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.

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.

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.

The Transpeptidase Sortase A Binds Nucleic Acids and Mediates Mammalian Cell Labeling.

Wild-type sortase A is an important virulence factor displaying a diverse array of proteins on the surface of bacteria. This protein display relies on the transpeptidase activity of sortase A, which is widely engineered to allow protein ligation and protein engineering based on the interaction between sortase A and peptides. Here an unknown interaction is found between sortase A from Staphylococcus aureus and nucleic acids, in which exogenously expressed engineered sortase A binds oligonucleotides in vitro and is independent of its canonical transpeptidase activity. When incubated with mammalian cells, engineered sortase A further mediates oligonucleotide labeling to the cell surface, where sortase A attaches itself and is part of the labeled moiety. The labeling reaction can also be mediated by many classes of wild-type sortases as well. Cell surface GAG appears involved in sortase-mediated oligonucleotide cell labeling, as demonstrated by CRISPR screening. This interaction property is utilized to develop a technique called CellID to facilitate sample multiplexing for scRNA-seq and shows the potential of using sortases to label cells with diverse oligonucleotides. Together, the binding between sortase A and nucleic acids opens a new avenue to understanding the virulence of wild-type sortases and exploring the application of sortases in biotechnology.

A pan-KRAS degrader for the treatment of KRAS-mutant cancers.

KRAS mutations are highly prevalent in a wide range of lethal cancers, and these mutant forms of KRAS play a crucial role in driving cancer progression and conferring resistance to treatment. While there have been advancements in the development of small molecules to target specific KRAS mutants, the presence of undruggable mutants and the emergence of secondary mutations continue to pose challenges in the clinical treatment of KRAS-mutant cancers. In this study, we developed a novel molecular tool called tumor-targeting KRAS degrader (TKD) that effectively targets a wide range of KRAS mutants. TKD is composed of a KRAS-binding nanobody, a cell-penetrating peptide selectively targeting cancer cells, and a lysosome-binding motif. Our data revealed that TKD selectively binds to KRAS in cancer cells and effectively induces KRAS degradation via a lysosome-dependent process. Functionally, TKD suppresses tumor growth with no obvious side effects and enhances the antitumor effects of PD-1 antibody and cetuximab. This study not only provides a strategy for developing drugs targeting "undruggable" proteins but also reveals that TKD is a promising therapeutic for treating KRAS-mutant cancers.

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. Another data attribute is the inherent variety. It follows, therefore, that semantic redundancy, which is the presence of similar or repetitive information, would tend to lower performance and limit generalizability to unseen data. In medical imaging data, semantic redundancy can occur due to the presence of multiple images that have highly similar presentations for the disease of interest. Further, the common use of augmentation methods to generate variety in DL training may be limiting performance when applied to semantically redundant data. We propose an entropy-based sample scoring approach to identify and remove semantically redundant training data. We 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.

Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.

We proposed a self-supervised machine learning method to automatically rate the severity of pulmonary edema in the frontal chest X-ray radiographs (CXR) which could be potentially related to COVID-19 viral pneumonia. For this we use the modified radiographic assessment of lung edema (mRALE) scoring system. The new model was first optimized with the simple Siamese network (SimSiam) architecture where a ResNet-50 pretrained by ImageNet database was used as the backbone. The encoder projected a 2048-dimension embedding as representation features to a downstream fully connected deep neural network for mRALE score prediction. A 5-fold cross-validation with 2,599 frontal CXRs was used to examine the new model's performance with comparison to a non-pretrained SimSiam encoder and a ResNet-50 trained from scratch. The mean absolute error (MAE) of the new model is 5.05 (95%CI 5.03-5.08), the mean squared error (MSE) is 66.67 (95%CI 66.29-67.06), and the Spearman's correlation coefficient (Spearman ρ) to the expert-annotated scores is 0.77 (95%CI 0.75-0.79). All the performance metrics of the new model are superior to the two comparators (P<0.01), and the scores of MSE and Spearman ρ of the two comparators have no statistical difference (P>0.05). The model also achieved a prediction probability concordance of 0.811 and a quadratic weighted kappa of 0.739 with the medical expert annotations in external validation. We conclude that the self-supervised contrastive learning method is an effective strategy for mRALE automated scoring. It provides a new approach to improve machine learning performance and minimize the expert knowledge involvement in quantitative medical image pattern learning.

Increased expression of microRNA-17 predicts poor prognosis in human glioma.

