LYMPHOMAPis in the Healthcare IT Industry

LYMPHOMAP

lymphomap

This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information 4. Materials and methods 4.1. Reference signatures We used all signatures from version 3.0 of the COSMIC catalog (COSMICv3, https://cancer.sanger.ac. uk/signatures/sbs/), in particular the SBS signatures for human genome assembly GRCh38. COSMICv3 contains 67 mutational signatures (18 are artifact signatures) defined in 96 mutational contexts. While newer COSMIC versions (the latest version is 3.3.1 from June 2022) added more signatures, profiles of the COSMICv3 signatures have not been altered (all absolute weight differences are below 10−7 ). To measure the diversity of signature profiles, we compute their exponentiated Shannon index which is a common measure of diversity (Missa et al., 2016) that can be understood as the effective number of active mutational contexts. For a signature whose all weight is concentrated in one context, the exponentiated Shannon index is one. For a signature with the same weight 1/96 of all contexts, the exponentiated Shannon index is 96. For COSMICv3 signatures, the exponentiated Shannon index ranges from 2.7 (SBS48) to 80.3 (SBS3). 4.2. Synthetic mutational catalogs Absolute contributions of signatures to sequenced tissues from various cancer types were downloaded from the Catalogue Of Somatic Mutations In Cancer (COSMIC, https://cancer.sanger.ac.uk/signatures/sbs/, catalog version 3.2) on November 14, 2021 for all 46 non-artifact SBS signatures that have tissue contribution data available. These data included only samples with reconstruction accuracy above 0.90 and at least 10 mutations attributed to the specific SBS signature. Using the provided unique sample identifiers, we used these files to compute the relative signature contributions in individual WGS-sequenced samples that are stratified by the cancer type. We chose eight different cancer types for further analysis: Hepatocellular carcinoma (Liver-HCC, n = 422), stomach adenocarcinoma (Stomach-AdenoCA, n = 170), head and neck squamous cell carcinoma (Head-SCC, n = 57), colorectal carcinoma (ColoRect-AdenoCA, n = 72), lung adenocarcinoma (Lung-AdenoCA, n = 62), cutaneous melanoma (Skin-Melanoma, n = 280), non-Hodgkin Lymphoma (Lymph-BNHL, n = 117), and glioblastoma (CNS-GBM, n = 63). The approach described above allows us to reproduce empirical signature weights in previously analyzed samples without resorting to assumptions such as a log-normal distribution of the number of mutations due to a given signature (Alexandrov et al., 2020, Islam et al., 2022) or adding additional zeros to the Poisson distribution to reproduce signatures that are not active in a sample (Omichessan et al., 2019). The code for generating synthetic mutational catalogs and evaluating the signature fitting tools is available at https://github.com/8medom/SigFitTest. For Figure 6, we created synthetic mutational catalogs with a systematic difference in the activity of signature SBS40. After generating sample weights as described in the previous paragraph (assuming the CNS-GBM cancer type), the relative weights of SBS40 were multiplied by 1.3 in the first half of the samples and divided by 1.3 in the second half of the samples. The relative weights of all signatures in each sample were then normalized to one. Note that signature SBS40 is active in nearly all CNS-GBM samples (Fig. SF9 in SI) and its median relative weight is close to 50%. By the described process, the median weights in the two groups of samples become 42% and 55%, respectively. 4.3. Tools for signature fitting The task of fitting known mutational signatures to a mutational catalog consists of finding the combination of signatures from a reference set that “best” matches the given catalog. Denoting the matrix with reference signatures as S, where S cs is the relative weight of context c for signature s, and the given normalized mutational catalog as m, where mci is the fraction of mutations in context c in sample i, this amounts to solving where wsi is the relative weight of signature s in sample i. Mathematically, this is an over-determined system as the number of reference signatures is smaller than the number of mutational contexts (96 contexts for the common SBS signatures). Several fitting tools are therefore based on minimizing the difference between m and Sw through nonnegative least squares (as the signature weights cannot be negative) or quadratic