Risk factors and prediction model for inpatient surgical site infection after elective abdominal surgery

BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors.AIM To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively.METHODS We retrospectively analysed the inpatient records of Shaanxi Provincial People’s Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002(NRS 2002)scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance(NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model.RESULTS A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus(42.2%), the liver(27.6%), the gastrointestinal tract(19.1%), the appendix(5.9%), the kidney(3.7%), and the groin area(1.4%). SSI occurred in 5% of the patients(n = 150). The risk factors associated with SSI were as follows: Age;gender;marital status;place of residence;history of diabetes;surgical season;surgical site;NRS 2002 score;preoperative white blood cell, procalcitonin(PCT), albumin, and low-density lipoprotein cholesterol(LDL) levels;preoperative antibiotic use;anaesthesia method;incision grade;NNIS score;intraoperative blood loss;intraoperative drainage tube placement;surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio(OR) = 5.698, 95% confidence interval(CI): 3.305-9.825, P = 0.001], antibiotic use(OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3(OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia(OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2(OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 μg/L(OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L(OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL(OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season(P < 0.05), surgical site(P < 0.05), and incision grade I or Ⅲ(P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score(0.662).CONCLUSION The patient’s condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.

A simulation-based nonlinear site amplification model for ground-motion prediction equations in Japan

In this manuscript we present a nonlinear site amplification model for ground-motion prediction equations(GMPEs)in Japan,using a site period-based site class and a site impedance ratio as site parameters.We used a large number of shear-wave velocity profiles from the Kiban-Kyoshin network(KiK-net)and the Kyoshin network(K-NET)to construct the one-dimensional(1D)numerical models.The strong-motion records from rock-sites in Japan with different earthquake categories and taken from the Pacific Earthquake Engineering Research Center dataset were used in this study.We fit a set of 1D site amplification models using the spectral amplification ratios derived from 1D equivalent linear analyses.Parameters of site impedance ratios for both linear and nonlinear site response were included in the 1D model.The 1D model could be implemented into GMPEs using a new proposed adjustment method.The adjusted site amplification ratios retain the nonlinear characteristics of the 1D model for strong motions and match the linear amplification ratio in GMPE for weak motions.The nonlinearity of the present site model is reasonably similar to that of the historical models,and the present site model could satisfactorily capture the nonlinear site response in empirical data.

Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyladenosine Site Identification

N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level.Although a variety of identification algorithms have been proposed recently,most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences,while ignoring the structural dependencies of nucleotides in their threedimensional structures.To overcome this issue,we propose a cross-species end-to-end deep learning model,namely CR-NSSD,which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification.Specifically,CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory.It then constructs a crossdomain reconstruction encoder to learn the sequential and structural dependencies between nucleotides.By minimizing the reconstruction and binary cross-entropy losses,CR-NSSD is trained to complete the task of m6A site identification.Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms.Moreover,the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species,thus improving the accuracy of cross-species identification.

Using the improved position specific scoring matrix and ensemble learning method to predict drug-binding residues from protein sequences

Identification of the drug-binding residues on the surface of proteins is a vital step in drug discovery and it is important for understanding protein function. Most previous researches are based on the structural information of proteins, but the structures of most proteins are not available. So in this article, a sequence-based method was proposed by combining the support vector machine (SVM)-based ensemble learning and the improved position specific scoring matrix (PSSM). In order to take the local environment information of a drug-binding site into account, an improved PSSM profile scaled by the sliding window and smoothing window was used to improve the prediction result. In addition, a new SVM-based ensemble learning method was developed to deal with the imbalanced data classification problem that commonly exists in the binding site predictions. When performed on the dataset of 985 drug-binding residues, the method achieved a very promising prediction result with the area under the curve (AUC) of 0.9264. Furthermore, an independent dataset of 349 drug- binding residues was used to evaluate the pre- diction model and the prediction accuracy is 84.68%. These results suggest that our method is effective for predicting the drug-binding sites in proteins. The code and all datasets used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/Ensem_DBS.zip.

Cancer Specific Non-Synonymous Single Nucleotide Polymorphism Prediction in the Context of Haplotype and Protein Interacting Sites

In this work, we study predicting the effect of non-synonymous SNPs on several cancers. We trained classifiers on both sequential and structural features extracted from the affected genes and assessed the predictions made by the trained classifiers using cross validation. Specifically, we investigated how the prediction performance can be improved by connecting SNPs in the context of haplotype and interacting sites of proteins encoded by affected genes. We found that accuracy was consistently enhanced by combining sequential and structural features, with increase ranging from a few percentage points up to more than 20 percentage points. The results for putting SNPs in the context of interacting sites were less consistent. Compared to individual SNPs, these that appear together in haplotype showed stronger correlation with one another and with the phenotype, and therefore led to significant improvement inprediction performance, with ROC score increased from 0.81 to 0.95. Although some similar effect has been expected for connecting SNPs to interacting sites in proteins, the performance actually got worse. This decrease in prediction accuracy may be caused by the small data set being used in the study, as many affected proteins in the study do not have known interacting sites.

Improved Protein Phosphorylation Site Prediction by a New Combination of Feature Set and Feature Selection

Phosphorylation of protein is an important post-translational modification that enables activation of various enzymes and receptors included in signaling pathways. To reduce the cost of identifying phosphorylation site by laborious experiments, computational prediction of it has been actively studied. In this study, by adopting a new set of features and applying feature selection by Random Forest with grid search before training by Support Vector Machine, our method achieved better or comparable performance of phosphorylation site prediction for two different data sets.

Computed tomography attenuation values of ascites are helpful to predict perforation site

AIM:To evaluate the effect of computed tomography(CT) attenuation values of ascites on gastrointestinal(GI) perforation site prediction.METHODS:The CT attenuation values of the ascites from 51 patients with GI perforations were measured by volume rendering to calculate the mean values.The effect of the CT attenuation values of the ascites on perforation site prediction and postoperative complications was evaluated.RESULTS:Of 24 patients with colorectal perforations,the CT attenuation values of ascites were significantly higher than those in patients with perforations at other sites [22.5 Hounsfield units(HU) vs 16.5 HU,respectively,P = 0.006].Colorectal perforation was significantly associated with postoperative complications(P = 0.038).The prediction rate of colorectal perforation using attenuation values as an auxiliary diagnosis improved by 9.8% compared to that of CT findings alone(92.2% vs 82.4%).CONCLUSION:The CT attenuation values of ascites could facilitate the prediction of perforation sites and postoperative complications in GI perforations,particularly in cases in which the perforation sites are difficult to predict by CT findings alone.

Use of Mutual Information Arrays to Predict Coevolving Sites in the Full Length HIV gp120 Protein for Subtypes B and C

It is well established that different sites within a protein evolve at different rates according to their role within the protein; identification of these correlated mutations can aid in tasks such as ab initio protein structure, structure function analysis or sequence alignment. Mutual Information is a standard measure for coevolution between two sites but its application is limited by signal to noise ratio. In this work we report a preliminary study to investigate whether larger sequence sets could circumvent this problem by calculating mutual information arrays for two sets of drug naive sequences from the HIV gpl20 protein for the B and C subtypes. Our results suggest that while the larger sequences sets can improve the signal to noise ratio, the gain is offset by the high mutation rate of the HIV virus which makes it more difficult to achieve consistent alignments. Nevertheless, we were able to predict a number of coevolving sites that were supported by previous experimental studies as well as a region close to the C terminal of the protein that was highly variable in the C subtype but highly conserved in the B subtype.

基于机器学习预测模型的现地警报级别地震预警试验——以2022年9月5日四川泸定6.8级地震为例

2022年9月5日12时52分四川省甘孜州泸定县发生6.8级地震,造成严重的经济损失和人员伤亡.本文利用此次地震中台站记录到的强震动数据,离线模拟基于机器学习预测模型的现地警报级别地震预警方法.该方法首先构建基于支持向量机的震级预测模型与现地地震动速度峰值(peak ground velocity,PGV)预测模型,而后将每个台站的震级和PGV预测值分别与震级阈值5.7和PGV阈值9.12 cm·s^(-1)做比较,进而得到现地警报级别(0,1,2,3),并用于判断台站附近是否发生潜在破坏.其中,警报级别3为预测震级和预测PGV都超过了阈值,表明在该台站附近有潜在地震破坏且震级偏大.此次地震的离线模拟结果表明:使用P波到达后3 s时间窗,基于支持向量机震级预测模型的单台震级估计标准差为0.35、平均绝对误差为0.27;基于支持向量机PGV预测模型的现地PGV预测标准差为0.34、平均绝对误差为0.32;震级估计误差和PGV预测误差主要分布在±2倍标准差范围内.在不考虑数据打包与传输延时的条件下,地震烈度Ⅶ度区域内的触发台站在震后8 s几乎都发布了警报级别3.在此次地震的震后初期,基于机器学习预测模型的现地警报级别地震预警方法可以得到可靠的警报预测结果,并为中国地震预警系统升级提供了潜在参考.

