NATURAL HYDROXYAPATITE AS AN ADSORBENT FOR MICROORGANISMS (MOS) FROM AQUEOUS SYSTEM

Natural hydroxyapatite (HAp) was isolated from calcination of waste caprine (goat) bone at 750 °C in muffle furnace. As produced material was characterized by using X-ray Diffraction (XRD) Fourier Transform Infrared (FTIR) spectroscopy and Scanning Electron Microscopy (SEM) analyses and ensured the synthesized material was nano rod hydroxyapatite. The pHPZC value of the HAp was 7.2 as determined by pH drift method. Adsorption of four different microorganisms (MOS) (E. coli, A. baumanii, S. aureus and C. albicans) onto natural HAp was investigated and found to adsorb onto HAp with the proportions greater than 25 % within the applied concentration ranges. Adsorption kinetics studies showed the adsorption process followed the pseudo-second order kinetics for all investigated MOS. Antimicrobial study revealed that three adsorbed species (E. coli, S. aureus and C. albicans) onto HAp remained viable form while HAp showed good antibacterial activity towards A. baumanii. Minimum inhibition concentration (MIC) and minimum biocidal concentration (MBC) values of HAp were found to be 12.5 and 100 mg·mL-1 respectively against A. baumanii. Thus, thermal treatment of waste bone powder is found to be cost-effective and environment-friendly method for the isolation of natural nano HAp and it can be applied as an adsorbent for different MOS from aqueous solution as well as a potential antibacterial agent

Infective Larvae of Brugia malayi Induce Polarization of Host Macrophages that Helps in Immune Evasion

Filarial parasites suppress, divert, or polarize the host immune response to aid their survival. However, mechanisms that govern the polarization of host MΦs during early filarial infection are not completely understood. In this study, we infected BALB/c mice with infective larvae stage-3 of Brugia malayi (Bm-L3) and studied their effect on the polarization of splenic MΦs. Results showed that MΦs displayed M2-phenotype by day 3 p.i. characterized by upregulated IL-4, but reduced IL-12 and Prostaglandin-D2 secretion. Increased arginase activity, higher arginase-1 but reduced NOS2 expression and poor phagocytic and antigen processing capacity was also observed. M2 MΦs supported T-cell proliferation and characteristically upregulated p-ERK but downregulated NF-κB-p65 and NF-κB-p50/105. Notably, Bm-L3 synergized with host regulatory T-cells (Tregs) and polarized M2 MΦs to regulatory MΦs (Mregs) by day 7 p.i., which secreted copious amounts of IL-10 and prostaglandin-E2. Mregs also showed upregulated expression levels of MHC-II, CD80, and CD86 and exhibited increased antigen-processing capacity but displayed impaired activation of NF-κB-p65 and NF-κB-p50/105. Neutralization of Tregs by anti-GITR + anti-CD25 antibodies checked the polarization of M2 MΦs to Mregs, decreased accumulation of regulatory B cells and inflammatory monocytes, and reduced secretion of IL-10, but enhanced IL-4 production and percentages of eosinophils, which led to Bm-L3 killing. In summary, we report hitherto undocumented effects of early Bm-L3 infection on the polarization of splenic MΦs and show how infective larvae deftly utilize the functional plasticity of host MΦs to establish themselves inside the host

Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

The aim of this work was to optimize the biomass production by Bifidobacterium bifidum 255 using the response surface methodology (RSM) and artificial neural network (ANN) both coupled with GA. To develop the empirical model for the yield of probiotic bacteria, additional carbon and nitrogen content, inoculum size, age, temperature and pH were selected as the parameters. Models were developed using ¼ fractional factorial design (FFD) of the experiments with the selected parameters. The normalized percentage mean squared error obtained from the ANN and RSM models were 0.05 and 0.1%, respectively. Regression coefficient (R²) of the ANN model showed higher prediction accuracy compared to that of the RSM model. The empirical yield model (for both ANN and RSM) obtained were utilized as the objective functions to be maximized with the help of genetic algorithm. The optimal conditions for the maximal biomass yield were 37.4 °C, pH 7.09, inoculum volume 1.97 ml, inoculum age 58.58 h, carbon content 41.74% (w/v), and nitrogen content 46.23% (w/v). The work reported is a novel concept of combining the statistical modeling and evolutionary optimization for an improved yield of cell mass of B. bifidum 255