Horúčka je častým javom na neurologických jednotkách intenzívnej starostlivosti. Etiologicky najčastejšie ide o infekčnú príčinu, v menšej miere prichádzajú následne do úvahy neinfekčné príčiny, ako trombembolizmus, medikamentózne navodený stav, postoperačné príčiny a v neposlednom rade centrálna neurogénna hypertermia. Ide o diagnózu per exclusionem, ktorá doteraz nemá štandardizované diagnostické kritériá ani liečbu. Rovnako tak nie je úplne objasnený ani patomechanizmus jej vzniku. Článok sa zaoberá prehľadom dostupných údajov o fyziológii termoregulácie, predostiera predpokladaný patofyziologický pôvod danej entity (s dôrazom na problematiku z pohľadu neurológa), zmieňuje sa o prejavoch a dôsledkoch ochorenia, napokon uvádza stručný prehľad možností liečby vrátane off­‐label preparátov.

Fever is a common phenomenon within neurological intensive care units. Etiologically, it is most often an infectious cause, to a lesser extent, non-infectious causes come into consideration such as thromboembolism, medically induced condition, postoperative causes and, last but not least, central neurogenic hyperthermia. This is a diagnosis per exclusionem, which does not yet have standardized diagnostic criteria or treatment. Likewise, the pathomechanism of its formation is not fully clarified. The article deals with an overview of available data from the physiology of thermoregulation, lays out the presumed pathophysiological background of the given entity (with an emphasis on the issue from the neurologist's point of view), mentions the symptoms and consequences of the disease, and finally gives a brief overview of treatment options, including off-label preparations.

• MeSH
• Hyperthermia * diagnosis   etiology   drug therapy   physiopathology   therapy   MeSH
• Hypothalamus physiology   MeSH
• Humans MeSH
• Brain physiopathology   MeSH
• Nervous System Diseases classification   physiopathology   MeSH
• Neurons physiology   classification   MeSH
• Risk Factors MeSH
• Body Temperature physiology   MeSH
• Body Temperature Regulation physiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Case Reports MeSH
• Review MeSH

• MeSH
• Circadian Rhythm * physiology   MeSH
• Sleep Duration MeSH
• Adult MeSH
• Endocrine System physiology   MeSH
• Respiratory Physiological Phenomena MeSH
• Humans MeSH
• Brain physiology   MeSH
• Neurons physiology   classification   MeSH
• Sleep Wake Disorders physiopathology   MeSH
• Dreams physiology   MeSH
• Sleep, REM physiology   MeSH
• Sleep * physiology   MeSH
• Sleep Stages physiology   MeSH
• Age Factors MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Publication type
• Review MeSH

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

• MeSH
• Single-Cell Analysis MeSH
• Cells classification   MeSH
• Humans MeSH
• Neocortex cytology   MeSH
• Neuroglia classification   MeSH
• Neurons classification   MeSH
• Terminology as Topic MeSH
• Transcriptome * MeSH
• Computational Biology MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

• Keywords
• Poznámky k vědeckým, populárně-vědeckým a dalším článkům a pořadům ve sdělovacích prostředcích,
• MeSH
• Astronomical Phenomena MeSH
• Hypoglycemic Agents administration & dosage   history   therapeutic use   MeSH
• Causality MeSH
• Clinical Trials as Topic MeSH
• Communications Media * trends   utilization   MeSH
• Humans MeSH
• Meta-Analysis as Topic MeSH
• Neurons physiology   classification   MeSH
• Motor Activity physiology   MeSH
• Work physiology   standards   psychology   MeSH
• Psychotropic Drugs administration & dosage   history   therapeutic use   MeSH
• Book Reviews as Topic * MeSH
• Statistics as Topic MeSH
• Volition MeSH
• Language Development MeSH
• Life Style MeSH
• Check Tag
• Humans MeSH

