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Purpose: To study the capability of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) on subtype classification and grade differentiation for small renal cell carcinoma (RCC). Histogram analysis for apparent diffusion coefficient (ADC) was studied for comparison. Materials and methods: A total of 61 patients with small RCC (< 4 cm) were included in the retrospective study. MRI data were reviewed, including a multi-b (0–1500 s/mm2) multi-TE (51–200 ms) diffusion weighted imaging (DWI) sequence. Region of interest (ROI) was delineated manually on DWI to include solid tumor. For each patient, a D-T2 spectrum was fitted and segmented into 5 compartments, and the volume fractions VA, VB, VC, VD, VE were obtained. ADC mapping was calculated, and histogram parameters ADC 90th, 10th, median, standard deviation, skewness and kurtosis were obtained. All MRI metrices were compared between clear cell RCC (ccRCC) and non-ccRCC group, and between high-grade and low-grade group. Receiver operator curve analysis was used to assess the corresponding diagnostic performance. Results: Significantly higher ADC 90th, ADC 10th and ADC median, and significantly lower DR-CSI VB was found for ccRCC compared to non-ccRCC. Significantly lower ADC 90th, ADC median and significantly higher VB was found for high-grade RCC compared to low-grade. For identifying ccRCC from non-ccRCC, VB showed the highest area under curve (AUC, 0.861) and specificity (0.882). For differentiating high- from low-grade, ADC 90th showed the highest AUC (0.726) and specificity (0.786), while VB also displayed a moderate AUC (0.715). Conclusion: DR-CSI may offer improved accuracy in subtype identification for small RCC, while do not show better performance for small RCC grading compared to ADC histogram. [ABSTRACT FROM AUTHOR]

Objective: To construct a prognostic model based on MR features and clinical data to evaluate the progression free survival (PFS), overall survival (OS) and objective response rate (ORR) of pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy. Methods: 105 pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy were assigned to the training set (n = 52), validation set (n = 22), and testing set (n = 31). Multi-lesion volume of interest were delineated, multi-sequence radiomics features were extracted, and the radiomics models for predicting PFS, OS and ORR were constructed, respectively. Clinical variables were extracted, and the clinical models for predicting PFS, OS and ORR were constructed, respectively. The nomogram was jointly constructed by radiomics model and clinical model. Result: The ORR exhibits no significant correlation with either PFS or OS. The area under the curve (AUC) of nomogram for predicting 6-month PFS reached 0.847 (0.737–0.957), 0.786 (0.566–1.000) and 0.864 (0.735–0.994) in the training set, validation set and testing set, respectively. The AUC of nomogram for predicting 1-year OS reached 0.770 (0.635–0.906), 0.743 (0.479–1.000) and 0.818 (0.630–1.000), respectively. The AUC of nomogram for predicting ORR reached 0.914 (0.828–1.00), 0.938 (0.840–1.00) and 0.846 (0.689–1.00), respectively. Conclusion: The prognostic models based on MR imaging features and clinical data are effective in predicting the PFS, OS and ORR of chemoimmunotherapy in pancreatic cancer patients with hepatic metastasis, and can be used to evaluate the prognosis of patients. [ABSTRACT FROM AUTHOR]

The effect of low voltage electrostatic field (LVEF, 2500 V, 0.2 mA) on oxidative denaturation of myofibrillar proteins (MPs) in repeatedly freeze–thaw (F-T, both 24 h) lamb was investigated in this study. F-T cycles intensified MP oxidative denaturation and structural destruction, with the influences becoming more severe after 1, 3, and 5 F-T cycles. The samples of the LVEF-assisted F-T cycles (LFT) exhibited lower carbonyl/dityrosine content, "r" value, and surface hydrophobicity, higher sulfhydryl content, solubility, Ca2+-ATPase activity, α-helix content, and fluorescence intensity under the identical F-T cycles (P < 0.05) in comparison with the common F-T cycles (CFT). Although the suppression of LVEF on protein oxidation decreased with the boost in the amount of F-T cycles, the effectiveness was still remarkable compared to samples without LVEF treatment. These original study findings would assist supply theoretical proof for the usage of LVEF in the freezing, storing, and transporting of frozen meat. [ABSTRACT FROM AUTHOR]

