中医医案方剂多维相似度算法设计与探索＊

中医医案方剂具有多维度的特性，单独采用一种相似度算法难以展现方剂数据在各维度的相似情况，具有一定的局限性。本研究基于中医医案方剂的多维数据特点，结合中医临床遣方用药的思维过程，设计了中医医案方剂的多维相似度算法，并以七则国医大师医案为示例，详细展示了该算法的计算过程，同时将多维相似度算法计算结果与传统算法计算结果进行了对比分析。通过对比可以看出本算法能够相对全面地反映不同方剂间的相似度，且结果更为合理也更贴合中医临床实际。     

Privacy-preserving matching of similar patients

The identification of similar entities represented by records in different databases has drawn considerable attention in many application areas, including in the health domain. One important type of entity matching application that is vital for quality healthcare analytics is the identification of similar patients, known as similar patient matching. A key component of identifying similar records is the calculation of similarity of the values in attributes (fields) between these records. Due to increasing privacy and confidentiality concerns, using the actual attribute values of patient records to identify similar records across different organizations is becoming non-trivial because the attributes in such records often contain highly sensitive information such as personal and medical details of patients. Therefore, the matching needs to be based on masked (encoded) values while being effective and efficient to allow matching of large databases.Bloom filter encoding has widely been used as an efficient masking technique for privacy-preserving matching of string and categorical values. However, no work on Bloom filter-based masking of numerical data, such as integer (e.g. age), floating point (e.g. body mass index), and modulus (numbers wrap around upon reaching a certain value, e.g. date and time), which are commonly required in the health domain, has been presented in the literature. We propose a framework with novel methods for masking numerical data using Bloom filters, thereby facilitating the calculation of similarities between records. We conduct an empirical study on publicly available real-world datasets which shows that our framework provides efficient masking and achieves similar matching accuracy compared to the matching of actual unencoded patient records.     

How do taxonomic versus thematic relations impact similarity and difference judgments? An ERP study

Taxonomically related concepts like “bee” and “butterfly” and thematically related concepts like “bee” and “honey” have different roles in similarity judgments. We examined the complex impact of taxonomic and thematic relations on similarity and difference judgments via ERPs in a S1–S2 paradigm. Subjects were required to remember a word denoting some object or animal (S1), and compare that to a second word (S2) that was either thematically related, taxonomically related or unrelated to S1, making a “high” or “low” similarity and difference judgments in separate blocks. We found two main differences that suggest thematic and taxonomic relations engage distinct neural processes. The first difference is an N400 effect peaking between 300 ms and 400 ms that is more negative for unrelated words than for thematically and taxonomically related words. The second difference is a frontally distributed P600 peaking between 500 ms and 600 ms that is larger for taxonomically related words than for both unrelated and thematically related words. These results suggest that the dual process model for perceiving similarity is superior to the comparison only model of similarity judgments, and furthermore, provide evidence that the thematic relations are dissociative from taxonomic relations in making similarity and difference judgments.     

A Novel Similarity Learning Method via Relative Comparison for Content-Based Medical Image Retrieval

Nowadays, the huge volume of medical images represents an enormous challenge towards health-care organizations, as it is often hard for clinicians and researchers to manage, access, and share the image database easily. Content-based medical image retrieval (CBMIR) techniques are employed to facilitate the above process. It is known that a few concrete factors, including visual attributes extracted from images, measures encoding the similarity between images, user interaction, etc. play important roles in determining the retrieval performance. This paper concentrates on the similarity learning problem of CBMIR. A novel similarity learning paradigm is proposed via relative comparison, and a large database composed of 5,000 images is utilized to evaluate the retrieval performance. Extensive experimental results and comprehensive statistical analysis demonstrate the superiority of adopting the newly introduced learning paradigm, compared with several conventional supervised and semi-supervised similarity learning methods, in the presented CBMIR application.     

In silico strategies on prion pathogenic conversion and inhibition from PrPC –PrPSc

Introduction: To date, various therapeutic strategies identified numerous anti-prion compounds and antibodies that stabilize PrP^C, block the conversion of PrP^C-PrP^Sc and increased effect on PrP^Sc clearance. However, no suitable drug has been identified clinically so far due to the poor oral absorption, low blood–brain-barrier [BBB] penetration, and high toxicity. Although some of the drugs were proven to be effective in prion-infected cell culture and whole animal models, none of them increased the rate of survival compared to placebo.

Areas covered: In this review, the authors highlight the importance of in silico approaches like molecular docking, virtual screening, pharmacophore analysis, molecular dynamics, QSAR, CoMFA and CoMSIA applied to detect molecular mechanisms of prion inhibition and conversion from PrP^C-PrP^Sc.

Expert opinion: Several in silico approaches combined with experimental studies have provided many structural and functional clues on the stability and physiological activity of prion mutants. Further, various studies of in silico and in vivo approaches were also shown to identify several new small organic anti-scrapie compounds to decrease the accumulation of PrP^res in cell culture, inhibit the aggregation of a PrP^C peptide, and possess pharmacokinetic characteristics that confirm the drug-likeness of these compounds.     

