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Author(s): Marco Massari 1, Stefania Spila Alegiani 1, Cristina Morciano 1,*, Matteo Spuri 2, Pasquale Marchione 3, Patrizia Felicetti 3, Valeria Belleudi 4, Francesca Romana Poggi 4, Marco Lazzeretti 5, [...], Background Myocarditis and pericarditis following the Coronavirus Disease 2019 (COVID-19) mRNA vaccines administration have been reported, but their frequency is still uncertain in the younger population. This study investigated the association between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) mRNA vaccines, BNT162b2, and mRNA-1273 and myocarditis/pericarditis in the population of vaccinated persons aged 12 to 39 years in Italy. Methods and findings We conducted a self-controlled case series study (SCCS) using national data on COVID-19 vaccination linked to emergency care/hospital discharge databases. The outcome was the first diagnosis of myocarditis/pericarditis between 27 December 2020 and 30 September 2021. Exposure risk period (0 to 21 days from the vaccination day, subdivided in 3 equal intervals) for first and second dose was compared with baseline period. The SCCS model, adapted to event-dependent exposures, was fitted using unbiased estimating equations to estimate relative incidences (RIs) and excess of cases (EC) per 100,000 vaccinated by dose, age, sex, and vaccine product. Calendar period was included as time-varying confounder in the model. During the study period 2,861,809 persons aged 12 to 39 years received mRNA vaccines (2,405,759 BNT162b2; 456,050 mRNA-1273); 441 participants developed myocarditis/pericarditis (346 BNT162b2; 95 mRNA-1273). Within the 21-day risk interval, 114 myocarditis/pericarditis events occurred, the RI was 1.99 (1.30 to 3.05) after second dose of BNT162b2 and 2.22 (1.00 to 4.91) and 2.63 (1.21 to 5.71) after first and second dose of mRNA-1273. During the [0 to 7) days risk period, an increased risk of myocarditis/pericarditis was observed after first dose of mRNA-1273, with RI of 6.55 (2.73 to 15.72), and after second dose of BNT162b2 and mRNA-1273, with RIs of 3.39 (2.02 to 5.68) and 7.59 (3.26 to 17.65). The number of EC for second dose of mRNA-1273 was 5.5 per 100,000 vaccinated (3.0 to 7.9). The highest risk was observed in males, at [0 to 7) days after first and second dose of mRNA-1273 with RI of 12.28 (4.09 to 36.83) and RI of 11.91 (3.88 to 36.53); the number of EC after the second dose of mRNA-1273 was 8.8 (4.9 to 12.9). Among those aged 12 to 17 years, the RI was of 5.74 (1.52 to 21.72) after second dose of BNT162b2; for this age group, the number of events was insufficient for estimating RIs after mRNA-1273. Among those aged 18 to 29 years, the RIs were 7.58 (2.62 to 21.94) after first dose of mRNA-1273 and 4.02 (1.81 to 8.91) and 9.58 (3.32 to 27.58) after second dose of BNT162b2 and mRNA-1273; the numbers of EC were 3.4 (1.1 to 6.0) and 8.6 (4.4 to 12.6) after first and second dose of mRNA-1273. The main study limitations were that the outcome was not validated through review of clinical records, and there was an absence of information on the length of hospitalization and, thus, the severity of the outcome. Conclusions This population-based study of about 3 millions of residents in Italy suggested that mRNA vaccines were associated with myocarditis/pericarditis in the population younger than 40 years. According to our results, increased risk of myocarditis/pericarditis was associated with the second dose of BNT162b2 and both doses of mRNA-1273. The highest risks were observed in males of 12 to 39 years and in males and females 18 to 29 years vaccinated with mRNA-1273. The public health implication of these findings should be considered in the light of the proven mRNA vaccine effectiveness in preventing serious COVID-19 disease and death.

