Development of preoperative nomograms to predict the risk of overall and multifocal positive surgical margin after radical prostatectomy.

OBJECTIVE: To develop preoperative nomograms using risk factors based on clinicopathological and MRI for predicting the risk of positive surgical margin (PSM) after radical prostatectomy (RP). PATIENTS AND METHODS: This study retrospectively enrolled patients who underwent prostate MRI before RP at our center between January 2015 and November 2022. Preoperative clinicopathological factors and MRI-based features were recorded for analysis. The presence of PSM (overall PSM [oPSM]) at pathology and the multifocality of PSM (mPSM) were evaluated. LASSO regression was employed for variable selection. For the final model construction, logistic regression was applied combined with the bootstrap method for internal verification. The risk probability of individual patients was visualized using a nomogram. RESULTS: In all, 259 patients were included in this study, and 76 (29.3%) patients had PSM, including 40 patients with mPSM. Final multivariate logistic regression revealed that the independent risk factors for oPSM were tumor diameter, frank extraprostatic extension, and annual surgery volume (all p < 0.05), and the nomogram for oPSM reached an area under the curve (AUC) of 0.717 in development and 0.716 in internal verification. The independent risk factors for mPSM included the percentage of positive cores, tumor diameter, apex depth, and annual surgery volume (all p < 0.05), and the AUC of the nomogram for mPSM was 0.790 in both development and internal verification. The calibration curve analysis showed that these nomograms were well-calibrated for both oPSM and mPSM. CONCLUSIONS: The proposed nomograms showed good performance and were feasible in predicting oPSM and mPSM, which might facilitate more individualized management of prostate cancer patients who are candidates for surgery.

Diagnostic performance of MRI in detecting prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL: a systematic review and meta-analysis.

OBJECTIVE: To investigate the diagnostic performance of MRI in detecting clinically significant prostate cancer (csPCa) and prostate cancer (PCa) in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL. METHODS: A computerized search of PubMed, Embase, Cochrane Library, Medline, and Web of Science was conducted from inception until October 31, 2023. We included articles on the use of MRI to detect csPCa or PCa at 4-10 ng/mL PSA. The primary and secondary outcomes were MRI performance in csPCa and PCa detection, respectively; the estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were pooled in a bivariate random-effects model. RESULTS: Among the 19 studies (3879 patients), there were 10 (2205 patients) and 13 studies (2965 patients) that reported MRI for detecting csPCa or PCa, respectively. The pooled sensitivity and specificity for csPCa detection were 0.84 (95% confidence interval [CI], 0.79-0.88) and 0.76 (95%CI, 0.65-0.84), respectively, for PCa detection were 0.82 (95%CI, 0.75-0.87) and 0.74 (95%CI, 0.65-0.82), respectively. The pooled NPV for csPCa detection was 0.91 (0.87-0.93). Biparametric magnetic resonance imaging also showed a significantly higher sensitivity and specificity relative to multiparametric magnetic resonance imaging (both p < 0.01). CONCLUSION: Prostate MRI enables the detection of csPCa and PCa with satisfactory performance in the PSA gray zone. The excellent NPV for csPCa detection indicates the possibility of biopsy decision-making in patients in the PSA gray zone, but substantial heterogeneity among the included studies should be taken into account. CLINICAL RELEVANCE STATEMENT: Prostate MRI can be considered a reliable and satisfactory tool for detecting csPCa and PCa in patients with PSA in the "gray zone", allowing for reducing unnecessary biopsy and optimizing the overall examination process. KEY POINTS: Prostate-specific antigen (PSA) is a common screening tool for prostate cancer but risks overdiagnosis. MRI demonstrated excellent negative predictive value for prostate cancer in the PSA gray zone. MRI can influence decision-making for these patients, and biparametric MRI should be further evaluated.

Postbiotics in colorectal cancer: intervention mechanisms and perspectives.

