Role of the Pectoralis Major Muscle Flap in the Multidisciplinary Treatment of Esophageal Cancer.

Background: Management of esophageal cancer is complex. Esophagectomy is associated with risk of significant complications. In this case series, we share the experience of our multidisciplinary team of thoracic surgeons and otolaryngologists in managing complications arising in the surgical treatment of esophageal cancer with the assistance of regional tissue transfer in the form of the pectoralis major flap.

Starting a surgical prehabilitation program: results from a pragmatic nonrandomized feasibility study.

Purpose: We sought to assess the feasibility and estimate the effects on outcomes of a multimodal prehabilitation service implemented as an ancillary surgical service.

Methods: We conducted a pragmatic, nonrandomized feasibility study of surgical prehabilitation. Patients were eligible if they were ≥ 18 yr of age, fluent in English, and referred by a health professional for prehabilitation.

La Substitution into the Melilite Derivative CaGaO: Prediction, Synthesis and Ionic Conductivity.

Melilite-type gallates of general formula REAEGaO are of interest for their ability to host mobile interstitial oxide ions in [GaO] layers. The crystal structure of CaGaO is closely related to melilite, with [GaO] layers stacked in a more complex way to accommodate an additional 0.5 interlayer cations per formula unit, suggesting the potential for similar oxide ion conduction behavior.

Integration of generative machine learning with the heuristic crystal structure prediction code FUSE.

The prediction of new compounds crystal structure prediction may transform how the materials chemistry community discovers new compounds. In the prediction of inorganic crystal structures there are three distinct classes of prediction: performing crystal structure prediction heuristic algorithms, using a range of established crystal structure prediction codes, an emerging community using generative machine learning models to predict crystal structures directly and the use of mathematical optimisation to solve crystal structures exactly. In this work, we demonstrate the combination of heuristic and generative machine learning, the use of a generative machine learning model to produce the starting population of crystal structures for a heuristic algorithm and discuss the benefits, demonstrating the method on eight known compounds with reported crystal structures and three hypothetical compounds.