Screening of electrode materials for ammonium ion batteries by high throughput calculation

Ammonium-ion batteries (AIBs) have attracted intense interest lately as promising energy storage systems due to their light weight, safe, inexpensive, and widely available advantages. It is of great significance to find a fast ammonium ion conductor for the electrode of AIBs that directly affects the electrochemical performance of the battery. Using high-throughput bond-valence calculation, we screened the electrode materials of AIBs with a low diffusion barrier from more than 8000 compounds in the ICSD database. Twenty-seven candidate materials were finally identified by the bond-valence sum method and density functional theory. Their electrochemical properties were further analyzed. Our results, which give the relationship between the structure and electrochemical properties of various important electrode materials which are suitable for AIBs development, may pave the way for next-generation energy storage systems.

Twenty-seven candidate materials which are suitable for ammonium ion battery electrodes, are identified by the bond-valence sum method and density functional theory from the ICSD database.     

44例肾移植术后多瘤病毒相关性肾病患者的临床病理特征和预后分析

目的  探讨肾移植术后多瘤病毒相关性肾病（PyVN）的临床病理学特征及预后。 方法  回顾性分析44例肾移植术后确诊PyVN患者的临床资料,分析患者穿刺原因及病理学诊断时间。按Banff 2018标准进行组织学分级,比较各级患者临床资料及病理特征。分析患者血、尿BK病毒DNA载量及移植肾功能。比较各组患者的预后情况,分析影响预后的危险因素。 结果  患者病理诊断PyVN距肾移植手术时间为16（8,29）个月,血清肌酐升高为穿刺的主要原因。44例患者中,PyVN 1级19例、2级21例、3级4例,各组光学显微镜下病毒包涵体阳性率差异无统计学意义（P=0.148）,2级患者炎症细胞浸润、间质纤维化、肾内多瘤病毒载量均多于或高于1级患者,3级患者炎症细胞浸润及肾内多瘤病毒载量多于或高于1级患者,3级患者肾内多瘤病毒载量多于或高于2级患者,差异均有统计学意义（均为P < 0.05/3）。确诊时39例患者具有血、尿BK病毒DNA载量检测记录,其中38例尿BK病毒阳性,30例血BK病毒阳性。患者确诊PyVN时血清肌酐较术后1个月升高,末次随访时血清肌酐较确诊PyVN时升高,差异均有统计学意义（P < 0.001、P=0.049）。不同级别PyVN患者术后1个月血清肌酐差异无统计学意义（P=0.554）,确诊时2级患者血清肌酐水平高于1级患者（P=0.007）。肾移植术后1、3、5年移植肾累积存活率分别为95%、69%、62%,不同分级PyVN间移植肾存活率差异有统计学意义,分级越高,存活率越低（P=0.014）。单因素和多因素Cox回归分析提示肾内多瘤病毒载量、确诊时血清肌酐水平为移植肾失功的独立危险因素（均为P < 0.05）。 结论  PyVN多发生在肾移植术后2年内,临床表现以血清肌酐升高、BK病毒血症和BK病毒尿症为主,术后常规监测BK病毒有利于早期诊断并保护移植肾,Banff 2018分级标准可有效预测移植肾预后。     

Canagliflozin primes antitumor immunity by triggering PD-L1 degradation in endocytic recycling

