A TRIBUTE TO ELENA KORNEVA, A PIONEER OF NEUROIMMUNE BIOLOGY, ON HER 85 th BIRTHDAY
Bibliographic record
Abstract
The scientific carrier of Elena Korneva is presented, who dedicated her life for research on Immuno-Physiology, as she likes to call her field. PubMed reports 102 papers over 52 years. We can safely assume that her total production, including those works, which are not reported by PubMed might be over 200-250 papers, books and book chapters. We discuss a total of 70 papers, as for many papers only the titles were available to us. Of this 70 papers 55 were reported by PubMed between 1962 and 2009. In addition we have collected 14 papers and one book which were not reported by PubMed. These are book chapters and journal articles. Her research subjects vary a great deal, but everything has to do with Immuno-Physiology. The first paper we discuss establishes that the hypothalamus regulates immune function [1]. This knowledge that the Neuroendocrine and Immunes Systems interact never was neglected in her papers. Here we list the various fields of her investigations and put the reference numbers in brackets to help the reader. Neuroimmune interaction [References No.: 1, 5-12]; Glucocorticoids [13-15]; Cytokines [13-25]; Defensins [26, 27, 55]; Signal Transduction [29-31]; Stress [31-37]; Cholinergic immunoregulation [38, 39]; Opioid peptides [40]; Thymus [41]; Pineal gland [42]; Natural killer (NK) cells [43]; Anesthesia [44]; Nonspecific resistance [45]; Autoimmunity [46, 47]; Chronic fatigue syndrome (CFS) [48]. Orexin and immunity [49-52]; History of Neuroimmune Biology [54]; Concluding remarks to NIB 6 [55]; Immune response in the CNS [56]. Signal Transduction by IL-1 and IL-2 [57, 58]. c-Fos gene and Il-2 expression in the brain [59]; Signaling mechanisms in stress [60]. Orexin in the CNS and in immune organs [61, 62]. Neuroimmune pathology of stress [63]. As we may see Korneva covered just about any subjects belonging to Immuno-Physiology. She is a leader, not a follower, so all of the papers present new discoveries, new aspects of various areas of the subject matter she investigated. Considering this fact and also the work that could not be reported here makes her a truly outstanding scientist, which has been appreciated by various institutions. She received numerous medals and awards and fulfilled leading positions for a lifetime.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.013 |
| Insufficient payload (model declined to judge) | 0.046 | 0.048 |
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".