AIM: To investigate the clinical significance of microRNA-17 (miR-17) expression in human gliomas. METHODS: Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was used to characterize the expression patterns of miR-17 in 108 glioma and 20 normal brain tissues. The associations of miR-17 expression with clinicopathological factors and prognosis of glioma patients were also statistically analyzed. RESULTS: Compared with normal brain tissues, miR-17 expression was significantly higher in glioma tissues (P < 0.001). In addition, the increased expression of miR-17 in glioma was significantly associated with advanced pathological grade (P = 0.006) and low Karnofsky performance score (KPS, P = 0.01). Moreover, Kaplan-Meier survival and Cox regression analyses showed that miR-17 overexpression (P = 0.008) and advanced pathological grade (P = 0.02) were independent factors predicting poor prognosis for gliomas. Furthermore, subgroup analyses showed that miR-17 expression was significantly associated with poor overall survival in glioma patients with high pathological grades (for grade III~IV: P < 0.001). CONCLUSIONS: Our data offer the convinced evidence that the increased expression of miR-17 may have potential value for predicting poor prognosis in glioma patients with high pathological grades, indicating that miR-17 may contribute to glioma progression and be a candidate therapeutic target for this disease.

Meta analysis of the curative effect of acupuncture on post-stroke depression.

OBJECTIVE: To systematically evaluate the curative effect of acupuncture on post-stroke depression (PSD). METHODS: The internet was used to retrieve the Wanfang Medical Data System, Chinese Periodical Net, the Weipu Information Resources System, PubMed and the Cochrane Library Database. Relevant articles, up to September 2010, were manually retrieved. These papers included studies that had performed random and semi-random control trials for the use of acupuncture to treat PSD. Fifteen random control tests involving 1096 patients were included in this study and individual cases, interventional measures and curative effects were extracted from this research. Grade methodological quality evaluation and meta-analysis were performed on these studies. RESULTS: Comparison between the acupuncture group and the Western medicine group for the curative rate on PSD revealed an OR of 1.48, 95% CI = [1.11 1.97] and P = 0.008. Comparison of obviously effective rate shows that OR = 1.39, 95% CI = [1.08 1.80] and P = 0.01. Comparison of effective rate shows that OR = 0.83, 95% CI = [0.631.09] and P = 0.18. CONCLUSION: Comparison between the acupuncture group and Western medicine group in treating PSD revealed that there is a statistical difference in curative rate and remarkably effective rate, but no difference in effective rate.

CycleGAN with Dynamic Criterion for Malaria Blood Cell Image Synthetization.

We present a cycle-consistent adversarial network (Cycle GAN) with dynamic criterion to synthesize blood cells parasitized by malaria plasmodia. The result shows 100% of the synthetic images are correctly classified by the pretrained classifier compared to 99.61% of the real images, 76.6% generated by the Cycle GAN without the dynamic criterion. The average score of Frechet Inception Distance (FID) of the generated images by the enhanced Cycle GAN is 0.0043 (Std=0.0005), which is significantly lower than the FID score of the variational autoencoder (VAE) model (0.0085 (Std=0.0007)). We conclude that the new Cycle GAN model with dynamic criterion can generate high quality malaria infected blood cell images with good diversity. The new method provides new augmentation technique to enhance the image diversity where the acquisition of well-annotated images is highly restricted, and to improve the robustness of medical image automatic processing by deep neural networks.

A SUMO1-Derived Peptide Targeting SUMO-Interacting Motif Inhibits α-Synuclein Aggregation.

The accumulation of α-synuclein amyloid fibrils in the brain is linked to Parkinson's disease and other synucleinopathies. The intermediate species in the early aggregation phase of α-synuclein are involved in the emergence of amyloid toxicity and considered to be the most neurotoxic. The N-terminal region flanking the non-amyloid-ß component domain of α-synuclein has been implicated in modulating its aggregation. Herein, we report the development of a SUMO1-derived peptide inhibitor (SUMO1(15-55)), which targets two SUMO-interacting motifs (SIMs) within this aggregation-regulating region and suppresses α-synuclein aggregation. Molecular modeling, site-directed mutagenesis, and binding studies are used to elucidate the mode of interaction, namely, via the binding of either of the two SIM sequences on α-synuclein to a putative hydrophobic binding groove on SUMO1(15-55). Subsequent studies show that SUMO1(15-55) also reduces α-synuclein-induced cytotoxicity in cell-based and Drosophila disease models.