Objectives: To develop an artificial intelligence (AI) model for prostate segmentation and prostate cancer (PCa) detection, and explore the added value of AI-based computer-aided diagnosis (CAD) compared to conventional PI-RADS assessment. Methods: A retrospective study was performed on multi-centers and included patients who underwent prostate biopsies and multiparametric MRI. A convolutional-neural-network-based AI model was trained and validated; the reliability of different CAD methods (concurrent read and AI-first read) were tested in an internal/external cohort. The diagnostic performance, consistency and efficiency of radiologists and AI-based CAD were compared. Results: The training/validation/internal test sets included 650 (400/100/150) cases from one center; the external test included 100 cases (25/25/50) from three centers. For diagnosis accuracy, AI-based CAD methods showed no significant differences and were equivalent to the radiologists in the internal test (127/150 vs. 130/150 vs. 125/150 for reader 1; 127/150 vs.132/150 vs. 131/150 for reader 2; all p > 0.05), whereas in the external test, concurrent-read methods were superior/equal to AI-first read (87/100 vs. 71/100, p < 0.001, for reader 2; 79/100 vs. 69/100, p = 0.076, for reader 1) and better than/equal to radiologists (79/100 vs. 72/100, p = 0.039, for reader 1; 87/100 vs. 86/100, p = 1.000, for reader 2). Moreover, AI-first read/concurrent read improved consistency in both internal test (κ = 1.000, 0.830) and external test (κ = 0.958, 0.713) compared to radiologists (κ = 0.747, 0.600); AI-first read method (8.54 s/7.66 s) was faster than readers (92.72 s/89.54 s) and concurrent-read method (29.15 s/28.92 s), respectively. Conclusion: AI-based CAD could improve the consistency and efficiency for accurate diagnosis; the concurrent-read method could enhance the diagnostic capabilities of an inexperienced radiologist in unfamiliar situations. Key Points: • For prostate cancer segmentation, the performance of multi-small Vnet displays optimal compared to small Vnet and Vnet (DSCmsvnet vs. DSCsvnet, p = 0.021; DSCmsvnet vs. DSCvnet, p < 0.001). • For prostate gland segmentation, the mean/median DSCs for fine and coarse segmentation were 0.91/0.91 and 0.88/0.89, respectively. Fine segmentation displays superior performance compared to coarse (DSCcoarse vs. DSCfine, p < 0.001). • For PCa diagnosis, AI-based CAD methods improve consistency in internal (κ = 1.000; 0.830) and external (κ = 0.958; 0.713) tests compared to radiologists (κ = 0.747; 0.600); the AI-first read (8.54 s/7.66 s) was faster than the readers (92.72 s/89.54 s) and the concurrent-read method (29.15 s/28.92 s). [ABSTRACT FROM AUTHOR]

Unsatisfied visible light response and high recombination probability of photogenerated charge carriers greatly limit the advanced application of graphitic carbon nitride (CN). Here, the novel Cu and C co-doping CN (Cu/C-CN) photocatalyst is successfully fabricated by thermal polymerization of the melamine and chlorophyll sodium copper salt as precursors, which significantly improves the photocatalytic performance than pure CN. A series of characterizations confirm that the co-doped Cu and C in CN not only endow the material with improved light harvest abilities and narrowed band gap, but also facilitate the charge carriers separation. As a result, the Cu/C-CN-0.3 photocatalyst exhibits a highly efficient photocatalytic degradation of RhB, the reaction rate constant of Cu/C-CN-0.3 (kapp=0.036 min−1) is almost 13 times higher than CN (kapp = 0.0027 min−1). This study may open a new perspective for constructing other dual-elements co-doped CN materials with high performance and application in wastewater treatment. [ABSTRACT FROM AUTHOR]

Introduction: To aim of this analysis was to investigate the extent and evaluate risk factors of residual hyperglycaemia in Chinese individuals with type 2 diabetes (T2D) initiating basal insulin. Methods: FPG GOAL was a 24-week, open-label, treat-to-target randomised controlled trial in Chinese individuals with T2D inadequately controlled with oral anti-hyperglycaemic drugs initiating treatment with basal insulin. This analysis categorised participants into the following glycaemic control categories: hyperglycaemia [glycated haemoglobin (HbA1c) ≥ 53 mmol/mol (≥ 7%), fasting plasma glucose (FPG) ≥ 7.0 mmol/L], residual hyperglycaemia [HbA1c ≥ 53 mmol/mol (≥ 7%), FPG < 7.0 mmol/L], discordant [HbA1c < 53 mmol/mol (< 7%), FPG ≥ 7.0 mmol/L] and at target [HbA1c < 53 mmol/mol (< 7%), FPG < 7.0 mmol/L]. The proportion of participants in each glycaemic control category was assessed at weeks 12 and 24. Multivariable regression analyses were conducted to evaluate risk factors for residual hyperglycaemia. Results: Of the 914 participants included, 22.1% had residual hyperglycaemia, 31.9% had hyperglycaemia, 11.1% were discordant and 29.3% were at target at week 24. More participants who were randomised to a fasting blood glucose (FBG) target of > 3.9 to ≤ 5.6 mmol/L had residual hyperglycaemia compared with participants randomised to a FBG target of > 3.9 to ≤ 6.1 mmol/L or > 3.9 to ≤ 7.0 mmol/L. Multivariable analysis indicated that higher HbA1c and lower FPG levels at baseline were associated with greater proportion of residual hyperglycaemia. Conclusion: Some Chinese individuals with T2D may have residual hyperglycaemia 3–6 months after initiating basal insulin treatment and require further intensified treatment. Higher HbA1c and lower FPG levels could be risk factors for residual hyperglycaemia. Trial Registration: ClinicalTrials.gov identifier NCT02545842. [ABSTRACT FROM AUTHOR]

