The remarkable impact of Opuntia Ficus Indica fruit administration on metabolic syndrome: Correlations between cognitive functions, oxidative stress and lipid dysmetabolism in the high-fat, diet-fed rat model.

BACKGROUND: A wealth of evidence underscores the bioactive properties of nutraceuticals and functional foods in addressing oxyinflammatory-based diseases with implications at both peripheral and central levels. Opuntia ficus-indica (OFI) is well-documented for its health-promoting attributes, though its fruit (OFIF) remains relatively understudied. Not only poses Metabolic Syndrome (MetS) cardiometabolic risks but also contributes significantly to cognitive impairment, especially in crucial brain areas such as hippocampus and hypothalamus. METHODS: Following 8 weeks of HFD to induce MetS, rats received OFIF oral supplementation for 4 weeks to evaluate cognitive and affective modifications using behavioural paradigms, i.e. open field, burrowing, white-dark box, novelty-suppressed feeding, and object recognition tests. Our investigation extended to biochemical evaluations of lipid homeostasis, central and peripheral oxidative stress and neurotrophic pathways, correlating these measures together with circulating leptin levels. RESULTS: Our data revealed that OFIF modulation of leptin positively correlates with systemic and brain oxidative stress, with markers of increased anxiety-like behaviour and impaired lipid homeostasis. On the other hand, leptin levels reduced by OFIF are associated with improved antioxidant barriers, declarative memory and neurotrophic signalling. DISCUSSION: This study underscores OFIF neuroactive potential in the context of MetS-associated cognitive impairment, offering insights into its mechanisms and implications for future therapeutic strategies.

A Combination of Polymethoxyflavones from Citrus sinensis and Prenylflavonoids from Humulus lupulus Counteracts IL-1ß-Induced Differentiated Caco-2 Cells Dysfunction via a Modulation of NF-κB/Nrf2 Activation.

We here investigated the anti-inflammatory activity of a polymethoxylated flavone-containing fraction (PMFF) from Citrus sinensis and of a prenylflavonoid-containing one (PFF) from Humulus lupulus, either alone or in combination (MIX). To this end, an in vitro model of inflammatory bowel disease (IBD), consisting of differentiated, interleukin (IL)-1ß-stimulated Caco-2 cells, was employed. We demonstrated that non-cytotoxic concentrations of either PMFF or PFF or MIX reduced nitric oxide (NO) production while PFF and MIX, but not PMFF, also inhibited prostaglandin E2 release. Coherently, MIX suppressed both inducible NO synthase and cyclooxygenase-2 over-expression besides NF-κB activation. Moreover, MIX increased nuclear factor erythroid 2-related factor 2 (Nrf2) activation, heme oxygenase-1 expression, restoring GSH and reactive oxygen and nitrogen species (RONs) levels. Remarkably, these effects with MIX were stronger than those produced by PMFF or PFF alone. Noteworthy, nobiletin (NOB) and xanthohumol (XTM), two of the most represented phytochemicals in PMFF and PFF, respectively, synergistically inhibited RONs production. Overall, our results demonstrate that MIX enhances the anti-inflammatory and anti-oxidative effects of the individual fractions in a model of IBD, via a mechanism involving modulation of NF-κB and Nrf2 signalling. Synergistic interactions between NOB and XTM emerge as a relevant aspect underlying this evidence.

Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology.

Malignant melanoma (MM) is the "great mime" of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed in a timely manner, it can even lead to death. In recent years, artificial intelligence has revolutionised much of what has been achieved in the biomedical field, and what once seemed distant is now almost incorporated into the diagnostic therapeutic flow chart. In this paper, we present the results of a machine learning approach that applies a fast random forest (FRF) algorithm to a cohort of naevoid melanomas in an attempt to understand if and how this approach could be incorporated into the business process modelling and notation (BPMN) approach. The FRF algorithm provides an innovative approach to formulating a clinical protocol oriented toward reducing the risk of NM misdiagnosis. The work provides the methodology to integrate FRF into a mapped clinical process.

Advanced Control Systems in Industry 5.0 Enabling Process Mining.

This paper merges new research topics in Industry 5.0 using the Business Process Modeling and Notation (BPMN) approach able to integrate Artificial Intelligence (AI) in production processes. The goal is to provide an innovative approach to model production management in industry, adopting a new "proof of concept" of advanced Process Mining (PM) automatizing decisions and optimizing machine setting and maintenance interventions. Advanced electronic sensing and actuation systems, integrating supervised and unsupervised AI algorithms, are embedded in the PM model as theoretical process workflows suggested by a Decision Support System (DSS) engine enabling an intelligent decision-making procedure. The paper discusses, as examples, two theoretical models applied to specific industry sectors, such as food processing and energy production. The proposed work provides important elements of engineering management related to the digitalization of production process matching with automated control systems setting production parameters, thus enabling the self-adapting of product quality supervision and production efficiency in modern industrial systems.

Characterization of the Aquaporin-9 Inhibitor RG100204 In Vitro and in db/db Mice.

