Combination of stem cell and repetitive transcranial magnetic stimulation in acute ischaemic stroke as a promising treatment: A case report
Bibliographic record
Abstract
Stroke is a major contributor to long-term disability and its incidence continues to rise annually. This case report aimed to explore the promising benefits of combining stem cell therapy with repetitive transcranial magnetic stimulation (rTMS) in managing acute ischaemic stroke patients. A 62-year-old male presented with left-sided hemiparesis and hemineglect, as well as a cognitive disturbance in the attention domain. His medical history included uncontrolled hypertension over decades and diabetes mellitus for five years. A non-contrast head computed tomography (CT) scan revealed infarction in the right middle cerebral artery (MCA), with an initial National Institutes of Health Stroke Scale (NIHSS) of 13 upon admission to the Emergency Room. The infection and tumour markers were conducted to confirm no contraindication in this patient receiving stem cell therapy. Following the acute phase, the patient underwent a comprehensive treatment regimen involving both stem cell therapy and serial rTMS. Clinical assessments included NIHSS, Barthel Index, and Fugl-Meyer Assesement to evaluate neurological deficits. Additionally, the Montreal Cognitive Assessment-Indonesian version (MoCA-INA) assessment, electroencephalography examination and motor threshold were conducted. The results of this case report revealed noteworthy improvements in NIHSS, motoric strength, and cognitive function post-treatment. In this case report, improvement in clinical outcomes was obtained in the form of motor strength and higher cortical function. Stem cell therapy combined with rTMS has good potential in treating various neuroregenerative and rehabilitative aspects in ischaemic stroke patients.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".