ТАУРИН В НОРМЕ И ПАТОЛОГИИ: РЕЗУЛЬТАТЫ ЭКСПЕРИМЕНТАЛЬНЫХ И ЭПИДЕМИОЛОГИЧЕСКИХ ИССЛЕДОВАНИЙ
Bibliographic record
Abstract
Taurine (T) was first noted as beneficial for stroke and cardiovascular diseases (CVD) prevention in genetic rat models, stroke-prone spontaneously hypertensive rats (SHRSP). The preventive mechanisms of T were ascribed to sympathetic modulation for reducing blood pressure (BP) and antiinflammatory action. Recent epidemiological surveys revealed the involvement of inflammatory mediators in the pathogenesis of stroke and also atherosclerosis for which T was proven to be effective experimentally. Arterio-lipidosis prone rats, a substrain of SHRSP selectively bred for higher reactive hypercholesterolemia, quickly develop not only arterial fat deposition but also fatty liver which could be attenuated by dietary T supplementation. CARDIAC (CVD and Alimentary Comparison) Study was a WHO-coordinated multi-center epidemiological survey on diets and CVD risks and mortalities in 61 populations. Twenty-four-hour urinary (24U) T was inversely related significantly with coronary heart disease mortality. Higher 24U-T excreters had significantly lower body mass index, systolic and diastolic BP, total cholesterol (T-Cho), and atherogenic index (AI: T-Cho/high density lipoprotein-cholesterol) than lower T excreters. T effects on CVD risks were intensified in individuals whose 24U-T and -magnesium (M) excretions were higher. Furthermore, higher Na excreters with higher heart rate whose BP were significantly higher than those with lower heart rate were divided into two groups by the mean of 24U-T, high and low T excreters. Since the former showed significantly lower BP than the latter, T may beneficially affect salt-sensitive BP rise. Included among the typical 61 populations, were Guiyang, China or St. John’s, Newfoundland, Canada, in which the means of both 24U-T and -M were high or low, respectively. The former and the latter had low and high CVD risks, respectively. Australian Aboriginals living at the coastal area in Victoria were supposed to eat T- and M-rich bush and sea foods and be free from CVD 200 years ago, but they presently have nearly the highest CVD risks indicating that T- and/or M-containing seafood, vegetables, fruits, nuts, milk, etc, similar to prehistoric hunters’ and gatherers’ food should be good for CVD prevention. The preventive effects of T, good for health and longevity, first noted experimentally, were also proven epidemiologically in humans.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; both teacher heads agree on what is shown here.
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".