Abstract Background Zingiber officinale, generally known as ginger, contains bioactive phytochemicals, including gingerols and shogaols, that may function as reducing agents and stabilizers for the formation of nickel nanoparticles (Ni-NPs). Ginger extract-mediated nickel nanoparticles were synthesized using an eco-friendly method, and their antibacterial, antioxidant, antiparasitic, antidiabetic, anticancer, dye degrading, and biocompatibility properties were investigated. Methods UV–visible spectroscopy, fourier transform infrared spectroscopy, X-ray powder diffraction, energy-dispersive X-ray spectroscopy, and scanning electron microscopy were used to validate and characterize the synthesis of Ni-NPs. Agar well diffusion assay, alpha-amylase and glucosidase inhibitory assay, free radical scavenging assay, biocompatibility assay, and MTT assay were used to analyse the biomedical importance of Ni-NPs. Results SEM micrograph examinations revealed almost aggregates of Ni-NPs; certain particles were monodispersed and spherical, with an average grain size of 74.85 ± 2.5 nm. Ni-NPs have successfully inhibited the growth of Pseudomonas aeruginosa , Escherichia coli , and Proteus vulgaris by inducing membrane damage, as shown by the absorbance at 260 nm (A260). DPPH (2,2-diphenyl-1-picrylhydrazyl) free radicals were successfully scavenged by Ni-NPs at an inhibition rate of 69.35 ± 0.81% at 800 µg/mL. A dose-dependent cytotoxicity of Ni-NPs was observed against amastigote and promastigote forms of Leishmania tropica , with significant mortality rates of 94.23 ± 1.10 and 92.27 ± 1.20% at 1.0 mg/mL, respectively. Biocompatibility studies revealed the biosafe nature of Ni-NPs by showing RBC hemolysis up to 1.53 ± 0.81% at 400 µg/mL, which is considered safe according to the American Society for Materials and Testing (ASTM). Furthermore, Ni-NPs showed antidiabetic activity by inhibiting α-amylase and α-glucosidase enzymes at an inhibition rate of 22.70 ± 0.16% and 31.23 ± 0.64% at 200 µg/mL, respectively. Ni-NPs have shown significant cytotoxic activity by inhibiting MCF-7 cancerous cells up to 68.82 ± 1.82% at a concentration of 400 µg/mL. The IC50 for Ni-NPs was almost 190 µg/mL. Ni-NPs also degraded crystal violet dye up to 86.1% at 2 h of exposure. Conclusions In conclusion, Zingiber officinale extract was found successful in producing stable nanoparticles. Ni-NPs have shown substantial biomedical activities, and as a result, we believe these nanoparticles have potential as a powerful therapeutic agent for use in nanomedicine.
Individual who have lost their lower limb because of amputation can use the prosthesis to restore daily living activities. The amputee intent recognition during locomotion modes can be used as source to control lower limb prosthesis. Due to continuous data recording from multiple sensors, the timely recognition of activities of daily living have become a challenging issue for traditional technology and conventional machine learning algorithms. This work hypothesize that parallel discriminant features can be learned from large amount of data generated by aggregating the neuromechanical signals from multiple subjects with parallel and distributed computing platform. Consequently, this paper apply three classifiers including support vector machine, decision tree and random forest on large data sets. The model performance is extensively evaluated in terms of different performance measurement parameters such as accuracy, efficiency, scalability and speedup in sequential and distributed environment. The experimental results show that the parallel approach achieved 3.9x computation speedup as compared to the sequential approach without affecting accuracy level. The parallel support vector machine algorithm demonstrated high speedup and scalability in comparison with random forest and decision tree algorithms. The outcome of this study could promote parallel based model for the unobtrusive recognition of lower limb locomotion modes and could promote the future design for the intelligent control of prostheses and exoskeleton.
