Novel Image-Guided Percutaneous Lung Tissue Excision Device With Integrated Sealing of Blood Vessels and Airways: An In Vivo Preclinical Study.

OBJECTIVE: This study evaluated the efficacy of the Minimally Invasive Targeted Resection (MiTR) device, a novel electrosurgical instrument that allows for targeted excision of a lung abnormality while using bipolar radiofrequency (RF) energy to seal blood vessels and airways. METHODS: The MiTR system was evaluated in 7 acute and 2 chronic porcine (7-day) models to evaluate the efficacy of tissue excision with bipolar RF sealing of blood vessels and airways and application of an autologous blood patch into the excised tissue cavity. Air leak was recorded for all evaluations. The study was approved by the institutional ethical board. RESULTS: Nineteen lung tissue samples, measuring 2.5 cm long × 1.2 cm diameter, were excised. In 8 of 9 animals (89%), hemostasis and pneumostasis were observed visually at the completion of the procedure. In 2 of 2 chronic animals (100%), hemostasis and pneumostasis persisted for the 7-day observation period. Histologic examination of the excised samples showed preservation of the core parenchymal architecture without evident tissue damage of the samples that would impair pathologic analysis. CONCLUSIONS: Percutaneous resection of targeted lung tissue with the MiTR system demonstrated hemostasis and pneumostasis while obtaining a histologically intact sample. After regulatory approval, the use of this device could offer more tissue for analysis than a transthoracic needle biopsy or bronchoscopy and a far less invasive alternative to video-assisted thoracic surgery or thoracotomy. This may also expand patient and physician options for the early diagnosis and treatment of lung cancer.

Ultra-high throughput mapping of genetic design space.

Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs, "composition-to-function" mappings could be created that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a generalizable genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pooled libraries of DNA constructs of arbitrary length. We show that CLASSIC can measure expression profiles of >10 5 drug-inducible gene circuit designs (ranging from 6-9 kb) in a single experiment in human cells. Using statistical inference and machine learning (ML) approaches, we demonstrate that data obtained with CLASSIC enables predictive modeling of an entire circuit design landscape, offering critical insight into underlying design principles. Our work shows that by expanding the throughput and understanding gained with each design-build-test-learn (DBTL) cycle, CLASSIC dramatically augments the pace and scale of synthetic biology and establishes an experimental basis for data-driven design of complex genetic systems.