. 2016;15.0(9):3158–3166. https://doi.org/10.1021/acs.jproteome.6b00356
  18. Influence of Mass Resolving Power in Orbital Ion-Trap Mass Spectrometry-Based Metabolomics. Najdekr et al.. Analytical Chemistry. 2016;88.0(23):11429–11435. https://doi.org/10.1021/acs.analchem.6b02319
  19. Citrulline as a biomarker of gastrointestinal toxicity in patients with rectal carcinoma treated with chemoradiation. Zezulova et al.. Clinical Chemistry And Laboratory Medicine. 2016;54.0(2):305–314. https://doi.org/10.1515/cclm-2015-0326
  20. Dr Jekyll and Mr Hyde: a strange case of 5-ethynyl-2′-deoxyuridine and 5-ethynyl-2′- deoxycytidine. Ligasova et al.. Open Biology. 2016;6.0(1):150172. https://doi.org/10.1098/rsob.150172
  21. A lower dosage of imatinib is sufficient to maintain undetectable disease in patients with chronic myeloid leukemia with long-term low-grade toxicity of the treatment. Faber et al.. Leukemia & Lymphoma. 2016;57.0(2):370–375. https://doi.org/10.3109/10428194.2015.1056184
  22. Equine atypical myopathy: A metabolic study. Karlikova et al.. Veterinary Journal. 2016;216.0:125–132. https://doi.org/10.1016/j.tvjl.2016.07.015
  23. Normalization techniques for PARAFAC modeling of urine metabolomic data. Gardlo et al.. Metabolomics. 2016;12.0(7):117. https://doi.org/10.1007/s11306-016-1059-9
  24. Ultrafast Online SPE-MS/MS Method for Quantification of 3 Tyrosine Kinase Inhibitors in Human Plasma. Vrobel et al.. Therapeutic Drug Monitoring. 2016;38.0(4):516–524. https://doi.org/10.1097/FTD.0000000000000309
  25. Amlodipine plasma levels and side effects in patients with arterial hypertension. Kocianova et al.. European Heart Journal. 2016;37.0:266–266.

  27. Oxidized phosphatidylcholines suggest oxidative stress in patients with medium-chain acyl-CoA dehydrogenase deficiency. Najdekr et al.. Talanta. 2015;139.0:62–66. https://doi.org/10.1016/j.talanta.2015.02.041
  28. A Fatal Combination: A Thymidylate Synthase Inhibitor with DNA Damaging Activity. Ligasova et al.. Plos One. 2015;10.0(2):e0117459. https://doi.org/10.1371/journal.pone.0117459
  29. Untargeted metabolomic analysis of urine samples in the diagnosis of some inherited metabolic disorders. Janeckova et al.. Biomedical Papers-Olomouc. 2015;159.0(4):582–585. https://doi.org/10.5507/bp.2014.048
  30. Detailed study of imatinib metabolization using high-resolution mass spectrometry. Friedecky et al.. Journal Of Chromatography A. 2015;1409.0:173–181. https://doi.org/10.1016/j.chroma.2015.07.033
  31. Fenretinide Prevents Inflammation and Airway Hyperresponsiveness in a Mouse Model of Allergic Asthma. Kanagaratham et al.. American Journal Of Respiratory Cell And Molecular Biology. 2014;51.0(6):783–792. https://doi.org/10.1165/rcmb.2014-0121OC
  32. INFLUENCE OF MASS SPECTROMETRY RESOLUTION ON METABOLITE COVERAGE IN PLASMA. Najdekr et al.. Cece 2014: 11Th International Interdisciplinary Meeting On Bioanalysis. 2014:92–93.
  33. A significant proportion of patients with chronic myeloid leukemia and suboptimal response according to European Leukemia Net criteria have excellent prognosis without treatment change. Rohon et al.. Biomedical Papers-Olomouc. 2013;157.0(2):181–188. https://doi.org/10.5507/bp.2011.059
  34. Metabolomic Techniques in Biomedicine. Wojtowicz et al.. Chemicke Listy. 2013;107.0(1):3–11.

