Rat Model of Staphylococcal Enterotoxin B-Induced Rhinosinusitis

ObjectivesIt has been proposed that microbial persistence, superantigen (SA) production, and host T-cell response may be involved in the development of chronic rhinosinusitis. According to the SA hypothesis, a single intranasal application of SA such as staphylococcal enterotoxin B (SEB) may induce chronic eosinophilic rhinosinusitis. This study aimed to develop a rat model of rhinosinusitis induced by intranasally applied SEB.MethodsForty µL of SEB (100 µg/mL) or phosphate buffered saline was applied intranasally through each naris in 4 week-old Sprague-Dawley test rats (N=36) and controls (N=16), respectively. Following sacrifice at 1, 5, 14, and 28 days, the obtained nasal cavity and sinuses were prepared for histologic investigation. The histologic sections were examined in a blind manner for the ratio of the sinus spaces occupied by inflammatory cell clusters and the number of inflammatory cells in the lamina propria.ResultsInfiltration of neutrophils in the lamina propria and appearance of neutrophil clusters in the sinus spaces were observed in the SEB-applied rats. The ratio of the sinus spaces occupied by neutrophil clusters and the number of neutrophils infiltrated in the lamina propria increased significantly at day 1 as compared with the control rats.ConclusionIntranasally applied SEB induces acute neutrophilic rhinosinusitis in rats. Eosinophilic inflammation was not demonstrated. The mere presence of SA in the nose does not necessarily induce SA-induced inflammation, as suggested by the SA hypothesis

Coupling of Semiconductor Nanowires with Neurons and Their Interfacial Structure

We report on the compatibility of various nanowires with hippocampal neurons and the structural study of the neuron–nanowire interface. Si, Ge, SiGe, and GaN nanowires are compatible with hippocampal neurons due to their native oxide, but ZnO nanowires are toxic to neuron due to a release of Zn ion. The interfaces of fixed Si nanowire and hippocampal neuron, cross-sectional samples, were prepared by focused ion beam and observed by transmission electron microscopy. The results showed that the processes of neuron were adhered well on the nanowire without cleft

Nanoscale Bonding between Human Bone and Titanium Surfaces: Osseohybridization

Until now, the chemical bonding between titanium and bone has been examined only through a few mechanical detachment tests. Therefore, in this study, a sandblasted and acid-etched titanium mini-implant was removed from a human patient after 2 months of placement in order to identify the chemical integration mechanism for nanoscale osseointegration of titanium implants. To prepare a transmission electron microscopy (TEM) specimen, the natural state was preserved as much as possible by cryofixation and scanning electron microscope/focused ion beam (SEM-FIB) milling without any chemical treatment. High-resolution TEM (HRTEM), energy dispersive X-ray spectroscopy (EDS), and scanning TEM (STEM)/electron energy loss spectroscopic analysis (EELS) were used to investigate the chemical composition and structure at the interface between the titanium and bone tissue. HRTEM and EDS data showed evidence of crystalline hydroxyapatite and intermixing of bone with the oxide layer of the implant. The STEM/EELS experiment provided particularly interesting results: carbon existed in polysaccharides, calcium and phosphorus existed as tricalcium phosphate (TCP), and titanium existed as oxidized titanium. In addition, the oxygen energy loss near edge structures (ELNESs) showed a possibility of the presence of CaTiO3. These STEM/EELS results can be explained by structures either with or without a chemical reaction layer. The possible existence of the osseohybridization area and the form of the carbon suggest that reconsideration of the standard definition of osseointegration is necessary

Athermal glass work at the nanoscale: Engineered electron-beam-induced viscoplasticity for mechanical shaping of brittle amorphous silica

© 2022 Acta Materialia Inc.Amorphous silica deforms viscoplastically at elevated temperatures, which is common for brittle glasses. The key mechanism of viscoplastic deformation involves interatomic bond switching, which is thermally activated. Here, we precisely control the mechanical shaping of brittle amorphous silica at the nanoscale via engineered electron–matter interactions without heating. We observe a ductile plastic deformation of amorphous silica under a focused scanning electron beam with low acceleration voltages (few to tens of kilovolts) during in-situ compression studies, with unique dependence on the acceleration voltage and beam current. By simulating the electron–matter interaction, we show that the deformation of amorphous silica depends strongly on the volume where inelastic scattering occurs. The electron–matter interaction via e-beam irradiation alters the Si–O interatomic bonds, enabling the high-temperature deformation behavior of amorphous silica to occur athermally. Finally, by systematically controlling the electron–matter interaction volume, it is possible to mechanically shape the brittle amorphous silica on a small scale at room temperature to a level comparable to glass shaping at high temperatures. The findings can be extended to develop new fabrication processes for nano- and microscale brittle glasses.N

Multi-View Convolutional Neural Networks in Rupture Risk Assessment of Small, Unruptured Intracranial Aneurysms

