Nano LIFE

This paper deals with the facile approach to the synthesis of different metal oxide nanoparticles and their comparative study for humidity sensing application at room temperature. The synthesis of these metal oxide nanoparticles is through co-precipitation method for nickel oxide and tin ferrite and hydrothermal route for cuprous oxide. The SEM and EDX reveal the porous morphology and confirmed composition of the synthesized metal oxides. FTIR detects the presence of functional groups like –OH and confirms the inverse spinal structure in tin ferrite. The optical band gap was determined by UV spectroscopy: 3.86eV for NiO, 4.13eV for Cu₂O, and 4.07eV for SnFe₂O₄. XRD gives the information about the average crystallite size for tin ferrite 2.42nm, cuprous oxide 12.88nm and nickel oxide 22.51nm as the size comes to nano range the surface area increases, which is a good indication for humidity sensing. The humidity sensing of materials was detected by electrical modes. The deposited thin films were prepared by spin coater and observed sensitivity of these films was 0.72M$\mathrm{\Omega }$/%RH for NiO, 1.59M$\mathrm{\Omega }$/%RH for Cu₂O, and 2.07M$\mathrm{\Omega }$/%RH for SnFe₂O₄. The experiments were repeated after few weeks and the aging effects of samples were found negligible which makes the sensor stable.

Efavirenz (EFV) suffers from poor aqueous solubility which results in low bioavailability of the drug. Nanocarrier-based drug delivery systems offer a suitable alternative for improving the physico-chemical properties of the drug and hence its efficacy. Nanosuspension (NS) of EFV was formulated by solvent-anti solvent precipitation method using PVP K-30 as stabilizer and sodium lauryl sulphate (SLS) as the wetting agent. Multi-level factorial design was applied to select the optimal formulation which was further characterized. The optimal batch exhibited mean particle size of 305nm and polydispersity index (PDI) of 0.345. Solid-state characterization studies of the NS conducted using scanning electron microscopy, Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction, and differential scanning calorimetry (DSC) revealed compatibility between the drug and the excipients and modest alteration in the crystallinity of the drug. There was progressive increase in the solubility of the drug when incorporated in NS from 17.39$\mu$g/ml to 256$\mu$g/ml. Further, drug release studies showed significantly better and controlled drug release pattern in comparison to the free drug due to the presence of nanosized particles in the formulation.

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN), a tumor suppressor protein with dual phosphatase activity, is found to be frequently mutated in various cancers. PTEN is post-translationally modulated at various amino acid residues which are crucial for sub-cellular localization as well as its catalytic functions rendering genomic stability. Recent reports suggest that PTEN also acts as a DNA repair protein. But how post-translational modulation of PTEN affects cytological damage and aneuploidy is not studied in detail. Here, we focus on the role of sumoylation of PTEN in context with DNA damage induced cytological damage like micronucleus (MNi), nuclear bud (NB), and nuclear bridge formation. Our data suggest that wild type PTEN but not sumo-dead PTEN significantly reduces cytological damage in PTEN mutant PC3 cells. In case of sumo-dead PTEN, the cytological parameters are increased during 24h recovery time point after DNA damage. Next, we measured the effectiveness of the sumo-dead (PTEN-K254R) mutant on aneuploidy, where we found that sumoylation is essential for maintaining chromosome number. As chromosome number variation in daughter cell is due to multiple spindle pole formation, we qualitatively and quantitatively evaluate the $\gamma$ tubulin pole formation in PTEN-K254R clone transfected cells. We found aberrant pole formation is significantly increased in PTEN-K254R transfected cells compared to wild-type PTEN. Further depletion of sumoylation activity of PTEN increases the expression of phosphorylated form of Aurora kinase A (AURKA) (T288) and PLK1 (T210) proteins with or without nocodazole, a microtubule depolymerizing agent compared to cells expressing wild-type PTEN. Thus, sumoylation of PTEN is essential for maintaining genomic stability.

An electrochemical detection of inositol content using platinum (Pt)-based noble metal electrode is investigated. In this work, the electrochemical behavior of the platinum electrode has been studied and analyzed using a three-electrode system against a silver–silver chloride (Ag/AgCl) reference electrode and a steel counter electrode. Differential pulse voltammetry technique has been employed for this experimental study. A satisfactory linear range of operation was obtained from 50 to 400$\mu$M with $\mathrm{\text{LOD}}=19.28\mu$M. Electrochemical responses for several inositol concentrations 50, 80, 100, 200, 300 and 400$\mu$M have also been analyzed using principal component analysis (PCA) with effective data clustering. A good class separability index (SI) was found to be 142.91. In addition, a prediction estimation of inositol contents using partial least square regression (PLSR) and principal component regression (PCR) algorithms were also evaluated and prediction accuracies of 93.69% and 93.71% were obtained, respectively. Moreover, the application of the Pt electrode over real orange juice sample extracts revealed satisfactory recovery rate of 96.18%. Thus, this technique of electrochemical system may be subjected for inositol detection in our daily-life food (especially juice, beverages) consumption.

Currently, the operational electroencephalography (EEG)-based brain–computer interfaces (BCIs) suffer from problems of BCI latency/lag issues, which restricts the use of interfaces impractical scenarios. One of the reasons behind the present challenges is the application of a purely data-driven approach to the BCI pipeline. Although BCI applications have improved significantly with the research in the fields of artificial intelligence (AI) and machine learning (ML), fundamental issues of data-driven training restrict the latency that can be achieved under current BCI paradigms. This work explores the possibility of future BCI using a combination of data-driven and theory-driven methods. In this study, an EEG-BCI dataset from steady-state visually evoked potentials (SSVEPs) is applied, where the SSVEP signals contain, source components from the occipital, parietal and frontal regions of the brain. Source reconstruction is done with the combination of independent component analysis (ICA) and low-resolution electromagnetic tomography analysis (LORETA). This method was able to predict BCI classification labels 5s earlier, based on pre-recorded signals from the scalp. The novelty of the current contribution lies in utilizing the source reconstructed EEG time-series for BCI classification, which allows for retention of classification accuracy up to 70% while working with the reduced data dimensionality. Implementation of this algorithm will allow a significant reduction in lag in online BCIs.