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  • Liver Stiffness and Fat Content May Lead to Coronary Heart Disease, Study Suggests

    Liver Stiffness and Fat Content May Lead to Coronary Heart Disease, Study Suggests

    A recent retrospective study has demonstrated the nonlinear positive correlation between liver stiffness and fat content and coronary heart disease (CHD), highlighting a new key indicator for CHD risk.

    Liver health has recently become a focal point of cardiovascular disease research, as it holds a central role in metabolism. Recent literature has indicated a correlation between liver disorders, such as fibrosis and fat accumulation, and cardiovascular risk. Liver stiffness, in turn, has been associated with inflammation, metabolic dysregulation, and endothelial dysfunction.1,2

    “By elucidating the connections between liver health and CHD, this research intends to provide novel biomarkers and tools for early detection and risk stratification, thereby informing targeted prevention and intervention strategies for cardiovascular disease,” wrote Shengnan Li, department of pharmacy, the Affiliated Cardiovascular Hospital of Qingdao University, and colleagues.1

    Investigators collected data from the NHANES database, extracting demographic information, physical examination data, and lab results from 27,493 participants from 2017-2020 and 2021-2023. Participants were excluded if they were missing liver stiffness values (n = 11,093), liver fat values (n = 3), CHD outcome data (n = 2902), educational attainment data (n = 8), or antibody data (n = 799).1

    After filtering for exclusion criteria, investigators included 12,684 participants with a mean age of 48.23 +/- 17.11 years. Among these, 539 were diagnosed with CHD. The association between liver stiffness and CHD was measured via transient elastography. It was categorized into quartiles: Q1 (lowest 25%), Q2 (25-50%), Q3 (50-75%), and Q4 (highest 25%).1

    Investigators found a consistent positive association between liver stiffness and CHD risk; in an unadjusted model, it was not significant in Q2 (odds ratio [OR] 1.244; P = .16) but was in Q3 (OR 1.514; P = .017) and Q4 (OR 2.303; P <.001). Disease development risk rose in Q3 and Q4 compared to Q1, indicating liver stiffness as a risk factor. Subgroup analysis found that the association between liver stiffness and CHD was significantly affected by race and blood parameters (P-interaction <.05).1

    Additionally, the team found a consistently positive association between liver fat and CHD risk. The unadjusted model also did not display significant risk in Q2 (OR 1.181; P = .382), but it did in Q3 (OR 1.689; P = .001) and Q4 (OR 2.336; P <.001). The risk of disease development also rose in Q3 and Q4 compared to Q1. Subgroup analysis showed significant interactions for CHD risk with sex, age, and cholesterol levels (P-interaction < .05).1

    Li and colleagues point out that these results challenge the longstanding belief that CHD is driven solely by traditional risk factors, such as hypertension, smoking, and dyslipidemia. The team advocates for a more integrated approach to risk stratification, which includes liver health markers in addition to standard predictors.1

    “Future research should prioritize longitudinal studies to elucidate causal pathways and assess the long-term impact of liver health interventions on cardiovascular outcomes,” Li and colleagues wrote. “Advances in omics technologies, including metabolomics, proteomics, and genomics, offer exciting opportunities to uncover novel biomarkers and mechanisms linking liver and cardiovascular health.”1

    References
    1. Li, S., Li, X., Xiao, H. et al. The relationship between liver stiffness, fat content measured by liver elastography, and coronary artery disease: a study based on the NHANES database. Sci Rep 15, 30010 (2025). https://doi.org/10.1038/s41598-025-15709-y
    2. Wu Y, Zhao Q. Refining risk assessment for intra-abdominal infections in immunocompromised intensive care unit patients. Eur J Intern Med. 2025;134:138-139. doi:10.1016/j.ejim.2024.12.025

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  • White House launches TikTok account with Trump saying 'I am your voice' – Reuters

