Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT.

Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) (P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) (P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) (P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Can Gender-Specific Renal and Visceral Fat Be Evaluated by CT Predict Fuhrman Nuclear Classification of Clear Cell Renal Cell Carcinoma?

BACKGROUND: Evidence of the association between obesity and renal cell carcinoma progression is contradictory. The effects of renal cell carcinoma on fat distribution are still unknown. OBJECTIVE: The goal of this study was to determine the ability of various forms of fat deposition to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma [ccRCC]. METHODS: This retrospective study included 320 patients with pathologically proven ccRCC [215 men and 105 women; 263 low-grade ccRCC and 57 highgrade ccRCC]. Based on computed tomography scans, adipose tissue in various body regions was classified into the perirenal fat area [PFA], visceral fat area [VFA], total fat area [TFA], subcutaneous fat area [SFA], and hepatic steatosis [HS]. Subsequently, the relative VFA [rVFA] was computed. Age, sex, body mass index, and maximal tumor diameter were also regarded as clinical factors. Univariate and multivariate logistic regression studies were conducted to evaluate whether there was an association between body fat composition and the Fuhrman classification and whether it was related to gender. RESULTS: After correcting for age, males with low-grade ccRCC exhibited higher TFA [257.6 vs. 203.0, p = 0.002], VFA [151.6 vs.115.5, p = 0.007], SFA [106.0 vs. 87.5, p = 0.015], PFA [55.1 vs. 30.4, p < 0.001], and HS [18% vs. 0%, p = 0.031] than those with high-grade ccRCC. There was no significant difference among rVFA in males. In females, there was no significant difference in any of the parameters. VFA and PFA remained independent predictors for high-grade ccRCC in males in both the monovariate [VFA: odds ratio [OR] 0.992, 95% confidence interval [CI] 0.987-0.997, p = 0.004; PFA: OR 0.949, 95% CI 0.930-0.970, p < 0.001] and multivariate [VFA: OR 1.028, 95% CI 1.001-1.074, p < 0.001; PFA: OR 0.878, 95% CI 0.833-0.926, p < 0.001] models. CONCLUSION: Gender-specific adipose tissue in different locations demonstrated varied values for predicting high-grade ccRCC and may be utilized as an independent predictor of high-grade ccRCC in male patients.

Realizing dendrite-free lithium deposition with three-dimensional soft-rigid nanofiber interlayers.

Lithium (Li) metal is regarded as the most desirable anode candidates for high-energy-density batteries by virtue of its lowest redox potential and ultrahigh theoretical specific capacity. However, uncontrollable Li dendritic growth, infinite volume variation and unstable solid electrolyte interface (SEI) ineluctably plague its commercialization process. Herein, the three-dimensional (3D) nanofiber functional layers with synergistic soft-rigid feature, consisting of tin oxide (SnO2)-anchored polyvinylidene fluoride (PVDF) nanofibers, are directly electrospun on copper current collector. This strategy can effectively regulate uniform Li deposition and strengthen SEI stability through the dual effect of physical accommodation and chemical ionic intervention. On the one hand, the nanofiber interlayers with excellent electrolyte affinity and well-distributed Li+ transport pathways can promote uniform Li+ flux distribution and large-size Li deposition. On the other hand, the rigid SnO2 contributes to reducing Li nucleation overpotential and stabilizing SEI layer assisted by its spontaneous reaction with Li. As a result, the smooth and dense Li deposition is achieved by such soft-rigid nanofiber interlayers, thereby extending the cycling life and improving the safety application of Li metal batteries. This work offers a new route for efficient protection of Li metal anodes and brings a new inspiration for developing high-energy-density Li metal batteries.

Esophageal cancer detection via non-contrast CT and deep learning.

