Pathologic complete response to chemoembolization improves survival outcomes after curative surgery for hepatocellular carcinoma: predictive factors of response.

BACKGROUND: We identified the predictive factors and prognostic significance of transarterial chemoembolization (TACE) for achieving pathologic complete response (pCR) before curative surgery for hepatocellular carcinoma (HCC) in hepatitis B-endemic areas. METHODS: Among 753 HCC patients treated with surgery, 124 patients underwent preoperative TACE before liver resection (LR), and 166 before liver transplantation (LT) between 2005 and 2016. Overall survival (OS) and recurrence-free survival (RFS) were analyzed. Pathologic response (PR) was defined as the mean percentage of necrotic area, and pCR was defined as the absence of viable tumor. RESULTS: A total of 34 (27%) and 38 (23%) patients had pCR before LR and LT, respectively. Alpha-fetoprotein (AFP) < 100 ng/mL and single tumor were significant preoperative predictors of pCR. OS and RFS were significantly improved in patients with pCR or a PR ≥ 90%, but not in patients with PR ≥ 50% after LR and LT. On multivariate analyses, PR ≥ 90% remained an independent predictor of better OS and RFS in LR and LT groups. CONCLUSION: Overall, our data clearly demonstrate that pCR predicts favorable prognosis after curative surgery for HCC, and predictors of pCR are AFP <100 ng/mL and single tumor.

All-atom de novo protein folding with a scalable evolutionary algorithm.

The search for efficient and predictive methods to describe the protein folding process at the all-atom level remains an important grand-computational challenge. The development of multi-teraflop architectures, such as the IBM BlueGene used in this study, has been motivated in part by the large computational requirements of such studies. Here we report the predictive all-atom folding of the forty-amino acid HIV accessory protein using an evolutionary stochastic optimization technique. We implemented the optimization method as a master-client model on an IBM BlueGene, where the algorithm scales near perfectly from 64 to 4096 processors in virtual processor mode. Starting from a completely extended conformation, we optimize a population of 64 conformations of the protein in our all-atom free-energy model PFF01. Using 2048 processors the algorithm predictively folds the protein to a near-native conformation with an RMS deviation of 3.43 A in < 24 h.