Second quarter ended June 30, 2026. A copy of this release is available through our website at lanternpharma.com, where you will also find a link to the slides management will be referencing on today's call. We would like to remind everyone that remarks about future expectations, performance, estimates, and prospects constitute forward-looking statements for purposes of safe harbor provisions under the Private Securities Litigation Reform Act of 1995. Lantern Pharma cautions that these forward-looking statements are subject to risks and uncertainties that may cause actual results to differ materially from those anticipated. A number of factors could cause actual results to differ materially from those indicated by forward-looking statements, including results of clinical trials and the impact of competition. Additional information concerning factors that could cause actual results to differ materially from those in the forward-looking statements can be found in our annual report on Form 10-K for the year ended December 31st, 2025, which is on file with the SEC and available on our website. Forward-looking statements made on this conference call are as of today, August 14th, 2026, and Lantern Pharma does not intend to update any of these forward-looking statements to reflect events or circumstances that occur after today, unless required by law. The webcast replay of the conference call and webinar will be available on Lantern's website. On today's webcast, we have Lantern Pharma Chief Executive Officer, Panna Sharma, and Chief Financial Officer, David Margrave. Panna will start things off with an overview of Lantern's strategy and business model and highlight recent achievements in our operations, after which David will discuss our financial results. This will be followed by some concluding comments from Panna, and then we'll open the call for question-and-answer. I'd now like to turn the call over to Panna Sharma, President and Chief Executive Officer of Lantern Pharma. Panna, please go ahead. Good morning, everyone, and thank you for joining us to discuss our second quarter 2026 results. As I've said before, AI and computationally driven approaches are now becoming central to how both large and emerging biopharma companies discover and develop drugs, but also how they allocate their resources and think about staffing their scientific teams. Today, we're at an inflection point that's actually accelerating, not just for Lantern, but for how science itself will be conducted. And we are watching it happen in real trials with real patients at Lantern. The golden age of artificial intelligence in medicine isn't beginning, it's actually accelerating. And this quarter, that idea has resulted in the development of a new company, Open-Medicine AI. In August, we established Open-Medicine AI as a separate company with commercial licenses and agreements with Lantern in place to take the AI data models to the next level. We'll spend some real time on that today because I think it's the most consequential structural decision we've made since starting Lantern. Let me first walk you through what got us here. A clinical signal that sharpened into a defined patient population, a signal that was actually validated using big data, a European regulatory clearance in a challenging recurrent cancer, an allowed patent on a patient selection method for one of our most valuable assets, LP-184, and an FDA-cleared trial in triple-negative breast cancer that's moving toward launch. All of these were backed by numerous observations in our trials, the LP-300 trial, the LP-184 trial, and even the LP-284 trial. What those observations were is that the mechanistic insights gained during our preclinical work actually have real-world parallels, and they could be the basis for meaningful activity in actual cancer patients. The remainder of 2026 is a defining year for Lantern Pharma, especially as we launch into 2027. We've achieved clinical validation across multiple programs while establishing the foundation for our next phase of growth in both of our engines, our drug development engine and also now our AI engine. In addition, our mid-year financial results reflect highly disciplined execution with a 25% reduction in total operating expenses year-over-year, even as we advanced multiple clinical programs through key inflection points and launched an entirely new company into one of the most promising and disruptive areas of AI, medicine. Our AI-driven clinical pipeline now encompasses multiple drug candidates across solid tumors, blood cancers, and now pediatric oncology, with a combined annual market potential estimated at over $15 billion. Let's start with our phase II program, LP-300 and the HARMONIC trial in never-smokers, non-small cell lung cancer who progress after TKI therapy. We believe there's about 400,000 patients- 500,000 patients diagnosed globally each year that have no specific therapy aimed at never-smokers that progress after TKI. In Asia, it's about 35%- 40%+ of non-small cell lung cancer cases. In the U.S. and Europe, it's between 15% and 20%. In June, we reported emerging data as of the May 11th cutoff, and it showed something we didn't expect to see this clearly, but that the benefit of LP-300 deepens the longer patients stay on it. Among L858R patients who completed six cycles, median progression-free survival reached 8.9 months. That's nine patients, three of whom hadn't progressed at analysis. Across the full cohort of L858R patients, median PFS was 8.4 months. The hazard ratio for that group was 0.37, with a confidence interval of 0.15- 0.89. That means also more than 70% of the L858R patients saw target lesion reduction, and some of the