Low-dose ketamine pretreatment reduces oxidative damage and inflammatory response following CO2 pneumoperitoneum in rats
Bibliographic record
Abstract
PURPOSE: The duration of pneumoperitoneum during laparoscopic procedures may contribute to post-surgical oxidative stress. Previous studies have shown that low-dose ketamine, an anesthetic with anti-inflammatory properties, protects various organs from ischemia-reperfusion injury. This study investigated the effects of low-dose ketamine on the overproduction of oxidants and the tissue damage caused by intra-abdominal pressure during CO2 pneumoperitoneum. METHODS: Male Sprague Dawley rats received a CO2 pneumoperitoneum of 15 mmHg and preceded by either low-dose ketamine (KP1, 5 mg/kg; KP2, 10 mg/kg) or 0.9% saline (PR, 3 ml). General anethesia was provided by pentobarbital and sevoflurane. The control group (CR) received an intraperitoneal saline injection and sham surgery. Three hours after pneumoperitoneum, serum concentrations of interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), malondialdehyde (MDA), superoxide dismutase (SOD) and intestinal fatty acid binding protein (iFABP) were measured and liver, kidney, lung, and intestine were evaluated for tissue damage. RESULTS: The highest plasma MDA, TNF-α, IL-6 and iFABP values were observed at T1 (after 3 hours of pneumoperitoneum) in the PR group, followed by the KP1, KP2, and CR groups (P < 0.01). SOD concentrations showed an opposite trend and were highest in the CR group, followed by the KP2, KP1, and PR groups (P < 0.01). TNF-α concentration was significantly lower in the KP2 than the KP1 group (P < 0.05). Histopathologic scoring of organ sections demonstrated the lowest scores in the KP2 group, followed by the KP1 and PR groups, in an increasing order (P < 0.05). CONCLUSION: Pretreatment with low-dose ketamine before general anaesthesia protects against potential oxidative damage and inflammatory response caused by CO2 pneumoperitoneum.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".