Hepatic-associated vascular morphological assessment to predict overt hepatic encephalopathy before TIPS: a multicenter study.

BACKGROUND: To provide patients the chance of accepting curative transjugular intrahepatic portosystemic shunt (TIPS) rather than palliative treatments for portal hypertension-related variceal bleeding and ascites, we aimed to assess hepatic-associated vascular morphological change to improve the predictive accuracy of overt hepatic encephalopathy (HE) risks. METHODS: In this multicenter study, 621 patients undergoing TIPS were subdivided into training (413 cases from 3 hospitals) and external validation datasets (208 cases from another 3 hospitals). In addition to traditional clinical factors, we assessed hepatic-associated vascular morphological changes using maximum diameter (including absolute and ratio values). Three predictive models (clinical, hepatic-associated vascular, and combined) were constructed using logistic regression. Their discrimination and calibration were compared to test the necessity of hepatic-associated vascular assessment and identify the optimal model. Furthermore, to verify the improved performance of ModelC-V, we compared it with four previous models, both in discrimination and calibration. RESULTS: The combined model outperformed the clinical and hepatic-associated vascular models (training: 0.814, 0.754, 0.727; validation: 0.781, 0.679, 0.776; p < 0.050) and had the best calibration. Compared to previous models, ModelC-V showed superior performance in discrimination. The high-, middle-, and low-risk populations displayed significantly different overt HE incidence (p < 0.001). Despite the limited ability of pre-TIPS ammonia to predict overt HE risks, the combined model displayed a satisfactory ability to predict overt HE risks, both in the low- and high-ammonia subgroups. CONCLUSION: Hepatic-associated vascular assessment improved the predictive accuracy of overt HE, ensuring curative chances by TIPS for suitable patients and providing insights for cirrhosis-related studies.

Myosteatosis is an independent risk factor for overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunting.

OBJECTIVE: The relationship between skeletal muscle and adipose tissue compositions and risk of overt hepatic encephalopathy (OHE) following transjugular intrahepatic portosystemic shunt (TIPS) treatment needs to be investigated. METHODS: A total of 282 patients were collected from two medical centres. The median time of follow-up was 48.23 + 1.36 months and the first-year results of all patients after TIPS therapy were collected. The muscle and adipose tissue indices were quantified at the third lumbar vertebra level. Sarcopenia and myosteatosis were defined according to previous researches. Receiver operating characteristic curves, chi-square test, univariate and multivariate logistic regression analyses were employed to investigate the potential association between muscle and adipose indices, sarcopenia, myosteatosis and the risk of developing post-TIPS OHE. RESULTS: All skeletal muscle indices, adipose tissue indices and sarcopenia had limited associations with post-TIPS OHE. Myosteatosis (148 cases, 52.5%, 55 with OHE, 37.2%) was identified as an independent risk factor for post-TIPS OHE. with P  < 0.001 in Chi-square test, P  < 0.001, odds ratio (OR): 2.854, 95% confidence interval (CI): 1.632-4.993 in univariate logistic regression analyses, and P  = 0.007, OR: 2.372, 95% CI: 1.268-4.438 in multivariate logistic regression analyses, respectively. CONCLUSION: Our results showed that myosteatosis was proven as an independent risk factor for the development of post-TIPS OHE.

A modified model for predicting mortality after transjugular intrahepatic portosystemic shunt: A multicentre study.

