Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center.

Purpose: To analyze the vaccine effect by comparing five groups: unvaccinated patients with Alpha variant, unvaccinated patients with Delta variant, vaccinated patients with Delta variant, unvaccinated patients with Omicron variant, and vaccinated patients with Omicron variant, assessing the "gravity" of COVID-19 pulmonary involvement, based on CT findings in critically ill patients admitted to Intensive Care Unit (ICU). Methods: Patients were selected by ICU database considering the period from December 2021 to 23 March 2022, according to the following inclusion criteria: patients with proven Omicron variant COVID-19 infection with known COVID-19 vaccination with at least two doses and with chest Computed Tomography (CT) study during ICU hospitalization. Wee also evaluated the ICU database considering the period from March 2020 to December 2021, to select unvaccinated consecutive patients with Alpha variant, subjected to CT study, consecutive unvaccinated and vaccinated patients with Delta variant, subjected to CT study, and, consecutive unvaccinated patients with Omicron variant, subjected to CT study. CT images were evaluated qualitatively using a severity score scale of 5 levels (none involvement, mild: ≤25% of involvement, moderate: 26−50% of involvement, severe: 51−75% of involvement, and critical involvement: 76−100%) and quantitatively, using the Philips IntelliSpace Portal clinical application CT COPD computer tool. For each patient the lung volumetry was performed identifying the percentage value of aerated residual lung volume. Non-parametric tests for continuous and categorical variables were performed to assess statistically significant differences among groups. Results: The patient study group was composed of 13 vaccinated patients affected by the Omicron variant (Omicron V). As control groups we identified: 20 unvaccinated patients with Alpha variant (Alpha NV); 20 unvaccinated patients with Delta variant (Delta NV); 18 vaccinated patients with Delta variant (Delta V); and 20 unvaccinated patients affected by the Omicron variant (Omicron NV). No differences between the groups under examination were found (p value > 0.05 at Chi square test) in terms of risk factors (age, cardiovascular diseases, diabetes, immunosuppression, chronic kidney, cardiac, pulmonary, neurologic, and liver disease, etc.). A different median value of aerated residual lung volume was observed in the Delta variant groups: median value of aerated residual lung volume was 46.70% in unvaccinated patients compared to 67.10% in vaccinated patients. In addition, in patients with Delta variant every other extracted volume by automatic tool showed a statistically significant difference between vaccinated and unvaccinated group. Statistically significant differences were observed for each extracted volume by automatic tool between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant of COVID-19. Good statistically significant correlations among volumes extracted by automatic tool for each lung lobe and overall radiological severity score were obtained (ICC range 0.71−0.86). GGO was the main sign of COVID-19 lesions on CT images found in 87 of the 91 (95.6%) patients. No statistically significant differences were observed in CT findings (ground glass opacities (GGO), consolidation or crazy paving sign) among patient groups. Conclusion: In our study, we showed that in critically ill patients no difference were observed in terms of severity of disease or exitus, between unvaccinated and vaccinated patients. The only statistically significant differences were observed, with regard to the severity of COVID-19 pulmonary parenchymal involvement, between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant, and between unvaccinated patients with Delta variant and vaccinated patients with Delta variant.

Field Assessment of the Host Range of Aculus mosoniensis (Acari: Eriophyidae), a Biological Control Agent of the Tree of Heaven (Ailanthus altissima).

Tree of heaven (Ailanthus altissima) is a fast-growing deciduous tree native to China, considered a serious invasive species worldwide, with several socio-economic and ecological impacts attributed to it. Chemical and mechanical methods have limited efficacy in its management, and biological controls may offer a suitable and sustainable option. Aculus mosoniensis (Ripka) is an eriophyid mite that has been recorded to attack tree of heaven in 13 European countries. This study aims to explore the host range of this mite by exposing 13 plant species, selected either for their phylogenetic and ecological similarity to the target weed or their economic importance. Shortly after inoculation with the mite, we recorded a quick decrease in mite number on all nontarget species and no sign of mite reproduction. Whereas, after just one month, the population of mites on tree of heaven numbered in the thousands, irrespective of the starting population, and included both adults and juveniles. Significantly, we observed evidence of damage due to the mite only on target plants. Due to the specificity, strong impact on the target, and the ability to increase its population to high levels in a relatively short amount of time, we find A. mosoniensis to be a very promising candidate for the biological control of tree of heaven.

