JWA binding to NCOA4 alleviates degeneration in dopaminergic neurons through suppression of ferritinophagy in Parkinson's disease.

Parkinson's disease (PD) poses a significant challenge in neurodegenerative disorders, characterized by the progressive loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNc). The intricate mechanisms orchestrating DA neurodegeneration in PD are not fully understood, necessitating the exploration of innovative therapeutic approaches. Recent studies have implicated ferroptosis as a major contributor to the loss of DA neurons, revealing a complex interplay between iron accumulation and neurodegeneration. However, the sophisticated nature of this process challenges the conventional belief that mere iron removal could effectively prevent DA neuronal ferroptosis. Here, we report JWA, alternatively referred to as ARL6IP5, as a negative regulator of ferroptosis, capable of ameliorating DA neuronal loss in the context of PD. In this study, synchronized expression patterns of JWA and tyrosine hydroxylase (TH) in PD patients and mice were observed, underscoring the importance of JWA for DA neuronal survival. Screening of ferroptosis-related genes unraveled the engagement of iron metabolism in the JWA-dependent inhibition of DA neuronal ferroptosis. Genetic manipulation of JWA provided compelling evidence linking its neuroprotective effects to the attenuation of NCOA4-mediated ferritinophagy. Molecular docking, co-immunoprecipitation, and immunofluorescence studies confirmed that JWA mitigated DA neuronal ferroptosis by occupying the ferritin binding site of NCOA4. Moreover, the JWA-activating compound, JAC4, demonstrated promising neuroprotective effects in cellular and animal PD models by elevating JWA expression, offering a potential avenue for neuroprotection in PD. Collectively, our work establishes JWA as a novel regulator of ferritinophagy, presenting a promising therapeutic target for addressing DA neuronal ferroptosis in PD.

Vagus nerve stimulation as a promising neuroprotection for ischemic stroke via α7nAchR-dependent inactivation of microglial NLRP3 inflammasome.

Ischemic stroke is a major cause of disability and death worldwide, and its management requires urgent attention. Previous studies have shown that vagus nerve stimulation (VNS) exerts neuroprotection in ischemic stroke by inhibiting neuroinflammation and apoptosis. In this study, we evaluated the timing for VNS intervention in ischemic stroke, and the underlying mechanisms  of VNS-induced neuroprotection. Mice were subjected to transient middle cerebral artery occlusion (tMCAO) for 60 min. The left vagus nerve at cervical level was exposed and attached to an electrode connected to a low-frequency electrical stimulator. Vagus nerve stimulation (VNS) was given for 60 min before, during and after tMCAO (Pre-VNS, Dur-VNS, Post-VNS). Neurological function was assessed 24 h after reperfusion. We found that all the three VNS significantly protected against the tMCAO-induced injury evidenced by improved neurological function and reduced infarct volume. Moreover, the Pre-VNS was the most effective against the ischemic injury. We found that tMCAO activated microglia in the ischemic core and penumbra regions of the brain, followed by the NLRP3 inflammasome activation-induced neuroinflammation, which finally triggered neuronal death. VNS treatment preserved α7nAChR expression in the penumbra regions, inhibited NLRP3 inflammasome activation and ensuing neuroinflammation, rescuing cerebral neurons. The role of α7nAChR in microglial NLRP3 inflammasome activation in ischemic stroke was further validated using genetic manipulations, including Chrna7 knockout mice and microglial Chrna7 overexpression mice, as well as pharmacological interventions using the α7nAChR inhibitor methyllycaconitine and agonist PNU-282987. Collectively, this study demonstrates the potential of VNS as a safe and effective strategy to treat ischemic stroke, and presents a new approach targeting microglial NLRP3 inflammasome, which might be therapeutic for other inflammation-related diseases.

Pleasantness Recognition Induced by Different Odor Concentrations Using Olfactory Electroencephalogram Signals.

Olfactory-induced emotion plays an important role in communication, decision-making, multimedia, and disorder treatment. Using electroencephalogram (EEG) technology, this paper focuses on (1) exploring the possibility of recognizing pleasantness induced by different concentrations of odors, (2) finding the EEG rhythm wave that is most suitable for the recognition of different odor concentrations, (3) analyzing recognition accuracies with concentration changes, and (4) selecting a suitable classifier for this classification task. To explore these issues, first, emotions induced by five different concentrations of rose or rotten odors are divided into five kinds of pleasantness by averaging subjective evaluation scores. Then, the power spectral density features of EEG signals and support vector machine (SVM) are used for classification tasks. Classification results on the EEG signals collected from 13 participants show that for pleasantness recognition induced by pleasant or disgusting odor concentrations, considerable average classification accuracies of 93.5% or 92.2% are obtained, respectively. The results indicate that (1) using EEG technology, pleasantness recognition induced by different odor concentrations is possible; (2) gamma frequency band outperformed other EEG rhythm-based frequency bands in terms of classification accuracy, and as the maximum frequency of the EEG spectrum increases, the pleasantness classification accuracy gradually increases; (3) for both rose and rotten odors, the highest concentration obtains the best classification accuracy, followed by the lowest concentration.

