Acute Rejection Rates in Vascularized Composite Allografts: A Systematic Review of Case Reports.

INTRODUCTION: Vascularized Composite Allografts (VCA) are usually performed in a full major histocompatibility complex mismatch setting, with a risk of acute rejection depending on factors such as the type of immunosuppression therapy and the quality of graft preservation. In this systematic review, we present the different immunosuppression protocols used in VCA and point out relationships between acute rejection rates and possible factors that might influence it. METHODS: This systematic review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We systematically searched Medline (PubMed), Embase, and The Cochrane Library between November 2022 and February 2023, using following Mesh Terms: Transplant, Transplantation, Hand, Face, Uterus, Penis, Abdominal Wall, Larynx, and Composite Tissue Allografts. All VCA case reports and reviews describing multiple case reports were included. RESULTS: We discovered 211 VCA cases reported. The preferred treatment was a combination of antithymocyte globulins, mycophenolate mofetil (MMF), tacrolimus, and steroids; and a combination of MMF, tacrolimus, and steroids for induction and maintenance treatment, respectively. Burn patients showed a higher acute rejection rate (P = 0.073) and were administered higher MMF doses (P = 0.020). CONCLUSIONS: In contrast to previous statements, the field of VCA is not rapidly evolving, as it has encountered challenges in addressing immune-related concerns. This is highlighted by the absence of a standardized immunosuppression regimen. Consequently, more substantial data are required to draw more conclusive results regarding the immunogenicity of VCAs and the potential superiority of one immunosuppressive treatment over another. Future efforts should be made to report the VCA surgeries comprehensively, and muti-institutional long-term prospective follow-up studies should be performed to compare the number of acute rejections with influencing factors.

Smartphone-based thermography in flap surgery: A systematic review and meta-analysis of perforator identification.

Background: Thermography can be used in pre-operative planning of free perforator flap surgeries. Thermography assesses skin temperature by measuring the quantity of infrared radiation observed. In this meta-analysis, authors assess the sensitivity of smartphone-based thermal imaging (SBTI) in the detection of perforators and analyze the difference between static and dynamic imaging. Materials and methods: Authors followed the PRISMA guidelines for systematic reviews and meta-analyses. The meta package in R was used to conduct the meta-analysis. The "metaprop" function was used to calculate the overall sensitivity estimate and 95% confidence interval. The "metaprop.one" function was used to calculate subgroup estimates for static and dynamic study types. The "metareg" function was used to conduct meta-regression analyses to explore sources of heterogeneity. Results: This study includes seven articles with 1429 perforators being evaluated. The overall proportion of the sensitivities was estimated to be 0.8754 (95% CI: 0.7542; 0.9414) using a random effects model. The heterogeneity of the studies was high, as indicated by the tau^2 value of 1.2500 (95% CI: 0.4497; 8.4060) and the I^2 value of 92.6% (95% CI: 88.1%; 95.4%). The pooled sensitivity for static imaging was 0.8636 (95%CI: 0.6238-0.9603) with a tau^2 of 2.0661 and a tau of 1.4374, while the pooled sensitivity for dynamic imaging was slightly higher (p = 0.7016) at 0.8993 (95%CI: 0.7412-0.9653) with a smaller tau^2 of 0.8403 and a tau of 0.9167. Conclusion: Further studies need to confirm that SBTI is a reliable and convenient technique for detecting perforators for the pre-operative planning of free perforator flap surgeries.

Tissue engineering strategies for breast reconstruction: a literature review of current advances and future directions.

