Laboratory Markers Predictive of Fulminant \u3ci\u3eClostridioides difficile\u3c/i\u3e Infection Refractory to Fluid Resuscitation

Background Old age, leucocytosis, hypoalbuminemia, and elevated creatinine have been identified as risk factors for fulminant Clostridioides difficile infection (CDI). High ATLAS scores have also been linked to fatal disease. The affiliated studies, however, involved patients prescribed metronidazole - a regimen no longer standard of care. The variables were thus reassessed in patients prescribed optimal therapy. Methods Adults hospitalized with CDI at University of Kentucky Medical Center were retrospectively reviewed. Enrolled subjects were separated according to disease classification i.e. non-severe/severe versus fulminant CDI. Fulminant patients were further subdivided into hypotensive persons responsive to fluid resuscitation, and those with sequent shock, ileus, or megacolon. Following partition, the cohorts underwent correlation analysis. Findings Forty-five subjects had non-severe/severe disease. Thirteen fulminant CDI patients responded to fluid resuscitation. Seventeen fulminant CDI patients developed shock, ileus, or megacolon. Median WBC counts, albumin values, and ATLAS scores varied among the cohorts. Although WBC counts were similar among the fulminant subsets, declining albumin values and increasing ATLAS scores mirrored disease worsening. Logistic regression revealed albumin values \u3c 20 g/L (odds ratio [OR] 3.91) and ATLAS scores ≥ 6 (OR 5.03) to predict critical illness in hypotensive persons. Conclusion Median WBC counts, albumin values, and ATLAS scores differed in patients separated by CDI severity. A notable variance in albumin values and ATLAS scores between fluid responsive fulminant disease and critical illness was moreover seen. The finding suggests hypoalbuminemia and high ATLAS scores in hypotensive CDI patients may herald shock, ileus, or megacolon

A Real Time System for Hand Gesture Recognition

In this paper we explore the various aspects of hand gesture recognition in real time using neural networks. Hand gesture can be a vital way for the user to interact with any system. In this system we capture a hand gesture from the user and then perform the action related to it. This provides us with an alternative to mouse and keyboard to control a system. Hand gesture recognition can be helpful in various fields and areas where interacting with the system without touch is important. Hand gesture recognition is incorporated along with image processing and to add additional accuracy we are using neural network. This combination of image processing and neural network in real time forms a really powerful tool, forming the base of our project