Understanding the regulatory mechanisms of PD-L1 expression in tumors provides key clues for improving immune checkpoint blockade efficacy or developing novel oncoimmunotherapy. Here, we showed that the FDA-approved sodium-glucose cotransporter-2 (SGLT2) inhibitor canagliflozin dramatically suppressed PD-L1 expression and enhanced T cell–mediated cytotoxicity. Mechanistic study revealed that SGLT2 colocalized with PD-L1 at the plasma membrane and recycling endosomes and thereby prevented PD-L1 from proteasome-mediated degradation. Canagliflozin disturbed the physical interaction between SGLT2 and PD-L1 and subsequently allowed the recognition of PD-L1 by Cullin3^SPOP E3 ligase, which triggered the ubiquitination and proteasome-mediated degradation of PD-L1. In mouse models and humanized immune-transformation models, either canagliflozin treatment or SGLT2 silencing significantly reduced PD-L1 expression and limited tumor progression — to a level equal to the PD-1 mAb — which was correlated with an increase in the activity of antitumor cytotoxic T cells. Notably, prolonged progression-free survival and overall survival curves were observed in the group of PD-1 mAb–treated patients with non–small cell lung cancer with high expression of SGLT2. Therefore, our study identifies a regulator of cell surface PD-L1, provides a ready-to-use small-molecule drug for PD-L1 degradation, and highlights a potential therapeutic target to overcome immune evasion by tumor cells.     

Construction of a prognostic model for radical esophagectomy based on immunohistochemical prognostic markers combined with clinicopathological factors

Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and lacks effective biomarkers to evaluate prognosis and treatment. Glycoprotein nonmetastatic melanoma protein B (GPNMB) is a protein highly expressed in ESCC tissues screened by isobaric tags for relative and absolute quantitation proteomics, which has significant prognostic value in a variety of malignant tumors, but its relationship with ESCC remains unclear. By immunohistochemical staining of 266 ESCC samples, we analyzed the relationship between GPNMB and ESCC. To explore how to improve the ability of ESCC prognostic assessment, we established a prognostic model of GPNMB and clinicopathological features. The results suggest that GPNMB expression is generally positive in ESCC tissues and is significantly associated with poorer differentiation, more advanced American Joint Council on Cancer (AJCC) stage, and higher tumor aggressiveness (P < .05). Multivariate Cox analysis indicated that GPNMB expression was an independent risk factor for ESCC patients. A total of 188 (70%) patients were randomly selected from the training cohort and the four variables were automatically screened by stepwise regression based on the AIC principle: GPNMB expression, nation, AJCC stage and nerve invasion. Through the weighted term, we calculate the risk score of each patient, and by drawing the receiver operating characteristic curve, we show that the model has good prognostic evaluation performance. The stability of the model was verified by test cohort. Conclusion: GPNMB is a prognostic marker consistent with the characteristics of tumor therapeutic targets. For the first time, we constructed a prognostic model combining immunohistochemical prognostic markers and clinicopathological features in ESCC, which showed higher prognostic efficacy than AJCC staging system in predicting the prognosis of ESCC patients in this region.     

Water dissociation at the water–rutile TiO2(110) interface from ab initio-based deep neural network simulations

The interaction of water with TiO₂ surfaces is of crucial importance in various scientific fields and applications, from photocatalysis for hydrogen production and the photooxidation of organic pollutants to self-cleaning surfaces and bio-medical devices. In particular, the equilibrium fraction of water dissociation at the TiO₂–water interface has a critical role in the surface chemistry of TiO₂, but is difficult to determine both experimentally and computationally. Among TiO₂ surfaces, rutile TiO₂(110) is of special interest as the most abundant surface of TiO₂’s stable rutile phase. While surface-science studies have provided detailed information on the interaction of rutile TiO₂(110) with gas-phase water, much less is known about the TiO₂(110)–water interface, which is more relevant to many applications. In this work, we characterize the structure of the aqueous TiO₂(110) interface using nanosecond timescale molecular dynamics simulations with ab initio-based deep neural network potentials that accurately describe water/TiO₂(110) interactions over a wide range of water coverages. Simulations on TiO₂(110) slab models of increasing thickness provide insight into the dynamic equilibrium between molecular and dissociated adsorbed water at the interface and allow us to obtain a reliable estimate of the equilibrium fraction of water dissociation. We find a dissociation fraction of 22 ± 6% with an associated average hydroxyl lifetime of 7.6 ± 1.8 ns. These quantities are both much larger than corresponding estimates for the aqueous anatase TiO₂(101) interface, consistent with the higher water photooxidation activity that is observed for rutile relative to anatase.

