Uncategorized - Relatix Health

May 13, 2026

AI chatbots are giving out people’s real phone numbers

People report that their personal contact info was surfaced by Google AI—and there’s apparently no easy way to prevent it.

A Redditor recently wrote that he was “desperate for help”: for about a month, he said, his phone had been inundated by calls from “strangers” who were “looking for a lawyer, a product designer, a locksmith.” Callers were apparently misdirected by Google’s generative AI.

In March, a software developer in Israel was contacted on WhatsApp after Google’s chatbot Gemini provided incorrect customer service instructions that included his number.

And in April, a PhD candidate at the University of Washington was messing around on Gemini and got it to cough up her colleague’s personal cell phone number.

AI researchers and online privacy experts have long warned of the myriad dangers generative AI poses for personal privacy. These cases give us yet another scenario to worry about: generative AI exposing people’s real phone numbers. (The Redditor did not respond to multiple requests for comment and we could not independently verify his story.)

Experts say that these privacy lapses are most likely due to personally identifiable information (PII) being used in training data, though it’s hard to understand the exact mechanism causing real phone numbers to show up in the AI-generated responses. But no matter the reason, the result is not fun for people on the receiving end—and, even more worryingly, there appears to be little that anyone can do to stop it.

A 400% increase in AI-related privacy requests

It’s impossible to know how often people’s phone numbers are exposed by AI chatbots, but experts say they believe that it is happening far more than is reported publicly.

DeleteMe, a company that helps customers remove their personal information from the internet, says customer queries about generative AI have increased by 400%—up to a few thousand—in the last seven months. These queries “specifically reference ChatGPT, Claude, Gemini … or other generative AI tools,” says Rob Shavell, the company’s cofounder and CEO. Specifically, 55% of these concerns about generative AI reference ChatGPT, 20% reference Gemini, 15% Claude, and 10% other AI tools, Shavell says. (MIT Technology Review has a business subscription to DeleteMe.)

Shavell says customer complaints about personal information being surfaced by LLMs usually take two forms: Either “a customer asks a chatbot something innocuous about themselves and gets back accurate home addresses, phone numbers, family members’ names, or employer details.” Alternatively, a customer may be confronted with and report the exposure of someone else’s personal data, when “the chatbot generates plausible-but-wrong contact information.”

This aligns with what happened to Daniel Abraham, a 28-year-old software engineer in Israel. In mid-March, he says, a stranger sent him a “weird WhatsApp message from an unknown number” asking for help with his account in PayBox, an Israeli payment app.

“I thought it was a spam message,” he wrote to MIT Technology Review in an email—“someone who was trying to troll me.”

But when he asked the stranger how they had found his number, they sent him a screenshot of Gemini’s instructions to contact PayBox customer service via WhatsApp—giving his personal number. Abraham does not work for PayBox, and PayBox does not have a WhatsApp customer service number, Elad Gabay, a customer service representative for the company, confirmed.

Later, Abraham asked Gemini how to contact PayBox, and it generated another person’s WhatsApp number. When I recently asked, Gemini again responded with an Israeli phone number—it belonged not to PayBox, but to a separate credit card company that works with PayBox.

Screenshot of the second part of a Google Gemini conversation. Gemini provides an incorrect phone number for PayBox. — Screenshot: Google Gemini provides *MIT Technology Review* with the incorrect number for PayBox.

Abraham’s exchange with the stranger ended quickly, but he said he was concerned about how other potential exchanges could quickly turn sour, including “harassment or other bad interactions.” “What if I asked for money in order to ‘solve’ that [customer service] issue?” he said.

To try to figure out how this happened, Abraham ran a regular Google search on his phone number, and he found that it had been shared online once, back in 2015, on a local site similar to Quora. Though he’s not sure who posted it there, it may explain how it ended up being reproduced by Gemini over a decade later.

