• 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • br Materials and methods br The case control study included


    2. Materials and methods
    The case-control study included 107 patients, which were divided into 3 groups: the main group (patients with diagnosed ovarian and endometrial cancer, n = 51), the reference group (patients with non-malignant ovarian and endometrial pathologies, n = 26) and the con-trol group (healthy individuals, n = 30). The average age of patients was 53.5 [46.3; 61.0] years for the main group, 49.0 [45.5; 57.0] years for the reference group, and 51.1 [42.9; 55.4] years for the control group. The inclusion in groups occurred in parallel. The inclusion cri-teria were: lack of any treatment including surgical, chemotherapeutic or radiation at the time of the study, the absence of signs of active infection (including purulent processes) and conduction of oral cavity sanation. The exclusion criterion was the absence of histological ver-ification PFK-158 of the diagnosis.
    2.2. Patient recruitment and sampling
    The patients of the main group were examined at the Clinical Oncology Dispensary (Omsk, Russia). The patients for the control group were recruited under the routine examination at the Omsk City Ambulatory-Care Clinical Hospital no. 4. All volunteers had to be free of fever and/or cold; non-smokers; and have good oral hygiene, while participants with gingival and periodontal PFK-158 were ex-cluded. To minimize any contamination of samples and to obtain a relatively constant baseline, participants were asked to refrain from brushing their teeth and eating or drinking in the 60 min prior to sample collection. 
    The study was carried out in accordance with the Helsinki Declaration (adopted in June 1964 in Helsinki, Finland and revised in October 2000 in Edinburgh, Scotland) and approved at a meeting of the Ethics Committee of the Omsk Regional Clinical Hospital "Clinical Oncology Center" on July 21, 2016 (Protocol No. 15). All volunteers provided written informed consent.
    2.4. Collection, processing and storage of saliva samples
    Unstimulated whole expectorated saliva (2 ml) was collected from each subject between 8 and 10 a.m., considering the circadian rhythm [60]. Subjects rinsed their mouth with water 10 min prior to sampling. The unstimulated whole saliva samples were centrifuged (10.000 × g for 10 min) to remove cellular debris and to minimize the turbidity of saliva, which could negatively impact on the accuracy of analysis [61]. The biochemical parameters were analyzed immediately after cen-trifugation (without freezing).
    2.5. IR spectroscopy of saliva samples
    Lipids from saliva samples were extracted using Folch solution (chloroform: ethanol = 2: 1, vol.) [62,63]. When analyzing biological material, 200 μl of the sample (saliva) was diluted with 800 μl of 0.9% NaCl, then the samples were extracted twice with 2 ml of Folch solu-tion. The combined organic phase was settled for 24 h, and then cen-trifuged (10.000 × g for 10 min) for a more complete phase separation. Carefully decant the upper layer and select the bottom layer for IR spectroscopy. Extracts with a volume of 50 μl were dried for 30 min on a substrate of zinc selenide in a thermostat at 37 °C. The infrared ab-sorption spectra were registered using an FT-801 Fourier IR spectro-meter (Simex, Russia) in the range of 500–4000 cm–1. Spectra were recorded with a scan number of 32 with a resolution of 4 cm−1. Three spectra were collated per sample/patient. The results were presented as an averaged (or levelled) spectrum.
    Pre-processing is perhaps one of the vital steps in FTIR of the bio-logical data analysis. It includes baseline correction, smoothing, and normalization of the original spectra. The pre-processing techniques are required for outlier rejection, reducing dimensionality, removal of ir-relevant or redundant information and improvements of interpret-ability, robustness and accuracy of subsequent quantitative or classifi-cation analysis tasks [64]. Baseline correction techniques have the common objective of minimizing unwanted spectral offsets, broad baseline distortions, positive or negative slopes, and other baseline ef-fects in FTIR spectra [65]. Smoothing is used for de-noising the FTIR spectra however, the exact technique has to be carefully chosen in order to avoid the smoothing out of useful information from the spectra [66]. It becomes very important to normalize FTIR spectra to account for confounding factors such as varying thickness of sample [65,67]. ZaIR 3.5 software (Simex) was used to carry out baseline correction and normalization of FTIR spectra. A background (air) measurement was taken for every sample processed. The peaks corresponding to CO2 vi-brations were removed using the “straight line generation” option in the ZaIR 3.5 software (Simex). Raw spectra were pre-processed using a simple two point linear subtraction baseline correction method. Two points, 900 and 1850 cm−1 were selected outside the wavenumber re-gion of interest that showed no variation across all samples. Spectra were then vector normalised. Spectrum smoothing was not performed.