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.

Chronic rhinosinusitis (CRS) is an important ENT pathology which affects about 5–12% of the general population. The treatment of CRS can be pharmacological (nasal sprays, douches, systemic antibiotics and steroids), surgical (endoscopic sinus surgery) or immunological according to established algorithms. CRS was divided for many years into CRS with nasal polyps (CRSwNP) and CRS without nasal polyps (CRSsNP). New ways of classifying CRS by endotypes (presence of neutrophilia, eosinophilia, fibrosis, glandular hypertrophy and epithelial dysmorphisms) appeared after the most recent understandings of the pathophysiology of the disease. Other classifications divide CRS into primary and secondary forms, localized/diffuse types and anatomical presentation. A new type of treatment has been administered in the last years, biologics. For the moment, biologics are indicated just in the cases of the patients who have undergone ESS or have contraindications for surgery and have bilateral polyps and meet a minimum of three of the following criteria: the necessity for systemic therapies with oral or parenteral corticosteroids or contraindications to systemic steroids, significant loss of smell or impaired QoL score, comorbid asthma and type 2 inflammation. This article aims to present the most relevant studies which used the three types of biologics (anti-IgE, anti-IL5 and anti-IL4/IL3) and wishes to increase the awareness of this new type of treatment that can be used in some CRS cases.

Respiratory allergies represent a major public health issue in the modern world. Pollens are among the most significant causes of seasonal allergic rhinitis, with pollens of wind‑pollinated trees representing an important cause. Members of the Platanaceae family (Platanus acerifolia, Platanus orientalis) are well‑recognized sources of allergenic pollens worldwide, due to their high capacity of sensitization and widespread usage as ornamental urban trees. Air pollution, characteristic to all important urban conglomerates in the world and provoked by diesel exhaust gases, industrial and domestic fumes, and biogenic volatile organic compounds represents another major public health issue. Plane trees, along with other species of trees, are one of the main sources of volatile compounds. Recent studies have demonstrated a strong correlation between air pollution and respiratory allergies, with airway chemical compounds intensifying the capacity of sensitization to allergenic pollens. This study presents an overview of the known negative elements on public health of the Platanus family.

Frontal bone trauma has an increasing incidence and prevalence due to the wide-scale use of personal mobility devices such as motorcycles, electric bicycles, and scooters. Usually, the patients are involved in high-velocity accidents and the resulting lesions could be life-threatening. Moreover, there are immediate and long-term aesthetic and functional deficits resulting from such pathology. The immediate complications range from local infections in the frontal sinus to infections propagating inside the central nervous system, or the presence of cerebrospinal fluid leaks and vision impairment. We review current trends and available guidelines regarding the management of cases with frontal bone trauma. Treatment options taken into consideration are a conservative attitude towards minor lesions or aggressive surgical management of complex fractures involving the anterior and posterior frontal sinus walls. We illustrate and propose different approaches in the management of cases with long-term complications after frontal bone trauma. The team attending to these patients should unite otorhinolaryngologists, neurosurgeons, ophthalmologists, and maxillofacial surgeons. Take-home message: Only such complex interdisciplinary teams of trained specialists can provide a higher standard of care for complex trauma cases and limit the possible exposure to further legal actions or even malpractice.

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.

Preoperative radiological planning is a key factor in the prediction of implant size and positioning that influences surgical time, the risk of complications, and functional outcomes. We have tested the accuracy of the digital templating performed in our hospital for a sample of 215 patients that underwent total hip arthroplasty. We assessed the accuracy of correctly predicting implant size for the femoral and acetabular components, as well as the stem neck length. We found that our method of templating proved accurate (within one size) in 95.8% of cases for the stem and 94.9% for the cup when using the anteroposterior view only, while the lateral view was accurate in 95.8% of cases for the stem and 97.2% for the cup. Exact prediction of the stem size was obtained in 77.7% of cases using the anteroposterior view and 67.0% of cases on the lateral view, and 73.0% and 74.4% of cases for the cup on the AP and LL views, respectively. Stem neck size was predicted exactly in 75.35% of cases and within one size in 93.49% of cases. We concluded that our method of digital templating using dedicated software is highly effective in accurately predicting implant size.