BACKGROUND AND AIMS: The transjugular intrahepatic portosystemic shunt has controversial survival benefits; thus, patient screening should be performed preoperatively. In this study, we aimed to develop a model to predict post-transjugular intrahepatic portosystemic shunt mortality to aid clinical decision making. METHODS: A total of 811 patients undergoing transjugular intrahepatic portosystemic shunt from five hospitals were divided into the training and external validation data sets. A modified prediction model of post-transjugular intrahepatic portosystemic shunt mortality (ModelMT ) was built after performing logistic regression. To verify the improved performance of ModelMT , we compared it with seven previous models, both in discrimination and calibration. Furthermore, patients were stratified into low-, medium-, high- and extremely high-risk subgroups. RESULTS: ModelMT demonstrated a satisfying predictive efficiency in both discrimination and calibration, with an area under the curve of .875 in the training set and .852 in the validation set. Compared to previous models (ALBI, BILI-PLT, MELD-Na, MOTS, FIPS, MELD, CLIF-C AD), ModelMT showed superior performance in discrimination by statistical difference in the Delong test, net reclassification improvement and integrated discrimination improvement (all p < .050). Similar results were observed in calibration. Low-, medium-, high- and extremely high-risk groups were defined by scores of ≤160, 160-180, 180-200 and >200, respectively. To facilitate future clinical application, we also built an applet for ModelMT . CONCLUSIONS: We successfully developed a predictive model with improved performance to assist in decision making for transjugular intrahepatic portosystemic shunt according to survival benefits.

Model containing sarcopenia and visceral adiposity can better predict the prognosis of hepatocellular carcinoma: a multicenter study.

AIM: This study aimed to explore whether the addition of sarcopenia and visceral adiposity could improve the accuracy of model predicting progression-free survival (PFS) in hepatocellular carcinoma (HCC). METHODS: In total, 394 patients with HCC from five hospitals were divided into the training and external validation datasets. Patients were initially treated by liver resection or transarterial chemoembolization. We evaluated adipose and skeletal muscle using preoperative computed tomography imaging and then constructed three predictive models, including metabolic (ModelMA), clinical-imaging (ModelCI), and combined (ModelMA-CI) models. Their discrimination, calibration, and decision curves were compared, to identify the best model. Nomogram and subgroup analysis was performed for the best model. RESULTS: ModelMA-CI containing sarcopenia and visceral adiposity had good discrimination and calibrations (integrate area under the curve for PFS was 0.708 in the training dataset and 0.706 in the validation dataset). ModelMA-CI had better accuracy than ModelCI and ModelMA. The performance of ModelMA-CI was not affected by treatments or disease stages. The high-risk subgroup (scored > 198) had a significantly shorter PFS (p < 0.001) and poorer OS (p < 0.001). CONCLUSIONS: The addition of sarcopenia and visceral adiposity improved accuracy in predicting PFS in HCC, which may provide additional insights in prognosis for HCC in subsequent studies.

3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study.

BACKGROUND: Overt hepatic encephalopathy (HE) should be predicted preoperatively to identify suitable candidates for transjugular intrahepatic portosystemic shunt (TIPS) instead of first-line treatment. This study aimed to construct a 3D assessment-based model to predict post-TIPS overt HE. METHODS: In this multi-center cohort study, 487 patients who underwent TIPS were subdivided into a training dataset (390 cases from three hospitals) and an external validation dataset (97 cases from another two hospitals). Candidate factors included clinical, vascular, and 2D and 3D data. Combining the least absolute shrinkage and operator method, support vector machine, and probability calibration by isotonic regression, we constructed four predictive models: clinical, 2D, 3D, and combined models. Their discrimination and calibration were compared to identify the optimal model, with subgroup analysis performed. RESULTS: The 3D model showed better discrimination than did the 2D model (training: 0.719 vs. 0.691; validation: 0.730 vs. 0.622). The model combining clinical and 3D factors outperformed the clinical and 3D models (training: 0.802 vs. 0.735 vs. 0.719; validation: 0.816 vs. 0.723 vs. 0.730; all p < 0.050). Moreover, the combined model had the best calibration. The performance of the best model was not affected by the total bilirubin level, Child-Pugh score, ammonia level, or the indication for TIPS. CONCLUSION: 3D assessment of the liver and the spleen provided additional information to predict overt HE, improving the chance of TIPS for suitable patients. 3D assessment could also be used in similar studies related to cirrhosis.

