This study analyzed gender differences in the progressive dopamine (DA) deficiency phenotype in the MitoPark (MP) mouse model of Parkinson’s disease (PD) with progressive loss of DA release and reuptake in midbrain DA pathways. We found that the progressive loss of these DA presynaptic parameters begins significantly earlier in male than female MP mice. This was correlated with behavioral gender differences of both forced and spontaneous motor behavior. The degeneration of the nigrostriatal DA system in MP mice is earlier and more marked than that of the mesolimbic DA system, with male MP mice again being more strongly affected than female MP mice. After ovariectomy, DA presynaptic and behavioral changes in female mice become very similar to those of male animals. Our results suggest that estrogen, either directly or indirectly, is neuroprotective in the midbrain DA system. Our results are compatible with epidemiological data on incidence and symptom progression in PD, showing that men are more strongly affected than women at early ages.

To determine the efficacy of PT320 on L-DOPA-induced dyskinetic behaviors, and neurochemistry in a progressive Parkinson’s disease (PD) MitoPark mouse model. To investigate the effects of PT320 on the manifestation of dyskinesia in L-DOPA-primed mice, a clinically translatable biweekly PT320 dose was administered starting at either 5 or 17-weeks-old mice. The early treatment group was given L-DOPA starting at 20 weeks of age and longitudinally evaluated up to 22 weeks. The late treatment group was given L-DOPA starting at 28 weeks of age and longitudinally observed up to 29 weeks. To explore dopaminergic transmission, fast scan cyclic voltammetry (FSCV) was utilized to measure presynaptic dopamine (DA) dynamics in striatal slices following drug treatments. Early administration of PT320 significantly mitigated the severity L-DOPA-induced abnormal involuntary movements; PT320 particularly improved excessive numbers of standing as well as abnormal paw movements, while it did not affect L-DOPA-induced locomotor hyperactivity. In contrast, late administration of PT320 did not attenuate any L-DOPA-induced dyskinesia measurements. Moreover, early treatment with PT320 was shown to not only increase tonic and phasic release of DA in striatal slices in L-DOPA-naïve MitoPark mice, but also in L-DOPA-primed animals. Early treatment with PT320 ameliorated L-DOPA-induced dyskinesia in MitoPark mice, which may be related to the progressive level of DA denervation in PD.

It is very important for the brain study to design a multi-channel and high signal-to-noise ratio (SNR) bio-medical signal capture, record, and analysis system, which can effectively enhance accuracy and precision of the signal capture under dozens to hundreds of microvolts. Unfortunately, the system for data acquisition is very easily interfered by the environment, the power, and the bio-amplifier, so that the results will lead to a failed promotion of capturing high SNR signals, especially in the tiny brain wave signal. In this study, it has been designed an inexpensive, purpose-built, high SNR brainwave signals measurement system. The system is composed of an improved capture system, record system, and analysis system. To better consider the strength and characteristics of the tiny brainwave signals, the system was designed to include a suitable bio-amplifier to make each channel of the invasive microelectrode able to collect the brainwave signals correctly and it provides recording and analysis software, which can not only extract the characteristics of brainwave signals, but also quickly classify signals. The system can collect biological signals from 10[ ][ ]V to 420[ ][ ]V and has a high SNR[ ]30. The proposed system is easy to make and can be fabricated for the relatively low cost of only US$203. The brain wave signals from the three actions can also be easily classified, with a correct rate up to 46.70%. The system has six improvements: good SNR, the ability to capture small signals, modularity, a low price, easy fabrication, and simple operation.

