AIMC Topic: Machine Learning

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Machine learning-enabled microfluidic ratiometric fluorescence sensor array based on lanthanide-gold nanoclusters for visual detection of multicomponent antibiotics.

Biosensors & bioelectronics
The accumulation of multiple antibiotics in the environment poses severe threats to ecosystems and human health, demanding rapid, sensitive, and portable detection methods. This study introduces a lanthanide-gold nanocluster (AuNCs)-based microfluidi...

Machine learning-assisted multi-channel nanozyme sensor arrays for multiple pesticide tracking, tracing and metabolism analysis.

Biosensors & bioelectronics
To achieve precise pesticide residue detection and metabolic analysis, we innovatively proposed a machine learning-assisted multi-channel nanozyme sensor array. Five Cu-carboxylate nanozymes with outstanding laccase-like and peroxidase-like activitie...

The SERS method based on the COF-ag substrate, combined with machine learning, is used for the detection of tetracycline and oxytetracycline in milk.

Food chemistry
Tetracycline antibiotics, valued for potent antibacterial effects, are widely used in livestock but raise concerns over unsafe residues in milk. In this study, a surface-enhanced Raman spectroscopy (SERS) method based on an amino-functionalized coval...

Predictive Modeling of DNA Damage Outcomes: Classification of Mutational Determinants Using Augmented Machine Learning Techniques.

Chemical research in toxicology
The mutational outcome of DNA damage as a direct result of constant chemical assault is governed by major factors, including the structure and nature of damage, replication, and repair machinery . The role of the size of the adduct, adduct-flanking b...

Metabolite Identification Data in Drug Discovery, Part 1: Data Generation and Trend Analysis.

Molecular pharmaceutics
In drug discovery, metabolite identification data are used to identify metabolic soft spots in research molecules to facilitate reduced metabolism in subsequently designed compounds. In addition, knowledge about exact metabolite structures enables th...

Ultrasensitive Detection of m A-Modified RNA Using CRISPR/Cas12a-Integrated Iontronic Biosensor with Hydrophobized Nanochannels: Toward Early Cancer Diagnosis by Machine Learning.

Analytical chemistry
N -methyladenosine (m A), the most prevalent internal modification in eukaryotic RNAs, has emerged as a focal point of intensive research in recent years owing to its pivotal regulatory roles in carcinogenesis, progression, and metastasis. However, c...

Prevalence, associated factors, and machine learning-based prediction of depression, anxiety, and stress among university students: a cross-sectional study from Bangladesh.

Journal of health, population, and nutrition
BACKGROUND: Mental health challenges are a growing global public health concern, with university students at elevated risk due to academic and social pressures. Although several studies have exmanined mental health among Bangladeshi students, few hav...

Variables for habitat and vertebrate hosts of Ixodes scapularis are the best ecological predictors of the spatial spread of Lyme disease in the United States (2010-2019).

Parasites & vectors
BACKGROUND: Lyme disease (LD) is a major public health concern in North America. The incidence of LD has increased in part due to the rapid expansion of Ixodes scapularis infected with Borrelia burgdorferi sensu lato (Bb), the causative agent of LD. ...

Gene association study between polycystic ovary syndrome and metabolic syndrome: a transcriptomic analysis and machine learning approach.

Journal of ovarian research
BACKGROUND: Patients with polycystic ovary syndrome (PCOS) often experience a range of metabolic comorbidities, suggesting a potential association between PCOS and metabolic syndrome (MetS). However, this potential link has not yet been fully elucida...

Advancing virulence factor prediction using protein language models.

BMC biology
BACKGROUND: Bacterial infections rank as the second leading cause of death globally, with virulence factors (VFs) being crucial to their pathogenicity. Predicting VFs accurately can uncover mechanisms of bacterial diseases and suggest new treatments....