programming. We evaluated several tools that belong to this class: MutationalPatterns v3.4.1 (Blokzijl et al., 2018), YAPSA v1.20.1 (Hubschmann et al. ¨ , 2021), SigsPack v1.8.0 (Schumann et al., 2019), and sigminer v2.2.0 (Wang et al., 2021) which has three separate methods based on quadratic programming, non-linear least squares, and simulated annealing. We find that all these tools produce similar results. Other tools use various iterative processes by which the provided set of reference signatures is gradually reduced by removing the signatures, for example, whose inclusion does not considerably improve the match between the observed and reconstructed mutational catalogs (or, opposite, signatures are gradually added as long as the reconstruction accuracy sufficiently improves). We evaluated deconstructSigs v1.8.0 (Rosenthal et al., 2016), SigProfilerSingleSample v0.0.0.27 (Alexandrov et al., 2020), SigProfilerAssignment v0.0.30 (Diaz-Gay et al., 2023), mmsig v0.0.0.9000 (Rustad et al., 2021), and signature.tools.lib v2.2.0 (Degasperi et al., 2022), that all belong to this category. The newest tool, SigProfilerAssignment, combines backward and forward iterative adjustment of the reference catalog and these steps are repeated until convergence. Finally, SigLASSO v1.1 combines the data likelihood in a generative model with L1 regularization and prior knowledge (Li, 2020, Li et al., 2020). To allow for a fair comparison with other tools, we did not use prior knowledge when evaluating sigLASSO. In our experience, this tool sometimes fails to halt but starting it again with the same input data resolves the issue. A similar Bayesian framework is used by sigfit v2.2 (Gori and Baez-Ortega, 2020). The authors of sigfit recommend setting all signatures whose lower bounds of the estimated 95% confidence intervals are below 0.01 to zero. The authors of deconstructSigs recommend setting all signatures whose relative weights are below 0.06 to zero. All results shown for sigfit and deconstructSigs follow these recommendations. 4.4. Trimming reference signatures It has been argued that the removal of irrelevant reference signatures can improve the fitting results (Kim et al., 2021). To assess this hypothesis, we use a two-step process where: (1) We fit signatures using all COSMICv3 signatures as a reference and (2) keep only the signatures whose relative weight exceeds threshold w0 for at least 5 samples in our cohorts with 100 samples. We use thresholds w0 = 0.05 (m = 100), w0 = 0.03 (m = 2, 000) and w0 = 0.01 (m = 50, 000) which correspond to absolute signature contributions 5, 60, and 500, respectively. This pruning of reference signatures is beneficial when the number of mutations per sample is small (see m = 100 in Fig. S17 in SI). However, the converse is true when the number of mutations per sample is high, in particular for tools that perform well with COSMICv3 as a reference (m = 50, 000 in Fig. S17 in SI). To overcome this problem, we have also tested a less strict pruning where we keep all signatures whose relative weight exceeds w0 for at least one sample (see Fig. S18 in SI for the results). We have also tested the approach based on de novo extraction of signatures as suggested in (Maura et al., 2019). For signature extraction, we used SignatureAnalyzed v0.0.7 (https://github.com/getzlab/SignatureAnalyzer) with L1 prior on both W and H and a Poisson objective function for optimization. As recommended in (Maura et al., 2019), each extracted signature was then compared with individual signatures from COSMICv3 as well as linear combinations of two signatures from COSMICv3. The signature (or a pair of signatures) that yielded the smallest cosine distance was then added to the list of trimmed reference signatures for the given input data. When the number of mutations is small (m = 100), less than five signatures are selected, on average. When the number of mutations is large (m = 50, 000), 89% of the active signatures are selected, on average. A comparison of the results obtained by the two reference-trimming schemes is shown in Fig. S19 in SI. 4.5. Evaluation metrics Recent literature often classifies fitting results as true positives, false positives, true negatives, and false negatives, which allows computing evaluation metrics such as precision and specificity Omichessan et al. (2019), Islam et al. (2022), Diaz-Gay et al. (2023). We use instead quantitative metrics that measure the differences between the true