Search

Abstract Cancer cells often exhibit fragmented mitochondria and dysregulated mitochondrial dynamics, but the underlying mechanism remains elusive. Here, we found that the mitochondrial protein death-associated protein 3 (DAP3) is localized to mitochondria and promotes the progression of hepatocellular carcinoma (HCC) by regulating mitochondrial function. DAP3 can promote the proliferation, migration, and invasion of HCC cells in vitro and in vivo by increasing mitochondrial respiration, inducing the epithelial-mesenchymal transition (EMT), and slowing cellular senescence. Mechanistically, DAP3 can increase mitochondrial complex I activity in HCC cells by regulating the translation and expression of MT-ND5. The phosphorylation of DAP3 at Ser185 mediated by AKT is the key event mediating the mitochondrial localization and function of DAP3 in HCC cells. In addition, the DAP3 expression in HCC samples is inversely correlated with patient survival. Our results revealed a mechanism by which DAP3 promotes mitochondrial function and HCC progression by regulating MT-ND5 translation and expression, indicating that DAP3 may be a therapeutic target for HCC.

Abstract Single image super‐resolution (SISR) is a promising research direction in computer vision and image processing for improving the visual perception of low‐quality images. In recent years, deep learning algorithms have driven tremendous development in SR, and SR methods based on various network architectures have significantly improved the quality of reconstructed images. Although there has been a large amount of reviews focusing on SISR, few studies have focused specifically on network architectures for SISR. This paper aims to provide a systematic overview of the design ideas of SISR using multiple architectures, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Transformer, and Diffusion model. In addition, an experimental analysis and comparison of state‐of‐the‐art SR algorithms have been performed on publicly available quantitative and qualitative datasets. Finally, some future directions are discussed that may help other community researchers.

Abstract The spatiotemporal regulation of inflammasome activation remains unclear. To examine the mechanism underlying the assembly and regulation of the inflammasome response, here we perform an immunoprecipitation-mass spectrometry analysis of apoptosis-associated speck-like protein containing a CARD (ASC) and identify NCF4/1/2 as ASC-binding proteins. Reduced NCF4 expression is associated with colorectal cancer development and decreased five-year survival rate in patients with colorectal cancer. NCF4 cooperates with NCF1 and NCF2 to promote NLRP3 and AIM2 inflammasome activation. Mechanistically, NCF4 phosphorylation and puncta distribution switches from the NADPH complex to the perinuclear region, mediating ASC oligomerization, speck formation and inflammasome activation. NCF4 functions as a sensor of ROS levels, to establish a balance between ROS production and inflammasome activation. NCF4 deficiency causes severe colorectal cancer in mice, increases transit-amplifying and precancerous cells, reduces the frequency and activation of CD8+ T and NK cells, and impairs the inflammasome-IL-18-IFN-γ axis during the early phase of colorectal tumorigenesis. Our study implicates NCF4 in determining the spatial positioning of inflammasome assembly and contributing to inflammasome-mediated anti-tumor responses.

A 12-bit time-interleaved (TI) analog-to-digital converter (ADC) with pipelined successive-approximation (SAR) channels is presented in this paper. The ADC consists of four TI channels, each incorporating a two-stage pipelined asynchronous SAR ADC. To facilitate clock distribution, a common bootstrapped sampler in front of the four channels is employed. The reset switch in the capacitive digital-to-analog converter (CDAC) of each channel is also bootstrapped to enhance the speed and linearity. A prototype ADC has been designed and implemented in a 22-nm FDSOI CMOS technology, with a core occupation area of 0.43 mm2. Measurements show that the ADC achieves a signal-to-noise-and-distortion ratio of 50dB with a low-frequency input, and of 48.5 dB at Nyquist. The total power consumption is 37.5 mW; the core ADC consumes 19.3 mW from a 0.8V supply. With a 1.4 GS/s sampling rate and input at Nyquist, the ADC achieves a Walden’s figure of merit of 114 fJ/conversion.

