The Multifaceted Role of Signal Peptide-CUB-EGF Domain-Containing Protein (SCUBE) in Cancer

Signal peptide, CUB, and EGF-like domain-containing proteins (SCUBE) are secretory cell surface glycoproteins that play key roles in the developmental process. SCUBE proteins participate in the progression of several diseases, including cancer, and are recognized for their oncogenic and tumor suppressor functions depending on the cellular context. SCUBE proteins promote cancer cell proliferation, angiogenesis, invasion, or metastasis, stemness or self-renewal, and drug resistance. The association of SCUBE with other proteins alters the expression of signaling pathways, including Hedgehog, Notch, TGF-β/Smad2/3, and β-catenin. Further, SCUBE proteins function as potential prognostic and diagnostic biomarkers for breast cancer, renal cell carcinoma, endometrial carcinoma, and nasopharyngeal carcinoma. This review presents key features of SCUBE family members, and their structure and functions, and highlights their contribution in the development and progression of cancer. A comprehensive understanding of the role of SCUBE family members offers novel strategies for cancer therapy.     

Wave-induced flow of pore fluid in a cracked porous solid containing penny-shaped inclusions

An aim of current study is to analyze the contribution of reflected longitudinal waves to wave-induced fluid flow(WIFF)in the cracked porous solid.Initially,we ...     

Artificial neural network-driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

In recent years, smart healthcare, artificial intelligence (AI)-aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL-based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre-established datasets that fail to capture real-life applicability. Therefore, this study proposes an AI-enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare-based wearable devices (WD) for real-time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL-based architecture aggregates the optimizer weights of many clients using a fifth-generation (5G) communication channel. Then, the performance of the proposed FL-based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.     

Conjugation of curcumin with PVP capped gold nanoparticles for improving bioavailability

Curcumin, a natural polyphenolic compound, has astounding therapeutic applications but lacks in bioavailability mainly due to its poor solubility in water. Polyvinyl pyrrolidone (PVP) which is a proven drug carrier has been used to facilitate the conjugation of curcumin with gold nanoparticles and to improve the solubility of curcumin in water. In this conjugate diaryl heptanoid chromophore group of curcumin which is a much needed group in biomedical applications remains intact as observed from FTIR and UV–vis spectroscopy analysis. The work shows good promise for such conjugates as therapeutic-cum-imaging materials in biomedical field.     

Effect of Pre-heat and Inert Gas Species on Cryogenic Aerosol Cleaning Performance in Semiconductor Manufacturing

Cryogenic aerosol cleaning is a dry cleaning method used in the back end of line (BEOL) semiconductor manufacturing to remove defects from planar hydrophobic surfaces such as SiCOH and SiCxNyHz. Cryogenic aerosol cleaning is preferred over conventional wet cleaning methods as it is a non-contact cleaning method, which uses inert gases to generate sub-micrometer-sized solid aerosol particles that physically remove nanometer-sized contaminants on wafer surfaces. Particle removal mechanism involves detachment of the particles upon impact with aerosol, diffusion, and finally entrainment away from the wafer. In BEOL metal line patterning, particles on the dielectric isolation surfaces translate through the subsequent lithography and copper fill steps in to single or multiple metal line open defects that are yield killers. In this study, we show that the particle removal performance of the standard aerosol cleaning can be enhanced by pre-heating the wafer and use of a higher molecular weight inert gas, namely Ar, for aerosol generation. Both the addition of a Pre-heat step and the use of Ar as the aerosol source showed 47–52% reduction in single and multiple line opens detected through wafer electrical tests during high volume semiconductor manufacturing process.     

2-Aminobenzothiazole as an efficient corrosion inhibitor of AA6061-T6 in 0.5 M HCl medium: electrochemical,surface morphological,and theoretical study

Journal of Applied Electrochemistry - The inhibitive action of 2-aminobenzothiazole (ABT) on the corrosion of AA6061-T6 was evaluated in 0.5 M HCl by electrochemical techniques. The...     