Aims: To assess the extent of off‐label drug use and the occurrence of suspected adverse drug reactions (ADRs) among paediatric patients in Italian hospitals. Methods: We conducted a 2‐year prospective cohort study across 22 Italian hospital wards from September 2020 to September 2022. As part of the surveillance project, we performed a 6‐month retrieval of all reported ADRs and evaluated all drug prescriptions for their possible off‐label use. Following an educational project on pharmacovigilance addressed to healthcare professionals in participating wards, the same data collection was performed. Results: Among the 892 patients included in the study, 64% were admitted to paediatric wards and 36% to neonatal wards. Fifty per cent of all drugs prescribed were used off‐label and mainly concerned the administration of a different dose from the one authorized. In neonatal wards, off‐label prescriptions occurred slightly more often, with antibacterials being the most frequently used off‐label drugs. A total of 35 reports of suspected ADRs were collected, five before the educational project and 30 afterwards. Based on product licence, 10 of the total 35 reports concerned at least one off‐label drug use. Conclusions: The off‐label use of drugs in treating paediatric patients was extensive in Italian hospitals. Regulatory interventions are needed to promote the use of drugs based on the latest available literature and improve ADR reporting on children. Paediatric indications and dosages of the drugs most commonly used in children should be supported by appropriate ad hoc studies. [ABSTRACT FROM AUTHOR]

Novel antidiabetic drugs have the ability to produce anti-inflammatory effects regardless of their glucose-lowering action. For this reason, these molecules (including GLP-1 RAs and DPP-4is) were hypothesized to be effective against COVID-19, which is characterized by cytokines hyperactivity and multiorgan inflammation. The aim of our work is to explore the potential protective role of GLP-1 RAs and DPP-4is in COVID-19 (with the disease intended to be a model of an acute stressor) and non-COVID-19 patients over a two-year observation period. Retrospective and one-versus-one analyses were conducted to assess the impact of antidiabetic drugs on the need for hospitalization (in both COVID-19- and non-COVID-19-related cases), in-hospital mortality, and two-year mortality. Logistic regression analyses were conducted to identify the variables associated with these outcomes. Additionally, log-rank tests were used to plot survival curves for each group of subjects, based on their antidiabetic treatment. The performed analyses revealed that despite similar hospitalization rates, subjects undergoing home therapy with GLP-1 RAs exhibited significantly lower mortality rates, even over a two-year period. These individuals demonstrated improved survival estimates both within hospital and non-hospital settings, even during a longer observation period. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.icarus.2007.04.023 Byline: Rosario Brunetto (a)(b), Ted L. Roush (c), Anna Cinzia Marra (a), Vincenzo Orofino (a) Keywords: Asteroids; surfaces; Centaurs; Spectroscopy; Experimental techniques Abstract: We perform an optical characterization of UV laser ablated silicates (olivine, pyroxene), starting from their reflectance spectra in the 0.3-2.5 [mu]m spectral range. The goal is to provide useful tools to model space weathering effects on surfaces of asteroids and TNOs (trans-neptunian objects). We determine that the reddening and darkening spectral trend is compatible with the Hapke's space weathering model, using the optical constants of metallic iron in a silicate matrix. This result is supported by new magnetic susceptibility measurements on laser ablated orthopyroxene. We also investigate the potential contribution of formation of amorphous silicates in the process. Applying our results to silicate-rich surfaces in the Solar System, we investigate the possibility of a weathered olivine component on the surface of Centaur 5145 Pholus. Inclusion of this component slightly decreases the amount of complex organics and water ice from those previously estimated. Thus, the current Pholus spectrum is consistent with the presence of either unweathered or weathered olivine, or potentially both materials. Author Affiliation: (a) Dipartimento di Fisica, Universita di Lecce, via Arnesano, I-73100 Lecce, Italy (b) INAF-Osservatorio Astrofisico di Catania, via S. Sofia 78, I-95123 Catania, Italy (c) NASA Ames Research Center, MS 245-3, Moffett Field, CA 94035-1000, USA Article History: Received 13 November 2006; Revised 28 February 2007

Considering the clinical significance for myocarditis and pericarditis after immunization with mRNA COVID-19 vaccines, the present pharmacovigilance study aimed to describe these events reported with mRNA COVID-19 vaccines in the Vaccine Adverse Events Reporting System (VAERS). From 1990 to July 2021, the mRNA vaccines were the most common suspected vaccines related to suspected cases of myocarditis and/or pericarditis (myocarditis: N = 1,165; 64.0%; pericarditis: N = 743; 55.1%), followed by smallpox vaccines (myocarditis: N = 222; 12.2%; pericarditis: N = 200; 14.8%). We assessed all suspected cases through the case definition and classification of the Brighton Collaboration Group, and only definitive, probable, and possible cases were included in the analysis. Our findings suggested that myocarditis and pericarditis mostly involve young male, especially after the second dose with a brief time to onset. Nevertheless, this risk is lower (0.38/100,000 vaccinated people; 95% CI 0.36–0.40) than the risk of developing myocarditis after SARS-CoV-2 infection (1000–4000 per 100,000 people) and the risk of developing "common" viral myocarditis (1–10 per 100,000 people/year). Comparing with the smallpox vaccine, for which is already well known the association with myocarditis and pericarditis, our analysis showed a lower probability of reporting myocarditis (ROR 0.12, 95% CI 0.10–0.14) and pericarditis (ROR 0.06, 95% CI 0.05–0.08) following immunization with mRNA COVID-19 vaccines. [ABSTRACT FROM AUTHOR]