Colorectal cancer (CRC) is a common malignancy affecting the gastrointestinal tract worldwide. The etiology and progression of CRC are related to factors such as environmental influences, dietary structure, and genetic susceptibility. Intestinal microbiota can influence the integrity of the intestinal mucosal barrier and modulate intestinal immunity by secreting various metabolites. Dysbiosis of the intestinal microbiota can affect the metabolites of the microbial, leading to the accumulation of toxic metabolites, which can trigger chronic inflammation or DNA damage and ultimately lead to cellular carcinogenesis and the development of CRC. Postbiotics are preparations of inanimate microorganisms or their components that are beneficial to the health of the host, with the main components including bacterial components (e.g., exopolysaccharides, teichoic acids, surface layer protein) and metabolites (e.g., short-chain fatty acids, tryptophan metabolite, bile acids, vitamins and enzymes). Compared with traditional probiotics, it has a more stable chemical structure and higher safety. In recent years, it has been demonstrated that postbiotics are involved in regulating intestinal microecology and improving the progression of CRC, which provides new ideas for the prevention and diagnosis of CRC. In this article, we review the changes in intestinal microbiota in different states of the gut and the mechanisms of anti-tumor activity of postbiotic-related components, and discuss the potential significance of postbiotics in the diagnosis and treatment of CRC. This reviews the changes and pathogenesis of intestinal microbiota in the development of CRC, and summarizes the relevant mechanisms of postbiotics in resisting the development of CRC in recent years, as well as the advantages and limitations of postbiotics in the treatment process of CRC.

Exploring a simplified way to diagnose pelvic lipomatosis: prediction of pelvic fat volume using a single cross-sectional image.

Background: Pelvic lipomatosis (PL) is a rare disease characterized by the overgrowth of pelvic adipose tissue (AT). We investigated the relationships between areas of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) and pelvic fat volume (PFV), and analyzed the feasibility of diagnosing PL from a single cross-sectional image. Methods: The study included 50 patients and 50 controls. We used nnU-Net to segment SAT and VAT automatically. L3 vertebra was set as the zero point (L0), and a total of 201 slices were obtained with a 1 mm interval (L-100 - L+100). We selected 5 pelvic slices, including slices of the anterior superior margin of the S1-S4 vertebrae and the slice above the bilateral femoral head (FH). SAT areas, VAT areas, and PFVs were calculated by computational software. Areas and volumes of 2 groups were compared by t-test or rank-sum test. The correlations among areas and PFV were calculated. Logistic regression models were developed to identify the best slice for predicting PL. Receiver operating characteristic (ROC) curves were performed, and the area under the curve (AUC) and thresholds [with sensitivity (SEN) and specificity (SPE)] were calculated. Results: VAT areas of L-94 - L-100, L+79 - L+100, S1-S4, and FH indicated statistical differences between patients and controls (P<0.05). The linear regression model with VAT area as the independent variable was established to estimate PFV (FH level: r=0.745, P<0.001, R2=0.555). Among the univariate logistic regression models, VAT area at FH as the independent variable had the highest performance in predicting PL (AUC: 0.893, SEN: 74%, SPE: 94%), followed by S4 level (AUC: 0.800, SEN: 88%, SPE: 66%). The overall accuracy of the logistic regression model including VAT areas at S4 and FH in predicting PL was 88% (AUC: 0.927, SEN: 90%, SPE: 88%). Conclusions: VAT areas at the level of FH can help estimate the value of PFV. VAT areas of S4 and FH provide greater power than a single image for the diagnosis of PL.

Effect of preoperative PI-RADS assessment on pathological outcomes in patients who underwent radical prostatectomy.

