Category: 3. Business

London: Cleveland Clinic brought together global leaders in biotechnology, healthcare, academia, government and industry at today’s Life Science Summit to explore the transformative forces shaping the future of therapeutics.

During the international event – held at the Lanesborough Hotel near Cleveland Clinic London – Cleveland Clinic leaders also announced several new or expanded collaborations and shared details about Cleveland Clinic London’s new cancer centre.

The summit highlighted advancements in areas such as AI-driven drug discovery, personalized medicine and cancer treatments, offering attendees unique insights into the science, strategy and societal impact of modern medicine. With a dynamic mix of keynote addresses, scientific presentations and panel discussions, the event was designed to foster collaboration and highlight opportunities to fast-track the translation of research into transformative therapies.

“The Life Science Summit reflects our commitment to advancing medicine across the globe through innovation and collaboration,” said Tom Mihaljevic, M.D., CEO and President of Cleveland Clinic and holder of the Morton L. Mandel CEO Chair. “By bringing together international leaders from across disciplines, we aim to foster meaningful dialogue and accelerate progress in developing treatments that improve the lives of patients around the world.”

Panel discussions focused on topics such as AI, precision medicine, and next-generation therapies, as well as trends in life sciences investment. Highlighted sessions included:

• Leveraging AI in Therapeutics: Use of generative models, automation and real-world data, as well as challenges like data quality, regulation and ethics. The session highlighted the importance of cross-sector collaboration in harnessing AI’s potential in transforming therapeutics.
• Therapeutics in Oncology: Key trends shaping cancer therapeutics, highlighting innovation, translation and clinical impact. Experts from academia, biotech and healthcare discussed breakthroughs, emerging targets and strategies to accelerate new treatments.
• Future of Tailored Therapeutics: A discussion on how genomics, AI and tailored therapeutics are advancing healthcare with precise diagnostics, personalized treatments and improved patient outcomes.
• Investors Forum: Insights into life sciences IPOs and the rising demand for breakthrough therapies and precision medicine.
• Biotech in Britain: A look at innovation, investment and policy fueling the UK’s biotech sector.

The summit also featured a Cleveland Clinic Portfolio Showcase, highlighting cutting-edge therapies and technologies being developed through Cleveland Clinic Innovations.

Other new partnerships and updates were shared during the event and highlighted Cleveland Clinic’s steadfast progress in expanding state-of-the-art clinical care and robust life sciences research in the UK. These included:

• New collaboration with Khosla Ventures: Cleveland Clinic and Khosla Ventures announced a strategic collaboration that connects the international health system with one of Silicon Valley’s leading investors in healthcare and technology. The new relationship combines the organizations’ unique strengths, aiming to reimagine healthcare delivery and create solutions that address some of the most pressing challenges in the field.
• Expanded collaboration with LifeArc: Building on a successful relationship since 2019, Cleveland Clinic and LifeArc, a UK-based self-funded medical research organisation, have identified new areas of mutual interest and will work together to drive innovation in key fields, including the development of new monoclonal antibody-based treatments and the provision of continuous education to clinician scientists. Through this new agreement, the organizations aim to create a robust pipeline for translating laboratory discoveries into life-changing treatments for patients.
• Discussion of cancer services in London with the construction of new cancer centre: As the latest Cleveland Clinic London location, offering one of the most comprehensive cancer programs in the UK Independent market, the new cancer centre will deliver the most advanced treatments. Multidisciplinary cancer care will include surgical oncology, medical oncology and haematology including systemic cancer therapies, such as immunotherapy, chemotherapy, targeted therapies and radiotherapy tailored to each patient. Construction is slated to begin in the fourth quarter of 2025, with completion anticipated by the end of 2027.

As part of a global research enterprise, Cleveland Clinic London has made clinical research an integral component of daily operations, achieving several firsts in the UK private healthcare sector. Currently, 46 investigator-sponsored and commercial studies are open across all major specialties, with over 1,000 patients recruited to clinical trials by the end of 2024, including NHS portfolio multicentre studies. Additionally, Cleveland Clinic London caregivers authored more than 1,100 peer-reviewed papers in 2024.

Building on Cleveland Clinic’s partnership with IBM and the Science and Technology Facilities Council, Cleveland Clinic London is advancing AI-driven research to improve patient outcomes. In a pilot study, a research team is examining how common hospital procedures impact overall health and quality of life. Researchers are using clinical and advanced imaging data provided by Cleveland Clinic London BioResource, a repository which provides patients with the opportunity to consent to enhanced longitudinal data collection and analysis. Researchers aim to develop sophisticated AI models for multi-disease analysis, ultimately enhancing understanding and care for patients.

