Evidence map›Paper›PMID 37028663›Full record

ReviewThe Science of the total environment2023

Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants.

Bakr Ahmed Taha, Yousif Al Mashhadany, Qussay Al-Jubouri, Affa Rozana Bt Abdul Rashid, Yunhan Luo, Zhe Chen, Sarvesh Rustagi, Vishal Chaudhary, Norhana Arsad

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The Science of the total environment, 2023. 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
5.8field-weighted citation impact, top 3% of its field
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, 53 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
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

9 authors at 7 institutions in 4 countries.

Bakr Ahmed TahaPhotonics Technology Laboratory, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia. Electronic address: p103537@siswa.ukm.edu.my.
Yousif Al MashhadanyDepartment of Electrical Engineering, College of Engineering, University of Anbar, Anbar 00964, Iraq.
Qussay Al-JubouriDepartment of Communication Engineering, University of Technology, Baghdad, Iraq.
Affa Rozana Bt Abdul RashidFaculty of Science and Technology, University Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, Malaysia.
Yunhan LuoGuangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Department of Optoelectronic Engineering, College of Science and Engineering, Jinan University, Guangzhou 510632, China.
Zhe ChenKey Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, Jinan University Guangzhou, 510632, China.
Sarvesh RustagiSchool of Applied and Life Sciences, Uttaranchal University, Dehradun, Uttarakhand, India.
Vishal ChaudharyDepartment of Physics, Bhagini Nivedita College, University of Delhi, New Delhi 110045, India. Electronic address: drvishal@bn.du.ac.in.
Norhana ArsadPhotonics Technology Laboratory, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia. Electronic address: noa@ukm.edu.my.
Jinan University · CNNational University of Malaysia · MYUniversiti Sains Islam Malaysia · MYUniversity of Anbar · IQUniversity of Delhi · INUniversity of Technology - Iraq · IQUttaranchal University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.

Indexed as

Biosensing TechniquesCOVID-19Artificial IntelligenceCOVID-19 TestingHumansIntelligenceSARS-CoV-2Artificial intelligenceBiosensorsMutations evolutionNanophononicsSARS-COV-2 variants

Identifiers

PMID37028663
PMCPMC10076079
OpenAlexW4362640991

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.