Fatal Adverse Events (FADEs) are a major public health problem, and some FADEs could be preventable. The aim of the present study is to describe the frequency, the drugs involved and the preventability in the FADEs collected through the MEREAFaPS Study between 2012 and 2018. All cases including the outcome "death" have been examined. We excluded cases with vaccine-related ADEs, overdose or suicide, and ADEs occurred during the hospitalisation. Two trained assessors evaluated all cases fulfilling the inclusion criteria. ADEs' preventability was evaluated applying the Schumock and Thornton algorithm. During the study period, we observed 429 cases of death, 92 of which were excluded. The remaining 337 cases involved 187 women and 150 men, with a mean age of 79 and of 77 years, respectively. For each report, the suspected drugs and concomitant ones were 1.26 and 4.20, respectively. Anticoagulants and antiplatelet agents account for more than 40% of FADE cases and the most frequent reactions are haemorrhages (37.5%). The 25% of the FADEs were preventable. This study confirms that FADEs are still a relevant clinical occurrence, and are often caused by widely used old drugs associated with adverse events. The death of one in four patients was preventable. Further efforts should be done to improve the appropriateness of the therapy, especially in older patients who are treated with anticoagulants. [ABSTRACT FROM AUTHOR]

The NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory (CO) carries, for the first time, a dual-frequency precipitation radar (DPR) designed to provide insights into the 3-D structure of precipitating clouds and rain intensity by using its Ka- and Ku-band frequencies. Single-frequency (SF) (both Ku- and Ka-only) and double-frequency (DF) based products provide particle-size distribution (PSD) parameters, as well as precipitation rates. Background surface type, precipitation type and phase, and vertical extension of the storm are also provided. In this paper, an intercomparison between the SF and DF DPR outputs over the Mediterranean area during rainfall events in the first four years of GPM-CO mission is carried out. The goal is to investigate the reliability of SF-based products by assessing their quality compared to the DF-based ones, treated as a reference. The vertical profiles and the near-surface values of the corrected reflectivity of the PSD parameters (mean mass-weighted diameter and normalized intercept) and of the rainfall rate have been analyzed. The data have been categorized for surface type (land and sea) and precipitation type (stratiform and convective). The results show a more marked difference between the DF and SF Ka-only based products than between DF and SF Ku-only based products. The feature is confirmed by the analysis of vertical profiles of the SF- and DF-based retrieved parameters. The statistical scores do not differ significantly between land and sea while they differ noticeably between stratiform and convective precipitation. [ABSTRACT FROM AUTHOR]

In this study, we present a new module for the Snow retrievaL ALgorithm fOr gMi (SLALOM) that retrieves surface snowfall rate using Global Precipitation Measurement (GPM) Microwave Imager measurements together with humidity and temperature vertical profiles. This module, named Surface Snowfall Rate Module, is tuned using colocated surface snowfall observations of the Cloud Profiling Radar onboard CloudSat. Using this new module, the SLALOM algorithm is able to predict surface snowfall rate with a relative bias of −13%, a root‐mean‐square error of 0.08 mm/hr, and a correlation coefficient of 0.7. Surface Snowfall Rate Module is then used to retrieve snowfall rate for three case studies and to provide a unique, 70°S to 70°N high‐resolution distribution of average surface snowfall rate from 2014 to 2017. This new product will be useful for surface precipitation analyses, global water budget estimation, and climatological analyses. Plain Language Summary: A comprehensive monitoring of Earth surface snowfall can only be reached using spaceborne instruments. However, estimating surface snowfall from space is challenging as the signal measured by space sensors is only indirectly related to surface snowfall rate. In this study, we developed a new algorithm that is able to estimate surface snowfall rate using observations from the Global Precipitation Measurement Microwave Imager together with humidity and temperature vertical profiles. We used this algorithm to provide a unique, 70°S to 70°N high‐resolution distribution of average surface snowfall rate from 2014 to 2017. This new product will be useful for surface precipitation analyses, global water budget estimation and climatological analyses. Key Points: We developed a surface snowfall rate retrieval algorithm for the Global Precipitation Measurement Microwave ImagerSurface snowfall rate is predicted with a relative bias of −13% and a correlation coefficient of 0.7 compare to CloudSat Cloud Profiling Radar observationsA unique, 70°S to 70°N high‐resolution distribution of average surface snowfall rate from 2014 to 2017 is provided [ABSTRACT FROM AUTHOR]