    1. White House launches TikTok account with Trump saying ‘I am your voice’  Reuters
    2. Chinese Officials Say They Won’t Sell TikTok’s Algorithm to US  Social Media Today
    3. White House belies need for TikTok shutdown: China Daily editorial  China Daily – Global Edition
    4. The White House is on TikTok now, which is technically banned in the US  TechCrunch
    5. Why China Is Dunking On The Trump Administration’s New TikTok Account  Forbes

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  • These Bananas Might Confirm Google Is Behind a Viral New AI Model

    These Bananas Might Confirm Google Is Behind a Viral New AI Model



    Andy Cross/MediaNews Group/The Denver Post via Getty Images

    • A mysterious new AI image model has been generating buzz for being so good.
    • There is speculation that Google is behind it, and the company may have just confirmed this.
    • It’s bananas!

    Nano Banana.

    Unless you’re deep in the weeds of AI models, those two words probably don’t belong together. But for several days, a mysterious new image model with that very name has been creating buzz among people who have gotten to try it — because it’s simply so good.

    The model has been showing up on LMArena, a benchmarking website that crowdsources user feedback. The site has a feature where you can “battle” two randomly selected models, which is where the model “nano-banana” has been appearing. When it has appeared, people have been remarking on just how good it is.

    There’s just one problem: we don’t know for certain who nano-banana belongs to.

    Enthusiasts have been trying to sleuth its maker and, so far, the most popular answer is Google, partly because the company started teasing something image-related earlier this month.

    Over the past week, posts have been popping up on Reddit and X from users who have been impressed by the model’s ability to generate images and edit them carefully when prompted.

    Business Insider managed to get nano-banana to appear on LMArena, and we found it to be pretty great at bringing our prompts to life, even if it still struggled with spelling the odd word correctly.

    Related stories

    Business Insider tells the innovative stories you want to know

    Business Insider tells the innovative stories you want to know

    Google hasn’t yet laid claim to the model — at least not directly. A Google spokesperson did not respond to Business a request for comment from Business Insider on it. On Tuesday, Logan Kilpatrick, Google’s head of product for AI Studio, posted a banana emoji on X. Naina Raisinghani, a Google DeepMind product manager, also posted a picture similar to Italian artist Maurizio Cattelan’s banana-taped-to-wall piece from 2019.

    The use of the word “nano” could suggest this is a model capable of running locally on a device (Google has in the past referred to its smaller models as “nano”). Coincidentally, Google is holding a big event for its new devices on Wednesday — will Jimmy Fallon reveal all?

    Have something to share? Contact this reporter via email at hlangley@businessinsider.com or Signal at 628-228-1836. Use a personal email address and a non-work device; here’s our guide to sharing information securely.


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  • We’ve been sending animals into space for 7 decades – yet there are still no rules to protect them from harm

    We’ve been sending animals into space for 7 decades – yet there are still no rules to protect them from harm

    This week, Russia is expected to launch its Bion-M No.2 biosatellite from the Baikonur Cosmodrome in Kazakhstan, carrying 75 mice and 1,500 fruit flies.

    While the mission underscores Russia’s ongoing investment in space medicine, it reignites ethical concerns over the treatment of animals in space research.

    Animals have played a pivotal role in space exploration since the 1950s. The former Soviet Union’s launch of the stray dog Laika aboard Sputnik 2 in 1957 marked the first living creature in orbit.

    Laika’s cramped, stressful conditions and eventual death from oxygen deprivation highlighted the harsh realities of early space missions.

    Laika became the first living creature in orbit.
    Wikimedia Commons, CC BY-SA

    The US followed suit in 1961 with Ham, a chimpanzee sent on a suborbital flight to test task performance in space. Ham endured invasive monitoring, electric shocks for incorrect responses and severe dehydration. Although he recovered physically, he showed signs of psychological trauma following the mission.

    As space exploration expands, the absence of legal protections for animals becomes increasingly problematic. International regulations are long overdue to formally recognise the sentience of animals in outer-space law and to safeguard their welfare before, during and after missions.

    Forgotten animal casualties of space exploration

    Despite technological advances, animal casualties persist. In 2019, Israel’s Beresheet spacecraft crash-landed on the Moon with thousands of tardigrades aboard. The fate of these eight-legged microscopic animals, also known as water bears or moss piglets, remains unknown.

