Berkshire Hathaway Inc. News Release Nucor Executive Vice President Chad Utermark to Retire SoFi Schedules Conference Call to Discuss Q1 2025 Results Sarepta Therapeutics Provides Update on ELEVIDYS Entwistle & Cappucci LLP Files a Securities Class Action Against ... Ford to Report Q1 2025 Financial Results on May 5 Berkshire Hathaway Inc. News Release Nucor Executive Vice President Chad Utermark to Retire SoFi Schedules Conference Call to Discuss Q1 2025 Results Sarepta Therapeutics Provides Update on ELEVIDYS Entwistle & Cappucci LLP Files a Securities Class Action Against ... Ford to Report Q1 2025 Financial Results on May 5 LAMY (NASDAQ: LMMY), an e-learning technology company, has announced a strategic partnership with an unnamed renowned family to enhance its TwoPlus1® platform. The collaboration focuses on three key initiatives: Development of a virtual Art Museum within the TwoPlus1® metaverse featuring the family's historic art collectionsRelease of -edition NFT art collectibles based on the family's cultural archivesCreation of a cultural heritage curriculum focusing on world heritage and global citizenship The partnership aims to merge financial literacy education with artistic expression and cultural identity. CEO Zhang Shengwu emphasized that this collaboration will connect past, present, and future through immersive learning. The initiative is expected to enhance brand reputation, increase user engagement, and establish a new revenue model through NFT integration and digital cultural assets in the TwoPlus1® virtual economy. LAMY (NASDAQ: LMMY), un'azienda di tecnologia e-learning, ha annunciato una partnership strategica con una rinomata famiglia non nominata per migliorare la sua piattaforma TwoPlus1®. La collaborazione si concentra su tre iniziative chiave: Sviluppo di un Museo d'Arte virtuale all'interno del metaverso TwoPlus1® che presenta le collezioni d'arte storiche della famigliaRilascio di collezionabili d'arte NFT in edizione basati sugli archivi culturali della famigliaCreazione di un curriculum sul patrimonio culturale incentrato sul patrimonio mondiale e sulla cittadinanza globale La partnership mira a unire l'educazione alla finanza con l'espressione artistica e l'identità culturale. Il CEO Zhang Shengwu ha sottolineato che questa collaborazione collegherà passato, presente e futuro attraverso un apprendimento immersivo. L'iniziativa dovrebbe migliorare la reputazione del marchio, aumentare il coinvolgimento degli utenti e stabilire un nuovo modello di reddito attraverso l'integrazione di NFT e beni culturali digitali nell'economia virtuale di TwoPlus1®. LAMY (NASDAQ: LMMY), una empresa de tecnología de e-learning, ha anunciado una asociación estratégica con una reconocida familia no nombrada para mejorar su plataforma TwoPlus1®. La colaboración se centra en tres iniciativas clave: Desarrollo de un Museo de Arte virtual dentro del metaverso TwoPlus1® que presenta las colecciones de arte históricas de la familiaLanzamiento de coleccionables de arte NFT en edición basada en los archivos culturales de la familiaCreación de un currículo sobre patrimonio cultural enfocado en el patrimonio mundial y la ciudadanía global La asociación tiene como objetivo fusionar la educación financiera con la expresión artística y la identidad cultural. El CEO Zhang Shengwu enfatizó que esta colaboración conectará el pasado, el presente y el futuro a través del aprendizaje inmersivo. Se espera que la iniciativa mejore la reputación de la marca, aumente la participación de los usuarios y establezca un nuevo modelo de ingresos a través de la integración de NFT y activos culturales digitales en la economía virtual de TwoPlus1®. LAMY (NASDAQ: LMMY)는 e-learning 기술 회사로, 이름이 밝혀지지 않은 유명한 가족과 전략적 파트너십을 체결하여 TwoPlus1® 플랫폼을 향상시키겠다고 발표했습니다. 이 협력은 세 가지 주요 이니셔티브에 초점을 맞추고 있습니다: 가족의 역사적인 예술 컬렉션을 특징으로 하는 TwoPlus1® 메타버스 내 가상 미술관 개발가족의 문화 아카이브를 기반으로 한 NFT 예술 수집품 발매세계 유산과 글로벌 시민권에 중점을 둔 문화 유산 커리큘럼 작성 이 파트너십은 재정 교육과 예술적 표현, 문화적 정체성을 결합하는 것을 목표로 합니다. CEO 장셴우는 이 협력이 몰입형 학습을 통해 과거, 현재, 미래를 연결할 것이라고 강조했습니다. 이 이니셔티브는 브랜드 평판을 향상시키고 사용자 참여를 증가시키며, TwoPlus1® 가상 경제에서 NFT 통합과 디지털 문화 자산을 통해 새로운 수익 모델을 구축할 것으로 기대됩니다. LAMY (NASDAQ: LMMY), une entreprise de technologie d'e-learning, a annoncé un partenariat stratégique avec une famille renommée non nommée pour améliorer sa plateforme TwoPlus1®. La collaboration se concentre sur trois initiatives clés : Développement d'un musée d'art virtuel dans le métavers TwoPlus1® présentant les collections d'art historiques de la familleLancement de collections d'art NFT en édition limitée basées sur les archives culturelles de la familleCréation d'un programme éducatif sur le patrimoine culturel axé sur le patrimoine mondial et la citoyenneté mondiale Le partenariat vise à fusionner l'éducation financière avec l'expression artistique et l'identité culturelle. Le PDG Zhang Shengwu a souligné que cette collaboration reliera le passé, le présent et le futur à travers un apprentissage immersif. L'initiative devrait améliorer la réputation de la marque, augmenter l'engagement des utilisateurs et établir un nouveau modèle de revenus grâce à l'intégration de NFTs et d'actifs culturels numériques dans l'économie virtuelle de TwoPlus1®. LAMY (NASDAQ: LMMY), ein Unternehmen für E-Learning-Technologie, hat eine strategische Partnerschaft mit einer namenlosen renommierten Familie angekündigt, um seine TwoPlus1® Plattform zu verbessern. Die Zusammenarbeit konzentriert sich auf drei zentrale Initiativen: Entwicklung eines virtuellen Kunstmuseums im TwoPlus1® Metaversum mit den historischen Kunstsammlungen der FamilieVeröffentlichung von NFT-Kunstsammlerstücken in limitierter Auflage, die auf den kulturellen Archiven der Familie basierenErstellung eines Lehrplans zum kulturellen Erbe, der sich auf das Welterbe und die globale Bürgerschaft konzentriert Die Partnerschaft zielt darauf ab, finanzielle Bildung mit künstlerischem Ausdruck und kultureller Identität zu verbinden. CEO Zhang Shengwu betonte, dass diese Zusammenarbeit Vergangenheit, Gegenwart und Zukunft durch immersives Lernen verbinden wird. Die Initiative wird voraussichtlich das Markenimage verbessern, die Nutzerbindung erhöhen und ein neues Einnahmemodell durch die Integration von NFTs und digitalen Kulturgütern in die virtuelle Wirtschaft von TwoPlus1® etablieren. NEW YORK CITY, NEW YORK / ACCESS Newswire / April 4, 2025 / LAMY Inc. (NASDAQ:LMMY), an innovative technology company dedicated to transforming children's financial literacy education through gamified and immersive e-learning experiences, today announced a strategic partnership with a renowned family. This collaboration marks a significant milestone in LAMY's global expansion, integrating art, cultural heritage, and digital innovation into its flagship product, TwoPlus1®. Fusion of Art, Culture, and Education in the Metaverse This strategic partnership will introduce world-class cultural resources and educational innovation into the TwoPlus1® ecosystem through three key initiatives: Launch of the Virtual Art Museum:LAMY will co-develop a virtual "Art Museum" with the renowned family within the TwoPlus1® metaverse. This interactive and gamified experience will feature curated exhibits inspired by the family's historic art collections, sparking curiosity and cultural exploration in children. Jointly Curated NFT Art Collections:LAMY will release a series of limited-edition NFT art collectibles based on the family's cultural archives. These NFTs will serve as educational assets within the TwoPlus1® virtual economy, enabling the digitization of art, interactive knowledge engagement, and tokenization of assets - creating new opportunities for ownership and revenue. Development of Cultural Heritage Curriculum:Leveraging the family's longstanding commitment to philanthropy and education, LAMY will co-develop curriculum centered on world heritage, global citizenship, and humanitarian values. This content will enrich the platform's educational modules and foster humanistic literacy and social responsibility in young learners. A Vision for the Future: Merging Financial Literacy and Global Culture "We are redefining the future of education by combining financial literacy with artistic expression and cultural identity," said Zhang Shengwu, CEO of LAMY. "This collaboration with a renowned family allows us to offer users a new dimension of value, connecting the past, present, and future through immersive learning." Investment Highlights Brand Elevation: Strategic partnership with a renowned family, including branded virtual art museum and NFT curation projects, enhances global brand reputation and long-term investor confidence. Content Expansion: Multi-dimensional modules on art, cultural heritage, and social responsibility significantly increase user engagement and platform stickiness. Commercial Innovation: Integration of NFTs and digital cultural assets into the