Salivary microbial signature highlighting actinomyces as a predictor of immune-checkpoint inhibitor monotherapy response in advanced non–small cell lung cancer Silvia Cavaliere, Marta Fogolari, Michele Iuliani, Simone Foderaro, Alessio Cortellini, Sonia Simonetti, Emanuele Claudio Mingo, Silvia Calagna, Marco Russano, Bruno Vincenzi, Giuseppe Tonini, Silvia Angeletti, Francesco Pantano Journal of Translational Medicine, 2026 Background Immune checkpoint inhibitors (ICIs) have improved survival in advanced non-small cell lung cancer (NSCLC), yet reliable biomarkers beyond programmed death-ligand 1 (PD-L1) expression remain limited. Increasing evidence links the gut microbiome to ICI activity, but the predictive value of the salivary microbiome is poorly defined. Methods We prospectively analyzed baseline saliva from 71 stage IV NSCLC patients treated with anti–PD-1/PD-L1 (ICI) monotherapy. After quality control, 70 samples underwent 16 S rRNA gene sequencing of the V1–V3 region. Microbial diversity, differential abundance (LEfSe, Mann-Whitney/Kruskal-Wallis with false discovery rate correction) and survival associations (Kaplan-Meier; Cox proportional-hazards with LASSO-based variable selection and 1000-fold bootstrap validation) were examined. In this cohort, an exploratory genus-level cut-off was derived by receiver operating characteristic (ROC) analysis. Results α-diversity and β-diversity did not differ between responders (progression-free survival (PFS) ≥ 12 months; n = 18) and non-responders ( n = 52). Differential‑abundance profiling revealed a graded enrichment of the phylum Actinobacteria across all lower ranks, class Actinobacteria, order Actinomycetales, family Actinomycetaceae and genus Actinomyces ,in non‑responders (LEfSe LDA > 3.5; p = 0.001 for each level; FDR ≤ 0.049). ROC analysis suggested an Actinomyces relative abundance of 11% (AUC = 0.768; sensitivity 0.94; specificity 0.44) as a data-driven threshold, classifying patients into low (≤ 11%, n = 46) and high (> 11%, n = 24) groups. High abundance was associated with shorter PFS (median 3 vs. 4 months; HR = 2.16, 95% CI 1.21–3.88, p = 0.009) and overall survival (OS) (median 5 vs. 9 months; HR = 2.61, 95% CI 1.48–4.61, p < 0.001) after multivariable adjustment for ECOG status, treatment line, corticosteroid and opioid use, smoking, histology and metastatic sites. Bootstrap validation supported model stability, with median bootstrap HRs of 2.56 (PFS) and 2.63 (OS), with narrow percentile CIs (PFS 1.57–4.49; OS 1.40–6.34) overlapping the original estimates. Conclusions In this exploratory cohort, salivary microbiome signature characterized by high Actinomyces abundance was independently associated with poorer ICI outcomes in NSCLC. Saliva profiling is non-invasive and, if validated in larger and independent cohorts, may complement tumour PD-L1 and clinical factors to refine patient stratification.
Pancreatic Steatosis as a Risk Phenotype for Pancreatic Ductal Adenocarcinoma: A Narrative Review Roberto Cammarata, Vincenzo La Vaccara, Lucrezia Bani, Federica Giordano, Pierpaolo Castagliuolo, Maria Vittoria Ristori, Sara Elsa Aita, Silvia Angeletti, Roberto Coppola, Damiano Caputo Medicina Lithuania, 2026 Background and Objectives: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related mortality, largely due to late-stage diagnosis and the absence of effective population-based screening. Intrapancreatic fat deposition (IPFD) has emerged as a potential risk phenotype. This narrative review critically appraises the clinical, metabolic, epidemiologic, and mechanistic evidence linking IPFD to PDAC and discusses its implications for risk stratification and prevention. Materials and Methods: A structured literature search was conducted in PubMed/MEDLINE and Scopus for studies published between 2007 and 2025 using predefined terms related to pancreatic steatosis and pancreatic cancer. After duplicate removal and screening according to predefined inclusion and exclusion criteria, 42 articles were included. Evidence was synthesized focusing on epidemiologic associations, mechanistic pathways, and imaging-based quantification methods. Results: A strong association between IPFD and PDAC was found. Although definitive causality remains unproven, some studies support temporal correlation between IPFD and PDAC, suggesting that IPFD precedes PDAC. A possible pathophysiological explanation to this correlation has been advanced in experimental models indicating IPFD as a pro-inflammatory factor cooperating with oncogenic KRAS to facilitate neoplastic progression. Finally, variability in IPFD definitions and heterogeneity in imaging assessment limit interpretability. Conclusions: Current evidence links IPFD to PDAC risk, suggesting a strong suspicion that pancreatic steatosis may represent an independent risk factor for PDAC. Still robust causal inference remains unproven. Well-designed prospective studies, standardized imaging protocols, and mechanistic investigations are required to clarify causality and determine whether pancreatic steatosis can be incorporated into risk-based screening and preventive strategies.
