scholarly journals Insight into metastatic oral cancer tissue from novel analyses using FTIR spectroscopy and aperture IR-SNOM

The Analyst ◽  
2021 ◽  
Author(s):  
Barnaby Ellis ◽  
Conor A Whitley ◽  
Safaa Al Jedani ◽  
Caroline Smith ◽  
Philip Gunning ◽  
...  
Keyword(s):  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Oral Cancer ◽  
Oral Squamous Cell Carcinoma ◽  
Lymphoid Tissue ◽  
Learning Algorithm ◽  
Cancer Tissue ◽  
Machine Learning Algorithm ◽  
Nodal Metastases ◽  
Insight Into

A novel machine learning algorithm is shown to accurately discriminate between oral squamous cell carcinoma (OSCC) nodal metastases and surrounding lymphoid tissue on the basis of a single metric, the...

scholarly journals Lack of Salivary Long Non-Coding RNA XIST Expression Is Associated with Increased Risk of Oral Squamous Cell Carcinoma: A Cross-Sectional Study

2021 ◽  
Vol 10 (19) ◽  
pp. 4622
Author(s):  
Tzong-Ming Shieh ◽  
Chung-Ji Liu ◽  
Shih-Min Hsia ◽  
Valendriyani Ningrum ◽  
Chiu-Chu Liao ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Cancer ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cancer Tissue ◽  
Xist Expression ◽  
Increased Risk ◽  
Cancer Morbidity ◽  
Lncrna Xist

Studies have shown that there is a disparity between males and females in south-east Asia with regard to oral cancer morbidity. A previous study found that oral cancer tissue showed loss of heterozygosity of the X-linked lncRNA XIST gene. We suggest that XIST may play an important role in oral cancer morbidity when associated with sex. Saliva contains proteins and RNAs that are potential biomarkers for the diagnosis of diseases. This study investigated salivary XIST expression and the correlation to clinical–pathological data among oral squamous cell carcinoma patients. Salivary XIST expression was only observed in females, and a high proportion of females with OSCC lack salivary lncRNA XIST expression (88%). The expression showed no correlation with alcohol consumption, betel quid chewing, or cigarette smoking habits. People lacking salivary lncRNA XIST expression had a significantly increased odds ratio of suffering from OSCC (OR = 19.556, p < 0.001), particularly females (OR = 33.733, p < 0.001). The ROC curve showed that salivary lncRNA XIST expression has acceptable discrimination accuracy to predict the risk of OSCC (AUC = 0.73, p < 0.01). Lack of salivary lncRNA XIST expression was associated with an increased risk of OSCC. We provided an insight into the role of salivary lncRNA XIST as a biomarker to predict the morbidity of OSCC.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

Zinc α-2 Glycoprotein (ZAG) a Novel Biomarker for the Detection of Oral Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Mehwish Feroz Ali
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Cancer ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Malignant Lesion ◽  
Tobacco Consumption ◽  
Constant Stimulus ◽  
Dysplastic Lesion ◽  
Cancerous Lesion

Oral cancer, the most challenging and life threatening disease in the field of dentistry, may start as a reactive lesion due to constant stimulus from tobacco consumption, transform into a pre-malignant lesion (dysplastic lesion) and ultimately develop into a cancerous lesion (Invasive carcinoma). There is a fundamental revolution taking place in the analyzing methods; extraction of biological protein from the saliva rather than from tissues or blood. Several of the biomarkers have been studied with pro-carcinogenic effects like Interleukins (ILs), tumor necrosis factor (TNF) and leptin, but only a few have been stated in the literature, which show anti-cancer characteristics like adiponectin and zinc alpha-2 glycoprotein. This review explored the diagnostic and prognostic values of a biomarkers zinc alpha-2 glycoprotein (ZAG) in adults suspected of oral squamous cell carcinoma (OSCC). The PubMed, EMBASE and Google Scholar were searched for scientific studies reported on the potential mechanism of zinc alpha-2 glycoprotein. All the research articles were selected in which ZAG is applied solely or in conjunction with other biomarkers in oral cancer and other cancers. These literatures were carefully assessed to find out and compile the diagnostic and prognostic values and to inquire therapeutic action of ZAG in the process of carcinogenesis.

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