Water is ubiquitous in the environment and its interaction with metal oxide surfaces has a key role in processes that range from wetting, dissolution, and corrosion to photocatalytic reactions (1, 2). The relative stability of molecularly vs. dissociatively adsorbed species can be of critical importance in these processes and has been intensely debated even for the simplest, low-index surfaces.One oxide surface of major fundamental and practical interest is rutile TiO₂(110) (R-110 in the following), that is widely considered the prototypical metal oxide surface (3–9). Extensive investigations of gas-phase water adsorption on R-110 have established that oxygen vacancies—the most common defects on this surface—dissociate water and become hydroxylated (10–13), while molecularly adsorbed water is only ~0.035 eV more stable than dissociated water at regular surface sites (14). Moreover, a ~20% fraction of dissociated water has been reported for one adsorbed monolayer on R-110 at T = 210 K (15). However, information is scarce for the interface between R-110 and liquid water, which is the system more relevant to photocatalysis and other practical applications.Experiments based on synchrotron X-ray diffraction revealed water layering with partial dissociation of the adsorbed water on the R-110 surface (16–18). In other experiments, various ordered structures with (2 × 1) periodicity at the interface were reported, either on R-110 in water (19) or after exposure to liquid water (20), but such structures were found to be induced by adsorbed organic contaminants rather than interfacial water in subsequent studies (21). On the theoretical side, density functional theory (DFT)-based ab initio molecular dynamics (AIMD) simulations reported that water remains intact at the interface with defect-free R-110 (22), but this prediction could not explain the results of X-ray experiments (18).While AIMD has provided invaluable information on the interaction of water with metal oxide surfaces (9, 23, 24), the length and time scales accessible by ab initio simulations are often insufficient to characterize the properties of aqueous oxide interfaces (25–28). For example, the duration of AIMD simulations was found to be too short to allow a reliable prediction of the equilibrium fraction of water dissociation at the aqueous anatase TiO₂ (101) (A-101) interface (25). To overcome these limitations, computationally efficient interatomic potentials with the accuracy of ab initio electronic structure methods are needed. As shown by several recent studies, this goal can be achieved using ab initio-based deep neural networks (DNNs) to represent the potential energy surface of the system of interest (29–34). Still, constructing a general DNN potential for heterogeneous systems can be challenging (35), and in fact relatively few applications to aqueous metal oxide interfaces have so far been reported (27, 35–37).In this work, we have developed and applied ab initio-based DNNs to elucidate the structure and average degree of water dissociation at the aqueous R-110 interface. Specifically, we used the “Deep Potential” (DP) scheme by Zhang et al. (30, 31) to construct a DNN potential that accurately reproduces the DFT results for both isolated gas-phase water molecules and liquid water interacting with defect-free R-110. For this surface, an additional difficulty is the significant dependence of the DFT predictions on the R-110 slab thickness (22, 38). To address this issue and be able to extrapolate the results to large slab thickness, the DP was trained on slab models of different numbers of layers and later used to run nanosecond timescale simulations of the aqueous interfaces of several different, 4 to 16 O-Ti-O trilayers thick, R-110 slabs. From these simulations, we estimated an equilibrium water dissociation fraction of 22 ± 6% and, correspondingly, a free energy difference between dissociatively and molecularly adsorbed water at the R-110/water interface of 0.040 ± 0.007 eV. Comparison of these estimates for R-110 to analogous results for aqueous A-101 (37) shows differences in the behavior of interfacial water that can significantly affect the functional properties of these two systems.     