responses sustained beyond two years. We've had a 77% clinical benefit rate, which is phenomenal for that line of therapy. I'll be direct. These are small exploratory cohorts, not powered for statistical significance yet, and a median from nine patients can move up or down. What makes us take it very seriously is that a Cox regression controlling for race, gender, TP53 status, which is very important, confirmed L858R as an independent predictor. This is not a demographic or statistical artifact, and safety was comparable between four and six cycles with no added toxicity from longer exposure. A drug that helps more the longer you stay on it without costing you more in side effects is a drug worth extending, especially where there is no other great therapy for these patients. That is actually the science and the data behind what we did next. We had a successful Type C meeting where no objections were raised to our key proposed amendments. We have concentrated the enrollment now on the L858R patients. These patients actually tend to do worse on current therapy regimens. That is why we also think there is a great need. We have extended the treatment from now six to up to eight cycles, and we have moved into a single-arm design, which should be more efficient and less costly. The trial continues enrolling in the U.S. and Taiwan, and we have used this data set and other observations, of course, about the future of the program in active partnering discussions. Let us talk a little bit about LP-184 this quarter. We have made several advances, all of which were driven by data and AI-leveraged methodologies. First, the EMA clearance. In July, we got clearance for an investigator-initiated phase I-B/II trial in advanced bladder cancer. This is in Copenhagen at Denmark's National Referral Center for Urologic Cancers, Rigshospitalet, and this is with Professor Rohrberg and Professor Pappot. They are the coordinating investigators. This will be a 39-patient trial and very uniquely on two biomarker, a dual biomarker strategy. One on PTGR1 overexpression, and then combining that with DNA damage repair deficiency. We are hoping to enroll patients, very importantly, that our platform has predicted should respond, and more importantly, have a mechanistic basis to be helped by that drug. Second major milestone is the 184 monotherapy in relapsed or refractory triple-negative breast cancer. That will be a phase I-B/II trial. That protocol has been FDA cleared and is now moving toward launch with a number of sites. We have also applied for grants for that trial, for that study as well, which we are pretty excited about. This drug targets tumors with DNA damage repair alterations, homologous recombination deficiency, or genomic loss of heterozygosity. We expect to enroll up to 40 patients across two dose cohorts, and we will follow by Simon two-stage efficacy read. Third, very important, is that we received a notice of allowance in July covering our three gene selection, where we use three genes, PTGR1, PTPN14, and ASPH for selection of patients most likely to respond to LP-184. We were issued a notice of allowance in four tumors, ovarian, liver, kidney, and thyroid cancer. That is a patent on the selection logic itself, which is one of the hardest parts of this to replicate, and then map that directly to a credible therapeutic intervention where safety is known and mechanism is beginning to be more and more observable. This all built on our 63-patient trial that we did for LP-184, and now that we have a dose of 0.39 mgs per kg. Very importantly, what we saw in that trial is that we saw tumor reduction in patients that were carrying these DNA repair deficiency genes, CHEK2, ATM, BRCA1, STK11, KEAP1. Those alterations conferred exceptional sensitivity to the drug. Unlike conventional chemotherapies and other DNA-damaging agents that indiscriminately target dividing cells, both LP-184 and LP-284 exploit specific genomic vulnerabilities in cancer cells. That precision is the thread that runs parallel through both programs and which we expect to give our programs a meaningful advantage in their development. LP-284 continues in hematologic malignancies and in adult soft tissue sarcomas, where we got orphan designation earlier this year. Starlight, briefly on the science, STAR-001, which is LP-184 in brain cancers. Our RADR platform identified that those particular brain tumors would be very sensitive if ERCC3 was removed as a protein, because that's involved in the repair mechanism. What we did is we characterized that with our group at Johns Hopkins that we collaborate with, and we're using spironolactone, which is already well-characterized, safe in pediatric and adults, and it actually does exactly that. It degrades the ERCC3 protein and shuts down the repair route. We've had great preclinical data, and now we're taking that now into the clinic. We're taking it into disease designations where we have orphan designations and also rare pediatric, such as ATRT, hepatoblastoma, rhabdomyosarcoma, and malignant rhabdoid tumors. Bear in mind that each of these is independently eligible for a priority review voucher upon approval, and they've recently transferred for $150 million-$200 million or more, and Lantern holds four of those. On the pediatric program specifically, I'm very excited and I want to give you an update. We're actively working with several pediatric oncology consortia to determine the best and most expedient path to bring these into a trial as soon as possible. We got two consortia that we're working with, and we'll have more data in this coming quarter. We're also working closely to enable compassionate use for the drug, especially in some