Translymphatic Chemotherapy by Intrapleural Placement of Gelatin Sponge Containing Biodegradable Paclitaxel Colloids Controls Lymphatic Metastasis in Lung Cancer
Bibliographic record
Abstract
As a means of treating lymphatic metastasis from lung cancer, the pharmacokinetics and therapeutic effects of an intrapleural (ipl) implantable drug delivery system consisting of a gelatin sponge impregnated with polylactide-co-glycolide paclitaxel (PLGA-PTX) microspheres were studied. PLGA-PTX with 7% (w/w) drug loading were incorporated into gelatin matrix. The pharmacokinetics were studied in rats with one of the following regimens: (a) Taxol 8 mg/kg by i.v. injection; (b) Taxol 8 mg/kg ipl; (c) PLGA-PTX (100 mg/kg) ipl; (d) sponge containing PLGA-PTX (100 mg/kg) ipl. PTX concentrations in lymph node and plasma were determined by liquid chromatography mass spectrometry, and the area under the curve (AUC) was calculated. Therapeutic efficacy was assessed in an orthotopic lung cancer model with tumor resection 14 days following tumor implantation. Animals were randomized to ipl placement of PLGA-PTX sponge, placebo sponge, or no treatment. Lymph node metastases were examined at 32 d. The results show that the mediastinal lymph node AUC was significantly higher with ipl. placement of PLGA-PTX sponge compared with i.v. and ipl administration of Taxol. This represents 100- to 400-fold increase of lymphatic drug exposure compared with i.v. dosing. Peak plasma concentration was significantly reduced in the PLGA-PTX sponge group compared with i.v. dosing. PLGA-PTX particles were microscopically identified in lymphatic tissue and resulted in an 80% reduction of lymphatic metastasis compared with controls. Translymphatic-targeted drug delivery significantly decreases lymphatic metastasis in an orthotopic lung cancer model. This effect may be attributable to the improved distribution of PTX to the lymphatic system.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".