Engineering the Properties of Transparent Hybrid Coating toward High Hardness, Excellent Flexibility, and Multifunction.

Transparent functional coatings with glass-like hardness and polymer-like flexibility are highly desirable for flexible and foldable displays. Although several coatings have been developed toward this goal, achieving a functional coating with 9H pencil hardness and extremely low bending radius of curvature (rc) remains a great challenge due to the inherent conflict between hardness and flexibility. To overcome this trade-off, a facile strategy is developed herein. The coating is an organic-inorganic hybrid nanocomposite that is prepared from thiol-acrylate polymerization of acrylo polyhedral oligomeric silsesquioxane and multifunctional thiols. The former provides the desired hardness, while the latter affords high flexibility and the maximum level of chemical bonding for organic-inorganic phases. Because of the good miscibility and varied functionality of monomers, we are able to manipulate the composition and internal structure of coating systematically, endowing it with high transparency (98%, 550 nm), super hardness (9H), excellent low modulus (1.85 GPa, the most flexible one to date), and the ability to withstand steel wool's abrasion and repeated bending (rc = 0.8 mm) 10 000 times on PET film. On the final coating, both antifouling and antibacterial abilities are integrated without sacrificing its other properties after postfunctionalizing a zwitterionic layer. This work balances the hardness-flexibility conflict effectively and provides some useful protective coatings for next-generation displays.

Preoperative prediction of overt hepatic encephalopathy caused by transjugular intrahepatic portosystemic shunt.

PURPOSE: Preoperative prediction of overt hepatic encephalopathy (OHE) should be performed in patients with variceal bleeding treated using the transjugular intrahepatic portosystemic shunt (TIPS) procedure. A reliable prediction tool is therefore required. METHOD: Patients with cirrhosis-related variceal bleeding treated using the TIPS procedure were screened at two hospitals. Patients classified as Child-Pugh Class B were identified. The least absolute shrinkage and selection operator method and the backward stepwise selection method were used to screen the clinical and radiological characteristics of participants. Then, models were constructed accordingly to predict OHE. Area under the receiver operating characteristic curves, calibration curves, and decision curves were performed to discover the optimal model. Finally, whether clinical factors influenced the performance of our optimal model was tested. RESULTS: A total of 191 patients were included (training cohort: 127 cases; validation cohort: 64 cases). Three novel radiological independent risk factors were found. The combined model outperformed the models containing clinical factors or radiological characteristics alone. The areas under the curve for the training and validation cohorts were 0.901 and 0.903, respectively, with satisfactory calibration and decision curves. The Model for End-Stage Liver Disease score, serum sodium, albumin, total bilirubin, and age exhibited limited influence on the performance of the combined model. CONCLUSIONS: These radiological characteristics are also independent risk factors for post-TIPS OHE. Combining clinical factors and radiological characteristics was an effective means of predicting OHE. This study's model could be used for preoperative selection of appropriate patients before the TIPS procedure is performed.

Prior Knowledge-Aware Fusion Network for Prediction of Macrovascular Invasion in Hepatocellular Carcinoma.

Macrovascular invasion (MaVI) is a major threat to survival in hepatocellular carcinoma (HCC), which should be treated as early as possible to ensure safety and efficacy. In this aspect, MaVI prediction can be helpful. However, MaVI prediction is difficult because of the inter-class similarity and intra-class variation of HCC in computed tomography (CT) images. Moreover, existing methods fail to include clinical priori knowledge associated with HCC, leading to incomprehensive information extraction. In this paper, we proposed a prior knowledge-aware fusion network (PKAFnet) to accurately achieve MaVI prediction in CT images. First, a perception module was presented to extract features related to tumor marginal heterogeneity in the graph domain, which contributed to rotation invariance and captured intensity variations of tumor margin. Second, a tumor segmentation network was built to obtain global information of a 3D tumor image and information associated with tumor internal heterogeneity in the image domain. Finally, multi-domain features associated with the tumor margin and tumor region were combined by using a multi-domain attentional feature fusion module. Thus, by incorporating MaVI-related prior knowledge, our PKAFnet can alleviate overfitting, which can improve the discriminative ability. The proposed PKAFnet was validated on a multi-center dataset, and remarkable performance was achieved in an independent testing set. Moreover, the interpretability of perception module and segmentation network were presented in our paper, which illustrated the effectiveness and credibility of PKAFnet. Therefore, the proposed method showed great application potential for MaVI prediction.

CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study.

BACKGROUND: Macrovascular invasion (MaVI) occurs in nearly half of hepatocellular carcinoma (HCC) patients at diagnosis or during follow-up, which causes severe disease deterioration, and limits the possibility of surgical approaches. This study aimed to investigate whether computed tomography (CT)-based radiomics analysis could help predict development of MaVI in HCC. METHODS: A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups. CT-based radiomics signature was built via multi-strategy machine learning methods. Afterwards, MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model (CRIM, clinical-radiomics integrated model) via random forest modeling. Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development. Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development, progression-free survival (PFS), and overall survival (OS) based on the selected risk factors. RESULTS: The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors (P < 0.001). CRIM could predict MaVI with satisfactory areas under the curve (AUC) of 0.986 and 0.979 in the training (n = 154) and external validation (n = 72) datasets, respectively. CRIM presented with excellent generalization with AUC of 0.956, 1.000, and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory. Peel9_fos_InterquartileRange [hazard ratio (HR) = 1.98; P < 0.001] was selected as the independent risk factor. The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development (P < 0.001), PFS (P < 0.001) and OS (P = 0.002). CONCLUSIONS: The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

Multi-task deep learning network to predict future macrovascular invasion in hepatocellular carcinoma.

BACKGROUND: Models predicting future macrovascular invasion in hepatocellular carcinoma are constructed to assist timely interventions. METHODS: A total of 366 HCC cases were retrospectively collected from five Chinese hospitals between April 2007 and November 2016: the training dataset comprised 281 patients from four hospitals; the external validation dataset comprised 85 patients from another hospital. Multi-task deep learning network-based models were constructed to predict future macrovascular invasion. The discrimination, calibration, and decision curves were compared to identify the best model. We compared the time to macrovascular invasion and overall survival using the best model and related image heterogeneity scores (H-score). Then, we determined the need for a segmentation subnet or the replacement deep learning algorithm by logistic regression in screening clinical/radiological factors. Finally, an applet was constructed for future application. FINDINGS: The best model combined clinical/radiological factors and radiomic features. It achieved best discrimination (areas under the curve: 0·877 in the training dataset and 0·836 in the validation dataset), calibration, and decision curve. Its performance was not affected by the treatments and disease stages. The subgroups had statistical significance for time to macrovascular invasion (training: hazard ratio [HR] = 0·073, 95% confidence interval [CI]: 0·032-0·167, p < 0·001 and validation: HR = 0·090, 95%CI: 0·022-0·366, p < 0·001) and overall survival (training: HR = 0·344, 95%CI: 0·246-0·547, p < 0·001 and validation: HR = 0·489, 95%CI: 0·279 - 0·859, p = 0·003). Similar results were achieved when the patients were subdivided by the H-score. The subnet for segmentation and end-to-end deep learning algorithms improved the performance of the model. INTERPRETATION: Our multi-task deep learning network-based model successfully predicted future macrovascular invasion. In high-risk populations, besides the current first-line treatments, more therapies may be explored for macrovascular invasion.

Deep Learning-Based Prediction of Future Extrahepatic Metastasis and Macrovascular Invasion in Hepatocellular Carcinoma.