Background: Esophageal cancer is the seventh most frequently diagnosed cancer with a high mortality rate and the sixth leading cause of cancer deaths in the world. Early detection of esophageal cancer is very vital for the patients. Traditionally, contrast computed tomography (CT) was used to detect esophageal carcinomas, but with the development of deep learning (DL) technology, it may now be possible for non-contrast CT to detect esophageal carcinomas. In this study, we aimed to establish a DL-based diagnostic system to stage esophageal cancer from non-contrast chest CT images. Methods: In this retrospective dual-center study, we included 397 primary esophageal cancer patients with pathologically confirmed non-contrast chest CT images, as well as 250 healthy individuals without esophageal tumors, confirmed through endoscopic examination. The images of these participants were treated as the training data. Additionally, images from 100 esophageal cancer patients and 100 healthy individuals were enrolled for model validation. The esophagus segmentation was performed using the no-new-Net (nnU-Net) model; based on the segmentation result and feature extraction, a decision tree was employed to classify whether cancer is present or not. We compared the diagnostic efficacy of the DL-based method with the performance of radiologists with various levels of experience. Meanwhile, a diagnostic performance comparison of radiologists with and without the aid of the DL-based method was also conducted. Results: In this study, the DL-based method demonstrated a high level of diagnostic efficacy in the detection of esophageal cancer, with a performance of AUC of 0.890, sensitivity of 0.900, specificity of 0.880, accuracy of 0.882, and F-score of 0.891. Furthermore, the incorporation of the DL-based method resulted in a significant improvement of the AUC values w.r.t. of three radiologists from 0.855/0.820/0.930 to 0.910/0.955/0.965 (p = 0.0004/<0.0001/0.0068, with DeLong's test). Conclusion: The DL-based method shows a satisfactory performance of sensitivity and specificity for detecting esophageal cancers from non-contrast chest CT images. With the aid of the DL-based method, radiologists can attain better diagnostic workup for esophageal cancer and minimize the chance of missing esophageal cancers in reading the CT scans acquired for health check-up purposes.

Electrocatalytic Degradation of Phenolic Wastewater Using a Zero-Gap Flow-Through Reactor Coupled with a 3D Ti/RuO2-TiO2@Pt Electrode.

In this study, the performance of a zero-gap flow-through reactor with three-dimensional (3D) porous Ti/RuO2-TiO2@Pt anodes was systematically investigated for the electrocatalytic oxidation of phenolic wastewater, considering phenol and 4-nitrophenol (4-NP) as the target pollutants. The optimum parameters for the electrochemical oxidation of phenol and 4-NP were examined. For phenol degradation, at an initial concentration of 50 mg/L, initial pH of 7, NaCl concentration of 10.0 g/L, current density of 10 mA/cm2, and retention time of 30 min, the degradation efficiency achieved was 95.05%, with an energy consumption of 15.39 kWh/kg; meanwhile, for 4-NP, the degradation efficiency was 98.42% and energy consumption was 19.21 kWh/kg (at an initial concentration of 40 mg/L, initial pH of 3, NaCl concentration of 10.0 g/L, current density of 10 mA/cm2, and retention time of 30 min). The electrocatalytic oxidation of phenol and 4-NP conformed to the pseudo-first-order kinetics model, and the k values were 0.2562 min-1 and 0.1736 min-1, respectively, which are 1.7 and 3.6-times higher than those of a conventional electrolyzer. Liquid chromatography-mass spectrometry (LC-MS) was used to verify the intermediates formed during the degradation of phenol or 4-NP and a possible degradation pathway was provided. The extremely narrow electrode distance and the flow-through configuration of the zero-gap flow-through reactor were thought to be essential for its lower energy consumption and higher mass transfer efficiency. The zero-gap flow-through reactor with a novel 3D porous Ti/RuO2-TiO2@Pt electrode is a superior alternative for the treatment of industrial wastewater.

Mucin-producing tumors of the ovary--preoperative differentiation between metastatic ovarian mucinous carcinoma and primary mucinous malignant tumors.

