Apex AI ($APEX)

No new good launches since $KLED and $MIXIE 16 & 17 days ago.

A real trench wasteland.

But this also means almost zero dilution, which concentrates capital to the 18 strongest ICM utility launches + memes from April/May.

1. #FARTCOIN $1B
2. $MIXIE $40M
3. $KLED $33M
4. #TITCOIN $15M
5. $JOS $14M
6. $BOTIFY at $13M
7. $PCULE $10M
8. $KNET $8M
9. $DARK $6M
10. $LENS $6M
11. $PWEASE $5M
12. $PROMPT $3M
13. $FAT $3M
14. $BRIX $2.5M
15. $APEX $2M
16. $EZ $2M
17. #BUCKAZOIDS $2M
18. $ASSDAQ $1M

Which ones will 3x first from these MCs?

That's the biggest question of June.

Partner Spotlight: Seoul National University Bundang Hospital (SNUBH)

We're excited to highlight our partnership with SNUBH, recently ranked #68 on Newsweek's "World's Best Hospitals 2025."

Our joint work focuses on using AI for early detection of stomach and colorectal cancers, preventive screenings, and research into Crohn's disease.

Special thanks to the dedicated team leading these efforts:

Dr. H. Yoon (Gastroenterology)

Dr. H. Yu (Clinical Research Support)

We're grateful to partner with teams like SNUBH to improve patient care.

More partner introductions coming soon!

非农的数据公布了，和预期一样，没有太大的变化，主要就是失业率仍然是维持在 4.2% ，和上个月相同，说明美国的就业情况起码没有变差，其次就是就业人数，和预期的差距也不大，虽然相比前值有小幅下降，但对市场的影响应该还是有限的。

另外还有工资，毕竟工资也能代表出美国经济的情况，得出的数据好于预期，失业率+工资说名了美国的经济起码目前还是可以的，当然这种数据要让美联储降息还是别想了。

总的来看是一份中性数据，今天的重点还是要看看川马经过一个晚上的平复是否可以做回表面上的朋友，或者是继续开战。

本推文由 @ApeXProtocolCN 赞助｜Dex With ApeX

写在今天的非农数据之前，就业和失业的数据应该是每个月的重点数据了，但在关税正式落地前，除非失业率出现了大幅的上涨，否则对美联储的货币政策应该不会有什么影响，最近一周多位美联储的官员已经讲的很明白了，一天正式的关税（对等）不落地，可能美联储就会更加谨慎。

所以市场对于第三季度前降息已经不报希望了，尤其是 GDP 的数据公布后，对于美国目前进入经济衰退的概率再次缩小，短时间没有什么分析师再去谈衰退了。

所以现在失业率的数据就又回到了不论好坏数据都是好数据的时候，失业率小幅上涨能增加一些美联储降息的预期，也不会过于刺激衰退的话题，而失业率下降说明美国的经济情况还是不错的，而且本身美联储就没有打算提前降息，所以经济好也能促进投资者的风险偏好。

然后就是就业人口，这个数据肯定是越多越好，但影响应该不会很大，只要不是大幅低于预期就行，另外就是工资的变化，工资增涨肯定就是经济效益不错，按照目前的市场预期来看，如果能符合预期，还是能提振一下投资者情绪的。

尤其是特斯拉盘前已经快回300美元了，市场需要一点利好来平复一下心情。当然如果非农数据炸了，这个周末就难过了。

本推文由 @ApeXProtocolCN 赞助｜Dex With ApeX

Here’s a video of @Official_ApexAI $APEX showing a demo of their AI Cancer detection tech

Live on @pumpdotfun, with 200+ viewers

They said many more amazing things s and gave good insights all stream and gave good clarity

Tech on pumpfun has a great future, I recommend

Realistically, not every hospital will have immediate access to Apex AI’s advanced cancer detection technology. But that shouldn’t mean patients miss out on life-saving insights.

The $APEX token bridges this critical gap:
- Patients whose hospitals aren't equipped with Apex AI can directly submit their endoscopy images online.
- Using $APEX tokens, they access AI-powered cancer diagnostics, bypassing institutional limitations.
- Apex AI’s B2B hospital technology becomes accessible as a B2C solution for everyone.

$APEX isn't just utility for the sake of it, it's democratizing access to cutting-edge medical technology. Right now, you can try this AI-powered cancer analysis completely free: just upload your endoscopy images on our website. After the trial period, this service will be exclusively accessible through the $APEX token.

HUGE @pumpdotfun @Official_ApexAI

We’re LIVE! Tune in to our tech demo on @pumpdotfun now!

2 hours to showtime. See our cancer-detection AI platform live on @pumpdotfun at 9 PM EST

Colorectal cancer diagnosis has long relied on invasive, subjective procedures. Apex AI's patent (No. 10-2021-0025707) introduces a powerful AI-driven system that objectively analyzes colonoscopy images to predict cancer risks—improving accuracy, efficiency, and patient outcomes.

Here's how it works:

Overview

The patent filed by Apex AI outlines a sophisticated system designed to predict the risk of colorectal cancer by analyzing colonoscopy images using artificial intelligence (AI). The system aims to overcome limitations of traditional diagnostic approaches, such as biopsies and manual image analyses, by providing a more objective, accurate, and efficient diagnostic tool.

Technical Background

Colorectal cancer (CRC) is a condition characterized by uncontrolled cell proliferation, which significantly affects bodily functions. Conventional diagnostic methods include tissue biopsy and imaging techniques (X-rays, MRI, etc.), both having limitations such as invasiveness, patient discomfort, potential inaccuracy, and high dependence on clinician experience.

Purpose of the Invention

The invention seeks to address these limitations by utilizing AI to analyze colonoscopy images systematically, providing quantitative risk assessments for colorectal cancer, thereby enhancing diagnostic reliability and accessibility.

System Components

The invention describes a colorectal cancer risk prediction system comprising three key modules (as depicted on page 14):

1. Base Condition Analysis Unit (기저질환 판단부)

- Utilizes AI algorithms to examine multiple colonoscopy images from various colon locations.

- Identifies and diagnoses basic colon-related diseases including inflammatory bowel disease (IBD), Crohn's disease, ulcerative colitis, polyps, low-grade adenomas, and high-grade adenomas.

2. Associated Disease Analysis Unit (유관질환 분석부)

- Analyzes the severity, location, size, color, and surface structure of detected colon diseases.

- Determines relationships between basic diseases and other associated conditions.

3. Colorectal Cancer Risk Calculation Unit (대장암 위험성 산출부)

- Synthesizes data from the Base Condition and Associated Disease Analysis units.

- Calculates individual risk scores for each identified disease, aggregating them into a final comprehensive colorectal cancer risk score.

Methodology

The AI-driven system employs the following methodology:

- Image Acquisition and AI-based Diagnosis: Acquires multiple colonoscopy images from distinct colon segments (rectum, sigmoid colon, descending colon, transverse colon, ascending colon, cecum, and appendix). AI algorithms classify the presence or absence of colorectal pathologies.

- Comprehensive Analysis of Pathologies: Evaluates pathology characteristics (location, severity, visual traits) to identify correlated disease patterns and potential progression pathways.

- Quantitative Risk Assessment: Computes risk scores for identified conditions, ultimately presenting an aggregate colorectal cancer risk profile that assists clinicians in making informed diagnostic and treatment decisions.

Advantages and Clinical Impact

This invention enhances early detection, diagnostic accuracy, and risk stratification of colorectal cancer, offering a substantial improvement over traditional diagnostic methods. The AI-driven approach significantly reduces subjectivity and human error, provides rapid assessments, and facilitates better clinical decision-making.