OBJECTIVE: To assess the effect of preoperative MRI with standardized Prostate Imaging-Reporting and Data System (PI-RADS) assessment on pathological outcomes in prostate cancer (PCa) patients who underwent radical prostatectomy (RP). PATIENTS AND METHODS: This retrospective cohort study included patients who had undergone prostate MRI and subsequent RP for PCa between January 2017 and December 2022. The patients were divided into the PI-RADS group and the non-PI-RADS group according to evaluation scheme of presurgery MRI. The preoperative characteristics and postoperative outcomes were retrieved and analyzed. The pathological outcomes included pathological T stage (pT2 vs. pT3-4) and positive surgical margins (PSMs). Patients were further stratified according to statistically significant preoperative variables to assess the difference in pathological outcomes. A propensity score matching based on the above preoperative characteristics was additionally performed. RESULTS: A total of 380 patients were included in this study, with 201 patients in the PI-RADS group and 179 in the non-PI-RADS group. The two groups had similar preoperative characteristics, except for clinical T stage (cT). As for pathological outcomes, the PI-RADS group showed a significantly lower percentage of pT3-4 (21.4% vs. 48.0%, p < 0.001), a lower percentage of PSMs (31.3% vs. 40.9%, p = 0.055), and a higher concordance between the cT and pT (79.1% vs. 64.8%, p = 0.003). The PI-RADS group also showed a lower proportion of pT3-4 (p < 0.001) in the cT1-2 subgroup and the cohort after propensity score matching. The PSM rate of cT3 patients was reduced by 39.2% in the PI-RADS group but without statistical significance (p = 0.089). CONCLUSIONS: Preoperative MRI with standardized PI-RADS assessment could benefit the decision-making of patients by reducing the rate of pathologically confirmed non-organ-confined PCa after RP and slightly reducing the PSM rate compared with non-PI-RADS assessment.

An MRI-based grading system for preoperative risk estimation of positive surgical margin after radical prostatectomy.

OBJECTIVE: To construct a simplified grading system based on MRI features to predict positive surgical margin (PSM) after radical prostatectomy (RP). METHODS: Patients who had undergone prostate MRI followed by RP between January 2017 and January 2021 were retrospectively enrolled as the derivation group, and those between February 2021 and November 2022 were enrolled as the validation group. One radiologist evaluated tumor-related MRI features, including the capsule contact length (CCL) of lesions, frank extraprostatic extension (EPE), apex abutting, etc. Binary logistic regression and decision tree analysis were used to select risk features for PSM. The area under the curve (AUC), sensitivity, and specificity of different systems were calculated. The interreader agreement of the scoring systems was evaluated using the kappa statistic. RESULTS: There were 29.8% (42/141) and 36.4% (32/88) of patients who had PSM in the derivation and validation cohorts, respectively. The first grading system was proposed (mrPSM1) using two imaging features, namely, CCL ≥ 20 mm and apex abutting, and then updated by adding frank EPE (mrPSM2). In the derivation group, the AUC was 0.705 for mrPSM1 and 0.713 for mrPSM2. In the validation group, our grading systems showed comparable AUC with Park et al.'s model (0.672-0.686 vs. 0.646, p > 0.05) and significantly higher specificity (0.732-0.750 vs. 0.411, p < 0.001). The kappa value was 0.764 for mrPSM1 and 0.776 for mrPSM2. Decision curve analysis showed a higher net benefit for mrPSM2. CONCLUSION: The proposed grading systems based on MRI could benefit the risk stratification of PSM and are easily interpretable. CRITICAL RELEVANCE STATEMENT: The proposed mrPSM grading systems for preoperative prediction of surgical margin status after radical prostatectomy are simplified compared to a previous model and show high specificity for identifying the risk of positive surgical margin, which might benefit the management of prostate cancer. KEY POINTS: • CCL ≥ 20 mm, apex abutting, and EPE were important MRI features for PSM. • Our proposed MRI-based grading systems showed the possibility to predict PSM with high specificity. • The MRI-based grading systems might facilitate a structured risk evaluation of PSM.

The feasibility of contrast-enhanced CT to identify the adhesive renal venous tumor thrombus of renal cell carcinoma.

