LaOx modified MnOx loaded biomass activated carbon and its enhanced performance for simultaneous abatement of NO and Hg0.

A battery of agricultural straw derived biomass activated carbons supported LaOx modified MnOx (LaMn/BACs) was prepared by a facile impregnation method and then tested for simultaneous abatement of NO and Hg0. 15%LaMn/BAC manifested excellent removal efficiency of Hg0 (100%) and NO (86.7%) at 180 °C, which also exhibited splendid resistance to SO2 and H2O. The interaction between Hg0 removal and NO removal was explored; thereinto, Hg0 removal had no influence on NO removal, while NO removal preponderated over Hg0 removal. The inhibitory effect of NH3 was greater than the accelerative effect of NO and O2 on Hg0 removal. The physicochemical characterization of related samples was characterized by SEM, XRD, BET, H2-TPR, NH3-TPD, and XPS. After incorporating suitable LaOx into 15%Mn/BAC, the synergistic effect between LaOx and MnOx contributed to the improvement of BET surface area and total pore volume, the promotion of redox ability, surface active oxygen species, and acid sites, inhibiting the crystallization of MnOx. 15%LaMn/BAC has the best catalytic oxidation activity at low temperature. That might be answerable for superior performance and preferable tolerance to SO2 and H2O. The results indicated that 15%LaMn/BAC was a promising catalyst for simultaneous abatement of Hg0 and NO at low temperature.

Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions.

PURPOSE: To compare the performance of histogram analysis and intra-perinodular textural transition (Ipris) for distinguishing between benign and malignant testicular lesions. PATIENTS AND METHODS: This retrospective study included 76 patients with 80 pathologically confirmed testicular lesions (55 malignant, 25 benign). All patients underwent preoperative T2-weighted imaging (T2WI) on a 3.0T MR scanner. All testicular lesions were manually segmented on axial T2WI, and histogram and Ipris features were extracted. Thirty enrolled patients were randomly selected to estimate the robustness of the features. We used intraclass correlation coefficients (ICCs) to evaluate intra- and interobserver agreement of features, independent t-test or Mann-Whitney U-test to compare features between benign and malignant lesions, and receiver operating characteristic curve analysis to evaluate the diagnostic performance of features. RESULTS: Eighteen histogram features and forty-eight Ipris features were extracted from T2WI of each lesion. Most (60/66) histogram and Ipris features had good robustness (ICC of both intra- and interobserver variabilities >0.6). Three histogram and nine Ipris features were significantly different between the benign and malignant groups. The area under the curve values for Energy, TotalEnergy, and Ipris_shell1_id_std were 0.807, 0.808, and 0.708, respectively, which were relatively higher than those of other features. CONCLUSION: Ipris features may be useful for identifying benign and malignant testicular tumors but have no significant advantage over conventional histogram features.

Noninvasive Differentiation of Obstructive Azoospermia and Nonobstructive Azoospermia Using Multimodel Diffusion Weighted Imaging.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of parameters derived from multimodel diffusion weighted imaging (monoexponential, stretched-exponential diffusion weighted imaging and diffusion kurtosis imaging [DKI]) from noninvasive magnetic resonance imaging in distinguishing obstructive azoospermia (OA) from nonobstructive azoospermia (NOA). MATERIALS AND METHODS: Forty-six patients with azoospermia were prospectively enrolled and classified into two groups (21 OA patients and 25 NOA patients). The multimodel parameters of diffusion-weighted imaging (DWI; apparent diffusion coefficient [ADC], distributed diffusion coefficient [DDC], diffusion heterogeneity [α], diffusion kurtosis diffusivity [Dapp], and diffusion kurtosis coefficient [Kapp]) were derived. The diagnostic performance of these parameters for the differentiation of OA and NOA patients were evaluated using receiver operating characteristic analysis. The area under the curve (AUC) was calculated to evaluate the diagnostic accuracy of each parameter. RESULTS: All the parameters (ADC, α, DDC, Dapp, and Kapp) values were significantly different between OA and NOA (P < 0.001 for all). For the differentiation of OA from NOA, Kapp showed the highest AUC value (0.965), followed by DDC (0.946), Dapp (0.933), ADC (0.922), and α (0.887). Kapp had a significantly higher AUC than the conventional ADC (P < 0.05). CONCLUSION: Parameters derived from multimodels of DWI have the potential for the noninvasive differentiation of OA and NOA. The Kapp value derived from the DKI model might serve as a useful imaging marker for the differentiation of azoospermia.

Discrimination between benign and malignant testicular lesions using volumetric apparent diffusion coefficient histogram analysis.

PURPOSE: This study aimed to evaluate the role of volumetric apparent diffusion coefficient (ADC) histogram analysis in discriminating between benign and malignant testicular masses. METHODS: In this retrospective study, fifty-nine patients with 61 pathologically confirmed testicular masses were consecutively enrolled, including 18 benign lesions and 43 malignant lesions. All patients conducted preoperative magnetic resonance imaging (MRI) with diffusion-weighted imaging. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. Comparisons were conducted by an independent t-test or Mann-Whitney U test, where appropriate. The classification performance of the parameters that showed significant differences between benign and malignant testicular disease were evaluated via receiver operating characteristic (ROC) curve analysis. RESULTS: Among the 18 histogram parameters we extracted, the energy, total energy, and range of ADC of testicular malignancies were all significantly increased compared with those of benignities. The minimum ADC and 10th percentile ADC of testicular malignancies were both significantly reduced compared with those of benignities. The minimum ADC value achieved the highest diagnostic performance in distinguishing between testicular benignities and malignancies, with an area under the ROC curve (AUC) of 0.822, sensitivity of 81.40 %, and specificity of 77.78 %. CONCLUSIONS: Volumetric ADC histogram analysis might be a useful tool to preoperatively discriminate between benign and malignant testicular masses.

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps.

OBJECTIVE: To evaluate a combination of texture features and machine learning-based analysis of apparent diffusion coefficient (ADC) maps for the prediction of Grade Group (GG) upgrading in Gleason score (GS) ≤6 prostate cancer (PCa) (GG1) and GS 3 + 4 PCa (GG2). MATERIALS AND METHODS: Fifty-nine patients who were biopsy-proven to have GG1 or GG2 and underwent MRI examination with the same MRI scanner prior to transrectal ultrasound (TRUS)-guided systemic biopsy were included. All these patients received radical prostatectomy to confirm the final GG. Patients were divided into training cohort and test cohort. 94 texture features were extracted from ADC maps for each patient. The independent sample t-test or Mann-Whitney U test was used to identify the texture features with statistically significant differences between GG upgrading group and GG non-upgrading group. Texture features of GG1 and GG2 were compared based on the final pathology of radical prostatectomy. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter features. Four supervised machine learning methods were employed. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The statistical comparison between AUCs was performed. RESULTS: Six texture features were selected for the machine learning models building. These texture features were significantly different between GG upgrading group and GG non-upgrading group (P < 0.05). The six features had no significant difference between GG1 and GG2 based on the final pathology of radical prostatectomy. All machine learning methods had satisfactory predictive efficacy. The diagnostic performance of nearest neighbor algorithm (NNA) and support vector machine (SVM) was better than random forests (RF) in the training cohort. The AUC, sensitivity, and specificity of NNA were 0.872 (95% CI: 0.750-0.994), 0.967, and 0.778, respectively. The AUC, sensitivity, and specificity of SVM were 0.861 (95%CI: 0.732-0.991), 1.000, and 0.722, respectively. There had no significant difference between AUCs in the test cohort. CONCLUSION: A combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.