Background: In an era where textured devices are being phased out due to concerns about BIA-ALCL, the Motiva SilkSurface breast implants intend to alleviate historical prosthesis-related complications. However, its safety and feasibility remain unelucidated. Methods: An analysis of Pubmed, Web of Science, Ovid, and Embase databases was performed. A total of 114 studies were identified initially, and 13 of these met the inclusion criteria and were assessed regarding postoperative parameters such as complication rate or follow-up period. Results: In 4784 patients who underwent breast augmentation with Motiva SilkSurface breast implants, a total of 250 (5.2%) complications were observed. Short- and medium-term complication rates ranged from 2.8–14.4% and 0.32–16.67%, respectively. The most common complication was early seroma (n = 52, overall incidence = 1.08%), followed by early hematoma (n = 28, overall incidence = 0.54%). The incidence of capsule contracture was 0.54% and breast implant-associated-anaplastic large cell lymphoma was not observed. Discussion: Although the majority of the studies in the current literature suggest the distinction of the Motiva SilkSurface breast implants in terms of postoperative complications and capsular contracture, its safety and feasibility need to be further elucidated with well-designed, large-scale, multicenter, prospective case-control studies. Other: No funding was received.

Skin cutaneous melanoma is characterized by high malignancy and prognostic heterogeneity. Immune cell networks are critical to the biological progression of melanoma through the tumor microenvironment. Thus, identifying effective biomarkers for skin cutaneous melanoma from the perspective of the tumor microenvironment may offer strategies for precise prognosis prediction and treatment selection. A total of 470 cases from The Cancer Genome Atlas and 214 from the Gene Expression Omnibus were systematically evaluated to construct an optimal independent immune cell risk model with predictive value using weighted gene co-expression network analysis, Cox regression, and least absolute shrinkage and selection operator assay. The predictive power of the developed model was estimated through receiver operating characteristic curves and Kaplan-Meier analysis. The association of the model with tumor microenvironment status, immune checkpoints, and mutation burden was assessed using multiple algorithms. Additionally, the sensitivity of immune and chemotherapeutics was evaluated using the ImmunophenScore and pRRophetic algorithm. Furthermore, the expression profiles of risk genes were validated using gene expression profiling interactive analysis and Human Protein Atlas resources. The risk model integrated seven immune-related genes: ARNTL, N4BP2L1, PARP11, NUB1, GSDMD, HAPLN3, and IRX3. The model demonstrated considerable predictive ability and was positively associated with clinical and molecular characteristics. It can be utilized as a prognostic factor for skin cutaneous melanoma, where a high-risk score was linked to a poor prognosis and indicated an immunosuppressive microenvironment. Furthermore, the model revealed several potential target checkpoints and predicted the therapeutic benefits of multiple clinically used drugs. Our findings provide a comprehensive landscape of the tumor immune microenvironment in skin cutaneous melanoma and identify prognostic markers that may serve as efficient clinical diagnosis and treatment selection tools.