scholarly journals Multi-centre Raman spectral mapping of oesophageal cancer tissues: a study to assess system transferability

2016 ◽  
Vol 187 ◽  
pp. 87-103 ◽  
Author(s):  
M. Isabelle ◽  
J. Dorney ◽  
A. Lewis ◽  
G. R. Lloyd ◽  
O. Old ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Clinical Information ◽  
Diagnostic Methods ◽  
Classification Model ◽  
Training Dataset ◽  
Clinical Implementation ◽  
Tissue Samples ◽  
Wide Range ◽  
Offset Correction ◽  
Spectrometer System

The potential for Raman spectroscopy to provide early and improved diagnosis on a wide range of tissue and biopsy samples in situ is well documented. The standard histopathology diagnostic methods of reviewing H&E and/or immunohistochemical (IHC) stained tissue sections provides valuable clinical information, but requires both logistics (review, analysis and interpretation by an expert) and costly processing and reagents. Vibrational spectroscopy offers a complimentary diagnostic tool providing specific and multiplexed information relating to molecular structure and composition, but is not yet used to a significant extent in a clinical setting. One of the challenges for clinical implementation is that each Raman spectrometer system will have different characteristics and therefore spectra are not readily compatible between systems. This is essential for clinical implementation where classification models are used to compare measured biochemical or tissue spectra against a library training dataset. In this study, we demonstrate the development and validation of a classification model to discriminate between adenocarcinoma (AC) and non-cancerous intraepithelial metaplasia (IM) oesophageal tissue samples, measured on three different Raman instruments across three different locations. Spectra were corrected using system transfer spectral correction algorithms including wavenumber shift (offset) correction, instrument response correction and baseline removal. The results from this study indicate that the combined correction methods do minimize the instrument and sample quality variations within and between the instrument sites. However, more tissue samples of varying pathology states and greater tissue area coverage (per sample) are needed to properly assess the ability of Raman spectroscopy and system transferability algorithms over multiple instrument sites.

scholarly journals Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Laurent James Livermore ◽  
Martin Isabelle ◽  
Ian Mac Bell ◽  
Connor Scott ◽  
John Walsby-Tickle ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Molecular Genetic ◽  
Classification Model ◽  
Wild Type ◽  
Genetic Classification ◽  
Fresh Tissue ◽  
Tissue Samples ◽  
Genetic Subtypes ◽  
Classification Of Gliomas

Abstract Background The molecular genetic classification of gliomas, particularly the identification of isocitrate dehydrogenase (IDH) mutations, is critical for clinical and surgical decision-making. Raman spectroscopy probes the unique molecular vibrations of a sample to accurately characterize its molecular composition. No sample processing is required allowing for rapid analysis of tissue. The aim of this study was to evaluate the ability of Raman spectroscopy to rapidly identify the common molecular genetic subtypes of diffuse glioma in the neurosurgical setting using fresh biopsy tissue. In addition, classification models were built using cryosections, formalin-fixed paraffin-embedded (FFPE) sections and LN-18 (IDH-mutated and wild-type parental cell) glioma cell lines. Methods Fresh tissue, straight from neurosurgical theatres, underwent Raman analysis and classification into astrocytoma, IDH-wild-type; astrocytoma, IDH-mutant; or oligodendroglioma. The genetic subtype was confirmed on a parallel section using immunohistochemistry and targeted genetic sequencing. Results Fresh tissue samples from 62 patients were collected (36 astrocytoma, IDH-wild-type; 21 astrocytoma, IDH-mutated; 5 oligodendroglioma). A principal component analysis fed linear discriminant analysis classification model demonstrated 79%–94% sensitivity and 90%–100% specificity for predicting the 3 glioma genetic subtypes. For the prediction of IDH mutation alone, the model gave 91% sensitivity and 95% specificity. Seventy-nine cryosections, 120 FFPE samples, and LN18 cells were also successfully classified. Meantime for Raman data collection was 9.5 min in the fresh tissue samples, with the process from intraoperative biopsy to genetic classification taking under 15 min. Conclusion These data demonstrate that Raman spectroscopy can be used for the rapid, intraoperative, classification of gliomas into common genetic subtypes.

