A Knowledge-based Clinical Toxicology Consultant for Diagnosing Multiple Exposures

Objective: This paper presents continued research toward the development of a knowledge-based system for the diagnosis of human toxic exposures. In particular, this research focuses on the challenging task of diagnosing exposures to multiple toxins. Although only 10% of toxic exposures in the United States involve multiple toxins, multiple exposures account for more than half of all toxin-related fatalities. Using simple medical mathematics, we seek to produce a practical decision support system capable of supplying useful information to aid in the diagnosis of complex cases involving multiple unknown substances. Methods: The system is automatically trained using data mining techniques to extract prior probabilities and likelihood ratios from a database managed by the Florida Poison Information Center (FPIC). When supplied with observed clinical effects, the system produces a ranked list of the most plausible toxic exposures. During testing, the system diagnosed toxins at three levels: identifying the substance, identifying the toxin’s major and minor categories, and identifying the toxin’s major category alone. To enable comparison between these three levels, accuracy was calculated as the percentage of exposures correctly identified in top 10% of trained diagnoses. Results: System evaluation utilized a dataset of 8,901 multiple exposure cases and 37,617 single exposure cases. Initial system testing using only multiple exposure cases yielded poor results, with diagnosis accuracies ranging from 18.5-50.1%. Further investigation revealed that the system’s inability to diagnose multiple disorders resulted from insufficient data and that the clinical effects observed in multiple exposures are dominated by a single substance. Including single exposures when training, the system achieved accuracies as high as 83.5% when 2 diagnosing the primary contributors in multiple exposure cases by substance, 86.9% when diagnosing by major and minor categories, and 79.9% when diagnosing by major category alone. Conclusions: Although the system failed to completely diagnose exposures to multiple toxins, the ability to identify the primary contributor in such cases may prove valuable in aiding medical personnel as they seek to diagnose and treat patients. As time passes and more cases are added to the FPIC database, we believe system accuracy will continue to improve, producing a viable decision support system for clinical toxicology

A Knowledge-based Clinical Toxicology Consultant for Diagnosing Single Exposures

Objective: Every year, toxic exposures kill twelve hundred Americans. To aid in the timely diagnosis and treatment of such exposures, this research investigates the feasibility of a knowledge-based system capable of generating differential diagnoses for human exposures involving unknown toxins. Methods: Data mining techniques automatically extract prior probabilities and likelihood ratios from a database managed by the Florida Poison Information Center. Using observed clinical effects, the trained system produces a ranked list of plausible toxic exposures. The resulting system was evaluated using 30,152 single exposure cases. In addition, the effects of two filters for refining diagnosis based on a minimum number of exposure cases and a minimum number of clinical effects were also explored. Results: The system achieved accuracies (calculated as the percentage of exposures correctly identified in top 10% of trained diagnoses) as high as 79.8% when diagnosing by substance and 78.9% when diagnosing by the major and minor categories of toxins. Conclusions: The results of this research are modest, yet promising. At this time, no similar systems are currently in use in the United States and it is hoped that these studies will yield an effective medical decision support system for clinical toxicology

VBASR: The Vision System Vision-Based Autonomous Security Robot

ABSTRACT The goal of this project is to develop a computer vision system that enables a robot to navigate the hallways of Bradley University's engineering building using a generic webcam as the only sensor. OpenCV2.0 programmed in C++ is the primary tool used to develop the vision system software. Three algorithms were developed to identify the center of the hallway and guide the robot in the correct direction. The first two algorithms use a generic filter (normal, median, or Gaussian) followed by edge detection and then corner detection on the edgedetected image. The first algorithm identifies the strongest vertical lines on an image. Averaging the horizontal coordinates of the vertical lines indicates the location of the center of the hallway relative to the robot. The second algorithm utilizes the trapezoidal shape of the hallway formed where the floor meets the walls, as seen from the perspective of the robot. The y-coordinates associated with the trapezoid's legs are then compared to estimate robot orientation with respect to the walls. The third algorithm uses color to segment the floor from the rest of the features in the image (walls, ceiling, and obstacles). Once again, the trapezoidal shape appears and the center of the hallway is determined based on the location of the highest y-valued pixels identified as floor pixels. Test data indicates that none of these algorithms is singularly sufficient; however, combining their results they can identify the direction a robot must turn to remain in the center of the hallway with 96.6% accuracy. Furthermore, leveraging the results of multiple algorithms produces more robust navigation, where one algorithm covers over the shortcomings of another. The vision system architecture is designed to execute algorithms in parallel. Such a structure enables the addition and removal of algorithms without adversely affecting the system as a whole. Further algorithms may be developed and easily added to improve navigation. Additionally, the system may intelligently ignore results from algorithms that are recognized as inappropriate for certain situations

A Pilot Study of Diffusion-Weighted MRI in Patients Undergoing Neoadjuvant Chemoradiation for Pancreatic Cancer

PURPOSE: In the current study we examined the ability of diffusion MRI (dMRI) to predict pathologic response in pancreatic cancer patients receiving neoadjuvant chemoradiation. METHODS: We performed a prospective pilot study of dMRI in patients with resectable pancreatic cancer. Patients underwent dMRI prior to neoadjuvant chemoradiation. Surgical specimens were graded according to the percent tumor cell destruction. Apparent diffusion coefficient (ADC) maps were used to generate whole-tumor derived ADC histogram distributions and mean ADC values. The primary objective of the study was to correlate ADC parameters with pathologic and CT response. RESULTS: Ten of the 12 patients enrolled on the study completed chemoradiation and had surgery. Three were found to be unresectable at the time of surgery and no specimen was obtained. Out of the 7 patients who underwent pancreaticoduodenectomy, 3 had a grade III histopathologic response (>90% tumor cell destruction), 2 had a grade IIB response (51% to 90% tumor cell destruction), 1 had a grade IIA response (11% to 50% tumor cell destruction), and 1 had a grade I response (>90% viable tumor). Median survival for patients with a grade III response, grade I-II response, and unresectable disease were 25.6, 18.7, and 6.1 months, respectively. There was a significant correlation between pre-treatment mean tumor ADC values and the amount of tumor cell destruction after chemoradiation with a Pearson correlation coefficient of 0.94 (P = .001). Mean pre-treatment ADC was 161 × 10−5 mm2/s (n = 3) in responding patients (>90% tumor cell destruction) compared to 125 × 10−5 mm2/s (n = 4) in non-responding patients (>10% viable tumor). CT imaging showed no significant change in tumor size in responders or non-responders. CONCLUSIONS: dMRI may be useful to predict response to chemoradiation in pancreatic cancer. In our study, tumors with a low ADC mean value at baseline responded poorly to standard chemoradiation and would be candidates for intensified therapy

Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci

Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder