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Deep learning (DL)-based categorization and detection methods for breast cancer diagnosis through medical images are utilized by computer-aided diagnosis (CAD) techniques. In this study, two DL-based ensemble models were proposed to improve the diagnostic efficiency of the system. The proposed models were evaluated on two mammography datasets: DDSM and CBIS-DDSM. To enhance the performance of breast lesion classification from mammographic scans, three pretrained convolutional neural network (CNN) models, namely VGG16, InceptionV3, and VGG19, were used as base classifiers, and two ensemble models were trained. Ensemble Model 1, using a linear meta-learner in the form of logistic regression for classification, and Ensemble Model 2, using a neural net as the meta-learner for classification. The DDSM dataset achieved accuracy, sensitivity, and specificity of 98.02%, 97.17%, and 98.87%, respectively, for Ensemble Model 1, and 98.10%, 97.01%, and 99.12%, respectively, for Ensemble Model 2. Furthermore, the performance of the proposed models was compared with existing state-of-the-art systems, and the results showed an increase in breast cancer classification performance with the proposed models. Hence, the proposed models have the potential to aid medical professionals in accurately classifying breast lesions. [ABSTRACT FROM AUTHOR]

This paper reports the development of bioactive fluorcanasite-fluorapatite nanoparticle (nFC-FAp) reinforced poly-(ε-caprolactone) (PCL) bio-nanocomposite bone scaffolds using a biomimetic approach. The filler-matrix combination was selected in particular, to facilitate biomineralization, tunable degradation and augmented cellular response. A novel hybrid technique was adopted to generate hierarchical porosity within the bone scaffolds to match the porous architecture of natural bone. The in-house synthesized nanostructured FC-FAp demonstrated presence of fluorcanasite and fluorapatite biominerals, conducive phases for bone formation. Fourier transform-infrared spectroscopy analysis of PCL/nFC-FAp scaffolds indicated interfacial compatibility between PCL matrix and nFC-FAp reinforcement. Microstructural analysis of scaffolds through field emission-scanning electron microscopy confirmed generation of interconnected hierarchical gradient porosity, while, synchrotron-based X-ray micro-computed tomography study revealed three-dimensional architectural details of the scaffolds, with anticipated favorable cellular environment for bone cell activities. Investigations of density and overall porosity of the scaffolds revealed that although, apparent densities increased with increasing loading of nFC-FAp, the variation in relative density and overall porosity values were minimal, establishing the efficacy of hybrid biofabrication approach. Water contact angle results indicated enhanced hydrophilic nature (surface wettability) of the bio-nanocomposite bone scaffolds, a conducive environment for enhanced cellular response. In-vitro biodegradation studies indicated tunable degradation and permissible pH stability of scaffolds with incorporation of nFC-FAp reinforcement. In-vitro biocompatibility studies based on MTT assay and fluorescence microscopy further established enhanced cell survival, viability, and proliferation with osteosarcoma bone cells. Overall, this study highlights a promising bioinspired strategy to develop composite bone scaffolds towards expedited repair of bone damages. [ABSTRACT FROM AUTHOR]

Increased use of ultra-wideband (UWB) in biomedical applications based on wireless body area networks (WBAN) opens a variety of options in the field of biomedical research. WBAN may aid in the continuous health monitoring of patients while they go about their everyday lives. Many studies and researchers were conducted several experimentations in the same field for the performance improvement. This study covered the hybridization of UWB technology, as well as on-body, off-body, and human-body ultra-wideband communication (HB-UWB). In this paper, the parameters considered are throughput, energy consumption, energy efficiency, energy used, network survival and delay. An improved model for design and assessment of power-saving UWB-WBAN was developed in this paper. A novel protocol model was introduced in this paper, namely low-power traffic-aware emergency based narrowband protocol (LTE-NBP) to overcome the major drawbacks of emergency, critical data transmission, reliability and the power issues in UWB-WBAN. It's the emergency-based low-power traffic-aware narrowband protocol. It is based on the dual-band physical layer technology. The suggested protocol considered an aware traffic model and an emergency medium access control (MAC) protocol. The proposed model's performance was evaluated and compared with the related algorithms on different performance parameters. The improved model is found to be efficient in throughput, energy efficiency, energy consumption, and delay. [ABSTRACT FROM AUTHOR]

