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Abstract Background The Cabrol procedure has undergone various modifications and developments since its invention. However, there is a notable gap in the literature regarding meta-analyses assessing it. Methods A systematic review and meta-analysis was conducted to evaluate the effectiveness and long-term outcomes of the Cabrol procedure and its modifications. Pooling was conducted using random effects model. Outcome events were reported as linearized occurrence rates (percentage per patient-year) with 95% confidence intervals. Results A total of 14 studies involving 833 patients (mean age: 50.8 years; 68.0% male) were included in this meta-analysis. The pooled all-cause early mortality was 9.0% (66 patients), and the combined rate of reoperation due to bleeding was 4.9% (17 patients). During the average 4.4-year follow-up (3,727.3 patient-years), the annual occurrence rates (linearized) for complications were as follows: 3.63% (2.79–4.73) for late mortality, 0.64% (0.35–1.16) for aortic root reoperation, 0.57% (0.25–1.31) for hemorrhage events, 0.66% (0.16–2.74) for thromboembolism, 0.60% (0.29–1.26) for endocarditis, 2.32% (1.04–5.16) for major valve-related adverse events, and 0.58% (0.34–1.00) for Cabrol-related coronary graft complications. Conclusion This systematic review provides evidence that the outcomes of the Cabrol procedure and its modifications are acceptable in terms of mortality, reoperation, anticoagulation, and valve-related complications, especially in Cabrol-related coronary graft complications. Notably, the majority of Cabrol procedures were performed in reoperations and complex cases. Furthermore, the design and anastomosis of the Dacron interposition graft for coronary reimplantation, considering natural anatomy and physiological hemodynamics, may promise future advancements in this field.

Abstract Background Intrahepatic cholangiocarcinoma (iCCA) is a highly lethal malignancy characterized by massive fibrosis and has ineffective adjuvant therapies. Here, we demonstrate the potential of angiotensin receptor blockers (ARBs) in targeting iCCA. Methods Masson's trichrome staining was used to assess the effect of ARBs in iCCA specimens, CCK8 and gel contraction assays in vitro and in xenograft models in vivo. RNA‐seq and ATAC‐seq were used for mechanistic investigations. Results Patients with iCCA who were administered ARBs had a better prognosis and a lower proportion of tumour stroma, indicating alleviated fibrosis. The presence of AGTR1, the ARBs receptor, is associated with a poor prognosis of iCCA and is highly expressed in tumour tissues and cancer‐associated fibroblasts (CAFs). The ARBs strongly attenuated the viability of AGTR1+CAFs in vitro and retarded tumour progression and fibrosis in xenograft models of co‐cultured CAFs and iCCA cells. Still, they did not have a significant effect on AGTR1−CAFs. Moreover, ARBs decreased the secretion of AGTR1+CAF‐derived MFAP5 via the Hippo pathway, weakened the interaction between CAFs and iCCA cells, and impaired the aggressiveness of iCCA cells by attenuating the activation of the Notch1 pathway in iCCA cells. Conclusions ARBs exhibit anti‐fibrotic function by inhibiting the viability of AGTR1+CAFs. These findings support using ARBs as a novel therapeutic option for targeting iCCA.

BackgroundAlthough advanced surgical and interventional treatments are available for advanced aortic valve calcification (AVC) with severe clinical symptoms, early diagnosis, and intervention is critical in order to reduce calcification progression and improve patient prognosis. The aim of this study was to develop therapeutic targets for improving outcomes for patients with AVC.Materials and methodsWe used the public expression profiles of individuals with AVC (GSE12644 and GSE51472) to identify potential diagnostic markers. First, the R software was used to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. Subsequently, artificial neural networks were employed to filter and model the diagnostic characteristics for AVC incidence. The diagnostic values were determined using the receiver operating characteristic (ROC) curves. Furthermore, CIBERSORT immune infiltration analysis was used to determine the expression of different immune cells in the AVC. Finally, the CMap database was used to predict candidate small compounds as prospective AVC therapeutics.ResultsA total of 78 strong DEGs were identified. The leukocyte migration and pid integrin 1 pathways were highly enriched for AVC-specific DEGs. CXCL16, GPM6A, BEX2, S100A9, and SCARA5 genes were all regarded diagnostic markers for AVC. The model was effectively constructed using a molecular diagnostic score system with significant diagnostic value (AUC = 0.987) and verified using the independent dataset GSE83453 (AUC = 0.986). Immune cell infiltration research revealed that B cell naive, B cell memory, plasma cells, NK cell activated, monocytes, and macrophage M0 may be involved in the development of AVC. Additionally, all diagnostic characteristics may have varying degrees of correlation with immune cells. The most promising small molecule medicines for reversing AVC gene expression are Doxazosin and Terfenadine.ConclusionIt was identified that CXCL16, GPM6A, BEX2, S100A9, and SCARA5 are potentially beneficial for diagnosing and treating AVC. A diagnostic model was constructed based on a molecular prognostic score system using machine learning. The aforementioned immune cell infiltration may have a significant influence on the development and incidence of AVC.