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Background: Statin, a lipid-lowering drug, has been suggested to confer anticancer efficacy. However, previous studies evaluating the association between statin use and prognosis in breast cancer showed inconsistent results. A meta-analysis was performed to evaluate the association between statin use and clinical outcome in women with breast cancer.Methods: Cohort studies comparing recurrence or disease-specific mortality in women with breast cancer with and without using of statins were identified by search of PubMed, Embase, and Cochrane's Library databases. A random-effect model, incorporating the inter-study heterogeneity, was used to combine the results. Subgroup analyses were performed to evaluate the influences of study characteristics on the outcomesResults: Seventeen cohort studies with 168,700 women with breast cancer were included. Pooled results showed that statin use was significantly associated with a lower risk of breast cancer recurrence (adjusted hazard ratio [HR] = 0.72, p < 0.001) and breast cancer mortality (HR = 0.80, p < 0.001). Subgroup analysis showed that timing of statin use, statin type, study design, sample size, or quality score did not significantly affect the outcomes. However, statin use was associated with more remarkably reduced breast cancer recurrence in studies with mean follow-up duration ≤ 5 years (HR = 0.55, p < 0.001) than that in studies of >5 years (HR = 0.83, p = 0.01).Conclusions: Statin use is associated with reduced recurrence and disease-specific mortality in women with breast cancer. These results should be validated in randomized controlled trials.

Background Metabolic syndrome (MetS) has been related to the pathogenesis of variety categories of cancers. This meta-analysis aimed to determine the association between MetS and the incidence of lung cancer. Methods Relevant cohort studies were identified by search of PubMed, Embase, and Cochrane’s Library databases. Cochrane’s Q test and I2 statistic were used to analyze the heterogeneity. Random-effect model which incorporates the potential heterogeneity was used for the meta-analysis. Results Five cohort studies with 188,970 participants were included. A total of 1,295 lung cancer cases occurred during follow-up. Meta-analyses showed that neither MetS defined by the revised NCEP-ATP III criteria (hazard ratio [HR]: 0.94, 95% confidence interval [CI]: 0.84 to 1.05, p = 0.25; I2 = 0) nor the IDF criteria (HR: 0.82, 95% CI: 0.61 to 1.11, p = 0.20; I2 = 0) was associated with an affected risk of lung cancer. Subgroup analyses showed consistent results in women and in men, in studies performed in Asian and non-Asian countries, and in prospective and retrospective cohorts (p all > 0.05). Meta-analysis limited to studies with the adjustment of smoking status also showed similar results (HR: 0.91, 95% CI: 0.80 to 1.05, p = 0.21; I2 = 0). No publication bias was detected based on the Egger regression test (p = 0.32). Conclusions Current evidence from cohort studies does not support that MetS is an independent risk factor for the incidence of lung cancer.

In literature, learning with expert advice methods usually assume that a learner always obtain the true label of every incoming training instance at the end of each trial. However, in many real-world applications, acquiring the true labels of all instances can be both costly and time consuming, especially for large-scale problems. For example, in the social media, data stream usually comes in a high speed and volume, and it is nearly impossible and highly costly to label all of the instances. In this article, we address this problem with active learning with expert advice, where the ground truth of an instance is disclosed only when it is requested by the proposed active query strategies. Our goal is to minimize the number of requests while training an online learning model without sacrificing the performance. To address this challenge, we propose a framework of active forecasters, which attempts to extend two fully supervised forecasters, Exponentially Weighted Average Forecaster and Greedy Forecaster, to tackle the task of online active learning (OAL) with expert advice. Specifically, we proposed two OAL with expert advice algorithms, named Active Exponentially Weighted Average Forecaster (AEWAF) and active greedy forecaster (AGF), by considering the difference of expert advices. To further improve the robustness of the proposed AEWAF and AGF algorithms in the noisy scenarios (where noisy experts exist), we also proposed two robust active learning with expert advice algorithms, named Robust Active Exponentially Weighted Average Forecaster and Robust Active Greedy Forecaster. We validate the efficacy of the proposed algorithms by an extensive set of experiments in both normal scenarios (where all of experts are comparably reliable) and noisy scenarios.

Background Investigations on the role of the time-mean serum uric acid (SUA) value in determining the risk of chronic kidney disease (CKD) are limited. We investigated whether the time-mean SUA value indicates the risk of CKD, and explored associations of the baseline and time-mean SUA levels with kidney function decline and incident CKD in a healthy population. Methods We initiated an inhabitant-based cohort study between January 2011 and December 2016. All participants completed a yearly medical check-up at the Zhejiang Province People’s Hospital and had baseline estimated glomerular filtration rates (eGFR) > 60 ml/min/1.73m2. The SUA level and eGFR were assessed every year in the follow-up period. A multivariate adjusted binary logistic regression analysis and Cox proportional hazards models were used to evaluate the risk of newly-developed CKD among different stratified groups. Results During the 6-year follow-up period, 227 (4.4%) participants developed CKD. In multivariable-adjusted analyses, the odds ratio (OR) for new-onset CKD increased, with higher time-mean SUA levels than at baseline (OR: 1.00 [reference], 2.709 [95% confidence interval: 1.836–5.293], 3.754 [1.898–7.428], and 7.462 [3.694–15.073]). After adjustment for potential cofounders, a multivariate Cox proportional hazard model showed that a higher SUA increased the risk of developing CKD (the adjusted hazard ratios of the highest and lowest quartiles for the baseline and time-mean SUA levels were 1.689 [1.058–2.696] and 6.320 [3.285–12.159], respectively). Conclusion An increased time-mean and single SUA value were independently associated with an increased likelihood of eGFR decline and development of new-onset CKD in the general population.

The urbanized world has brought social, economic, and environmental sustainability into challenged surroundings in rapidly rising countries, thereby requiring the exploration of their intertwined relationships. This study regarded Brazil, India, and China as “BIC” countries to be the representative study areas for our investigation of sustainability in the context of rapid urbanization. In general, our work was synthesized into a comparison framework in four aspects: rural–urban relation, industrial development, city development, and urban landscape pattern. We determined that rural–urban dichotomy exists in all study areas, with India and China having a high degree. China was identified as a manufacturing-based country in the past half-century, whereas Brazil and India have the service sector as their primary industry. The distribution of large cities follows a regional pattern, with Brazil being northeast-focused, China being southeast-focused, and India being comparatively balanced. The Amazon forest in the north brings great challenges to Brazil with respect to the conservation of its biodiversity and eco-environment. India and China have encountered tremendous urban expansion or sprawl in the past several decades. The sustainability issues in social, economic, and environmental aspects for Brazil, India, and China were summarized in the context of rapid urbanization to provide references for other countries.