For research use only. Not for human consumption. Not medical advice — consult a licensed clinician.

Immune Modulator

Adamax

Also known as: N-acetyl Semax amidate adamantylated

Adamax is a grey-market nootropic peptide developed and sold by the Mexico-based vendor Ceretropic. It is marketed as a Semax derivative: N-acetyl Semax amide (Ac-MEHFPGP-NH2) with an adamantane (adamantyl-glycine) moiety appended at the C-terminus, reportedly intended to improve metabolic stability and blood-brain-barrier penetration. It is NOT an ADNP / NAP / davunetide analog despite some online descriptions conflating it with those compounds. Adamax has no indexed peer-reviewed literature of its own; all claimed effects are extrapolated from Semax data plus anecdotal user reports.

Real-time market data

Pricing for Adamax

Live vendor pricing, normalized to $/mg so sizes compare fairly — fused with each seller's Merit trust score and latest independent COA purity. Prices refresh daily.

Average price

$8.48/mg

range $5.00–$18.00/mg

Sellers

17

from $42.00

45-day trend

-8.3%

vs 45-day avg $/mg

15 of 17 sellers have a current price· 8 stale listings hidden (not seen in 7 days)

Prices observed from public storefronts (last 24h), normalized to $/mg. "Evidence" is Merit's 0–100 Merit Score, derived only from observable verification evidence (methodology on /about); "Purity" is the latest independent COA. Some buy links are affiliate links — Merit may earn a commission at no extra cost to you, and where a vendor offers one, the code shown gets you a discount at their checkout. Affiliate status never affects price data, ranking, or the Merit Score (full policy on /disclosure). Research use only.

Independent evidence

COAs for Adamax

12 third-party tests across 2 vendors. Each card links to the full report.

All 12 COAs for Adamax →
Measured purity 99.6%–100.0% across 7 COAs
Measured purity over time
Research depth

12 citations indexed for Adamax

All research on Adamax →

Study · 2026

A robust stacked ensemble strategy with multi-optimizer CNN models for skin cancer classification

Skin cancer is one of the most prevalent and potentially life-threatening cancers globally, making it a critical area of focus in medical research. Early detection, followed by timely and appropriate treatment, can significantly enhance patient survival outcomes.

Study · 2026

NRLC-YOLO for lightweight detection and grasp positioning of latex cups in rubber plantations

Natural rubber harvesting remains highly dependent on manual labor, particularly during latex cup collection, which limits efficiency and increases operational costs. Intelligent robotic harvesting systems require accurate visual perception and reliable grasp point positioning under rubber plantation environments.

Study · 2026

BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection

The noise of Magnetic Resonance Imaging (MRI) poses challenges for Deep Learning (DL) when tumor boundaries are obscured, tumor location and appearance are complex due to overlap between tumor and non-tumor cells, and modality identification is difficult because tumor features vanish in the later layers of the DL.

Study · 2026

Diabetes Management Through Glucose Dynamics Analysis Network: A Novel Approach for Accurate Blood Glucose Level Forecasting

Background Accurate real-time prediction of blood glucose (BG) levels is essential for improving insulin-dosing decision support systems, including closed-loop insulin delivery and bolus calculators.

Study · 2026

Neural Controlled Differential Equation and Its Application in Pharmacokinetics and Pharmacodynamics

With the recent advances in machine learning (ML) and artificial intelligence (AI), data-driven modeling approaches for pharmacokinetics (PK) and pharmacodynamics (PD) have gained popularity due to their versatility in diverse settings and reduced reliance on prior assumptions.

Study · 2026

A comparative evaluation of gradient-based optimization algorithms for short-term load forecasting using deep residual networks

Short-Term Load Forecasting (STLF) is essential for the reliable and economic operation of modern power systems. Deep Residual Networks (DRNs) have emerged as an effective framework for STLF due to their ability to model nonlinear and multi-scale load patterns.