Obesity is an important condition affecting the quality of life of numerous patients and increasing their associated risk for multiple diseases, including tumors and immune-mediated disorders. Inflammation appears to play a major role in the development of obesity and represents a central point for the activity of cellular and humoral components in the adipose tissue. Macrophages play a key role as the main cellular component of the adipose tissue regulating the chronic inflammation and modulating the secretion and differentiation of various pro- and anti-inflammatory cytokines. Inflammation also involves a series of signaling pathways that might represent the focus for new therapies and interventions. Weight loss is essential in decreasing cardiometabolic risks and the degree of associated inflammation; however, the latter can persist for long after the excess weight is lost, and can involve changes in macrophage phenotypes that can ensure the metabolic adjustment. A clear understanding of the pathophysiological processes in the adipose tissue and the interplay between obesity and chronic inflammation can lead to a better understanding of the development of comorbidities and may ensure future targets for the treatment of obesity.

Along with the rapid and extensive advancements in the 3D printing field, a diverse range of uses for 3D printing have appeared in the spectrum of medical applications. Vat photopolymerization (VPP) stands out as one of the most extensively researched methods of 3D printing, with its main advantages being a high printing speed and the ability to produce high-resolution structures. A major challenge in using VPP 3D-printed materials in medicine is the general incompatibility of standard VPP resin mixtures with the requirements of biocompatibility and biofunctionality. Instead of developing completely new materials, an alternate approach to solving this problem involves adapting existing biomaterials. These materials are incompatible with VPP 3D printing in their pure form but can be adapted to the VPP chemistry and general process through the use of innovative mixtures and the addition of specific pre- and post-printing steps. This review’s primary objective is to highlight biofunctional and biocompatible materials that have been adapted to VPP. We present and compare the suitability of these adapted materials to different medical applications and propose other biomaterials that could be further adapted to the VPP 3D printing process in order to fulfill patient-specific medical requirements.

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.

The incidence of microbial infections in orthopedic prosthetic surgeries is a perennial problem that increases morbidity and mortality, representing one of the major complications of such medical interventions. The emergence of novel technologies, especially 3D printing, represents a promising avenue of development for reducing the risk of such eventualities. There are already a host of biomaterials, suitable for 3D printing, that are being tested for antimicrobial properties when they are coated with bioactive compounds, such as antibiotics, or combined with hydrogels with antimicrobial and antioxidant properties, such as chitosan and metal nanoparticles, among others. The materials discussed in the context of this paper comprise beta-tricalcium phosphate (β-TCP), biphasic calcium phosphate (BCP), hydroxyapatite, lithium disilicate glass, polyetheretherketone (PEEK), poly(propylene fumarate) (PPF), poly(trimethylene carbonate) (PTMC), and zirconia. While the recent research results are promising, further development is required to address the increasing antibiotic resistance exhibited by several common pathogens, the potential for fungal infections, and the potential toxicity of some metal nanoparticles. Other solutions, like the incorporation of phytochemicals, should also be explored. Incorporating artificial intelligence (AI) in the development of certain orthopedic implants and the potential use of AI against bacterial infections might represent viable solutions to these problems. Finally, there are some legal considerations associated with the use of biomaterials and the widespread use of 3D printing, which must be taken into account.

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.