OBJECTIVE: To investigate the clinical and magnetic resonance imaging (MRI) features for preoperatively discriminating  primary ovarian mucinous malignant tumors (POMTs) and metastatic mucinous carcinomas involving the ovary (MOMCs). METHODS: This retrospective multicenter study enrolled 61 patients with 22 POMTs and 49 MOMCs, which were pathologically proved between November 2014 to Jane 2023. The clinical and MRI features were evaluated and compared between POMTs and MOMCs. Univariate and multivariate analyses were performed to identify the significant variables between the two groups, which were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: 35.9% patients with MOMCs were discovered synchronously with the primary carcinomas; 25.6% patients with MOMCs were bilateral, and all of the patients with POMTs were unilateral. The biomarker CEA was significantly different between the two groups (p = 0.002). There were significant differences in the following MRI features: tumor size, configuration, enhanced pattern, the number of cysts, honeycomb sign, stained-glass appearance, ascites, size diversity ratio, signal diversity ratio. The locular size diversity ratio (p = 0.005, OR = 1.31), and signal intensity diversity ratio (p = 0.10, OR = 4.01) were independent predictors for MOMCs. The combination of above independent criteria yielded the largest area under curve of 0.922 with a sensitivity of 82.3% and specificity of 88.9%. CONCLUSIONS: Patients with MOMCs were more commonly bilaterally and having higher levels of CEA, but did not always had a malignant tumor history. For ovarian mucin-producing tumors, the uniform locular sizes and signal intensities were more predict MOMCs.

Effects of Wheat Oligopeptide on the Baking and Retrogradation Properties of Bread Rolls: Evaluation of Crumb Hardness, Moisture Content, and Starch Crystallization.

Delaying the deterioration of bakery goods is necessary in the food industry. The objective of this study was to determine the effects of wheat oligopeptide (WOP) on the qualities of bread rolls. The effects of WOP on the baking properties, moisture content, and starch crystallization of rolls during the storage process were investigated in this study. The results showed that WOP effectively improved the degree of gluten cross-linking, thereby improving the specific volume and the internal structure of rolls. The FTIR and XRD results showed that the addition of WOP hindered the formation of the starch double helix structure and decreased its relative crystallinity. The DSC results revealed a decrease in the enthalpy change (ΔH) from 0.812 to 0.608 J/g after 7 days of storage with 1.0% WOP addition, further indicating that WOP reduced the availability of water for crystal lattice formation and hindered the rearrangement of starch molecules. The addition of WOP also improved the microstructure of the rolls that were observed using SEM analysis. In summary, WOP is expected to be an effective natural additive to inhibit starch staling and provide new insights into starchy food products.

Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis.

This study proposes a recognition model for different drying methods of grain using hyperspectral imaging technology (HSI) and multivariate analysis. Fresh harvested grain samples were dried using three different methods: rotating ventilation drying, mechanical drying, and natural drying. Hyperspectral images of the samples were collected within the 388-1065 nm band range. The spectral features of the samples were extracted using principal component analysis (PCA), while the texture features were extracted using second-order probability statistical filtering. Partial least squares regression (PLSR) drying models with different characteristics were established. At the same time, a BPNN (Back-propagation neural network, BPNN) based on spectral texture fusion features was established to compare the recognition effects of different models. Texture analysis indicated that the mean-image had the clearest contour, and the texture characteristics of mechanical drying were smaller than those of rotating ventilation drying and natural drying. The BPNN model established using spectral-texture feature variables showed the best performance in distinguishing grain in different drying modes, with a prediction model obtained based on the correlation coefficients of special variables. The spectral and texture feature values were fused for pseudo-color visualization expression, and the three drying methods of grain showed different colors. This study provides a reference for non-destructive and rapid detection of grain with different drying methods.

Analysis of factors related to osteoporotic vertebral fracture in prostate cancer patients.

