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3%, and was more sensitive than chromogranin (33.5%) and CD56 (16.4%) but less than synaptophysin (94.6%). Interestingly, INSM1 expression segregated IBC with neuroendocrine differentiation into different prognostic subgroups, particularly within luminal B subtype. Among the synaptophysin/chromogranin+ luminal B cancers, INSM1 expression was associated with significantly better survival (DFS χ2=8.009, p=0.004; BCSS χ2=3.873, p=0.049). Multivariate analysis showed that synaptophysin/chromogranin+ INSM1- status was an independent adverse factor for DFS (HR=2.282, 95%CI=1.196-4.356, p=0.012) in the luminal B subtype. Our data supported the usefulness of INSM1 in detecting neuroendocrine differentiation in IBC. Furthermore, INSM1 expression suggested a favourable prognostic impact; thus, it could be useful for stratifying neuroendocrine tumours with different prognosis.

In pancreatic cancer, extensive tumor involvement of the mesenteric venous system poses formidable challenges to operative resection. Such involvement can result from cavernous collateral veins leading to increased intraoperative blood loss or long-segment vascular defects of not only just the superior mesenteric vein but also even jejunal/ileal branches. Strategies to facilitate margin-free resection and safe vascular reconstruction in pancreatic surgery are important, particularly because systemic control of the tumor is improving with multi-agent chemotherapy regimens.

We describe a systematic, multidisciplinary assessment for patients with pancreatic cancer that involves the superior mesenteric vein, as well as the preoperative planning of those undergoing operative resection. In addition, detailed descriptions of operative approaches and technical strategies, which evolved with increasing experience at a high-volume center, are presented.

For the preoperative evaluation of tumor-free, vascular locaith curative intent.

Herein we share operative strategies to overcome involvement of the superior mesenteric vein in pancreatic cancer. Improvements in preoperative planning and operative technique can address common barriers to resection with curative intent.

Few studies evaluate the impact of unhealthy alcohol and drug use on the risk and severity of postoperative outcomes after upper gastrointestinal and pancreatic oncologic resections.

The National Inpatient Sample was queried to identify patients undergoing total gastrectomy, esophagectomy, total pancreatectomy, and pancreaticoduodenectomy between 2012 and 2015. Unhealthy alcohol and drug use was assessed by the International Classification of Diseases, Ninth Revision, and National Inpatient Sample coder designation. Multivariable regression was used to identify associations between alcohol and drug use and postoperative complication, duration of stay, hospital cost, and mortality.

In the study, 59,490 patients met inclusion criteria; 2,060 (3.5%) had unhealthy alcohol use; 1,265 (2.1%) had unhealthy drug use. Postoperative complication rates were higher in patients with alcohol and drug use than in abstainers (67.5% vs 62.8% vs 57.2%; P < .01). On multivariable regression, alcohol use was independently associated with increased risk of a nonwithdrawal complication (odds ratio 1.33 [1.05, 1.68]), and alcohol and drug use were independently associated with increased length of stay (1.54 [0.12, 2.96]) and 2.22 [0.90, 3.55] days) and cost ($5,471 [$60, $10,881] and $4,022 [$402, $7,643]), but not mortality.

Unhealthy substance use is associated with increased rates of postoperative complications, prolonged length of stay, and costs in patients undergoing major upper gastrointestinal and pancreatic oncologic resections. Screening and abstinence interventions should be incorporated into the preoperative care pathways for these patients.

Unhealthy substance use is associated with increased rates of postoperative complications, prolonged length of stay, and costs in patients undergoing major upper gastrointestinal and pancreatic oncologic resections. Screening and abstinence interventions should be incorporated into the preoperative care pathways for these patients.

We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these patients.

The National Surgery Quality Improvement Program database was used to identify patients undergoing appendectomy between 2005 and 2017. Logistic regression, support vector machines, random forest decision trees, and extreme gradient boosting machines were used to model the occurrence of postoperative sepsis.

In the study, 223,214 appendectomies were identified; 2,143 (0.96%) were indicated as having postoperative sepsis. Logistic regression (area under the curve 0.70; 95% confidence interval, 0.68-0.73), random forest decision trees (area under the curve 0.70; 95% confidence interval, 0.68-0.73), and extreme gradient boosting (area under the curve 0.70; 95% confidence interval, 0.68-0.73) afforded similar performance, while support vector machines (area under the curve 0.51; 95% confidence interval, 0.50-0.52) had worse performance. Variable importance analyses identified preoperative congestive heart failure, transfusion, and acute renal failure as predictors of postoperative sepsis.

Machine learning methods can be used to predict the development of sepsis after appendectomy with moderate accuracy. Such predictive modeling has potential to ultimately allow for preoperative recognition of patients at risk for developing postoperative sepsis after appendectomy thus facilitating early intervention and reducing morbidity.

Machine learning methods can be used to predict the development of sepsis after appendectomy with moderate accuracy. mTOR inhibitor Such predictive modeling has potential to ultimately allow for preoperative recognition of patients at risk for developing postoperative sepsis after appendectomy thus facilitating early intervention and reducing morbidity.Plastics contain a complex mixture of known and unknown chemicals; some of which can be toxic. Bioplastics and plant-based materials are marketed as sustainable alternative to conventional plastics. However, little is known with regard to the chemicals they contain and the safety of these compounds. Thus, we extracted 43 everyday bio-based and/or biodegradable products as well as their precursors, covering mostly food contact materials made of nine material types, and characterized these extracts using in vitro bioassays and non-target high-resolution mass spectrometry. Two-third (67%) of the samples induced baseline toxicity, 42% oxidative stress, 23% antiandrogenicity and one sample estrogenicity. In total, we detected 41,395 chemical features with 186-20,965 features present in the individual samples. 80% of the extracts contained >1000 features, most of them unique to one sample. We tentatively identified 343 priority compounds including monomers, oligomers, plastic additives, lubricants and non-intentionally added substances.