A critically ill polytrauma patient is one of the most complex cases to be admitted to the intensive care unit, due to both the primary traumatic complications and the secondary post-traumatic interactions. From a molecular, genetic, and epigenetic point of view, numerous biochemical interactions are responsible for the deterioration of the clinical status of a patient, and increased mortality rates. From a molecular viewpoint, microRNAs are one of the most complex macromolecular systems due to the numerous modular reactions and interactions that they are involved in. Regarding the expression and activity of microRNAs in sepsis, their usefulness has reached new levels of significance. MicroRNAs can be used both as an early biomarker for sepsis, and as a therapeutic target because of their ability to block the complex reactions involved in the initiation, maintenance, and augmentation of the clinical status.

Background: Hemodialysis (HD) patients have significantly higher mortality rates compared to the general population, primarily due to complex comorbidities. This systematic review and meta-analysis aimed to evaluate and compare the performance of various machine learning (ML) models in predicting mortality among HD patients. Methods: The analysis followed PRISMA guidelines, including studies that assessed the predictive capabilities of ML models for mortality in HD patients. Review Manager software version 5.4.1. was used for meta-analysis, and the performance of ML models was compared, including logistic regression, XGBoost, and Random Forest models. Results: The meta-analysis indicated that the logistic regression model predicted a true positive mortality rate of 8.23%, close to the actual rate of 10.53%. In contrast, the XGBoost and Random Forest models predicted rates of 9.93% and 8.94%, respectively, compared to the actual mortality rate of 13.73%. The highest area under the curve (AUC) was reported for the Random Forest model at a 3-year follow-up (AUC = 0.89). No significant difference was found between the performance of logistic regression and Random Forest models (p = 0.82). Conclusions: ML models, particularly Random Forest and logistic regression, demonstrated effective predictive capabilities for mortality in HD patients. These models can help identify high-risk patients early, facilitating personalized treatment strategies and potentially improving long-term outcomes. However, the observed heterogeneity among studies indicates a need for further research to refine model performance and standardize predictive features.

In the past few years, elastography has gained ground as a complementary method to ultrasonography in noninvasive breast cancer screening. Despite positive outcomes, there is a further need to refine the method, especially regarding BIRADS scores 3 and 4A, where the distinction between benignancy and malignancy is established. The aim of the present study was to evaluate the best risk-stratification system using both qualitative and semiquantitative elastographic methods for solid breast nodules. A total of 1405 solid nodules, described in 657 female patients, were examined in our endocrine unit between January 2018 and December 2019. The inclusion criterion for our retrospective study was the presence of any solid breast mass in women of all ages (mean, 40.85 ± SD 27.11), detected during ultrasound examination using a HITACHI PREIRUS machine (Hitachi Medical Corporation, Tokyo, Japan). The Breast Imaging Reporting and Data System (BIRADS)–US criteria were used in the assessment of each nodule by conventional US (gray-scale mode) and Doppler evaluation. The Ueno score and strain ratio were also measured for all the described lesions. We considered multiple algorithms for the risk reassessment of solid breast nodules: classical BIRADS–US, EFSUMB BIRADS, worst-case scenario BIRADS and BIRADS TM. There were 93 malignant nodules out of 1405. The diagnosis was based on histopathological results for all the malignant lesions. Benign lesions were diagnosed based on histopathological results, Tru-Cut biopsy, mammography and MRI. The Sensitivity (Se), Specificity (Sp), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy (Acc) were obtained for all the proposed risk-stratification reporting systems: conventional BIRADS-US (Se, 74.23%; Sp, 63.95%; PPV, 13.53%; NPV, 97.79%; Acc, 65%); EFSUMB BIRADS (Se, 71.23%; Sp, 81.55%; PPV, 22.68%; NPV, 97.99%; Acc, 81%); worst-case scenario BIRADS (Se, 84.23%; Sp, 58.23%; PPV, 13.29%; NPV, 98.84%; Acc, 60%); BIRADS TM (Se, 81.23%; Sp, 75.84%; PPV, 20.35%; NPV, 98.81%; Acc, 77%). We found that the most efficient risk-stratification reporting system was the proposed one, BIRADS TM, which considers both upgrading and downgrading the conventional BIRADS-US, followed by the worst-case scenario BIRADS and EFSUMB BIRADS.