Extracellular and intracellular intermittent magnetic-fluid hyperthermia treatment of SK-Hep1 hepatocellular carcinoma cells based on magnetic nanoparticles coated with polystyrene sulfonic acid.

The use of magnetic nanoparticles (MNPs) magnetized on applying an alternating magnetic field (AMF) to stimulate the thermal characteristics and to induce tumor apoptosis is a currently active area of research in cancer treatment. In previous work, we developed biocompatible and superparamagnetic polystyrene-sulfonic-acid-coated magnetic nanoparticles (PSS-MNPs) as applications for magnetically labeled cell trapping, but without assessment of treatment effects on tumor diseases. In the present work, we examined PSS-MNP-induced magnetic fluid hyperthermia (MFH) on SK-Hep1 hepatocellular carcinoma (HCC) cells for lethal thermal effects with a self-made AMF system; an adjustable AMF frequency generated a variable intensity of magnetic field and induced MNP relaxation. The extracellular and intracellular MFH treatments on a SK-Hep1 cell line were implemented in vitro; the result indicates that the lethal effects were efficient and caused a significantly decreased cell viability of SK-Hep1 cells. As the PSS-MNP concentration decreased, especially in intracellular MFH treatments, the MFH effects on cells, however, largely decreased through heat spreading to the culture medium. On controlling and decreasing the volume of culture medium, the problem of heat spreading was solved. It can be consequently expected that PSS-MNPs would be a prospective agent for intracellular cancer magnetotherapy.

Development of a telehealthcare decision support system for patients discharged from the hospital.

OBJECTIVE: This article presents the development of a telehealthcare decision support system (TDSS) for patients discharged from the hospital, where symptom data are important indications of the recovery progress for patients. Symptom data are difficult to quantify in a telehealthcare application scenario because the observations and perceptions on symptoms by the patient themselves are subjective. In the TDSS, both symptom data from patients and clinical histories from the hospital information system are collected. Machine learning algorithms are used to build a predictive model for classifying patients according to their symptom data and clinical histories, to provide a degree of urgency for the patient to return to the hospital. MATERIALS AND METHODS: During a 1-year period, 1,467 patient cases were collected. Symptom data and clinical histories were preprocessed into 49 parameters for machine learning. The training data of patients were validated manually with their actual clinical histories of returning to the hospital. The performances of predictive models trained by five different machine learning algorithms were evaluated and compared. RESULTS: The Bayesian network algorithm had the best performance among the machine learning algorithms tested in this application scenario and was selected to be implemented in the TDSS. On the 1,467 patient cases collected, its precision in 10-fold cross-validation was 79.3%. The most important six parameters were also selected from the 49 parameters by feature selection. The performance of correct prediction by the TDSS is comparable to that by the nursing team at the call center. CONCLUSIONS: The TDSS provides a degree of urgency for patients to return to the hospital and thereby assists the telehealthcare nursing team in making such decisions. The performance of the TDSS is expected to improve as more cases of patient data are collected and input into the TDSS. The TDSS has been implemented in one of the largest commercialized telehealthcare practices in Taiwan administered by Min-Sheng General Hospital.

Development and practice of a Telehealthcare Expert System (TES).

OBJECTIVE: Expert systems have been widely used in medical and healthcare practice for various purposes. In addition to vital sign data, important concerns in telehealthcare include the compliance with the measurement prescription, the accuracy of vital sign measurements, and the functioning of vital sign meters and home gateways. However, few expert system applications are found in the telehealthcare domain to address these issues. MATERIALS AND METHODS: This article presents an expert system application for one of the largest commercialized telehealthcare practices in Taiwan by Min-Sheng General Hospital. The main function of the Telehealthcare Expert System (TES) developed in this research is to detect and classify events based on the measurement data transmitted to the database at the call center, including abnormality of vital signs, violation of vital sign measurement prescriptions, and malfunction of hardware devices (home gateway and vital sign meter). When the expert system detects an abnormal event, it assigns an "urgent degree" and alerts the nursing team in the call center to take action, such as phoning the patient for counseling or to urge the patient to return to the hospital for further tests. RESULTS: During 2 years of clinical practice, from 2009 to 2011, 19,182 patients were served by the expert system. The expert system detected 41,755 events, of which 22.9% indicated abnormality of vital signs, 75.2% indicated violation of measurement prescription, and 1.9% indicated malfunction of devices. On average, the expert system reduced by 76.5% the time that the nursing team in the call center spent in handling the events. CONCLUSIONS: The expert system helped to reduce cost and improve quality of the telehealthcare service.