Background and objectives: Our study aimed to assess the learning curve for robot-assisted (RA) total knee arthroplasty (TKA) in our hospital, compare operative times between RA-TKAs and manual TKAs, and assess the early complications rate between the two approaches. Methods: We included 39 patients who underwent RA-TKA and 45 control patients subjected to manual TKA in the same period and operated on by the same surgical staff. We collected demographic and patient-related data to assess potential differences between the two groups. Results: No statistical differences were recorded in regard to age, BMI, sex, Kellgren–Lawrence classification, or limb alignment between patients undergoing RA-TKA and manual TKA, respectively. Three surgeons transitioned from the learning to the proficiency phase in our study after a number of 6, 4, and 3 cases, respectively. The overall operative time for the learning phase was 111.54 ± 20.45 min, significantly longer compared to the average of 86.43 ± 19.09 min in the proficiency phase (p = 0.0154) and 80.56 ± 17.03 min for manual TKAs (p < 0.0001). No statistically significant difference was recorded between the global operative time for the proficiency phase TKAs versus the controls. No major complications were recorded in either RA-TKA or manual TKA groups. Conclusions: Our results suggest that experienced surgeons may adopt RA-TKA using this platform and quickly adapt without significant complications.

In unstable pertrochanteric fractures, there are still debates regarding the complications and long‑term benefits after internal fixation using short or long cephalomedullary nails. Therefore, a study was developed regarding this idea. From May 2017 to April 2020, 61 patients with unstable (AO 31‑A2) and intertrochanteric fractures (AO 31‑A3) were surgically operated on. During follow‑up, 8 patients were excluded (lost or deceased). A total of 26 patients received internal short nail system fixation and 27 received a long nail system. All cases followed the standard 6‑week rehabilitation protocol. Follow‑up was at 3, 6 weeks, 3, 6 and 12 months, and clinical and functional assessment were determined by a different surgeon using the Visual Analogue Scale (VAS), Harris Hip Score (HHS) and Functional Ambulation Categories (FAC). A total of 42 (79.2%) had a 31.A2 fracture (21 in the long nail group and 21 in the short nail group) and 11 (20.8%) had a 31.A3 fracture (6 in long nail group and 5 in the short nail group). Surgical time was significantly longer (P<0.05) in the long nail group (an average of 81.38±12.01 min), compared with the short nail group (53.11±8.36 min). Blood loss was significantly higher (P<0.05) in the long nail group (210±12.1 ml) compared to the short nail group (75.4±14.8 ml). No statistical differences were noted regarding tip‑apex distance (TAD) and VAS score. At 6 months, HHS was better for the short nail group (84.76±3.68) (P<0.05). Regarding the FAC scale, no significant statistical differences were identified. Cut‑out occurred in 2 cases in the short nail group and 1 case from the long nail group. Only 1 peri‑implant fracture occurred in a patient with a long cephalomedullary nail. In conclusion, the long cephalomedullary nail requires a longer surgical time and is associated with an increase in intraoperative blood loss without improving the functional outcome after 12 months postoperatively. A larger sample of cases is required to thoroughly analyze the postoperative complications.

The relationship between diabetes mellitus and ocular complications has been extensively studied by many authors. Diabetic keratopathy has already been well characterized and defined as a clinical entity. This review focuses on exploring corneal epithelial changes in diabetic patients, aiming to provide a pragmatic overview of the existing knowledge on this topic. The paper systematically examines alterations in corneal epithelial structure and their impact on diabetic patients. Advanced imaging techniques are also discussed for their role in precise characterization and improved diagnostics. Additionally, the paper explores the mechanisms behind corneal epithelial changes in diabetes, looking at factors such as hyperglycemia, oxidative stress, and Advanced Glycation End-Products. The impact of altered corneal epithelial integrity on barrier function and susceptibility to external issues is considered, addressing potential links to heightened proteolytic enzyme activities and delayed wound healing observed in diabetic individuals. The review also covers the practical implications of corneal epithelial changes, including the association with corneal erosions, persistent epithelial defects, and an increased risk of dry eye syndrome in diabetic patients.