    Often, animals used in missions are deemed surplus afterwards, with little legal obligation for their continued care. For example, France’s Félicette, a cat sent into orbit in 1963, was euthanised post-mission for brain study, despite surviving re-entry.

    Unlike military working animals, which undergo transition programmes for civilian life, space animals lack formal exit protocols.

    Records of their fates are scarce, and their legal status remains ambiguous. This gap stems partly from the absence of animal considerations in outer-space law.

    International responses and ethical shifts

    Activist pressure, particularly from People for the Ethical Treatment of Animals (PETA), has led to some reforms.

    In 1996, NASA withdrew from the BION programme and introduced the Principles for the Ethical Care and Use of Animals. These guidelines were prompted by the Belmont Report which was commissioned in 1974 by the US Congress following controversy about unethical practices in research.

    The guidelines emphasise bioethical responsibility, acknowledging that animals “warrant moral concern”.

    NASA committed to stewardship of research animals, encompassing acquisition, care and what happens to them after the space mission. The principles outline three core ethical tenets:

    1. Respect for life: use only appropriate species in minimal numbers necessary for valid results.

    2. Societal benefit: weigh the ethical value of animal use against potential societal gains.

    3. Non-maleficence: minimise pain and distress, recognising that animals may suffer similarly to humans.

    While these principles don’t ban animal use, they promote ethical reflection and accountability.

    Some agencies have followed suit. In 2010, the European Space Agency rejected primate research, opting for simulation technologies to study astronaut health risks.

    NASA briefly considered resuming primate experiments in 2010, but PETA’s lobbying led to the cancellation of proposed research at Brookhaven National Laboratory.

    Nonetheless, NASA continues to use mice in space studies. In 2024, a group of mice was sent to the International Space Station to examine the effects of space on bodily systems.

    Private companies have also faced scrutiny. In 2022, KEKA Aerospace in the Democratic Republic of Congo pledged to stop using animals after a rat named Kavira died aboard its Troposphere 5 rocket.

    Legal gaps in outer-space law

    Despite growing awareness, the legal and ethical frameworks surrounding animals in space remain underdeveloped. The lack of formal protections and transparency continues to raise questions about the ethical and moral cost of scientific progress.

    There are five core outer space treaties, covering issues such as the peaceful use of outer space, the rescue of astronauts, the registration of space objects and liability for damage. But despite the long history of animals participating in space missions, none offers formal protections, focusing solely on human and state interests.

    A common argument is that prioritising animal welfare could hinder scientific progress. While violence against humans is prohibited, harm to animals for food, research, medicine and other purposes remains widely accepted on Earth. Some question whether it would be inconsistent to restrict harm to animals in space, where human casualties are more likely.

    However, two key observations challenge this view.

    First, many countries, including New Zealand, now legally recognise animals as sentient beings, deserving moral and legal consideration. Just as human rights evolved after the second world war, the animal welfare movement has gradually secured protections against cruelty and neglect. Yet, space law remains largely silent on the physical and psychological harm animals endure during missions.

    Second, concerns that animal welfare might overshadow human safety are unfounded. Outer-space law is already flexible enough to ensure human protection takes precedence. The real question is whether space law can evolve to safeguard both human and animal interests without conflict.

    Importantly, the types of harm animals face in space – stress, injury and death – are not fundamentally different from those permitted on Earth in service of human needs. In both contexts, animals are used to advance human survival or ambition. Thus, the perceived inconsistency in protecting animals in space may be less significant than it appears.

    We need a more balanced framework – one that acknowledges animals as sentient participants and ensures their welfare is considered alongside human interests.

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  • Effect of discontinuing antipsychotic medications on the risk of hospitalization in long-term care: a machine learning-based analysis | BMC Medicine

    Effect of discontinuing antipsychotic medications on the risk of hospitalization in long-term care: a machine learning-based analysis | BMC Medicine

    Study design and data

    The current report adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for cohort studies [15].