  36. Targeted metabolomic analysis of plasma samples for the diagnosis of inherited metabolic disorders. Janeckova et al.. Journal Of Chromatography A. 2012;1226.0:11–17. https://doi.org/10.1016/j.chroma.2011.09.074
  37. Isotope dilution direct injection mass spectrometry method for determination of four tyrosine kinase inhibitors in human plasma. Micova et al.. Talanta. 2012;93.0:307–313. https://doi.org/10.1016/j.talanta.2012.02.038
  38. Imatinib trough plasma levels do not correlate with the response to therapy in patients with chronic myeloid leukemia in routine clinical setting. Faber et al.. Annals Of Hematology. 2012;91.0(6):923–929. https://doi.org/10.1007/s00277-011-1394-x
  39. Analysis of cytokinin nucleotides by capillary zone electrophoresis with diode array and mass spectrometric detection in a recombinant enzyme in vitro reaction. Beres et al.. Analytica Chimica Acta. 2012;751.0:176–181. https://doi.org/10.1016/j.aca.2012.08.049
  40. High lapatinib plasma levels in breast cancer patients: risk or benefit?. Cizkova et al.. Tumori Journal. 2012;98.0(1):162–165. https://doi.org/10.1177/030089161209800123
  41. Analysis of Nucleotides in Dry Blood Spots. Baresova et al.. Chemicke Listy. 2011;105.0(3):207–211.
  42. Flow injection analysis vs. ultra high performance liquid chromatography coupled with tandem mass spectrometry for determination of imatinib in human plasma. Micova et al.. Clinica Chimica Acta. 2010;411.0(23-24):1957–1962. https://doi.org/10.1016/j.cca.2010.08.014
  43. LAPATINIB IN BREAST CANCER - THE PREDICTIVE SIGNIFICANCE OF HER1 (EGFR), HER2, PTEN AND PIK3CA GENES AND LAPATINIB PLASMA LEVEL ASSESSMENT. Bouchalova et al.. Biomedical Papers-Olomouc. 2010;154.0(4):281–288. https://doi.org/10.5507/bp.2010.043
  44. Imatinib dose escalation in two patients with chronic myeloid leukemia, with low trough imatinib plasma levels measured at various intervals from the beginning of therapy and with suboptimal treatment response, leads to the achievement of higher plasma levels and major molecular response. Faber et al.. International Journal Of Hematology. 2010;91.0(5):897–902. https://doi.org/10.1007/s12185-010-0576-y
  45. Determination of Tyrosine Kinase Inhibitors in Human Plasma by UHPLC-MS/MS. Micova et al.. Chemicke Listy. 2010;104.0:S31–S34.
  46. Oligodendroglia from ADSL-deficient patient produce SAICAribotide and SAMP. Zidkova et al.. Molecular Genetics And Metabolism. 2010;101.0(2-3):286–288. https://doi.org/10.1016/j.ymgme.2010.06.014
  47. MEASUREMENT OF TROUGH IMATINIB PLASMA LEVELS IN PATIENTS WITH CML DOES NOT SIGNIFICANTLY CORRELATE WITH TREATMENT RESPONSE BUT MAY BE SUCCESSFULLY USED IN SELECTED PATIENTS FOR DOSAGE ADJUSTMENT. Faber et al.. Haematologica-The Hematology Journal. 2010;95.0:343–344.
  48. NUCLEOTIDE ANALYSIS IN BLOOD SPOTS. Baresova et al.. Chemicke Listy. 2010;104.0:S3–S5.

1998 - 2009

  1. Capillary electrophoresis determination of thiopurine methyl transferase activity in erythrocytes. Tomkova et al.. Journal Of Chromatography B-Analytical Technologies In The Biomedical And Life Sciences. 2009;877.0(20-21):1943–1945. https://doi.org/10.1016/j.jchromb.2009.05.005
  2. IMATINIB PLASMA LEVELS CORRELATE WITH MOLECULAR RESPONSE IN CIVIL PATIENTS. Faber et al.. Haematologica-The Hematology Journal. 2008;93.0:219–220.
  3. ITPase activity in dry blood spots is comparable with that in fresh erythrocytes. Tomkova et al.. Nucleosides Nucleotides & Nucleic Acids. 2008;27.0(6-7):656–660. https://doi.org/10.1080/15257770802143897
  4. DETERMINATION OF IMATINIB IN PLASMA BY HIGH PERFORMANCE CAPILLARY ELECTROPHORESIS. Faber et al.. Haematologica-The Hematology Journal. 2008;93.0:442–443.
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