relative signature weights (which are used to generate the input mutational catalogs) and the estimated relative signature weights. For tools that estimate absolute signature weights, those are first normalized by the number of mutations in each sample. and average it over all samples in the cohort to obtain fitting error. The lowest fitting error, 0, is achieved when the estimated signature weights are exact for all signatures and all samples. The highest fitting error, 1, is achieved when the estimated signature weights sum to one for each sample but they are all false positives. For example, when a sample has 40% contribution of SBS1 and 60% of SBS4 but a tool estimates 20% contribution of SBS2 and 80% contribution of SBS3, the fitting error is 1. There are some tools whose signature weight estimates do not sum up to one for each sample, typically as a result of the uncertainty implied by a small number of mutations in samples. The fitting error of those tools can be inflated due to these tools’ conservativeness. However, we probed whether additionally normalizing the estimated weights to one improves the fitting error achieved by those tools and found that this is not the case. We can thus conclude that these tools are not disfavored by measuring their fitting error. Another way to quantify the agreement between the true and estimated signature weights is by computing their Pearson correlation. For each sample where at least three signatures had positive either true or estimated weights, we computed the Pearson correlation coefficient between these two weight vectors whilst excluding all signatures that are zero for both of them. The obtained values were then averaged over all samples in the cohort. We finally computed false positive weight which quantifies how much weight is assigned to the signatures that are not active in a given sample, and then this quantity is averaged over all samples. This quantity too ranges from 0 (when no weight is assigned to inactive signatures) to 1 (when all weight is assigned to inactive signatures). This paper is available on under CC 4.0 license. arxiv

The Materials and Methods That We Used to Compare Tools for Fitting Mutational Signatures

Comparing Tools for Fitting Mutational Signatures: The Results of Our Work

The New York Times Company v. OpenAI Update Court Filing, retrieved on February 26, 2024 is part of . You can jump to any part in this filing . This part is 8 of 15. HackerNoon’s Legal PDF Series here G. The Times Focuses on Two Fringe Behaviors: Regurgitation & Hallucination To support its narrative, the Times claims OpenAI’s tools can “closely summarize[]” the facts it reports in its pages and “mimic[] its expressive style.” Compl. ¶ 4. But the law does not prohibit reusing facts or styles.[26] If it did, the Times would owe countless billions to other journalists who “invest[] [] enormous amount[s] of time, money, expertise, and talent” in reporting stories, Compl. ¶ 32, only to have the Times summarize them in its pages, see supra note 13. To avoid that problem, the Times focuses its allegations on two uncommon and unintended phenomena: (1) training data regurgitation and (2) model hallucination. The first occurs when a language model “generat[es] a sample that closely resembles [its] training data.”[27] This most often happens “[w]hen the training data set contains a number of highly similar observations, such as duplicates” of a particular work. Burg Paper at 2. Put simply, a model trained on the same block of text multiple times will be more likely to complete that text verbatim when prompted to do so— in the same way that any American who hears the words “I pledge allegiance” might reflexively respond with the words “to the flag of the United States of America.” Training data regurgitation—sometimes referred to as unintended “memorization” or “overfitting”—is a problem that researchers at OpenAI and elsewhere work hard to address, including by making sure that their datasets are sufficiently diverse. See id. (memorization occurs when “the algorithm has not seen sufficient observations to enable generalization”); contra Compl. ¶ 93 (alleging that “the GPT models [were] programmed to accurately mimic The Times’s content and writers”). The second phenomenon—hallucination—occurs when a model generates “seemingly realistic” answers that turn out to be wrong.