Study on the pharmacokinetics of mulberry fruit polysaccharides through fluorescence labeling

Biothiols are extremely powerful antioxidants that protect cells against the effects of oxidative stress. They are also considered relevant disease biomarkers, specifically risk factors for cardiovascular disease. In this paper, a new procedure for the simultaneous determination of human serum albumin and low-molecular-weight thiols in plasma is described. The method is based on the pre-column derivatization of analytes with a thiol-specific fluorescence labeling reagent, monobromobimane, followed by separation and quantification through reversed-phase high-performance liquid chromatography with fluorescence detection (excitation, 378 nm; emission, 492 nm). Prior to the derivatization step, the oxidized thiols are converted to their reduced forms by reductive cleavage with sodium borohydride. Linearity in the detector response for total thiols was observed in the following ranges: 1.76–30.0 mg mL−1 for human serum albumin, 0.29–5.0 nmol mL−1 for α-lipoic acid, 1.16–35 nmol mL−1 for glutathione, 9.83–450.0 nmol mL−1 for cysteine, 0.55–40.0 nmol mL−1 for homocysteine, 0.34–50.0 nmol mL−1 for N-acetyl-L-cysteine, and 1.45–45.0 nmol mL−1 for cysteinylglycine. Recovery values of 85.16–119.48% were recorded for all the analytes. The developed method is sensitive, repeatable, and linear within the expected ranges of total thiols. The devised procedure can be applied to plasma samples to monitor biochemical processes in various pathophysiological states.

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Unsupervised content-preserving transformation for optical microscopy

Light Science & Applications ◽

10.1038/s41377-021-00484-y ◽

2021 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Xinyang Li ◽

Guoxun Zhang ◽

Hui Qiao ◽

Feng Bao ◽

Yue Deng ◽

...

Keyword(s):

Deep Learning ◽

Optical Microscopy ◽

Training Data ◽

Fluorescence Labeling ◽

Imaging Data ◽

Image Transformation ◽

General Applicability ◽

Data Annotation ◽

Biomedical Image ◽

Wide Range

AbstractThe development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

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Boronic acid complexes with amino phenolic N,O-ligands and their use for non-covalent protein fluorescence labeling

Bioorganic Chemistry ◽

10.1016/j.bioorg.2021.104993 ◽

2021 ◽

pp. 104993

Author(s):

Mayte A. Martínez-Aguirre ◽

Marcos Flores Alamo ◽

Karla Elisa Trejo-Huizar ◽

Anatoly K. Yatsimirsky

Keyword(s):

Boronic Acid ◽

Fluorescence Labeling ◽

Protein Fluorescence

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Integrated Transcriptomic and Un-Targeted Metabolomics Analysis Reveals Mulberry Fruit (Morus atropurpurea) in Response to Sclerotiniose Pathogen Ciboria shiraiana Infection

International Journal of Molecular Sciences ◽

10.3390/ijms21051789 ◽

2020 ◽

Vol 21 (5) ◽

pp. 1789 ◽

Cited By ~ 2

Author(s):

Lijun Bao ◽

Hongpeng Gao ◽

Zelin Zheng ◽

Xiaoxiao Zhao ◽

Minjuan Zhang ◽

...

Keyword(s):

Molecular Mechanisms ◽

Early Stage ◽

Metabolite Profile ◽

Northwest China ◽

Targeted Metabolomics ◽

Mulberry Fruit ◽

Phenylpropanoid Biosynthesis ◽

Mapman Analysis ◽

Mulberry Fruits ◽

Prevention Methods

Mulberry sclerotiniose caused by Ciboria shiraiana is a devastating disease of mulberry (Morus alba L.) fruit in Northwest China. At present, no disease-resistant varieties are used in production, as the molecular mechanisms of this disease are not well understood. In this study, to explore new prevention methods and provide direction for molecular breeding, transcriptomic sequencing and un-targeted metabolomics were performed on healthy (CK), early-stage diseased (HB1), and middle-stage diseased (HB2) mulberry fruits. Functional annotation, gene ontology, a Kyoto encyclopedia of genes and genomes (KEGG) analysis, and a Mapman analysis of the differentially expressed genes revealed differential regulation of genes related to plant hormone signal transduction, transcription factors, and phenylpropanoid biosynthesis. A correspondence between the transcript pattern and metabolite profile was observed in the phenylpropanoid biosynthesis pathway. It should be noted that the log2 ratio of eugenol (isoeugenol) in HB1 and HB2 are 85 times and 23 times higher than CK, respectively. Our study shows that phenylpropanoid biosynthesis may play an essential role in response to sclerotiniose pathogen infection and eugenol(isoeugenol) enrichment in mulberry fruit, which may provide a novel method for mulberry sclerotiniose control.

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