Background and Objectives: Diabetic ketoacidosis (DKA) represents the most prevalent hyperglycemic emergency and poses a significant life-threatening metabolic risk for individuals with diabetes. The present study examines the predictive role of the leukocyte glucose index (LGI) values at baseline in diagnosing the severity of DKA and their correlation with the presence of diabetes-related microvascular complications. Materials and Methods: A retrospective observational study was conducted involving a total of 94 patients who had previously confirmed diagnoses of either Type I or Type II diabetes mellitus and presented with ketoacidosis upon emergency admission to the Department of Diabetology, Nutrition, and Metabolic Disease. Demographic information, values of arterial systolic and diastolic pressure, known duration and type of diabetes, severity of ketoacidosis, routine laboratory results, and blood gas analyses were retrieved from the hospital’s electronic database. Results: Higher diastolic blood pressure (DBP) values were observed in both mild (p = 0.021) and severe DKA (p = 0.035) compared to moderate DKA. When examining laboratory data, elevated white blood cell (WBC) counts were observed in severe DKA when compared to mild DKA (p = 0.009), as well as increased neutrophil counts in both moderate (p = 0.038) and severe (p = 0.011) DKA relative to mild DKA. Furthermore, patients with severe DKA exhibited lower values of venous blood pH, partial pressure of carbon dioxide (pvCO₂), base excess (BE), and bicarbonate than the other groups (all p < 0.05), alongside higher levels of lactate, anion gap, and LGI (all p < 0.05). Regarding the parameters of arterial blood gas, we identified a negative correlation between LGI values and venous blood pH (r = −0.383, p < 0.001), serum bicarbonate (r = −0.352, p < 0.001), pCO₂ (r = −0.271, p = 0.009), and BE (r = −0.330, p < 0.001). At univariate analysis, elevated LGI values are associated with the severity of DKA (OR: 1.87, p = 0.016) and diabetes-related microvascular complications (OR: 2.16, p = 0.010). Conclusions: The positive correlation between LGI and DKA severity and between LGI and diabetes microvascular complications highlights the potential utility of LGI as a predictive marker, facilitating early risk stratification and clinical decision-making.

Background and Objectives: Acute limb ischemia (ALI) is a life-threatening vascular emergency that requires immediate intervention to restore perfusion and prevent limb loss or mortality. Management strategies primarily include thrombolysis and surgical revascularization, each with distinct risks and benefits. This review evaluates and compares the outcomes of thrombolysis and surgical revascularization in ALI management, emphasizing their efficacy, safety, and patient selection criteria. Materials and Methods: A systematic review was conducted in adherence to PRISMA guidelines, analyzing data from 15 studies, including randomized controlled trials and large retrospective analyses, encompassing over 3500 patients with varying demographics and clinical presentations. Study quality was assessed using the Cochrane risk of bias tool and the Newcastle–Ottawa Scale. Results: Thrombolysis, utilizing agents such as urokinase or recombinant tissue plasminogen activator (rt-PA), demonstrated limb salvage rates up to 90% in acute cases, with 30-day mortality rates of 4–6%. It was particularly effective in patients with embolic occlusions or short symptom durations. However, bleeding complications associated with thrombolysis were reported in up to 47% of cases. Conversely, surgical revascularization remains crucial for those with advanced ischemia or contraindications to thrombolysis, offering reliable perfusion restoration but with higher perioperative morbidity, especially in older patients with significant comorbidities. Recent advancements, including hybrid approaches combining catheter-directed thrombolysis with percutaneous mechanical thrombectomy, have shown promise in improving outcomes by reducing procedure times and enhancing clot resolution. Conclusions: While thrombolysis and surgical revascularization are effective, optimizing patient selection remains a key challenge. Future research should focus on refining treatment algorithms, investigating novel thrombolytic agents, and expanding the role of minimally invasive techniques to improve long-term outcomes while mitigating complications such as bleeding and reperfusion injuries.

Intensive care medicine represents one of the most challenging setting in modern healthcare, where specific mechanisms intertwine and form a dynamic biological model, where organ dysfunction can easily evolve to multi-organ dysfunction, continuously reshaping the patient’s clinical course. The critically ill patient represents a biological system resulted from interaction between maladaptive and adaptative mechanisms, therefore generates a large volume of data that can exceeds human cognitive capacity. Artificial intelligence can integrate multimodal physiological, laboratory, and clinical data into a dynamic representation of the patient’s biological trajectory. AI-tools and machine learning technologies have evolved to potential clinical support tools, with great perspectives for future implementation, but currently with limited use in clinical practice. This article is a narrative review of artificial intelligence in ICU, aiming to present current evidence and limitations.

