Anti-nociceptive effect of Faecalibacterium prausnitzii in non-inflammatory IBS-like models

International audienceVisceral pain and intestinal dysbiosis are associated with Irritable Bowel Syndrome (IBS), a common functional gastrointestinal disorder without available efficient therapies. In this study, a decrease of Faecalibacterium prausnitzii presence has been observed in an IBS-like rodent model induced by a neonatal maternal separation (NMS) stress. Moreover, it was investigated whether F. prausnitzii may have an impact on colonic sensitivity. The A2-165 reference strain, but not its supernatant, significantly decreased colonic hypersensitivity induced by either NMS in mice or partial restraint stress in rats. This effect was associated with a reinforcement of intestinal epithelial barrier. Thus, F. prausnitzii exhibits anti-nociceptive properties, indicating its potential to treat abdominal pain in IBS patients

Comparison of [18F] fluorocholine PET/CT with [99mTc] sestamibi and ultrasonography to detect parathyroid lesions in primary hyperparathyroidism: a prospective study.

Background: Primary hyperparathyroidism is a common endocrine disorder produced by the increase of parathyroid hormone (PTH) due to a benign adenoma of a single parathyroid gland, or as multiple gland hyperplasia, or as a rare malignant tumor. Preoperative imaging scans are frequently necessary for the minimally invasive parathyroidectomies to identify the location of enlarged parathyroid glands and to design the procedure. Methods: The diagnostic reliability of [18F]fluorocholine positron emission tomography/computed tomography (FCH PET/CT), [99mTc]sestamibi [multiplexed ion beam imaging (MIBI)] and cervical ultrasonography was analyzed in 37 patients diagnosed with primary hyperparathyroidism undergoing minimally invasive parathyroidectomy. The three preoperative imaging techniques were correlated with intraoperative and histopathological findings as well as changes in biochemical parameters (serum PTH and calcium levels). Statistical analysis was carried out with SPSS version 24.0. Results: In 30 of 37 patients (81.1%), FCH PET/CT correctly localized the pathological gland. In 3 cases of ectopic adenomas, the accuracy of the techniques was 100% (3/3) for FCH PET/CT, 66.7% (2/3) for MIBI, and 33.3% (1/3) for neck ultrasonography. Neither neck ultrasonography nor MIBI were able to locate pathological parathyroid glands in those patients with multiglandular disease, while FCH PET/CT correctly located one patient (1/3, 33.3%) with two adenomas and 3 patients (3/6, 50.0%) with hyperplasia. The three imaging techniques, FCH PET/CT, MIBI and neck ultrasound yielded a sensitivity of 92.1%, 57.9% and 32.4%, a positive predictive value of 94.6%, 84.6% and 78.6%, and a diagnostic accuracy of 96.4%, 85.7% and 79.0%, respectively. Conclusions: In this group of patients diagnosed with primary hyperparathyroidism, FCH PET/CT was superior to MIBI and neck ultrasound in detecting adenomas, particularly in the presence of ectopic glands or multiglandular disease

Architecture and performance of the KM3NeT front-end firmware

The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine

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

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

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Multi-messenger Observations of a Binary Neutron Star Merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ∼ 1.7 {{s}} with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of {40}-8+8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 {M}ȯ . An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ∼ 40 {{Mpc}}) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ∼10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ∼ 9 and ∼ 16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta.</p

Impact of intestinal dysbiosis on mouse models of colonic hypersensitivity

Impact of intestinal dysbiosis on mouse models of colonic hypersensitivity. NeuroGASTRO 201