The Eclipta alba plant is considered hepatoprotective, owing to its phytoconstituents wedelolactone. In the current study, effect of elevated ultraviolet-B (eUV-B) radiation was investigated on biochemical, phytochemical, and antioxidative enzymatic activities of E. alba (Bhringraj) plant. The UV-B exposure resulted in an increase in oxidative stress, which has caused an imbalance in phytochemical, biochemical constituents, and induced antioxidative enzymatic activities. It was observed that the UV-B exposure promoted wedelolactone yield by 23.64%. Further, the leaf extract of UV-B-exposed plants was used for the synthesis of carbon quantum dots (CQDs) using low cost, one-step hydrothermal technique and its biocompatibility was studied using in vitro MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay on HepG2 liver cell line. It revealed no toxicity in any treatment groups in comparison to the control. Both CQDs and leaf extract were orally administered to the golden hamster suffering from alcohol-induced liver cirrhosis. In the morphometric study, it was clearly observed that a combination of UV-B-exposed leaf extract and synthesized CQDs delivered the best result with maximum recovery of liver tissues. The present study reveals the positive impact of UV-B exposure on the medicinally important plant, increased yield of wedelolactone, and its enhanced hepatoprotective efficacy for the treatment of damaged liver tissues. [ABSTRACT FROM AUTHOR]

The electronic and electrical industrial sector is exponentially growing throughout the globe, and sometimes, these wastes are being disposed of and discarded with a faster rate in comparison to the past era due to technology advancements. As the application of electronic devices is increasing due to the digitalization of the world (IT sector, medical, domestic, etc.), a heap of discarded e-waste is also being generated. Per-capita e-waste generation is very high in developed countries as compared to developing countries. Expansion of the global population and advancement of technologies are mainly responsible to increase the e-waste volume in our surroundings. E-waste is responsible for environmental threats as it may contain dangerous and toxic substances like metals which may have harmful effects on the biodiversity and environment. Furthermore, the life span and types of e-waste determine their harmful effects on nature, and unscientific practices of their disposal may elevate the level of threats as observed in most developing countries like India, Nigeria, Pakistan, and China. In the present review paper, many possible approaches have been discussed for effective e-waste management, such as recycling, recovery of precious metals, adopting the concepts of circular economy, formulating relevant policies, and use of advance computational techniques. On the other hand, it may also provide potential secondary resources valuable/critical materials whose primary sources are at significant supply risk. Furthermore, the use of machine learning approaches can also be useful in the monitoring and treatment/processing of e-wastes. Highlights: In 2019, ~ 53.6 million tons of e-wastes generated worldwide. Discarded e-wastes may be hazardous in nature due to presence of heavy metal compositions. Precious metals like gold, silver, and copper can also be procured from e-wastes. Advance tools like artificial intelligence/machine learning can be useful in the management of e-wastes. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common diseases in women; it can have long-term implications and can even be fatal. However, early detection, achieved through recent advancements in technology, can help reduce mortality. In this paper, different machine intelligence techniques [machine learning (ML), and deep learning (DL)] were analysed in the context of breast cancer. In addition, the classification of breast cancer into malignant and benign using different breast cancer image modalities were discussed. Furthermore, the diagnosis of breast cancer using various publicly and privately available image datasets, pre-processing techniques, feature extraction techniques, comparison between conventional ML and different convolutional neural network (CNN) architectures, and transfer learning techniques were discussed in detail. It also correlates the parameters and attributes impact in case of different methods applied. Advantages and the limitations of the machine intelligence approaches were highlighted based on the discussion and analysis. A total of 162 research publications was considered for the time period of 2015–2021. These are in the chronological order of their appearance. This systematic literature review will be helpful to the researchers due to the detailed analysis of different methodologies and in conducting further investigations. [ABSTRACT FROM AUTHOR]