of these rare pediatric brain tumors, where there's an exceptional need. Again, Starlight is 100% owned by Lantern. We expect to raise additional funding for it as a separate funding. It holds its own INDs now, its own regulatory designations, and it's not just a program status, it's actually a way to monetize it independently of the rest of Lantern, and more importantly, it's a template. We're about to use that same template again, this time with the underlying platform itself. Now, going back to Open-Medicine, and this is we believe the structural news of the quarter. In August, we formally established Open-Medicine, OMAI, as a separate company, executed our board-approved commercial licensing agreements, and more importantly, OMAI now can operate the multi-agentic AI co-scientist that we launched as withZeta.ai and use it in the commercial setting. Here's the logic. Most people using AI drug development today ask one model a question and get an answer. We now see that things are moving well beyond a single line of questioning or querying. So we built an orchestrated system, and this orchestra brings together specialized agents for literature synthesis, medicinal chemistry, pathway analysis, data curation, literature analysis, portfolio prioritization, clinical trial development, and they challenge each other, and they pass information and ideas, and they cross-validate before delivering hardened results or ask the scientist or drug developer to get more engaged and ask them questions. This, we believe, this multi-agentic, non-monolithic model is really the standard infrastructure for specialized domains that are multi-disciplinary, and we think it'll be the standard infrastructure for drug discovery. We think this is something that will be critical. In addition to that, we believe that the computational biology model and the computational chemistry model that run deep and in their own large quantitative models is critical, and more importantly, it can generate publication-quality results with a full audit trail. As a platform gets smarter and more users use it and data flows through it, each engagement for a user will feed the next, and this is exactly the kind of dynamic that deserves its own capital structure. Clinical drug development and enterprise software are priced by different investors and different metrics. Held inside a clinical-stage oncology company, a software business may or may not get the credit for what it's worth because investors who price AI and software generally don't own clinical-stage biotech, and vice versa. That's the entire rationale for separating and racing forward with Open-Medicine AI. Open-Medicine AI is 100% owned by Lantern today. It intends to raise capital at its own level in exchange for Open-Medicine AI equity with a longer-term objective of becoming a separately listed company. Lantern expects to remain one of its largest shareholders. Lantern continues to retain the rights to the full access to the platform for our own drugs, and this changes nothing about those programs' priority or timing, and we believe that the market there is much, much larger than just early oncology companies like ourselves. Analysts project the market to reach about $10 billion by 2030, 2031, with oncology as one of its largest segments. Even doing my own bottoms-up analysis on companies and drug discovery, drug discovery technology, AI-enabled, I expect it to easily reach $9 billion- $10 billion+ by 2031. We'll host a dedicated informational call in mid-September on Open-Medicine AI's market opportunity platform, roadmap, commercial model. But putting all this together, a clinically validated platform with three drugs and trials, a commercially accessible AI platform and software company with models and state-of-the-art tools, and a drug pipeline, these all feed each other. You get a business model that extends well beyond just the clinical assets. We think it's a very powerful complement to have both of these engines, an AI engine that can be separated and power dozens of companies, and drug assets that are going after meaningful, challenging, rare, and aggressive diseases, and we think these are very complementary. The AI tools and services we think can grow to being several hundred million dollars in standalone value as part of this larger $10 billion market. We think a nice chunk of that $10 billion market will be agentic in nature, and Open-Medicine AI will have a real chance at grabbing a significant piece of that. These are two great growth engines in the company, and I'll let David talk a little bit, David Margrave, to discuss our financials, our key metrics, and also dig into the details behind the non-cash expenses that are related to warrants that drive a higher net operating loss than what's actually underneath the hood. David, I'll turn it over to you. Thank you, Panna, and good morning, everyone. I'll now share some financial highlights from our second quarter ended June 30, 2026. Before getting into the details of the quarter, I want to note that this quarter was different from prior quarters because we had a substantial non-cash expense related to the issuance of warrants in connection with our May financing transaction and the way those warrants are treated for accounting purposes. I'll discuss this topic in detail later in my discussion. Cash, cash equivalents, and marketable securities were approximately $7.4 million at June 30, 2026, consisting of approximately $6.7 million in cash and cash equivalents and approximately $0.7 million in marketable securities, compared to approximately $10.1 million in cash equivalents, and marketable securities as of December 31, 2025. Funding received