Neurons in anterior cingulate and prefrontal cortex (ACC/PFC) carry information about behaviorally relevant target stimuli. This information is believed to affect behavior by exerting a top-down attentional bias on stimulus selection. However, attention information may not necessarily be a biasing signal but could be a corollary signal that is not directly related to ongoing behavioral success, or it could reflect the monitoring of targets similar to an eligibility trace useful for later attentional adjustment. To test this suggestion we quantified how attention information relates to behavioral success in neurons recorded in multiple subfields in macaque ACC/PFC during a cued attention task. We found that attention cues activated three separable neuronal groups that encoded spatial attention information but were differently linked to behavioral success. A first group encoded attention targets on correct and error trials. This group spread across ACC/PFC and represented targets transiently after cue onset, irrespective of behavior. A second group encoded attention targets on correct trials only, closely predicting behavior. These neurons were not only prevalent in lateral prefrontal but also in anterior cingulate cortex. A third group encoded target locations only on error trials. This group was evident in ACC and PFC and was activated in error trials "as if" attention was shifted to the target location but without evidence for such behavior. These results show that only a portion of neuronaly available information about attention targets biases behavior. We speculate that additionally a unique neural subnetwork encodes counterfactual attention information.

• MeSH
• Action Potentials physiology   MeSH
• Analysis of Variance MeSH
• Time Factors MeSH
• Gyrus Cinguli cytology   MeSH
• Macaca mulatta MeSH
• Neurons classification   physiology   MeSH
• Cues MeSH
• Attention physiology   MeSH
• Prefrontal Cortex cytology   MeSH
• Reaction Time physiology   MeSH
• Photic Stimulation MeSH
• Space Perception physiology   MeSH
• Choice Behavior physiology   MeSH
• Bias MeSH
• Animals MeSH
• Check Tag
• Male MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

• MeSH
• Cells MeSH
• History, 19th Century * MeSH
• History of Medicine * MeSH
• Ganglia * anatomy & histology   secretion   MeSH
• Humans MeSH
• Spinal Cord anatomy & histology   cytology   MeSH
• Microscopy * methods   standards   instrumentation   utilization   MeSH
• Brain * anatomy & histology   MeSH
• Nerve Tissue * anatomy & histology   pathology   MeSH
• Neuroglia * MeSH
• Neurons cytology   physiology   classification   pathology   ultrastructure   MeSH
• Neurosciences * history   MeSH
• Research history   MeSH
• Famous Persons * MeSH
• Check Tag
• History, 19th Century * MeSH
• Humans MeSH
• Publication type
• Biography MeSH
• Historical Article MeSH
• Review MeSH

• About
• Purkyně, Jan Evangelista, 1787-1869 Authority
• Pappenheim, Samuel, 1811-1882 Authority
• Rosenthal, David, 1821-1875 Authority
• Čermák, Jan Nepomuk 1828-1873 Authority
• Schwann, Theodor, 1810-1882 Authority
• Hannover, Adolph, 1814-1894 Authority
• Gerber, Friedrich, 1797-1872 Authority
• Bruns, Victor von, 1812-1883 Authority
• Henle, Jakob, 1809-1885 Authority
• Helmholtz, Hermann von, 1821-1894 Authority
• Stilling, Benedict, 1810-1879 Authority
• Hassall, Arthur Hill, 1817-1894 Authority
• Rokitanský, Karel, 1804-1878 Authority
• Virchow, Rudolf, 1821-1902 Authority
• Wagner, Rudolf, 1805-1864 Authority
• Volkmann, Alfred 1801-1894
• Bidder, Georg 1810-1894
• Kölliker, Albert 1817-1905
• Gerlach, Joseph von, 1820-1896
• Waller, Augustus 1816-1870
• Leydig, Franz 1821-1908
• Clarke, Lockhart 1817-1880
• Corti, Alfonso 1822-1876
• Müller, Heinrich 1820-1864
• Schultze, Max 1825-1874
• Bergmann, Carl 1814-1865
• Gratiolet, Louis 1815-1865
• Schroeder, van der Kolk, Jacobus 1797-1862
• Remak, Robert 1815-1865
• Mauthner, Ludwig 1840-1894

