Risk Factors of Post-Operative Extracorporeal Membrane Oxygenation Support after Lung Transplantation: A Retrospective Analysis

Background: Extracorporeal membrane oxygenation (ECMO) is an effective adjuvant therapy for cardiopulmonary support during the period of lung transplantation (LTx). However, factors associated with the application of ECMO after LTx remain controversial. The purpose of this study is to clarify the risk factors of post-operative ECMO support and to evaluate the outcomes. Methods: It was a hospital, single-center, retrospective study. 266 patients underwent LTx supported by ECMO were included. According to whether or not the patients received continourly ECMO support after the surgery, the enrolled patients were further divided into intra-operative ECMO group (group I, 105 cases) and post-operative ECMO group (group P, 161 cases). The peri-operative data of the donors and recipients were collected. The independent risk factors associated with post-operative ECMO support during LTx were identified. The relationship between primary graft dysfunction (PGD)/post-operative survival and duration of ECMO support was also analyzed. Results: Prolonged donor ventilation ≥ 5 days, pre-operative recipient mechanical ventilation, bilateral lung transplantation (BLT), veno-venous (V-V) ECMO and PGD in recipient were independent risk factors for post-operative ECMO support. The risk of PGD and post-operative death increased along with the increase of ECMO bypass time, and the mortality risk in group P was 2.33 (95% confidence interval: 1.16 - 4.67) times as that in group I. Conclusions: Mechanical ventilation for donor ≥ 5 days, pre-operative mechanical ventilation, BLT, V-V-ECMO and PGD in recipient were independent risk factors for post-operative ECMO support after LTx, and post-operative ECMO could not reduce recipients’ hospital mortality.

Predicting oral disintegrating tablet formulations by neural network techniques

Oral disintegrating tablets(ODTs) are a novel dosage form that can be dissolved on thetongue within 3 min or less especially for geriatric and pediatric patients. Current ODT for-mulation studies usually rely on the personal experience of pharmaceutical experts andtrial-and-error in the laboratory, which is inefficient and time-consuming. The aim of cur-rent research was to establish the prediction model of ODT formulations with direct com-pression process by artificial neural network(ANN) and deep neural network(DNN) tech-niques. 145 formulation data were extracted from Web of Science. All datasets were dividedinto three parts: training set(105 data), validation set(20) and testing set(20). ANN andDNN were compared for the prediction of the disintegrating time. The accuracy of the ANNmodel have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testingset respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respec-tively. Compared with the ANN, DNN showed the better prediction for ODT formulations.It is the first time that deep neural network with the improved dataset selection algorithmis applied to formulation prediction on small data. The proposed predictive approach couldevaluate the critical parameters about quality control of formulation, and guide researchand process development. The implementation of this prediction model could effectivelyreduce drug product development timeline and material usage, and proactively facilitatethe development of a robust drug product.

两种含氯消毒副产物对草履虫急性毒性效应初探

氯化消毒是我国普遍采用的城市污水消毒措施,然而,氯化消毒的同时,氯与水中的有机物发生反应生成一系列的卤代烃类化合物,即氯化消毒副产物。经研究证实大部分氯化消毒副产物具有致癌、致突变、生殖毒性效应。为研究消毒废水中一些副产物的毒性,本研究采用消毒废水中的有害副产物三氯甲烷(CHCl3)和三溴甲烷(CHBr3)为毒物材料,以草履虫为受试生物,对其种群进行单一急性毒性和联合毒性实验,探究CHCl3和CHBr3对草履虫的急性毒性和联合毒性。实验结果表明,两种毒物单独作用时,随着浓度增大,毒性作用也增大,CHCl3的1 h半致死浓度(LC50)为1585.27 μg/L,CHBr3的1 h半致死浓度(LC50)为656.37 μg/L,两种毒物共同作用时表现为协同作用。由于草履虫对两种毒物毒性较为敏感,其可作为CHCl3和CHBr3的毒性评价生物。

