No abstract

No abstract

Dear Editor, Colorectal cancer (CRC) is a major cause of cancer-related mortality worldwide. 1 Shifting the detection of CRC to earlier stages via massive screening has considerably reduced mortality. 2 Metabolomics has shown great potential in the identification of noninvasive biomarkers of CRC. However, the huge number and inconsistent reports of putative markers make choosing the most appropriate biomarkers difficult. The aim of this study was to identify the metabolic markers of CRC by a multi-step strategy. We first identified and verified differential plasma metabolites of CRC by a two-stage case-control design. The tumour specificity of plasma markers was then confirmed by comparison with tumor-adjacent non-malignant paired tissue. Moreover, we conducted a systematic review of metabolomics studies of CRC to affirm the markers in multiple populations. Finally, the metabolites were quantitatively evaluated in an independent case-control population (Figure 1).A two-stage case-control study involving 170 cases and 197 controls was performed. All patients were diagnosed at the Third Affiliated Hospital of Harbin Medical University. Controls were recruited from patients in the orthopaedic and ophthalmology departments and volunteers from Xiangfang District of Harbin City during the same period. Fasting peripheral venous blood was obtained in the morning in the hospital or medical examination centre. Metabolic profiling analysis was conducted on a ultra performance liquid chromatography (UPLC)/ Q-time-of-flight (TOF)-mass spectrometry (MS)/MS platform. Principal component analysis and orthogonal projections to latent structures discriminant analysis were performed to check the separation tendency. Student's t-test or Wilcoxon's rank-sum test with an adjusted P-value was applied to test the metabolites between cases and controls (details in Supporting Information, Section 1.1).No significant differences in age, sex or body mass index were observed between the cases and controls in This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

SummarySelf‐Monitoring, Analysis, and Reporting Technology (SMART) is a technology in hard disk drives to predict impending disk failures for data repair in advance. As the prediction accuracy of SMART is unsatisfactory, recently, machine learning techniques have been explored to improve the prediction accuracy. Those approaches treat disk failure prediction as a binary classification problem and take SMART attributes as features, and some of them achieve satisfactory prediction accuracy. However, there is no uniform metric to measure the financial impact of these methods whose primary objective is to reduce disk failure recovery costs via disk failure prediction. In this article, from a financial impact perspective, we propose a simple, yet practical, metric Mean‐Cost‐To‐Recovery (MCTR) for disk failure prediction in data centers. Specifically, by assigning different weights to mispredicted healthy disks and failed disks, we measure the entire misprediction costs, that is, MCTR. In addition, we argue that the commonly used threshold 0.5 for disk failure prediction is suboptimal because of the fact of data imbalance, that is, failed disks are much fewer than healthy ones. To find the optimal threshold which renders minimal MCTR, we wrap a cost‐minimizing procedure around disk failure prediction and use a threshold‐moving technique for searching. Moreover, to map sample‐level prediction results to disk‐level prediction results, a modified leaky‐bucket algorithm is design to determine the disk health state by considering its multiple sample‐level prediction results. To evaluate the effectiveness of our approach, we conduct extensive experiments using three real‐world datasets. The experimental results show that compared with reactive data protection schemes, we can reduce MCTR by up to 86.9%, and compared with cost‐blind failure predictions, we can reduce MCTR by up to 22.3%.