Drug-eluting bead transarterial chemoembolization for hepatocellular carcinoma: does size really matter?

PURPOSEWe aimed to compare the safety and effectiveness of 100–300 μm versus 300–500 μm drug-eluting bead transarterial chemoembolization (DEB-TACE) and to investigate the impact of tumor and feeding artery size on treatment outcome of different particle sizes in the treatment of hepatocellular carcinoma (HCC).METHODSThis retrospective cohort study enrolled 234 consecutive patients who underwent TACE using 100–300 μm DEB (Group A, n=75) and 300–500 μm DEB (Group B, n=159) in a tertiary center between August 2012 and March 2017. Initial treatment response and adverse events were assessed using modified Response Evaluation Criteria in Solid Tumors (mRECIST) and National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) version 5.0, respectively.RESULTSA total of 704 HCCs in 234 patients were evaluated. The average index tumor size was 3.8 cm. Multivariate analysis showed that tumor size, lobe involvement, particle size, and tumor location were significant predictive factors of complete response. The overall rate of complete response in groups A and B were 56.0% and 33.3% (P = 0.001), respectively. Group A had higher complete response rate than group B in the subgroup of BCLC B with tumor <3 cm (57.9% vs. 21.1%; P = 0.020) and subgroup of feeding artery ≥0.9 mm (55.2% vs. 30.9%; P = 0.014). There were fewer major complications in group A compared with group B (0% vs. 6.9%, P = 0.018).CONCLUSIONTACE with 100–300 μm DEB is associated with better initial treatment response and fewer major complications compared with 300–500 μm. Our study also highlights the impact of tumor characteristics on treatment outcome of different DEB size, which might help to select the optimal sphere size for TACE in the treatment of HCC

Efficacy and safety of splenic artery embolization for intractable ascites using Amplatzer vascular plug versus coil after living donor liver transplantation

PURPOSEIntractable ascites (IA) is an uncommon but challenging complication after liver transplantation. Splenic artery embolization (SAE) modulates the splenic artery and regulates portal flow. This study aimed to evaluate the efficacy and safety of SAE using the Amplatzer vascular plug (AVP) versus coil embolization for post-living-donor liver transplantation (LDLT) IA.METHODSThis retrospective study evaluated consecutive patients from 1 center who received LDLT (n=1410) between March 2006 and August 2019. The inclusion criteria for SAE were splenomegaly with IA after LDLT.RESULTSTotally 15 patients underwent SAE for post-LDLT IA. Eleven patients who received AVP embolization (age, 51.2 ± 15.1 years; range, 8-63 years; 5 men and 6 women) were compared with 4 patients receiving coil embolization (age, 30.8 ± 30.8 years; range, 1.5-63 years; 2 men and 2 women). AVP and coil embolization both significantly reduced portal vein hyperflow (plug/ coil; P <.001/.006) and decreased ascites volume (plug/coil; P <.003/.042). The benefits of AVP embolization included shorter procedure time (P =.029), significantly reduced splenic volume (P =.012), increased liver volume (P =.012), decreased spleen/liver ratio (P =.012), and improvement of pancytopenia (P =.008) due to secondary hypersplenism. No significant differences were found between the two groups in the length of hospital stay or complications such as splenic infarction, pancreatitis, or sepsis.CONCLUSIONSAE using AVP and coil embolization provide effective and safe methods for managing patients with IA after LDLT. AVP embolization may be more efficient than coil embolization, providing more effective reduction of ascites volume and the advantages of shortened procedure time and improvement of hypersplenism

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Ultraviolet Photodetecting and Plasmon-to-Electric Conversion of Controlled Inkjet-Printing Thin-Film Transistors

Direct ink-jet printing of a zinc-oxide-based thin-film transistor (ZnO-based TFT) with a three-dimensional (3-D) channel structure was demonstrated for ultraviolet light (UV) and visible light photodetection. Here, we demonstrated the channel structures by which temperature-induced Marangoni flow can be used to narrow the channel width from 318.9 &plusmn; 44.1 &mu;m to 180.1 &plusmn; 13.9 &mu;m via a temperature gradient. Furthermore, a simple and efficient oxygen plasma treatment was used to enhance the electrical characteristics of switching ION/IOFF ratio of approximately 105. Therefore, the stable and excellent gate bias-controlled photo-transistors were fabricated and characterized in detail for ultraviolet (UV) and visible light sensing. The photodetector exhibited a superior photoresponse with a significant increase of more than 2 orders of magnitude larger drain current generated upon UV illumination. The results could be useful for the development of UV photodetectors by the direct-patterning ink-jet printing technique. Additionally, we also have successfully demonstrated that a metal-semiconductor junction structure that enables plasmon energy detection by using the plasmonic effects is an efficient conversion of plasmon energy to an electrical signal. The device showed a significant variations negative shift of threshold voltage under different light power density with exposure of visible light. With the ZnO-based TFTs, only ultraviolet light detection extends to the visible light wavelength

Highly Transparent and Surface-Plasmon-Enhanced Visible-Photodetector Based on Zinc Oxide Thin-Film Transistors with Heterojunction Structure

