Urinary Concentrations of Metals and Metalloids in Malaysian Adults
Bibliographic record
Abstract
Exposure to environmental pollutants in humans can be conducted through direct measurement of biological media such as blood, urine or hair. Assessment studies of metals and metalloids in Malaysia is very scarce although cross-sectional nationwide human biomonitoring surveys have been established by the USA, Canada, Germany, Spain, France, and Korea. This study aims to assess urinary metal levels namely cadmium (Cd), nickel (Ni), lead (Pb) and arsenic (As) among Malaysian adults. This was a cross-sectional study involving 1440 adults between the age of 18 and 88 years old. After excluding those with 24 h urine samples of less than 500 ml, urine creatinine levels < 0.3 or > 3.0 g/L and those who refuse to participate in the study, a total of 817 respondents were included for analysis. A questionnaire with socio-demographic information such as age, gender, occupation, ethnic, academic qualification and medical history was administered to the respondents. Twenty-four-hour urine samples were collected in a container before being transported at 4 °C to the laboratory. Samples were then aliquoted into 15 ml tubes and kept at - 80 °C until further analysis. Urine was diluted ten-fold with ultrapure water, filtered and analysed for metals and metalloids using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). The geometric mean of urinary As, Ni, Cd and Pb concentrations among adults in Malaysia was 48.21, 4.37, 0.32, and 0.80 µg/L, respectively. Males showed significantly higher urinary metal concentrations compared to females for As, Cd and Pb except for Ni. Those who resided in rural areas exhibited significantly higher As, Cd and Pb urinary concentrations than those who resided in urban areas. As there are no nationwide data on urinary metals, findings from this study could be used to identify high exposure groups, thus enabling policy makers to improve public health strategically.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".