Do acute phase markers explain body temperature and brain temperature after ischemic stroke?

Whiteley, William; Thomas, Ruth; Lowe, G. D. O.; Rumley, Ann; Karaszewski, Bartosz; Armitage, Paul A.; Marshall, Ian; Lymer, Katherine; Dennis, Martin; Wardlaw, Joanna M.

doi:10.1212/wnl.0b013e31825f04d8

Cited by 19 publications

(15 citation statements)

References 18 publications

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“…The mean temperature in the ischaemic area was 38.4 o C and 37.7 o C in the remaining part, while the body temperature oscillated around 36.6 o C. An increased concentration of interleukin 6 correlated with higher temperature of normal brain areas both on admission and after 5 days post-admission. No similar correlation was found with respect to the temperature in the ischaemic area [21].…”

Section: Central Fever In Various Clinical Conditions Head Injurymentioning

confidence: 79%

“…Hyperthermia is a common complication of AIS occurring in up to 50% of patients and is associated with a poorer prognosis [19]. The processes determining the brain temperature in AIS have not been fully elucidated [20,21]. Animal studies have demonstrated that under normal health conditions, the balance between the generation of heat in various metabolic processes and cooling of the brain by To facilitate the diagnosis of CF, it is necessary to determine the diagnostic criteria or find the diagnostic markers.…”

Section: Strokementioning

confidence: 99%

“…Increases in temperature seem to be caused by local inflammatory response, including intracerebral synthesis of Il-6 and other proinflammatory cytokines and their effects on the hypothalamus [21]. Moreover, the temperature of an ischaemic area has been found to be higher than that in the remaining part of the brain [21,23]. Whitley et al [21] assessed the brain temperature using magnetic resonance spectroscopy in 44 patients with acute ischemic stroke.…”

Section: Central Fever In Various Clinical Conditions Head Injurymentioning

confidence: 99%

“…Moreover, the temperature of an ischaemic area has been found to be higher than that in the remaining part of the brain [21,23]. Whitley et al [21] assessed the brain temperature using magnetic resonance spectroscopy in 44 patients with acute ischemic stroke. Their measurements were performed on the first and fifth day after stroke.…”

Section: Central Fever In Various Clinical Conditions Head Injurymentioning

confidence: 99%

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Zaburzenia termoregulacji pochodzenia ośrodkowego — jak diagnozować i leczyć

Zawadzka¹,

Szmuda²,

Mazurkiewicz-Bełdzińska³

2017

Anaesthesiol Intensive Ther

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Fever is a common symptom in the Intensive Care Unit. At least half of febrile episodes are caused by infection. Excluding infectious etiology and other non-infectious causes of fever, especially in patients with central nervous system (CNS) disorders, attention should be paid to disturbances of thermoregulatory centre. In particular, subarachnoid haemorrhage, cerebral trauma, along with ischaemic or haemorrhagic stroke are strongly associated with the development of central fever. Proper, speedy diagnosis of the cause of fever makes it possible to implement preventive measures against the harmful effects of hyperthermia on the CNS and to avoid the consequences of inappropriate treatment. The aim of this review is to present the current treatment options for the management of central fever and to analyze recent recommendations for the treatment of hyperthermia, including the use of hypothermia. The recommendations of American and European associations are inconsistent, mainly due to the lack of randomized clinical trials confirming the effectiveness of such treatment. The diagnosis of central fever is still made by the exclusion of other causes. The authors of the review intended to present the characteristic features of central fever, differentiating this state from infectious fever and also analyze the presence of central fever in particular neurological diseases. It seems particularly important to establish diagnostic criteria for central fever or to find diagnostic markers. It is also necessary to conduct further randomized clinical trials evaluating the indications for treatment of hyperthermia.

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Section: Central Fever In Various Clinical Conditions Head Injurymentioning

confidence: 79%

Section: Strokementioning

confidence: 99%

Section: Central Fever In Various Clinical Conditions Head Injurymentioning

confidence: 99%

Section: Central Fever In Various Clinical Conditions Head Injurymentioning

confidence: 99%

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Zaburzenia termoregulacji pochodzenia ośrodkowego — jak diagnozować i leczyć

Zawadzka¹,

Szmuda²,

Mazurkiewicz-Bełdzińska³

2017

Anaesthesiol Intensive Ther

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“…Clinically, MRSI can detect metabolic changes in strokes [77], autism [78], brain tumors [79], multiple sclerosis [80], seizure [81], depression [82], Parkinson's disease [83], Kimura disease [84], etc. Table 4 lists the identifiable metabolites (N-acetyl aspartate, choline, creatine, lipid, lactate, myoinositol, glutamate and glutamine), and gives their peak positions and indications of change.…”

