Proteome Expression Signatures: Differences between Orbital and Subcutaneous Abdominal Adipose Tissues.

Differences between orbital and subcutaneous abdominal fat in the same patient have been noted but not formally investigated, previously. The objective of this research was to compare the differential expression of protein profiles in subcutaneous abdominal and orbital adipose tissues. In this cross-sectional, observational study, orbital fat tissue was sampled from 10 patients who underwent blepharoplasty and agreed to provide a small sample of subcutaneous abdominal fat. Shotgun mass spectrometry was performed on the extracted proteome. Data were analyzed using protein appearance patterns, differential expression and statistical enrichment. Protein analysis revealed significant differences in proteomics and differential expression between the orbital and subcutaneous abdominal adipose tissues, which presented five proteins that were uniquely expressed in the orbital fat and 18 in the subcutaneous abdominal fat. Gene Ontology analysis identified significantly different cellular processes and components related to the extracellular matrix or basement membrane components. This analysis shows the differences between orbital and subcutaneous abdominal fat found in proteomics differential expression, uniquely expressed proteins, and cellular processes. Further research is needed to correlate specific proteins and cellular processes to the mechanism of fat accumulation and obesity.

Predicting Mohs surgery complexity by applying machine learning to patient demographics and tumor characteristics.

Mohs micrographic surgery (MMS) is considered the gold standard for difficult-to-treat malignant skin tumors, whose incidence is on the rise. Currently, there are no agreed upon classifiers to predict complex MMS procedures. Such classifiers could enable better patient scheduling, reduce staff burnout and improve patient education. Our goal was to create an accessible and interpretable classifier(s) that would predict complex MMS procedures. A retrospective study applying machine learning models to a dataset of 8644 MMS procedures to predict complex wound reconstruction and number of MMS procedure stages. Each procedure record contained preoperative data on patient demographics, estimated clinical tumor size prior to surgery (mean diameter), tumor characteristics and tumor location, and postoperative procedure outcomes included the wound reconstruction technique and the number of MMS stages performed in order to achieve tumor-free margins. For the number of stages complexity classification model, the area under the receiver operating characteristic curve (AUROC) was 0.79 (good performance), with model accuracy of 77%, sensitivity of 71%, specificity of 77%, positive prediction value (PPV) of 14% and negative prediction value (NPV) of 98%. The results for the wound reconstruction complexity classification model were 0.84 for the AUROC (excellent performance), with model accuracy of 75%, sensitivity of 72%, specificity of 76%, PPV of 39% and NPV of 93%. The ML models we created predict the complexity of the components that comprise the MMS procedure. Using the accessible and interpretable tool we provide online, clinicians can improve the management and well-being of their patients. Study limitation is that models are based on data generated from a single surgeon.