Pembuatan Niosom Berbasis Maltodekstrin De 5-10 Dari Pati Singkong (Manihot Utilissima)

Niosomes are non ionic surfactant vesicles that have potential application in the delivery of hydrophobic or amphilic drugs. We developed proniosomes, a dry formulation using a maltodextrin as a carrier coated with non ionic surfactant, which can be used to produce niosomes within a minutes by addition of hot water followed by agitation. A novel method is reported here for rapid preparation of proniosomes with wide range of surfactant loading. Maltodextrin DE 5-10 was hidrolyzed from tapioca starch using Thermamyl L 120 da Novo at 85o C. The result from SEM analyses shown that proniosomes appear very similar to the maltodextrin, but the surface was more smooth. Niosome suspensions which was observed under the optical microscopy and particle size analyzer were evaluated as drug carrier using ibuprofen as a model. The result provide an indication of maltodextrin DE 5-10 from tapioca starch are potentialy carrier in the proniosome preparation which can be used for producing niosomes

Performance of our proposed method in four scenarios (I to IV) using naïve simulated data with the complete version.

(A) Synthetic data of correlated genes were generated from multivariate normal distribution with a mean value of 0 and a covariance matrix whose entries are 0.6. (B) Synthetic data of correlated genes were generated from multivariate normal distribution with a mean value of 0 and a covariance matrix whose entries are 0.3. The numbers in blue represent the average number of identified ceRNA-miRNA triplets after 100 simulations. (TIFF)</p

ceRNA regulatory network observed in TCGA-LUAD.

The size of each dot represents the number of bridged miRNAs per ceRNA. (TIFF)</p

Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.5 to 0.7.

(XLS)</p

A Composite Model for Subgroup Identification and Prediction via Bicluster Analysis

<div><p>Background</p><p>A major challenges in the analysis of large and complex biomedical data is to develop an approach for 1) identifying distinct subgroups in the sampled populations, 2) characterizing their relationships among subgroups, and 3) developing a prediction model to classify subgroup memberships of new samples by finding a set of predictors. Each subgroup can represent different pathogen serotypes of microorganisms, different tumor subtypes in cancer patients, or different genetic makeups of patients related to treatment response.</p><p>Methods</p><p>This paper proposes a composite model for subgroup identification and prediction using biclusters. A biclustering technique is first used to identify a set of biclusters from the sampled data. For each bicluster, a subgroup-specific binary classifier is built to determine if a particular sample is either inside or outside the bicluster. A composite model, which consists of all binary classifiers, is constructed to classify samples into several disjoint subgroups. The proposed composite model neither depends on any specific biclustering algorithm or patterns of biclusters, nor on any classification algorithms.</p><p>Results</p><p>The composite model was shown to have an overall accuracy of 97.4% for a synthetic dataset consisting of four subgroups. The model was applied to two datasets where the sample’s subgroup memberships were known. The procedure showed 83.7% accuracy in discriminating lung cancer adenocarcinoma and squamous carcinoma subtypes, and was able to identify 5 serotypes and several subtypes with about 94% accuracy in a pathogen dataset.</p><p>Conclusion</p><p>The composite model presents a novel approach to developing a biclustering-based classification model from unlabeled sampled data. The proposed approach combines unsupervised biclustering and supervised classification techniques to classify samples into disjoint subgroups based on their associated attributes, such as genotypic factors, phenotypic outcomes, efficacy/safety measures, or responses to treatments. The procedure is useful for identification of unknown species or new biomarkers for targeted therapy.</p></div

The prediction model divided the 97 patients into four subgroups using SVM.

<p>The logrank test for differences among the four subgroups (0,0), (0,1), (1,0), and (1,1) was 0.003.</p

Subgroup classification for the 111 lung cancer patients of the GSE3141 dataset using the composite model with the SVM, RF and LDA algorithms, and K-means (2-means, 3-means and 4-means) cluster analysis.

<p>Subgroup classification for the 111 lung cancer patients of the GSE3141 dataset using the composite model with the SVM, RF and LDA algorithms, and K-means (2-means, 3-means and 4-means) cluster analysis.</p

Hierarchical cluster analysis of the 14 subgroups identified from the test dataset using the average linkage distance.

<p>The 14 subgroups consist of 5 major subgroups: 1. Thompson (0010000000); 2. Typhimurium (0100000000); 3. Decoy (0000000000); 4. Oranienburg (0001000000, 0000100000, 0001100000, 0001110000, 0000110000); 5. Hadar (0000010000, 1000010000) and I4,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111318#pone.0111318-Langreth1" target="_blank">[5]</a>,12:i- (1000000000, 1000000100, 1000001000, 0000001000).</p

Frequency distributions of subgroup patterns identified by the SVM composite model for the Salmonella PFGE test dataset, which consisted of 5,055 isolates from five training serotypes and 1,000 additional “Decoy” isolates.

<p>Frequency distributions of subgroup patterns identified by the SVM composite model for the Salmonella PFGE test dataset, which consisted of 5,055 isolates from five training serotypes and 1,000 additional “Decoy” isolates.</p

Frequency distributions of classification patterns identified by the SVM composite model (m₁–m₁₀) for the <i>Salmonella</i> PFGE training dataset consisting of five serotypes.

<p>Frequency distributions of classification patterns identified by the SVM composite model (m₁–m₁₀) for the <i>Salmonella</i> PFGE training dataset consisting of five serotypes.</p