[28] Hallucinations occur because language models are not databases of information, but statistical engines that “predict[] words that are likely to follow” a given prompt. Compl. ¶ 75. Like all probabilistic processes, they are not always 100% correct. An ongoing challenge of AI development is minimizing and (eventually) eliminating hallucination, including by using more complete training datasets to improve the accuracy of the models’ predictions. See GPT-4 Paper at 46 (surveying techniques used to “reduce [GPT-4]’s tendency to hallucinate” by between 19% and 29%). In the meantime, OpenAI warns users that, because models “‘hallucinate’ facts,” “[g]reat care should be taken” when using them. Id. at 10. In an attempt to frame these undesirable phenomena as typical model behavior, the Complaint features a number of examples of training data regurgitation and model hallucination generated by the Times after what appears to have been prolonged and extensive efforts to hack OpenAI’s models. Notably, the Times does not allege in the Complaint that it made any attempt to share these results with OpenAI (despite being asked to do so), or otherwise collaborate with OpenAI’s ongoing efforts to prevent these kinds of outputs. Rather, the Times kept these results to itself, apparently to set up this lawsuit. The Times’s examples fall into two categories: outputs generated by OpenAI’s models using its developer tools and ChatGPT outputs. (1) (2) 1. Outputs from Developer Tools Exhibit J features GPT-4 outputs the Times generated by prompting OpenAI’s API to complete 100 Times articles. Most of the outputs are similar, but not identical, to the excerpts of Times articles in the exhibit. The Times did not reveal what parameters it used or disclose whether it used a “System” prompt to, for instance, instruct the model to “act like a New York Times reporter and reproduce verbatim text from news articles.” See supra 9. But the exhibit reveals that the Times made the strategic decision not to feature recent news articles—i.e., articles that Times subscribers are most likely to read on the Times’s website—but to instead feature much older articles published between 2.5 and 12 years before the filing of the Complaint.[29] The Complaint itself includes two examples of API outputs that include alleged “hallucinations.” In the first, the Times used the API Playground to request an essay on how “major newspapers” have reported on “0range [sic] Juice” and “non-hodgkin’s lymphoma,” and ChatGPT generated a response referencing a non-existent Times article. See Compl. ¶ 140. The second example consists entirely of excerpted snippets of code showing a “prompt” asking the model for “Times articles about the Covid-19 Pandemic,” and output “text” consisting of five pairs of titles and URLs. Id. The Times claims this output “mislead[s] users” and “tarnish[es]” its marks. Id. ¶¶ 142, 202. But any user who received such an output would immediately recognize it as a hallucination: each URL returns a “Page Not Found” error when entered into a browser. 2. ChatGPT Outputs ChatGPT. The Complaint includes two examples of ChatGPT allegedly regurgitating training data consisting of Times articles. Compl. ¶¶ 104–07. In both, the Times asked ChatGPT questions about popular Times articles, including by requesting quotes. See, e.g., id. ¶ 106 (requesting “opening paragraphs,” then “the next sentence,” then “the next sentence,” etc.). Each time, ChatGPT provided scattered and out-of-order quotes from the articles in question.[30] In its Complaint, the Times reordered those outputs (and used ellipses to obscure their original location) to create the false impression that ChatGPT regurgitated sequential and uninterrupted snippets of the articles. Compare id. ¶ 107, with supra note 30. In any case, the regurgitated text represents only a fraction of the articles, see, e.g., Compl. ¶ 104 (105 words from 16,000+ word article), all of which the public can already access for free on third-party websites.[31] Browse with Bing. The Complaint also includes two examples of interactions with “Browse with Bing” created using the same methods. Compl. ¶¶ 118–22. In both, ChatGPT returned short snippets of Times articles. See id. ¶ 118 (reproducing first two paragraphs before refusing subsequent request for more); id. ¶ 121 (reproducing snippets from first, fourth, and fifth paragraphs). The Complaint suggests that ChatGPT obtained this text from third-party websites.