AIMC Topic: Fluoroquinolones

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Predicting the quantum yield of O generation for pteridines and fluoroquinolones using machine learning.

Physical chemistry chemical physics : PCCP
Fluoroquinolones (FQs) are a family of antibiotic drugs well-known for their high photochemical activity: upon UV-vis excitation FQs may produce singlet oxygen and/or lose the fluorine atom. Pterins (Ptrs) are a class of organic photosensitizers with...

Exploring multidrug resistance patterns in community-acquired urinary tract infections with machine learning.

Antimicrobial agents and chemotherapy
While associations of antibiotic resistance traits are not random in multidrug-resistant (MDR) bacteria, clinically relevant resistance patterns remain underexplored. This study used association-set mining to explore resistance associations within i...

AI One-Click-Processing-Assisted Ratiometric RTP Paper-Based Sensor Array for the Rapid Discrimination and Detection of Mixtures of Oxolinic Acid and Flumequine.

Analytical chemistry
The rapid and precise detection and discrimination of structurally analogous analytes remain highly desirable yet challenging. In this work, a ratiometric room-temperature phosphorescence (RTP) sensor array integrated with phosphorescence amplificati...

Exploration of the fluorine-fluorine interaction mechanism in fluoroquinolone antibiotics recognition and ciprofloxacin detection on the basis of fluorine-doped carbon quantum dots and machine learning.

Food chemistry
The uncontrolled use of antibiotics poses a significant threat to human health and ecosystems. Accurate differentiation and trace detection of fluoroquinolone antibiotics (FQs) in foods are imperative. Fluorine-doped carbon quantum dots chelated with...

Machine learning-driven 3D-QSAR models facilitated rapid on-site broad-spectrum immunoassay of (fluoro)quinolones using evanescent wave fiber-embedded optofluidic biochip.

Biosensors & bioelectronics
(Fluoro)quinolones (FQs) pose significant threats to public health due to their widespread use and persistence in food and water sources. Given the extensive variety of FQs, testing each compound individually is prohibitively expensive and time-consu...

Investigating long-term risk of aortic aneurysm and dissection from fluoroquinolones and the key contributing factors using machine learning methods.

Scientific reports
The connection between fluoroquinolones and severe heart conditions, such as aortic aneurysm (AA) and aortic dissection (AD), has been acknowledged, but the full extent of long-term risks remains uncertain. Addressing this knowledge deficit, a retros...

Use of Machine Learning to Assess the Management of Uncomplicated Urinary Tract Infection.

JAMA network open
IMPORTANCE: Uncomplicated urinary tract infection (UTI) is a common indication for outpatient antimicrobial therapy. National guidelines for the management of uncomplicated UTI were published in 2011, but the extent to which they align with current p...

Effect of palladium(II) complexes on NorA efflux pump inhibition and resensitization of fluoroquinolone-resistant : and approach.

Frontiers in cellular and infection microbiology
leads to diverse infections, and their treatment relies on the use of antibiotics. Nevertheless, the rise of antibiotic resistance poses an escalating challenge and various mechanisms contribute to antibiotic resistance, including modifications to d...

Comparing LASSO and random forest models for predicting neurological dysfunction among fluoroquinolone users.

Pharmacoepidemiology and drug safety
BACKGROUND: Fluoroquinolones are associated with central (CNS) and peripheral (PNS) nervous system symptoms, and predicting the risk of these outcomes may have important clinical implications. Both LASSO and random forest are appealing modeling metho...