AIMC Journal:
Vector borne and zoonotic diseases (Larchmont, N.Y.)

Showing 1 to 3 of 3 articles

Single-Cell Transcriptomic Profiling and Machine Learning Reveal Stage-Specific Gene Dynamics in Brucellosis.

Vector borne and zoonotic diseases (Larchmont, N.Y.)
BACKGROUND: Accurate prediction of the progression of brucellosis is critical for optimizing therapeutic interventions, yet reliable biomarkers for this transition remain elusive. METHOD: Single-cell RNA sequencing (scRNA-seq) of peripheral blood mon...

Integrating Microfluidics Chips into Vector-Borne Disease Surveillance: Technological Breakthroughs and Persistent Hurdles.

Vector borne and zoonotic diseases (Larchmont, N.Y.)
BACKGROUND: Vector-borne diseases (VBDs), such as malaria, dengue, and Zika virus infections, remain a critical global health burden, particularly in resource-limited regions. Conventional diagnostic methods, including microscopy, enzyme-linked immun...

The Role of Artificial Intelligence and Machine Learning in Predictive Virology: Forecasting, Tracking, and Combating Viral Threats.

Vector borne and zoonotic diseases (Larchmont, N.Y.)
The escalating threat of viral pandemics, dramatically illustrated by the COVID-19 crisis, has exposed the critical shortcomings of conventional reactive virology in addressing rapidly evolving pathogens. This review introduces predictive virology (P...