Constructing stochastic models from deterministic process equations by propensity adjustment

Background

Models of biochemical systems are typically complex, which may complicate the discovery of cardinal biochemical principles. It is therefore important to single out the parts of a model that are essential for the function of the system, so that the remaining non-essential parts can be eliminated. However, each component of a mechanistic model has a clear biochemical interpretation, and it is desirable to conserve as much of this interpretability as possible in the reduction process. Furthermore, it is of great advantage if we can translate predictions from the reduced model to the original model.

Results

In this paper we present a novel method for model reduction that generates reduced models with a clear biochemical interpretation. Unlike conventional methods for model reduction our method enables the mapping of predictions by the reduced model to the corresponding detailed predictions by the original model. The method is based on proper lumping of state variables interacting on short time scales and on the computation of fraction parameters, which serve as the link between the reduced model and the original model. We illustrate the advantages of the proposed method by applying it to two biochemical models. The first model is of modest size and is commonly occurring as a part of larger models. The second model describes glucose transport across the cell membrane in baker's yeast. Both models can be significantly reduced with the proposed method, at the same time as the interpretability is conserved.

Conclusions

We introduce a novel method for reduction of biochemical models that is compatible with the concept of zooming. Zooming allows the modeler to work on different levels of model granularity, and enables a direct interpretation of how modifications to the model on one level affect the model on other levels in the hierarchy. The method extends the applicability of the method that was previously developed for zooming of linear biochemical models to nonlinear models.     

基于机器学习的环氧合酶-2抑制剂分类模型的构建

摘要 目的：构建环氧合酶-2（Cyclooxygenase-2,COX-2）抑制剂分类模型,用以筛选和优化COX-2抑制剂。方法：基于八种机器学习算法构建模型,比较不同模型的预测性能,筛选出最优模型后利用Y随机验证法对其进行测试,最后运用SHAP（Shapley Additive eXplanation）算法对最优模型进行可解释性分析。结果：八种不同模型的性能比较结果显示,基于随机森林算法建立的模型最优,其预测准确率、平衡准确率、马修斯相关系数、特征曲线下面积和F1分数（分别为0.893、0.825、0.673、0.909和0.933）最高;Y随机验证结果表明最优模型的预测结果并非偶然;此外,通过SHAP算法挖掘出20个最有可能影响COX-2抑制剂活性的结构片段。结论：本研究为新型COX-2抑制剂的开发提供理论依据,可供本领域其他研究人员对先导化合物进行优化或设计更好的COX-2抑制剂。     

F-Actin is associated with a worsening qSOFA score and intensive care unit admission in emergency department patients at risk for sepsis

Abstract

Objective: We previously demonstrated that plasma levels of F-actin and Thymosin Beta 4 differs among patients with septic shock, non-infectious systemic inflammatory syndrome and healthy controls and may serve as biomarkers for the diagnosis of sepsis. The current study aims to determine if these proteins are associated with or predictive of illness severity in patients at risk for sepsis in the Emergency Department (ED).

Methods: Prospective, biomarker study enrolling patients (>18?years) who met the Shock Precautions on Triage Sepsis rule placing them at-risk for sepsis.

Results: In this study of 203 ED patients, F-actin plasma levels had a linear trend of increase when the quick Sequential Organ Failure Assessment (qSOFA) score increased. F-actin was also increased in patients who were admitted to the Intensive Care Unit (ICU) from the ED, and in those with positive urine cultures. Thymosin Beta 4 was not associated with or predictive of any significant outcome measures.

Conclusion: Increased levels of plasma F-actin measured in the ED were associated with incremental illness severity as measured by the qSOFA score and need for ICU admission. F-actin may have utility in risk stratification of undifferentiated patients in the ED presenting with signs and symptoms of sepsis.     