scholarly journals Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms

2021 ◽  
Vol 7 ◽  
pp. e547
Author(s):  
Aijaz Ahmad Reshi ◽  
Imran Ashraf ◽  
Furqan Rustam ◽  
Hina Fatima Shahzad ◽  
Arif Mehmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medical Diagnosis ◽  
Sampling Technique ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Classification Model ◽  
Superior Performance ◽  
Training Dataset ◽  
Tree Classifier ◽  
The Impact

Medical diagnosis through the classification of biomedical attributes is one of the exponentially growing fields in bioinformatics. Although a large number of approaches have been presented in the past, wide use and superior performance of the machine learning (ML) methods in medical diagnosis necessitates significant consideration for automatic diagnostic methods. This study proposes a novel approach called concatenated resampling (CR) to increase the efficacy of traditional ML algorithms. The performance is analyzed leveraging four ML approaches like tree-based ensemble approaches, and linear machine learning approach for automatic diagnosis of inter-vertebral pathologies with increased. Besides, undersampling, over-sampling, and proposed CR techniques have been applied to unbalanced training dataset to analyze the impact of these techniques on the accuracy of each of the classification model. Extensive experiments have been conducted to make comparisons among different classification models using several metrics including accuracy, precision, recall, and F1 score. Comparative analysis has been performed on the experimental results to identify the best performing classifier along with the application of the re-sampling technique. The results show that the extra tree classifier achieves an accuracy of 0.99 in association with the proposed CR technique.

Extraction of DNA from a Wide Range of Objects by Treatment with Ammonium Salts

2020 ◽  
Vol 36 (6) ◽  
pp. 98-106
Author(s):  
E.I. Levitin ◽  
B.V. Sviridov ◽  
O.V. Piksasova ◽  
T.E. Shustikova
Keyword(s):  
Dna Extraction ◽  
Dna Isolation ◽  
Tissue Samples ◽  
New Approach ◽  
Cell Wall Disruption ◽  
Wide Range ◽  
Low Salt ◽  
Mammalian Blood ◽  
Plasmid Extraction ◽  
Two Hybrid

Currently, simple, rapid, and efficient techniques for DNA isolation from a wide range of organisms are in demand in biotechnology and bioinformatics. A key (and often limiting) step is the cell wall disruption and subsequent DNA extraction from the disintegrated cells. We have developed a new approach to DNA isolation from organisms with robust cell walls. The protocol includes the following steps: treatment of cells or tissue samples with ammonium acetate followed by cell lysis in low-salt buffer with the addition of SDS. Further DNA extraction is carried out according to standard methods. This approach is efficient for high-molecular native DNA isolation from bacteria, ascomycetes, yeast, and mammalian blood; it is also useful for express analysis of environmental microbial isolates and for plasmid extraction for two-hybrid library screening. express method for DNA isolation; ammonium salt treatment (в русских ключевых такой порядок), osmotic breakage of cells This study was financially supported by the NRC "Kurchatov Institute"-GOSNIIGENETIKA Kurchatov Genomic Center.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yan ◽  
Zuotian Huang ◽  
Tong Mou ◽  
Yunhai Luo ◽  
Yanyao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Competing Endogenous Rna ◽  
Tissue Samples ◽  
Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

scholarly journals 332 The diagnostic role of shunt series radiographs (SSR) in children presenting to the children’s emergency department (CED) with suspected ventriculoperitoneal (VP) shunt failure

2020 ◽  
Vol 37 (12) ◽  
pp. 852.3-853
Author(s):  
Angharad Griffiths ◽  
Ikechukwu Okafor ◽  
Thomas Beattie
Keyword(s):  
Clinical Outcome ◽  
Sensitivity And Specificity ◽  
Clinical Information ◽  
Flow Diagram ◽  
List Type ◽  
Shunt Failure ◽  
Vp Shunt ◽  
Wide Range ◽  
Diagnostic Role