Microscopic entities, microorganisms that drastically affect human health need to be thoroughly investigated. A biofilm is an architectural colony of microorganisms, within a matrix of extracellular polymeric substance that they produce. Biofilm contains microbial cells adherent to one-another and to a static surface (living or non-living). Bacterial biofilms are usually pathogenic in nature and can cause nosocomial infections. The National Institutes of Health (NIH) revealed that among all microbial and chronic infections, 65% and 80%, respectively, are associated with biofilm formation. The process of biofilm formation consists of many steps, starting with attachment to a living or non-living surface that will lead to formation of micro-colony, giving rise to three-dimensional structures and ending up, after maturation, with detachment. During formation of biofilm several species of bacteria communicate with one another, employing quorum sensing. In general, bacterial biofilms show resistance against human immune system, as well as against antibiotics. Health related concerns speak loud due to the biofilm potential to cause diseases, utilizing both device-related and non-device-related infections. In summary, the understanding of bacterial biofilm is important to manage and/or to eradicate biofilm-related diseases. The current review is, therefore, an effort to encompass the current concepts in biofilm formation and its implications in human health and disease.
Field studies were carried out to evaluate various green manuring crops viz. sesbania, guara and sunnhemp and grain legumes viz. mungbean and cowpeas for their biomass production and N contribution to soil. Plant sampling was done at 30, 45 and 60 days after sowing. Maximum, plant height and fresh shoot and root biomass were produced by sunnhemp at all the three growth periods but the grain yield and total biomass production at harvest stage (60 DAS) were maximum in case of Pakistani Janter (Sesbania aculeata), The yield of the follow-up wheat crop was also affected significantly by the residual effect of green manures and grain legumes and the maximum wheat grain and straw were recorded from the plots where Pakistani Janter was planted.
In recent years green nanotechnology gained significant importance to synthesize nanoparticles due to their cost effectiveness and biosafety. In the current study, silver nanoparticles were synthesized by using extract of Spirogyra hyalina as a capping and reducing agent. The synthesized nanoparticles were characterized by UV-Visible spectroscopy, Fourier transform infrared spectroscopy, Scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray diffractive analysis. Silver nanoparticles give a characteristic Surface Plasmon Resonance peak of 451 nm at 2.21 a.u (arbitrary unit). SEM micrograph revealed the spherical morphology and average grain size of 52.7 nm. Furthermore, antibacterial, antifungal, insecticidal, antioxidant and membrane damage activities were determined. The maximum antibacterial and antifungal activity was observed for Pseudomonas aeruginosa (18 ± 1.2 mm) and Fusarium solani (14.3 ± 0.6 mm), respectively. In membrane damage assay, Pseudomonas aeruginosa absorbed A260 wavelength and gave maximum peak values of 0.286, 0.434 and 0.629 at 25, 35 and 45 µg/mL of silver nanoparticles. The membrane damage assay confirmed that nanoparticles are involved in bacterial cell membrane damage. At 500 ppm silver nanoparticles showed 30% mortality against Tribolium castaneum (a common grain pest). The silver nanoparticles also showed potent antioxidant activity and successfully scavenged the DPPH free radicals upto 53.43 ± 0.17, 43.26 ± 0.97, 31.39 ± 0.33, 24.62 ± 0.85, and 14.13 ± 0.12% at a concentration of 400, 200, 100, 50, and 25 µg/mL of nanoparticles, respectively. It is concluded that silver nanoparticles can easily be synthesized by using green algae Spirogyra hyalina as a capping and reducing agent. Silver nanoparticles showed potent biomedical activities and thus can be used for therapeutic applications invitro and invivo.
Walking mode recognition through surface electromyography (sEMG) sensors is an active field of smart prostheses technologies. This work presents the mel frequency cepstral coefficients of the nonlinear sEMG signals as an effective feature for the recognition of different walking modes. Principal component analysis and mutual information were used for feature reduction and optimum sEMG channel selection respectively. The proposed recognition system identifies five walking modes such as normal walking, slow walking, fast walking, ramp ascending and ramp descending. The proposed method was evaluated using 11 channels of lower limb sEMG signals recorded from six subjects including four able-bodied, one unilateral transtibial and one unilateral transfemoral amputee. Several classifiers were trained with a pool of collected data. The experimental result exhibits that the proposed system achieved the highest accuracy of 97.50% using support vector machine. The promising results of this work could promote the future developments of neural-controlled lower limb prosthetics.
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