Designing Temperature-Insensitive Solvated Electrolytes for Low-Temperature Lithium Metal Batteries.

Lithium metal batteries face problems from sluggish charge transfer at interfaces, as well as parasitic reactions between lithium metal anodes and electrolytes, due to the strong electronegativity of oxygen donor solvents. These factors constrain the reversibility and kinetics of lithium metal batteries at low temperatures. Here, a nonsolvating cosolvent is applied to weaken the electronegativity of donor oxygen in ether solvents, enabling the participation of anionic donors in the solvation structure of Li+. This strategy significantly accelerates the desolvation process of Li+ and reduces the side effects of solvents on interfacial transport and stability. The designed anion-aggregated electrolyte has a unique temperature-insensitive solvation structure and enables lithium metal anodes to achieve a high average Coulombic efficiency at room temperature and -20 °C. A high-loading LiFePO4||Li cell exhibited high reversibility with a 100% capacity retention after 150 cycles at room temperature, -20, and -40 °C. The practical 1 Ah-level LiFePO4||Li pouch-cell delivered 81% and 61% of the capacity at room temperature when charged and discharged at -20 and -40 °C, respectively. This strategy of constructing temperature-insensitive solvation by electronegativity regulation offers a novel approach for developing electrolytes of low-temperature batteries.

Competitive Coordination of Sodium Ions for High-Voltage Sodium Metal Batteries with Fast Reaction Speed.

The unstable electrode-electrolyte interface and the narrow electrochemical window of normal electrolytes hinder the potential application of high-voltage sodium metal batteries. These problems are actually related to the solvation structure of the electrolyte, which is determined by the competition between cations coordinated with anions or solvent molecules. Herein, we design an electrolyte incorporating ethyl (2,2,2-trifluoroethyl) carbonate and fluoroethylene carbonate, which facilitates a pronounced level of cation-anion coordination within the solvation sheath by enthalpy changes to reduce the overall coordination of cation-solvents and increase sensitivity to salt concentration. Such an electrolyte regulated by competitive coordination leads to highly reversible sodium plating/stripping with extended cycle life and a high Coulombic efficiency of 98.0%, which is the highest reported so far in Na||Cu cells with ester-based electrolytes. Moreover, 4.5 V high-voltage Na||Na3V2(PO4)2F3 cells exhibit a high rate capability up to 20 C and an impressive cycling stability with an 87.1% capacity retention after 250 cycles with limited Na. The proposed strategy of solvation structure modification by regulating the competitive coordination of the cation provides a new direction to achieve stable sodium metal batteries with high energy density and can be further extended to other battery systems by controlling enthalpy changes of the solvation structure.

Improved discrimination of COVID-19 based on data enhancement technology and an information balance feature selection (INB) method.

The coronavirus disease (COVID-19) ravaged the world in late 2019 and posed a serious threat to human life and property destruction on a global scale. In this paper, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) method was selected for balancing the data sample, and an information balance feature selection (INB) method was first proposed to realize the accurate discrimination of COVID-19 saliva samples based on the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. The results of the experiment showed that the INB method obtained higher classification accuracy than the traditional feature selection methods in both the original spectrum and the second-order derivative spectrum, especially in the second-order derivative spectrum where all the indexes reached about 85 %. In addition, the combination of WGAN_GP data augmentation and the INB method resulted in an accuracy of 88.7 % for the original spectrum and even 90.6 % for the second-order derivative spectrum. According to these findings, classification research using the WGAN_GP data enhancement model may increase classification accuracy. Additionally, the ability to successfully separate COVID-19 indicates that the INB method to identify spectral data features is a workable method, which also offers a fresh viewpoint on feature selection.

Assessing the inflammatory severity of the terminal ileum in Crohn disease using radiomics based on MRI.

