scholarly journals Identification of Two-Dimensional Copper Signatures in Human Blood for Bladder Cancer with Machine Learning

2022 ◽  
Author(s):  
Weichao Wang ◽  
Xian Liu ◽  
Changwen Zhang ◽  
Fei Sheng ◽  
Shanjun Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bladder Cancer ◽  
Isotopic Composition ◽  
Human Blood ◽  
Cancer Biomarkers ◽  
Two Dimensional ◽  
Negative Results ◽  
Stable Isotopic ◽  
Low Sensitivity ◽  
Almost All

Currently, almost all available cancer biomarkers are based on concentrations of compounds, often suffering from low sensitivity, poor specificity, and false positive or negative results. Stable isotopic composition of elements...

scholarly journals Controls on the isotopic composition of microbial methane

2021 ◽  
Author(s):  
Jonathan Gropp ◽  
Qusheng Jin ◽  
Itay Halevy
Keyword(s):  
Isotopic Composition ◽  
Isotopic Signature ◽  
Environmental Data ◽  
Isotopic Equilibrium ◽  
Sources And Sinks ◽  
Stable Isotopic ◽  
Isotopic Model ◽  
Almost All ◽  
Natural Settings

AbstractMicrobial methane production (methanogenesis) is responsible for more than half of the annual emission of this major greenhouse gas to the atmosphere. Though the stable isotopic composition of methane is often used to characterize its sources and sinks, empirical descriptions of the isotopic signature of methanogenesis currently limit such attempts. We developed a biochemical-isotopic model of methanogenesis by CO2 reduction, which predicts carbon and hydrogen isotopic fractionations, and clumped isotopologue distributions, as functions of the cell’s environment. We mechanistically explain multiple-isotopic patterns in laboratory and natural settings and show that such patterns constrain the in-situ energetics of methanogenesis. Combining our model with environmental data, we infer that in almost all marine environments and gas deposits, energy-limited methanogenesis operates close to chemical and isotopic equilibrium.

Seasonal trends in the stable isotopic composition of snow and meltwater runoff in a subarctic catchment at Okstindan, Norway

2004 ◽  
Vol 35 (2) ◽  
pp. 119-137 ◽  
Author(s):  
S.D. Gurney ◽  
D.S.L. Lawrence
Keyword(s):  
Isotopic Composition ◽  
Mean Value ◽  
Mean Values ◽  
Stable Isotopic Composition ◽  
Meltwater Runoff ◽  
Stable Isotopic ◽  
The Mean ◽  
The Difference ◽  
Δ18o And Δd

Seasonal variations in the stable isotopic composition of snow and meltwater were investigated in a sub-arctic, mountainous, but non-glacial, catchment at Okstindan in northern Norway based on analyses of δ18O and δD. Samples were collected during four field periods (August 1998; April 1999; June 1999 and August 1999) at three sites lying on an altitudinal transect (740–970 m a.s.l.). Snowpack data display an increase in the mean values of δ18O (increasing from a mean value of −13.51 to −11.49‰ between April and August), as well as a decrease in variability through the melt period. Comparison with a regional meteoric water line indicates that the slope of the δ18O–δD line for the snowpacks decreases over the same period, dropping from 7.49 to approximately 6.2.This change points to the role of evaporation in snowpack ablation and is confirmed by the vertical profile of deuterium excess. Snowpack seepage data, although limited, also suggest reduced values of δD, as might be associated with local evaporation during meltwater generation. In general, meltwaters were depleted in δ18O relative to the source snowpack at the peak of the melt (June), but later in the year (August) the difference between the two was not statistically significant. The diurnal pattern of isotopic composition indicates that the most depleted meltwaters coincide with the peak in temperature and, hence, meltwater production.