PURPOSE: For timely treatment of extrahepatic metastasis and macrovascular invasion (aggressive progressive disease [PD]) in hepatocellular carcinoma, models aimed at stratifying the risks of subsequent aggressive PD should be constructed. PATIENTS AND METHODS: After dividing 332 patients from five hospitals into training (n = 236) and validation (n = 96) datasets, non-invasive models, including clinical/semantic factors (ModelCS), deep learning radiomics (ModelD), and both (ModelCSD), were constructed to stratify patients according to the risk of aggressive PD. We examined the discrimination and calibration; similarly, we plotted a decision curve and devised a nomogram. Furthermore, we performed analyses of subgroups who received different treatments or those in different disease stages and compared time to aggressive PD and overall survival in the high- and low-risk subgroups. RESULTS: Among the constructed models, ModelCSD, combining clinical/semantic factors and deep learning radiomics, outperformed ModelCS and ModelD (areas under the curve [AUCs] for the training dataset: 0.741, 0.815, and 0.856; validation dataset: 0.780, 0.836, and 0.862), with statistical difference per the net reclassification improvement, the integrated discrimination improvement, and/or the DeLong test in both datasets. Besides, ModelCSD had the best calibration and decision curves. The performance of ModelCSD was not affected by treatment types (AUC: resection = 0.839; transarterial chemoembolization = 0.895; p = 0.183) or disease stages (AUC: BCLC [Barcelona Clinic Liver Cancer] stage 0 and A = 0.827; BCLC stage AB &B = 0.861; p = 0.537). Moreover, the high-risk group had a significantly shorter median time to aggressive PD than the low-risk group (training dataset hazard ratio [HR] = 0.108, p < 0.001; validation dataset HR = 0.058, p < 0.001) and poorer overall survival (training dataset HR = 0.357, p < 0.001; validation dataset HR = 0.204, p < 0.001). CONCLUSION: Our deep learning-based model successfully stratified the risks of aggressive PD. In the high-risk population, current guideline indicates that first-line treatments are insufficient to prevent extrahepatic metastasis and macrovascular invasion and ensure survival benefits, so more therapies may be explored for these patients.

Prediction of overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt treatment: a cohort study.

BACKGROUND/PURPOSE: Overt hepatic encephalopathy (HE) risk should be preoperatively predicted to identify patients suitable for curative transjugular intrahepatic portosystemic shunt (TIPS) instead of palliative treatments. METHODS: A total of 185 patients who underwent TIPS procedure were randomised (130 in the training dataset and 55 in the validation dataset). Clinical factors and imaging characteristics were assessed. Three different models were established by logistic regression analyses based on clinical factors (ModelC), imaging characteristics (ModelI), and a combination of both (ModelCI). Their discrimination, calibration, and decision curves were compared, to identify the best model. Subgroup analysis was performed for the best model. RESULTS: ModelCI, which contained two clinical factors and two imaging characteristics, was identified as the best model. The areas under the curve of ModelC, ModelI, and ModelCI were 0.870, 0.963, and 0.978 for the training dataset and 0.831, 0.971, and 0.969 for the validation dataset. The combined model outperformed the clinical and imaging models in terms of calibration and decision curves. The performance of ModelCI was not influenced by total bilirubin, Child-Pugh stages, model of end-stage liver disease score, or ammonia. The subgroup with a risk score ≥ 0.88 exhibited a higher proportion of overt HE (training dataset: 13.3% vs. 97.4%, p < 0.001; validation dataset: 0.0% vs. 87.5%, p < 0.001). CONCLUSION: Our combination model can successfully predict the risk of overt HE post-TIPS. For the low-risk subgroup, TIPS can be performed safely; however, for the high-risk subgroup, it should be considered more carefully.

cMET promotes metastasis and epithelial-mesenchymal transition in colorectal carcinoma by repressing RKIP.

