Machine learning perioperative applications in visceral surgery: a narrative review.

Artificial intelligence in surgery has seen an expansive rise in research and clinical implementation in recent years, with many of the models being driven by machine learning. In the preoperative setting, machine learning models have been utilized to guide indications for surgery, appropriate timing of operations, calculation of risks and prognostication, along with improving estimations of time and resources required for surgeries. Intraoperative applications that have been demonstrated are visual annotations of the surgical field, automated classification of surgical phases and prediction of intraoperative patient decompensation.

Biologically informed NeuralODEs for genome-wide regulatory dynamics.

Background: Gene regulatory network (GRN) models that are formulated as ordinary differential equations (ODEs) can accurately explain temporal gene expression patterns and promise to yield new insights into important cellular processes, disease progression, and intervention design. Learning such gene regulatory ODEs is challenging, since we want to predict the evolution of gene expression in a way that accurately encodes the underlying GRN governing the dynamics and the nonlinear functional relationships between genes. Most widely used ODE estimation methods either impose too many parametric restrictions or are not guided by meaningful biological insights, both of which impede either scalability, explainability, or both.

An estimation of the endoscopist's musculoskeletal injury risk for right and left lateral decubitus positions during colonoscopy: a field-based ergonomic study.

Background: Colonoscopy exposes endoscopists to awkward postures and prolonged forces, which increases their risk of musculoskeletal injury. Patient positioning has a significant impact on the ergonomics of colonoscopy. Recent trials have found the right lateral decubitus position is associated with quicker insertion, higher adenoma detection rates, and greater patient comfort compared to the left lateral decubitus position.

Biologically informed NeuralODEs for genome-wide regulatory dynamics.

Models that are formulated as ordinary differential equations (ODEs) can accurately explain temporal gene expression patterns and promise to yield new insights into important cellular processes, disease progression, and intervention design. Learning such ODEs is challenging, since we want to predict the evolution of gene expression in a way that accurately encodes the causal gene-regulatory network (GRN) governing the dynamics and the nonlinear functional relationships between genes. Most widely used ODE estimation methods either impose too many parametric restrictions or are not guided by meaningful biological insights, both of which impedes scalability and/or explainability.

Ursodeoxycholic acid for prevention of gallstone disease after laparoscopic sleeve gastrectomy: an Atlantic Canada perspective.

Background: Prophylactic ursodeoxycholic acid (UDCA) may be beneficial in reducing gallstone disease after bariatric surgery. The American Society for Metabolic and Bariatric Surgery (ASMBS) 2019 guidelines recommend a 6-month course of UDCA for patients undergoing laparoscopic sleeve gastrectomy (LSG). This has not been adopted broadly.

Biologically informed NeuralODEs for genome-wide regulatory dynamics.

Modeling dynamics of gene regulatory networks using ordinary differential equations (ODEs) allow a deeper understanding of disease progression and response to therapy, thus aiding in intervention optimization. Although there exist methods to infer regulatory ODEs, these are generally limited to small networks, rely on dimensional reduction, or impose non-biological parametric restrictions - all impeding scalability and explainability. PHOENIX is a neural ODE framework incorporating prior domain knowledge as soft constraints to infer sparse, biologically interpretable dynamics.

Morbid Obesity and Severe Knee Osteoarthritis: Which Should Be Treated First?

Background: There are limited prospective data, and conflicting retrospective data, providing guidance on how to optimally manage patients with morbid obesity and severe knee osteoarthritis. This study sought to review the effect of bariatric surgery on knee pain and knee surgery 30-day outcomes, as well as assess whether the sequence of bariatric and knee surgery has any effect on 30-day complications.

Methods: A retrospective chart review of all patients undergoing laparoscopic sleeve gastrectomy (LSG) from July 2006 to July 2016 at a university hospital was performed.

Prognostic factors for changes in the timed 4-stair climb in patients with Duchenne muscular dystrophy, and implications for measuring drug efficacy: A multi-institutional collaboration.

The timed 4-stair climb (4SC) assessment has been used to measure function in Duchenne muscular dystrophy (DMD) practice and research. We sought to identify prognostic factors for changes in 4SC, assess their consistency across data sources, and the extent to which prognostic scores could be useful in DMD clinical trial design and analysis. Data from patients with DMD in the placebo arm of a phase 3 trial (Tadalafil DMD trial) and two real-world sources (Universitaire Ziekenhuizen, Leuven, Belgium [Leuven] and Cincinnati Children's Hospital Medical Center [CCHMC]) were analyzed.

Lysophosphatidic acid receptor mRNA levels in heart and white adipose tissue are associated with obesity in mice and humans.

Background: Lysophosphatidic acid (LPA) receptor signaling has been implicated in cardiovascular and obesity-related metabolic disease. However, the distribution and regulation of LPA receptors in the myocardium and adipose tissue remain unclear.

Objectives: This study aimed to characterize the mRNA expression of LPA receptors (LPA1-6) in the murine and human myocardium and adipose tissue, and its regulation in response to obesity.

Enzyme-Mediated Modification of Single-Domain Antibodies for Imaging Modalities with Different Characteristics.

Antibodies are currently the fastest-growing class of therapeutics. Although naked antibodies have proven valuable as pharmaceutical agents, they have some limitations, such as low tissue penetration and a long circulatory half-life. They have been conjugated to toxic payloads, PEGs, or radioisotopes to increase and optimize their therapeutic efficacy.