Claytonnymann4728
The current metastatic category (M) of nasopharyngeal carcinoma (NPC) is a "catch-all" category, we previously successfully established a M1 subdivision system based on prognostic metastatic characteristics in epidemic areas. We aimed to figure out metastatic characteristics associated with survival outcomes of NPC in non-epidemic areas.
A total of 428 newly diagnosed de novo metastatic NPC patients from 2010 to 2016 were analyzed from the population-based Surveillance, Epidemiology, and End Results program. Cox proportional hazard ratios (HRs) were used to identify independent prognostic factors for survival.
The most frequently involved metastatic locations were the bones (53.04%), the lungs (36.68%), the livers (29.21%) and the distant lymph nodes (24.07%). Univariate analysis indicated that bone involvement (HR=1.39, 95% CI=1.09-1.77), liver involvement (HR=1.44, 95% CI=1.12-1.85) and multiple metastatic locations (HR=1.32, 95% CI=1.04-1.67) were negative prognostic factors of overall survival (OS) for patients with synchronous metastasis. We established a new M1 subdivision system based on metastatic characteristics M1a, without bone and liver involvement; M1b, single bone or liver involvement; M1c, multiple metastatic locations including bone and/or liver. selleck chemicals Multivariate analysis confirmed that our new subcategories were associated with significantly different OS (M1b vs M1a HR=1.54, 95% CI=1.11-2.16; M1c vs M1a HR=2.03, 95% CI=1.47-2.78).
Synchronous metastatic NPC patients with multiple metastatic locations involved bone and/or liver were prone to suffer from dismal OS and might need more attentions for selection of treatment modality.
Synchronous metastatic NPC patients with multiple metastatic locations involved bone and/or liver were prone to suffer from dismal OS and might need more attentions for selection of treatment modality.In multistability, perceptual interpretations ("percepts") of ambiguous stimuli alternate over time. There is considerable debate as to whether similar regularities govern the first percept after stimulus onset and percepts during prolonged presentation. We address this question in a visual pattern-component rivalry paradigm by presenting two overlaid drifting gratings, which participants perceived as individual gratings passing in front of each other ("segregated") or as a plaid ("integrated"). We varied the enclosed angle ("opening angle") between the gratings (experiments 1 and 2) and stimulus orientation (experiment 2). The relative number of integrated percepts increased monotonically with opening angle. The point of equality, where half of the percepts were integrated, was at a smaller opening angle at onset than during prolonged viewing. The functional dependence of the relative number of integrated percepts on opening angle showed a steeper curve at onset than during prolonged viewing. Dominance durations of integrated percepts were longer at onset than during prolonged viewing and increased with opening angle. The general pattern persisted when stimuli were rotated (experiment 2), despite some perceptual preference for cardinal motion directions over oblique directions. Analysis of eye movements, specifically the slow phase of the optokinetic nystagmus (OKN), confirmed the veridicality of participants' reports and provided a temporal characterization of percept formation after stimulus onset. Together, our results show that the first percept after stimulus onset exhibits a different dependence on stimulus parameters than percepts during prolonged viewing. This underlines the distinct role of the first percept in multistability.
Chest X-ray radiography (CXR) has been widely considered as an accessible, feasible, and convenient method to evaluate suspected patients' lung involvement during the COVID-19 pandemic. However, with the escalating number of suspected cases, traditional diagnosis via CXR fails to deliver results within a short period of time. Therefore, it is crucial to employ artificial intelligence (AI) to enhance CXRs for obtaining quick and accurate diagnoses. Previous studies have reported the feasibility of utilizing deep learning methods to screen for COVID-19 using CXR and CT results. However, these models only use a single deep learning network for chest radiograph detection; the accuracy of this approach required further improvement.
In this study, we propose a three-step hybrid ensemble model, including a feature extractor, a feature selector, and a classifier. First, a pre-trained AlexNet with an improved structure extracts the original image features. Then, the ReliefF algorithm is adopted to sort the extracts efficiently on small sample sizes.
The model proposed in this article is practical and effective, and can provide high-precision COVID-19 CXR detection. We demonstrated its suitability to aid medical professionals in distinguishing normal CXRs, viral pneumonia CXRs and COVID-19 CXRs efficiently on small sample sizes.
To evaluate the automatic determination method for the arch form in cone-beam computed tomography (CBCT) images with cubic B-spline approximation on digital dental models using various types of missing teeth.
The maxilla and mandible from eight dental CBCT images with Class I occlusion and no missing teeth were used in this study. The dental arch determination algorithm using cubic B-spline approximation was modified by applying a smoothing function for reliable curve fitting to the digital dental models with various types of missing teeth. For evaluation, 31 scenarios with missing teeth were simulated, and cases with 1-8 missing teeth were divided into three groups solitary, consecutive, and multiple (more than 4) missing teeth. The prediction accuracies of the dental arch forms were evaluated through comparisons with the gold standards for the digital dental models by two expert orthodontists.
The distance errors between the gold standards and the estimated results of the dental arch forms in all types of models were 0.237-1.740mm. The mean distance errors of the solitary, consecutive, and multiple groups were 0.436±0.124mm (0.237-0.964mm), 0.591±0.250mm (0.256-1.482mm), and 0.679±0.310mm (0.254-1.740mm), respectively.
The algorithm for predicting the arch form functioned reliably, even for digital dental models with various types of missing teeth, and could be applied to digital dentistry for applications such as orthodontic tooth setup, artificial tooth arrangement for denture fabrication, and implant guides.
The algorithm for predicting the arch form functioned reliably, even for digital dental models with various types of missing teeth, and could be applied to digital dentistry for applications such as orthodontic tooth setup, artificial tooth arrangement for denture fabrication, and implant guides.
