Background: The aim of this study was to investigate how the systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), taken individually and combined, are associated with overall survival (OS) in patients surgically treated for malignant salivary gland tumors (MSGTs). Methods: A retrospective analysis of 74 cases following surgery at our department between January 2011 and June 2018 was performed. The Receiver Operating Characteristic (ROC) curve was used to calculate the optimal cutoff values for SII, SIRI, PLR, and NLR. Survival curves of different groups at 1–3–5 years were estimated using the Kaplan–Meier method. Results: The optimal thresholds with the highest sensitivity and specificity were 3.95 for NLR, 187.6 for PLR, 917.585 for SII, and 2.045 for SIRI. The ROC curves revealed that the best combination with AUC = 0.884 was SII + SIRI. The estimated 5-year OS probability in patients with SII+ SIRI scores of 0, 1, and 2 was 96%, 87.5% and 12.5%, respectively (p < 0.001). Conclusion: SII+ SIRI can independently predict the OS of patients after MSGT surgery. The prognostic score system based on SII+ SIRI may be good clinical practice as a reference for clinical decision-making.

Compelling new support has been provided for histone deacetylase isoform 6 (HDAC6) as a common thread in the generation of the dysregulated proinflammatory and fibrotic phenotype in cystic fibrosis (CF). HDAC6 also plays a crucial role in bacterial clearance or killing as a direct consequence of its effects on CF immune responses. Inhibiting HDAC6 functions thus eventually represents an innovative and effective strategy to tackle multiple aspects of CF-associated lung disease. In this Perspective, we not only showcase the latest evidence linking HDAC(6) activity and expression with CF phenotype but also track the new dawn of HDAC(6) modulators in CF and explore potentialities and future perspectives in the field.

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Background: The purpose of this study was to investigate how the systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR), and radiomic metrics (quantitative descriptors of image content) extracted from MRI sequences by machine learning increase the efficacy of proper presurgical differentiation between benign and malignant salivary gland tumors. Methods: A retrospective study of 117 patients with salivary gland tumors was conducted between January 2015 and November 2022. Univariate analyses with nonparametric tests and multivariate analyses with machine learning approaches were used. Results: Inflammatory biomarkers showed statistically significant differences (p < 0.05) in the Kruskal–Wallis test based on median values in discriminating Warthin tumors from pleomorphic adenoma and malignancies. The accuracy of NLR, PLR, SII, and SIRI was 0.88, 0.74, 0.76, and 0.83, respectively. Analysis of radiomic metrics to discriminate Warthin tumors from pleomorphic adenoma and malignancies showed statistically significant differences (p < 0.05) in nine radiomic features. The best multivariate analysis result was obtained from an SVM model with 86% accuracy, 68% sensitivity, and 91% specificity for six features. Conclusions: Inflammatory biomarkers and radiomic features can comparably support a pre-surgical differential diagnosis.

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Background: The aim of this study has been to investigate if inflammatory biomarkers such as the systemic immune-inflammation index (SII), neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) have any correlation with the recurrence of pleomorphic adenomas (PA) in patients afflicted by this disease. Methods: A retrospective multicentre study involving two Italian centres, the Maxillofacial Sur-gery at the Policlinico Federico II in Naples and the ENT and Maxillofacial Surgery at the IRCCS G. Pascale National Cancer Institute in Naples, has been performed. The study, involving 608 pa-tients who had received a diagnosis of PA, was conducted between January 2000 and January 2020. A Receiver Operating Characteristic (ROC) curve was used to calculate the optimal cut-off values for the SII, PLR and NLR. A linear regression model (LRM) and a non-linear logistic re-gression model (NLRM) were realized with respect to the NLR, PLR and SII. Results: The highest performance was achieved by the NLR (cut-off 2.960) with the AUC = 0.91, with an accuracy of 96.1%, sensitivity of 78.4% and specificity of 97.2%, PPV = 64.4% and NPV = 98.6%. The LRM and NLRM including all the haematological parameters did not increase the per-formance in terms of recurrence detection. Conclusion: The NLR is an excellent support tool in predicting the recurrence of PA with a cut-off of 2.960. It can be a good reference point for surgical decision-making and follow-up in clinical practice.

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