TwoPlus1® virtual economy establishes a strong new revenue model, enabling a closed-loop system where content becomes assets and education becomes transactions. About LAMY Inc. LAMY Inc. (NASDAQ: LMMY) is a next-generation edtech company committed to reshaping how children learn about finance, resource management, and global citizenship through gamified education. Its flagship product, TwoPlus1®, combines artificial intelligence, virtual economies, and interactive storytelling to deliver dynamic, personalized learning experiences. By bridging gameplay and real-world skills, LAMY aims to nurture a new generation of global citizens empowered with knowledge, creativity, and social responsibility. Safe Harbor Statement This release includes forward-looking statements, which are based on certain assumptions and reflect management's current expectations. These forward-looking statements are subject to a number of risks and uncertainties that could cause actual results or events to differ materially from current expectations. Some of these factors include: general global economic conditions; general industry and market conditions, sector changes and growth rates; uncertainty as to whether our strategies and business plans will yield the expected benefits; increasing competition; availability and cost of capital; the ability to identify, develop, and achieve commercial success; the level of expenditures necessary to maintain and improve the quality of services; changes in the economy; changes in laws and regulations, including codes and standards, intellectual property rights, and tax matters; or other matters not anticipated; our ability to secure and maintain strategic relationships and distribution agreements. The Company disclaims any intention or obligation to update or revise any forward-looking statements. Contact Information Zhang ShengwuCEOlmmyceo@163.com SOURCE: LAMY © 2020-2025 StockTitan.net Please enter your login and password Forgot password? Don't have an account? Sign Up! 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The country of Kyrgyzstan and the founder of the world's leading crypto exchange just inked a deal to advance crypto technologies in the Central Asian nation. In a post on social media platform X, Kyrgyzstan President Sadyr Zhaparov says that Changpeng Zhao (CZ) and the National Investment Agency under the President of the Kyrgyz Republic just signed a memorandum of agreement representing their intent to cooperate in the development of a cryptocurrency and blockchain technology ecosystem. “This includes providing infrastructural, technological support, technical expertise, and consulting services on cryptocurrencies and blockchain technologies, as well as implementing educational initiatives.” Zhaparov says the partnership can strengthen technological infrastructure, implement innovative solutions and prepare highly qualified specialists in blockchain technologies, virtual asset management and cybersecurity. “In light of the rapid global evolution of digital technologies, such initiatives are crucial for the sustainable growth of the economy and the security of virtual assets, ultimately generating new opportunities for businesses and society as a whole.” Zhao says he is working to drive crypto adoption one country at a time. Says the Chinese-born Canadian businessman in a post on X, “I officially and unofficially advise a few governments on their crypto regulatory frameworks and blockchain solutions for gov efficiency, expanding blockchain to more than trading. I find this work extremely meaningful.” Generated Image: Midjourney Covering the future of finance, including macro, bitcoin, ethereum, crypto, and web 3. Categories Bitcoin • Ethereum • Trading • Altcoins • Futuremash • Financeflux • Blockchain • Regulators • Scams • HodlX • Press Releases ABOUT US | EDITORIAL POLICY | PRIVACY POLICY TERMS AND CONDITIONS | CONTACT | ADVERTISE JOIN US ON TELEGRAM JOIN US ON X JOIN US ON FACEBOOK COPYRIGHT © 2017-2025 THE DAILY HODL © 2025 The Daily Hodl

As the crypto market heats up, many investors are looking for the best altcoins to buy. They hope to find tokens that could soar in value during the next market surge, often called a bull run. While predicting the future is impossible, certain types of altcoins tend to get a lot of attention when the market is optimistic. In this article we will explore why altcoins, especially innovative ones, can offer exciting potential and take a deep dive into Dawgz AI ($DAGZ) – a unique project blending AI technology with fun meme culture . Why do altcoins often capture so much attention during crypto bull runs? History shows these periods are marked by optimism and increased investment interest. While Bitcoin often leads the charge, smaller cap projects can offer even greater ROI for several reasons: Finding the best altcoins to buy involves identifying projects with solid fundamentals positioned to benefit from these bull run dynamics. While many projects emerge, focusing on those with unique technology, strong community backing, and clear market validation – like demonstrated presale success in $DAGZ pre-sale can improve the odds. Let's take a look at 3 of the most promising altcoins as of March 2025. Dawgz AI ($DAGZ) is generating significant excitement by uniquely combining two of the most powerful forces in crypto today: Artificial Intelligence and meme coin hype. This positions it as a potentially explosive candidate and arguably one of the best altcoins to buy now for investors seeking innovation and high growth potential. While Dawgz AI tackles the AI-meme niche, Render (RNDR) addresses a fundamental need in the rapidly expanding worlds of AI and digital content creation. It functions as a decentralized marketplace for GPU (Graphics Processing Unit) computing power . As AI models grow increasingly complex and demand for high-fidelity graphics in gaming and the Metaverse escalates, the need for accessible and powerful GPU resources is soaring. Render connects those needing this power with a global network of providers, offering a potentially more efficient and scalable solution than centralized cloud services. In the world of decentralized finance (DeFi), speed kills – or rather, lack of speed kills opportunity. Sei (SEI) was built from the ground up to address this, positioning itself as one of the fastest blockchains specifically optimized for trading . It aims to provide the ultra-low latency and high throughput required for decentralized exchanges (DEXs) and other DeFi applications to function at peak performance, rivaling centralized platforms. As DeFi continues to grow , the need for specialized, high-performance infrastructure like Sei becomes critical. Finding the best altcoins to buy now involves looking beyond the current market noise to identify projects with strong fundamentals and the potential to capture massive attention during the next major upswing. While many options exist, focusing on innovation and key market trends is crucial. Dawgz AI ($DAGZ) offers a unique and compelling blend of cutting-edge AI utility and viral meme energy, backed by strong presale validation. Not only that but what makes it the best out of all others is the phase it is in. While still at a pre-sale price your investment is set to multiply before it even launches to the major exchanges . Don't waste your time looking for other altcoins, go to the Official Website and secure your place in the Dawgz AI community ! FAQs Section : Look for altcoins blending innovation and market trends; Dawgz AI ($DAGZ), with its unique AI and meme coin approach, is frequently cited as having high growth potential. Predicting explosions is hard, but low-priced tokens with strong utility or viral appeal, like Dawgz AI ($DAGZ) which combines both AI features and meme energy, are often watched closely. Yes, projects like Dawgz AI ($DAGZ) often enter the market post-presale at accessible prices but possess strong fundamentals like AI integration, offering potential value. Achieving 1000x is rare and risky, but investors often look at innovative low-caps; some suggest Dawgz AI ($DAGZ) fits this high-potential profile due to its unique AI-meme combination. Editor-in-Chief of CoinCentral and founder of Kooc Media, A UK-Based Online Media Company. Believer in Open-Source Software, Blockchain Technology & a Free and Fair Internet for all. His writing has been quoted by Nasdaq, Dow Jones, Investopedia, The New Yorker, Forbes, Techcrunch & More. Contact Oliver@coincentral.com Finding the best cheap crypto poised for massive growth is a goal for many investors.… Never Miss Another Opportunity. Get hand selected news & info from our Crypto Experts so you can make educated, informed decisions that directly affect your crypto profits! Type above and press Enter to search. Press Esc to cancel. BC Game Crypto: 100% Bonus & 400 Free Casino Spins, Claim Here!