An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans.

COVID-19 infection caused by SARS-CoV-2 pathogen has been a catastrophic pandemic outbreak all over the world, with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at automatically identifying lung parenchyma and lobes. Next, we combine the segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the model's classification results with those obtained by three expert radiologists on a dataset of 166 CT scans. Results showed a sensitivity of 90.3% and a specificity of 93.5% for COVID-19 detection, at least on par with those yielded by the expert radiologists, and an average lesion categorization accuracy of about 84%. Moreover, a significant role is played by prior lung and lobe segmentation, that allowed us to enhance classification performance by over 6 percent points. The interpretation of the trained AI models reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai. The whole AI system is unique since, to the best of our knowledge, it is the first AI-based software, publicly available, that attempts to explain to radiologists what information is used by AI methods for making decisions and that proactively involves them in the decision loop to further improve the COVID-19 understanding.

Sonographic assessment of facial HIV-related lypoatrophy.

OBJECTIVE: To investigate the utility of ultrasonography (US) for assessing and grading facial lypoatrophy (FLA) in patients with HIV. DESIGN: The social effect of FLA is huge and may reduce antiretroviral therapy adherence. Strategies for the early detection of FLA are crucial, because complete correction of FLA in late stages is unlikely. METHODS: Fifty-two HIV-positive patients undergoing highly active antiretroviral therapy underwent US with nasogenian transversal scan using a high-frequency broadband transducer (5-17 MHz) to detect FLA. Intra- and interobserver variability were calculated to assess US reproducibility. Concerning FLA grading, patients were categorized in five clinical classes and four US classes. RESULTS: Our results regarding inter- and intraobserver coefficients of variation permit the validation of US as a reproducible technique (p<.001), and a high correlation between US and clinical classification was obtained, with complete concordance for more advanced FLA classes. CONCLUSIONS: The lack of a reference objective method to quantify subcutaneous fat is a major difficulty in measuring HIV-related FLA. Our results, in accordance with data from the literature, suggest that US is an ideal tool for assessing and grading FLA. Furthermore, US may be suitable for routine evaluation in HIV-infected patients for early detection of FLA and to select its optimal management.

Assessment of risk in radiology using malpractice RVU.

INTRODUCTION: Analysis on the causes and remedies needed to reduce the incidence of malpractice has been under continual studies, although limited data is available regarding quantitative evaluation of the risk. OBJECTIVES: To determine radiological risk in a preventive and quantitative manner and verify if the malpractice relative value units (MP-RVU) are a good indicator of associated risk factors. MATERIALS AND METHODS: Radiological examinations executed by our Radiology Department in 2000-2004 have been codified according to nomenclature HCPCS (Healthcare Common Procedure Coding System) used by United States of America Centers for Medicare and Medicaid Services (CMS). For every examination was calculated the annual weight of malpractice. The data has been groupped in macroaggregates by methodology. The ratio MP-RVU/no. examinations has been considered as an index of insurance risk (MP index) RESULTS: A total of 133,005 examinations were performed, which produced 25,252 MP-RVU points, the total mp index was 0.193. Traditional radiology represents 38% of the examinations, accounting for 8% of MP-RVU with a MP index=0.039. Ultrasound represents 35% of the examinations, accounting for 23% of MP-RVU with a MP index=0.125. CT represents 13% of the examinations, accounting for 28% of MP-RVU with a MP index=0.434. MR represents 11% of the examinations, accounting for 39% of MP-RVU with a MP index=0.667. CONCLUSIONS: Malpractice relative value units (MP-RVU) are indicative of the risk considered globally and when subgrouped. MP index correlates this risk with number of exams carried out divided by methodology. This model providing quantitative data for projects concerning risk management and in allowing the correlation between data obtained in different departments.