Abstract Mitochondria dysfunction contributes to acute liver injuries, and mitochondrial regulators, such as PGC-1α and MCJ, affect liver regeneration. Therefore, identification of mitochondrial modulators may pave the way for developing therapeutic strategies. Here, ZHX2 is identified as a mitochondrial regulator during acute liver injury. ZHX2 both transcriptionally inhibits expression of several mitochondrial electron transport chain genes and decreases PGC-1α stability, leading to reduction of mitochondrial mass and OXPHOS. Loss of Zhx2 promotes liver recovery by increasing mitochondrial OXPHOS in mice with partial hepatectomy or CCl4-induced liver injury, and inhibition of PGC-1α or electron transport chain abolishes these effects. Notably, ZHX2 expression is higher in liver tissues from patients with drug-induced liver injury and is negatively correlated with mitochondrial mass marker TOM20. Delivery of shRNA targeting Zhx2 effectively protects mice from CCl4-induced liver injury. Together, our data clarify ZHX2 as a negative regulator of mitochondrial OXPHOS and a potential target for developing strategies for improving liver recovery after acute injuries.

Liver-resident natural killer cells develop locally and have multiple immunological roles in situ. Here the authors investigate the gut-liver axis and show the impact of the intestinal microbiota on the development of liver-resident natural killer cells.

The automatic extraction of airport runway areas from high-resolution Synthetic Aperture Radar (SAR) images is of great research significance in the military and civilian fields. However, it is still challenging to distinguish the airport from surrounding objects in SAR images. In this article, a new framework is proposed to extract airport runway areas (runways, taxiways, packing lots, and aircrafts) in a fast and automatic manner. The framework is based on the Geospatial Contextual Attention Mechanism (GCAM) for geospatial feature learning and classification, which is employed together with the down-sampling and coordinate mapping modules. To evaluate the performance of the proposed framework, three large-scale Gaofen-3 SAR images with 1m resolution are utilized in the experiment. According to the results, Mean Pixels Accuracy (MPA) and Mean Intersection Over Union (MIOU) of the GCAM are 0.9850 and 0.9536, respectively, which outperform RefineNet, DeepLabV3+, and MDDA. The training time of GCAM for the dataset is 2.25h, and the average testing time for the five SAR images is only 18.15s. Therefore, GCAM can offer rapid and automatic airport detection from high-resolution SAR images with high accuracy, which can further be employed to mark the airport to greatly improve the detection accuracy of the aircrafts.

Ti3+ self-doped rutile/anatase/TiO2(B) mixed-crystal tri-phase heterojunctions photocatalysts are fabricated via hydrothermal approach and mechanochemical process, followed by in-situ solid-state chemical reduction approach. The as-prepared samples are characterized by X-ray diffraction, Raman, scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and UV–vis diffuse reflectance spectroscopy. The photocatalysts are consisted by anatase/rutile TiO2 nanoparticles and 1D TiO2(B) single-crystalline nanorods, which form rutile/anatase/TiO2(B) tri-phase heterojunctions. The visible-light-driven photocatalytic degradation rates of methyl orange and phenol are up to ∼98 and 97%, which are 2.2 and 1.8 times higher than that of commercial Degussa P25, 2.3 and 2.2 times higher than that of pure TiO2(B) as well. The enhancement can be attributed to the synergistic effect of special nanostructure, tri-phase heterojunctions, oxygen vacancy and Ti3+ self-doping, which facilitates the absorption of visible light and the spatial separation of photo-generated charge carriers. This work provides a new perspective for designing high-active visible-light-driven photocatalyst in future. Keywords: TiO2, Visible-light-driven photocatalysis, Tri-phase heterojunction, Ti3+ self-doped, Pollutants degradation

Radiofrequency ablation (RFA) is a widely used and effective treatment for primary or metastatic liver cancer with small-size lesions. However, the therapeutic effectiveness of RFA in controlling metastatic lesion or recurrence is still limited. As the major cell population in tumor microenvironment (TME), macrophages have been reported to be recruited to RFA-treated lesion, but their roles are still unclear. Herein, we successfully established the mouse model mimicking RFA-induced abscopal effect, in which RFA eliminated the local orthotopic liver tumor but failed to control growth of distant tumor. Correspondently, RFA suppressed protumoral activation of local tumor-associated macrophages (TAMs), but failed to reprogram TAMs in distance. Importantly, although RFA led to reduced proportion of hepatic CD169+ macrophages in local and decreased expression of immune inhibitory molecules Tim-3 and PD-L1, these alterations were not observed for CD169+ macrophages in distant TME. Further RNA-seq and flow cytometry analysis showed that hepatic CD169+ macrophages contributed to reprograming TME through recruiting CD8+ T/NK cells and suppressing accumulation of MDSCs/Tregs. Consistently, depletion of CD169+ macrophages in CD169-DTR mouse greatly promoted liver tumor progression and largely dampened RFA-induced tumor suppression. Notably, transfer of CD169+ macrophages synergistically enhanced RFA-induced inhibition of distant tumor. To our knowledge, this is the first study which demonstrates hepatic CD169+ macrophages as a key factor responsible for RFA-induced abscopal effect. Our data suggest RFA with transfer of CD169+ macrophages as a promising combination therapy to lessen metastasis or recurrence of liver cancer in patients.