    This study is a registry-based retrospective cohort study that was conducted as part of the analytical tasks of the I-CARE4OLD project, a European Union (EU) funded program aimed at improving prognostication in older adults with complex chronic conditions through the use of ML methods [16]. The study was approved by the Finnish Institute for Health and Welfare (THL) (permission no. THL/1118/6.02.00/2021). The data sources were the RAI-LTC (Resident Assessment Instrument for Long-Term Care) based comprehensive geriatric assessments of LTCF residents and the Finnish Care Register for Health Care. Trained assessors, usually registered nurses, collected data using the Minimum Data Set (MDS) 2.0 version of the RAI-LTC instrument. All LTCF residents in Finland are regularly assessed with this instrument at least twice per year, as defined by the Elderly Care Act 980/2012. RAI assessments are delivered twice a year to the THL either by the service provider themselves or by their authorized application provider. The national RAI database is maintained by THL, responsible by legislation for keeping social and health records based on the Act on the Institute of Health and Welfare 31.10.2008/668. The validity and reliability of the RAI-LTC instrument has been demonstrated in previous studies [17].

    Data collected over the years 2014 to 2018 were used in the present study. The RAI-LTC instrument collects information on each resident’s demographic, functional, medical, and cognitive status and drug prescriptions. Several scales to measure clinically relevant indicators such as cognition (cognitive performance scale, CPS) [18], self-function (activity of daily living, ADL) [19], behavior (aggressive behavior scale, ABS) [20], or depression (depression rating scale, DRS) [21] are embedded in the instrument.

    Data preprocessing steps and the operational definitions of variables and scales are described in details in the supplementary material (see Additional file 1) [22,23,24,25,26].

    Definition of study groups

    Antipsychotic use was identified from the RAI-LTC section dedicated to drug prescription using the ATC code N05A, excluding N05AN. According to previous estimates from RAI data, the overall prevalence of antipsychotic use among LTCF residents in Finland ranges from 28% to 35% with atypical antipsychotics being the most frequently prescribed agents and risperidone accounting for the majority of prescriptions followed by quetiapine and olanzapine [27]. Residents who were 65 years of age or older were selected for this study. To be included in the study, residents had to have at least four consecutive RAI assessments, each conducted at 6-month intervals. Time period of assessments 1 and 2 was defined as the baseline period. Follow-up period started from the third assessment. Residents were classified in the discontinuing group, if antipsychotic medications were prescribed at the baseline period (assessments 1 and 2) but not at the follow-up period (assessments 3 and 4). Residents were classified in the group of chronic users, if antipsychotic medications were prescribed both at the baseline and follow-up period (assessments 1 to 4). The input variables (or candidate predictors) in the models were collected from the second assessment of the baseline period. For residents with more than one valid group of four RAI assessments, the assessment group was randomly selected. Those residents who died during the study period were excluded from the analyses. Definition of study groups is further described and illustrated in Additional file 1: Fig. S1.

    Definition of study outcome

    The main outcome in this study was hospitalization for any cause. Information on hospitalizations was obtained from the Finnish Care Register for Health Care. The operational definition adopted was any number of hospitalizations within 360 days of the first follow-up assessment (i.e., the third assessment) and it was categorized on a binary scale (yes/no).

    Individual treatment effect models

    A causal ML approach was adopted to assess the effect of antipsychotic discontinuation on the risk of hospitalization. Causal ML models, particularly ITE models, estimate how an intervention would affect outcomes at the individual level. Unlike standard supervised ML models, which predict the risk of an outcome, ITE models aim to estimate the causal effect of a treatment. This makes them well-suited for evaluating pharmacological and non-pharmacological interventions in older adults with complex chronic conditions, as they account for patient heterogeneity and enable personalized effect estimates. The ITE when antipsychotic medication is stopped can be represented by equation:

    $$tau (x)=Eleft[{Y}_{i}left(1right)-{Y}_{i}left(0right)|{X}_{i}=xright]$$

    (1)

