Is atezolizumab plus bevacizumab as first-line therapy for unresectable hepatocellular carcinoma superior to lenvatinib? a systematic review and meta­analysis.

BACKGROUND: This meta-analysis was dedicated to evaluating the effectiveness and safety of Atezolizumab plus Bevacizumab (Atez/Bev) and Lenvatinib (LEN) as first-line systematic therapy for unresectable hepatocellular carcinoma (u-HCC). METHODS: The prospective protocol for this study was registered with the PROSPERO (Registration number: CRD42022356874). Literature searches were conducted in PubMed, EMBASE database Cochrane Library, and Web Science to determine all clinical controlled studies that reported Atez/Bev and LEN for treating u-HCC. We. evaluated as primary end-point overall survival (OS) and progression-free survival (PFS), as well as other outcomes such as tumor response and adverse events (AEs).Quality assessment and data extraction of studies were conducted independently by three reviewers. Mean difference (MD) and odds ratio (OR) with 95% confidence interval (CI) were calculated using a fixed-effects or random-effects model. The meta-analysis was performed with RevMan 5.3 software. RESULTS: 12 retrospective cohort studies (RCSs) involving a total of 4948 patients were finally included. The results showed that compared with LEN, Atez/Bev can improve the patient's PFS (HR = 0.80, 95% CI: 0.72 ~ 0.88; p < 0.0001) and reduce the rate of overall AEs (OR = 0.46 95% CI: 0.38 ~ 0.55, p < 0.00001) and grade ≥ 3 AEs (OR = 0.43; 95% CI: 0.36 ~ 0.51, p < 0.00001), while there is no difference between OS and treatment responses rate (objective response rate, disease control rate, complete response, partial response, progressive disease, and stable disease) between two groups. In addition, the subgroup analysis shows that Atez/Bev can promote the OS of patients with viral hepatitis. (HR = 0.79, 95% CI: 0.67 ~ 0.95; p = 0.01), while LEN has an advantage in improving OS in patients with Child-Pugh grade B liver function (HR = 1.98, 95% CI: 1.50 ~ 2.63; p < 0.00001). CONCLUSION: Current evidence shows that compared with LEN, Atez/Bev has more advantages in PFS and safety in treating u-HCC and can improve the OS of patients with viral. LEN has advantages in improving the OS of patients with grade B liver function. However, more multicenter randomized controlled experiments are needed in the future to verify our results.

Application of indocyanine green-mediated fluorescence molecular imaging technology in liver tumors resection: a systematic review and meta-analysis.

Background: This meta-analysis was dedicated to evaluating the safety and effectiveness of indocyanine green (ICG) -mediated fluorescence molecular imaging (FMI) technology in liver tumors resection. Methods: A literature search of PubMed, Embase databases, Cochrane Library, and Web of Science was performed to identify all clinical controlled studies exploring the effects of fluorescence imaging on liver tumors resection. Quality assessment and data extraction of studies were conducted independently by 3 reviewers. Mean difference (MD) and odds ratio (OR) with 95% confidence interval (CI) were calculated using a fixed-effects or random-effects model. The meta-analysis was performed with RevMan 5.3 software. Results: 14 retrospective cohort studies (RCSs) involving a total of 1227 patients were finally included. The results showed that Fluorescence-assisted liver tumors resection could improve the R0 resection rate (OR = 2.63; 95% CI: 1.46~4.73, p = 0.001), reduce overall complications (OR = 0.66; 95% CI: 0.44~0.97, p = 0.04), biliary fistula (OR = 0.20; 95% CI: 0.05~0.77, p = 0.02), intraoperative blood loss (MD = -70.76, 95% CI: -106.11 to -35.41; p < 0.0001), and shortens hospital stay (MD = -1.41, 95% CI: -1.90 to -0.92; p < 0.00001). There were no significant differences in the incidences of operative time (MD = -8.68, 95% CI: -18.59 to -1.22; p = 0.09), complications of grade III or above (OR = 0.73; 95% CI: 0.43~1.25, p = 0.26), liver failure (OR = 0.86; 95% CI: 0.39~1.89, p = 0.71), and blood transfusion (OR = 0.66; 95% CI: 0.42~1.03, p = 0.07). Conclusion: Current evidence suggests that ICG-mediated FMI technology could enhance the clinical effectiveness of patients with liver tumors resection and is clinically worthy of promotion. Systematic review registration: PROSPERO, identifier CRD42022368387.

The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis.

In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.