Introduction: Critically ill patients in intensive care units (ICUs) are at high risk of malnutrition, which can result in muscle atrophy, polyneuropathy, increased mortality, or prolonged hospitalizations with complications and higher costs during the recovery period. They often develop ICU-acquired weakness, exacerbated by sepsis, immobilization, and drug treatments, leading to rapid muscle mass loss and long-term complications. Studies indicate that adequate protein and calorie intake can decrease mortality and improve prognosis and recovery. However, optimal implementation remains a critical challenge. Objectives: This narrative review aims to summarize recent advances in nutritional strategies for critically ill patients. It highlights the benefits and limitations of current approaches including enteral (EN) and parenteral nutrition (PN) and examines their impact on clinical outcomes and overall mortality. Additionally, the review explores the emerging role of precision nutrition in critical care using technologies such as metabolomics and artificial intelligence (AI) to provide valuable insights into optimizing nutritional care in critically ill patients. Methods: A comprehensive literature search was conducted to identify recent studies, clinical guidelines, and expert consensus papers on nutritional support for ICU patients. The investigation focused on critical aspects such as the optimal timing for intervention, the route of administration, specific protein and energy targets, and technological innovations to support personalized nutrition, ensuring that each patient receives tailored support based on their unique needs. Results: Guidelines recommend initiating EN or PN nutrition within the first 48 h of admission, using indirect calorimetry (IC) to estimate energy needs, and supplementing protein up to 1.2 g/kg/day after stabilization. IC has gained importance in assessing energy needs but is still underused in the ICU. EN is preferred because it maintains intestinal integrity, reduces the risk of infections, and is recommended within the first 48 h of ICU admission. PN is used when EN is infeasible, but it increases the risk of infection. By integrating metabolomics with transcriptomic and genomic data, we can gain a deeper understanding of the effect of nutrition on cellular homeostasis, facilitating personalized treatments and enhancing the recovery of critically ill patients. Conclusions: AI is becoming increasingly important in monitoring and evaluating artificial nutrition, providing a more accurate and efficient alternative to traditional methods. AI can assist in identifying and managing malnutrition and is effective for estimating caloric and nutrient intake. AI minimizes human error, enables continuous monitoring, and integrates various data sources. The nutritional care of critically ill patients requires collaboration among specialists from diverse fields, including physicians, nutritionists, pharmacists, radiologists, IT experts, and policymakers.

There is an increasing prevalence of diabetes mellitus (DM), particularly type 2 DM (T2DM), and its associated complications. T2DM is linked to insulin resistance, chronic inflammation, and oxidative stress, which can lead to both macrovascular and microvascular complications, including peripheral diabetic neuropathy (PDN). Inflammatory processes play a key role in the development and progression of T2DM and its complications, with specific markers like C-reactive protein (CRP), interleukins (ILs), and tumor necrosis factor (TNF)-α being associated with increased risk. Other key inflammatory markers such as nuclear factor kappa B (NF-κB) are activated under hyperglycemic and oxidative stress conditions and contribute to the aggravation of PDN by regulating inflammatory gene expression and enhancing endothelial dysfunction. Other important roles in the inflammatory processes are played by Toll-like receptors (TLRs), caveolin 1 (CAV1), and monocyte chemoattractant protein 1 (MCP1). There is a relationship between vitamin D deficiency and PDN, highlighting the critical role of vitamin D in regulating inflammation and immune responses. The involvement of macrophages in PDN is also suspected, emphasizing their role in chronic inflammation and nerve damage in diabetic patients. Vitamin D supplementation has been found to reduce neuropathy severity, decrease inflammatory markers, and improve glycemic control. These findings suggest that addressing vitamin D deficiency could offer therapeutic benefits for PDN. These molecular pathways are critical in understanding the pathogenesis of DM complications and may offer potential biomarkers or therapeutic targets including anti-inflammatory treatments, vitamin D supplementation, macrophage phenotype modulation, and lifestyle modifications, aimed at reducing inflammation and preventing PDN. Ongoing and more extensive clinical trials with the aim of investigating anti-inflammatory agents, TNF-α inhibitors, and antioxidants are needed to advance deeper into the understanding and treatment of painful diabetic neuropathy.

Background/Objectives: Ventilator-associated pneumonia (VAP) is the most common nosocomial infection encountered in the intensive care unit (ICU) and is associated with prolonged hospitalization and increased mortality. We evaluated the causative pathogens involved and their resistance to the major classes of antibiotics in patients with VAP and assessed the differences between patients with and without coronavirus disease 2019 (COVID-19). Materials and Methods: This study was a single-center, cross-sectional, retrospective analysis involving 122 patients who were hospitalized in the ICU of Târgu Mureș County Clinical Hospital from 1 April 2021, to 1 April 2023. This study compares patients with VAP in COVID-19 and non-COVID-19 groups, examining the clinical progression, duration of ventilation and hospitalization, mortality, pathogen distribution, and the emergence of multidrug-resistant strains. Results: A length of stay in the ICU exceeding 11.5 days was associated with the development of multidrug-resistant (MDR) infections (AUC: 0.708, p < 0.001). Similarly, a duration of MV exceeding 196 h was associated with MDR acquisition (AUC: 0.695, p = 0.002). Additionally, a Clinical Pulmonary Infection Score (CPIS) greater than 5 was associated with MDR development (AUC: 0.854, p < 0.001) in the whole group of patients. The most commonly isolated strains were Acinetobacter spp., Pseudomonas spp., Klebsiella spp., and Staphylococcus aureus. Among non-COVID-19 patients, there was a notably higher frequency of MDR Acinetobacter baumannii. A bacterial resistance to carbapenems was found in Acinetobacter spp. (51.6%), Klebsiella spp. (22.6%), and Pseudomonas spp. (25.8%). Conclusions: COVID-19 patients experienced longer ventilation, higher mortality, and an increased risk of developing MDR. Carbapenem resistance was universal in Acinetobacter spp. and Klebsiella pneumoniae, whereas resistance in Pseudomonas aeruginosa was more prevalent among non-COVID-19 patients. The Clinical Pulmonary Infection Score (CPIS) strongly correlates with developing MDR pathogens in both patient groups.