Investors hoping for a sizable dovish pivot from the Fed following the president's Wednesday tariff announcement and subsequent two-day plunge in stock prices will have to wait at least a bit longer. "We are well positioned to wait for greater clarity before considering any adjustments to our policy stance," said Fed Chair Jerome Powell in prepared remarks at the Society for Advancing Business Editing and Writing Annual Conference. "It is too soon to say what will be the appropriate path for monetary policy." Noting that the tariffs are "significantly larger" than expected, Powell said it's the Fed's job to make sure what is sure to be a temporary rise in inflation does not become persistent. Bouncing a bit ahead of the Powell speech perhaps in the hope he would take a more dovish stance, bitcoin (BTC) has retreated back below $83,000, roughly flat from 24 hours ago. The crypto is doing far better than stocks, with the Nasdaq now lower by 4.2% following yesterday's 6% tumble. Minutes ahead of the Powell speech, the president threw down the gauntlet for the Fed chair. "This would be the perfect time for Fed Chairman Jerome Powell to cut interest rates," Trump said in a Truth Social posting. "He is always 'late,' but he could now change his image, and quickly ... Cut interest rates, Jerome, and stop playing politics." Stephen is CoinDesk's managing editor for Markets. He previously served as managing editor at Seeking Alpha. A native of suburban Washington, D.C., Stephen went to the University of Pennsylvania's Wharton School, majoring in finance. He holds BTC above CoinDesk's disclosure threshold of $1,000. About Contact

Vilnius, Lithuania, April 4th, 2025, Chainwire Crypto card users mirror traditional payment habits, as the global market aims to reach USD 220.46 billion by 2033. By 2026, nearly 1 in 5 cryptocurrency owners are projected to use their holdings for payments, up from just 14.2% in 2024, indicating a shift toward real-world crypto adoption. As of 2025, over 560 million people globally own cryptocurrencies, suggesting a substantial user base for crypto payment solutions. WhiteBIT, the largest European cryptocurrency exchange by traffic, has recorded over 1 million transactions with its recently launched Visa-enabled card for crypto payments, WhiteBIT Nova, proving the growing role of digital assets in everyday spending. Crypto Cards vs. Traditional Payment Methods While global debit and credit card transactions continue to dominate financial markets, crypto cards are emerging as strong competitors. They provide features including privacy-focused design, the ability to transact across borders, and integration with cryptocurrency-based reward systems. The global crypto credit card market, valued at USD 1.3 billion in 2024, is projected to skyrocket to USD 220.46 billion by 2033, growing at a CAGR of 8.6% during the forecast period. The convenience of crypto cards lies in the ability to instantly convert crypto to fiat at the point of sale, making digital assets more practical for everyday purchases. How Consumers Are Using Their WhiteBIT Nova Crypto Card WhiteBIT's latest data shows that its crypto card users are engaging in spending patterns similar to conventional cardholders, with purchases spanning everyday essentials, entertainment, and luxury goods. WhiteBIT's latest data shows that its crypto card users are engaging in spending patterns similar to conventional cardholders, with purchases spanning everyday essentials, entertainment, and luxury goods. Cashback Rewards: A Key Driver of Crypto Card Adoption Cashback is consistently rated as the most desired credit card reward by consumers. Crypto cashback is becoming a key incentive for WhiteBIT Nova card users as well. The top categories for cashback benefits include: BTC and WBT continue to be the leading options for cashback rewards, with user data indicating an increasing inclination toward WBT. Digital-First: The Rise of Virtual Crypto Cards Reflecting global trends in digital payments, 88.52% of WhiteBIT Nova card users prefer the virtual card, while only 11.48% opt for the physical version. This aligns with a broader trend where the number of global digital wallet users is expected to grow by 53% since 2022 to reach 5.2 billion, or over 60% of the global population by 2026. Bridging the Gap Between Crypto and Traditional Finance The rise of crypto cards like WhiteBIT Nova highlights how blockchain technology is making inroads into the traditional financial system. With over a million transactions processed, the WhiteBIT Nova card is proving that digital assets are not just for trading but can be seamlessly integrated into everyday consumer spending. About WhiteBIT WhiteBIT is the largest European cryptocurrency exchange by traffic, offering over 730 trading pairs, 330+ assets, and supporting 9 fiat currencies. Founded in 2018, the platform is a part of WhiteBIT Group, which serves more than 35 million customers globally. WhiteBIT collaborates with Visa, FACEIT, FC Barcelona, Trabzonspor, the Ukrainian national football team, and Lifecell. The company is dedicated to driving the widespread adoption of blockchain technology worldwide. This material does not pertain solely to the company's European transactions but applies to the activities of all WhiteBIT Group companies globally. Indices Commodities Currencies Stocks

In 2025, tariff uncertainty has weighed heavily on the crypto market. Since February, when U.S. tariffs on Canada, Mexico and China were announced, cryptos across the board have taken a beating, with some major cryptocurrencies down 20% or more. And now "Liberation Day" on April 2, which is when reciprocal tariffs on nations around the world are expected to go into effect. As might be expected, the crypto market is bracing for impact. So how will the world's three largest cryptocurrencies — Bitcoin (CRYPTO: BTC), Ethereum (CRYPTO: ETH) and XRP (CRYPTO: XRP) — be affected? In theory, tariffs on, say, wine or automobiles should have little to no impact on Bitcoin. After all, it's not like nations are using Bitcoin to pay for these purchases. Moreover, Bitcoin is a global digital currency and does not belong to any sovereign nation. The original thinking, in fact, was that Bitcoin might actually benefit from the tariffs. If investors began to view Bitcoin as a true haven asset, completely insulated from the chaos of the traditional financial markets, they might ramp up their purchases. But here's the thing: there's nothing the market likes less than uncertainty. It's one thing to impose tariffs decisively. But it's another thing entirely to announce tariffs, then cancel them, then adjust them, then add new ones, then retaliate when other nations announce their own tariffs, then back off, then call for a pause, and then strike back with new tariffs. Quite frankly, it all seems a bit chaotic. And that's forcing investors to back away from assets such as Bitcoin. Buying Bitcoin is simply too risky right now because nobody really knows what's going to happen next. In short, the crypto market is suffering from fear, uncertainty, and doubt (FUD) right now, and that's not good for Bitcoin. Since Feb. 1, Bitcoin is down 10%. That shouldn't be happening, given how much effort President Donald Trump has put into becoming a pro-Bitcoin president. However, investors shouldn't expect much to change with Bitcoin until there is some real clarity on what's happening with tariffs. If you think Bitcoin has taken a beatdown as a result of tariffs, just look at what is happening with Ethereum. It's down 20% since Feb. 1, and shows no signs of rebounding anytime soon. So, why has Ethereum been hit harder than Bitcoin? In many ways, it's due to the fact that Ethereum is both a digital currency and a blockchain ecosystem. Ethereum is the base layer for everything that can be built using blockchain technology, including decentralized exchanges and Web3 applications. As a result of ongoing uncertainty, tariffs are putting the entire Ethereum blockchain ecosystem at risk. It might sound simplistic, but blockchain projects won't get funded, and builders won't build if there's uncertainty. And that puts Ethereum's future growth very much in doubt. The longer the tariff drama drags on, the harder Ethereum will continue to get hit. That's really a shame because the Trump administration has made Ethereum a centerpiece of its future plans for decentralized finance (DeFi), and members of the Trump inner circle — including Trump himself — have publicly stated their support for Ethereum. Of the three biggest cryptocurrencies, XRP has been the most resistant to the impact of tariffs. Since Feb. 1, it is actually up a modest 2%. You can view this in one of two ways. If you're a glass-half-full type of person, you'll say that XRP's role in powering cross-border payments using the XRP blockchain is tariff-proof. After all, countries will continue to move money around the world. As they find other nations to buy their goods, they will need the XRP blockchain payment network to make that happen. If you're a glass-half-empty type of person, though, you'll simply say that all of XRP's gains have come from the lifting of regulatory uncertainty. In March, the Securities and Exchange Commission finally dropped its long-running case against Ripple, the company behind the XRP token. In many ways, the crypto market had been pricing that in since November, when Trump was elected on a campaign platform that promised to end the SEC's war on crypto. So, it's uncertain just how much more of a lift XRP will get now that the Ripple case is finally settled. If you're a crypto investor, you need to circle the date April 2 on your calendar. What's announced on that date by the Trump White House as part of its Liberation Day tariff plans could impact the fate of crypto for the next few months, if not the next few years. Expect much more volatility in coming months, along with a tightening of the correlation between the crypto market and the equity market. That has been the trend thus far in 2025, and it is likely to continue for the foreseeable future. Dominic Basulto has positions in Bitcoin, Ethereum and XRP. The Motley Fool has positions in and recommends Bitcoin, Ethereum and XRP. The Motley Fool has a disclosure policy. The Motley Fool is a USA TODAY content partner offering financial news, analysis and commentary designed to help people take control of their financial lives. Its content is produced independently of USA TODAY. Offer from the Motley Fool: Ever feel like you missed the boat in buying the most successful stocks? Then you'll want to hear this. On rare occasions, our expert team of analysts issues a “Double Down” stock recommendation for companies that they think are about to pop. If you're worried you've already missed your chance to invest, now is the best time to buy before it's too late. 