FRACTAL ANGLE BASED DIFFERENTIATION OF TERM PREGNANCIES USING UTERINE ELECTROMYOGRAPHIC SIGNALS

Uterine Electromyography (uEMG) is a non-invasive technique that provides quantitative measure of uterine activity from the abdominal surface. In this work, an attempt has been made to investigate Term (gestational age > 37 weeks) uEMG signals using Adaptive Fractal Analysis (AFA). For this, the signals obtained in second and third trimesters are considered and subjected to AFA. The fluctuation function is computed and the corresponding linear scaling regions are identified based on Chi-square statistic, standard error of slope, and coefficient of determination. Angle-based features from multiple scaling regions namely, inter-fractal angle and, short- and long-term fractal angles are extracted and are used for further analysis. The obtained results demonstrates that AFA approach can characterize the Term signals during varied gestational ages. All features show significant differences (p < 0.05) in both groups. Feature values suggest that the third trimester signals possess more correlated and smoother fluctuations when compared to second trimester signals. This is attributed to the increased coordination of uterine contractions as delivery approaches. Hence, it appears that the proposed adaptive angle-based fractal features could be potential biomarkers in analyzing the muscle contractions associated with Term pregnancies.

PROBING CLINICAL RELEVANCE: ESTABLISHING THE EFFICACY OF C. NOVYI AGAINST A PANEL OF 2D CULTURED PANCREATIC CANCER CELLS

Pancreatic cancer presents a unique challenge for the development of effective oncotherapies. The tumor microenvironment (TME) of this type of tumor typically contains a dense desmoplastic barrier composed of aberrant extracellular matrix proteins, as well as an acidic, hypoxic and necrotic core. Additionally, the immune system surrounding this type of tumor has often been suppressed by the TME. Hence, choosing the correct model of the tumor microenvironment within which to test a potential anti-cancer therapy is a critical experimental design decision. While the typical solid tumor contains a complex microenvironment including both phenotypic and genotypic heterogeneity, the methods used to model this disease state often do not reflect this complexity. This simplistic approach may have contributed to stagnant five-year survival rates experienced over the past four decades. Oncolytic bacteria, a class of bacteria with the innate ability to seek and destroy solid tumors has been revived from historical anecdotes in an attempt to overcome these challenges. Regardless of the promise of oncolytic bacteria, accurate assessment of their potential requires choosing the proper tumor model. This study explores the impact of cancer cell lines co-cultured with Wild-Type C. novyi to establish the efficacy of this oncolytic bacteria in a monolayer culture.

COMORBIDITY CHARACTERISTICS OF ADULT PATIENTS WITH REPORTED NECK AND LOW BACK COMPLAINTS - AN OUTPATIENT CLINICAL POPULATION-BASED COHORT STUDY

In clinical practice, a comprehensive history and examination often includes questionnaires covering all systems. Physical examination is augmented by functional assessment using subjective (neck and lumbar disability indexes, NDI and LBDI respectively) and objective measures (computerized dynamic posturography, CDP). In this retrospective chart review of patients with complaints of postural instability and neck or low back pain, the presence and number of comorbidities and their classification were analyzed by age, gender, and severity of the disability. In general subjects showed higher disability in the NDI than in the LBDI (with more significant impact of the proximal vs. distal pain, joint and receptor dysfunction); they had a wide range of CDP results (the more difficult the test, the higher the number of subjects that were not able to complete it and the lower the number that had healthy balance); and on average 3.84 comorbid conditions were present, with 21 subjects presenting with 5 or more, 3 with 10 or more, and one reporting 15 comorbidities. No statistically significant differences were found for age, BMI and LBDI; sex and NDI affected metabolic comorbidities; certain tests of the CDP affected the Musculoskeletal and Other type of comorbidities. It was difficult to detect strong correlation trends that could be easily explained. Complex subjects cases complicate the possibility of doing practice based clinical research, but more importantly they create a challenge for the clinician in deciding the best course of action for treating the patient. New algorithmic assessments and integrated approaches are needed.