Chatbots like Gemini, Open AI’s ChatGPT, and Anthropic’s Claude are built on LLMs that are trained on huge amounts of data scraped from across the web. This inevitably includes hundreds of millions of instances of PII. As we reported last summer, for example, the large popular open-source data set DataComp CommonPool, which has been used to train image-generation models, included copies of résumés, driver’s licenses, and credit cards.

The likelihood of PII appearing in AI training data is only increasing as public data “runs out” and AI companies look for new sources of high-quality training data. This includes information from data brokers and people-search websites. According to the California data broker registry, for instance, 31 of 578 registered data brokers operating in the state self-reported that they had “shared or sold consumers’ data to a developer of a GenAI system or model in the past year.”

Furthermore, models are known to memorize and reproduce data verbatim from training data sets—and recent research suggests that it is not just frequently appearing data that is most likely to be memorized.

Imperfect Measures

It’s standard practice now to build guardrails into an LLM’s design to constrain certain outputs, ranging from content filters meant to identify and prevent chatbots from releasing PII to Anthropic’s instructions to Claude to choose responses that contain “the least personal, private, or confidential information belonging to others.”

But as a pair of University of Washington PhD students researching privacy and technology saw firsthand recently, these safeguards don’t always work.

“One day, I was just playing around on Gemini, and I searched for Yael Eiger, my friend and collaborator,” Meira Gilbert says. She typed in “Yael Eiger contact info,” and after Gemini provided an overview of Eiger’s research, which Gilbert had expected, Gemini also returned her friend’s personal phone number. “It was shocking,” Gilbert says.

When she saw the Gemini result, Eiger remembered that she had, in fact, shared her phone number online in the previous year, for a technology workshop. But she had not expected it to be so visible to everyone on the internet.

Have you had your PII revealed by generative AI? Reach the reporter on Signal at eileenguo.15 or tips@technologyreview.com.

“Having your information be … accessible to one audience, and then Gemini making it accessible to anyone” feels completely different, Eiger says—especially when she found that the information was buried in a normal Google search.

“It was severely downgraded,” Gilbert confirms. “I never would have found it if I was just looking through Google results.” (I tried the same prompt in Gemini earlier this month, and after an initial denial, the tool also gave me Eiger’s number.)

After this experience, Eiger, Gilbert, and another UW PhD student, Anna-Maria Gueorguieva, decided to test ChatGPT to see what it would surface about a professor.

At first, OpenAI’s guardrails kicked in, and ChatGPT responded that the information was unavailable. But in the same response, the chatbot suggested, “if you want to go deeper, I can still try a more ‘investigative-style’ approach.” Their inquiry just had to help “narrow things down,” ChatGPT said, by providing “a neighborhood guess” for where the professor might live, or “a possible co-owner name” for the professor’s home. ChatGPT continued: “That’s usually the only way to surface newer or intentionally less-visible property records.”

The students provided this information, leading ChatGPT to produce the professor’s home address, home purchase price, and spouse’s name from city property records.

(Taya Christianson, an OpenAI representative, said she was not able to comment on what happened in this case without seeing screenshots or knowing which model the students had tested, though we pointed out that many users may not know which model they were using in the ChatGPT interface. In response to questions about the exposure of PII, she sent links to documents describing how OpenAI handles privacy, including filtering out PII, and other tools.)

This reveals one of the fundamental problems with chatbots, says DeleteMe’s Shavell. AI companies “can build in guardrails, but [their chatbots] are also designed to be effective and to answer customer questions.”

The exposure issue is not limited to Gemini or ChatGPT. Last year, Futurism found that if you prompted xAI’s chatbot Grok with “[name] address,” in almost all cases, it provided not only residential addresses but also often the person’s phone numbers, work addresses, and addresses for people with similar-sounding names. (xAI did not respond to a request for comment.)

No clear answers

There aren’t straightforward solutions to this problem—there’s no easy way to either verify whether someone’s personal information is in a given model’s training set or to compel the models to remove PII.

