Background: Recent research has evaluated psychological and biological characteristics associated with pain in survivors of breast cancer (BC). Few studies consider their relationship with inflammatory activity. Voluntary facial expressions modify the autonomic activity and this may be useful in the hospital environment for clinical biopsychosocial assessment of pain. Methods: This research compared a BC survivors group under integral treatment (Oncology, Psychology, Nutrition) with a control group to assess the intensity of pain, behavioral interference, anxiety, depression, temperament-expression, anger control, social isolation, emotional regulation, and alexithymia and inflammatory activity, with salivary interleukin 6 (IL-6). Then, a psychophysiological evaluation through repeated measures of facial infrared thermal imaging (IRT) and hands in baseline—positive facial expression (joy)—negative facial expression (pain)—relaxation (diaphragmatic breathing). Results: The results showed changes in the IRT (p < 0.05) during the execution of facial expressions in the chin, perinasal, periorbital, frontal, nose, and fingers areas in both groups. No differences were found in the IL-6 level among the aforementioned groups, but an association with baseline nasal temperature (p < 0.001) was observable. The BC group had higher alexithymia score (p < 0.01) but lower social isolation (p < 0.05), in comparison to the control group. Conclusions: In the low- and medium-concentration groups of IL-6, the psychophysiological intervention proposed in this study has a greater effect than on the high concentration group of IL-6. This will be considered in the design of psychological and psychosocial interventions for the treatment of pain.

This project proposes the use of Digital Signal Processing (DSP) for real-time capture and analysis of pathological slide images to improve accuracy and efficiency. Analyzing cell density statistics and average cell nuclei diameters of a slide image is useful to determine the abnormality of slide sample. Being tedious as it is in counting/measuring hundreds to thousands of cells in one sample slide under a microscope, the manual result, typically can be achieved by a pathologist, is often limited by human eye precision/efficiency. Millions of biopsy samples obtained daily around the world, from minor skin lesions to major tumors, are anxiously waiting to be screened/examined. As a high-level, interactive environment for data visualization/analysis/computation, MATLAB ® is utilized currently to perform automatic image analysis and segmentation of brain cells on a computer. By comparing cell concentration and cell nuclei sizes between cancerous and normal image groups, MATLAB ® can be programmed to distinguish normal brain cells from questionable ones. In general, pathological image analysis using a computer-based application could demonstrate great precision and efficiency for screening large quantities of cells on one or numerous sample slides. Currently, MATLAB ® image analysis works on captured/digitized slide images and takes a minute per image to automatically pre-screen abnormalities that require further human expert analysis. With future realtime/parallel/machine-intelligent improvements, we hope that DSP can help physicians/pathologists/patients everywhere to get immediate diagnosis for effective/timely treatment, and can show accuracy within acceptable levels that are comparable to human pathologists in dealing with cell-overlapping and non-cell objects existing in slide images.

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