RADIOMICS BASED SINGLE AND MULTI-CLASS GLIOMA CLASSIFICATION USING SUPPORT VECTOR MACHINE VARIANTS

The common type of primary brain tumor is glioma. The mortality rate of glioma patients is high due to delayed diagnosis, incorrect grading and treatment planning. Traditionally, gliomas were classified into Low Grade (grade-I and grade-II) and High Grade (grade-III and grade-IV). However, World Health Organization has insisted to classify the grades into grade-I(G-I), grade II(G-II), grade III(G-III) and grade IV(G-IV) individually to aid the physicians in clinical decision-making. Although there are limited number of studies reported to differentiate individual grades, the classification accuracy was low. Consequently, in this work single-class (G-II vs. G-III, G-II vs. G-IV and G-III vs. G-IV) and multi-class (G-II vs. G-III+IV, G-III vs. G-II+IV and G-IV vs. G-II+III) analysis was performed using specific region of tumor and whole brain as Regions of Interest(ROI) by extracting radiomic features. The images for this study (N=75) were obtained from The Cancer Imaging Archive. Further, the statistically significant features were used in the classification of individual grades by implementing variants of Support Vector Machine (SVM) algorithm: SVM, Linear-SVM and Least-Squared SVM. Among these, Linear-SVM resulted in the highest classification accuracy (>80%) with average sensitivity, specificity and AUC values of >70%. The comparative analysis of whole brain versus tumor ROI showed that the latter yielded better classification accuracy.

HUMAN FACTORS LEARNING CURVE FOR THUMB-OPERATED TRIGGER RIFLE USING GYROSCOPIC FEEDBACK INSTRUMENTATION

Researchers interested in evaluating the biomechanics and human factors associated with using a new product recognize that skill development with the novel design is time-dependent. A learning curve is a plot that shows the time to complete a task using the product decreases as the number of training repetitions increases. A novel thumb-operated trigger system (Iron Horse, Blackwater Worldwide™) has been developed for the AR-15 style rifle with the intent to shorten the learning curve. The purpose of this research effort is to quantify the learning curve for the new device and to compare it to that of a standard mil-spec AR-15 trigger system. A previously-trained shooter dry-fire trained with both rifle systems for twenty consecutive days alternating lower receivers each day. The rifles were equipped with a gyroscopic instrument (Mantis X™) that tracked the movement of the firearm during the trigger pull process. The instrument has a timer to record the reaction time to an auditory signal for each shot, records the magnitude and direction of movement of the firearm, and calculates an accuracy score. There was not a significant difference (p>0.05) between the thumb operated and mil-spec triggers’ cycle times. However, the accuracy scores with the thumb operated trigger were significantly higher (p<0.05) than those with the mil-spec trigger.

STUDY OF THE BILATERAL ASYMMETRY OF PLANTAR MECHANICAL PROPERTIES AS A BIOMARKER FOR THE DIFFERENTIATION OF DIABETIC CONDITION

Diabetes mellitus is a globally prevalent metabolic disease which results in altered plantar mechanical properties and foot ulcer. In this study, the bilateral asymmetry of mechanical properties for plantar soft tissue is investigated in healthy and diabetic conditions. Myotonometric signals are acquired from sub-metatarsal region of the plantar faces of healthy subjects and patients with varied diabetic age. Mechanical parameters such as dynamic stiffness and logarithmic decrement are extracted from the recorded signal. The asymmetry indices between right and left feet are computed. Statistical analysis shows that the spatial pattern of dynamic stiffness and logarithmic decrement varies significantly between healthy and diabetic subjects. The asymmetry index of dynamic stiffness in the fifth sub-metatarsal head can differentiate between healthy subjects and patients with both high and low diabetic age (p<0.05). The asymmetry index of logarithmic decrement is found to vary significantly between the healthy subjects and patients with higher diabetic age (p<0.05). These results indicate that bilateral asymmetry of myotonometric parameters can be exploited as a possible biomarker to differentiate diabetic patients from healthy subjects and can aid in the early detection of foot ulcer.