Ideally, individual consumers should be able to request that their PII be removed, says Jennifer King, the privacy and data fellow at Stanford University Institute for Human-Centered Artificial Intelligence. But this is typically interpreted to apply only to the data that people have directly given to companies—like when they interact with a chatbot, King explains.

“I don’t know if Google even has the infrastructure … to say to me, ‘Yes, we have your data in our training data, we can summarize what we know about you, and then we can delete or correct things that are wrong or things that you don’t want in there,’” she says.

Existing privacy legislation, like the California Consumer Privacy Act or Europe’s GDPR, does not cover the “publicly available” information that has already been scraped and used to train LLMs, especially since much of this is anonymized (though multiple studies have also shown how easy it is to infer identities and PII from anonymized and pseudonymous data).

As to “whether they [AI companies] have ever systematically tried to go back through data that had already been collected from the public internet and minimized that stuff?” King adds. “No idea.”

The next best solution would be that the companies are “taking out everybody’s phone numbers or all data that resembles [phone numbers],” King says, but “nobody’s been willing to say” they’re doing that.

Hugging Face, a platform that hosts open-source data sets and AI models, has a tool that allows people to search how often a piece of data—like their phone number—has appeared in open-source LLM training data sets, but this does not necessarily represent what has been used to train closed LLMs that power popular chatbots like Claude, ChatGPT, and Gemini. (Eiger’s number, for example, did not show up in Hugging Face’s tool.)

Alex Joseph, the head of communications for Gemini apps and Google Labs, did not respond to specific questions, but he said that “the team” is “looking into” the particular cases flagged by MIT Technology Review. He also provided a link to a support document that describes how users can “object to the processing of your personal data” or “ask for inaccurate personal data in Gemini Apps’ responses to be corrected.” The page notes that the company’s response will depend on the privacy laws of your jurisdiction.

OpenAI has a privacy portal that allows people to submit requests to remove their personal information from ChatGPT responses, but notes that it balances privacy requests with the public interest and “may decline a request if we have a lawful reason for doing so.”

Anthropic describes how it uses personal data in model training, but it does not have a clear way for people to request its removal. The company did not respond to a request for comment.

The best option for anyone who wants to protect their private data right now is to “start upstream: get personal data off the public web before it ends up in the next scrape,” says Shavell. Since the start of the year, for instance, California has offered its residents a web portal to request that data brokers delete their information. Still, this doesn’t guarantee that your data hasn’t already been used for training—and will therefore not appear in a chatbot’s response.

The Redditor who received incessant calls posted that he had “submitted an official Legal Removal/Privacy Request to Google, asking them to urgently blacklist my number from their LLM outputs,” but had not yet received a response. He also wrote last month that “the harassment continues daily.”

Abraham, the Israeli software developer, says he contacted Google’s customer service on March 17, the day after his phone number was exposed. He says he did not receive a response until May 4, and it simply asked for documentation that he had already provided.

Meanwhile, inspired by her own exposure on Gemini, Eiger, along with Gilbert and Gueorguieva, is designing a research project to further study what personal information is being surfaced by various AI chatbots—and what they may know, even if they’re not telling us.

Some of that information may “technically be public,” says Gilbert, but chatbots may be altering “the amount of effort you would put into finding” it. Now instead of searching through 10 pages of Google search results, or paying for the information from a data broker site, “does generative AI just lower the barrier to entry to target people?”

This piece has been updated to clarify OpenAI’s response.

May 13, 2026

Rate of New Late-Stage Breast Cancers Increases

The incidence of stage IV breast cancer increased significantly overall, across ages, and for both sexes from 2010 through 2021, according to research from a Dana Farber-led team. The percentage of patients with stage IV breast cancers, versus those with stages I to III diagnoses also increased.

Notably, this increase was seen for all tumor subtypes in both sexes.

The researchers write, “These findings suggest that efforts are needed to determine factors contributing to these increases and to identify breast cancer before patients present with de novo stage IV disease.”