脓毒症患儿预后的影响因素分析及pSOFA评分、PCIS评分及早期血乳酸测定的预测价值探讨

摘要 目的：分析脓毒症患儿预后的影响因素,并探讨儿科序贯器官衰竭评估（pSOFA）评分、小儿危重病例评分法（PCIS）评分及早期血乳酸（Lac）测定对预后的预测价值。方法：选取2020年1月～2022年5月我院儿童医学中心收治的107例脓毒症患儿,根据脓毒症患儿28 d生存情况分为死亡组48例和存活组59例。收集患儿临床资料,对患儿进行pSOFA评分、PCIS评分评价和血Lac检测。采用单因素和多因素Logistic回归分析脓毒症患儿死亡的影响因素,受试者工作特征（ROC）曲线分析pSOFA评分、PCIS评分和血Lac水平对脓毒症患儿死亡的预测价值。结果：107例脓毒症患儿28 d死亡率为44.86％（48/107）。死亡组脓毒症分级、合并器官损伤≥3个比例、机械通气比例、pSOFA评分、白细胞计数、D-二聚体、C反应蛋白、降钙素原、血Lac水平高于存活组,机械通气时间长于存活组,PCIS评分、血小板计数、白蛋白水平低于存活组（P＜0.05）。多因素Logistic回归分析显示,严重脓毒症、脓毒性休克、合并≥3个器官损伤、机械通气、pSOFA评分增加、D-二聚体升高、血Lac升高为脓毒症患儿死亡的独立危险因素,PCIS评分增加、白蛋白升高为独立保护因素（P＜0.05）。ROC曲线分析显示,pSOFA评分、PCIS评分和血Lac水平联合预测脓毒症患儿死亡的曲线下面积大于各指标单独预测。结论：脓毒症分级、合并器官损伤、机械通气、D-二聚体、白蛋白、pSOFA评分、PCIS评分、血Lac为脓毒症患儿预后的影响因素,pSOFA评分、PCIS评分和血Lac水平联合预测脓毒症患儿死亡风险的价值较高。     

Peripheral blood values as predictors of autoimmune status in oral cavity squamous cell carcinoma

BackgroundRecent literature has highlighted the role of the host in prognosis in oral squamous cell carcinoma (OSCC). Autoimmune (AI) disease represents a macroscopic depiction of host status. The goal of this study was to predict an AI “status” and to analyze the utility of this “status” as a prognostic indicator in OSCC.MethodsFrom a departmental database of OSCC patients (n = 1377), 125 patients with an AI disorder were identified. PBL values were obtained and standardized for analysis. A LASSO regression model was used to determine the best predictors of AI status and an AI score was developed. The score was then analyzed across various survival endpoints.ResultsWhen AI score was divided into a binary variable, patients in the highest quartile had a significantly worse overall survival (OS), local recurrence-free (LRFP) and distant recurrence-free probability (DRFP). Survival curves showed significant differences for OS, DSS, LRFP, and DRFP.ConclusionsAI diseases are immune dysregulations that could play a role in prognosis. Therefore, development of an AI score is necessary to depict host status in a ubiquitous manner. AI score as a binary variable may be more utilitarian in a clinical setting, compared to the continuous score. This novel tool needs validation and integration into more tumor and host characteristics. This investigation showed utility of such a score, similar to PBL data in OSCC prognosis. Future studies should incorporate other relevant variables known to affect outcome and implement a more comprehensive predictive model.     

Peripheral blood values as predictors of autoimmune status in oral cavity squamous cell carcinoma

BackgroundRecent literature has highlighted the role of the host in prognosis in oral squamous cell carcinoma (OSCC). Autoimmune (AI) disease represents a macroscopic depiction of host status. The goal of this study was to predict an AI “status” and to analyze the utility of this “status” as a prognostic indicator in OSCC.MethodsFrom a departmental database of OSCC patients (n = 1377), 125 patients with an AI disorder were identified. PBL values were obtained and standardized for analysis. A LASSO regression model was used to determine the best predictors of AI status and an AI score was developed. The score was then analyzed across various survival endpoints.ResultsWhen AI score was divided into a binary variable, patients in the highest quartile had a significantly worse overall survival (OS), local recurrence-free (LRFP) and distant recurrence-free probability (DRFP). Survival curves showed significant differences for OS, DSS, LRFP, and DRFP.ConclusionsAI diseases are immune dysregulations that could play a role in prognosis. Therefore, development of an AI score is necessary to depict host status in a ubiquitous manner. AI score as a binary variable may be more utilitarian in a clinical setting, compared to the continuous score. This novel tool needs validation and integration into more tumor and host characteristics. This investigation showed utility of such a score, similar to PBL data in OSCC prognosis. Future studies should incorporate other relevant variables known to affect outcome and implement a more comprehensive predictive model.     

