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Natural extracts containing high polyphenolic concentrations may act as good antimicrobials for their antibacterial and antibiofilm activity. The present research characterizes two hydro-organic extracts with high polyphenolic content, obtained from the shrub as antipathogenic candidates. As a result of their own composition, both extracts, LE050 and PG050, have shown pronounced bioactivities with potential uses, especially in agricultural, livestock production, food manufacturing, and pharmaceutical industries. Polyphenolic compounds were extracted by using adjusted hydro-organic solvent mixtures. These extracts' in vitro antimicrobial activity was evaluated on Gram-positive and Gram-negative pathogenic bacteria, giving special attention to those involved in food contamination. Due to this, the biofilm dispersion was assessed on , and . The extracts showed antimicrobial activity against the pathogenic species tested, presenting IC50 values between 0.625-20% /. Different behaviors have been detected between both extracts, probably linked to their distinct polyphenol composition, being LE050 extract the one with most promising bioactive applications. Finally, the results from the biofilm dispersion assays reveal that the extracts exhibit a good antibiofilm activity against the pathogenic bacteria tested.
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http://dx.doi.org/10.3390/antibiotics12111645 | DOI Listing |
J Chem Inf Model
September 2025
Key Laboratory of Micro-nano Sensing and IoT of Wenzhou, Wenzhou Institute of Hangzhou Dianzi University, Wenzhou 325038, China.
Transcription factors (TFs) are essential proteins that regulate gene expression by specifically binding to transcription factor binding sites (TFBSs) within DNA sequences. Their ability to precisely control the transcription process is crucial for understanding gene regulatory networks, uncovering disease mechanisms, and designing synthetic biology tools. Accurate TFBS prediction, therefore, holds significant importance in advancing these areas of research.
View Article and Find Full Text PDFJ Food Sci
September 2025
College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou, China.
Primary agricultural products are closely related to our daily lives, as they serve not only as raw materials for food processing but also as products directly purchased by consumers. These products face the issue of freshness decline and spoilage during both production and consumption. Freshness degradation induces sensory deterioration and nutritional loss and promotes harmful substance accumulation, causing gastrointestinal issues or even endangering life.
View Article and Find Full Text PDFInt J Phytoremediation
September 2025
Department of Environmental Engineering, Faculty of Engineering and Architecture, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey.
The green synthesis method is a significant approach that offers several advantages, including simplicity, rapidity, and cost-effectiveness in the synthesis of nanoparticles. Iron nanoparticles were synthesized in this work using waste banana peel extract as a capping and reducing agent. The produced nanoparticles were then subjected to a number of characterization procedures, such as Raman spectroscopy, X-ray diffractometry (XRD), zeta potential analysis, Fourier transform infrared (FT-IR) spectroscopy, ultraviolet-visible (UV-VIS) absorption spectroscopy, field scanning electron microscopy (FE-SEM), energy dispersive X-ray analysis (EDX), and thermogravimetric analysis (TGA).
View Article and Find Full Text PDFWorld J Surg Oncol
September 2025
Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Background: Inflammation impacts the prognosis of numerous types of tumors. Inflammatory indicators such as the neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and neutrophil-to-eosinophil ratio (NER) have emerged as potential prognostic markers and are closely correlated with the outcomes of cancer patients. However, the connection between NER and cancer prognosis remains incompletely understood.
View Article and Find Full Text PDFISA Trans
August 2025
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, PR China; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China. Electronic addr
With the deep digital transformation of traditional manufacturing industry and the continuous automation level improvement of production lines, it is more important to predict the Key Performance Indicators (KPIs) of processes in a timely and accurate manner. The traditional laboratory destructive test method for obtaining KPIs consumes a large amount of time and incurs high costs, which not only fails to provide timely and effective guidance for production processes but also results in significant losses for manufacturing enterprises. To address these issues, an online prediction soft sensor model for KPIs based on a serial-parallel gated recurrent unit with self-attention mechanism (SPGRU-SA) soft sensor model is proposed.
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