In vitro and ex vivo models of microbial keratitis: Present and future.

Microbial keratitis (MK) is an infection of the cornea, caused by bacteria, fungi, parasites, or viruses. MK leads to significant morbidity, being the fifth leading cause of blindness worldwide. There is an urgent requirement to better understand pathogenesis in order to develop novel diagnostic and therapeutic approaches to improve patient outcomes. Many in vitro, ex vivo and in vivo MK models have been developed and implemented to meet this aim. Here, we present current in vitro and ex vivo MK model systems, examining their varied design, outputs, reporting standards, and strengths and limitations. Major limitations include their relative simplicity and the perceived inability to study the immune response in these MK models, an aspect widely accepted to play a significant role in MK pathogenesis. Consequently, there remains a dependence on in vivo models to study this aspect of MK. However, looking to the future, we draw from the broader field of corneal disease modelling, which utilises, for example, three-dimensional co-culture models and dynamic environments observed in bioreactors and organ-on-a-chip scenarios. These remain unexplored in MK research, but incorporation of these approaches will offer further advances in the field of MK corneal modelling, in particular with the focus of incorporation of immune components which we anticipate will better recapitulate pathogenesis and yield novel findings, therefore contributing to the enhancement of MK outcomes.

Photosensitizer-Amplified Antimicrobial Materials for Broad-Spectrum Ablation of Resistant Pathogens in Ocular Infections.

The emergence of multidrug resistant (MDR) pathogens and the scarcity of new potent antibiotics and antifungals are one of the biggest threats to human health. Antimicrobial photodynamic therapy (aPDT) combines light and photosensitizers to kill drug-resistant pathogens; however, there are limited materials that can effectively ablate different classes of infective pathogens. In the present work, a new class of benzodiazole-paired materials is designed as highly potent PDT agents with broad-spectrum antimicrobial activity upon illumination with nontoxic light. The results mechanistically demonstrate that the energy transfer and electron transfer between nonphotosensitive and photosensitive benzodiazole moieties embedded within pathogen-binding peptide sequences result in increased singlet oxygen generation and enhanced phototoxicity. Chemical optimization renders PEP3 as a novel PDT agent with remarkable activity against MDR bacteria and fungi as well as pathogens at different stages of development (e.g., biofilms, spores, and fungal hyphae), which also prove effective in an ex vivo porcine model of microbial keratitis. The chemical modularity of this strategy and its general compatibility with peptide-based targeting agents will accelerate the design of highly photosensitive materials for antimicrobial PDT.

A Novel Fit-Flexible Fluorescence Soft Imager: Tri-Sensing of Intensity, Fall-Time, and Life Profile.

Time-resolved fluorescence imaging techniques, like confocal fluorescence lifetime imaging microscopy, are powerful photonic instrumentation tools of modern science with diverse applications, including: biology, medicine, and chemistry. However, complexities of the systems, both at specimen and device levels, cause difficulties in quantifying soft biomarkers. To address the problems, we first aim to understand and model the underlying photophysics of fluorescence decay curves. For this purpose, we provide a set of mathematical functions, called "life models", fittable with the real temporal recordings of histogram of photon counts. For each model, an equivalent electrical circuit, called a "life circuit", is derived for explaining the whole process. In confocal endomicroscopy, the components of excitation laser, specimen, and fluorescence-emission signal as the histogram of photon counts are modelled by a power source, network of resistor-inductor-capacitor circuitry, and multimetre, respectively. We then design a novel pixel-level temporal classification algorithm, called a "fit-flexible approach", where qualities of "intensity", "fall-time", and "life profile" are identified for each point. A model selection mechanism is used at each pixel to flexibly choose the best representative life model based on a proposed Misfit-percent metric. A two-dimensional arrangement of the quantified information detects some kind of structural information. This approach showed a potential of separating microbeads from lung tissue, distinguishing the tri-sensing from conventional methods. We alleviated by 7% the error of the Misfit-percent for recovering the histograms on real samples than the best state-of-the-art competitor. Codes are available online.

Bayesian Statistical Analysis for Bacterial Detection in Pulmonary Endomicroscopic Fluorescence Lifetime Imaging.

Pneumonia, a respiratory disease often caused by bacterial infection in the distal lung, requires rapid and accurate identification, especially in settings such as critical care. Initiating or de-escalating antimicrobials should ideally be guided by the quantification of pathogenic bacteria for effective treatment. Optical endomicroscopy is an emerging technology with the potential to expedite bacterial detection in the distal lung by enabling in vivo and in situ optical tissue characterisation. With advancements in detector technology, optical endomicroscopy can utilize fluorescence lifetime imaging (FLIM) to help detect events that were previously challenging or impossible to identify using fluorescence intensity imaging. In this paper, we propose an iterative Bayesian approach for bacterial detection in FLIM. We model the FLIM image as a linear combination of background intensity, Gaussian noise, and additive outliers (labelled bacteria). While previous bacteria detection methods model anomalous pixels as bacteria, here the FLIM outliers are modelled as circularly symmetric Gaussian-shaped objects, based on their discrete shape observed through visual analysis and the physical nature of the imaging modality. A Hierarchical Bayesian model is used to solve the bacterial detection problem where prior distributions are assigned to unknown parameters. A Metropolis-Hastings within Gibbs sampler draws samples from the posterior distribution. The proposed method's detection performance is initially measured using synthetic images, and shows significant improvement over existing approaches. Further analysis is conducted on real optical endomicroscopy FLIM images annotated by trained personnel. The experiments show the proposed approach outperforms existing methods by a margin of +16.85% ( F1 ) for detection accuracy.