Synthesis,microstructure and optical characterization of zirconium oxide nanostructures

Zirconium oxide (ZrO₂) nanostructures were synthesized by hydrothermal route. Surface morphology analysis depicts the formation of the nanobars and hexagonal-shaped nanodiscs at different synthesis conditions. The structural analysis confirms that the as-synthesized ZrO₂ product is of pure monoclinic phase (m-ZrO₂) with crystallite size of about 25 nm. The product consists of monodispersed nanoparticles of uniform composition, high purity, and crystallinity. The Raman spectra are quantitatively analyzed and the observed peaks are attributed to various vibration modes of the m-ZrO₂. The UV–vis absorption spectrum showed a strong absorption peak at about 292 nm and the estimated optical band gap was around 3.57 eV. Photoluminescence (PL) spectrum of ZrO₂ nanostructure showed a strong and broad emission peak at around 410 nm at room temperature, which can be attributed to the ionized oxygen vacancy in the material.     

SAR image despeckling using heavy-tailed Burr distribution

Multiplicative speckle noise diminishes the radiometric resolution of the synthetic aperture radar (SAR) images and all the coherent images. Speckle removal adds an extra value to an automated SAR image interpretation and analysis. In this paper, dual-tree complex wavelet-transform-based Bayesian method is proposed for despeckling the SAR images. In each subband, the reflectance and noise of the logarithmically transformed wavelet coefficients are modeled using heavy-tailed Burr and zero-mean Gaussian distributions. The closed-form expression for the shape parameter of Burr distribution is derived by employing the Mellin transform. The resultant complex-free quadratic maximum a posteriori solution with suitable shrinkage function yields despeckled SAR images. Extensive experiments are carried out using real SAR images as well as simulated images. The proposed method performs well in terms of equivalent number of looks with 3.5751 dB improvement in homogeneous region1 of Pipe river SAR image, edge preservation with 0.6158 improvement, peak signal to noise ratio of 51.3305 dB, and mean structural similarity index measure of 0.9397 at 0.05 noise variance for synthetically speckled image in comparison to the existing methods and takes averagely 2.3461 times less computing time. The proposed method provides a computationally efficient better speckle reduction in homogeneous regions while still preserving the edge.     

Antibacterial and dye degradation potential of zero‐valent silver nanoparticles synthesised using the leaf extract of Spondias dulcis

The present study reports a simple and low cost synthesis of zero‐valent silver nanoparticles (ZVSNPs) from silver nitrate using the leaf extract of Spondias dulcis. The ZVSNPs showed a unique peak at 420 nm in UV–vis spectrum. The SEM image portrayed cuboidal shaped particles. The EDX spectrum designated the elemental silver peak at 3 keV. In XRD, a sharp peak at 32.47° denoted the existence of (1 0 1) lattice plane and the average crystallite size was calculated as 48.61 nm. The lattice parameter was determined as 0.39 nm. The FTIR spectra of the leaf extract and ZVSNPs showed shifts in the specific functional group bands which ascertained the involvement of phytoconstituents in the formation and capping of nanoparticles. The average hydrodynamic size was measured as 59.66 nm by DLS method. A low PDI, 0.187 witnessed the monodispersity. A negative zeta potential value of −15.7 mV indicated the negative surface charges of the nanoparticles. The bactericidal action of ZVSNPs was demonstrated against two pathogens S.typhimurium and E.coli during which a dosage dependent zone of inhibition results was observed. Additionally, the catalytic potential of ZVSNPs was examined for the degradation of methylene blue dye in which an accelerated degradation of the dye was observed.Inspec keywords: antibacterial activity, crystallites, electrokinetic effects, scanning electron microscopy, nanoparticles, particle size, ultraviolet spectra, X‐ray chemical analysis, microorganisms, light scattering, nanofabrication, materials preparation, X‐ray diffraction, visible spectra, silver, dyes, Fourier transform infrared spectraOther keywords: wavelength 420.0 nm, Ag, voltage ‐15.7 mV, size 59.66 nm, size 0.39 nm, size 48.61 nm, electron volt energy 3.0 keV, Fourier transform infrared spectra, methylene blue dye, bactericidal action, dynamic light scattering, lattice parameter, Escherichia coli, Salmonella typhimurium, Spondias dulcis, negative zeta potential, polydispersity index, crystallite size, leaf extract, X‐ray diffraction, energy dispersive X‐ray spectrum, cuboidal‐shaped particles, scanning electron microscopy image, ultraviolet–visible spectrum, silver nitrate, zero‐valent silver nanoparticles