AGEs and Glucose Levels Modulate Type I and III Procollagen mRNA Synthesis in Dermal Fibroblasts Cells Culture

In the dermis, fibroblasts play an important role in the turnover of the dermal extracellular matrix. Collagen I and III, the most important dermal proteins of the extracellular matrix, are progressively altered during ageing and diabetes. For mimicking diabetic conditions, the cultured human dermal fibroblasts were incubated with increasing amounts of AGE-modified BSA and D-glucose for 24 hours. The expression of procollagen α2(I) and procollagen α1(III) mRNA was analyzed by quantitative real-time PCR. Our data revealed that the treatment of fibroblasts with AGE-modified BSA upregulated the expression of procollagen α2(I) and procollagen α1(III) mRNA in a dose-dependent manner. High glucose levels mildly induced a profibrogenic pattern, increasing the procollagen α2(I) mRNA expression whereas there was a downregulation tendency of procollagen α1(III) mRNA

Explaining the Image Content via Machine Learning Techniques: Extending the Experience on Semantic Labeling from Remote Sensing to Medical Imaging

This presentation explores the application of machine learning (ML) techniques to interpret content from Earth observation (EO) and medical imaging data. Our objective was to leverage Support Vector Machine (SVM) algorithms to discover semantic relations in diverse image datasets. We collated EO images utilizing multispectral and radar sensors from four urban areas and procured medical images via camera, microscope, and computed tomography (CT). Our methodology underwent rigorous testing by domain experts, with classification results validated against reference datasets and expert evaluations, achieving 95% accuracy for satellite images and 85% for medical images. We applied our approach to correlate EO images detailing environmental data impacting quality of life with medical images indicating disease phenotypes, aiming to enhance epidemiological studies' precision

Approaching Complexity in OneHealth by Mathematical Modeling and Machine Learning

The term OneHealth was coined approximately two decades ago and it is still conceptualized until this day. Most definitions have some common points, while exhibiting sometimes notable differences. Independently of these definitions OneHealth is an attempt to cover the complexity of several ecosystems coexistence such as human, animal, environmental, microbial, socio-economics, global health and governance ecosystems. Depending on these OneHealth definitions, the interactions between these ecosystems can in turn be defined with more or less detail. This paper will attempt to present the main models of interactions between ecosystems and the computational challenges and needs to achieve some consistent and quantitative results illustrated by some case studies such as the interaction between human, animal and microbial ecosystems leading to new infectious disease, the impact of the environmental ecosystem on human health through environmental risk factors etc. All these interactions need detailed and multiscale modeling, various computational strategies and massive input data to get insightful and relevant results. Furthermore, we argue that data (especially extracted from images, e.g. Earth observation images) should be extracted through automatic algorithms taking advantage of the newly developed machine and/or deep learning (ML/DL) strategies. Predictive models, as well should be developed based on these ML/DL techniques. It will quickly emerge that diversity and complexity of OneHealth issues need a series of methods as diverse and complex as the problems raised by this concept

Mycotoxins and Essential Oils—From a Meat Industry Hazard to a Possible Solution: A Brief Review

The preservation of food supplies has been humankind’s priority since ancient times, and it is arguably more relevant today than ever before. Food sustainability and safety have been heavily prioritized by consumers, producers, and government entities alike. In this regard, filamentous fungi have always been a health hazard due to their contamination of the food substrate with mycotoxins. Additionally, mycotoxins are proven resilient to technological processing. This study aims to identify the main mycotoxins that may occur in the meat and meat products “Farm to Fork” chain, along with their effect on the consumers’ health, and also to identify effective methods of prevention through the use of essential oils (EO). At the same time, the antifungal and antimycotoxigenic potential of essential oils was considered in order to provide an overview of the subject. Targeting the main ways of meat products’ contamination, the use of essential oils with proven in vitro or in situ efficacy against certain fungal species can be an effective alternative if all the associated challenges are addressed (e.g., application methods, suitability for certain products, toxicity)

