Our study shows that symptoms of asthma, rhinitis, and eczema are increasing, reflecting a change in the morbidity of these conditions in our population.

The extraction of disease specific information from Fourier transform infrared (FTIR) spectra of human body fluids demands the highest standards of accuracy and reproducibility of measurements because the expected spectral differences between healthy and diseased subjects are very small in relation to a large background absorbance of the whole sample. Here, we demonstrate that with the increased sensitivity of modern FTIR spectrometers, automatisation of sample preparation and modern bioinformatics, it is possible to identify and validate spectral biomarker candidates for distinguishing between urinary bladder cancer (UBC) and inflammation in suspected bladder cancer patients. The current dataset contains spectra of blood serum and plasma samples of 135 patients. All patients underwent cytology and pathological biopsy characterization to distinguish between patients without UBC (46) and confirmed UBC cases (89). A minimally invasive blood test could spare control patients a repeated cystoscopy including a transurethral biopsy, and three-day stationary hospitalisation. Blood serum, EDTA and citrate plasma were collected from each patient and processed following predefined strict standard operating procedures. Highly reproducible dry films were obtained by spotting sub-nanoliter biofluid droplets in defined patterns, which were compared and optimized. Particular attention was paid to the automatisation of sample preparation and spectral preprocessing to exclude errors by manual handling. Spectral biomarker candidates were identified from absorbance spectra and their 1(st) and 2(nd) derivative spectra using an advanced Random Forest (RF) approach. It turned out that the 2(nd) derivative spectra were most useful for classification. Repeat validation on 21% of the dataset not included in predictor training with Linear Discriminant Analysis (LDA) classifiers and Random Forests (RFs) yielded a sensitivity of 93 ± 10% and a specificity of 46 ± 18% for bladder cancer. The low specificity can be most likely attributed to the unbalanced and small number of control samples. Using this approach, spectral biomarker candidates in blood-derived biofluids were identified, which allow us to distinguish between cancer and inflammation, but the observed differences were tiny. Obviously, a much larger sample number has to be investigated to reliably validate such candidates.

By integration of FTIR imaging and a novel trained random forest classifier, lung tumour classes and subtypes of adenocarcinoma are identified in fresh-frozen tissue slides automated and marker-free. The tissue slices are collected under standard operation procedures within our consortium and characterized by current gold standards in histopathology. In addition, meta data of the patients are taken. The improved standards on sample collection and characterization results in higher accuracy and reproducibility as compared to former studies and allows here for the first time the identification of adenocarcinoma subtypes by this approach. The differentiation of subtypes is especially important for prognosis and therapeutic decision.

Blood samples of urinary bladder cancer (UBC) patients and patients with urinary tract infection were analysed with advanced automated high throughput Fourier transform infrared (HT-FTIR)-spectroscopy. Thin dried film samples were robotically prepared on multi-well titer plates (MTP) for absorbance measurements in transmission mode. Within the absorbance, 1st and 2nd derivative spectra of serum and two plasma preparations, discriminative patterns were identified and validated using bioinformatic tools. The optimal spectral resolution for data acquisition was determined. An accurate discrimination of the patient groups was achieved with three different independent spectral variable sets. The HT-FTIR blood test may support future clinical diagnostics.

BackgroundAn association between low socioeconomic status (SES) and lung cancer has been observed in several studies, but often without adequate control for smoking behavior. We studied the association between lung cancer and occupationally derived SES, using data from the international pooled SYNERGY study.MethodsTwelve case-control studies from Europe and Canada were included in the analysis. Based on occupational histories of study participants we measured SES using the International Socio-Economic Index of Occupational Status (ISEI) and the European Socio-economic Classification (ESeC). We divided the ISEI range into categories, using various criteria. Stratifying by gender, we calculated odds ratios (OR) and 95% confidence intervals (CI) by unconditional logistic regression, adjusting for age, study, and smoking behavior. We conducted analyses by histological subtypes of lung cancer and subgroup analyses by study region, birth cohort, education and occupational exposure to known lung carcinogens.ResultsThe analysis dataset included 17,021 cases and 20,885 controls. There was a strong elevated OR between lung cancer and low SES, which was attenuated substantially after adjustment for smoking, however a social gradient persisted. SES differences in lung cancer risk were higher among men (lowest vs. highest SES category: ISEI OR 1.84 (95% CI 1.61–2.09); ESeC OR 1.53 (95% CI 1.44–1.63)), than among women (lowest vs. highest SES category: ISEI OR 1.54 (95% CI 1.20–1.98); ESeC OR 1.34 (95% CI 1.19–1.52)).ConclusionSES remained a risk factor for lung cancer after adjustment for smoking behavior.

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