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Institution

China Pharmaceutical University

EducationNanjing, China
About: China Pharmaceutical University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Apoptosis & Drug delivery. The organization has 19947 authors who have published 16209 publications receiving 275951 citations.


Papers
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Journal ArticleDOI
TL;DR: The PKSolver provided pharmacokinetic researchers with a fast and easy-to-use tool for routine and basic PK and PD data analysis with a more user-friendly interface and its output could be generated in Microsoft Word in the form of an integrated report.

1,493 citations

Journal ArticleDOI
TL;DR: The data indicate that SARS-CoV-2 may infect other tissues aside from the lungs and infect persons with different sexes, ages, and races equally, and may partially explain why males and females, young and old persons infected with this virus have markedly distinct disease severity.
Abstract: Since its discovery in December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 2 180 000 people worldwide and has caused more than 150 000 deaths as of April 16, 2020. SARS-CoV-2, which is the virus causing coronavirus disease 2019 (COVID-19), uses the angiotensin-converting enzyme 2 (ACE2) as a cell receptor to invade human cells. Thus, ACE2 is the key to understanding the mechanism of SARS-CoV-2 infection. This study is to investigate the ACE2 expression in various human tissues in order to provide insights into the mechanism of SARS-CoV-2 infection. We compared ACE2 expression levels across 31 normal human tissues between males and females and between younger (ages ≤ 49 years) and older (ages > 49 years) persons using two-sided Student’s t test. We also investigated the correlations between ACE2 expression and immune signatures in various tissues using Pearson’s correlation test. ACE2 expression levels were the highest in the small intestine, testis, kidneys, heart, thyroid, and adipose tissue, and were the lowest in the blood, spleen, bone marrow, brain, blood vessels, and muscle. ACE2 showed medium expression levels in the lungs, colon, liver, bladder, and adrenal gland. ACE2 was not differentially expressed between males and females or between younger and older persons in any tissue. In the skin, digestive system, brain, and blood vessels, ACE2 expression levels were positively associated with immune signatures in both males and females. In the thyroid and lungs, ACE2 expression levels were positively and negatively associated with immune signatures in males and females, respectively, and in the lungs they had a positive and a negative correlation in the older and younger groups, respectively. Our data indicate that SARS-CoV-2 may infect other tissues aside from the lungs and infect persons with different sexes, ages, and races equally. The different host immune responses to SARS-CoV-2 infection may partially explain why males and females, young and old persons infected with this virus have markedly distinct disease severity. This study provides new insights into the role of ACE2 in the SARS-CoV-2 pandemic.

1,143 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes.
Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

1,129 citations

Journal ArticleDOI
TL;DR: The development of a software program, called DDSolver, for facilitating the assessment of similarity between drug dissolution data and to establish a model library for fitting dissolution data using a nonlinear optimization method is described.
Abstract: In recent years, several mathematical models have been developed for analysis of drug dissolution data, and many different mathematical approaches have been proposed to assess the similarity between two drug dissolution profiles. However, until now, no computer program has been reported for simplifying the calculations involved in the modeling and comparison of dissolution profiles. The purposes of this article are: (1) to describe the development of a software program, called DDSolver, for facilitating the assessment of similarity between drug dissolution data; (2) to establish a model library for fitting dissolution data using a nonlinear optimization method; and (3) to provide a brief review of available approaches for comparing drug dissolution profiles. DDSolver is a freely available program which is capable of performing most existing techniques for comparing drug release data, including exploratory data analysis, univariate ANOVA, ratio test procedures, the difference factor f 1, the similarity factor f 2, the Rescigno indices, the 90% confidence interval (CI) of difference method, the multivariate statistical distance method, the model-dependent method, the bootstrap f 2 method, and Chow and Ki’s time series method. Sample runs of the program demonstrated that the results were satisfactory, and DDSolver could be served as a useful tool for dissolution data analysis.

1,045 citations

Journal ArticleDOI
19 Jun 2020-Science
TL;DR: Two peptidomimetic aldehydes were designed, synthesized, and evaluated as antiviral drug candidates, and both exhibited excellent inhibitory activity and potent anti-SARS-CoV-2 infection activity.
Abstract: SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is the etiological agent responsible for the global COVID-19 (coronavirus disease 2019) outbreak. The main protease of SARS-CoV-2, Mpro, is a key enzyme that plays a pivotal role in mediating viral replication and transcription. We designed and synthesized two lead compounds (11a and 11b) targeting Mpro Both exhibited excellent inhibitory activity and potent anti-SARS-CoV-2 infection activity. The x-ray crystal structures of SARS-CoV-2 Mpro in complex with 11a or 11b, both determined at a resolution of 1.5 angstroms, showed that the aldehyde groups of 11a and 11b are covalently bound to cysteine 145 of Mpro Both compounds showed good pharmacokinetic properties in vivo, and 11a also exhibited low toxicity, which suggests that these compounds are promising drug candidates.

1,023 citations


Authors

Showing all 20080 results

NameH-indexPapersCitations
Jian Yang1421818111166
Lei Zhang130231286950
Hong Liu100190557561
Muhammad Farooq92134137533
Bin Li92175542835
Jia Li85148734168
Lei Xing7990524057
Hualiang Jiang7992731944
Wei Zhao7747930589
Yuan Xie7673924155
Masayuki Yoshikawa7660821537
Hisashi Matsuda7350417575
Ke Zen7125422538
Zhaohui Wang6940219737
Tong Wu6659119325
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202354
2022260
20211,614
20201,535
20191,456
20181,301