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.

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.