Whole-Voltage-Range Solid-Solution Reaction in Layered Oxide Cathode of Sodium-Ion Batteries.

Layered manganese-based oxides (LMOs) are promising cathode materials for sodium-ion batteries (SIBs) due to their versatile structures. However, the Jahn-Teller effect of Mn3+ induces severe distortion of MnO6 octahedra, and the resultant low symmetry is responsible for the gliding of MnO2 layers and then inferior multiple-phase transitions upon Na+ extraction/insertion. Here, hexagonal P2-Na0.643 Li0.078 Mn0.827 Ti0.095 O2 is synthesized through the incorporation of Li and Ti into the distorted orthorhombic P'2-Na0.67 MnO2 to function as a phase-transition-free oxide cathode. It is revealed that Li in both the transition-metal and Na layers enhances the covalency of Mn-O bonds and allows degeneracy of Mn 3d eg orbitals to favor the formation of hexagonal phase, and the high strength of Ti-O bonds reduces the electrostatic interaction between Na and O for suppressed Na+ /vacancy rearrangements. These collectively lead to a whole-voltage-range solid-solution reaction between 1.8 and 4.3 V with a small volume variation of 1.49%. This rewards its excellent cycling stability (capacity retention of 90% after 500 cycles) and rate capability (89 mAh g-1 at 2000 mA g-1 ).

Clinical and Dosimetric Predictors for Postoperative Cardiopulmonary Complications in Esophageal Squamous Cell Carcinoma Patients Receiving Neoadjuvant Chemoradiotherapy and Surgery.

BACKGROUND: Neoadjuvant chemoradiotherapy followed by esophagectomy is the standard treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). This study explored correlations of clinical factors and dose-volume histogram (DVH) parameters with postoperative cardiopulmonary complications and predicted their risk by establishing a nomogram model. METHODS: Clinical and DVH parameters of ESCC patients who underwent trimodality treatment from 2002 to 2020 were collected. Postoperative cardiopulmonary complications were recorded. Logistic regression analysis was applied, and a nomogram model was constructed. Area under the receiver operating characteristic (AUC) curve, calibration curve, and decision curve analyses were performed to evaluate the performance of the nomogram. RESULTS: Of the 307 ESCC patients enrolled in this study, 65 (21.2%) experienced pulmonary complications and 57 (18.6%) experienced cardiac complications. The following six risk factors were identified as independent risk factors for pulmonary complications by multivariate logistic regression analyses in the integrated model: male sex (odds ratio [OR], 3.26; 95% confidence interval [CI], 1.27-9.70; P = 0.021), post-radiation therapy (RT) forced expiratory volume in 1 s (FEV1) (OR, 0.51; 95% CI 0.28-0.90; P = 0.023), mean lung dose (MLD) (OR, 1.13; 95% CI 1.01-1.28; P = 0.041), and pre-RT monocyte (OR, 8.36; 95% CI 1.23-11.7; P = 0.03). The AUC of this integrated model was 0.705 (95% CI 0.64-0.77). The paclitaxel and cisplatin (TP) concurrent chemotherapy regimen was the independent predictor of cardiac complication (OR, 2.50; 95% CI 1.22-5.55; P = 0.016). CONCLUSIONS: For ESCC patients who underwent trimodality treatment, male sex, post-RT FEV1, MLD, and pre-RT monocyte were confirmed as significant predictors of postoperative pulmonary complications. A nomogram model including six risk factors was further established. The independent predictor of cardiac complication was TP concurrent chemotherapy.

Knockdown of PHLDA2 promotes apoptosis and autophagy of glioma cells through the AKT/mTOR pathway.

Pleckstrin homology like domain family A member 2 (PHLDA2) is an imprinted gene expressed in placenta and has been shown to be associated with tumor progression. However, the effect of PHLDA2 on glioma cell growth has not been reported yet. Data based on TCGA database showed that PHLDA2 was up-regulated in glioma tissues. Moreover, PHLDA2 was also elevated in glioma cells. Functional assays showed that siRNA-mediated knockdown of PHLDA2 reduced cell viability of glioma cells and suppressed the cell proliferation. Cell apoptosis of glioma cells was promoted by silencing of PHLDA2 with increased Bax and decreased Bcl-2. Silencing of PHLDA2 reduced protein expression of p62, enhanced LC3 and Beclin1 to promote autophagy. Phosphorylated AKT and mTOR were down-regulated in glioma cells by interference of PHLDA2. In conclusion, downregulation of PHLDA2 inhibited glioma cell proliferation, and promoted cell apoptosis and autophagy through inactivation of AKT/mTOR signaling.

Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN.

