63,234 research outputs found
TLQP-21, a VGF-derived peptide, stimulates exocrine pancreatic secretion in the rat
No full textThe aims of this paper were to study: (1) the effects of TLQP-21 (non-acronic name), the C-terminal region of the VGF (non-acronic name), polypeptide (from residue 557 to 576 of VGF), on in vitro amylase release from rat isolated pancreatic lobules and acinar cells; (2) the mechanism through which TLQP-21 regulates exocrine pancreatic secretion, by using the muscarinic receptor antagonist atropine (10 -6 M) and the cyclo-oxygenase inhibitor, indomethacin (10 -6 M). On pancreatic lobules of rats, concentrations of TLQP-21 from 10 -7 to 10 -5 M significantly (p < 0.05) induced a 2-3-fold increase of baseline pancreatic amylase release, measured at the end of 60 min incubation period. Co-incubation with atropine 10 -6 M did not antagonise the enzyme outflow induced by the peptide. On the contrary, co-incubation of TLQP-21 (10 -7 and 10 -6 M) with indomethacin, at concentration of 10 -6 M, which alone did not modify enzyme secretion, completely suppressed the increase of amylase evoked by TLQP-21 on pancreatic lobules. On rat pancreatic acinar cells, TLQP-21, at all the concentrations tested, was unable to affect exocrine pancreatic secretion, indicating an indirect mechanism of action on acinar cells. These results put in evidence, for the first time, that TLQP-21, a VGF-derived peptide, modulates exocrine pancreatic secretion in rats through a stimulatory mechanism involving prostaglandin release. In conclusion, TLQP-21 could be included among the neurohumoral signals regulating pancreatic exocrine secretion, and increases the knowledge concerning the systems controlling this function. © 2012 Elsevier Inc
An application of DMAIC methodology for reducing voluntary departures from an Emergency Department
No full textThe Emergency Department (ED) is a zone of a hospital where usually the healthcare staff has to face sudden and serious heathy problems. On the other side, patients find themselves in a situation of great emotion, discomfort and anxiety, aggravated by the expectation that, in cases of lesser severity, it can be long and badly tolerated. Furthermore, during access, patients and caregivers must undertake visits, assessments, consultations and bureaucratic procedures that can be long and complicated, due to inadequate or inaccurate information. In these conditions, waiting times can often be very long and / or patients can decide to abandon the ED. In this work, a combination of important managerial methodologies, Lean Six Sigma and DMAIC cycle (Define, Measure, Analyse, Improve, Control) are employed at an important hospital of South Italy in order to improve the emergency care process as well as the patient experience. It is a retrospective study, for which the historical times of access to the ED were analysed for the years 2015 and 2016. During this period, to improve the reception of patients and to reduce voluntary departures, two digital monitors were being designed and installed in the waiting room of the ED, which allow the visualization of the overall situation at the ED itself. The visualization is of course totally dynamic, i.e. the layout varies according to the data taken into consideration. In conclusion, the adopted approach impacts on the entrance and waiting times of patients in the ED and improves the general patient experience
Indagine chimica sullo stato di salute del Fiume Sordo: inquinanti organici ed inorganici
No full textIl Fiume Sordo nasce dalla Costa Grande, alle pendici del monte Cunorre (m 1257) e, dopo un percorso di 8 km, si immette da destra nel Fiume Cavaliere subito dopo aver attraversato la città di Isernia. Lo scopo dello studio è: a) la caratterizzazione chimica del corso d’acqua; b) la determinazione degli inquinanti inorganici nelle acque e nei sedimenti; c) formulare una proposta per un piano di bonifica del fiume.
Allo scopo è stato definito un piano di campionamento periodico per l’arco di un anno scegliendo quattro diverse stazioni dove effettuare i campionamenti distribuite lungo tutto il corso del fiume, dalla sorgente di S. Martino fino alla parte terminale del fiume prima della sua confluenza con il Cavaliere
Gastrointestinal effects of intracerebroventricularly injected nociceptin/orphaninFQ in rats
No full textNociceptin/orphanin FQ/(N/OFQ), a novel heptadecapeptide recently isolated from porcine and rat brain, is the endogenous ligand of
the N/OFQ peptide receptor (NOP, previously known as ORL-1). In this study we examined the effects of intracerebroventricularly (icv) injected
N/OFQ on gastric emptying, gastrointestinal transit, colonic propulsion and gastric acid secretion in rats. N/OFQ (0.01–10 nmol/rat)
significantly delayed gastric emptying of a phenol red meal, inhibited transit of a non-absorbable charcoal marker through the small
intestine and increased the mean colonic bead expulsion time. These N/OFQ-motor effects were abolished by the NOP receptor selective
antagonist [NPhe1]N/OFQ(1–13)-NH2 (50 nmol/rat), but were unaltered by the classical opioid receptor antagonist, naloxone
(9.2mol/kg). Icv injected N/OFQ (10 nmol/rat) decreased gastric acid secretion in 2-h pylorus ligated rats in a naloxone sensitive manner.
