Alessandro Melo Deana

Antibiotics (ATBs) remain a cornerstone in the treatment of bacterial infections, yet clinical practice increasingly incorporates photonic therapies such as photobiomodulation (PBM) and photodynamic therapy (PDT). These modalities, widely used in infectious and inflammatory conditions, are frequently administered concomitantly with ATBs. However, the potential photochemical interactions between β-lactam ATBs and light-based therapies remain poorly understood, raising questions about efficacy and safety in combined treatment settings. In this study, we characterized the optical and photochemical properties of representative β-lactam ATBs, including penicillins (amoxicillin, oxacillin), cephalosporins (cephalothin, cefazolin, ceftazidime, ceftriaxone, cefuroxime), and a carbapenem (meropenem). Using diffuse transmittance and reflectance spectroscopy, we calculated absorption and scattering coefficients via the Kubelka–Munk function. Reactive oxygen species (ROS) generation was assessed under direct blue light (460 nm) illumination and red light exposure in the presence of methylene blue (MB), a clinically employed photosensitizer. Cephalosporins demonstrated significant ROS generation under blue light, whereas penicillins and meropenem did not. In PDT-like conditions, meropenem enhanced MB-mediated ROS production and promoted MB photobleaching, suggesting potentiation of PDT effects. In contrast, penicillins suppressed MB-driven ROS generation, potentially limiting PDT efficacy. Cephalosporins showed heterogeneous effects, either enhancing, reducing, or leaving MB-induced ROS unchanged. These findings indicate that β-lactam ATBs exert distinct photobiological behaviors with potential clinical consequences. Cephalosporins may act as intrinsic photosensitizers, meropenem may potentiate PDT, and penicillins may attenuate phototherapy efficacy. Recognizing such interactions is critical to optimizing combined ATB–phototherapy regimens and avoiding unintended antagonism or toxicity in clinical practice.

Background: The growing demand for automated microorganism classification in the context of Laboratory 4.0 highlights the potential of convolutional neural networks (CNNs) for accurate and efficient image analysis. However, their effectiveness remains limited by the scarcity of large, labeled datasets. This study addresses a key gap in the literature by investigating how commonly used image preprocessing techniques, such as lossy compression, non-uniform scaling (typically applied to fit input images to CNN input layers), and data augmentation, affect the performance of CNNs in automated microorganism classification. Methods: Using two well-established CNN architectures, AlexNet and DenseNet-121, both frequently applied in biomedical image analysis, we conducted a series of computational experiments on a standardized dataset of high-resolution bacterial images. Results: Our results demonstrate under which conditions these preprocessing strategies degrade or improve CNN performance. Using the findings from this research to optimize hyperparameters and train the CNNs, we achieved classification accuracies of 98.61% with AlexNet and 99.82% with DenseNet-121, surpassing the performance reported in current state-of-the-art studies. Conclusions: This study advances laboratory digitalization by reducing data preparation effort, training time, and computational costs, while improving the accuracy of microorganism classification with deep learning. Its contributions also benefit broader biomedical fields such as automated diagnostics, digital pathology, clinical decision support, and point-of-care imaging.

This study aimed to evaluate microhardness of a dentifrice containing fluoride and arginine compared to a positive control (fluoride only) and a negative control (no fluoride) on sound and demineralized bovine enamel surfaces. Specimens were randomly assigned to different treatments that included daily pH cycling and brushing three times a day with one of the following dentifrices (n = 8): Neutraçucar (arginine and fluoride), Colgate Total 12 (fluoride) and My First Colgate (no fluoride). Enamel carious lesions were artificially created one week before the beginning of these treatments (demineralized bovine enamel (DE) groups). The same groups were also tested in sound enamel (sound bovine enamel (SE) groups). Microhardness was measured at baseline and after one, two, and five weeks of treatment using a Knoop indenter. Statistical analysis involved two-way Analysis of Variance (ANOVA) and Tukey’s test. After five weeks, both Total 12 and Neutraçucar had increased the microhardness of DE specimens (p < 0.05). Only Neutraçucar had increased the microhardness of the sound enamel after five weeks of treatment. Thus, it could be concluded that arginine-based dentifrices increase the microhardness of sound and demineralized bovine enamel surfaces.