    where Yi(1) and Yi(0) are potential outcomes [28] after the medication is discontinued or continued and Xi are the covariates of resident i. This measure (ITE) can be interpreted as the absolute risk reduction (ARR). For example, it can be interpreted that (widehat{tau }<0) indicates that discontinuing antipsychotic medications reduces the risk of hospitalization, while (widehat{tau }>0) implies an increased the risk. Currently, there is no generally accepted standard algorithm for estimating ITE. Therefore, we used several different algorithms (DML, DR-learner, X-learner, and causal forest) and compared their estimates. For training and evaluating causal ML models, the dataset was split into the training/validation set and test set. The split was based on the index day (June 1, 2016), which divided the data set in the ratio of 70% for training/validation (before the index date) and 30% for testing (after the index date). The parameters of the models were searched and the models were trained in the training/validation set. Then, the trained models were evaluated on the test set. The workflow of causal ML model training and evaluation and confounder selection is illustrated in Fig. 1.

    Fig. 1

    Workflow of ML model training and evaluation

    Confounders

    From an overall set of 298 variables available from the data source, a subset of potential confounders was selected using both a data-driven and knowledge-based selection approach. A complete list of processed variables has been included in Additional file 1: Table S1. Based on data-driven approach, candidate confounders were searched through univariate logistic regression models that were trained for predicting hospitalization and exposure. The relevance sorting criterion was the area under curve (AUROC) value for both outcomes. A group of three study researchers (DF, HF, RL), who are experts in the field of clinical geriatrics and clinical pharmacy, reviewed the list of potential confounders and included additional variables that, although not considered relevant based on the logistic regression, were considered potential confounders because they were deemed good proxies for unmeasured factors associated with antipsychotic discontinuation and influencing the probability of being hospitalized. The final list of potential confounders included: age, gender, body mass index (BMI), number of medications, number of comorbidities, cognitive decline (CPS score) [18], functional status (ADLH score) [19], depression (DRS score) [21], presence and severity of behavioral symptoms (ABS score) [20], delirium symptoms, delusions or hallucinations, unsteady gait, acute episode or flare-up of recurrent or chronic problem or monitoring acute medical condition, recent hospital visits or emergency department visits, chemotherapy or end-stage disease, problems with eating and swallowing, any restraints used, physician visits in last 14 days or doctor orders changed or abnormal laboratory tests. Detailed definitions of confounders can be found in Additional file 1: Table S2.

    Model evaluation

    A fundamental problem in evaluating causal inference models is that a given individual can never be observed in both treated and untreated conditions (Eq. 1). Therefore, metrics that calculate the difference to true treatment effect can only work in a simulation where you know both possible outcomes. However, we can accept that if model found heterogeneity in the data, then model-assisted recommendations are better than random treatment assignment. In this study, we used the area under uplift curve (AUUC) [29] and c-for-benefit [30] metrics to verify this property. Both metrics have been increasingly adopted in the literature to evaluate the discriminative ability of ITE models [31]. Furthermore, we analyzed treatment effect distributions for presenting information about what models have learned from data and conducted a set of sensitivity analyses.

    Model interpretation

    For the interpretation ITE models, we used SHAP values (SHapley Additive exPlanations) [32, 33], partial dependence plots (PDP) [34], and surrogate models [35]. PDP plots were calculated for the variables with the highest absolute sum of SHAP values. Our surrogate models were decision trees that were trained to predict the estimations of the trained ITE models when the input were the confounders.

    Software

    All analyses were performed using Python version 3.9.7 and the following libraries: Scikit-learn package [36] version 1.0.2 for all data processing steps, EconML [37] version 0.14.0 for ITE models, and SHAP [32, 33] version 0.40.0 for model interpretation.

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  • Innovative approaches to combat antibiotic resistance: integrating CRISPR/Cas9 and nanoparticles against biofilm-driven infections | BMC Medicine

    Innovative approaches to combat antibiotic resistance: integrating CRISPR/Cas9 and nanoparticles against biofilm-driven infections | BMC Medicine

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  • US Open: Francesca Jones begins qualifiers with win

    US Open: Francesca Jones begins qualifiers with win

    Top seed Francesca Jones took the first step to reaching the US Open main draw by winning her opening qualifier, but Heather Watson was among four Britons to lose on Tuesday.

