AGING, ATHEROSCLEROSIS, AUTOIMMUNOINFLAMMATION, AND SYNERGISTIC PHENOMENON
Bibliographic record
Abstract
În lucrare se analizează cele mai recente date privind rolul proceselor autoimune şi modifi cările de vârstă ale sistemului arterial în aterogeneză. Procesele de senescenţă contribuie la creşterea permeabilităţii arterelor, dereglarea barierei endoteliale, favorizând insudaţia componentelor plasmatice, inclusiv lipoproteinele, şi infi ltraţia peretelui vascular cu monocite. Microscopia electronică cu baleiaj a suprafeţei interne a arterelor coronariene umane a demonstrat că cel mai precoce indice al leziunilor aterosclerotice este tumefi erea endoteliocitelor. Sinergismul “aterogeneza-gerontogeneza” reprezintă un fenomen fundamental în evoluţia aterosclerozei, denumită şi “rugina vieţii”. Este expus detaliat rolul mecanismelor autoimune în patomorfogeneza aterosclerozei. Infl amaţia imună în peretele arterial, ca reacţie la depunerea complexelor autoimune ce includ lipoproteinele modifi cate în calitate de antigen, este considerată un component important al aterosclerozei. Implicarea imunoinfl amaţiei în mecanismele aterogenezei este apreciată drept o concepţie complet nouă, originală, descrisă recent în monografi a lansată în cadrul International Academy of Pathology (Amsterdam, Montreal). \nПрезидиум Российской Академии Медицинских Наук постановлением № 72 от 28 марта 2007 присудил авторам: Диплом премии им. А.И. Струкова, оценив как “лучшую научную работу по патологической анатомии”. \nÎn Federaţia Rusă premiat – Vladimir Nagornev; în RM premiaţi membrii A.Ş.M. – Vasile Anestiadi şi Eremia Zota.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".