METHOD: A set of three psychophysics conditions of hearing (critical band spectral estimation, equal loudness hearing curve, and the intensity loudness power law of hearing) is used to estimate the auditory spectrum. The auditory spectrum and all-pole models of the auditory spectrums are computed and analyzed and used in a Gaussian mixture model for an automatic decision.
RESULTS: In the experiments using the Massachusetts Eye & Ear Infirmary database, an ACC of 99.56% is obtained for pathology detection, and an ACC of 93.33% is obtained for the pathology classification system. The results of the proposed systems outperform the existing running-speech-based systems.
DISCUSSION: The developed system can effectively be used in voice pathology detection and classification systems, and the proposed features can visually differentiate between normal and pathological samples.
Materials and Methods: SF1 was produced by optimized methodology for bioassay-guided fractionation. Fourier transform infrared (FTIR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) were carried out to characterize the SF1. SF1 was screened for cytotoxicity activity toward HeLa, SiHa, and normal cells (NIH) cells by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) assay. The anticancer mechanism of SF1 was evaluated toward SiHa cells, which showed highest cytotoxicity toward SF1 treatment. The mechanism includes cell cycle progression and protein expression, which was detected using specific antibody-conjugated fluorescent dye, p53-FITC, by flow cytometry.
Results: Major constituents of SF1 were alkaloids with amines as functional group. SF1 showed highest cytotoxic activity against SiHa (half-maximal inhibitory concentration [IC50] < 10 µg/mL) compared to HeLa cells. Cytoselectivity of SF1 was observed with no IC50 detected on normal NIH cells. On flow cytometry analysis, SF1 was able to induce apoptosis on SiHa cells by arresting cell cycle at G1/S and upregulation of p53 protein.
Conclusion: SF1 showed anticancer activity by inducing apoptosis through arrested G1/S cell cycle checkpoint-mediated mitochondrial pathway.
OBJECTIVE: The main objective of this paper is to develop a robust algorithm to extract respiration rate using the contactless displacement sensor.
METHODS: In this study, chest movements were used as an indicative of inspiration and expiration to measure respiratory rate using the contactless displacement sensor. The contactless optical signals were recorded from 32 healthy subjects in four different controlled breathing conditions: rest, coughing, talking and hand movement to obtain the motion artifacts that the patients may have in the emergency department. The Empirical mode decomposition (EMD) algorithm was used to derive continuous RR signal from the contactless optical signal.
RESULTS: The analysis showed that there is a good correlation (0.9702) with RMSE of 0.33 breaths per minutes between the contact respiration rate and contactless respiration rate using empirical mode decomposition method.
CONCLUSION: It can be concluded that the empirical mode decomposition method can extract the respiration rate of the contactless optical signal from chest movement.