    British number four Jones beat Slovakia’s Viktoria Hruncakova 6-3 6-4. She will face Ekaterine Gorgodze of Georgia on Wednesday as she looks to reach the US Open main draw for the first time in her career.

    Jones has never received direct entry to a Grand Slam through her ranking and narrowly missed out this time, despite having climbed to a career-high 86th in the world since the US Open cut-off point.

    The 24-year-old served six aces in a match lasted one hour 22 minutes and notched an 87% win rate on her first serves.

    Meanwhile, former British number one Watson was defeated 6-3 3-6 4-6 by Gorgodze as she failed to reach the main draw for a fourth successive year.

    British women’s number seven Harriet Dart edged out Romanian Anca Todoni – who sits 112 places above her in the rankings – 7-5 6-7 (3-7) 7-6 (10-6).

    British men’s number five Jan Choinski lost 7-6 (7-4) 6-4 to Argentina’s Andrea Collarini, but Oliver Crawford beat Alex Bolt of Australia 5-7 6-4 6-4.

    George Loffhagen was beaten 4-6 5-7 by Jason Kubler, while 22-year-old Jack Pinnington Jones lost 6-3 7-6 (8-6) 7-6 (14-12).

    Jodie Burrage began her qualifying campaign with a win on Monday, but British number five Dan Evans was eliminated and will miss out on the main draw of a Grand Slam for the first time since 2018.

    There are six Brits in action on Wednesday in the second round of qualifying:

    • Jodie Burrage v Arantxa Rus

    • Jay Clarke v Arthur Cazaux

    • Billy Harris v Mikhail Kukushkin

    • Harriet Dart v Ayana Akli

    • Oliver Crawford v Garrett Johns

    • Fran Jones v Ekaterine Gorgodze

    Players must win three matches in qualifying to advance to the main singles draws for the tournament, which begins on Sunday.

    Jack Draper, Cameron Norrie and Jacob Fearnley have direct entry into the men’s singles, with Emma Raducanu, Katie Boulter and Sonay Kartal in the women’s.

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  • Pure Wallet Delivers First Mobile Experience for Gas-Free,

    Pure Wallet Delivers First Mobile Experience for Gas-Free,

    Pure Wallet sets a new standard for mobile crypto security, offering ISO 27001-certified, offline transactions with zero gas fees. Its proprietary blockchain enables ultra-fast, self-custodial transfers, while a successful in-app pre-sale and growing global adoption underscore its practical, internet-independent utility.

    Photo Courtesy of Pure Wallet

    HOUSTON, Aug. 19, 2025 (GLOBE NEWSWIRE) —  Pure Wallet, a mobile application built for secure, offline crypto transactions, has become the first to deliver a fully mobile experience that requires no internet connection and incurs zero gas fees. Built to operate without any internet connection or additional hardware, Pure Wallet introduces a mobile-based cold wallet solution that allows users to store and transfer crypto directly from their smartphones.

    Unlike traditional wallets that require constant network connectivity, Pure Wallet uses proprietary offline blockchain technology to execute crypto transfers securely and instantly with transmission speeds under one millisecond and no gas fees. Tokens are stored directly on the user’s device, creating a secure self-custodial experience that doesn’t rely on cloud storage or third-party intermediaries.

    The application has achieved ISO 27001 certification, underscoring its commitment to high-level information security. This makes Pure Wallet one of the few mobile-based cold wallets to meet globally recognized cybersecurity standards.

    “Offline is the most disruptive technology of our time, serving as the missing link between quantum, AI, and global adoption,” said Andrew Cha, the company’s Founder.

    Since launching its pre-sale in February 2025, the Pure Wallet team has onboarded over 2,000 users and raised more than $2 million in early token participation. Uniquely, the pre-sale was conducted directly within the wallet itself, with no online interfaces or exchanges required, a first-of-its-kind achievement in the space.

