With the rise of antibiotic resistance, the drive to discover novel antimicrobial substances and standard testing methods with the aim of controlling transmissive diseases are substantially high. In healthcare sectors and industries, although methods for testing antibiotics and other aqueous-based reagents are well established, methods for testing nanomaterials, non-polar and other particle-based suspensions are still debatable. Hence, utilities of ISO standard validations of such substances have been recalled where corrective actions had to be taken. This paper reports a serial analysis obtained from testing the antimicrobial activities of 10 metallic-based nanomaterials against 10 different pathogens using five different in vitro assays, where the technique, limitation and robustness of each method were evaluated. To confirm antimicrobial activities of metallic-based nanomaterial suspensions, it was found that at least two methods must be used, one being the agar well diffusion method, which was found to be the most reliable method. The agar well diffusion method provided not only information on antimicrobial efficacy through the size of the inhibitory zones, but it also identified antimicrobial ions and synergistic effects released by the test materials. To ascertain the effective inhibitory concentration of nanoparticles, the resazurin broth dilution method is recommended, as MIC can be determined visually without utilising any equipment. This method also overcomes the limit of detection (LoD) and absorbance interference issues, which are often found in the overexpression of cell debris and nanoparticles or quantum dots with optical profiles. In this study, bimetallic AgCu was found to be the most effective antimicrobial nanoparticle tested against across the bacterial (MIC 7 µg/mL) and fungal (MIC 62.5 µg/mL) species.
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http://dx.doi.org/10.3390/bioengineering10111259 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
Biomed Phys Eng Express
January 2025
Shandong University of Traditional Chinese Medicine, Qingdao Academy of Chinese Medical Sciences, Jinan, Shandong, 250355, CHINA.
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease, and it can be used as an important indicator of disease progression. However, many existing methods focus mainly on the image itself when processing brain imaging data, ignoring other non-imaging data (e.g.
View Article and Find Full Text PDFJ Neurosurg
January 2025
Departments of1Neurological Surgery.
Objective: Tumor consistency, or fibrosity, affects the ability to optimally resect meningiomas, especially with recent trends evolving toward minimally invasive approaches. The authors' team previously validated a practical 5-point scale for intraoperative grading of meningioma consistency. The impact of meningioma consistency on surgical management and outcomes, however, has yet to be explored.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Psychiatry, Yongin Severance Hospital, Yongin, Republic of Korea.
Background: The COVID-19 pandemic has accelerated the digitalization of modern society, extending digital transformation to daily life and psychological evaluation and treatment. However, the development of competencies and literacy in handling digital technology has not kept pace, resulting in a significant disparity among individuals. Existing measurements of digital literacy were developed before widespread information and communications technology device adoption, mainly focusing on one's perceptions of their proficiency and the utility of device operation.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Institute of Data Science, National University of Singapore, 117602, Singapore.
Objectives: This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.
Materials And Methods: Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL.
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