Metallic biomaterials are engineered systems designed to provide internal support to biological tissues and they are being used largely in joint replacements, dental implants, orthopaedic fixations and stents. Higher biomaterial usage is associated with an increased incidence of implant-related complications due to poor implant integration, inflammation, mechanical instability, necrosis and infections, and associated prolonged patient care, pain and loss of function. In this review, we will briefly explore major representatives of metallic biomaterials along with the key existing and emerging strategies for surface and bulk modification used to improve biointegration, mechanical strength and flexibility of biometals, and discuss their compatibility with the concept of 3D printing.

The NASA Task Load Index (TLX) is a popular technique for measuring subjective mental workload. It relies on a multidimensional construct to derive an overall workload score based on a weighted average of ratings on six subscales: mental demand, physical demand, temporal demand, performance, effort, and frustration level. A program for implementing a computerized version of the NASA TLX is described. The software version assists in simplifying collection, postprocessing, and storage of raw data. The program collects raw data from the subject and calculates the weighted (or unweighted) workload score, which is output to a text file. The program can also be tailored to a specific experiment using a simple input text file, if desired. The program was designed in Visual Studio 2005 and is capable of running on a Pocket PC with Windows CE or on a PC with Windows 2000 or higher. The NASA TLX program is available for free download.

Raman spectroscopy shows potential in differentiating tumors from normal tissue. We used Raman spectroscopy with near‐infrared light excitation to study normal breast tissue and tumors from 11 mice injected with a cancer cell line. Spectra were collected from 17 tumors, 18 samples of adjacent breast tissue and lymph nodes, and 17 tissue samples from the contralateral breast and its adjacent lymph nodes. Discriminant function analysis was used for classification with principal component analysis scores as input data. Tissues were examined by light microscopy following formalin fixation and hematoxylin and eosin staining. Discriminant function analysis and histology agreed on the diagnosis of all contralateral normal, tumor, and mastitis samples, except one tumor which was found to be more similar to normal tissue. Normal tissue adjacent to each tumor was examined as a separate data group called tumor bed. Scattered morphologically suspicious atypical cells not definite for tumor were present in the tumor bed samples. Classification of tumor bed tissue showed that some tumor bed tissues are diagnostically different from normal, tumor, and mastitis tissue. This may reflect malignant molecular alterations prior to morphologic changes, as expected in preneoplastic processes. Raman spectroscopy not only distinguishes tumor from normal breast tissue, but also detects early neoplastic changes prior to definite morphologic alteration. © 2007 Wiley Periodicals, Inc. Biopolymers 89: 235–241, 2008. This article was originally published online as an accepted preprint. The “Published Online”date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com

This paper introduces a new robust method for the removal of background tissue fluorescence from Raman spectra. Raman spectra consist of noise, fluorescence and Raman scattering. In order to extract the Raman scattering, both noise and background fluorescence must be removed, ideally without human intervention and preserving the original data. We describe the rationale behind our robust background subtraction method, determine the parameters of the method and validate it using a Raman phantom against other methods currently used. We also statistically compare the methods using the residual mean square (RMS) with a fluorescence-to-signal (F/S) ratio ranging from 0.1 to 1000. The method, 'adaptive minmax', chooses the subtraction method based on the F/S ratio. It uses multiple fits of different orders to maximize each polynomial fit. The results show that the adaptive minmax method was significantly better than any single polynomial fit across all F/S ratios. This method can be implemented as part of a modular automated real-time diagnostic in vivo Raman system.