<i>In silico and in vivo</i> toxicity assessment of cysteamine-modified nanoparticles: implications for pharmacotherapy application
Bibliographic record
Abstract
Background Given the increasing therapeutic potential of cysteamine (CYST) at appropriate doses and expert concerns regarding the toxicity of nanoparticles, this study aimed to assess the toxicity profile of both CYST and silver nanoparticles conjugated with cysteamine (CYST-AgNPs).Methods For the acute study, a 300 mg/kg starting dose of CYST (i.p administration) produced a toxic response in some mice (n = 3/group), and a 300 mg/kg beginning dose of CYST-AgNPs produced delayed mild toxicity. Lower doses of CYST and CYST-AgNPs (50, 100, and 200 mg/kg; n = 3/group) were administered (i.p) for further acute toxicological evaluation. The sub-acute toxicity test was conducted for 21 days, and female mice (n = 5/group) were divided into control, CYST (25 and 50 mg/kg), and CYST-AgNPs (25 and 50 mg/kg). AgNPs and CYST-AgNPs were characterized with FTIR spectroscopy, UV spectrophotometer, HR-TEM, and SEM-EDX. Blood samples were collected via cardiac puncture and processed according to the standard hematological analysis protocols.Results The UV-vis absorbance wavelength range of 400-800 nm was observed. HR-TEM showed mostly spherical nanoparticles ranging from 30 to 90 nm. FTIR showed a functional group of O-H, C = C stretching vibration for AgNPs and O-H, S-H, N-H, C = C stretching vibration for CYST-AgNPs. EDX spectroscopy showed silver, carbon, oxygen, sodium, and chloride elements for AgNPs and CYST-AgNPs. The CYST decreased the WBC, RBC, and platelet counts significantly (p < 0.05), while CYST-AgNPs (25 and 50 mg/kg) reduced only the RBC counts (p < 0.05).Conclusion This investigation presents the in vivo safety analysis and pharmacological potential of cysteamine-modified silver nanoparticles (CYST-AgNPs), suggesting enhanced therapeutic activity.
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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".