Bioengineering

Silicone implants are widely used in medical applications, particularly for breast augmentation and reconstruction. However, ongoing concerns regarding their long-term safety and biocompatibility necessitate comprehensive characterization. This review critically evaluates the chemical, physical, and biological testing approaches currently used to assess silicone implants, and specifically silicone breast implants, biocompatibility, and highlights the limitations of existing ISO 10993-based protocols, which often apply a one-size-fits-all model. We propose an application-specific framework to improve the relevance and precision of biocompatibility assessments. Chemical analyses, including Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy, provide essential information on polymer structure, integrity, and composition, thereby supporting quality control and market surveillance. Physical characterization methods, such as scanning electron microscopy (SEM), atomic force microscopy (AFM), and contact angle measurements, assess the surface morphology, hydrophobicity, and potential defects that may influence the host response. Mechanical testing, which evaluates properties such as tensile strength and fatigue resistance, simulates in vivo stress conditions to predict the long-term durability. Biological evaluations guided by ISO 10993 use in vitro and in vivo models to assess cytotoxicity, adhesion, inflammation, and tissue integration. However, these are often not tailored to the implant type, surface features, or duration of exposure. Emerging tools, such as organ-on-a-chip platforms and machine learning models, offer new possibilities for predictive and context-specific evaluation. We advocate a standardized, modular strategy that integrates chemical, physical, and biological testing with clinical data to bridge preclinical assessments and real-world outcomes, with a specific focus on silicone breast implants. The aim of this approach is to improve patient safety, regulatory clarity, and device innovation across the global landscape of silicone implant development.

This retrospective study aimed to evaluate the survival rate of hybrid dental implants in different patient profiles and clinical conditions. A total of 1215 patients’ files were analyzed from patients with at least one hybrid dental implant inserted at ILAPEO College (Curitiba, Brazil) from 2018 to 2024. The data collection was performed from 2021 to 2024. Parameters related to patients, implants, and surgical characteristics were collected. Descriptive summary statistics were estimated for all parameters. The associations between the dependent variables “implant survival” and patient, procedure, and implant characteristics were assessed by the Cox proportional hazards model. A total of 4783 hybrid dental implants (Helix, GM, Neodent) were placed in 1215 patients with a mean age of 57.17 ± 12.09 years. The most frequent patients’ medical conditions were diabetes, hypertension, thyroid dysfunction, use of steroids (corticoids), psychological limitations, and bruxism and clenching. Patients were followed for a mean period of 29.54 ± 18.95 months. Immediate loading was applied in 2302 (48.13%) implants and conventional loading in 1735 (36.27%). One hundred and fifty-one implants were lost due to a lack of osseointegration, resulting in an implant survival rate of 95.4% (CI: 94.4%; 96.6%). Adverse events were reported in 389 (8.13%) implants. Uncontrolled hypertension, hypertension without information on control, absence of final abutment, replacement implant, and adverse event occurrence were associated with implant loss. Treatment using a hybrid macrogeometry dental implant is an option for total or partial edentulous patients with compromised health and different clinical conditions.

Screening for retinal disease is increasingly performed by general practitioners and other non-ophthalmologist clinicians in primary care, especially where access to ophthalmology is limited and diagnostic accuracy may be suboptimal. To investigate the role of an automated fundus-interpretation support solution in improving general physicians’ screening accuracy and referral decisions, we conducted a paired before–after study evaluating an AI-based decision support tool. Four non-ophthalmologists who have been involved in screen fundus images in clinical practice reviewed 500 de-identified color fundus photographs twice—first unaided and, after a washout period, with AI assistance. With AI support, diagnostic accuracy improved significantly from 82.8% to 91.1% (p < 0.0001), with the greatest benefit observed in glaucoma-suspect and multi-pathology cases. Clinicians retained final diagnostic authority, and a favorable safety profile was observed. These results demonstrate that AI-assisted diagnosis aid can meaningfully augment non-ophthalmologist screening and referral decision-making in real-world primary care, while underscoring the need for broader validation and implementation studies.