during the second quarter of 2026 consisted of approximately $4.4 million in gross proceeds from our registered direct offering that closed on May 14, 2026. Additional funding is a top priority, and we intend to pursue additional capital raises, collaborations, and other opportunities to extend our operating runway. R&D expenses were approximately $1.8 million for the three months ended June 30, 2026, compared to approximately $3.1 million for the three months ended June 30, 2025. This was a decrease of approximately $1.3 million, or 42%. The decrease was primarily attributable to reductions of approximately $1 million in research studies and materials expenses relating to the conduct of our clinical trials and decreases of approximately $0.3 million in salaries and benefit expenses. G&A expenses were approximately $1.7 million for the three months ended June 30, 2026, compared to approximately $1.6 million for the three months ended June 30, 2025. This was an increase of approximately $0.13 million, or 8%. The increase was primarily attributable to increases in business development and investor relations expenses of approximately $0.36 million and salaries and benefit expense increases of approximately $0.14 million, offset in part by decreases in other professional fees of approximately $0.35 million. Loss from operations was approximately $3.5 million for the three months ended June 30, 2026, compared to a loss from operations of approximately $4.7 million for the three months ended June 30, 2025, representing a decrease of approximately 25%. In connection with our May 2026 registered direct offering, in which we raised approximately $4.4 million in gross proceeds, the company issued investor warrants to purchase up to 2,135,923 shares of common stock at an exercise price of $2.27 per share and placement agent warrants to purchase up to 106,796 shares of common stock at an exercise price of $2.575 per share. These warrants are accounted for as liabilities due to a settlement feature that may be triggered in the event of a fundamental transaction. During the three months ended June 30, 2026, the company recorded an aggregate of approximately $3.6 million of expense related to these warrants. The main component of this was non-cash expense arising from an increase in the fair value of the warrants that was driven primarily by a substantial increase in the company's stock price between the May 14, 2026, warrant issuance date and June 30, 2026. Other components related to warrant expense were loss on issuance of the warrants and warrant issuance costs. After including the non-cash and other items related to warrants, our net loss was approximately $7.1 million, or $0.57 per share for the three months ended June 30, 2026, compared to a net loss of approximately $4.3 million, or $0.40 per share, for the three months ended June 30, 2025. For the six months ended June 30, 2026, our net loss was approximately $10.4 million, or $0.88 per share, compared to a net loss of approximately $8.9 million, or $0.82 per share, for the six months ended June 30, 2025. From a capitalization standpoint, as of June 30, 2026, the company had 12,759,146 shares of common stock outstanding. As we described, in May 2026, we closed a registered direct offering and concurrent private placement comprising 1,454,175 shares of common stock, pre-funded warrants to purchase up to 681,748 shares of common stock, investor warrants to purchase up to 2,135,923 shares of common stock at an exercise price of $2.27 per share, and placement agent warrants to purchase up to 106,796 shares of common stock at an exercise price of $2.575 per share. There was no activity under our at-the-market sales facility during the three months ended June 30, 2026. I will now turn the call back over to Panna for an additional update on our programs and operations. Panna? Thank you, David. Two closing points. First, the number I want all of you to remember is that we advanced programs from AI-derived insights to first in human clinical trials in a timeline under three years, roughly two to three years, at approximately $2 million- $3 million each. The industry norm to reach that same point is 5- 10 years at $25 million- $100 million. We have three molecules in clinical trials of dosed over 100 patients, and at the same time have been able to advance an AI platform that is launching commercially. Those numbers are not a marketing claim. It is actually our operating model, and it is a key part of our core advantage. Secondly, what we now have structurally that we did not have just in April is a lung cancer trial refined around a specific patient population, L858R mutations. We have European clearance for a dual biomarker trial, which will be led by investigators in Denmark in a challenging recurrent bladder cancer setting. An FDA-cleared second trial in triple-negative breast cancer, postpartum refractory patients moving toward launch, an AI and software company with executed licenses, multiple engineering centers, and a growing user base. As David just walked you through, we actually did all that while our actual operating losses or loss from operations were down approximately 25% year-over-year. We did all of this while continuing to advance both engines of growth. We believe that's a really important and smart way to build, and that's the argument for continuing to operate this way. We're not just building better tools, we're reimagining what's possible in precision oncology and building the tools to support it. We believe this will be the standard for the rest of the industry, and more importantly, it's the platform that we think will be positioned to scale. Want to thank our team, our