Review of the indexes to assess the ecological quality of coralligenous reefs: towards a unified approach

There is an urgent need to better understand the stressors, namely heatwaves, changes in thermohaline circulation and mucilage events, that are rapidly reshaping bioconstructions, such as coralligenous assemblages. This calls for increased monitoring efforts in these invaluable habitats that will improve our understanding of the resistance and resilience of bioconstructions. Since 2009, 16 indexes have been designed to assess the ecological quality of Mediterranean coralligenous reefs. The main objective of this work is to propose a framework to support the development of a shared, cost-effective, and practical index to assess the status of the coralligenous biocenosis. To achieve this, studies conceiving these 16 indexes were reviewed: comparing their objectives, metrics, and applied methodologies. A standardized nomenclature of anthropogenic pressures is supplied, using, when possible, definitions from the European Habitat Directive, Marine Strategy Framework Directive and Water Framework Directive. Additionally, given the unprecedented climatic conditions, we highlight that a common index should give particular attention to the response of the coralligenous to thermal stress and mucilage. A list of priority anthropogenic pressures/environmental stressors and relative indicators and metrics are suggested. This review stresses the urgency to align the methodologies at basin scale and highlights the pros and cons of the preexisting indexes that must be considered in the design of a new, shared procedure to evaluate the status of coralligenous assemblages

The role of a data manager at a clinical trial center: the experience of the Alessandria hospital, Italy

Objectives: To define the Data Manager (DM) job description within the Clinical Trial Center (CTC) of the Alessandria Hospital (AO AL). To identify the number of authorized clinical studies after the implementation of three DMs in the CTC of the AO AL. Methods: The activities of the DM within the CTC of the AO AL take place in the activation, management and conclusion of clinical trials. The activities were monitored through specific indicators from June 01st, 2019 to May 31st, 2020. Results: During the reference period, an increased authorized studies were observed. Conclusion: The implementation of DMs in the CTC of AO AL has been demonstrated the importance of the figure itself, which, although it has not professionally recognized yet, is found to be fundamental in clinical research

Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients

Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk

Rapid RT-PCR identification of SARS-CoV-2 in screening donors of fecal microbiota transplantation

Since its first appearance in late 2019 in Wuhan, China, severe acute respiratory syndrome caused by Coronavirus 2 (SARS-CoV-2) has had a major impact on healthcare facilities around the world. Although in the past year, mass vaccination and the development of monoclonal antibody treatments have reduced the number of deaths and severe cases, the circulation of SARS-CoV-2 remains high. Over the past two years, diagnostics have played a crucial role in virus containment both in health care facilities and at the community level. For SARS-CoV-2 detection, the commonly used specimen type is the nasopharyngeal swab, although the virus can be identified in other matrices such as feces. Since fecal microbiota transplantation (FMT) assumes significant importance in the treatment of chronic gut infections and that feces may be a potential vehicle for transmission of SARS-CoV-2, in this study we have evaluated the performance of the rapid cartridge-based RT-PCR test STANDARD™ M10 SARS-CoV-2 (SD Biosensor Inc., Suwon, South Korea) using fecal samples. The results obtained indicates that STANDARD™ M10 SARS-CoV-2 can detect SARS-CoV-2 in stool samples even at low concentration. For this reason, STANDARD™ M10 SARS-CoV-2 could be used as reliable methods for the detection of SARS-CoV-2 in fecal samples and for the screening of FMT donors

A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2

Abstract Background This study aimed to explore whether explainable Artificial Intelligence methods can be fruitfully used to improve the medical management of patients suffering from complex diseases, and in particular to predict the death risk in hospitalized patients with SARS-Cov-2 based on admission data. Methods This work is based on an observational ambispective study that comprised patients older than 18 years with a positive SARS-Cov-2 diagnosis that were admitted to the hospital Azienda Ospedaliera “SS Antonio e Biagio e Cesare Arrigo”, Alessandria, Italy from February, 24 2020 to May, 31 2021, and that completed the disease treatment inside this structure. The patients’medical history, demographic, epidemiologic and clinical data were collected from the electronic medical records system and paper based medical records, entered and managed by the Clinical Study Coordinators using the REDCap electronic data capture tool patient chart. The dataset was used to train and to evaluate predictive ML models. Results We overall trained, analysed and evaluated 19 predictive models (both supervised and unsupervised) on data from 824 patients described by 43 features. We focused our attention on models that provide an explanation that is understandable and directly usable by domain experts, and compared the results against other classical machine learning approaches. Among the former, JRIP showed the best performance in 10-fold cross validation, and the best average performance in a further validation test using a different patient dataset from the beginning of the third COVID-19 wave. Moreover, JRIP showed comparable performances with other approaches that do not provide a clear and/or understandable explanation. Conclusions The ML supervised models showed to correctly discern between low-risk and high-risk patients, even when the medical disease context is complex and the list of features is limited to information available at admission time. Furthermore, the models demonstrated to reasonably perform on a dataset from the third COVID-19 wave that was not used in the training phase. Overall, these results are remarkable: (i) from a medical point of view, these models evaluate good predictions despite the possible differences entitled with different care protocols and the possible influence of other viral variants (i.e. delta variant); (ii) from the organizational point of view, they could be used to optimize the management of health-care path at the admission time

Clinical Evaluation and Comparison of Two Microfluidic Antigenic Assays for Detection of SARS-CoV-2 Virus