Increasing evidence indicates that c-mesenchymal-epithelial transition factor (cMET) plays an important role in the malignant progression of colorectal cancer (CRC). However, the underlying mechanism is not fully understood. As a metastasis suppressor, raf kinase inhibitory protein (RKIP) loss has been reported in many cancer types. In this study, the expression levels of cMET and RKIP in CRC tissues and cell lines were determined, and their crosstalk and potential biological effects were explored in vitro and in vivo. Our results showed that cMET was inversely correlated with RKIP. Both cMET upregulation and RKIP downregulation indicated poor clinical outcomes. Moreover, the MAPK/ERK signaling pathway was implicated in the regulation of cMET and RKIP. Overexpression of cMET promoted tumor cell epithelial-mesenchymal transition, invasion, migration, and chemoresistance, whereas the effects could be efficiently inhibited by increased RKIP. Notably, small hairpin RNA-mediated cMET knockdown dramatically suppressed cell proliferation, although no RKIP-induced influence on cell growth was observed in CRC. Altogether, cMET overexpression may contribute to tumor progression by inhibiting the antioncogene RKIP, providing preclinical justification for targeting RKIP to treat cMET-induced metastasis of CRC.

SUMO1 modification of methyltransferase-like 3 promotes tumor progression via regulating Snail mRNA homeostasis in hepatocellular carcinoma.

Rationale: Hepatocellular carcinoma (HCC) is one of the leading causes of mortality worldwide. Methyltransferase-like 3 (Mettl3), an RNA N6-methyladenosine (m6A) methyltransferase, has been shown to act as an oncogene in several human cancers. However, the regulatory role of posttranslational modifications of Mettl3 in liver cancer remains elusive. Methods: SUMOylation was analyzed using immunoprecipitation and western blot assays. In vitro and in vivo biological functions were examined using MTS, colony formation, wound healing, transwell, apoptosis, and viability assays and the BALB/c nude mouse model, respectively. Immunohistochemistry was conducted to evaluate the prognostic value of Mettl3 expression in HCC. The regulatory mechanism of Mettl3 in HCC was investigated by m6A dot blot, immunofluorescence, dual luciferase reporter, protein stability, and RNA stability assays. Results: Mettl3 was found to be SUMOylated by a small ubiquitin-like modifier SUMO1. Further, SUMOylation of Mettl3 was increased upon mitogen stimulation, which correlated with UBC9 upregulation, and was positively correlated with high metastatic potential of liver cancer. Finally, SUMOylation of Mettl3 was found to regulate HCC progression via controlling Snail mRNA homeostasis in an m6A methyltransferase activity-dependent manner. Conclusions: This study revealed a novel mechanism of SUMOylated Mettl3-mediated Snail mRNA homeostasis, identifying the UBC9/SUMOylated Mettl3/Snail axis as a novel mediator of the SUMO pathway involved in HCC progression.

Selection Between Liver Resection Versus Transarterial Chemoembolization in Hepatocellular Carcinoma: A Multicenter Study.

OBJECTIVES: Models should be developed to assist choice between liver resection (LR) and transarterial chemoembolization (TACE) for hepatocellular carcinoma. METHODS: After separating 520 cases from 5 hospitals into training (n = 302) and validation (n = 218) data sets, we weighted the cases to control baseline difference and ensured the causal effect between treatments (LR and TACE) and estimated progression-free survival (PFS) difference. A noninvasive PFS model was constructed with clinical factors, radiological characteristics, and radiomic features. We compared our model with other 4 state-of-the-art models. Finally, patients were classified into subgroups with and without significant PFS difference between treatments. RESULTS: Our model included treatments, age, sex, modified Barcelona Clinic Liver Cancer stage, fusion lesions, hepatocellular carcinoma capsule, and 3 radiomic features, with good discrimination and calibrations (area under the curve for 3-year PFS was 0.80 in the training data set and 0.75 in the validation data set; similar results were achieved in 1- and 2-year PFS). The model had better accuracy than the other 4 models. A nomogram was built, with different scores assigned for LR and TACE. Separated by the threshold of score difference between treatments, for some patients, LR provided longer PFS and might be the better option (training: hazard ratio [HR] = 0.50, P = 0.014; validation: HR = 0.52, P = 0.026); in the others, LR provided similar PFS with TACE (training: HR = 0.84, P = 0.388; validation: HR = 1.14, P = 0.614). TACE may be better because it was less invasive. DISCUSSION: We propose an individualized model predicting PFS difference between LR and TACE to assist in the optimal treatment choice.

