Hyaluronic acid (HA) is a well-known key extracellular matrix component, which as therapeutic agent is believed to participate in healing and may be used for treatment delivery to the injury site. Burns pose significant clinical and aesthetic challenges requiring rapid skin restoration to avoid complications. This study compares HA efficacy in burn healing in rats against commonly used panthenol-containing gel (PCG).

Deep burns were induced in rats with 200°C copper plate. Pharmaceutical-grade HA (1.8%) or PCG were applied 24 hours post-injury, with spontaneous healing as control. Wound recovery was assessed over 28 days. Collagens of I and III types were quantified using PicroSirius Red staining and ImageJ software.

Control and PCG-treated burns closed significantly just by day 21, remaining open till the end. HA decreased wound area a little faster from day 3, slightly outperforming PCG. Both treatments showed granulation from day 7 and epithelialization by day 28. Initial collagen content in HA-group dropped paradoxically by day 3, matched PCG by day 14 in total number, but with abnormal distribution in derma. By day 28, both groups exceeded control collagen levels. HA suppressed systemic leukocyte number to normal levels by day 14, while drastically enhancing local inflammation till this observation point.

Observed strong stimulation of local inflammatory reaction with HA can be explained by some data suggesting that HA can either stimulate or suppress immune response in skin, depending on its source and physicochemical characteristics; therefore, further research is needed for wide clinical applications of HA as a wound cover.

Introduction: Integrating machine learning algorithms into the medical field has become essential for improving disease diagnosis and predicting conditions at an early stage. However, conventional machine learning techniques often struggle with large, complex datasets, limiting their effectiveness. This work proposes a novel deep-learning approach to enhance disease prediction accuracy using medical databases to address this. Methods: This study introduces a two-stage deep learning model utilizing a Convolutional Neural Network (CNN) for disease prediction. CNNs, known for their strengths in pattern recognition and regression, are applied to classify medical data. In the first stage, initial classification is performed, while the second stage focuses on analysing the experimental dataset to assess accuracy. To evaluate the performance of the proposed CNN model, a comparative study is conducted against two established models: VGG16 and Recurrent Neural Network (RNN). Results: The proposed CNN model achieved an accuracy of 98.5%, significantly surpassing the performance of both VGG16 (85%) and RNN (90%). The CNN's ability to handle complex datasets with diverse medical parameters effectively highlights its superiority in disease prediction tasks. Conclusion: The study demonstrates that the CNN-based deep learning model offers a highly accurate and efficient solution for early disease prediction, outperforming traditional models. With its improved accuracy and robustness, the proposed approach has the potential to enhance diagnostic capabilities, enabling timely medical interventions and better patient care outcomes.

For decades, coordination compounds have strongly demonstrated their importance in many daily disciplines and industries. Their applications include catalysts, magnetic materials, porous materials, biomedical applications, drug delivery, etc. Coordination compounds are also highly present in stereochemistry as chiral luminescent materials, homochiral, chiral liquid crystals, enantioselective sensors, chiroptical switches, and magnetochiral compounds. For all these reasons and many more, tremendous effort has been devoted to the study of coordination compounds experimentally and theoretically. The development of new potential theoretical approaches has made it fruitful to study these kinds of compounds. In this contribution, a theoretical DFT-based study is performed on complexes of an organic ligand with different metal ions in order to reveal the energetics of such reactions. First of all, the electronic structure of the studied ligand as well as the formed complex was optimized at DFT//B3LYP/6-31G(d) level of theory. Then, using various approaches such as Fukui functions, and based on the obtained optimized structure of the studied complexes at the level of B3LYP/6-31G(d), the possible sites responsible for chelation with the metal ions were determined. Finally, the energetics based on thermodynamic quantities calculations, such as enthalpy, were also investigated in order to predict the stability and thermochemistry of the studied coordination compounds. It was found that the stability and the structure of the complexes not only depend on the ligand but also on the nature of the metal ion.

Pollution is a concern in modern society, with pharmaceutical compounds being increasingly recognized as a major cause. Their improper disposal, along with their increased use, makes them reach the aquatic environment, causing potential harm to the aquatic ecosystem and consequently to human health. As a result, it is of extreme importance to develop sensors capable of monitoring pharmaceutical compounds in a sustainable and affordable way, with a rapid response [1]. The aim of this work is the development of an electrochemical sensor for the determination of venlafaxine in environmental waters, a widely prescribed antidepressant drug. The sensor is based on a carbon paper transducer modified with an iron-based metal-organic framework (MOF), MIL-100, by electrodeposition. Cyclic voltammetry analysis showed an irreversible oxidation peak at around 0.7 V (vs Ag/AgCl), with higher intensity compared with the unmodified carbon paper. Square-wave voltammetry was then applied to perform the optimization studies regarding electrolyte pH, technique parameters (frequency, step potential, amplitude), and analyte deposition, as well the study of its analytical performance. This electrochemical sensor shows promising analytical features in the determination of venlafaxine, taking advantage of the higher porosity and surface area of the MOF material, resulting in higher adsorption of the drug and thus higher electrochemical efficiency.

