Literature DB >> 23429795

Combined studies of chemical composition of urine sediments and kidney stones by means of infrared microspectroscopy.

Sandra Tamošaitytė1, Vaiva Hendrixson, Arūnas Želvys, Ramūnas Tyla, Zita A Kučinskienė, Feliksas Jankevičius, Milda Pučetaitė, Valerija Jablonskienė, Valdas Šablinskas.   

Abstract

Results of the structural analysis of urinary sediments by means of infrared spectral microscopy are presented. The results are in good agreement with the results of standard optical microscopy in the case of single-component and crystalline urinary sediments. It is found that for noncrystalline or multicomponent sediments, the suggested spectroscopic method is superior to optical microscopy. The chemical structure of sediments of any molecular origin can be elucidated by this spectroscopic method. The method is sensitive enough to identify solid particles of drugs present in urine. Sulfamethoxazole and traces of other medicines are revealed in this study among the other sediments. We also show that a rather good correlation exists between the type of urinary sediments and the renal stones removed from the same patient. Spectroscopic studies of urinary stones and corresponding sediments from 76 patients suffering from renal stone disease reveal that in 73% of cases such correlation exists. This finding is a strong argument for the use of infrared spectral microscopy to prevent kidney stone disease because stones can be found in an early stage of formation by using the nonintrusive spectroscopic investigation of urinary sediments. Some medical recommendations concerning the overdosing of certain pharmaceuticals can also be derived from the spectroscopic studies of urinary sediments.

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Year:  2013        PMID: 23429795     DOI: 10.1117/1.JBO.18.2.027011

Source DB:  PubMed          Journal:  J Biomed Opt        ISSN: 1083-3668            Impact factor:   3.170


  2 in total

1.  Composition of urinary calculi in infants: a report from an endemic country.

Authors:  Mirza Naqi Zafar; Salma Ayub; Hafsa Tanwri; Syed Ali Anwar Naqvi; Syed Adibul Hasan Rizvi
Journal:  Urolithiasis       Date:  2017-11-03       Impact factor: 3.436

2.  Neural Network Analysis of Crystalluria Content to Predict Urinary Stone Type.

Authors:  Raed M Almannie; Abdullah K Alsufyani; Abdullah U Alturki; Mana Almuhaideb; Saleh Binsaleh; Abdulaziz M Althunayan; Mohammed A Alomar; Khalid M Albarraq; Fahad A Alyami
Journal:  Res Rep Urol       Date:  2021-12-29
  2 in total

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