Highly transparent zinc oxide (ZnO)-based thin-film transistors (TFTs) with gold nanoparticles (AuNPs) capable of detecting visible light were fabricated through spray pyrolysis on a fluorine-doped tin oxide substrate. The spray-deposited channel layer of ZnO had a thickness of approximately 15 nm, and the thickness exhibited a linear increase with an increasing number of sprays. Furthermore, the ZnO thin-film exhibited a markedly smoother channel layer with a significantly lower surface roughness of 1.84 nm when the substrate was 20 cm from the spray nozzle compared with when it was 10 cm away. Finally, a ZnO and Au-NP heterojunction nanohybrid structure using plasmonic energy detection as an electrical signal, constitutes an ideal combination for a visible-light photodetector. The ZnO-based TFTs convert localized surface plasmon energy into an electrical signal, thereby extending the wide band-gap of materials used for photodetectors to achieve visible-light wavelength detection. The photo-transistors demonstrate an elevated on-current with an increase of the AuNP density in the concentration of 1.26, 12.6, and 126 pM and reach values of 3.75, 5.18, and 9.79 &times; 10&minus;7 A with applied gate and drain voltages. Moreover, the threshold voltage (Vth) also drifts to negative values as the AuNP density increases

Development of Novel Residual-Dense-Attention (RDA) U-Net Network Architecture for Hepatocellular Carcinoma Segmentation

The research was based on the image recognition technology of artificial intelligence, which is expected to assist physicians in making correct decisions through deep learning. The liver dataset used in this study was derived from the open source website (LiTS) and the data provided by the Kaohsiung Chang Gung Memorial Hospital. CT images were used for organ recognition and lesion segmentation; the proposed Residual-Dense-Attention (RDA) U-Net can achieve high accuracy without the use of contrast. In this study, U-Net neural network was used to combine ResBlock in ResNet with Dense Block in DenseNet in the coder part, allowing the training to maintain the parameters while reducing the overall recognition computation time. The decoder was equipped with Attention Gates to suppress the irrelevant areas of the image while focusing on the significant features. The RDA model was used to identify and segment liver organs and lesions from CT images of the abdominal cavity, and excellent segmentation was achieved for the liver located on the left side, right side, near the heart, and near the lower abdomen with other organs. Better recognition was also achieved for large, small, and single and multiple lesions. The study was able to reduce the overall computation time by about 28% compared to other convolutions, and the accuracy of liver and lesion segmentation reached 96% and 94.8%, with IoU values of 89.5% and 87%, and AVGDIST of 0.28 and 0.80, respectively

Macro-regenerative nodules in biliary atresia: CT/MRI findings and their pathological relations

AIM: To describe the radiological findings of a macro-regenerative nodule (MRN) in the liver of pre-transplantation biliary atresia (BA) patients and to correlate it with histological findings

In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition

Cancer is one of the common diseases. Quantitative biomarkers extracted from standard-of-care computed tomography (CT) scan can create a robust clinical decision tool for the diagnosis of hepatocellular carcinoma (HCC). According to the current clinical methods, the situation usually accounts for high expenditure of time and resources. To improve the current clinical diagnosis and therapeutic procedure, this paper proposes a deep learning-based approach, called Successive Encoder-Decoder (SED), to assist in the automatic interpretation of liver lesion/tumor segmentation through CT images. The SED framework consists of two different encoder-decoder networks connected in series. The first network aims to remove unwanted voxels and organs and to extract liver locations from CT images. The second network uses the results of the first network to further segment the lesions. For practical purpose, the predicted lesions on individual CTs were extracted and reconstructed on 3D images. The experiments conducted on 4300 CT images and LiTS dataset demonstrate that the liver segmentation and the tumor prediction achieved 0.92 and 0.75 in Dice score, respectively, by as-proposed SED method

A Scoring System for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Quantitative Functional MRI

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a histopathological marker and risk factor for HCC recurrence. We integrated diffusion-weighted imaging (DWI) and magnetic resonance (MR) image findings of tumors into a scoring system for predicting MVI. In total, 228 HCC patients with pathologically confirmed MVI who underwent surgical resection or liver transplant between November 2012 and March 2021 were enrolled retrospectively. Patients were divided into a right liver lobe group (n = 173, 75.9%) as the model dataset and a left liver lobe group (n = 55, 24.1%) as the model validation dataset. Multivariate logistic regression identified two-segment involved tumor (Score: 1; OR: 3.14; 95% CI: 1.22 to 8.06; p = 0.017); ADCmin &le; 0.95 &times; 10&minus;3 mm2/s (Score: 2; OR: 10.88; 95% CI: 4.61 to 25.68; p = 0.000); and largest single tumor diameter &ge; 3 cm (Score: 1; OR: 5.05; 95% CI: 2.25 to 11.30; p = 0.000), as predictive factors for the scoring model. Among all patients, sensitivity was 89.66%, specificity 58.04%, positive predictive value 68.87%, and negative predictive value 84.41%. For validation of left lobe group, sensitivity was 80.64%, specificity 70.83%, positive predictive value 78.12%, and negative predictive value 73.91%. The scoring model using ADCmin, largest tumor diameter, and two-segment involved tumor provides high sensitivity and negative predictive value in MVI prediction for use in routine functional MR