血清LXA4联合MMP-9预测脓毒症患者发生多器官功能障碍综合征的临床研究

摘要 目的：探讨血清脂氧素A4（LXA4）联合基质金属蛋白酶-9（MMP-9）对脓毒症患者发生多器官功能障碍综合征（MODS）的预测价值。方法：选择2020年1月-2023年1月期间中国人民解放军空军军医大学第二附属医院接受治疗的140例脓毒症患者作为研究对象。根据患者入院28 d内是否发生MODS将其分为MODS组（n=41）和非MODS组（n=99）。检测并对比两组血清LXA4、MMP-9水平。采用单因素及多因素Logistic回归模型分析脓毒症患者发生MODS的影响因素。采用受试者工作特征（ROC）曲线分析血清LXA4、MMP-9对脓毒症患者发生MODS的预测价值。结果：本次纳入的140例脓毒症患者，入院28 d内共有41例发生MODS，发生率为29.29%（41/140）。MODS组血清LXA4水平低于非MODS组，MMP-9水平高于非MODS组（P<0.05）。单因素分析结果显示：脓毒症患者发生MODS与合并高血压、脓毒症病程、存在休克、年龄、合并糖尿病、存在细菌感染、APACHE II评分、疾病严重程度、SOFA评分、存在低血钙、PCT有关（P<0.05）。多因素Logistic回归分析结果显示：年龄偏大、MMP-9偏高、脓毒症病程偏长、LXA4偏低、APACHE II评分偏高、PCT偏高、SOFA评分偏高、存在休克、合并糖尿病、存在低血钙、合并高血压、疾病严重程度为危重、存在细菌感染均是脓毒症患者发生MODS的危险因素（P<0.05）。血清LXA4、MMP-9单独及联合检测预测脓毒症患者发生MODS的曲线下面积（AUC）分别为0.815、0.821和0.898，联合检测的效能优于单独检测。结论：脓毒症并发MODS患者血清LXA4下降，MMP-9升高，二者联合检测对脓毒症并发MODS中具有较好的预测价值。年龄、休克、脓毒症病程、低血钙、APACHE II评分、疾病严重程度、SOFA评分、细菌感染、合并糖尿病、PCT、LXA4、MMP-9、合并高血压均是脓毒症患者发生MODS的影响因素。     

Development and validation of a novel model incorporating MRI-based radiomics signature with clinical biomarkers for distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis

PurposeIt is difficult to make a clear differential diagnosis of pancreatic carcinoma (PC) and mass-forming chronic pancreatitis (MFCP) via conventional examinations. We aimed to develop a novel model incorporating an MRI-based radiomics signature with clinical biomarkers for distinguishing the two lesions.MethodsA total of 102 patients were retrospectively enrolled and randomly divided into the training and validation cohorts. Radiomics features were extracted from four different sequences. Individual imaging modality radiomics signature, multiparametric MRI (mp-MRI) radiomics signature, and a final mixed model based on mp-MRI and clinically independent risk factors were established to discriminate between PC and MFCP. The diagnostic performance of each model and model discrimination were assessed in both the training and validation cohorts.ResultsADC had the best predictive performance among the four individual radiomics models, but there were no significant differences between the pairs of models (all p > 0.05). Six potential radiomics features were finally selected from the 960 texture features to formulate the radiomics score (rad-score) of the mp-MRI model. In addition, the boxplot results of the distributions of rad-scores identified the rad-score as an independent predictive factor for the differentiation of PC and MFCP (p< 0.001). Notably, the nomogram integrating rad-score and clinically independent risk factors had a better diagnostic performance than the mp-MRI and clinical models. These results were further confirmed by the validation group.ConclusionThe mixed model was developed and preliminarily validated to distinguish PC from MFCP, which may benefit the formulation of treatment strategies and nonsurgical procedures.     

