Exploring immune evasion of SARS-CoV-2 variants using a pseudotyped system.

In the United States, coronavirus disease 2019 (COVID-19) cases have consistently been linked to the prevailing variant XBB.1.5 of SARS-CoV-2 since late 2022. A system has been developed for producing and infecting cells with a pseudovirus (PsV) of SARS-CoV-2 to investigate the infection in a Biosafety Level 2 (BSL-2) laboratory. This system utilizes a lentiviral vector carrying ZsGreen1 and Firefly luciferase (Fluc) dual reporter genes, facilitating the analysis of experimental results. In addition, we have created a panel of PsV variants that depict both previous and presently circulating mutations found in circulating SARS-CoV-2 strains. A series of PsVs includes the prototype SARS-CoV-2, Delta B.1.617.2, BA.5, XBB.1, and XBB.1.5. To facilitate the study of infections caused by different variants of SARS-CoV-2 PsV, we have developed a HEK-293T cell line expressing mCherry and human angiotensin converting enzyme 2 (ACE2). To validate whether different SARS-CoV-2 PsV variants can be used for neutralization assays, we employed serum from rats immunized with the PF-D-Trimer protein vaccine to investigate its inhibitory effect on the infectivity of various SARS-CoV-2 PsV variants. According to our observations, the XBB variant, particularly XBB.1.5, exhibits stronger immune evasion capabilities than the prototype SARS-CoV-2, Delta B.1.617.2, and BA.5 PsV variants. Hence, utilizing the neutralization test, this study has the capability to forecast the effectiveness in preventing future SARS-CoV-2 variants infections.

Interpretable deep learning insights: Unveiling the role of 1 Gy volume on lymphopenia after radiotherapy in breast cancer.

BACKGROUND: Lymphopenia is known for its significance on poor survivals in breast cancer (BC) patients. Considering full dosimetric data, this study aimed to develop and validate predictive models for lymphopenia after radiotherapy (RT) in BC. MATERIAL AND METHODS: BC patients treated with adjuvant RT were eligible in this multicenter study. The study endpoint was lympopenia, defined as the reduction in absolute lymphocytes and graded lymphopenia after RT. The dose-volume histogram (DVH) data of related critical structures and clinical factors were taken into account for the development of dense neural network (DNN) predictive models. The developed DNN models were validated using external patient cohorts. RESULTS: Totally 918 consecutive patients with invasive BC enrolled. The training, testing, and external validating datasets consisted of 589, 203, and 126 patients, respectively. Treatment volumes at nearly all dose levels of the DVH were significant predictors for lymphopenia following RT, including volumes at very low-dose 1 Gy (V1) of organs at risk (OARs) including lung, heart and body, especially ipsilateral-lung V1. A final DNN model, combining full DVH dosimetric parameters of OARs and three key clinical factors, achieved a predictive accuracy of 75 % or higher. CONCLUSION: This study demonstrated and externally validated the significance of full dosimetric data, particularly the volume of low dose at as low as 1 Gy of critical structures on lymphopenia after radiation in BC patients. The significance of V1 deserves special attention, as modern VMAT RT technology often has a relatively high value of this parameter. Further study warranted for RT plan optimization.

Integrated Optimization of Structure and Control for Fast Steering Mirrors.

This study concerns the problem of integrated optimization of structure and control based on a fast steering mirror, aiming to achieve simultaneous optimization of the mechanical structure and control system. The traditional research and development path of the fast steering mirror involves a lengthy process from the initial design to the final physical manufacture. In the prior process, it was necessary to produce physical prototypes for repeated debugging and iterative optimization to achieve design requirements, but this approach consumes a significant amount of time and cost. To expedite this process and reduce unnecessary experimental costs, this study proposes an integrated optimization of structure and control (IOSC) method. With the use of IOSC, it is possible to achieve simultaneous optimization of structure and control. Specifically, the use of non-dominated sorting genetic algorithm II (NSGA-II) obtains globally optimal controller parameters and mechanical structure parameters under certain performance indices. This achieves an effective balance between the resonance frequency generated by the system and the working bandwidth, providing a high-precision reference for the research and development of fast steering mirrors.

