Immobilization of Enzymes on a Phospholipid Bionically Modified Polysulfone Gradient-Pore Membrane for the Enhanced Performance of Enzymatic Membrane Bioreactors

Enzymatic membrane bioreactors (EMBRs), with synergistic catalysis-separation performance, have increasingly been used for practical applications. Generally, the membrane properties, particularly the pore structures and interface interactions, have a significant impact on the catalytic efficiency of the EMBR. Therefore, a biomimetic interface based on a phospholipid assembled onto a polysulfone hollow-fiber membrane with perfect radial gradient pores (RGM-PSF) has been prepared in this work to construct a highly efficient and stable EMBR. On account of the special pore structure of the RGM-PSF with the apertures decreasing gradually from the inner side to the outer side, the enzyme molecules could be evenly distributed on the three-dimensional skeleton of the membrane. In addition, the supported phospholipid layer in the membrane, prepared by physical adsorption, was used for the immobilization of the enzymes, which provides sufficient linkage to prevent the enzymes from leaching but also accommodates as many enzyme molecules as possible to retain high bioactivity. The properties of the EMBR were studied by using lipase from Candida rugosa for the hydrolysis of glycerol triacetate as a model. Energy-dispersive X-ray and circular dichroism spectroscopy were employed to observe the effect of lecithin on the membrane and structure changes in the enzyme, respectively. The operational conditions were investigated to optimize the performance of the EMBR by testing substrate concentrations from 0.05 to 0.25 M, membrane fluxes from 25.5 to 350.0 L·m−2·h−1, and temperatures from 15 to 55 °C. As a result, the obtained EMBR showed a desirable performance with 42% improved enzymatic activity and 78% improved catalytic efficiency relative to the unmodified membrane

Diagnostic Accuracy of Liver and Spleen Stiffness in Magnetic Resonance Elastography for the Detection of Gastroesophageal Varices: A Systematic Review and Meta-Analysis

Background: The aim of this meta-analysis was to assess the performance of magnetic resonance elastography (MRE) in detecting gastroesophageal varices (GEV) in patients with chronic liver disease (CLD). Methods: A literature search in English and Chinese databases such as PubMed, EMBASE, Cochrane Library, Web of Science, and China National Knowledge Infrastructure was conducted. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver-operating characteristic (SROC) curve with a 95% CI were calculated. A quality analysis of the included study was conducted using the QUADAS-2 tool, and a meta-analysis was performed using Stata16. The clinical practical value of MRE in detecting GEV was evaluated using the Fagan plot. Heterogeneity across studies was explored through meta-regression and subgroup analyses. Results: A total of nine relevant articles that compared liver stiffness (LS) or spleen stiffness (SS) using MRE with esophagogastroduodenoscopy (EGD) to detect the existence of GEV were identified. The pooled summary sensitivity, specificity, PLR, NLR, and DOR of LS or SS for the detection of GEV were 81% (95% CI: 74%, 87%), 72% (95% CI: 62%, 80%), 2.89 (95% CI: 2.12, 3.94), 0.26 (95% CI: 0.19, 0.36), and 10.91 (95% CI: 6.53, 18.24), respectively. The year of publication, study design, and MR equipment are the sources of heterogeneity. There was no significant difference in the publication bias (p > 0.05). Conclusions: Based on these findings, MRE demonstrates good diagnostic accuracy for detecting GEV in patients with CLD

The MR Imaging of Primary Intrahepatic Lymphoepithelioma-like Cholangiocarcinoma: A Diagnostic Challenge

Purpose: To characterize the magnetic resonance imaging features of primary intrahepatic lymphoepithelioma-like cholangiocarcinoma (LELCC). Materials and Methods: Thirty-four patients with 38 histologically confirmed LELCCs were enrolled retrospectively from January 2014 to August 2022. We evaluated the clinical features, histologic findings, and imaging manifestations on dynamic enhanced MRI. Results: 74% (25/34) of the cases were associated with EBV infection. Moreover, patients infected with EBV exhibited a lower level of Ki-67 proliferation. The serum CA199 level was elevated in 10 patients. The median tumor diameter was 2.8 cm (range, 1.1–8.7 cm). Most tumors were well-defined with a smooth or lobulated margin and showed peripheral hyperintensity and central hypointensity on T2-weighted imaging (T2WI). T2 hyperintense foci were recognized in 8 patients. In the dynamic enhanced MRI, 21 tumors demonstrated Type A enhancement pattern (rim enhancement), 10 demonstrated Type B (rapid wash-in and wash-out), and seven demonstrated Type C (rapid wash-in without wash-out). Capsular enhancement in PVP or DP was found in 22 tumors. A few patients had satellite lesions, portal vein thrombosis, bile duct dilatation, and distal metastasis. Lymph node metastases were discovered pathologically in 11 patients. Conclusions: MRI findings of LELCC vary and are non-specific. While a majority of LELCCs exhibit typical features of intrahepatic cholangiocarcinoma (iCCA), unique findings like T2 hyperintense foci or capsular enhancement could suggest LELCC. EBV infection and elevated tumor markers can aid in differentiation. However, given the mimics of some cases of liver hypervascular lesions, histological examination remains essential for definitive diagnosis

Construction and Evaluation of Fe₃O₄‑Based PLGA Nanoparticles Carrying rtPA Used in the Detection of Thrombosis and in Targeted Thrombolysis

