Investigation of Imbalance of Trace Elements in Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Type 2 diabetes is a chronic disorder that is associated with the imbalance of trace elements which are involved in many functions especially enzyme activities. Changes in the levels of serum elements probably can create some complications in type 2 diabetes.The objective of this study was to evaluate the serum levels of the trace elements of zinc and copper, Body mass index, Glucose and HbA1c levels in patients with type 2 diabetes compared to a normal group. This case-control study was performed on 60 men with type 2 diabetes and 60 healthy men. Glucose levels were measured by glucose oxidase method. Body mass index was calculated from each person’s weight and height. The serum levels of zinc and copper were measured by atomic absorption spectrometry and the levels of HbA1c were measured by ion exchange chromatography. The results were analyzed using the SPSS software version 21, and the data were reported as Mean ± SD. In this study, the serum zinc level in the diabetic group was significantly lower than in the control group. The levels of serum copper in diabetic patients were higher than in normal subjects, but the difference was not significant. Our results suggested that the measurement of trace elements along with determination of other biochemical parameters can be performed for monitoring diabetic complications. Also, it seems impaired metabolism of these trace elementsand antagonistic interaction between them may have a contributory role in the progression of DM and its complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".