Detection of lipoprotein(a)- cholesterol expression in Bangladeshi adults with dyslipidemia
Bibliographic record
Abstract
Lipoprotein(a)-cholesterol (Lp(a)-C), a low-density lipoprotein (LDL)-like particle is considered as a risk factor for cardiovascular diseases (CVDs). We aimed to investigate the association of Lp(a)-C expression with dyslipidemia among the Bangladeshi population and assess the relationship with cardiovascular risks. In this cross-sectional comparative study, a total of 180 urban males and females between ages 19-65 years were included who were enrolled in a hospital setting of Bangladesh. Participants were selected based on their total cholesterol (TC) level≥200 mg/dl, high density lipoprotein (HDL)-C<40 mg/dl, LDL-C≥140 mg/dl, and triacylglycerol (TG)≥150 mg/dl regardless of race, religion and socioeconomic status. Venous blood was collected from all participants and analyzed. Further, participants’ socio-demographics and body mass index (BMI) were collected. Expression of Lp(a)-C was detected in 22.86% patients with desirable levels (<14 mg/dL) of serum Lp(a)-C. This study suggests that the prevalence of hyperlipidemia and hypertriglyceridemia is high in the Bangladeshi population. Males were found to have lower HDL-C and higher TG than females. and, similar to other ethnic groups, a negative correlation between BMI and HDL-C was found in this population. In addition, Lp(a)-C had a positive correlation with TG which may recommend routine clinical investigation of Lp(a)-C as a biomarker for CVD risk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".