The study appears this week (May 12 issue) in JAMA Network Open. The senior author is José P. Leone, MD, department of medical oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston.

In their analysis of data from over 700,000 U.S. patients, the incidence of stage IV breast cancer increased significantly by 1.2% per year, and the percentage of people with stage IV also increased significantly. Stage IV incidence increased widely across all ages, races, sexes, and tumor subtypes. Still, survival improved significantly from 2010 through 2021.

Stage IV incidence increased across all tumor subtypes in both sexes. In women, those subtypes include hormone receptor (HR)–positive/ERBB2-negative, HR-positive/ERBB2-positive, HR-negative/ERBB2-positive, and triple-negative disease.

Trends in the incidence of de novo stage IV breast cancer “remain underreported,” these authors write. A previous study evaluating incidence of distant disease in the U.S. before 2010 showed a statistically significant increase in incidence for younger patients and a statistically significant decrease in older patients. But, this current study’s authors said, a meta-analysis reported a decreasing percentage of stage IV presentation over time.

Breast cancer is the second most common cancer in women, behind skin cancer. It is the most common cancer diagnosed in females worldwide and an estimated 30% of patients develop metastases. The American Cancer Society estimates 42,140 U.S. women will die from breast cancer in 2026.

The incidence of breast cancer in younger women, in particular, has been rising. In August 2025, the CDC reported that: “Most breast cancers occur in older women, but rates have been increasing slowly among women younger than 45 years in all racial and ethnic groups.” The agency added that survival from breast cancer is improving “among women in most racial and ethnic groups.”

Breast cancer in men remains rare, but the rate is increasing also.

This population-based cohort study used data from the Surveillance, Epidemiology, and End Results (SEER) program to identify patients diagnosed with de novo invasive breast cancer between January 1, 2010, and December 31, 2021. Data analyses were conducted from January 2024 to June 2025.

Of 761,471 breast cancer diagnoses, 43,934 (5.8%) were stage IV. Stage IV incidence increased from 9.5 cases per 100,000 females in 2010 to 11.2 cases per 100,000 females in 2021. The incidence of stages I to III disease also increased, from 163 cases per 100,000 females in 2010 to 177.4 cases per 100,000 females in 2021.

Among males, there was also a statistically significant increase in stage IV incidence.

The researchers noted that, “Although overall survival improved, research is warranted to determine factors contributing to increased incidence, including potential changes in natural history of breast cancer, disease screening, and incidence and mortality of other conditions.”

The post Rate of New Late-Stage Breast Cancers Increases appeared first on Inside Precision Medicine.

May 13, 2026

<![CDATA[Rajesh Tampi, MD, previews APA talks on geriatric psychiatry.]]>

May 13, 2026

Why AI Alone Isn’t Enough for Oligonucleotide Discovery

AI is reshaping drug discovery, and nucleic acid–based medicines, including mRNAs, gene therapy, and oligonucleotide therapeutics, are no exception. By optimizing sequences and chemical modifications for experimental testing, AI accelerates discovery timelines, which is particularly critical for oligo therapeutics, a modality central to the n=1 rare diseases, which afflict mostly young patients for whom there is additional urgency.

However, a familiar caveat remains: AI is only as powerful as the data from which it learns. How can we provide enough high-quality input data to fuel this engine and design next-generation precision medicine?

A typical workflow for developing an AI-powered oligo predictive model begins with collecting experimental outcomes of oligo sequences, with each sequence annotated using a defined set of features. This data is then used to train AI models that identify patterns associated with improved activity and safety.

However, as is often the case with pioneering technologies such as oligonucleotides, the scarcity of data is a major problem. To overcome this limitation, scientists trawl public resources such as publications and patents to extract this data. ASOptimizer, OligoAI, and eSkip-Finder are examples of newer oligo-predicting AI models that are trained using publicly available data.

While these models are advancing in the right direction, relying primarily on this data comes with several disadvantages, such as:

inconsistent experimental conditions between the datasets,
limited diversity in sequences and chemistries,
lack of negative data, and
insufficient coverage of critical information such as toxicity and off-target effects.

