An Organizational Perspective on Free and Open Source Software Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The incorporation of nanoparticles (NPs) in industrial and biomedical applications has increased significantly in recent years, yet their hazardous and toxic effects have not been studied extensively. Here, we studied the effects of 24 nm silver NPs (AgNPs) on a panel of bacteria isolated from medical devices used in a hospital intensive care unit. The cytotoxic effects were evaluated in macrophages and the expression of the inflammatory cytokines IL-6, IL-10 and TNF-α were quantified. The effects of NPs on coagulation were tested in vitro in plasma-based assays. We demonstrated that 24 nm AgNPs were effective in suppressing the growth of clinically relevant bacteria with moderate to high levels of antibiotic resistance. The NPs had a moderate inhibitory effect when coagulation was initiated through the intrinsic pathway. However, these NPs are cytotoxic to macrophages and are able to elicit an inflammatory response. Thus, beneficial and potential harmful effects of 24 nm AgNPs on biomedical devices must be weighed in further studies in vivo. From the Clinical Editor: The authors of this study demonstrate that gallic acid reduced 24 nm Ag NPs are effective in suppressing growth of clinically relevant antibiotic resistant bacteria. However, these NPs also exhibit cytotoxic properties to macrophages and may trigger an inflammatory response. Thus, the balance of beneficial and potential harmful effects must be weighed carefully in further studies.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it