The Life Science Summit was supported by sponsorships from AstraZeneca, Bank of America, and Flagship Pioneering, as well as Jones Day and LifeArc.

The American hardware industry is undergoing a renaissance. New players are emerging across aerospace, automotive, defense, and beyond—and they must iterate more quickly, and with more complexity, than ever before. Just as software developers needed new tools to move from a waterfall framework to the more flexible agile approach, hardware now needs a different type of collaborative platform—one that can instantly align requirements across systems to build safely, reliably, and quickly. 

That’s why Flow Engineering is poised to have a generational impact.

Whether it’s an electric vehicle, a rocket, or a robot, building complex hardware requires full context throughout the system: changing the thermal parameters for a single component, for example, can have a ripple effect through the rest of the design. But the legacy products used to track these requirements are clunky artifacts of a bygone era and simply don’t measure up. Engineers are reluctant to use them, and they make development cycles, already famously long in hardware, even longer.

Today’s electric vehicles have more in common with computers than internal combustion vehicles. And Flow founder Pari Singh knows that, like software, hardware’s future is agile. His insights are making Flow the default requirements management platform for the next generation of hardware companies.

Built specifically for agile hardware teams, Flow serves as a modern system of record, automatically verifying changes and propagating them downstream. Its magic lies in the collaboration it enables. Flow feels natural and intuitive for systems engineers and their domain-specific colleagues alike, and integrates with a full suite of engineering tools. Teams can break down silos and iterate continuously, working together and with outside partners in real time, all from a single source of truth. As with software development, these shorter feedback loops dramatically reduce time to market—which gives Flow’s customers a critical competitive advantage.

Born and raised in London and obsessed with engineering from a young age, Pari was a 22-year-old Imperial College graduate when he launched a design consultancy for hybrid rocket engines. He built a tool to do in two hours what took others twelve weeks. This led to the innovative idea behind Flow. As we at Sequoia have gotten to know Pari, we’ve found him to be clear-thinking, driven, and resilient. He is also relentless in his pursuit of top talent and building a truly special team—which is fortunate given the company’s rapid growth.

Flow is working with top companies across space, defense, nuclear, aero, and robotics, including Rivian, Joby, Astranis, and Radiant Nuclear. As this next generation of hardware businesses continues to scale, we at Sequoia are pleased to lead the Series A to help Flow accelerate their success. The team is hiring now both in London and at their new HQ in San Francisco.

Through the combined effort of systems engineers and their colleagues, a collection of pumps, valves, and pipes can become a spaceship—and the future of hardware engineering itself will require the same kind of extraordinary collaboration. In this industry-wide effort to build the hardware of tomorrow, we believe Pari and the team at Flow are leading the way.

Check Point Software Technologies Ltd. (NASDAQ: CHKP), a pioneer and global leader of cyber security solutions, today announced its inclusion in Fast

Company’s Next Big Things in Tech 2025 list. This prestigious recognition highlights Check Point’s innovation in securing and protecting public blockchains from emerging cyber threats.

“At Check Point, innovation isn’t just about keeping up — it’s about staying ahead,” said Roi Karo, Chief Strategy Officer at Check Point. “Just as Check Point’s original Firewall-1 secured the early internet, our blockchain solution protects the decentralized future, offering enterprise-grade, compliance-ready security for a safer blockchain ecosystem. We’re proud that Fast Company recognizes our mission to bring real-time threat detection and prevention to the blockchain, ensuring trust, transparency, and safety across the decentralized world.”

Check Point’s inclusion on the list highlights its groundbreaking work in real-time blockchain threat prevention — a first-of-its-kind capability now active on the Cardano mainnet, among others. This innovation marks a fundamental shift from reactive threat detection to proactive, in-chain prevention, protecting users, digital assets, and infrastructure before cyber attacks can occur. Check Point’s solution combines bold thinking with deep technical commitment to adapt enterprise-grade protection to decentralized ecosystems.

Fast Company’s Next Big Things in Tech 2025 honorees represent a diverse array of technologies developed by established companies, startups, and research teams. These innovations are featured for their potential to revolutionize the lives of consumers, businesses, and society overall.