OBJECTIVE: This study was aimed at exploring the osteoporotic vertebral fracture rate and the related causal factors in prostate cancer patients before and after treatment. METHODS: One hundred prostate cancer patients were recruited in this study. One hundred men without prostate cancer history were selected as the control group. The study was approved by the Medical Ethics Committee under Ethics number B2021-373R and the requirement for the informed consent was waived. The T4-L1 vertebral body of the case group and the control group before and after treatment was evaluated according to Genant's semi-quantitative method. The difference in vertebral body fracture rate between the case group and the control group and the changes in vertebral body fracture rate before and after treatment among the case group were compared. They were grouped according to age, body mass index (BMI), prostate-specific antigen (PSA) levels, Gleason grade, and androgen deprivation therapy (ADT). Univariate and multivariate logistic regression models were used to determine the factors significantly associated with vertebral fracture rate in prostate cancer patients. RESULTS: The prevalence of vertebral fracture was 16% and 31% in prostate cancer patients before and after treatment, respectively, and 29% in the control group. The vertebral fracture rate of the patients before treatment significantly differed that of the control group and the patients after treatment. Univariate analysis showed that age, PSA levels, and treatment parameters were the significant influencing factors of vertebral fracture rates. Multivariate logistic regression analysis showed that age was the main influencing factor of vertebral fracture rates. CONCLUSION: Osteoporotic vertebral fractures in patients with prostate cancer was associated with many factors. And the incidence of vertebral fracture in prostate cancer patients after ADT was significantly higher than that before treatment.

Liver fat volume fraction measurements based on multi-material decomposition algorithm in patients with nonalcoholic fatty liver disease: the influences of blood vessel, location, and iodine contrast.

BACKGROUND: In recent years, spectral CT-derived liver fat quantification method named multi-material decomposition (MMD) is playing an increasingly important role as an imaging biomarker of hepatic steatosis. However, there are various measurement ways with various results among different researches, and the impact of measurement methods on the research results is unknown. The aim of this study is to evaluate the reproducibility of liver fat volume fraction (FVF) using MMD algorithm in nonalcoholic fatty liver disease (NAFLD) patients when taking blood vessel, location, and iodine contrast into account during measurement. METHODS: This retrospective study was approved by the institutional ethics committee, and the requirement for informed consent was waived because of the retrospective nature of the study. 101 patients with NAFLD were enrolled in this study. Participants underwent non-contrast phase (NCP) and two-phase enhanced CT scanning (late arterial phase (LAP) and portal vein phase (PVP)) with spectral mode. Regions of interest (ROIs) were placed at right posterior lobe (RPL), right anterior lobe (RAL) and left lateral lobe (LLL) to obtain FVF values on liver fat images without and with the reference of enhanced CT images. The differences of FVF values measured under different conditions (ROI locations, with/without enhancement reference, NCP and enhanced phases) were compared. Friedman test was used to compare FVF values among three phases for each lobe, while the consistency of FVF values was assessed between each two phases using Bland-Altman analysis. RESULTS: Significant difference was found between FVF values obtained without and with the reference of enhanced CT images. There was no significant difference about FVF values obtained from NCP images under the reference of enhanced CT images between any two lobes or among three lobes. The FVF value increased after the contrast injection, and there were significant differences in the FVF values among three scanning phases. Poor consistencies of FVF values between each two phases were found in each lobe by Bland-Altman analysis. CONCLUSION: MMD algorithm quantifying hepatic fat was reproducible among different lobes, while was influenced by blood vessel and iodine contrast.

Imaging Characteristics of Clear Cell Papillary Renal Cell Carcinoma: Identifying the Sheep in Wolf's Clothing.