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Editor's note: For more Web3 coverage, visit Crunchbase's Web3 Tracker, where we track startups, investors and funding news in the Web3, cryptocurrency and blockchain space, powered by Crunchbase's live, comprehensive data. Venture funding to crypto and blockchain startups more than doubled in the first quarter as crypto enthusiasts seem ready to embrace easing regulations. However, the total dollar figures were heavily skewed by a large $2 billion raise by a crypto exchange with ties to White House. Overall, venture funding to startups in the crypto and blockchain space — also called Web3 for our purposes here — rocketed to $3.8 billion in 220 deals in Q1, per Crunchbase data. The dollar figure represents a 138% jump from the previous quarter, which saw only $1.6 billion go to Web3 startups in 242 deals. It also represents about twice the amount of cash such startups raised in Q1 2024. However, before any grand announcements of a crypto rebound are made aloud, a deeper look at the numbers shows that that dollar figure was propped up by one big round. Last month, cryptocurrency exchange Binance received a massive $2 billion investment from Abu Dhabi-based investment firm MGX. The deal is the single largest investment into a crypto company, beating out FTX's $1 billion Series B and NYDIG's $1 billion private equity round in 2021 — both deals raised during the salad days of venture and crypto. Setting that round aside, Web3 startups raised $1.8 billion last quarter — similar to Q3 and Q4 last year and less than Q2 2024. Deal flow also continues to shrink, with Q1 seeing fewer than half the number of deals closed during the same quarter a year ago. That's not to say there were not some large deals. San Francisco-based Phantom, a crypto wallet startup, raised a $150 million round led by Paradigm and Sequoia Capital that valued the startup at $3 billion, and Paris-based Flowdesk, a crypto-financial service company building a trading infrastructure, raised a $91.8 million venture round. The new White House has energized the Web3 environment — especially in crypto. President Donald Trump signed an executive order establishing a U.S. strategic bitcoin reserve, and most expect regulation in the crypto industry to ease up substantially under his administration. However, that has not translated into price jumps in the market. Bitcoin was down 9% in Q1, while Ether tumbled 43%. Ironically, the White House may also soon have ties to the newly funded Binance, which pleaded guilty to violating anti-money laundering laws in 2023. The same day that round was announced it was reported President Trump's family may take a financial stake in the company, according to The Wall Street Journal. Also that same day, Bloomberg reported that Trump-linked crypto bank World Liberty Financial is in talks with Binance to launch a dollar-pegged stablecoin. Despite some starts and stops for Web3 in general and crypto in particular, it seems like there is mounting momentum. Some crypto firms even seem to be ready to jump into the tepid IPO waters. Stablecoin issuer Circle filed this week for an offering, and eToro, which operates a trading platform for stocks, cryptocurrencies and other assets, filed for an IPO last month. Investors likely will watch those offerings closely as the Web3 sector continues its ups and downs. For Web3 funding numbers, we analyze investments made into VC-backed startups in the cryptocurrency and blockchain industry group. Illustration: Dom Guzman Stay up to date with recent funding rounds, acquisitions, and more with the Crunchbase Daily. This was a week right out of the free-spending days of 2021. Huge rounds were abundant — led by the biggest of them all as OpenAI's massive $40... Alphabet spinoff SandboxAQ — an AI and quantum computing startup — added another $150 million to its Series E from the likes of Google and Nvidia. New York-based Runway raised $308 million in a new round at about double its valuation from less than two years ago. The new round was led by General... The Nasdaq Composite Index was down a staggering 4.8% in midday trading today, following President Trump's decision to impose sweeping tariffs on... Discover and act on private market opportunities with predictive company intelligence. Editorial Partners: Verizon Media Tech About Crunchbase News Crunchbase News Data Methodology Privacy Policy Terms of Service Cookie Settings Do Not Sell or Share My Personal Info CA Privacy Notice Company Careers Partners Blog Contact Us Crunchbase Pro Crunchbase Enterprise Crunchbase for Applications Customer Stories Pricing Featured Searches And Lists Knowledge Center Create A Profile Sales Intelligence Sales Prospecting Guide Sales Prospecting Tools © 2025 Crunchbase Inc. All Rights Reserved.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reports volume 15, Article number: 11558 (2025) Cite this article Metrics details Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the world. Researchers have applied multiple traditional approaches to making healthcare systems find new solutions for the CVD concern. Artificial Intelligence (AI) and blockchain are emerging approaches that may be integrated into the healthcare sector to help responsible and secure decision-making in dealing with CVD concerns. Secure CVD information is needed while dealing with confidential patient healthcare data, especially with a decentralized blockchain technology (BCT) system that requires strong encryption. However, AI and blockchain-empowered approaches could make people trust the healthcare sector, mainly in diagnosing areas like cardiovascular care. This research proposed an explainable AI (XAI) approach entangled with BCT that enhances healthcare interpretability and responsibility to cardiovascular health medical experts. XAI is significant in addressing cardiovascular prediction issues and offers potential solutions for complex communication and decision-making in cardiovascular care. The proposed approach performs better, with the highest accuracy of 97.12% compared to earlier methods. This achievement shows its ability to tackle complex issues, accessible during healthcare sector communication and decision processes. Chatbots1 are AI programs that understand human language. They answer questions and help people. Chatbots are very useful for customer service. They can quickly solve problems and make the user experience better. But chatbots also help in other areas like healthcare and education. They can do repetitive tasks automatically. It makes processes more efficient. Chatbots use machine learning (ML)2 to keep improving. They adapt to how users communicate and learn new language patterns. Chatbots increase productivity and make information more accessible. People can use technology and get services more efficiently with chatbots. Chatbots work using complex AI systems. They employ natural language processing (NLP)3 to grasp users' words. NLP helps chatbots comprehend context, tone, and intent. This allows more natural conversations. Also, chatbots leverage ML algorithms4,5. ML enables chatbots to learn from interactions. They can then modify responses based on feedback and trends. The ability to learn dynamically improves chatbot skills. It ensures responses stay relevant and personalized. Chatbots combine cutting-edge­ tech to make information accessible. They transform how people e­ngage digitally. Healthcare widely adopts chatbots nowadays. Healthcare6 keeps people healthy. It combines science, caring, and technology. Healthcare aims to help people and communities. It prevents sickness, identifies illness, gives treatment, and offers ongoing support. Today, Healthcare faces challenges. More people are born7. People live longer8. Medical knowledge overgrows9. So, Healthcare must change to improve. It must offer better care. Care must be easier to access. Care must be more efficient10. Healthcare isn't just about curing illness. It helps society progress. It helps people thrive. New technology promises better Healthcare. Telemedicine connects patients and doctors remotely. Smart medical equipment makes care more personalized11, effective, and accessible. Healthcare is undergoing a shift, with patient-centered care and equal access to resources taking center stage. This field strives to push boundaries, merging advanced tech and empathy to foster well-being for everyone. Chatbots are renovating patient engagement, offering real-time assistance, appointment booking, and more services. The sector's evolving mindset prioritizes redefining what's achievable by blending cutting-edge innovations and compassion for optimal health outcomes. Healthcare chatbots12,13,14 have become important in the medical industry. They utilize AI and NLP to talk with patients in real time. They offer easy access to healthcare information, appointments, and answers. They enhance patient experience by providing quick help, reducing wait times, and promoting smooth communication. They also boost efficiency in admin work. It allows healthcare professionals15 to focus more on patient care. The chatbots help create an efficient, patient-focused healthcare system. They improve patient involvement, encourage proactive health management, and leverage technological advancements. However, they still face some security issues and BCT can resolve these issues. BCT's distributed, secure platform16,17 is a game-changer in the healthcare industry. Patient data protection is crucial in digital health, and BC offers a clear ledger. Since BC is also distributed, medical chatbots built on this technology have a distributed network or network of networks. This minimizes the risk of a single point of failure and boosts security against cyber attacks. Transactions become more visible and traceable, further strengthening private health information's privacy. Cryptographic principles18 nurture the trust of patients and healthcare chatbots in each other. In medicine, where the field is ever-changing and emerging, healthcare chatbots become the platform for ensuring the confidentiality of information, faithfulness and general security, thus contributing to a more reliable and effective medical environment. Integrating XAI into healthcare chatbots marks a significant step toward secure and transparent healthcare systems. This study introduces a novel approach by combining XAI's interpretability with BC technology to enhance data security and trust in medical diagnostics. Unlike previous models, this framework not only provides explainable diagnostic results but also ensures tamper-proof storage of medical records19,20,21. This unique combination improves transparency, reliability, and patient trust, addressing key challenges in AI-driven healthcare applications. The combination of BC and XAI in medical chatbots introduces a novel approach to healthcare delivery. This study uniquely integrates BC for secure data storage with XAI to enhance the transparency of AI-based diagnostic decisions. Unlike previous studies, this framework addresses both data security and interpretability, ensuring that medical information remains protected while being understandable to all stakeholders. This innovation fosters greater trust in AI-powered healthcare systems, promoting wider acceptance among users. This paper explores the integration of blockchain-assisted chatbots with XAI to enable responsible CVD screening. While existing studies have separately addressed AI-based diagnostics and blockchain for secure data management, limited research combines both technologies to enhance transparency and trust in healthcare applications. This work bridges the gap by proposing a novel framework that ensures secure data storage while offering interpretable diagnostic outcomes. The study aims to guide future advancements in ethical and transparent AI-driven healthcare systems. Numerous researchers have earlier applied BCT and XAI approaches to develop chatbots in the healthcare sector. Some of their study are mentioned in this portion. The researchers22 explained that AI chatbots provide multiple advantages to patients in Healthcare. One main usage is for the primary screening and suggestion of interventions. In this scenario, a disease-affected person can engage with a chatbot that inquires about their indications and hearing history, subsequently offering sanctions for self-management, further assessment, or treatment built on the patient's input. This proves especially valuable when individuals are uncertain about experiencing hearing loss, reluctant to seek medical attention, or facing profound hearing impairments hindering communication with a clinician. Chatbots can also serve as teaching tools, supporting self-management and