Difficulties in the representation of rainfall variability using ground-based sensors over mountainous areas necessitate the use of high-resolution satellite precipitation data sets from combined passive microwave (PMW) and geostationary infrared observations. The accuracy of these data sets depends greatly on the uncertainty characteristics of the PMW sensor retrievals and sampling frequency. In this paper, we evaluate the retrieval error characteristics from different PMW sensors over mountainous terrain using high temporal and spatial resolution ground-based radar (GR) reference rainfall data sets from two field experiments: one in the mid-Atlantic East Coast of the United States and the second in the Northeastern Italian Alps. We extracted matchups of PMW/GR rainfall based on a matching methodology that identifies GR volume scans coincident with PMW overpasses, and scales GR parameters to the satellite products’ nominal spatial resolution. Different PMW precipitation retrieval algorithms are evaluated for four PMW sensors. The error analysis shows varying error characteristics across different PMW retrievals. Moreover, the magnitude-dependent systematic error, going from overestimation or weak underestimation of light precipitation to mainly underestimation of heavier precipitation, shows weak covariation with the ground reference. Detection capabilities for all sensors increase markedly with an increasing reference rate and convective occurrence because of the more pronounced ice-scattering signal associated with deeper convection and high rain rates. [ABSTRACT FROM AUTHOR]

The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 DTB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 DTB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg.m-2, IWP > 0.24 kg.m-2 over land, and SIC > 57%, TPW > 5.1 kg.m-2 over sea). The complex combined 166 DTB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms. [ABSTRACT FROM AUTHOR]

A new precipitation retrieval algorithm for the AMSR2 is described. The algorithm is based on the cloud dynamics and radiation database (CDRD) Bayesian approach and represents an evolution of the previous version applied to SSMIS observations, and used operationally within the EUMETSAT H-SAF program. This new product presents as main innovation the use of a very large database entirely empirical, derived from coincident radar and radiometer observations from the NASA/JAXA GPM-CO launched on February 28, 2014. The other new aspects are: 1) a new rain-/no-rain screening approach; 2) use of EOF and CCA for dimensionality reduction; 3) use of new ancillary variables to categorize the database and mitigate the problem of non-uniqueness of the retrieval solution; and 4) development and implementations of modules for computation time minimization. A verification study for case studies over Italy and for coincident AMSR2/GPM-CO observations over the MSG full disk area has been carried out. Results show remarkable AMSR2 capabilities for RR retrieval over ocean (for RR > 0.1 mm/h), good capabilities over vegetated land (for RR > 1 mm/h), while for coastal areas the results are less certain. Comparisons with NASA GPM products, and with ground-based radar data, show that the new CDRD for AMSR2 is able to depict very well the areas of high precipitation over all surface types. The algorithm is also able to handle an extremely large observational database available from GPM-CO and to provide rainfall estimate with minimum latency, making it suitable for NRT hydrological and operational applications. [ABSTRACT FROM PUBLISHER]

The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS) radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation-sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013–2014) dataset of coincident observations over a regular grid at 0.5°  ×  0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with generally better estimation of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR. [ABSTRACT FROM AUTHOR]

