Near-Infrared Light Photocontrolled Targeting, Bioimaging, and Chemotherapy with Caged Upconversion Nanoparticles in Vitro and in Vivo

ACS Nano ◽  
2013 ◽  
Vol 7 (10) ◽  
pp. 8516-8528 ◽  
Author(s):  
Yi-Hsin Chien ◽  
Yu-Lin Chou ◽  
Shu-Wen Wang ◽  
Shu-Ting Hung ◽  
Min-Chiau Liau ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Light ◽  
Upconversion Nanoparticles ◽  
Near Infrared Light

scholarly journals Powering rotary molecular motors with low-intensity near-infrared light

2020 ◽  
Vol 6 (44) ◽  
pp. eabb6165
Author(s):  
Lukas Pfeifer ◽  
Nong V. Hoang ◽  
Maximilian Scherübl ◽  
Maxim S. Pshenichnikov ◽  
Ben L. Feringa
Keyword(s):  
Smart Materials ◽  
Molecular Motors ◽  
Near Infrared ◽  
In Vivo Studies ◽  
Infrared Light ◽  
Near Infrared Light ◽  
Two Photon ◽  
Low Intensity

Light-controlled artificial molecular machines hold tremendous potential to revolutionize molecular sciences as autonomous motion allows the design of smart materials and systems whose properties can respond, adapt, and be modified on command. One long-standing challenge toward future applicability has been the need to develop methods using low-energy, low-intensity, near-infrared light to power these nanomachines. Here, we describe a rotary molecular motor sensitized by a two-photon absorber, which efficiently operates under near-infrared light at intensities and wavelengths compatible with in vivo studies. Time-resolved spectroscopy was used to gain insight into the mechanism of energy transfer to the motor following initial two-photon excitation. Our results offer prospects toward in vitro and in vivo applications of artificial molecular motors.

scholarly journals Hyaluronate modified upconversion nanoparticles for near infrared light-triggered on–off tattoo systems

RSC Advances ◽  
2017 ◽  
Vol 7 (24) ◽  
pp. 14805-14808 ◽  
Author(s):  
Seulgi Han ◽  
Songeun Beack ◽  
Sanghwa Jeong ◽  
Byung Woo Hwang ◽  
Myeong Hwan Shin ◽  
...  
Keyword(s):  
Biomedical Applications ◽  
Near Infrared ◽  
Infrared Light ◽  
Upconversion Nanoparticles ◽  
Near Infrared Light ◽  
Nir Light

We successfully developed an NIR light-triggered in vivo on–off tattoo system using hyaluronate modified upconversion nanoparticles for various biomedical applications.

scholarly journals Generalizability of Deep Learning Models for Caries Detection in Near-Infrared Light Transillumination Images

2021 ◽  
Vol 10 (5) ◽  
pp. 961 ◽  
Author(s):  
Agnes Holtkamp ◽  
Karim Elhennawy ◽  
José E. Cejudo Grano de Oro ◽  
Joachim Krois ◽  
Sebastian Paris ◽  
...  
Keyword(s):  
Deep Learning ◽  
Near Infrared ◽  
Infrared Light ◽  
Learning Models ◽  
Near Infrared Light ◽  
In Vivo Models ◽  
Lower Accuracy ◽  
Caries Lesions

Objectives: The present study aimed to train deep convolutional neural networks (CNNs) to detect caries lesions on Near-Infrared Light Transillumination (NILT) imagery obtained either in vitro or in vivo and to assess the models’ generalizability. Methods: In vitro, 226 extracted posterior permanent human teeth were mounted in a diagnostic model in a dummy head. Then, NILT images were generated (DIAGNOcam, KaVo, Biberach), and images were segmented tooth-wise. In vivo, 1319 teeth from 56 patients were obtained and segmented similarly. Proximal caries lesions were annotated pixel-wise by three experienced dentists, reviewed by a fourth dentist, and then transformed into binary labels. We trained ResNet classification models on both in vivo and in vitro datasets and used 10-fold cross-validation for estimating the performance and generalizability of the models. We used GradCAM to increase explainability. Results: The tooth-level prevalence of caries lesions was 41% in vitro and 49% in vivo, respectively. Models trained and tested on in vivo data performed significantly better (mean ± SD accuracy: 0.78 ± 0.04) than those trained and tested on in vitro data (accuracy: 0.64 ± 0.15; p < 0.05). When tested in vitro, the models trained in vivo showed significantly lower accuracy (0.70 ± 0.01; p < 0.01). Similarly, when tested in vivo, models trained in vitro showed significantly lower accuracy (0.61 ± 0.04; p < 0.05). In both cases, this was due to decreases in sensitivity (by −27% for models trained in vivo and −10% for models trained in vitro). Conclusions: Using in vitro setups for generating NILT imagery and training CNNs comes with low accuracy and generalizability. Clinical significance: Studies employing in vitro imagery for developing deep learning models should be critically appraised for their generalizability. Applicable deep learning models for assessing NILT imagery should be trained on in vivo data.

scholarly journals 754 Near-infrared light 790-1000 nm causes photoaging in vivo and in vitro

2019 ◽  
Vol 139 (5) ◽  
pp. S130
Author(s):  
H. Sakaue ◽  
T. Sato
Keyword(s):  
Near Infrared ◽  
Infrared Light ◽  
Near Infrared Light

HP-01-005 Regulation of the relaxation of the corpus cavernosum by a near-infrared light-controlled NO donor in in vivo and in vitro studies

2019 ◽  
Vol 16 (5) ◽  
pp. S31-S32
Author(s):  
Y. Hotta ◽  
A. Nishikawa ◽  
N. Ieda ◽  
T. Kataoka ◽  
H. Nakagawa ◽  
...  
Keyword(s):  
Near Infrared ◽  
Corpus Cavernosum ◽  
In Vitro Studies ◽  
Infrared Light ◽  
No Donor ◽  
Near Infrared Light

scholarly journals Near-infrared light driven tissue-penetrating cardiac optogenetics via upconversion nanoparticles in vivo

2020 ◽  
Vol 11 (3) ◽  
pp. 1401 ◽  
Author(s):  
Panpan Rao ◽  
Long Wang ◽  
Yue Cheng ◽  
Xi Wang ◽  
Haitao Li ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Light ◽  
Upconversion Nanoparticles ◽  
Near Infrared Light

Near-infrared light induced in vivo photodynamic therapy of cancer based on upconversion nanoparticles

Biomaterials ◽  
2011 ◽  
Vol 32 (26) ◽  
pp. 6145-6154 ◽  
Author(s):  
Chao Wang ◽  
Huiquan Tao ◽  
Liang Cheng ◽  
Zhuang Liu
Keyword(s):  
Photodynamic Therapy ◽  
Near Infrared ◽  
Infrared Light ◽  
Upconversion Nanoparticles ◽  
Near Infrared Light ◽  
Photodynamic Therapy Of Cancer

Amphiphilic chitosan modified upconversion nanoparticles for in vivo photodynamic therapy induced by near-infrared light

2012 ◽  
Vol 22 (11) ◽  
pp. 4861 ◽  
Author(s):  
Sisi Cui ◽  
Haiyan Chen ◽  
Hongyan Zhu ◽  
Junmei Tian ◽  
Xuemei Chi ◽  
...  
Keyword(s):  
Photodynamic Therapy ◽  
Near Infrared ◽  
Infrared Light ◽  
Upconversion Nanoparticles ◽  
Near Infrared Light
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