Investigation on Pulse Wave Forward Peak Detection and Its Applications in Cardiovascular Health.

OBJECTIVE: The contours of the pulse wave vary greatly, which affect the accuracy of pulse wave peak detection and the reliability of subsequent peak-based cardiovascular health analyses. We proposed an algorithm to reliably detect the peak of forward pulse wave (forward peak) and proposed to use it for improving the accuracy in cardiovascular health analysis. METHODS: A method based on Gaussian fitting was proposed to detect the forward peak. Then, the forward peak was utilized for instantaneous heart rate (HR), heart rate variability (HRV), and augmentation index (a cardiovascular risk marker reflecting arterial stiffness) estimations. The accuracy of HR/HRV obtained by forward peak was compared with that obtained by other photoplethymogram (PPG) characteristic points previously reported, using electrocardiogram-derived HR/HRV as gold standard. The correlation between augmentation index and age was calculated. The performance was verified using PPG-based pulse wave data with different contours while they were recorded at different locations from subjects with a wide range of age. RESULTS: The proposed forward peak detection method had smaller estimation error when compared with the gold standard, than other PPG characteristic points in estimating HR/HRV. The augmentation index extracted from the proposed forward peak was significantly correlated with age (p < 0.01). CONCLUSIONS: The proposed algorithm can relatively reliably detect the forward peak and has a wide application prospect in cardiovascular health. SIGNIFICANCE: Due to the convenience of PPG measurements, this proposed forward peak detection method has the potential to be widely used in the fields of wearable devices and telemedicine.

Bioreductive prodrugs as cancer therapeutics: targeting tumor hypoxia / 癌症

Hypoxia, a state of low oxygen, is a common feature of solid tumors and is associated with disease progression as well as resistance to radiotherapy and certain chemotherapeutic drugs. Hypoxic regions in tumors, therefore, represent attractive targets for cancer therapy. To date, five distinct classes of bioreactive prodrugs have been developed to target hypoxic cells in solid tumors. These hypoxia-activated prodrugs, including nitro compounds, N-oxides, quinones, and metal complexes, generally share a common mechanism of activation whereby they are reduced by intracellular oxidoreductases in an oxygen-sensitive manner to form cytotoxins. Several examples including PR-104, TH-302, and EO9 are currently undergoing phase II and phase III clinical evaluation. In this review, we discuss the nature of tumor hypoxia as a therapeutic target, focusing on the development of bioreductive prodrugs. We also describe the current knowledge of how each prodrug class is activated and detail the clinical progress of leading examples.

Retrieving novel C5aR antagonists using a hybrid ligand-based virtual screening protocol based on SVM classification and pharmacophore models.

C5aR antagonists have been thought as potential immune mediators in various inflammatory and autoimmune diseases, and discovery of C5aR antagonists has attracted much attention in recent years. The discovery of C5aR antagonists was usually achieved through high-throughput screening, which usually suffered a high cost and a low success rate. Currently, the fast developing computer-aided virtual screening (VS) methods provide economic and rapid approaches to the lead discovery. In this account, we proposed a hybrid ligand-based VS protocol that is based on support vector machine (SVM) classification and pharmacophore models for retrieving novel C5aR antagonists. Performance evaluation of this hybrid VS protocol in virtual screening against a large independent test set, T-CHEM, showed that the hybrid VS approach significantly increased the hit rate and enrichment factor compared with the individual SVM classification model-based VS and pharmacophore model-based VS, as well as molecular docking-based VS in that the receptor structure was created by homology modeling. The hybrid VS approach was then used to screen several large chemical libraries including PubChem, Specs, and Enamine. Finally, a total of 20 compounds were selected from the top ranking hits, and shifted to the subsequent in vitro and in vivo studies, which results will be reported in the near future.

Discovery of novel Bruton's tyrosine kinase inhibitors using a hybrid protocol of virtual screening approaches based on SVM model, pharmacophore and molecular docking.

Bruton's tyrosine kinase has emerged as a potential target for the treatment for B-cell malignancies and autoimmune diseases. Discovery of Bruton's tyrosine kinase inhibitors has thus attracted much attention recently. In this investigation, we introduced a hybrid protocol of virtual screening methods including support vector machine model-based virtual screening, pharmacophore model-based virtual screening and docking-based virtual screening for retrieving new Bruton's tyrosine kinase inhibitors from commercially available chemical databases. Performances of the hybrid virtual screening approach were evaluated against a test set, which results showed that the hybrid virtual screening approach significantly shortened the overall screening time, and considerably increased the hit rate and enrichment factor compared with the individual method (SB-VS, PB-VS and DB-VS) or their combinations by twos. This hybrid virtual screening approach was then applied to screen several chemical databases including Specs (202,408 compounds) and Enamine (980,000 compounds) databases. Thirty-nine compounds were selected from the final hits and have been shifted to experimental studies.

A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA-CG-SVM method.

Breast cancer resistance protein (BCRP) is one of the key multi-drug resistance proteins, which significantly influences the therapeutic effects of many drugs, particularly anti-cancer drugs. Thus, distinguishing between substrates and non-substrates of BCRP is important not only for clinical use but also for drug discovery and development. In this study, a prediction model of the substrates and non-substrates of BCRP was developed using a modified support vector machine (SVM) method, namely GA-CG-SVM. The overall prediction accuracy of the established GA-CG-SVM model is 91.3% for the training set and 85.0% for an independent validation set. For comparison, two other machine learning methods, namely, C4.5 DT and k-NN, were also adopted to build prediction models. The results show that the GA-CG-SVM model is significantly superior to C4.5 DT and k-NN models in terms of the prediction accuracy. To sum up, the prediction model of BCRP substrates and non-substrates generated by the GA-CG-SVM method is sufficiently good and could be used as a screening tool for identifying the substrates and non-substrates of BCRP.