The Ti600/TC18 dissimilar titanium alloy joints were prepared by inertia friction welding (IFW). Then, stress-relief annealing and two-stage annealing were performed to optimize the microstructure and properties of the original joints, the purpose of them is to improve the structure and performance of the joints. Then, the microstructure, phase composition, tensile properties, microhardness, and fracture morphology of the joints after heat treatments were investigated. The results showed that after stress-relief annealing, the microstructure of the joints was almost similar to that of the specimen before annealing; the weld zone (WZ) of the joints was composed of fine recrystallized grains and α′, and the more β phases underwent a martensitic transformation. The shapes and sizes of αp phases were increased after two-stage annealing; its percentage content was decreased. The tensile properties and the microhardness values of the joints undergoing stress-relief annealing were relatively higher than that of the joints undergoing two-stage annealing; there was no obvious change in the plasticity of the joints. It was confirmed that the stress-relief annealing microstructure was composed of α′ and β phases, which were beneficial to the properties of the joints. However, the αs phases were coarsened after two-stage annealing, and the properties of the joints were reduced.

Automatic river bridge detection is a typical and valuable application for SAR image analysis. However, the background information of SAR image is complex, and there are many specious targets with similar features, such as road, ponds and ridges, which usually cause false alarms. And current river bridge detection methods fail to handle these interference efficiently. Therefore, this paper applies deep learning to SAR and proposes a new river bridge detection algorithm, which is named as Single Short Detection-Adaptively Effective Feature Fusion (SSD-AEFF). It can effectively reduce the interference of noisy information, and achieve fast and high-precision detection of river bridges in complex SAR imagery. SSD-AEFF is based on SSD, and AEFF module is innovated to enhance the multi-scale feature maps together with effective Squeeze-Excitation (eSE) module to further fuse effective features and decrease the interference of background information. Additionally, Non-Maximum Suppression (NMS) is used to screen out redundant candidate boxes to produce the final detection result. Moreover, Gradient Harmonizing Mechanism (GHM) loss function is introduced to solve the problem of sample imbalance in the training process. Experimental results on TerraSAR data compared with existing baseline models demonstrate the superiority of the proposed SSD-AEFF algorithm.

The mismatch of geometric parameters in a nanotomography system bears a significant impact on the reconstructed images. Moreover, projection image noise is increased due to limitations of the X-ray power source. The accuracy of the existing self-calibration method, which uses only the grayscale information of the projected image, is easily affected by noise and leads to reduced accuracy. This paper proposes a geometric parameter self-calibration method based on feature matching of mirror projection images. Firstly, the fast extraction and matching feature points in the mirror projection image are performed by speeded-up robust features (SURF). The feature triangle is then designed according to the stable position of the system’s rotation axis to further filter the feature points. In turn, the influence of the mismatched points on the calculation accuracy is reduced. Finally, the straight line where the rotation axis is located is fitted by the midpoint coordinates of the filtered feature points, thereby realizing geometric parameter calibration of the system. Simulation and actual data from the experimental results show that the proposed method effectively realizes the calibration of geometric parameters, and the blurring and ghosting caused by geometric artifacts are corrected. Compared with existing methods, the image clarity can be improved by up to 14.4%.

The resolution of 3D structure reconstructed by laboratory nanoCT is often affected by changes in ambient temperature. Although correction methods based on projection alignment have been widely used, they are time-consuming and complex. Especially in piecewise samples (e.g., chips), the existing methods are semi-automatic because the projections lose attenuation information at some rotation angles. Herein, we propose a fast correction method that directly processes the reconstructed slices. Thus, the limitations of the existing methods are addressed. The method is named multiscale dense U-Net (MD-Unet), which is based on MIMO-Unet and achieves state-of-the-art artifacts correction performance in nanoCT. Experiments show that MD-Unet can significantly boost the correction performance (e.g., with three orders of magnitude improvement in correction speed compared with traditional methods), and MD-Unet+ improves 0.92 dB compared with MIMO-Unet in the chip dataset.

