Unknown

Dataset Information

0

Erratum: Author Correction: History and progress of hypotheses and clinical trials for Alzheimer's disease.

ABSTRACT: [This corrects the article DOI: 10.1038/s41392-019-0063-8.].

SUBMITTER: Liu PP 

PROVIDER: S-EPMC6799801 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

Json Xml
altmetric image

Publications

Erratum: Author Correction: History and progress of hypotheses and clinical trials for Alzheimer's disease.

Liu Pei-Pei PP   Xie Yi Y   Meng Xiao-Yan XY   Kang Jian-Sheng JS  

Signal transduction and targeted therapy 20190923

[This corrects the article DOI: 10.1038/s41392-019-0063-8.]. ...[more]

Similar Datasets

Unknown History and progress of hypotheses and clinical trials for Alzheimer's disease.
| S-EPMC6799833 | biostudies-literature
Unknown Erratum: Author Correction: CRISPR-Cas12a-assisted nucleic acid detection.
| S-EPMC6411867 | biostudies-literature
Unknown Erratum: Author Correction: Bcl11b controls odorant receptor class choice in mice.
| S-EPMC6742633 | biostudies-literature
Unknown Erratum: Author Correction: Updated annotation of the wild strawberry Fragaria vesca V4 genome.
| S-EPMC6804729 | biostudies-literature
Unknown Author Correction: Divergence in the metabolome between natural aging and Alzheimer's disease.
| S-EPMC7655935 | biostudies-literature
Unknown Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.
| S-EPMC6818723 | biostudies-literature
Unknown Author Correction: Spread of pathological tau proteins through communicating neurons in human Alzheimer's disease.
| S-EPMC8342451 | biostudies-literature
Unknown Clinical Research on Alzheimer's Disease: Progress and Perspectives.
| S-EPMC6246849 | biostudies-literature
Unknown Erratum: Author Correction: Improved Brassica rapa reference genome by single-molecule sequencing and chromosome conformation capture technologies.
| S-EPMC6851084 | biostudies-literature
Unknown Erratum: Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand.
| S-EPMC7543031 | biostudies-literature