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First-in-class therapy targets ‘undruggable’ protein in hard-to-treat blood cancers

A first-in-class therapy to target MYC, one of the most sought-after and difficult targets in cancer biology, showed promise in hard-to-treat blood cancers, according to a new preclinical study from The University of Texas MD Anderson Cancer Center published in Blood.

Researchers led by Michael Andreeff, M.D., Ph.D., professor, and Yuki Nishida, M.D., Ph.D., assistant professor, both of Leukemia, found that the experimental drug GT19630 interrupts a newly discovered cycle between MYC and GSPT1, resulting in strong anticancer activity in preclinical models of leukemia, lymphoma and multiple myeloma, including treatment-resistant and TP53-mutated disease.

“For decades, scientists have struggled to develop therapies that successfully block MYC, leading many in the field to describe it as undruggable,” Andreeff said. “By identifying a vulnerability in the relationship between MYC and GSPT1, we found a way to eliminate both proteins and disable a pathway many cancers depend on for survival.”

The silent data leak hidden inside encrypted reasoning traces of frontier AI models

To hide this reasoning while maintaining conversational context across multiple API turns, major providers such as Anthropic and OpenAI use Authenticated Encryption with Associated Data (AEAD) envelopes. AEAD is a cryptographic standard that encrypts data while attaching metadata to verify that the payload has not been modified in transit.

Providers designed these encrypted envelopes around three goals:

1. Confidentiality: Prevent clients from reading the intermediate reasoning tokens to protect proprietary information.

The dangers of trusting blackbox machine learning

The success of deep learning in the past decade has increased interest in the field of artificial intelligence. But the rising popularity of AI has also highlighted some of the key problems of the field, including the “black box problem,” the challenge of making sense of the way complex machine learning algorithms make decisions. The Apple Card disaster is one of many manifestations of the black-box problem coming to light in the past years.

The increased attention to black-box machine learning has given rise to a body of research on explainable AI. And a lot of the work done in the field involves developing techniques that try to explain the decision made by a machine learning algorithm without breaking open the black box. But explaining AI decisions after they happen can have dangerous implications, argues Cynthia Rudin, professor of computer science at Duke University, in a paper published in the Nature Machine Intelligence journal.

“Rather than trying to create models that are inherently interpretable, there has been a recent explosion of work on ‘explainable ML’, where a second (post hoc) model is created to explain the first black box model. This is problematic. Explanations are often not reliable,” Rudin writes. and can be misleading, as we discuss below.

Moving beyond passive RAG: How to implement active memory reconstruction for AI agents

To see how this works in practice, consider a concrete enterprise use case: a customer success agent that needs to answer, “Why was this customer promised a different renewal price, and should we honor it?”

The answer may require reconstructing a causal chain across a support conversation, a sales exception, a contract clause, a billing-system update, and a later internal note. Traditional similarity-based RAG might retrieve the most recent billing document or the most semantically similar support ticket, but fail to connect the causal chain.

With MRAgent’s active reconstruction, the agent can extract cues like the customer’s name, follow associative tags to the original support ticket, retrieve the sales exception, and link it to the billing update. It navigates these semantic relationships, prunes irrelevant branches, and iteratively stops once it has enough evidence to answer the query.

US authorities say Siemens controllers used for water and other infrastructure are being targeted by hackers threat actors use AI tools to generate exploitation scripts

Attacks on these industrial controllers could lead to sabotage of critical infrastructure.

This yeast supplement may boost cancer-fighting immunity

A simple yeast-based food supplement may help restore the immune system’s ability to fight cancer, according to new research in mice. Scientists found that yeast beta-glucan reprogrammed early immune cells in the bone marrow, leading to stronger, longer-lasting cancer-fighting responses against colorectal, skin, and breast cancer cells.

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