Recent progress in hydrogen:From solar to solar cell

Hydrogen,meeting the requirements of sustainable development,is regarded as the ultimate energy in the 21st century.Due to the inexhaustible and feasible of solar energy,solar water splitting is an immensely promising strategy for environmental-friendly hydrogen production,which not only overcomes the fluctuation and intermittency but also contributes to achieving the mission of global“Carbon Neutrality and Carbon Peaking”.However,there is still a lack of a comprehensive overview focusing on hydrogen progress with a discussion of development from solar energy to solar cells.Herein,we emphasize several solar-to-hydrogen pathways from the basic concepts and principles and focus on photovoltaic-electrolysis and photoelectrochemical/photovoltaic systems,which have achieved solar-to-hydrogen(STH)efficiency of over 10%and have extremely promising for large-scale application.In addition,we summarize the challenges and opportunities faced in this field including configuration design,electrode materials,and performance evaluation.Finally,perspectives on the potential commercial application and scientific research for the further development of solar-to-hydrogen are analyzed and presented.

联通主义视阈下的cMOOC知识生产的实证研究——基于机器学习的对比分析

强调以学习者共同体为核心来开展知识的生产和传播活动是联通主义区别于行为主义、认知主义和建构主义的重要特征之一。学习者共同体已成为联通主义学习范式下的知识生产群体,但其知识生产属性尚不明确,亟待更深一步探究。由此,本研究借助机器学习领域中的词向量(Word2vec)语义分析工具,以中文核心期刊论文为代表的传统知识生产为客观参照物,在同一个主题下对联通主义视阈下的c MOOC "互联网+教育:理论与实践的对话"的知识生产属性进行实证研究,结果显示:基于c MOOC的联通主义知识生产呈现出三种类别的属性,分别是:主题聚焦性、理念时新性和视野广角性。而以中文核心期刊论文为代表的传统知识生产则呈现出分层结构性、特定群体关注性和物质供给侧重性三种类别的属性。基于以上结果,本文进一步从生产群体、思维理念以及实践应用等层面对比和剖析两类知识生产的影响和启示。作为以国内首门c MOOC为研究对象开展的联通主义知识生产属性与传统知识生产属性的对比实证研究,其结果不仅为国内、国际上以c MOOC为表现形式的联通主义知识生产实践提供了客观证据和直接指导,同时也首次探明了联通主义学习范式下的知识生产属性,为联通主义知识观及其知识生产理论的构建和充实提供参考依据。

Deep learning for in vitro prediction of pharmaceutical formulations

Current pharmaceutical formulation development still strongly relies on the traditional trialand-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly.Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies.

肝移植受者围手术期真菌性眼内炎一例

眼内炎是由细菌或真菌感染累及玻璃体和前房水的严重感染性眼病,如未及时有效治疗可导致患者视力丧失[1]。几乎所有眼内炎患者均会出现视力下降,部分合并眼部疼痛。真菌性眼内炎常与细菌性、非感染性和肿瘤等病因重叠,确诊较为困难,如缺乏临床经验、经验性使用糖皮质激素以及未采用有效的抗真菌药物治疗,则可能增加患者视力丧失的风险[2]。本文回顾性分析昆明市第一人民医院1例肝移植受者围手术期真菌性眼内炎患者临床资料,现报道如下。

rCOS： a formal model-driven engineering method for component-based software

Model-driven architecture （MDA） has become a main stream technology for software-intensive system design. The main engineering principle behind it is that the inherent complexity of software development can only be mastered by building, analyzing and manipulating system models. MDA also deals with system complexity by provid- ing component-based design techniques, allowing indepen- dent component design, implementation and deployment, and then system integration and reconfiguration based on com- ponent interfaces. The model of a system in any stage is an integration of models of different viewpoints. Therefore, for a model-driven method to be applied effectively, it must pro- vide a body of techniques and an integrated suite of tools for model construction, validation, and transformation. This requires a number of modeling notations for the specifica- tion of different concerns and viewpoints of the system. These notations should have formally defined syntaxes and a unified theory of semantics. The underlying theory of the method is needed to underpin the development of tools and correct use of tools in software development, as well as to formally ver- ify and reason about properties of systems in mission-critical applications. The modeling notations, techniques, and tools must be designed so that they can be used seamlessly in sup- porting development activities and documentation of artifactsin software design processes. This article presents such a method, called the rCOS, focusing on the models of a system at different stages in a software development process, their se- mantic integration, and how they are constructed, analyzed, transformed, validated, and verified.