A Study of Raman Spectroscopy as a Clinical Diagnostic Tool for the Detection of Lynch Syndrome/Hereditary NonPolyposis Colorectal Cancer (HNPCC)

Lynch syndrome also known as hereditary non-polyposis colorectal cancer (HNPCC) is a highly penetrant hereditary form of colorectal cancer that accounts for approximately 3% of all cases. It is caused by mutations in DNA mismatch repair resulting in accelerated adenoma to carcinoma progression. The current clinical guidelines used to identify Lynch Syndrome (LS) are known to be too stringent resulting in overall underdiagnoses. Raman spectroscopy is a powerful analytical tool used to probe the molecular vibrations of a sample to provide a unique chemical fingerprint. The potential of using Raman as a diagnostic tool for discriminating LS from sporadic adenocarcinoma is explored within this thesis. A number of experimental parameters were initially optimized for use with formalin fixed paraffin embedded colonic tissue (FFPE). This has resulted in the development of a novel cost-effective backing substrate shown to be superior to the conventionally used calcium fluoride (CaF2). This substrate is a form of silanized super mirror stainless steel that was found to have a much lower Raman background, enhanced Raman signal and complete paraffin removal from FFPE tissues. Performance of the novel substrate was compared against CaF2 by acquiring large high resolution Raman maps from FFPE rat and human colonic tissue. All of the major histological features were discerned from steel mounted tissue with the benefit of clear lipid signals without paraffin obstruction. Biochemical signals were comparable to those obtained on CaF2 with no detectable irregularities. By using principal component analysis to reduce the dimensionality of the dataset it was then possible to use linear discriminant analysis to build a classification model for the discrimination of normal colonic tissue (n=10) from two pathological groups: LS (n=10) and sporadic adenocarcinoma (n=10). Using leaveone-map-out cross-validation of the model classifier has shown that LS was predicted with a sensitivity of 63% and a specificity of 89% - values that are competitive with classification techniques applied routinely in clinical practice

Label-free Raman spectroscopic imaging to extract morphological and chemical information from a formalin-fixed, paraffin-embedded rat colon tissue section.

Animal models and archived human biobank tissues are useful resources for research in disease development, diagnostics and therapeutics. For the preservation of microscopic anatomical features and to facilitate long-term storage, a majority of tissue samples are denatured by the chemical treatments required for fixation, paraffin embedding and subsequent deparaffinization. These aggressive chemical processes are thought to modify the biochemical composition of the sample and potentially compromise reliable spectroscopic examination useful for the diagnosis or biomarking. As a result, spectroscopy is often conducted on fresh/frozen samples. In this study, we provide an extensive characterization of the biochemical signals remaining in processed samples (formalin fixation and paraffin embedding, FFPE) and especially those originating from the anatomical layers of a healthy rat colon. The application of chemometric analytical methods (unsupervised and supervised) was shown to eliminate the need for tissue staining and easily revealed microscopic features consistent with goblet cells and the dense populations of cells within the mucosa, principally via strong nucleic acid signals. We were also able to identify the collagenous submucosa- and serosa- as well as the muscle-associated signals from the muscular regions and blood vessels. Applying linear regression analysis to the data, we were able to corroborate this initial assignment of cell and tissue types by confirming the biological origin of each layer by reference to a subset of authentic biomolecular standards. Our results demonstrate the potential of using label-free Raman microspectroscopy to obtain superior imaging contrast in FFPE sections when compared directly to conventional haematoxylin and eosin (H&E) staining

Deep Learning Applied to Raman Spectroscopy for the Detection of Microsatellite Instability/MMR Deficient Colorectal Cancer

Defective DNA mismatch repair is one pathogenic pathway to colorectal cancer. It is characterised by microsatellite instability which provides a molecular biomarker for its detection. Clinical guidelines for universal testing of this biomarker are not met due to resource limitations; thus, there is interest in developing novel methods for its detection. Raman spectroscopy (RS) is an analytical tool able to interrogate the molecular vibrations of a sample to provide a unique biochemical fingerprint. The resulting datasets are complex and high-dimensional, making them an ideal candidate for deep learning, though this may be limited by small sample sizes. This study investigates the potential of using RS to distinguish between normal, microsatellite stable (MSS) and microsatellite unstable (MSI-H) adenocarcinoma in human colorectal samples and whether deep learning provides any benefit to this end over traditional machine learning models. A 1D convolutional neural network (CNN) was developed to discriminate between healthy, MSI-H and MSS in human tissue and compared to a principal component analysis-linear discriminant analysis (PCA-LDA) and a support vector machine (SVM) model. A nested cross-validation strategy was used to train 30 samples, 10 from each group, with a total of 1490 Raman spectra. The CNN achieved a sensitivity and specificity of 83% and 45% compared to PCA-LDA, which achieved a sensitivity and specificity of 82% and 51%, respectively. These are competitive with existing guidelines, despite the low sample size, speaking to the molecular discriminative power of RS combined with deep learning. A number of biochemical antecedents responsible for this discrimination are also explored, with Raman peaks associated with nucleic acids and collagen being implicated

