Evidence map›Paper›PMID 42069833›Full record

ArticleScientific reports2026

Smart label-free SPR biosensing platform for hemoglobin and urine glucose detection via machine learning.

Parvathala Siva Kumar Reddy, Saleh Chebaane, Yesudasu Vasimalla, Sana Ben Khalifa, Norah A M Alsaif, Ravi Sankar Puppala, Bhishma Karki, Santosh Kumar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Parvathala Siva Kumar ReddyCentre of Excellence for Nanotechnology, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522302, India.
Saleh ChebaaneDepartment of Physics, College of Science, University of Ha'il, P.O. Box 2440, Ha'il, Saudi Arabia.
Yesudasu VasimallaCentre of Excellence for Nanotechnology, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522302, India.
Sana Ben KhalifaDepartment of Physics, College of Science, Qassim University, P.O. Box 6644, 51452, Buraydah, Almolaydah, Saudi Arabia.
Norah A M AlsaifDepartment of Physics, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Ravi Sankar PuppalaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522020, India.
Bhishma KarkiDepartment of Physics, Tri-Chandra Multiple Campus, Tribhuvan University, Kathmandu, 44600, Nepal. Bhishma.karki@trc.tu.edu.np.
Santosh KumarCentre of Excellence for Nanotechnology, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522302, India.

Funding

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia PNURSP2026R60
6 · The paper itself

Abstract

This article presents the design and the numerical analysis of a smart label-free Surface Plasmon Resonance (SPR) sensor to detect the concentration of haemoglobin in blood and the concentration of glucose in the urine samples. The suggested sensor uses a thin film of silver (Ag) on the prism's surface to excite the surface plasmons. The finite element method (FEM) was used to do numerical simulations to optimize the layer thickness and to analyse the sensor's performance in terms of the sensitivity and the Figure of merit (FOM). The results of the simulation showed that there is a linear correlation between resonance wavelength shift and change in analyte refractive index. The optimised design obtained a sensitivity of 288.29 °/RIU, QF of 780.80 [Formula: see text], SNR of 15.62, FoM of 492.51 [Formula: see text] and CSF of 539.20. The label-free methodology involves no chemical tagging and thus allows biosensing that is quick, real-time and economical. The suggested SPR sensor has great possibilities to be implemented in the non-invasive biomedical applications, diagnostics and point-of-care monitoring. In addition, machine learning models were employed to predict sensor sensitivity based on structural and optical parameters, demonstrating the strong capability of data-driven approaches for rapid performance estimation and design optimization.

Indexed as

Biosensing TechniquesGlucoseHemoglobinsMachine LearningSurface Plasmon ResonanceHumansGlucoseHemoglobinsBiomedical applicationsGlucose sensingHaemoglobin detectionLabel-free biosensorMachine learningSurface plasmon resonance

Identifiers

PMID42069833
PMCPMC13328546

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Registered trials

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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.