Correlations between Lymphocytes, Mid-Cell Fractions and Granulocytes with Human Blood Characteristics Using LowPower Carbon Dioxide Laser Radiation
Bibliographic record
Abstract
In this research, the subpopulations of human blood parameters including lymphocytes, monocytes, and granulocytes were determined byelectronic sizing in the Health Centre of UniversitiSains Malaysia. These parameters have beencorrelated with human blood characteristics such as age, gender, ethnicity, blood types, body mass index, medical history, number of chronic diseases, and type of chronic diseases; before and after irradiation with 15 W carbon dioxide laser (?=10,600 nm). The correlations were obtained byfinding patterns in changes of blood parameters using paired non-parametric tests, and an independent non-parametric test using the SPSS version 17. Irradiation of blood samples with carbon dioxide lasershowedsignificant changes in lymphocytes, monocytes, and granulocytes before and after irradiation.These analysis revealed that a significant increaseinlymphocyte before and after irradiation among different body mass index (p-value = 0.031).There is significant increase in monocyte before and after irradiation between medical history (p-value = 0.052), and number of chronic diseases (p-value = 0.022).And there is significant decrease in granulocyte before and after irradiation, among different body mass index (p-value = 0.021), and number of chronic diseases (p-value = 0.018).The correlation between changes in human blood parameters and a patient’s characteristics were very much correlated and can become a significant indicator for blood analyses.This study considered as a new finding for the increase inlymphocyte, monocyte, and the decrease in granulocyte by using low power Carbon dioxide laser radiation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".