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Record W3197329212 · doi:10.29169/1927-5129.2019.15.07

The Environmental Effects of Lead Concentrations on Protein and DNA Structures in Epileptic Patients from an Infrared Spectroscopic Study

2019· article· en· W3197329212 on OpenAlexvenueno aff
Maria Kyriakidou, Pavlos Nisianakis, George Papatheodorou, Michail Rallis, T. Théophanides

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2019
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsnot available
Fundersnot available
KeywordsLead (geology)InfraredDNAChemistryEnvironmental chemistryBiologyBiochemistryOpticsPhysicsPaleontology

Abstract

fetched live from OpenAlex

Fourier transform infrared (FT-IR) and inductively coupled plasma mass spectrometry (ICP-MS) elementary analysis were used to investigate the environmental effects of lead blood serum levels on the life metal ions (Cu2+ and Zn2+), protein secondary structure and DNA structure in epileptic patients. By increasing the lead concentration an increased intensity of the band at 1744 cm-1 was observed due to induced oxidative stress. The shifts of the amide I and amide II bands of the peptide group, -CONH- from 1655 cm-1 and 1550 cm-1, respectively, to lower frequencies is due to the change of protein molecular structure from α-helix to β-sheets. An important change in the spectral region between 1200-900 cm-1, where the phosphates and phosphate-ribose groups of DNA and RNA are absorbing, is suggesting an attack on the DNA backbone as a function of the increase of lead concentration. The characteristic band at 1170 cm-1 could be used as a “marker band” for the damaged DNA backbone structure upon lead exposure. The ICP-MS elementary analysis showed a decrease of the ratio [Cu/Zn] by increasing the lead levels in blood serum is linked to oxidative stress and is confirming the FT-IR data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.269
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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