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Category: bioengineering

Ultra-flat optic pushes beyond what was previously thought possible

Cameras are everywhere. For over two centuries, these devices have grown increasingly popular and proven to be so useful, they have become an indispensable part of modern life.

Today, they are included in a vast range of applications—everything from smartphones and laptops to security and to cars, aircraft, and satellites imaging Earth from high above. And as an overarching trend toward miniaturizing mechanical, optical, and electronic products continues, scientists and engineers are looking for ways to create smaller, lighter, and more energy-efficient cameras for these technologies.

Ultra-flat optics have been proposed as a solution for this engineering challenge, as they are an alternative to the relatively bulky lenses found in cameras today. Instead of using a curved lens made out of glass or plastic, many ultra-flat optics, such as metalenses, use a thin, flat plane of microscopic nanostructures to manipulate light, which makes them hundreds or even thousands of times smaller and lighter than conventional camera lenses.

Rice research team on quest to engineer computing systems from living cells

Rice University biosciences professor Matthew Bennett has received a $1.99 million grant from the National Science Foundation to lead research on engineered bacterial consortia that could form the basis of biological computing systems. The four-year project will also involve co-principal investigators Kirstin Matthews, Caroline Ajo-Franklin and Anastasios Kyrillidis from Rice along with Krešimir Josić from the University of Houston. The research team aims to develop platforms that integrate microbial sensing and communication with electronic networks, paving the way for computing systems constructed from living cells instead of traditional silicon-based hardware.

The project highlights the growing potential of synthetic biology, where microbes are examined not just as living organisms but as processors of information. If successful, Bennett’s research could accelerate medical diagnostics, environmental monitoring and the development of next-generation computing applications.

“Microbes are remarkable information processors, and we want to understand how to connect them into networks that behave intelligently,” Bennett said. “By integrating biology with electronics, we hope to create a new class of computing platforms that can adapt, learn and respond to their environments.”

Scientists Turned Our Cells Into Quantum Computers—Sort Of

For the protein qubit to “encode” more information about what is going on inside a cell, the fluorescent protein needs to be genetically engineered to match the protein scientists want to observe in a given cell. The glowing protein is then attached to the target protein and zapped with a laser so it reaches a state of superposition, turning it into a nano-probe that picks up what is happening in the cell. From there, scientists can infer how a certain biological process happens, what the beginnings of a genetic disease look like, or how cells respond to certain treatments.

And eventually, this kind of sensing could be used in non-biological applications as well.

“Directed evolution on our EYFP qubit could be used to optimize its optical and spin properties and even reveal unexpected insights into qubit physics,” the researchers said. “Protein-based qubits are positioned to take advantage of techniques from both quantum information sciences and bioengineering, with potentially transformative possibilities in both fields.”

Sustainable AI: Physical neural networks exploit light to train more efficiently

Artificial intelligence is now part of our daily lives, with the subsequent pressing need for larger, more complex models. However, the demand for ever-increasing power and computing capacity is rising faster than the performance traditional computers can provide.

To overcome these limitations, research is moving towards innovative technologies such as physical neural networks, analog circuits that directly exploit the laws of physics (properties of light beams, quantum phenomena) to process information. Their potential is at the heart of the study published in the journal Nature. It is the outcome of collaboration between several international institutes, including the Politecnico di Milano, the École Polytechnique Fédérale in Lausanne, Stanford University, the University of Cambridge, and the Max Planck Institute.

The article entitled “Training of Physical Neural Networks” discusses the steps of research on training physical neural networks, carried out with the collaboration of Francesco Morichetti, professor at DEIB—Department of Electronics, Information and Bioengineering, and head of the university’s Photonic Devices Lab.

AI turns printer into a partner in tissue engineering

In 3D bioprinting, researchers use living cells to create functional tissues and organs. Instead of printing with plastic, they print with living cells. This comes with great challenges. Cells are fragile and wouldn’t survive a regular 3D . That’s why Levato’s team developed a special bio-ink, a mix of living cells and nourishing gels that protect the cells during the printing process.

