Investigation of the status quo of massive blood transfusion in China

The aim of this study was to provide an overview of massive transfusion in Chinese hospitals, identify the important indications for massive transfusion and corrective therapies based on clinical evidences and supporting experimental studies, and propose guidelines for the management of massive transfusion. This multi-region, multi-center retrospective study involved a Massive Blood Transfusion Coordination Group composed of 50 clinical experts specializing in blood transfusion, cardiac surgery, anesthesiology, obstetrics, general surgery, and medical statistics from 20 tertiary general hospitals across five regions in China. Data were collected for all patients, who received ≥ 10 U red blood cell transfusion within 24 hours in the participating hospitals from January 1 2009 to December 31 2010, including patient’s demographics, pre-, peri-, and post-operative clinical characteristics, laboratory test results before, during, and after transfusion, and patient mortality at post-transfusion and discharge. We also designed an in vitro hemodilution model to investigate the changes of blood coagulation indices during massive transfusion and the correction of coagulopathy through supplement blood components under different hemodilutions. The experimental data in combination with the clinical evidences were used to determine the optimal proportion and timing for blood component supplementation during massive transfusion. Based on the findings from the present study, together with an extensive review of domestic and international transfusion-related literature and consensus feedback from the 50 experts, we drafted the guidelines on massive blood transfusion that may help Chinese hospitals to develop standardized protocols for massive blood transfusion.     

不同光谱类型对银川平原土壤含盐量反演精度的影响与校正

土壤盐渍化是导致土壤质量下降、耕地减产的重要因素之一。为准确快速评价银川平原土壤含盐量,本研究对野外高光谱数据和室内高光谱数据进行一阶微分(FDR)变换,逐步回归(SR)筛选特征波段,利用偏最小二乘回归(PLSR)与支持向量机(SVM)进行建模,明确适用于本地区土壤含盐量准确反演的光谱类型,并对较差光谱类型进行分段校正与全局校正,尝试提高土壤含盐量反演精度。结果表明: 基于野外光谱的土壤含盐量反演模型精度比室内光谱平均高58.9%;对室内光谱进行分段校正、全局校正后反演精度均有提高,其中,PLSR以分段校正精度更高,建模决定系数(R_c²)、验证决定系数(R_p²)和相对分析误差(RPD)分别为0.790、0.633和1.64,而SVM以全局校正精度更高,R_c²、R_p²和RPD分别为0.927、0.947和3.87;SVM模型的反演精度高于PLSR,其中,野外光谱建模效果最佳,室内全局校正光谱与室内分段校正光谱次之,室内光谱最差。因此,野外高光谱可实现对银川平原土壤表层含盐量的定量反演,经校正的室内光谱对土壤含盐量反演精度显著提升,均可为粮食安全与生态环境高质量发展提供保障。     

Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods

With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.     

一测多评法测定番石榴叶中6种黄酮类成分的含量

为建立同时测定番石榴叶中6种黄酮类成分(金丝桃苷、异槲皮苷、瑞诺苷、番石榴苷、萹蓄苷、槲皮素)的一测多评法。本研究以金丝桃苷为内参物,采用HPLC法,确定金丝桃苷与另外5种成分的相对校正因子,并通过获得的校正因子计算后5种成分的量;同时采用外标法测定11批番石榴叶的含量。结果表明11批番石榴叶的QAMS法与外标法测定结果间无显著性差异,证明该法可用于番石榴叶6种黄酮类成分的定量分析。     

The interprotein scoring noises in glide docking scores

Small molecule drugs are rarely selective enough to interact solely with their designated targets. Unintended "off-target" interactions often lead to side effects, but also serendipitously lead to new therapeutic uses. Identification of the off-targets of a compound is therefore of significant value to the evaluation of its developmental potential. In computational biology, the strategy of "reverse docking" has been introduced to predict the targets of a compound, which uses a compound to virtually screen a library of proteins, reversing the bait and prey in "normal" docking screenings. The present study shows that, in reverse docking, additional optimization of the scoring function may help to improve the target prediction accuracy. In a case study with the Glide scores, we found that only 57% of the ligand-protein relationships could be correctly identified in a library of 58 complexes whose crystal binding conformations were all able to be accurately reproduced. This was likely a result of the constant over- or under-estimation of the scores for specific proteins. In other words, there were interprotein noises in the Glide scores. Introducing a correction term based on protein characteristics improved the target-prediction accuracy by 27% (57-72%). It is our hope that this focused discussion on the Glide scores would invite further efforts to characterize and normalize this type of interprotein noises in all docking scores, so that better target prediction accuracy can be achieved with the strategy of reverse docking.     

