Numerical simulation of air pollution mitigation by means of photocatalytic coatings in real-world street canyons

Motivated by the increasing interest on passive control solutions to lower pollutant concentrations in cities, this paper introduces a novel methodology to demonstrate the potential of photocatalytic coatings in abating air pollution in real-world urban environments. The methodology introduced in this paper is based on an original application of Computational Fluid Dynamic (CFD) modelling to simulate the effect of photocatalytic coatings in real yet simplified urban setting. The numerical approach is validated against observations gathered during an ad-hoc designed intensive experimental campaign performed in a real urban area in the city of Bologna, Italy (44.5075 N, 11.3514E), under semi-controlled conditions. Comparison of the model output with observations show a concentration reduction in the range 10\u201320%. After validation and choice of the proper model set-up, numerical simulations are analyzed by focusing on the mechanisms enhancing the flow circulation within the canyon, an effect that may increase the effect of coatings within street canyons. Results show that application of photocatalytic coatings can give pollutant reductions up to 50% in a confined region close to the walls. A parametrization for the pollutant reduction within the street canyon is suggested to summarize these results, providing a characterization of the photocatalytic coatings performances as a function of the geometric char-acteristic of the street canyon

"Diagnosis on the Dock" project: A proactive screening program for diagnosing pulmonary tuberculosis in disembarking refugees and new SEI model.

Abstract Objective From 2011 to 2017, the total number of refugees arriving in Europe, particularly in Italy, climbed dramatically. Our aim was to diagnose pulmonary TB in migrants coming from the African coast using a clinical-based port of arrival (PoA) screening program. Methods From 2016 to 2018, migrants coming via the Mediterranean Route were screened for body temperature and the presence of cough directly on the dock: if they were feverish with productive cough, their sputum was examined with NAAT; with a dry cough, they underwent Chest-X-ray (CXR). Those migrants with positive NAAT or CXR suggestive for TB were admitted to our ward. In addition, we plotted an SEI simulation of our project to evaluate the epidemiological impact of our screening. Results Out of 33.676 disembarking migrants, 314 (0.9%) had fever and cough: 80 (25.47%) with productive cough underwent NAAT in sputum, and 16 were positive for TB; 234 (74.52%) with dry cough had a CXR examination, and 39 were suggestive of TB, later confirmed by mycobacterial culture. The SEI-new model analysis demonstrated that our screening program significantly reduced TB spreading all over the country. Conclusions For possible future high migrant flows, PoA screening for TB has to be considered feasible and effective in decreasing TB spreading

SARS-CoV-2 monoclonal antibody combination therapy in patients with COVID-19 and primary antibody deficiency

: Previous reports highlighted the efficacy of SARS-CoV-2 specific monoclonal antibodies (mAbs) against COVID-19. Here we conducted a prospective study on clinical outcome and antiviral effect of mAbs added to standard of care therapy in SARS-CoV-2 infected patients with Primary Antibody Defects. Median time of SARS-CoV-2 qPCR positivity was shorter in eight patients treated with mAbs (22 days) than in ten patients treated with standard of care therapy only (37 days, p=0.026). Median time of SARS-CoV-2 qPCR positivity from mAbs administration was 10 days. SARS-CoV-2 mAbs treatment was effective and well-tolerated in patients with Primary Antibody Defects

SARS-CoV-2 Vaccine Induced Atypical Immune Responses in Antibody Defects: Everybody Does their Best