The Ornstein-Uhlenbeck neuronal model is specified by two types of parameters. One type corresponds to the properties of the neuronal membrane, whereas the second type (local average rate of the membrane depolarization and its variability) corresponds to the input of the neuron. In this article, we estimate the parameters of the second type from an intracellular record during neuronal firing caused by stimulation (audio signal). We compare the obtained estimates with those from the spontaneous part of the record. As predicted from the model construction, the values of the input parameters are larger for the periods when neuron is stimulated than for the spontaneous ones. Finally, the firing regimen of the model is checked. It is confirmed that the neuron is in the suprathreshold regimen during the stimulation.

• MeSH
• Action Potentials physiology   MeSH
• Acoustic Stimulation methods   MeSH
• Time Factors MeSH
• Electroencephalography MeSH
• Membrane Potentials physiology   MeSH
• Models, Neurological MeSH
• Guinea Pigs MeSH
• Neural Pathways physiology   MeSH
• Neurons classification   physiology   MeSH
• Signal Processing, Computer-Assisted MeSH
• Reaction Time physiology   MeSH
• Stochastic Processes MeSH
• Animals MeSH
• Check Tag
• Guinea Pigs MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Normalized entropy as a measure of randomness is explored. It is employed to characterize those properties of neuronal firing that cannot be described by the first two statistical moments. We analyze randomness of firing of the Ornstein-Uhlenbeck (OU) neuronal model with respect either to the variability of interspike intervals (coefficient of variation) or the model parameters. A new form of the Siegert's equation for first-passage time of the OU process is given. The parametric space of the model is divided into two parts (sub-and supra-threshold) depending upon the neuron activity in the absence of noise. In the supra-threshold regime there are many similarities of the model with the Wiener process model. The sub-threshold behavior differs qualitatively both from the Wiener model and from the supra-threshold regime. For very low input the firing regularity increases (due to increase of noise) cannot be observed by employing the entropy, while it is clearly observable by employing the coefficient of variation. Finally, we introduce and quantify the converse effect of firing regularity decrease by employing the normalized entropy.

• MeSH
• Action Potentials physiology   MeSH
• Time Factors MeSH
• Entropy MeSH
• Financing, Organized MeSH
• Models, Neurological MeSH
• Neurons physiology   classification   MeSH
• Models, Statistical MeSH
• Animals MeSH
• Check Tag
• Animals MeSH

We propose a measure of the information rate of a single stationary neuronal activity with respect to the state of null information. The measure is based on the Kullback-Leibler distance between two interspike interval distributions. The selected activity is compared with the Poisson model with the same mean firing frequency. We show that the approach is related to the notion of specific information and that the method allows us to judge the relative encoding efficiency. Two classes of neuronal activity models are classified according to their information rate: the renewal process models and the first-order Markov chain models. It has been proven that information can be transmitted changing neither the spike rate nor the coefficient of variation and that the increase in serial correlation does not necessarily increase the information gain. We employ the simple, but powerful, Vasicek's estimator of differential entropy to illustrate an application on the experimental data coming from olfactory sensory neurons of rats.

• MeSH
• Action Potentials physiology   MeSH
• Time Factors MeSH
• Entropy MeSH
• Financing, Organized MeSH
• Markov Chains MeSH
• Models, Neurological MeSH
• Neural Pathways physiology   MeSH
• Neurons physiology   classification   MeSH
• Signal Processing, Computer-Assisted MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Comparative Study MeSH

• MeSH
• Adenine Nucleotides MeSH
• Rabbits MeSH
• Spinal Cord cytology   metabolism   MeSH
• Neurons enzymology   classification   ultrastructure   MeSH
• Animals MeSH
• Check Tag
• Rabbits MeSH
• Animals MeSH