Fear behavior is under tight control of the prefrontal cortex, but the underlying microcircuit mechanism remains elusive. In particular, it is unclear how distinct subtypes of inhibitory interneurons (INs) within prefrontal cortex interact and contribute to fear expression. We employed a social fear conditioning paradigm and induced robust social fear in mice. We found that social fear is characterized by activation of dorsal medial prefrontal cortex (dmPFC) and is largely diminished by dmPFC inactivation. With a combination of in vivo electrophysiological recordings and fiber photometry together with celltype-specific pharmacogenetics, we further demonstrated that somatostatin (SST) INs suppressed parvalbumin (PV) INs and disinhibited pyramidal cells and consequently enhanced dmPFC output to mediate social fear responses. These results reveal a previously unknown disinhibitory microcircuit in prefrontal cortex through interactions between IN subtypes and suggest that SST INs-mediated disinhibition represents an important circuit mechanism in gating social fear behavior.

The optical differential pathlength factor (DPF) is an important parameter for physiological measurement using near infrared spectroscopy, but for the human adult head it has been available only for the forehead. Here we report measured DPF results for the forehead, somatosensory motor and occipital regions from measurements on 11 adult volunteers using a time-resolved optical imaging system. The optode separation was about 30 mm and the wavelengths used were 759 nm, 799 nm and 834 nm. Measured DPFs were 7.25 for the central forehead and 6.25 for the temple region at 799 nm. For the central somatosensory and occipital areas (10 mm above the inion), DPFs at 799 nm are 7.5 and 8.75, respectively. Less than 10% decreases of DPF for all these regions were observed when the wavelength increased from 759 nm to 834 nm. To compare these DPF maps with the anatomical structure of the head, a Monte Carlo simulation was carried out to calculate DPF for these regions by using a two-layered semi-infinite model and assuming the thickness of the upper layer to be the sum of the thicknesses of scalp and skull, which was measured from MRI images of a subject's head. The DPF data will be useful for quantitative monitoring of the haemodynamic changes occurring in adult heads.

Fluorescence diffuse optical tomography (DOT) has attracted many attentions from the community of biomedical imaging, since it provides effective enhancement in imaging contrast. This modality is now rapidly evolving as a potential means of monitoring molecular events in small living organisms with help of molecule-specific contrast agents, referred to as fluorescence molecular tomography (FMT). FMT could greatly promote pathogenesis research, drug development, and therapeutic intervention. Although FMT in steady-state and frequency-domain modes have been heavily investigated, the extension to time-domain scheme is imminent for its several unique advantages over the others. By extending the previously developed generalized pulse spectrum technique for time-domain DOT, we propose a linear, featured-data image reconstruction algorithm for time-domain FMT that can simultaneously reconstruct both fluorescent yield and lifetime images of multiple fluorophores, and validate the methodology with simulated data.

As a result of this study, the authors verified that it is necessary and feasible to get better NPS estimate by appropriate background trend removal. Subtraction of a 2-D second-order polynomial fit to the image was the most appropriate technique for background detrending without consideration of processing time.

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We present in vivo images of near-infrared (NIR) diffuse optical tomography (DOT) of human lower legs and forearm to validate the dual functions of a time-resolved (TR) NIR DOT in clinical diagnosis, i.e., to provide anatomical and functional information simultaneously. The NIR DOT system is composed of time-correlated single-photon-counting channels, and the image reconstruction algorithm is based on the modified generalized pulsed spectral technique, which effectively incorporates the TR data with reasonable computation time. The reconstructed scattering images of both the lower legs and the forearm revealed their anatomies, in which the bones were clearly distinguished from the muscles. In the absorption images, some of the blood vessels were observable. In the functional imaging, a subject was requested to do handgripping exercise to stimulate physiological changes in the forearm tissue. The images of oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentration changes in the forearm were obtained from the differential images of the absorption at three wavelengths between the exercise and the rest states, which were reconstructed with a differential imaging scheme. These images showed increases in both blood volume and oxyhemoglobin concentration in the arteries and simultaneously showed hypoxia in the corresponding muscles. All the results have demonstrated the capability of TR NIR DOT by reconstruction of the absolute images of the scattering and the absorption with a high spatial resolution that finally provided both the anatomical and functional information inside bulky biological tissues.

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the “Smart Healthcare” era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.

Acetic acid was used to enhance the efficiency of chemical vapor generation (CVG) of copper. The volatile product was formed at room temperature following merging of the sample solution with a flowing stream of 0.1% NaBH4; ICP-OES was used for detection. A precision of 1.6% RSD (n=11, 0.2 mg/L) was realized at sampling frequencies of about 90/h. A detection limit of 0.4 ng/mL (3sigma) was obtained. Sensitivity was enhanced approximately 8-fold over conventional pneumatic nebulization sample introduction. Compared with CVG in an optimized HCl or HNO3 medium, sensitivity was increased by an order of magnitude using acetic acid and the reductant concentration was reduced to only 10% of that required with HCl or HNO3. The generation efficiency was estimated to be 18% or 70%, depending on the method used to evaluate it. The methodology was successfully used to the determine copper in human hair and low alloy steel certified reference materials.