Background: Data on immune responses to SARS-CoV-2 in patients with Primary Antibody Deficiencies (PAD) are limited to infected patients and to heterogeneous cohorts after immunization. Methods: Forty-one patients with Common Variable Immune Deficiencies (CVID), six patients with X-linked Agammaglobulinemia (XLA), and 28 healthy age-matched controls (HD) were analyzed for anti-Spike and anti-receptor binding domain (RBD) antibody production, generation of Spike-specific memory B-cells, and Spike-specific T-cells before vaccination and one week after the second dose of BNT162b2 vaccine. Results: The vaccine induced Spike-specific IgG and IgA antibody responses in all HD and in 20% of SARS-CoV-2 naive CVID patients. Anti-Spike IgG were detectable before vaccination in 4 out 7 CVID previously infected with SARS-CoV-2 and were boosted in six out of seven patients by the subsequent immunization raising higher levels than patients naïve to infection. While HD generated Spike-specific memory B-cells, and RBD-specific B-cells, CVID generated Spike-specific atypical B-cells, while RBD-specific B-cells were undetectable in all patients, indicating the incapability to generate this new specificity. Specific T-cell responses were evident in all HD and defective in 30% of CVID. All but one patient with XLA responded by specific T-cell only. Conclusion: In PAD patients, early atypical immune responses after BNT162b2 immunization occurred, possibly by extra-follicular or incomplete germinal center reactions. If these responses to vaccination might result in a partial protection from infection or reinfection is now unknown. Our data suggests that SARS-CoV-2 infection more effectively primes the immune response than the immunization alone, possibly suggesting the need for a third vaccine dose for patients not previously infected

Multi-institutional development and external validation of a nomogram to predict recurrence after curative resection of pancreatic neuroendocrine tumors

Objective: To develop a nomogram estimating the probability of recurrence free at 5 years after resection for localized grade 1 (G1)/ grade 2 (G2) pancreatic neuroendocrine tumors (PanNETs). Background: Among patients undergoing resection of PanNETs, approximately 17% experience recurrence. It is not established which patients are at risk, with no consensus on optimal follow-up. Method: A multi-institutional database of patients with G1/G2 PanNETs treated at 2 institutions was used to develop a nomogram estimating the rate of freedom from recurrence at 5 years after curative resection. A second cohort of patients from 3 additional institutions was used to validate the nomogram. Prognostic factors were assessed by univariate analysis using Cox regression model. The nomogram was internally validated using bootstrap resampling method and on the external cohort. Performance was assessed by concordance index (c-index) and a calibration curve. Results: The nomogram was constructed using a cohort of 632 patients. Overall, 68% of PanNETs were G1, the median follow-up was 51 months, and we observed 74 recurrences. Variables included in the nomogram were the number of positive nodes, tumor diameter, Ki-67, and vascular/perineural invasion. The model bias-corrected c-index from the internal validation was 0.85, which was higher than European Neuroendocrine Tumors Society/American Joint Committee on Cancer 8th staging scheme (c-index 0.76, P = <0.001). On the external cohort of 328 patients, the nomogram c-index was 0.84 (95% confidence interval 0.79–0.88). Conclusion: Our externally validated nomogram predicts the probability of recurrence-free survival at 5 years after PanNETs curative resection, with improved accuracy over current staging systems. Estimating individual recurrence risk will guide the development of personalized surveillance programs after surgery

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

Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)

The third edition of Flow Cytometry Guidelines provides the key aspects to consider when performing flow cytometry experiments and includes comprehensive sections describing phenotypes and functional assays of all major human and murine immune cell subsets. Notably, the Guidelines contain helpful tables highlighting phenotypes and key differences between human and murine cells. Another useful feature of this edition is the flow cytometry analysis of clinical samples with examples of flow cytometry applications in the context of autoimmune diseases, cancers as well as acute and chronic infectious diseases. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid. All sections are written and peer‐reviewed by leading flow cytometry experts and immunologists, making this edition an essential and state‐of‐the‐art handbook for basic and clinical researchers.DFG, 389687267, Kompartimentalisierung, Aufrechterhaltung und Reaktivierung humaner Gedächtnis-T-Lymphozyten aus Knochenmark und peripherem BlutDFG, 80750187, SFB 841: Leberentzündungen: Infektion, Immunregulation und KonsequenzenEC/H2020/800924/EU/International Cancer Research Fellowships - 2/iCARE-2DFG, 252623821, Die Rolle von follikulären T-Helferzellen in T-Helferzell-Differenzierung, Funktion und PlastizitätDFG, 390873048, EXC 2151: ImmunoSensation2 - the immune sensory syste