A self-correction method for the drift artifacts of laboratory cone-beam nanoscale X-ray computed tomography (nano-CT) based on the trajectory of projection centroid (TPC) is proposed. This method does not require additional correction phantoms, simplifying the correction process. The whole TPC is estimated by the partial TPC in the optimal projection set. The projection drift is calculated by the measured TPC and the estimated TPC. The interval search method is used so that the proposed method can adapt to the case of a truncated projection due to drift. The fixed-angle scanning experiment of the Siemens star and the partial derivative analysis of the projection position show the necessity of correcting drift artifacts. Further, the Shepp–Logan phantoms with different drift levels are simulated. The results show that the proposed method can effectively estimate the horizontal and vertical drifts within the projection drift range of ±2 mm (27 pixels) with high accuracy. Experiments were conducted on tomato seed and bamboo stick to validate the feasibility of the proposed method for samples with different textures. The correction effect on different reconstructed slices indicates that the proposed method provides performance superior to the reference scanning method (RSM) and global fitting. In addition, the proposed method requires no extra scanning, which improves the acquisition efficiency, as well as radiation utilization.

Background: Liver cancer stem cells (CSCs) are critical determinants of HCC relapse and therapeutic resistance, but the mechanisms underlying the maintenance of CSCs are poorly understood. We aimed to explore the role of tumor repressor Zinc-fingers and homeoboxes 2 (ZHX2) in liver CSCs. Methods: CD133+ or EPCAM+ stem-like liver cancer cells were sorted from tumor tissues of HCC patients and HCC cell lines by flow cytometry. In addition, sorafenib-resistant cells, tumor-sphere forming cells and side population (SP) cells were respectively cultured and isolated as hepatic CSCs. The tumor-initiating and chemoresistance properties of ZHX2-overexpressing and ZHX2-knockdown cells were analyzed in vivo and in vitro. Microarray, luciferase reporter assay, chromatin immunoprecipitation (ChIP) and ChIP-on-chip analyses were performed to explore ZHX2 target genes. The expression of ZHX2 and its target gene were determined by quantitative RT-PCR, western blot, immunofluorescence and immunohistochemical staining in hepatoma cells and tumor and adjacent tissues from HCC patients. Results: ZHX2 expression was significantly reduced in liver CSCs from different origins. ZHX2 deficiency led to enhanced liver tumor progression and expansion of CSC populations in vitro and in vivo. Re-expression of ZHX2 restricted capabilities of hepatic CSCs in supporting tumor initiation, self-renewal and sorafenib-resistance. Mechanically, ZHX2 suppressed liver CSCs via inhibiting KDM2A-mediated demethylation of histone H3 lysine 36 (H3K36) at the promoter regions of stemness-associated transcription factors, such as NANOG, SOX4 and OCT4. Moreover, patients with lower expression of ZHX2 and higher expression of KDM2A in tumor tissues showed significantly poorer survival. Conclusion: ZHX2 counteracts stem cell traits through transcriptionally repressing KDM2A in HCC. Our data will aid in a better understanding of molecular mechanisms underlying HCC relapse and drug resistance. Keywords: ZHX2, Tumor suppressor, Epigenetics, Cancer stem cells, Oncotherapy