The present article critically reviewed the potentiality of Mg–Ca silicate-based crystalline bioceramics such as MgSiO3, Mg2SiO4, CaSiO3, Ca2SiO4, Ca3SiO5, CaMgSi2O6, Ca2MgSi2O7, Ca7MgSi4O16, CaMgSiO4 and Ca3MgSi2O8 as new generation orthopedic prosthetic implants. Mg2+, Ca2+ and Si4+ ions are abundant in bone and play a crucial role in various bone metabolic activities such as enhancing osteogenesis and inhibiting osteoporosis. The release rate of Mg2+, Ca2+ and Si4+ ions from these bioceramics depends on the crystal structure which consequently, influences their bioactivity and biocompatibility. In addition, the release rate of these ions can be tuned by tailoring the processing parameters/routes and compositional modifications and subsequently, bioactivity, cellular response as well as bone regeneration ability can be improved. Toward this end, the present article thoroughly reviewed and analyzed the influence of crystal structure, processing parameters/routes and compositional alteration on in vitro/in vivo biocompatibility and degradation behavior of the above ceramics. Further, a correlation between structure, processing and properties has been established. [ABSTRACT FROM AUTHOR]

Big data is prominent for the systematic extraction and analysis of a huge or complex dataset. It is also helpful in the management of data as compared to the traditional data-processing mechanisms. In this paper, an efficient ant colony optimization (ACO) and particle swarm optimization (PSO)-based framework have been proposed for data classification and preprocessing in the big data environment. It shows that the content part can be collaborated and fetched for analysis from the volume and velocity integration. Then weight marking has been done through the volume and the data variety. In the end, the ranking has been done through the velocity and variety aspects of big data. Data preprocessing has been performed from weights assigned on the basis of size, content, and keywords. ACO and PSO are then applied considering different computation aspects like uniform distribution, random initialization, epochs, iterations, and time constraint in case of both minimization and maximization. The weight assignments have been done automatically and through an unbiased random mechanism. It has been done on a scale of 0–1 for all the separated data. Then simple adaptive weight (SAW) method has been applied for prioritization and ranking. The overall average classification accuracy obtained in the case of PSO-SAW is 98%, and in the case of ACO-SAW, it is 95%. PSO-SAW approach outperforms in all cases, in comparison to ACO-SAW. [ABSTRACT FROM AUTHOR]

The piezoelectric properties of ferroelectric ceramics have been widely investigated in the materials science community, but relatively less is known about such properties of natural living system. Inspired by the knowledge that the piezoelectric properties influence the metabolic activities of natural bone, the design and development of bone-mimicking electrically active synthetic orthopedic implant materials has gained an excellent distinction in the biomaterials community. In this perspective, the present article briefly reviews the origin of fundamental electrical responses in natural bone along with their biological consequences. In this sequence, the potentiality of multifunctional electrically active perovskites (CaTiO3, BaTiO3, Na0.5K0.5NbO3 etc.) as promising bone substitute has been discussed. The results of several published studies from the author's research group are summarized to highlight the cytocompatibility and histocompatibility of those perovskites. Further, the influence of incorporation of these piezoelectric materials as secondary phases in a bioactive matrix in improving the electromechanical response of composite has also been discussed briefly. It is perceived that this review will stimulate further research to explore the biomedical applications of these functional oxides. [ABSTRACT FROM AUTHOR]

The present work reports the effect of charged surfaces of BaTiO3 substrate on the synthesis of ZnO. The unpolarized and polarized BaTiO3 substrates were immersed in ZnO precursor solution and synthesis behavior of ZnO on both the surfaces was compared. The polarized BaTiO3 substrates have been observed to accelerate ZnO crystal growth. The surface charge density of the polarized substrate was measured to be 1.6 μC/cm2. To understand the effect of amount of substrate charge on growth behavior of ZnO crystals, the substrates with varying charge densities were prepared through partial depolarization of polarized substrates. With increase in the amount of surface charge, crystallization of ZnO increases due to enhanced electrostatic interactions. Overall, the polarization-assisted deposition method has been suggested as a simple, efficient, and rapid technique for the synthesis of multifunctional ZnO. [ABSTRACT FROM AUTHOR]