Gut flora “the second brain” connects Eastern and Western medicine: intestinal hyper-permeability or Qi deficiency can affect brain, mind, and whole body
Bibliographic record
Abstract
There are two famous “the Father of Modern Medicine”—Hippocrates (a Greek physician) said “all disease begins in the gut” and Sir William Osler (a Canadian physician) said “a man is as old as his arteries.” It is known that gut microbiota imbalance (dysbiosis) and intestinal hyper-permeability are related to high level of advanced glycation end-products (AGEs) which damage blood vessel’s endothelial function and potentially cause multi-organ dysfunction. On the other hand, Traditional Chinese Medicine (TCM) doctors already knew the importance of digestive system thousands of years ago since the production of “Qi” which is vital energy for whole body largely rely on abdominal condition, and Qi deficiency triggers multiple mental and physical symptoms. It may sound two leading Integrative Medicine (IM) systems, Western IM and TCM use “different languages,” but it is not well known that their essence is very similar (illness is a result of “dis-ease” or “dysfunction of Qi.”). Genome analysis technology, such as DNA sequencing and microbiota researches have enabled us to understand how intestinal dysbiosis is related to obesity, metabolic syndrome, brain disorders, autoimmune diseases, or mood disorders (Brain-Mind-Gut axis). In addition, making the most of artificial intelligence’s (AI) “deep learning” technology might be a potential “prescription” to heal worldwide medical-economical crisis, spend more time with patients (for medical providers), and prevent providers’ burnout. AI-assisted TCM-style history taking computer and portable wrist pulse diagnostic device are about to be used in China. In the new era of preventive medicine, gut microbiota research, precision medicine with genome analysis, AI technology and TCM ancient wisdom can be combined together. Blending the new and the old will make it possible to detect and treat “pre-clinical disease” before real diseases happen since Western IM and TCM are actually using a “same language.”
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".