Intercostal Nerve Cryoablation During Lobectomy for Postsurgical Pain: A Safe and Cost-Effective Intervention.

Introduction: The cost benefit of intercostal nerve cryoablation during surgical lobectomy for postoperative pain management is unknown. The current study compared hospital economics, resource use, and clinical outcomes during the index stay and accompanying short-term follow-up. Patients who underwent lobectomy with standard of care treatment for postsurgical pain management and cryoablation were compared to those with standard of care treatment only.

Engineering a "muco-trapping" ACE2-immunoglobulin hybrid with picomolar affinity as an inhaled, pan-variant immunotherapy for COVID-19.

Soluble angiotensin-converting enzyme 2 (ACE2) can act as a decoy molecule that neutralizes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by blocking spike (S) proteins on virions from binding ACE2 on host cells. Based on structural insights of ACE2 and S proteins, we designed a "muco-trapping" ACE2-Fc conjugate, termed ACE2-(GS)-Fc, comprised of the extracellular segment of ACE2 (lacking the C-terminal collectrin domain) that is linked to mucin-binding IgG1-Fc via an extended glycine-serine flexible linker. ACE2-(GS)-Fc exhibits substantially greater binding affinity and neutralization potency than conventional full length ACE2-Fc decoys or similar truncated ACE2-Fc decoys without flexible linkers, possessing picomolar binding affinity and strong neutralization potency against pseudovirus and live virus.

Evaluation of Cervical Vertebral Motion and Foraminal Changes During the Spurling Test Using Zero Echo Time Magnetic Resonance Imaging and Computed Tomography-Based Micromotion Analysis.

Study Design: Clinical experimental diagnostic study.

Objective: The objective of the study was to investigate cervical spine dynamics including changes in the cervical foramina in patients experiencing intermittent arm radiculopathy.

Background: Cervical foraminal stenosis is a frequent cause of radicular arm pain.

Machine learning-powered compact modeling of stochastic electronic devices using mixture density networks.

The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation-how to accurately account for the inherent stochastic nature of certain devices. While conventional deterministic models have served as indispensable tools for circuit designers, they fall short when it comes to capturing the subtle yet critical variability exhibited by many electronic components. In this paper, we present an innovative approach that transcends the limitations of traditional modeling techniques by harnessing the power of machine learning, specifically Mixture Density Networks (MDNs), to faithfully represent and simulate the stochastic behavior of electronic devices.