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.

A top-down/bottom-up integrated proteomic approach based on LC-MS and 2-DE analysis was applied for comparative characterization of medulloblastoma and pilocytic astrocytoma posterior cranial fossa pediatric brain tumor tissues. Although rare, primary brain tumors are the most frequent solid tumors in the pediatric age. Among them the medulloblastoma is the prevalent malignant tumor in childhood while pilocytic astrocytoma is the most common, rarely showing a malignant progression. Due to the limited availability of this kind of sample, the study was applied to pooled tumor tissues for a preliminary investigation. The results showed different proteomic profiles of the two tumors and evidenced interesting differential expression of several proteins and peptides. Top-down proteomics of acid-soluble fractions of brain tumor homogenates ascribed a potential biomarker role of malignancy to β- and α-thymosins and their truncated proteoforms and to C-terminal truncated (des-GG) ubiquitin, resulting exclusively detected or over-expressed in the highly malignant medulloblastoma. The bottom-up proteomics of the acid-soluble fraction identified several proteins, some of them in common with 2-DE analysis of acid-insoluble pellets. Peroxiredoxin-1, peptidyl-prolyl cis-trans isomerase A, triosephosphate isomerase, pyruvate kinase PKM, tubulin beta and alpha chains, heat shock protein HSP-90-beta and different histones characterized the medulloblastoma while the Ig kappa chain C region, serotransferrin, tubulin beta 2A chain and vimentin the pilocytic astrocytoma. The two proteomic strategies, with their pros and cons, well complemented each other in characterizing the proteome of brain tumor tissues and in disclosing potential disease biomarkers to be validated in a future study on individual samples of both tumor histotypes.

Introduction. The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. Materials and Methods. In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients ( F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. Results. At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) ( χ 2 10.4; p < 0.001 ), neutrophil-to-lymphocyte (NL) ratio ( χ 2 7.6; p = 0.006 ), and platelet count ( χ 2 5.39; p = 0.02 ), along with age ( χ 2 87.6; p < 0.001 ) and gender ( χ 2 17.3; p < 0.001 ), accurately predicted in-hospital mortality. Hemoglobin levels were not associated with mortality. We also identified the best cut-off for mortality prediction: a NL   ratio > 4.68 was characterized by an odds ratio for in-hospital mortality   OR = 3.40 (2.40-4.82), while the OR for a RDW > 13.7 % was 4.09 (2.87-5.83); a platelet   count > 166,000 /μL was, conversely, protective (OR: 0.45 (0.32-0.63)). Conclusion. Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment.

The lipoaspirate fluid (LAF) is emerging as a potentially valuable source in regenerative medicine. In particular, our group recently demonstrated that it is able to exert osteoinductive properties in vitro. This original observation stimulated the investigation of the proteomic component of LAF, by means of LC-ESI-LTQ-Orbitrap-MS top-down/bottom-up integrated approach, which represents the object of the present study. Top-down analyses required the optimization of sample pretreatment procedures to enable the correct investigation of the intact proteome. Bottom-up analyses have been directly applied to untreated samples after monodimensional SDS-PAGE separation. The analysis of the acid-soluble fraction of LAF by top-down approach allowed demonstrating the presence of albumin and hemoglobin fragments (i.e. VV- and LVV-hemorphin-7), thymosins β4 and β10 peptides, ubiquitin and acyl-CoA binding protein; adipogenesis regulatory factor, perilipin-1 fragments, and S100A6, along with their PTMs. Part of the bottom-up proteomic profile was reproducibly found in both tested samples. The bottom-up approach allowed demonstrating the presence of proteins, listed among the components of adipose tissue and/or comprised within the ASCs intracellular content and secreted proteome. Our data provide a first glance on the LAF molecular profile, which is consistent with its tissue environment. LAF appeared to contain bioactive proteins, peptides and paracrine factors, suggesting its potential translational exploitation.

Although histologically benign, adamantinomatous craniopharyngioma (AC) pediatric brain tumor is a locally aggressive disease that frequently determines symptoms and hormonal dysfunctions related to the mass effect on the surrounding structures. Another typical feature of this benign neoplasm is the presence of voluminous liquid cysts frequently associated with the solid component. Even if studies have been devoted to the proteomic characterization of the tumor intracystic fluid, poor explorations have been performed on its solid part, principally investigated by transcriptomics technologies. In the present study, seven specimens of AC whole tumor tissue have been analyzed by LC-MS for a preliminary assessment of the proteomic profile by a top-down/bottom-up integrated approach. Thymosin beta 4, ubiquitin, calmodulin, S100 proteins, prothymosin α isoform 2, alpha-defensins 1-4, and fragments largely belonging to vimentin, hemoglobin, and glial fibrillary acidic protein characterized the intact proteome. The identification of alpha-defensins, formerly characterized in AC intracystic fluid, reinforces the hypothesis of a role for inflammation in tumor pathogenesis. A total number of 1798 unique elements were identified by a bottom-up approach with a special focus on the 433 proteins commonly characterized in the 85.7% of the samples analyzed. Their gene ontology classification evidenced the involvement of the adherence system, intermediate filaments, and actin cytoskeleton in tumor pathogenesis and of elements part of the Wnt, FGF, and EGFR signaling pathways. In addition, proteins involved in calcium modulation, innate immunity, inflammation, CCKR and integrin signaling, and gonadotropin-releasing hormone receptor pathways were also outlined. Further than confirming proteomic data previously obtained on AC intracystic fluid, these results offer a preliminary overview of the AC whole tissue protein phenotype, adding new hints towards the comprehension of this still obscure pediatric brain tumor.

Liquid chromatography in coupling with high-resolution ESI-LTQ-Orbitrap mass spectrometry was applied for a proteomic study of pediatric pilocytic astrocytoma brain tumor intracystic fluid by an integrated top-down/bottom-up platform. Both of the proteomic strategies resulted complementary and support each other in contributing to a wide characterization of the protein and peptide content of the tumor fluid. Top-down approach allowed to identify several proteins and peptides involved in different biological activities together with the characterization of interesting proteoforms such as fibrinopeptide A and its truncated form, fibrinopeptide B, complement C3f fragments, β-thymosin peptides, ubiquitin, several apolipoproteins belonging to A and C families, apolipoprotein J and D, and cystatin C. Of particular interest resulted the identification of a N-terminal truncated cystatin C proteoform, likely involved in immune response mechanism modulations and the identification of oxidized and glycosylated apolipoproteins including disulfide bridge dimeric forms. The bottom-up approach confirmed some of the experimental data findings together with adding the characterization of high-molecular-mass proteins in the samples. These data could contribute to elucidate the molecular mechanisms involved in onset and progression of the disease and cyst development.

The clinical and prognostic significance and the origin of these proteoforms have to be deeply investigated.

Machine learning has been used for distinct purposes in the science field but no applications on illegal drug have been done before. This study proposes a new web-based system for cocaine classification, profiling relations and comparison, that is capable of producing meaningful output based on a large amount of chemical profiling’s data. In particular, the Profiling Relations In Drug trafficking in Europe (PRIDE) system, offers several advantages to intelligence actions across Europe. Thus, it provides a standardized, broad methodology which uses machine learning algorithms to classify and compare drug profiles, highlight how similar drug samples are, and how probable it is that they share a common origin, batch, or preparation process. We evaluated the proposed algorithms using precision and recall metrics and analyzed the quality of predictions performed by the algorithms, with respect to our gold standard. In our experiments, we reached a value of 88% for F0.5-measure, 91% for precision, and 78% for recall, confirming our main hypothesis: machine learning can learn and be applied to have an automatic classification of cocaine profiles.