BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences.

The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.

Integrated multimodal artificial intelligence framework for healthcare applications.

Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs.

Wearable materials with embedded synthetic biology sensors for biomolecule detection.

Integrating synthetic biology into wearables could expand opportunities for noninvasive monitoring of physiological status, disease states and exposure to pathogens or toxins. However, the operation of synthetic circuits generally requires the presence of living, engineered bacteria, which has limited their application in wearables. Here we report lightweight, flexible substrates and textiles functionalized with freeze-dried, cell-free synthetic circuits, including CRISPR-based tools, that detect metabolites, chemicals and pathogen nucleic acid signatures.

Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images.

A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to improved melanoma prognosis and a possible 20-fold reduction in treatment cost. Despite this clinical and economic value, efficient tools for SPL detection are mostly absent.

A deep learning approach to programmable RNA switches.

Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology.

Creating CRISPR-responsive smart materials for diagnostics and programmable cargo release.

Materials that sense and respond to biological signals in their environment have a broad range of potential applications in drug delivery, medical devices and diagnostics. Nucleic acids are important biological cues that encode information about organismal identity and clinically relevant phenotypes such as drug resistance. We recently developed a strategy to design nucleic acid-responsive materials using the CRISPR-associated nuclease Cas12a as a user-programmable sensor and material actuator.

Programmable CRISPR-responsive smart materials.

Stimuli-responsive materials activated by biological signals play an increasingly important role in biotechnology applications. We exploit the programmability of CRISPR-associated nucleases to actuate hydrogels containing DNA as a structural element or as an anchor for pendant groups. After activation by guide RNA-defined inputs, Cas12a cleaves DNA in the gels, thereby converting biological information into changes in material properties.

Interconnected Microphysiological Systems for Quantitative Biology and Pharmacology Studies.

Microphysiological systems (MPSs) are in vitro models that capture facets of in vivo organ function through use of specialized culture microenvironments, including 3D matrices and microperfusion. Here, we report an approach to co-culture multiple different MPSs linked together physiologically on re-useable, open-system microfluidic platforms that are compatible with the quantitative study of a range of compounds, including lipophilic drugs. We describe three different platform designs - "4-way", "7-way", and "10-way" - each accommodating a mixing chamber and up to 4, 7, or 10 MPSs.

Analysis of white blood cell dynamics in nailfold capillaries.

Based on video data acquired with low-cost, portable microscopy equipment, we introduce a semi-automatic method to count visual gaps in the blood flow as a proxy for white blood cells (WBC) passing through nailfold capillaries. Following minimal user interaction and a pre-processing stage, our method consists in the spatio-temporal segmentation and analysis of capillary profiles. Besides the mere count information, it also estimates the speed associated with every WBC event.

Efficacy and safety evaluation of systemic extremely low frequency magnetic fields used in the healing of diabetic foot ulcers--phase II data.

Background And Aims: Cellular and animal models investigating extremely low frequency magnetic fields (ELF-MF) have reported promotion of leukocyte-endothelial interactions, angiogenesis, myofibroblast and keratinocyte proliferation, improvement of peripheral neuropathy and diabetic wound healing. In humans, it has also been reported that systemic exposure to ELF-MF stimulates peripheral blood mononuclear cells, promoting angiogenesis and healing of chronic leg ulcers. The aim of the study was to investigate the effect of exposing different blood volumes to specific ELF-MFs (120 Hz sinusoidal waves of 0.