Comparison between indicators for the measurement of radiology activity volumes (Italy-USA).

PURPOSE: Different evaluation systems and indicators have recently been used to measure the activity volumes of Italian hospital departments, and in particular of Diagnostic Imaging Units. These measurements have mostly been based on more or less complex and repeatable indicators such as total accesses, accesses per imaging modality, type and number of exams. The aim of this study was to compare four models for measuring and evaluating productivity to assess their features and propose a common method for measuring activity volumes in a Diagnostic Imaging Unit. The models considered are: a) the numerical count, b) the model proposed by SNR-SAGO-SIRM, c) the model based on transfer prices in use in the Emilia Romagna Region (RER), d) the model used by the U.S. Health Care Financing Agency (HCFA-USA), based on a complex system of weights named RVUs (Relative Value Units). MATERIALS AND METHODS: The period under review considers two years of activity (2000-2001) at our Diagnostic Imaging Unit. The data were collected by grouping the radiological procedures into homogeneous groups (macroaggregates) which were then assessed with the four models. The reference parameters considered in order to produce homogeneous data were: the number of procedures per physician hour, the score per hour according to the SNR-SAGO-SIRM model, the score per hour according to the RER model, the number of work-RVUs per hour worked. With regard to the HCFA-USA system, the following indicators were used: the work component (work-RVU), the insurance component (malpractice RVU) and the technical component (practice expense-RVU), the equivalent units of physician time (FTE: Full Time Equivalent), such as the number of procedures per FTE, the difficulty index, and the number of RVUs per FTE. RESULTS: a) The total number of procedures was 55,884, the number of procedures per hour ranged from 2.43 (August 2000) to 4.20 (March 2000); based on the numerical count conventional radiology accounted for the most of the Unit's activity (40%). b) The total score according to the SNR-SAGO-SIRM model was 147,358; the weight of each physician hour ranged from 6.37 (August 2000) to 9.80 (October 2001). The SNR-SAGO-SIRM model indicates that the most significant macroaggregate in the Unit's activity was ultrasound (42%). c) The total score according to the RER model was 4,313,047, the weight of each physician hour varied between 159 (August 2000) and 316 (April 2000). Based on the RER model, CT (42%) accounted for most of the Unit's activity. d) According to the RVU model, the total number of work-RVUs was 37,619, and the physician weight per hour ranged from 1.45 (August 2000) to 2.86 (March 2000). The predominant method was ultrasound (35%); the number of total practice expense-RVUs was 192,749; the month with the highest score was March 2000 (9,398), while the one with the lowest score was August 2000 (4,710); the total number of malpractice RVUs was 9,940, and the months with the highest scores were April 2000 (487) and March 2000 (487), while the month with the lowest score was August 2000 (243), and the modality carrying the highest insurance risks was MRI (38%). We also calculated the number of procedures per FTE (6,141), the number of work-RVUs per FTE (4,134); the difficulty index resulting from the ratio between work-RVUs and number of procedures (0.67); the number of work-RVUs per hour worked (3.06). CONCLUSIONS: Based on the numerical count, conventional radiology and ultrasound play a predominant role (40% and 34%, respectively, total 74%). This approach therefore fails to reflect the weight of more technologically advanced procedures. The SNR-SAGO-SIRM model gives adequate importance to the combination ''number- weight of patients'' among the macroaggregates analysed. The RER model rewards the use of more expensive technologies, as it assesses the overall weight of the service and not only the weight of the radiologist's activity. The RVU model, with its distribution of weights, differentiates the different work, cost, and insurance components of the macroaggregates. It also introduces an important aspect that is new to our professional and scientific culture: evaluation of the ''insurance component'', whose role will become increasingly important in Italy. The difficulty index (work-RVUs/no. of procedures), which expresses the ratio between the number of modalities and their complexity, is particularly interesting. This index, adjusted to reflect the Italian situation, might help to assess the true technological and scientific content of the department's activity.