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Ullah F.,Science and Technology Unit | Khelil A.,TU Darmstadt | Sheikh A.A.,Science and Technology Unit | Felemban E.,UQU | Bojan H.M.A.,Ministry of Health
2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013 | Year: 2013

In emergency health cases such as mass casualty incidents, the death ratio is still high due to lack of an automatic and intelligent system which timely observes and reports patient criticality. Indeed, the existing criticality assessment approaches are manual such as the established Simple Triage and Rapid Treatment (START). Accordingly, it is difficult for care givers to provide optimal healthcare, in particular, if the number of casualties outnumbers the responders. A challenge is how to automatically tag a possibly large number of victims with various types of disorders immediately after an incident and before the arrival of the paramedics. Such an automated tagging would provide for more optimized emergency response. We propose an automatic self-tagging methodology using body sensor networks that deliver relevant vital signs, i.e., respiratory rate, heart rate and mental status. We present three approaches to recognize and grade the criticality level of patients. The proposed approaches are generic and can be easily adapted to different scenario such as patients in intensive care units, patients in surgery and elderlies being monitored in their home. Being fully automated, our methodology is able to provide real-time tagging with higher accuracy and fine-granularity than the simplistic manual current systems. We demonstrate the viability of our self-tagging approaches by statistically demonstrating their accuracy compared to that of experts manual tagging. © 2013 IEEE.

Mohamed A.Q.,UQU | Mohamed A.Q.,Cairo University | Ramadan N.K.,UQU | Ramadan N.K.,Cairo University | And 4 more authors.
Journal of Applied Pharmaceutical Science | Year: 2014

Three simple, accurate and sensitive methods were developed for simultaneous determination of oxyclozanide and levamisole. Method (A) was depending on zero-order absorption spectrophotometry for measuring oxyclozanide at 300 nm and derivative ratio spectrophotometry for levamisole using oxyclozanide as a divisor, then measuring the peak amplitude at 246 nm. Method (B) was TLC method, using silica gel 60 F254 plates; the optimized mobile phase was ethyl acetate/ methanol/ ammonium hydroxide 33% (8:2:0.2 by volume). The spots were scanned densitometrically at 300 nm for oxyclozanide and 220 nm for levamisole. Method (C) was an HPLC method, performed on C18 column using acetonitrile/ methanol/ 0.05M potassium dihydrogen phosphate (60:20:20 by volume), the pH was adjusted to 3.5±0.2 with ortho-phosphoric acid as a mobile phase with a flow rate of 1 ml/min. Detection was performed at 220 nm. Linearity ranges were 5 - 40 μg/ml of oxyclozanide and levamisole for method (A), 1 - 6 μg/band of oxyclozanide and 2 - 10 μg/ band of levamisole for method (B) and 0.5 - 10 μg/ml of both drugs for method (C), the mean percentage recoveries were 100.21±0.844% for oxyclozanide and 99.53±0.920% for levamisole in case of method (A), 99.72±1.348% for oxyclozanide and 99.14±1.277% for levamisole in case of method (B) and 99.81±0.852% for oxyclozanide and 100.20±0.886% for levamisole in case of method (C). The proposed methods were found to be specific for both drugs in their binary mixture. Statistical comparison between the results obtained by these methods and the manufacturer's method for oxyclozanide and the official method for levamisole was done, and no significance difference was observed. © 2014 Afaf Osman Mohamed et al.

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