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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Related Experiment Video

Updated: Jan 23, 2026

A 96 Well Microtiter Plate-based Method for Monitoring Formation and Antifungal Susceptibility Testing of Candida albicans Biofilms
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Rapid Single-Cell Phenotypic Antifungal Susceptibility Testing on a SlipChip Enabled by Deep Learning.

Yan'an Ren1, Juanxiu Qin2, Min Li2,3,4

  • 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.

Analytical Chemistry
|January 22, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a rapid, single-cell antifungal susceptibility testing (AFST) method using microfluidics and AI. It delivers results in 4 hours, significantly faster than traditional methods for fungal infections.

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Area of Science:

  • Medical Microbiology
  • Biotechnology
  • Computational Biology

Background:

  • Fungal infections are a major global health threat, causing over 1.5 million deaths annually.
  • Current antifungal susceptibility testing (AFST) methods are time-consuming, requiring 24-48 hours, which delays critical treatment decisions.
  • There is an urgent need for rapid and accurate AFST methods to guide antifungal therapy.

Purpose of the Study:

  • To develop and validate a rapid, label-free, single-cell AFST (sc-AFST) platform.
  • To significantly reduce the time required for AFST from days to hours.
  • To enable high-resolution single-cell growth analysis and population-level drug response profiling.

Main Methods:

  • Integration of a SlipChip microfluidic device with deep learning-based image analysis (ResNet-34 classifier and U-Net segmentation model).
  • Partitioning of fungal cells into picoliter droplets for exposure to antifungal gradients.
  • Automated identification and quantification of cellular growth from bright-field images.

Main Results:

  • Achieved >98.8% sensitivity in cell detection and >93% segmentation accuracy.
  • Demonstrated 100% categorical agreement with the gold standard broth microdilution test across 10 clinical isolates.
  • Successfully determined minimal inhibitory concentrations (MICs) within 4 hours.

Conclusions:

  • The developed sc-AFST platform provides a scalable, rapid, and clinically actionable tool for antifungal susceptibility testing.
  • This technology enables precise single-cell analysis and population-level drug response assessment.
  • The rapid turnaround time of sc-AFST can significantly improve patient outcomes by enabling timely treatment decisions for fungal infections.