Floating Zone Growth of Sr Substituted Han Purple: Ba0.9_{0.9}Sr0.1_{0.1}CuSi2_{2}O6_{6}

We present a route to grow single crystals of Ba$_{0.9}$Sr$_{0.1}$CuSi$_{2}$O$_{6}$ suitable for inelastic neutron studies via the floating zone technique. Neutron single crystal diffraction was utilized to check their bulk quality and orientation. Finally, the high quality of the grown crystals was proven by X-ray diffraction and magnetic susceptibility

Acute phase proteins and white blood cell levels for prediction of infectious complications in status epilepticus

ABSTRACT: INTRODUCTION: Infections in status epilepticus (SE) patients result in severe morbidity making early diagnosis crucial. As SE may lead to inflammatory reaction, the value of acute phase proteins and white blood cells (WBC) for diagnosis of infections during SE may be important. We examined the reliability of C-reactive protein (CRP), procalcitonin (PCT), and WBC for diagnosis of infections during SE. METHODS: All consecutive SE patients treated in the ICU from 2005 to 2009 were included. Clinical and microbiological records, measurements of CRP and WBC during SE were analyzed. Subgroup analysis was performed for additional PCT measurements in the first 48 hours of SE. RESULTS: 22.5% of 160 consecutive SE patients had infections during SE. Single levels of CRP and WBC had no association with the presence of infections. Their linear changes over the first three days after SE onset were significantly associated with the presence of infections (p=0.0012 for CRP, p=0.0137 for WBC). Levels of PCT were available for 31 patients and did not differ significantly in patients with and without infections. Sensitivity of PCT and CRP was high (94% and 83%) and the negative predictive value of CRP increased over the first three days to 97%. Specificity was low, without improvement for different cut-offs. CONCLUSIONS: Single levels of CRP and WBC are not reliable for diagnosis of infections during SE, while their linear changes over time significantly correlated with the presence of infections. In addition, low levels of CRP and PCT rule out hospital-acquired infections in SE patient

Cerebral perfusion in sepsis-associated delirium

INTRODUCTION: The pathophysiology of sepsis-associated delirium is not completely understood and the data on cerebral perfusion in sepsis are conflicting. We tested the hypothesis that cerebral perfusion and selected serum markers of inflammation and delirium differ in septic patients with and without sepsis-associated delirium. METHODS: We investigated 23 adult patients with sepsis, severe sepsis, or septic shock with an extracranial focus of infection and no history of intracranial pathology. Patients were investigated after stabilisation within 48 hours after admission to the intensive care unit. Sepsis-associated delirium was diagnosed using the confusion assessment method for the intensive care unit. Mean arterial pressure (MAP), blood flow velocity (FV) in the middle cerebral artery using transcranial Doppler, and cerebral tissue oxygenation using near-infrared spectroscopy were monitored for 1 hour. An index of cerebrovascular autoregulation was calculated from MAP and FV data. C-reactive protein (CRP), interleukin-6 (IL-6), S-100beta, and cortisol were measured during each data acquisition. RESULTS: Data from 16 patients, of whom 12 had sepsis-associated delirium, were analysed. There were no significant correlations or associations between MAP, cerebral blood FV, or tissue oxygenation and sepsis-associated delirium. However, we found a significant association between sepsis-associated delirium and disturbed autoregulation (P = 0.015). IL-6 did not differ between patients with and without sepsis-associated delirium, but we found a significant association between elevated CRP (P = 0.008), S-100beta (P = 0.029), and cortisol (P = 0.011) and sepsis-associated delirium. Elevated CRP was significantly correlated with disturbed autoregulation (Spearman rho = 0.62, P = 0.010). CONCLUSION: In this small group of patients, cerebral perfusion assessed with transcranial Doppler and near-infrared spectroscopy did not differ between patients with and without sepsis-associated delirium. However, the state of autoregulation differed between the two groups. This may be due to inflammation impeding cerebrovascular endothelial function. Further investigations defining the role of S-100beta and cortisol in the diagnosis of sepsis-associated delirium are warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT00410111

EEG recording latency in critically ill patients: Impact on outcome. An analysis of a randomized controlled trial (CERTA).

OBJECTIVE To assess, in adults with acute consciousness impairment, the impact of latency between hospital admission and EEG recording start, and their outcome. METHODS We reviewed data of the CERTA trial (NCT03129438) and explored correlations between EEG recording latency and mortality, Cerebral Performance Categories (CPC), and modified Rankin Scale (mRS) at 6 months, considering other variables, using uni- and multivariable analyses. RESULTS In univariable analysis of 364 adults, median latency between admission and EEG recordings was comparable between surviving (61.1 h; IQR: 24.3-137.7) and deceased patients (57.5 h; IQR: 22.3-141.1); p = 0.727. This did not change after adjusting for potential confounders, such as lower Glasgow Coma Score on enrolment (p < 0.001) and seizure or status epilepticus detection (p < 0.001). There was neither any correlation between EEG latency and mRS (rho 0.087, p 0.236), nor with CPC (rho = 0.027, p = 0.603). CONCLUSION This analysis shows no correlation between delays of EEG recordings and mortality or functional outcomes at 6 months in critically ill adults. SIGNIFICANCE These findings might suggest that in critically ill adults mortality correlates with underlying brain injury rather than EEG delay

Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study.

Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Computer-based approaches, especially deep learning, could potentially overcome these limitations. However, most deep learning studies focus on a specific question or a single pathology. Here we explore the potential of deep learning for EEG-based diagnostic and prognostic assessment of patients with acute consciousness impairment (ACI) of various etiologies. EEGs from 358 adults from a randomized controlled trial (CERTA, NCT03129438) were retrospectively analyzed. A convolutional neural network was used to predict the clinical outcome (based either on survival or on best cerebral performance category) and to determine the etiology (four diagnostic categories). The largest probability output served as marker for the confidence of the network in its prediction ("certainty factor"); we also systematically compared the predictions with raw EEG data, and used a visualization algorithm (Grad-CAM) to highlight discriminative patterns. When all patients were considered, the area under the receiver operating characteristic curve (AUC) was 0.721 for predicting survival and 0.703 for predicting the outcome based on best CPC; for patients with certainty factor ≥ 60 % the AUCs increased to 0.776 and 0.755 respectively; and for certainty factor ≥ 75 % to 0.852 and 0.879. The accuracy for predicting the etiology was 54.5 %; the accuracy increased to 67.7 %, 70.3 % and 84.1 % for patients with certainty factor of 50 %, 60 % and 75 % respectively. Visual analysis showed that the network learnt EEG patterns typically recognized by human experts, and suggested new criteria. This work demonstrates for the first time the potential of deep learning-based EEG analysis in critically ill patients with various etiologies of ACI. Certainty factor and post-hoc correlation of input data with prediction help to better characterize the method and pave the route for future implementations in clinical routine

Nonlinear quantum magnetophononics in SrCu2_2(BO3_3)2_2

Harnessing the most advanced capabilities of quantum technologies will require the ability to control macroscopic quantum states of matter. Quantum magnetic materials provide a valuable platform for realizing highly entangled many-body quantum systems, and have been used to investigate phenomena ranging from quantum phase transitions (QPTs) to fractionalization, topological order and the entanglement structure of the quantum wavefunction. Although multiple studies have controlled their properties by static applied pressures or magnetic fields, dynamical control at the fundamental timescales of their magnetic interactions remains completely unexplored. However, major progress in the technology of ultrafast laser pulses has enabled the dynamical modification of electronic properties, and now we demonstrate the ultrafast control of quantum magnetism. This we achieve by a magnetophononic mechanism, the driving of coherent lattice displacements to produce a resonant excitation of the quantum spin dynamics. Specifically, we apply intense terahertz laser pulses to excite a collective spin state of the quantum antiferromagnet SrCu$_2$(BO$_3$)$_2$ by resonance with the nonlinear mixing frequency of the driven phonons that modulate the magnetic interactions. Our observations indicate a universal mechanism for controlling nonequilibrium quantum many-body physics on timescales many orders of magnitude faster than those achieved to date.Comment: 24 pages, 9 figure

Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies.

Early prognostication in patients with acute consciousness impairment is a challenging but essential task. Current prognostic guidelines vary with the underlying etiology. In particular, electroencephalography (EEG) is the most important paraclinical examination tool in patients with hypoxic ischemic encephalopathy (HIE), whereas it is not routinely used for outcome prediction in patients with traumatic brain injury (TBI). Data from 364 critically ill patients with acute consciousness impairment (GCS ≤ 11 or FOUR ≤ 12) of various etiologies and without recent signs of seizures from a prospective randomized trial were retrospectively analyzed. Random forest classifiers were trained using 8 visual EEG features-first alone, then in combination with clinical features-to predict survival at 6 months or favorable functional outcome (defined as cerebral performance category 1-2). The area under the ROC curve was 0.812 for predicting survival and 0.790 for predicting favorable outcome using EEG features. Adding clinical features did not improve the overall performance of the classifier (for survival: AUC = 0.806, p = 0.926; for favorable outcome: AUC = 0.777, p = 0.844). Survival could be predicted in all etiology groups: the AUC was 0.958 for patients with HIE, 0.955 for patients with TBI and other neurosurgical diagnoses, 0.697 for patients with metabolic, inflammatory or infectious causes for consciousness impairment and 0.695 for patients with stroke. Training the classifier separately on subgroups of patients with a given etiology (and thus using less training data) leads to poorer classification performance. While prognostication was best for patients with HIE and TBI, our study demonstrates that similar EEG criteria can be used in patients with various causes of consciousness impairment, and that the size of the training set is more important than homogeneity of ACI etiology

Innate and adaptive immunity in human epilepsies

Inflammatory mechanisms have been increasingly implicated in the origin of seizures and epilepsy. These mechanisms are involved in the genesis of encephalitides in which seizures are a common complaint. Experimental and clinical evidence suggests different inflammatory responses in the brains of patients with epilepsy depending on the etiology. In general, activation of both innate and adaptive immunity plays a role in refractory forms of epilepsy. Epilepsies in which seizures develop after infiltration of cells of the adaptive immune system in the central nervous system (CNS) include a broad range of epileptic disorders with different (known or unknown) etiologies. Infiltration of lymphocytes is observed in autoimmune epilepsies, especially the classical paraneoplastic encephalitides with antibodies against intracellular tumor antigens. The presence of lymphocytes in the CNS also has been found in focal cerebral dysplasia type 2 and in cortical tubers. Various autoantibodies have been shown to be associated with temporal lobe epilepsy (TLE) and hippocampal sclerosis of unknown etiology, which may be due to the presence of viral DNA. During the last decade, an increasing number of antineuronal autoantibodies directed against membranous epitopes have been discovered and are associated with various neurologic syndromes, including limbic encephalitis. A major challenge in epilepsy is to define biomarkers, which would allow the recognition of patient populations who might benefit from immune-modulatory therapies. Some peripheral inflammatory markers appear to be differentially expressed in patients with medically controlled and medic