Formulation and In Vitro Evaluation of Ofloxacin Tablets using Natural Gums as Binders

Natural gums are economical, easily available, and useful as tablet binders. In the present investigation, an attempt was made to formulate Ofloxacin tablets using three natural binders, namely Acacia arabica, Hibiscus esculentus, and xanthan gum. Such six batches of Ofloxacin tablets were prepared by using different types and amounts of the natural binders by the wet granulation method. The tablets were analyzed for their hardness, friability, and weight variation, and in vitro release was performed in a phosphate buffer at pH 6.8. The prepared tablets were also evaluated for their various release kinetics and similarity factors f₂. The physical properties of the tablets containing the natural binders showed sufficient hardness, desirable disintegration time, and low friability. Their better percentage of drug release was observed as compared to the marketed formulation showing more than 85% drug release within 45 minutes. The in vitro release data was well-fitted into zero-order and the values of release exponent ‘n’ were between 0.303 and 0.514. The high similarity factor f₂ of 64.50 was achieved with the best batch in comparison to the marketed tablets. The results obtained indicated that the gum Acacia arabica performed as well as gelatin compared to the other binders for the Ofloxacin tablet formulation.     

Comparison of Internal Transcribed Spacers and Intergenic Spacer Regions of Five Common Iranian Sheep Bursate Nematodes

Background

Accurate identification of sheep nematodes is a critical point in epidemiological studies and monitoring of drug resistance in flocks. However, due to a close morphological similarity between the eggs and larval stages of many of these nematodes, such identification is not a trivial task. There are a number of studies showing that molecular targets in ribosomal DNA (Internal transcribed spacer 1, 2 and Intergenic spacer) are suitable for accurate identification of sheep bursate nematodes. The objective of present study was to compare the ITS1, ITS2 and IGS regions of Iranian common bursate nematodes in order to choose best target for specific identification methods.

Methods

The first and second internal transcribed spacers (ITS1and ITS2) and intergenic spacer (IGS) of the ribosomal DNA (rDNA) of 5 common Iranian bursate nematodes of sheep were sequenced. The sequences of some non–Iranian isolates were used for comparison in order to evaluate the variation in sequence homology between geographically different nematode populations.

Results

Comparison of the ITS1 and ITS2 sequences of Iranian nematodes showed greatest similarity among Teladorsagia circumcincta and Marshallagia marshalli of 94% and 88%, respectively. While Trichostrongylus colubriformis and M. marshalli showed the highest homology (99%) in the IGS sequences. Comparison of the spacer sequences of Iranian with non-Iranian isolates showed significantly higher variation in Haemonchus contortus compared to the other species.

Conclusion

Both the ITS1 and ITS2 sequences are convenient targets to have species-specific identification of Iranian bursate nematodes. On the other hand the IGS region may be a less suitable molecular target.     

Similarity preserving low-rank representation for enhanced data representation and effective subspace learning

Latent Low-Rank Representation (LatLRR) delivers robust and promising results for subspace recovery and feature extraction through mining the so-called hidden effects, but the locality of both similar principal and salient features cannot be preserved in the optimizations. To solve this issue for achieving enhanced performance, a boosted version of LatLRR, referred to as Regularized Low-Rank Representation (rLRR), is proposed through explicitly including an appropriate Laplacian regularization that can maximally preserve the similarity among local features. Resembling LatLRR, rLRR decomposes given data matrix from two directions by seeking a pair of low-rank matrices. But the similarities of principal and salient features can be effectively preserved by rLRR. As a result, the correlated features are well grouped and the robustness of representations is also enhanced. Based on the outputted bi-directional low-rank codes by rLRR, an unsupervised subspace learning framework termed Low-rank Similarity Preserving Projections (LSPP) is also derived for feature learning. The supervised extension of LSPP is also discussed for discriminant subspace learning. The validity of rLRR is examined by robust representation and decomposition of real images. Results demonstrated the superiority of our rLRR and LSPP in comparison to other related state-of-the-art algorithms.     

Influence of the roll compactor parameter settings and the compression pressure on the buccal bio-adhesive tablet properties

Miconazole buccal tablets were prepared via a dry granulation process. By applying a factorial design (2⁴), the roll compactor parameters (compaction force, gap between the rolls, type of the rolls (smooth, ribbed) and the sieve aperture) were optimised for the tablet strength. The compaction force and the roll type significantly affected the tablet strength. Afterwards, a quarter fractional factorial design (2^5-2) was applied, consisting of the four compactor parameters and additionally the compression pressure, in order to optimise these parameters for the dissolution profile and the buccal bio-adhesion characteristics (bio-adhesive force and energy). In order to evaluate the dissolution profiles properly, the similarity factor between sample and a zero-order release reference profile was used. The compression pressure and the roll type significantly affected the dissolution profile. The sieve aperture had a significant effect on the buccal adhesion properties and the compaction force had a significant effect on the dissolution profile and the bio-adhesive energy. The gap between the rolls affected the bio-adhesive force significantly.     

Prediction of the stability of polymeric matrix tablets containing famotidine from the positron annihilation lifetime distributions of their physical mixtures

The aim of the present work was to elucidate the impact of the structural changes of polymeric excipients during the course of storage on the drug release stability of tablets containing different polymers. Matrix tablets were formulated with famotidine as a model drug, using polyvinylpyrrolidone and carbopol matrix. Dissolution tests were carried out before and after storing the tablets under stress conditions for different time intervals. Parameters characterizing the release kinetics of matrix tablets, just as difference and similarity factors, were calculated to compare the release profiles as a function of storage time. Positron annihilation lifetime measurements were carried out to track the structural changes of the physical mixtures containing polymers during the course of storage. The changes in the positron lifetime distribution curves of the famotidine-polymer mixtures were in good correlation with the significant changes of release parameters of tablets. Thus the method would be a valuable tool for the screening of possible destabilizing interactions in the preformulation phase.