Narrow band imaging (NBI) endoscopy is an optical image enhancing technology that allows a detailed inspection of vascular and mucosal patterns, providing the ability to predict histology during real-time endoscopy. By combining NBI with magnification endoscopy (NBI-ME), the accurate assessment of lesions in the gastrointestinal tract can be achieved, as well as the early detection of neoplasia by emphasizing neovascularization. Promising results of the method in the diagnosis of premalignant and malignant lesions of gastrointestinal tract have been reported in clinical studies. The usefulness of NBI-ME as an adjunct to endoscopic therapy in clinical practice, the potential to improve diagnostic accuracy, surveillance strategies and cost-saving strategies based on this method are summarized in this review. Various classification systems of mucosal and vascular patterns used to differentiate preneoplastic and neoplastic lesions have been reviewed. We concluded that the clinical applicability of NBI-ME has increased, but standardization of endoscopic criteria and classification systems, validation in randomized multicenter trials and training programs to improve the diagnostic performance are all needed before the widespread acceptance of the method in routine practice. However, published data regarding the usefulness of NBI endoscopy are relevant in order to recommend the method as a reliable tool in diagnostic and therapy, even for less experienced endoscopists.

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: Carotid endarterectomy (CEA) is the first-line surgical intervention for cases of severe carotid stenoses. Unfortunately, the restenosis rate is high after CEA. This study aims to demonstrate the predictive role of carotid plaque features and inflammatory biomarkers (monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), and Aggregate Index of Systemic Inflammation (AISI)) in carotid restenosis and mortality at 12 months following CEA. Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a minimum of 70% carotid stenosis and surgical indications for CEA admitted to the Vascular Surgery Clinic, Emergency County Hospital of Targu Mures, Romania between 2018 and 2021. Results: According to our results, the high pre-operative values of inflammatory biomarkers—MLR (OR: 10.37 and OR: 6.11; p < 0.001), NLR (OR: 34.22 and OR: 37.62; p < 0.001), PLR (OR: 12.02 and OR: 16.06; p < 0.001), SII (OR: 18.11 and OR: 31.70; p < 0.001), SIRI (OR: 16.64 and OR: 9.89; p < 0.001), and AISI (OR: 16.80 and OR: 8.24; p < 0.001)—are strong independent factors predicting the risk of 12-month restenosis and mortality following CEA. Moreover, unstable plaque (OR: 2.83, p < 0.001 and OR: 2.40, p = 0.04) and MI (OR: 3.16, p < 0.001 and OR: 2.83, p = 0.005) were independent predictors of all outcomes. Furthermore, AH (OR: 2.30; p = 0.006), AF (OR: 1.74; p = 0.02), tobacco (OR: 2.25; p < 0.001), obesity (OR: 1.90; p = 0.02), and thrombotic plaques (OR: 2.77; p < 0.001) were all independent predictors of restenosis, but not for mortality in all patients. In contrast, antiplatelet (OR: 0.46; p = 0.004), statin (OR: 0.59; p = 0.04), and ezetimibe (OR:0.45; p = 0.03) therapy were protective factors against restenosis, but not for mortality. Conclusions: Our data revealed that higher preoperative inflammatory biomarker values highly predict 12-month restenosis and mortality following CEA. Furthermore, age above 70, unstable plaque, cardiovascular disease, and dyslipidemia were risk factors for all outcomes. Additionally, AH, AF, smoking, and obesity were all independent predictors of restenosis but not of mortality in all patients. Antiplatelet and statin medication, on the other hand, were protective against restenosis but not against mortality.

Cardiac autonomic neuropathy (CAN) in diabetes is among the strongest risk markers for future global and cardiovascular mortality. The aim of this study was to analyse CAN prevalence and to compare the associations between CAN, the glycaemic control, cardiovascular risk factors, peripheral neuropathy, retinopathy and macroangiopathy in patients with type 1 (T1DM) and type 2 diabetes mellitus (T2DM). One hundred ninety-five diabetic patients were included in this study. All patients were evaluated for detection of CAN (with standardised cardiovascular reflex tests), diabetes-related microvascular complications (polyneuropathy, retinopathy), common carotid artery intima-media thickness (IMT) and ankle-brachial index (ABI). The prevalence of CAN was 39.1% in T2DM and 61.8% in T1DM patients. Multivariate logistic regression analysis demonstrated that in T2DM, the odds [OR (95% confidence intervals)] of CAN increased with diabetes duration [1.67(1.42–1.92)], HbA1c [1.74(1.34–2.27)], cholesterol [1.01(1.00–1.01)], triglycerides [1.01(0.99–1.00)], smoking [2.35(1.23–4.49)], systolic blood pressure [1.01(1.00–1.03)], BMI [1.16(1.08–1.24)], glomerular filtration rate [0.91(0.88–0.93)], peripheral neuropathy [25.94(11.04–44.25)], retinopathy [13.13(3.03–84.73)] and IMT [10.12 (7.21–15.32)]. In T1DM, the odds of CAN increased with diabetes duration [1.62(1.13–2.31)], HbA1c [4.49(1.27–15.9)], age of patients [1.14(1.03–1.27)], glomerular filtration rate [0.94(0.89–0.99)], peripheral neuropathy [31.6(4.5–45.8)] and IMT [5.5(2.3–8.3)]. This study indicated that CAN is a more frequent complication in T1DM. Apart from glycaemic control, the existence of CAN is associated with potentially modifiable cardiovascular risk only in T2DM patients. The presence of other micro- and macrovascular complications increases the probability of having CAN in both types of DM (but more pronounced in T2DM).

Background and Aims. Diabetic neuropathy is a frequent complication of type 2 diabetes mellitus (T2DM). Genetic susceptibility and oxidative stress may play a role in the appearance of T2DM and diabetic neuropathy. We investigated the relation between polymorphism in genes related to oxidative stress such asGSTM1,GSTT1, andGSTP1and the presence of T2DM and diabetic neuropathy (DN).Methods. Samples were collected from 84 patients with T2DM (42 patients with DN and 42 patients without DN) and 98 healthy controls and genotyped by using polymerase chain reaction and restriction fragment length polymorphism method.Results.GSTP1Ile105Val polymorphism was associated with the risk of developing T2DM (p=0.05) but not with the risk of developing DN in diabetic cases.GSTM1andGSTT1gene polymorphisms were associated with neither the risk of developing T2DM nor the risk of DN occurrence in diabetic patients. No association was observed between the patients with T2DM and DSPN (diabetic sensorimotor peripheral neuropathy) and T2DM without DSPN regarding investigated polymorphism.Conclusion. Our data suggest thatGSTP1gene polymorphisms may contribute to the development of T2DM in Romanian population.GSTM1,GSTT1, andGSTP1gene polymorphisms are not associated with susceptibility of developing diabetic neuropathy in T2DM patients.