双歧杆菌三联活菌胶囊联合营养支持治疗重症急性胰腺炎的临床研究

目的:探讨双歧杆菌三联活菌胶囊联合营养支持治疗重症急性胰腺炎的临床疗效。方法:选择2014年1月至2015年6月我院收治的90例重症急性胰腺炎患者作为研究对象,按照随机数字表法分为治疗组和对照组,每组45例患者。对照组在常规治疗基础上采用营养支持治疗,治疗组在对照组的基础上采用双歧杆菌三联活菌胶囊治疗。分别于治疗前后取患者静脉血进行C反应蛋白、白细胞介素-8、肿瘤坏死因子、白细胞计数、淀粉酶、脂肪酶、乳酸脱氢酶的检测,并对治疗效果和胃肠功能进行评分。结果:治疗组的总有效率95.56%显著高于对照组的80.00%,差异有统计学意义(P0.05)。治疗14 d后,两组患者的C反应蛋白、白细胞介素-8、肿瘤坏死因子、白细胞计数、淀粉酶、脂肪酶、乳酸脱氢酶水平均降低,且治疗组的下降幅度显著大于对照组,差异均有统计学意义(P0.05)。治疗后两组患者的胃肠功能评分均低于治疗前,且治疗组胃肠功能评分明显优于对照组,差异有统计学意义(P0.05)。结论:双歧杆菌三联活菌胶囊联合营养支持治疗重症急性胰腺炎可降低患者的血液指标,增强肠道功能,具有较好的临床疗效,临床可推广应用。     

GGIP: Structure and sequence‐based GPCR–GPCR interaction pair predictor

G Protein‐Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo‐ or hetero‐dimers or higher‐order molecular complexes. Many GPCRs exert a wide variety of molecular functions by forming specific combinations of GPCR subtypes. In addition, some GPCRs are reportedly associated with diseases. GPCR oligomerization is now recognized as an important event in various biological phenomena, and many researchers are investigating this subject. We have developed a support vector machine (SVM)‐based method to predict interacting pairs for GPCR oligomerization, by integrating the structure and sequence information of GPCRs. The performance of our method was evaluated by the Receiver Operating Characteristic (ROC) curve. The corresponding area under the curve was 0.938. As far as we know, this is the only prediction method for interacting pairs among GPCRs. Our method could accelerate the analyses of these interactions, and contribute to the elucidation of the global structures of the GPCR networks in membranes. Proteins 2016; 84:1224–1233. © 2016 Wiley Periodicals, Inc.     

Combining information from multiple bone turnover markers as diagnostic indices for osteoporosis using support vector machines

Context: Osteoporosis (OP) is a progressive systemic bone disease. Dual-energy X-ray absorptiometry (DXA) is routinely employed and is considered the gold standard method for the diagnosis of OP.

Objective: We aimed to investigate the potential use of combined information from multiple bone turnover markers (BTMs) as a clinical diagnostic tool for OP.

Materials and methods: A total of 9053 Chinese postmenopausal women (2464 primary OP patients and 6589 healthy controls) were recruited. Serum levels of six common BTMs, including BAP, BSP, CTX, OPG, OST and sRANKL were assayed. Models based on support vector machine (SVM) were constructed to explore the efficiency of different combinations of multiple BTMs for OP diagnosis.

Results: Increasing the number of BTMs used in generating the models increased the predictive power of the SVM models for determining the disease status of study subjects. The highest kappa coefficient for the model with one BTM (BAP) compared to DXA was 0.7783. The full model incorporating all six BTMs resulted in a high kappa coefficient of 0.9786.

Conclusion: Our findings showed that although single BTMs were not sufficient for OP diagnosis, appropriate combinations of multiple BTMs incorporated into the SVM models showed almost perfect agreement with the DXA.     

VPMCD: variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.     

Sequence analysis and rule development of predicting protein stability change upon mutation using decision tree model

Understanding the mechanism of the protein stability change is one of the most challenging tasks. Recently, the prediction of protein stability change affected by single point mutations has become an interesting topic in molecular biology. However, it is desirable to further acquire knowledge from large databases to provide new insights into the nature of them. This paper presents an interpretable prediction tree method (named iPTREE-2) that can accurately predict changes of protein stability upon mutations from sequence based information and analyze sequence characteristics from the viewpoint of composition and order. Therefore, iPTREE-2 based on a regression tree algorithm exhibits the ability of finding important factors and developing rules for the purpose of data mining. On a dataset of 1859 different single point mutations from thermodynamic database, ProTherm, iPTREE-2 yields a correlation coefficient of 0.70 between predicted and experimental values. In the task of data mining, detailed analysis of sequences reveals the possibility of the compositional specificity of residues in different ranges of stability change and implies the existence of certain patterns. As building rules, we found that the mutation residues in wild type and in mutant protein play an important role. The present study demonstrates that iPTREE-2 can serve the purpose of predicting protein stability change, especially when one requires more understandable knowledge.     

Machine Learning in Healthcare

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.     

Effects of recent experience on foraging decisions by bumble bees

The temporal and spatial scales employed by foraging bees in sampling their environment and making foraging decisions should depend both on the limits of bumble bee memory and on the spatial and temporal pattern of rewards in the habitat. We analyzed data from previous experiments to determine how recent foraging experience by bumble bees affects their flight distances to subsequent flowers. A single visit to a flower as sufficient to affect the flight distance to the next flower. However, longer sequences of two or three visits had an additional effect on the subsequent flight distance of individual foragers. This suggests that bumble bees can integrate information from at least three flowers for making a subsequent foraging decision. The existence of memory for floral characteristics at least at this scale may have significance for floral selection in natural environments.     

The dual role of sclerites in a gorgonian coral: Conflicting functions of support and defence

Sclerites in the gorgonian coral Briareum asbestinum perform the dual role of skeletal support against wave action and structural defence against predators. Local populations of B. asbestinum vary along gradients of decreasing water movement and decreasing predator abundance with increasing depth, such that sclerite length increases and sclerite density decreases with depth. Based on this pattern, I explored a possible trade-off between the sclerite composition that is most resistant to tearing versus most deterrent to predatory gastropods. Feeding assays revealed that artificial foods containing longer sclerites and those containing higher volume fractions of sclerites are less palatable to the gastropod Cyphoma gibbosum. However, real colonies appear constrained, in that they do not contain both long sclerites and high volume fractions at the same time. Given a choice among real colonies, snails prefer shallow-water colonies with shorter sclerites e ven though the sclerite volume fractions are high. Although least deterrent to snails, shallow-water colonies are 56% more resistant to tearing than their deep-water counterparts. Hence, variation in sclerite composition among local populations of B. asbestinum may be maintained by opposing selection for support versus defense.