Microglia, rapidly activated following peripheral nerve injury (PNI), accumulate within the spinal cord and adopt inflammation that contributes to development and maintenance of neuropathic pain. Microglia express functional Toll-like receptors (TLRs), which play pivotal roles in regulating inflammatory processes. However, little is known about the role of TLR3 in regulating neuropathic pain after PNI. Here TLR3 expression and autophagy activation was assayed in dorsal root ganglions and in microglia following PNI by using realtime PCR, western blot and immunohistochemistry. The role of TLR3/autophagy signaling in regulating tactile allodynia was evaluated by assaying paw mechanical withdrawal threshold and cold allodynia after intrathecal administration of Poly (I:C) and 3-methyladenine (3-MA). We found that L5 spinal nerve ligation (SNL) induces the expression of TLR3 in dorsal root ganglions and in primary rat microglia at the mRNA and protein level. Meanwhile, L5 SNL results in an increased activation of autophagy, which contributes to microglial activation and subsequent inflammatory response. Intrathecal administration of Poly (I:C), a TLR3 agonist, significantly increases the activation of microglial autophagy, whereas TLR3 knockdown markedly inhibits L5 SNL-induced microglial autophagy. Poly (I:C) treatment promotes the expression of proinflammatory mediators, whereas 3-MA (a specific inhibitor of autophagy) suppresses Poly (I:C)-induced secretion of proinflammatory cytokines. Autophagy inhibition further inhibits TLR3-mediated mechanical and cold hypersensitivity following SNL. These results suggest that inhibition of TLR3/autophagy signaling contributes to alleviate neurophathic pain triggered by SNL.

Background Surgical site infection (SSI) is one of the most common types of health care–associated infections. It increases mortality, prolongs hospital length of stay, and raises health care costs. Many institutions developed risk assessment models for SSI to help surgeons preoperatively identify high-risk patients and guide clinical intervention. However, most of these models had low accuracies. Objective We aimed to provide a solution in the form of an Artificial intelligence–based Multimodal Risk Assessment Model for Surgical site infection (AMRAMS) for inpatients undergoing operations, using routinely collected clinical data. We internally and externally validated the discriminations of the models, which combined various machine learning and natural language processing techniques, and compared them with the National Nosocomial Infections Surveillance (NNIS) risk index. Methods We retrieved inpatient records between January 1, 2014, and June 30, 2019, from the electronic medical record (EMR) system of Rui Jin Hospital, Luwan Branch, Shanghai, China. We used data from before July 1, 2018, as the development set for internal validation and the remaining data as the test set for external validation. We included patient demographics, preoperative lab results, and free-text preoperative notes as our features. We used word-embedding techniques to encode text information, and we trained the LASSO (least absolute shrinkage and selection operator) model, random forest model, gradient boosting decision tree (GBDT) model, convolutional neural network (CNN) model, and self-attention network model using the combined data. Surgeons manually scored the NNIS risk index values. Results For internal bootstrapping validation, CNN yielded the highest mean area under the receiver operating characteristic curve (AUROC) of 0.889 (95% CI 0.886-0.892), and the paired-sample t test revealed statistically significant advantages as compared with other models (P<.001). The self-attention network yielded the second-highest mean AUROC of 0.882 (95% CI 0.878-0.886), but the AUROC was only numerically higher than the AUROC of the third-best model, GBDT with text embeddings (mean AUROC 0.881, 95% CI 0.878-0.884, P=.47). The AUROCs of LASSO, random forest, and GBDT models using text embeddings were statistically higher than the AUROCs of models not using text embeddings (P<.001). For external validation, the self-attention network yielded the highest AUROC of 0.879. CNN was the second-best model (AUROC 0.878), and GBDT with text embeddings was the third-best model (AUROC 0.872). The NNIS risk index scored by surgeons had an AUROC of 0.651. Conclusions Our AMRAMS based on EMR data and deep learning methods—CNN and self-attention network—had significant advantages in terms of accuracy compared with other conventional machine learning methods and the NNIS risk index. Moreover, the semantic embeddings of preoperative notes improved the model performance further. Our models could replace the NNIS risk index to provide personalized guidance for the preoperative intervention of SSIs. Through this case, we offered an easy-to-implement solution for building multimodal RAMs for other similar scenarios.

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Herein, a high‐performance β‐gallium oxide (β‐Ga2O3) metal–oxide–semiconductor field‐effect transistor (MOSFET) on sapphire substrate with a high breakdown voltage of more than 800 V and a high‐power figure of merit of more than 86.3 MV cm−2 is demonstrated. The atomic force microscopy (AFM) image and Raman peaks that are first characterized to ensure a nanomembrane with high quality are used for the device fabrication. A saturation drain current of 231.8 mA mm−1, an RON,sp of 7.41 mΩ cm2, an ON/OFF ratio of 108, and a subthreshold swing of 86 mV dec−1 are obtained at a channel doping concentration of 4.47 × 1017 cm−3 and a source‐to‐drain distance of 11.4 μm. Furthermore, a high breakdown voltage over 800 V is also achieved, corresponding to a record‐high direct current (DC) power figure of merit of 86.3 MW cm−2. Technology computer aided design (TCAD) simulation is also performed to extract the distribution of the electric field along the β‐Ga2O3 channel surface.

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