Health-related quality of life in pilots of a Chinese commercial airline.

Health-related quality of life (HRQOL) is currently an important issue in the medical industry. However, data on HRQOL in commercial airline pilots are lacking. This study aimed to investigate HRQOL and its related factors in a sample of commercial pilots. A purposive sample of 373 participants was recruited from a Chinese Commercial Airline. The median (IQR) score for physical health, psychological health and social relationship were 64.3 (75-53.6), 62.5 (70.8-54.2) and 75 (75-58.3), respectively. The mean (SD) score for Environment was 62.2 (16). After controlling for demographics, the multiple linear regression analyses showed that physical activity, fruit intake and vegetable intake were positively correlated with HRQOL score (p < .05), while time-zone flights, smoking, alcohol drinking and being dyslipidemic showed a negative correlation with HRQOL score (p < .05). Healthcare providers should consider time-zone flights, behavioral factors and dyslipidemia when planning related health promotion and disease prevention programs for commercial pilots in the future.

Astrocytes constitute the major TNF-α-producing cell population in the infarct cortex in dMCAO rats receiving intravenous MSC infusion.

Recent studies report that inhibiting TNF-α might be a novel therapeutic strategy for managing brain ischemia. Our previous study reported that mesenchymal stem cell (MSC) transplantation could suppress TNF-α level in both serum and brain. However, the cell type(s) that contribute to the production of TNF-α during ischemia following MSC transplantation has not been well studied. In the present study, we found by fluorescent immunohistochemistry, that 7.95 ± 6.17% of TNF-α+ cells co-expressed Iba-1 in the infarct area of dMCAO rats, a majority of which were found to be CD68+ (activated microglia), suggesting that resident microglial population were not the major source of TNF-α expression. 68.49 ± 5.12% of the TNF-α+ cells in the infarct area could be labeled by GFAP, a specific marker for astrocytes, indicating that resident GFAP+ astrocytes might be the major source of TNF-α expression in the infarct area. In addition to the infarct area, the GFAP+/TNF-α+ double-positive astrocytes accounted for 73.68 ± 7.48% of the TNF-α+ cells in striatum and corpus callosum. The infiltrating cells, including monocytes and lymphocytes, were not the main source of TNF-α either. In response to MSC transplantation, the total TNF-α+ cells as well as the percentage of TNF-α-expressing astrocytes were significantly reduced in the infarct area, suggesting that MSC transplantation could suppress the expression of TNF-α by astrocytes. Taken together, the results demonstrated that resident astrocytes, but not microglia, were the major source of TNF-α expression and could be suppressed by MSC infusion.

Optimized acupuncture treatment (acupuncture and intradermal needling) for cervical spondylosis-related neck pain: a multicenter randomized controlled trial.

ABSTRACT: Cervical spondylosis (CS)-related neck pain is difficult to treat because of its degenerative nature. The aim of this 9-center, single-blinded, randomized controlled trial was to evaluate the efficacy of optimized acupuncture for CS-related neck pain. Participants who met the inclusion criteria were randomized to optimized, shallow, and sham acupuncture groups (1:1:1). The primary outcome was the change from baseline in the Northwick Park Neck Pain Questionnaire score at week 4. Participants were followed up until week 16. Of the 896 randomized participants, 857 received ≥1 intervention session; 280, 286, and 291 received optimized, shallow, and sham acupuncture, respectively. A total of 835 (93.2%) participants completed the study. At week 4, significant differences (P < 0.001) were observed in the changes in Northwick Park Neck Pain Questionnaire scores between the optimized acupuncture group and both the shallow {7.72 (95% confidence interval [CI], 5.57-9.86)} and sham acupuncture (10.38 [95% CI, 8.25-12.52]) groups. The difference in the scores at week 16 between the optimized acupuncture group and the shallow (8.84 [95% CI, 6.34-11.34]) and sham acupuncture (10.81 [95% CI, 8.32-13.30]) groups were significant. The center effect indicated wide variability in the treatment effects (Cohen's d = 0.01-2.19). Most SF-36 scores were higher in the optimized acupuncture group than those in the other groups. These results suggest that 4-week optimized acupuncture treatment alleviates CS-related neck pain and improves the quality of life, with the effects persisting for minimum 3 months. Therefore, acupuncture can have positive effects on CS-related neck pain, although the effect size may vary widely.