Evidence map›Paper›PMID 39359838›Full record

ReviewAnnals of medicine and surgery (2012)2024

Revolution in malaria detection: unveiling current breakthroughs and tomorrow's possibilities in biomarker innovation.

Emmanuel Ifeanyi Obeagu, G I A Okoroiwu, N I Ubosi, Getrude U Obeagu, Hope Onohuean, Tukur Muhammad, Teddy C Adias

Abstract readReview
In one paragraph

Review in Annals of medicine and surgery (2012), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Life (Basel, Switzerland) · 2025
    Review
  9. Article
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

7 authors.

Emmanuel Ifeanyi ObeaguDepartment of Medical Laboratory Science, Kampala International University.
G I A OkoroiwuDepartment of Public Health Science, Faculty of Health Sciences, National Open University of Nigeria, Jabi, Abuja.
N I UbosiDepartment of Public Health Science, Faculty of Health Sciences, National Open University of Nigeria, Jabi, Abuja.
Getrude U ObeaguSchool of Nursing Science, Kampala International University.
Hope OnohueanBiopharmaceutics Unit, Department of Pharmacology and Toxicology, School of Pharmacy, Kampala International University, Kampala.
Tukur MuhammadDepartment of Science Education & Educational Foundations, Faculty of Education Kampala International University Western Campus.
Teddy C AdiasDepartment of Haematology and Blood Transfusion Science, Faculty of Medical Laboratory Science, Federal University Otuoke, Bayelsa State, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ongoing battle against malaria has seen significant advancements in diagnostic methodologies, particularly through the discovery and application of novel biomarkers. Traditional diagnostic techniques, such as microscopy and rapid diagnostic tests, have their limitations in terms of sensitivity, specificity, and the ability to detect low-level infections. Recent breakthroughs in biomarker research promise to overcome these challenges, providing more accurate, rapid, and non-invasive detection methods. These advancements are critical in enhancing early detection, guiding effective treatment, and ultimately reducing the global malaria burden. Innovative approaches in biomarker detection are leveraging cutting-edge technologies like next-generation sequencing, proteomics, and metabolomics. These techniques have led to the identification of new biomarkers that can be detected in blood, saliva, or urine, offering less invasive and more scalable options for widespread screening. For instance, the discovery of specific volatile organic compounds in the breath of infected individuals presents a revolutionary non-invasive diagnostic tool. Additionally, the integration of machine learning algorithms with biomarker data is enhancing the precision and predictive power of malaria diagnostics, making it possible to distinguish between different stages of infection and identify drug-resistant strains. Looking ahead, the future of malaria detection lies in the continued exploration of multi-biomarker panels and the development of portable, point-of-care diagnostic devices. The incorporation of smartphone-based technologies and wearable biosensors promises to bring real-time monitoring and remote diagnostics to even the most resource-limited settings.

Indexed as

biomarkersdiagnosismalariamonitoringtreatment

Identifiers

PMID39359838
PMCPMC11444567

What Socratic holds

Textmetadata
LicenceCC BY
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.