We propose a new generative model named adaptive cycle-consistent generative adversarial network, or Ad CycleGAN to perform image translation between normal and COVID-19 positive chest X-ray images. An independent pre-trained criterion is added to the conventional Cycle GAN architecture to exert adaptive control on image translation. The performance of Ad CycleGAN is compared with the Cycle GAN without the external criterion. The quality of the synthetic images is evaluated by quantitative metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Universal Image Quality Index (UIQI), visual information fidelity (VIF), Frechet Inception Distance (FID), and translation accuracy. The experimental results indicate that the synthetic images generated either by the Cycle GAN or by the Ad CycleGAN have lower MSE and RMSE, and higher scores in PSNR, UIQI, and VIF in homogenous image translation (i.e., Y → Y) compared to the heterogenous image translation process (i.e., X → Y). The synthetic images by Ad CycleGAN through the heterogeneous image translation have significantly higher FID score compared to Cycle GAN (p < 0.01). The image translation accuracy of Ad CycleGAN is higher than that of Cycle GAN when normal images are converted to COVID-19 positive images (p < 0.01). Therefore, we conclude that the Ad CycleGAN with the independent criterion can improve the accuracy of GAN image translation. The new architecture has more control on image synthesis and can help address the common class imbalance issue in machine learning methods and artificial intelligence applications with medical images.

Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation.

Background: Large vision models (LVM) pretrained by large datasets have demonstrated their enormous capacity to understand visual patterns and capture semantic information from images. We proposed a novel method of knowledge domain adaptation with pretrained LVM for a low-cost artificial intelligence (AI) model to quantify the severity of SARS-CoV-2 pneumonia based on frontal chest X-ray (CXR) images. Methods: Our method used the pretrained LVMs as the primary feature extractor and self-supervised contrastive learning for domain adaptation. An encoder with a 2048-dimensional feature vector output was first trained by self-supervised learning for knowledge domain adaptation. Then a multi-layer perceptron (MLP) was trained for the final severity prediction. A dataset with 2599 CXR images was used for model training and evaluation. Results: The model based on the pretrained vision transformer (ViT) and self-supervised learning achieved the best performance in cross validation, with mean squared error (MSE) of 23.83 (95 % CI 22.67-25.00) and mean absolute error (MAE) of 3.64 (95 % CI 3.54-3.73). Its prediction correlation has the R 2 of 0.81 (95 % CI 0.79-0.82) and Spearman ρ of 0.80 (95 % CI 0.77-0.81), which are comparable to the current state-of-the-art (SOTA) methods trained by much larger CXR datasets. Conclusion: The proposed new method has achieved the SOTA performance to quantify the severity of SARS-CoV-2 pneumonia at a significantly lower cost. The method can be extended to other infectious disease detection or quantification to expedite the application of AI in medical research.

Noise-induced modality-specific pretext learning for pediatric chest X-ray image classification.

Introduction: Deep learning (DL) has significantly advanced medical image classification. However, it often relies on transfer learning (TL) from models pretrained on large, generic non-medical image datasets like ImageNet. Conversely, medical images possess unique visual characteristics that such general models may not adequately capture. Methods: This study examines the effectiveness of modality-specific pretext learning strengthened by image denoising and deblurring in enhancing the classification of pediatric chest X-ray (CXR) images into those exhibiting no findings, i.e., normal lungs, or with cardiopulmonary disease manifestations. Specifically, we use a VGG-16-Sharp-U-Net architecture and leverage its encoder in conjunction with a classification head to distinguish normal from abnormal pediatric CXR findings. We benchmark this performance against the traditional TL approach, viz., the VGG-16 model pretrained only on ImageNet. Measures used for performance evaluation are balanced accuracy, sensitivity, specificity, F-score, Matthew's Correlation Coefficient (MCC), Kappa statistic, and Youden's index. Results: Our findings reveal that models developed from CXR modality-specific pretext encoders substantially outperform the ImageNet-only pretrained model, viz., Baseline, and achieve significantly higher sensitivity (p < 0.05) with marked improvements in balanced accuracy, F-score, MCC, Kappa statistic, and Youden's index. A novel attention-based fuzzy ensemble of the pretext-learned models further improves performance across these metrics (Balanced accuracy: 0.6376; Sensitivity: 0.4991; F-score: 0.5102; MCC: 0.2783; Kappa: 0.2782, and Youden's index:0.2751), compared to Baseline (Balanced accuracy: 0.5654; Sensitivity: 0.1983; F-score: 0.2977; MCC: 0.1998; Kappa: 0.1599, and Youden's index:0.1327). Discussion: The superior results of CXR modality-specific pretext learning and their ensemble underscore its potential as a viable alternative to conventional ImageNet pretraining for medical image classification. Results from this study promote further exploration of medical modality-specific TL techniques in the development of DL models for various medical imaging applications.

Semantically redundant training data removal and deep model classification performance: A study with chest X-rays.

Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its performance scales up with the amount of training data. However, the data must also exhibit variety to enable improved learning. In medical imaging data, semantic redundancy, which is the presence of similar or repetitive information, can occur due to the presence of multiple images that have highly similar presentations for the disease of interest. Also, the common use of augmentation methods to generate variety in DL training could limit performance when indiscriminately applied to such data. We hypothesize that semantic redundancy would therefore tend to lower performance and limit generalizability to unseen data and question its impact on classifier performance even with large data. We propose an entropy-based sample scoring approach to identify and remove semantically redundant training data and demonstrate using the publicly available NIH chest X-ray dataset that the model trained on the resulting informative subset of training data significantly outperforms the model trained on the full training set, during both internal (recall: 0.7164 vs 0.6597, p<0.05) and external testing (recall: 0.3185 vs 0.2589, p<0.05). Our findings emphasize the importance of information-oriented training sample selection as opposed to the conventional practice of using all available training data.

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images.

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pretrained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.