Porcine-derived collagen peptides promote re-epithelialisation through activation of integrin signalling.

Chronic non-healing cutaneous wounds represent a major burden to patients and healthcare providers worldwide, emphasising the continued unmet need for credible and efficacious therapeutic approaches for wound healing. We have recently shown the potential for collagen peptides to promote proliferation and migration during cutaneous wound healing. In the present study, we demonstrate that the application of porcine-derived collagen peptides significantly increases keratinocyte and dermal fibroblast expression of integrin α2ß1 and activation of an extracellular signal-related kinase (ERK)-focal adhesion kinase (FAK) signalling cascade during wound closure in vitro. SiRNA-mediated knockdown of integrin ß1 impaired porcine-derived collagen peptide-induced wound closure and activation of ERK-FAK signalling in keratinocytes but did not impair ERK or FAK signalling in dermal fibroblasts, implying the activation of differing downstream signalling pathways. Studies in ex vivo human 3D skin equivalents subjected to punch biopsy-induced wounding confirmed the ability of porcine-derived collagen peptides to promote wound closure by enhancing re-epithelialisation. Collectively, these data highlight the translational and clinical potential for porcine-derived collagen peptides as a viable therapeutic approach to promote re-epithelialisation of superficial cutaneous wounds.

Validation of epidermal AMBRA1 and loricrin (AMBLor) as a prognostic biomarker for nonulcerated American Joint Committee on Cancer stage I/II cutaneous melanoma.

BACKGROUND: Combined expression of the autophagy-regulatory protein AMBRA1 (activating molecule in Beclin1-regulated autophagy) and the terminal differentiation marker loricrin in the peritumoral epidermis of stage I melanomas can identify tumour subsets at low risk of -metastasis. OBJECTIVES: To validate the combined expression of peritumoral AMBRA1 and loricrin (AMBLor) as a prognostic biomarker able to identify both stage I and II melanomas at low risk of tumour recurrence. METHODS: Automated immunohistochemistry was used to analyse peritumoral AMBRA1 and loricrin expression in geographically distinct discovery (n = 540) and validation (n = 300) cohorts of nonulcerated American Joint Committee on Cancer (AJCC) stage I and II melanomas. AMBLor status was correlated with clinical outcomes in the discovery and validation cohorts separately and combined. RESULTS: Analysis of AMBLor in the discovery cohort revealed a recurrence-free survival (RFS) rate of 95.5% in the AMBLor low-risk group vs. 81.7% in the AMBLor at-risk group (multivariate log-rank, P < 0.001) and a negative predictive value (NPV) of 96.0%. In the validation cohort, AMBLor analysis revealed a RFS rate of 97.6% in the AMBLor low-risk group vs. 78.3% in the at-risk group (multivariate log-rank, P < 0.001) and a NPV of 97.6%. In a multivariate model considering AMBLor, Breslow thickness, age and sex, analysis of the combined discovery and validation cohorts showed that the estimated effect of AMBLor was statistically significant, with a hazard ratio of 3.469 (95% confidence interval 1.403-8.580, P = 0.007) and an overall NPV of 96.5%. CONCLUSIONS: These data provide further evidence validating AMBLor as a prognostic biomarker to identify nonulcerated AJCC stage I and II melanoma tumours at low risk of disease recurrence.

PhenoBot: an automated system for leaf area analysis using deep learning.

MAIN CONCLUSION: A low-cost dynamic image capturing and analysis pipeline using color-based deep learning segmentation was developed for direct leaf area estimation of multiple crop types in a commercial environment. Crop yield is largely driven by intercepted radiation of the leaf canopy, making the leaf area index (LAI) a critical parameter for estimating yields. The growth rate of leaves at different growth stages determines the overall LAI, which is used by crop growth models (CGM) for simulating yield. Consequently, precise phenotyping of the leaves can help elucidate phenological processes relating to resource capturing. A stable system for acquiring images and a strong data processing backend play a vital role in reducing throughput time and increasing accuracy of calculations, compared to manual analysis. However, most available solutions are not dynamic, as they use color-based segmentation, which fails to capture leaves with varying shades and shapes. We have developed a system that uses a low-cost setup to acquire images and an automated pipeline to manage the data storage on the device and in the cloud. The system is powered by virtual machines that run multiple custom-trained deep learning models to segment out leaves, calculate leaf area (LA) for the whole set and at the individual leaf level, overlays important information on the images, and appends them on a compatible file used for CGMs with very high accuracy. The pipeline is dynamic and can be used for multiple crops. The use of open-source hardware, platforms, and algorithms makes this system affordable and reproducible.

C3 Glomerulopathy and Related Disorders in Children: Etiology-Phenotype Correlation and Outcomes.

BACKGROUND AND OBJECTIVES: Membranoproliferative GN and C3 glomerulopathy are rare and overlapping disorders associated with dysregulation of the alternative complement pathway. Specific etiologic data for pediatric membranoproliferative GN/C3 glomerulopathy are lacking, and outcome data are based on retrospective studies without etiologic data. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A total of 80 prevalent pediatric patients with membranoproliferative GN/C3 glomerulopathy underwent detailed phenotyping and long-term follow-up within the National Registry of Rare Kidney Diseases (RaDaR). Risk factors for kidney survival were determined using a Cox proportional hazards model. Kidney and transplant graft survival was determined using the Kaplan-Meier method. RESULTS: Central histology review determined 39 patients with C3 glomerulopathy, 31 with immune-complex membranoproliferative GN, and ten with immune-complex GN. Patients were aged 2-15 (median, 9; interquartile range, 7-11) years. Median complement C3 and C4 levels were 0.31 g/L and 0.14 g/L, respectively; acquired (anticomplement autoantibodies) or genetic alternative pathway abnormalities were detected in 46% and 9% of patients, respectively, across all groups, including those with immune-complex GN. Median follow-up was 5.18 (interquartile range, 2.13-8.08) years. Eleven patients (14%) progressed to kidney failure, with nine transplants performed in eight patients, two of which failed due to recurrent disease. Presence of >50% crescents on the initial biopsy specimen was the sole variable associated with kidney failure in multivariable analysis (hazard ratio, 6.2; 95% confidence interval, 1.05 to 36.6; P<0.05). Three distinct C3 glomerulopathy prognostic groups were identified according to presenting eGFR and >50% crescents on the initial biopsy specimen. CONCLUSIONS: Crescentic disease was a key risk factor associated with kidney failure in a national cohort of pediatric patients with membranoproliferative GN/C3 glomerulopathy and immune-complex GN. Presenting eGFR and crescentic disease help define prognostic groups in pediatric C3 glomerulopathy. Acquired abnormalities of the alternative pathway were commonly identified but not a risk factor for kidney failure.