Microbial Contamination During Long-term Ex Vivo Normothermic Machine Perfusion of Human Livers.

BACKGROUND: Normothermic machine perfusion permits the ex vivo preservation of human livers before transplantation. Long-term perfusion for days-to-weeks provides the opportunity for enhanced pretransplant assessment and potential regeneration of organs. However, this risks microbial contamination and infection of the recipient if the organ is transplanted. An understanding of perfusate microbial contamination is required to inform infection control procedures and antimicrobial prophylaxis for this technology. METHODS: We modified a liver perfusion machine for long-term use by adding long-term oxygenators and a dialysis filter. Human livers that were not suitable for transplantation were perfused using a red-cell-based perfusate under aseptic and normothermic conditions (36 °C) with a goal of 14 d. Cephazolin was added to the perfusate for antimicrobial prophylaxis. Perfusate and bile were sampled every 72 h for microbial culture. RESULTS: Eighteen partial human livers (9 left lateral segment grafts and 9 extended right grafts) were perfused using our perfusion system. The median survival was 7.2 d. All organs surviving longer than 7 d (9/18) had negative perfusate cultures at 24 and 48 h. Half of the grafts (9/18) became culture-positive by the end of perfusion. Microbial contaminants included Gram-negative ( Pseudomonas species, Proteus mirabilis, Stenotrophomonas maltophilia ) and Gram-positive bacteria ( Staphylococcus epidermidis , Enterococcus faecalis , and Bacillus species) as well as yeast ( Candida albicans ). CONCLUSIONS: Microbial contamination of perfusate is common during long-term perfusion of human livers with both exogenous and endogenous sources. Enhanced infection control practices and review of targeted antimicrobial prophylaxis are likely to be necessary for translation into the clinical arena.

Automatic Refractive Error Estimation Using Deep Learning-Based Analysis of Red Reflex Images.

Purpose/Background: We evaluate how a deep learning model can be applied to extract refractive error metrics from pupillary red reflex images taken by a low-cost handheld fundus camera. This could potentially provide a rapid and economical vision-screening method, allowing for early intervention to prevent myopic progression and reduce the socioeconomic burden associated with vision impairment in the later stages of life. Methods: Infrared and color images of pupillary crescents were extracted from eccentric photorefraction images of participants from Choithram Hospital in India and Dargaville Medical Center in New Zealand. The pre-processed images were then used to train different convolutional neural networks to predict refractive error in terms of spherical power and cylindrical power metrics. Results: The best-performing trained model achieved an overall accuracy of 75% for predicting spherical power using infrared images and a multiclass classifier. Conclusions: Even though the model's performance is not superior, the proposed method showed good usability of using red reflex images in estimating refractive error. Such an approach has never been experimented with before and can help guide researchers, especially when the future of eye care is moving towards highly portable and smartphone-based devices.