Reference:

[1] Miguel Tavares, Simone Morais, Álvaro Torrinha, Metal-organic frameworks based electrochemical sensors for emerging pharmaceutical contaminants in aquatic environment, Trends in Environmental Analytical Chemistry 47 (2025) e00271.

The continuous increase in the number of legal cases submitted to the judiciary has imposed a significant burden on the court system, making the analysis, identification, and classification of similar actions within large and complex datasets increasingly challenging. When performed manually, this task becomes not only time-consuming but also highly prone to human error, potentially compromising the efficiency and reliability of judicial procedures. This study investigates the application of Natural Language Processing (NLP) techniques to automate and enhance the classification of procedural acts into high-frequency categories. Specifically, the Continuous Bag of Words (CBOW) and Skip-gram models—both based on word embedding strategies—were implemented in conjunction with the Logistic Regression algorithm for supervised classification. The dataset, comprising approximately 311,000 legal documents from the Court of Justice of the State of Amazonas (TJAM), was processed through a robust pipeline, including automated web scraping, advanced text preprocessing, vocabulary construction, and model training. The experimental results were highly promising: the models achieved an accuracy rate of 95% and an F1-score of 95%, demonstrating the strong potential of integrating NLP with machine learning to optimize procedural management. By automating repetitive and labor-intensive classification tasks, the proposed approach not only reduces processing time and human workload but also enables judicial institutions to allocate more resources to complex cases requiring expert human judgment, thereby improving efficiency, reducing backlog, and enhancing access to justice.

This experimental work explores the application of an innovative, eco-friendly, and highly-reproducible nanofabrication technique, namely Pulse-Atomic Force Lithography (P-AFL), as the starting point of a process that employs eco-sustainable materials to manufacture nanowires. A thin film made of chitosan, a biopolymer, was obtained by spin-coating a solution of medium-molecular-weight chitosan at a concentration of 0.8% w/v in acetic acid 1% v/v on a silicon oxide substrate. The surface morphology and thickness of the resulting chitosan film was characterized by Atomic Force Microscopy (AFM): the film was homogeneous, with a roughness of about 1 nm and a thickness of about 52 nm. Then, the P-AFL technique was optimized to pattern a set of nanogrooves on chitosan. A metal layer of chrome and titanium, with a 1:10 ratio, was deposited on the chitosan film by Electron Bem Evaporation (EBE). Successively, the samples were submitted to a lift-off process by an acetic acid solution (1% v/v) with the aim of dissolving the chitosan layer. The resulting nanowires were then characterized by AFM and Scanning Electron Microscopy (SEM): the nanostructures appeared well-fabricated, with a length of 10 μm and a height of (71 ± 20) nm. The entire process is based on the use of the P-AFL technique, which does not require the use of toxic chemicals, i.e., the developer resists for conventional optical lithography. Moreover, chitosan is eco-sustainable, as it is derived from natural sources, bio-compatible, and bio-degradable, and the lift-off step is performed by using acetic acid, a non-harmful chemical.

Scalable animal re-identification without labels is essential for wildlife monitoring, especially in resource-limited settings; however, most unsupervised re-ID frameworks remain limited to human datasets. This study systematically evaluates three state-of-the-art frameworks—Self-paced Contrastive Learning (SpCL), Cluster Contrast (CC), and Transformer-Based Multi-Granular Features (TMGF)—on the NDD20 dolphin dataset, a curated underwater image collection featuring white-beaked dolphins (Lagenorhynchus albirostris).

Dolphin viewpoints were manually annotated to address the lack of camera ID labels and camera-aware proxies were substituted with pose-aware proxies to isolate pose variation. All frameworks were trained fully unsupervised using clustering-derived pseudo-labels and contrastive objectives, with ground-truth identities reserved solely for evaluation. Retrieval performance was assessed using mean Average Precision (mAP) and Cumulative Matching Characteristic (CMC) scores. TMGF consistently outperformed SpCL and CC, boosting mAP by 3% and demonstrating greater robustness to pose variation and intra-class variability. View-specific evaluation outperformed aggregated retrieval, suggesting that flank-dependent identity cues are significant for dolphin re-ID. In contrast, SpCL and CC, despite competitive Top-10 accuracy, exhibited lower mAP, indicating reduced consistency.

