A Miniature Bio-Photonics Companion Diagnostics Platform for Reliable Cancer Treatment Monitoring in Blood Fluids.

In this paper, we present the development of a photonic biosensor device for cancer treatment monitoring as a complementary diagnostics tool. The proposed device combines multidisciplinary concepts from the photonic, nano-biochemical, micro-fluidic and reader/packaging platforms aiming to overcome limitations related to detection reliability, sensitivity, specificity, compactness and cost issues. The photonic sensor is based on an array of six asymmetric Mach Zender Interferometer (aMZI) waveguides on silicon nitride substrates and the sensing is performed by measuring the phase shift of the output signal, caused by the binding of the analyte on the functionalized aMZI surface. According to the morphological design of the waveguides, an improved sensitivity is achieved in comparison to the current technologies (<5000 nm/RIU). This platform is combined with a novel biofunctionalization methodology that involves material-selective surface chemistries and the high-resolution laser printing of biomaterials resulting in the development of an integrated photonics biosensor device that employs disposable microfluidics cartridges. The device is tested with cancer patient blood serum samples. The detection of periostin (POSTN) and transforming growth factor beta-induced protein (TGFBI), two circulating biomarkers overexpressed by cancer stem cells, is achieved in cancer patient serum with the use of the device.

NeuMonD: a tool for the development of new indicators of anaesthetic effect.

Electroencephalogram (EEG) signals and auditory evoked potentials (AEPs) have been suggested as a measure of depth of anaesthesia, because they reflect activity of the main target organ of anaesthesia, the brain. The online signal processing module NeuMonD is part of a PC-based development platform for monitoring "depth" of anaesthesia using EEG and AEP data. NeuMonD allows collection of signals from different clinical monitors, and calculation and simultaneous visualisation of several potentially useful parameters indicating "depth" of anaesthesia using different signal processing methods. The main advantage of NeuMonD is the possibility of early evaluation of the performance of parameters or indicators by the anaesthetist in the clinical environment which may accelerate the process of developing new, multiparametric indicators of anaesthetic "depth".

Adapted variable precision rough set approach for EEG analysis.

OBJECTIVE: Rough set theory (RST) provides powerful methods for reduction of attributes and creation of decision rules, which have successfully been applied in numerous medical applications. The variable precision rough set model (VPRS model), an extension of the original rough set approach, tolerates some degree of misclassification of the training data. The basic idea of the VPRS model is to change the class information of those objects whose class information cannot be induced without contradiction from the available attributes. Thereafter, original methods of RST are applied. An approach of this model is presented that allows uncertain objects to change class information during the process of attribute reduction and rule generation. This method is referred to as variable precision rough set approach with flexible classification of uncertain objects (VPRS(FC) approach) and needs only slight modifications of the original VPRS model. METHODS AND MATERIAL: To compare the VPRS model and VPRS(FC) approach both methods are applied to a clinical data set based on electroencephalogram of awake and anesthetized patients. For comparison, a second data set obtained from the UCI machine learning repository is used. It describes the shape of different vehicle types. Further well known feature selection methods were applied to both data sets to compare their results with the results provided by rough set based approaches. RESULTS: The VPRS(FC) approach requires higher computational effort, but is able to achieve better reduction of attributes for noisy or inconsistent data and provides smaller rule sets. CONCLUSION: The presented approach is a useful method for substantial attribute reduction in noisy and inconsistent data sets.

Detection of consciousness by electroencephalogram and auditory evoked potentials.

BACKGROUND: A set of electroencephalographic and auditory evoked potential (AEP) parameters should be identified that allows separation of consciousness from unconsciousness (reflected by responsiveness/unresponsiveness to command). METHODS: Forty unpremedicated patients received anesthesia with remifentanil and either sevoflurane or propofol. With remifentanil infusion (0.2 microg . kg . min), patients were asked every 30 s to squeeze the investigator's hand. Sevoflurane or propofol was given until loss of consciousness. After intubation, propofol or sevoflurane was stopped until patients followed the command (return of consciousness). Thereafter, propofol or sevoflurane was started again (loss of consciousness), and surgery was performed. Return of consciousness was observed after surgery. The electroencephalogram and AEP from immediately before and after the transitions were selected. Logistic regression was calculated to identify models for the separation between consciousness and unconsciousness. For the top 10 models, 1,000-fold cross-validation was performed. Backward variable selection was applied to identify a minimal model. Prediction probability was calculated. The digitized electroencephalogram was replayed, and the Bispectral Index was measured and accordingly analyzed. RESULTS: The best full model (prediction probability 0.89) contained 15 AEP and 4 electroencephalographic parameters. The best minimal model (prediction probability 0.87) contained 2 AEP and 2 electroencephalographic parameters (median frequency of the amplitude spectrum from 8-30 Hz and approximate entropy). The prediction probability of the Bispectral Index was 0.737. CONCLUSIONS: A combination of electroencephalographic and AEP parameters can be used to differentiate between consciousness and unconsciousness even in a very challenging data set. The minimal model contains a combination of AEP and electroencephalographic parameters and has a higher prediction probability than Bispectral Index for the separation between consciousness and unconsciousness.

Narcotrend does not adequately detect the transition between awareness and unconsciousness in surgical patients.

BACKGROUND: The Narcotrend index (MonitorTechnik, Bad Bramstedt, Germany) is a dimensionless number between 0 and 100 that is calculated from the electroencephalogram and inversely correlates with depth of hypnosis. The current study evaluates the capability of the Narcotrend to separate awareness from unconsciousness at the transition between these levels. METHODS: Electroencephalographic recordings of 40 unpremedicated patients undergoing elective surgery were analyzed. Patients were randomly assigned to receive (1) sevoflurane-remifentanil (/= 0.2 microg . kg . min), (3) propofol-remifentanil (/= 0.2 microg . kg . min). Remifentanil and sevoflurane or propofol were given until loss of consciousness. After tracheal intubation, propofol or sevoflurane was stopped until return of consciousness and then restarted to induce loss of consciousness. After surgery, drugs were discontinued. Narcotrend values at loss and return of consciousness were compared with each other, and anesthetic groups were compared. Prediction probability was calculated from values at the last command before and at loss and return of consciousness. RESULTS: At 105 of 316 analyzed time points, the Narcotrend did not calculate an index, and the closest calculated value was analyzed. No significant differences between loss and return of consciousness were found. In group 1, Narcotrend values were significantly higher than in group 3. Prediction probability was 0.501. CONCLUSIONS: In these challenging data, the Narcotrend did not differentiate between awareness and unconsciousness. In addition, Narcotrend values were not independent from the anesthetic regimen.