Diffusion and Perfusion: The Keys to Fat Grafting

Background: Fat grafting is now widely used in plastic surgery. Long-term graft retention can be unpredictable. Fat grafts must obtain oxygen via diffusion until neovascularization occurs, so oxygen delivery may be the overarching variable in graft retention. Methods: We studied the peer-reviewed literature to determine which aspects of a fat graft and the microenvironment surrounding a fat graft affect oxygen delivery and created 3 models relating distinct variables to oxygen delivery and graft retention. Results: Our models confirm that thin microribbons of fat maximize oxygen transport when injected into a large, compliant, well-vascularized recipient site. The “Microribbon Model” predicts that, in a typical human, fat injections larger than 0.16 cm in radius will have a region of central necrosis. Our “Fluid Accommodation Model” predicts that once grafted tissues approach a critical interstitial fluid pressure of 9 mm Hg, any additional fluid will drastically increase interstitial fluid pressure and reduce capillary perfusion and oxygen delivery. Our “External Volume Expansion Effect Model” predicts the effect of vascular changes induced by preoperative external volume expansion that allow for greater volumes of fat to be successfully grafted. Conclusions: These models confirm that initial fat grafting survival is limited by oxygen diffusion. Preoperative expansion increases oxygen diffusion capacity allowing for additional graft retention. These models provide a scientific framework for testing the current fat grafting theories

Recurrence of Dupuytren’s contracture: A consensus-based definition

Purpose: One of the major determinants of Dupyutren disease (DD) treatment efficacy is recurrence of the contracture. Unfortunately, lack of agreement in the literature on what constitutes recurrence makes it nearly impossible to compare the multiple treatments alternatives available today. The aim of this study is to bring an unbiased pool of experts to agree upon what would be considered a recurrence of DD after treatment; and from that consensus establish a much-needed definition for DD recurrence. Methods: To reach an expert consensus on the definition of recurrence we used the Delphi method and invited 43 Dupuytren’s research and treatment experts from 10 countries to participate by answering a series of questionnaire rounds. After each round the answers were analyzed and the experts received a feedback report with another questionnaire round to further hone in of the definition. We defined consensus when at least 70% of the experts agreed on a topic. Results: Twenty-one experts agreed to participate in this study. After four consensus rounds, we agreed that DD recurrence should be defined as “more than 20 degrees of contracture recurrence in any treated joint at one year post-treatment compared to six weeks post-treatment”. In addition, “recurrence should be reported individually for every treated joint” and afterwards measurements should be repeated and reported yearly. Conclusion: This study provides the most comprehensive to date definition of what should be considered recurrence of DD. These standardized criteria should allow us to better evaluate the many treatment alternatives

Trends and predictors of transmitted drug resistance (TDR) and clusters with TDR in a local Belgian HIV-1 epidemic

We aimed to study epidemic trends and predictors for transmitted drug resistance (TDR) in our region, its clinical impact and its association with transmission clusters. We included 778 patients from the AIDS Reference Center in Leuven (Belgium) diagnosed from 1998 to 2012. Resistance testing was performed using population-based sequencing and TDR was estimated using the WHO-2009 surveillance list. Phylogenetic analysis was performed using maximum likelihood and Bayesian techniques. The cohort was predominantly Belgian (58.4%), men who have sex with men (MSM) (42.8%), and chronically infected (86.5%). The overall TDR prevalence was 9.6% (95% confidence interval (CI): 7.7-11.9), 6.5% (CI: 5.0-8.5) for nucleoside reverse transcriptase inhibitors (NRTI), 2.2% (CI: 1.4-3.5) for non-NRTI (NNRTI), and 2.2% (CI: 1.4-3.5) for protease inhibitors. A significant parabolic trend of NNRTI-TDR was found (p = 0.019). Factors significantly associated with TDR in univariate analysis were male gender, Belgian origin, MSM, recent infection, transmission clusters and subtype B, while multivariate and Bayesian network analysis singled out subtype B as the most predictive factor of TDR. Subtype B was related with transmission clusters with TDR that included 42.6% of the TDR patients. Thanks to resistance testing, 83% of the patients with TDR who started therapy had undetectable viral load whereas half of the patients would likely have received a suboptimal therapy without this test. In conclusion, TDR remained stable and a NNRTI up-and-down trend was observed. While the presence of clusters with TDR is worrying, we could not identify an independent, non-sequence based predictor for TDR or transmission clusters with TDR that could help with guidelines or public health measures

Method for analyzing neuronal network activity

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 73).Investigating the development of neuronal networks can help us to identify new therapies and treatments for conditions that affect the brain, such as autism and Alzheimer's disease. Two-photon calcium imaging has been a powerful tool for the investigation of the development of neuronal networks. However, one of the major challenges of working with two-photon calcium images is processing the large data sets, which often requires manual analysis by a skilled researcher. Here, we introduce a machine learning (ML) pipeline for the analysis of two-photon calcium image sequences. This semi-autonomous ML pipeline includes proposed methods for automatically identifying neurons, signal extraction, signal processing, event detection, feature extraction, and analysis. We run our ML pipeline on a dataset of two-photon calcium image sequences extracted by our team. This dataset includes two-photon calcium image sequences of spontaneous network activity from primary cortical cultures of Mecp2-deficient and wild-type mice. Loss-of-function mutation in the MECP2 gene, causes 95% of Rett syndrome cases and some cases of autism. We evaluate our ML pipeline using this dataset. Our ML pipeline reduces the time required to analyze two-photon calcium images from over 10 minutes to about 30 seconds per sample. Our goal is to accelerate the analysis of neuronal network function to aid in our understanding of neurological disorders and the identification of novel therapeutic targets.by Raoul-Emil Roger Khouri.M. Eng