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Materials for 3D Printing in Medicine
Published in Harish Kumar Banga, Rajesh Kumar, Parveen Kalra, Rajendra M. Belokar, Additive Manufacturing with Medical Applications, 2023
Kamal Kishore, Roopak Varshney, Param Singh, Manoj Kumar Sinha
In old age, health problems like bone fracture and tissue losses are more common. These problems require metals for fixation, replacement and reconstruction of tissue and bone. These metals provide support to the patient for immediate mobilisation (Niinomi, 2007). Metals and their alloys have better mechanical characteristics such as high strength, high elasticity, better anti-wear and corrosion resistance. Nowadays, around 75 per cent of medically used implants are made of metals like stainless steel, titanium alloys (Banerjee & Williams, 2013), cobalt-chromium alloys (Hsu et al., 2005), niobium, nitinol and tantalum (Black, 1994). But in recent years, biodegradable metals such as magnesium (N. Li & Zheng, 2013), zinc (Xiang et al., 2014), iron (Vorndran et al., 2011) and calcium are more in use for the manufacturing of implants. Table 5.2 presents a brief of different types of metals used in the biomedical field. For medical applications, AM-based metal and their alloys are classified into three groups as mentioned below:Conventional metals and their alloysBiodegradable metalsShape-memory alloys
Mechanical circulatory support device selection for bridging to cardiac transplantation: a clinical guide
Published in Expert Review of Medical Devices, 2023
Tamari Miller, Veli K. Topkara
The implementation of the new UNOS heart allocation system in 2018 has impacted trends in the use of the durable LVADs for bridging to transplant. In this new system, patients with a device complication may be designated status 4 or higher. However, UNOS data suggests a 90% decline in the number of patients listed for transplant with durable VADs in the new allocation system with more recent analyses demonstrating a smaller though significant decline of about 30% [41,42]. With MOMENTUM 3 5-year follow-up data showing a 54% survival to transplant, recovery, or LVAD support free of debilitating strokes or reoperation and 58% overall survival in patients with HM3 LVAD, the only durable LVAD currently commercially available, more data is needed to understand what bridging to transplant will look like in this new landscape. Early analyses have demonstrated that bridging to transplant with HM3 LVAD is safe in carefully selected patients. Our analysis of the UNOS registry showed comparable wait times in patients supported with HM3 in the old and new heart allocation systems and suggests individualized decision-making about transplant for patients with HM3 LVAD who are high risk for transplant [42]. High-risk features for bridging with HM3 LVAD included old age, ischemic etiology, poor functional status, elevated creatine, pulmonary hypertension, and obesity, which should be taken under consideration for decision-making.
Prediction of the Probability and Risk Factors of Early Abdominal Aortic Aneurysm Using the Gradient Boosted Decision Trees Model
Published in Applied Artificial Intelligence, 2022
Risk factors causing AAA were selected based on the clinical experience of the doctors and data from various studies. The data from both AAA patients and healthy individuals were selected and collected to form an AAA sample set. It is generally known that male, smoking, old age (older than 65 years), obesity, hypertension, coronary heart disease, diabetes, hyperlipidemia, peripheral artery disease, and related family history are risk factors for AAA(Perrin, Badel, and Ogeas 2016). A total of 15 features were selected, including age, sex, blood pressure (BP), triglycerides (TG), Low density lipoprotein cholesterol (LDL-C), blood glucose (Glu), smoking, alcohol consumption (Drink), family history (FH), body mass index (BMI), homocysteine (Hcy), uric acid (UA), chronic obstructive pulmonary disease (COPD), history of coronary heart disease (CHD), and history of cerebrovascular disease (CVD), as shown in Table 1.
Relating mechanical properties of vertebral trabecular bones to osteoporosis
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2020
R. Cesar, J. Bravo-Castillero, R. R. Ramos, C. A. M. Pereira, H. Zanin, J. M. D. A. Rollo
Osteoporosis is the most common osteometabolic disease in old age, especially in women, resulting in higher morbidity and mortality rates (Schweser and Crist 2017; Sozen et al. 2017; Kanis et al. 2018). This disease occurs when there is an imbalance between bone formation and reabsorption (turnover). This process causes a progressive reduction in mineral density (BMD) and mechanical resistance, increased fragility and risk of bone fracture ( (Fonseca et al. 2014; Chandran 2017; Sandino et al. 2017 ). Usually, fractures associated with this disorder have a higher occurrence in regions of the trabecular bone as the neck of the femur and vertebrae (Ballane et al. 2017; Borges et al. 2017; Loures et al. 2017). DXA is considered the gold standard for estimating the BMD, mechanical resistance and risk of bone fracture (Hans and Baim 2017; Michael Lewiecki and Binkley 2017; Subramaniam et al. 2018). However, BMD by itself does not fully explain the variation in bone strength and fracture risk in the clinical treatments (Griffith and Genant 2008; Tawara et al. 2010; Perilli et al. 2012). Therefore, complementary evaluation is fundamental to the success in determining fracture risk associated with osteoporosis (Curtis et al. 2017; Sandino et al. 2017; Ripamonti et al. 2018; Tomasevic-Todorovic et al. 2018). Osteopenia is a condition in which bone density levels are lower than normal but not as low as in osteoporosis. The World Health Organization (WHO) used the BMD as a T-score to classify individuals young normal (> -1,0), osteopenic (between -1.0 and -2.5), and osteoporotic (≤ -2.5) (Karaguzel and Holick 2010; Dimai 2017).