Evaluation of the potential of Delta-aminolevulinic acid for simultaneous detection of bioburden and anti-microbial photodynamic therapy of MRSA infected wounds in Swiss albino mice.

BACKGROUND: The dramatic increase of drug-resistant bacteria necessitates urgent development of platforms to simultaneously detect and inactivate bacteria causing wound infections, but are confronted with various challenges. Delta amino levulinic acid (ALA) induced protoporphyrin IX (PpIX) can be a promising modality for simultaneous bioburden diagnostics and therapeutics. Herein, we report utility of ALA induced protoporphyrin (PpIX) based simultaneous bioburden detection, photoinactivation and therapeutic outcome assessment in methicillin resistant Staphylococcus aureus (MRSA) infected wounds of mice. METHODS: MRSA infected wounds treated with 10% ALA were imaged with help of a blue LED (∼405 nm) based, USB powered, hand held device integrated with a modular graphic user interface (GUI). Effect of ALA application time, bacteria load, post bacteria application time points on wound fluorescence studied. PpIX fluorescence observed after excitation with blue LEDs was used to detect bioburden, start red light mediated antimicrobial photodynamic therapy (aPDT), determine aPDT effectiveness and assess selectivity of the approach. RESULTS: ALA-PpIX fluorescence of wound bed discriminates infected from uninfected wounds and detects clinically relevant load. While wound fluorescence pattern changes as a function of ALA incubation and post infection time, intra-wound inhomogeneity in fluorescence correlates with the Gram staining data on presence of biofilms foci. Lack of red fluorescence from wound granulation tissue treated with ALA suggests selectivity of the approach. Further, significant reduction (∼50%) in red fluorescence, quantified using the GUI, relates well with bacteria load reduction observed post topical aPDT. CONCLUSION: The potential of ALA induced PpIX for simultaneous detection of bioburden, photodynamic inactivation and "florescence-guided aPDT assessment" is demonstrated in MRSA infected wounds of mice.

Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging.

Significance: Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring additional adjuvant systemic therapy. Identification of this latter cohort that is likely to recur following surgery is key to better personalized therapy selection, but there is a lack of proper quantitative assessment tools for potential clinical adoption. Aim: The purpose of this study is to employ Mueller matrix (MM) polarized light microscopy in combination with supervised machine learning (ML) to quantitatively analyze the prognostic value of peri-tumoral collagen in CRC in relation to 5-year local recurrence (LR). Approach: A simple MM microscope setup was used to image surgical resection samples acquired from stage III CRC patients. Various potential biomarkers of LR were derived from MM elements via decomposition and transformation operations. These were used as features by different supervised ML models to distinguish samples from patients that locally recurred 5 years later from those that did not. Results: Using the top five most prognostic polarimetric biomarkers ranked by their relevant feature importances, the best-performing XGBoost model achieved a patient-level accuracy of 86%. When the patient pool was further stratified, 96% accuracy was achieved within a tumor-stage-III sub-cohort. Conclusions: ML-aided polarimetric analysis of collagenous stroma may provide prognostic value toward improving the clinical management of CRC patients.

Mueller matrix polarization parameters correlate with local recurrence in patients with stage III colorectal cancer.

The peri-tumoural stroma has been explored as a useful source of prognostic information in colorectal cancer. Using Mueller matrix (MM) polarized light microscopy for quantification of unstained histology slides, the current study assesses the prognostic potential of polarimetric characteristics of peri-tumoural collagenous stroma architecture in 38 human stage III colorectal cancer (CRC) patient samples. Specifically, Mueller matrix transformation and polar decomposition parameters were tested for association with 5-year patient local recurrence outcomes. The results show that some of these polarimetric parameters were significantly different (p value < 0.05) for the recurrence versus the no-recurrence patient cohorts (Mann-Whitney U test). MM parameters may thus be prognostically valuable towards improving clinical management/treatment stratification in CRC patients.

Binary dose level classification of tumour microvascular response to radiotherapy using artificial intelligence analysis of optical coherence tomography images.

The dominant consequence of irradiating biological systems is cellular damage, yet microvascular damage begins to assume an increasingly important role as the radiation dose levels increase. This is currently becoming more relevant in radiation medicine with its pivot towards higher-dose-per-fraction/fewer fractions treatment paradigm (e.g., stereotactic body radiotherapy (SBRT)). We have thus developed a 3D preclinical imaging platform based on speckle-variance optical coherence tomography (svOCT) for longitudinal monitoring of tumour microvascular radiation responses in vivo. Here we present an artificial intelligence (AI) approach to analyze the resultant microvascular data. In this initial study, we show that AI can successfully classify SBRT-relevant clinical radiation dose levels at multiple timepoints (t = 2-4 weeks) following irradiation (10 Gy and 30 Gy cohorts) based on induced changes in the detected microvascular networks. Practicality of the obtained results, challenges associated with modest number of animals, their successful mitigation via augmented data approaches, and advantages of using 3D deep learning methodologies, are discussed. Extension of this encouraging initial study to longitudinal AI-based time-series analysis for treatment outcome predictions at finer dose level gradations is envisioned.