Auto-detection of cerebral aneurysms via convolutional neural network (CNN) is being increasingly reported. However, few studies to date have accurately predicted the risk, but not the diagnosis itself. We developed a multi-view CNN for the prediction of rupture risk involving small unruptured intracranial aneurysms (UIAs) based on three-dimensional (3D) digital subtraction angiography (DSA). The performance of a multi-view CNN-ResNet50 in accurately predicting the rupture risk (high vs. non-high) of UIAs in the anterior circulation measuring less than 7 mm in size was compared with various CNN architectures (AlexNet and VGG16), with similar type but different layers (ResNet101 and ResNet152), and single image-based CNN (single-view ResNet50). The sensitivity, specificity, and overall accuracy of risk prediction were estimated and compared according to CNN architecture. The study included 364 UIAs in training and 93 in test datasets. A multi-view CNN-ResNet50 exhibited a sensitivity of 81.82 (66.76–91.29)%, a specificity of 81.63 (67.50–90.76)%, and an overall accuracy of 81.72 (66.98–90.92)% for risk prediction. AlexNet, VGG16, ResNet101, ResNet152, and single-view CNN-ResNet50 showed similar specificity. However, the sensitivity and overall accuracy were decreased (AlexNet, 63.64% and 76.34%; VGG16, 68.18% and 74.19%; ResNet101, 68.18% and 73.12%; ResNet152, 54.55% and 72.04%; and single-view CNN-ResNet50, 50.00% and 64.52%) compared with multi-view CNN-ResNet50. Regarding F1 score, it was the highest in multi-view CNN-ResNet50 (80.90 (67.29–91.81)%). Our study suggests that multi-view CNN-ResNet50 may be feasible to assess the rupture risk in small-sized UIAs

Incidence of Gastric Cancer in Patients with Laryngeal Cancer

Background/Aims: Gastric cancer (GC) is the second most common cancer in Korea and the most common in men in the south of the country. We investigated the incidence of synchronous GC in patients with head and neck squamous cell carcinoma (HNSCC) in the southern part of Korea. Materials and Methods: We retrospectively reviewed the medical records of HNSCC patients treated between 2011 and 2014. In patients with synchronous GC, evaluation included a history of smoking and alcohol consumption, endoscopic findings, Campylobacter-like organism (CLO) test, and immunohistochemical analysis of preserved HNSCC tissues. Results: Analysis of the records of 153 HNSCC patients revealed tumors of the larynx in 56 patients (36.6%), of the pharynx in 74 patients (48.4%), and tumors at other locations in 23 patients (15.0%). The mean age of patients was 66.0 years, and the men:women ratio was 8:1. Synchronous cancers were detected in 12 patients. We observed esophageal squamous cell carcinoma (SCC) in five patients (3.3%), and gastric adenocarcinoma in seven patients (4.6%). Synchronous GC was detected in patients with laryngeal SCC. All cases of GC were classified as early GC. Conclusions: Synchronous GC was as frequent as esophageal SCC in patients with HNSCC, and all cases of GC were observed to be early stage cancers in this study. Thorough endoscopic examination should be performed in patients with laryngeal cancer to detect the presence of synchronous GC

Another Look at Obesity Paradox in Acute Ischemic Stroke: Association Rule Mining

Though obesity is generally associated with the development of cardiovascular disease (CVD) risk factors, previous reports have also reported that obesity has a beneficial effect on CVD outcomes. We aimed to verify the existing obesity paradox through binary logistic regression (BLR) and clarify the paradox via association rule mining (ARM). Patients with acute ischemic stroke (AIS) were assessed for their 3-month functional outcome using the modified Rankin Scale (mRS) score. Predictors for poor outcome (mRS 3–6) were analyzed through BLR, and ARM was performed to find out which combination of risk factors was concurrently associated with good outcomes using maximal support, confidence, and lift values. Among 2580 patients with AIS, being obese (OR [odds ratio], 0.78; 95% CI, 0.62–0.99) had beneficial effects on the outcome at 3 months in BLR analysis. In addition, the ARM algorithm showed obese patients with good outcomes were also associated with an age less than 55 years and mild stroke severity. While BLR analysis showed a beneficial effect of obesity on stroke outcome, in ARM analysis, obese patients had a relatively good combination of risk factor profiles compared to normal BMI patients. These results may partially explain the obesity paradox phenomenon in AIS patients

Machine Learning Application for Rupture Risk Assessment in Small-Sized Intracranial Aneurysm

The assessment of rupture probability is crucial to identifying at risk intracranial aneurysms (IA) in patients harboring multiple aneurysms. We aimed to develop a computer-assisted detection system for small-sized aneurysm ruptures using a convolutional neural network (CNN) based on images of three-dimensional digital subtraction angiography. A retrospective data set, including 368 patients, was used as a training cohort for the CNN using the TensorFlow platform. Aneurysm images in six directions were obtained from each patient and the region-of-interest in each image was extracted. The resulting CNN was prospectively tested in 272 patients and the sensitivity, specificity, overall accuracy, and receiver operating characteristics (ROC) were compared to a human evaluator. Our system showed a sensitivity of 78.76% (95% CI: 72.30%–84.30%), a specificity of 72.15% (95% CI: 60.93%–81.65%), and an overall diagnostic accuracy of 76.84% (95% CI: 71.36%–81.72%) in aneurysm rupture predictions. The area under the ROC (AUROC) in the CNN was 0.755 (95% CI: 0.699%–0.805%), better than that obtained from a human evaluator (AUROC: 0.537; p < 0.001). The CNN-based prediction system was feasible to assess rupture risk in small-sized aneurysms with diagnostic accuracy superior to human evaluators. Additional studies based on a large data set are necessary to enhance diagnostic accuracy and to facilitate clinical application