Quantification of blockiness in pectins—A comparative study using vibrational spectroscopy and chemometrics

The gelling properties of pectins are related not only to the degree of esterification (DE), but also to the distribution of the ester groups. In this study, we have examined an experimentally designed series of 31 pectins originating from the same mother pectin and de-esterified using combinations of two different enzymatic mechanisms. The potential of using infrared (IR), Raman, and near infrared (NIR) spectroscopies combined with chemometrics for reliable and rapid determination of the DE and distribution patterns of methyl ester groups in a designed set of pectin powders was investigated. Quantitative calibration models using partial least squares (PLS) regression were developed and compared. The calibration models for prediction of DE obtained on extended inverse signal correction (EISC)-treated spectra of all three spectroscopic methods yielded models with cross-validated prediction errors (RMSECV) between 1.1%p and 1.6%p DE and correlation coefficients of 0.99. A calibration model predicting degree of random de-esterification (R) and block de-esterification (B) was developed for each spectroscopic method, yielding RMSECV values between 4.4 and 6.7 and correlation coefficients (r) between 0.79 and 0.92. Variable selection using interval PLS (iPLS) significantly improved the prediction of R for IR spectroscopy, yielding RMSECV of 3.5 and correlation coefficients of 0.95. All three spectroscopic methods were able to distinguish the spectral patterns of pectins with different enzyme treatments in simple classification models by principal component analysis (PCA). Extended canonical variate analysis revealed one specific signal in the Raman (1045 cm⁻¹) spectrum and one significant area (1250-1400 cm⁻¹) in the IR spectrum which are able to classify the pectin samples according to the four different enzyme treatments. In both Raman and IR spectra, the signal intensity decreased in the sequence R-B > B > B-R > R > re-methylated pectin.     

Bias‐Corrected Diagonal Discriminant Rules for High‐Dimensional Classification

Summary Diagonal discriminant rules have been successfully used for high‐dimensional classification problems, but suffer from the serious drawback of biased discriminant scores. In this article, we propose improved diagonal discriminant rules with bias‐corrected discriminant scores for high‐dimensional classification. We show that the proposed discriminant scores dominate the standard ones under the quadratic loss function. Analytical results on why the bias‐corrected rules can potentially improve the predication accuracy are also provided. Finally, we demonstrate the improvement of the proposed rules over the original ones through extensive simulation studies and real case studies.     

Sample Size Considerations for GEE Analyses of Three‐Level Cluster Randomized Trials

Summary Cluster randomized trials in health care may involve three instead of two levels, for instance, in trials where different interventions to improve quality of care are compared. In such trials, the intervention is implemented in health care units (“clusters”) and aims at changing the behavior of health care professionals working in this unit (“subjects”), while the effects are measured at the patient level (“evaluations”). Within the generalized estimating equations approach, we derive a sample size formula that accounts for two levels of clustering: that of subjects within clusters and that of evaluations within subjects. The formula reveals that sample size is inflated, relative to a design with completely independent evaluations, by a multiplicative term that can be expressed as a product of two variance inflation factors, one that quantifies the impact of within‐subject correlation of evaluations on the variance of subject‐level means and the other that quantifies the impact of the correlation between subject‐level means on the variance of the cluster means. Power levels as predicted by the sample size formula agreed well with the simulated power for more than 10 clusters in total, when data were analyzed using bias‐corrected estimating equations for the correlation parameters in combination with the model‐based covariance estimator or the sandwich estimator with a finite sample correction.