Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT.

OBJECTIVE: The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterative reconstruction-Veo (ASIR-V). METHODS: One hundred eighty-three patients with pulmonary nodules underwent standard-dose computed tomography (SDCT) (4.30 ± 0.36 mSv) and ULD-CT (UL-A, 0.57 ± 0.09 mSv or UL-B, 0.33 ± 0.04 mSv). SDCT was the reference standard using (ASIR-V) at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). Radiomics analysis extracted 102 features, and the intraclass correlation coefficient (ICC) quantified reproducibility between ULD-CT and SDCT reconstructed by 50%ASIR-V, DLIR-M, and DLIR-H for each feature. RESULTS: Among 102 radiomic features, the percentages of reproducibility of 50%ASIR-V, DLIR-M, and DLIR-H were 48.04% (49/102), 49.02% (50/102), and 52.94% (54/102), respectively. Shape and first order features demonstrated high reproducibility across different reconstruction algorithms and radiation doses, with mean ICC values exceeding 0.75. In texture features, DLIR-M and DLIR-H showed improved mean ICC values for pure ground glass nodules (pGGNs) from 0.69 ± 0.23 to 0.75 ± 0.18 and 0.81 ± 0.12, respectively, compared with 50%ASIR-V. Similarly, the mean ICC values for solid nodules (SNs) increased from 0.60 ± 0.19 to 0.66 ± 0.14 and 0.69 ± 0.13, respectively. Additionally, the mean ICC values of texture features for pGGNs and SNs in both ULD-CT groups decreased with reduced radiation dose. CONCLUSIONS: DLIR can improve the reproducibility of radiomic features at ultra-low doses compared with ASIR-V. In addition, pGGNs showed better reproducibility at ultra-low doses than SNs.

Enhancing diagnostic performance and image quality in coronary CT angiography: Impact of SnapShot Freeze 2 algorithm across varied heart rates in stent patients.

PURPOSE: To investigate the enhancement of image quality achieved through the utilization of SnapShot Freeze 2 (SSF2), a comparison was made against the results obtained from the original SnapShot Freeze algorithm (SSF) and standard motion correction (STND) in stent patients undergoing coronary CT angiography (CCTA) across the entire range of heart rates. MATERIALS AND METHODS: A total of 118 patients who underwent CCTA, were retrospectively included in this study. Images of these patients were reconstructed using three different algorithms: SSF2, SSF, and STND. Objective assessments include signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), diameters of stents and artifact index (AI). The image quality was subjectively evaluated by two readers. RESULTS: Compared with SSF and STND, SSF2 had similar or even higher quality in the parameters (AI, SNR, CNR, inner diameters) of coronary artery, stent, myocardium, MV (mitral valve), TV (tricuspid valve), AV (aorta valve), and PV (pulmonary valve), and aortic root (AO). Besides the above structures, SSF2 also demonstrated comparable or even higher subjective scores in atrial septum (AS), ventricular septum (VS), and pulmonary artery root (PA). Furthermore, the enhancement in image quality with SSF2 was significantly greater in the high heart rate group compared to the low heart rate group. Moreover, the improvement in both high and low heart rate groups was better in the SSF2 group compared to the SSF and STND group. Besides, when using the three algorithms, an effect of heart rate variability on stent image quality was not detected. CONCLUSION: Compared to SSF and STND, SSF2 can enhance the image quality of whole-heart structures and mitigate artifacts of coronary stents. Furthermore, SSF2 has demonstrated a significant improvement in the image quality for patients with a heart rate equal to or higher than 85 bpm.

Alkyl-Doping Enables Significant Suppression of Conformational Relaxation and Intermolecular Nonradiative Decay for Improved Near-Infrared Fluorescence Imaging.

Despite various efforts to optimize the near-infrared (NIR) performance of perylene diimide (PDI) derivatives for bio-imaging, convenient and efficient strategies to amplify the fluorescence of PDI derivatives in biological environment and the intrinsic mechanism studies are still lacking. Herein, we propose an alkyl-doping strategy to amplify the fluorescence of PDI derivative-based nanoparticles for improved NIR fluorescence imaging. The developed PDI derivative, OPE-PDI, shows much brighter in n-Hexane (HE) compared with that in other organic media, and the excited state dynamics investigation experimentally elucidates the solvent effect-induced suppression of intermolecular energy transfer and intramolecular nonradiative decay as the underlying mechanism for the fluorescence improvement. Theoretical calculations reveal the lowest reorganization energies of OPE-PDI in HE among various solvents, indicating the effectively suppressed conformational relaxation to support the strongest radiative decay. Inspired by this, an alkyl atmosphere mimicking HE is constructed by incorporating the octadecane into OPE-PDI-based nanoparticles, permitting up to 3-fold fluorescence improvement compared with the counterpart nanoparticles. Owing to the merits of high brightness, anti-photobleaching, and low biotoxicity for the optimal nanoparticles, they have been employed for probing and long-term monitoring of tumor. This work highlights a facile strategy for the fluorescence enhancement of PDI derivative-based nanoparticles.

