Anti-dipeptidyl-peptidase-like protein 6 encephalitis with pure cerebellar ataxia: a case report.

BACKGROUND: Anti-dipeptidyl-peptidase-like protein 6 (DPPX) encephalitis is a rare autoimmune encephalitis. The clinical symptoms of anti-DPPX encephalitis are often severe, manifested as diarrhea/weight loss, central nervous system hyperexcitability and cognitive dysfunction. CASE PRESENTATION: An 18-year-old boy was admitted for 1-week-long cerebellar symptoms including dizziness, unsteady gait and frequent vomiting. Magnetic resonance imaging (MRI) displayed no abnormal findings. However, autoimmune encephalitis panel revealed anti-DPPX antibody was positive in the serum. This patient completely recovered after immunoglobulin and corticoids therapy. In addition, repeat serum antibody test for DPPX was negative within one month. CONCLUSION: In addition to the classic triad, anti-DPPX encephalitis may manifest as mild and rare symptoms due to lower antibody titers. Fast identification of rare symptoms can help to quickly diagnosis and effective treatment.

Monitoring Anomalies in 3D Bioprinting with Deep Neural Networks.

Additive manufacturing technologies have progressed in the past decades, especially when used to print biofunctional structures such as scaffolds and vessels with living cells for tissue engineering applications. Part quality and reliability are essential to maintaining the biocompatibility and structural integrity needed for engineered tissue constructs. As a result, it is critical to detect for any anomalies that may occur in the 3D-bioprinting process that can cause a mismatch between the desired designs and printed shapes. However, challenges exist in detecting the imperfections within oftentimes transparent bioprinted and complex printing features accurately and efficiently. In this study, an anomaly detection system based on layer-by-layer sensor images and machine learning algorithms is developed to distinguish and classify imperfections for transparent hydrogel-based bioprinted materials. High anomaly detection accuracy is obtained by utilizing convolutional neural network methods as well as advanced image processing and augmentation techniques on extracted small image patches. Along with the prediction of various anomalies, the category of infill pattern and location information on the image patches can be accurately determined. It is envisioned that using our detection system to categorize and localize printing anomalies, real-time autonomous correction of process parameters can be realized to achieve high-quality tissue constructs in 3D-bioprinting processes.

Knockout of Rnf213 Ameliorates Cerebral Ischemic-reperfusion Injury by Inhibiting Neuronal Apoptosis Through the Akt/GSK-3ß/ß-catenin/Bcl-2 Pathway.

Previous studies by us and others have shown that RING finger protein 213 (RNF213) is associated with cerebrovascular disease and systemic vasculopathy. Indeed, Rnf213 mRNA expression is increased in cerebral ischemia reperfusion injury (CIRI). The purpose of the present study was to investigate the role of Rnf213 in CIRI. Using the middle cerebral artery occlusion (MCAO) model, we confirmed that the expression of RNF213 protein was significantly upregulated in neurons in the ischemic penumbra. Rnf213 knockout mice were successfully generated using CRISPR/Cas9 technology. According to TTC staining and Bederson neurological scale, removal of Rnf213 decreased brain infarct volume and improved neurological deficit score, although the restoration of cerebral blood flow after MCAO was similar in WT and Rnf213-/- mice. In addition, the levels of p-Akt, p-GSK-3ß, ß-catenin and Bcl-2 were significantly increased 24 h after MCAO in the ischemic penumbra of the Rnf213-/- mice compared to WT mice, indicating that Rnf213 removal may ameliorate neuronal apoptosis by regulating the Akt/GSK-3ß/ß-catenin/Bcl-2 signaling pathway. Taken together, our study reveals that Rnf213 regulates neuronal apoptosis in CIRI, therefore impacting on brain infarct volume in brain ischemia.

Design and validation of a ten nanosecond resolved resistive thermometer for Gaussian laser beam heating.

Pulsed laser processing plays a crucial role in additive manufacturing and nanomaterial processing. However, probing the transient temperature field during the pulsed laser interaction with the processed materials is challenging as it requires both high spatial and temporal resolution. Previous transient thermometry studies have measured neither sub-100 µm spatial resolution nor sub-10 ns temporal resolution. The temperature field induced by Gaussian laser beam profiles has also not been accounted for. Here, we demonstrate a 9 ns rise time, 50 µm sized Pt thin-film sensor for probing the temperature field generated by a nanosecond pulsed laser on a semiconductor thin film. The measurement error sources and associated improvements in the thin film fabrication, sensor patterning, and electrical circuitry are discussed. We carried out the first experimental and theoretical analysis of spatial resolution and accuracy for measuring a Gaussian pulse on the serpentine structure. Transparent silica and sapphire substrates, as well as 7-45 nm insulation layer thicknesses, are compared for sensing accuracy and temporal resolution. Finally, the measured absolute temperature magnitude is validated through the laser-induced melting of the 40 nm thick amorphous silicon film. Preliminary study shows its potential application for probing heat conduction among ultrathin films.