TASSER_low‐zsc: An approach to improve structure prediction using low z‐score–ranked templates

In a variety of threading methods, often poorly ranked (low z‐score) templates have good alignments. Here, a new method, TASSER_low‐zsc that identifies these low z‐score–ranked templates to improve protein structure prediction accuracy, is described. The approach consists of clustering of threading templates by affinity propagation on the basis of structural similarity (thread_cluster) followed by TASSER modeling, with final models selected by using a TASSER_QA variant. To establish the generality of the approach, templates provided by two threading methods, SP³ and SPARKS², are examined. The SP³ and SPARKS² benchmark datasets consist of 351 and 357 medium/hard proteins (those with moderate to poor quality templates and/or alignments) of length ≤250 residues, respectively. For SP³ medium and hard targets, using thread_cluster, the TM‐scores of the best template improve by ～4 and 9% over the original set (without low z‐score templates) respectively; after TASSER modeling/refinement and ranking, the best model improves by ～7 and 9% over the best model generated with the original template set. Moreover, TASSER_low‐zsc generates 22% (43%) more foldable medium (hard) targets. Similar improvements are observed with low‐ranked templates from SPARKS². The template clustering approach could be applied to other modeling methods that utilize multiple templates to improve structure prediction. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

三级医院医疗质量评价指标体系的构建及优化

目的 在原有医疗质量评价体系的基础上,建立一套科学合理、可操作性强的三级综合性医院医疗质量评价指标体系。方法 采用典型研究方法,通过文献研究筛选初始指标,通过知情人访谈法确定质量评价框架,运用德尔菲法并借鉴平衡积分卡的原理和思想优化并确定指标及权重系数。结果 确定了以医疗基础质量、环节质量和终末质量为基本结构的三级综合性医院质量评价框架和指标体系,由3个一级维度、8个二级维度、24个三级维度指标体系及其相应权重。结论 医疗质量评价指标体系关注医院质量管理中的难点和重点环节,能够满足上海市三级综合性医院医疗质量管理的实际需要。     

共刺激分子CD86对急诊脓毒症发生的预测价值

摘要 目的：：探讨共刺激分子CD86评估急诊感染患者发生脓毒症的价值。方法：以2019年9月至2020年9月上海交通大学医学院附属仁济医院急诊科监护室和病房收治的年龄≥18岁且存在感染或临床高度怀疑感染的患者为研究对象。按照Sepsis-3.0标准,将患者分为脓毒症组和非脓毒症组。记录所有患者的临床资料,入院时24小时内检测所有患者的静脉血血常规、外周血单核细胞（PBMC）的CD86和HLA-DR表达水平、C反应蛋白（CRP）、降钙素原（PCT）、白蛋白（ALB）、乳酸(Lac)、血清白介素水平,计算CRP/ALB比值、中性粒细胞/淋巴细胞比值(NLR),血小板/淋巴细胞比值(PLR),淋巴细胞/单核细胞比值(LMR)并记录。组间比较上述各指标的差异,然后进一步采用Logistics回归模型进行多因素分析。结果：入选患者共91例,脓毒症患者49例（死亡15例,占30.6%）,非脓毒症患者42例（0死亡）;脓毒症组CD86%、N（中性粒细胞）、CRP/ALB、NLR、IL-6、IL-10、SIL-2R、CRP、PCT均高于非脓毒症感染（P<0.05）,而HLA-DR、LY（淋巴细胞）、PLT（血小板）、ALB、LMR在脓毒症组中则较非脓毒症组降低（P<0.05）。多因素Logistics回归分析建立CD86模型和HLA-DR模型,发现在CD86模型中,CD86,呼吸频率（RR）,LY,PCT是发生脓毒症的独立预测因子（P<0.05）,OR值分别为1.539(1.148-2.064),1.141(1.009-1.290),0.280(0.097-0.811)和1.036(1.005-1.068);在HLA-DR模型中,HLA-DR和LY是发生脓毒症的独立预测因子（P<0.05）,OR值分别为0.971(0.953-0.988)和0.290（0.117-0.718）。最后将CD86模型、HLA-DR模型和APACHII评分进行ROC曲线分析,曲线下面积（AUC）分别为0.870（0.796-0.944）、0.793（0.700-0.887）、0.754（0.653-0.855）。结论：脓毒症患者PBMC的CD86水平升高,HLA-DR水平降低,CD86和HLA-DR可以早期预测急诊脓毒症的发生。     

