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Based on the energy ratio method, an automatic picking method with strong noise resistance is proposed. It considers the influence of the current point's position on the first-arrival characteristic value. Specifically, an outlier detection technique is proposed to eliminate abnormal first arrivals for low signal-to-noise ratio (SNR) seismic data. First, the first arrivals of adjacent shots obtained by the new method are arranged according to the offsets. Then, combined with the distribution characteristics of the first arrivals, a symmetric window centered on the current point is established as the calculation range, and the distance-based outlier detection method is adopted for the abnormal first arrivals. The size of the calculation time window is determined by scanning the given value range. In order to optimize the processing results, we further propose an outlier detection method based on grid density. After this step, the abnormal first arrivals will be further eliminated. Following these steps, the abnormal first arrivals of all shots can be removed effectively. The actual data processing results show that the proposed program can accurately pick up the first arrivals and has a good performance in detecting the abnormal first arrivals.

BackgroundIt is not clear whether there are valuable inflammatory markers for prognosis judgment in the intensive care unit (ICU). We therefore conducted a multicenter, prospective, observational study to evaluate the prognostic role of inflammatory markers.MethodsThe clinical and laboratory data of patients at admission, including C-reactive protein (CRP), were collected in four general ICUs from September 1, 2018, to August 1, 2019. Multivariate logistic regression was used to identify factors independently associated with nonsurvival. The area under the receiver operating characteristic curve (AUC-ROC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the effect size of different factors in predicting mortality during ICU stay. 3 -knots were used to assess whether alternative cut points for these biomarkers were more appropriate.ResultsA total of 813 patients were recruited, among whom 121 patients (14.88%) died during the ICU stay. The AUC-ROC values of PCT and CRP for discriminating ICU mortality were 0.696 (95% confidence interval [CI], 0.650–0.743) and 0.684 (95% CI, 0.633–0.735), respectively. In the multivariable analysis, only APACHE II score (odds ratio, 1.166; 95% CI, 1.129–1.203;P = 0.000) and CRP concentration > 62.8 mg/L (odds ratio, 2.145; 95% CI, 1.343–3.427;P = 0.001), were significantly associated with an increased risk of ICU mortality. Moreover, the combination of APACHE II score and CRP > 62.8 mg/L significantly improved risk reclassification over the APACHE II score alone, with NRI (0.556) and IDI (0.013). Restricted cubic spline analysis confirmed that CRP concentration > 62.8 mg/L was the optimal cut-off value for differentiating between surviving and nonsurviving patients.ConclusionCRP markedly improved risk reclassification for prognosis prediction.

Long non-coding RNAs (lncRNAs) have been demonstrated that they not only play important roles in tumorgenicity but also associate with cancer prognosis. Recently, highly up-regulated in liver cancer (HULC) is abnormally expressed in liver cancer and other cancers, and participated in cancers progression; however, it is unclear whether its expression is associated with prognosis. Here, we performed a meta-analysis and systematic review to evaluate the prognostic value and metastasis of HULC in various cancer patients. The meta-analysis was performed using a systematic search of PubMed, Web of Science, ScienceDirect and Wiley Online Library database to eligible studies. The pooled hazard ratios (HRs) with a 95% confidence interval (95% CI) were calculated to assess its prognosis and metastasis in human cancer. A total of 1134 patients from 11 studies were included. The results indicated that overexpression of HULC was associated with poor overall survival (OS) (HR = 1.89, 95% CI: 1.32-2.47). Furthermore, subgroup analysis showed that cancer type (digestive system cancer or non-digestive system cancers) and sample size (more or less than 100) significantly associated between HULC and OS. In addition, overexpression of HULC expression was significantly associated with metastasis in cancers (HR = 2.67, 95% CI: 0.94-4.39). The meta-analysis indicated that lncRNA HULC could serve as a new molecular marker for cancer prognosis and metastasis.

Background Although serum cystatin C (sCysC), urinary N-acetyl-β-d-glucosaminidase (uNAG), and urinary albumin/creatinine ratio (uACR) are clinically available, their optimal combination for acute kidney injury (AKI) detection and prognosis prediction remains unclear. We aimed to assess the discriminative abilities of these biomarkers and their possible combinations for AKI detection and intensive care unit (ICU) mortality prediction in critically ill adults. Methods A multicenter, prospective observational study was conducted in mixed medical-surgical ICUs at three tertiary care hospitals. One thousand eighty-four adult critically ill patients admitted to the ICUs were studied. We assessed the use of individual biomarkers (sCysC, uNAG, and uACR) measured at ICU admission and their combinations with regard to AKI detection and prognosis prediction. Results AUC-ROCs for sCysC, uNAG, and uACR were calculated for total AKI (0.738, 0.650, and 0.683, respectively), severe AKI (0.839, 0.706, and 0.771, respectively), and ICU mortality (0.727, 0.793, and 0.777, respectively). The panel of sCysC plus uNAG detected total and severe AKI with significantly higher accuracy than either individual biomarkers or the other two panels (uNAG plus uACR or sCysC plus uACR). For detecting total AKI, severe AKI, and ICU mortality at ICU admission, this panel yielded AUC-ROCs of 0.756, 0.863, and 0.811, respectively; positive predictive values of 0.71, 0.31, and 0.17, respectively; and negative predictive values of 0.81, 0.97, and 0.98, respectively. Moreover, this panel significantly contributed to the accuracy of the clinical models for AKI detection and ICU mortality prediction, as measured by the AUC-ROC, continuous net reclassification index, and incremental discrimination improvement index. The comparable performance of this panel was further confirmed with bootstrap internal validation. Conclusions The combination of a functional marker (sCysC) and a tubular damage marker (uNAG) revealed significantly superior discriminative performance for AKI detection and yielded additional prognostic information on ICU mortality. Electronic supplementary material The online version of this article (doi:10.1186/s13054-017-1626-0) contains supplementary material, which is available to authorized users.