Composition-Based Risk Estimation of Mycotoxins in Dry Dog Foods

The risk of mycotoxins co-occurrence in extrusion-produced dry foods increases due to their composition based on various grains and vegetables. This study aimed to validate a risk estimation for the association between ingredients and the ELISA-detected levels of DON, FUM, ZEA, AFs, T2, and OTA in 34 dry dog food products. The main ingredients were corn, beet, and oil of different origins (of equal frequency, 79.41%), rice (67.6%), and wheat (50%). DON and FUM had the strongest positive correlation (0.635, p = 0.001). The presence of corn in the sample composition increased the median DON and ZEA levels, respectively, by 99.45 μg/kg and 65.64 μg/kg, p = 0.011. In addition to DON and ZEA levels, integral corn presence increased the FUM median levels by 886.61 μg/kg, p = 0.005. For corn gluten flour-containing samples, DON, FUM, and ZEA median differences still existed, and OTA levels also differed by 1.99 μg/kg, p 403.06 μg/kg (OR = 38.4, RR = 9.90, p = 0.002), FUM levels > 1097.56 μg/kg (OR = 5.56, RR = 1.45, p = 0.048), ZEA levels > 136.88 μg/kg (OR = 23.00, RR = 3.09, p = 0.002), and OTA levels > 3.93 μg/kg (OR = 24.00, RR = 3.09, p = 0.002). Our results suggest that some ingredients or combinations should be avoided due to their risk of increasing mycotoxin levels

Amorphous Silica Nanoparticles Obtained by Laser Ablation Induce Inflammatory Response in Human Lung Fibroblasts

Silica nanoparticles (SiO2 NPs) represent environmentally born nanomaterials that are used in multiple biomedical applications. Our aim was to study the amorphous SiO2 NP-induced inflammatory response in MRC-5 human lung fibroblasts up to 72 hours of exposure. The intracellular distribution of SiO2 NPs was measured by transmission electron microscopy (TEM). The lactate dehydrogenase (LDH) test was used for cellular viability evaluation. We have also investigated the lysosomes formation, protein expression of interleukins (IL-1β, IL-2, IL-6, IL-8, and IL-18), COX-2, Nrf2, TNF-α, and nitric oxide (NO) production. Our results showed that the level of lysosomes increased in time after exposure to the SiO2 NPs. The expressions of interleukins and COX-2 were upregulated, whereas the expressions and activities of MMP-2 and MMP-9 decreased in a time-dependent manner. Our findings demonstrated that the exposure of MRC-5 cells to 62.5 µg/mL of SiO2 NPs induced an inflammatory response

Increased RAGE, latent TGF-β1, collagen I and III gene and protein expression in CCD-1070Sk fibroblasts after 12 h of AGE-BSA exposure.

<p>Representative immunoblots (a) and the corresponding densitometry analysis. Each immunoreactive band was normalized to the total proteins transferred in the corresponding lane. Data are relative to controls (BSA treated cells) and represent means ± SD (b). Relative gene expression for RAGE, TGF-β1, collagen (Col) I and III levels is shown in (c). <i>p</i> values indicate statistically significant changes * for <i>p</i>< 0.05; ** for <i>p</i>< 0.01; *** for <i>p</i>< 0.001.</p

MMP-2 activity and protein expression levels in AGEs-BSA exposed CCD-1070Sk cells.

<p>MMP-2 gelatinolytic bands after 12 h and 24 h of AGEs exposure (a). Densitometry analysis of gelatinolytic bands is shown in (b). MMP-2 immunoreactive protein bands after 12 h and 24 h AGEs exposure (c). Densitometry analysis of MMP-2 immunoreactive bands is shown in (d). Densitometry data represent means ± SD. * <i>p</i>< 0.05; ** <i>p</i>< 0.01; *** <i>p</i>< 0.001.</p

The target genes and the primer sequences used for semi quantitative real-time RT- PCR.

<p>The target genes and the primer sequences used for semi quantitative real-time RT- PCR.</p