脓毒症患者炎性因子、凝血功能与APACHEⅡ评分和预后的关系

目的:研究脓毒症患者炎性因子、凝血功能与急性生理学和慢性健康状况Ⅱ(APACHEⅡ)评分和预后的关系。方法:选取2017年1月至2018年1月我院收治的脓毒症患者150例为脓毒症组,所有患者根据预后结果分为死亡组(n=49)和存活组(n=101),选择同期在我院住院的非脓毒症患者98例为非脓毒症组,比较脓毒症组与非脓毒症组患者炎性因子、凝血功能指标水平及APACHEⅡ评分,同时比较死亡组和存活组患者炎性因子、凝血功能指标水平及APACHEⅡ评分,并分析脓毒症患者APACHEⅡ评分与炎性因子、凝血功能指标的相关性。结果:脓毒症组患者降钙素原(PCT)、活化部分凝血酶原时间(APTT)、凝血酶原时间(PT)水平及APACHEⅡ评分均高于非脓毒症组,血小板计数(PLT)低于非脓毒症组(P0.05),两组C-反应蛋白(CRP)比较差异无统计学意义(P0.05)。死亡组患者PCT、APTT、PT水平及APACHEⅡ评分均高于存活组,PLT水平低于存活组(P0.05),两组CRP比较差异无统计学意义(P0.05)。经Spearman相关性分析结果显示,脓毒症患者APACHEⅡ评分与PCT、APTT、PT均呈正相关关系,与PLT呈负相关关系(P0.05),与CRP无相关性(P0.05)。结论:脓毒症患者PCT、APTT、PT水平明显上升,PLT水平明显下降,且均与患者的APACHEⅡ评分密切相关,临床可通过调节炎症因子水平及凝血功能指标从而改善患者的病情和预后。     

Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach

PurposeTo evaluate the potential of 2D texture features extracted from magnetic resonance (MR) images for differentiating brain metastasis (BM) and glioblastomas (GBM) following a radiomics approach.MethodsThis retrospective study included 50 patients with BM and 50 with GBM who underwent T1-weighted MRI between December 2010 and January 2017. Eighty-eight rotation-invariant texture features were computed for each segmented lesion using six texture analysis methods. These features were also extracted from the four images obtained after applying the discrete wavelet transform (88 features × 4 images). Three feature selection methods and five predictive models were evaluated. A 5-fold cross-validation scheme was used to randomly split the study group into training (80 patients) and testing (20 patients), repeating the process ten times. Classification was evaluated computing the average area under the receiver operating characteristic curve. Sensibility, specificity and accuracy were also computed. The whole process was tested quantizing the images with different gray-level values to evaluate their influence in the final results.ResultsHighest classification accuracy was obtained using the original images quantized with 128 gray-levels and a feature selection method based on the p-value. The best overall performance was achieved using a support vector machine model with a subset of 32 features (AUC = 0.896 ± 0.067, sensitivity of 82% and specificity of 80%). Naïve Bayes and k-nearest neighbors models showed also valuable results (AUC ≈ 0.8) with a lower number of features (<13), thus suggesting that these models may be more generalizable when using external validations.ConclusionThe proposed radiomics MRI approach is able to discriminate between GBM and BM with high accuracy employing a set of 2D texture features, thus helping in the diagnosis of brain lesions in a fast and non-invasive way.     