Periprosthetic joint infection (PJI) is a serious complication that can occur after joint arthroplasty, such as hip or knee replacement surgeries. It involves the invasion of the periprosthetic space by pathogens, leading to severe inflammation and often requiring complex medical intervention. PJI is associated with significant morbidity, increased healthcare costs, and a reduced quality of life for patients. This study aims to evaluate the performance of multiple supervised machine learning models in predicting PJI using clinical and demographic data collected from patients who underwent joint arthroplasty. Eight supervised machine learning models—Logistic Regression, Random Forest, XGBoost, Artificial Neural Network (ANN), k-Nearest Neighbors (KNN), AdaBoost, Gaussian Naive Bayes (GNB), and Stochastic Gradient Descent (SGD)—were trained and tested on a dataset of 27,854 patients. Models were evaluated using accuracy, precision, recall, specificity, F1 score, and area under the ROC curve (AUC). Random Forest and XGBoost showed the best overall performance, with high accuracy and balanced metrics across all evaluation criteria. KNN also performed strongly, particularly in minimizing misclassifications. GNB and SGD yielded weaker results, with higher error rates. Random Forest, XGBoost, and KNN are the most promising models for clinical implementation in PJI prediction. Their robust performance may support earlier diagnosis and improved patient outcomes in orthopedic care. This study demonstrates that machine learning models—particularly Random Forest and XGBoost—can accurately predict periprosthetic joint infection (PJI) using structured electronic health record data. By integrating these models into preoperative assessment workflows, clinicians may be able to identify high-risk patients earlier, personalize prophylactic strategies, and reduce infection-related morbidity. The implementation of these predictive tools has the potential to enhance clinical decision-making, improve surgical outcomes, and optimize the use of healthcare resources in orthopedic practice.

Cholesterol is an essential element for the development and normal function of the central nervous system. While peripheral cholesterol is influenced by liver metabolism and diet, brain cholesterol metabolism takes place in an isolated system due to the impermeability of the blood–brain barrier (BBB). However, cross-talk occurs between the brain and periphery, specifically through metabolites such as oxysterols that play key roles in regulating cholesterol balance. Several neurodegenerative conditions such as Alzheimer’s disease or Parkinson’s disease are considered to be affected by the loss of this balance. Also, the treatment of hypercholesterolemia needs to consider these discrete interferences between brain and peripheral cholesterol and the possible implications of each therapeutic approach. This is particularly important because of 27-hydroxycholesterol and 24-hydroxycholesterol, which can cross the BBB and are involved in cholesterol metabolism. This paper examines the metabolic pathways of cholesterol metabolism in the brain and periphery and focuses on the complex cross-talk between these metabolisms. Also, we emphasize the regulatory role of the BBB and the need for an integrated approach to cholesterol management.

Stilbenoids are a category of plant compounds exhibiting notable health-related benefits. After resveratrol, perhaps the most well-known stilbenoid is pinosylvin, a major phytochemical constituent of most plants characterised by the pine spines among others. Pinosylvin and its derivatives have been found to exert potent antibacterial and antifungal effects, while their antiparasitic and antiviral properties are still a subject of ongoing research. The antioxidant properties of pinosylvin are mostly based on its scavenging of free radicals, inhibition of iNOS and protein kinase C, and promotion of HO-1 expression. Its anti-inflammatory properties are based on a variety of mechanisms, such as COX-2 inhibition, NF-κB and TRPA1 activation inhibition, and reduction in IL-6 levels. Its anticancer properties are partly associated with its antioxidant and anti-inflammatory potential, although a number of other mechanisms are described, such as apoptosis induction and matrix metalloproteinase inhibition. A couple of experiments have also suggested a neuroprotective potential. A multitude of ethnomedical and ethnobotanical effects of pinosylvin-containing plants are reported, like antimicrobial, antioxidant, anti-inflammatory, hepatoprotective, and prokinetic actions; many of these are corroborated by recent research. The advent of novel methods of artificial pinosylvin synthesis may facilitate its mass production and adoption as a medical compound. Finally, pinosylvin may be a tool in promoting environmentally friendly pesticide and insecticide policies and be used in land remediation schemes.