Background: Patients undergoing surgery and general anesthesia often experience anxiety, fear and stress, with negative bodily responses. These may be managed by the pre-procedural application of anxiolytic, analgesic, and anesthetic drugs that have, however, potential risks or side effects. Music therapy (MT) can be used as a complementary no-drug intervention alongside standard surgical care before, during and after medical procedures. The aim of this study was to evaluate the effects of preoperative MT intervention compared to premedication with midazolam on levels of anxiety, sedation and stress during general anesthesia for elective stomatology surgery. Methods: A two-arm randomized and controlled single-center, parallel-group, pre–post event study was conducted. In total, 70 patients affected by stage I or II (both clinically and instrumentally N0) micro-invasive oral cancer and undergoing elective surgery under general anesthesia were assigned to the control group (CG) or to the music therapy group (MTG). MTG patients received preoperative music therapy intervention (MT) from a certified music therapist before surgery, while the CG patients did not receive MT but instead received premedication with intravenous midazolam, 0.02 mg/kg. Anesthesia was the same in both groups. The systolic blood pressure (SBP), diastolic blood pressure (DBP) and heart rate (HR) were recorded at the entrance to the operating room, just before the induction of anesthesia and every 5 min until the end of surgery. An anxiety visual analogues scale (A-VAS) was used to evaluate the level of anxiety. The bispectral index (BIS) monitor was used to measure the depth of sedation just before and 10 min after both music intervention and midazolam administration. Stress response was assessed 5 min before and 20 min after surgery via the control of plasma prolactin (PRL), growth hormone (GH), and cortisol levels. The patient global impression of satisfaction (PGIS) was tested 1 h after surgery. Participants in the MTG were asked to answer 3 questions concerning their experience with MT. Results: No statistical differences among the PRL, GH and cortisol levels between the two groups were registered before and after the treatment, as well as for PAS, PAD and HR. Significant differences in the A-VAS scores between the MTG and CG (p < 0.01) was observed. Compared to the CG, MTG patients had a statistically significantly lower BIS score (p = 0.02) before induction. A PGIS score of 86.7% revealed that patients in the MTG were very satisfied, versus 80% in the CG (p < 0.05). Conclusion: Preoperative music therapy could be an alternative to intravenous midazolam when aiming to promote a preoperative and post-operative state of anxiolysis and sedation in stomatology surgery, even if no differences were found in terms of the surgery-related stress response according to physiological and hormonal determinations.

One of the main problems in the analysis of real data is often related to the presence of anomalies. Namely, anomalous cases can both spoil the resulting analysis and contain valuable information at the same time. In both cases, the ability to detect these occurrences is very important. In the biomedical field, a correct identification of outliers could allow the development of new biological hypotheses that are not considered when looking at experimental biological data. In this work, we address the problem of detecting outliers in gene expression data, focusing on microarray analysis. We propose an ensemble approach for detecting anomalies in gene expression matrices based on the use of Hierarchical Clustering and Robust Principal Component Analysis, which allows us to derive a novel pseudo-mathematical classification of anomalies.

Learning processes play an important role in enhancing understanding and analyzing real phenomena. Most of these methodologies revolve around solving penalized optimization problems. A significant challenge arises in the choice of the penalty hyperparameter, which is typically user-specified or determined through Grid search approaches. There is a lack of automated tuning procedures for the estimation of these hyperparameters, particularly in unsupervised learning scenarios. In this paper, we focus on the unsupervised context and propose a bi-level strategy to address the issue of tuning the penalty hyperparameter. We establish suitable conditions for the existence of a minimizer in an infinite-dimensional Hilbert space, along with presenting some theoretical considerations. These results can be applied in situations where obtaining an exact minimizer is unfeasible. Working on the estimation of the hyperparameter with the gradient-based method, we also introduce a modified version of Ekeland’s principle as a stopping criterion for these methods. Our approach distinguishes from conventional techniques by reducing reliance on random or black-box strategies, resulting in stronger mathematical generalization.