Near-infrared <i>in vivo</i> imaging system for dynamic visualization of lung-colonizing bacteria in mouse pneumonia

Conventional animal models of infectious diseases have traditionally relied upon average assessments involving numerous individuals, meaning they do not directly reflect changes in the pathology of an individual. Moreover, in recent years, ethical concerns have resulted in the demand to reduce the number of animals used in such models. Although <i>in vivo</i> imaging offers an effective approach for longitudinally evaluating the pathogenesis of infectious diseases in individual animals, a standardized method has not yet been established. To our knowledge, this study is the first to develop a highly versatile <i>in vivo</i> pulmonary bacterial quantification system utilizing near-infrared luminescence, plasmid-mediated expression of firefly luciferase in bacteria, and a scientific complementary metal-oxide semiconductor camera. Our research holds promise as a useful tool for assessing the efficacy of therapeutic drugs and pathogenesis of infectious diseases.

Authors

Institutions

Publication Details

Journal
Microbiology Spectrum
Published
2024-09-17
DOI
https://doi.org/10.1128/spectrum.00828-24
Citations
3
Primary Topic
bioluminescence and chemiluminescence research
Type
article
Field-Weighted Citation Impact
0.70

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Near-infrared in vivo imaging system for dynamic visualization of lung-colonizing bacteria in mouse pneumonia

Yuji Morita, Noriteru Yamada, Ryuichi Nakano, Hisakazu Yano et al.
3 citations
Microbiology Spectrum
bioluminescence and chemiluminescence research
0.70
article

Near-infrared in vivo imaging system for dynamic visualization of lung-colonizing bacteria in mouse pneumonia

Yuji Morita, Noriteru Yamada, Ryuichi Nakano, Hisakazu Yano, Daiki Yamaguchi, Shôjirô Maki, Nobuo Kitada, Kinnosuke Yahiro, Go Kamoshida, Ryohei Saito‐Moriya, Shinichi Kato, Syun Kawakubo, Ayane Okuda, Keisuke Ueyama, R. Tanaka, Saki Azuma, Takamitsu Tsuji, S Isaka, Manaha Tomita
article en
3 citations

Abstract

Conventional animal models of infectious diseases have traditionally relied upon average assessments involving numerous individuals, meaning they do not directly reflect changes in the pathology of an individual. Moreover, in recent years, ethical concerns have resulted in the demand to reduce the number of animals used in such models. Although <i>in vivo</i> imaging offers an effective approach for longitudinally evaluating the pathogenesis of infectious diseases in individual animals, a standardized method has not yet been established. To our knowledge, this study is the first to develop a highly versatile <i>in vivo</i> pulmonary bacterial quantification system utilizing near-infrared luminescence, plasmid-mediated expression of firefly luciferase in bacteria, and a scientific complementary metal-oxide semiconductor camera. Our research holds promise as a useful tool for assessing the efficacy of therapeutic drugs and pathogenesis of infectious diseases.

Microbiology SpectrumVol. 12(11)
University of Electro-Communications (JP), Meiji Pharmaceutical University (JP), Kyoto Pharmaceutical University (JP), Japan Women's University (JP), Nara Medical University (JP)
Japan Agency for Medical Research and Development, National Center for Global Health and Medicine, Kyoto Pharmaceutical University, Ichiro Kanehara Foundation for the Promotion of Medical Sciences and Medical Care, Japan Society for the Promotion of Science
Good health and well-being
Openalex Percentile: Top 30%
bioluminescence and chemiluminescence research
0.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.