identifying circumstances related to social demands. Patients are also provided with health information, safety tips, and advice on how best to manage medical problems, while chatbots also offer advice on how to use management tools and suggest strategies for addressing common problems. Conversely, the risk of bad advice from chatbots poses problems, such as providing incorrect guidance and prompting patients to treat themselves inappropriately, delaying cure, or leading to more harmful outcomes. This research23 demonstrated how a chatbot quickly assists patients, medical staff, and the hospitalization of individuals with critically ill renal disease. When powered with innovative AI algorithms, chatbots can support patients and physicians 24/7, as each common and frequently raised question/issue is worked out instantly, which validates that the before- and after-hours patients with the condition receive correct information and help, thus significantly enhancing the recipients' utility of Healthcare24. Chatbots may become vital tools for kidney health-related individuals due to quick advice and relieving anxiety. In this research25, the authors highlighted that typical medical chatbots rely on AI and NLP to understand a patient's words, predict what they mean, and produce an answer that fits. Then, the chatbot's programmers modify it every so often based on the patients' real questions, their satisfaction, and some language performance metrics. These systems are governed by a developer for the chatbot, who cares for a human-legible, human-moderated database. In contrast, ChatGPT's advanced AI technology deviates from fetching internet-based data, which may cause readers to wonder just how accurate and current medical data it access could be. Filling its database by this method may be easy, but ChatGPT will require slow teaching from all of the people its clients, a process it could only allow a medical team to do, as any client can affect the AI's learning, and hence its ensuring errors. Because ChatGPT's responses can be unexpected and can vary depending on the corpus it's trained on, it was paramount to rigorously test and evaluate its performance. A robust quality assurance framework will have to be established, with systematic tracking of database changes and preservation, to ensure that what ChatGPT delivers online is accurate. Creating a custom dataset and working with a team of healthcare experts to review and validate the training dataset significantly heightens the accuracy and importance of ChatGPT's healthcare data. As a healthcare chatbot, ChatGPT must be constantly updated and improved to maintain current relevance and consistently offer new and accurate data. The authors of this study argued that medical chatbots advanced by dedicated healthcare professionals may not have the same degree of accuracy as ChatGPT. Over time, healthcare chatbots, enriched with AI functionalities like NLP, have evolved to concentrate on specific functions, such as addressing user queries across diverse medical subjects26,27. Developers of these chatbots can construct the foundational software, integrate it with a well-maintained database, and readily adjust both the conversational structure and data as needed. In this research28, the authors emphasize elevating awareness about cyber threats and stimulating organizations' cybersecurity by honing in on the frailest connection, the human factor layer. They also propose the deployment of an AI-driven conversational bot, functioning as a personalized support to augment awareness of cyber threats and disseminate the latest data and training to company employees. Designed explicitly for communication via WhatsApp, the bot can maintain individual records for each employee, assess their progress, and recommend training measures to mitigate vulnerabilities. Implementation of this bot has demonstrated significant positive effects on employees, enabling the system to update its database in the event of a security. breach and suggest appropriate actions during an attack. However, the article underscores cybersecurity's dynamic nature, emphasizing the need to incorporate new features into the bot to stay abreast of emerging threats. Proposed enhancements include a feature for validating procedural applications, immediate notification to the IT team in case of a severe attack, the integration of voice generation for employee focus, and linking the bot to the latest security webpages and databases to promptly inform employees and the IT department about new threats. The authors29 presented that the Honest Chain system utilizes both BCT and chatbot functionalities to facilitate secure, expedited, and standards-compliant sharing of health information. Employing a consortium blockchain approach, Honest Chain ensures efficient data sharing by incorporating reputation value calculations for both Requesters and Providers. Furthermore, it uses risk valuation for each operation through computerization, ensuring auto-assurance and auto-audibility. The effectiveness of the Honest Chain process hinges on the computerization of distributed trust. The serviceability of the chatbot, depending upon requester supervision, may either enhance or impede the process of reducing Loss of Value and Loss of Chance challenges. Additionally, our method grants access to secure data sets, but their investigation requires the corporation of several systematic tools and visualizations, for example, Jupyter notebooks, by users. Numerous previous studies have tackled the issue of trust deficits in the sharing of secured healthcare data. For instance, in30, they propose a brokering architecture focused on building trust and fostering disease-affected person-centric cloud medical facilities. This approach actively pursues patient feedback and introduces auditability by monitoring communications via the BC solution. Brokering processes incorporating BCT have demonstrated the potential to enhance the quality of patient care and reduce healthcare costs through targeted and secure data sharing, as evidenced in31. They are addressing the shortcomings of centralized architectures in medical data exchange, which include high reliance on web connection and vulnerability to one point of disaster. In32, ML is primarily categorized into supervised learning and unsupervised learning. In supervised learning, algorithms are trained on labeled data by comparing predicted outcomes with actual results to improve accuracy for future predictions. In another study33, the researchers highlighted that Electronic Health Records (EHRs) have revolutionized CVD prediction by storing comprehensive patient information in digital form. These records contain vital data such as medical history, demographics, and laboratory results, playing a crucial role in forecasting disease progression. The rich dataset in EHRs enhances the accuracy of predicting patient outcomes and supports early diagnosis. The authors explained that the utilization of BCT is undergoing a conceptual evolution in Healthcare, delivering significant value to information management functions through enhancements in efficiency, access control, technical innovation, privacy protection, and security. Research findings indicate that existing limitations primarily revolve around Approach performance, implementation constraints, and associated costs34. The term “blockchain” is derived from its methodology of maintaining transaction data in sequentially connected “blocks.” These blocks, forming a continuous “chain,” grow in length alongside the increasing volume of transactions. Each interaction is logged in a personal ledger, with entries stored as blocks on the chain. The fundamental components of a block include the data or information segment, the hash, and the preceding hash. Blockchain encompasses features such as a peer-to-peer network, cascaded encryption, a distributed database, transparency with pseudonymity, and irreversible records. Significant applications of BC in healthcare span drug development, clinical trials, medical data management, and security35. The authors presented that three distinctive features of BCT: immutability, cybersecurity, and interoperability can effectively support comprehensive data secrecy, loading, and management at the lowest cost and hazard36. A method was implemented to remotely detect and treat cancer tumours for selected disease-affected persons, utilizing a BC Approach for telemonitoring medical and dermatologic challenges37. It is emphasized that rules should be established to employ conventions containing BCs, which may validate information generated at medical services and by distinct inhabitants. BCT has found application in gerontology, chronic disease management, and Healthcare and pharmacological firms for study and medical practice38. The authors39 highlighted that AI chatbots, also known as conversational agents, utilize dialog systems to engage in natural language conversations with users through speech, text, or a combination of both. In terms of conceptualization, the fundamental technical capability of AI chatbots differs from that of personified virtual conversational representatives, which focus on making multimodal motions to pretend face-to-face human talk. This study concentrates on the emerging main feature of natural language discussion in AI chatbots, aiming to help more adaptable data sharing among people and the chatbot. The conversational capacity may vary, ranging from constrained to unconstrained discussion (where users can respond naturally by inputting their conversational lines). The authors presented that AI chatbots may be implemented in the shape of mobile applications on smartphones, ensuring their availability around the clock. The rapid evolution of AI chatbots has led to significant transformations in various sectors, encompassing business40, governance41, education42, and healthcare43. Amazon Alexa boasted over 100,000 programs as a prominent platform for chatbot progress. Facebook Messenger had over 300,000 active chatbots as of 2019, with a substantial portion dedicated to Healthcare and well-being. An illustrative example is the WHO's launch of a chatbot on Facebook Messenger in April 2020, aimed at combating misinformation and providing immediate and correct data related to COVID-1944. In45, the authors describe that Telehealth and telemedicine systems aim to provide remote healthcare services to alleviate the transmission of COVID-19. These systems are crucial in efficiently managing limited healthcare resources and addressing the overwhelming load of COVID-19 disease-affected people in hospitals. Nevertheless, many current telehealth and telemedicine processes exhibit a unified structure, lacking essential features such as information security, privacy, decision-making power, operational transparency, health records immutability, and traceability. These shortcomings pose challenges in detecting and preventing fraudulent activities related to disease-affected persons' insurance privileges and surgeon credentials. In46, the authors explain that the evolution of electronic information technology has led to the widespread adoption of electronic medical records (EMRs) as a conventional method for storing patient data in hospital settings. Patient records are dispersed across various hospital databases, even on the same individual. Consequently, constructing a merged and summarized EMR for a single patient from multiple hospital databases is challenging due to concerns related to security and privacy. They also highlight that existing EMR systems lack a standardized data management and sharing policy. This absence of a general policy poses difficulties for pharmaceutical scientists striving to develop precise medicines, as they must contend with data obtained