Machine Learning Models for Sepsis: From Early Detection to Short- and Long-Term Prognosis Maria Vittoria Ristori, Filippo Ruffini, Silvia Spoto, Roberto Cammarata, Vincenzo La Vaccara, Lucrezia Bani, Damiano Caputo, Paolo Soda, Valerio Guarrasi, Silvia Angeletti International Journal of Molecular Sciences, 2026 Sepsis is a leading cause of morbidity and mortality worldwide, and its outcomes depend on early recognition and timely intervention. Conventional clinical scores and biomarkers provide prognostic value but often lack accuracy for individualized prediction. Machine learning (ML) offers the ability to integrate multidimensional data to improve risk stratification. We analyzed 477 patients admitted to our hospital, including 251 with sepsis, 100 with septic shock, and 126 controls. Demographic, clinical, and laboratory data were collected. Univariate correlation analyses explored associations with sepsis severity and mortality (in-hospital, 30-day, and 90-day). Several ML models were tested, with performance assessed by area under the receiver operating characteristic curve (AUC-ROC) and Matthews’s correlation coefficient (MCC). Model interpretability was evaluated using SHAP (SHapley Additive exPlanations). Sepsis severity and mortality correlated with biomarkers (procalcitonin, mid-regional pro-adrenomedullin, lactate) and clinical scores (SOFA, qSOFA). In-hospital mortality was associated with ADM, catecholamine use, and SOFA, while 90-day mortality involved smoking and Gram-negative or polymicrobial infections. Different machine learning models were evaluated, and the model achieving the highest performance on the validation set was selected. The selected model either outperformed or demonstrated comparable performance to logistic regression, depending on the specific prediction task (AUC 0.99 for sepsis, 0.96 for septic shock, 0.70 for ICU admission; 0.90, 0.72, and 0.87 for in-hospital, 30-day, and 90-day mortality). SHAP confirmed the clinical relevance of these predictors. ML models integrating clinical and biochemical data outperform conventional methods in predicting sepsis progression and mortality, while maintaining interpretability. These findings support the use of ML-based tools for early diagnosis and personalized risk stratification in sepsis, though external validation is required before clinical application.
