Lifeboat Foundation

When it comes to delivering drugs to the body, a major challenge is ensuring that they remain in the area they’re treating and continuing to deliver their payload accurately. While major strides have been made in delivering drugs, monitoring them is a challenge that often requires invasive procedures like biopsies.

Researchers at NYU Tandon led by Jin Kim Montclare, Professor of Chemical and Biomolecular Engineering, have developed proteins that can assemble themselves into fibers to be used as therapeutic agents for the potential treatments of multiple diseases.

These biomaterials can encapsulate and deliver therapeutics for a host of diseases. But while Montclare’s lab has long worked on producing these materials, there was once a challenge that was hard to overcome—how to make sure that these proteins continued to deliver their therapeutics at the correct location in the body for the necessary amount of time.

The delivery of drugs to specific target tissues and cells in the brain poses a significant challenge in brain therapeutics, primarily due to limited understanding of how nanoparticle (NP) properties influence drug biodistribution and off-target organ accumulation. This study addresses the limitations of previous research by using various predictive models based on collection of large data sets of 403 data points incorporating both numerical and categorical features. Machine learning techniques and comprehensive literature data analysis were used to develop models for predicting NP delivery to the brain. Furthermore, the physicochemical properties of loaded drugs and NPs were analyzed through a systematic analysis of pharmacodynamic parameters such as plasma area under the curve. The analysis employed various linear models, with a particular emphasis on linear mixed-effect models (LMEMs) that demonstrated exceptional accuracy. The model was validated via the preparation and administration of two distinct NP formulations via the intranasal and intravenous routes. Among the various modeling approaches, LMEMs exhibited superior performance in capturing underlying patterns. Factors such as the release rate and molecular weight had a negative impact on brain targeting. The model also suggests a slightly positive impact on brain targeting when the drug is a P-glycoprotein substrate.

Optogenetics has revolutionized neuroscience understanding by allowing spatiotemporal control over cell-type specific neurons in neural circuits. However, the sluggish development of noninvasive photon delivery in the brain has limited the clinical application of optogenetics. Focused ultrasound (FUS)-derived mechanoluminescence has emerged as a promising tool for in situ photon emission, but there is not yet a biocompatible liquid-phase mechanoluminescence system for spatiotemporal optogenetics. To achieve noninvasive optogenetics with a high temporal resolution and desirable biocompatibility, we have developed liposome (Lipo@IR780/L012) nanoparticles for FUS-triggered mechanoluminescence in brain photon delivery. Synchronized and stable blue light emission was generated in solution under FUS irradiation due to the cascade reactions in liposomes.

The fusion of biological principles with technological innovation has resulted in significant advancements in artificial intelligence (AI) through the development of Brainoware. Developed by researchers at Indiana University, Bloomington, this innovative system leverages clusters of lab-raised brain cells to achieve elementary speech recognition and solve mathematical problems.

The crux of this technological leap lies in the cultivation of specialized stem cells that mature into neurons—the fundamental units of the brain. While a typical human brain comprises a staggering 86 billion neurons interconnected extensively, the team managed to engineer a minute organoid, merely a nanometer wide. This tiny but powerful structure was connected to a circuit board through an array of electrodes, allowing machine-learning algorithms to decode responses from the brain tissue.

Termed Brainoware, this amalgamation of biological neurons and computational circuits exhibited remarkable capabilities after a brief training period. It was discerned between eight subjects based on their diverse pronunciation of vowels with an accuracy rate of 78%. Impressively, Brainoware outperformed artificial networks in predicting the Henon map, a complex mathematical construct within chaotic dynamics.

For instance, the pegRNA molecules used in prime editing are difficult and expensive to chemically synthesise or laborious to clone, which hampers the crucial optimisation of prime-editing efficiency. Additionally, the reverse transcriptase (RT) enzymes used in prime editing are relatively error-prone and have low processivity, which may limit the precision and size of edits that can be introduced. Furthermore, RTs have a low affinity for dNTPs, which can impact prime-editing efficiency in non-dividing and differentiated cells.

To address these issues, two research groups led by Dr. Ben Kleinstiver at Mass General Hospital (MGH) & Harvard Medical School, and Dr. Erik Sontheimer at the RNA Therapeutics Institute (UMass Chan Medical School) have independently developed new approaches that build upon prime editing by replacing RT with another type of enzyme, namely a DNA-dependent DNA polymerase. This change permits the use of DNA instead of RNA as a template for editing, potentially addressing some of the main limitations of prime editing by allowing higher efficiency and adaptability.

Signal processing is key to communications and video image processing for astronomy, medical diagnosis, autonomous driving, big data and AI. Menxi Tan and colleagues report a photonic processor operating at 17Tb/s for ultrafast robotic vision and machine learning.

A research team led by Prof. Sun Zhong at Peking University has reported an analog hardware solution for real-time compressed sensing recovery. It has been published as an article titled, “In-memory analog solution of compressed sensing recovery in one step” in Science Advances.

In this work, a design based on a resistive memory (also known as memristor) array for performing instantaneous matrix-matrix-vector multiplication (MMVM) is introduced. Based on this module, an analog matrix computing circuit that solves compressed sensing (CS) recovery in one step (within a few microseconds) is disclosed.

CS has been the cornerstone of modern signal and image processing, across many important fields such as medical imaging, wireless communications, object tracking, and single-pixel cameras. In CS, sparse signals can be highly undersampled in the front-end sensor, which breaks through the Nyquist rate and thus significantly improving sampling efficiency.

A game-changer in prosthetics has been introduced to the world, and for the first time, amputees are regaining sensation through an electrical signal from their prosthetic arm. Max Ortiz-Catalan, a professor of bionics, explains the process of implanting these mind-controlled bionic arms through direct skeletal attachment. The researcher takes us through every step of this groundbreaking advancement in bionic medicine, from surgically implanting electrodes to fitting the prosthesis and training for everyday use.\r\
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Harvard researchers have realized a key milestone in the quest for stable, scalable quantum computing, an ultra-high-speed technology that will enable game-changing advances in a variety of fields, including medicine, science, and finance.

The team, led by Mikhail Lukin, the Joshua and Beth Friedman University Professor in physics and co-director of the Harvard Quantum Initiative, has created the first programmable, logical quantum processor, capable of encoding up to 48 logical qubits, and executing hundreds of logical gate operations, a vast improvement over prior efforts.

Published in Nature, the work was performed in collaboration with Markus Greiner, the George Vasmer Leverett Professor of Physics; colleagues from MIT; and QuEra Computing, a Boston company founded on technology from Harvard labs.