[NPhe1]N/OFQ(1–13)-NH2 (100 nmol/rat) icv administered alone stimulated gastric acid secretion. These results indicate that N/OFQ activates
via NOP receptor stimulation a central inhibitory pathway modulating gastrointestinal propulsive activity and gastric acid secretion
in rats
Classifying the type of delivery from cardiotocographic signals: A machine learning approach
No full textBackground and objective: Cardiotocography (CTG) is the most employed methodology to monitor the foetus in the prenatal phase. Since the evaluation of CTG is often visual, and hence qualitative and too subjective, some automated methods have been introduced for its assessment. Methods: In this paper, a custom-made software is exploited to extract 17 features from the available CTG. A preliminary univariate statistical analysis is performed; then, five machine learning algorithms, exploiting ensemble learning, were implemented (J48, Random Forests (RF), Ada-boosting of decision tree (ADA-B), Gradient Boosting and Decorate) through Knime analytics platform to classify patients according to their delivery: vaginal or caesarean section. The dataset is composed by 370 signals collected between 2000 and 2009 in both public and private hospitals. The performance of the algorithms was evaluated using 10 folds cross validation with different evaluation metrics: accuracy, precision, sensitivity, specificity, area under the curve receiver operating characteristic (AUCROC). Results: While only two features were significantly different (gestation week and power expressed by the high frequency band of FHR power spectrum), from the statistical point of view, machine learning results were great. The RF obtained the best results: accuracy (91.1%), sensitivity (90.0%) and AUCROC (96.7%). The ADA-B achieved the highest precision (92.6%) and specificity (93.1%). As expected, the lowest scores were obtained by J48 that was the base classifier employed in all the others empowered implementations. Excluding the J48 results, the AUCROC of all the algorithms was greater than 94.9%. Conclusion: In the light of the obtained results, that are greater than those ones found in the literature from comparable researches, it can be stated that the machine learning approach can actually help the physicians in their decision process when evaluating the foetal well-being
Feasibility of Machine Learning in Predicting Features Related to Congenital Nystagmus
No full textCongenital nystagmus is an ocular-motor disease affecting people’s visual acuity since their first years of life. Electrooculography is used to perform eye tracking in these patients, giving the possibility to extract a wide variety of parameters. The relationships among all these variables were analysed in the past and the aim of this paper is to perform a new analysis employing more recent techniques, those of machine learning. The electrooculography of 20 patients was recorded, signals were pre-processed, and some parameters were extracted through a custom-made software. Knime analytics platform was chosen in order to build predictive models using Random Forests and Logistic Regression Tree algorithms and some evaluation metrics were computed. The visual acuity and the variability of eye positioning were predicted employing five and six variables, respectively. In terms of coefficient of determination, visual acuity had values over 0.72 and variability of eye positioning over 0.70. Compared to the results obtained without machine learning algorithms during the past years, these values become more valuable. In conclusion, this approach showed its feasibility in detecting relationships among variables related to congenital nystagmus; it could be tested in order to find new and stronger relationships among these variables and be of support for clinicians
Is It Possible to Predict Cardiac Death?
No full textCardiovascular diseases are the leading cause of death in all the world; despite having the knowledge of the main risk factors, they keep on being complicated pathologies to deal with. Cardiovascular management has introduced a lot of parameters as regards patients’ state of health; particularly, nuclear cardiology with Stress single-photon emission computed tomography myocardial perfusion imaging can carry out interesting parameters that have encouraged researchers to apply machine learning techniques to predict whether patients will die due to a cardiac event or not. The dataset consisted of 661 patients that were evaluated for suspected of known coronary artery disease at the Department of Advanced Biomedical Sciences of the University Hospital “Federico II” in Naples. Knime analytics platform was employed to implement a decision tree and Random forests. After a procedure of features reduction, 29 features were included, and the overall accuracy was 91.0%, while recall, precision, sensitivity and specificity overcame the value of 90.0%. This implementation shows the feasibility of machine learning combined with data coming from nuclear cardiology. Moreover, the possibility to predict cardiac death exploiting clinical data and parameters carried out from instrumental exams would help clinicians to provide patients with the best treatments and interventions
Cod. 122 Differenza in termini di funzione muscolare e compromissione ossea in pazienti affetti da Rettocolite ulcerosa e Morbo di Crohn
No full textIndagine sui corpi idrici superficiali dei Campi Flegrei: il Lago d’Averno
No full textL’occasione per promuovere questo studio è stato l’episodio occorso a metà aprile del 2005 quando nel lago di Averno si è prodotta un’ingente moria di pesci. L’obiettivo dello studio è stato pertanto così definito: a) Determinazione i principali parametri chimici di acque e sedimenti; b) Studio dei risultati allo scopo di verificare lo stato di salubrità del lago; c) Individuazione delle possibili cause della moria dei pesci. E’ stata definita una griglia di campionamento con 18 stazioni di prelievo in modo da poter georeferenziare i parametri ottenuti. In campagna ci siamo serviti di un GPS per le coordinate geografiche e di una zattera per gli spostamenti sulla superficie del lago. I prelievi di campioni lungo la colonna d’acqua sono stati effettuati con campionatore per acque di profondità. Le metodiche di analisi utilizzate sono quelle dell’IRSA-CNR. I sedimenti sono stati prelevati in corrispondenza delle stesse stazioni di campionamento delle acque, sul fondo del lago, con l’ausilio di una benna di Petersen
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