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Effect of Chromium, Cadmium and Arsenic on Growth and Morphology of HeLa Cells

2012· article· en· W2324353915 on OpenAlexvenueno aff
Aftab Ahmad, Bushra Muneer, Abdul Rauf Shakoori

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicChromium effects and bioremediation
Canadian institutionsnot available
Fundersnot available
KeywordsChromiumCadmiumHeLaMetallothioneinChemistryArsenicCell growthCellMolecular biologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Rapid industrialization and anthropogenic activities are main causes of environmental pollution and level of heavy metals is on the increase in biosphere. These heavy metals have deleterious effects on human health and cause many abnormalities. In the present study, we investigated the effects of arsenic, chromium and cadmium on the growth and morphology of HeLa cell. The total protein profile of control as well as treated cells was checked by SDS-PAGE. Chromium was used to induce the expression of metallothionein protein and expression of protein was detected by SDSPAGE. There was reduction in proliferation of cells in chromium, cadmium and arsenic containing medium. Cell necrosis was observed with the increase in the concentration of chromium and at 0.10 µg/mL concentration of chromium complete cell lysis was observed. There was change in morphology of cells with increase in concentration of cadmium and at 1.0 µg/ml cells became round. Arsenic also proved to be deleterious for the growth of HeLa cells and there was change in morphology of cells at 1.0 µg/ml but it was not as toxic as chromium and cadmium. There was no difference in protein profile of control and chromium treated cells except lower in concentration of protein due to less number of cells. Metallothionein were not observed in treated cells by SDS-PAGE. Heavy metal have very deleterious effects on human cells and with increase in metal concentration there was change in morphology of cells and also great reduction in proliferation

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.223
Teacher spread0.217 · 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 designBench or experimental
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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

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