Insulin – Overview, Infections and Benefits of Immunization and Insurance
Bibliographic record
Abstract
From the time the earliest description of diabetes appeared around 552 BC in the Ebers Papyrus, people have been searching for a cure for it. The term ‘diabetes’ was introduced by Aretaeus of Cappadocia, a Greek physician, (129–199 AD). Before the availability of insulin, any child who had diabetes had a very poor life expectancy, and the prognosis was also poor for any adult having the disease. In 1921, Canadian physician Frederick Banting and medical student Charles H. Best discovered the hormone insulin in the pancreatic extracts of dogs. Earlier, several injections of insulin were required daily; in the 1930s, H.C. Hagedorn, a chemist in Denmark, added protamine to the preparation and found that it prolonged the action of insulin. It was not until 1978 that the first recombinant DNA human insulin was prepared by combining the insulin A- and B-chains. Credit goes to David Goeddel and his colleagues (of Genentech) for this. Some innate (cytokines, complement) humoral immune functions are decreased and some remain the same in patients with diabetes mellitus (DM) compared to those without DM. We all know why diabetics are more prone to infections by this point. The science keeps the mind going but what keeps the heart going? Probably a big size chocolate bar for a diabetic with a blood sugar of. But no! Resistance is a key. If we are not going to resist that candy bar, nor are the bacteria or other organisms causing infection going to. There is not a lot we can do after this point, but there is quite a lot that could be done before—immunisation. Diabetes is an uphill battle for those that suffer from it; but the new health insurance schemes offered by the government should ease a little bit of the pressure. Insulin is not a definitive cure for diabetes but is definitely a form of life support. As of now, there is no complete cure for diabetes, but there may be one to wipe it out in the future due to the advancing technologies in the field of science.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".