Absolute Quantification of Drug‐Vector Delivery to the Cytosol

Accurate quantification of biological resistance has been impossible so far. Among the various forms of biological resistance which exist in nature, pathogen resistance to drugs is a familiar one. However, as in the case of other forms of resistance, accurately quantifying drug resistance in pathogens has been impossible up to now. Here, we introduce a mathematically-defined and uniform procedure for the absolute quantification of biological resistance deployed by any living organism in the biological realm, including and beyond drug resistance in medicine. The scheme introduced makes possible the exact measurement or computation of the extent to which resistance is deployed by any living organism regardless of kingdom and regardless of the mechanism of resistance involved. Furthermore, the Second Law of Resistance indicating that resistance has the potential to increase to infinite levels, and the Third Law of Resistance indicating that resistance comes to an end once interaction stops, the resistance unit function introduced here is fully compatible with both the Second and Third Laws of Resistance.

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Precolumn Isotope Dilution Analysis in nanoHPLC−ICPMS for Absolute Quantification of Sulfur-Containing Peptides

Analytical Chemistry ◽

10.1021/ac061864r ◽

2007 ◽

Vol 79 (7) ◽

pp. 2859-2868 ◽

Cited By ~ 58

Author(s):

Dirk Schaumlöffel ◽

Pierre Giusti ◽

Hugues Preud'Homme ◽

Joanna Szpunar ◽

Ryszard Łobiński

Keyword(s):

Isotope Dilution ◽

Absolute Quantification ◽

Isotope Dilution Analysis ◽

Sulfur Containing

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Absolute Quantification of MicroRNAs in a Single Cell with Chemiluminescence Detection Based on Rolling Circle Amplification on a Microchip Platform

Analytical Chemistry ◽

10.1021/acs.analchem.1c01463 ◽

2021 ◽

Author(s):

Shengyu Chen ◽

Jingjin Zhao ◽

Chunhuan Xu ◽

Ivan Yu. Sakharov ◽

Shulin Zhao

Keyword(s):

Single Cell ◽

Rolling Circle Amplification ◽

Absolute Quantification ◽

Chemiluminescence Detection ◽

Rolling Circle

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Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

Scientific Reports ◽

10.1038/s41598-021-92014-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Gabriel A. Colozza-Gama ◽

Fabiano Callegari ◽

Nikola Bešič ◽

Ana C. de J. Paviza ◽

Janete M. Cerutti

Keyword(s):

Machine Learning ◽

Sanger Sequencing ◽

Learning Algorithm ◽

Absolute Quantification ◽

Braf V600e Mutation ◽

Braf V600e ◽

Driver Genes ◽

Quantitative Classification ◽

Cancer Driver

AbstractSomatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice.

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Simultaneous absolute quantification and sequencing of fish environmental DNA in a mesocosm by quantitative sequencing technique

Scientific Reports ◽

10.1038/s41598-021-83318-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tatsuhiko Hoshino ◽

Ryohei Nakao ◽

Hideyuki Doi ◽

Toshifumi Minamoto

Keyword(s):

Fish Species ◽

High Throughput Sequencing ◽

Correlation Coefficients ◽

Environmental Dna ◽

Digital Pcr ◽

Absolute Quantification ◽

Taqman Probe ◽

Individual Species ◽

Natural Environments ◽

Target Sequence

AbstractThe combination of high-throughput sequencing technology and environmental DNA (eDNA) analysis has the potential to be a powerful tool for comprehensive, non-invasive monitoring of species in the environment. To understand the correlation between the abundance of eDNA and that of species in natural environments, we have to obtain quantitative eDNA data, usually via individual assays for each species. The recently developed quantitative sequencing (qSeq) technique enables simultaneous phylogenetic identification and quantification of individual species by counting random tags added to the 5′ end of the target sequence during the first DNA synthesis. Here, we applied qSeq to eDNA analysis to test its effectiveness in biodiversity monitoring. eDNA was extracted from water samples taken over 4 days from aquaria containing five fish species (Hemigrammocypris neglectus, Candidia temminckii, Oryzias latipes, Rhinogobius flumineus, and Misgurnus anguillicaudatus), and quantified by qSeq and microfluidic digital PCR (dPCR) using a TaqMan probe. The eDNA abundance quantified by qSeq was consistent with that quantified by dPCR for each fish species at each sampling time. The correlation coefficients between qSeq and dPCR were 0.643, 0.859, and 0.786 for H. neglectus, O. latipes, and M. anguillicaudatus, respectively, indicating that qSeq accurately quantifies fish eDNA.

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