Hyperthermia by near infrared radiation induced immune cells activation and infiltration in breast tumor.

Breast cancer is the most common cancer that causes death in women. Conventional therapies, including surgery and chemotherapy, have different therapeutic effects and are commonly associated with risks and side effects. Near infrared radiation is a technique with few side effects that is used for local hyperthermia, typically as an adjuvant to other cancer therapies. The understanding of the use of near NIR as a monotherapy, and its effects on the immune cells activation and infiltration, are limited. In this study, we investigate the effects of HT treatment using NIR on tumor regression and on the immune cells and molecules in breast tumors. Results from this study demonstrated that local HT by NIR at 43 °C reduced tumor progression and significantly increased the median survival of tumor-bearing mice. Immunohistochemical analysis revealed a significant reduction in cells proliferation in treated tumor, which was accompanied by an abundance of heat shock protein 70 (Hsp70). Increased numbers of activated dendritic cells were observed in the draining lymph nodes of the mice, along with infiltration of T cells, NK cells and B cells into the tumor. In contrast, tumor-infiltrated regulatory T cells were largely diminished from the tumor. In addition, higher IFN-γ and IL-2 secretion was observed in tumor of treated mice. Overall, results from this present study extends the understanding of using local HT by NIR to stimulate a favourable immune response against breast cancer.

Measurement of bioelectric and acoustic profile of breast tissue using hybrid magnetoacoustic method for cancer detection.

This paper proposes a novel hybrid magnetoacoustic measurement (HMM) system aiming at breast cancer detection. HMM combines ultrasound and magnetism for the simultaneous assessment of bioelectric and acoustic profiles of breast tissue. HMM is demonstrated on breast tissue samples, which are exposed to 9.8 MHz ultrasound wave with the presence of a 0.25 Tesla static magnetic field. The interaction between the ultrasound wave and the magnetic field in the breast tissue results in Lorentz Force that produces a magnetoacoustic voltage output, proportional to breast tissue conductivity. Simultaneously, the ultrasound wave is sensed back by the ultrasound receiver for tissue acoustic evaluation. Experiments are performed on gel phantoms and real breast tissue samples harvested from laboratory mice. Ultrasound wave characterization results show that normal breast tissue experiences higher attenuation compared with cancerous tissue. The mean magnetoacoustic voltage results for normal tissue are lower than that for the cancerous tissue group. In conclusion, the combination of acoustic and bioelectric measurements is a promising approach for breast cancer diagnosis.

Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network.

This paper presents a new approach to diagnose and classify early risk in dengue patients using bioelectrical impedance analysis (BIA) and artificial neural network (ANN). A total of 223 healthy subjects and 207 hospitalized dengue patients were prospectively studied. The dengue risk severity criteria was determined and grouped based on three blood investigations, namely, platelet (PLT) count (less than or equal to 30,000 cells per mm(3)), hematocrit (HCT) (increase by more than or equal to 20%), and either aspartate aminotransferase (AST) level (raised by fivefold the normal upper limit) or alanine aminotransferase (ALT) level (raised by fivefold the normal upper limit). The dengue patients were classified according to their risk groups and the corresponding BIA parameters were subsequently obtained and quantified. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group's quantification. Day of fever was defined as the day of fever subsided, i.e., when the body temperature fell below 37.5°C. The blood investigation and the BIA data were taken for 5 days. The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network's performance indicator. The best ANN architecture of 3-6-1 (3 inputs, 6 neurons in the hidden layer, and 1 output), learning rate of 0.1, momentum constant of 0.2, and iteration rate of 20,000 was pruned using a weight-eliminating method. Eliminating a weight of 0.05 enhances the dengue's prediction risk classification accuracy of 95.88% for high risk and 96.83% for low risk groups. As a result, the system is able to classify and diagnose the risk in the dengue patients with an overall prediction accuracy of 96.27%.