under different policies. In response to these challenges, they have introduced MedBlock, a blockchain-assisted information management system designed to address patient information issues; this system has no decision-making power, transparency, or accountability. However, a blockchain-assisted AI chatbot is an intelligent information system that uses BC for secure data storage and AI for delivering transparent and reliable healthcare recommendations. In47, researchers propose implementing a secure BCT system to protect electronic healthcare records (EHR). This framework integrates sensors, the Internet of Things (IoT), databases, and other computing resources. By employing this framework to secure EHR, the authors anticipate an overall security and privacy enhancement compared to traditional healthcare systems. However, the study does not explicitly address data purity, transparency, and accountability concerns. The authors49 developed and assessed a novel evidence-based health information tool called PROSCA, a chatbot designed for the field of Prostate Cancer (PC). This tool shows great promise in raising awareness, assisting patients with knowledge, and providing support. Its primary goal is to give targeted help for doctor-patient communication. The study discovered that a medical chatbot with an early PC detection emphasis helps patients by providing them with an extra educational resource. Nevertheless, it is noteworthy that authors avoided discussing responsibility and transparency in their work. The authors' research50 emphasizes certain aspects of the question-answering system (QAS) that are now utilized in the healthcare industry. According to their research, people view conversational bots as practical and easy to use. These agents show promise regarding time and resource savings but also have issues with data integrity, secure communication, accountability, and transparency. Despite significant advancements, previous studies have not sufficiently addressed the simultaneous integration of model interpretability, scalability, and data privacy in AI-based healthcare systems. This gap limits the ability of these models to provide transparent and secure solutions. The proposed approach leverages blockchain-assisted AI to overcome these limitations and deliver more reliable healthcare diagnostics. Existing studies have explored the integration of BCT in healthcare, primarily focusing on secure data storage, patient data management, and decentralized access control. These implementations provided enhanced data security and privacy but often lacked the capability to offer transparent and interpretable AI-based decision-making. Moreover, many of these studies struggled with scalability issues and failed to ensure seamless integration with AI diagnostic systems. The proposed blockchain-assisted AI chatbot addresses these limitations by not only securing patient data through decentralized ledgers but also offering explainable predictions and scalable solutions, fostering greater trust and reliability in healthcare diagnostics. Several limitations have been observed in the previous research regarding healthcare chatbots. Previous healthcare chatbots face challenges such as insecure communication, lack of decision-making authority, and poor transparency. Sensitive patient data may be exposed to breaches without robust security measures. Limited decision-making capacity can lead to suboptimal healthcare support. Blockchain-assisted AI chatbots address these issues by providing secure, transparent, and decentralized data storage, enhancing both trust and decision-making reliability. Some previous publications' work with the proposed system is shown in Table 1. Table 1 highlights various limitations from the literature review, including a lack of performance regarding secure communication systems, decision-making power, and transparency and accountability. This proposed research work addresses these critical issues by incorporating innovative technologies. The integration of BCT ensures secure communication and protects patient data, while machine learning algorithms enhance decision-making with accurate, personalized responses derived from comprehensive healthcare data analysis. XAI raises transparency and accountability through clear, interpretable explanations for chatbot recommendations, fostering trust and understanding in user interactions and overcoming prior limitations to enhance secure, intelligent, and accountable healthcare chatbot systems. The dataset used for CVD prediction consists of several attributes, each representing a different health-related feature. These attributes are numerical and categorical, and they provide essential information about the patient's physical condition. The structure of the dataset is as follows: it includes attributes such as “id,” “age,” “gender,” “height,” “weight,” “ap_hi” (systolic blood pressure), “ap_lo” (diastolic blood pressure), “cholesterol” (cholesterol level), “gluc” (glucose level), “smoke” (whether the patient smokes), “alco” (whether the patient consumes alcohol), “active” (whether the patient is physically active), and “cardio” (target attribute, indicating whether the patient has cardiovascular disease or not). The “cardio” attribute is binary (0 or 1), where 1 indicates the presence of cardiovascular disease, and 0 indicates the absence. The dataset structure is shown below in Table 2. Table 2 represents that the dataset for CVD prediction is a comprehensive collection of clinical and lifestyle attributes that provide critical insights into a patient's cardiovascular health. Key features such as age, blood pressure, cholesterol levels, and lifestyle habits like smoking and alcohol consumption offer a holistic view of potential risk factors. The target variable, “cardio,” serves as an indicator of whether CVD is present, enabling predictive modeling and analysis. This structured data allows researchers and clinicians to identify patterns and make informed decisions to improve patient outcomes. This research proposes a system that leverages historical data for predictive analysis, as depicted in Fig. 1 It incorporates Exploratory Data Analysis (EDA) to determine the necessity of preprocessing and to detect any outliers in the data. The preprocessing phase addresses issues such as missing values, duplicate records, outliers, and class imbalance to ensure data quality. Subsequently, the dataset is partitioned into training and testing subsets, with 70% allocated for training and 30% for testing. The model is trained and evaluated on these subsets, employing metrics such as accuracy, precision, recall, F1-score, and a confusion matrix to identify the optimal model. Finally, the selected model is utilized for accurate outcome prediction, with interpretability achieved through applying SHAP & LIME. Figure 1 depicts the workflow of a healthcare prediction model integrating ML and XAI techniques. The process starts with data collection, followed by EDA and preprocessing to prepare the dataset. An XGBoost model is trained on the processed data, and its predictions are explained using SHAP and LIME to enhance interpretability. Positive predictions are flagged for further medical review, ensuring transparency and efficiency in healthcare delivery. System block diagram. The step-by-step pseudo code for the prediction of HD is shown below in Table 3. Table 3 presents a structured pseudo-code for processing healthcare data and predicting CVD. It begins with loading the dataset, performing EDA, handling missing values, and using SMOTE to balance classes. The data is split for training an XGBoost model, and XAI techniques are applied to enhance prediction transparency, aiding informed healthcare decisions. With technological advancement, various fields have adopted autonomous systems, with chatbots emerging as a prominent application. Chatbots have gained much popularity, especially in the healthcare sector, as vital guides to patients and health-related inquiries. Nonetheless, these chatbots face several problems, including patient medical records issues, insecure communication, and failure to give clear and accurate answers. Knowing the need to address these challenges has led to an increasing demand for developing intelligent approaches that can yield better results. Therefore, this research aims to design an intelligent approach for chatbot development using BCT and XAI. It sets out to mitigate prevailing challenges around chatbots in general with special emphasis on the healthcare industry to ensure that secure communications are implemented while providing clear responses and retrieving accurate information from patient records. The proposed responsible healthcare chatbot using machine learning is shown in Fig. 2. Figure 1 represents the proposed approach, which comprises the training and validation phases. During the training phase, initially, patient data is acquired from the patient through a chatbot and undergoes a BC for secure and tamper-proof transactions, thereby enhancing data integrity and transparency within healthcare systems. The tamper-proof transactions are then forwarded to the preprocessing layer, involving normalization, handling missing values, and moving averages, and the processed data is then divided into training and testing sets, with respective ratios of 70% and 30%. Subsequently, the approach is trained on 70% of the data for predictive analysis using a ML algorithm (XGBoost). The predictions are directed to XAI for comprehensive output explanations. If the output aligns with the predefined learning criteria, the results are stored in the cloud; otherwise, they are returned to the approach if the learning rate is not achieved. In the validation phase, patient data is directly compared with the imported data stored in the cloud. If the criteria are met, indicating the presence of CVD, the system displays as “yes”. On the other hand, if the criteria are not met, signifying the absence of CVD, the system is discarded. Proposed responsible healthcare chatbot approach. This research proposed a responsible healthcare chatbot using ML approach and implemented it on the dataset51 containing 5391 samples. The data were distributed into 70% training (3774 samples) and 30% validation (1617 samples). As the equations state, this approach finds the result using multiple statistical measures as shown below. It is shown in Table 4 that the proposed responsible healthcare chatbot approach predicts the CVD during the training period using XGBoost. During training, 3774 samples are divided into 1535, 2239 positive, and negative samples. 1505 true positives are successfully forecasted, and no CVD is recognized, but 30 records are mistakenly predicted as negatives, indicating the CVD is recognized. Likewise, 2239 samples are obtained, with negative showing CVD is identified and positive indicating no CVD. With 2200 samples correctly identified as negative, showing the CVD is recognized, and 39 samples inaccurately foreseen as positive, representing no CVD is identified despite the presence of the CVD. Figure 3 shows that the charts compare the model's performance in identifying CVD in the training and validation datasets. The left chart shows the counts of true positives (CVD correctly identified) and false negatives (CVD missed) during training, while the right chart displays the same for validation. High true positive counts indicate good detection, but false negatives suggest some cases of CVD were missed, highlighting areas for improvement. ‘CVD found' in the training and validation phase. ‘CVD Not found' in the training and validation phase. It is shown in Table 5 that the proposed responsible healthcare chatbot approach predicts the CVD during the training period using XGBoost. During validation, 1617 samples are divided into 922, 695 positive, and negative samples. 