Chronic ethanol exposure increases microtubule content in PC12 cells

BACKGROUND: Chronic ethanol exposure has been shown to result in changes in neuronal cyto-architecture such as aberrant sprouting and alteration of neurite outgrowth. In PC12 cells, chronic ethanol treatment produces an increase in Nerve Growth Factor (NGF)-induced neurite outgrowth that appears to require the epsilon, but not delta, isoform of Protein Kinase C (PKC). Neurites contain a core of microtubules that are formed from polymerization of free-tubulin. Therefore, it would be expected that an increase in neurite outgrowth would correlate with an increase in microtubule content. We examined the effect of chronic ethanol exposure on microtubule content in PC12 cells and the role of PKC epsilon and delta in ethanol's effect on microtubule levels. RESULTS: Chronic ethanol exposure of wild-type and vector control PC12 cells resulted in a significant increase in microtubule content and a corresponding decrease in free tubulin. There was also a significant increase in microtubule content in PC12 cells expressing a dominate-negative inhibitor of epsilon PKC; cells which have previously been shown to have no ethanol-induced increase in neurite outgrowth. In contrast, ethanol had no effect on microtubule content in PC12 cells expressing a dominate-negative inhibitor of delta PKC. CONCLUSION: These results suggest that chronic ethanol exposure alters the relative ratio of free tubulin to microtubule-associated tubulin, an important component of the cytoskeleton. Further, the data from the PKC dominant-negative cell lines suggest that the effects of ethanol on microtubule content do not correlate with the effects of ethanol on neurite outgrowth. The delta isoform of PKC appears to be necessary for the ethanol-induced increase in microtubule content. These studies demonstrate an effect of chronic ethanol exposure which may contribute to previously documented alterations of neuronal cyto-architecture

BT-Nurse : computer generation of natural language shift summaries from complex heterogeneous medical data

Objective: To determine if a computer system can automatically generate a useful natural language nursing shift summary solely from an electronic patient record system, in a neonatal intensive care unit (NICU). Design: A system was built which automatically generates NICU shift summaries, using datato- text technology. The system was tested for two months in the Royal Infirmary of Edinburgh NICU. Measurements: Nurses were asked to rate the understandability, accuracy, and helpfulness of the computer-generated summaries; they were also asked for free-text comments about the summaries. Results: The nurses found the majority of the summaries to be understandable, accurate, and helpful (p < .001 for all measures). However, nurses also pointed out many deficiencies, especially with regard to extra content they wanted to see in the computer-generated summaries. Conclusions: Natural language NICU shift summaries can be automatically generated from an electronic patient record. However our proof-of-concept software needs considerable additional development work.peer-reviewe

What is in a text and what does it do: Qualitative Evaluations of an NLG system – the BT-Nurse – using content analysis and discourse analysis.

Evaluations of NLG systems generally are quantiative, that is, based on corpus comparison statistics and/or results of experiments with people. Outcomes of such evaluations are important in demonstrating whether or not an NLG system is successful, but leave gaps in understanding why this is the case. Alternatively, qualitative evaluations carried out by experts provide knowledge on where a system needs to be improved. In this paper we describe two such evaluations carried out for the BT-Nurse system, using two different methodologies (content analysis and discourse analysis). The outcomes of such evaluations are discussed in comparison to what was learnt from a quantitiave evaluation of BT-Nurse. Implications for the role of similar evaluations in NLG are also discussed.peer-reviewe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Melatonin inhibition of nicotine-stimulated dopamine release in PC12 cells

Melatonin, a pineal hormone, modifies numerous physiologic processes including circadian rhythms and sleep. In specific tissues, melatonin appears to have an inverse relationship with dopamine. To examine this relationship, a pheochromocytoma cell line (PC12) was used to determine the extent of melatonin’s ability to inhibit nicotine-stimulated dopamine release. Multiple experiments were conducted that examined: (1) the dose response of acute melatonin (5 min); (2) the effects of chronic melatonin (16 h pre-exposure); (3) the effects of prior nicotine or melatonin exposure (5 min) on melatonin’s ability to alter dopamine release from a second 5-min nicotine exposure; and (4) the role of melatonin receptors (by pertussis toxin inhibition) on nicotine-stimulated dopamine release. In the dose response studies, melatonin inhibited nicotine-stimulated dopamine release with an ED50 of 8.6 μM. Chronic exposure to melatonin had no effect on melatonin’s acute inhibition of nicotine-stimulated dopamine release. Prior nicotine or melatonin exposure had little effect on subsequent melatonin or nicotine exposure, except that the cells exposed to nicotine were not responsive to a second exposure to nicotine. Blockade of melatonin receptor function by pre-exposure to pertussis toxin (16 h) did not prevent melatonin’s inhibition of nicotine-stimulated dopamine release. However, the toxin-treated cells were less inhibited by melatonin when compared to control cells suggesting a partial role for melatonin receptors. These results indicate that melatonin can acutely inhibit nicotine-stimulated dopamine release in PC12 cells. This model system allows detailed examination of melatonin’s cellular actions as well as supporting a role for melatonin on neuronal dopamine release

Automatic generation of natural language nursing shift summaries in neonatal intensive care : BT-Nurse

automatically generate helpful natural language nursing shift summaries solely from an electronic patient record system, in a neonatal intensive care unit (NICU). Methods: A system was developed which automatically generates partial NICU shift summaries (for the respiratory and cardiovascular systems), using data-to-text technology. It was evaluated for 2 months in the NICU at the Royal Infirmary of Edinburgh, under supervision. Results: In an on-ward evaluation, a substantial majority of the summaries was found by outgoing and incoming nurses to be understandable (90%), and a majority was found to be accurate (70%), and helpful (59%). The evaluation also served to identify some outstanding issues, especially with regard to extra content the nurses wanted to see in the computer-generated summaries. Conclusions: It is technically possible automatically to generate limited natural language NICU shift summaries from an electronic patient record. However, it proved difficult to handle electronic data that was intended primarily for display to the medical staff, and considerable engineering effort would be required to create a deployable system from our proof-of-concept software.peer-reviewe