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The current landscape is characterized by a dearth of effective, safe, simple, and noninvasive methods for preventing pain following craniotomy. This clinical trial seeks to evaluate the efficacy profile of preemptive application of a topical 5% lidocaine patch in alleviating post-craniotomy pain. Methods: This was a multi-centric, prospective randomized placebo-controlled triple-blind clinical trial. Eligible patients were randomly assigned to either the masked intervention group, who received lidocaine 5% white hydrogel plasters (N = 90), or the placebo control group who received plain hydrogel plasters of the same pattern, size, appearance and material as L5Ps, but free of lidocaine (N = 90). Primary outcome was the pain intensity (Visual Analogue Scale at 24 h) after craniotomy. Secondary outcomes included: intra-operative analgesics consumption, pain intensity, cumulative rescue analgesics consumption, sleeping scores, adverse effects such as skin reactions, etc. The intention-to-treat analyses and the per-protocol analyses were used. Results: There were no statistically significant differences in the VAS scores at 24 h after craniotomy (P = 0.539). However, subgroup analysis for female and male patients showed that statistically significant differences were found in VAS scores in male patients (0 [0, 3] in the masked intervention group at 24 h after craniotomy and 3 [1, 4.5] in the placebo control group, P = 0.017). There were no obvious side effects directly associated with preemptive L5P. Conclusion: Preemptive lidocaine 5% plaster as a safe technique was not found to be effective in reducing post-craniotomy pain, but potential gender disparities in the outcomes of this method warrant further investigation. Trial Registration: ClinicalTrials.Gov (NCT 04169854). [ABSTRACT FROM AUTHOR]

Key Clinical Message: Maxillary canines are often impacted, which can result in tooth disorders and adversely affect occlusal and facial development. The case report describes complete bilateral impaction of maxillary canines and significant root resorption of a central incisor. The multidisciplinary approach is the optimal strategy for addressing impacted maxillary canines. [ABSTRACT FROM AUTHOR]

Deep learning is applied to many complex tasks in the field of wireless communication, such as modulation recognition of spectrum waveforms, because of its convenience and efficiency. This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform. Some existing works address this problem directly using the concept of adversarial examples in the computer vision field without fully considering the characteristics of the waveform transmission in the physical world. Therefore, we propose two low‐interception waveforms (LIWs) generation methods, the LIW and ULIW algorithms, which can reduce the probability of the modulation being recognized by a third party without affecting the reliable communication of the friendly party. Among them, ULIW improves LIW algorithm by simulating channel noise during training cycle, and substantially reduces the perturbation magnitude while maintaining low interception accuracy. Our LIW and ULIW exhibit significant low‐interception performance in both numerical simulations and hardware experiments. Key Points: Define the low‐interception question, and give the mathematical model, application scenarios, and evaluation criteriaPropose the ULIW algorithm based on our LIW algorithm, which substantially reduces perturbation while maintaining low interception accuracyVerify the low‐interception performance of LIW and ULIW in both the digital and physical worlds [ABSTRACT FROM AUTHOR]

BackgroundThe radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients. ObjectiveThis research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans. MethodsIn this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN) (ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons. ResultsDNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (P=.02 in sensitivity and P