Relationship between hyperacute blood pressure and outcome after ischemic stroke: data from the VISTA Collaboration

Background and Purpose—High blood pressure (BP) is associated independently with poor outcome after acute ischemic stroke, although in most analyses “baseline” BP was measured 24 hours or more postictus, and not during the hyperacute period. Methods—Analyses included 1722 patients in hyperacute trials (recruitment 8 hours) from the Virtual Stroke International Stroke Trial Archive (VISTA) Collaboration. Data on BP at enrolment and after 1, 2, 16, 24, 48, and 72 hours, neurological impairment at 7 days (NIHSS), and functional outcome at 90 days (modified Rankin scale) were assessed using logistic regression models, adjusted for confounding variables; results are for 10-mm Hg change in BP. Results—Mean time to enrolment was 3.7 hours (range 1.0 to 7.9). High systolic BP (SBP) was significantly associated with increased neurological impairment (odds ratio, OR 1.06, 95% confidence interval, 95% CI 1.01 to 1.12), and poor functional outcome; odds ratios for both increased with later BP measurements made at up to 24 hours poststroke. Smaller (versus larger) declines in SBP over the first 24 hours were significantly associated with poor NIHSS scores (OR 1.16, 95% CI 1.05 to 1.27) and functional outcome (OR 1.23, 95% CI 1.13 to 1.34). A large variability in SBP was also associated with poor functional outcome. Conclusions—High SBP and large variability in SBP in the hyperacute stages of ischemic stroke are associated with increased neurological impairment and poor functional outcome, as are small falls in SBP over the first 24 hours.

The MATCH project: collaboration between academia and industry

The university sector offers innovative research initiatives which industry should be tapping. Michael Craven, Senior Research Fellow at the University of Nottingham, reports on a unique collaboration between academia and industry that is helping companies assess the value of medical technology.

Behavioural correlation for detecting P2P bots

In the past few years, IRC bots, malicious programs which are remotely controlled by the attacker through IRC servers, have become a major threat to the Internet and users. These bots can be used in different malicious ways such as issuing distributed denial of services attacks to shutdown other networks and services, keystrokes logging, spamming, traffic sniffing cause serious disruption on networks and users. New bots use peer to peer (P2P) protocols start to appear as the upcoming threat to Internet security due to the fact that P2P bots do not have a centralized point to shutdown or traceback, thus making the detection of P2P bots is a real challenge. In response to these threats, we present an algorithm to detect an individual P2P bot running on a system by correlating its activities. Our evaluation shows that correlating different activities generated by P2P bots within a specified time period can detect these kind of bots.