An Improved Method for Developing Injury Risk Curves Using the Brier Metric Score

Injury Risk for the Hand and Forearm Under Loading Representative of Behind Shield Blunt Trauma

Ballistic shields protect users from a variety of threats, including projectiles. Shield back-face deformation (BFD) is the result of the shield absorbing energy from a projectile and deforming towards the user. Back-face deformation can result in localized blunt loading to the upper extremity, where the shield is supported by the user and may cause injury through behind armour blunt trauma (BABT) mechanisms. Post-mortem human subject (PMHS) responses are critical to identify the injury risk in these high-rate scenarios and are used to quantify the injury tolerance. Two vulnerable locations along the upper extremity were investigated-the hand and forearm-using eight PMHS to identify the fracture threshold resulting from shield BABT loading conditions. Impacts delivered to the hand at 16.4 ± 0.8 m/s resulted in failure loads of 3818 ± 897 N, whilst the forearm impacts delivered at a similar velocity of 16.9 ± 1.9 m/s had lower failure loads at 3011 ± 656 N. The corresponding 10% risk of hand and forearm fractures (as measured on a modified WorldSID Anthropomorphic Test Device) were identified as 11.0 kN and 8.1 kN, respectively, which should be used when evaluating future designs of composite ballistic shields. This study is the first known investigation of the upper extremity to this high loading rate scenario and provides the foundation for future biomechanical research in the area of behind shield blunt trauma.

Lower Extremity Injury Risk Curve Development for a Human Body Model in the Underbody Blast Environment

Computational human body models (HBMs) provide the ability to explore numerous candidate injury metrics ranging from local strain based criteria to global combined criteria such as the Tibia Index. Despite these efforts, there have been relatively few studies that focus on determining predicted injury risk from HBMs based on observed postmortem human subjects (PMHS) injury data. Additionally, HBMs provide an opportunity to construct risk curves using measures that are difficult or impossible to obtain experimentally. The Global Human Body Models Consortium (GHBMC) M50-O v 6.0 lower extremity was simulated in 181 different loading conditions based on previous PMHS tests in the underbody blast (UBB) environment and 43 different biomechanical metrics were output. The Brier Metric Score were used to determine the most appropriate metric for injury risk curve development. Using survival analysis, three different injury risk curves (IRC) were developed: "any injury," "calcaneus injury," and "tibia injury." For each injury risk curve, the top three metrics selected using the Brier Metric Score were tested for significant covariates including boot use and posture. The best performing metric for the "any injury," "calcaneus injury" and "tibia injury" cases were calcaneus strain, calcaneus force, and lower tibia force, respectively. For the six different injury risk curves where covariates were considered, the presence of the boot was found to be a significant covariate reducing injury risk in five out of six cases. Posture was significant for only one curve. The injury risk curves developed from this study can serve as a baseline for model injury prediction, personal protective equipment (PPE) evaluation, and can aid in larger scale testing and experimental protocols.

Application of survival analysis to model proliferation likelihood of Escherichia coli biofilm following laser-induced hyperthermia treatment

Eighty percent of bacterial infections associated with living tissue and medical devices are linked to drug-resistant biofilms, leading to lengthy and costly recoveries. Laser-induced hyperthermia can disrupt cell proliferation within biofilms and increase susceptibility to antibiotics. However, there can be bacterial survival differences dependent upon laser irradiation times, and prolonged time at elevated temperature can damage healthy tissue. The objective of this study was to use survival analysis to model the impact of temperature increases on reducing viable biofilm bacteria. In vitro biofilms of Escherichia coli were grown on silicone discs or silicone doped with photothermal poly(3,4-ethylenedioxythiophene) hydrate (PEDOT) nanotubes, and subjected to laser-induced hyperthermia, using a 3 W continuous wave laser at 800 nm for varying times. The number of colony forming units per milliliter (CFU/mL) and maximum temperature were measured after each trial. Survival analysis was employed to estimate bacterial cell proliferation post-treatment to provide a quantitative framework for future studies evaluating photothermal inactivation of bacterial biofilms. The results demonstrate the first application of survival analysis for predicting the likelihood of bacterial cell proliferation based on temperature.

A more novel and powerful prognostic gene signature of lung adenocarcinoma determined from the immune cell infiltration landscape

Background

Lung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high mortality each year. The tumor immune cell infiltration (ICI) is closely associated with clinical outcome with LUAD patients. The present study was designed to construct a gene signature based on the ICI of LUAD to predict prognosis.

Methods

Downloaded the raw data of three cohorts of the TCGA-LUAD, GSE72094, and GSE68465 and treat them as training cohort, validation cohort one, and validation cohort two for this research. Unsupervised clustering detailed grouped LUAD cases of the training cohort based on the ICI profile. The univariate Cox regression and Kaplan-Meier was adopted to identify potential prognostic genes from the differentially expressed genes recognized from the ICI clusters. A risk score-based prognostic signature was subsequently developed using LASSO-penalized Cox regression analysis. The Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS were constructed to assess the ability to predict the prognosis and effects of clinical variables in another two independent validation cohorts. More innovatively, we searched similar papers in the most recent year and made comprehensive comparisons with ours. GSEA was used to discover the related signaling pathway. The immune relevant signature correlation identification and immune infiltrating analysis were used to evaluate the potential role of the signature for immunotherapy and recognize the critical immune cell that can influence the signature's prognosis capability.

Results

A signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients. The thirteen-gene signature exhibited independence in evaluating the prognosis of LUAD patients in our training and validation cohorts. Compared to our predecessors, our model has an advantage in predictive power. Nine well know immunotherapy targets, including TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, and PDCD1 were recognized correlating with our signature. The mast cells were found to play vital parts in backing on the thirteen-gene signature's outcome predictive capacity.

Conclusions

Collectively, the current study indicated a robust thirteen-gene signature that can accurately predict LUAD prognosis, which is superior to our predecessors in predictive ability. The immune relevant signatures, TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, PDCD1, and mast cells infiltrating were found closely correlate with the thirteen-gene signature's power.