R0 surgical resection of giant dedifferentiated retroperitoneal liposarcomas in the COVID era with and without nephrectomy: A case report

: Retroperitoneal sarcomas (RPSs) are rare findings that can grow into large masses without eliciting severe symptoms. At present, surgical resection is the only radical therapy, whenever it can be performed with the aim to achieve a complete removal of the tumor. The present report describes two consecutive cases of RPSs that resulted in dedifferentiated liposarcomas (DDLPSs) and these patients underwent R0 surgical resection with and without a nephron-sparing procedure. The diagnostic workup, the surgical approach, the impact of late surgical management due to the COVID pandemic and the latest literature on the topic are discussed and analyzed. The patients, who refused to undergo any medical examination during the prior 2 years due to the COVID pandemic, were admitted to Federico II University Hospital (Naples, Italy) complaining about weight loss and general abdominal discomfort. In the first case, a primitive giant abdominal right neoplasm of retroperitoneal origin enveloping and medializing the right kidney was observed. The second patient had a similar primitive retroperitoneal giant left neoplasm, which did not affect the kidney. Given the characteristics of the masses and the absence of distant metastases, after a multidisciplinary discussion, radical surgical removal was carried out for both patients. The lesions appeared well-defined from the surrounding tissues, and markedly compressed all the adjacent organs, without signs of infiltration. In the first patient, the right kidney was surrounded and undetachable from the tumor and it was removed en bloc with the mass. The second patient benefited from a nephron-sparing resection, due to the existence of a clear cleavage plane. The postoperative courses were uneventful. Both the histological examinations were oriented towards a DDLPS and both patients benefited from adjuvant chemotherapy. In conclusion, the treatment of giant RPS is still challenging and requires multidisciplinary treatment as well as, when possible, radical surgical removal. The lack of tissue infiltration and the avoidance of excision or reconstruction of major organs (including the kidney) could lead to an easier postoperative course and an improved prognosis. When possible, surgical management of recurrences or incompletely resected masses must be pursued. Since the COVID pandemic caused limited medicalization of a number of population groups and delayed diagnosis of other oncologic diseases, an increased number of DDLPSs could be expected in the near future

Outcomes of COVID-19 patients treated with continuous positive airway pressure outside ICU

Aim We aim at characterizing a large population of Coronavirus 19 (COVID-19) patients with moderate-to-severe hypoxemic acute respiratory failure (ARF) receiving CPAP outside intensive care unit (ICU), and ascertaining whether the duration of CPAP application increased the risk of mortality for patients requiring intubation. Methods In this retrospective, multicentre cohort study, we included COVID-19 adult patients, treated with CPAP outside ICU for hypoxemic ARF from March 1 st to April 15th, 2020. We collected demographic and clinical data, including CPAP therapeutic goal, hospital length of stay (LOS), and 60- day in-hospital mortality. Results The study includes 537 patients with a median age of 69 (IQR, 60-76) years. Males were 391 (73%). According to predefined CPAP therapeutic goal, 397 (74%) patients were included in full treatment subgroup, and 140 (26%) in the do-not intubate (DNI) subgroup. Median CPAP duration was 4 (IQR, 1-8) days, while hospital LOS 16 (IQR, 9-27) days. Sixty-day in-hospital mortality was overall 34% (95%CI, 0.304-0.384), and 21% (95%CI, 0.169-0.249) and 73% (95%CI, 0.648-0.787) for full treatment and DNI subgroups, respectively. In the full treatment subgroup, in-hospital mortality was 42% (95%CI, 0.345-0.488) for 180 (45%) CPAP failures requiring intubation, while 2% (95%CI, 0.008- 0.035) for the remaining 217 (55%) patients who succeeded. Delaying intubation was associated with increased mortality [HR, 1.093 (95%CI, 1.010-1.184)]. Conclusions We described a large population of COVID-19 patients treated with CPAP outside ICU. Intubation delay represents a risk factor for mortality. Further investigation is needed for early identification of CPAP failures

Serum Albumin Is Inversely Associated With Portal Vein Thrombosis in Cirrhosis

We analyzed whether serum albumin is independently associated with portal vein thrombosis (PVT) in liver cirrhosis (LC) and if a biologic plausibility exists. This study was divided into three parts. In part 1 (retrospective analysis), 753 consecutive patients with LC with ultrasound-detected PVT were retrospectively analyzed. In part 2, 112 patients with LC and 56 matched controls were entered in the cross-sectional study. In part 3, 5 patients with cirrhosis were entered in the in vivo study and 4 healthy subjects (HSs) were entered in the in vitro study to explore if albumin may affect platelet activation by modulating oxidative stress. In the 753 patients with LC, the prevalence of PVT was 16.7%; logistic analysis showed that only age (odds ratio [OR], 1.024; P = 0.012) and serum albumin (OR, -0.422; P = 0.0001) significantly predicted patients with PVT. Analyzing the 112 patients with LC and controls, soluble clusters of differentiation (CD)40-ligand (P = 0.0238), soluble Nox2-derived peptide (sNox2-dp; P < 0.0001), and urinary excretion of isoprostanes (P = 0.0078) were higher in patients with LC. In LC, albumin was correlated with sCD4OL (Spearman's rank correlation coefficient [r(s)], -0.33; P < 0.001), sNox2-dp (r(s), -0.57; P < 0.0001), and urinary excretion of isoprostanes (r(s), -0.48; P < 0.0001) levels. The in vivo study showed a progressive decrease in platelet aggregation, sNox2-dp, and urinary 8-iso prostaglandin F2 alpha-III formation 2 hours and 3 days after albumin infusion. Finally, platelet aggregation, sNox2-dp, and isoprostane formation significantly decreased in platelets from HSs incubated with scalar concentrations of albumin. Conclusion: Low serum albumin in LC is associated with PVT, suggesting that albumin could be a modulator of the hemostatic system through interference with mechanisms regulating platelet activation

The Large Observatory For X-ray Timing: LOFT

LOFT, the Large Observatory for X-ray Timing, is a new space mission concept devoted to observations of Galactic and extra-Galactic sources in the X-ray domain with the main goals of probing gravity theory in the very strong field environment of black holes and other compact objects, and investigating the state of matter at supra-nuclear densities in neutron stars. The instruments on-board LOFT, the Large area detector and the Wide Field Monitor combine for the first time an unprecedented large effective area (~10 m2 at 8 keV) sensitive to X-ray photons mainly in the 2-30 keV energy range and a spectral resolution approaching that of CCD-based telescopes (down to 200 eV at 6 keV). LOFT is currently competing for a launch of opportunity in 2022 together with the other M3 mission candidates of the ESA Cosmic Vision Progra

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