Serial assessment and long-term radiographic evaluation of the maxillary sinus after inferior maxillectomy and soft tissue free flap reconstruction in patients with oral cancer.

PURPOSE: This study evaluates serial radiographic changes in the maxillary sinus of patients with oral cancer after an inferior maxillectomy and a soft tissue free flap reconstruction. METHODS: Fifty-six patients were evaluated between Oct 2005 and Mar 2017 from an institutional database. Preoperative and surveillance imaging was reviewed at set time-points. Maxillary sinus scores were allotted based on a modification of the Lund-MacKay staging system. Patients were evaluated for change in sinus score. A univariate (UV) and multivariate (MV) analysis was performed. RESULTS: There were 53.5% T3/T4 category tumors and 68% received adjuvant treatment. Median follow-up was 24.4 months. Preoperative mean sinus score was 0.27 ± 0.44 and postoperative mean sinus score at 24 months was 1.2 ± 1.3 (p = <0.001). On UV analysis advanced T-stage at 12 months (OR 6.7, 95% CI 1.2-50.3, p = 0.01) and 24 months (OR 5.2, 95% CI 1.03-36.8, p = 0.04) was associated with significantly higher sinus score. On MV analysis, advanced T-stage continued to be associated with increased odds for higher sinus score (OR 4.9, 95% CI 1.1-26.8, p = 0.039). CONCLUSION: A mild increase in postoperative sinus score is seen in this cohort of patients. Advanced T-stage is associated with increased odds for higher sinus scores.

Single-nucleotide polymorphism at alcohol dehydrogenase 1B: A susceptible gene marker in oro-/hypopharyngeal cancers from genome-wide association study.

INTRODUCTION: In the era of precision preventive medicine, susceptible genetic markers for oro-/hypopharyngeal squamous cell carcinoma (OPSCC) have been investigated for genome-wide associations. MATERIALS AND METHODS: A case-control study including 659 male head and neck squamous cell carcinoma (HNSCC) patients, including 331 oropharyngeal cancer, treated between March 1996 and December 2016 and 2400 normal controls was performed. A single-nucleotide polymorphism (SNP) array was used to determine genetic loci that increase susceptibility to OPSCC. RESULTS: We analyzed the allele frequencies of 664,994 autosomal SNPs in 659 HNSCC cases; 7 SNPs scattered in loci of chromosomes 5, 7, 9, 11, and 19 were significant in genome-wide association analysis (Pc < 1.0669 × 10-7 ). In OPSCCs (n = 331), two clustered regions in chromosomes 4 and 6 were significantly different from the controls. We successfully identified a missense alteration of the SNP region in alcohol dehydrogenase 1B (ADH1B) (https://genome.ucsc.edu; hg38); the top correlated locus was rs1229984 (p = 1 × 10-11 ). Adjusted for environmental exposure, including smoking, alcohol, and areca quid, a region in chromosome 12, related to alcohol metabolism, was found to independently increase the susceptibility to OPSCC. The ADH1B rs1229984 AA genotype had better overall survival compared to the AG and GG genotypes (p = 0.042) in OPSCC. The GG genotype in rs1229984 was significantly associated with a younger age of onset than other genotypes (p = 0.001 and <0.001, respectively) in OPSCC patients who consumed alcohol. CONCLUSION: ADH1B was an important genetic locus that significantly correlated with the development of OPSCCs and patient survival.

Indoor Positioning Using Magnetic Fingerprint Map Captured by Magnetic Sensor Array.

By collecting the magnetic field information of each spatial point, we can build a magnetic field fingerprint map. When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user's location. However, since the magnetic field is easily affected by external magnetic fields and magnetic storms, which can lead to "local temporal-spatial variation", it is difficult to construct a stable and accurate magnetic field fingerprint map for indoor positioning. This research proposes a new magnetic indoor positioning method, which combines a magnetic sensor array composed of three magnetic sensors and a recurrent probabilistic neural network (RPNN) to realize a high-precision indoor positioning system. The magnetic sensor array can detect subtle magnetic anomalies and spatial variations to improve the stability and accuracy of magnetic field fingerprint maps, and the RPNN model is built for recognizing magnetic field fingerprint. We implement an embedded magnetic sensor array positioning system, which is evaluated in an experimental environment. Our method can reduce the noise caused by the spatial-temporal variation of the magnetic field, thus greatly improving the indoor positioning accuracy, reaching an average positioning accuracy of 0.78 m.

Intelligent Brushing Monitoring Using a Smart Toothbrush with Recurrent Probabilistic Neural Network.

Smart toothbrushes equipped with inertial sensors are emerging as high-tech oral health products in personalized health care. The real-time signal processing of nine-axis inertial sensing and toothbrush posture recognition requires high computational resources. This paper proposes a recurrent probabilistic neural network (RPNN) for toothbrush posture recognition that demonstrates the advantages of low computational resources as a requirement, along with high recognition accuracy and efficiency. The RPNN model is trained for toothbrush posture recognition and brushing position and then monitors the correctness and integrity of the Bass Brushing Technique. Compared to conventional deep learning models, the recognition accuracy of RPNN is 99.08% in our experiments, which is 16.2% higher than that of the Convolutional Neural Network (CNN) and 21.21% higher than the Long Short-Term Memory (LSTM) model. The model we used can greatly reduce the computing power of hardware devices, and thus, our system can be used directly on smartphones.

Stereo Imaging Using Hardwired Self-Organizing Object Segmentation.

Stereo vision utilizes two cameras to acquire two respective images, and then determines the depth map by calculating the disparity between two images. In general, object segmentation and stereo matching are some of the important technologies that are often used in establishing stereo vision systems. In this study, we implement a highly efficient self-organizing map (SOM) neural network hardware accelerator as unsupervised color segmentation for real-time stereo imaging. The stereo imaging system is established by pipelined, hierarchical architecture, which includes an SOM neural network module, a connected component labeling module, and a sum-of-absolute-difference-based stereo matching module. The experiment is conducted on a hardware resources-constrained embedded system. The performance of stereo imaging system is able to achieve 13.8 frames per second of 640 × 480 resolution color images.