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Aims: Emerging evidence is demonstrating that rapid regeneration of remnant liver elicited by associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) may be attenuated in fibrotic livers. However, the molecular mechanisms responsible for this process are largely unknown. It is widely acknowledged that the TGFβ1 signaling axis plays a major role in liver fibrosis. Therefore, the aims of this study were to elucidate the underlying mechanism of liver regeneration during ALPPS with or without fibrosis, specifically focusing on TGFβ1 signaling. Approach: ALPPS was performed in rat models with N -diethylnitrosamine-induced liver fibrosis and no fibrosis. Functional liver remnant regeneration and expression of TGFβ1 were analyzed during the ALPPS procedures. Adeno-associated virus-shTGFβ1 and the small molecule inhibitor LY2157299 (galunisertib) were used separately or in combination to inhibit TGFβ1 signaling in fibrotic rats. Results: Liver regeneration following ALPPS was lower in fibrotic rats than non-fibrotic rats. TGFβ1 was a key mediator of postoperative regeneration in fibrotic liver. Interestingly, AAV-shTGFβ1 accelerated the regeneration of fibrotic functional liver remnant and improved fibrosis, while LY2157299 only enhanced liver regeneration. Moreover, combination treatment elicited a stronger effect. Conclusions: Inhibition of TGFβ1 accelerated regeneration of fibrotic liver, ameliorated liver fibrosis, and improved liver function following ALPPS. Therefore, TGFβ1 is a promising therapeutic target in ALPPS to improve fibrotic liver reserve function and prognosis., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Objective: Understanding the three-dimensional (3D) spatial position and orientation of vessels and tumor(s) is vital in laparoscopic liver resection procedures. Augmented reality (AR) techniques can help surgeons see the patient's internal anatomy in conjunction with laparoscopic video images., Method: In this paper, we present an AR-assisted navigation system for liver resection based on a rigid stereoscopic laparoscope. The stereo image pairs from the laparoscope are used by an unsupervised convolutional network (CNN) framework to estimate depth and generate an intraoperative 3D liver surface. Meanwhile, 3D models of the patient's surgical field are segmented from preoperative CT images using V-Net architecture for volumetric image data in an end-to-end predictive style. A globally optimal iterative closest point (Go-ICP) algorithm is adopted to register the pre- and intraoperative models into a unified coordinate space; then, the preoperative 3D models are superimposed on the live laparoscopic images to provide the surgeon with detailed information about the subsurface of the patient's anatomy, including tumors, their resection margins and vessels., Results: The proposed navigation system is tested on four laboratory ex vivo porcine livers and five operating theatre in vivo porcine experiments to validate its accuracy. The ex vivo and in vivo reprojection errors (RPE) are 6.04 ± 1.85 mm and 8.73 ± 2.43 mm, respectively., Conclusion and Significance: Both the qualitative and quantitative results indicate that our AR-assisted navigation system shows promise and has the potential to be highly useful in clinical practice., Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest., (Copyright © 2019 Elsevier B.V. All rights reserved.)