Urine headspace analysis with field asymmetric ion mobility spectrometry for detection of chronic kidney disease.

Electronic noses (eNoses) are an emerging class of experimental diagnostic tools. They are based on the detection of volatile organic compounds. Urine is used as sample medium in several publications but neither the effect of chronic kidney disease (CKD) on the analysis nor the potential to detect CKD has been explored. Materials & methods: We utilized an eNose based on field asymmetric ion mobility spectrometry (FAIMS) technology to classify urine samples from CKD patients and controls. Results: We were able to differentiate extremes of kidney function with an accuracy of 81.4%. Conclusion: In this preliminary study, applying eNose technology we were able to distinguish the patients with impaired kidney function from those with normal kidney function.

Differential mobility spectrometry classification of bacteria.

Aim: Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost-effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials & methods: Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-validation. Results: Differential mobility spectrometry achieved a correct classification rate 70.7% for all bacterial species. For bacterial genera, the rate was 77.6% and between Gram status, 89.1%. Conclusion: Largest difficulties arose in distinguishing bacteria of the same genus. Future improvement of the sensor characteristics may improve the classification accuracy.

Detection of Pancreatic Cancer by Urine Volatile Organic Compound Analysis.

BACKGROUND/AIM: Most pancreatic cancer patients are diagnosed at an advanced stage, since the diagnosis is demanding. Field asymmetric waveform ion mobility spectrometry (FAIMS) is a sensitive technique used for the detection of volatile organic compounds (VOC). We evaluated the ability of FAIMS to discriminate between pancreatic cancer and healthy controls from a urine sample. PATIENTS AND METHODS: For a proof-of-concept study in three Finnish hospitals, 68 patients with pancreatic cancer, 36 with acute pancreatitis, 18 with chronic pancreatitis, 8 with pancreatic pre-malign lesions and 52 healthy controls were prospectively recruited. Urine samples were collected at the time of diagnosis and stored at -70°C. The samples were subsequently measured with FAIMS. The data were processed with linear discriminant analysis and cross-validated with leave-one-out cross-validation. RESULTS: FAIMS distinguished pancreatic cancer from controls with a sensitivity of 79% and specificity of 79%. CONCLUSION: As a non-invasive and rapid urine test, FAIMS can discriminate patients with pancreatic cancer from healthy controls.

In vitro detection of common rhinosinusitis bacteria by the eNose utilising differential mobility spectrometry.

Acute rhinosinusitis (ARS) is a sudden, symptomatic inflammation of the nasal and paranasal mucosa. It is usually caused by respiratory virus infection, but bacteria complicate for a small number of ARS patients. The differential diagnostics between viral and bacterial pathogens is difficult and currently no rapid methodology exists, so antibiotics are overprescribed. The electronic nose (eNose) has shown the ability to detect diseases from gas mixtures. Differential mobility spectrometry (DMS) is a next-generation device that can separate ions based on their different mobility in high and low electric fields. Five common rhinosinusitis bacteria (Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, Staphylococcus aureus, and Pseudomonas aeruginosa) were analysed in vitro with DMS. Classification was done using linear discriminant analysis (LDA) and k-nearest neighbour (KNN). The results were validated using leave-one-out cross-validation and separate train and test sets. With the latter, 77% of the bacteria were classified correctly with LDA. The comparative figure with KNN was 79%. In one train-test set, P. aeruginosa was excluded and the four most common ARS bacteria were analysed with LDA and KNN; the correct classification rate was 83 and 85%, respectively. DMS has shown its potential in detecting rhinosinusitis bacteria in vitro. The applicability of DMS needs to be studied with rhinosinusitis patients.

Electronic Nose in the Detection of Wound Infection Bacteria from Bacterial Cultures: A Proof-of-Principle Study.

BACKGROUND: Soft tissue infections, including postoperative wound infections, result in a significant burden for modern society. Rapid diagnosis of wound infections is based on bacterial stains, cultures, and polymerase chain reaction assays, and the results are available earliest after several hours, but more often not until days after. Therefore, antibiotic treatment is often administered empirically without a specific diagnosis. METHODS: We employed our electronic nose (eNose) system for this proof-of-concept study, aiming to differentiate the most relevant bacteria causing wound infections utilizing a set of clinical bacterial cultures on identical blood culture dishes, and established bacterial lines from the gaseous headspace. RESULTS: Our eNose system was capable of differentiating both methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pyogenes, Escherichia coli, Pseudomonas aeruginosa, and Clostridium perfringens with an accuracy of 78% within minutes without prior sample preparation. Most importantly, the system was capable of differentiating MRSA from MSSA with a sensitivity of 83%, a specificity of 100%, and an overall accuracy of 91%. CONCLUSIONS: Our results support the concept of rapid detection of the most relevant bacteria causing wound infections and ultimately differentiating MRSA from MSSA utilizing gaseous headspace sampling with an eNose.