OBJECTIVE: To identify adhesive renal venous tumor thrombus (RVTT) of renal cell carcinoma (RCC) by contrast-enhancement CT (CECT). MATERIALS AND METHODS: Our retrospective study included 53 patients who underwent preoperative CECT and pathologically confirmed RCC combined with RVTT. They were divided into two groups based on the intra-operative findings of RVTT adhesion to the venous wall, with 26 cases in the adhesive RVTT group (ARVTT) and 27 cases in the non-adhesive group (NRVTT). The location, maximum diameter (MD) and CT values of tumors, the maximum length (ML) and width (MW) of RVTT, and length of inferior vena cava tumor thrombus were compared between the two groups. The presence of renal venous wall involvement, renal venous wall inflammation, and enlarged retroperitoneal lymph node was compared between the two groups. A receiver operating characteristic curve was used to analyze the diagnostic performance. RESULTS: The MD of RCC and the ML and MW of the RVTT were all larger in the ARVTT group than in the NRVTT group (p = 0.042, p < 0.001, and p = 0.002). The proportion of renal vein wall involvement and renal vein wall inflammation were higher in the ARVTT group than in NRVTT groups (both p < 0.001). The multivariable model including ML and vascular wall inflammation to predict ARVTT could achieve the best diagnostic performance with the area under the curve, sensitivity, specificity, and accuracy of 0.91, 88.5%, 96.3%, and 92.5%, respectively. CONCLUSION: The multivariable model acquired by CECT images could be used to predict RVTT adhesion. CLINICAL RELEVANCE STATEMENT: For RCC patients with tumor thrombus, contrast-enhanced CT could noninvasively predict the adhesion of tumor thrombus, thus predicting the difficulty of surgery and contributing to the selection of an appropriate treatment plan. KEY POINTS: • The length and width of the tumor thrombus could be used to predict its adhesion to the vessel wall. • Adhesion of the tumor thrombus can be reflected by inflammation of the renal vein wall. • The multivariable model from CECT can well predict whether the tumor thrombus adhered to the vein wall.

USP33 promotes nonalcoholic fatty acid disease-associated fibrosis in gerbils via the c-myc signaling.

Nonalcoholic fatty acid disease (NAFLD) is a common complication of obesity associated with liver fibrosis. The underlying molecular mechanisms involved in the progression from normal to fibrosis remain unclear. Liver tissues from the liver fibrosis model identified the USP33 gene as a key gene in NAFLD-associated fibrosis. USP33 knockdown inhibited hepatic stellate cell activation and glycolysis in gerbils with NAFLD-associated fibrosis. Conversely, overexpression of USP33 caused a contrast function on hepatic stellate cell activation and glycolysis activation, which was inhibited by c-Myc inhibitor 10058-F4. The copy number of short-chain fatty acids-producing bacterium Alistipes sp. AL-1, Mucispirillum schaedleri, Helicobacter hepaticus in the feces, and the total bile acid level in serum were higher in gerbils with NAFLD-associated fibrosis. Bile acid promoted USP33 expression and inhibiting its receptor reversed hepatic stellate cell activation in gerbils with NAFLD-associated fibrosis. These results suggest that the expression of USP33, an important deubiquitinating enzyme, is increased in NAFLD fibrosis. These data also point to hepatic stellate cells as a key cell type that may respond to liver fibrosis via USP33-induced cell activation and glycolysis.

Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study.

OBJECTIVES: To automatically segment prostate central gland (CG) and peripheral zone (PZ) on T2-weighted imaging using deep learning and assess the model's clinical utility by comparing it with a radiologist annotation and analyzing relevant influencing factors, especially the prostate zonal volume. METHODS: A 3D U-Net-based model was trained with 223 patients from one institution and tested using one internal testing group (n = 93) and two external testing datasets, including one public dataset (ETDpub, n = 141) and one private dataset from two centers (ETDpri, n = 59). The Dice similarity coefficients (DSCs), 95th Hausdorff distance (95HD), and average boundary distance (ABD) were calculated to evaluate the model's performance and further compared with a junior radiologist's performance in ETDpub. To investigate factors influencing the model performance, patients' clinical characteristics, prostate morphology, and image parameters in ETDpri were collected and analyzed using beta regression. RESULTS: The DSCs in the internal testing group, ETDpub, and ETDpri were 0.909, 0.889, and 0.869 for CG, and 0.844, 0.755, and 0.764 for PZ, respectively. The mean 95HD and ABD were less than 7.0 and 1.3 for both zones. The U-Net model outperformed the junior radiologist, having a higher DSC (0.769 vs. 0.706) and higher intraclass correlation coefficient for volume estimation in PZ (0.836 vs. 0.668). CG volume and Magnetic Resonance (MR) vendor were significant influencing factors for CG and PZ segmentation. CONCLUSIONS: The 3D U-Net model showed good performance for CG and PZ auto-segmentation in all the testing groups and outperformed the junior radiologist for PZ segmentation. The model performance was susceptible to prostate morphology and MR scanner parameters.

