Prediction of the remnant liver hypertrophy ratio after preoperative portal vein embolization

BACKGROUND

Portal vein embolization (PVE) is considered to improve the safety of major hepatectomy. Various conditions might affect remnant liver hypertrophy after PVE. The aim of the present study was to clarify the factors that affect remnant liver hypertrophy and to establish a prediction formula for the hypertrophy ratio.

METHODS

Fifty-nine patients who underwent preoperative PVE for cholangiocarcinoma (39 patients), metastatic carcinoma (10 patients), hepatocellular carcinoma (8 patients), and other diseases (2 patients) were enrolled in this study. For the prediction of the hypertrophy ratio, a formula with stepwise multiple regression analysis was set up. The following parameters were used: age, gender, future liver remnant ratio to total liver (FLR%), plasma disappearance rate of indocyanine green (ICGK), platelet count, prothrombin activity, serum albumin, serum total bilirubin at the time of PVE and the maximum value before PVE (Max Bil), as well as a history of cholangitis, diabetes mellitus, and chemotherapy.

RESULTS

The mean hypertrophy ratio was 28.8%. The 5 parameters detected as predictive factors were age (p = 0.015), FLR% (p < 0.001), ICGK (p = 0.112), Max Bil (p < 0.001), and history of chemotherapy (p = 0.007). The following prediction formula was established: 101.6 - 0.78 × age - 0.88 × FLR% + 128 × ICGK - 1.48 × Max Bil (mg/dl) - 21.2 × chemotherapy. The value obtained using this formula significantly correlated with the actual value (r = 0.72, p < 0.001). A 10-fold cross validation also showed significant correlation (r = 0.62, p < 0.001), and a hypertrophy ratio <20% was predictable with a sensitivity of 100% and a specificity of 90.9%. Moreover, technetium-99m-diethylenetriaminepentaacetic acid-galactosyl human serum albumin scintigraphy showed a significantly smaller increase in the uptake ratio of the remnant liver in patients with prediction values <20% than in those with values ≥20% (6.8 vs. 20.8%, p = 0.030).

CONCLUSIONS

The prediction formula can prognosticate the hypertrophy ratio after PVE, which may provide a new therapeutic strategy for major hepatectomy.

Breakdown of the interlayer coherence in twisted bilayer graphene

Coherent motion of electrons in Bloch states is one of the fundamental concepts of charge conduction in solid-state physics. In layered materials, however, such a condition often breaks down for the interlayer conduction, when the interlayer coupling is significantly reduced by, e.g., a large interlayer separation. We report that complete suppression of coherent conduction is realized even in an atomic length scale of layer separation in twisted bilayer graphene. The interlayer resistivity of twisted bilayer graphene is much higher than the c-axis resistivity of Bernal-stacked graphite and exhibits strong dependence on temperature as well as on external electric fields. These results suggest that the graphene layers are significantly decoupled by rotation and incoherent conduction is a main transport channel between the layers of twisted bilayer graphene.

Response-guided telaprevir therapy in prior relapsers? The role of bridging data from treatment-naïve and experienced subjects

The purpose of this report is to illustrate the US Food and Drug Administration's rationale for approving response-guided therapy (RGT) for telaprevir (TVR) in combination with pegylated interferon-α and ribavirin (P/R) for the treatment of adults with genotype 1 chronic hepatitis C who were prior relapsers. RGT was prospectively evaluated in two registration trials of treatment-naïve subjects. In these studies, RGT allowed subjects who achieved undetectable hepatitis C virus RNA from weeks 4 and 12, known as extended rapid virologic response (eRVR), to stop all treatments at 24 weeks. A patient without eRVR received an additional 36 weeks of P/R after 12 weeks of a TVR triple regimen (total of 48 weeks). However, RGT in prior P/R relapsers was not prospectively evaluated. Empirical cross-trial data indicated high sustained virologic response rates (>90%) in prior relapsers achieving eRVR, irrespective of P/R duration (24 or 48 weeks). Further analyses demonstrated that interferon responsiveness does not change in P/R-experienced subjects with a second round of P/R. The comparability in interferon responsiveness across treatment courses allowed us to bridge data between treatment-naïve and P/R-experienced subjects to support the approval of RGT in prior relapse subjects.

