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Label-free detection of thalassemia and other ROS impairing diseases

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Abstract

Pathogenesis of different diseases showed that some of them, especially thalassemia (T) and rheumatoid arthritis (RA) have an implicit association with oxidative stress and altered levels of reactive oxygen species (ROS). Introducing ROS level and the balance between ROS and antioxidants as essential metrics, an attempt was made to classify T and RA from normal individuals (treated as controls)(C) using synchronous fluorescence spectroscopy (SFS) and Raman line intensity of water. This non-invasive and label-free approach was backed up by a categorization algorithm that helped in the prediction of disease types from serum samples. The predictive system constituted principal component analysis (PCA) with four parameters, namely spectral intensity ratios of reduced nicotinamide adenine dinucleotide (NADH) to tryptophan (Trp) (NADH/Trp), kynurenine (Kyn) to tryptophan (Kyn/Trp), kynurenine to NADH (Kyn/NADH), and logarithmic changes in Raman line intensity of water (Rline), with the index headers containing the disease types. Rline has a positive correlation with both Kyn/Trp and Kyn/NADH and a negative correlation with NADH/Trp ratio, implying its direct or indirect association with oxidative stress. In addition to the classification of T, RA, and C a sub-classification of T into beta major and E-beta in their post and pre-splenectomized surgical stages could also be realized. Furthermore, receiver operating characteristic (ROC) analysis was deployed to ascertain that the misclassification error (ME) was negligible for the disease types.

A schematic representation of the workflow converging into the categorical classification of disease classes.

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References

  1. Agresti A (2003) Categorical data analysis, vol 482. Wiley, New York

    Google Scholar 

  2. Badawy AAB (2017) Kynurenine pathway of tryptophan metabolism: regulatory and functional aspects. Int J Tryptophan Res. https://doi.org/10:1178646917691938

  3. Baker A (2002) Fluorescence properties of some farm wastes: implications for water quality monitoring. Water Res 36(1):189–195

    Article  CAS  Google Scholar 

  4. Borisova E, Gisbrecht A, Genova-Hristova T, Troyanova P, Pavlova E, Penkov N, Bratchenko I, Zakharov V, Lihachova I, Kuzmina I et al (2019) Multispectral autoflourescence detection of skin neoplasia using steady-state techniques. In: 20th International conference and school on quantum electronics: laser physics and applications, vol 11047. International Society for Optics and Photonics, p 1104704

  5. Hoge FE, Swift RN (1981) Airborne simultaneous spectroscopic detection of laser-induced water raman backscatter and fluorescence from chlorophyll a and other naturally occurring pigments. Appl Optics 20(18):3197–3205

    Article  CAS  Google Scholar 

  6. Ivanov AV, Valuev-Elliston VT, Tyurina DA, Ivanova ON, Kochetkov SN, Bartosch B, Isaguliants MG (2017) Oxidative stress, a trigger of hepatitis c and b virus-induced liver carcinogenesis. Oncotarget 8(3):3895

    Article  Google Scholar 

  7. Lin W, Wu G, Li S, Weinberg EM, Kumthip K, Peng LF, Méndez-Navarro J, Chen W-C, Jilg N, Zhao H et al (2011) Hiv and hcv cooperatively promote hepatic fibrogenesis via induction of reactive oxygen species and nfκ b. J Biol Chem 286(4):2665– 2674

    Article  CAS  Google Scholar 

  8. Mejía SÁ, Gutman LAB, Camarillo CO, Navarro RM, Becerra MCS, Santana LD, Pérez M C EH, Flores MD (2018) Nicotinamide prevents sweet beverage-induced hepatic steatosis in rats by regulating the g6pd, nadph/nadp+ and gsh/gssg ratios and reducing oxidative and inflammatory stress. Eur J Pharmacol 818:499–507

    Article  Google Scholar 

  9. Modesto C, Anton J, Rodriguez B, Bou R, Arnal C, Ros J, Tena X, Rodrigo C, Rotes I, Hermosilla E et al (2010) Incidence and prevalence of juvenile idiopathic arthritis in Catalonia (Spain). Scand J Rheumatol 39(6):472–479

    Article  CAS  Google Scholar 

  10. Pacheco ME, Bruzzone L (2013) Synchronous fluorescence spectrometry: conformational investigation or inner filter effect?. J Lumin 137:138–142

    Article  CAS  Google Scholar 

  11. Pershin SM, Grishin MYa, Lednev VN, Garnov SV, Bukin VV, Chizhov PA, Khodasevich IA, Oshurko VB (2018) Quantification of distortion of the water oh-band using picosecond raman spectroscopy. Laser Phys Lett 15(3):035701

