An Imaging-Based Computational and Experimental Study of Skull Fracture: Finite Element Model Development

Bandak, Faris A.; Vorst, M. J. Vander; Stuhmiller, Louise M.; Mlakar, P. F.; Chilton, W. E.; Stuhmiller, James H.

doi:10.1089/neu.1995.12.679

Cited by 43 publications

(18 citation statements)

References 14 publications

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“…These criteria are computed using the intracranial mechanical behavior simulated by the finite element head model described in Bandak et al (1994), Takhounts et al (2003) and illustrated in Fig. 2.…”

Section: Simon Criteriamentioning

confidence: 99%

“…Hence, new injury criteria are proposed. In the past few decades, more than ten different three-dimensional finite element head models (FEHM) have been reported in the literature by Ward et al (1980), Shugar (1977), Hosey and Liu (1980), Dimasi et al (1991), Mendis (1992), Ruan et al (1991), Bandak et al (1994), Zhou et al (1995), Al-Bsharat et al (1999), Willinger et al (1999) and Zhang et al (2001). Fully documented head impact cases can be simulated in order to compute mechanical loading sustained by brain tissue as well as other tissues in the surrounding areas and we compare it to the real injuries described in the medical reports.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Head injury prediction capability of the HIC, HIP, SIMon and ULP criteria

Marjoux¹,

Baumgartner²,

Deck³

et al. 2008

Accident Analysis & Prevention

190

132

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Section: Simon Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Head injury prediction capability of the HIC, HIP, SIMon and ULP criteria

Marjoux¹,

Baumgartner²,

Deck³

et al. 2008

Accident Analysis & Prevention

190

132

View full text Add to dashboard Cite

“…Consequently, the non-invasive methods with high accuracy are urgently needed for MCD diagnosis are needed. The mathematical model based on the statistical analysis and computer technique has been used in clinic, such as computed tomography (CT) and nuclear magnetic resonance (NMR) (Bandak et al 1995;Hu et al 2011a). Levery et al established a new equation of estimated glomerular filtration rate (eGFR), named CKD-EPI, which could assess the stages of kidney diseases (CKD stage) (Levey et al 2009).…”

Section: Discussionmentioning

confidence: 99%

Peer Review #2 of "A diagnostic model for minimal change disease based on biological parameters (v0.2)"

2018

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Background: Minimal change disease (MCD) is a kind of nephrotic syndrome (NS). In this study, we aimed to establish a mathematical diagnostic model based on biological parameters to classify MCD. Methods: A total of 798 NS patients were divided into MCD group and control group. The comparison of biological indicators between two groups were performed with t-tests. Logistic regression was used to establish the diagnostic model, and the diagnostic value of the model was estimated using receiver operating characteristic (ROC) analysis. Results: 13 indicators including Anti-phospholipase A2 receptor (anti-PLA2R) (P=0.000), Total protein (TP) (P=0.000), Albumin (ALB) (P=0.000), Direct bilirubin (DB) (P=0.002), Creatinine (Cr) (P=0.000), Total cholesterol (CH) (P=0.000), Lactate dehydrogenase (LDH) (P=0.007), High density lipoprotein cholesterol (HDL) (P=0.000), Low density lipoprotein cholesterol (LDL) (P=0.000), Thrombin time (TT) (P=0.000), Plasma fibrinogen (FIB) (P=0.000), Immunoglobulin A (IgA) (P=0.008) and Complement 3 (C3) (P=0.019) were significantly correlated with MCD. Furthermore, the area under ROC curves of CH, HDL, LDL, TT and FIB were more than 0.70. Logistic analysis demonstrated that CH and TT were risk factors for MCD. According to the ROC of "CH+TT", the AUC was 0.827, with the sensitivity of 83.0% and the specificity of 69.8% (P=0.000). Conclusion: The established diagnostic model with CH and TT could be used for classified diagnosis of MCD.

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“…Loboa and colleagues extended such approaches to address new bone and other skeletal tissue formation in distraction osteogenesis and fracture healing . The use of high‐resolution images, such as magnetic resonance imaging (MRI) and computed tomography (CT) scans, has allowed for models to more accurately depict the natural anatomical geometries of tissues . Taken together, biomechanicians showed that the kinetics of bone remodeling in response to applied forces could be predicted by computational modeling.…”

Section: Biomechanics and Computational Analysismentioning

confidence: 99%

Key developments that impacted the field of mechanobiology and mechanotransduction

Wall

Butler

Haj

et al. 2017

Journal Orthopaedic Research

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Advances in mechanobiology have evolved through insights from multiple disciplines including structural engineering, biomechanics, vascular biology, and orthopaedics. In this paper, we reviewed the impact of key reports related to the study of applied loads on tissues and cells and the resulting signal transduction pathways. We addressed how technology has helped advance the burgeoning field of mechanobiology (over 33,600 publications from 1970 to 2016). We analyzed the impact of critical ideas and then determined how these concepts influenced the mechanobiology field by looking at the citation frequency of these reports as well as tracking how the overall number of citations within the field changed over time. These data allowed us to understand how a key publication, idea, or technology guided or enabled the field. Initial observations of how forces acted on bone and soft tissues stimulated the development of computational solutions defining how forces affect tissue modeling and remodeling. Enabling technologies, such as cell and tissue stretching, compression, and shear stress devices, allowed more researchers to explore how deformation and fluid flow affect cells. Observation of the cell as a tensegrity structure and advanced methods to study genetic regulation in cells further advanced knowledge of specific mechanisms of mechanotransduction. The future of the field will involve developing gene and drug therapies to simulate or augment beneficial load regimens in patients and in mechanically conditioning organs for implantation. Here, we addressed a history of the field, but we limited our discussions to advances in musculoskeletal mechanobiology, primarily in bone, tendon, and ligament tissues. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:605-619, 2018.

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An Imaging-Based Computational and Experimental Study of Skull Fracture: Finite Element Model Development

Cited by 43 publications

References 14 publications

Head injury prediction capability of the HIC, HIP, SIMon and ULP criteria

Head injury prediction capability of the HIC, HIP, SIMon and ULP criteria

Peer Review #2 of "A diagnostic model for minimal change disease based on biological parameters (v0.2)"

Key developments that impacted the field of mechanobiology and mechanotransduction

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