JN.1 variants circulating in Italy from October 2023 to April 2024: genetic diversity and immune recognition Emanuela Giombini, Ilaria Schiavoni, Luigina Ambrosio, Alessandra Lo Presti, Angela Di Martino, Stefano Fiore, Pasqualina Leone, Francesca Fortunato, Rosa Prato, Giorgio Fedele, Anna Teresa Palamara, Paola Stefanelli, , Liborio Stuppia, Federico Anaclerio, Giovanni Savini, Cesare Cammà, Luigi Possenti, Domenico Dell’Edera, Antonio Picerno, Teresa Lopizzo, Maria Teresa Fiorillo, Rosaria Oteri, Giuseppe Viglietto, Pasquale Minchella, Francesca Greco, Antonio Limone, Giovanna Fusco, Claudia Tiberio, Luigi Atripaldi, Mariagrazia Coppola, Davide Cacchiarelli, Antonio Grimaldi, Stefano Pongolini, Erika Scaltriti, Vittorio Sambri, Giorgio Dirani, Silvia Zannoli, Tiziana Lazzarotto, Giada Rossini, Federica Baldan, Sabrina Lombino, Pierlanfranco D’Agaro, Ludovica Segat, Fabio Barbone, Raffaella Koncan, Antonio Battisti, Patricia Alba, Maria Teresa Scicluna, Silvia Angeletti, Elisabetta Riva, Fulvia Pimpinelli, Maurizio Fanciulli, Alice Massacci, Maurizio Sanguinetti, Fabrizio Maggi, Martina Rueca, Cesare Ernesto Maria Gruber, Ombretta Turriziani, Carlo Federico Perno, Francesca Ceccherini-Silberstein, Maria Concetta Bellocchi, Bianca Bruzzone, Giancarlo Icardi, Andrea Orsi, Rea Valaperta, Maria Oggionni, Sophie Testa, Fabio Sagradi, Arnaldo Caruso, Serena Messali, Diana Fanti, Alice Nava, Sergio Malandrin, Annalisa Cavallero, Claudio Francesco Farina, Marco Arosio, Ferruccio Ceriotti, Sara Colonia Uceda Renteria, Stefania Paganini, Anna Maria Di Blasio, Erminio Torresani, Maria Beatrice Boniotti, Cristina Bertasio, Nicola Clementi, Michela Sampaolo, Federica Novazzi, Nicasio Mancini, Maria Rita Gismondo, Valeria Micheli, Fausto Baldanti, Federica AM Giardina, Antonio Piralla, Federica Zavaglio, Francesca Rovida, Elena Pariani, Cristina Galli, Laura Pellegrinelli, Stefano Menzo, Massimiliano Scutellà, Valentina Felice, Elisabetta Pagani, Irene Bianconi, Angela Maria Di Pierro, Lucia Collini, Giovanni Lorenzin, Valeria Ghisetti, Sara Gilardi, Alice Bartolini, Daniela Cantarella, Simone Peletto, Giuseppe Ru, Pier Luigi Acutis, Elena Bozzetta, Maria Chironna, Daniela Loconsole, Antonio Parisi, Fabio Arena, Rossella De Nittis, Giuseppina Iannelli, Florigio Romano Lista, Ferdinando Coghe, Sergio Uzzau, Salvatore Rubino, Flavia Angioj, Gabriele Ibba, Caterina Serra, Giovanna Piras, Giuseppe Mameli, Rosanna Asproni, Francesca Di Gaudio, Stefano Vullo, Stefano Reale, Teresa Pollicino, Francesco Vitale, Fabio Tramuto, Stefania Stefani, Guido Scalia, Concetta Ilenia Palermo, Giuseppe Mancuso, Vincenzo Bramanti, Carmelo Fidone, Giuseppe Barrano, Mauro Pistello, Gian Maria Rossolini, Francesca Malentacchi, Maria Grazia Cusi, Antonella Mencacci, Barbara Camilloni, Calogero Terregino, Alice Fusaro, Isabella Monne, Edoardo Giussani, Davide Gibellini, Emil Tonon, Riccardo Cecchetto, Laura Squarzon, Mosè Favarato, Valeria Biscaro, Elisa Vian, Silvia Ragolia, Michela Pascarella, Fabio Buffoli, Isabella Cerbaro BMC Infectious Diseases, 2025 BACKGROUND: The continuous emergence of SARS-CoV-2 variants and subvariants poses significant public health challenges. The latest designated subvariant JN.1, with all its descendants, shows more than 30 mutations in the spike gene. JN.1 has raised concerns due to its genomic diversity and its potential to enhance transmissibility and immune evasion. This study aims to analyse the molecular characteristics of JN.1-related lineages (JN.1*) identified in Italy from October 2023 to April 2024 and to evaluate the neutralization activity against JN.1 of a subsample of sera from individuals vaccinated with XBB.1.5 mRNA. METHODS: The genomic diversity of the spike gene of 794 JN.1* strain was evaluated and phylogenetic analysis was conducted to compare the distance to XBB.1.5. Moreover, serum neutralization assays were performed on a subsample of 19 healthcare workers (HCWs) vaccinated with the monovalent XBB.1.5 mRNA booster to assess neutralizing capacity against JN.1. RESULTS: Sequence analysis displayed high spike variability between JN.1* and phylogenetic investigation confirmed a substantial differentiation between JN.1* and XBB.1.5 spike regions with 29 shared mutations, of which 17 were located within the RBD region. Pre-booster neutralization activity against JN.1 was observed in 42% of HCWs sera, increasing significantly post-booster, with all HCWs showing neutralization capacity three months after vaccination. A significant correlation was found between anti-trimeric Spike IgG levels and neutralizing titers against JN.1. CONCLUSIONS: The study highlights the variability of JN.1* in Italy. Results on a subsample of sera from HCWs vaccinated with XBB.1.5 mRNA booster vaccine suggested enhanced neutralization activity against JN.1.