Thrombotic disease is extremely harmful to human health, but early detection and treatment can help improve prognoses and reduce mortality. To date, few studies have used MR molecular imaging in the early detection of thrombi and in the dynamic monitoring of the thrombolytic efficiency. In this article, we construct Fe₃O₄-based poly­(lactic-co-glycolic acid) (PLGA) nanoparticles to use in the detection of thrombi and in targeted thrombolysis using MRI monitoring. Cyclic arginine-glycine-aspartic peptide (cRGD) was grafted onto the chitosan (CS) surface to synthesize a CS-cRGD film using carbodiimide-mediated amide bond formation. A double emulsion solvent evaporation method (water in oil in water [W/O/W]) was used to construct Fe₃O₄-based PLGA nanoparticles carrying recombinant tissue plasminogen activator (rtPA) (Fe₃O₄–PLGA-rtPA/CS-cRGD). Fe₃O₄–PLGA, Fe₃O₄–PLGA-rtPA, and Fe₃O₄–PLGA-rtPA/CS nanoparticles were constructed using the same W/O/W method. The results showed that the Fe₃O₄-based nanoparticles were constructed successfully and have a regular shape, a relatively uniform size, a high carrier rate of Fe₃O₄ and encapsulation efficiency of rtPA, and a relatively high activity of released rtPA. Transmission electron microscope (TEM) images revealed that the iron oxide particles were relatively uniformly distributed in the nano-spherical shell. The Fe₃O₄-based nanoparticles could be imaged using a clinical MRI scanner, and there were no significant differences in the transverse relaxation rate (<i>R</i>₂*) or in the signal-to-noise ratio (SNR) values between the Fe₃O₄-based nanoparticles and an Fe₃O₄ solution with the same concentration of Fe₃O₄. In vitro and in vivo experiments confirmed that the Fe₃O₄–PLGA-rtPA/CS-cRGD nanoparticles specifically accumulated on the edge of the thrombus and that they had a significant effect on the thrombolysis compared with the Fe₃O₄–PLGA, Fe₃O₄–PLGA-rtPA, and Fe₃O₄–PLGA-rtPA/CS nanoparticles and with free rtPA solution. These results suggest the potential of the Fe₃O₄–PLGA-rtPA/CS-cRGD nanoparticles as a dual-function tool in the early detection of a thrombus and in the dynamic monitoring of the thrombolytic efficiency using MRI

Structural Brain Network Alteration and its Correlation With Structural Impairments in Patients With Depression in de novo and Drug-Naïve Parkinson's Disease

Purpose: Depression is common in Parkinson's disease (PD) and is correlated with the severity of motor deficits and quality of life. The present study aimed to investigate alterations in the structural brain network related to depression in Parkinson's disease (d-PD) and their correlations with structural impairments of white matter (WM).Materials and Methods: Data were acquired from the Parkinson Progression Markers Initiative (PPMI) database. A total of 84 de novo and drug-naïve PD patients were screened and classified into two groups according to the 15-item Geriatric Depression Scale (GDS-15): d-PD (n = 28) and nondepression in PD (nd-PD, n = 56). Additionally, 37 healthy controls (HC) were screened. All subjects underwent DTI and 3D-T1WI on a 3.0 T MR scanner. Individual structural brain networks were constructed and analyses were performed using graph theory and network-based statistics (NBS) at both global and local levels. Differences in global topological properties were explored among the three groups. The association models between node and edge changes and the GDS-15 were constructed to detect regions that were specifically correlated with d-PD. Tract-based spatial statistics (TBSS) was used to detect structural impairments of WM between the d-PD and nd-PD groups. The correlations between altered global topological properties and structural impairments were analyzed in the d-PD group.Results: The global efficiency and characteristic path length of the structural brain network were impaired in the d-PD group compared with those in the nd-PD and HC groups. Thirteen nodes and 1 subnetwork with 10 nodes and 12 edges specifically correlated with d-PD were detected. The left hippocampus, left parahippocampal, left lingual, left middle occipital, left inferior occipital, left fusiform, left middle temporal, and left inferior temporal regions were all involved in the results of node and edge analysis. No WM microstructural impairments were identified in the d-PD group.Conclusion: Our study suggests that the integration of the structural brain network is impaired with disrupted connectivity of limbic system and visual system in the de novo and drug-naïve d-PD patients.The topological properties assessing integration of the structural brain network can serve as a potential objective neuroimaging marker for early diagnosis of d-PD

MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018

Differentiating hepatocellular carcinoma (HCC) from other primary liver malignancies in the Liver Imaging Reporting and Data System (LI-RADS) M (LR-M) tumours noninvasively is critical for patient treatment options, but visual evaluation based on medical images is a very challenging task. This study aimed to evaluate whether magnetic resonance imaging (MRI) models based on radiomics features could further improve the ability to classify LR-M tumour subtypes. A total of 102 liver tumours were defined as LR-M by two radiologists based on LI-RADS and were confirmed to be HCC (n = 31) and non-HCC (n = 71) by surgery. A radiomics signature was constructed based on reproducible features using the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression algorithms with tenfold cross-validation. Logistic regression modelling was applied to establish different models based on T2-weighted imaging (T2WI), arterial phase (AP), portal vein phase (PVP), and combined models. These models were verified independently in the validation cohort. The area under the curve (AUC) of the models based on T2WI, AP, PVP, T2WI + AP, T2WI + PVP, AP + PVP, and T2WI + AP + PVP were 0.768, 0.838, 0.778, 0.880, 0.818, 0.832, and 0.884, respectively. The combined model based on T2WI + AP + PVP showed the best performance in the training cohort and validation cohort. The discrimination efficiency of each radiomics model was significantly better than that of junior radiologists’ visual assessment (p < 0.05; Delong). Therefore, the MRI-based radiomics models had a good ability to discriminate between HCC and non-HCC in LR-M tumours, providing more options to improve the accuracy of LI-RADS classification