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Staphylococcus aureus (S. aureus), as a Gram-positive bacterium, is commonly encountered in various infectious diseases, such as acute skin and soft tissue infections. Despite that many efforts have been made, sensitive and reliable quantitative determination of S. aureus remains a huge challenge. Here, we depict a novel colorimetric approach for sensitive and accurate detection by combining allosteric probe-based target recognition and chain extension-based dual signal recycling. The single-strand DNA (ssDNA) products generated by the chain extension process lead to the liberation of G-quadruplex sequences, which can fold into active DNAzyme under the assistance of hemin. The active DNAzyme can work as peroxidase mimics to catalyze the 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS)-HO system, causing the color change of the system. Eventually, the method exhibits a wide detection range from 10 cfu/mL to 10 cfu/mL. The limit of detection of the approach was determined 232 cfu/mL. Considering the robust capability of the approach in S. aureus detection, we believe that it will be a potential alternative tool for biomedical research and clinical molecular diagnostics.
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http://dx.doi.org/10.1007/s12033-023-00791-2 | DOI Listing |
J Med Internet Res
September 2025
School of Nursing, University of Minho, Braga, Portugal.
Background: The spread of misinformation on social media poses significant risks to public health and individual decision-making. Despite growing recognition of these threats, instruments that assess resilience to misinformation on social media, particularly among families who are central to making decisions on behalf of children, remain scarce.
Objective: This study aimed to develop and evaluate the psychometric properties of a novel instrument that measures resilience to misinformation in the context of social media among parents of school-age children.
Eur J Case Rep Intern Med
August 2025
Nephrology Department, Unidade Local de Saúde de Braga, Braga, Portugal.
Introduction: Bevacizumab is a monoclonal antibody that targets vascular endothelial growth factor (VEGF) and is widely used in oncology for its anti-angiogenic properties. However, VEGF inhibition may result in significant nephrotoxicity, including thrombotic microangiopathy (TMA). While systemic TMA is well-described, isolated renal-limited TMA remains under recognised.
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August 2025
Division of Hematology and Oncology, UNM Comprehensive Cancer Center, Albuquerque, USA.
Background: Blinatumomab and inotuzumab ozogamicin (InO) are B-cell targeted agents used in the frontline and relapsed/refractory treatment of B-cell acute lymphoblastic leukaemia (B-ALL). Blinatumomab, a bispecific T-cell engager that targets CD19 and CD3, and InO, an antibody-drug conjugate targeting CD22, have both shown efficacy. However, recent reports have noted lineage conversion as a complication when these agents are used individually or sequentially.
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September 2025
Department of Personnel Strategies, Institute of Management, Collegium of Management and Finance, SGH Warsaw School of Economics, Warsaw, Poland.
Introduction: Organizational resilience is of paramount importance for coping with adversity, particularly in the healthcare sector during crises. The objective of the present study was to evaluate the impact of resilience-based interventions on the well-being of healthcare employees during the pandemic. In this study, resilience-based interventions are defined as organizational actions that strengthen a healthcare institution's capacity to cope with crises-such as ensuring adequate personal protective equipment and staff testing, clear risk-communication, alternative care pathways (e.
View Article and Find Full Text PDFChem Sci
August 2025
Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University Shanghai 200240 China
Predicting Antibody-Antigen (Ab-Ag) docking and structure-based design represent significant long-term and therapeutically important challenges in computational biology. We present SAGERank, a general, configurable deep learning framework for antibody design using Graph Sample and Aggregate Networks. SAGERank successfully predicted the majority of epitopes in a cancer target dataset.
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