“Next Big Things in Tech is both a snapshot of the most interesting tech of the moment and a crystal ball that predicts the next several years,” said Brendan Vaughan, Editor-in-Chief of Fast Company. “We’re excited to share this list with our readers, and we congratulate the winners for their vision and innovation.”

In addition to being honored on Fast Company’s Next Big Things in Tech 2025 list, Check Point has been recognized as one of the World’s Best Companies of 2025 by TIME and Statista, one of America’s Best Cybersecurity Companies by Newsweek and Statista and included on Fast Company’s World Changing Ideas 2024 list, among other accolades.

Learn more about Check Point’s Blockchain Security solution here.

Follow Check Point on LinkedIn, X (formerly Twitter), Facebook, YouTube and our blog. 

About Check Point Software Technologies Ltd. 

Check Point Software Technologies Ltd. (www.checkpoint.com) is a leading protector of digital trust, utilizing AI-powered cyber security solutions to safeguard over 100,000 organizations globally. Through its Infinity Platform and an open garden ecosystem, Check Point’s prevention-first approach delivers industry-leading security efficacy while reducing risk. Employing a hybrid mesh network architecture with SASE at its core, the Infinity Platform unifies the management of on-premises, cloud, and workspace environments to offer flexibility, simplicity and scale for enterprises and service providers.

Legal Notice Regarding Forward-Looking Statements

This press release contains forward-looking statements. Forward-looking statements generally relate to future events or our future financial or operating performance. Forward-looking statements in this press release include, but are not limited to, statements related to our expectations regarding  our products and solutions and Lakera’s products and solutions, our ability to leverage Lakera’s capabilities and integrate them into Check Point, our ability to deliver end-to-end AI security stack, our foundation of the new Check Point’s Global Center of Excellence for AI Security, and the consummation of the acquisition. Our expectations and beliefs regarding these matters may not materialize, and actual results or events in the future are subject to risks and uncertainties that could cause actual results or events to differ materially from those projected. The forward-looking statements contained in this press release are also subject to other risks and uncertainties, including those more fully described in our filings with the Securities and Exchange Commission, including our Annual Report on Form 20-F filed with the Securities and Exchange Commission on March 17, 2025. The forward-looking statements in this press release are based on information available to Check Point as of the date hereof, and Check Point disclaims any obligation to update any forward-looking statements, except as required by law.

LAS VEGAS, Oct. 14, 2025 /PRNewswire/ — Collins Aerospace, an RTX (NYSE: RTX) business, has announced its Venue™ smart monitor integrated with Airshow^® HD will enter service on a Dassault Falcon 7X in November. The smart monitor with Airshow HD is an all-in-one entertainment solution for business aviation, providing 4K resolutions, flight information and streaming entertainment to the cabin. Dassault Falcon Jet’s service center in Little Rock, Arkansas will install two smart monitors with Airshow HD into the cabin bulkhead.

The Venue smart monitor streamlines Airshow HD cabin entertainment into one consolidated unit, providing customers an elegant in-flight entertainment (IFE) entry point. With the ability to be installed on very light jets to heavy aircraft, the system is designed to eliminate excess hardware, reducing aircraft power consumption and simplifying on-going maintenance.

“The activation of the first Venue smart monitor with Airshow HD marks a significant shift in business aviation in-flight entertainment, bringing pristine resolutions, streaming content and an enhanced user interface to more jets than ever before,” said Craig Bries, vice president and general manager of Commercial Avionics at Collins Aerospace. “The standalone system is flexible and simple to install, enabling phased upgrade paths to meet a variety of entertainment needs, cabin layouts and budgets.”

The solution includes the Airshow HD mobile application, available for download on user mobile devices. Designed with familiar iconography and intuitive functionality, the application extends Airshow HD access to passenger fingertips anywhere within the cabin.

The Venue smart monitor with Airshow HD is available in five sizes with touchscreen and ultra-high-definition options available. Demonstrations of the system are on display at RTX booth #2245 at the 2025 National Business Aviation Association (NBAA) convention and exhibition in Las Vegas.

About Collins Aerospace 
Collins Aerospace, an RTX business, is a leader in integrated and intelligent solutions for the global aerospace and defense industry. Our 80,000 employees are dedicated to delivering future-focused technologies to advance sustainable and connected aviation, passenger safety and comfort, mission success, space exploration, and more.