Furthermore, since data sourcing and annotating often require the use of automated, AI-powered tools, there is a risk of mislabeling and misinterpretation. As such, correlation statistics between predicted and experimental values for these models are not too high, generally hovering between 0.4 and 0.7.^{1,2, 3}

Building the data foundation for AI drug discovery

The most valuable training data is:

designed to span broad chemical space and probe critical safety features,
produced under controlled conditions,
consistently processed and annotated, and
generated in a controlled environment, ideally internally.

Large-scale screening campaigns are essential in that context as they provide the dense, reliable, datasets required to train AI models and extract meaningful insights for sequence and chemistry prediction.

Brett Monia, CEO of Ionis Pharmaceuticals, describes this reality as “hard, brutal screening–screening a lot of oligonucleotides with different decorations, different amounts of chemistries, different sequences. We have plenty of (design) rules, but we still don’t have enough.”⁴

One way to address this challenge is through intentional screening design: deliberately varying sequence motifs and positional chemistries within screening libraries to systematically explore chemical landscapes and expand the empirical foundation on which both rules and AI models are built.

With the advent of faster and more affordable transcriptomic technologies, high-throughput RNA-seq can now be incorporated into oligonucleotide screening workflows. This method enables the systematic detection of off-target effects, including those that arise through mechanisms beyond straightforward.^5,6

While these approaches generate large and complex datasets, they represent a critical investment—one that lays the foundation for a faster, more efficient, and ultimately more cost‑effective future of oligo discovery.

Digital infrastructure, engineering AI-ready data at scale

While generating large datasets may be hard and brutal, managing, curating, and analyzing them doesn’t need to be. For data to be truly reliable and trustworthy, quality must be engineered from the start. Important aspects to consider include:

A single source of truth–a centralized FAIR data repository, where all data is systematically stored and governed for controlled access and use;

Comprehensive metadata capture, including protocols, batch numbers, and reagent references to ensure results can be interpreted correctly and are not driven by experimental artifacts;
Automated quality control and data analysis of large screens for large‑scale screens, ensuring consistent, efficient, and reproducible data processing; and
Consistent ontology and nomenclature for oligo sequences and their chemistries, as exemplified by Roche’s open-source tool (HelmShaker) for translating molecules into HELM notation.

In practice, these principles are implemented through integrated digital infrastructures that combine molecular registration systems with automated analytics across diverse experimental modalities such as high‑throughput screening, next‑generation sequencing, mass spectrometry, and chromatography.

Such approaches are increasingly used across the pharmaceutical and biotechnology sectors to manage oligonucleotide ADME, process development, and screening data, thus helping teams maintain data integrity and continuity throughout the oligo discovery and development lifecycle.

AI promises to redefine what is possible in oligo discovery, and the field is already beginning to see its impact. But AI alone is not the breakthrough—data is. Only large, high‑quality experimental datasets, generated intentionally and prospectively, can unlock AI’s full predictive power.

Organizations that invest early in both systematic data generation and robust data infrastructure will be best positioned to lead the next wave of oligonucleotide discovery. This shift is especially urgent for n = 1 rare diseases, where speed, precision, and learning from every experiment can make the difference between possibility and progress.

Ming Wang, PhD, is scientific business manager at Genedata.