Clinical and survival outcomes of colectomy for transverse colon cancer in elderly patients

It remains controversial whether elderly patients with transverse colon cancer present worse prognoses. Our study utilized evidence from multi-center databases to evaluate the perioperative and oncology outcomes of radical resection of colon cancer in elderly and nonelderly patients. In this study, we analyzed 416 patients with transverse colon cancer who underwent radical surgery from January 2004 to May 2017, including 151 elderly (aged ≥ 65 years) and 265 nonelderly (aged < 65 years) patients. We retrospectively compared the perioperative and oncological outcomes between these 2 groups. The median follow-up in the elderly and nonelderly groups was 52 and 64 months, respectively. There were no significant differences in the overall survival (OS) (P = .300) and disease-free survival (DFS) (P = .380) between the elderly and nonelderly groups. However, the elderly group had longer hospital stays (P < .001), a higher complication rate (P = .027), and fewer lymph nodes harvested (P = .002). The N classification and differentiation were significantly associated with OS based on univariate analysis, and the N classification was an independent prognostic factor for OS based on multivariate analysis (P < .05). Similarly, the N classification and differentiation were significantly correlated with the DFS based on univariate analysis. However, multivariate analysis indicated that the N classification was an independent prognostic factor for DFS (P < .05). In conclusion, the survival and surgical outcomes in elderly patients were similar to nonelderly patients. The N classification was an independent factor for OS and DFS. Even though elderly patients with transverse colon cancer present a higher surgical risk than nonelderly patients, performing radical resection in elderly patients can be an appropriate choice for treatment.     

Use of machine learning models to predict in‐hospital mortality in patients with acute coronary syndrome

BackgroundCardiovascular diseases are a significant health burden with the prevalence increasing worldwide. Thus, a highly accurate assessment and prediction of death risk are crucial to meet the clinical demand. This study sought to develop and validate a model to predict in‐hospital mortality among patients with the acute coronary syndrome (ACS) using nonlinear algorithms.MethodsA total of 2414 ACS patients were enrolled in this study. All samples were divided into five groups for cross‐validation. The logistic regression (LR) model and XGboost model were applied to predict in‐hospital mortality. The results of two models were compared between the variable set by the global registry of acute coronary events (GRACE) score and the selected variable set.ResultsThe in‐hospital mortality rate was 3.5% in the dataset. Model performance on the selected variable set was better than that on GRACE variables: a 3% increase in area under the receiver operating characteristic (ROC) curve (AUC) for LR and 1.3% for XGBoost. The AUC of XGBoost is 0.913 (95% confidence interval [CI]: 0.910–0.916), demonstrating a better discrimination ability than LR (AUC = 0.904, 95% CI: 0.902–0.905) on the selected variable set. Almost perfect calibration was found in XGBoost (slope of predicted to observed events, 1.08; intercept, −0.103; p < .001).ConclusionsXGboost modeling, an advanced machine learning algorithm, identifies new variables and provides high accuracy for the prediction of in‐hospital mortality in ACS patients.     

An Innovative Approach to Prepare Liquid-Solid Dual-Phase Flowable Tritium Breeder with Low MHD Effect

In present paper, a novel flowable tritium breeder is prepared by mixing the Li₂TiO₃ micro-powders and liquid GaInSn alloy, where GaInSn alloy is used to simulate the fluid behaviors of lithium-based liquid tritium breeder, forming a type of composite characterized by liquid-solid dual phase. In detail, the effects of the volume fraction of ceramic micro-powders on viscosity and conductivity of the composite in magnetic field are the focus. The XRD results prove that the obtained Li₂TiO₃ micro-powders contained Li₂TiO₃ phase without impurities. The results shows that once the magnetic field intensity exceeds the critical value, the viscosity of liquid GaInSn metal becomes significantly greater than that of liquid-solid dual-phase composites. Furthermore, the addition of Li₂TiO₃ micro-powders could effectively reduce the magneto hydro dynamic (MHD) fluid effect, and the dual-phase composites exhibit comparatively lower flow resistance under the strong magnetic field. Moreover, the conductivity of the tritium breeder composites decreases rapidly with the addition of Li₂TiO₃ micro-powders. The MHD pressure-drop-increasing rate decreases with the increase of viscosity, which indicates that the addition of Li₂TiO₃ micro-powders effectively reduces the MHD effect. The conductivity of the composites increased slightly and then remained stable after static placing for several tens of minutes. The present investigation provides a novel insight into the fabrication strategy of tritium breeder materials with low MHD effect.