Non-scanning fiber-optic near-infrared beam led to two-photon optogenetic stimulation in-vivo.

Stimulation of specific neurons expressing opsins in a targeted region to manipulate brain function has proved to be a powerful tool in neuroscience. However, the use of visible light for optogenetic stimulation is invasive due to low penetration depth and tissue damage owing to larger absorption and scattering. Here, we report, for the first time, in-depth non-scanning fiber-optic two-photon optogenetic stimulation (FO-TPOS) of neurons in-vivo in transgenic mouse models. In order to optimize the deep-brain stimulation strategy, we characterized two-photon activation efficacy at different near-infrared laser parameters. The significantly-enhanced in-depth stimulation efficiency of FO-TPOS as compared to conventional single-photon beam was demonstrated both by experiments and Monte Carlo simulation. The non-scanning FO-TPOS technology will lead to better understanding of the in-vivo neural circuitry because this technology permits more precise and less invasive anatomical delivery of stimulation.

Auto-fluorescence lifetime and light reflectance spectroscopy for breast cancer diagnosis: potential tools for intraoperative margin detection.

This study investigates the use of two spectroscopic techniques, auto-fluorescence lifetime measurement (AFLM) and light reflectance spectroscopy (LRS), for detecting invasive ductal carcinoma (IDC) in human ex vivo breast specimens. AFLM used excitation at 447 nm with multiple emission wavelengths (532, 562, 632, and 644 nm), at which auto-fluorescence lifetimes and their weight factors were analyzed using a double exponent model. LRS measured reflectance spectra in the range of 500-840 nm and analyzed the spectral slopes empirically at several distinct spectral regions. Our preliminary results based on 93 measured locations (i.e., 34 IDC, 31 benign fibrous, 28 adipose) from 6 specimens show significant differences in 5 AFLM-derived parameters and 9 LRS-based spectral slopes between benign and malignant breast samples. Multinomial logistic regression with a 10-fold cross validation approach was implemented with selected features to classify IDC from benign fibrous and adipose tissues for the two techniques independently as well as for the combined dual-modality approach. The accuracy for classifying IDC was found to be 96.4 ± 0.8%, 92.3 ± 0.8% and 96 ± 1.3% for LRS, AFLM, and dual-modality, respectively.