Extracellular Vesicles in Tumor Diagnosis: A Mini-Review

2020 ◽  
Vol 20 ◽  
Author(s):  
Si Yu ◽  
Menglin Huang ◽  
Jingyu Wang ◽  
Yongchang Zheng ◽  
Haifeng Xu
Keyword(s):  
Clinical Diagnosis ◽  
Sensitivity And Specificity ◽  
Cancer Diagnosis ◽  
Cancer Biomarkers ◽  
Related Substances ◽  
Physiological Processes ◽  
Cancer Pathogenesis ◽  
Limited Application ◽  
Low Sensitivity ◽  
Shed Light

: Widely exploration of noninvasive tumor/cancer biomarkers has shed light on clinical diagnosis. However, many under-investigated biomarkers showed limited application potency due to low sensitivity and specificity, while extracellular vehicles (EVs) were gradually recognized as promising candidates. EVs are small vesicles transporting bioactive cargos between cells in multiple physiological processes and also in tumor/cancer pathogenesis. This review aimed to offer recent studies of EVs on structure, classification, physiological functions, as well as changes in tumor initiation and progression. Furthermore, we focused on advances of EVs and/or EV-related substances in cancer diagnosis, and summarized ongoing studies of promising candidates for future investigations.

scholarly journals Terahertz characterization of two-dimensional low-conductive layers enabled by metal gratings

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Prashanth Gopalan ◽  
Yunshan Wang ◽  
Berardi Sensale-Rodriguez
Keyword(s):  
Optical Transmission ◽  
Terahertz Spectroscopy ◽  
Design Guidelines ◽  
Two Dimensional ◽  
Highly Sensitive ◽  
Transmission Measurements ◽  
Metal Gratings ◽  
Low Sensitivity ◽  
Non Destructive

AbstractWhile terahertz spectroscopy can provide valuable information regarding the charge transport properties in semiconductors, its application for the characterization of low-conductive two-dimensional layers, i.e., σs <  < 1 mS, remains elusive. This is primarily due to the low sensitivity of direct transmission measurements to such small sheet conductivity levels. In this work, we discuss harnessing the extraordinary optical transmission through gratings consisting of metallic stripes to characterize such low-conductive two-dimensional layers. We analyze the geometric tradeoffs in these structures and provide physical insights, ultimately leading to general design guidelines for experiments enabling non-contact, non-destructive, highly sensitive characterization of such layers.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

scholarly journals Imaging of Bladder Cancer: Standard Applications and Future Trends

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 220
Author(s):  
Rasha Taha Abouelkheir ◽  
Abdalla Abdelhamid ◽  
Mohamed Abou El-Ghar ◽  
Tarek El-Diasty
Keyword(s):  
Bladder Cancer ◽  
Malignant Tumors ◽  
Cancer Staging ◽  
Low Grade ◽  
Diagnostic Modality ◽  
Positron Emission ◽  
Standard Application ◽  
Muscle Invasive ◽  
Muscle Invasion ◽  
Low Sensitivity

The evolution in imaging has had an increasing role in the diagnosis, staging and follow up of bladder cancer. Conventional cystoscopy is crucial in the diagnosis of bladder cancer. However, a cystoscopic procedure cannot always depict carcinoma in situ (CIS) or differentiate benign from malignant tumors prior to biopsy. This review will discuss the standard application, novel imaging modalities and their additive role in patients with bladder cancer. Staging can be performed with CT, but distinguishing between T1 and T2 BCa (bladder cancer) cannot be assessed. MRI can distinguish muscle-invasive from non-muscle-invasive tumors with accurate local staging. Vesical Imaging-Reporting and Data System (VI-RADS) score is a new diagnostic modality used for the prediction of tumor aggressiveness and therapeutic response. Bone scintigraphy is recommended in patients with muscle-invasive BCa with suspected bony metastases. CT shows low sensitivity for nodal staging; however, PET (Positron Emission Tomography)/CT is superior and highly recommended for restaging and determining therapeutic effect. PET/MRI is a new imaging technique in bladder cancer imaging and its role is promising. Texture analysis has shown significant steps in discriminating low-grade from high-grade bladder cancer. Radiomics could be a reliable method for quantitative assessment of the muscle invasion of bladder cancer.

scholarly journals Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 742
Author(s):  
Rima Hajjo ◽  
Dima A. Sabbah ◽  
Sanaa K. Bardaweel ◽  
Alexander Tropsha
Keyword(s):  
Machine Learning ◽  
High Specificity ◽  
Cancer Biomarkers ◽  
Cancer Prognosis ◽  
Current Status ◽  
Imaging Data ◽  
Non Invasive ◽  
Machine Learning Methods ◽  
Cancer Types ◽  
Magnetic Resonance Imaging Mri

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

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