ArticleTalanta2022
Characterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classifications.
Article in Talanta, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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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.
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Who cites it
13 citing papers in PubMed.
- One-Drop Serum Screening Test to Monitor Tissue Iron Accumulation.Analytical chemistry · 2025Article
- Detection of serum composition in pediatric inflammatory multisystem syndrome associated with SARS-CoV-2 and the response for the treatment by FTIR.Scientific reports · 2025Article
- A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells.Micromachines · 2023Review
- Increased levels of nerve growth factor accompany oxidative load in recurrent pregnancy loss. Machine learning applied to FT-Raman spectra study.Bioprocess and biosystems engineering · 2023Article
- Infrared Spectroscopy as a Potential Diagnostic Tool for Medulloblastoma.Molecules (Basel, Switzerland) · 2023Article
- Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19.Computer methods and programs in biomedicine · 2023Article
- Article
- Blood serum lipid profiling may improve the management of recurrent miscarriage: a combination of machine learning of mid-infrared spectra and biochemical assays.Analytical and bioanalytical chemistry · 2022Article
- Spectroscopic methods for COVID-19 detection and early diagnosis.Virology journal · 2022Review
- Article
- Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level.Measurement : journal of the International Measurement Confederation · 2022Article
- COVID-19 antibody level analysis with feature selection approach.Procedia computer science · 2022Article
- Novel photonic methods for diagnosis of SARS-CoV-2 infection.Translational biophotonicsReview
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Herein, we show differences in blood serum of asymptomatic and symptomatic pregnant women infected with COVID-19 and correlate them with laboratory indexes, ATR FTIR and multivariate machine learning methods. We collected the sera of COVID-19 diagnosed pregnant women, in the second trimester (n = 12), third-trimester (n = 7), and second-trimester with severe symptoms (n = 7) compared to the healthy pregnant (n = 11) women, which makes a total of 37 participants. To assign the accuracy of FTIR spectra regions where peak shifts occurred, the Random Forest algorithm, traditional C5.0 single decision tree algorithm and deep neural network approach were used. We verified the correspondence between the FTIR results and the laboratory indexes such as: the count of peripheral blood cells, biochemical parameters, and coagulation indicators of pregnant women. CH
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Registered trials
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.