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Background Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments. Methods This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol. Results Among 199 patients subject to study (median [interquartile range] age 65 [46–78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity. Conclusion Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

Background and aims To validate a set of indicators for monitoring the quality of care of patients with diabetes in ‘real-life’ practice through its relationship with measurable clinical outcomes and healthcare costs. Methods and results A population-based cohort study was carried out by including the 20,635 patients, residents in the Lombardy Region (Italy), who in the year 2012 were newly taken-in-care for diabetes. Adherence with clinical recommendations (i.e., controls for glycated haemoglobin, lipid profile, urine albumin excretion and serum creatinine) was recorded during the first year after the patient was taken-in-care, and categorized according whether he/she complied with none or almost none (0 or 1), just some (2) or all or almost all (3 or 4) the recommendations, respectively denoted as poor, intermediate and high adherence. Short- and long-term complications of diabetes, and healthcare cost incurred by the National Health Service, were assessed during follow-up. Compared with patients with poor adherence, those with intermediate and high adherence respectively showed (i) a delay in outcome occurrence of 13 days (95% CI, -2 to 27) and 23 days (9 to 38), and (ii) a lower healthcare cost of 54 € and 77 €. In average, a gain of 18 Euros and 15 Euros for each day free from diabetic complication by increasing adherence respectively from poor to intermediate and from poor to high were observed. Conclusion Close control of patients with diabetes through regular clinical examinations must be considered the cornerstone of national guidance, national audits, and quality improvement incentive schemes.

Background Multimorbidity is a growing concern for healthcare systems, with many countries experiencing demographic transition to older population profiles. A simple multisource comorbidity score (MCS) has been recently developed and validated. A very large real-world investigation was conducted with the aim of measuring inequalities in the MCS distribution across Italy. Methods Beneficiaries of the Italian National Health Service aged 50–85 years who in 2018 were resident in one of the 10 participant regions formed the study population (15.7 million of the 24.9 million overall resident in Italy). MCS was assigned to each beneficiary by categorizing the individual sum of the comorbid values (i.e. the weights corresponding to the comorbid conditions of which the individual suffered) into one of the six categories denoting a progressive worsening comorbidity status. MCS distributions in women and men across geographic partitions were compared. Results Compared with beneficiaries from northern Italy, those from centre and south showed worse comorbidity profile for both women and men. MCS median age (i.e. the age above which half of the beneficiaries suffered at least one comorbidity) ranged from 60 (centre and south) to 68 years (north) in women and from 63 (centre and south) to 68 years (north) in men. The percentage of comorbid population was lower than 50% for northern population, whereas it was around 60% for central and southern ones. Conclusion MCS allowed of capturing geographic variability of multimorbidity prevalence, thus showing up its value for addressing health policy in order to guide national health planning.

The increasing number of Malignant Mesothelioma (MM) cases that arrive for expert examinations to court for compensation reasons in subjects exposed to asbestos, in many instances rely exclusively on cytological smears of pleural effusion. We evaluated the accuracy and reproducibility of cytological pleural effusions, based on morphological criteria alone. Nine pathologists and eight residents from seven institutions in north-east Italy blindly examined 45 smears of MM (17), metastases (14) and benign effusions (14), in two rounds. Diagnoses had been confirmed by immunohistochemical and clinical follow-up, and eventually at autopsy. Diagnostic accuracy, interobserver and intraobserver agreement in the distinction of benign vs malignant cases, and in the differentiation of primary from metastatic malignancies, were evaluated. The distinction of benign from malignant smears resulted rather satisfactory ( k =0.514), but markedly decreased in differentiation of MM from metastases (overall agreement: k =0.343), as well as when readings from residents were analyzed ( k =0.132). Cytology is a useful and reliable tool in the identification of malignancies, but when the distinction of primary from metastatic tumors is addressed morphological criteria alone are not sufficient for a definite diagnosis of MM and the use of cell blocks, immunohistochemistry (IHC) and molecular ancillary techniques are recommended.

Markers of skin wound vitality and the research methodology used for their determination are still matters of debate in forensic pathology. Cathepsin-D, a lysosomal enzyme, is the most expressed cathepsin in human skin, and although it seems to have the necessary requirements to be utilized as a vitality marker, past research has provided no definitive and clear response on its potential usefulness. Immunohistochemistry with monoclonal antibodies and image analysis has been employed to detect and quantify the expression of Cathepsin-D in human skin wounds. We analyzed skin fragments obtained from 20 living individuals (group A) and 20 persons deceased from natural causes (group B). For each case, five skin fragments were withdrawn at 0′, 5′, 10′, 30′, and 90′ after abdominal incision. Once the samples were formalin-fixed and paraffin-embedded, we analyzed the expression of Cathepsin-D through the quantification of the immunohistochemistry signal by image analysis. Immunoreactivity was displayed in Pixels of positive area measured by image analysis and converted in micrometer squares. The average levels of Cathepsin-D were higher in group B than in group A, except in three cases which showed a lower expression, with a statistically significant difference of Cathepsin-D expression between the two groups ( p

For pathologists, pneumologists, and occupational and environmental physicians it is relevant to know silica levels in lung tissue to better define limits of exposure. Environmental Scanning Electron Microscopy (ESEM) has been employed to detect silica particles and to compare silica levels in subjects with and without Lung Cancer (LC). We investigated 25 paraffin-embedded tissue samples of patients with LC adenocarcinoma, and 20 fresh samples of subjects without LC deceased for extra-pulmonary diseases. Silica levels were quantified considering the Number of Spots of silica particles (NS), and the Number of Positive Zones (NPZ) in which there was at least one spot. Levels of NS and NPZ were assessed with Poisson-type regression models, and in two samples of silica-exposed workers with LC the performance of models were evaluated. LC patients displayed higher silica levels, as compared to controls; smoking, age and gender had no significant effects on this relationship. Values of NS and NPZ for the exposed workers were in agreement with model estimates. The fitted model between NS and NPZ might be useful in evaluating new observations and in the development of threshold limit values of silica in biological tissues. ESEM is a rapid, simple and valid tool for the determination of silica levels in lung tissues.