Background and Objectives: Impaired cognition and pain after surgery contribute to prolonged hospital stays and increased mortality rates. Thus, the development of preemptive algorithms for reducing their impact should be prioritized. The main objectives of the present study were to evaluate the efficiency of using virtual reality (VR) to treat postoperative cognitive decline and pain perception. Materials and Methods: The study was a prospective, monocentric, clinical study that included 51 patients who have undergone major abdominal surgery. The patients were divided into two groups: Control (n = 25) and VR (n = 26). The VR sessions consisted of 5–8 min exposure at 24–48 h after surgery. We considered the outcome variables, the mini-mental state examination, and visual analogue scale at 24–48 h after surgery. The dependent variables were age, social status, educational level, and duration of surgery. Results: We did not observe any differences in postoperative cognition deficit with regard to VR. The VR, however, successfully reduced postoperative pain intensity. Moreover, the patients’ age, surgery duration, level of education, and social status influenced the MMSE score at 24–48 h after surgery. Conclusions: Even if using VR does not alleviate short-term postoperative cognitive impairments, it could affect pain perception. Further studies are needed to support the use of VR in perioperative contexts.

Sepsis is one of the most common causes of morbidity and mortality worldwide. Despite the remarkable advances in modern medicine throughout the last century, the mortality rates associated with sepsis have remained significantly elevated, both in high- and low-income countries. The main difficulty in the diagnosis and treatment of septic patients is the tremendous heterogeneity of this condition. The vast heterogeneity that characterizes sepsis ranges from the clinical presentation to the biological aspects of the disease. Evidence-based medicine approaches sepsis as a homogenous syndrome and does not consider the individual discrepancies between septic patients. This approach may contribute to the poor outcomes of septic patients. In recent years, personalized medicine has gained significant interest. This novel form of medicine underlines the importance of understanding the genetic, epigenetic, and molecular basis of a disease in order to provide a more tailored approach for the patient. The study of “omics”, such as cytomics, genomics, epigenomics, transcriptomics, proteomics, and metabolomics, provides a deeper comprehension of the complex interactions between the host, the disease, and the environment. The aim of this review is to summarize the potential role of a personalized approach in sepsis management, considering the interactions between various “omics”.

Sepsis and septic shock remain major causes of mortality worldwide, affecting approximately 49 million people annually and accounting for an estimated 11 million deaths, representing nearly 20% of all global deaths. Despite sustained efforts in early diagnosis and treatment, sepsis continues to impose a substantial global health burden. Sepsis is defined as a life-threatening organ dysfunction secondary to a dysregulation of the host response to infection. Despite guidelines-based approaches to its diagnosis and therapy, the mortality rates remain higher even in developed countries, reaching up to 30% in intensive care units. High mortality rates in sepsis are largely driven by clinical heterogeneity, highlighting the critical role of epigenetic modulation mechanisms in shaping diverse clinical phenotypes. The main epigenetic mechanisms that modulate the immune response and outcome in sepsis are: DNA methylation, microRNA synthesis, histone modulation, and RNA methylation. This narrative review examines the role of epigenetic mechanisms in sepsis heterogeneity, phenotyping, management, and outcomes, with particular emphasis on the potential contribution of artificial intelligence to the integration of molecular and clinical data for precision medicine. Consequently, the study of AI-integrated strategies for better tailoring of the diversity of epigenetic changes should be prioritized. Thus, machine learning algorithms, deep learning frameworks, and large language models offer helpful insights in redefining the role of AI in sepsis management from this epigenetic perspective. Despite the support of AI in integrating the epigenetic mechanisms into clinical practice, some disadvantages should be also considered like: black box configuration for deep learning frameworks, inequalities from the past data used for training the models, data privacy, ethical concerns, cohort size, number of parameters considered etc. In conclusion, AI tools represent important means in tailoring the personalized decision tree, especially considering the epigenetic influence on the disease course.