Objective: Aimed to asses the role of PT estimation in early diagnosis and predicting the extent and the outcome of head injury with ICerH and/ or Contusion
Method :PT was measured by Digiclot 818
Group –1: One hundred consecutive head injured patients admitted at Neurosurgical and Al Ramadi teaching hospitals were initially estimated for prothrombin time and subsequently scanned
Group-2 : Two hundred twenty five consecutive non scanned head injured patients admitted to Neurosurgical and Al Ramadi teaching hospitals were estimated with prothrombin time at the time of insult and subsequently for the next two weeks Al – Kindy Col Med J 2012; Vol. 8 No. 1 P: 54
Clinical and neurological evaluation (GCS) score in addition to computerized tomography scan was done for both groups
Result :Group -1: Twenty eight (28%) of the initially estimated head injured patients had prolonged prothrombin time in which their subsequent CT scan revealed an ICer H and/or contusion
Seventy six (76%) percent of those with severely head injured patients (GCS≤8) were having an initial prothrombin time prolongation..While those with mild head injury (GCS≥12) only thirty (30%) of them were having the initial prothrombin time prolongation
Group-2 Those patients who survived the initial insult and subsequently thereafter (surgically or conservatively treated) even with a deficit.. Daily estimation of prothrombin time showed a decline within the 7-14 days
Those patients who finally died (surgically or conservatively treated) daily estimation of prothrombin time showed a persistent elevation or a fluctuation within the next 7-14 day
Conclusion Prothrmbin time is an important
parameter in :
1-Reflecting the severity of head injury (GCS)
3-Early detection of ICerH and /or contusion
2-Prognostic value in cases of of ICerH and /or contusion
Abb : ICerH = Intracerebral hematomas,SDH=subdural hematomas,EDH=epidural hematomas,SAH=subarachnoid hemorrhages,PT=Prothrombin time,Pts=Patients,CT scan=computarized tomography.
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The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices
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