About RTX
RTX is the world’s largest aerospace and defense company. With more than 185,000 global employees, we push the limits of technology and science to redefine how we connect and protect our world. Through industry-leading businesses – Collins Aerospace, Pratt & Whitney, and Raytheon – we are advancing aviation, engineering integrated defense systems for operational success, and developing next-generation technology solutions and manufacturing to help global customers address their most critical challenges. The company, with 2024 sales of more than $80 billion, is headquartered in Arlington, Virginia.

For questions or to schedule an interview, please contact [email protected]

SOURCE RTX

Diagnostic delay remains a critical barrier impacting the lives of those living with PCD. We demonstrated the feasibility of integrating confirmed patient records from disease registries into large health insurance databases with national-level coverage, enabling the development of ML systems to identify individuals at high risk for PCD (as an example of a screening tool for rare disease). This effort was made possible through a multidisciplinary effort led by patient advocates, researchers, and clinicians to develop a detailed knowledge engineered representation of the insurance claims profile of patients with PCD. While unvalidated, this work may serve as the basis for future ML efforts in rare disease detection.

We developed a screening cohort of cases in the claims database with diagnostic, drug, and procedural codes associated with PCD (Appendix 1). Analysis of this screening cohort identified clinical features of this cohort that clinicians may consider (Table 2). For example, clinicians who encounter patients with situs anomalies, including those with isolated congenital heart malformations, should screen for classic PCD-related symptoms, including year-round wet cough and year-round nasal congestion since infancy. When these issues are present, evaluation for cystic fibrosis (CF) may be necessary, but a diagnosis of PCD seems more likely to explain these symptoms, especially when CF newborn screening is negative.

Although we did not validate our model on an independent dataset or recall patients for diagnostic testing, we did evaluate its performance using several key metrics. We assessed positive predictive value and sensitivity within a fivefold cross-validation framework to measure how well the random forest model generalizes to unseen data. Notably, the inclusion of patients with Q34.8 and EM codes led to improved model performance (Fig. 3C), suggesting that expanding the positive case pool can help mitigate the challenges of imbalanced datasets. Important to note is that this likely led to the inclusion of false positives in the training set, and therefore is not a valuable strategy for building robust training sets in future studies. This highlights the critical need for gold-standard confirmed cohorts, as readily available claims data, while abundant, introduce significant diagnostic noise that can undermine model accuracy. This can be achieved through collaborations with patient organizations, medical centers, or potentially through AI-driven approaches, such as generating synthetic positive cases. Unexpectedly, ADASYN augmentation did not improve model performance in our tests, regardless of the threshold or training set composition, possibly due to the small size of the training data and complexity of the feature space (Fig. 3B, D).

Another way to assess model performance is to compare the number of patients predicted to have PCD by the model with the expected number of cases in the screening cohort. The model classified 7705 patients as positive, which aligns with 8667 patients with PCD we anticipated based on our initial calculations (Fig. 2). This result is promising; however, we cannot determine the true positive predictive value through actual PCD diagnostic testing since these patients remain deidentified.

We reviewed the relative importance of clinical features in the final model (Fig. 3D). Initially, features were treated without predefined weighting. Features selected more frequently and contributing more to impurity reduction were assigned higher importance scores. Reducing impurity improves the model’s ability to correctly classify patients based on their PCD likelihood. These top 10 features suggest that patients investigated for suppurative respiratory disease or using chronic therapies for this may have unrecognized PCD and should undergo PCD diagnostic testing (Fig. 4) [8]. The top feature was situs inversus, which led to an 8.76% reduction in impurity, compared to the average reduction of 1.56% across all features. This result is unsurprising, as slightly less than 50% of patients with PCD have situs inversus totalis, and an additional 12% have more complex laterality defects with situs ambiguus [19]. The emergence of hypertonic saline prescription as a prominent feature, despite its limited demonstrated efficacy in confirmed PCD cohorts, suggests its role as a surrogate marker for generalized chronic airway disease management in patients before or during their diagnostic journey. This highlights how claims-based features can capture aspects of clinical suspicion or treatment patterns rather than solely definitive interventions for a specific diagnosis. Finally, several features that we might expect to see contributing to positive predictions were not among the top, such as airway clearance or inhaled hypertonic saline, which we speculate may have resulted from manual grouping of codes to form the broad feature categories. For example, we may have instead grouped “inhaled hypertonic saline” with “DRUG-mucolytics” as a broad feature category. Modern deep learning models have the advantage of automatically inferring optimal feature sets and thus would avoid this issue [20].

