Impaired redox environment modulates cardiogenic and ion-channel gene expression in cardiac-resident and non-resident mesenchymal stem cells

Redox homeostasis plays a crucial role in the regulation of self-renewal and differentiation of stem cells. However, the behavioral actions of mesenchymal stem cells in redox imbalance state remain elusive. In the present study, the effect of redox imbalance that was induced by either hydrogen peroxide (H2O2) or ascorbic acid on human cardiac-resident (hC-MSCs) and non-resident (umbilical cord) mesenchymal stem cells (hUC-MSCs) was evaluated. Both cells were sensitive and responsive when exposed to either H2O2 or ascorbic acid at a concentration of 400 µmol/L. Ascorbic acid pre-treated cells remarkably ameliorated the reactive oxygen species level when treated with H2O2. The endogenous antioxidative enzyme gene (Sod1, Sod2, TRXR1 and Gpx1) expressions were escalated in both MSCs in response to reactive oxygen species elevation. In contrast, ascorbic acid pre-treated hUC-MSCs attenuated considerable anti-oxidative gene (TRXR1 and Gpx1) expressions, but not the hC-MSCs. Similarly, the cardiogenic gene (Nkx 2.5, Gata4, Mlc2a and β-MHC) and ion-channel gene (IKDR, IKCa, Ito and INa.TTX) expressions were significantly increased in both MSCs on the oxidative state. On the contrary, reduced environment could not alter the ion-channel gene expression and negatively regulated the cardiogenic gene expressions except for troponin-1 in both cells. In conclusion, redox imbalance potently alters the cardiac-resident and non-resident MSCs stemness, cardiogenic, and ion-channel gene expressions. In comparison with cardiac-resident MSC, non-resident umbilical cord-MSC has great potential to tolerate the redox imbalance and positively respond to cardiac regeneration

Effective Biosurfactants production by Pseudomonas aeruginosa and its efficacy on different Oils

A rhamnolipid producing bacterium, Pseudomonas aeruginosa was isolated from contaminated soil with oily wastes. The Pseudomonas aeruginosa has grown with glucose and corn oil as a carbon source produced biosurfactant. This biosurfactant was purified by procedures that included chloroform-ethanol extraction and 0.05M bicarbonate treatments. The active compound was identified as rhamnolipid by using thin layer chromatography. The biosurfactant efficacy was tested on coconut oil, groundnut oil, sunflower oil and olive oil. The coconut oil responded better and gave a maximum level of 1cm than the olive oil, groundnut oil and sunflower oil

Generation and characterization of human cardiac resident and non-resident mesenchymal stem cell

Despite the surgical and other insertional interventions, the complete recuperation of myocardial disorders is still elusive due to the insufficiency of functioning myocardiocytes. Thus, the use of stem cells to regenerate the affected region of heart becomes a prime important. In line with this human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) have gained considerable interest due to their potential use for mesodermal cell based replacement therapy and tissue engineering. Since MSCs are harvested from various organs and anatomical locations of same organism, thus the cardiac regenerative potential of human cardiac-derived MSCs (hC-MSCs) and human umbilical cord Wharton’s Jelly derived MSC (hUC-MSCs) were tested concurrently. At in vitro culture, both hUC-MSCs and hC-MSCs assumed spindle shape morphology with expression of typical MSC markers namely CD105, CD73, CD90 and CD44. Although, hUC-MSCs and hC-MSCs are identical in term of morphology and immunophenotype, yet hUC-MSCs harbored a higher cell growth as compared to the hC-MSCs. The inherent cardiac regenerative potential of both cells were further investigated with mRNA expression of ion channels. The RT-PCR results demonstrated that both MSCs were expressing a notable level of delayed rectifier-like K+ current (I KDR ) ion channel, yet the relative expression level was considerably varied between hUC-MSCs and hC-MSCs that Kv1.1(39 ± 0.6 vs 31 ± 0.8), Kv2.1 (6 ± 0.2 vs 21 ± 0.12), Kv1.5 (7.4 ± 0.1 vs 6.8 ± 0.06) and Kv7.3 (27 ± 0.8 vs 13.8 ± 0.6). Similarly, the Ca2+-activated K+ current (I KCa ) channel encoding gene, transient outward K+ current (I to ) and TTX-sensitive transient inward sodium current (I Na.TTX ) encoding gene (Kv4.2, Kv4.3 and hNE-Na) expressions were detected in both groups as well. Despite the morphological and phenotypical similarity, the present study also confirms the existence of multiple functional ion channel currents IKDR, IKCa, Ito, and INa.TTX in undifferentiated hUC-MSCs as of hC-MSCs. Thus, the hUC-MSCs can be exploited as a potential candidate for future cardiac regeneration

Enhancement of Electroluminescent Green Emission by Far-Field Coupling of Au Nanoparticles in Organic Light Emitting Diodes

Far-field surface plasmon enhanced green electroluminescence in organic light-emitting devices (OLEDs) is harvested by tuning the gold nanoparticles (Au NPs) density at the interface of the anode:hole transport layer (HTL) in OLEDs using Ir­(DMSPI)₂(acac) as emissive layer. Au NPs increases the hole injection with 35/μm² density at the indium tin oxide (ITO):<i>N</i>,<i>N</i>′-di-1-naphthyl-<i>N</i>,<i>N</i>′-diphenylbenzidine (NPB) interface and leads to enhanced emission intensity as a result of increased radiative rate (<i>k</i>_r). The Au NPs modified anode in OLEDs injects holes effectively into the NPB layer and stabilizes its energy level which results in an increase of current density. The reduced hole injection barrier (HIB) was analyzed by using the Richardson–Schottky equation. The far-field plasmonic coupling with hole injection ability of Au NPs at the ITO:HTL interface enhanced the device efficiencies at low turn-on voltage in this work. However, the anode with 6.0/μm² density of Au NPs shows poor hole injection ability into the HTL due to trapping of holes at the interface

Multi-Layered Non-Local Bayes Model for Lung Cancer Early Diagnosis Prediction with the Internet of Medical Things

The Internet of Things (IoT) has been influential in predicting major diseases in current practice. The deep learning (DL) technique is vital in monitoring and controlling the functioning of the healthcare system and ensuring an effective decision-making process. In this study, we aimed to develop a framework implementing the IoT and DL to identify lung cancer. The accurate and efficient prediction of disease is a challenging task. The proposed model deploys a DL process with a multi-layered non-local Bayes (NL Bayes) model to manage the process of early diagnosis. The Internet of Medical Things (IoMT) could be useful in determining factors that could enable the effective sorting of quality values through the use of sensors and image processing techniques. We studied the proposed model by analyzing its results with regard to specific attributes such as accuracy, quality, and system process efficiency. In this study, we aimed to overcome problems in the existing process through the practical results of a computational comparison process. The proposed model provided a low error rate (2%, 5%) and an increase in the number of instance values. The experimental results led us to conclude that the proposed model can make predictions based on images with high sensitivity and better precision values compared to other specific results. The proposed model achieved the expected accuracy (81%, 95%), the expected specificity (80%, 98%), and the expected sensitivity (80%, 99%). This model is adequate for real-time health monitoring systems in the prediction of lung cancer and can enable effective decision-making with the use of DL techniques