Fine particulate matter (PM _2.5 ) is widely concerned for its harmful impacts on global environment and human health, making air pollution monitoring so crucial and indispensable. As the world’s first open, real-time, and historical air quality platform, OpenAQ collects and provides government measurement and research-level data from various channels. However, despite OpenAQ’s innovation in providing us with ground-measured PM _2.5 worldwide, we find significant data gaps in time series for most of the sites. The incompleteness of the data directly affects the public perception of PM _2.5 concentration levels and hinders the progress of research related to air pollution. To address these issues, a two-step hybrid model named ST-SILM, i.e. spatio-temporal model with single exponential smoothing-inverse distance weighted (SES-IDW) and long short-term memory (LSTM), is proposed to repair the missing data from PM _2.5 sites worldwide collected from OpenAQ from 2017 to 2019. Both spatio-temporal correlation and neighborhood fields are considered and established in the model. To be specific, SES-IDW were firstly used to repair missing values, and secondly, the LSTM network was employed to reconstruct the time series of continuous missing data. After the global ground-measured PM _2.5 was reconstructed, the light gradient boosting machine model was applied to remote sensing estimation of the original ground-measured PM _2.5 and of the reconstructed ground-measured PM _2.5 to further verify the performance of ST-SILM. Experiment results show that the estimation accuracy of the reconstructed dataset is better ( R ^2 from 2017 to 2019 increased by 0.02, 0.02, and 0.01 compared with the original dataset). Therefore, it is concluded that the proposed model can effectively reconstruct data from PM _2.5 sites worldwide.

Thermal drift of nano-computed tomography (CT) adversely affects the accurate reconstruction of objects. However, feature-based reference scan correction methods are sometimes unstable for images with similar texture and low contrast. In this study, based on the geometric position of features and the structural similarity (SSIM) of projections, a rough-to-refined rigid alignment method is proposed to align the projection. Using the proposed method, the thermal drift artifacts in reconstructed slices are reduced. Firstly, the initial features are obtained by speeded up robust features (SURF). Then, the outliers are roughly eliminated by the geometric position of global features. The features are refined by the SSIM between the main and reference projections. Subsequently, the SSIM between the neighborhood images of features are used to relocate the features. Finally, the new features are used to align the projections. The two-dimensional (2D) transmission imaging experiments reveal that the proposed method provides more accurate and robust results than the random sample consensus (RANSAC) and locality preserving matching (LPM) methods. For three-dimensional (3D) imaging correction, the proposed method is compared with the commonly used enhanced correlation coefficient (ECC) method and single-step discrete Fourier transform (DFT) algorithm. The results reveal that proposed method can retain the details more faithfully.

In aircraft detection from synthetic aperture radar (SAR) images, there are several major challenges: the shattered features of the aircraft, the size heterogeneity and the interference of a complex background. To address these problems, an Efficient Bidirectional Path Aggregation Attention Network (EBPA2N) is proposed. In EBPA2N, YOLOv5s is used as the base network and then the Involution Enhanced Path Aggregation (IEPA) module and Effective Residual Shuffle Attention (ERSA) module are proposed and systematically integrated to improve the detection accuracy of the aircraft. The IEPA module aims to effectively extract advanced semantic and spatial information to better capture multi-scale scattering features of aircraft. Then, the lightweight ERSA module further enhances the extracted features to overcome the interference of complex background and speckle noise, so as to reduce false alarms. To verify the effectiveness of the proposed network, Gaofen-3 airports SAR data with 1 m resolution are utilized in the experiment. The detection rate and false alarm rate of our EBPA2N algorithm are 93.05% and 4.49%, respectively, which is superior to the latest networks of EfficientDet-D0 and YOLOv5s, and it also has an advantage of detection speed.

The detection of airports from Synthetic Aperture Radar (SAR) images is of great significance in various research fields. However, it is challenging to distinguish the airport from surrounding objects in SAR images. In this paper, a new framework, multi-level and densely dual attention (MDDA) network is proposed to extract airport runway areas (runways, taxiways, and parking lots) in SAR images to achieve automatic airport detection. The framework consists of three parts: down-sampling of original SAR images, MDDA network for feature extraction and classification, and up-sampling of airports extraction results. First, down-sampling is employed to obtain a medium-resolution SAR image from the high-resolution SAR images to ensure the samples (500 × 500) can contain adequate information about airports. The dataset is then input to the MDDA network, which contains an encoder and a decoder. The encoder uses ResNet_101 to extract four-level features with different resolutions, and the decoder performs fusion and further feature extraction on these features. The decoder integrates the chained residual pooling network (CRP_Net) and the dual attention fusion and extraction (DAFE) module. The CRP_Net module mainly uses chained residual pooling and multi-feature fusion to extract advanced semantic features. In the DAFE module, position attention module (PAM) and channel attention mechanism (CAM) are combined with weighted filtering. The entire decoding network is constructed in a densely connected manner to enhance the gradient transmission among features and take full advantage of them. Finally, the airport results extracted by the decoding network were up-sampled by bilinear interpolation to accomplish airport extraction from high-resolution SAR images. To verify the proposed framework, experiments were performed using Gaofen-3 SAR images with 1 m resolution, and three different airports were selected for accuracy evaluation. The results showed that the mean pixels accuracy (MPA) and mean intersection over union (MIoU) of the MDDA network was 0.98 and 0.97, respectively, which is much higher than RefineNet and DeepLabV3. Therefore, MDDA can achieve automatic airport extraction from high-resolution SAR images with satisfying accuracy.