辽西地区新石器时代文化空间分布特征及其演进预测研究

以辽西地区新石器时期的小河西文化、兴隆洼文化、赵宝沟文化、红山文化和小河沿文化遗址为研究对象,使用ArcGIS软件对各文化时期遗址数据进行提取,借助Logistic回归分析方法构建辽西地区新石器时期遗址的分布预测模型,得出遗址分布与海拔高程等自然地理要素之间的关系,生成辽西地区新石器时期遗址分布概率图。研究结果表明,辽西地区新石器时期聚落选址与自然地理要素关系密切,河流对遗址分布影响较大,海拔高程、坡度影响其次,坡向影响最弱。遗址预测模型显示较高概率分布区集中分布于距离河流较近的河谷、丘陵地带和地势较高的台地。预测模型可为探讨辽西地区史前人地关系演变提供重要参考,为考古发掘工作提供科学判断与决策支持,提高考古工作效率。

基坑降水引发的沉降理论计算及预测研究

为有效掌握基坑沉降变形规律,以基坑所处地质条件为基础,先结合有效应力原理,利用分层综合法计算基坑降水引发的沉降理论值;其次,再以基坑施工过程中的现场沉降监测数据为基础,通过变形预测来佐证理论计算值的准确性。分析结果表明:在基坑沉降的理论计算结果中,沉降理论计算值均不同程度的小于沉降监测值,且随着与降水井中心距离的增加,沉降值具持续减小特征;在基坑沉降的预测研究结果中,所得预测结果的相对误差均值介于2.02%~2.09%,说明ILSO-RVM-CT在基坑沉降变形预测中具有较强的预测能力,且预测结果显示基坑沉降变形后续还会具小速率增加特征,说明基坑沉降变形趋于稳定。

离子电喷雾推力器束电流数学模型与敏感性分析

虽然阵列式结构的离子电喷雾推力器具有小体积、高比冲、高推力分辨率等优点,但是其发展受到了缺乏理论研究的限制。针对该问题,基于多点发射现象发展了描述推力器束电流的数学模型,并通过智能优化算法对模型中的经验系数进行了辨识,研究了高、低电压下束电流及发射行为存在的不同特征。基于Sobol方法进行了全局敏感性分析,研究了发射体结构的几何参数对束电流的影响程度。模型计算结果与试验结果基本一致,当电压大于1.5 kV时,发射点基底半径与多孔储层孔隙半径相当,外加电场主要影响束电流的非线性增加;电压小于1.5 kV时,发射点数量较少,基底半径是多孔储层孔隙半径的1.5倍,发射点数量是影响束电流大小的关键因素。敏感性分析结果表明,发射体尖端与提取极之间的距离对束电流的影响程度最大,其1阶敏感性指数为0.841,在加工制造中须严格控制其公差大小。

盾构下穿古城区地面沉降预测及现场监测分析

【目的】为了获取武汉市和平大道南延线盾构段地面的沉降数据,本研究通过Peck公式对现场沉降监测结果进行了拟合分析,并对Peck公式进行了修正。【方法】每30 m设置沿隧道上部对称分布的监测点进行现场地表沉降监测,选取其中4个最具代表性的断面沉降结果进行Peck公式拟合分析和验证,并根据实际沉降情况对Peck公式进行修正。【结果】研究结果表明,在隧道轴线上部存在异常沉降,因此通过对后续掘进段掘进参数进行调整,加强壁后同步注浆和监控测量控制沉降,并且将得到的沉降槽曲线与Peck公式进行拟合,得到4个断面的实测沉降数据与拟合曲线的拟合优度R2均高于0.85。【结结论论】盾构段沉降槽曲线与Peck公式计算结果高度拟合,可以用Peck公式对研究区沉降进行预测,选取地层体积损失率Vl为0.95%,沉降槽宽度系数k为0.55对Peck公式进行修正后,可以更加简单高效地预测后续沉降。