Clinicopathologic predictors of early relapse in advanced epithelial ovarian cancer: development of prediction models using nationwide data

ObjectiveTo identify clinicopathologic factors predictive of early relapse (platinum-free interval (PFI) of ≤6 months) in advanced epithelial ovarian cancer (EOC) in first-line treatment, and to develop and internally validate risk prediction models for early relapse.MethodsAll consecutive patients diagnosed with advanced stage EOC between 01-01-2008 and 31-12-2015 were identified from the Netherlands Cancer Registry. Patients who underwent cytoreductive surgery and platinum-based chemotherapy as initial EOC treatment were selected. Two prediction models, i.e. pretreatment and postoperative, were developed. Candidate predictors of early relapse were fitted into multivariable logistic regression models. Model performance was assessed on calibration and discrimination. Internal validation was performed through bootstrapping to correct for model optimism.ResultsA total of 4,557 advanced EOC patients were identified, including 1,302 early relapsers and 3,171 late or non-relapsers. Early relapsers were more likely to have FIGO stage IV, mucinous or clear cell type EOC, ascites, >1 cm residual disease, and to have undergone NACT-ICS. The final pretreatment model demonstrated subpar model performance (AUC = 0.64 [95 %-CI 0.62−0.66]). The final postoperative model based on age, FIGO stage, pretreatment CA-125 level, histologic subtype, presence of ascites, treatment approach, and residual disease after debulking, demonstrated adequate model performance (AUC = 0.72 [95 %-CI 0.71−0.74]). Bootstrap validation revealed minimal optimism of the final postoperative model.ConclusionA (postoperative) discriminative model has been developed and presented online that predicts the risk of early relapse in advanced EOC patients. Although external validation is still required, this prediction model can support patient counselling in daily clinical practice.     

Preterm or Not – An Evaluation of Estimates of Gestational Age in a Cohort of Women from Rural Papua New Guinea

BackgroundKnowledge of accurate gestational age is required for comprehensive pregnancy care and is an essential component of research evaluating causes of preterm birth. In industrialised countries gestational age is determined with the help of fetal biometry in early pregnancy. Lack of ultrasound and late presentation to antenatal clinic limits this practice in low-resource settings. Instead, clinical estimators of gestational age are used, but their accuracy remains a matter of debate.MethodsIn a cohort of 688 singleton pregnancies from rural Papua New Guinea, delivery gestational age was calculated from Ballard score, last menstrual period, symphysis-pubis fundal height at first visit and quickening as well as mid- and late pregnancy fetal biometry. Published models using sequential fundal height measurements and corrected last menstrual period to estimate gestational age were also tested. Novel linear models that combined clinical measurements for gestational age estimation were developed. Predictions were compared with the reference early pregnancy ultrasound (<25 gestational weeks) using correlation, regression and Bland-Altman analyses and ranked for their capability to predict preterm birth using the harmonic mean of recall and precision (F-measure).ResultsAverage bias between reference ultrasound and clinical methods ranged from 0–11 days (95% confidence levels: 14–42 days). Preterm birth was best predicted by mid-pregnancy ultrasound (F-measure: 0.72), and neuromuscular Ballard score provided the least reliable preterm birth prediction (F-measure: 0.17). The best clinical methods to predict gestational age and preterm birth were last menstrual period and fundal height (F-measures 0.35). A linear model combining both measures improved prediction of preterm birth (F-measure: 0.58).ConclusionsEstimation of gestational age without ultrasound is prone to significant error. In the absence of ultrasound facilities, last menstrual period and fundal height are among the more reliable clinical measures. This study underlines the importance of strengthening ultrasound facilities and developing novel ways to estimate gestational age.     

