The ADAM17 inhibitor ZLDI-8 sensitized hepatocellular carcinoma cells to sorafenib through Notch1-integrin ß-talk.

ZLDI-8 is an A disintegrin and metalloproteinase domain 17 (ADAM17) inhibitor that suppresses the shedding of Notch1 to the Notch1 intracellular domain (NICD). In previous studies, we found that ZLDI-8 was able to sensitize HCC to sorafenib, but the mechanism of action remains unclear. The sensitizing effects of ZLDI-8 were tested both in vitro and in vivo. EMT-related factors, sorafenib sensitivity-related proteins and ECM-related gene expression were assessed using immunohistochemistry, RTPCR and Western blotting. Knockdown assays were conducted to determine the relationship between the Notch and Integrin pathways. CoIP assays, nuclear and cytoplasmic fractionation and immunofluorescence colocalization were applied to explore the interaction between the Notch and Integrin pathways. Appropriate statistical analysis methods were used to assess the significance of the experimental results and to ensure the scientific validity and reliability of the experimental design. We found that ECM- and EMT-related proteins were downregulated after ZLDI-8 treatment (P<0.05). ZLDI-8 significantly downregulated Integrinß1 and Integrinß3 in HCC in vitro and in vivo (P<0.05), possibly through Foxc2-dependent regulation. Mechanistically, interfering with the expression of both Integrin-linked kinase (ILK) and the NICD may downregulate the expression of proteins targeted by sorafenib, thereby sensitizing cells to sorafenib. The retroregulation of Integrinß by ILK may occur through the interaction between the NICD and ILK and may be the result of the translocation of the complexus. Our study indicates that blocking the Notch pathway may affect Integrinß through crosstalk between the Notch1 and Integrinß/ILK signaling pathways, thus providing a potential therapeutic strategy for HCC.

Which is the best TACE agent for patients with different NLR hepatocellular carcinomas? A systematic review and network meta-analysis.

Background: Transarterial chemoembolization (TACE) is a common treatment for hepatocellular carcinoma (HCC), but the best therapeutic agent for TACE treatment has not been determined. The neutrophil/lymphocyte ratio (NLR) is a systemic immune system marker; however, the ability of the NLR to predict the prognosis of patients with HCC is unknown, and no studies have been conducted to determine the most appropriate TACE regimen for HCC patients with different NLRs. Methods: The PubMed, Embase, Web of Science, and CNKI databases were searched through May 28, 2023. Comparisons of overall survival (OS) among cohort studies with different NLRs and different TACE treatment regimens were performed with a random effects model. Findings: Thirty-five studies involving 9210 patients were included in this meta-analysis. The results showed that Group 3-4 (NLR<2.5) patients had a significantly longer OS than Group 1-2 (NLR 2.5-5.0). Among the patients, Group 1-3 (NLR 2.0-5.0) patients had the best survival after treatment with adriamycin (lnHR (95 % CI = 0.48 [0.31, 0.75] and lnHR (95 % CI = 0.41 [0.19, 0.91]). Among the Group 4 patients (NLR<2.0), the best outcome was obtained with platinum + adriamycin (lnHR (95 % CI = 0.59 [0.45, 0.78]), followed by adriamycin. A subgroup analysis of TACE combined with other treatments showed that adriamycin combined with sorafenib was the most effective and superior to the other treatment agents. Interpretation: The NLR can be used to predict the prognosis of HCC patients treated with TACE; the higher the NLR is, the worse the prognosis. Adriamycin may be the best therapeutic agent for HCC patients treated with TACE.

Is tumor necrosis factor-α monoclonal therapy with proactive therapeutic drug monitoring optimized for inflammatory bowel disease? Network meta-analysis.