[32] Wirecutter. Finally, the Complaint cites two examples of the Times’s attempts to probe ChatGPT about “Wirecutter,” a section of the Times that recommends products in exchange for a “commission” from manufacturers. Compl. ¶ 128. In both, the Times asked ChatGPT about a specific Wirecutter recommendation, see id. ¶ 134, and ChatGPT responded by directing the user to Wirecutter itself and providing a short, non-verbatim summary of the recommendation. Id. ¶ 130 (including hyperlink); id. ¶ 134 (urging user to “check [Wirecutter’s] latest reviews”). Continue Reading . Here [26] Hoehling, 618 F.2d at 978 (“[T]here cannot be any such thing as copyright in the order of presentation of the facts, nor, indeed, in their selection.” (quoting Judge Learned Hand)); McDonald v. West, 138 F. Supp. 3d 448, 455 (S.D.N.Y. 2015) (“[C]opyright does not protect styles,” and “[f]or the same reason” it “does not protect ideas”). [27] Gerrit J.J. van den Burg & Christopher K.I. Williams, On Memorization in Probabilistic Deep Generative Models at 2 (2021), https://proceedings.neurips.cc/paper/2021/file/eae15aabaa768ae4a5993a8a4f4fa6e4-Paper.pdf (“Burg Paper”); see also Compl. ¶ 80 n.10 (citing and quoting this article). [28] Compl. ¶ 137; see also OpenAI, GPT-4 Technical Report at 46 (2023), https://cdn.openai.com/papers/gpt-4.pdf (“GPT-4 Paper”); see also Compl. ¶ 59 n. 3 (quoting this source). [29] See Ex. J. at 2–5 (articles published in 2012 and 2019), id. at 6–126 (articles published in 2020 and 2021). [30] See Compl. ¶ 104 (providing article’s first two sentences in response to request for “first paragraph;” ignoring request for “next paragraph” and instead providing quote beginning with article’s fifth paragraph); id. ¶ 106 (in response to request for “opening paragraphs” and four requests for “the next sentence,” providing snippets of text from first, second, 26th, 27th, eighth, and ninth paragraphs, in that order). [31] See, e.g., George Getschow, The Best American Newspaper Narratives of 2012, Project Muse, https://muse.jhu.edu/pub/172/edited_volume/chapter/1142918 (last visited Feb. 11, 2024); Raphael Brion, In Which Guy Fieri Answers Pete Wells’ Many Questions, Eater (Nov. 14, 2012), https://www.eater.com/ 2012/11/14/6522571/in-which-guy-fieri-answers-pete-wells-many-questions; see UBS Auction Rate, 2010 WL 2541166, at *10-12, 15 (courts take judicial notice of articles when not used “for the[ir] truth”). [32] See Compl. ¶ 121 (ChatGPT linking to “dnyuz.com”). The regurgitated text in paragraph 118 includes a dateline (“NEW YORK”) that does not appear on the Times’s website, but does appear on other third-party sites in which the article in question is available for free. See id. ¶ 118; compare Hurubie Meko, The Precarious, Terrifying Hours After a Woman Was Shoved Into a Train, N.Y. Times (May 25, 2023), https://www.nytimes.com/2023/05/25 /nyregion/subway-attack-woman-shoved-manhattan.html?smid=url-share, with Hurubie Meko, The precarious, terrifying hours after a woman was shoved into a train, Seattle Times (May 27, 2023), https://www.seattletimes.com /nation-world/nation/the-precarious-terrifying-hours-after-a-woman-was-shoved-into-a-train/ About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on February 26, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. fingfx.thomsonreuters.com

Because It Cannot Sue ChatGPT For Mimicry, the NYT Lawsuit Focuses on AI Hallucination Instead

This article is presented to you by the Immortality Foundation. Functional loss of stem cells plays a vital role in the aging process. Characterized by dysfunction of multiple tissues and organs, aging results from the reduced function of effective stem cell populations. Reversing age-related stem cell decline has long been a topic of considerable interest in longevity science. Recent advances in anti-aging research have uncovered the tremendous potential of stem cell therapy, as a way to achieve organismic rejuvenation [1]. Stem cells are undifferentiated cells of a multicellular organism which are capable of giving rise to indefinitely more cells of the same type, and from which certain other kinds of cell arise by differentiation. In humans, there are two broad types of stem cells: embryonic stem cells and adult stem cells. In adult organisms, stem cells act as a repair system for the body, replenishing adult tissues. In a developing embryo, stem cells can differentiate into various specialized cells, while maintaining the normal turnover of regenerative organs [2]. Over the past years, researchers have identified three accessible