Reliability analysis of inverse Gaussian processes with two-stage degenerate paths.

For randomly degraded products undergoing a two-stage degradation process, traditional random effects models assume that the degradation rate follows a symmetrically normal distribution. However, certain products exhibit asymmetric degradation rates. In light of this, this paper proposes an approach for reliability analysis based on the inverse Gaussian (IG) degeneration process, which considers both asymmetric random effects and the two-stage nature simultaneously. To begin with, we establish a two-stage IG degradation process model that incorporates a skew normal random effect. Subsequently, we determine the location of change points using the Schwarz Information Criterion (SIC). The estimation of parameters is then conducted by combining Maximum Likelihood Estimations (MLEs) with the Genetic Algorithm (GA). Finally, we validate and demonstrate the practicality for the proposed model through Monte Carlo (MC) simulation and examples involving lithium batteries.

Pathogenesis and virulence of coronavirus disease: Comparative pathology of animal models for COVID-19.

Animal models that can replicate clinical and pathologic features of severe human coronavirus infections have been instrumental in the development of novel vaccines and therapeutics. The goal of this review is to summarize our current understanding of the pathogenesis of coronavirus disease 2019 (COVID-19) and the pathologic features that can be observed in several currently available animal models. Knowledge gained from studying these animal models of SARS-CoV-2 infection can help inform appropriate model selection for disease modelling as well as for vaccine and therapeutic developments.

Vegetation net primary productivity in urban areas of China responded positively to the COVID-19 lockdown in spring 2020.

To prevent the spread of COVID-19, China implemented large-scale lockdown measures in early 2020, resulting in a marked reduction in human activities over a short period. Studies have explored environmental changes during lockdowns, lacking analysis of response of net primary productivity (NPP) to lockdowns, especially for diverse vegetation types. Correlation between NPP and impact factors during lockdowns remains unclear. Through Google Earth Engine, we evaluated spatial-temporal changes in spring NPP at multiple scales during lockdown period (LD, 2020) compared with unlocked period (UL, 2017-2019) by remote sensing data in urban areas of China. Changes in four impact factors, aerosol optical depth (AOD) and photosynthetically active radiation (PAR) (via remote sensing data), alongside temperature (TEM) and precipitation (PRE) (via meteorological data) were explored. Additionally, geodetector, a valuable statistical tool for detecting the driving ability of various elements, was employed to explore the underlying causes of vegetation changes during LD. In the spring of LD: 1) National urban NPP generally increased (+6.50 %), notably in Northeast China (NE), North China (N) and East China (E). Besides, overall urban AOD decreased (-3.64 %), notably in N and Central China (C). National urban PAR increased (+2.7 %), particularly in C and Northwest China (NW). However, overall urban TEM (-0.06 %) and PRE (-1.21 %) changed negatively. 2) NPP in all three vegetation types in urban areas enhanced, with change rates: croplands > forests > grasslands. Evident enhancements occurred in the forests and croplands in N, and the grasslands in NE. 3) Through geodetector, during LD, AOD (q = 0.223) and TEM (q = 0.272) emerged as the dominant factors for NPP. Compared with UL, the explanatory power of AOD and PAR on NPP increased during LD. This study provides valuable insights into understanding the effects of short-term human activities on vegetation productivity, offering reference for the formulation of ecological and environmental policies.

Depression and risk of arthritis: A Mendelian randomization study.