Precipitation retrievals exploiting the available passive microwave (PMW) observations by cross-track and conically scanning satellite-borne radiometers in the Global Precipitation Measurement (GPM) mission era are used to monitor and characterize heavy precipitation events that occurred during the Fall 2014 in Italy. Different physically based PMW precipitation retrieval algorithms are used: the Cloud Dynamics and Radiation Database (CDRD) and Passive microwave Neural network Precipitation Retrieval (PNPR), used operationally in the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on support to Operational Hydrology and Water Management (H-SAF), and the National Aeronautics and Space Administration (NASA) Goddard PROFiling algorithm (GPROF). Results show that PMW precipitation retrievals from the GPM constellation of radiometers provide a reliable and quantitative description of the precipitation (instantaneous and on the daily scale) throughout the evolution of the precipitation systems in the Mediterranean region. The comparable relative errors among gauges, radar, and combination of radiometer overpasses legitimize the use of PMW estimates as a valuable and independent tool for monitoring precipitation. The pixel-based comparison with dual-polarization radars and raingauges indicates the ability of the different sensors to identify different precipitation areas and regimes (0.60 < \,\textPOD < 0.76; 0.28 < FAR < 0.45; 0.42 < ETS < 0.59;-1.6\;\text{mm}/\text{h} < ME < 1.1\;\text{mm}/\text{h}$, with values depending on the radiometer and on the precipitation product). This is particularly relevant in the presence of complex orography in proximity of coastal areas, as for the analyzed cases. The different characteristics of the radiometers (i.e., viewing geometry, spatial resolution, channel assortment) and of retrieval techniques, as well as the limitations of the ground-based reference datasets, are taken into consideration in the evaluation of the accuracy and consistency of the retrievals. [ABSTRACT FROM PUBLISHER]

The spaceborne W-band (94 GHz) Cloud Profiling Radar (CPR) onboard the CloudSat (CS) satellite, which was launched in 2006, is providing valuable information about global cloud properties. This work aims at interpreting collocated time/space observations from CPR on CS and a ground C-band (5.6 GHz) Radar (GR), with the help of numerical simulations of electromagnetic scattering returns from populations of monodisperse spheres of ice and liquid water. Two cloud systems over Apulia region are investigated. CPR and GR images have been geo-referenced, then combined and displayed for analysis. The numerical simulations of the two radar reflectivities are used as a tool in the inversion procedure, aiming at identifying the hydrometeors, in their phase and size distribution, in the cloud volume simultaneously observed by the two radars. The possible vertical profiles of hydrometeors are presented. [ABSTRACT FROM PUBLISHER]

This study shows how satellite-based passive and active microwave (MW) sensors can be used in conjunction with high-resolution Numerical Weather Prediction (NWP) simulations to provide insights of the precipitation structure of the tropical-like cyclone (TLC) Numa, which occurred on 15–19 November 2017. The goal of the paper is to characterize and monitor the precipitation at the different stages of its evolution from development to TLC phase, throughout the storm transition over the Mediterranean Sea. Observations by the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) and by the GPM constellation of MW radiometers are used, in conjunction with the Regional Atmospheric Modeling System (RAMS) simulations. The GPM-CO measurements are used to analyze the passive MW radiometric response to the microphysical structure of the storm, while the comparison between successive MW radiometer overpasses shows the evolution of Numa precipitation structure from its early development stage on the Ionian Sea into its TLC phase, as it persists over southern coast of Italy (Apulia region) for several hours. Measurements evidence stronger convective activity at the development phase compared to the TLC phase, when strengthening or weakening phases in the eye development, and the occurrence of warm rain processes in the areas surrounding the eye, are identified. The weak scattering and polarization signal at and above 89 GHz, the lack of scattering signal at 37 GHz, and the absence of electrical activity in correspondence of the rainbands during the TLC phase, indicate weak convection and the presence of supercooled cloud droplets at high levels. RAMS high-resolution simulations support what inferred from the observations, evidencing Numa TLC characteristics (closed circulation around a warm core, low vertical wind shear, intense surface winds, heavy precipitation), persisting for more than 24 h. Moreover, the implementation of DPR 3D reflectivity field in the RAMS data assimilation system shows a small (but non negligible) impact on the precipitation forecast over the sea up to a few hours after the DPR overpass. [ABSTRACT FROM AUTHOR]

