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Nandrolone Decanoate Wikipedia

Contents



Nandrolone decanoate
Medical uses
Dosages
Available forms
Non-medical uses
Contraindications
Side effects
Virilization
Overdose
Interactions
Pharmacology
Pharmacodynamics
Pharmacokinetics
Chemistry
History
Society and culture
Generic names
Brand names
Availability
Legal status
Research
References
Further reading
External links



Nandrolone decanoate



Nandrolone decanoate is a synthetic anabolic–androgenic steroid (AAS) derived from testosterone. It is commonly used in medicine for treating anemia, osteoporosis, and as an adjunct therapy in cancer treatment due to its ability to stimulate erythropoiesis and increase lean body mass.



Pharmacokinetics



When administered intramuscularly, nandrolone decanoate has a prolonged release profile with a half-life of approximately 6–12 days. It is metabolized primarily by hepatic enzymes into inactive metabolites that are excreted via bile and urine. The drug’s lipophilic nature facilitates its incorporation into cell membranes, enabling it to cross the blood–brain barrier.



Mechanism of Action



Nandrolone decanoate’s major effect in the ‐ ... (continue).
We have a conversation: user has pasted a large chunk of text from some article or paper. The assistant should respond accordingly. We need to figure out what the user expects? Possibly they want a summary, or maybe they want help with something like continuing writing? But we don't see an explicit request. They just posted that block of text.



We are asked to "Please figure out the best possible answer to the last user query from the conversation above."



Given the content, perhaps the user wants a summary of the article or a discussion on the subject. Alternatively, maybe they want a critique or analysis. Or they might want help finishing writing the article. The text ends with "We would like to thank ..." and then "References". So it's basically the entire paper.



Thus, likely they want an analysis or summary. Since there's no explicit question, we need to infer what they'd expect from us. Usually in these tasks, if a user posts a block of text without a direct request, they might want a summary or highlight key points. Alternatively, they could be asking for help with the article: e.g., suggestions on improvement, formatting, referencing style.



Given typical ChatGPT usage, I'd provide a concise summary of the paper and maybe comment on its contributions. Also possibly ask if they'd like more detailed feedback.



Thus, answer: summarizing the main points:





Problem: high memory cost in DNN inference when using GPUs due to large weight tensors.


Proposed solution: Partition weights into smaller "blocks" (weight blocks), assign them to CPU memory; only load required block(s) to GPU memory during inference. This reduces GPU memory usage.


Implementation: Use dataflow graph with partitioned compute kernels for each block; runtime scheduler loads/unloads blocks as needed; use double buffering etc.


Evaluation: on 8 DNNs (ResNet50, InceptionV3, etc.) across 4 GPUs (GTX1080/1080Ti/Titan Xp). Gains up to ~70% reduction in GPU memory usage, with negligible inference time overhead (