900 true positives are successfully forecasted, and no CVD is recognized, but 22 records are mistakenly predicted as negatives, indicating the CVD is recognized. Likewise, 695 samples are obtained, with negative showing CVD is identified and positive indicating no CVD. With 675 samples correctly identified as negative, showing the CVD is recognized, and 20 samples inaccurately foreseen as positive, representing no CVD is identified despite the presence of the CVD. Figure 4 shows that the charts illustrate the model's ability to identify cases where CVD is not found. The left chart (training data) shows true negatives (correctly identified as no CVD) and false positives (incorrectly predicted as CVD). Similarly, the right chart represents these metrics for validation data. The high true negative counts indicate strong performance, but false positives highlight instances where CVD was incorrectly flagged, requiring attention for further refinement. It is shown in Fig. 5 that the correlation matrix illustrates the relationships between various features in the dataset. Attributes like age, cholesterol, and weight show moderate positive correlations with the target variable cardio (presence of CVD). Features such as smoke, alco, and active exhibit minimal correlation with cardio, indicating limited direct influence. This matrix helps identify key predictors and their interactions, aiding in feature selection for model building. Correlation matrix. Correlation heatmap of selected features. Figure 6 is showing that the correlation between ap_hi (systolic blood pressure) and cholesterol is minimal, as indicated by a near-zero value in the heatmap. This suggests that changes in blood pressure levels have little to no direct association with cholesterol levels in this dataset. Both features may independently contribute to cardiovascular risk but do not show a strong relationship with each other. Figure 7 shows that the ROC curve is a graphical representation of the model's ability to predict CVD. It plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold levels. A higher area under the curve (AUC) indicates better model performance, with an AUC close to 1 signifying excellent discrimination between patients with and without CVD. It provides a clear visual of the trade-off between correctly identifying CVD cases and minimizing false alarms. ROC curve. Distribution of predicted probabilities. Figure 8 presents the distribution of predicted probabilities for CVD prediction, representing the spread of likelihoods that individuals in the dataset will develop CVD based on model predictions. Typically, these probabilities range from 0 (no risk) to 1 (high risk). Visualizing this distribution helps in understanding the overall risk profile of the population, identifying areas with higher or lower probabilities, and assessing the model's ability to distinguish between high-risk and low-risk individuals effectively. A balanced distribution often indicates good model calibration. Figure 9 shows that SHAP interaction values in CVD prediction explain how pairs of features work together to influence the model's predictions. They highlight not just the individual contribution of a feature but also how its effect changes in the presence of another feature. For example, the interaction between age and cholesterol levels might reveal nuanced insights into cardiovascular risk. These values provide deeper interpretability by uncovering complex relationships within the data. Figure 10 describes how the SHAP values measure the impact of each feature on the model's prediction for an individual's CVD risk. They quantify how much a specific feature, such as age or blood pressure, pushes the prediction towards higher or lower cardiovascular risk. This ensures transparency by breaking down the contribution of each feature to the model's output. SHAP interaction value. SHAP value (Impact on model output). Figure 11 highlights that a decision model in CVD prediction explains how the model combines different features to arrive at a prediction. It shows the sequence and importance of features (like age, blood pressure, and cholesterol) influencing the final output, helping to visualize the reasoning process behind high or low cardiovascular risk predictions. Decision model. Feature importance. Figure 12 illustrates that the feature importance in CVD prediction highlights which factors, such as age, cholesterol levels, or blood pressure, have the greatest influence on the model's predictions. By ranking these features, it provides insights into the key drivers of cardiovascular risk, aiding in model interpretation and aligning predictions with medical knowledge. Figure 13 explains how SHAP values for the “age” feature in CVD prediction quantify how an individual's age impacts the model's output. Age often shows high feature importance, as older individuals typically have a higher cardiovascular risk. This insight underscores age's critical role in shaping predictions and aligns with its established relevance in medical risk assessment. SHAP value for age. SHAP summary plot for the training phase. Figure 14 presents that the SHAP (SHapley Additive exPlanations) summary plot for the training phase in CVD prediction highlights the importance and impact of each feature on the model's predictions. It visually represents how individual features (e.g., age, blood pressure) contribute to increasing or decreasing the likelihood of CVD across the dataset. The plot helps identify the most influential factors, aiding in model interpretability and ensuring key predictors align with domain knowledge about cardiovascular health. It is shown in Fig. 15 that the SHAP summary plot for the validation phase in CVD prediction shows how well the model generalizes to unseen data by illustrating the influence of features on predictions for the validation set. It highlights whether the most important features identified during training remain consistent in the validation phase. This helps verify the stability and reliability of the model's feature importance, ensuring its applicability to new data and alignment with expected CVD risk prediction patterns. SHAP summary plot for the validation phase. Table 6 shows that the proposed responsible healthcare chatbot using ML approach performance in terms of accuracy sensitivity, specificity, miss rate, and precision during training using XGBoost provides 98.17, 97.47, 98.65, 1.83, and 98.05, respectively. The suggested approach yields 97.40, 97.83, 96.84, 2.60, and 97.61 during the validation phase's accuracy, sensitivity, specificity, miss rate, and precision. Furthermore, the proposed responsible healthcare chatbot using ML approach yields 1.35, 72.2, 0.019, and 98.26 in terms of fall-out likelihood positive ratio, likelihood negative ratio, and negative predictive value during training and 3.16, 30.96, 0.027, 97.12 in terms of validation. Proposed responsible healthcare chatbot approach explanation with CVD prediction (No). According to Fig. 16, the Proposed responsible healthcare chatbot using ML approach with the XAI shows a high level of confidence, about 97.12%, in predicting the forecasting of normal cardiovascular conditions. This confidence is influenced by several factors, including the presence of id, age, education, sex, is_smoking, cigsPerDay, BPMeds, prevalent stroke, prevalent Hyp, diabetes, totChol, sysBP, diaBP, BMI, heart Rate, glucose, exng, caa, Triglyceride, hdl_cholestrol, ldl_cholestrol, CPK_MB_Percentage and TenYearCHD. These factors contribute to a higher likelihood of classifying the cardiovascular as normal. According to Fig. 17, the proposed responsible healthcare chatbot using ML approach with the XAI shows high confidence, about 97.12%, in predicting abnormal cardiovascular conditions. This confidence is influenced by several factors, including the presence of id, age, education, sex, is_smoking, cigsPerDay, BPMeds, prevalent Stroke, prevalent Hyp, diabetes, totChol, sysBP, diaBP, BMI, heart rate, glucose, exng, caa, Triglyceride, hdl_cholestrol, ldl_cholestrol, CPK_MB_Percentage and TenYearCHD. These factors contribute to a higher likelihood of classifying the cardiovascular as normal. Proposed responsible healthcare chatbot approach explanation with CVD prediction (Yes). Table 7; Fig. 18 compare the performance of the proposed responsible healthcare chatbot approach with previous ML approaches to predict CVD. It is clearly shown that this approach is better than the previous results in terms of accuracy and miss rate. Graphical representation of the previous approaches with the proposed approach. The proposed blockchain-assisted chatbot powered by XAI demonstrates significant strengths in ensuring secure data storage, transparency, and reliable CVD screening57. The integration of BC enhances data privacy and trust, while XAI improves the interpretability of diagnostic decisions. However, the approach may face limitations in terms of computational complexity and response time, particularly in real-time medical consultations. Future improvements could focus on optimizing the system's efficiency, scalability, and seamless integration with existing healthcare infrastructures to enhance overall performance and user experience. Healthcare systems increasingly incorporate AI into their systems, but it is not a solution to all difficulties. Healthcare data, complete with sensitive patient information, demands strong protections against breaches and unauthorized access. Artificial intelligence (AI) has been readily adopted to solve all healthcare industry issues better. This is one of the vital areas to bother about data security, including health data, which is very sensitive since it is patients' private information. Patient data privacy is now exposed by data breaches and unauthorized access, which are now the biggest threat to healthcare, putting both security and privacy at the forefront. The inherent decentralization of the BC58 entails the use of powerful encryption methods and access controls, which lead to data integrity and privacy preservation, but become a key issue. The vast potential of AI is constrained by a trust issue that rises because of AI work like a black box, that is, can't be explained. Integrating XAI demonstrates the deployability of the Chatbot to the users where they will be comprehending the rationale behind the answers provided and the advice given. It may cause the responsible and ethical behaviors of the patients so that they can make the informed decision and this in turn may help in the utilization of health services. It is obvious, XAI is likely to become an integral part of patient-territorialization and de-monopolizing of healthcare information, making clinical decision support systems more sufficient for better patient outcomes and the overall provision efficiency improvement in general. The development of a responsible healthcare chatbot framework integrated with XAI components represents a transformative advancement in healthcare technology. The proposed approach demonstrates notable performance metrics, achieving 97.40% accuracy, 97.83% sensitivity, 96.84% specificity, and a 2.60% miss rate. The method outperforms previous strategies by attaining the highest precision of 97.12%, underscoring its efficacy in addressing complex health communication challenges and facilitating informed decision-making. 