With the advancements in bio-inks, layer-by-layer 3D bioprinting became possible. But this method is still time-consuming and puts a lot of stress on the cells. Researchers from Utrecht came up with a solution: volumetric bioprinting.

Volumetric bioprinting is faster and gentler on cells. Using cell-friendly laser light, a 3D structure is created all at once. “To build a structure, we project a series of light patterns into a spinning tube filled with light-sensitive gel and cells,” Levato explains. “Where the light beams converge, the material solidifies. This creates a full 3D object in one go, without having to touch the cells.” To do this, it is crucial to know exactly where the cells are in the gel. GRACE now makes that possible.

Apertura Gene Therapy and Rett Syndrome Research Trust Collaborate to Pioneer Advanced Genetic Medicines for Rett Syndrome Using TfR1-Targeted AAV Capsid

NEW YORK and TRUMBULL, Conn., April 30, 2025 /PRNewswire/ — Apertura Gene Therapy, a biotechnology company focused on innovative gene therapy solutions, and the Rett Syndrome Research Trust (RSRT), an organization working to cure Rett Syndrome, today announced a collaboration to license Apertura’s human transferrin receptor 1 capsid (TfR1 CapX). This partnership aims to advance innovative genetic medicine approaches for the treatment of Rett Syndrome, a rare genetic neurological disorder caused by random mutations in the MECP2 gene on the X chromosome that primarily affect females, causing developmental regression and severe motor and language impairments.

Apertura’s TfR1 CapX is an intravenously delivered adeno-associated virus (AAV) capsid engineered to bind the transferrin receptor 1(TfR1), enabling efficient delivery of genetic medicines across the blood-brain barrier (BBB). TfR1 is a well-characterized BBB-crossing receptor, broadly and consistently expressed throughout life—even in the context of neurological disease—making it an attractive target for CNS delivery in disorders like Rett syndrome. Developed by Apertura’s academic founder, Dr. Ben Deverman, Director of Vector Engineering at the Broad Institute, TfR1 CapX has shown strong CNS selectivity in preclinical studies, achieving over 50% neuronal and 90% astrocyte transduction across multiple brain regions. Because Rett syndrome affects the brain diffusely, broader cellular transduction may correlate with greater symptomatic improvement.

DNA-based neural network learns from examples to solve problems

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead of electronic parts that carries out computation through chemical reactions rather than digital signals.

An important property of any neural network is the ability to learn by taking in information and retaining it for future decisions. Now, researchers in the laboratory of Lulu Qian, professor of bioengineering, have created a DNA-based neural network that can learn. The work represents a first step toward demonstrating more complex learning behaviors in .

A paper describing the research appears in the journal Nature on September 3. Kevin Cherry, Ph.D., is the study’s first author.

A light-programmable, dynamic ultrasound wavefront

The notion of a phased array was initially articulated by Nobel Prize recipient K. F. Braun. Phased arrays have subsequently evolved into a formidable mechanism for wave manipulation. This assertion holds particularly true in the realm of ultrasound, wherein arrays composed of ultrasound-generating transducers are employed in various applications, including therapeutic ultrasound, tissue engineering, and particle manipulation.

Importantly, these applications—contrary to those aimed at imaging—demand high-intensity ultrasound, which complicates the electrical driving requirements, as each channel necessitates its own independently operational pulse circuitry and amplifier. Consequently, the majority of phased array transducers (PATs) are constrained to several hundred elements, thereby restricting the capability to shape intricate ultrasound beams.

To date, there exists no scalable methodology for the powering and control of phased array transducers.

Tiny 3D-Printed Device Supercharges Tissue Engineering With Unprecedented Precision

The device is compact enough to rest on a fingertip and is compatible with current tissue-engineering technology. A newly developed 3D-printed device offers scientists the ability to build human tissue models with far greater precision and complexity. The tool, created by an interdisciplinary tea

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