Genomic Surveillance and Resistance Profiling of Multidrug-Resistant Acinetobacter baumannii Clinical Isolates: Clonal Diversity and Virulence Insights Maria Vittoria Ristori, Ilaria Pirona, Lucia De Florio, Sara Elsa Aita, Gabriele Macari, Silvia Spoto, Raffaele Antonelli Incalzi, Silvia Angeletti Microorganisms, 2025 Acinetobacter baumannii is a multidrug-resistant opportunistic pathogen that poses critical challenges in hospital settings due to its environmental resilience and high resistance to antibiotics. Genomic surveillance has become essential for identifying transmission patterns, guiding antimicrobial stewardship, and informing infection control policies. We conducted whole-genome sequencing on 44 A. baumannii isolates collected between 2022 and 2023 from diverse wards in an Italian hospital. Illumina-based sequencing was followed by a comprehensive bioinformatics pipeline, including genome assembly, taxonomic validation, MLST, SNP-based phylogeny, pan-genome analysis, antimicrobial resistance (AMR) gene profiling, and virulence factor prediction. Most isolates were classified as ST2; SAMPLE-34 was ST1 and genetically distinct. Phylogenetic analysis revealed four clonal clusters with cluster-specific AMR and accessory gene content. The pan-genome included 5050 genes, with notable variation linked to hospital ward origin. ICU and internal medicine strains carried higher loads of AMR genes, especially against aminoglycosides, β-lactams, and quinolones. Virulence profiling highlighted widespread immune evasion mechanisms; “Acenovactin” was predominant, while some isolates lacked key adhesion or toxin factors. Our findings underscore the clinical relevance of integrating genomic epidemiology into routine hospital surveillance. Identifying clonal clusters and resistance signatures supports real-time outbreak detection, risk stratification, and targeted infection prevention strategies.
Investigating the evolutionary dynamics and mutational pattern of SARS-CoV-2 spike gene on selected SARS-CoV-2 variants Bachir Balech, Alessandra Lo Presti, Claudia Telegrafo, Lucia Maisto, Emanuela Giombini, Angela Di Martino, Luigina Ambrosio, Apollonia Tullo, Paola Stefanelli, and Plos One, 2025 The continuous evolution of SARS-CoV-2 has led to the emergence of several variants representing significant challenges for public health. Many studies highlight the relevance of phylogenetic inference or mutational pattern analysis to understand the evolutionary relatedness of viral variants and to estimate the potential effect of new mutations on viral transmission, virulence and antigenicity. Here we describe an evolutionary investigation approach combined with mutational analyses of SARS-CoV-2 Spike gene to annotate and potentially track important amino acid site variation of specific functional domain relevant for viral survival. This approach was applied on XBB*, EG* and BA* and their sub-lineages (see materials and methods) available from GISAID. In addition, we considered the major variants of concern (Alpha, Delta, Omicron) and Wuhan-Hu-1 strain as references. Maximum likelihood phylogenetic tree was constructed from the complete dataset while selection pressure and mutational analyses were conducted on single variants separately. The obtained phylogenetic tree of Spike amino acid gene sequence showed a clear separation of viral variants as well as their expected appearance order. This result supported the significance of selection pressure analyses outcomes combined with amino acid mutational frequencies where in many cases they showed a linear and parallel trend. This allowed also to hypothesize the potential importance of low-frequency mutations in new potential virus variants. This study constitutes an asset of important insights to be considered in regular monitoring programs. In addition, the analysis framework described here introduces a starting point for further standardization, optimization and application on different data types and in large-scale studies.
Proteomic Insights into Bacterial Responses to Antibiotics: A Narrative Review Sara Elsa Aita, Maria Vittoria Ristori, Antonio Cristiano, Tiziana Marfoli, Marina De Cesaris, Vincenzo La Vaccara, Roberto Cammarata, Damiano Caputo, Silvia Spoto, Silvia Angeletti International Journal of Molecular Sciences, 2025 Antimicrobial resistance is an escalating global threat that undermines the efficacy of modern antibiotics and places a substantial economic burden on healthcare systems—costing Europe alone over EUR 11.7 billion each year due to rising medical expenses and productivity losses. While genomics and transcriptomics have significantly advanced our understanding of the genetic foundations of resistance, they often fail to capture the dynamic, real-time adaptations that enable bacterial survival. Proteomics, particularly mass spectrometry-based strategies, bridges this gap by uncovering the functional protein-level changes that drive resistance, persistence, and tolerance under antibiotic pressure. In this review, we examine how proteomic approaches provide new insights into resistance mechanisms across various antibiotic classes, with a particular focus on β-lactams, aminoglycosides, and fluoroquinolones, highlighting clinically relevant pathogens, especially members of the ESKAPE group. Finally, we examine future directions, including the integration of proteomics with other omic technologies and the growing role of artificial intelligence in resistance prediction, paving the way for more predictive, personalized, and effective solutions to combat antimicrobial resistance.
The Use of Self-Sampling Devices via a Smartphone Application to Encourage Participation in Cervical Cancer Screening: A Pilot Study Francesco Plotti, Fernando Ficarola, Giuseppina Fais, Carlo De Cicco Nardone, Roberto Montera, Daniela Luvero, Gianna Barbara Cundari, Alice Avian, Elisabetta Riva, Santina Castriciano, Silvia Angeletti, Massimo Ciccozzi, Roberto Angioli, Corrado Terranova Journal of Clinical Medicine, 2025 Background: Cervical cancer ranks among the most prevalent tumors in low-income countries, with the Pap test as one of the primary screening tools. The Pap smear detects abnormal cells, the CLART test identifies specific HPV genotypes, and HPV self-sampling allows for self-collected HPV testing. This study aimed to evaluate the feasibility of the first smartphone-based health device for home-collection HPV testing. Methods: Enrolled patients during the gynecological examination underwent three different samplings: Pap smear, HPV DNA genotyping test CLART, and vaginal HPV-Selfy swab. Each patient received a kit including an activation code, vaginal swab, and instructions. After performing the self-sample, patients returned the kit to our laboratory. Both the samples collected by the gynecologist and those collected by the patients themselves were analyzed. Results: A total of 277 patients were enrolled, with 226 self-collected swabs received for analysis. The assay yielded valid results for both self-collected and clinician-collected swabs in 190 patients. When comparing these results with paired clinician-taken vaginal swabs, we observed an agreement of 95.2% (Cohen’s Kappa: 0.845). We report an agreement of 93.7% (Cohen’s Kappa: 0.798). Conclusions: The study demonstrated the feasibility of HPV-Selfy as a complementary tool in cervical cancer screening, especially where adherence to traditional surveillance is low.
An Antibiotic Stewardship Program in Pancreatic Surgery Matteo De Pastena, Salvatore Paiella, Erica Secchettin, Damiano Caputo, Luca Moraldi, Danila Azzolina, Laura Addari, Elena Carrara, Anna Maria Azzini, Luca Tirloni, Roberto Coppola, Ilenia Bartolini, Vincenzo La Vaccara, Matteo Risaliti, Roberto Cammarata, Irene Urciuoli, Tommaso Giani, Alessandro Esposito, Luca Casetti, Luca Landoni, Antonio Pea, Martina Fontana, Giuseppe Malleo, Annarita Mazzariol, Dario Gregori, Silvia Angeletti, Massimo Ciccozzi, Carlotta Fiammenghi, Evelina Tacconelli, Roberto Salvia JAMA Network Open, 2025 ImportanceAntimicrobial stewardship (AMS) programs optimize antibiotic use and mitigate antimicrobial resistance. The literature on the efficacy of AMS programs in pancreatic surgery is limited.ObjectiveTo investigate the association of a multifaceted AMS intervention targeting surgical antibiotic prophylaxis (SAP) with the rate of surgical site infections (SSIs) following pancreatic surgery.Design, Setting, and ParticipantsThis cross-sectional study was a multicenter, before-and-after analysis conducted at 3 Italian centers. The intervention cohort included adult patients aged 18 years or older who underwent pancreatectomy between January 1, 2020, and December 31, 2022, while the historical cohort included patients from January 1, 2015, to December 31, 2019.ExposureA multiprofessional, multidimensional ASM program that included a bundle of interventions and pivoted on preoperative rectal screening for multidrug-resistant bacteria and targeted SAP.Main Outcomes and MeasuresThe primary outcomes were SSI incidence and SAP appropriateness, assessed through the coverage rate of rectal and biliary isolates. Data were analyzed using propensity score weighting. Secondary outcomes evaluated were other postoperative outcomes (eg, pancreatic fistula rate, length of stay), antibiotic use, and costs.ResultsA total of 3387 patients (median [IQR] age, 66 [66-73] years; 1788 male [52.8%]) were included, with 1219 in the intervention cohort and 2168 in the historical cohort. After implementing the AMS program, a statistically significant reduction was found in rates of overall (30.1% vs 20.6%), superficial (5.8% vs 2.5%), deep (0.9% vs 0.3%), and organ-space (26.3% vs 19.3%) SSIs. After propensity score weighting, the odds ratios for the estimated mean treatment effect were 0.92 (95% CI, 0.89-0.96) for overall, 0.85 (95% CI, 0.78-0.93) for superficial, and 0.95 (95% CI, 0.92-0.99) for organ-space SSIs. Surgical antibiotic prophylaxis coverage increased significantly for rectal screening (87.2% vs 100%) and biliary bacterial colonization (59.7% vs 68.7%). Complications, infections, length of stay, and antibiotic consumption also decreased, with an overall cost savings of 247 460 euros.Conclusions and RelevanceThese findings suggest that a multifaceted, pancreatic surgery–specific AMS program is associated with decreased rates of SSIs, increased coverage of isolated bacteria, improved clinical outcomes, more judicious antibiotic use, and lower costs.
Machine Learning for Predicting the Low Risk of Postoperative Pancreatic Fistula After Pancreaticoduodenectomy: Toward a Dynamic and Personalized Postoperative Management Strategy Roberto Cammarata, Filippo Ruffini, Alberto Catamerò, Gennaro Melone, Gianluca Costa, Silvia Angeletti, Federico Seghetti, Vincenzo La Vaccara, Roberto Coppola, Paolo Soda, Valerio Guarrasi, Damiano Caputo Cancers, 2025 Background. Postoperative pancreatic fistula (POPF) remains one of the most relevant complications following pancreaticoduodenectomy (PD), significantly impacting short-term outcomes and delaying adjuvant therapies. Current predictive models offer limited accuracy, often failing to incorporate early postoperative data. This retrospective study aimed to develop and validate machine learning (ML) models to predict the absence and severity of POPF using clinical, surgical, and early postoperative variables. Methods. Data from 216 patients undergoing PD were analyzed. A total of twenty-four machine learning (ML) algorithms were systematically evaluated using the Matthews Correlation Coefficient (MCC) and AUC-ROC metrics. Among these, the GradientBoostingClassifier consistently outperformed all other models, demonstrating the best predictive performance, particularly in identifying patients at low risk of postoperative pancreatic fistula (POPF) during the early postoperative period. To enhance transparency and interpretability, a SHAP (SHapley Additive exPlanations) analysis was applied, highlighting the key role of early postoperative biomarkers in the model predictions. Results. The performance of the GradientBoostingClassifier was also directly compared to that of a traditional logistic regression model, confirming the superior predictive performance over conventional approaches. This study demonstrates that ML can effectively stratify POPF risk, potentially supporting early drain removal and optimizing postoperative management. Conclusions. While the model showed promising performance in a single-center cohort, external validation across different surgical settings will be essential to confirm its generalizability and clinical utility. The integration of ML into clinical workflows may represent a step forward in delivering personalized and dynamic care after pancreatic surgery.
Intestinal Microbiota and Derived Metabolites in Myocardial Fibrosis and Postoperative Atrial Fibrillation Antonio Nenna, Alice Laudisio, Chiara Taffon, Marta Fogolari, Cristiano Spadaccio, Chiara Ferrisi, Francesco Loreni, Omar Giacinto, Ciro Mastroianni, Raffaele Barbato, David Rose, Antonio Salsano, Francesco Santini, Silvia Angeletti, Anna Crescenzi, Raffaele Antonelli Incalzi, Massimo Chello, Mario Lusini International Journal of Molecular Sciences, 2024
Mid-Regional Pro-Adrenomedullin Can Predict Organ Failure and Prognosis in Sepsis? Silvia Spoto, Stefania Basili, Roberto Cangemi, Giorgio D’Avanzo, Domenica Marika Lupoi, Giulio Francesco Romiti, Josepmaria Argemi, José Ramón Yuste, Felipe Lucena, Luciana Locorriere, Francesco Masini, Giulia Testorio, Rodolfo Calarco, Marta Fogolari, Maria Francesconi, Giulia Battifoglia, Sebastiano Costantino, Silvia Angeletti International Journal of Molecular Sciences, 2023
Associated effects of clinical characteristics, risk factors, and comorbidity on disease severity and mortality among patients with COVID-19 in Sulaimani City/ Kurdistan Region of Iraq New Microbiologica, 2023
On the SARS-CoV-2 BA.2.75 variant: A genetic and structural point of view Fabio Scarpa, Daria Sanna, Ilenia Azzena, Marta Giovanetti, Domenico Benvenuto, Silvia Angeletti, Giancarlo Ceccarelli, Stefano Pascarella, Marco Casu, Pier Luigi Fiori, Massimo Ciccozzi Journal of Medical Virology, 2023
Clinical validation of full HR-HPV genotyping HPV Selfy assay according to the international guidelines for HPV test requirements for cervical cancer screening on clinician-collected and self-collected samples Alice Avian, Nicolò Clemente, Elisabetta Mauro, Erica Isidoro, Michela Di Napoli, Sandra Dudine, Anna Del Fabro, Stefano Morini, Tiziana Perin, Fabiola Giudici, Tamara Cammisuli, Nicola Foschi, Marco Mocenigo, Michele Montrone, Chiara Modena, Martina Polenghi, Luca Puzzi, Vjekoslav Tomaic, Giulio Valenti, Riccardo Sola, Shivani Zanolla, Enea Vogrig, Elisabetta Riva, Silvia Angeletti, Massimo Ciccozzi, Santina Castriciano, Maria Pachetti, Matteo Petti, Sandro Centonze, Daniela Gerin, Lawrence Banks, Bruna Marini, Vincenzo Canzonieri, Francesco Sopracordevole, Fabrizio Zanconati, Rudy Ippodrino Journal of Translational Medicine, 2022
Genetic Variability of the Monkeypox Virus Clade IIb B.1 Fabio Scarpa, Daria Sanna, Ilenia Azzena, Piero Cossu, Chiara Locci, Silvia Angeletti, Antonello Maruotti, Giancarlo Ceccarelli, Marco Casu, Pier Luigi Fiori, Nicola Petrosillo, Massimo Ciccozzi Journal of Clinical Medicine, 2022
The variants question: What is the problem? Davide Zella, Marta Giovanetti, Francesca Benedetti, Francesco Unali, Silvia Spoto, Michele Guarino, Silvia Angeletti, Massimo Ciccozzi Journal of Medical Virology, 2021
Human immunodeficiency virus type 2: The neglected threat Giancarlo Ceccarelli, Marta Giovanetti, Caterina Sagnelli, Alessandra Ciccozzi, Gabriella d’Ettorre, Silvia Angeletti, Alessandra Borsetti, Massimo Ciccozzi Pathogens, 2021
Laparoscopic total mesorectal excision (L-TME) for rectal cancer surgery: Does elective diverting ileostomy really protect? An observational retrospective cohort study Annali Italiani Di Chirurgia, 2021