References:

¹Hwang, G., Kwon, M., Seo, D., Kim, DH., Lee, D., Lee, K., Kim, E., Kang, M., Ryu, JH., ASOptimizer: Optimizing antisense oligonucleotides through deep learning for IDO1 gene regulation. Mol Ther Nucleic Acids. 2024 Apr 6;35(2):102186. doi: 10.1016/j.omtn.2024.102186

²Chiba, S., Lim, KRQ., Sheri, N., Anwar, S., Erkut, E., Shah, MNA., Aslesh, T., Woo, S., Sheikh, O., Maruyama, R., Takano, H., Kunitake, K., Duddy, W., Okuno, Y., Aoki, Y., Yokota, T. eSkip-Finder: a machine learning-based web application and database to identify the optimal sequences of antisense oligonucleotides for exon skipping. Nucleic Acids Res. 2021 Jul 2;49(W1):W193-W198. doi: 10.1093/nar/gkab442

³Hill, B., Jaques, M.R., Nair, RR., Whiffin, N., Wood, MJA., Sanders, SJ., Oliver, PL., Hill, AC., Rinaldi, C. Accurately modelling RNase H-mediated antisense oligonucleotide efficacy. bioRxiv. 2025 Oct 30. https://doi.org/10.1101/2025.10.29.685292

⁴Accelerating Oligonucleotide Therapeutics. Evotec eBook.

⁵Pekker, D., Kuntz, S., McArthur, M., Nicholson-Shaw, T., Yanke, S., Mukhopadhyay, S. A Dose-Response Model for Accurate Detection and Quantification of Transcriptome-Wide Gene Knockdown for Oligonucleotide-Based Medicines. bioRxiv. 2024 May 29. https://www.biorxiv.org/content/10.1101/2024.05.28.596270v1.full.pdf

⁶In-silico siRNA Off-Target Predictions: What Should We Be Looking For? OTS Oligonucleotide Therapeutics Society, Webinar, 2024 May 2

The post Why AI Alone Isn’t Enough for Oligonucleotide Discovery appeared first on GEN – Genetic Engineering and Biotechnology News.

May 13, 2026

Usage of the Tablet-Based “Keep On Keep Up” Digital Program and Resulting Changes in Physical Capacity and Real-World Walking in Community-Dwelling Older Adults: Process Evaluation

Background: “Keep On Keep Up” (KOKU) is a tablet-based digital program based on the well-validated Otago and Fitness and Mobility Exercise programs for older adults to decrease the risk of falling. Objective: This substudy involved a process evaluation in order to analyze the usage patterns of the KOKU digital program, specifically training frequency, volume, and intensity among older adults over a 3-month self-managed training period. Pre-post changes in physical capacity and real-world walking were examined. Methods: This study is a nested cohort study within the three-armed randomized controlled SMART-AGE trial conducted in Germany (German Clinical Trials Register ID: DRKS00034316). Participants aged 67 years or older with basic digital literacy were included. KOKU provided guided but unsupervised progressive strength and balance training for 3 months. The data on training adherence, engagement, and progression were collected. Instrumented assessments included the Timed Up and Go Test, the 30-Second Chair Rise Test, and real-world walking monitoring using wearable sensors. Results: A total of 113 participants (n=63, 56% female; mean age 74.02, SD 5.36 y) were included in the analysis. During the 3-month period, participants used KOKU for 24 (SD 15) days, that is, 2 to 3 times per week. Over the entire study period, no falls or other adverse events were reported due to KOKU usage. The number of exercises performed per participant ranged from 2 to 213, with a median value of 70. The instrumented Timed Up and Go Test results revealed a prolonged total duration (=0.26; =.009). In the instrumented 30-Second Chair Rise Test, improvements were observed in the number of completed repetitions (=0.21; =.04) and frequency of repetitions (=0.23; =.03). This was mainly due to a reduction in inactive time (=−0.60; <.001). Real-world walking parameters remained unchanged, except for a slower walking speed during walking bouts of less than 30 seconds (=0.49; <.001). All changes did not meet the criteria for minimally important differences. Conclusions: KOKU is a novel digital intervention for older adults, promoting balance and strength exercises. Physical capacity improvements were small. However, the use of instrumented assessments provided further insights into participants’ capacity and mobility that would not have been identifiable with conventional assessments. Future improvements to the program should focus on incorporating more challenging exercises for individuals with varying levels of physical capacity. Trial Registration: German Clinical Trials Register DRKS00034316; https://drks.de/search/en/trial/DRKS00034316

