Regression analysis on forward modeling of diffuse optical tomography system for carcinoma cell detection.

The forward model design was employed in the Diffuse Optical Tomography (DOT) system to determine the optimal photonic flux in soft tissues like the brain and breast. Absorption coefficient (mua), reduced scattering coefficient (mus), and photonic flux (phi) were the parameters subjected to optimization. The Box-Behnken Design (BBD) method of the Response Surface Methodology (RSM) was applied to enhance the Diffuse Optical Tomography experimental system. The DC modulation voltages applied to different laser diodes of 850 nm and 780 nm wavelengths and spacing between the source and detector are the two factors operating on three optimization parameters that predicted the result through two-dimensional tissue image contours. The analysis of the Variance (ANOVA) model developed was substantial (R2 = > 0.954). The experimental results indicate that spacing and wavelength were more influential factors for rebuilding image contour. The position of the tumor in soft tissues is inspired by parameters like absorption coefficient and scattering coefficient, which depend on DC voltages applied to the Laser diode. This regression method predicted the values throughout the studied parameter space and was suitable for enhancement learning of diffuse optical tomography systems. The range of residual error percentage evaluated between experimental and predicted values for mua, mus, and phi was 0.301%, 0.287%, and 0.1%, respectively.

"Combination of Thoracic Epidural Anesthesia, Supraclavicular Brachial Plexus Block and Supraclavicular Nerve Block as Surgical Anesthesia for Modified Radical Mastectomy-A Case Series".

Sensory innervation of the breast is complex, thereby making it difficult to perform any surgical procedure with a single regional anesthesia technique. Because of the involvement of pectoral muscles and extension of the incision into the axilla, a modified radical mastectomy makes it further challenging and requires multiple techniques. We have used a new combination of regional techniques in this case series and found that it provided complete surgical anesthesia with a smaller volume of local anesthetic.

Serum total triiodothyronine (T3) as a predictor of mortality and morbidity in critically ill patients and its correlation of predictability with acute physiology and chronic health evaluation II score: A prospective observational study.

BACKGROUND: The aim is to assess the prognostic value of total T3, total T4, and thyroid-stimulating hormone among critically ill patients admitted to the medical intensive care unit (ICU) in association with mortality and its correlation with the acute physiology and chronic health evaluation II (APACHE II) score. METHODS: : Our prospective observational study consists of 257 patients without known thyroid diseases admitted to the medical ICU. The baseline characteristics of the patients were recorded, including the APACHE II score and thyroid hormone levels at ICU admission. Based on the primary outcome of mortality, we analyzed the data by appropriate statistical methods. A P < 0.05 was considered significant. RESULTS: Of the 257 patients included in the study, 47 (18.28%) succumbed to their illnesses. A significant difference in T3 levels (P < 0.001), T4 levels (P < 0.001), and APACHE II score (P < 0.001) was found between the survivors and the nonsurvivors. There was negative correlation observed between T3 and APACHE II score (r = -0.448, P < 0.001) and T4 and APACHE II score (r = -0.221, P ≤ 0.001). Multivariate logistic regression analysis determined T3 to be the only independent predictor of ICU mortality among thyroid hormones. The area under the curve (AUC) for T3 (0.811 ± 0.04) was almost equal to that of the APACHE II score (0.858 ± 0.029). The duration of ICU stay and hospital stay in patients with low T3 was significantly higher compared to patients with normal T3. CONCLUSION: Serum T3 is a good indicator for predicting mortality and morbidity among critically ill patients.