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Background: Aberrant expression of caveolin-1 (CAV-1) is involved in the pathogenesis of hepatocellular carcinoma (HCC); however, the results have been inconsistent due to the small size of sample in the individual study. Methods: We performed a meta-analysis and evaluated the association of CAV-1 protein overexpression and clinicopathological significance by using Review Manager 5.2. Pooled ORs and HR with corresponding CIs were calculated. Results: Nine studies were included in the meta-analysis with 810 HCC and 172 cirrhosis patients. CAV-1 protein overexpression was correlated with the risk of cirrhosis; OR was 3.25, p=0.01. Furthermore, the rate of CAV-1 protein overexpression was significantly higher in HCC with cirrhosis than HCC without cirrhosis, suggesting that the CAV-1 protein overexpression likely initiated carcinogenesis in liver with cirrhosis and subsequently contributed to the progression of HCC. In addition, CAV-1 protein overexpression was strongly associated with poor differentiated HCC and invasion; ORs were 2.61 and 2.71, respectively. CAV-1 protein overexpression was strongly correlated with poor overall survival in patients with HCC; HR was 0.4, p=0.03. Conclusions: In summary, CAV-1 protein overexpression is at risk for liver cirrhosis and HCC derived from cirrhosis, and CAV-1 is also a promising prognostic predictor in HCC.

This paper deals with the problem of preemptive scheduling in a two-stage supply chain framework. The supply chain environment contains two stages: production and transportation. In the production stage jobs are processed on a manufacturer's bounded serial batching machine, preemptions are allowed, and set-up time is required before a new batch is processed. In the transportation stage each batch is delivered to a customer by a single vehicle. The objective is to minimize the makespan by making decisions for both mutually coordinated stages. Specifically, two versions are studied. The first one is that all jobs are available to be processed at time zero, and the second one is that jobs have different release times. An time algorithm is developed for the first version, and we show that it can produce the optimal schedule for the entire problem. For the second version, based on some useful properties we have designed an time heuristic and a novel lower bound. The worst-case performance ratio of our algorithm is bounded by 2. Our computational study with random instances of different scales shows high-quality solutions for either small-scale or large-scale instances returned in a reasonable PC times.

Motivated by applications in manufacturing industry, we consider a supply chain scheduling problem, where each job is characterised by non-identical sizes, different release times and unequal processing times. The objective is to minimise the makespan by making batching and sequencing decisions. The problem is formalised as a mixed integer programming model and proved to be strongly NP-hard. Some structural properties are presented for both the general case and a special case. Based on these properties, a lower bound is derived, and a novel two-phase heuristic TP-H is developed to solve the problem, which guarantees to obtain a worst case performance ratio of . Computational experiments with a set of different sizes of random instances are conducted to evaluate the proposed approach TP-H, which is superior to another two heuristics proposed in the literature. Furthermore, the experimental results indicate that TP-H can effectively and efficiently solve large-size problems in a reasonable time.