An Experimental Study: Does the Neuroprotective Effect Increase When Hypothermia Deepens After Traumatic Brain Injury?

Abstract

Background:Experimental approaches have been promising with the use of therapeutic hypothermia after Traumatic Brain Injury (TBI) whereas clinical data have not supported its efficacy.Objectives:This study aimed to investigate whether using selective deeper brain cooling correlates with a more neuroprotective effect on Intracranial Pressure (ICP) increments following TBI in rats.Materials and Methods:Adult male Sprague-Dawley rats (mean weight = 300 g; n = 25) were subjected to brain injury using a modified Marmarou method. Immediately after the onset of TBI, rats were randomized into three groups. Selective brain cooling was applied around the head using ice packages. Intracranial Temperature (ICT) and ICP were continuously measured at 0, 30, 60, 120, and 180 minutes and recorded for all groups. Group 1 (n = 5) was normothermia and was assigned as the control group. Group 2 (n = 10) received moderate hypothermia with a target ICT of between 32°C - 33°C and Group 3 (n = 10) was given a deeper hypothermia with a target ICT of below 32°C.Results:All subjects reached the target ICT by the 30th minute of hypothermia induction. The ICT was significantly different in Group 2 compared to Group 1 only at the 120th minute (P = 0.017), while ICP was significantly lower starting from the 30th minute (P = 0.015). The ICT was significantly lower in Group 3 compared to Groups 1 and 2 starting from the 30th minute (P = 0.001 and P = 0.003, respectively). The ICP was significantly lower in Group 3 compared to Group 1 starting from 30th minute (P = 0.001); however, a significant difference in ICP between Group 3 and Group 2 was observed only at the 180th minute (P = 0.047).Conclusions:Results of this study indicate that selective brain cooling is an effective method of decreasing ICP in rats; however, the deeper hypothermia caused a greater decrease in ICP three hours after hypothermia induction.

Formative assessment methods for intelligent learning systems

“…The family of bandit algorithms is designed to cope with uncertainty by balancing exploration and exploitation (Girisgin et al, 2015). However, when applied to formative assessment, the exploitation component is non-obvious, as ultimately, the goal is to explore the knowledge of the student.…”

“…where C is a constant that can be chosen to regulate the impact the second exploration component has on the choice of the topic, and N t is the number of questions on the topic that has been asked so far. As the number of questions on the topic increases, so the uncertainty and the exploration term of the formula decrease (Girisgin et al, 2015). Thus, the algorithm will seek out the weakest topics of knowledge for a student; once identified, it will thoroughly question the student on said topics.…”

“…The second parameter in brackets describes uncertainty in difficulty estimation and is controlled by exploration constant C, a total number of questions asked n, and a number of questions asked on the topic N t . The uncertainty parameter decreases as the algorithm probes student's knowledge on the topic and sElo t estimation becomes more accurate (Girisgin et al, 2015). Each student is assigned a starting Elo rating on each topic t, sElo t = 1000.…”

“…čia C -konstanta, kurią galima pasirinkti siekiant reguliuoti antrojo tyrimo komponento įtaką temos pasirinkimui; N t -iki šiol užduotų klausimų šia tema skaičius. Daugėjant temos klausimų, mažėja formulės neapibrėžtumas ir tyrinėjimo komponentas (Girisgin et al, 2015). Taigi algoritmas ieškos silpniausių mokinio žinių temų, jas identifikavęs nuodugniai apklaus mokinį minėtomis temomis.…”

“…Pirmiausia algoritmas bando pasirinkti mažiausiai žinomą temą, tada užduoda lengviausią tos temos klausimą. Silpniausia tema parenkama pagal šią lygtį: Neapibrėžtumo parametras mažėja, nes algoritmas tiria mokinio žinias apie temą ir sElo t įvertis tampa tikslesnis (Girisgin et al, 2015). Kiekvienam mokiniui priskiriamas pradinis Elo įvertinimas kiekvienoje temoje t, sElo t = 1000.…”