Intra-operative Raman spectroscopy and ex vivo Raman mapping for assessment of cartilage degradation

The development of a label-free, non-destructive and safe analytical method such as Raman spectroscopy for assessing cartilage degradation is highly desirable. Compared to non-optical imaging modalities, Raman mapping offers a more sensitive means of directly assessing the chemical composition of cartilage in three-dimensional space and the potential to monitor cartilage degeneration to inform intervention and treatment strategies. Herein, we report the application of Raman spectroscopic methods ex vivo and at arthroscopy to identify molecular alterations in cartilage specimens containing minor focal lesions characteristic of the early disease phase. Our initial ex vivo analysis, obtained by single-point Raman spectroscopy of cartilage samples, supports previous findings based on S-O stretching vibration bands associated with sulphated glycosaminoglycans (sGAGs). We extended the analyses to the high-wavenumber region where we observed that vibrational bands assigned to C-H and O-H stretching modes discriminated early cartilage alterations from healthy cartilage samples. Furthermore, we performed a proof-of-concept in-clinic study using a custom-built optical probe to acquire Raman spectral measurements for the first time in patients undergoing arthroscopy of knee joints. Spectra were obtained with adequate signal-to-noise ratios that similarly discriminated between lesion and adjacent cartilage sites and identified reductions in sGAGs in apparently healthy cartilage. Building on this, we present initial results from Raman mapping to spatially resolve the molecular constituents of cartilage through its depth and across a lesion. Mapping revealed a non-uniform and reduced sGAG distribution within the lesion and peripheral cartilage that was otherwise visually normal, similar to the in-clinic observations, showing that the degradative influence of the lesion extended beyond its border. This was accompanied by a decreased fluorescence signal intensity, which suggests that fluorescence may provide valuable information as an adjunct to the Raman signal in discriminating normal and degenerating cartilage. This work demonstrates the value of Raman mapping over single-point Raman measurements for the analysis of the anisotropy of articular cartilage and highlights the potential of the technology for in vivo articular joint arthroscopy applications

Интеллектуальная система аналитической обработки цифрового сетевого контента для защиты от нежелательной информации

В настоящее время Интернет и социальные сети как среда распространения цифрового сетевого контента становятся одной их важнейших угроз персональной, общественной и государственной информационной безопасности. Возникает необходимость защиты личности, общества и государства от нежелательной информации. В научно-методическом плане проблема защиты от нежелательной информации имеет крайне небольшое количество решений. Этим определяется актуальность представленных в статье результатов, направленных на разработку интеллектуальной системы аналитической обработки цифрового сетевого контента для защиты от нежелательной информации. В статье рассматриваются концептуальные основы построения такой системы, раскрывающие содержание понятия нежелательной информации и представляющие общую архитектуру системы. Приводятся модели и алгоритмы функционирования наиболее характерных компонентов системы, таких как компонент распределенного сканирования сети, компонент многоаспектной классификации сетевых информационных объектов, компонент устранения неполноты и противоречивости и компонент принятия решений. Представлены результаты реализации и экспериментальной оценки системных компонентов, которые продемонстрировали способность системы отвечать предъявляемым требованиям по полноте и точности обнаружения и противодействию нежелательной информации в условиях ее неполноты и противоречивости

Rapid and complete paraffin removal from human tissue sections delivers enhanced Raman spectroscopic and histopathological analysis

