Analysis of the antioxidative function of the radioprotective Japanese traditional (Kampo) medicine, hangeshashinto, in an aqueous phase
Bibliographic record
Abstract
Oral mucositis (OM) is a common and painful complication of radiotherapy for head and neck cancer. Hangeshashinto (HST), a Japanese traditional medicine, is known to alleviate radiotherapy- and/or chemotherapy-induced OM; however, the detailed mechanism has not yet been clarified. The aim of the present study was to clarify the details of the antioxidative functions of HST against reactive oxygen species (ROS) produced by radiation. The hydroxyl radical (•OH)-scavenging ability and the reduction ability was simultaneously measured using a modified electron paramagnetic resonance (EPR) spin-trapping method. The superoxide (O(2) (•-))-scavenging ability was estimated by an EPR redox probing method. Water suspensions of powdered HST and of its seven constitutive crude drugs were tested. In addition, some of the main water-soluble ingredients of the crude drugs were also tested. HST was found to scavenge both •OH and O(2) (•-). Furthermore, HST was observed to reduce relatively stable nitroxyl radicals. Glycyrrhizae Radix (kanzo), Ginseng Radix (ninjin), Zizyphi Fructus (taiso) and glycyrrhizin (an ingredient of kanzo) were all found to be relatively good •OH scavengers. Scutellariae Radix (ogon) and Coptidis Rhizoma (oren) demonstrated reducing ability. In addition, acteoside and berberine chloride, which are water-soluble ingredients of ogon and oren, respectively, also demonstrated reducing ability. Oren exhibited oxidative ability at higher concentrations, which may have a function in maintaining catalytic redox action. The antioxidative function of HST probably worked via a balance of scavenging ROS, reducing stable free radicals, and some minor oxidizing activities.
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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 | 0.000 |
| 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".