Multi-marker diagnosis method for early Hepatocellular Carcinoma based on surface plasmon resonance.

Early diagnosis of Hepatocellular Carcinoma (HCC) is an important means to raise the survival rate of patients. Multi-marker combined detection is a powerful tool of early HCC diagnosis. Traditional detection methods are not effective and accurate because it is difficult to achieve combined detection of multiple markers. In this paper, we selected Alpha Fetoprotein (AFP) and miRNA-125b as the combined detection markers to improve the simultaneously diagnostic sensitivity and specificity. The anti-AFP monoclonal antibody and the DNA probes paired with the miRNA-125b were modified on the surface of surface plasmon resonance (SPR) sensor respectively to specifically recognize AFP and miRNA-125b in serum. In order to enhance the SPR response signal and detection sensitivity, Double Antibody Sandwich Method (DASM) and S9.6 antibody enhanced method were applied to achieve low detection limit of the two markers. Experimental results showed that AFP (25-400 ng/mL) was accurately detected by DASM and the detection limit of miRNA-125b by S9.6 antibody enhanced method reached 123.044 pM. These results verified the feasibility of the multi-marker detection method in early diagnosis of HCC.

A flexible enzyme-electrode sensor with cylindrical working electrode modified with a 3D nanostructure for implantable continuous glucose monitoring.

A novel cylindrical flexible enzyme-electrode sensor was fabricated with a bigger working electrode (WE) surface than the traditional pin-like one for implantable continuous glucose monitoring. On the WE surface, a 3D nanostructure consisting of graphene and platinum nanoparticles was constructed to enhance the sensitivity; in conjunction with the bigger WE, this nanostructure enabled hypoglycemia detection, which is still a big challenge in clinics. The cylindrical sensor was fabricated by rotated inkjet printing which enabled direct patterning of microstructures on a curved surface, thus overcoming the restriction of the traditional planar micromachining by photolithography. Specifically, the cylindrical substrate (polyetheretherketone, PEEK) was modified by (3-aminopropyl) trimethoxysilane and (3-mercaptopropyl) trimethoxysilane to facilitate surface wettability, which discourages the coalescence of adjacent droplets, and to facilitate the adhesion of metals to PEEK in order to construct robust electrodes. A synchronous heating method was used to evaporate the solvent of the droplets quickly to prevent them from running along the cylindrical surface, which affects the formation of the printed electrode significantly. In vitro experimental results showed that the proposed sensor was able to detect the glucose concentration ranging from 0 to 570 mg dL-1 which demonstrated its capability for physiological glucose detection. In vivo experiments were conducted with rats, and the measurement results recorded using the implanted cylindrical sensor showed great compliance to those recorded using a commercial glucometer which exhibited the viability of the proposed sensor for implantable continuous glucose monitoring, even under the hypoglycemic conditions.

Intrinsic excitability state of local neuronal population modulates signal propagation in feed-forward neural networks.

Reliable signal propagation across distributed brain areas is an essential requirement for cognitive function, and it has been investigated extensively in computational studies where feed-forward network (FFN) is taken as a generic model. But it is still unclear how distinct local network states, which are intrinsically generated by synaptic interactions within each layer, would affect the ability of FFN to transmit information. Here we investigate the impact of such network states on propagating transient synchrony (synfire) and firing rate by a combination of numerical simulations and analytical approach. Specifically, local network dynamics is attributed to the competition between excitatory and inhibitory neurons within each layer. Our results show that concomitant with different local network states, the performance of signal propagation differs dramatically. For both synfire propagation and firing rate propagation, there exists an optimal local excitability state, respectively, that optimizes the performance of signal propagation. Furthermore, we find that long-range connections strongly change the dependence of spiking activity propagation on local network state and propose that these two factors work jointly to determine information transmission across distributed networks. Finally, a simple mean field approach that bridges response properties of long-range connectivity and local subnetworks is utilized to reveal the underlying mechanism.