We have developed QUANTAS (QUANTification by Artificial Signal), which is a software-based protocol for concentration measurement by NMR. QUANTAS is an absolute intensity external standard method for quantification by NMR that compensates for various experimental parameters. It is applicable to all nuclei and modern spectrometers. QUANTAS is demonstrated here for (1)H and (19)F NMR, enabling heteronuclear integrals to be compared. It can be applied using fixed probe tuning, matching and pulse length, for samples with the same effective loading on the probe coil as the appropriate reference spectrum. Otherwise, an optimised tuning and matching approach is adopted for every sample together with explicit PULCON (PUlse Length-based CONcentration measurements) absolute intensity corrections.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/mrc.2647 | DOI Listing |
J Hazard Mater
January 2025
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430070, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430070, China. Electronic address:
Artificial intelligence-assisted imaging biosensors have attracted increasing attention due to their flexibility, allowing for the digital image analysis and quantification of biomarkers. While deep learning methods have led to advancements in biomarker identification, the diversity in the density and adherence of targets still poses a serious challenge. In this regard, we propose CellNet, a neural network model specifically designed for detecting dense targets.
View Article and Find Full Text PDFJCO Precis Oncol
January 2025
Translational Research Support Office, National Cancer Center Hospital East, Chiba, Japan.
Purpose: Human epidermal growth factor receptor 2 (HER2)-targeted therapies have shown promise in treating -amplified metastatic colorectal cancer (mCRC). Identifying optimal biomarkers for treatment decisions remains challenging. This study explores the potential of artificial intelligence (AI) in predicting treatment responses to trastuzumab plus pertuzumab (TP) in patients with -amplified mCRC from the phase II TRIUMPH trial.
View Article and Find Full Text PDFAnal Chem
January 2025
China-Croatia Belt and Road Joint Laboratory on Biodiversity and Ecosystem Services, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610213, China.
The accurate quantification of multicomponents using LC-MS is pivotal for ensuring the quality control of herbal medicine, as well as the investigation of their analysis of biological tissue distribution. However, two significant challenges persist: the scarcity of authentic standards and the selection of appropriate internal standards. In this study, we present a highly sensitive isotope-coded equivalent reporter ion assay (iERIA) that combines equivalently quantitative ion and isotope-coded derivatization strategies.
View Article and Find Full Text PDFALTEX
January 2025
National Institutes of Health, National Institute for Environmental Health Sciences, DTT/NICEATM, Durham, NC, USA.
The integration of artificial intelligence (AI) into new approach methods (NAMs) for toxicology rep-resents a paradigm shift in chemical safety assessment. Harnessing AI appropriately has enormous potential to streamline validation efforts. This review explores the challenges, opportunities, and future directions for validating AI-based NAMs, highlighting their transformative potential while acknowledging the complexities involved in their implementation and acceptance.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
School of Light Industry and Chemical Engineering, Dalian Polytechnic University, Dalian 116034, China. Electronic address:
Accurate, specific, and cost-effective detection of toxic cyanogenic glycosides is crucial for ensuring biological health and food safety. In this study, a novel biosensor based on co-immobilized multi-enzyme system was constructed by artificial antibody-antigen-directed immobilization for the colorimetric detection of amygdalin through a cascade reaction catalyzed by β-glucosidase, glucose oxidase, and horseradish peroxidase. Artificial antibodies and antigens were prepared using catechol and 3,4-dihydroxybenzaldehyde, respectively, to generate mutual affinity recognition ability for enzyme immobilization.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!