ANALYSIS OF TRUNK POWER AND JOINT STRESSES BETWEEN PROFESSIONAL AND COLLEGIATE PITCHERS

The dynamic motion of a baseball pitch generates high elbow and shoulder torques that can result in injury. Previous research has noted the importance of properly transferring energy from the lower extremities through the throwing arm to decrease joint stress. The goal of this study was to compare segmental powers between two levels of pitchers at various moments throughout the pitching cycle and observe their influence on upper extremity torques. Thirteen professional and thirteen collegiate pitchers participated in this study. Forty-seven reflective markers were attached to the subjects at specific landmarks. An 8-camera motion analysis system was set up surrounding an artificial pitching mound, where participants threw 10 fastballs. Data were exported and processed using Visual 3D software. Welch’s T-tests compared the means between groups with a significance set at p < 0.05. Professional pitchers were found to have significantly greater torso power at foot contact, maximum shoulder external rotation, ball release, and overall peak torso power. They also demonstrated significantly greater pitch velocity. Professional pitchers generated similar elbow varus torque and shoulder internal rotation torque compared to collegiate pitchers. These findings suggest professional pitchers more effectively use torso power to help increase pitch speed without increasing overall joint torques.

ANALYSIS ON THE EFFECT OF HALF ANGLE ON THE DISPLACEMENT OF PEDICLE SCREW DURING AXIAL PULL-OUT TEST IN CANCELLOUS BONE USING 2D AXISYMMETRIC FE MODEL

Pedicle screw fixations are commonly used in the treatment of spinal pathologies. For effective treatment, stable anchorage between the screw and bone is necessary. In this study, the influence of proximal and distal half angle of the screw, on the displacement of fixation and stress transfer are simulated using a 2D axisymmetric finite element model. A parametric study was performed by varying the proximal half-angle between 0° and 60° in steps of 10° and the distal half angles are considered as 30° and 40°. The material properties and boundary conditions are applied based on previous studies. Frictional contact is considered between the bone and screw. Results show that, displacement of fixation is observed to be minimum at a proximal half angle of 0° and maximum at an angle of 60°. High stress concentration is observed in first few threads with highest maximum von Mises stress at an angle of 60°. High stress transfer was obtained for proximal half-angles of 40° and 50°. It is observed that, this method might aid to develop better pedicle screws for treatment of Scoliosis.

DIFFERENTIATION OF DICHOTOMOUS EMOTIONAL STATES IN ELECTRODERMAL ACTIVITY SIGNALS USING HIGHER-ORDER CROSSING FEATURES AND PARAMETRIC CLASSIFIERS

Prediction and recognition of happy and sad emotional states play important roles in many aspects of human life. In this work, an attempt has been made to classify them using Electrodermal Activity (EDA). For this, EDA signals are obtained from a public database and decomposed into tonic and phasic components. Features, namely Hjorth and higher-order crossing, are extracted from the phasic component of the signal. Further, these extracted features are fed to four parametric classifiers, namely, linear discriminant analysis, logistic regression, multilayer perceptron, and naive bayes for the classification. The results show that the proposed approach can classify the dichotomous happy and sad emotional states. The multilayer perceptron classifier is accurate (85.7%) in classifying happy and sad emotional states. The proposed method is robust in handling the dynamic variation of EDA signals for happy and sad emotional states. Thus, it appears that the proposed method could be able to understand the neurological, psychiatrical, and biobehavioural mechanisms of happy and sad emotional states.

LATERAL VENTRICLE TEXTURE ANALYSIS IN ALZHEIMER BRAIN MR IMAGES USING KERNEL DENSITY ESTIMATION

Alzheimer's Disease (AD) is an irreversible, progressive neurodegenerative disorder affecting a large population worldwide. Automated diagnosis of AD using Magnetic Resonance (MR) imaging-based biomarkers plays a crucial role in disease management. Compositional changes in cerebrospinal fluid due to AD might induce textural variations in Lateral Ventricles (LV) of the brain. In this work, an attempt has been made to differentiate Alzheimer's condition by quantifying the textural changes in LV using Kernel Density Estimation (KDE) technique. Reaction-Diffusion level set method is used to segment the LV from T1-weighted trans-axial brain MR images obtained from a publicly available database. Spatial KDE is used to analyze the local intensity variations within the segmented LV. The optimal kernel function and bandwidth are selected for KDE. The statistical features such as mean, median, standard deviation, variance, kurtosis, skewness and entropy, representing the distribution of KDE values within LV, are evaluated. The extracted KDE-based statistical features show significant discrimination between normal and AD subjects (p<0.01). An accuracy of 86.20% and sensitivity of 96% are obtained using SVM classifier. The results indicate that KDE seems to be a potential tool for analyzing the textural changes in brain, and thus can be clinically relevant for diagnosis of AD.