BACKGROUND: The efficacy and safety of anti-tumor necrosis factor-α (TNF-α) monoclonal antibody therapy [adalimumab (ADA) and infliximab (IFX)] with therapeutic drug monitoring (TDM), which has been proposed for inflammatory bowel disease (IBD) patients, are still controversial. AIM: To determine the efficacy and safety of anti-TNF-α monoclonal antibody therapy with proactive TDM in patients with IBD and to determine which subtype of IBD patients is most suitable for proactive TDM interventions. METHODS: As of July 2023, we searched for randomized controlled trials (RCTs) and observational studies in PubMed, Embase, and the Cochrane Library to compare anti-TNF-α monoclonal antibody therapy with proactive TDM with therapy with reactive TDM or empiric therapy. Pairwise and network meta-analyses were used to determine the IBD patient subtype that achieved clinical remission and to determine the need for surgery. RESULTS: This systematic review and meta-analysis yielded 13 studies after exclusion, and the baseline indicators were balanced. We found a significant increase in the number of patients who achieved clinical remission in the ADA [odds ratio (OR) = 1.416, 95% confidence interval (CI): 1.196-1.676] and RCT (OR = 1.393, 95%CI: 1.182-1.641) subgroups and a significant decrease in the number of patients who needed surgery in the proactive vs reactive (OR = 0.237, 95%CI: 0.101-0.558) and IFX + ADA (OR = 0.137, 95%CI: 0.032-0.588) subgroups, and the overall risk of adverse events was reduced (OR = 0.579, 95%CI: 0.391-0.858) according to the pairwise meta-analysis. Moreover, the network meta-analysis results suggested that patients with IBD treated with ADA (OR = 1.39, 95%CI: 1.19-1.63) were more likely to undergo TDM, especially in comparison with patients with reactive TDM (OR = 1.38, 95%CI: 1.07-1.77). CONCLUSION: Proactive TDM is more suitable for IBD patients treated with ADA and has obvious advantages over reactive TDM. We recommend proactive TDM in IBD patients who are treated with ADA.

The efficacy and safety of PI3K and AKT inhibitors for patients with cancer: A systematic review and network meta-analysis.

BACKGROUND: Inhibiting PI3K/AKT pathway activation may hinder the occurrence and progression of cancer. The aim of this study was to evaluate the efficacy and safety of the PI3K/AKT inhibitors and determine the most appropriate inhibitor for different cancer types. METHODS: Electronic databases up to June 2024 were used to examine the efficacy and safety of PI3K inhibitors (alpelisib, copanlisib, duvelisib, and idelalisib) and AKT inhibitors (capivasertib, ipatasertib and MK-2206) for the treatment of cancer. Data was assessed with a random-effect pairwise and network meta-analysis. Randomized controlled trials and retrospective studies were eligible if they compared PI3K or AKT inhibitors with non-PI3K/AKT controls with no restriction. RESULTS: The results were based on 34 studies from 34 published articles and 6 online registration trials (6710 patients). According to pairwise meta-analysis, PI3K/AKT inhibitors showed to be highly effective, especially for treating mutant cancers, but had poor safety profiles. According to our network meta-analysis, PI3K/AKT inhibitors, especially the AKT inhibitor capivasertib, are effective for treating solid cancers such as breast cancer (BC). Moreover, PI3K inhibitors, especially idelalisib, were effective for treating hematologic cancers such as chronic lymphocytic leukemia (CLL). CONCLUSIONS: The PI3K/AKT inhibitors are effective in patients with genetic mutations. For solid cancers such as BC, capivasertib was efficacy and safety. For hematological cancers represented by CLL, idelalisib was efficacy and safety. The above studies can be used when recommending appropriate targeted therapies for patients with different cancer types.

A new intelligent bearing fault diagnosis model based on triplet network and SVM.

Separating sensitive characteristic signals from original vibration data is an important challenge for rolling bearing fault diagnosis. Because it is difficult to obtain large number of damaged bearings, Rolling bearing fault datasets are often small sample datasets. For the classification of small sample rolling bearing fault datasets, we propose a coupling vibration data classification method based on triplet embedding. The method is divided into two steps: feature extraction and fault identification. First, build a triple embedding based on the CNN model to reduce the original vibration signal, and then train the SVM model for classification. Compared with traditional features and autoencoder, triplet network can learn the differences between samples. Make classification training easier and more accurate. We have evaluated the performance of this method through two bearing experiment examples. The experimental results show that this method is superior to stacked autoencoder, stacked denoising autoencoder and CNN.