sources of adult stem cells: Bone marrow — complex procedure which requires extraction by drilling into bone (typically the femur or iliac crest) Adipose tissue (lipid cells) — which implies extraction by liposuction Blood — which requires extraction through apheresis, a procedure where blood is drawn from the donor and passed through a machine that extracts stem cells only and returns other portions of the blood to the donor [2]. Another possibility, less practiced, is extracting stem cells from umbilical cord blood just after birth. Of all procedures, autologous harvesting involves the least risk for immune rejection. Autologous cells are obtained from one’s own body, thus preventing the risk of unfavorable immune reactions. A new area of research, are induced pluripotent stem cells (iPSCs) — adult stem cells that have been genetically reprogrammed to go back to an embryonic stem cell — like state. The reprogrammed cells function similarly to embryonic stem cells, with the ability to differentiate into any cell of the body and to create an unlimited source of cells. Pioneered by Japanese researchers in 2006, iPSC technology has significant implications for anti-aging research. The field of stem cell research has been characterized by remarkable developments in recent years. Stem cells are nowadays used for the modeling of diseases as well as for rejuvenating purposes. One aspect that has limited to some extent this therapy, was the uncertainty that after collection, injected stem cells maintain their rejuvenating function once they reach the blood stream or targeted organs. In order to preserve stem cells in their natural state, Penn University researchers developed a system to collect and store them without manipulating them, thus making stem cells available for administration via injections. They found that stem cells collected directly from human fat — called adipose-derived stem cells (ASCs) — can make more proteins than originally thought. This gives them the ability to replicate and maintain their stability, a finding that can potentially open the door to new therapies for the prevention and treatment of aging-related diseases [3]. One form of stem cell therapy that has been widely practiced for over 30 years is bone marrow transplantation, and has been used to treat cancer patients with conditions such as leukemia and lymphoma. Another stem-cell therapy called Prochymal, was approved in Canada in 2012 for the treatment of acute graft-vs-host disease in children unresponsive to steroids [4]. Moreover, FDA has already approved five hematopoietic stem-cell products derived from umbilical cord blood, for the treatment of blood and immunological diseases. The number of clinical trials increases exponentially, as researchers try to tap into the potential of this revolutionary technique [5]. According to a study, conducted by the Cedars-Sinai Heart Institute, stem cells taken from younger hearts can be used to reverse the aging process, thus giving “old hearts” the chance to rejuvenate. All treated mice had improved heart functionality, were able to exercise 20 percent longer, regrew hair at a faster rate, and had longer heart cell telomeres (telomeres are compounds found at the ends of chromosomes whose shortening is directly correlated to the aging process) [6]. Moreover, scientists recently proved that stem cell brain injections could reverse aging. Researchers at Albert Einstein College of Medicine gave old mice a new lease on life by transplanting stem cells straight into hormone-producing regions of their brains. This study could open a new perspective in the quest for immortality. While stem cells have long held promise for regenerating specific tissues, the new findings suggest they could be used to override the entire aging process. Research leader Dongsheng Cai explains that the discovery that lays the ground for this work is that the hypothalamus — a section of the brain which secretes key hormones — controls the speed of aging throughout the body. This function is mainly attributed to a tiny group of adult stem cells, which restock the brain with neurons. Studies in animals showed that by replenishing those stem cells it’s possible to slow and even reverse various aspects of aging throughout the body [7]. Another study, conducted at the University of Rochester, found that the loss of stem cells in muscles leads to muscular decline in older mice, something that also happens to humans as they grow