Sepsis is a major medical emergency, characterized by a dysfunctional immune response to infection, which often progresses to multiple organ failure and death. Early diagnosis and prognostic evaluation present significant challenges due to limitations in the specificity and sensitivity of traditional biomarkers. This narrative review summarizes recent evidence on the potential of circulating microRNAs (miRNAs) such as miR-150, miR-146a, miR-223, miR-155, miR-122, and miR-4772-5p and plasma gelsolin (pGSN) as diagnostic and prognostic markers in sepsis. We discuss mechanisms involved and their potential for integration with artificial intelligence (AI) in personalized medicine. PubMed, Embase, and Web of Science databases were searched for relevant literature. Original research, systematic reviews, and meta-analyses focused on the diagnostic or prognostic value of circulating miRNAs or pGSN in sepsis were included; opinion papers and case reports were excluded. Altered expression of certain circulating microRNAs correlates with disease severity and mortality. Among circulating microRNAs (miRNAs), miR-122 and miR-150 have become the most consistently validated biomarkers in clinical studies, associated with sepsis severity and death rates. Additionally, other miRNAs such as miR-146a, miR-155, and miR-223 play roles in modulating immune and endothelial responses, highlighting the complex regulation of sepsis pathophysiology. Low pGSN concentrations at admission are associated with severe sepsis and acute respiratory distress syndrome, and serve as an independent predictor of mortality. Preclinical studies suggest that supplementation with exogenous pGSN could increase survival. AI algorithms show promising results for early sepsis detection and optimization of therapeutic decisions. However, combining circulating miRNAs and plasma gelsolin (pGSN) into AI-based models is still an exploratory idea that needs prospective validation, assay standardization, and multicenter studies before it can be used clinically.

Background: Heart transplantation represents the treatment for patients with end-stage heart failure (HF) being symptomatic despite optimal medical therapy. We investigated the role of NMR (neutrophil-to-monocyte ratio), NLR (neutrophil-to-lymphocyte ratio), NPR (neutrophil-to-platelet ratio), NWR (neutrophil-to-white cells ratio), MLR (monocyte-to-lymphocyte ratio), PLR (platelet-to-lymphocyte ratio), MWR (neutrophil-to-white cells ratio), and LWR (lymphocyte-to-white cells ratio) at the same cut-off values previously studied, to predict complications after heart transplant within 2 months after surgery. Methods: From May 2014 to January 2021, was included 38 patients in our study from the Cardiovascular and Transplant Emergency Institute of Târgu Mureș. Results: Preoperative NMR > 8.9 (OR: 70.71, 95% CI: 3.39–1473.64; p = 0.006) was a risk factor for the apparition of post-operative paroxysmal atrial fibrillation (Afib). In contrast, preoperative MWR > 0.09 (OR: 0.04, 95% CI: 0.003–0.58; p = 0.0182) represented a protective factor against AFib, but being the risk of complications of any cause (OR: 14.74, 95% CI: 1.05–206.59, p = 0.0458). Conclusion: Preoperative elevated levels of NMR were associated with the apparition of documented AFib, with high levels of MWR as a protective factor. High MWR was a risk factor in developing complications of any cause in the first 2 months after heart transplantation.

Atherosclerosis is now recognized as a chronic inflammatory disease of the arterial wall, in which perivascular adipose tissue (PVAT) has evolved from a passive structural component to a key player in regulating vascular homeostasis and the pathophysiology of atherosclerosis, playing an active, not just structural, role. PVAT surrounds blood vessels and influences them metabolically, immunologically, and vascularly by secreting adipokines, cytokines, and other bioactive mediators. Under physiological conditions, PVAT has protective roles, as it produces adiponectin, nitric oxide (NO), and other vasodilatory factors that help maintain vascular tone and reduce inflammation. In particular, brown-like PVAT (rich in Uncoupling Protein-1 (UCP1) and mitochondria) offers significant vasoprotective effects. Under pathological conditions (obesity, dyslipidemia, insulin resistance), PVAT undergoes a phenotypic transition towards a pro-inflammatory profile by increasing leptin, tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6) secretion and decreasing adiponectin, contributing to endothelial dysfunction, vascular smooth muscle cell (VSMC) proliferation, local immune cell recruitment, extracellular matrix (ECM) remodeling, and fibrosis. PVAT plays a complex role in vascular health and disease, interacting with systemic metabolism through the secretion of bioactive molecules. Metabolic imbalances can promote PVAT inflammation. Epigenetic alterations and micro ribonucleic acid (miRNAs) can influence PVAT inflammation, and modern imaging methods for PVAT assessment, such as the fat attenuation index (FAI) and artificial intelligence-assisted radiomic profiling, may become predictive biomarkers of cardiac risk. Future directions aim to identify biomarkers and develop targeted therapies that modulate PVAT inflammation and dysfunction in the context of cardiovascular diseases.