Supplementary_Table - Chinese Herbal Medicine Versus Other Interventions in the Treatment of Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials

<p>Supplementary_Table for Chinese Herbal Medicine Versus Other Interventions in the Treatment of Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials by Ao Yu, David Adelson, and David Mills in Journal of Evidence-Based Integrative Medicine</p

Supplemental_Material_2 - Chinese Herbal Medicine Versus Other Interventions in the Treatment of Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials

<p>Supplemental_Material_2 for Chinese Herbal Medicine Versus Other Interventions in the Treatment of Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials by Ao Yu, David Adelson, and David Mills in Journal of Evidence-Based Integrative Medicine</p

Additional file 2: of Horizontal transfer of BovB and L1 retrotransposons in eukaryotes

Figures S1. and S2. show additional discordant L1 clusters. Figures S3–S54. show Kimura divergence plots for species containing both L1 and BovB elements. Figure S55 shows the location of the chimeric L1-BovB element in the cow genome (Bos taurus), showing that there is little evidence of transcription. (PDF 823 kb

Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia

<div><p>Background</p><p>Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction ‘rules’ for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia.</p><p>Principle Findings</p><p>The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models.</p></div

The categories for each predictive factor used to transform data into categorical data and number of patients.

<p>The categories for each predictive factor used to transform data into categorical data and number of patients.</p

Predictive factor descriptions, factor type and median with range values.

<p>Predictive factor descriptions, factor type and median with range values.</p

Model D structure.

<p>The final model in the tree graph that achieved high accuracy performance.</p

TIDEL II patients in this study.

<p>Inclusion and exclusion criteria.</p

The schema for the CML predictive model, building, evaluation, and final model selection.

<p>To build the predictive model, we studied a clinical trial, preparing data for analysis by imputing missing values and reformatting factors using comprehensive standard boundaries to create subcategories for each predictive factor based on domain knowledge. For evaluation and final model selection, the nested design was used to split the dataset into training, validation and testing sets. The model was trained on the training set, features were selected on the validation set, and performance was evaluated on the test set. The final models were compared with previous methods.</p

The comparison between previous methods and our recommended model on Saudi population.

<p>The comparison between previous methods and our recommended model on Saudi population.</p