old. Therefore, stem cells might hold the key to people maintaining strength despite their muscles’ decline that starts in their 30s. The scientists are now focusing to build upon their research and discover a drug which could help individuals maintain the stem cell pools within their muscles [8]. Injections of stem cells derived from human embryos have been successfully used to treat multiple sclerosis in mice. The study revealed that stem cell-treated mice were able to walk and run around normally while untreated animals continued to be paralyzed. The researchers believe the findings are promising enough to move to clinical trials on patients in the hope of developing effective treatments for this degenerative disease [9]. In the cosmetic industry, stem cell injections are referred to as the ultimate anti-aging treatment. Studies showed that stem cells can replace, regrow, rejuvenate and repair the skin at a cellular level. Moreover, participants who have undergone stem cell infusion to slow down aging report other beneficial effects, such as: relief from aches and pains, dramatic boost in energy level, weight loss to normal level, increase in ability to do physical exercise, etc [10]. The numerous stem cell studies in progress across the globe are only a first step on the long road towards curing degenerative and age-related diseases. Because of their unlimited ability to self-renew and to differentiate, embryonic stem cells remain of great significance for regenerative medicine. There are still challenges to overcome before stem cells can be safely, effectively, and routinely used in the clinical setting. However, their potential benefits hold tremendous promise for an array of new anti-aging therapies and treatments. This article was presented to you by Immortality Foundation. References: Honoki, K. (2017). Preventing aging with stem cell rejuvenation: Feasible or infeasible? World J Stem Cells, 9(1), 1–8. Stem cells. (accessed on 30.10.2017). https://en.wikipedia.org/wiki/Stem_cell Stem Cell Therapy is Future of Anti-Ageing. (accessed on 30.10.2017). http://bangaloremirror.indiatimes.com/others/sci-tech/stem-cell-therapy-is-future-of-anti-ageing/articleshow/57237567.cms A Stem-Cell-Based Drug Gets Approval in Canada. (accessed on 30.10.2017). http://www.nytimes.com/2012/05/18/health/a-stem-cell-based-drug-gets-approval-in-canada.html Reisman, M., & Adams, K. T. (2014). Stem cell therapy: A look at current research, regulations, and remaining hurdles. P & T?: A Peer-Reviewed Journal for Formulary Management, 39(12), 846–57. New Study Reveals Stem Cells from Young Hearts May Help Reverse the Aging Process. (accessed on 30.10.2017). https://futurism.com/new-study-reveals-stem-cells-from-young-hearts-may-help-reverse-the-aging-process Injection of stem cells could reverse ageing: New York scientists. (accessed on 30.10.2017). http://www.theaustralian.com.au/news/health-science/injection-of-stem-cells-could-reverse-ageing-new-york-scientists/news-story/9a3daaa9b47caa9bc0c7ec8daf24dfb9 Study on Mice Shows Stem Cells Key to Maintaining Strength in Old Age. (accessed on 30.10.2017). https://www.stemaid.com/news/study-on-mice-shows-stem-cells-key-to-maintaining-strength-in-old-age.html Human stem cells successfully treat multiple sclerosis in mice. (accessed on 30.10.2017). http://www.independent.co.uk/news/science/human-stem-cells-successfully-treat-multiple-sclerosis-in-mice-9496400.html Stem Cell Therapy for Your Skin. (accessed on 30.10.2017). https://www.neomatrixmedical.com/stem-cell-medicine-in-florida/stem-cell-therapy-for-your-skin

Stem Cell Therapy — Triggering Human Body’s Ability to Heal Itself

The mythical fountain of youth. The tales of it appeared in writings by Herodotus, it was sought by the emperors of ancient China, for its sake alchemists spent years trying to create a philosopher’s stone. Centuries have passed, technological progress inexorably moves forward, but the issue of reversing the age is still hanging at poise. All over the world hundreds of clinics and laboratories spend millions of dollars searching for a way to slow down biological time. Now, in the heyday of blockchain and cryptocurrencies, high-tech startups are also joining the race for eternal youth. Chameleon-like cells According to the World Bank, about 8.7 percent of the world’s population is people over the age of 65. This is about 652 million humans! The are even more discouraging: the ageing population grows at an unprecedented