Advanced nanomaterials targeting hypoxia to enhance radiotherapy.

Hypoxia within solid tumors is often responsible for the failure of radiotherapy. The development of hypoxia-targeting nanomaterials - aimed at enhancing the effect of radiotherapy by electrical or heat effects and at modulating hypoxia in the tumor microenvironment - is a promising strategy to address this issue. We provide an overview of recently developed advanced materials that potentiate radiotherapy. First, we summarize novel materials for oxygen delivery or production to modify the tumor microenvironment, thus improving the effects of ionizing radiation. Second, we present new approaches for the design of high-Z element-based multifunctional nanoplatforms to enhance radiotherapy. Third, novel drug delivery systems for hypoxic regions and hypoxia-inducible factor-1-targeted therapies are discussed. Fourth, we establish the effectiveness of X-ray- or near-infrared-responsive nanoparticles for selectively triggering therapeutic effects under hypoxic conditions. Finally, this review emphasizes the importance of research in the field of nanomedicine focused on tumor hypoxia to improve clinical outcomes.

Morphology and internal structure control over PLA microspheres by compounding PLLA and PDLA and effects on drug release behavior.

The applications of Polylactide (PLA) microspheres in biomedical areas are greatly determined by the size, morphology and internal structure. Taking advantage of the formation of stereocomplex (SC) crystallites between poly(L-lactide) (PLLA) and poly(D-lactide) (PDLA), we propose a facile strategy to prepare PLA microspheres with tunable morphology and crystalline structure by compounding PLLA and PDLA. With increasing PDLA content, the crystallinity of SC-PLA rose gradually until the ratio of PLLA and PDLA reached 1:1 and then fell. Correspondingly, the morphology of the microspheres were varied (smooth, porous, golf-ball like, guava like) and higher crystallinity of SC-PLA would lead to a more coarse and porous structure. Finally, three typical kinds of Rifampicin-loaded microspheres with different ratio of PLLA and PDLA (7:3, 3:7, 10:0, sorted by porosity from high to low) were prepared and the release behavior was compared. At 30 h, the cumulative release of 7:3, 3:7 and 10:0 microspheres were 32.6%, 17.8% and 6.0% respectively, indicating that the release profiles were generally determined by the porosity of the microspheres. Our findings not only provide a new strategy to prepare PLA microspheres with controllable morphology but offer additional possibilities for the applications of SC-PLA products in biomedical area.

Surface markers of liver cancer stem cells and innovative targeted-therapy strategies for HCC.

Liver cancer stem cells (LCSCs) have important roles in the occurrence, development, recurrence, therapy resistance and metastasis of hepatocellular carcinoma (HCC). Therefore, intensive studies are undergoing to identify the mechanisms by which LCSCs contribute to HCC invasion and metastasis, and to design more efficient treatments for this disease. With continuous efforts in LCSC research over the years, therapies targeting LCSCs are thought to have great potential for the clinical treatment and prognosis of liver cancer. Novel LCSC surface markers are continuously discovered and several have been used in targeted therapies to reduce HCC recurrence, metastasis, and drug resistance following tumor resection. The present review describes the surface markers characterizing LCSCs and the recent progress in therapies targeting these markers, including antibodies and polypeptides.

Combination of the neutrophil to lymphocyte ratio and the platelet to lymphocyte ratio as a useful predictor for recurrence following radiofrequency ablation of hepatocellular carcinoma.

