Fibrillar level fracture in bone beyond the yield point

The nanoscale deformation and fracture mechanisms of parallel fibered bone are investigated using a novel combination of in-situ tensile testing to failure combined with high brilliance synchrotron X-ray scattering. The technique enables the simultaneous measurement of strain at two length scales – in the mineralized collagen fibrils (~100 nm diameter) along with the macroscopic strain (~1 mm diameter). Under constant rate tensile loading, we find that fibril strain saturates beyond the macroscopic yield point of bone at ~0.5 %, providing a correlation between the failure mechanisms at the nanoscale and the bulk structural properties. When bone stretched beyond the yield point is unloaded back to zero stress, the fibrils are contracted relative to their original state. We examine the findings in the context of a fiber – matrix shearing model at the nanometer level.     

FFCAEs: An efficient feature fusion framework using cascaded autoencoders for the identification of gliomas

Intracranial tumors arise from constituents of the brain and its meninges. Glioblastoma (GBM) is the most common adult primary intracranial neoplasm and is categorized as high-grade astrocytoma according to the World Health Organization (WHO). The survival rate for 5 and 10 years after diagnosis is under 10%, contributing to its grave prognosis. Early detection of GBM enables early intervention, prognostication, and treatment monitoring. Computer-aided diagnostics (CAD) is a computerized process that helps to differentiate between GBM and low-grade gliomas (LGG), using the perceptible analysis of magnetic resonance (MR) of the brain. This study proposes a framework consisting of a feature fusion algorithm with cascaded autoencoders (CAEs), referred to as FFCAEs. Here we utilized two CAEs and extracted the relevant features from multiple CAEs. Inspired by the existing work on fusion algorithms, the obtained features are then fused by using a novel fusion algorithm. Finally, the resultant fused features are classified with the Softmax classifier to arrive at an average classification accuracy of 96.7%, which is 2.45% more than the previously best-performing model. The method is shown to be efficacious thus, it can be useful as a utility program for doctors.     

Systemic Evaluation of Mechanism of Cytotoxicity in Human Colon Cancer HCT-116 Cells of Silver Nanoparticles Synthesized Using Marine Algae Ulva lactuca Extract

In the current study, biogenic silver nanoparticles (U-AgNPs) were synthesized using marine green macro-algal Ulva lactuca extract, and evaluated mechanism behind its anticancer activity against the Human colon cancer (HCT-116). The biogenic U-AgNPs were characterized using various physiochemical techniques. The TEM micrographs confirmed the spherical morphology of synthesized U-AgNPs, with a mean size of 8–14 nm. EDX spectrum as well as ICP-OES confirmed that AgNPs was nearly 90% purity for silver. FTIR Spectra analysis of U-AgNPs confirmed U. lactuca extract bioactive molecules presence over U-AgNPs surface as a stabilizing agent, thereby improving biocompatibility. The cytotoxicity study revealed the dose dependent cell death in colon cancer cells with no loss of viability in normal human colon epithelial cells. Furthermore, the fluorescence micrographs of nucleus staining assay revealed the DNA fragmentation and nucleus condensation of cancer cells treated with U-AgNPs, indicating an apoptosis-mediated cell death. The western bolt and RT-PCR analysis of U-AgNPs treated cancer cells showed the rise in proapoptotic markers (P53, Bax, and P21) and decline in anti-apoptotic markers (Bcl-2), thus confirming the p53-dependent apoptosis mediated cell death in HCT-116. Overall, our study concluded that novel biogenic U-AgNPs nanoparticles, synthesized using marine green macro-algal U. lactuca extract showed efficient anticancer activity against HCT-116 cell line and hence could work as potential therapeutic agent for targeted anti-cancer therapy.

     

Retrieval of parameters in micromixer with obstacle by cascade-forward-type artificial neural network: Comparison of four training algorithms under noisy data

Two parameters are retrieved in a passive Y-type micromixer with circular obstacle by cascade-forward-type artificial neural network (CFANN). The governing equations are solved by the finite volume method, under specific boundary conditions. The numerical model is then used to compute velocity profile and mixing efficiency, for different values of the Reynolds number. Thus, the velocity profiles along with Reynolds number (Re) and mixing efficiency (η) constitute the input–output pair of data. These data are used to train CFANN, and the network is monitored through different means, like, histograms, performance curves, and so forth. For inverse analysis, the trained CFANN model is fed with a new velocity profile as input, and corresponding values of Reynolds number and mixing efficiency are obtained as output. In an attempt to construct the optimum CFANN model, various combinations were explored, like, (1) different numbers of neurons in the hidden layer, (2) different noise levels in input data, and (3) different algorithms in the training stage. Finally, the CFANN with 10 hidden layer neurons with Levenberg–Marquardt (LM) algorithm was found to give retrieved values with up to 0.96% absolute error for all levels of noise in the input data. Also, the CFANN model with the LM algorithm has a very high value of regression coefficient of greater than 0.998, under all the noise values. Scaled conjugate gradient algorithm gives good results for the no-noise case, but fails poorly with the rise of noise. Other algorithms, like, Bayesian regularization and resilient backpropagation, perform poorly even in the no-noise case. The present approach is highly simple, accurate, and time efficient for applying inverse analysis in micromixers.     

Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis

Noise corrupts ultrasound images and degrades spatial and contrast resolutions. Hence, it is challenging to characterize the lesions precisely using ultrasound images. The present study aims to evaluate 67 denoising filters and select the best one for ultrasound image denoising. Seven test images were synthesized to evaluate the performance of filters at three different noise levels. Eleven full-reference quantitative image quality metrics (IQMs) were employed to evaluate the performance of the filters. A new filter evaluation method, Rank Analysis, was introduced and utilized at each noise level. The ten best filters with the smallest mean rank in all noise levels were defined for further analysis on real ultrasound images. The Rank Analysis was also employed for real ultrasound images, and filters were evaluated based on 14 IQMs (11 full-reference and three no-reference). Finally, the best filter was defined using the repeated measures analysis statistical test. According to the Rank Analysis results, the Spatial correlation (SCorr) filter obtained the best results with the mean rank scores±SD of 1 ± 0, which was significantly better than the other nine filters (p < 0.001). The second-best results were achieved by three filters, Bitonic, most homogeneous neighborhood, and Lee diffusion (p < 0.05). We concluded that SCorr is the best filter for ultrasound image denoising. It can be used in the pre-processing step before segmentation and diagnostic procedures. In addition, a new filter evaluation method, Rank Analysis, was introduced in this study, which is easy to use, fast, and provides reliable results. So, it can be used to evaluate newly developed filters in the future studies.     

Stratum-Confined Solid-State Reaction (SC-SSR) toward Colloidal Silicon-Based Hollow Nanostructures for Bioapplications

Silicon nanostructures (SiNSs) can provide multifaceted bioapplications; but preserving their subhundred nm size during high-temperature silica-to-silicon conversion is the major bottleneck. The SC-SSR utilizes an interior metal-silicide stratum space at a predetermined radial distance inside silica nanosphere to guide the magnesiothermic reduction reaction (MTR)-mediated synthesis of hollow and porous SiNSs. In depth mechanistic study explores solid-to-hollow transformation encompassing predefined radial boundary through the participation of metal-silicide species directing the in-situ formed Si-phase accumulation within the narrow stratum. Evolving thin-porous Si-shell remains well protected by the in-situ segregated MgO emerging as a protective cast against the heat-induced deformation and interparticle sintering. Retrieved hydrophilic SiNSs (<100 nm) can be conveniently processed in different biomedia as colloidal solutions and endocytosized inside cells as photoluminescence (PL)-based bioimaging probes. Inside the cell, rattle-like SiNSs encapsulated with Pd nanocrystals can function as biorthogonal nanoreactors to catalyze intracellular synthesis of probe molecules through C-C cross coupling reaction.     

Severe hypertriglyceridemia in an infant on chronic hemodialysis

Severe hyperlipidemia is a risk factor for cardiovascular disease. Children with chronic kidney disease and end stage renal disease are at risk for development of hyperlipidemia. In this report, we describe a 7-month-old male infant with Denys–Drash syndrome who was found to have a “milky-layer” floating on the deaerator of the hemodialysis machine. Investigations showed severe hypertriglyceridemia of >1000 mg/dl. The patient had been on chronic continuous manual peritoneal dialysis until 6 months of age and recently had been switched to hemodialysis. Management included lowering of caloric intake and addition of medium chain triglyceride with reduction of the serum triglyceride levels to 300–400 mg/dl. Close monitoring of serum lipids and timely intervention is important to prevent serious complications associated with dyslipidemia. Observation of the “milky layer” in the deaerator of the hemodialysis machine may be an interesting visual clue of underlying severe hypertriglyceridemia.     

Modelling and Optimization of Phase Locked Loop under Constrained Channel Length and Width of MOSFETs

Silicon - CMOS integrated circuits consisting of MOSFETs have tradeoffs among their performance parameters. Hence they need minimization in those tradeoffs calling for multi objective optimization...