Background: Recently, microglia, being the determinant of environment in peri-infarct tissue and strongly influence the potential for neuronal plasticity, has been implicated in post-ischemic secondary injury and functional recovery. However, the specific role of peri-infarct microglia and the timing of its morphological changes following ischemic stroke are not well understood. Valproic acid (VPA) can protect against ischemic damage and promote recovery. These effects are usually attributed, at least partially, to the anti-inflammatory ability of this drug to suppress microglial activation. In this study, we explored whether a low dose of VPA after stroke could modify reactive responses in microglia/macrophages and optimize peri-infarct microenvironments to improve functional recovery.Methods: Male Sprague-Dawley rats were subjected to distal middle cerebral artery occlusion (dMCAo) for 90 minutes, followed by reperfusion. Some received a single injection of VPA (200 mg/kg) 90 minutes after the induction of ischemia, while vehicle-treated animals underwent the same procedure with physiological saline. Infarction volume was calculated at 48 hours after reperfusion, and neurological symptoms were evaluated through 48 hours thereafter. The production of cytokines and biomarkers after insult was determined using enzyme-linked immunosorbent assays (ELISAs) and western blot. The effects of VPA on the activation of peri-infarct CD11b-positive cells were assessed based on cellular density and quality observation of morphology with fractal analysis and circularity index. The expressions of genes within peri-infarct zones at 3 days were determined by RNA-sequencing analysis. To determine whether VPA modulates microglial polarization, gentleMACS Dissociator was used to isolate CD11b-positive cells from the peri-infarct cortex of rats treated with or without VPA 3 days after dMCAo, after which the cells were subjected to qPCR analysis.Results: 200 mg/kg of VPA injected 90 minutes after ischemia induction did not significantly reduce infarct volume but did improve neurological deficit at least partially compared with vehicle. Meanwhile, VPA significantly reduced dMCAo-induced elevation of IL-6 at 24 hours post-stroke and significantly decreased the number of CD11b-positive microglia/macrophages within peri-infarct cortex at 7 but not at 2 days. Morphological analysis revealed that VPA therapy leads to higher fractal dimensions and lower circularity index of CD11b-positive cells within peri-infarct cortex at both 2 days and 7 days, suggesting that VPA has core effects on microglial activation. The attenuation of microglia/macrophage activation caused by VPA might involve HDAC inhibition-mediated suppression of galectin-3 production. Analysis of VPA-induced changes to gene expressions in the peri-infarct cortex at 3 days post-stroke indicated the upregulation of wound healing, collagen trimmer, and extracellular matrix genes. Furthermore, qPCR analysis of CD11b-positive cells suggested that VPA could partially enhance M2 subset polarization of microglia/macrophages in peri-infarct cortex.Conclusions: Our results are the first to show that a low dose of VPA promotes short-term functional recovery but does not alter infarct volume. The decreases in the expression of both IL-6 and galectin-3 might influence the extracellular matrix remodeling and morphological characteristics and transcriptional profiles of microglia/macrophages, which could contribute to the improved recovery.

Understanding the neurons that transmit messages in the brain while we thinking, feeling, or acting is critical for research on the causes of neurological disease and treatment strategies. This research focuses on the primary motor cortex M1 region, which is involved in human motor function as an activity command center. Understanding this region can help us to determine the mechanism of movement control by the brain, with applicability to other activity mechanisms. A time delay neural network (TDNN) is a suitable model for studying brain signals. TDNN can analyze comprehensive information for a period of successive signals, which is similar to the transmission mechanism of the M1 region. Therefore, this study used a TDNN to build a three-stage encoding system corresponding to the signal type, type arrangement, and time sequence of the brainwave signal from the M1 region and the encoded results were defined as codes, symbols, and commands, respectively. This study aimed to understand the relationship between movement and the M1 region by decoding the signal when the rat undertakes an action. First, we recorded the M1 signal from three rat action types (walk, stand up, and shift head) and performed signal processing. This included using a nonlinear energy operator to find the response points of each action signal. The signals were separated into several sections according to the response time points and independent component analysis was then used to extract the features of the signal (the signal of interest). Finally, we found 16 representative sample signals through a dynamic dimension increasing algorithm to train a three-stage TDNN. We then input the remaining feature signals of interest into the three-stage TDNN for encoding and classification. The results showed an accuracy rate for the three actions of 51.4%, 80.0%, and 54.3%, which means that it is feasible to explain the brain signal of M1 from the free-moving animal using a three-stage TDNN encoding model.

Nuclear medicine could provide an accurate estimate of the biochemical composition, metabolism, and capability of the kidney. Using nuclear medicine imaging to scan the organ, we can find the time-activity curve, called a renogram, by the intensity of the image. The activity curve can be employed to describe organ capability. Traditional methods of observing the activity curve of the whole kidney can obscure some small details. This paper proposes an improved renogram based on blood vessel distribution to estimate the kidney capability automatically. Every patient must acquire 180 renal dynamic images from the renal scan. First, acquiring region of the kidney in the No. 68 kidney image and correcting the background of this sample image, the sample image will be applied to the rest of the images to find the region of the kidney. The kidney image is then divided into several sub-regions according to the paths of the blood vessels from No. 50 kidney image. For each sub-region, we find the time–intensity curve, called the sub-renogram. Then, we observe the activity curves of the sub-region and the whole kidney separately to assist the diagnosis. The results of the automatic analysis are similar to the traditional methods used on the whole kidney, which ignores any abnormal sub-region activity. The proposed method can improve the accuracy of the doctor’s diagnosis.