Sensitive predictor-

Author information: 1 Department of Anaesthesiology, Seth G. Medical College and K. Hospital, Parel, Mumbai - , India. AIMS: To evaluate the ink impression made by the palm of the dominant hand as a screening tool for difficult laryngoscopy in diabetic patients. Following induction of anaesthesia and neuromuscular relaxation, laryngoscopy was performed and the laryngoscopic view scored.

Sensitive predictor

Latest Issue Alert. This article may be too technical for most readers to understand. Information about reproducing material from RSC articles with different licences is available on our Permission Requests page. Future studies may Sensitive predictor this association. The high value of the beta coefficient indicates that lower theta band SL decreases the odds of being diagnosed with epilepsy, corroborating the abovementioned difference between epilepsy and non-epilepsy patients. Fecal occult blood screen test outcome.

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Unlike other tests on our list, these tests are strips, and they come in a pack of 25, although a pack of 50 can be purchased. Wait a week and check again if you believe you may be pregnant. In previous research, SL has been used to distinguish between sleep terrors and nocturnal frontal lobe epilepsy seizures [27]. Sensitive tests will produce accurate results faster than non-sensitive tests. It is during the fertile time that pregnancy is Sensitive predictor likely to occur. Impedance was kept below 5 KOhm. Sensitive predictor Second Chance Pass it on, trade it in, give it a second life. The 1 best seller category across the U. Cross-sectional study vs. Amazon Inspire Digital Educational Resources. By 9 dpo they started getting darker and was confirmed 10dpo. Power was also analysed Sensitive predictor Fast Fourier Transformations. Value Buy.

Patients were selected from a database with standard EEGs of patients after a first suspected seizure.

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Causes for the suspected seizure in these patients are listed in table 1 ; no other diagnosis was reached in three patients, but epilepsy was ruled out as a diagnosis. Once more we stumble across an example of a 2 in one kit that offers you the chance of evaluating both ovulation as well as pregnancy in a single go, but this time with the stamp of a brand behind it. For those women wondering what the best ovulation test is and how it differs from the others, the answer is in accuracy. Schweizerischen Aerztezeitung. Suppose a 'bogus' test kit is designed to always give a positive reading. The clinical data were collected within 48 h after occurrence of the accident. Our Choice Preview.

Sensitive predictor

Sensitive predictor

Sensitive predictor. Author notes

At present, diagnosis of epilepsy is mainly based on clinical judgment, but there is a need for reliable diagnostic tools to aid diagnosis and classification of epilepsy syndromes and therapeutic decisions. In EEGs recorded for the differential diagnosis of epilepsy, the presence of interictal epileptiform discharges IEDs is an important feature.

In previous research, SL has been used to distinguish between sleep terrors and nocturnal frontal lobe epilepsy seizures [27]. The SL proved able to detect seizures while disregarding parasomnias associated with sleep terrors. Measures of functional connectivity have been investigated with respect to their ability to predict seizures [28] , but these methods are only applied to patients already diagnosed with epilepsy. Previous studies used functional connectivity to differentiate between children with epileptic seizures and healthy children based on their resting-state EEG [17] , [18].

To our knowledge, functional connectivity has never been used as a method of differentiating between new adult epilepsy patients and patients with a first seizure who are later not diagnosed with epilepsy.

The current study has some limitations. The actual predictive power of functional connectivity when diagnosing epilepsy can only be proven in prospective studies, whereas the current patient data were acquired retrospectively. This also limited our sample size, since strict criteria were applied to determine whether patients were diagnosed with epilepsy or not. In addition, in-depth analysis of variables such as epilepsy type was impossible because of missing data.

Also, participants were heterogeneous with respect to radiological abnormalities and CNS medication use. Prospective studies with more homogeneous patients and elaborate data collection are needed to confirm our findings. Furthermore, results of power analysis show that theta band power was also a significant predictor of diagnosis.

This is not surprising, as synchronization likelihood is sensitive to volume conduction and is closely related to power. However, power performed poorer than theta band functional connectivity in terms of specificity and sensitivity in our regression models, indicating the added value of connectivity over power. Moreover, the commonly held idea that connectivity differences are a result of power alterations may not be correct. It is possible that the opposite is true, namely that changes in connectivity may induce power changes.

Future studies may address this association. If connectivity could be used as a predictive tool in this patient group, this would imply great benefits in clinical practice.

Time and resources would be saved when patients do not have IEDs on their EEG and would in the current situation undergo a second EEG after sleep deprivation, which is still not very sensitive. First and foremost, however, correct diagnosis directly after a first seizure would mean great health benefit. At this time, many patients cannot be diagnosed with epilepsy until they experience a second seizure or have IEDs on their first or second EEG. If correct diagnosis could be reached earlier and antiepileptic drugs could be prescribed immediately, this would minimize the risk of epilepsy-related accidents.

Furthermore, better diagnosis would result in less unnecessary AED use in patients who do not have epilepsy. In conclusion, functional connectivity is a promising new tool to diagnose epilepsy, especially in those patients who have a normal first EEG. Competing Interests: L. Douw and E. None of these funders were involved in study design, acquisition and interpretation of data, or writing of this manuscript.

