Evidence mapPaperPMID 39713646Full record

ReviewACS omega2024

Sensing the Future-Frontiers in Biosensors: Exploring Classifications, Principles, and Recent Advances.

Sumitha Manoharan Nair Sudha Kumari, Xavier Thankappan Suryabai

Abstract readReview
In one paragraph

Review in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sumitha Manoharan Nair Sudha KumariCentre for Advanced Materials Research, Department of Physics, Government College for Women, Thiruvananthapuram, University of Kerala, Kerala 695014, India.
Xavier Thankappan SuryabaiCentre for Advanced Materials Research, Department of Physics, Government College for Women, Thiruvananthapuram, University of Kerala, Kerala 695014, India.ORCID https://orcid.org/0000-0001-8955-0743

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biosensors are transforming healthcare by delivering swift, precise, and economical diagnostic solutions. These analytical instruments combine biological indicators with physical transducers to identify and quantify biomarkers, thereby improving illness detection, management, and patient surveillance. Biosensors are widely utilized in healthcare for the diagnosis of chronic and infectious diseases, tailored treatment, and real-time health monitoring. This thorough overview examines several categories of biosensors and their uses in the detection of numerous biomarkers, including glucose, proteins, nucleic acids, and infections. Biosensors are commonly classified based on the type of transducer employed or the specific biorecognition element utilized. This review introduces a novel classification based on substrate morphology, offering a comprehensive perspective on biosensor categorization. Considerable emphasis is placed on the advancement of point-of-care biosensors, facilitating decentralized diagnostics and alleviating the strain on centralized healthcare systems. Recent advancements in nanotechnology have significantly improved the sensitivity, selectivity, and downsizing of biosensors, rendering them more efficient and accessible. The study examines problems such as stability, reproducibility, and regulatory approval that must be addressed to enable the widespread implementation of biosensors in clinical environments. The study examines the amalgamation of biosensors with wearable devices and smartphones, emphasizing the prospects for ongoing health surveillance and individualized medical care. This viewpoint clarifies the distinct types of biosensors and their particular roles, together with recent developments in the "smart biosensor" sector, facilitated by artificial intelligence and the Internet of Medical Things (IoMT). This novel approach seeks to deliver a comprehensive evaluation of the present condition of biosensor technology in healthcare, recent developments, and prospective paths, emphasizing their significance in influencing the future of medical diagnostics and patient care.

Identifiers

PMID39713646
PMCPMC11656264

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.