OBJECTIVE: This study aimed to describe the characteristics of computed tomography (CT) and magnetic resonance imaging (MRI) of clear cell papillary renal cell carcinoma (CCPRCC). METHODS: This retrospective study comprised 27 patients diagnosed with 29 tumors of CCPRCC. The study was approved by the Medical Ethics Committee and the requirement for the informed consent was waived. The inclusion criteria stipulated pathology-confirmed CCPRCCs with at least one preoperative imaging examination, including CT or MRI. Two experienced radiologists independently analyzed the imaging characteristics, including size, location, growth mode, morphology, texture, density, and enhancement pattern. Paired t-test was used to compare differences in CT Hounsfield unit values and apparent diffusion coefficient (ADC) imaging between the tumor and the renal cortex. RESULTS: The mean age of the 27 patients was 57.0 ± 14.2 years. Nineteen patients underwent CT, while 12 underwent MRI (There are 4 patients underwent not only CT but also MRI). Among the cases, 26 (96%) were single, and 1 (4%) was multiple, consisting of three lesions. Out of the 29 tumors, 15 (52%) were located in the left kidney and 14 (48%) in the right kidney. The mean tumor diameter was 3.3 ± 1.7 cm. Furthermore, 19 (66%), 3 (10%), and 7 (24%) tumors were solid, cystic, mixed solid, and cystic type, respectively. The growth mode was endogenous and exogenous in 8 (28%) and 21 (72%) tumors, respectively. The tumor shape was irregular and round in 5 (17%) and 24 (83%) tumors, respectively. The CT value of the tumor was approximately 33.2 ± 9.8 HU, which was not significantly different from that of the renal cortex(31.1 ± 6.3HU)(p = 0.343). Furthermore, 7 (24%), 12 (41%), and 3 (10%) had calcification, cystic degeneration, and hemorrhage, respectively. In 12 tumors, hypointense and hyperintense were predominant on T1 and T2-weighted images, respectively. The tumor capsule was found at the edge of 12 tumors. The average ADC value of the tumor (1.54 ± 0.74 × 10-3 mm2/s) and that of the renal cortex(1.68 ± 0.63×10-3mm2 /s) was not statistically significantly different (p = 0.260). The enhancement scanning revealed "wash-in and wash-out" enhancement in 19 (68%) tumors, continuous or progressive enhancement in 6 (21%) tumors, and enhanced cystic wall and central separation in 3 (11%) tumors. CONCLUSION: CCPRCC occurs more likely in middle-aged and elderly individuals, and the tumor is prone to cystic degeneration, with rare bleeding and calcification, and no obvious limitation on MRI diffusion-weighted imaging, which enhancement form performs as mainly "wash-in and washout," but the final diagnosis depends on histopathology.

A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts extensive research interests. Recent deep learning has shown its great potential in improving image quality and reconstruction speed. Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network, called JDSI. During the image artifacts removal, it gradually provides more faithful sensitivity maps with high-frequency information, leading to improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. Results on in vivo datasets and radiologist reader study demonstrate that, for both calibration-based and calibrationless reconstruction, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the acceleration factor is high. Additionally, JDSI owns nice robustness to patients and autocalibration signals.

Efficient and Selective Electrochemical Nitrate Reduction to N2 Using a Flow-Through Zero-Gap Electrochemical Reactor with a Reconstructed Cu(OH)2 Cathode: Insights into the Importance of Inter-Electrode Distance.

Electrochemically converting nitrate, a widely distributed nitrogen contaminant, into harmless N2 is a feasible and environmentally friendly route to close the anthropogenic nitrogen-based cycle. However, it is currently hindered by sluggish kinetics and low N2 selectivity, as well as scarce attention to reactor configuration. Here, we report a flow-through zero-gap electrochemical reactor that shows a high performance of nitrate reduction with 100% conversion and 80.36% selectivity of desired N2 in the chlorine-free system at 100 mg-N·L-1 NO3- while maintaining a rapid reduction kinetics of 0.07676 min-1. More importantly, the mass transport and current utilization efficiency are significantly improved by shortening the inter-electrode distance, especially in the zero-gap electrocatalytic system where the current efficiency reached 50.15% at 5 mA·cm-2. Detailed characterizations demonstrated that during the electroreduction process, partial Cu(OH)2 on the cathode surface was reconstructed into stable Cu/Cu2O as the active phase for efficient nitrate reduction. In situ characterizations revealed that the highly selective *NO to *N conversion and the N-N coupling step played crucial roles during the selective reduction of NO3- to N2 in the zero-gap electrochemical system. In addition, theoretical calculations demonstrated that improving the key intermediate *N coverage could effectively facilitate the N-N coupling step, thereby promoting N2 selectivity. Moreover, the environmental and economic benefits and long-term stability shown by the treatment of real nitrate-containing wastewater make our proposed electrocatalytic system more attractive for practical applications.