Incomplete removal of paraffin and organic contaminants from tissues processed for diagnostic histology has been a profound barrier to the introduction of Raman spectroscopic techniques into clinical practice. We report a route to rapid and complete paraffin removal from a range of formalin-fixed paraffin embedded tissues using super mirror stainless steel slides. The method is equally effective on a range of human and animal tissues, performs equally well with archived and new samples and is compatible with standard pathology lab procedures. We describe a general enhancement of the Raman scatter and enhanced staining with antibodies used in immunohistochemistry for clinical diagnosis. We conclude that these novel slide substrates have the power to improve diagnosis through anatomical pathology by facilitating the simultaneous combination of improved, more sensitive immunohistochemical staining and simplified, more reliable Raman spectroscopic imaging, analysis and signal processing

Intra-operative Raman spectroscopy and mapping for assessment of cartilage degradation

The development of a label-free, non-destructive and safe analytical method such as Raman spectroscopy for assessing cartilage degradation is highly desirable. Compared to non-optical imaging modalities, Raman mapping offers a more sensitive means of directly assessing the chemical composition of cartilage in three-dimensional space and the potential to monitor cartilage degeneration to inform intervention and treatment strategies. Herein, we report the application of Raman spectroscopic methods ex vivo and at arthroscopy to identify molecular alterations in cartilage specimens containing minor focal lesions characteristic of the early disease phase. Our initial ex vivo analysis, obtained by single-point Raman spectroscopy of cartilage samples, supports previous findings based on S-O stretching vibration bands associated with sulphated glycosaminoglycans (sGAGs). We extended the analyses to the high-wavenumber region where we observed that vibrational bands assigned to C-H and O-H stretching modes discriminated early cartilage alterations from healthy cartilage samples. Furthermore, we performed a proof-of-concept in-clinic study using a custom-built optical probe to acquire Raman spectral measurements for the first time in patients undergoing arthroscopy of knee joints. Spectra were obtained with adequate signal-to-noise ratios that similarly discriminated between lesion and adjacent cartilage sites and identified reductions in sGAGs in apparently healthy cartilage. Building on this, we present initial results from Raman mapping to spatially resolve the molecular constituents of cartilage through its depth and across a lesion. Mapping revealed a non-uniform and reduced sGAG distribution within the lesion and peripheral cartilage that was otherwise visually normal, similar to the in-clinic observations, showing that the degradative influence of the lesion extended beyond its border. This was accompanied by a decreased fluorescence signal intensity, which suggests that fluorescence may provide valuable information as an adjunct to the Raman signal in discriminating normal and degenerating cartilage. This work demonstrates the value of Raman mapping over single-point Raman measurements for the analysis of the anisotropy of articular cartilage and highlights the potential of the technology for in vivo articular joint arthroscopy applications

COVID-19 infection in pregnant women in Sverdlovsk region

The article examines the features of the clinical course of COVID19 infection in pregnant women in the Sverdlovsk region. The study included 362 women who had a positive PCR result for the SARS-CoV-2 during pregnancy. In pregnant women, in most cases, there is an asymptomatic course of a new coronavirus infection, or a state of mild severity. In the presence of bilateral pneumonia, in most cases, pregnant women have a state of moderate severity. As pregnancy progresses, SARS-CoV-2 increases the risk of pneumonia and more severe diseaseВ статье рассматриваются особенности клинического течения инфекции COVID-19 у беременных в Свердловской области. В исследование включено 362 женщины, у которых в течение беременности был положительный результат ПЦР на наличие SARS-CoV-2. У беременных в большинстве случаев наблюдается бессимптомное течение новой коронавирусной инфекции, либо состояние легкой степени тяжести. При наличии двусторонней пневмонии в большинстве случаев у беременных наблюдается состояние средней степени тяжести. С увеличением срока беременности при наличии SARS-CoV-2 повышается риск пневмонии и более тяжелого течения заболевани

Construction and Analysis of Integral User-Oriented Trustworthiness Metrics

Trustworthiness metrics help users to understand information system’s or a device’s security, safety, privacy, resilience, and reliability level. These metrics have different types and natures. The challenge consists of the integration of these metrics into one clear, scalable, sensitive, and reasonable metric representing overall trustworthiness level, useful for understanding if the users can trust the system or for the comparison of the devices and information systems. In this research, the authors propose a novel algorithm for calculation of an integral trustworthiness risk score that is scalable to any number of metrics, considers their criticality, and does not perform averaging in a case when all metrics are of equal importance. The obtained trustworthiness risk score could be further transformed to trustworthiness level. The authors analyze the resulting integral metric sensitivity and demonstrate its advantages on the series of experiments