Background: Glioblastomas are the most common tumors of the central nervous system. The incidence of brain tumors is higher in developed countries and is constantly increasing. Aim of the study: The purpose of this study was to analyze the clinicopathological data of patients diagnosed with glioblastomas from a selected case series. Material and methods: This retrospective, single-center study was conducted on glioblastoma cases diagnosed between 2014 and 2018 at the Department of Pathology of the County Emergency Clinical Hospital of Târgu Mureș, Romania. The information was centralized from histopathology reports and focused on the location of glioblastomas and demographic parameters (gender and age of patients). Results: In the studied period, there were 154 patients diagnosed with glioblastoma, of which 50.65% were men. The most affected age group was 61–70 years (31.17% of cases). In most cases (49.35%), the glioblastoma was located in the left cerebral hemisphere, and the most affected lobes were the frontal (29.87%), temporal (26.62%), and parietal (14.94%). We could not find a statistically significant association between the distribution of cases by years, gender, and location of the tumor. Conclusions: Glioblastoma is a tumor that involves the left cerebral hemisphere most often and affects both genders over the age of sixty.

Skull base pneumatization is anatomically variable and clinically relevant to temporal bone and transsphenoidal surgical corridors, but manual volumetric segmentation is time-consuming. This retrospective pilot study evaluated a clinician-guided deep learning workflow for mastoid and sphenoid sinus compartment segmentation on bone computed tomography. Images were curated and annotated in 3D Slicer using MONAI Label and separate three-dimensional SegResNet models. The mastoid development dataset comprised 122 side-cases, with 28 reserved side-cases; the sphenoid dataset comprised 117 development and 17 reserved examinations. The best internal-validation Dice scores were 0.8660 for the mastoid model and 0.8750 for the sphenoid model. In AI-assisted correction cohorts, mean Dice ranged from 0.9539 to 0.9691 for mastoid and from 0.9347 to 0.9426 for sphenoid. In independently annotated subsets, AI-to-expert Dice was 0.8083–0.8097 for mastoid and 0.8339–0.8473 for sphenoid, while interobserver Dice was 0.8003 and 0.9136, respectively. AI assistance reduced mean segmentation time by 86.5% for mastoid and 81.4% for sphenoid. These findings support clinician-supervised AI segmentation as an efficient starting point for volumetric assessment.

Background and Objectives: Atherosclerosis is a chronic inflammatory disease significantly contributing to cardiovascular morbidity and mortality. This study primarily aims to evaluate the role of baseline blood glucose levels and inflammatory markers in the histopathological features of atherosclerotic plaques in the carotid and femoro-popliteal arteries. Materials and Methods: In this retrospective, observational, and monocentric study, 165 patients diagnosed with infrainguinal peripheral arterial disease or carotid artery disease hospitalized in the Vascular Surgery Clinic, between January 2019 and December 2023, were included. From the electronic database of the hospital, we documented demographic data, cardiovascular comorbidities, including hypertension, atrial fibrillation, ischemic heart disease, and chronic heart failure, as well as chronic kidney disease, diabetes, and prevalent risk factors such as active smoking, dyslipidemia, and obesity. Additionally, we recorded the arterial site from which the atherosclerotic plaque was obtained, along with laboratory data obtained at the time of admission prior to the surgery. The patients were divided into “Carotid Artery” and “Femoro-Popliteal Axis” based on anatomical location. Results: A greater prevalence of male patients (p = 0.008) and dyslipidemia (p = 0.002) was found in the group with atherosclerotic plaques from the femoro-popliteal axis. Laboratory data also showed increased lymphocyte (p = 0.020) and PLT (p = 0.028) levels in this group. There was no significant difference in the types of atherosclerotic plaques between the two patient groups. However, those in the Carotid Artery group showed a higher rate of antiaggregant treatment and a reduced incidence of dual therapy (p < 0.001). The Spearman correlation analysis revealed a positive correlation between baseline glucose levels and NLR (r = 0.402, p < 0.001), MLR (r = 0.217, p = 0.005), PLR (r = 0.306, p < 0.001), and LGI (r = 0.693, p < 0.001). Furthermore, the predictive roles of glucose, NLR, MLR, and LGI were assessed through multivariate analysis. Consequently, elevated baseline values of the parameters above were associated with unstable atherosclerotic plaques, independent of demo-graphic data, standard cardiovascular risk factors, site of artery harvest, and chronic vascular treatments at the time of admission (for all p < 0.05). Conclusions: This study highlights the significant relationships between glucose levels and various inflammatory markers in patients with different histopathological diagnoses of atherosclerotic plaques. Additionally, elevated glycemic and systemic inflammation biomarkers were associated with unstable atherosclerotic plaque, independent of demographic data, comorbidities, cardiovascular risk factors, anatomical artery harvest, and vascular chronic medication at the time of admission.