MRI for Hepatitis B-Associated Intrahepatic Cholangiocarcinoma: A Multicenter Comparative Study.

BACKGROUND: The diagnosis of intrahepatic cholangiocarcinoma (iCCA) is challenging in hepatitis B virus (HBV)-infected patients, due to the overlapping clinical manifestations and atypical imaging patterns compared to patients without HBV. PURPOSE: To investigate the preoperative imaging characteristics of iCCA in patients with HBV in comparison to those without HBV. STUDY TYPE: Retrospective. SUBJECTS: 431 patients with histopathologically confirmed iCCA (143 HBV-positive and 288 HBV-negative patients) were retrospectively enrolled from three institutes, and patients were allocated to the training (n = 302) and validation (n = 129) cohorts from different institutes or time period; 100 matching HBV-positive hepatocellular carcinoma (HCC) patients were also enrolled. FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T, including T1- and T2-weighted, diffusion-weighted and dynamic gadopentetate dimeglumine-enhanced imaging. ASSESSMENT: Clinical and MRI features were analyzed and compared between HBV-positive and HBV-negative patients with iCCA, and between HBV-positive patients with iCCA and HCC. STATISTICAL TESTS: Univariate and multivariate logistic regression analyses with odds ratio (OR) to identify independent features for discriminating HBV-associated iCCA. Diagnostic model generation by incorporating independent features, and the performance for discrimination was evaluated by receiver operating characteristics with the area under the curve (AUC) and 95% confidence interval (CI). AUCs were compared by the DeLong's method. A P-value <0.05 was considered statistically significant. RESULTS: Compared to patients without HBV, washout or degressive enhancement pattern (OR = 51.837), well-defined tumor margin (OR = 8.758) and no peritumoral bile duct dilation (OR = 4.651) were independent significant features for discriminating HBV-associated iCCAs. All these features were also the predominant MRI manifestations for HBV-associated HCC. The combined index showed an AUC of 0.798 (95% CI 0.748-0.842) in the training cohort and an AUC of 0.789 (95% CI 0.708-0.856) in the validation cohort for discrimination. The sensitivity, specificity, and accuracy were all >70%, which was superior to each single feature alone in both cohorts. [Correction added after first online publication on 29 June 2023. The Field Strength/Sequence has been updated from 5-T to 1.5-T.] DATA CONCLUSION: Preoperative MRI may help to discriminate HBV-associated iCCA. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY STAGE: 2.

In Situ Forming Gel Polymer Electrolyte for High Energy-Density Lithium Metal Batteries.

In situ forming gel polymer electrolyte (GPE) is one of the most feasible ways to improve the safety and cycle performances of lithium metal batteries with high energy density. However, most of the in situ formed GPEs are not compatible with high-voltage cathode materials. Here, this work provides a novel strategy to in situ form GPE based on the mechanism of Ritter reaction. The Ritter reaction in liquid electrolyte has the advantage of appropriate reaction temperature and no additional additives. The polymer chains are cross-linked by amide groups with the formation of GPE with superior electrochemical properties. The GPE has high ionic conductivity (1.84 mS cm-1 ), wide electrochemical stability window (>5.25 V) and high lithium ion transference number (≈0.78), compatible with high-voltage cathode materials. The Li|LiNi0.6 Co0.2 Mn0.2 O2 batteries with in situ formed GPE show excellent long-term cycle stability (93.4%, 300 cycles). The density functional theory calculation and X-ray photoelectron spectroscopy results verify that the amide and nitrile groups are beneficial for stabilizing cathode structure and promoting uniform Li deposition on Li anode. Furthermore, the in situ formed GPE exhibits excellent electrochemical performance in Graphite|LiMn2 O4 and Graphite|LiNi0.5 Co0.2 Mn0.3 O2 pouch batteries. This approach is adaptable to current battery technologies, which will be sure to promote the development of high energy-density lithium-ion batteries.