Retraction: Expression of vascular endothelial growth factor C in renal cell carcinoma and its correlation with pathological parameters and prognosis.

[This retracts the article DOI: 10.21037/tau-20-970.].

Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images.

Background: Pulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach. Methods: The proposed segmentation method for pulmonary segments using the BT-based approach comprised the following five essential steps: (I) segmentation of the lung using a U-Net (R231) (public access) model; (II) segmentation of the lobes using a V-Net (self-developed) model; (III) segmentation of the airway using a combination of a differential geometric approach method and a BronchiNet (public access) model; (IV) labeling of the BT branches based on anatomical position; and (V) segmentation of the pulmonary segments based on the distance of each voxel to the labeled BT branches. This five-step process was applied to 14 high-resolution breath-hold CT images and compared against manual segmentations for evaluation. Results: For the lung segmentation, the lung mask had a mean dice similarity coefficient (DSC) of 0.98±0.03. For the lobe segmentation, the V-Net model had a mean DSC of 0.94±0.06. For the airway segmentation, the average total length of the segmented airway trees per image scan was 1,902.8±502.1 mm, and the average number of the maximum airway tree generations was 8.5±1.3. For the segmentation of the pulmonary segments, the proposed method had a DSC of 0.73±0.11 and a mean surface distance of 6.1±2.9 mm. Conclusions: This study demonstrated the feasibility of combining multiple deep-learning models for the auxiliary segmentation of pulmonary segments on CT images using a BT-based approach. The results highlighted the potential of the BT-based method for the semi-automatic segmentation of the pulmonary segment.

Pseudo-medical image-guided technology based on 'CBCT-only' mode in esophageal cancer radiotherapy.

Purpose To minimize the various errors introduced by image-guided radiotherapy (IGRT) in the application of esophageal cancer treatment, this study proposes a novel technique based on the 'CBCT-only' mode of pseudo-medical image guidance. Methods The framework of this technology consists of two pseudo-medical image synthesis models in the CBCT→CT and the CT→PET direction. The former utilizes a dual-domain parallel deep learning model called AWM-PNet, which incorporates attention waning mechanisms. This model effectively suppresses artifacts in CBCT images in both the sinogram and spatial domains while efficiently capturing important image features and contextual information. The latter leverages tumor location and shape information provided by clinical experts. It introduces a PRAM-GAN model based on a prior region aware mechanism to establish a non-linear mapping relationship between CT and PET image domains.  As a result, it enables the generation of pseudo-PET images that meet the clinical requirements for radiotherapy. Results The NRMSE and multi-scale SSIM (MS-SSIM) were utilized to evaluate the test set, and the results were presented as median values with lower quartile and upper quartile ranges. For the AWM-PNet model, the NRMSE and MS-SSIM values were 0.0218 (0.0143, 0.0255) and 0.9325 (0.9141, 0.9410), respectively. The PRAM-GAN model produced NRMSE and MS-SSIM values of 0.0404 (0.0356, 0.0476) and 0.9154 (0.8971, 0.9294), respectively. Statistical analysis revealed significant differences (p < 0.05) between these models and others. The numerical results of dose metrics, including D98 %, Dmean, and D2 %, validated the accuracy of HU values in the pseudo-CT images synthesized by the AWM-PNet. Furthermore, the Dice coefficient results confirmed statistically significant differences (p < 0.05) in GTV delineation between the pseudo-PET images synthesized using the PRAM-GAN model and other compared methods. Conclusion The AWM-PNet and PRAM-GAN models have the capability to generate accurate pseudo-CT and pseudo-PET images, respectively. The pseudo-image-guided technique based on the 'CBCT-only' mode shows promising prospects for application in esophageal cancer radiotherapy.

Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks: A Proof-of-Concept Study.