Given the ongoing pandemic, there is a need to identify SARS-CoV-2 and differentiate it from other respiratory viral infections in various critical settings. Since its introduction, rapid antigen testing is spreading worldwide, but diagnostic accuracy is extremely variable and often in disagreement with the manufacturer’s specifications. Our study compared the clinical performances of two microfluidic rapid antigen tests towards a molecular assay, starting from positive samples. A total of 151 swabs collected at the Microbiology and Virology Laboratory of A.O. “SS Antonio e Biagio e C. Arrigo” (Alessandria, Italy) for the diagnosis of SARS-CoV-2 were simultaneously tested to evaluate accuracy, specificity, and agreement with the RT-qPCR results. Both assays showed an overall agreement of 100% for negative specimens, while positive accuracy comprised between 45.10% and 54.90%. According to the manufacturer’s instructions, the greatest correlation between the antigenic and molecular assays was observed for the subset with high viral load (18/19, 94.74%), while it dramatically decreased for other subsets. Moreover, the ability to differentiate between SARS-CoV-2 and Flu provides an added value and could be addressed in an epidemic context. However, an in-house validation should be performed due to differences observed in performance declared by manufacturers and those actually obtained

SARS‐CoV‐2 infection as a trigger of autoimmune response

Abstract Currently, few evidences have shown the possible involvement of autoimmunity in patients affected by coronavirus disease 2019 (COVID‐19). In this study, we elucidate whether severe acute respiratory syndrome coronavirus disease 2 (SARS‐CoV‐2) stimulates autoantibody production and contributes to autoimmunity activation. We enrolled 40 adult patients (66.8 years mean age) admitted to Alessandria Hospital between March and April 2020. All the patients had a confirmed COVID‐19 diagnosis and no previously clinical record of autoimmune disease. Forty blood donors were analyzed for the same markers and considered as healthy controls. Our patients had high levels of common inflammatory markers, such as C reactive protein, lactate dehydrogenase, ferritin, and creatinine. Interleukin‐6 concentrations were also increased, supporting the major role of this interleukin during COVID‐19 infection. Lymphocyte numbers were generally lower compared with healthy individuals. All the patients were also screened for the most common autoantibodies. We found a significant prevalence of antinuclear antibodies, antineutrophil cytoplasmic antibodies, and ASCA immunoglobulin A antibodies. We observed that patients having a de novo autoimmune response had the worst acute viral disease prognosis and outcome. Our results sustain the hypothesis that COVID‐19 infection correlates with the autoimmunity markers. Our study might help clinicians to: (a) better understand the heterogeneity of this pathology and (b) correctly evaluate COVID‐19 clinical manifestations. Our data explained why drugs used to treat autoimmune diseases may also be useful for SARS‐CoV‐2 infection. In addition, we highly recommend checking patients with COVID‐19 for autoimmunity markers, mainly when deciding on whether to treat them with plasma transfer therapy. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? ☑ Recent data sustain the idea that autoimmune phenomena exist in patients with coronavirus disease 2019 (COVID‐19), but other investigations are necessary to define the possible link between severe acute respiratory syndrome coronavirus disease 2 (SARS‐CoV‐2) infection and autoimmune disease onset. WHAT QUESTION DID THIS STUDY ADDRESS? ☑ In this monocentric study, we demonstrated how SARS‐CoV‐2 infection could be associated with an autoimmune response and development of autoantibodies. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? ☑ Patients with COVID‐19 having an increased level of inflammatory markers and strong autoantibodies positivity (i.e., antinuclear antibodies and antineutrophil cytoplasmic antibodies) presented the worst clinical outcome. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? ☑ These results suggest that the drugs normally used to treat autoimmune diseases should also be considered during SARS‐CoV‐2, improving public health. In addition, before starting a transfer plasma therapy, it is important to also evaluate the autoimmunity conditions of the patients with COVID‐19. Transferring antibodies or trying to neutralize them should be done with precaution. It is possible that the risk of developing or increasing the autoimmune response may enhance

Baseline clinical characteristics and prognostic factors in hospitalized COVID-19 patients aged ≤ 65 years: A retrospective observational study.

BackgroundIndividual differences in susceptibility to SARS-CoV-2 infection, symptomatology and clinical manifestation of COVID-19 have thus far been observed but little is known about the prognostic factors of young patients.MethodsA retrospective observational study was conducted on 171 patients aged ≤ 65 years hospitalized in Alessandria's Hospital from 1st March to 30th April 2020 with laboratory confirmed COVID-19. Epidemiological data, symptoms at onset, clinical manifestations, Charlson Comorbidity Index, laboratory parameters, radiological findings and complications were considered. Patients were divided into two groups on the basis of COVID-19 severity. Multivariable logistic regression analysis was used to establish factors associated with the development of a moderate or severe disease.FindingsA total of 171 patients (89 with mild/moderate disease, 82 with severe/critical disease), of which 61% males and a mean age (± SD) of 53.6 (± 9.7) were included. The multivariable logistic model identified age (50-65 vs 18-49; OR = 3.23 CI95% 1.42-7.37), platelet count (per 100 units of increase OR = 0.61 CI95% 0.42-0.89), c-reactive protein (CPR) (per unit of increase OR = 1.12 CI95% 1.06-1.20) as risk factors for severe or critical disease. The multivariable logistic model showed a good discriminating capacity with a C-index value of 0.76.InterpretationPatients aged ≥ 50 years with low platelet count and high CRP are more likely to develop severe or critical illness. These findings might contribute to improved clinical management