A multi-center diagnostic system for intrahepatic mass-forming cholangiocarcinoma based on preoperative MRI and clinical features.

OBJECTIVES: To establish a non-invasive diagnostic system for intrahepatic mass-forming cholangiocarcinoma (IMCC) via decision tree analysis. METHODS: Totally 1008 patients with 504 pathologically confirmed IMCCs and proportional hepatocellular carcinomas (HCC) and combined hepatocellular cholangiocarcinomas (cHCC-CC) from multi-centers were retrospectively included (internal cohort n = 700, external cohort n = 308). Univariate and multivariate logistic regression analyses were applied to evaluate the independent clinical and MRI predictors for IMCC, and the selected features were used to develop a decision tree-based diagnostic system. Diagnostic efficacy of the established system was calculated by the receiver operating characteristic curve analysis in the internal training-testing and external validation cohorts, and also in small lesions ≤ 3 cm. RESULTS: Multivariate analysis revealed that female, no chronic liver disease or cirrhosis, elevated carbohydrate antigen 19-9 (CA19-9) level, normal alpha-fetoprotein (AFP) level, lobulated tumor shape, progressive or persistent enhancement pattern, no enhancing tumor capsule, targetoid appearance, and liver surface retraction were independent characteristics favoring the diagnosis of IMCC over HCC or cHCC-CC (odds ratio = 3.273-25.00, p < 0.001 to p = 0.021). Among which enhancement pattern had the highest weight of 0.816. The diagnostic system incorporating significant characteristics above showed excellent performance in the internal training (area under the curve (AUC) 0.971), internal testing (AUC 0.956), and external validation (AUC 0.945) cohorts, as well as in small lesions ≤ 3 cm (AUC 0.956). CONCLUSIONS: In consideration of the great generalizability and clinical efficacy in multi-centers, the proposed diagnostic system may serve as a non-invasive, reliable, and easy-to-operate tool in IMCC diagnosis, providing an efficient approach to discriminate IMCC from other HCC-containing primary liver cancers. CLINICAL RELEVANCE STATEMENT: This study established a non-invasive, easy-to-operate, and explainable decision tree-based diagnostic system for intrahepatic mass-forming cholangiocarcinoma, which may provide essential information for clinical decision-making. KEY POINTS: • Distinguishing intrahepatic mass-forming cholangiocarcinoma (IMCC) from other primary liver cancers is important for both treatment planning and outcome prediction. • The MRI-based diagnostic system showed great performance with satisfying generalization ability in the diagnosis and discrimination of IMCC. • The diagnostic system may serve as a non-invasive, easy-to-operate, and explainable tool in the diagnosis and risk stratification for IMCC.

CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis.

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectrum plots and metabolite quantification, the spread of clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: (1) Automatically statistical analysis to find biomarkers for diseases; (2) Consistency verification between the classic and artificial intelligence quantification algorithms; (3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, data of both healthy subjects and patients with mild cognitive impairment are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing at least two years of free access and service. If you are interested, please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.

Value of T1 Mapping in the Non-invasive Assessment of Renal Pathologic Injury for Chronic Kidney Disease Patients.

