Concordance between preoperative ultrasound arterial mapping in the femoropopliteal and distal sector and intraoperative angiography.

Aims: The present study analyzes the concordance between arterial mapping of this sector using duplex ultrasound and intraoperative angiography.

Methods: A retrospective, single-center observational study was carried out. The study sample consisted of patients subjected to open or endovascular surgery of the femoropopliteal and distal sector with prior ultrasound arterial mapping from January 2017 to December 2022.

Synergistic Polymer Blending Informs Efficient Terpolymer Design and Machine Learning Discerns Performance Trends for pDNA Delivery.

Cationic polymers offer an alternative to viral vectors in nucleic acid delivery. However, the development of polymer vehicles capable of high transfection efficiency and minimal toxicity has remained elusive, and continued exploration of the vast design space is required. Traditional single polymer syntheses with large monomer bases are very time-intensive, limiting the speed at which new formulations are identified.

Polymer design SHAP and Bayesian machine learning optimizes pDNA and CRISPR ribonucleoprotein delivery.

We present the facile synthesis of a clickable polymer library with systematic variations in length, binary composition, p, and hydrophobicity (clog ) to optimize intracellular pDNA and CRISPR-Cas9 ribonucleoprotein (RNP) performance. We couple physicochemical characterization and machine learning to interpret quantitative structure-property relationships within the combinatorial design space. For the first time, we reveal unexpected disparate design parameters for nucleic acid carriers; explainable machine learning on 432 formulations, we discover that lower polymer p and higher percentages of benzimidazole ethanethiol enhance pDNA delivery, yet polymer length and captamine cation identity improve RNP delivery.

Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics.

Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties and . However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome.