PURPOSE: To analyze the prevalence of homologous recombination deficiency (HRD) in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: We conducted a systematic review and meta-analysis of the prevalence of HRD in PDAC from PubMed, Scopus, and Cochrane Library databases, and online cancer genomic data sets. The main outcome was pooled prevalence of somatic and germline mutations in the better characterized HRD genes ( BRCA1, BRCA2, PALB2, ATM, ATR, CHEK2, RAD51, and the FANC genes). The secondary outcomes were prevalence of germline mutations overall, and in sporadic and familial cases; prevalence of germline BRCA1/2 mutations in Ashkenazi Jewish (AJ); and prevalence of HRD based on other definitions (ie, alterations in other genes, genomic scars, and mutational signatures). Random-effects modeling with the Freeman-Tukey transformation was used for the analyses. PROSPERO registration number: (CRD42020190813). RESULTS: Sixty studies with 21,842 participants were included in the systematic review and 57 in the meta-analysis. Prevalence of germline and somatic mutations was BRCA1: 0.9%, BRCA2: 3.5%, PALB2: 0.2%, ATM: 2.2%, CHEK2: 0.3%, FANC: 0.5%, RAD51: 0.0%, and ATR: 0.1%. Prevalence of germline mutations was BRCA1: 0.9% (2.4% in AJ), BRCA2: 3.8% (8.2% in AJ), PALB2: 0.2%, ATM: 2%, CHEK2: 0.3%, and FANC: 0.4%. No significant differences between sporadic and familial cases were identified. HRD prevalence ranged between 14.5%-16.5% through targeted next-generation sequencing and 24%-44% through whole-genome or whole-exome sequencing allowing complementary genomic analysis, including genomic scars and other signatures (surrogate markers of HRD). CONCLUSION: Surrogate readouts of HRD identify a greater proportion of patients with HRD than analyses limited to gene-level approaches. There is a clear need to harmonize HRD definitions and to validate the optimal biomarker for treatment selection. Universal HRD screening including integrated somatic and germline analysis should be offered to all patients with PDAC.

To provide an epidemiological framework of symptoms related to Foreign Body (FB) injuries due to Button Battery (BB)

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.

BACKGROUND: Thrombosis is linked to neutrophil release of neutrophil extracellular traps (NETs). NETs are proposed as a mechanism of resistance to thrombolysis. This study intends to analyze the composition of thrombi retrieved after mechanical thrombectomy, estimate the age and organization of thrombi, and evaluate associations with the use of thrombolysis, antiplatelets, and heparin. METHODS: This retrospective observational study involved 72 samples (44 from cerebral and 28 coronary arteries), which were stained with hematoxylin and eosin, anti-NE (neutrophil elastase) antibody, and anti-histone H2B (histone H2B) antibody, representing different components in NET formation, all detectable during the later stages of NETosis, for histochemical and digital quantification of NET content. The histological and morphological evaluations of the specimens were correlated, through univariate and mediation analyses, with clinical information and therapy administered before intervention. RESULTS: The results demonstrated that the composition of cerebral and coronary thrombi differs, and there were significantly more lytic cerebral thrombi than coronary thrombi (66% versus 14%; P =0.005). There was a considerably higher expression of NETs in the cerebral thrombi as testified by the higher expression of H2B ( P =0.031). Thrombolysis was remarkably associated with higher NE positivity (average marginal effect, 6.461 [95% CI, 0.7901–12.13]; P =0.02555), regardless of the origin of thrombi. There was no notable association between the administration of antiaggregant therapy/heparin and H2B/NE amount when adjusted for the thrombus location. Importantly, the age of the thrombus was the only independent predictor of NET content without any mediation of the thrombolytic treatment ( P =0.014). CONCLUSIONS: The age of the thrombus is the driving force for NET content, which correlates with impaired clinical outcomes. The therapy that is currently administered does not modify NET content. This study supports the need to investigate new pharmacological approaches added to thrombolysis to prevent NET formation or enhance their disruption, such as recombinant human DNase I (deoxyribonuclease I).

Background: Pathophysiological features of coronavirus disease 2019-associated acute respiratory distress syndrome (COVID-19 ARDS) were indicated to be somewhat different from those described in nonCOVID-19 ARDS, because of relatively preserved compliance of the respiratory system despite marked hypoxemia. We aim ascertaining whether respiratory system static compliance (Crs), driving pressure (DP), and tidal volume normalized for ideal body weight (VT/kg IBW) at the 1st day of controlled mechanical ventilation are associated with intensive care unit (ICU) mortality in COVID-19 ARDS. Methods: Observational multicenter cohort study. All consecutive COVID-19 adult patients admitted to 25 ICUs belonging to the COVID-19 VENETO ICU network (February 28th–April 28th, 2020), who received controlled mechanical ventilation, were screened. Only patients fulfilling ARDS criteria and with complete records of Crs, DP and VT/kg IBW within the 1st day of controlled mechanical ventilation were included. Crs, DP and VT/kg IBW were collected in sedated, paralyzed and supine patients. Results: A total of 704 COVID-19 patients were screened and 241 enrolled. Seventy-one patients (29%) died in ICU. The logistic regression analysis showed that: (1) Crs was not linearly associated with ICU mortality (p value for nonlinearity = 0.01), with a greater risk of death for values < 48 ml/cmH₂O; (2) the association between DP and ICU mortality was linear (p value for nonlinearity = 0.68), and increasing DP from 10 to 14 cmH₂O caused significant higher odds of in-ICU death (OR 1.45, 95% CI 1.06–1.99); (3) VT/kg IBW was not associated with a significant increase of the risk of death (OR 0.92, 95% CI 0.55–1.52). Multivariable analysis confirmed these findings. Conclusions: Crs < 48 ml/cmH₂O was associated with ICU mortality, while DP was linearly associated with mortality. DP should be kept as low as possible, even in the case of relatively preserved Crs, irrespective of VT/kg IBW, to reduce the risk of death.