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Background:The severity of hepatic encephalopathy is currently graded clinically using West Haven criteria and psychometric tests.Objective:To assess the discriminative power of the bispectral index (BIS) monitor to classify the degree and progression of hepatic encephalopathy.Design:A consecutive, multicentre, observer blinded validation study.Setting:Medical University of Graz (Graz, Austria), Zhejiang University First Affiliated Hospital (Hang Zhou, China), and Cairo University (Cairo, Egypt).Patients:28 consecutive patients with hepatic encephalopathy were first enrolled at Medical University of Graz as a test set. The estimated BIS cut off values were subsequently tested in a validation set of 31 patients at Zhejiang University First Affiliated Hospital and 26 patients at Cairo University; 18 patients were reassessed later in a longitudinal study. Fifteen of 85 patients (18%) were excluded from the final analysis (11 became too agitated with high electromyographic activity; four fell asleep during the recording).Results:Applying the Austrian BIS cut off values of 85, 70, and 55 for discriminating West Haven grades 1 to 4 yielded agreement between BIS classification and West Haven grades in 40 of the 46 validation patients (87%), and in 16 of the 18 follow up patients (89%). Mean (SD) BIS values differed significantly between patients with West Haven grade 1 (90.2 (2.5)), grade 2 (78.4 (6.6)), grade 3 (63.2 (4.8)), and grade 4 (45.4 (5.0)).Conclusions:BIS is a useful measure for grading and monitoring the degree of involvement of the central nervous system in patients with chronic liver disease.

Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning–based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications. Raman spectroscopy is increasingly being used in biological assays and studies. This protocol provides guidance for performing chemometric analysis to detect and extract information relating to the chemical differences between biological samples.

The aim of this study was to describe behaviour, kinetics, time courses and limitations of the six different fully automated spectrometric methods - DPPH, TEAC, FRAP, DMPD, Free Radicals and Blue CrO5. Absorption curves were measured and absorbance maxima were found. All methods were calibrated using the standard compounds Trolox® and/or gallic acid. Calibration curves were determined (relative standard deviation was within the range from 1.5 to 2.5 %). The obtained characteristics were compared and discussed. Moreover, the data obtained were applied to optimize and to automate all mentioned protocols. Automatic analyzer allowed us to analyse simultaneously larger set of samples, to decrease the measurement time, to eliminate the errors and to provide data of higher quality in comparison to manual analysis. The total time of analysis for one sample was decreased to 10 min for all six methods. In contrary, the total time of manual spectrometric determination was approximately 120 min. The obtained data provided good correlations between studied methods (R = 0.97 – 0.99).

Bacterial biofilms constitute a major source of sanitary problems and economic losses in the food industry. Indeed, biofilm removal may require intense mechanical cleaning procedures or very high concentrations of disinfectants or both, which can be damaging to the environment and human health. This study assessed the efficacy of a technique based on spectroscopy in the visible, near-infrared, and short-wavelength infrared range for the quick detection of biofilms formed on polystyrene by the pathogenic bacterium . To do that, biofilms corresponding to three strains, which differed in biofilm-forming ability and composition of the extracellular matrix, were allowed to develop for 5 or 24 h, representing an active formation stage and mature biofilms, respectively. Spectral analysis of the samples, corresponding to three biological replicates of each condition, was then performed by using a portable device. The results of these experiments showed that partial least-squares discriminant analysis of the spectral profile could discriminate between surfaces containing attached bacterial biomass and noninoculated ones. In this model, the two first principal components accounted for 39 and 19% of the variance and the estimated error rate stabilized after four components. Cross-validation accuracy of this assessment was 100%. This work lays the foundation for subsequent development of a spectroscopy-based protocol that allows biofilm detection on food industrial surfaces.

Hyper spectral imaging is a possible way for disease detection. However, for carcinoma detection most of the results are ex-vivo . However, in-vivo results of endoscopic studies still show fairly low accuracies in contrast to the good results of many ex-vivo studies. To overcome this problem and to provide a reasonable explanation, Monte-Carlo simulations of photon trajectories are proposed as a tool to generate multi spectral images including inter patient variations to simulate 40 patients. Furthermore, these simulations have the huge advantage that the position of the carcinoma is known. Due to this, the effect of mislabelled data can be studied. As shown in this study, a percentage of 30–35% of mislabelled data might lead to significant decrease of the accuracy from around 90% to around 70–75%. Therefore, the main focus of hyper spectral imaging has to be the exact characterization of the training data in the future.