In this study, we present an innovative approach that harnesses deep neural networks to simulate respiratory lung motion and extract local functional information from single-phase chest X-rays, thus providing valuable auxiliary data for early diagnosis of lung cancer. A novel radiograph motion simulation (RMS) network was developed by combining a U-Net and a long short-term memory (LSTM) network for image generation and sequential prediction. By utilizing a spatial transformer network to deform input images, our proposed network ensures accurate image generation. We conducted both qualitative and quantitative assessments to evaluate the effectiveness and accuracy of our proposed network. The simulated respiratory motion closely aligns with pulmonary biomechanics and reveals enhanced details of pulmonary diseases. The proposed network demonstrates precise prediction of respiratory motion in the test cases, achieving remarkable average Dice scores exceeding 0.96 across all phases. The maximum variation in lung length prediction was observed during the end-exhale phase, with average deviation of 4.76 mm (±6.64) for the left lung and 4.77 mm (±7.00) for the right lung. This research validates the feasibility of generating patient-specific respiratory motion profiles from single-phase chest radiographs.

Weak non-line-of-sight target echoes extraction without accumulation.

Non-line-of-sight (NLOS) technology has been rapidly developed in recent years, allowing us to visualize or localize hidden objects by analyzing the returned photons, which is expected to be applied to autonomous driving, field rescue, etc. Due to the laser attenuation and multiple reflections, it is inevitable for future applications to separate the returned extremely weak signal from noise. However, current methods find signals by direct accumulation, causing noise to be accumulated simultaneously and inability of extracting weak targets. Herein, we explore two denoising methods without accumulation to detect the weak target echoes, relying on the temporal correlation feature. In one aspect, we propose a dual-detector method based on software operations to improve the detection ability for weak signals. In the other aspect, we introduce the pipeline method for NLOS target tracking in sequential histograms. Ultimately, we experimentally demonstrated these two methods and extracted the motion trajectory of the hidden object. The results may be useful for practical applications in the future.

Corrigendum to "Image fusion of multi-vision system with one high resolution image for the measurement of long distance" [Heliyon 9(3) (March 2023) e14433].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e14433.].

Boundary delineation in transrectal ultrasound images for region of interest of prostate.

Accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for ultrasound-guided brachytherapy for prostate cancer. However, the current practice of manual segmentation is difficult, time-consuming, and prone to errors. To overcome these challenges, we developed an accurate prostate segmentation framework (A-ProSeg) for TRUS images. The proposed segmentation method includes three innovation steps: (1) acquiring the sequence of vertices by using an improved polygonal segment-based method with a small number of radiologist-defined seed points as prior points; (2) establishing an optimal machine learning-based method by using the improved evolutionary neural network; and (3) obtaining smooth contours of the prostate region of interest using the optimized machine learning-based method. The proposed method was evaluated on 266 patients who underwent prostate cancer brachytherapy. The proposed method achieved a high performance against the ground truth with a Dice similarity coefficient of 96.2% ± 2.4%, a Jaccard similarity coefficient of 94.4% ± 3.3%, and an accuracy of 95.7% ± 2.7%; these values are all higher than those obtained using state-of-the-art methods. A sensitivity evaluation on different noise levels demonstrated that our method achieved high robustness against changes in image quality. Meanwhile, an ablation study was performed, and the significance of all the key components of the proposed method was demonstrated.

How to drive strategic green innovation behavior? A qualitative comparative analysis (QCA) based on Chinese manufacturing enterprises.

Green innovation is an important way for manufacturing enterprises to achieve green and high-quality development. The existing literature has ignored the heterogeneous motivations for enterprise green innovation. The strategic green innovation behavior (SGIB) aiming at seeking strategic differences and building core competitiveness is the necessary measure for enterprises to realize green transformation. Furthermore, the existing research studies the influencing factors of green innovation from a single perspective, ignoring the interaction of institutional and resource factors. Based on this, this study uses QCA to identify the driving mechanism of enterprise SGIB. The research collected questionnaire data from 199 manufacturing enterprises in China. The main results show that a single factor will not constitute a necessary condition for positive SGIB, but the not-high green dynamic capability is a necessary condition for negative SGIB. Positive SGIB includes four configuration paths: institution-ethics synergy, normative pressure oriented, environmental ethics oriented, and institutional incentive oriented. Lack of institutional pressure is the only configuration path that leads to negative SGIB. Further analysis found that environmental ethics and institutional pressure are the main forces to promote SGIB. After a series of robustness tests, the above basic conclusions did not change significantly. Based on the above conclusions, we believe that government departments, industry associations, and enterprises should form a joint force to enhance the level of institutional pressure and enterprise environmental ethics, promoting the implementation of strategic green innovation.

Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization.

PURPOSE: The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements. MATERIALS AND METHODS: Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation ( VNM ) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV 1 ; forced vital capacity, FVC; and FEV 1 /FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM- VNM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons. RESULTS: At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference VNM . The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P <0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV 1 (FEV 1 /FVC). CONCLUSIONS: The results provide further insight into the underlying association of specific pulmonary textures with both local ( VNM ) and global (FEV 1 /FVC, FEV 1 ) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.

Stray light control for the space-agile optical system with a pointing mirror.

The space-agile optical composite detection (SOCD) system with a pointing mirror possesses flexible and fast response ability. Like other space telescopes, if the stray light is not properly eliminated, it may result in a false response or noise that floods the real light signal due to the low illuminance and large dynamic range of the target. The paper shows the optical structure layout, the decomposition of the optical processing index and roughness control index, the stray light suppression requirements, and the detailed stray light analysis process. The pointing mirror and ultra-long afocal optical path increase the difficulty of stray light suppression in the SOCD system. This paper presents the design method of a special-shaped aperture diaphragm and entrance baffle, black baffle surface testing, simulating, selection, and stray light suppression analysis process. The special-shaped entrance baffle has a significant effect on the suppression of stray light and reduced dependence on the platform posture of the SOCD system.

A super-voxel-based method for generating surrogate lung ventilation images from CT.

Purpose: This study aimed to develop and evaluate CTVISVD , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D mean) and mean ventilation values (Vent mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D mean values to yield CTVISVD. For the performance evaluation, the voxel- and region-wise differences between CTVISVD and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, CTVIHU and CTVIJac, and compared with the SPECT images. Results: The correlation between the D mean and Vent mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the CTVISVD method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the CTVIHU (0.33 ± 0.14, p < 0.05) and CTVIJac (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for CTVISVD (0.63 ± 0.07) was significantly higher than the corresponding values for the CTVIHU (0.43 ± 0.08, p < 0.05) and CTVIJac (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between CTVISVD and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.

A comprehensive investigation on source apportionment and multi-directional regional transport of volatile organic compounds and ozone in urban Zhengzhou.

To understand the characteristics, source apportionment, and regional transport of volatile organic compounds (VOCs) and ozone (O3) in a typical city with severe air pollution in central China, we observed and analyzed 115 VOC species at an urban site in Zhengzhou from 29 July to 26 September 2021. During this period, observation- and emission-based approaches revealed that Zhengzhou was in a VOC-limited regime. The average concentration of total VOCs (TVOCs) was 162.25 ± 71.42 µg/m3, dominated by oxygenated VOCs (OVOCs, 34.49%), alkanes (24.29%), and aromatics (19.49%). Six VOC sources were identified using positive matrix factorization (PMF) model, including paint solvent usage (25.32%), secondary production (24.11%), industrial production (19.22%), vehicle exhaust (16.18%), biogenic emission (8.87%), and combustion (6.30%). To assess the regional contribution and source apportionment of VOCs and O3, Comprehensive Air Quality Model with Extensions (CAMx) with the Ozone Source Apportionment Technology (OSAT) was used for simulation. Results showed that the VOCs were significantly affected by local emissions (about 70%), while O3 was mainly attributed to regional and super-regional transport. Regarding multi-directional regional transport of VOCs and O3, dominant contributions were from the northeast and east-northeast directions, and O3 contributions were also predominantly from the east and east-southeast directions. In terms of source apportionment, the transportation and industrial sectors (including solvent usage) were the major contributors to O3 and VOCs. To alleviate VOCs and O3 pollution, transportation and industrial emission reduction should be strengthened, and regional coordination, especially from the northeast to east-southeast directions, should be emphasized in addition to local management.

