| Autor: |
Manuela Lualdi, Adalberto Cavalleri, Luigi Battaglia, Ambrogio Colombo, Giulia Garrone, Daniele Morelli, Emanuele Pignoli, Elisa Sottotetti, Ermanno Leo |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
BMC Cancer, Vol 18, Iss 1, Pp 1-9 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2407 |
| DOI: |
10.1186/s12885-018-4754-2 |
| Popis: |
Abstract Background An increase in naturally-occurring porphyrins has been described in the blood of subjects bearing different kinds of tumors, including colorectal, and this is probably related to a systemic alteration of heme metabolism induced by tumor cells. The aim of our study was to develop an artificial neural network (ANN) classifier for early detection of colorectal adenocarcinoma based on plasma porphyrin accumulation and risk factors. Methods We measured the endogenous fluorescence of blood plasma in 100 colorectal adenocarcinoma patients and 112 controls using a conventional spectrofluorometer. Height, weight, personal and family medical history, use of alcohol, red meat, vegetables and tobacco were all recorded. An ANN model was built up from demographic data and from the integral of the fluorescence emission peak in the range 610–650 nm. We used the Receiver Operating Characteristic (ROC) curve to assess performance in distinguishing colorectal adenocarcinoma patients and controls. A liquid chromatography-high resolution mass spectrometry (LC-HRMS) analytical method was employed to identify the agents responsible for native fluorescence. Results The fluorescence analysis indicated that the integral of the fluorescence emission peak in the range 610–650 nm was significantly higher in colorectal adenocarcinoma patients than controls (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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