Association of ischemic stroke onset time with presenting severity,acute progression,and long-term outcome: A cohort study

BackgroundPreclinical data suggest circadian variation in ischemic stroke progression, with more active cell death and infarct growth in rodent models with inactive phase (daytime) than active phase (nighttime) stroke onset. We aimed to examine the association of stroke onset time with presenting severity, early neurological deterioration (END), and long-term functional outcome in human ischemic stroke.Methods and findingsIn a Korean nationwide multicenter observational cohort study from May 2011 to July 2020, we assessed circadian effects on initial stroke severity (National Institutes of Health Stroke Scale [NIHSS] score at admission), END, and favorable functional outcome (3-month modified Rankin Scale [mRS] score 0 to 2 versus 3 to 6). We included 17,461 consecutive patients with witnessed ischemic stroke within 6 hours of onset. Stroke onset time was divided into 2 groups (day-onset [06:00 to 18:00] versus night-onset [18:00 to 06:00]) and into 6 groups by 4-hour intervals. We used mixed-effects ordered or logistic regression models while accounting for clustering by hospitals. Mean age was 66.9 (SD 13.4) years, and 6,900 (39.5%) were women. END occurred in 2,219 (12.7%) patients. After adjusting for covariates including age, sex, previous stroke, prestroke mRS score, admission NIHSS score, hypertension, diabetes, hyperlipidemia, smoking, atrial fibrillation, prestroke antiplatelet use, prestroke statin use, revascularization, season of stroke onset, and time from onset to hospital arrival, night-onset stroke was more prone to END (adjusted incidence 14.4% versus 12.8%, p = 0.006) and had a lower likelihood of favorable outcome (adjusted odds ratio, 0.88 [95% CI, 0.79 to 0.98]; p = 0.03) compared with day-onset stroke. When stroke onset times were grouped by 4-hour intervals, a monotonic gradient in presenting NIHSS score was noted, rising from a nadir in 06:00 to 10:00 to a peak in 02:00 to 06:00. The 18:00 to 22:00 and 22:00 to 02:00 onset stroke patients were more likely to experience END than the 06:00 to 10:00 onset stroke patients. At 3 months, there was a monotonic gradient in the rate of favorable functional outcome, falling from a peak at 06:00 to 10:00 to a nadir at 22:00 to 02:00. Study limitations include the lack of information on sleep disorders and patient work/activity schedules.ConclusionsNight-onset strokes, compared with day-onset strokes, are associated with higher presenting neurologic severity, more frequent END, and worse 3-month functional outcome. These findings suggest that circadian time of onset is an important additional variable for inclusion in epidemiologic natural history studies and in treatment trials of neuroprotective and reperfusion agents for acute ischemic stroke.

Wi-Sun Ryu and colleagues investigate the association of stroke onset time with presenting severity, early neurological deterioration (END), and long-term functional outcome in ischemic stroke.     

Circulating soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) as diagnostic and prognostic marker in neonatal sepsis

ObjectiveTriggering receptor expressed on myeloid cells-1 (TREM-1) is an important receptor involved in the innate inflammatory response and sepsis. We assessed soluble TREM-1 (sTREM-1) in 112 septic neonates (63 culture-positive and 49 culture-negative) and 40 healthy controls as a potential early diagnostic and prognostic marker for neonatal sepsis (NS).MethodsStudied neonates were evaluated for early- or late-onset sepsis using clinical and laboratory indicators upon admission. sTREM-1 was measured on initial sepsis evaluation and at 48 h after antibiotic therapy. For ethical reasons, cord blood samples were collected from control neonates and only samples from neonates that proved to be healthy by clinical examination and laboratory analysis were further analyzed for sTREM-1.ResultsBaseline sTREM-1 levels were significantly elevated in culture-proven (1461.1 ± 523 pg/mL) and culture-negative sepsis (1194 ± 485 pg/mL) compared to controls (162.2 ± 61 pg/mL) with no significant difference between both septic groups. Culture-positive or negative septic preterm neonates had significantly higher sTREM-1 compared to full term neonates. sTREM-1 was significantly higher in neonates with early sepsis than late sepsis and was associated with high mortality. sTREM-1 was significantly decreased 48 h after antibiotic therapy compared to baseline or levels in neonates with persistently positive cultures. sTREM-1 was positively correlated to white blood cells (WBCs), absolute neutrophil count, immature/total neutrophil (I/T) ratio, C-reactive protein (hs-CRP) and sepsis score while negatively correlated to gestational age and weight. hs-CRP and sepsis score were independently related to sTREM-1 in multiregression analysis. sTREM-1 cutoff value of 310 pg/mL could be diagnostic for NS with 100% sensitivity and specificity (AUC, 1.0 and 95% confidence interval [CI], 0.696–1.015) while the cutoff value 1100 pg/mL was predictive of survival with 100% sensitivity and 97% specificity (AUC, 0.978 and 95% CI, 0.853–1.13). However, hs-CRP cutoff 13.5 mg/L could be diagnostic for NS with a sensitivity of 76% and specificity of 72% (AUC, 0.762 and 95% CI, 0.612–0.925) and levels were not related to survival as no significant difference was found between dead and alive septic neonates.ConclusionsElevated sTREM-1 could be considered an early marker for NS that reflects sepsis severity and poor prognosis.     

