Preparation of multifunctional upconversion nanoconstruct for in vitro and in vivo imaging and photodynamic therapy induced by near-infrared light

2014 ◽  
Author(s):  
Deyan Yin ◽  
Shanshan Huang ◽  
Ying Zhang ◽  
Yueqing Gu
Keyword(s):  
Photodynamic Therapy ◽  
In Vivo Imaging ◽  
Near Infrared ◽  
Infrared Light ◽  
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 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 Polychromatic in vivo imaging of multiple targets using visible and near infrared light

2013 ◽  
Vol 65 (8) ◽  
pp. 1112-1119 ◽  
Author(s):  
Hisataka Kobayashi ◽  
Michelle R. Longmire ◽  
Peter L. Choyke
Keyword(s):  
In Vivo Imaging ◽  
Near Infrared ◽  
Infrared Light ◽  
Multiple Targets ◽  
Near Infrared Light

Near-infrared light excited UCNP-DNAzyme nanosensor for selective detection of Pb2+ and in vivo imaging

Talanta ◽  
2021 ◽  
Vol 227 ◽  
pp. 122156
Author(s):  
Linna Huang ◽  
Feng Chen ◽  
Xia Zong ◽  
Qiujun Lu ◽  
Cuiyan Wu ◽  
...  
Keyword(s):  
In Vivo Imaging ◽  
Near Infrared ◽  
Infrared Light ◽  
Selective Detection ◽  
Near Infrared Light

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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