INTRODUCTION: Observational studies have found that most patients with arthritis have depression. We aimed to determine the causal relationship between various types of arthritis and depression. METHODS: We conducted a two-sample bidirectional Mendelian randomized (MR) analysis to determine whether there was a significant causal relationship between depression and multiple types of arthritis. The data of our study were derived from the publicly released genome-wide association studies (GWASs) and the largest GWAS meta-analysis. MR analysis mainly used inverse-variance weighted method; supplementary methods included weighted median, weighted mode, and MR-Egger using MR pleiotropy residual sum and outlier to detect and correct for the presence of pleiotropy. RESULTS: After adjusting for heterogeneity and horizontal pleiotropy, we found that depression was associated with an increased risk of osteoarthritis (OA) (OR = 1.02, 95%CI: 1.01-1.02, p = 2.96 × E - 5). In the reverse analysis, OA was also found to increase the risk of depression (OR = 1.10, 95%CI: 1.04-1.15, p = .0002). Depression only increased the risk of knee OA (KOA) (OR = 1.25, 95%CI: 1.10-1.42, p = 6.46 × E - 4). Depression could potentially increase the risk of spondyloarthritis (OR = 1.52, 95%CI: 1.19-1.94, p ≤ 8.94 × E - 4). CONCLUSION: There is a bidirectional causal relationship of depression with OA. However, depression only augments the risk of developing KOA. Depression may increase the risk of spondyloarthritis and gout.

Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features.

Purpose: We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats. Methods: A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment. Results: Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level. Conclusions: MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.

Comparative Pathology of Animal Models for Influenza A Virus Infection.

Animal models are essential for studying disease pathogenesis and to test the efficacy and safety of new vaccines and therapeutics. For most diseases, there is no single model that can recapitulate all features of the human condition, so it is vital to understand the advantages and disadvantages of each. The purpose of this review is to describe popular comparative animal models, including mice, ferrets, hamsters, and non-human primates (NHPs), that are being used to study clinical and pathological changes caused by influenza A virus infection with the aim to aid in appropriate model selection for disease modeling.

A Comparative Assessment of Different Aerogel-Insulated Building Walls for Enhanced Thermal Insulation Performance.

Aerogel is widely recognized as a superinsulating material with great potential for enhancing the thermal insulation performance of building walls. It can be applied in various forms such as aerogel plasters (AP), aerogel fibrous composites (AFC), and aerogel concrete (AC) in practical engineering applications. This study aims to investigate the most efficient application form for maximizing building insulation performance while minimizing the amount of aerogel used. To predict the thermal insulation performance of aerogel-insulated walls, a resistance-capacitance network model integrating the aerogels' effective thermal conductivity model was developed and was validated by comparing it with Fluent simulation software results in terms of surface temperature. Using the validated models, the thermophysical parameters, transient thermal properties, and transmission load were predicted and compared among AP, AFC, and AC walls. The results indicate that using AFC can result in approximately 50% cost savings to achieve the same thermal resistance. After adding a 20 mm thickness of aerogel to the reference wall without aerogel, the AFC wall exhibited the highest improvement in thermal insulation performance, reaching 46.0-53.5%, followed by the AP wall, and then the AC wall, aligning with considerations of microstructural perspectives, thermal resistance distributions, and thermal non-uniformity factors. Therefore, giving priority to AFC use could reduce the required amount of silica aerogel and enhance economic efficiency. These results provide valuable insights for theoretical models and the application of aerogel-insulated walls in building engineering insulation.

Assessment of Immune Responses to a Trivalent Pichinde Virus-Vectored Vaccine Expressing Hemagglutinin Genes from Three Co-Circulating Influenza A Virus Subtypes in Pigs.

Pichinde virus (PICV) can infect several animal species and has been developed as a safe and effective vaccine vector. Our previous study showed that pigs vaccinated with a recombinant PICV-vectored vaccine expressing the hemagglutinin (HA) gene of an H3N2 influenza A virus of swine (IAV-S) developed virus-neutralizing antibodies and were protected against infection by the homologous H3N2 strain. The objective of the current study was to evaluate the immunogenicity and protective efficacy of a trivalent PICV-vectored vaccine expressing HA antigens from the three co-circulating IAV-S subtypes: H1N1, H1N2, and H3N2. Pigs immunized with the trivalent PICV vaccine developed virus-neutralizing (VN) and hemagglutination inhibition (HI) antibodies against all three matching IAV-S. Following challenge infection with the H1N1 strain, five of the six pigs vaccinated with the trivalent vaccine had no evidence of IAV-S RNA genomes in nasal swabs and bronchoalveolar lavage fluid, while all non-vaccinated control pigs showed high number of copies of IAV-S genomic RNA in these two types of samples. Overall, our results demonstrate that the trivalent PICV-vectored vaccine elicits antibody responses against the three targeted IAV-S strains and provides protection against homologous virus challenges in pigs. Therefore, PICV exhibits the potential to be explored as a viral vector for delivering multiple vaccine antigens in swine.