Throughout the last decade, the expansion of food testing has been gradually moving towards ordinary high throughput screening methods performed on-site. The demand for point-of-care testing, able to distinguish molecular signatures with high accuracy, sensitivity and specificity has been significantly increasing. This new requirement relies on the on-site detection and monitorization of molecular signatures suitable for the surveillance of food production and processing. The widespread use of antibiotics has contributed to disease control of livestock but has also created problems for the dairy industry and consumers. Its therapeutic and subtherapeutic use has increased the risk of contamination in milk in enough concentrations to cause economic losses to the dairy industry and have a health impact in highly sensitive individuals. This study focuses on the development of a simple Surface-Enhanced Raman Spectroscopy (SERS) method for fast high throughput screening of tetracycline (TET) in milk. For this, we integrate a paper-based low-cost, fully recyclable and highly stable SERS platform, with a minimal sample preparation protocol. A two-microliter sample of milk solutions spiked with TET (from 0.01 to 1000 ppm) is dried on a silver nanoparticle coated cardboard substrate and measured via a Raman spectrophotometer. The SERS substrate showed to be extremely stable with a shelf life of several months. A global spectrum principal component analysis approach was used to test all the detected vibrational modes and their correlation with TET concentration. Peak intensity ratios (455 cm −1 /1280 cm −1 and 874 cm −1 /1397 cm −1 ) were found to be correlated with TET concentrations in milk, achieving a sensitivity as low as 0.1 ppm. Results indicate that this SERS method combined with portable Raman spectrometer is a potential tool that can be used on-site for the monitoring of TET residues and other antibiotics.

This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy (LRS), can distinguish healthy and tumor tissue. We now report that combining LRS with two machine learning algorithms, unsupervised k-means and stochastic nonlinear neural networks (NN), provides rapid, quantitative, probabilistic tumor assessment with real-time error analysis. NNs were first trained on Raman spectra using human expert histopathology diagnostics as gold standard (74 spectra, 5 patients). K-means predictions using spectral data when compared to histopathology produced clustering models with 93.2–94.6% accuracy, 89.8–91.8% sensitivity, and 100% specificity. NNs trained on k-means predictions generated probabilities of correctness for the autonomous classification. Finally, the autonomous system characterized an extended dataset (203 spectra, 8 patients). Our results show that an increase in DNA|RNA signal intensity in the fingerprint region (600–1800 cm −1 ) and global loss of high wavenumber signal (2800–3200 cm −1 ) are particularly sensitive LRS warning signs of tumor. The stochastic nature of NNs made it possible to rapidly generate multiple models of target tissue classification and calculate the inherent error in the probabilistic estimates for each target.

In the last decade, Raman Spectroscopy has demonstrated to be a label-free and non-destructive optical spectroscopy able to improve diagnostic accuracy in cancer diagnosis. This is because Raman spectroscopic measurements can reveal a deep molecular understanding of the biochemical changes in cancer tissues in comparison with non-cancer tissues. In this pilot study, we apply Raman spectroscopy imaging to the diagnosis and grading of chondrogenic tumors, including enchondroma and chondrosarcomas of increasing histologic grades. The investigation included the analysis of areas of 50×50 μm 2 to approximately 200×200 μm 2 , respectively. Multivariate statistical analysis, based on unsupervised (Principal Analysis Components) and supervised (Linear Discriminant Analysis) methods, differentiated between the various tumor samples, between cells and extracellular matrix, and between collagen and non-collagenous components. The results dealt out basic biochemical information on tumor progression giving the possibility to grade with certainty the malignant cartilaginous tumors under investigation. The basic processes revealed by Raman Spectroscopy are the progressive degrading of collagen type-II components, the formation of calcifications and the cell proliferation in tissues ranging from enchondroma to chondrosarcomas. This study highlights that Raman spectroscopy is particularly effective when cartilaginous tumors need to be subjected to histopathological analysis.

The potential of laser-induced breakdown spectroscopy (LIBS) to discriminate biological and chemical threat simulant residues prepared on multiple substrates and in the presence of interferents has been explored. The simulant samples tested include Bacillus atrophaeus spores, Escherichia coli, MS-2 bacteriophage, α-hemolysin from Staphylococcus aureus, 2-chloroethyl ethyl sulfide, and dimethyl methylphosphonate. The residue samples were prepared on polycarbonate, stainless steel and aluminum foil substrates by Battelle Eastern Science and Technology Center. LIBS spectra were collected by Battelle on a portable LIBS instrument developed by A3 Technologies. This paper presents the chemometric analysis of the LIBS spectra using partial least-squares discriminant analysis (PLS-DA). The performance of PLS-DA models developed based on the full LIBS spectra, and selected emission intensities and ratios have been compared. The full-spectra models generally provided better classification results based on the inclusion of substrate emission features; however, the intensity/ratio models were able to correctly identify more types of simulant residues in the presence of interferents. The fusion of the two types of PLS-DA models resulted in a significant improvement in classification performance for models built using multiple substrates. In addition to identifying the major components of residue mixtures, minor components such as growth media and solvents can be identified with an appropriately designed PLS-DA model.