BACKGROUND: Evaluating inflammatory severity using imaging is essential for Crohn's disease, but it is limited by potential interobserver variation and subjectivity. We compared the efficiency of magnetic resonance index of activity (MaRIA) collected by radiologists and a radiomics model in assessing the inflammatory severity of terminal ileum (TI). METHODS: 121 patients were collected from two centers. Patients were divided into ulcerative group and mucosal remission group based on the TI Crohn's disease Endoscopic Severity Index. The consistency of bowel wall thickness (BWT), relative contrast enhancement (RCE), edema, ulcer, MaRIA and features of the region of interest between radiologists were described by weighted Kappa test and intraclass correlation coefficient (ICC), and developed receiver operating curve of MaRIA. The radiomics model was established using reproducible features of logistic regression based on arterial staging of T1WI sequences. Delong test was used to compare radiomics with MaRIA. RESULTS: The consistency between radiologists were moderate in BWT (ICC = 0.638), fair in edema (κ = 0.541), RCE (ICC = 0.461), MaRIA (ICC = 0.579) and poor in ulcer (κ = 0.271). Radiomics model was developed by 6 reproducible features (ICC = 0.93-0.96) and equivalent to MaRIA which evaluated by the senior radiologist (0.872 vs 0.883 in training group, 0.824 vs 0.783 in validation group, P = 0.847, 0.471), both of which were significantly higher than MaRIA evaluated by junior radiologist (AUC: 0.621 in training group, 0.557 in validation group, all, P < 0.05). CONCLUSION: The evaluation of inflammatory severity could be performed by radiomics objectively and reproducibly, and was comparable to MaRIA evaluated by the senior radiologist. Radiomics may be an important method to assist junior radiologists to assess the severity of inflammation objectively and accurately.

Changes in the anterior cingulate cortex in Crohn's disease: A neuroimaging perspective.

INTRODUCTION: Evidence suggests that Crohn's disease (CD) pathophysiology goes beyond the gastrointestinal tract and is also strongly associated with the brain. In particular, the anterior cingulate cortex (ACC), which plays an integral role in the first brain as part of the default mode network (DMN) and pain matrix, shows abnormalities using multiple neuroimaging modalities. This review summarizes nine related studies that investigated changes in the ACC using structural magnetic resonance imaging, resting-state functional magnetic resonance imaging, and magnetic resonance spectroscopy. METHODS: An extensive PubMed literature search was conducted from 1980 to August 2020. In a review of the articles identified, particular attention was paid to analysis methods, technical protocol characteristics, and specific changes in the ACC. RESULTS: In terms of morphology, a decrease in gray matter volume and cortical thickness was observed along with an increase in local gyrification index. In terms of function, functional connectivity (FC) within the DMN was increased. FC between the ACC and the amygdala was decreased. Higher amplitudes of low-frequency fluctuation and graph theory results, including connectivity strength, clustering coefficient, and local efficiency, were detected. In terms of neurotransmitter changes, the concentrations of glutamate increased along with a decrease in gamma-aminobutyric acid, providing a rational explanation for abdominal pain. These changes may be attributed to stress, pain, and negative emotions, as well as changes in gut microbiota. CONCLUSIONS: For patients with CD, the ACC demonstrates structural, functional, and metabolic changes. In terms of clinical findings, the ACC plays an important role in the onset of depression/anxiety and abdominal pain. Therefore, successful modulation of this pathway may guide treatment.

Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics.

Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients. Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1,305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator (LASSO) logistic regression and learning vector quantization (LVQ) methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Results: A two-feature radiomic signature was found significantly associated with LNM (p < 0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve of 0.850 [95% confidence interval (CI), 0.758-0.942] in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714-1.000) in internal validation cohort and 0.878 (95% CI, 0.696-1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram. Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.

A novel surgical predictive model for Chinese Crohn's disease patients.