Aquaporin-9 (AQP9) is a facilitator of glycerol and other small neutral solute transmembrane diffusion. Identification of specific inhibitors for aquaporin family proteins has been difficult, due to high sequence similarity between the 13 human isoforms, and due to the limited channel surface areas that permit inhibitor binding. The few AQP9 inhibitor molecules described to date were not suitable for in vivo experiments. We now describe the characterization of a new small molecule AQP9 inhibitor, RG100204 in cell-based calcein-quenching assays, and by stopped-flow light-scattering recordings of AQP9 permeability in proteoliposomes. Moreover, we investigated the effects of RG100204 on glycerol metabolism in mice. In cell-based assays, RG100204 blocked AQP9 water permeability and glycerol permeability with similar, high potency (~5 × 10-8 M). AQP9 channel blocking by RG100204 was confirmed in proteoliposomes. After oral gavage of db/db mice with RG100204, a dose-dependent elevation of plasma glycerol was observed. A blood glucose-lowering effect was not statistically significant. These experiments establish RG100204 as a direct blocker of the AQP9 channel, and suggest its use as an experimental tool for in vivo experiments on AQP9 function.

Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience.

The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as basal cell carcinoma (BCC), seborrheic keratosis (SK) and dermal nevus. Furthermore, the difficulty in diagnosing particular melanocytic lesions, such as Spitz nevi and melanoma, considering the grade of interobserver variability among dermatopathologists, has led to an objective difficulty in training machine learning (ML) algorithms to a totally reliable, reportable and repeatable level. In this work we tried to train a fast random forest (FRF) algorithm, typically used for the classification of clusters of pixels in images, to highlight anomalous areas classified as melanoma "defects" following the Allen-Spitz criteria. The adopted image vision diagnostic protocol was structured in the following steps: image acquisition by selecting the best zoom level of the microscope; preliminary selection of an image with a good resolution; preliminary identification of macro-areas of defect in each preselected image; identification of a class of a defect in the selected macro-area; training of the supervised machine learning FRF algorithm by selecting the micro-defect in the macro-area; execution of the FRF algorithm to find an image vision performance indicator; and analysis of the output images by enhancing lesion defects. The precision achieved by the FRF algorithm proved to be appropriate with a discordance of 17% with respect to the dermatopathologist, allowing this type of supervised algorithm to be nominated as a help to the dermatopathologist in the challenging diagnosis of malignant melanoma.

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria.

Complex energy monitoring and control systems have been widely studied as the related topics include different approaches, advanced sensors, and technologies applied to a strongly varying amount of application fields. This paper is a systematic review of what has been done regarding energy metering system issues about (i) sensors, (ii) the choice of their technology and their characterization depending on the application fields, (iii) advanced measurement approaches and methodologies, and (iv) the setup of energy Key Performance Indicators (KPIs). The paper provides models about KPI estimation, by highlighting design criteria of complex energy networks. The proposed study is carried out to give useful elements to build models and to simulate in detail energy systems for performance prediction purposes. Some examples of energy complex KPIs based on the integration of the Artificial Intelligence (AI) concept and on basic KPIs or variables are provided in order to define innovative formulation criteria depending on the application field. The proposed examples highlight how modeling a complex KPI as a function of basic variables or KPIs is possible, by means of graph models of architectures.

Case study on city-airports: Datasets and calculation models.

Data have been collected over time, belonging the 2018th and 2019th, from airports owners, from stakeholders, from universities, from the net, and performing under GIS evaluation processes. Most of the collected data are geographic, economic, and financial statements of the different ownerships, maps about the airport and urban planning, and data about carriers and routes. Specifically, the GIS has been useful to the Network Analysis evaluations. The analysis results can be collected and used in the most comprehensive analysis of similar systems. The results summarize data about four different couples of small remote airports in the EU and their specific network systems [1], [2], [3], [4]. Therefore, the ongoing analysis wait to be extended to other similar systems.

Self-assembled pillar-like structures in nanodiamond layers by pulsed spray technique.

Pillar-like structures of nanodiamonds on a silicon substrate are self-assembled for the first time by a pulsed spray technique. This technique allows us to deposit nanodiamond layers by using high quality nanocrystals of 250 nm dispersed in 1,2-dichloroethane (DCE) solvent. The analysis of 2D/3D confocal and atomic force microscopy images evidences the presence of self-assembled pillar-like structures distributed in an irregular way. The proposed method is simple, easy and cheap, and does not require complex growth processes or structured materials, ideal for upscaling toward industrial biochip implementation and photonic applications. The suggested formation mechanisms of self-assembly are based on the so-called coffee stain effect, i.e., on the time evolution of DCE evaporation.

In situ formation and size control of gold nanoparticles into chitosan for nanocomposite surfaces with tailored wettability.

The in situ formation of gold nanoparticles into the natural polymer chitosan is described upon pulsed laser irradiation. In particular, hydrogel-type films of chitosan get loaded with the gold precursor, chloroauric acid salt (HAuCl(4)), by immersion in its aqueous solution. After the irradiation of this system with increasing number of ultraviolet laser pulses, we observe the formation of gold nanoparticles with increasing density and decreasing size. Analytical studies using absorption measurements, atomic force microscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy of the nanocomposite samples throughout the irradiation procedure reveal that under the specific irradiation conditions there are two competing mechanisms responsible for the nanoparticles production: the photoreduction of the precursor responsible for the rising growth of gold particles with increasing size and the subsequent photofragmentation of these particles into smaller ones. The described method allows the localized formation of gold nanoparticles into specific areas of the polymeric films, expanding its potential applications due to its patterning capability. The size and density control of the gold nanoparticles, obtained by the accurate increase of the laser irradiation time, is accompanied by the simultaneously controlled increase of the wettability of the obtained gold nanocomposite surfaces. The capability of tailoring the hydrophilicity of nanocomposite materials based on natural polymer and biocompatible gold nanoparticles provides new potentialities in microfluidics or lab on chip devices for blood analysis or drugs transport, as well as in scaffold development for preferential cells growth.