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Statement of Problem: Soft liners are essential for denture wearers, which aids in the healing of soft tissue injuries caused by rough denture base surfaces. Silicone soft liners, while effective, can accumulate biofilm over time, necessitating enhancement., Purpose: This in vitro study aimed to assess the efficacy of silicone soft liners incorporating varying concentrations of cerium oxide nanoparticles., Materials and Methods: A stainless-steel die as per ISO standard 10139-2-2018 (35 × 6 mm), Using G*Power 3.0.10 software, 400 samples were prepared with 95 % confidence interval and 80 % power. Samples were divided into five groups: surface morphology (Group A), surface hardness (Group B), wettability (Group C), cytotoxicity (Group D), and antifungal property (Group E). Each group was subdivided based on cerium oxide nanoparticle concentrations. Samples were stored in artificial saliva until evaluation. Surface morphology was examined via scanning electron microscopy (SEM), surface hardness using Shore A Durometer, wettability by drop shape analysis, cytotoxicity via MTT assay, and antifungal properties using crystal violet staining.Data were assessed for normal distribution using Kolmogorov-Smirnov and Shapiro-Wilk tests., Results: SEM analysis showed optimal nanoparticle dispersion in Group A2(0.25 %) and A3 (0.5 %). Group B2 (0.25 %) exhibited the lowest mean surface hardness, decreasing from day 1 to day 30. Group C3 demonstrated the most hydrophobic surface across days. Group D2 exhibited the least cytotoxicity at all time intervals. Group E4 displayed the highest antifungal activity., Conclusion: Within study limitations, silicone soft liners modified with 0.25 % and 0.5 % cerium oxide nanoparticles exhibited superior properties in surface hardness and cytotoxicity. Optimal surface morphology and wettability were observed with 0.5 % concentration, while antifungal efficacy peaked at 1 %. These findings suggest clinical potential for treating damaged oral tissues., Clinical Implications: Soft liners modified with 0.25 % and 0.5 % cerium oxide nanoparticles may benefit patients with oral tissue abuse, offering enhanced therapeutic properties., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Purpose: The aim of the study was to determine whether a novel computed tomography (CT) postprocessing software technique (CALIPER) is superior to visual CT scoring as judged by functional correlations in idiopathic pulmonary fibrosis (IPF)., Materials and Methods: A total of 283 consecutive patients with IPF had CT parenchymal patterns evaluated quantitatively with CALIPER and by visual scoring. These 2 techniques were evaluated against: forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), diffusing capacity for carbon monoxide (DLco), carbon monoxide transfer coefficient (Kco), and a composite physiological index (CPI), with regard to extent of interstitial lung disease (ILD), extent of emphysema, and pulmonary vascular abnormalities., Results: CALIPER-derived estimates of ILD extent demonstrated stronger univariate correlations than visual scores for most pulmonary function tests (PFTs): (FEV1: CALIPER R=0.29, visual R=0.18; FVC: CALIPER R=0.41, visual R=0.27; DLco: CALIPER R=0.31, visual R=0.35; CPI: CALIPER R=0.48, visual R=0.44). Correlations between CT measures of emphysema extent and PFTs were weak and did not differ significantly between CALIPER and visual scoring. Intriguingly, the pulmonary vessel volume provided similar correlations to total ILD extent scored by CALIPER for FVC, DLco, and CPI (FVC: R=0.45; DLco: R=0.34; CPI: R=0.53)., Conclusions: CALIPER was superior to visual scoring as validated by functional correlations with PFTs. The pulmonary vessel volume, a novel CALIPER CT parameter with no visual scoring equivalent, has the potential to be a CT feature in the assessment of patients with IPF and requires further exploration.

Increased clinical use of chest high-resolution computed tomography results in increased identification of lung adenocarcinomas and persistent subsolid opacities. However, these lesions range from very indolent to extremely aggressive tumors. Clinically relevant diagnostic tools to noninvasively risk stratify and guide individualized management of these lesions are lacking. Research efforts investigating semiquantitative measures to decrease interrater and intrarater variability are emerging, and in some cases steps have been taken to automate this process. However, many such methods currently are still suboptimal, require validation and are not yet clinically applicable. The computer-aided nodule assessment and risk yield software application represents a validated tool for the automated, quantitative, and noninvasive tool for risk stratification of adenocarcinoma lung nodules. Computer-aided nodule assessment and risk yield correlates well with consensus histology and postsurgical patient outcomes, and therefore may help to guide individualized patient management, for example, in identification of nodules amenable to radiological surveillance, or in need of adjunctive therapy., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Rationale: Screening for lung cancer using low-dose computed tomography (CT) reduces lung cancer mortality. However, in addition to a high rate of benign nodules, lung cancer screening detects a large number of indolent cancers that generally belong to the adenocarcinoma spectrum. Individualized management of screen-detected adenocarcinomas would be facilitated by noninvasive risk stratification., Objectives: To validate that Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel image analysis software, successfully risk stratifies screen-detected lung adenocarcinomas based on clinical disease outcomes., Methods: We identified retrospective 294 eligible patients diagnosed with lung adenocarcinoma spectrum lesions in the low-dose CT arm of the National Lung Screening Trial. The last low-dose CT scan before the diagnosis of lung adenocarcinoma was analyzed using CANARY blinded to clinical data. Based on their parametric CANARY signatures, all the lung adenocarcinoma nodules were risk stratified into three groups. CANARY risk groups were compared using survival analysis for progression-free survival., Measurements and Main Results: A total of 294 patients were included in the analysis. Kaplan-Meier analysis of all the 294 adenocarcinoma nodules stratified into the Good, Intermediate, and Poor CANARY risk groups yielded distinct progression-free survival curves (P < 0.0001). This observation was confirmed in the unadjusted and adjusted (age, sex, race, and smoking status) progression-free survival analysis of all stage I cases., Conclusions: CANARY allows the noninvasive risk stratification of lung adenocarcinomas into three groups with distinct post-treatment progression-free survival. Our results suggest that CANARY could ultimately facilitate individualized management of incidentally or screen-detected lung adenocarcinomas.

Pulmonary nodules are commonly detected in computed tomography (CT) chest screening of a high-risk population. The specific visual or quantitative features on CT or other modalities can be used to characterize the likelihood that a nodule is benign or malignant. Visual features on CT such as size, attenuation, location, morphology, edge characteristics, and other distinctive "signs" can be highly suggestive of a specific diagnosis and, in general, be used to determine the probability that a specific nodule is benign or malignant. Change in size, attenuation, and morphology on serial follow-up CT, or features on other modalities such as nuclear medicine studies or MRI, can also contribute to the characterization of lung nodules. Imaging analytics can objectively and reproducibly quantify nodule features on CT, nuclear medicine, and magnetic resonance imaging. Some quantitative techniques show great promise in helping to differentiate benign from malignant lesions or to stratify the risk of aggressive versus indolent neoplasm. In this article, we (1) summarize the visual characteristics, descriptors, and signs that may be helpful in management of nodules identified on screening CT, (2) discuss current quantitative and multimodality techniques that aid in the differentiation of nodules, and (3) highlight the power, pitfalls, and limitations of these various techniques.