The aim of the present study was to investigate the prognostic potential of a novel inflammation-based system, the combination of the neutrophil to lymphocyte ratio (NLR) and the platelet to lymphocyte ratio (PLR) (CNP), for predicting the survival time of patients with hepatocellular carcinoma (HCC) who had received radiofrequency ablation (RFA). A total of 287 HCC patients treated with RFA were enrolled in the study. Patients with an elevated NLR (>2.58) and an elevated PLR (>131.78) were allocated a score of 2, and patients exhibiting one or neither of these characteristics were allocated a score of 1 or 0, respectively. The association between the CNP and various HCC clinicopathological factors, patterns of recurrence and prognoses were analyzed. The CNP was associated with liver cirrhosis (P=0.015), Child-Pugh class (P=0.024), total bilirubin level (P=0.028), neutrophil count (P<0.001), lymphocyte count (P<0.001) and platelet count (P<0.001). Compared with their low-CNP counterparts, patients with an elevated CNP were more likely to develop distant intrahepatic recurrence [52.3% (CNP 2) vs. 33.9% (CNP 0) and 34.6% (CNP 1), P=0.015; CNP 0 vs. CNP 1, P=0.922; CNP 1 vs. CNP 2, P=0.020] and extrahepatic metastasis [25.0% (CNP 2) vs. 7.6% (CNP 0) and 18.5% (CNP 1), P=0.003; CNP 0 vs. CNP 1, P=0.020; CNP 1 vs. CNP 2, P=0.309], and had shorter overall survival (OS) time (CNP 0 vs. CNP 1, P<0.001; CNP 1 vs. CNP 2, P<0.001) and recurrence-free survival (RFS; CNP 0 vs. CNP 1, P=0.012; CNP 1 vs. CNP 2, P=0.004). Moreover, multivariate analysis revealed that the CNP was superior to the NLR and the PLR as an independent prognostic marker of OS and RFS. Therefore, it was concluded that the CNP may represent a useful predictor for recurrence and prognosis in patients with HCC treated with RFA.

Texture analysis of intermediate-advanced hepatocellular carcinoma: prognosis and patients' selection of transcatheter arterial chemoembolization and sorafenib.

Transcatheter arterial chemoembolization (TACE) and sorafenib combination treatment for unselected hepatocellular carcinoma (HCC) is controversial. We explored the potential of texture analysis for appropriate patient selection. There were 261 HCCs included (TACE group: n = 197; TACE plus sorafenib (TACE+Sorafenib) group n = 64). We applied a Gabor filter and wavelet transform with 3 band-width responses (filter 0, 1.0, and 1.5) to portal-phase computed tomography (CT) images of the TACE group. Twenty-one textural parameters per filter were extracted from the region of interests delineated around tumor outline. After testing survival correlations, the TACE group was subdivided according to parameter thresholds in receiver operating characteristic curves and compared to TACE+Sorafenib group survival. The Gabor-1-90 (filter 0) was most significantly correlated with TTP. The TACE group was accordingly divided into the TACE-1 (Gabor-1-90 ≤ 3.6190) and TACE-2 (Gabor-1-90 > 3.6190) subgroups; TTP was similar in the TACE-1 subgroup and TACE+Sorafenib group, but shorter in the TACE-2 subgroup. Only wavelet-3-D (filter 1.0) correlated with overall survival (OS), and was used for subgrouping. The TACE-5 (wavelet-3-D ≤ 12.2620) subgroup and the TACE+Sorafenib group showed similar OS, while the TACE-6 (wavelet-3-D > 12.2620) subgroup had shorter OS. Gabor-1-90 and wavelet-3-D were consistent.Independent of tumor number or size, CT textural parameters are correlated with TTP and OS. Patients with lower Gabor-1-90 (filter 0) and wavelet-3-D (filter 1.0) should be treated with TACE and sorafenib. Texture analysis holds promise for appropriate selection of HCCs for this combination therapy.