An Overview of Additive Manufacturing Technologies-A Review to Technical Synthesis in Numerical Study of Selective Laser Melting.

Additive Manufacturing (AM) processes enable their deployment in broad applications from aerospace to art, design, and architecture. Part quality and performance are the main concerns during AM processes execution that the achievement of adequate characteristics can be guaranteed, considering a wide range of influencing factors, such as process parameters, material, environment, measurement, and operators training. Investigating the effects of not only the influential AM processes variables but also their interactions and coupled impacts are essential to process optimization which requires huge efforts to be made. Therefore, numerical simulation can be an effective tool that facilities the evaluation of the AM processes principles. Selective Laser Melting (SLM) is a widespread Powder Bed Fusion (PBF) AM process that due to its superior advantages, such as capability to print complex and highly customized components, which leads to an increasing attention paid by industries and academia. Temperature distribution and melt pool dynamics have paramount importance to be well simulated and correlated by part quality in terms of surface finish, induced residual stress and microstructure evolution during SLM. Summarizing numerical simulations of SLM in this survey is pointed out as one important research perspective as well as exploring the contribution of adopted approaches and practices. This review survey has been organized to give an overview of AM processes such as extrusion, photopolymerization, material jetting, laminated object manufacturing, and powder bed fusion. And in particular is targeted to discuss the conducted numerical simulation of SLM to illustrate a uniform picture of existing nonproprietary approaches to predict the heat transfer, melt pool behavior, microstructure and residual stresses analysis.

Anthropometric variables accurately predict dual energy x-ray absorptiometric-derived body composition and can be used to screen for diabetes.

The current world-wide epidemic of obesity has stimulated interest in developing simple screening methods to identify individuals with undiagnosed diabetes mellitus type 2 (DM2) or metabolic syndrome (MS). Prior work utilizing body composition obtained by sophisticated technology has shown that the ratio of abdominal fat to total fat is a good predictor for DM2 or MS. The goals of this study were to determine how well simple anthropometric variables predict the fat mass distribution as determined by dual energy x-ray absorptometry (DXA), and whether these are useful to screen for DM2 or MS within a population. To accomplish this, the body composition of 341 females spanning a wide range of body mass indices and with a 23% prevalence of DM2 and MS was determined using DXA. Stepwise linear regression models incorporating age, weight, height, waistline, and hipline predicted DXA body composition (i.e., fat mass, trunk fat, fat free mass, and total mass) with good accuracy. Using body composition as independent variables, nominal logistic regression was then performed to estimate the probability of DM2. The results show good discrimination with the receiver operating characteristic (ROC) having an area under the curve (AUC) of 0.78. The anthropometrically-derived body composition equations derived from the full DXA study group were then applied to a group of 1153 female patients selected from a general endocrinology practice. Similar to the smaller study group, the ROC from logistical regression using body composition had an AUC of 0.81 for the detection of DM2. These results are superior to screening based on questionnaires and compare favorably with published data derived from invasive testing, e.g., hemoglobin A1c. This anthropometric approach offers promise for the development of simple, inexpensive, non-invasive screening to identify individuals with metabolic dysfunction within large populations.

A Reg family protein is overexpressed in islets from a patient with new-onset type 1 diabetes and acts as T-cell autoantigen in NOD mice.

Genes overexpressed in pancreatic islets of patients with new-onset type 1 diabetes are potential candidates for novel disease-related autoantigens. RT-PCR-based subtractive hybridization was used on islets from a patient who died at the onset of type 1 diabetes, and it identified a type 1 diabetes-related cDNA encoding hepatocarcinoma-intestine-pancreas/pancreatic-associated protein (HIP/PAP). This protein belongs to the family of Reg proteins implicated in islet regeneration; its gene contains a putative interleukin-6 (IL-6) response element. Islets from healthy cadaveric human donors released HIP/PAP protein into the culture medium, and this release was enhanced by the addition of IL-6. The expression pattern of mouse homologues of HIP/PAP was determined in pancreata of prediabetic and diabetic NOD mice. Both groups showed positive immunostaining for HIP/PAP in islets and ductal epithelium. To test whether HIP/PAP is a target of islet-directed autoimmunity, we measured splenic T-cell responses against HIP/PAP in NOD mice. Spontaneous proliferation was detected after 4 weeks. Lymphocytes from islet infiltrates and pancreatic lymph nodes from 7- to 10-week-old NOD mice were used to establish an HIP/PAP-specific I-A(g7)-restricted T-cell line, termed WY1, that also responded to mouse islets. WY1 cells homed to islets of NOD-SCID mice and adoptively transferred disease when coinjected with purified CD8(+) cells from diabetic NOD mice. Our conclusion was that differential cloning of Reg from islets of a type 1 diabetic patient and the response of Reg to the cytokine IL-6 suggests that HIP/PAP becomes overexpressed in human diabetic islets because of the local inflammatory response. HIP/PAP acts as a T-cell autoantigen in NOD mice. Therefore, autoimmunity to HIP/PAP might create a vicious cycle, accelerating the immune process leading to diabetes.