Due to the complexity of Crohn's disease (CD), it is difficult to predict disease course with a single stratification factor or biomarker. A logistic regression (LR) model has been proposed by Guizzetti et al to stratify patients with CD-related surgical risk, which could help decision-making on disease treatment. However, there are no reports on relevant studies on Chinese population. The aim of the study is to present and validate a novel surgical predictive model to facilitate therapeutic decision-making for Chinese CD patients. Data was extracted from retrospective full-mode electronic medical records, which contained 239 CD patients and 1524 instances. Two sub-datasets were generated according to different attribute selection strategies, both of which were split into training and testing sets randomly. The imbalanced data in the training sets was addressed by synthetic minority over-sampling technique (SMOTE) algorithm before model development. Seven predictive models were employed using 5 popular machine learning algorithms: random forest (RF), LR, support vector machine (SVM), decision tree (DT) and artificial neural networks (ANN). The performance of each model was evaluated by accuracy, precision, F1-score, true negative (TN) rate, and the area under the receiver operating characteristic curve (AuROC). The result revealed that RF outperformed all other baseline models on both sub-datasets. The 10 leading risk factors for CD-related surgery returned from RF for attribute ranking were changes of radiology, presence of a fistula, presence of an abscess, no infliximab use, enteroscopy findings, C-reactive protein, abdominal pain, white blood cells, erythrocyte sedimentation rate and platelet count. The proposed machine learning model can accurately predict the risk of surgical intervention in Chinese CD patients, which could be used to tailor and modify the treatment strategies for CD patients in clinical practice.

[Correlation of MRI apparent diffusion coeffcient with molecular marker Ki-67 in gastric cancer].

OBJECTIVES: To study the correlation between MRI apparent diffusion coefficient (ADC) and expression of Ki-67 in gastric cancers, and to investigate the application of ADC value in diagnosing the malignance of gastric cancer. METHODS: A retrospective cohort analysis was performed on 87 gastric cancer patients who received MRI examination and radical resection at Zhejiang Provincial Hospital of Traditional Chinese Medicine from November 2014 to August 2015. All the postoperative resected samples were confirmed as gastric cancer. Preoperative MRI examination was performed by using Siemens 3.0-T Verio MRI with following parameters: section thickness 3 mm, gap 1 mm, matrix 182×320, field of view 40 cm. Plain scan was followed by T1-weighted fat suppression technique VIBE 3D(TR3.92/TE1.39,90degree) scans at arterial phase (the 30th second), portal venous phase (the 60th second), lag period (the 90th second), axial planes and coronal planes (the 180th second), and sagittal planes (the 210th second), respectively. ADC value of tumor was measured at b-factor of 800 s/mm2 and ADC map was generated from DWI data on the work station. The expression of Ki-67 in cancer tissue was detected by routine immunohistochemical (SP) staining after surgery. Correlation between ADC value and the expression of Ki-67 in gastric cancer was analyzed. RESULTS: Irregular thickening of the gastric wall and inhomogeneous enhancement of the tumor after injection of the contrast material appeared in gastric cancer. Gastric cancer tissue presented hyperintensity and normal gastric wall presented isointensity in DWI image (b=800 s/mm2). Compared with normal gastric tissue, mean ADC value of gastric cancer tissue was significant lower [(1.114±0.265)×10-3 mm2/s vs. (2.032±0.202)×10-3 mm2/s, t=26.209, P=0.000]. The ADC values of high-middle differentiation group, middle-low differentiation group, low differentiation group and signet ring cell carcinoma/mucinous adenocarcinoma group were (1.347±0.234)×10-3 mm2/s, (1.179±0.257)×10-3 mm2/s, (0.996±0.185)×10-3 mm2/s and (1.082±0.230)×10-3 mm2/s, respectively. The difference of mean ADC value among different tumor stages was significant(F=8.498, P=0.000). Along with the Ki-67 expression up-regulated, the ADC value decreased in cancer tissue. The Ki-67 expressions in cancer tissue was negatively correlated with cancer ADC values (r=-0.570, P=0.000). Furthermore, negative correlations of Ki-67 expressions with ADC values of high-middle differentiation group (r=-0.627, P=0.016), low differentiation group (r=-0.787, P=0.000) and signet ring cell carcinoma/mucinous adenocarcinoma group (r=-0.792, P=0.000) were observed respectively, while Ki-67 expression was not correlated with ADC value of middle-low differentiation group. CONCLUSION: The ADC value of gastric cancer can reflect the level of tumor differentiation, and is negatively correlated with Ki-67 expression in cancer tissues.