    Article  Google Scholar 

  12. Phull A-R, Nasir B, ul Haq I, Kim SJ (2018) Oxidative stress, consequences and ros mediated cellular signaling in rheumatoid arthritis. Chemico-Biol Interact 281:121–136

    Article  CAS  Google Scholar 

  13. Pootrakul P, Vongsmasa V, La-Ongpanich P, Wasi P (1981) Serum ferritin levels in thalassemias and the effect of splenectomy. Acta Haematol 66(4):244–250

    Article  CAS  Google Scholar 

  14. Raja SO, Shaw J, Chattopadhyay A, Chatterjee S, Bhattacharya M, Dasgupta AK (2014) Synchronous fluorescence based one step optical method for assessing oxidative stress and its dependence on serum ferritin. Anal Methods 6(16):6228–6231

    Article  CAS  Google Scholar 

  15. Risoluti R, Materazzi S, Sorrentino F, Maffei L, Caprari P (2016) Thermogravimetric analysis coupled with chemometrics as a powerful predictive tool for ß-thalassemia screening. Talanta 159:425–432

    Article  CAS  Google Scholar 

  16. Rivella S (2009) Ineffective erythropoiesis and thalassemias. Curr Opin Hematol 16(3):187

    Article  CAS  Google Scholar 

  17. Robertson MP, Caithness N, Villet MH (2001) A pca-based modelling technique for predicting environmental suitability for organisms from presence records. Divers Distrib 7(1–2):15–27

    Article  Google Scholar 

  18. Rybka J, Kdziora-Kornatowska K, BanasLezanska P, Majsterek I, Carvalho LA, Cattaneo A, Anacker C, Kedziora J (2013) Interplay between the pro-oxidant and antioxidant systems and proinflammatory cytokine levels, in relation to iron metabolism and the erythron in depression. Free Radic Biol Med 63:187–194

    Article  CAS  Google Scholar 

  19. Sas K, Szabó E, Vécsei L (2018) Mitochondria, oxidative stress and the kynurenine system, with a focus on ageing and neuroprotection. Molecules 23(1):191

    Article  Google Scholar 

  20. Shaw J, Chakraborty A, Nag A, Chattopadyay A, Dasgupta AK, Bhattacharyya M (2017) Intracellular iron overload leading to dna damage of lymphocytes and immune dysfunction in thalassemia major patients. Eur J Haematol 99(5):399–408

    Article  CAS  Google Scholar 

  21. Shaw J, Chakraborty A, Chatterjee S, Bhattacharyya M, Dasgupta AK (2018) Chelation therapy using static magnetic field. bioRxiv p 429597

  22. Shipley B (2016) Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inference with R. Cambridge University Press, Cambridge

    Book  Google Scholar 

  23. Sing T, Sander O, Beerenwinkel N, Lengauer T (2005) Rocr: visualizing classifier performance in r. Bioinformatics 21(20):3940–3941

    Article  CAS  Google Scholar 

  24. Trott PA (1977) International classification of diseases for oncology. J Clin Pathol 30(8):782

    Article  Google Scholar 

  25. Venepalli NK, Shergill A, Dorestani P, Boyd AD (2014) Conducting retrospective ontological clinical trials in icd-9-cm in the age of icd-10-cm. Cancer Inform 13:CIN–S14032

    Google Scholar 

  26. Vlahos R, Stambas J, Selemidis S (2012) Suppressing production of reactive oxygen species (ros) for influenza a virus therapy. Trends Pharmacol Sci 33(1):3–8

    Article  CAS  Google Scholar 

  27. Wellens HLL, BeGole EA, Kuijpers-Jagtman AM (2017) Roc surface assessment of the anb angle and wits appraisal’?s diagnostic performance with a statistically derived ‘gold standard’: does normalizing measurements have any merit?. Eur J Orthod 39(4):358–364

    Article  Google Scholar 

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Acknowledgements

We thank Calcutta Medical College from where all the blood samples were collected.

Funding

We thank the Indian Council of Medical Research (ICMR) Project file no. 5/3/8/322/2016 for funding this work.

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Authors and Affiliations

  1. Department of Biochemistry, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, 700019, India

    Ayan Chakraborty & Anjan Kr Dasgupta

  2. Department of Medicine, Calcutta Medical College, Kolkata, 700073, India

    Sanjoy Kumar Chatterjee

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  1. Ayan Chakraborty
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  2. Sanjoy Kumar Chatterjee
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  3. Anjan Kr Dasgupta
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Correspondence to Anjan Kr Dasgupta.

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Chakraborty, A., Chatterjee, S.K. & Dasgupta, A.K. Label-free detection of thalassemia and other ROS impairing diseases. Med Biol Eng Comput 58, 2143–2159 (2020). https://doi.org/10.1007/s11517-020-02191-z

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