Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Microbial Biosensors01:17

Microbial Biosensors

88
Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
88
Automated Microbial Diagnostics01:24

Automated Microbial Diagnostics

77
Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
77

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Non-ionizing radiation and cancer: A review on current evidence, mechanistic insights, and public health implications.

Biochemistry and biophysics reports·2026
Same author

Postbiotics as Emerging Therapeutics for Allergic Diseases: A Novel Approach Beyond Live Biologics.

Probiotics and antimicrobial proteins·2026
Same author

From compliance to prediction: Clinical laboratories as digital infrastructure for health-system quality and safety.

International journal for quality in health care : journal of the International Society for Quality in Health Care·2026
Same author

Plazomicin susceptibility profile in carbapenem-resistant Enterobacterales (CRE): a laboratory-based cross-sectional study from a tertiary care hospital in North India.

Indian journal of medical microbiology·2026
Same author

DHHC3 interferes with antitumor immunity in melanoma cells.

Oncotarget·2026
Same author

Prevalence and serotype analysis of Diarrhoeagenic Escherichia coli in children less than 10 years at a tertiary hospital in New Delhi, India.

Indian journal of medical microbiology·2026

Related Experiment Video

Updated: May 1, 2026

Dry Film Photoresist-based Electrochemical Microfluidic Biosensor Platform: Device Fabrication, On-chip Assay Preparation, and System Operation
13:42

Dry Film Photoresist-based Electrochemical Microfluidic Biosensor Platform: Device Fabrication, On-chip Assay Preparation, and System Operation

Published on: September 19, 2017

12.6K

Sepsis Diagnostics via Biosensors: Engineering Platforms, Artificial Intelligence Integration, and Clinical

Chaitali Singhal1, Sudarshana Chatterjee2, Shruti Gupta1

  • 1Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd Milestone, Gurugram Expressway, Faridabad 121001, India.

ACS Sensors
|February 16, 2026
PubMed
Summary

Biosensors offer real-time sepsis diagnosis, overcoming challenges of traditional methods. This review details advances in biosensor technology and artificial intelligence for improved sepsis detection and clinical application.

Keywords:
AI-integrationclinical matrix compatibilitycontextual diagnostic intelligencemultiplexed sepsis biomarker detectionpoint-of-care biosensingregulatory and reimbursement barrierstemporal diagnosticstranslational biosensor readinesswearable sepsis sensors

More Related Videos

Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics
10:42

Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics

Published on: June 17, 2022

3.5K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

553

Related Experiment Videos

Last Updated: May 1, 2026

Dry Film Photoresist-based Electrochemical Microfluidic Biosensor Platform: Device Fabrication, On-chip Assay Preparation, and System Operation
13:42

Dry Film Photoresist-based Electrochemical Microfluidic Biosensor Platform: Device Fabrication, On-chip Assay Preparation, and System Operation

Published on: September 19, 2017

12.6K
Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics
10:42

Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics

Published on: June 17, 2022

3.5K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

553

Area of Science:

  • Biomedical Engineering
  • Clinical Diagnostics
  • Translational Medicine

Background:

  • Sepsis diagnosis is complex due to heterogeneity and lack of biomarkers.
  • Current culture-based methods are slow and lack real-time capabilities.
  • Biosensors offer a promising alternative for rapid, multiplexed sepsis detection.

Purpose of the Study:

  • To review recent advances in biosensor technology for sepsis diagnostics.
  • To explore the role of artificial intelligence in augmenting biosensor performance.
  • To identify and address translational bottlenecks hindering clinical deployment.

Main Methods:

  • Synthesis of advances in substrate engineering, nanomaterial amplification, and biorecognition elements (aptamers, AMPs, PNAs, XNAs, CRISPR).
  • Analysis of real-world case studies demonstrating clinical feasibility.
  • Delineation of AI models for biosensor signal interpretation versus EHR-driven prediction.

Main Results:

  • Significant progress in biosensor components and amplification strategies.
  • Demonstrated clinical feasibility through case studies.
  • Distinction between AI for signal interpretation and broader predictive frameworks.

Conclusions:

  • Biosensors, enhanced by AI, represent a paradigm shift in sepsis diagnostics.
  • Addressing translational gaps (performance, regulatory, reimbursement) is crucial for deployment.
  • A collaborative approach is needed to accelerate biosensor integration into sepsis care.