Prognostic Value of Facial Nerve Antidromic Evoked Potentials in Bell Palsy: A Preliminary Study

To analyze the value of facial nerve antidromic evoked potentials (FNAEPs) in predicting recovery from Bell palsy. Study Design. Retrospective study using electrodiagnostic data and medical chart review. Methods. A series of 46 patients with unilateral Bell palsy treated were included. According to taste test, 26 cases were associated with taste disorder (Group 1) and 20 cases were not (Group 2). Facial function was established clinically by the Stennert system after monthly follow-up. The result was evaluated with clinical recovery rate (CRR) and FNAEP. FNAEPs were recorded at the posterior wall of the external auditory meatus of both sides. Results. Mean CRR of Group 1 and Group 2 was 61.63% and 75.50%. We discovered a statistical difference between two groups and also in the amplitude difference (AD) of FNAEP. Mean ± SD of AD was −6.96% ± 12.66% in patients with excellent result, −27.67% ± 27.70% with good result, and −66.05% ± 31.76% with poor result. Conclusions. FNAEP should be monitored in patients with intratemporal facial palsy at the early stage. FNAEP at posterior wall of external auditory meatus was sensitive to detect signs of taste disorder. There was close relativity between FNAEPs and facial nerve recovery

A Statistical Decomposition Based Neural Network For Multivariate Time Series Forecasting

Machine learning based time series forecasting methods are popular and can match the performance of statistical models, in terms of accuracy, scalability, speed, etc. This disclosure presents techniques that incorporate statistical modeling into a neural network framework. The hybrid time series forecasting model described herein is named Seasonality Trend AutoRegressive Residual Yeo-Johnson power transformation Neural Network (STARRY-N). STARRY-N combines the advantages of residual neural network structure (such as N-BEATS) and explainable statistical forecasting models (such as TBATS). The model utilizes a neural network structure with separate stacks for trend, power transformed trend, seasonality, residual correction, and covariate adoption such as holiday effects. STARRY-N has good accuracy and is an explainable forecasting model

Enlarging the Stokes Shift by Weakening the π-Conjugation of Cyanines for High Signal-To-Noise Ratiometric Imaging

The signal-to-noise ratio (SNR) is one of the key features of a fluorescent probe and one that often defines its potential utility for in vivo labeling and analyte detection applications. Here, it is reported that introducing a pyridine group into traditional cyanine-7 dyes in an asymmetric manner provides a series of tunable NIR fluorescent dyes (Cy-Mu-7) characterized by enhanced Stokes shifts (≈230 nm) compared to the parent cyanine 7 dye (nm). The observed Stokes shift increase is ascribed to symmetry breaking of the Cy-Mu-7 core and a reduction in the extent of conjugation. The fluorescence signals of the Cy-Mu-7 dyes are enhanced upon confinement within the hydrophobic cavity of albumin or via spontaneous encapsulation within micelles in aqueous media. Utilizing the Cy-Mu-7, ultra-fast in vivo kidney labeling in mice is realized, and it is found that the liver injury will aggravate the burden of kidney by monitoring the fluorescence intensity ratio of kidney to liver. In addition, Cy-Mu-7 could be used as efficient chemiluminescence resonance energy transfer acceptor for the reaction between H O and bisoxalate. The potential utility of Cy-Mu-7 is illustrated via direct monitoring fluctuations in endogenous H O levels in a mouse model to mimic emergency room trauma

DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for various tasks in medical imaging. However, it is difficult to extend diffusion models for 3D image reconstructions due to the memory burden. Directly stacking 2D slices together to create 3D image volumes would results in severe inconsistencies between slices. Previous works tried to either apply a penalty term along the z-axis to remove inconsistencies or reconstruct the 3D image volumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless, these previous methods failed to produce satisfactory results in challenging cases for PET image denoising. In addition to administered dose, the noise levels in PET images are affected by several other factors in clinical settings, e.g. scan time, medical history, patient size, and weight, etc. Therefore, a method to simultaneously denoise PET images with different noise-levels is needed. Here, we proposed a Dose-aware Diffusion model for 3D low-dose PET imaging (DDPET-3D) to address these challenges. We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models. The code is available at: https://github.com/xxx/xxx.Comment: Paper under review. 16 pages, 11 figures, 4 table

Targeting the BRD4/FOXO3a/CDK6 Axis Sensitizes AKT Inhibition in Luminal Breast Cancer

BRD4 assembles transcriptional machinery at gene super-enhancer regions and governs the expression of genes that are critical for cancer progression. However, it remains unclear whether BRD4-mediated gene transcription is required for tumor cells to develop drug resistance. Our data show that prolonged treatment of luminal breast cancer cells with AKT inhibitors induces FOXO3a dephosphorylation, nuclear translocation, and disrupts its association with SirT6, eventually leading to FOXO3a acetylation as well as BRD4 recognition. Acetylated FOXO3a recognizes the BD2 domain of BRD4, recruits the BRD4/RNAPII complex to the CDK6 gene promoter, and induces its transcription. Pharmacological inhibition of either BRD4/FOXO3a association or CDK6 significantly overcomes the resistance of luminal breast cancer cells to AKT inhibitors in vitro and in vivo. Our study reports the involvement of BRD4/FOXO3a/CDK6 axis in AKTi resistance and provides potential therapeutic strategies for treating resistant breast cancer

FARIMA model-based communication traffic anomaly detection in intelligent electric power substations

The technological advances of intelligent electric substations have significantly improved the operational performance of power utilities by incorporating advanced monitoring and control functionalities. The data traffic patterns in substation communication network (SCN) need to be better understood to improve the SCN performance against different forms of cyber-attacks. To this end, this study presents a fractional auto-regressive integrated moving average (FARIMA)-based threshold model to characterise the SCN traffic flow based on the IEC 61850 protocol and carry out anomaly detection. The performance of the proposed anomaly detection solution is assessed and validated through numerical analysis under the condition of the cyber storm based on the collected SCN data traffic from a real 110 kV substation, and the numerical results clearly confirmed its effectiveness