Ethanol intoxication treated by haemodialysis

Prikazan je tijek bolesti 68-godišnjeg bolesnika koji je u suicidalnoj namjeri popio 1,5 litara žestokog alkoholnog pića (570 mL etanola). Primljen je u komi III. stupnja, s koncentracijom etanola u serumu 5,09 g/L. Primijenjen je postupak ekstrakorporalne hemodijalize, tijekom kojega je postao kontaktibilan. Koncentracija etanola u serumu snizila se na 3,46 g/L nakon 2 sata, a nakon iduća 2 sata na 0,82 g/L. Klirens etanola iznosio je 260 mL/min. Bolesnik je otpušten kući četvrtog dana nakon prijema.The paper deals with the course of illness in a 68-year-old man who attempted to commit suicide by drinking 1.5 L of a concentrated alcoholic drink (570 ml of ethanol). He was admitted to hospital in a state of the III degree coma, with a serum ethanol concentration of 5.09 g/L. He was treated by the method of extracorporeal haemodialysis for four hours. At the end of two hours ethanol concentration in the serum decreased to 3.46 g/L and at the end of the treatment it was 0.82 g/L. The rate of ethanol clearance reached 260 ml/min. The patients was dismissed from hospital on the fourth day of admittance

Does your accurate process predictive monitoring model give reliable predictions?

The evaluation of business process predictive monitoring models usually focuses on accuracy of predictions. While accuracy aggregates performance across a set of process cases, in many practical scenarios decision makers are interested in the reliability of an individual prediction, that is, an indication of how likely is a given prediction to be eventually correct. This paper proposes a first definition of business process prediction reliability and shows, through the experimental evaluation, that metrics that include features defining the variability of a process case often give a better prediction reliability indication than metrics that include the probability estimation computed by the machine learning model used to make predictions alone

Assessment of machine learning reliability methods for quantifying the applicability domain of QSAR regression models

The vastness of chemical space and the relatively small coverage by experimental data recording molecular properties require us to identify subspaces, or domains, for which we can confidently apply QSAR models. The prediction of QSAR models in these domains is reliable, and potential subsequent investigations of such compounds would find that the predictions closely match the experimental values. Standard approaches in QSAR assume that predictions are more reliable for compounds that are "similar" to those in subspaces with denser experimental data. Here, we report on a study of an alternative set of techniques recently proposed in the machine learning community. These methods quantify prediction confidence through estimation of the prediction error at the point of interest. Our study includes 20 public QSAR data sets with continuous response and assesses the quality of 10 reliability scoring methods by observing their correlation with prediction error. We show that these new alternative approaches can outperform standard reliability scores that rely only on similarity to compounds in the training set. The results also indicate that the quality of reliability scoring methods is sensitive to data set characteristics and to the regression method used in QSAR. We demonstrate that at the cost of increased computational complexity these dependencies can be leveraged by integration of scores from various reliability estimation approaches. The reliability estimation techniques described in this paper have been implemented in an open source add-on package (https://bitbucket.org/biolab/orange-reliability ) to the Orange data mining suite

Juvenile myasthenia gravis associated with autoimmune channelopathy and mixed connective tissue disease

Background/Aims: Ion channels are crucial elements in neuronal signaling and synaptic transmission. Autoantibodies against voltage-gated ion channels cause disorders in neuromuscular transmission. Autoantibodies against voltage-gated potassium channels (VGKC-Abs) are associated with acquired neuromyotonia and related disorders such as Morvan's syndrome and limbic encephalitis. The symptoms of myasthenia gravis reflect dysfunction of neuromuscular transmission. Methods and results: We present a 15-year-old girl with ptosis, proximal muscle weakness, neuromyotonia, hyperhidrosis, short memory loss and confusion. AntinAChR antibodies were positive. Electromyography showed neuromyotonic discharges. Cerebrospinal fluid analysis revealed oligoclonal bands. The plasma VGKC-antibody titer was elevated (176 pM, controls 100 pM), along with positive antiganglioside antibodies (GM1, asialo GM1, GM2, GD1a,b) and SS-A 211 U/mL, SS-B 157 U/mL, U1-RNP 188 U/mL, DNA-topo 128 U/mL (control <100). Brain magnetic resonance imaging was normal. The girl was treated with pyridostigmine, steroids, intravenous immunoglobulins and azathioprine, and repetitive plasma exchanges. Neurological impairments and myasthenic crisis occurred in periods of 5-21 days. Conclusion: The neuromyotonia and some of the dysautonomic features are likely to be directly related to the VGKC antibodies in the peripheral nervous system. The central nervous system symptoms are very likely to be due to the direct effects of VGKC antibodies, although there can be some other autoantibodies. A severe clinical course might be related to myasthenia gravis associated with autoimmune disease of the central and peripheral nervous system overlapping probably with mixed connective tissue disease

Application of Conformal Prediction in QSAR

Part 4: First Conformal Prediction and Its Applications Workshop (COPA 2012)International audienceQSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using statistical learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity. However, predictions from a QSAR model are difficult to assess if their prediction intervals are unknown. In this paper we introduce conformal prediction into the QSAR field to address this issue. We apply support vector machine regression in combination with two nonconformity measures to five datasets of different sizes to demonstrate the usefulness of conformal prediction in QSAR modeling. One of the nonconformity measures provides prediction intervals with almost the same width as the size of the QSAR models’ prediction errors, showing that the prediction intervals obtained by conformal prediction are efficient and useful