Aluminum is the most widely distributed metal in the environment and is extensively used in daily life, although it has no known biological functions. Its pro-oxidant activity, along with its ability to mimicry essential metals, elicits oxidative stress and mitochondrial dysfunction that culminate in apoptosis. Remarkably, aluminum affects iron-sulfur cluster proteins and antioxidant enzymes, triggering a cascade of events that contribute to adenosine triphosphate depletion and requiring the cell to shift the metabolism to mitigate its deficiency. Disturbances in cell homeostasis are then expressed as biochemical and histological alterations in several organs. For instance, tissue damage induced by aluminum can lead to liver and heart dysfunction as well as neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. Therefore this chapter aims to provide a broader view of the mechanism of aluminum toxicity in cell metabolism with the mitochondria as a target organelle, with a focus on tissues with high energy requirements.
Early diagnosis of Dengue fever is critical for effective patient management and outbreak control. This study presents an electrochemical biosensor based on a mimetic peptide (MPDV) covalently immobilized on a gold electrode modified with reduced graphene oxide (r-GO) and poly(3-hydroxyphenylacetic acid). The sensing mechanism is based on the specific binding of anti-DENV IgM antibodies by the LLRNAST core epitopemimicking recognition motif within the MPDV peptide. This biological interaction modulates the intrinsic electrooxidation of the peptide's cysteine residues, resulting in a sensitive label-free and mediator-free signal. Surface characterization by AFM, CV, and EIS confirmed the successful layer-by-layer assembly of the sensing interface and effective peptide immobilization. The r-GO/poly(3-HPA) nanocomposite promoted a 19-fold enhancement of the electroactive charge, significantly increasing sensitivity. Molecular dynamics simulations demonstrated stable peptide orientation, ensuring the accessibility of the binding motif. The biosensor exhibited a linear response toward anti-DENV IgM in human serum over the dilution range from 1:50 to 1:10,000, with a correlation coefficient (R2) of 0.999. The limit of detection (LOD) was 0.30 ng mL-1 and the limit of quantification (LOQ) was 0.99 ng mL-1. Selectivity was validated against symptomatic negative controls and cocirculating flaviviruses, supported by preliminary immunoassay data, demonstrating negligible non-specific interactions. The biosensor retained approximately 91% of its initial response after 40 days of storage under dry storage at 8 degrees C. By integrating a structurally stable mimetic peptide with a nanostructured conductive interface, this study establishes a highly sensitive strategy for selective anti-DENV IgM detection. The combination of experimental and theoretical modeling provides a robust platform for early dengue diagnosis and highlights the potential of this mediator-free biosensor for point-of-care applications.
In recent years, energy transition has boosted the expansion of new electrical generation sources, most of which are inverter-based, significantly altering the behavior of voltage and current signals. Thus, fault diagnosis methods should be validated and adapted to operate in this new scenario. This paper evaluates the performance of five classic fault detection methods and, addressing their identified limitations, proposes a fault detection algorithm based on ATs combined with a rule-based internal and external fault discriminator for medium-voltage collector networks in onshore wind farms. The method is robust, eliminating the need for prior parameterization in the primary detection stage. The proposed methodology was validated on an actual system located in Northeast Brazil, modeled in PSCAD/EMTDC and through hardware-in-the-loop tests using the RTDS interfaced with a Texas Instruments F28379D development board. The performance was tested under diverse operating conditions, including variations in fault resistance, different circuit topologies, and event types, while also considering wind turbine shutdowns and fluctuations in wind penetration levels. The results are highly promising and compatible with modern protection requirements, demonstrating an average primary pickup of under 1.36 ms, confirmed within 18 ms, and an accuracy rate exceeding 99.8%, even in the presence of noisy signal conditions.
Wood residues from sustainable forest management in the Amazon represent an underexploited feedstock for thermochemical conversion, yet the pyrolysis kinetics of these materials remain poorly characterized. Eleven native Amazonian species were physicochemically characterized and grouped by cluster analysis into five classes with distinct lignocellulosic and energy profiles; one species per cluster (Pourouma spp., Nectandra cf. amazonum, Dinizia excelsa, Caryocar glabrum, and Pouteria cf. oblanceolata), selected to span the physicochemical diversity identified among the eleven species, was subjected to kinetic study. Non-isothermal thermogravimetric analyses were performed under nitrogen at five heating rates (2.5-20 K min-1), and kinetic triplets were determined using isoconversional methods (FDM, FWO, KAS, STK, VZK) combined with the kinetic compensation effect. Average apparent activation energies ranged from 156.31 to 172.27 kJ mol-1, with the lowest and highest values associated with the holocellulose and lignin/extractive-richest biomasses, respectively, and global pre-exponential factors on the order of 1010-1011 s-1. The two-and one-dimensional diffusion models (D2 and D1) best described the devolatilization behavior of the five residues. Reconstruction of the experimental curves validated the estimated kinetic triplets, with mean percentage deviations below 1% for conversion and below 10% for conversion rate across all heating rates. These results link chemical composition to pyrolysis reactivity in Amazonian hardwood residues and provide a kinetic dataset for subsequent the design, simulation, and scale-up of thermochemical conversion routes for their energy valorization.
Gastric cancer (GC) remains a major global health burden, with poor survival rates. HER2 is a key biomarker for targeted therapy, but discordance between primary tumors and metastases may impact treatment decisions. This meta-analysis evaluated the prevalence and clinical relevance of HER2 status differences between primary gastric cancers and metastatic lesions. A systematic search of PubMed, Embase, and Cochrane was conducted for studies assessing HER2 status in matched primary and metastatic gastric cancer samples. Pooled proportions and 95