This study offers the first comprehensive assessment of unsupervised re-ID models on a marine wildlife dataset. It reveals that pose-aware proxies are effective for species with view-invariant or bilaterally consistent identifiers (e.g., humans, dorsal fins, tail flukes), but less so for species with asymmetric or view-dependent cues (e.g., flank markings). These findings underscore the importance of species-aware design when adapting unsupervised learning to ecological domains, advancing the development of AI-driven tools for biodiversity monitoring and marine conservation.

The objective of this study was to develop and evaluate a nanosuspension of Abacavir, a poorly water-soluble antiretroviral drug classified under BCS Class II, to enhance its solubility and oral bioavailability. Nanosuspensions were prepared using the quasi-emulsification solvent diffusion method, employing Eudragit RS100 and RL100 polymers in combination with Poloxamer 407 (Pluronic F127) as a stabilizer. A total of eight formulations were developed by varying polymer and stabilizer ratios. The prepared nanosuspensions were characterized for particle size, zeta potential, drug entrapment efficiency, saturation solubility, and in vitro drug release.

Among the tested formulations, ABC-F4 (Drug:Polymer:Stabilizer ratio of 1:2:1 using Eudragit RS100) exhibited optimal characteristics, including a particle size of 92.20 nm, zeta potential of –14.55 mV, and drug entrapment efficiency of 91.21%. In vitro dissolution studies revealed a sustained drug release of 99.87% over 10 hours, indicating effective control of drug release. Saturation solubility of the nanosized Abacavir increased nearly fivefold compared to the pure drug, confirming significant solubility enhancement. Compatibility studies using FTIR and DSC showed no interaction between the drug and excipients.

The study concludes that nanosuspension technology using Eudragit RS100 and Poloxamer 407 is a promising strategy to improve the solubility, stability, and oral bioavailability of Abacavir. Such a formulation could contribute to improved therapeutic outcomes in HIV treatment by enabling better absorption and sustained drug release.

Sweet orange is one of the world’s most important fruit crops. However, increased demand and growing pressure on the sector have created an ideal scenario for the emergence and spread of diseases. Specifically, Penicillium digitatum, the causal agent of green mold disease, is responsible for 90% of postharvest losses in the citrus industry. Seaweeds produce numerous bioactive compounds with antimicrobial properties.

This study aims to evaluate the antifungal activity of extracts from two invasive seaweeds from the Portuguese coast, Asparagopsis armata Harvey and Sargassum muticum (Yendo) Fensholt, obtained using green solvents (water and ethanol), against the above-mentioned pathogen. Antifungal activity was assessed in vitro using the poisoned food technique and microdilution method and in vivo using fruit immersion assays. The cell lysate of A. armata (CL) at 1 mg/mL showed strong antifungal activity against P. digitatum in vitro, resulting in morphological changes in mycelial growth and complete inhibition of spore germination. To improve the bioavailability, solubility, and release of the CL bioactive compounds, encapsulation in pyrogenic amorphous silica and maltodextrin–pectin (1:0.12) was explored and successfully achieved. Comparative analyses of the antifungal efficacy of the encapsulated and free CL revealed that the maltodextrin–pectin formulation preserved the inhibitory activity. In vivo assays on sweet orange fruits demonstrated a 36% reduction in mycelial growth area and a 42% reduction in sporulation area following CL application, while maintaining fruit quality.

These findings highlight the potential of invasive seaweeds as eco-friendly alternatives to synthetic pesticides, while also contributing to marine environment restoration through a circular economy approach.

An accelerated oxidation assay was performed at 37 °C under dark conditions on binary blends of olive oil and hemp seed oil containing either 25% or 50% hemp seed oil (AC). The blends were fortified with tocopherols and gallic acid at several concentrations to evaluate their potential to modulate oxidative deterioration. Fortification with these antioxidants did not measurably alter classical primary quality parameters such as free acidity and peroxide valu, when compared with the corresponding non‑enriched controls. In contrast, the proportion of hemp seed oil itself was decisive: the higher inclusion level (50% AC) was associated with less favorable outcomes for both acidity and peroxide index, indicating an adverse effect on primary oxidative stability. A similar dependence on hemp seed oil content was recorded for the specific extinction coefficient at 232 nm (K232), which reflects conjugated dienes formed during early oxidation. Conversely, neither antioxidant enrichment nor hemp seed oil proportion produced discernible changes in the extinction coefficient at 270 nm (K270) linked to conjugated trienes and secondary oxidative species. Notably, the accumulation of secondary oxidation products increased with rising tocopherol dose, suggesting a pro-oxidant shift or depletion dynamics at elevated levels. Finally, the antioxidant capacity assays employed did not yield a straightforward correlation between measured oxidative stability and the compositional variables of the blends, underscoring the limitations of these in vitro methods for predicting real oxidative behavior in such complex lipid matrices.