Diagnostic Accuracy of Lipopolysaccharide-Binding Protein as Biomarker for Sepsis in Adult Patients: A Systematic Review and Meta-Analysis

IntroductionLipopolysaccharide-binding protein (LBP) is widely reported as a biomarker to differentiate infected from non-infected patients. The diagnostic use of LBP for sepsis remains a matter of debate. We aimed to perform a systematic review and meta-analysis to assess the diagnostic accuracy of serum LBP for sepsis in adult patients.MethodsWe performed a systematic review and meta-analysis to assess the accuracy of LBP for sepsis diagnosis. A systematic search in PubMed and EMBASE for studies that evaluated the diagnostic role of LBP for sepsis through December 2015 was conducted. We searched these databases for original, English language, research articles that studied the diagnostic accuracy between septic and non-septic adult patients. Sensitivity, specificity, and other measures of accuracy, such as diagnostic odds ratio (DOR) and area under the receiver operating characteristic curve (AUC) of LBP were pooled using the Hierarchical Summary Receiver Operating Characteristic (HSROC) method.ResultsOur search returned 53 reports, of which 8 fulfilled the inclusion criteria, accounting for 1684 patients. The pooled sensitivity and specificity of LBP for diagnosis of sepsis by the HSROC method were 0.64 (95% CI: 0.56–0.72) and 0.63 (95% CI: 0.53–0.73), respectively. The value of the DOR was 3.0 (95% CI: 2.0–4.0) and the AUC was 0.68 (95% CI: 0.64–0.72). Meta-regression analysis revealed that cut-off values accounted for the heterogeneity of sensitivity and sample size (> = 150) accounted for the heterogeneity of specificity.ConclusionsBased on the results of our meta-analysis, LBP had weak sensitivity and specificity in the detection of sepsis. LBP may not be practically recommended for clinical utilization as a single biomarker.     

Integrating life cycle assessment (LCA) and life cycle costing (LCC) in the early phases of aircraft structural design: an elevator case study

Purpose

The main objective of this paper is to develop a model that will combine economic and environmental assessment tools to support the composite material selection of aircraft structures in the early phases of design and application of the tool for an aircraft elevator.

Methods

An integrated life cycle cost (LCC) and life cycle assessment (LCA) methodology was used as part of the sustainable design approach for the laminate stacking sequence design. The model considered is the aircraft structure made of carbon fiber reinforce plastic prepreg and processed via hand layup-autoclave process which is the preferred method for the aircraft industry. The model was applied to a cargo aircraft elevator case study by comparing six different laminate configurations and two different carbon fiber prepreg materials across aircraft’s entire life cycle.

Results and discussion

The results show, in line with other studies using different methodologies (e.g., life cycle engineering, or LCE), that the combination of LCA with LCC is a worthwhile approach for comparing the different laminate configurations in terms of cost and environmental impact to support composite laminate stacking design by providing the best trade-off between cost and environment. Elevator LCC reduces 19% by changing the material type and applying different ply orientations. Elevator LCA score reduces 53% by selecting the optimum instead of best technical solution that minimizes the displacement. Improving the structural performance does not always lead to an increase in the cost.