Differential Attachment of Pulmonary Cells on PDMS Substrate with Varied Features.

Cancer is now a global concern, and control of the function of cancer cells is recognized as an important challenge. Although many aggressive chemical and radiation methods are in practice to eliminate cancer cells, most of them imply severe adverse toxic effects on patients. Taking advantage of natural physical differences between cancer and normal cells might benefit the patient with more specific cytotoxicity and fewer adverse effects. Physical factors are the main means that can influence cell-biomaterial interaction. To explore the importance of attachment phenomena on cancer cells in this research, polydimethylsiloxane (PDMS) substrates with varied stiffness and roughness were synthesized and lung cancer cell's behavior on these surfaces was examined. To achieve diverse surface topography SDBD plasma was used at various exposure times, and different stiffness was obtained by changing in curing agent amount. Atomic force microscopy (AFM) and tensile modulus were employed to the characterization of roughness and stiffness respectively. Lung cancer cell survival and growth were studied by MTT and image processing analysis. The results indicated that softer and rougher surface made lung cancer cells to die. The number of detached cells, mean space of the detached cells, cellular coverage of surface, and the ratio of detached/ all cellular coverage were significantly affected by roughness and stiffness. Therefore, physical factors can control cell function, especially in lung cancer cells and these results might provide a strong base to help cancer cell removal.

Comparison of transmission FTIR and ATR spectra for discrimination between beef and chicken meat and quantification of chicken in beef meat mixture using ATR-FTIR combined with chemometrics.

Detecting meat adulteration for quality control and accurate labeling is important and needs convenient analytical methods. This study aimed to investigate and compare the application of the transmission and ATR approaches of FTIR followed by principal component analysis (PCA) to not only discriminate between chicken and beef meat but also quantizing chicken portion of mixtures. Two different approaches are presented; spectra preprocessing with focus on wavenumber region of 1700-1071 cm-1, and no preprocessed where PCA was applied on the whole spectra range of mid-FTIR. The results suggest that applying PCA on specified preprocessed spectra could detect hidden relationships between variables in chicken and beef in both approaches. PCA successfully clustered these kinds of meats when applied on transmission mode spectra without any preprocessing treatment, while applying it on ATR mode's raw spectra failed to cluster them. Additionally, the preprocessed ATR-FTIR spectrum was used to prepare regression models by Partial Least Square Regression (PLS-R) and artificial neural networks (ANN) for predicting presence and percentage of chicken meat in the beef meat mixture. The results demonstrated the superiority of ANN over PLS-R in this assessment with an R2 of 0.999.

Optimization of induction parameters, structure quality assessment by ATR-FTIR and in silico characterization of expressed recombinant polcalcin in three different strains of Escherichia coli.

The inhalation of Chenopodium album (C. album) pollen, especially polcalcin (Che a 3), has been reported as a significant source of allergic respiratory symptoms. This study was conducted to characterize biophysical differences of recombinant polcalcin come from three different Escherichia coli strains using attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), optimize recombinant polcalcin expression, and linear B-cell epitopes identification using in silico methods. ATR-FTIR results of purified proteins showed spectra intensity variance in the amide I region, while there were no changes in pick position and shape of the bands. SHuffle® T7 Express was selected for subsequent optimization due to ability in the correction of the mis-oxidized bonds and promotes proper folding which was validated by ATR-FTIR analysis results. Then, Response Surface Methodology was performed to optimize critical factors including induction temperature, duration of induction, and concentration of inducer. The best partitioning conditions were achieved in 1.5 mM IPTG for 10.97 h at 29.9 °C. Finally, prediction of polcalcin B-cell epitopes was carried out which indicated the presence of 4 different epitopes. Together, the results may help to the development of diagnostic approaches and also vaccine manufacture for desensitization and modulation of the allergic response in patients.

Identification and Quantification of Texture Soy Protein in A Mixture with Beef Meat Using ATR-FTIR Spectroscopy in Combination with Chemometric Methods.

Meat, as an important source of protein, is one of the main parts of many people's diet. Due to economic interests and thereupon adulteration, there are special concerns on its accurate labeling. In this study Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometric techniques (principal component analysis (PCA), artificial neural networks (ANNs), and partial least square regression (PLS-R)) were employed for discrimination of pure beef meat from textured soy protein plus detection and quantification of texture soy protein in a mixture with beef meat. Spectral preprocessing was carried out on each spectra including Savitzki-Golay (SG) smoothing filter, Standard Normal Vitiate (SNV), scatter correction (MSC), and min-max normalization. Spectral range 1700-1071 cm-1 was selected for further analysis. Principal component analysis showed discrete clustering of pure samples. In the next step, supervised artificial neural networks (ANNs) were performed for classification and discrimination. The results showed classification accuracy of 100% using this model. Furthermore, PLS-R model correlated the actual and FTIR estimated values of texture soy protein in beef meat mixture with coefficient of determination (R2) of 0.976. In conclusion, it was demonstrated that ATR-FTIR spectroscopy along with PCA and ANNs analysis might potentially replace traditional laborious and time-consuming analytical techniques to detect adulteration in beef meat as a rapid, low cost, and highly accurate method.