The Mediterranean Sea is a unique meteorological environment and a weather forecasting challenge, where severe weather events of different nature often originate and develop to hit coastal regions, causing major damages and casualties. Conventional ground-based instruments (e.g., raingauges and weather radars) are often inadequate to monitor events during their offshore development. On the other hand, in spite of their irregular time sampling, Low Earth Orbit satellites, carrying passive and active microwave (MW) sensors, can be fully exploited for the characterization and monitoring of heavy precipitation systems over this area. In this respect, the NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory (CO), equipped with the most advanced MW radiometer (GPM Microwave Imager, GMI) and the first spaceborne Dual-frequency Precipitation Radar (DPR), allows for the 3-D analysis of precipitation structures. Moreover the GPM constellation of MW radiometers ensures 1-3 hourly global coverage of precipitation. In this presentation, we show the GPM mission role in the characterization and monitoring of precipitation associated to extreme events, that hit Italy in the last years. Moreover, we test the capabilities and limitations of advanced satellite precipitation products in the GPM era for quantification and monitoring of precipitation associated with very intense and localized convective cells. To this aim, we analyze the flash flood occurred in Livorno, on the coast of Tuscany, Italy, in the night between 9 and 10 Sept. 2017. In this case, the precipitation products based on the GPM constellation of MW radiometers offer a wide spectrum of results, showing that better performances come from algorithms tailored for the specific region, while algorithms designed for global application are less sensitive to such small-scale thunderstorms. An analogous study is carried out for Numa, a Mediterranean tropical-like cyclone occurred on 16-18 November 2017 over southern Italy. GPM-CO measurements are used, in conjunction with LINET (LIghtning NETwork) data, to show how the rain-band structure evolves as the storm develops into its mature phase. High-resolution simulations were carried out with the Regional Atmospheric Modeling System (RAMS) to analyze the key mechanisms leading to the formation and evolution of Numa. We also show the impact of the assimilation of DPR reflectivity on the precipitation forecast. Long data records of reliable satellite precipitation-related measurements and products are becoming available in the GPM era. Here we make a first attempt to exploit them for a preliminary analysis of occurrence and intensity of extreme events in the Mediterranean, where they can be recognized as climate change signatures. This study evidences the need to fully exploit satellite data and products for studying and monitoring severe weather in the Mediterranean area, and to improve forecasting (and nowcasting) capabilities in this complex region. Thus efforts should be undertaken to develop satellite products tailored for these areas (working on algorithm calibration), and to provide the error structure of the products (working on validation). This would enable users to apply the most suitable product for a specific need, including hydrological applications, and for data assimilation of precipitation-related fields over the sea, where these systems often initiate. [ABSTRACT FROM AUTHOR]

This work proposes a multi-parameter method for the detection of cloud-to-ground stroke rate (SRCG) associated to convective cells, based on the measurements of a low-cost single-polarization X-band weather radar. To train and test our procedure, we built up a multi-year dataset, collecting 1575 radar reflectivity volumes that were acquired in the pilot study area of Naples metropolitan environment matched with the LIghtning NETwork (LINET) strokes and meteorological in-situ data. Three radar-based variables are extracted simultaneously for each rain cell and properly merged together, using "ad hoc" classification methods, to produce an estimation of the expected lightning activity for each rain cell. These variables, proxies of mixed-phase particles and ice amount into a convective cell, are combined into a single label to cluster the SRCG into two categories: SRCG = 0 (no production of strokes) or SRCG > 0 (stroke production), respectively. Overall, the main results are comparable with those that were obtained from more advanced radar systems, showing a Critical Success Index of 0.53, an Equitable Threat Score of 0.34, a Frequency Bias Index of 1.00, a Heidke Skill Score of 0.42, a Hanssen-Kuiper Skill Score of 0.42, and an area under the curve of probability of detection as a function of false alarm rate (usually referred as ROC curve) equal to 0.78. The developed technique, although with some limitations, outperforms those based on the use of single stroke proxy parameters. [ABSTRACT FROM AUTHOR]

The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades; however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Paraíba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Paraíba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values. [ABSTRACT FROM AUTHOR]

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI. [ABSTRACT FROM AUTHOR]

This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME < −0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications. [ABSTRACT FROM AUTHOR]

Introduction: The therapeutic approach to the patient with acute bacterial skin and skin structure infection (ABSSSI) and complicated infections often involves the early transition from intravenous to oral therapy (early switch) or early discharge. Our study aimed to evaluate sustainable and innovative care models that can be transferred to community healthcare and the economic impact of dalbavancin therapy vs Standard of Care (SoC) therapy for the treatment of ABSSSI and other Gram-positive infections including those by multidrug-resistant organisms. We also described the organization of an infectious disease network that allows optimizing the treatment of ABSSSI and other complex infections with dalbavancin., Materials and Methods: We retrospectively studied all patients treated with dalbavancin in the University Hospital "S. Anna" of Ferrara, Italy, between November 2016 and December 2022. The clinical information of each patient was collected from the hospital's SAP database and used to evaluate the impact of dalbavancin in early discharge with reduction of length of stay promoting dehospitalization and in improving adherence to antibiotic therapy., Results: A total of 287 patients (165 males and 122 females) were included in the study of which 62 were treated with dalbavancin. In 13/62 patients dalbavancin was administered in a single dose at the completion of therapy to facilitate early discharge. Assuming a 12-day hospitalization required for the treatment of ABSSSI or to complete the treatment of osteomyelitis or spondilodiscitis, the treatment with dalbavancin results in a cost reduction of more than €3,200 per single patient compared to SoC (dancomycin, linezolid or vancomycin)., Conclusions: Dalbavancin has proven to be a valid therapeutic aid in the organization of a territorial infectious disease network given its prolonged action, which allows the dehospitalization with management of even patients with complex infections in outpatient parenteral antimicrobial therapy., (© 2024 The Authors.)

