Quantitative and Qualitative Investigation of Traditional Chinese Medicine Nursing Protocols for Type 2 Diabetes Mellitus

Objective:To analyze the implementation status of traditional Chinese medicine(TCM)nursing protocols for type 2 diabetes mellitus.Methods:Totally 420 hospitalized patients with type 2 diabetes mellitus were given TCM nursing protocols by syndrome differentiation.A quantitative and qualitative investigation was conducted to patients who received TCM nursing and nurses who performed TCM nursing techniques.Results:According to the self-evaluation outcomes of patients,TCM nursing protocols were effective to relieve polydipsia,polyuria,fatigue and other symptoms.Patients exhibited a high level of compliance with nursing techniques such as auricular point pressing,acupoint massage,Chinese herbal foot-bath and moxibustion.The interview showed that patients could benefit from the TCM nursing protocols,and nurses considered that application of TCM nursing protocols could enhance their awareness of TCM knowledge and practice skills.Conclusion:The application of TCM nursing protocols is effective to relieve clinical symptoms of type 2 diabetes mellitus.It can also improve the knowledge level and practice skills of nurses in TCM hospitals.

Recommending Personalized POIs from Location Based Social Network

Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.

Asymmetric synthesis of syn-aryl-(2S,3R)-2-chloro-3-hydroxy esters via an engineered ketoreductase-catalyzed dynamic reductive kinetic resolution

We report here a generic,green synthesis of 17 valuable syn-aryl-(2S,3R)-2–chloro-3–hydroxy esters(syn-(2S,3R)-1)in 73%-99%isolated yields along with 6.1:1–83:1 dr and 31%~>99%ee,through dynamic reductive kinetic resolution of racemic arylα–chloroβ-keto esters(2)catalyzed by an engineered ketoreductase which was obtained via ep PCR-based directed evolution.The hectogram scale synthesis of syn-(2S,3R)-1b at a substrate concentration of 120 g/L showcased the application potential of the biocatalytic method developed presently.