中晚期口腔癌组织瓣移植术区感染发生的危险因素及Nomogram预测模型构建

目的分析中晚期口腔癌患者手术同期组织瓣移植术区感染危险因素,并构建列线图Nomogram预测模型。方法收集2015年3月至2020年3月,首都医科大学附属北京口腔医院住院接受游离组织瓣移植手术的中晚期口腔癌患者,建立研究队列,收集患者基线和临床资料。观察术区感染发生率,以多因素Logistic回归分析术区感染的主要危险因素,并基于回归分析结果,建立Nomogram预测模型,以模型一致性指数和ROC曲线的曲线下面积(area under curve,AUC)验证模型的预测区分度;绘制校准曲线并进行Hosmer-Lemeshow验证模型的概率一致性;通过决策曲线分析(decision curve analysis,DCA)验证预测模型的临床效益。结果所有纳入研究患者中83例(23.1%)发生术区感染。Logistic回归分析证实,糖尿病、肿瘤部位(非上颌区域)、T4b分期、手术时间≥8 h及气管切开术是术区感染发生的独立危险因素。构建的Nomogram预测模型,经内部验证,C指数为0.682,Hosmer-Lemeshow拟合优度检验P=0.974,证实模型具有较好的区分度和模型概率一致性。临床决策曲线显示,在0.1-0.55阈值范围内,模型能够提供临床净收益。结论本研究通过临床分析,获得中晚期口腔癌患者接受同期游离组织瓣移植术后术区感染的主要危险因素,并构建了Nomogram预测模型,模型具有较好的临床效能。

基于BP神经网络的场地等效剪切波速变化预测研究

利用日本KiK-net台网提供的407个台站的30952条地震动记录,提出了一种基于BP神经网络的场地等效剪切波速比变化预测模型。模型采用了均方误差函数及Adam优化算法,由3个输入参数、5个隐藏神经元及1个输出参数组成。输入参数为地面峰值加速度PGA、Arias烈度I_(a)及场地剪切波速V_(S30),输出为场地等效剪切波速比(V_(S r))。研究结果表明:该神经网络模型残差对于各输入变量整体呈现出无偏的特性,在大部分的软硬场地中均有较好的预测性能,该模型预测得到的PGA关于V_(S30)的相关系数曲线与用传统的最小二乘法回归得到的函数曲线相比,其相关系数有更好的表现。该模型预测曲线显示,B类场地在PGA达到175 cm/s^(2)时,场地剪切波速下降5%,D、E类场地在PGA达到140 cm/s^(2)时,场地剪切波速下降5%,多数场地的非线性阈值为50~100 cm/s^(2)。PGA在该网络模型中占据着较高的权重,为场地等效剪切波速变化的最主要控制参数。该网络模型捕捉到场地等效剪切波速比随PGA的增大有下降的趋势,而较为松软的D、E类场地受PGA影响更大,下降幅度更大。

融合时空特征的城市多站点PM2.5浓度预测

本文提出一种融合时空特征的城市多站点PM2.5预测方法,该方法可以捕捉PM2.5在时间和空间上的相关性,通过将区域多个站点的PM2.5数据转换为一系列静态图像,将其输入到卷积长短期记忆(ConvLSTM)模型中,采用端对端的方式进行训练,预测城市未来多个站点多个时段的PM2.5浓度。以北京多个站点的PM2.5数据进行实验验证。结果表明:考虑了时空特征的ConvLSTM方法在短期预测方面优于其他4种时序方法,该方法可为PM2.5预测提供新的思路。