Introduction: The purpose of TheShinISS-Vax|Flu study is to examine the association between influenza vaccines and adverse events requiring hospital admission or emergency care during the influenza vaccination campaigns 2021/2022 and 2022/2023 in Italy., Methods and Analysis: This is a Self-Controlled Case Series multiregional study using linked routinely collected data from regional healthcare databases of the participating regions. Study participants will be persons aged ≥6 months, unvaccinated or who have received influenza vaccine during the influenza vaccination campaigns in the seasons 2021/2022 and 2022/2023 in Italy and who have experienced the outcome of interest for the first time during the study period (1 September 2021-30 June 2022 and 1 September 2022-30 June 2023 for the first and second vaccination campaigns, respectively). Risk periods will be specifically defined for each outcome and further subdivided into periods of 7 days. The exposures will be the first or second dose of the influenza vaccines administered during the two vaccination campaigns. Statistical analysis will be conducted separately for the data of the two campaigns. Exposure risk period will be compared with baseline risk period defined as any time of observation out of the risk periods. The modified SCCS method will be applied to handle event-dependent exposure and mortality and fitted using unbiased estimating equations to estimate relative incidences and excess of cases per 100 000 vaccinated by dose, age, sex and type of vaccine. Calendar period will be included as time-varying confounder in the model, where appropriate., Ethics and Dissemination: The study received the approval from the National ethics committee for clinical trials of public research bodies and other national public institutions (PRE BIO CE n.0036723, 23/09/2022). Results will be published in peer-reviewed journals and reports in accordance with the publication policies of the Italian National Institute of Health and of the Italian Medicines Agency., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Vaccine vigilance implies the collection, evaluation, analysis and communication of adverse events following immunization (AEFI) and is a useful tool for vaccine monitoring allowing, even after approval and marketing, to check its safety/tolerability. The multiregional project "Active surveillance of adverse vaccine reactions", joined by the AUSL of Ferrara, is aimed at making parents of children, who have undergone at least one vaccination provided by the regional vaccination calendar in the first 24 months of life, aware of the reporting of any AEFI via mobile phone-SMS., Methods: An analysis of the project data, collected in the period March 2018 - May 2019, was carried out, to evaluate the effectiveness of the reporting tool and the type and frequency of AEFI. Anonymized data were analyzed by number, gender, distribution by age, type of vaccine, adverse event, severity and outcome., Results: A total of 1,494 consents and 983 SMS messages were obtained from parents. The vaccine doses carried out were 1,984 (28.3% hexavalent, 28% PCV13, 17% anti-rotavirus, 14.3% Men-B). Almost all (99.5%) AEFI were classified as "not serious". Based on the Organ System Class (SOC), most reports are related to "General Disorders and Administration Site Conditions" (52.3%), followed by "Psychiatric Disorders" (26.5%) and "Metabolic and nutrition disorders" (12.5%)., Conclusions: The reported AEFI are in line with the ones reported in the literature. Reporting via SMS is a valid vaccine surveillance tool contributing to the qualitative and quantitative improvement of the information transmitted., Competing Interests: Conflicts of interest statement The other authors declare no conflict of interest related to this manuscript. GG declares that he does not have a specific conflict of interest related to this paper; however, he reports grants from Sanofi Pasteur MSD, GSK Biologicals SA, Pfizer, Sanofi Pasteur Italy, MSD Italy, Emergent BioSolutions and Seqirus for taking part to advisory boards, expert meetings, for acting as speaker and/or organizer of meetings/congresses and as principal investigator and chief of O.U. in RCTs., (©2022 Pacini Editore SRL, Pisa, Italy.)