A randomized, double-blind, placebo-controlled trial of paracetamol and ketoprofren lysine salt for pain control in children with pharyngotonsillitis cared by family pediatricians

<p>Abstract</p> <p>Background</p> <p>To evaluate the analgesic effect and tolerability of paracetamol syrup compared to placebo and ketoprofen lysine salt in children with pharyngotonsillitis cared by family pediatricians.</p> <p>Methods</p> <p>A double-blind, randomized, placebo-controlled trial of a 12 mg/kg single dose of paracetamol paralleled by open-label ketoprofren lysine salt sachet 40 mg. Six to 12 years old children with diagnosis of pharyngo-tonsillitis and a Children's Sore Throat Pain (CSTP) Thermometer score > 120 mm were enrolled. Primary endpoint was the Sum of Pain Intensity Differences (SPID) of the CSTP Intensity scale by the child.</p> <p>Results</p> <p>97 children were equally randomized to paracetamol, placebo or ketoprofen. Paracetamol was significantly more effective than placebo in the SPID of children and parents (<it>P </it>< 0.05) but not in the SPID reported by investigators, 1 hour after drug administration. Global evaluation of efficacy showed a statistically significant advantage of paracetamol over placebo after 1 hour either for children, parents or investigators. Patients treated in open fashion with ketoprofen lysine salt, showed similar improvement in pain over time. All treatments were well-tolerated.</p> <p>Conclusions</p> <p>A single oral dose of paracetamol or ketoprofen lysine salt are safe and effective analgesic treatments for children with sore throat in daily pediatric ambulatory care.</p

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

DivagAzioni

In questo libro Ornella Martini propone nuove esplorazioni di territori educativi già percorsi, di cui al contempo disegna una mappa più aggiornata e sempre più mobile: quanto più il movimento del suo pensiero-azione si fa circostanziato e preciso tanto più risulta parziale e temporaneo. Le declinazioni necessarie e mai sufficienti del rapporto tra naturale e artificiale, la centralità del corpo per ogni esperienza, il pollice digitale che fa e conosce insieme alla mano che lo ospita, la natura e la cultura dell'apprendimento sono alcuni degli itinerari ridisegnati da queste nuove immersioni, tra le quali l'immagine come linguaggio aspiratutto e la narrazione del sé dentro specchi-finestre digitali e multimediali