investigators, and our shareholders, as we light our way through precision oncology solutions. We expect to have a lot of great additional results over the coming quarters. I want to especially thank our own team here at Lantern, especially a longtime member of our team who's moving on to a new leadership opportunity in media and technology after five years with us. Five years of building this company's brand, voice, communications, and also being an amazing colleague. So thank you very much. With that, I would like to now open the call to questions. You can type your question using the question-and-answer tool or raise your hand, and we'll try to unmute your line and repeat your question. Any questions with the remaining time that we have? You want to go to the question-and-answer. Okay. I think, Michael, you should be unmuted. Can you hear me? Yeah. Good morning. Two questions, Panna. One on LP-300 and then the other on OMAI. On LP-300, can you talk about where are you in the data analysis? It's obviously nice to see the PFS stretching out a little bit more, but how mature is this data set? Will it mature further? When do you plan to update us again? The next question related to that is, now that you got the protocol amendment in place, have any patients been enrolled under the new protocol? All right. Let's go. A lot of questions. Once we had sufficient confidence that the protocol would be amended and the data was trending that way, we wanted to get the new IRBs approved at all the sites, and that's all been done now. So we expect enrollment to resume under the new eight cycles, which is important. We think that'll extend durability and maybe even deepen response. So we expect to be enrolling patients in Taiwan and the U.S. specifically under the new amended protocol. We hope to expect another 15 patients, 16 patients. That'll give us meaningful data, and we expect to enroll those over the next four to six months, both in the U.S. and Taiwan. That's the initial focus. Will there be any other updates coming on the current cohort? We may have an update toward the end of the year. I think other than just extending PFS, you're really relying on the next batch of patients coming in to see what kind of responses that we continue getting. Okay, very good. Thanks for that update. On Open-Medicine AI, can you talk about, I think, most of us that come from a therapeutics background are not AI experts. Most of the technology is a black box because the companies, like Insilico Medicine and others, don't open their kimono to see what's actually operating internally. Maybe you can help us understand what your system looks like or how it compares. How should we think about it in the context of the other tools that are out there that the pharma industry seems to be taking advantage of? Yeah. I'm working on something for our mid-September webinar. The AI cycle in drug development, we're on our fourth cycle. If you go back to early days of supercomputers and molecular modeling and large install bases, it was the first wave. Limited compute resource, but infrastructure-heavy. We're almost at the opposite end of that now, where we have almost limitless compute resource and infrastructure install super light, and there are two waves caught in between that. We really didn't have the capability to get the transparency that you would want real-time until after an algorithm was run, and oftentimes those algorithms would take days or weekends or long-term. Now those can be done in seconds, so you can get real-time, what is the process that happened? We also didn't have the software and tools to do large-scale algorithm mapping and analysis, because it was just extra overhead. Now we have the ability to do that, so we get transparency that we didn't have. That was a luxury in the past. Now it's commonplace, and people expect it. A lot of the large-scale AI providers, including the Anthropic and OpenAI of the world, and even to some extent, DeepSeek, have made some levels of transparency into how the system operates more expected. That is something that we rest on the shoulders of. We can do it very differently, so that's a platform that we've built. More importantly, once you see the transparency, you as an enterprise user or end user can actually tweak it and alter it, and that just didn't exist before. We're in a different wave of how AI, and I expect, and I'll mention this in the webinar in September, is that the people who are going to be hit the hardest are going to be two. Number one, people who provide professional knowledge labor, basically. Second, it's going to be the existing install base of software providers into pharma. Those days of going in and being able to charge $100,000, $500,000, $300,000 for some very, very specific functionality of an install base, those days are going to be gone. They're all going to go to providers like Open-Medicine AI. Also, you're not going to hire teams of bioinformaticians and teams of data analytics people. You can do all that now in the cloud with one smart engineer, data science person. You can launch swarms of people, swarms of agents doing this work for you, and that is especially what we have proven with Open-Medicine AI. I think that is the future, and I think that is where leading-edge providers like Cloud Science Labs and others are going toward. People are going to expect greater transparency, and if you really want to democratize the development of drugs, you are going to have to be able to allow people to go to a URL, to go to an app, and start their inquiry. That is exactly where I see Open-Medicine AI playing, is a new category that just has not been valued in price. I am writing a piece you