scholarly journals Lymph node pathology using optical spectroscopy in cancer diagnostics

2008 ◽  
Vol 22 (2-3) ◽  
pp. 97-104 ◽  
Author(s):  
M. Isabelle ◽  
N. Stone ◽  
H. Barr ◽  
M. Vipond ◽  
N. Shepherd ◽  
...  

Raman and infrared spectroscopy are optical spectroscopic techniques that use light scattering (Raman) and light absorption (infrared) to probe the vibrational energy levels of molecules in tissue samples. Using these techniques, one can gain an insight into the biochemical composition of cells and tissues by looking at the spectra produced and comparing them with spectra obtained from standards such as proteins, nucleic acids, lipids and carbohydrates. As a result of optical spectroscopy being able to measure these biochemical changes, diagnosis of cancer could take place faster than current diagnostic methods, assisting and offering pathologists and cytologists a novel technology in cancer screening and diagnosis.The purpose of this study is to use both spectroscopic techniques, in combination with multivariate statistical analysis tools, to analyze some of the major biochemical and morphological changes taking place during carcinogenesis and metastasis in lymph nodes and to develop a predictive model to correctly differentiate cancerous from benign lymph nodes taken from oesophageal cancer patients.The results of this study showed that Raman and infrared spectroscopy managed to correctly differentiate between cancerous and benign oesophageal lymph nodes with a training performance greater than 94% using principal component analysis (PCA)-fed linear discriminant analysis (LDA). Cancerous nodes had higher nucleic acid but lower lipid and carbohydrate content compared to benign nodes which is indicative of increased cell proliferation and loss of differentiation.With better understanding of the molecular mechanisms of carcinogenesis and metastasis together with use of multivariate statistical analysis tools, these spectroscopic studies will provide a platform for future development of real-time (in surgery) non-invasive diagnostic tools in medical research.

2014 ◽  
Vol 926-930 ◽  
pp. 1116-1119 ◽  
Author(s):  
Li Jun Yang ◽  
Jing Wang ◽  
Zhao Jie Li ◽  
Xiao Hua Song ◽  
Yu Min Liu ◽  
...  

Fourier transform infrared spectroscopy (FTIR) combined with multivariate statistical analysis was applied to differentiate and identify Shigella sonnei and Escherichiacoli O157: H7. FTIR absorption spectra from 4000-600 cm-1 were collected from sampling 10 μL of bacterial suspention. The spectra between 1800 and 900 cm-1 highlighted the most distinctive variations and were the most useful for characterizing the selected microorganisms. Spectra of the two bacteria were noticeably segregated with distinct clustering by principal component analysis (PCA). Further more, another cluster model of hierarchical cluster analysis (HCA) was established and could also gave a good separation between the two bacteria. These results demonstrate that FTIR technology has considerable potential as a rapid, accurate and simple method for differentiating and identifying bacteria.


2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ewelina Dziurkowska ◽  
Marek Wesolowski

Multivariate statistical analysis is widely used in medical studies as a profitable tool facilitating diagnosis of some diseases, for instance, cancer, allergy, pneumonia, or Alzheimer’s and psychiatric diseases. Taking this in consideration, the aim of this study was to use two multivariate techniques, hierarchical cluster analysis (HCA) and principal component analysis (PCA), to disclose the relationship between the drugs used in the therapy of major depressive disorder and the salivary cortisol level and the period of hospitalization. The cortisol contents in saliva of depressed women were quantified by HPLC with UV detection day-to-day during the whole period of hospitalization. A data set with 16 variables (e.g., the patients’ age, multiplicity and period of hospitalization, initial and final cortisol level, highest and lowest hormone level, mean contents, and medians) characterizing 97 subjects was used for HCA and PCA calculations. Multivariate statistical analysis reveals that various groups of antidepressants affect at the varying degree the salivary cortisol level. The SSRIs, SNRIs, and the polypragmasy reduce most effectively the hormone secretion. Thus, both unsupervised pattern recognition methods, HCA and PCA, can be used as complementary tools for interpretation of the results obtained by laboratory diagnostic methods.


Author(s):  
Galina V. Volynets ◽  
A. S. Potapov ◽  
A. K. Gevorkyan ◽  
I. E. Smirnov ◽  
A. V. Nikitin ◽  
...  

Introduction. Alagille Syndrome (arteriohepatic dysplasia) is the genetically determined, multisystemic autosomal dominant disease characterized by the formation of the pathology of the liver, heart, eyes, kidneys, central nervous system, ear and possessing specific phenotypic characteristics. In connection with this the great importance is belonged to the early diagnosis and timely initiation of the pathogenetic treatment of the disease. Aim. On the base on multivariate statistical analysis of the clinical diagnostic indices to create stepwise algorithm for diagnosis of the of the Alagille syndrome in infants for the timely administration of adjuvant therapy, organization of the monitoring for the patient and to reduce the level of disability. Materials and methods. Under observation there was 21 child (10 boys and 11 girls) with Alagille syndrome, there was performed continuous examination, analysis of the patient history and clinical diagnostic methods at the onset and during the dynamics of the disease. Results. With the aid of the multivariate statistical analysis there were revealed clinical and laboratory criteria for the diagnosis of Alagille syndrome in infants, with consequent composition of the step-by-step algorithm of the diagnosis of the disease. There was made an estimation of the severity of the liver dysfunction.


2009 ◽  
Vol 48 (2) ◽  
pp. 134-141 ◽  
Author(s):  
Sunil Kumar Singh ◽  
Sunil Kumar Jha ◽  
Anand Chaudhary ◽  
R. D. S. Yadava ◽  
S. B. Rai

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