查看更多>>摘要：? 2021Background: The eighth edition of the American Joint Committee on Cancer (AJCC) staging manual's TNM staging classification for gastric neuroendocrine tumors has been shown to have poor prognostic discriminability. The aim of present study was to propose a modified T-stage classification, and externally validate its performance in a separate population data registry. Methods: A modified T-stage classification with tumor size and extent of tumor invasion was generated from the National Cancer Database between 2004 and 2014 (n = 1249). External validation was performed using the Surveillance, Epidemiology, and End Results registry between 1973 and 2013 (n = 539). Results: In the National Cancer Database population, using the AJCC T-stage classification, the 5-y survival rates were 85.7%, 80.8%, 64.5%, and 46.1% in T1, T2, T3, and T4 patients respectively (P < 0.001). These rates were more contrasting with the modified T-stage (mT) classification at 87.0%, 78.2%, 59.0%, and 40.3% respectively (P < 0.001). When patients within each of the AJCC T stages were stratified by mT stages, significant survival heterogeneity was observed within each of the AJCC T2 to T4 stages (P < 0.01). Conversely, when mT stages were stratified by AJCC T stage, no survival difference was observed in any of the mT stages (P > 0.05). The same analyses were performed using Surveillance, Epidemiology, and End Results data, and all the observed results were validated. Conclusion: The current AJCC T stage classification categorizes patients into groups with heterogenous prognosis, thus failing to serve as an effective staging tool. A modified T-stage classification demonstrated significantly improved stratification for patients with gastric neuroendocrine tumors.
查看更多>>摘要：? 2021 Elsevier Inc.Background: Central and systemic immune dysfunction after traumatic brain injury (TBI) can lead to infectious-related complications, which may result in delayed mortality. The role of early empiric antibiotics after TBI has not been characterized to date, but is recommended in select cases to decrease complications. We aimed to determine the relationship between early antibiotic use and in-hospital mortality in TBI patients. Methods: A retrospective review was conducted of TBI patients requiring ICU admission at an urban, academic, Level I trauma center from 01/2014 to 08/2016. Data collection included demographics, injury characteristics, details regarding antibiotic use, and outcomes. Early antibiotic administration was defined as any antibiotic given within 48 hs from admission. Patients given early antibiotics (EARLY) were compared to those who received their first dose later or did not receive any antibiotics (non-EARLY). Results: Of the 488 TBI patients meeting inclusion criteria, 189 (38.7%) received early antibiotics. EARLY patients were younger (EARLY 54.2 versus non-EARLY 61.5 ys, P <0.01) and more likely to be male (71.4% versus 60.9%, P = 0.02). Injury severity scores (23.6 versus 17.2, P <0.01) and regional head abbreviated injury scale scores (3.9 versus 3.7, P <0.01) were significantly higher in patients who received early antibiotics. Unadjusted in-hospital mortality rates were similar, however EARLY was associated with a lower mortality rate (AOR 0.17, 95% CI: 0.07 – 0.43, adjusted P <0.01) after adjusting for confounders. Conclusions: Despite presenting with a higher injury burden, TBI patients who received early antibiotics had a lower associated mortality rate compared to their counterparts. Future investigations are necessary to understand the underlying mechanisms that result in this potential survival benefit.
查看更多>>摘要：? 2021Introduction: Suicide rates for sexual minorities are higher than the heterosexual population. The purpose of this study is to explore circumstances surrounding suicide completion to inform future intervention strategies for suicide among lesbian, gay, bisexual and transgender (LGBT) individuals. Materials and Methods: We completed a retrospective analysis of data from the National Violent Death Reporting System (NVDRS) from 2013-2017. Victims identified as transgender were considered separately. We stratified analysis by identified sex of the victim for the LGB population. Results: Of the 16,831 victims whose sexual orientation or transgender status was known: 3886 (23.1%) were identified as female, 12,945 (76.9%) were identified as male. 479 (2.8%) were identified as LGBT; of these, 53 (11%) were transgender. LGBT victims were younger than non-LGBT victims. Male LGB victims were more likely to have a history of prior suicide attempts, past or current mental illness diagnosis, and were less likely to use firearms than male heterosexual victims. Female LGB victims were more likely to have problems in an intimate partner relationship than heterosexual women, while LGB men were more likely to have problems in family or other relationships. Transgender victims were again more likely to have mental health problems and a history of prior attempts, but less likely to have intimate partner problems and more likely to have a history of child abuse. Conclusions: These results highlight the importance of promoting suicide interventions that recognize the complex intersection between stated gender, sex, and sexuality and the different cultural impacts these identities can have.
查看更多>>摘要：? 2021 The AuthorsBackground: The clinical characterization of the biological status of complex wounds remains a considerable challenge. Digital photography provides a non–invasive means of obtaining wound information and is currently employed to assess wounds qualitatively. Advances in machine learning (ML) image processing provide a means of identifying “hidden” features in pictures. This pilot study trains a convolutional neural network (CNN) to predict gene expression based on digital photographs of wounds in a canine model of volumetric muscle loss (VML). Materials and Methods: Images of volumetric muscle loss injuries and tissue biopsies were obtained in a canine model of VML. A CNN was trained to regress gene expression values as a function of the extracted image segment (color and spatial distribution). Performance of the CNN was assessed in a held-back test set of images using Mean Absolute Percentage Error (MAPE). Results: The CNN was able to predict the gene expression of certain genes based on digital images, with a MAPE ranging from ～10% to ～30%, indicating the presence and identification of distinct, and identifiable patterns in gene expression throughout the wound. Conclusions: These initial results suggest promise for further research regarding this novel use of ML regression on medical images. Specifically, the use of CNNs to determine the mechanistic biological state of a VML wound could aid both the design of future mechanistic interventions and the design of trials to test those therapies. Future work will expand the CNN training and/or test set, with potential expansion to predicting functional gene modules.
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