This feasibility study showed that this portable e-nose can properly differentiate between patients with lung cancer and healthy controls. This result could have important implications for future lung cancer screening. Further studies with larger cohorts, including also more participants with early-stage tumors, should be performed to increase the robustness of this noninvasive diagnostic tool and to determine its added value in the diagnostic chain for lung cancer.

Electronic nose (e-nose) technology has the potential to detect cancer at an early stage and can differentiate between cancer origins. Our objective was to compare patients who had head and neck squamous cell carcinoma (HNSCC) with patients who had colon or bladder cancer to determine the distinctive diagnostic characteristics of the e-nose. Feasibility study An e-nose device was used to collect samples of exhaled breath from patients who had HNSCC and those who had bladder or colon cancer, after which the samples were analyzed and compared. One hundred patients with HNSCC, 40 patients with bladder cancer, and 28 patients with colon cancer exhaled through an e-nose for 5 min. An artificial neural network was used for the analysis, and double cross-validation to validate the model. In differentiating HNSCC from colon cancer, a diagnostic accuracy of 81 % was found. When comparing HNSCC with bladder cancer, the diagnostic accuracy was 84 %. A diagnostic accuracy of 84 % was found between bladder cancer and colon cancer. The e-nose technique using double cross-validation is able to discriminate between HNSCC and colon cancer and between HNSCC and bladder cancer. Furthermore, the e-nose technique can distinguish colon cancer from bladder cancer.

An increased number of treatment options has become available for patients with single sided deafness (SSD), who are seeking hearing rehabilitation. For example, bone-conduction devices that employ contralateral routing of sound (CROS), by transmitting acoustic bone vibrations from the deaf side to the cochlea of the hearing ear, are widely used. However, in some countries, cochlear implantation is becoming the standard treatment. The present study investigated whether CROS intervention, by means of a CROS bone-conduction device (C-BCD), affected sound-localization performance of patients with SSD. Several studies have reported unexpected moderate to good unilateral sound-localization abilities in unaided SSD listeners. Listening with a C-BCD might deteriorate these localization abilities because sounds are transmitted, through bone conduction to the contralateral normal hearing ear, and could thus interfere with monaural level cues (i.e. ambiguous monaural head-shadow cues), or with the subtle spectral localization cues, on which the listener has learned to rely on. The present study included nineteen SSD patients who were using their C-BCD for more than five months. To assess the use of the different localization cues, we investigated their localization abilities to broadband (BB, 0.5-20 kHz), low-pass (LP, 0.5-1.5 kHz), and high-pass filtered noises (HP, 3-20 kHz) of varying intensities. Experiments were performed in complete darkness, by measuring orienting head-movement responses under open-loop localization conditions. We demonstrate that a minority of listeners with SSD (5 out of 19) could localize BB and HP (but not LP) sounds in the horizontal plane in the unaided condition, and that a C-BCD did not deteriorate their localization abilities.

Background: Oral squamous cell carcinoma (OSCC) is increasing at an alarming rate particularly in low-income countries. This urges for research into noninvasive, user-friendly diagnostic tools that can be used in limited-resource settings. This study aims to test and validate the feasibility of e-nose technology for detecting OSCC in the limited-resource settings of the Sudanese population. Methods: Two e-nose devices (Aeonose™, eNose Company, Zutphen, The Netherlands) were used to collect breath samples from OSCC (n = 49) and control (n = 35) patients. Patients were divided into a training group for building an artificial neural network (ANN) model and a blinded control group for model validation. The Statistical Package for the Social Sciences (SPSS) software was used for the analysis of baseline characteristics and regression. Aethena proprietary software was used for data analysis using artificial neural networks based on patterns of volatile organic compounds. Results: A diagnostic accuracy of 81% was observed, with 88% sensitivity and 71% specificity. Conclusions: This study demonstrates that e-nose is an efficient tool for OSCC detection in limited-resource settings, where it offers a valuable cost-effective strategy to tackle the burden posed by OSCC.