A smart bioreactor (HPs-Ag), designed by protein-silver nanoclusters-PNIPAAm, could control the membrane permeability to determine the different molecular weight of materials and the speed of catalysis of enzyme reaction. Studies also showed that the molecular weight cutoff of the HPs-Ag membrane could be continuously turned up from 12 to 70 kDa. The catalytic reaction rate of the ALP loaded bared HPs-Ag by using pNPP as substrates was increased (18.14- and 9.23-fold) with different concentrations of H2O2. [ABSTRACT FROM AUTHOR]

Objectives: To develop a radiomics model using preoperative multiphasic CT for predicting distant metastasis after surgical resection in patients with localized clear cell renal cell carcinoma (ccRCC) and to identify key biological pathways underlying the predictive radiomics features using RNA sequencing data. Methods: In this multi-institutional retrospective study, a CT radiomics metastasis score (RMS) was developed from a radiomics analysis cohort (n = 184) for distant metastasis prediction. Using a gene expression analysis cohort (n = 326), radiomics-associated gene modules were identified. Based on a radiogenomics discovery cohort (n = 42), key biological pathways were enriched from the gene modules. Furthermore, a multigene signature associated with RMS was constructed and validated on an independent radiogenomics validation cohort (n = 37). Results: The 9-feature-based RMS predicted distant metastasis with an AUC of 0.861 in validation set and was independent with clinical factors (p < 0.001). A gene module comprising 114 genes was identified to be associated with all nine radiomics features (p < 0.05). Four enriched pathways were identified, including ECM-receptor interaction, focal adhesion, protein digestion and absorption, and PI3K-Akt pathways. Most of them play important roles in tumor progression and metastasis. A 19-gene signature was constructed from the radiomics-associated gene module and predicted metastasis with an AUC of 0.843 in the radiogenomics validation cohort. Conclusions: CT radiomics features can predict distant metastasis after surgical resection of localized ccRCC while the predictive radiomics phenotypes may be driven by key biological pathways related to cancer progression and metastasis. Key Points: • Radiomics features from primary tumor in preoperative CT predicted distant metastasis after surgical resection in patients with localized ccRCC. • CT radiomics features predictive of distant metastasis were associated with key signaling pathways related to tumor progression and metastasis. • Gene signature associated with radiomics metastasis score predicted distant metastasis in localized ccRCC. [ABSTRACT FROM AUTHOR]

A unique polymeric carbon nitride (CN) tubular sponge with an edge-functionalized phosphonic group (CNPC) has been obtained using a simple template-free method. Using phenylphosphonic acid as an additive promotes the formation of a cyanuric acid–melamine supramolecular precursor. Meanwhile, phosphonic groups are grafted to heptazine units. Experimental and characterization investigations reveal that the functional phosphonic group and tubular sponge morphology endow CNPCx with additional reaction active sites, negative conduction band levels, and highly efficient charge separation. The obtained CNPCx photocatalysts exhibit excellent catalytic performance with regard to H2 evolution and contaminant degradation. This study provides a new design based on the synergetic regulating effect of macro and microstructures of CN with high performance in energy conversion and environmental purification. [ABSTRACT FROM AUTHOR]

Background: To evaluate the performance of diffusion-relaxation correlation spectrum imaging (DR-CSI) with support vector machine (SVM) in detecting prostate cancer (PCa)., Methods: In total, 114 patients (mean age, 66 years, range, 48-87 years) who received a prostate MRI and underwent biopsy were enrolled in three stages. Thirty-nine were assigned for the exploration stage to establish the model, 18 for the validation stage to choose the appropriate scale for mapping and 57 for the test stage to compare the diagnostic performance of the DR-CSI and PI-RADS., Results: In the exploration stage, the DR-CSI model was established and performed better than the ADC and T 2 values (both P < 0.001). The validation result shows that at least 2 pixels were required for both the long-axis and short-axis in the mapping procedure. In the test stage, DR-CSI had higher accuracy than PI-RADS ≥ 3 as a positive finding based on patient (84.2% vs. 63.2%, P = 0.004) and lesion (78.8% vs. 57.6%, P = 0.001) as well as PI-RADS ≥ 4 on lesion (76.5% vs. 64.7%, P = 0.029), while there was no significant difference between DR-CSI and PI-RADS ≥ 4 based on patient (P = 0.508). For clinically significant PCa, DR-CSI had higher accuracy than PI-RADS ≥ 3 based on patients (84.2% vs. 63.2%, P = 0.004) and lesions (62.4% vs. 48.2%, P = 0.036). There was no significant difference between DR-CSI and PI-RADS ≥ 4 (P = 1.000 and 0.845 for the patient and lesion levels, respectively)., Conclusions: DR-CSI combined with the SVM model may improve the diagnostic accuracy of PCa., Trial Registration: This study was approved by the Ethics Committee of our institute (Approval No. KY2018-213). Written informed consent was obtained from all participants., (© 2022. The Author(s).)