Aims/Objectives/BackgroundVP shunts are used to drain CSF from the cranial vault because of a wide range of pathologies and, like any piece of hardware, can fail. Traditionally investigations include SSR and CT. This project examines the role of SSR in evaluating children with suspected VP shunt failure.Primary outcome: Sensitivity and specificity of SSR in children presenting to the CED with concern for shunt failure.Methods/DesignConducted in a single centre, tertiary CED of the national Irish Neurosurgical(NS) referral centre (ED attendance:>50,000 patients/year). 100 sequential SSR requested by the CED were reviewed. Clinical information was extracted from electronic requests. Shunt failure was defined by the need for NS intervention(Revision).Abstract 332 Figure 1Abstract 332 Figure 2Results/ConclusionsSensitivity and specificity is presented in figure 1 (two by two table).100 radiographs performed in 84 children.22% shunts revised (see flow diagram).7 SSR’s were abnormal.85% (n=6) shunts revised. [5 following abnormal CT].Of the normal SSR’s; 16 had abnormal CT and revised.85/100 received CT.64 of 85 CT’s (75%) were normal.□6 of the 64 had focal shunt concern.SSR’s shouldn’t be used in isolation. NPV&PPV, Sensitivity&Specificity is low.SSR’s are beneficial where there’s concern over focal shunt problems (injury/pain/swelling) or following abnormal CT.VP shunt failure is not well investigated with SSR alone.SSR’s could be omitted where there is no focal shunt concern/after normal CT (without impacting clinical outcome) reducing radiation exposure and reduce impact on CED’s.59 SSR’s could have been avoided without adverse clinical outcome.

scholarly journals Trends in Molecular Diagnosis and Diversity Studies for Phytosanitary Regulated Xanthomonas

2021 ◽  
Vol 9 (4) ◽  
pp. 862
Author(s):  
Vittoria Catara ◽  
Jaime Cubero ◽  
Joël F. Pothier ◽  
Eran Bosis ◽  
Claude Bragard ◽  
...  
Keyword(s):  
Molecular Diagnosis ◽  
Bacterial Species ◽  
Management Strategies ◽  
Diagnostic Methods ◽  
Plant Diseases ◽  
Wild Plants ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Wide Range ◽  
Diversity Studies

Bacteria in the genus Xanthomonas infect a wide range of crops and wild plants, with most species responsible for plant diseases that have a global economic and environmental impact on the seed, plant, and food trade. Infections by Xanthomonas spp. cause a wide variety of non-specific symptoms, making their identification difficult. The coexistence of phylogenetically close strains, but drastically different in their phenotype, poses an added challenge to diagnosis. Data on future climate change scenarios predict an increase in the severity of epidemics and a geographical expansion of pathogens, increasing pressure on plant health services. In this context, the effectiveness of integrated disease management strategies strongly depends on the availability of rapid, sensitive, and specific diagnostic methods. The accumulation of genomic information in recent years has facilitated the identification of new DNA markers, a cornerstone for the development of more sensitive and specific methods. Nevertheless, the challenges that the taxonomic complexity of this genus represents in terms of diagnosis together with the fact that within the same bacterial species, groups of strains may interact with distinct host species demonstrate that there is still a long way to go. In this review, we describe and discuss the current molecular-based methods for the diagnosis and detection of regulated Xanthomonas, taxonomic and diversity studies in Xanthomonas and genomic approaches for molecular diagnosis.

scholarly journals Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Training Dataset ◽  
Independent Dataset ◽  
X Ray ◽  
Learning Classifier ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

scholarly journals Raman Spectroscopy Investigation of Graphene Oxide Reduction by Laser Scribing

2021 ◽  
Vol 7 (2) ◽  
pp. 48
Author(s):  
Vittorio Scardaci ◽  
Giuseppe Compagnini
Keyword(s):  
Raman Spectroscopy ◽  
Graphene Oxide ◽  
Reduce Graphene Oxide ◽  
Oxide Reduction ◽  
Material Parameters ◽  
Laser Scribing ◽  
Wide Range ◽  
Scan Speed ◽  
Laser Reduction

Laser scribing has been proposed as a fast and easy tool to reduce graphene oxide (GO) for a wide range of applications. Here, we investigate laser reduction of GO under a range of processing and material parameters, such as laser scan speed, number of laser passes, and material coverage. We use Raman spectroscopy for the characterization of the obtained materials. We demonstrate that laser scan speed is the most influential parameter, as a slower scan speed yields poor GO reduction. The number of laser passes is influential where the material coverage is higher, producing a significant improvement of GO reduction on a second pass. Material coverage is the least influential parameter, as it affects GO reduction only under restricted conditions.

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