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The treatment of skin with a low-power continuous-wave (CW) near-infrared (NIR) laser prior to vaccination is an emerging strategy to augment the immune response to intradermal vaccine, potentially substituting for chemical adjuvant, which has been linked to adverse effects of vaccines. This approach proved to be low cost, simple, small, and readily translatable compared with the previously explored pulsed-wave medical lasers. However, little is known on the mode of laser–tissue interaction eliciting the adjuvant effect. In this study, we sought to identify the pathways leading to the immunological events by examining the alteration of responses resulting from genetic ablation of innate subsets including mast cells and specific dendritic cell populations in an established model of intradermal vaccination and analyzing functional changes of skin microcirculation upon the CW NIR laser treatment in mice. We found that a CW NIR laser transiently stimulates mast cells via generation of reactive oxygen species, establishes an immunostimulatory milieu in the exposed tissue, and provides migration cues for dermal CD103+ dendritic cells without inducing prolonged inflammation, ultimately augmenting the adaptive immune response. These results indicate that use of an NIR laser with distinct wavelength and power is a safe and effective tool to reproducibly modulate innate programs in skin. These mechanistic findings would accelerate the clinical translation of this technology and warrant further explorations into the broader application of NIR lasers to the treatment of immune-related skin diseases.

OBJECTIVE A critical feature for the maintenance of precise skeletal muscle force production by the human brain is its ability to configure motor function activity dynamically and adaptively in response to visual and somatosensory information. Existing studies have concluded that not only the sensorimotor area but also distributed cortical areas act cooperatively in the generation of motor commands for voluntary force production to the desired level. However, less attention has been paid to such physiological mechanisms in conventional brain-computer interface (BCI) design and implementation. We proposed a new, physiologically inspired two-stage decoding method to see its contribution on accuracy improvement of BCI. APPROACH We performed whole-head high-density scalp electroencephalographic (EEG) recording during a right finger force-matching task at three strength levels (20%, 40%, and 60% maximal voluntary contraction following a resting state). A two-stage regression approach was employed that decodes muscle contraction level from EEG signals in the multi-level force-matching task and translates them into: (1) presence/absence of muscle contraction as a first stage; and (2) muscle contraction level as a second stage. Dimensionality reduction of the EEG signals, using principal component analysis, avoided multicollinearity during multiple regression, and data-driven stepwise multiple regression identified EEG components that were involved in the multi-level force-matching task. MAIN RESULTS An alternatively tuned two-stage regressor accurately decoded muscle contraction level with online processing rather than the conventional decoders, and identified EEG components that were related to voluntary force production. Relaxation/contraction state-dependent EEG components were localized dominantly in the contralateral parieto-temporal regions, whereas multi-level force regulation-dependent EEG components came from the fronto-parietal regions. SIGNIFICANCE Our findings identify respective cortical signalings during relaxation/contraction and multi-level force regulation using a sensor-based approach with EEG. Simulation-based assessment of the current physiologically inspired decoding technique proved improved accuracy in online BCI control.