Supervised machine learning to predict non-home discharge following surgical treatment of pelvic fractures

Background: Decision-tree-based machine learning (ML) algorithms such as random forest (RF) are useful for their ability to predict outcomes and rank variables according to their utility in the decision-making process. This study utilizes RF to identify important predictors of discharge to facility following surgical stabilization of pelvis fractures, a traumatic injury that often precludes mortality and diminished quality of life. Methods: The American College of Surgeons national surgical quality improvement program (ACS-NSQIP) database was queried for patients aged 16 to 70 undergoing surgical fixation of pelvis fractures between 2008 and 2018. Outcome of interest was discharge home versus to facility. RF was trained with surgical variables, comorbidities, and other patient factors and tasked with predicting discharge location. Permutation feature importance (PFI) was then generated to identify important variables. Results: Out of 492 patients, 184 patients were discharged to facility, and 308 patients were discharged home. RF identified age, American Society of Anesthesiologists (ASA) classification, and preoperative hematocrit as top predictors for discharge to facility. Patients being discharged home were younger, had lower ASA scores, and had higher preoperative hematocrit. Conclusions: RF identified age, ASA classification, and preoperative hematocrit as top predictors for discharge destination following pelvic surgery. Knowledge of the impact of these variables can inform preoperative planning for both patients and their care team, while highlighting the opportunity to address preoperative hematocrit to both reduce cost and improve quality of care

Working group 5: space transportation

The disruption of the traditionally stable launch vehicle market by new commercial players is driving the space transportation sector through its greatest period of change. Although this unprecedented level of growth is aiding in increasing the accessibility of space, it does not come without its challenges. In order to identify, analyse, and address the challenges facing the current and future launch sector, the Space Transportation Working Group at the 2017 Space Generation Congress assessed the existing and incoming stakeholders, their changing needs, and the roles each could play in meeting these challenges.This aim was encapsulated in the following goal statement:Addressing future challenges to foster an economically sustainable launch market,The primary stakeholders in the sector (government space agencies, commercial industry, and launch customers) are undergoing changes in their traditional roles, enabling increasedcooperation. In parallel, upcoming stakeholders, such as academic institutions and nongovernment organisations, may provide support in brokering these developing partnerships. These interactions almost always involve compromise, and from this analysis the following trade off challenges were focused on:1. Innovation and risk2. Global collaboration vs National interestsa. Global collaboration - commercial vs institutionalb. Addressing security issue

Defective NOD2 peptidoglycan sensing promotes diet‐induced inflammation, dysbiosis, and insulin resistance

Abstract Pattern recognition receptors link metabolite and bacteria‐derived inflammation to insulin resistance during obesity. We demonstrate that NOD2 detection of bacterial cell wall peptidoglycan (PGN) regulates metabolic inflammation and insulin sensitivity. An obesity‐promoting high‐fat diet (HFD) increased NOD2 in hepatocytes and adipocytes, and NOD2−/− mice have increased adipose tissue and liver inflammation and exacerbated insulin resistance during a HFD. This effect is independent of altered adiposity or NOD2 in hematopoietic‐derived immune cells. Instead, increased metabolic inflammation and insulin resistance in NOD2−/− mice is associated with increased commensal bacterial translocation from the gut into adipose tissue and liver. An intact PGN‐NOD2 sensing system regulated gut mucosal bacterial colonization and a metabolic tissue dysbiosis that is a potential trigger for increased metabolic inflammation and insulin resistance. Gut dysbiosis in HFD‐fed NOD2−/− mice is an independent and transmissible factor that contributes to metabolic inflammation and insulin resistance when transferred to WT, germ‐free mice. These findings warrant scrutiny of bacterial component detection, dysbiosis, and protective immune responses in the links between inflammatory gut and metabolic diseases, including diabetes