    Pure Wallet is currently available for download on the Apple App Store, holding a 4.3-star average rating, and has surpassed 1,000 installs globally. Adoption has been strongest in Vietnam, Nigeria, and the United States, where offline functionality and mobile-first access resonate with growing demand for secure, independent digital asset tools.

    Transactions within Pure Wallet are signed and executed completely offline using advanced cryptographic protocols. Assets remain stored on the mobile device, and private keys are never exposed to a network. This architecture eliminates common vulnerabilities associated with online wallets, browser extensions, or cloud-based services.

    The company has also successfully deployed its mainnet, laying the foundation for a broader ecosystem anchored by Pure Chain, a proprietary blockchain designed to support decentralized applications that function without internet connectivity. Planned services include Pure Contract, Pure Certificate, and Pure Ticket, each offering real-world utility in both connected and disconnected environments.

    In addition to its technical foundation, Pure Wallet is designed for accessibility. Unlike hardware wallets that require physical devices and setup procedures, Pure Wallet is a mobile app available to anyone with a smartphone. It removes the barriers of online infrastructure while providing enterprise-grade protection for everyday users.

    The product has gained attention at global blockchain events including Bitcoin 2025 and Blockchain Life 2025, where it was recognized for its fully offline, gas-free transaction model.

    Over the next 12 months, the team plans to continue developing Pure Chain and onboarding developers interested in building decentralized applications that operate in offline or constrained environments. The longer-term goal is to establish Pure Wallet as the gateway to a full ecosystem of offline-capable Web3 tools.

    With growing concerns over gas fees, data privacy, and online threats, Pure Wallet offers a simple and effective alternative: a mobile cold wallet that combines military-grade security with full offline functionality.

    Pure Wallet is currently available for download on the Apple App Store and Google Play. For more information, kindly visit https://purewallet.ai/

    About Pure Wallet

    Pure Wallet is a next-generation mobile application that enables secure, offline storage and transfer of cryptocurrency assets. Powered by proprietary offline blockchain technology, the app allows users to manage digital assets without an internet connection, gas fees, or external hardware.

    Certified under ISO 27001, Pure Wallet combines cold wallet security with mobile convenience, offering ultra-fast transaction speeds and complete self-custody. The app serves as the foundation of a broader decentralized ecosystem under development, including the offline-capable blockchain network, Pure Chain.

    Contact Information

    DaHye Kim
    Marketing Manager
    Pure Wallet LLC
    d-h.kim@purewallet.ai
    https://purewallet.ai/
    Texas, USA

    A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/1807d578-e3df-49dc-acc0-2630a5eb2de3

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  • Andreeva/Medvedev defeat Danilovic/Djokovic in 2025 US Open mixed doubles – US Open Tennis

    1. Andreeva/Medvedev defeat Danilovic/Djokovic in 2025 US Open mixed doubles  US Open Tennis
    2. U.S. Open mixed doubles live updates: New-look format down to semifinals – The Athletic  The New York Times
    3. US Open mixed doubles: Novak Djokovic, Olga Danilovic fall to Andreeva, Medvedev  Tennis World USA
    4. Watch: Novak Djokovic arrives at US Open. The game is on!  MSN
    5. Novak Djokovic Highlights US Open Mixed Doubles Problem Behind Carlos Alcaraz and Others’ Early Exit  EssentiallySports

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  • Collins/Harrison beat Townsend/Shelton in all-American MXD QF at 2025 US Open – US Open Tennis

    1. Collins/Harrison beat Townsend/Shelton in all-American MXD QF at 2025 US Open  US Open Tennis
    2. Danielle Collins’ dog Quincy crashes US Open mixed doubles press conference  Tennis.com
    3. Collins/Harrison Vs Bencic/Zverev Live Streaming, US Open 2025: When, Where To Watch Round Of 16 Match  Outlook India
    4. Danielle Collins Makes Priorities Clear as She Eyes US Open Mixed Doubles’ Biggest Prize  EssentiallySports
    5. Americans Collins/Harrison shock Bencic/Zverev in 2025 US Open Mixed Doubles R1  US Open Tennis

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