Receiving these fundings does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.

Funding: L. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Center for Biotechnology Information , U. PLoS One. Published online May Heimans , 1 Hanneke E. Ronner , 2 Cornelis J. Stam , 2 and Jaap C.

Reijneveld 1. Jan J. Hanneke E. Cornelis J. Jaap C. Olaf Sporns, Editor. Author information Article notes Copyright and License information Disclaimer.

Received Mar 10; Accepted May 5. Copyright Douw et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

This article has been cited by other articles in PMC. Methods Ethics Statement All data used in this study were collected as part of standard medical care and were analyzed anonymously. Functional connectivity The synchronization likelihood SL [19] was used as an index of functional connectivity.

Results Patient characteristics The database with EEG recordings because of suspected epilepsy after a first seizure contained patients. Open in a separate window. Figure 1. SL differences Significant differences in functional connectivity were present between epilepsy and non-epilepsy patients in the theta band see figure 2.

Figure 2. Mean SLs of epilepsy and non-epilepsy patients in all seven frequency bands. SL as predictor of diagnosis In order to explore whether SL was a useful tool to classify individual patients in the epilepsy or non-epilepsy group, we performed logistic regression with diagnosis epilepsy versus no epilepsy as the dependent variable. Figure 3. Figure 4. Discussion Differences in EEG functional connectivity between epilepsy and non-epilepsy patients after a first suspected seizure were found: patients diagnosed with epilepsy showed increased synchronization likelihood SL in the theta band when compared to patients who were not diagnosed with epilepsy.

Footnotes Competing Interests: L. References 1. Litt B, Echauz J. Prediction of epileptic seizures. Lancet Neurol. Timofeev I, Steriade M. Neocortical seizures: initiation, development and cessation. Epileptology of the first-seizure presentation: a clinical, electroencephalographic, and magnetic resonance imaging study of consecutive patients. Noachtar S, Remi J. The role of EEG in epilepsy: a critical review. Epilepsy Behav. Factors related to the occurrence of typical paroxysmal abnormalities in the EEG records of epileptic patients.

Ten-year experience with abnormal EEGs in asymptomatic adult males. Aviat Space Environ Med. Electroencephalogram epileptiform abnormalities in candidates for aircrew training. Electroencephalogr Clin Neurophysiol. The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci. Epileptic seizures are preceded by a decrease in synchronization. Epilepsy Res. Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG.

J Clin Neurophysiol. Does spatiotemporal synchronization of EEG change prior to absence seizures? Brain Res. Small-world networks and epilepsy: Graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures.

Clin Neurophysiol. Assessing seizure dynamics by analysing the correlation structure of multichannel intracranial EEG. Enhanced EEG functional connectivity in mesial temporal lobe epilepsy. Cortical abnormalities in epilepsy revealed by local EEG synchrony. Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity.

J Neurosci Methods. Detection of signs of brain dysfunction in epileptic children by recognition of transient changes in the correlation of seizure-free EEG. Brain Topogr. Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets.

Physica D. Generalized synchronization of chaos in directionally coupled chaotic systems. Blood ; 11 : Background: Previous studies have proposed combination of various factors that predict poor mobilization after GCSF stimulation. However, there is no single clinical or laboratory test that reliably correlates with bone marrow reserve and PBPC mobilization.

Methods: We have prospectively followed 36 autologous PBPC transplant candidates, from the time of initial evaluation to transplant, to evaluate factors the correlate with bone marrow reserve and autologous PBPC mobilization. Results: The patients median age was 63 range 26 — Further analysis showed low baseline platelet count predicts poor PBPC mobilization.

Conclusion: Baseline platelet count is a sensitive predictor of bone marrow reserve and can be used prior to growth factor administration to predict poor mobilization. Sign In or Create an Account. User Tools. Sign In. Article Navigation. Abstracts Not Selected for Presentation November 16, This Site.

The fungal transamidase complex that executes glycosylphosphatidylinositol GPI lipid anchoring of precursor proteins has overlapping but distinct sequence specificity compared with the animal system. Therefore, a taxon-specific prediction tool for the recognition of the C-terminal signal in fungal sequences is necessary. We have collected a learning set of fungal precursor protein sequences from the literature and fungal proteomes.

Although the general four segment scheme of the recognition signal is maintained also in fungal precursors, there are taxon specificities in details. A fungal big-Pi predictor has been developed for the assessment of query sequence concordance with fungi-specific recognition signal requirements. The rate of false positive prediction is in the range of 0. The fungal big-Pi tool successfully predicts the Gas1 mutation series described by C.

Nuoffer and co-workers, and recognizes that the human PLAP C terminus is not a target for the fungal transamidase complex. Lists of potentially GPI lipid anchored proteins for five fungal proteomes have been generated and the hits have been functionally classified.

The fungal big-Pi prediction WWW server as well as precursor lists are available at.

Sensitive predictor

Sensitive predictor

Sensitive predictor