Publications by authors named "H G Vullings"

The intracisternal granules in locust adipokinetic cells appear to represent accumulations of secretory material within cisternae of the rough endoplasmic reticulum. An important question is whether these granules are destined for degradation or represent stores of (pro)hormones. Two strategies were used to answer this question.

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The distribution and actions of FMRFamide-related peptides (FaRPs) in the corpora cardiaca of the locust Locusta migratoria were studied. Antisera to FMRFamide and SchistoFLRFamide (PDVDHVFLRFamide) label neuronal processes that impinge on glandular cells in the glandular lobe of the corpora cardiaca known to produce adipokinetic hormones. Electron microscopic immunocytochemistry revealed that these FaRP-containing processes form synaptoid contacts with the glandular cells.

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The retrocerebral complex of locusts consists of the corpus cardiacum, the corpora allata, and the nerves that connect these glands with the central nervous system. Both corpus cardiacum and corpora allata are neuroendocrine organs and consist of a glandular part, which synthesizes adipokinetic hormones and juvenile hormone, respectively, and of a neurohemal part. The glandular adipokinetic cells in the corpus cardiacum appear to be subjected to a multitude of regulatory stimulating, inhibiting, and modulating substances.

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Four locustatachykinins (LomTK I-IV) were identified in about equal amounts in extracts of corpora cardiaca of locusts, using reverse-phase high-performance liquid chromatography and radioimmunoassay with synthetic LomTK I-IV as standards. Brain extracts also contained the four isoforms in roughly equimolar concentrations. Retrograde tracing of the nervi corporis cardiaci II (NCC II) in vitro with Lucifer yellow in combination with LomTK immunocytochemistry revealed that about half of the secretomotor neurons in the lateral part of the protocerebrum projecting into the glandular lobe of the corpora cardiaca (CCG) contain LomTK-immunoreactive material.

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We present a new approach to the design of a decision support system (DSS) in anesthesia which converts the available data to relevant information. Instead of a patient-driven design (patient modelling), we use a decision-driven design (anesthetist modelling). This approach results in a system consisting of three stages.

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