Section: Other Modalitiesmentioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

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Abstract-(Aim) Pathological brain detection (PBD) systems aim to assist and even replace neuroradiologists to make decisions for patients. This review offers a comprehensive and quantitative comparison for PBD systems by artificial intelligence in magnetic resonance imaging (MRI) scanning. (Method) We first investigated four categories of brain diseases, including neoplastic disease, neurodegenerative disease, cerebrovascular disease, and inflammation. Next, we introduced important MRI techniques, such as the shimming, water and fat suppression, and three advanced imaging modalities (functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopic imaging). Then, we discussed four image preprocessing techniques (image denoising, slice selection, brain extraction, spatial normalization, and intensity normalization), seven feature representation techniques (shape, moment, wavelet, statistics, entropy, gray level co-occurrence matrix, and Fourier transform), and two dimension reduction techniques (feature selection and feature extraction). Afterwards, we studied classification related methods: six learning models (decision tree, extreme learning machine, k-nearest neighbors, naive Bayes classifier, support vector machine, feed-forward neural network), five kernel functions (linear, homogeneous and inhomogeneous polynomial, radial basis function, and sigmoid), and three types of optimization methods (evolutionary algorithm, stochastic optimization, and swarm intelligence). (Results) We introduced three benchmark datasets and used K-fold stratified cross validation to avoid overfitting. We presented a detailed quantitative comparison among 44 state-of-theart PBD algorithms and discussed their advantages and limitations. (Discussions) Artificial intelligence is now making stride in the PBD field and enjoys a fair amount of success. In the future, semi-supervised learning and transfer learning techniques may be potential breakthroughs to develop PBD systems.

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Reliability of MRSI brain temperature mapping at 1.5 and 3 T

et al. 2013

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MRSI permits the non-invasive mapping of brain temperature in vivo, but information regarding its reliability is lacking. We obtained MRSI data from 31 healthy male volunteers [age range, 22–40 years; mean ± standard deviation (SD), 30.5 ± 5.0 years]. Eleven subjects (age range, 23–40 years; mean ± SD, 30.5 ± 5.2 years) were invited to receive four point-resolved spectroscopy MRSI scans on each of 3 days in both 1.5-T (TR/TE = 1000/144 ms) and 3-T (TR/TE = 1700/144 ms) clinical scanners; a further 20 subjects (age range, 22–40 years; mean ± SD, 30.5 ± 4.9 years) were scanned on a single occasion at 3 T. Data were fitted in the time domain to determine the water–N-acetylaspartate chemical shift difference, from which the temperature was estimated. Temperature data were analysed using a linear mixed effects model to determine variance components and systematic temperature changes during the scanning sessions. To characterise the effects of instrumental drift on apparent MRSI brain temperature, a temperature-controlled phantom was constructed and scanned on multiple occasions. Components of apparent in vivo temperature variability at 1.5 T/3 T caused by inter-subject (0.18/0.17 °C), inter-session (0.18/0.15 °C) and within-session (0.36/0.14 °C) effects, as well as voxel-to-voxel variation (0.59/0.54 °C), were determined. There was a brain cooling effect during in vivo MRSI of 0.10 °C [95% confidence interval (CI): –0.110, –0.094 °C; p < 0.001] and 0.051 °C (95% CI: –0.054, –0.048 °C; p < 0.001) per scan at 1.5 T and 3 T, respectively, whereas phantom measurements revealed minimal drift in apparent MRSI temperature relative to fibre-optic temperature measurements. The mean brain temperature at 3 T was weakly associated with aural (R = 0.55, p = 0.002) and oral (R = 0.62, p < 0.001) measurements of head temperature. In conclusion, the variability associated with MRSI brain temperature mapping was quantified. Repeatability was somewhat higher at 3 T than at 1.5 T, although subtle spatial and temporal variations in apparent temperature were demonstrated at both field strengths. Such data should assist in the efficient design of future clinical studies. © 2013 The Authors. NMR in Biomedicine published by John Wiley & Sons, Ltd.

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Do acute phase markers explain body temperature and brain temperature after ischemic stroke?

Cited by 19 publications

References 18 publications

Zaburzenia termoregulacji pochodzenia ośrodkowego — jak diagnozować i leczyć

Zaburzenia termoregulacji pochodzenia ośrodkowego — jak diagnozować i leczyć

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Reliability of MRSI brain temperature mapping at 1.5 and 3 T

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