MétaCan
Menu
Back to cohort
Record W2553952900 · doi:10.5539/ijc.v9n1p1

Determination of Some Trace Elements in Breast Cancer Serum by Atomic Absorption Spectroscopy

2016· article· en· W2553952900 on OpenAlexvenueno aff
Safaa Sabri Najim

Bibliographic record

VenueInternational Journal of Chemistry · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsChemistrySeleniumCadmiumZincManganeseAtomic absorption spectroscopyBreast cancerCobaltChromiumCopperInternal medicineCancerInorganic chemistryMedicine

Abstract

fetched live from OpenAlex

In the breast cancer significant differences occurs in the normal distribution of the trace elements, playing an important role in carcinogenic process. The aim of this study was to investigate the serum levels of some trace elements (Chromium, Cadmium, Manganese, Cobalt, Nickel, Selenium, Zinc, Iron, Copper and Magnesium) by using flame atomic absorption spectroscopy (FAAS). The present study included 150 females, the participates were divided into two main groups, control group which consisted of 75 apparently healthy females, 75patients with breast cancer group .The serum levels of Chromium (tcal2.9631 ,ttab1.960), Cadmium (tcal2.0798 ,ttab1.960), Manganese (tcal18.5676, ttab1.960), Selenium (tcal2.2759,ttab1.960), Iron (tcal2.9296,ttab1.960), Copper (tcal4.0869, ttab1.960),Magnesium (tcal2.6648, ttab1.960), Cobalt (tcal3.8615 , ttab1.960) and Zinc (tcal7.0160, ttab1.960) were statistically significant higher in the breast cancer patients group than the control group. Similarly, Nickel (tcal1.5988,ttab1.960) levels showed significant lower level in breast cancer patients group. On the other hand, the higher levels of trace elements could lead to formation of free radicals or other reactive oxygen species. The evaluation of these trace elements in serum maybe used as helpful tool in diagnosis of the breast cancer.

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 categoriesInsufficient payload (model declined to judge)
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 score0.028
Threshold uncertainty score0.998

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)0.0030.000

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 head0.260
Teacher spread0.255 · 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.

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

Citations10
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of ChemistrySame topicHeavy Metal Exposure and ToxicityFrench-language works237,207