Limitations

There are several important limitations to this approach. First is the small size of our training set expressed as a set of positive cases linked from the PCDFR. Given the small size of our training dataset (82 pediatric patients) relative to the over 55 genes associated with PCD and its phenotypic heterogeneity, it is possible that certain rare PCD subtypes, such as those with MCIDAS, CCNO, or FOXJ1 mutations and hydrocephalus as a predominant feature, are underrepresented or entirely absent, potentially impacting the generalizability of our findings. In addition, we utilized hashing technology to identify confirmed cases from the PCDFR in the claims database, which increases the overall accuracy of identifying patients but also incurs an increased risk of ‘collisions’ where separate patients are incorrectly conflated into a single patient record. We also assumed that the background cohort in the claims database was PCD-negative. Further, in accordance with patient privacy protections, we were unable to reidentify and confirm the presence or absence of a PCD diagnosis in the Q34.8 + EM sub-cohort of the positive class. This lack of individual-level confirmation introduces a limitation, as it is possible that EM was conducted due to suggestive history but ultimately yielded negative results, a scenario not captured in our population-level data.

The claims database on which we developed the training and screening cohorts was a unified, national-scale insurance claim database that provided widespread coverage to include many relevant populations, but did not capture neonatal populations. Given that situs inversus totalis with neonatal respiratory distress is sensitive and specific for PCD, future machine-learning methods should aim to include neonates in the study population. Medicare patients are not represented in the claims database. The inclusion of these populations may improve model performance in a general population [21]. There are also limitations inherent to the use of claims data. Notably, the presence of a procedure code does not guarantee a specific outcome or result, and the reporting of a drug code does not confirm if the prescription was actually filled and adhered to by the patient. Furthermore, claims data may lack granular clinical details, temporal information beyond service dates, and insights into patient behavior or lifestyle factors that could influence health outcomes.

This work serves as a foundational methodology, designed with a lightweight implementation to ensure it operates efficiently on a small-scale analysis platform. We used a tabulated approach that resulted in a reduced set of features that summarized patients’ clinical experiences in any given year and then used only the maximum feature value across all available years as the final feature used in the analysis.

Future directions

We demonstrated the feasibility of ML methods for patient screening for PCD based on national-level insurance claim data in the absence of an ICD code [22]. While the approach used closed claims data and manual feature categorization for a random forest model, future ML models could leverage more powerful algorithms, trained on hundreds of features, including time-series data, to further improve classification accuracy. Future efforts could explore the use of national electronic medical record (EMR) data to train neural networks, as this data more accurately reflects the clinical environments in which such screening tools will be applied and no longer require the curation of features by a clinical audience, which inherently presents challenges with selection of features to include or exclude, and can instead consider the totality of data [23, 24]. For example, we did not include pulmonary nontuberculous mycobacterial (NTM) infections (ICD-10-CM: A31.0) in the list of features for machine learning (Table 1). This was an unintentional omission that could be corrected in future work since isolated pulmonary nontuberculous mycobacterial (PNTM) infections are associated with PCD. An EMR-based approach would allow for the inclusion and automatic weighting of a much broader range of clinical variables, including those like asthma diagnoses, allowing the model to discern patterns without a priori human exclusion based on potentially outdated or evolving clinical paradigms. We believe this will be crucial for the development of even more robust and adaptable screening tools.

The key challenge when developing ML-based tools for rare diseases is the relatively small number of available patients. Patient-led organizations are making rapid strides towards the development and utilization of research-ready, rare disease patient data for natural history and clinical studies. The PCDF is one such example, establishing a clinical registry in 2020 to collect rigorous and detailed diagnostic and phenotypic data on individuals with genetically confirmed PCD through the PCDF Clinical and Research Centers Network, and expanding the PCDFR from approximately 150 patient participants at the time of linkage and analysis in this study, to now over 600 participants from 36 North American specialty centers accredited in diagnosis and management of patients with PCD. These efforts are providing crucial infrastructure to drive research partnerships and will be instrumental in the pursuit of improved screening, diagnosis, and care for the PCD community.

As patient organizations and their partners continue to develop comprehensive registries and datasets, there is a profound opportunity to scale ML-based approaches for screening many of the estimated 300 million people worldwide living with rare diseases. Once validated, these tools could be deployed in diverse clinical settings, including in international communities with significant disparities in access to diagnosis and care [25], enabling rapid identification of patients for referral and significantly reducing the time from first clinic visit to diagnostic testing. ML has the potential to transform the diagnostic landscape, bringing timely and accurate diagnoses to those who have long faced a complex diagnostic journey.