Glucose determination behaviour of gold microspheres-electrodeposited carbon cloth flexible electrodes in neutral media.

Despite numerous advances in the field of nonenzymatic glucose detection, monitoring glucose in physiological applications is still a challenge and is mostly limited to electrode surface modification. This study proposes a simple method for electrodepositing cotton-like gold microspheres (CGMs) on a carbon cloth (CC) flexible electrode, with the potential for the functional supporting substrate to monitor glucose in a neutral environment. It was demonstrated that the voltammetric response of glucose oxidation increased with increases in glucose concentration in the 3D functional flexible substrate; moreover, the amperometric response of glucose oxidation increased over time. The results indicate that the functional flexible electrode-made of gold microspheres-based carbon cloth with a predefined geometry and pore-architecture network to promote the medium-permeation and synergetic effects between CGMs and CC-can be a suitable platform for measuring glucose variation in environments with neutral pH. This is particularly relevant because the oxygen-containing functional groups on the CC surface increase the dehydrogenation rate of glucose oxidation in neutral phosphate-buffered saline.

In-situ growth of 3D rosette-like copper nanoparticles on carbon cloth for enhanced sensing of ammonia based on copper electrodissolution.

Copper is an attractive candidate for sensing ammonia. Here, an electrodissolution mechanism for measuring liquid-phase ammonia was developed via a novel three-dimensional rosette-like structure of copper nanoparticles (CuNPs) integrated onto carbon cloth (CuNPs/CC). A one-step hydrothermal synthetic procedure was employed to construct the metallic CuNPs with a stereo rosette-like pattern on flexible CC substrate. The morphology, composition and sensing performance of the as-prepared composite were characterised in detail. The CuNPs/CC composite showed excellent sensing performance to ammonia, which is attributed to the electrodissolution of CuNPs being promoted by ammonia to form a stabilised copper-ammonia complex. This electrochemical response occurs without the electro-oxidation of ammonia, thus avoiding the energy barrier of the N-N bond and the toxicity of N-adsorbates, which is advantageous for ammonia detection. In addition, the sensor also shows very high sensitivity to ammonia with a low detection limit, as well as good anti-interference performance, repeatability and stability. The high accuracy and precision for the quantification of ammonia concentration in a variety of real samples indicate that the CuNPs/CC composition has potential in the development of high-performance ammonia sensors.

Copper Nanowires Modified with Graphene Oxide Nanosheets for Simultaneous Voltammetric Determination of Ascorbic Acid, Dopamine and Acetaminophen.

Copper nanowires (Cu NWs) were modified with graphene oxide (GO) nanosheets to obtain a sensor for simultaneous voltammetric determination of ascorbic acid (AA), dopamine (DA) and acetaminophen (AC). The nanocomposite was obtained via sonication, and its structures were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDS). The electrochemical oxidation activity of the materials (placed on a glassy carbon electrode) was studied by cyclic voltammetry and differential pulse voltammetry. Due to the synergistic effect of Cu NWs and GO, the specific surface, electrochemical oxidation performance and conductivity are improved when compared to each individual component. The peaks for AA (-0.08 V), DA (+0.16 V), and AC (+0.38 V) are well separated. The sensor has wide linear ranges which are from 1-60 µM, 1-100 µM, and 1-100 µM for AA, DA, and AC, respectively, when operated in the differential pulse voltammetric mode. The detection limits are 50, 410 and 40 nM, respectively. Potential interferences by uric acid (20 µM), glucose (10 mM), NaCl (1 mM), and KCl (1 mM) were tested for AA (1 µΜ), DA (1 µΜ), and AC (1 µΜ) and were found to be insignificant. The method was successfully applied to the quantification of AA, DA, and AC in spiked serum samples.