Comparative Efficacy and Safety of Phosphate Binders in Hyperphosphatemia Patients With Chronic Kidney Disease

Background: In this study, we coordinated a network meta‐analysis to establish the efficacy and safety of different agents used in the treatment of hyperphosphatemia patients with chronic kidney disease. Methods: PubMed, CNKI, and Embase were systematically searched to retrieve relevant studies. Outcomes were presented by mean differences, odds ratios, and corresponding 95% credible intervals for continuous outcomes and binary outcomes, respectively. Each therapy was ranked according to the value of surface under the cumulative ranking curve. Consistencies between direct and indirect comparisons were assessed with a node‐splitting plot. Results: In terms of efficacy end points (including levels of serum phosphate, serum calcium, serum intact parathyroid hormone, and serum calcium × phosphorus product), all 7 kinds of agents outperformed or performed at least equally to placebo, with iron‐based phosphate‐binding agents being potentially the most effective. As for safety end points (including mortality, adverse events, and all‐cause discontinuation), almost all agents were equivalent in term of mortality and all‐cause discontinuation except in the comparison between iron‐based phosphate‐binding agents and placebo. Meanwhile, iron‐based phosphate‐binding agents colestilan and nicotinic acid performed poorly compared with placebo in terms of adverse events. Furthermore, iron‐based phosphate‐binding agents were potentially the safest agents followed sequentially by calcium‐based phosphate‐binding agents and placebo. Conclusion: Iron‐based phosphate‐binding agents were the preferable agents when considering efficacy and safety simultaneously.     

Singular value based characterization and analysis of thermal patches for early breast abnormality detection

The purpose of this study is to develop a novel breast abnormality detection system by utilizing the potential of infrared breast thermography (IBT) in early breast abnormality detection. Since the temperature distributions are different in normal and abnormal thermograms and hot thermal patches are visible in abnormal thermograms, the abnormal thermograms possess more complex information than the normal thermograms. Here, the proposed method exploits the presence of hot thermal patches and vascular changes by using the power law transformation for pre-processing and singular value decomposition to characterize the thermal patches. The extracted singular values are found to be statistically significant (p?<?0.001) in breast abnormality detection. The discriminability of the singular values is evaluated by using seven different classifiers incorporating tenfold cross-validations, where the thermograms of the Department of Biotechnology-Tripura University-Jadavpur University (DBT-TU-JU) and Database of Mastology Research (DMR) databases are used. In DMR database, the highest classification accuracy of 98.00% with the area under the ROC curve (AUC) of 0.9862 is achieved with the support vector machine using polynomial kernel. The same for the DBT-TU-JU database is 92.50% with AUC of 0.9680 using the same classifier. The comparison of the proposed method with the other reported methods concludes that the proposed method outperforms the other existing methods as well as other traditional feature sets used in IBT based breast abnormality detection. Moreover, by using Rank1 and Rank2 singular values, a breast abnormality grading (BAG) index has also been developed for grading the thermograms based on their degree of abnormality.