The realization of broadband absorption in the terahertz regime is of significant interest in high-sensitive signal detection and modulation. Previously, multi-layer metamaterials have been proposed and demonstrated to expand the absorption bandwidth by clustering multiple closely-positioned structures with different absorption peaks. Here, we proposed an alternative design for broadband metamaterial absorption by incorporating two interlaced fishnet layers, and achieved a broadband absorber with a full-width at half-maximum of about 0.99 THz. We also investigated the impact of the relative position of the two fishnet layers in the form of frequency tuning and expanding the absorption band.

Plasmon-induced transparency in terahertz metamaterials markedly modifies the dispersive properties of an otherwise opaque medium and reveals unprecedented prospects on novel functional components. However, plasmon-induced transparency in metamaterials so far exists in a narrow frequency band or without actively tunable abilities. Here, we demonstrate optical control of a broadband plasmon-induced transparency in a hybrid metamaterial made from integrated silicon-metal unit cells. Attributed to the modification in damping rate of the dark mode resonators under optical excitation, a giant dynamic amplitude modulation of the broadband transparency window is observed. The scheme suggested here is promising in developing broadband active slow-light devices and realizing on-to-off switching responses of the terahertz radiation at room temperature.

Deep learning techniques have significantly improved image restoration tasks in recent years. As a crucial component of deep learning, the loss function plays a key role in network optimization and performance enhancement. However, the currently prevalent loss functions assign equal weight to each pixel point during loss calculation, which hampers the ability to reflect the roles of different pixel points and fails to exploit the image’s characteristics fully. To address this issue, this study proposes an asymmetric loss function based on the image and data characteristics of the image recovery task. This novel loss function can adjust the weight of the reconstruction loss based on the grey value of different pixel points, thereby effectively optimizing the network training by differentially utilizing the grey information from the original image. Specifically, we calculate a weight factor for each pixel point based on its grey value and combine it with the reconstruction loss to create a new loss function. This ensures that pixel points with smaller grey values receive greater attention, improving network recovery. In order to verify the effectiveness of the proposed asymmetric loss function, we conducted experimental tests in the image super-resolution task. The experimental results show that the model with the introduction of asymmetric loss weights improves all the indexes of the processing results without increasing the training time. In the typical superresolution network SRCNN, by introducing asymmetric weights, it is possible to improve the peak signal-to-noise ratio (PSNR) by up to about 0.5%, the structural similarity index (SSIM) by up to about 0.3%, and reduce the rootmean- square error (RMSE) by up to about 1.7% with essentially no increase in training time. In addition, we also further tested the performance of the proposed method in the denoising task to verify the potential applicability of the method in the image restoration task. [ABSTRACT FROM AUTHOR]

Abstract Background Postoperative time to extubation plays a role in prognosis after heart valve surgery; however, its exact impact has not been clarified. This study compared the postoperative outcomes of minimally invasive surgery and conventional sternotomy, focusing on early extubation and factors influencing prolonged mechanical ventilation. Methods Data from 744 patients who underwent heart valve surgery at the Zhejiang Provincial People’s Hospital between August 2019 and June 2022 were retrospectively analyzed. The outcomes in patients who underwent conventional median sternotomy (MS) and minimally invasive (MI) video-assisted thoracoscopic surgery were compared using inverse probability of treatment weighting (IPTW) and Kaplan–Meier curves. Clinical data, including surgical data, postoperative cardiac function, postoperative complications, and intensive care monitoring data, were analyzed. Results After propensity score matching and IPTW, 196 cases of conventional MS were compared with 196 cases of MI video-assisted thoracoscopic surgery. Compared to patients in the conventional MS group, those in the MI video-assisted thoracoscopic surgery group in the matched cohort had a higher early postoperative extubation rate (P