Abstract P1-05-03: A requirement for neural precursor cell-expressed developmentally downregulated gene 9 during the initiation of mammary tumorigenesis in MMTV-neu mice

The Neural precursor cell-Expressed Developmentally Downregulated gene 9 (NEDD9; also HEF1, CAS-L) scaffolding protein regulates many signaling pathways associated with mitosis, survival, migration, and ciliary integrity. Within the last few years, elevated NEDD9 expression has been implicated in progression and metastasis of several types of cancer, including those of the lung, skin, and brain. We have investigated the consequences of introducing a Nedd9 genotype into the MMTV-neu mouse mammary tumor model, which in many respects recapitulates features of human HER2+ breast cancer. 80% of wild-type MMTV-Neu; Nedd9+/+ animals developed tumors with an average latency of 339 days, but only 18% of MMTV-Neu;Nedd9−/− animals developed tumors with an average latency of 416 days. This highly significant difference indicates that the Nedd9−/− genotype significantly prevents neu-dependent mammary tumor formation.

HER2-positive human breast tumors and MMTV-neu-induced tumors originate from mammary luminal epithelial progenitor cells. We evaluated mammary progenitor cell populations from non-tumor bearing MMTV-neu;Nedd9−/− versus MMTV-neu;Nedd9+/+ 4-month old mice (2 months prior to appearance of tumors). Flow cytometry analysis indicated a significantly reduced CD24high;CD49flow luminal progenitor subpopulation in MMTV-neu;Nedd9−/− and Nedd9−/− mammary glands, that could be contributed to the tumorigenesis resistance in the MMTV-neu;Nedd9−/− model. The MMTV-neu;Nedd9−/−genotype negatively affected the Matrigel mammosphere colony-forming potential of luminal progenitor cells compared to MMTV-neu; Nedd9+/+cells, causing formation of fewer colonies with aberrant size and morphology. Quantification of mitotic division planes of MMTV-neu;Nedd9−/− mammospheres revealed no contribution to the observed defects. However, MMTV-neu;Nedd9−/− mammospheres had defective expression of signaling proteins governing cell attachment, with reduced levels of FAK and more cytoplasmic localization of SRC.

The results reported above, together with other data, support three main conclusions. First, they revealed a very substantial requirement for Nedd9 during early stages of HER2/neu-dependent tumor formation. Second, these results are the first to demonstrate a role for Nedd9 in supporting the abundance and colony-forming potential of mammary luminal progenitor cells. Third, they indicate that the defects in mammosphere growth likely involve perturbation of crucial cellular attachment signaling pathways involving FAK and SRC. In sum, these data provide a strong justification for future analysis of NEDD9 in the defective signaling of transformed mammary epithelial progenitor cell populations that initiate human breast cancer.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P1-05-03.

Soft nearest prototype classification

We propose a new method for the construction of nearest prototype classifiers which is based on a Gaussian mixture ansatz and which can be interpreted as an annealed version of learning vector quantization (LVQ). The algorithm performs a gradient descent on a cost-function minimizing the classification error on the training set. We investigate the properties of the algorithm and assess its performance for several toy data sets and for an optical letter classification task. Results show 1) that annealing in the dispersion parameter of the Gaussian kernels improves classification accuracy; 2) that classification results are better than those obtained with standard learning vector quantization (LVQ 2.1, LVQ 3) for equal numbers of prototypes; and 3) that annealing of the width parameter improved the classification capability. Additionally, the principled approach provides an explanation of a number of features of the (heuristic) LVQ methods.

Spatial learning-induced accumulation of agmatine and glutamate at hippocampal CA1 synaptic terminals

Agmatine, the decarboxylated metabolite of l-arginine, is considered to be a novel putative neurotransmitter. Recent studies have demonstrated that endogenous agmatine may directly participate in the processes of spatial learning and memory. Agmatine-immunoreactivity has been observed within synaptic terminals of asymmetric excitatory synapses in the hippocampal CA1 stratum radiatum (SR), suggesting that agmatine may be colocalized with glutamate. In the present study we demonstrate, using immunofluorescence confocal microscopy, that agmatine is colocalized with glutamate within CA1-CA3 hippocampal pyramidal cell bodies, in young Sprague-Dawley rats. Subcellular investigation, using postembedding electron microscopy-immunogold cytochemistry, has also revealed that agmatine is colocalized with glutamate in most synaptic terminals in the SR region of CA1. Ninety-seven percent of all agmatinergic profiles were found to contain glutamate, and 92% of all glutamatergic profiles contained agmatine (n=6; 300 terminals). Alterations in colocalized agmatine and glutamate levels in the SR synaptic terminals, following 4 days Morris water maze training, were also investigated. Compared with swim only control rats, water maze-trained rats had statistically significant increases in both agmatine (78%; P<0.01) and glutamate (41%; P<0.05) levels within SR terminals synapsing onto CA1 dendrites. These findings provide the first evidence that agmatine and glutamate are colocalized in synaptic terminals in the hippocampal CA1 region, and may co-participate in spatial learning and memory processing.