Human Embryonic Stem Cells in the Treatment of Patients With Down Syndrome: A Case Report
Bibliographic record
Abstract
Down syndrome (DS) is a common chromosomal disorder caused by trisomy of chromosome 21 (HSA21q). Individuals with DS suffer from various congenital and progressive diseases. There is no standard treatment for DS. Due to overexpression of genes in trisomic cells, abnormal neuronal development and functional changes occur in the central nervous system of DS patients. The key deficiencies in neural stem and progenitor cell expansion lead to cognitive disabilities in DS. Human embryonic stem cells (hESCs) could serve as an expandable source for neurons production, which could be applied for the treatment of various diseases affecting brain. In this article, we report a case of DS patient treated with hESC therapy. A DS child had delayed milestones, with no speech, subnormal understanding and subnormal motor skills. Following the treatment, patient showed remarkable changes and improvement in the clinical conditions such as better understanding, improved muscle tone of limbs and ability to recognize near ones. hESC therapy was found to be beneficial in the treatment of DS. Further studies are needed to understand the clinical utility of hESC therapy in the patients with DS. J Med Cases. 2016;7(4):123-125 doi: http://dx.doi.org/10.14740/jmc2455w
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".