Estimation of total antioxidant capacity in type 2 diabetic and normal healthy subjects
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) is a highly prevalent non-communicable disease in the world. Current investigations evolved that oxidative stress is also a major risk factor to cause type 2 diabetes mellitus due to impairment of antioxidant defense system in various biological fluids.
 Methodology: In this cross-sectional study, 70 type 2 diabetes mellitus subjects and 30 normal healthy subjects of both genders were selected from various health care centers of Karachi, Pakistan for a study period of six months June 2017 – December 2017. The total antioxidant capacity (TAC) concentration was measured in serum by enzyme-linked immunosorbent assay (ELISA) technique using Caymans Antioxidant Assay. The biochemical parameters and anthropometric measurements were estimated by standardized methods. Data was analyzed using the statistical program Statistical Package for the Social Sciences (SPSS) version 10.0.
 Results: According to the study results TAC was significantly reduced (**0.05 ± 0.00 mmol /L) in type 2 diabetes mellitus subjects compared to normal healthy subjects (0.13 ± 0.02 mmol /L). It was noted that diastolic blood pressure (DBP), body mass index (BMI), and triglycerides (TG’s) were significantly increased while high density lipoprotein-cholesterol (HDL-C) was significantly reduced in diabetic subjects than the comparative healthy individuals.
 Conclusion: This study showed that decreased levels of TAC and HDL-C in type 2 DM patients with increased levels of BMI, systolic blood pressure (SBP), fasting blood sugar (FBS), DBP, and total cholesterol (TC) which may cause oxidative stress and increase the progression of cardiovascular disease (CVD) and other metabolic diseases. Modifications in dietary habits and intake of antioxidant foods or supplements may diminish the process of oxidative stress which may consequently decrease CVD and other severe clinical outcomes.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".