Urologic surgery in a safe hospital during the COVID-19 pandemic scenario Rocco PAPALIA, Rita CATALDO, Rossana ALLONI, Karl H. PANG, Antonio ALCINI, Gerardo FLAMMIA, Annamaria SALERNO, Maria G. NOTARANGELO, Silvia ANGELETTI, Antonella VENDITTI, Lorenzo SOMMELLA, Roberto M. SCARPA, Francesco ESPERTO Minerva Urology and Nephrology, 2021
Targeting Microbiome: An Alternative Strategy for Fighting SARS-CoV-2 Infection Ornella Spagnolello, Claudia Pinacchio, Letizia Santinelli, Paolo Vassalini, G. P. Innocenti, Gabriella De Girolamo, Silvia Fabris, M. Giovanetti, S. Angeletti, A. Russo, C. Mastroianni, M. Ciccozzi, G. Ceccarelli, G. d'Ettorre Chemotherapy, 2021
The Pregnancy Outcomes Among Newly Arrived Asylum-Seekers in Italy: Implications of Public Health Lucia Fontanelli Sulekova, Martina Spaziante, Serena Vita, Paola Zuccalà, Valentina Mazzocato, Ornella Spagnolello, Maurizio Lopalco, Laura Elena Pacifici, Luca Bello, Cristian Borrazzo, Silvia Angeletti, Massimo Ciccozzi, Giancarlo Ceccarelli Journal of Immigrant and Minority Health, 2021
Safety and tolerability of a novel oral nutritional supplement in healthy volunteers Annalisa Bonelli, Pierantonio Menna, Giorgio Minotti, Silvia Angeletti, Alessandro Comandini, Rossella Picollo, Elisa Quarchioni, Vincenzo Russo, Enrica Salvatori, Francesca Ferravante, Sara Emerenziani, Michele Cicala, Maurizio Muscaritoli Clinical Nutrition, 2021
The importance of genomic analysis in cracking the coronavirus pandemic Davide Zella, Marta Giovanetti, Eleonora Cella, Alessandra Borsetti, Marco Ciotti, Giancarlo Ceccarelli, Gabriella D’Ettorre, Aldo Pezzuto, Vittoradolfo Tambone, Laura Campanozzi, Marco Magheri, Francesco Unali, Martina Bianchi, Francesca Benedetti, Stefano Pascarella, Silvia Angeletti, Massimo Ciccozzi Expert Review of Molecular Diagnostics, 2021
Malaria in an asylum seeker paediatric liver transplant recipient: Diagnostic challenges for migrant population Serena Vita, Simona Gabrielli, Lucia Fontanelli Sulekova, Maurizio De Angelis, Francesco Alessandri, Francesco Pugliese, Franco Ruberto, Ornella Spagnolello, Valentina Mazzocato, Luigi Celani, Maurizio Lopalco, Simonetta Mattiucci, Riccardo Bazzardi, Silvia Angeletti, Massimo Ciccozzi, Gabriella D’Ettorre, Giancarlo Ceccarelli Journal of Infection in Developing Countries, 2021
The von Willebrand factor antigen plasma concentration: A monitoring marker in the treatment of aortic and mitral valve diseases Folia Biologica Czech Republic, 2020
Origin and evolution of Nipah virus Alessandra Lo Presti, Eleonora Cella, Marta Giovanetti, Alessia Lai, Silvia Angeletti, et al. Journal of Medical Virology, 2016
MALDI-TOF mass spectrometry and blakpc gene phylogenetic analysis of an outbreak of carbapenem resistant K. pneumoniae strains New Microbiologica, 2016
MALDI-TOF mass spectrometry and blakpc gene phylogenetic analysis of an outbreak of carbapenem-resistant K. pneumoniae strains New Microbiologica, 2015
Turnaround time of positive blood cultures after the introduction of matrix-assisted laser desorption-ionization time-of-flight mass spectrometry New Microbiologica, 2015
Real-time polymerase chain reaction with melting analysis of positive blood culture specimens in bloodstream infections: Diagnostic value and turnaround time New Microbiologica, 2013