will see by mid-September. It is called "The Deflation of Discovery and the Birth of a New Category," and that specifically talks to agentic AI in drug development and drug discovery. Okay. Thank you. Another question I will take. Someone is asking, "Any interest with withZeta.ai from large pharma?" The quick answer is yes. We have got a lot of pharma companies, both biologic groups as well as small molecule groups. We have had some have several calls with us, some visit. The answer is yes. Large pharma is definitely interested. This is something that they are all evaluating, cutting deals on, looking at. Large pharma will have to partner with agentic AI to make it commonplace. It is transforming the economics of early development and also late-stage development. Yes, very much increasing interest. The more marketing, the more dollars we can put behind driving awareness of Open-Medicine AI and withZeta.ai, the more I expect. The one thing that we have seen that has been solid is that once we put the tool in front of people, it gets very sticky. Yes. Thank you. Take another important question. Let us see if we can do this one live. We are trying to do some live. So I do not know. Go ahead. Hit the live. I think Beau Parsons, you should be on live. I can read it also if you do not want to do it live, but Okay. This is another question, is our models we expect will be standards in computational biology and drug development. What are you doing to ensure that, and that other competitors do not copy your methods? First of all, everyone will copy one another. That is part of putting Open-Medicine AI separately is to allow it to move faster, further, and have its own independent balance sheet to ensure that you always stay one or two steps ahead. There are definitely companies that have more capital. More capital doesn't necessarily mean you're going to be the surviving entity. You can look at any industry, and category by category, but capital efficiency is important long-term, which we've proven to be very capital efficient at. But we're at a point where it needs to be a separate entity and raise its own capital to stay ahead of the curve. The things that we're doing, in addition to continuing to train our models and try to grow intelligently using our center in Bangalore, India, those are things that we're doing. We're also constantly benchmarking, like we did with our blood-brain barrier algorithm, like we're doing with our biocomputational tools. We're trying to pick some of the toughest challenges and go deep as opposed to go broad, and that's one of the things that we're big components of is going deep in certain categories versus broad across all of science. I don't think we ever would've claimed, "Hey, we're going to be Claude Science and do all of science." I think that just makes no sense to me. You can pick specific categories like rare cancers, specific areas like biocomputational tools, specific problems like blood-brain barrier or penetration into any tissue type, and do it and resolve it really, really well. So we're going to go after certain diseases that we think require that kind of depth and then march forward in that fashion. But capital, no doubt, more capital is needed to drive that. Let's go ahead and get to the next question. Let's go to this. Sorry. Let's go to Baird and Redshift team. Maybe we can answer that one live. Baird and Redshift team, if you guys want to ask your question live. Oh, no, they can't ask their question live. You have to read the question. Oh, okay. All right. David asking a question on what does adoption and feedback look like. The adoption is very sticky, like I've said before, once we get it in front of users. We're taking certain measures to make sure that users get the benefit of the full platform. We've introduced a new code called withZeta.ai 14 that people can sign up for, and get the full professional edition. People who play with the professional edition, especially generative chemistry, biocomputational tools, the investigator mode, it tends to be very, very sticky. So that's the exciting news. Key is getting them to that point, so we're also beginning to implement some more aggressive email campaigns to drive the awareness and specialized codes for certain larger pharma companies. But yeah, great question. Okay. Another question is, anonymous, what would you contemplate the biggest benefit of the Open-Medicine AI spin-out will be for shareholders? Lantern owns 100% of Open-Medicine AI today. We think it is poised to be very disruptive. Disruptive companies can be valued higher. We are going to raise capital. Lantern will continue being the largest shareholder, we think, for a while, and we may explore ways to distribute the underlying shares to all shareholders in Lantern. Those are things that we are talking about, and potentially distribution of the shares of Open-Medicine AI to all Lantern shareholders. Again, we are having discussions. We are looking at the most efficient ways to do that, but I expect Lantern shareholders to continue being beneficiaries of that asset as we monetize it, both in private financings and, very importantly, as it potentially goes into an exchange, public exchange. I think we are coming up, it is almost 45 minutes into the call, and we look forward to answering questions and one-on-ones as it continues. I know we have a couple of requests for some one-on-one follow-up meetings. We will take those as well. Thank you guys for participating. I want to thank all the Lantern investors, people who are interested, and I look forward to giving you guys more updates as the year continues. Thank you, and I thank you again to our team as well. Thanks a lot.