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Background and Purpose: Asthma is characterized by airway inflammation, mucus hypersecretion, and airway hyperresponsiveness. The use of nicotinic agents to mimic the cholinergic anti‐inflammatory pathway (CAP) controls experimental asthma. Yet, the effects of vagus nerve stimulation (VNS)‐induced CAP on allergic inflammation remain unknown. Experimental Approach: BALB/c mice were sensitized and challenged with house dust mite (HDM) extract and treated with active VNS (5 Hz, 0.5 ms, 0.05–1 mA). Bronchoalveolar lavage (BAL) fluid was assessed for total and differential cell counts and cytokine levels. Lungs were examined by histopathology and electron microscopy. Key Results: In the HDM mouse asthma model, VNS at intensities equal to or above 0.1 mA (VNS 0.1) but not sham VNS reduced BAL fluid differential cell counts and alveolar macrophages expressing α7 nicotinic receptors (α7nAChR), goblet cell hyperplasia, and collagen deposition. Besides, VNS 0.1 also abated HDM‐induced elevation of type 2 cytokines IL‐4 and IL‐5 and was found to block the phosphorylation of transcription factor STAT6 and expression level of IRF4 in total lung lysates. Finally, VNS 0.1 abrogated methacholine‐induced hyperresponsiveness in asthma mice. Prior administration of α‐bungarotoxin, a specific inhibitor of α7nAChR, but not propranolol, a specific inhibitor of β2‐adrenoceptors, abolished the therapeutic effects of VNS 0.1. Conclusion and Implications: Our data revealed the protective effects of VNS on various clinical features in allergic airway inflammation model. VNS, a clinically approved therapy for depression and epilepsy, appears to be a promising new strategy for controlling allergic asthma. [ABSTRACT FROM AUTHOR]

Physics has been an important part of the science curriculum in high schools. Without the appropriate high school physics background, it is difficult for a student subsequently to pursue an electronics engineering program at the university level since a good understanding of many concepts in physics is required to comprehend the material covered in electronics-related courses. Unfortunately, a phenomenon observed in today's high schools is a clear diminution of student interest in physics, leading to dwindling physics class sizes compared to a decade ago. The lack of interest in and passion for physics at the high school level ultimately leads to smaller intakes at the university level for electronics engineering courses and/or lower qualification cutoffs for students entering the programs. This results in a significant reduction in good students graduating from engineering programs. This paper will first share survey results that reveal possible reasons behind the decreasing number of students entering engineering programs. Two concerted efforts launched from the National University of Singapore (NUS) to arrest the declining interest in high schools will then be presented. (Contains 3 tables and 17 figures.)

Parkinson’s disease (PD) is a chronic, non-reversible neurodegenerative disorder, and freezing of gait (FOG) is one of the most disabling symptoms in PD as it is often the leading cause of falls and injuries that drastically reduces patients’ quality of life. In order to monitor continuously and objectively PD patients who suffer from FOG and enable the possibility of on-demand cueing assistance, a sensor-based FOG detection solution can help clinicians manage the disease and help patients overcome freezing episodes. Many recent studies have leveraged deep learning models to detect FOG using signals extracted from inertial measurement unit (IMU) devices. Usually, the latent features and patterns of FOG are discovered from either the time or frequency domain. In this study, we investigated the use of the time-frequency domain by applying the Continuous Wavelet Transform to signals from IMUs placed on the lower limbs of 63 PD patients who suffered from FOG. We built convolutional neural networks to detect the FOG occurrences, and employed the Bayesian Optimisation approach to obtain the hyper-parameters. The results showed that the proposed subject-independent model was able to achieve a geometric mean of 90.7% and a F1 score of 91.5%. [ABSTRACT FROM AUTHOR]

Objective: Peripheral neural interface (PNI) with a stable integration of synthetic elements with neural tissue is key for successfulneuro-prosthetic applications. An inevitable phenomenon of reactive fibrosis is a primary hurdle for long term functionality of PNIs. This proof-of-concept study aimed to fabricate and test a novel, stable PNI that harnesses fibro-axonal outgrowth at the nerve end and includes fibrosis in the design. Methods: Two non-human primates were implanted with Substrate-guided, Tissue-Electrode Encapsulation and Integration (STEER) PNIs. The implant included a 3D printed guide that strove to steer the regrowing nerve towards encapsulation of the electrodes into a fibro-axonal tissue. After four months from implantation, we performed electrophysiological measurements to test STEER's functionality and examined the macro and micro- morphology of the outgrowth tissue. Results: We observed a highly structured fibro-axonal composite within the STEER PNI. A conduction of intracranially generated action potentials was successfully recorded across the neural interface. Immunohistology demonstrated uniquely configured laminae of myelinated axons encasing the implant. Conclusion: STEER PNI reconfigured the structure of the fibro-axonal tissue and facilitated long-term functionality and stability of the neural interface. Significance: The results point to the feasibility of our concept for creating a stable PNI with long-term electrophysiologic functionality by using simple design and materials. [ABSTRACT FROM AUTHOR]