Background and Objective: Mastectomy is a primary treatment for breast cancer patients, and both autologous and implant-based reconstructive techniques have shown excellent results. In recent years, advancements in bioengineering have led to a proliferation of innovative approaches to breast reconstruction. This article comprehensively explores the promising perspectives offered by bioengineering and tissue engineering in the field of breast reconstruction. Methods: A literature review was conducted between April and June 2023 on PubMed and Google Scholar Databases. All English and French articles related to bioengineering applied to the field of breast reconstruction were included. We used the Evidence-Based Veterinary Medicine Association (EBVM) Toolkit 14 checklist for narrative reviews as a quality assurance measure and the Scale for the Assessment of Narrative Review Articles (SANRA) tool to self-assess our methodology. Key Content and Findings: Over 130 references related to breast bioengineering were included. The analysis revealed four key applications: enhancing the quality of the skin envelope, improving the viability of fat grafting, creating breast shape and volume via bio-printing, and optimizing nipple reconstruction through engineering techniques. The primary identified approaches revolved around establishing structural support and enhancing cellular viability. Structural techniques predominantly involved the implementation of 3D printed, decellularized, or biocompatible material scaffolds. Meanwhile, promoting cellular content trophicity primarily focused on harnessing the regenerative potential of adipose-derived stem cells (ADSCs) and increasing the tissue's survivability and cell trophicity. Conclusions: Tissue and bioengineering hold immense promise in the field of breast reconstruction, offering a diverse array of approaches. By combining existing techniques with novel advancements, they have the potential to significantly enhance the therapeutic options available to plastic and reconstructive surgeons.

Improved sexuality and satisfactory lubrication after genital affirmation surgery using penile skin inversion in transgender women: A satisfaction study.

INTRODUCTION: Genital affirmation surgery (GAS) requests are consistently increasing in demand. The gold standard is penile skin inversion, using cutaneous grafting for neovagina creation. The aim is to achieve the most realistic results both physically and functionally. Different studies have contradictory results, and the use of lubrication is insufficiently clear, while the use of sigmoidoplasty has been defended for constant lubrication. AIMS: Our aim was to evaluate transgender women's sexual function and lubrication after vaginoplasty by penile skin inversion. METHODS: We performed a prospective study on 45 patients who underwent primary penile inversion vaginoplasty. Participants answered two questionnaires during the follow-up consultation: the Female Sexual Function Index (FSFI) and an 18-item custom questionnaire. RESULTS: The average FSFI score of our patients was 28.9, up to the cut-off defining a sexual disorder. Compared to the Wylomanski control group, no differences were found for the FSFI score and in the subgroups. Considering lubrication, 69% of the patients were satisfied or very satisfied with their lubrication. Furthermore, 53% reported a fluid release at each orgasm. CONCLUSION: This study reported high satisfaction for both sexuality and lubrication, proning against penile inversion's GAS bad reputation concerning postoperative lubrication. A squirting effect was described for the first time and was present in 53% of our transpatients.

Unveiling the power of convolutional neural networks in melanoma diagnosis.

Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identification of melanomas. Multiple studies have evaluated the performance of convolutional neural networks, and most algorithms match or even surpass the accuracy of dermatologists. However, only 23.8% of dermatologists have good or excellent knowledge of the topic. We believe that the lack of knowledge physicians experience regarding artificial intelligence is an obstacle to its clinical implementation. We describe how a convolutional neural network differentiates a benign from a malignant lesion. We systematically searched the Web of Science, Medline (PubMed), and The Cochrane Library on the 9th February, 2022. We focused on articles describing the role and use of artificial intelligence in melanoma recognition between 2017 and 2022, using the following MeSH terms: "melanoma," "diagnosis," and "artificial intelligence". Traditional machine learning algorithms comprise different parts which must preprocess, segment, extract features and classify the lesion into benign or malignant. Deep learning algorithms can perform these steps simultaneously, which significantly enhances efficiency. Convolutional neural networks include a convolutional layer, a pooling layer, and a fully connected layer. Convolutional and pooling layers extract features from the lesion and reduce computational power, whereas fully connected layers classify the image into two or more categories. Additionally, we suggest that further studies should be performed to accelerate the clinical implementation of artificial intelligence, to create comprehensive datasets and to generate explainable algorithms.