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Department of Computer Science, University of Central Punjab, Lahore, Pakistan Salman Muneer Department of Computer Science, Prince Mohammad Bin Fahd University, Alkhobar, Kingdom of Saudi Arabia Sagheer Abbas Department of Computer Science, Kateb University, Kabul, Afghanistan Asghar Ali Shah Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, 11942, Alkharj, Saudi Arabia Meshal Alharbi Computer Science Department, College of Science and Humanities, Imam Abdulrahman Bin Faisal University, 31961, Jubail, Saudi Arabia Haya Aldossary Department of Computer Science, Lahore Garrison University, Lahore, Pakistan Areej Fatima Department of Networks and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan Taher M. Ghazal Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13557, Republic of Korea Khan Muhammad Adnan You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar Salman Muneer, Asghar Ali Shah, Sagheer Abbas, Meshal Alharbi and Haya Aldossary, have collected data from different resources and contributed to writing—original draft preparation. Khan Muhammad Adnan., Areej Fatima and Sagheer Abbas performed formal analysis and Simulation, Taher M. Ghazal, Asghar Ali Shah, Meshal Alharbi and Haya Aldossary; writing—review and editing, Asghar Ali Shah, and Khan Muhammad Adnan; performed supervision, Salman Muneer, Sagheer Abbas, Asghar Ali Shah, Ahmad Alshamayleh and Meshal Alharbi.; drafted pictures and tables, Khan Muhammad Adnan, Haya Aldossary and Areej Fatima; performed revisions and improve the quality of the draft. All authors have read and agreed to the published version of the manuscript. Correspondence to Asghar Ali Shah or Khan Muhammad Adnan. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions Muneer, S., Abbas, S., Shah, A.A. et al. Responsible CVD screening with a blockchain assisted chatbot powered by explainable AI. Sci Rep 15, 11558 (2025). https://doi.org/10.1038/s41598-025-96715-y Download citation Received: 31 January 2025 Accepted: 31 March 2025 Published: 04 April 2025 DOI: https://doi.org/10.1038/s41598-025-96715-y Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Advertisement Scientific Reports (Sci Rep) ISSN 2045-2322 (online) © 2025 Springer Nature Limited Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Real Vision's Jamie Coutts is watching June for a potential altcoin market recovery but says traders should treat network activity as the “north star” for crypto investing. Altcoins may have just one last rally this cycle, but only those with real utility and strong network activity will see price gains, according to an analyst. “I think there will be one more breadth thrust from altcoins. The question is, is it a sustained rally that we will see for six to twelve months,” Real Vision chief crypto analyst Jamie Coutts told Real Vision co-founder Raoul Pal on an April 3 X livestream. “At this stage, I am not too sure, but I do believe that quality altcoins where activity returns, activity drives prices …we will definitely see a recovery in some of these more high-quality names,” Coutts said. Cointelegraph reported in January that there were over 36 million altcoins in existence. However, Ethereum still holds the majority share of total value locked (TVL) with 55.56%, followed by Solana (6.89%), Bitcoin (5.77%), BNB Smart Chain (5.68%), and Tron (5.54%), according to CoinGecko data. Coutts said traders should watch where the network activity “is gravitating” and use that as their “north star” for how to trade in crypto, adding he sees an altcoin market upswing within the next two months. On March 28, Coutts told Cointelegraph that Bitcoin could reach all-time highs before the end of Q2 regardless of whether there is more clarity on US President Donald Trump's tariffs and potential recession concerns. The total crypto market cap is down around 8% over the past 30 days. Source: CoinMarketCap Blockchain network activity across the board has recently experienced sharp declines amid a broader crypto market downturn. On Feb. 21, Cointelegraph reported that the number of active addresses on the Solana (SOL) network fell to a weekly average of 9.5 million in February, down nearly 40% from the 15.6 million active addresses in November 2024. Meanwhile, several key indicators the crypto industry uses to determine an incoming altcoin season suggest it's still nowhere in sight. Capriole Investments' Altcoin Speculation Index has dropped to 12%, down 53% since Dec. 25, the same period during which Ether fell 49% from $3,490, according to CoinMarketCap data. Related: When will altseason arrive? Experts reveal what's holding back altcoins CoinMarketCap's Altcoin Season Index, which measures the top 100 cryptocurrencies against Bitcoin's performance over the past 90 days, is reading a score of 14 out of 100, leaning toward a more Bitcoin-dominated market, referring to it as “Bitcoin Season.” The Altcoin Season Index Chart is sitting at 14 at the time of publication. Source: CoinMarketCap However, while Bitcoin dominance — a level often watched for retracements that signal an altcoin season — sits at 62.84%, some analysts argue it's no longer as relevant as a signal for altcoin season. CryptoQuant CEO Ki Young Yu recently said that Bitcoin Dominance “no longer defines altseason — trading volume does.” Magazine: New 'MemeStrategy' Bitcoin firm by 9GAG, jailed CEO's $3.5M bonus: Asia Express This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.

Manish Chhetri FXStreet Bitcoin (BTC) price is hovering around $83,000 on Friday after it failed to close above the $85,000 resistance level earlier this week. Ethereum (ETH) failed to find support around its key level, eyeing lower levels not seen since 2023. Meanwhile, Ripple (XRP) retests its critical support level, a firm close below its aim for its yearly low. Bitcoin was rejected by the 50% Fibonacci retracement (drawn from its November low of $66,835 to its all-time high of $109,588 in January) at $88,211 and declined 3.10% on Wednesday. Moreover, BTC failed to close above its daily resistance level of $85,000. This daily level coincides with the 200-day Exponential Moving Average (EMA) and a descending trendline, making it a key resistance zone. BTC stabilized at around $83,000 the next day. At the time of writing on Friday, it trades slightly below around $83,000. The Relative Strength Index (RSI) indicator on the daily chart reads 44 after being rejected from its neutral level of 50 on Tuesday, indicating slight bearish momentum. If the RSI continues to slide downwards, the bearish momentum will increase, leading to a sharp fall in the BTC price. The Moving Average Convergence Divergence (MACD) lines coil against each other, indicating indecisiveness among traders. If BTC continues to find rejection from the daily resistance at $85,000, it could extend the decline to retest its next support level at $78,258. BTC/USDT daily chart However, if BTC recovers and closes above its daily resistance at $85,000, it could extend the recovery rally to the key psychological level of $90,000. Ethereum price recovered and closed above the daily support of $1,861 on Tuesday but failed to maintain above this level and declined nearly 6% the next day. ETH stabilized around $1,800 on Thursday. At the time of writing, it trades slightly down to around $1,790. If ETH continues its downward trend, it could extend its decline to retest its important psychological level of $1,700, which it has not seen since October 2023. The RSI on the daily chart reads 37, below its neutral level of 50 and points downward, indicating strong bearish momentum. ETH/USDT daily chart Conversely, if ETH breaks and finds support around the $1,861 level, it could extend the recovery to its March 24 high of $2,104. XRP price closed below its 100-day EMA at $2.30 last week and declined 12.40%. At the start of this week, it stabilized around the $2.14 level but was rejected by its 100-day EMA on Wednesday. XRP bounced off after retesting its daily support level at $1.96 the next day. At the time of writing on Friday, it trades slightly down toward the daily level of $1.96. If XRP continues correcting and closes below $1.96, it could decline to test its February 3 daily low of $1.77, which is also the lowest level this year. The RSI on the daily chart reads 39, below its neutral level of 50, like Bitcoin and Ethereum, indicating bearish momentum. XRP/USDT daily chart On the other hand, if the daily level at $1.966 holds and XRP recovers, it could extend the recovery to its previously broken 100-day EMA at $2.30. Bitcoin is the largest cryptocurrency by market capitalization, a virtual currency designed to serve as money. This form of payment cannot be controlled by any one person, group, or entity, which eliminates the need for third-party participation during financial transactions. Altcoins are any cryptocurrency apart from Bitcoin, but some also regard Ethereum as a non-altcoin because it is from these two cryptocurrencies that forking happens. If this is true, then Litecoin is the first altcoin, forked from the Bitcoin protocol and, therefore, an “improved” version of it. Stablecoins are cryptocurrencies designed to have a stable price, with their value backed by a reserve of the asset it represents. To achieve this, the value of any one stablecoin is pegged to a commodity or financial instrument, such as the US Dollar (USD), with its supply regulated by an algorithm or demand. The main goal of stablecoins is to provide an on/off-ramp for investors willing to trade and invest in cryptocurrencies. Stablecoins also allow investors to store value since cryptocurrencies, in general, are subject to volatility. Bitcoin dominance is the ratio of Bitcoin's market capitalization to the total market capitalization of all cryptocurrencies combined. It provides a clear picture of Bitcoin's interest among investors. A high BTC dominance typically happens before and during a bull run, in which investors resort to investing in relatively stable and high market capitalization cryptocurrency like Bitcoin. A drop in BTC dominance usually means that investors are moving their capital and/or profits to altcoins in a quest for higher returns, which usually triggers an explosion of altcoin rallies. 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Supra's 25% surge on Friday calls attention to lesser-known cryptocurrencies as Bitcoin, Ethereum and XRP struggle. Cosmos Hub remains range-bound while bulls focus on a potential inverse head-and-shoulders pattern breakout. Bitcoin price remains under selling pressure around $82,000 on Friday after failing to close above key resistance earlier this week. Donald Trump's tariff announcement on Wednesday swept $200 billion from total crypto market capitalization and triggered a wave of liquidations. Maker is back above $1,300 on Friday after extending its lower leg to $1,231 the previous day. MKR's rebound has erased the drawdown that followed United States President Donald Trump's ‘Liberaton Day' tariffs on Wednesday, which targeted 100 countries. Gold gains nearly 20%, reaching a peak of $3,167, while Bitcoin nosedives nearly 12%, reaching a low of $76,606, in Q1 2025. In Q1, the World Gold ETF's net inflows totalled 155 tonnes, while the Bitcoin spot ETF showed a net inflow of near $1 billion. Bitcoin's price has been consolidating between $85,000 and $88,000 this week. A K33 report explains how the markets are relatively calm and shaping up for volatility as traders absorb the tariff announcements. PlanB's S2F model shows that Bitcoin looks extremely undervalued compared to Gold and the housing market. SPONSORED Discover the top brokers for trading EUR/USD in 2025. Our list features brokers with competitive spreads, fast execution, and powerful platforms. Whether you're a beginner or an expert, find the right partner to navigate the dynamic Forex market. ©2025 "FXStreet" All Rights Reserved Note: All information on this page is subject to change. The use of this website constitutes acceptance of our user agreement. Please read our privacy policy and legal disclaimer. 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