断层影响下深井工作面底板破坏深度预测

底板破坏深度的预测是底板突水防治研究中的重要一环。为研究断层影响下深井工作面底板采动破坏深度,基于现场实测数据,运用多元线性回归分析方法,构建了断层影响下深井工作面底板破坏深度预测模型,并检验了模型的准确性。通过现场监测方法,验证了预测模型的合理性。结果表明,与传统的统计公式相比,提出的预测模型相对误差平均值至少降低了25.49%,采用新预测模型预测含断层的工作面底板破坏深度为44.96 m,现场监测底板破坏深度为45.7 m,新预测模型预测值与现场监测基本一致。该预测模型对预测断层影响下深井底板破坏深度和防治底板突水具有一定的实用价值。

血清wnt5a和LC3-Ⅱ联合MELD评分预测慢加急性乙型肝炎肝衰竭患者短期预后价值探讨

目的探讨血清无翅型MMTV整合位点5a(wnt5a)和微血管相关蛋白1轻链3-Ⅱ(LC3-Ⅱ)水平联合终末期肝病模型(MELD)评分预测慢加急性乙型肝炎肝衰竭(HBV-ACLF)患者短期预后的价值。方法2018年1月~2022年12月我院诊治的152例HBV-ACLF患者,常规内科和人工肝治疗,计算MELD评分,采用ELISA法检测血清wnt5a和LC3-Ⅱ水平,应用Logistic回归分析影响HBV-ACLF患者短期预后的因素,应用受试者工作特征(ROC)曲线评估血清wnt5a和LC3-Ⅱ联合MELD评分预测HBV-ACLF患者短期预后的效能。结果在治疗3个月内,本组HBV-ACLF患者死亡40例(26.3%);死亡组年龄、肝性脑病发生率、INR、血清总胆红素水平、MELD评分和wnt5a水平分别为(51.3±5.1)岁、70.0%、(3.2±0.9)、(441.5±89.7)μmol/L、(28.2±4.3)分和(2.8±1.5)ng/mL,均显著大于生存组【分别为(45.4±4.6)岁、20.0%、(1.7±0.3)、(280.6±73.1)μmol/L、(19.7±2.8)分和(1.3±0.2)ng/mL,P<0.05】,而外周血血小板计数和血清LC3-Ⅱ水平分别为(77.8±10.3)×10^(9)/L和(20.6±2.1)μg/mL,显著低于生存组【分别为(116.4±11.7)×10^(9)/L和(32.5±3.9)μg/mL,P<0.05】;多因素Logistic回归分析显示,年龄大、并发肝性脑病和血清wnt5a升高或血清LC3-Ⅱ水平降低为影响HBV-ACLF患者短期预后的独立影响因素(P<0.05);以MELD评分大于26.9分为截断点,其预测ACLF患者短期死亡的灵敏度和特异度分别为80.0%和92.9%,而检测血清wnt5a和LC3-Ⅱ水平也可以协助判断。结论血清wnt5a和LC3-Ⅱ联合MELD评分对HBV-ACLF患者短期预后具有较好的预测价值。

基于双模块卷积神经网络的TCR-多肽结合位点预测

TCR(T细胞受体)-多肽结合位点的准确预测对免疫治疗和相关药物发现具有重要意义。文章综合多个文献及数据库整理了一个TCR-多肽结合位点数据集,并引入了一种基于卷积神经网络的预测方法Propep-TCR。该方法综合考虑了输入TCR的序列特征和结构特征,通过采用残基可变滑动窗口方法提取每个目标残基的特征向量。为解决数据集中正负样本不平衡的问题,还采用了改进的损失函数和过采样技术。实验结果表明,Propep-TCR可以成功预测出TCR序列中的潜在结合位点,取得了优于传统算法的性能,其预测准确度达到0.98,AUROC达到了0.95。

基于EMD-VMD-LSTM预测算法的高桩码头结构安全预警方法

基于高桩码头健康监测数据,针对存在复杂环境因素影响监测精度问题,提出EMD-VMD-LSTM组合算法处理并预测高桩码头结构中的监测信息,通过对比分析验证在高桩码头结构现场监测大数据的预测预警分析中采用EMD-VMD-LSTM组合算法的可行性。同时,基于EMD-VMD-LSTM方法和固定预警阈值,提出动态预警阈值的确定方法,设计动静态预警结合的高桩码头结构预警方法,构建多指标、多层次的高桩码头结构安全预警体系以及相应的预警流程。研究成果可以提升高桩码头结构安全预警的准确性和效率,也可为类似工程结构的安全预警管理提供一定的参考。