RESUMO

Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.

Assuntos

RESUMO

Photoaging (PA) is considered a silent disease affecting millions of people globally and is defined as skin damage due to prolonged exposure to ultraviolet radiation (UVR) from the sun. Physiologically, the skin is in a state of renewal and synthesis of components of the extracellular matrix (ECM). However, exposure to UVR affects the production of the ECM, and the functioning and response of skin cells to UVR begins to change, thus expressing clinical and phenotypic characteristics of PA. The primary mechanisms involved in PA are direct damage to the DNA of skin cells, increases in oxidative stress, the activation of cell signaling pathways responsible for the loss of skin integrity, and cytotoxicity. The medical and scientific community has been researching new therapeutic tools that counteract PA, considering that the damage caused by UVR exceeds the antioxidant defense mechanisms of the skin. Thus, in recent years, certain nutraceuticals and phytochemicals have been found to exhibit potential antioxidant and photoprotective effects. Therefore, the main objective of this review is to elucidate the molecular bases of PA and the latest pharmaceutical industry findings on antioxidant treatment against the progression of PA.

Assuntos

RESUMO

The growing emergence of microbes resistant to commercially available antibiotic therapies poses a threat to healthcare systems worldwide. Multiple factors have been associated with the increasing incidence of hospital-acquired infections caused by antibiotic-resistant pathogens, including the indiscriminate use of broad-spectrum antibiotics, the massive application of antibiotics in hospitals as a prophylactic measure, self-medication, and nonadherence to pharmacological therapies by patients. In this study, we developed a novel treatment to mitigate the impact of microbial resistance. We synthesized a benzoporphyrin derivative, 5,10,15,20-tetrakis (4-ethylphenyl) porphyrin (TEtPP), with a reaction yield close to 50%. TEtPP exhibited excellent photophysical properties (Φf = 0.12 ± 0.04 and ΦΔ = 0.81 ± 0.23) and was thereby assessed as a potential agent for antibacterial photodynamic therapy. The photophysical properties of the synthesized porphyrin derivative were correlated with the assayed antimicrobial activity. TEtPP showed higher activity against the MRSA strain under irradiation than in the absence of irradiation (minimum inhibitory concentration (MIC) = 69.42 µg/mL vs. MIC = 109.30 µg/mL, p < 0.0001). Similar behavior was observed against P. aeruginosa (irradiated MIC = 54.71 µg/mL vs. nonirradiated MIC = 402.90 µg/mL, p < 0.0001). TEtPP exhibited high activity against S. aureus in both the irradiated and nonirradiated assays (MIC = 67.68 µg/mL vs. MIC = 58.26 µg/mL, p = 0.87).

Assuntos

RESUMO

Bacterial resistance is responsible for a wide variety of health problems, both in children and adults. The persistence of symptoms and infections are mainly treated with ß-lactam antibiotics. The increasing resistance to those antibiotics by bacterial pathogens generated the emergence of extended-spectrum ß-lactamases (ESBLs), an actual public health problem. This is due to rapid mutations of bacteria when exposed to antibiotics. In this case, ß-lactamases are enzymes used by bacteria to hydrolyze the beta-lactam rings present in the antibiotics. Therefore, it was necessary to explore novel molecules as potential ß-lactamases inhibitors to find antibacterial compounds against infection caused by ESBLs. A computational methodology based on molecular docking and molecular dynamic simulations was used to find new microalgae metabolites inhibitors of ß-lactamase. Six 3D ß-lactamase proteins were selected, and the molecular docking revealed that the metabolites belonging to the same structural families, such as phenylacridine (4-Ph), quercetin (Qn), and cryptophycin (Cryp), exhibit a better binding score and binding energy than commercial clinical medicine ß-lactamase inhibitors, such as clavulanic acid, sulbactam, and tazobactam. These results indicate that 4-Ph, Qn, and Cryp molecules, homologous from microalgae metabolites, could be used, likely as novel ß-lactamase inhibitors or as structural templates for new in-silico pharmaceutical designs, with the possibility of combatting ß-lactam resistance.

Assuntos

RESUMO

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

Assuntos

RESUMO

Quantum cascade laser spectroscopy was used to quantify active pharmaceutical ingredient content in a model formulation. The analyses were conducted in non-contact mode by mid-infrared diffuse reflectance. Measurements were carried out at a distance of 15 cm, covering the spectral range 1000-1600 cm(-1) Calibrations were generated by applying multivariate analysis using partial least squares models. Among the figures of merit of the proposed methodology are the high analytical sensitivity equivalent to 0.05% active pharmaceutical ingredient in the formulation, high repeatability (2.7%), high reproducibility (5.4%), and low limit of detection (1%). The relatively high power of the quantum-cascade-laser-based spectroscopic system resulted in the design of detection and quantification methodologies for pharmaceutical applications with high accuracy and precision that are comparable to those of methodologies based on near-infrared spectroscopy, attenuated total reflection mid-infrared Fourier transform infrared spectroscopy, and Raman spectroscopy.

Assuntos