Therapeutically Targeting Neuroinflammation and Microglia after Acute Ischemic Stroke

Inflammation has a pivotal role in the pathogenesis of ischemic stroke, and recent studies posit that inflammation acts as a double-edged sword, not only detrimentally augmenting secondary injury, but also potentially promoting recovery. An initial event of inflammation in ischemic stroke is the activation of microglia, leading to production of both pro- and anti-inflammatory mediators acting through multiple receptor signaling pathways. In this review, we discuss the role of microglial mediators in acute ischemic stroke and elaborate on preclinical and clinical studies focused on microglia in stroke models. Understanding how microglia can lead to both pro- and anti-inflammatory responses may be essential to implement therapeutic strategies using immunomodulatory interventions in ischemic stroke

Beneficial Effects of Melatonin Combined with Exercise on Endogenous Neural Stem/Progenitor Cells Proliferation after Spinal Cord Injury

Endogenous neural stem/progenitor cells (eNSPCs) proliferate and differentiate into neurons and glial cells after spinal cord injury (SCI). We have previously shown that melatonin (MT) plus exercise (Ex) had a synergistic effect on functional recovery after SCI. Thus, we hypothesized that combined therapy including melatonin and exercise might exert a beneficial effect on eNSPCs after SCI. Melatonin was administered twice a day and exercise was performed on a treadmill for 15 min, six days per week for 3 weeks after SCI. Immunohistochemistry and RT-PCR analysis were used to determine cell population for late response, in conjunction with histological examination and motor function test. There was marked improvement in hindlimb function in SCI+MT+Ex group at day 14 and 21 after injury, as documented by the reduced size of the spinal lesion and a higher density of dendritic spines and axons; such functional improvements were associated with increased numbers of BrdU-positive cells. Furthermore, MAP2 was increased in the injured thoracic segment, while GFAP was increased in the cervical segment, along with elevated numbers of BrdU-positive nestin-expressing eNSPCs in the SCI+MT+Ex group. The dendritic spine density was augmented markedly in SCI+MT and SCI+MT+Ex groups.These results suggest a synergistic effect of SCI+MT+Ex might create a microenvironment to facilitate proliferation of eNSPCs to effectively replace injured cells and to improve regeneration in SCI

Development of an assessment method for freely moving nonhuman primates’ eating behavior using manual and deep learning analysis

Purpose: Although eating is imperative for survival, few comprehensive methods have been developed to assess freely moving nonhuman primates' eating behavior. In the current study, we distinguished eating behavior into appetitive and consummatory phases and developed nine indices to study them using manual and deep learning-based (DeepLabCut) techniques. Method: The indices were utilized to three rhesus macaques by different palatability and hunger levels to validate their utility. To execute the experiment, we designed the eating behavior cage and manufactured the artificial food. The total number of trials was 3, with 1 trial conducted using natural food and 2 trials using artificial food. Result: As a result, the indices of highest utility for hunger effect were approach frequency and consummatory duration. Appetitive composite score and consummatory duration showed the highest utility for palatability effect. To elucidate the effects of hunger and palatability, we developed 2D visualization plots based on manual indices. These 2D visualization methods could intuitively depict the palatability perception and hunger internal state. Furthermore, the developed deep learning-based analysis proved accurate and comparable with manual analysis. When comparing the time required for analysis, deep learning-based analysis was 24-times faster than manual analysis. Moreover, temporal and spatial dynamics were visualized via manual and deep learning-based analysis. Based on temporal dynamics analysis, the patterns were classified into four categories: early decline, steady decline, mid-peak with early incline, and late decline. Heatmap of spatial dynamics and trajectory-related visualization could elucidate a consumption posture and a higher spatial occupancy of food zone in hunger and with palatable food. Discussion: Collectively, this study describes a newly developed and validated multi-phase method for assessing freely moving nonhuman primate eating behavior using manual and deep learning-based analyses. These effective tools will prove valuable in food reward (palatability effect) and homeostasis (hunger effect) research

A Novel, Automated, and Real-Time Method for the Analysis of Non-Human Primate Behavioral Patterns Using a Depth Image Sensor

By virtue of their upright locomotion, similar to that of humans, motion analysis of non-human primates has been widely used in order to better understand musculoskeletal biomechanics and neuroscience problems. Given the difficulty of conducting a marker-based infrared optical tracking system for the behavior analysis of primates, a 2-dimensional (D) video analysis has been applied. Distinct from a conventional marker-based optical tracking system, a depth image sensor system provides 3-D information on movement without any skin markers. The specific aim of this study was to develop a novel algorithm to analyze the behavioral patterns of non-human primates in a home cage using a depth image sensor. The behavioral patterns of nine monkeys in their home cage, including sitting, standing, and pacing, were captured using a depth image sensor. Thereafter, these were analyzed by observers’ manual assessment and the newly written automated program. We confirmed that the measurement results from the observers’ manual assessments and the automated program with depth image analysis were statistically identical