PURPOSE: The objective of this study was to evaluate renal function and pathologic injury in chronic kidney disease (CKD) using T1 mapping. METHODS: We recruited fifteen healthy volunteers (HV) and seventy-five CKD patients to undergo T1 mapping examination, and renal parenchymal T1 values were measured. Spearman correlation analysis was used to evaluate the relevance between the pathologic injury score, estimated glomerular filtration rate (eGFR), and renal parenchymal T1 values. The diagnostic efficiency of T1 value in evaluating renal pathologic impairment was assessed. RESULTS: In all subjects, renal cortical T1 value was remarkably lower than renal medullary T1 value (P < 0.01). The renal medullary T1 value of HV was considerably lower than that of CKD patients in all stages (P < 0.05). The T1 values were negatively correlated with eGFR (cortex, r = -0.718; medulla, r = -0.645). The T1 values were positively correlated with glomerular injury score (cortex, r = 0.692; medulla, r = 0.632), tubulointerstitial injury score (cortex, r = 0.758; medulla, r = 0.690) (all P < 0.01). The area under the curve (AUC) of renal cortical and medullary T1 values were 0.914 and 0.880 to distinguish moderate-severe from mild renal injury groups. To differentiate mild renal injury group from control group, the AUC values of renal cortical and medullary T1 values were 0.879 and 0.856. CONCLUSION: T1 mapping has potential application value in non-invasively assessing renal pathologic injury in CKD.

Targeted IFNγ induction by a genetically engineered Salmonella typhimurium is the key to the liver metastasis inhibition in a mouse model of pancreatic neuroendocrine tumor.

Background: Liver metastasis is one of the primary causes of death for the patients with pancreatic neuroendocrine tumors (PNETs). However, no curative therapy has been developed so far. Methods: The anti-tumor efficacy of a genetically engineered tumor-targeting Salmonella typhimurium YB1 was evaluated on a non-functional INR1G9 liver metastasis model. Differential inflammatory factors were screened by Cytometric Bead Array. Antibody depletion assay and liver-targeted AAV2/8 expression vector were used for functional evaluation of the differential inflammatory factors. Results: We demonstrated that YB1 showed significant anti-tumor efficacy as a monotherapy. Since YB1 cannot infect INR1G9 cells, its anti-tumor effect was possibly due to the modulation of the tumor immune microenvironment. Two inflammatory factors IFNγ and CCL2 were elevated in the liver after YB1 administration, but only IFNγ was found to be responsible for the anti-tumor effect. Liver-targeted expression of IFNγ caused the activation of macrophages and NK cells, and reproduced the therapeutic effect of YB1 on liver metastasis. Conclusion: We demonstrated that YB1 may exhibit anti-tumor effect mainly based on IFNγ induction. Targeted IFNγ therapy can replace YB1 for treating liver metastasis of PNETs.

The Added Value of ADC-based Nomogram in Assessing the Depth of Myometrial Invasion of Endometrial Endometrioid Adenocarcinoma.

RATIONALE AND OBJECTIVES: To explore the potential value of the apparent diffusion coefficient (ADC)-based nomogram models in preoperatively assessing the depth of myometrial invasion of endometrial endometrioid adenocarcinoma (EEA). MATERIALS AND METHODS: Preoperative magnetic resonance imaging (MRI) of 210 EEA patients were retrospectively analyzed. ADC histogram metrics derive from the whole-tumor regions of interest. Univariate and multivariate analyses were used to screen the ADC histogram metrics and clinical characteristics for nomogram model building. The diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of two radiologists without and with the assistance of models were calculated and compared. RESULTS: Two nomogram models were developed for predicting no myometrial invasion (NMI) and deep myometrial invasion (DMI) with area under the curves of 0.85 and 0.82, respectively. With the assistance of models, the overall accuracies were significantly improved [radiologist_1, 73.3% vs 86.2% (p = 0.001); radiologist_2, 80.0% vs 91.0% (p = 0.002)]. In determining NMI, the sensitivity and PPV were greatly improved but not significant for radiologist_1 (51.9% vs 77.8% and 46.7% vs 75.0%, p = 0.229 and 0.511), and under/near the significance level for radiologist_2 (59.3% vs 88.9% and 57.1% vs 82.8%, p = 0.041 and 0.065), while the specificity, accuracy, and NPV were significantly improved (all p < 0.001). In determining DMI, all sensitivity, specificity, accuracy, PPV, and NPV were significantly improved (all p < 0.001). CONCLUSION: The ADC-based nomogram models can improve the diagnostic performance of radiologist in preoperatively assessing the depth of myometrial invasion and facilitate optimizing clinical individualized treatment decisions.