Image fusion of multi-vision system with one high resolution image for the measurement of long distance.

The mutual restriction between angle detection accuracy and spatial resolution of image will restrict the detection of long distance by multi-vision system. One higher resolution image of a multi-vision system can be used to improve the resolution of the images of each camera. In present paper, a new fusion method with the help of one higher resolution image is proposed and analytically studied. As an example, an image fusion for the images captured by the combining optical system of camera arrays and the synthetic aperture is studied. Both of the simulation and experiment shows that the proposed method is simple and accuracy.

Niche-mediated bacterial community composition in continental glacier alluvial valleys under cold and arid environments.

Introduction: Bacteria are an essential component of glacier-fed ecosystems and play a dominant role in driving elemental cycling in the hydrosphere and pedosphere. However, studies of bacterial community composition mechanisms and their potential ecological functions from the alluvial valley of mountain glaciers are extremely scarce under cold and arid environments. Methods: Here, we analyzed the effects of major physicochemical parameters related to soil on the bacterial community compositions in an alluvial valley of the Laohugou Glacier No. 12 from the perspective of core, other, and unique taxa and explored their functional composition characteristics. Results and discussion: The different characteristics of core, other, and unique taxa highlighted the conservation and difference in bacterial community composition. The bacterial community structure of the glacial alluvial valley was mainly affected by the above sea level, soil organic carbon, and water holding capacity. In addition, the most common and active carbon metabolic pathways and their spatial distribution patterns along the glacial alluvial valley were revealed by FAPTOTAX. Collectively, this study provides new insights into the comprehensive assessment of glacier-fed ecosystems in glacial meltwater ceasing or glacier disappearance.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

Gut Microbiota and Metabolites May Play a Crucial Role in Sea Cucumber Apostichopus japonicus Aestivation.

The constant increase in temperatures under global warming has led to a prolonged aestivation period for Apostichopus japonicus, resulting in considerable losses in production and economic benefits. However, the specific mechanism of aestivation has not been fully elucidated. In this study, we first tried to illustrate the biological mechanisms of aestivation from the perspective of the gut microbiota and metabolites. Significant differences were found in the gut microbiota of aestivating adult A. japonicus (AAJSD group) compared with nonaestivating adult A. japonicus (AAJRT group) and young A. japonicus (YAJRT and YAJSD groups) based on 16S rRNA gene high-throughput sequencing analysis. The abundances of Desulfobacterota, Myxococcota, Bdellovibrionota, and Firmicutes (4 phyla) in the AAJSD group significantly increased. Moreover, the levels of Pseudoalteromonas, Fusibacter, Labilibacter, Litorilituus, Flammeovirga, Polaribacter, Ferrimonas, PB19, and Blfdi19 genera were significantly higher in the AAJSD group than in the other three groups. Further analysis of the LDA effect size showed that species with significant variation in abundance in the AAJSD group, including the phylum Firmicutes and the genera Litorilituus, Fusibacter, and Abilibacter, might be important biomarkers for aestivating adult A. japonicus. In addition, the results of metabolomics analysis showed that there were three distinct metabolic pathways, namely biosynthesis of secondary metabolites, tryptophan metabolism, and sesquiterpenoid and triterpenoid biosynthesis in the AAJSD group compared with the other three groups. Notably, 5-hydroxytryptophan was significantly upregulated in the AAJSD group in the tryptophan metabolism pathway. Moreover, the genera Labilibacter, Litorilituus, Ferrimonas, Flammeovirga, Blfdi19, Fusibacter, Pseudoalteromonas, and PB19 with high abundance in the gut of aestivating adult A. japonicus were positively correlated with the metabolite 5-HTP. These findings suggest that there may be potential biological associations among the gut microbiota, metabolites, and aestivation in A. japonicus. This work may provide a new perspective for further understanding the aestivation mechanism of A. japonicus.

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

Background: The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Methods: Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Results: Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. Conclusions: The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.