May 13, 2026

Equitable Digital Frailty Screening for Marginalized Older Adults Using Audio Computer-Assisted Self-Interview: Collaborative Development Guide and User Testing Study

Background: Older adults facing social or structural marginalization for reasons such as lower literacy, digital exclusion, financial constraints, restricted living environments, or complex health histories, face persistent barriers to much-needed health screening. Digital health tools, particularly those using audio computer-assisted self-interview (ACASI) technology, offer potential to overcome these barriers (audio-delivered and self-administrable), but their application to marginalized populations remains underexplored. Moreover, guidance is limited for developing such tools which require collaboration within cross-disciplinary teams. This paper presents development insights and user testing findings from the ASCAPE (Audio App-Delivered Screening for Cognition and Age-Related Health in Prisoners) project, which aimed to develop equitable digital frailty and cognition screening for older people in Australian prisons. Objective: This study aims to describe the collaborative development of the “ASCAPE-HS” prototype, a tablet-based ACASI-delivered Frailty Index and aging screener, and to synthesize key lessons from the project that can inform equitable digital health tool development in hard-to-reach older adults. Also, to present findings on the usability and acceptability of ASCAPE-HS in a diverse community sample. Methods: The ASCAPE-HS prototype was developed through an iterative process involving researchers, clinicians, software developers, and end users under a digital health equity framework. The prototype included a self-administered, audio-delivered Frailty Index, alongside other items relevant to aging. The design process prioritized accessibility, sociocultural relevance, and technical feasibility, with regular multidisciplinary consultation and iterative refinement. Exploratory user testing with 20 older adults (aged 47‐93 years, including n=5 who had not finished secondary schooling, n=3 people with previous imprisonment history, and n=9 with mild or moderate cognitive impairment) provided feedback on usability and acceptability. Results: A 50-item Frailty Index was developed, alongside an additional selection of holistic questions that could meaningfully capture age-related health, and transferred to an iOS app (Apple, Inc), with ACASI features. Key elements included lay wording, consistent interface, simple “tapping” response options with repeatable audio feedback, a tutorial, and artificial intelligence–generated audio guidance. Key development considerations were synthesized into a checklist for teams undertaking similar projects. Successful strategies for the collaborative design process included diverse teams abreast of emerging literature and policy with varying expectations for engagement during development, and dedicating time to flexible, iterative development processes. Acceptability (median scores ≥4 out of 5 across 6 constructs) and usability (mean System Usability Scale score 79.0, SD 8.8) were high. Conclusions: A collaborative approach can produce ACASI-based health screening tools that are well-received by older adults. We highlight the feasibility of integrating frailty and aging assessment into a usable and acceptable digital tool, and offer actionable principles for collaborative, evidence-based development of equitable health screening tools in diverse, hard-to-reach populations.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/30699dc5ac2d0ad05f7755da3210033e" />

May 13, 2026

Cellares and ProTgen Automate Manufacturing of Progenitor T-Cell Therapy for Blood Cancer

Cellares, an Integrated Development and Manufacturing Organization (IDMO) that combines automated manufacturing platforms with global Smart Factory infrastructure, and ProTgen, a therapeutic company pioneering targeted Notch activators to reactivate the thymus and reconstitute the adaptive immune system, have announced a partnership to automate manufacturing and quality control of ProT-096, ProTgen’s personalized progenitor T-cell therapy for patients with refractory leukemia and other hematologic malignancies. In this collaboration, Cellares will apply the company’s Cell Shuttle and Cell Q platforms to ProT-096 while providing regulatory support toward IND submission.

Fabian Gerlinghaus, co-founder and CEO of Cellares, says hematologic malignancies have waited too long for cell therapy to deliver on its promise, with manufacturing complexity being one of the main bottlenecks. ProT-096 represents “exactly the kind of innovative program” for which Cellares was founded.

“Early-stage developers should not have to choose between advancing their science and securing the manufacturing foundation they need to scale,” said Gerlinghaus. “By automating the manufacturing process and providing regulatory expertise toward IND submission, we can help ProTgen move faster and with greater confidence toward the clinic.”

Patients with refractory hematologic malignancies often face a compromised immune system following intensive treatment. While ProT-096 mandates precision manufacturing at scale, achieving the reproducibility, process consistency, and cost efficiency needed to support clinical development requires advanced manufacturing approaches.

Cell Shuttle’s automated, end-to-end manufacturing workflow reduces manual touchpoints, minimizes variability, and enables standardized execution across runs, equipment, and facilities. Combined with Cell Q, the workflow is designed to meet the demands of clinical- and commercial-scale production while maintaining quality standards for GMP manufacturing. The partnership also adds personalized progenitor T cells to Cellares’ platform capabilities across CAR T-cell therapies and HSC programs.

ProTgen’s proprietary targeted Notch activator platform programs cell fate both in vivo and ex vivo. The company’s initial focus is to reactivate the thymus and rebuild a diverse, functional immune repertoire for patients with compromised or aging immune systems.

Carter Cliff, CEO of ProTgen says ProT-096 represents a new approach to immune reconstitution, with the potential to address a significant unmet need for patients whose immune systems have been severely compromised by hematologic malignancy and prior treatment.

“This partnership allows us to pair our targeted Notch activator platform with an automated, scalable manufacturing foundation designed to support the path toward IND submission and, ultimately, clinical development,” he said.

The post Cellares and ProTgen Automate Manufacturing of Progenitor T-Cell Therapy for Blood Cancer appeared first on GEN – Genetic Engineering and Biotechnology News.

May 13, 2026

<![CDATA[New report reveals private practice delivers 113.6M mental health sessions, yet business training gaps and access mismatches persist.]]>

May 13, 2026

Adopting Creative Chemistry to Optimize Bioprocessing Workflow

Taking a creative approach to chemistry can help developers of antibody-drug conjugates (ADCs) improve the stability and purity of their products. That’s the view of Sunny Zhou, PhD, professor of chemistry and chemical biology at Northeastern University. Zhou will be speaking at the Bioprocessing Summit in Boston in August.

According to Zhou, the structure of ADCs can make them vulnerable to bioprocessing issues that don’t affect traditional antibodies. As one example, he says, the payloads of antibody drug conjugates often significantly absorb above 280 nm, making them markedly more sensitive to light.

“There’ll be photochemistry induced by the payload that can damage both the antibodies and payloads, such as crosslinking that likely leads to aggregation,” he says. “We’ve already published some work showing light-induced protein modifications, crosslinking, and aggregation.”

According to Zhou, some initiatives are already underway to address this issue. For example, by engaging in antibody production and downstream processing in dim or safe light (e.g., yellow or red light) instead of the more commonly used bright white light.

Another issue, he says, is that the linker connecting the antibody and drug payload is designed to be cleaved by enzymes in human patients. On the other hand, it also means that similar enzymes in host cell proteins (HCPs) may prematurely cleave the linker during production and storage, thereby decomposing the drug and contaminating the final product.

“Many host cell proteins contain such enzymes, but they don’t cleave antibodies. With these ADC linkers, however, enzymes that didn’t create problems before might do so now,” he says.

Zhou explains that premature cleavage of ADC linkers has been observed in an industrial setting. Fortunately, he says, his research team, in collaboration with companies like Takeda, is already creating universal platforms and workflows to identify and effectively remove these potential HCP contaminants, as well as working to better understand the stability of the linkers.

“These drugs circulate in the body for maybe two to three weeks, and stability issues can be amplified during circulation,” he says. “So, making the linker more stable [during manufacturing] may also help improve stability during circulation, further down the line.”

Zhou’s team is now hoping to look at other creative chemistries in bioprocessing. Among these is, for example, removing reagents, by-products, and impurities by filtration, which may be faster than relying on chromatography, he says.

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