rate and the percentage is projected to reach nearly 17 percent by 2050. forecasts The life expectancy increases as well. But a longer life does not necessarily mean a better life. With age, a person inevitably loses strength, beauty and is prone to serious chronic diseases. With the latter years comes an ever-growing risk factor for degenerative illnesses and the development of malignant tumors, Alzheimer’s disease, or dementia. By defeating ageing, we might overcome many diseases. And vice versa — preventing and curing these illnesses we may finally find longevity. For more than a decade, stem cell therapy has been at the forefront of a struggle against ageing and severe diseases. Stem cells are the “universal” building blocks — progenitors of all cell types in our body. They are able to repeatedly divide and transform into cells of any organ. Simply put, if the body needs to regenerate a tissue, stem cells have the capacity to transform into these required tissue cells. The same happens with bones, muscles, tendons. So, the body repairs itself with no medications or surgeries involved. Stem cell therapy is already being used to treat tendon ruptures and serious diseases like leukemia and lymphoma. If stem cells have such huge potential and have already proved their effectiveness in treating a number of diseases, then what is the problem? Accelerating the progress In fact, there are several problems. Firstly, the high price. For example, American clinics charge nearly $10,000 per treatment. Most patients need not just one therapy session, but an entire round. Sometimes the final cost can reach more than $100,000, not including possible travel expenses or follow-up care. In addition, the availability of stem cell therapies worldwide is much more limited than of other medical treatments. Due to this, there is a growing phenomenon of stem cell tourism. The most popular destinations are the Asian countries of China, Singapore, Malaysia, Japan, and Thailand. Stem cell therapy is relatively new and promising, but still largely underexplored. Hundreds of biotech companies around the world are engaged in research of biotechnologies, including stem cell transplantation. Scientists are actively pursuing studies on the use of cell therapy in restoring vision for the blind, regrowing teeth and treating Alzheimer’s and Parkinson’s diseases. With all these companies involved, there is no surprise that the global market of cell therapy is growing rapidly. According to performed by Transparency Market Research, the industry is expected to reach $270.5 billion by 2025. the analysis Today the struggle against ageing ceases to be just a lot for enthusiasts, donors, and philanthropists but draws the attention of large investors and venture capitalists. The stem cell market already has its high-tech unicorns. These include biotechnology company Sangamo Therapeutics Inc. with a market capitalization of $1.34 billion; Athersys with a cap of $264.88 million, Cellular Biomedicine Group Inc. with a stock price of more than $20. The booming crypto industry also doesn’t stand aloof and makes its own contribution to resolving the global problem. In February 2018, one of the most famous people in the crypto universe — the founder of Ethereum Vitalik Buterin — to the SENS Research Foundation, a charity funding research for treating age-related diseases. donated $2.4 million donated $2.4 million Blockchain companies are not just donating money, some of them plan to facilitate the research of stem cell treatment and expose this innovative therapy to more people. This is what the Japanese blockchain venture is working on. It seeks to extend the healthy life expectancy and make its own contribution to the regenerative medicine — with the help of blockchain and cryptocurrency. Stem Cell Project The project mainly focuses on two directions: making stem cell treatment possible to pay in cryptocurrency and accelerating the research of its usage in treating various illnesses. With many high-tech ventures already involved, and blockchain companies joining the race, our dreams of a long-lasting life have the potential to finally become a reality. Vitalik Buterin and the Stem Cell Project are among those ‘crypto pioneers’ who stand at the frontier and make their own contribution to modern regenerative medicine. And who knows, maybe their activities will help radically change our human lives.

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