Potential Use of 3D CORAGRAF-Loaded PDGF-BB in PLGA Microsphere Seeded Mesenchymal Stromal Cells in Enhancing the Repair of Calvaria Critical-Size Bone Defect in Rat Model

Our previous study evidenced that the 3D CORAGRAF loaded with PLGA microsphere constitutes PDGF-BB can support cell attachment and proliferation and can induce an osteogenic commitment of mesenchymal stromal cells in the in vitro condition. However, how this construct can perform in pathophysiological conditions in terms of repairing critical bone defects is yet to be understood. A study was therefore conducted to investigate the regeneration potential of calvaria critical-size defects using CORAGRAF + PLGA with PDGF-BB + mesenchymal stromal cells (MSCs) in a rat model. A 5 mm critical bone defect was created on calvaria of 40 male Sprague-Dawley rats. CORAGRAF incorporated either with or without PDGF-BB and seeded with rat bone-marrow-derived MSCs was implanted at the defect region. The bone regeneration potential of implanted constructs was assessed using micro-CT imaging and histological staining in weeks 4 and 8. The micro-CT images indicated a significant closure of defects in the cranial bone of the rats treated with 3D CORAGRAF + PLGA with PDGF-BB + MSCs on week 4 and 8 post-implantation. This finding, further supported with the histology outcome where the rat cranial defect treated with CORAGRAF + PLGA with PDGF-BB + MSCs indicated neo-bony ingrowth with organized and mature bone-like morphology as compared with other groups. The previous in vitro results substantiated with our pre-clinical findings demonstrate that the combination of CORAGRAF + PLGA with PDGF-BB + MSCs could be an ideal construct to support bone regeneration in critical bone defects. Hence, this construct can be further investigated for its safety and efficacy in large animal models, or it can be skipped to human trial prior for commercialization.     

Pathogenicity of indole-3-acetic acid producing fungus <Emphasis Type="Italic">Fusarium delphinoides</Emphasis> strain GPK towards chickpea and pigeon pea

An indole-3-acetic acid (IAA) producing fungal strain was isolated from chickpea grown rhizospheric soil samples. Based on morphological and Internal Transcribed Spacer (ITS) region sequence analysis the new isolate was identified as Fusarium delphinoides. The Fusarium delphinoides strain produces and secretes IAA in-vitro as identified by HPLC and Mass spectrometry. The IAA production is dependent on tryptophan (Trp) as a nitrogen source in the medium. The IAA production is influenced by growth conditions such as pH of the medium, concentration of Trp and the nature of the carbon source. Additional nitrogen sources repress Trp dependent IAA production. Glucose and Trp served as the best carbon and nitrogen sources respectively. Pathogenicity of Fusarium delphinoides towards the plants was tested by electrolyte, nutrient leakage analysis and also by scoring the disease symptoms. Two cultivars of chickpea (ICCV-10 and L-550) and two cultivars of pigeon pea (Maruti and PT-221) were assessed for the pathogenicity by inoculating with spores of Fusarium delphinoides. The inoculation induced symptoms of Fusarium wilt as in the case of Fusarium oxysporum f. sp. ciceris (FOC), a known pathogen causing Fusarium wilt in chickpea. Electrolyte and nutrient leakage from the infected plants were used to assess the resistance, tolerance (moderately resistance) and susceptibility of the plants to the infection. Based on the results, both the pigeon pea cultivars (Maruti and PT-221) were rated as resistant, and ICCV-10 was rated as a tolerant cultivar of chickpea. However, chickpea cultivar L −550 was found to be a susceptible host for infection by Fusarium delphinoides. These results suggest that Fusarium delphinoides, which belongs to the Fusarium dimerum species group, is an IAA producing plant pathogen and causes wilt in chickpea. Further, along with pathogenicity tests, electrolyte and nutrient leakage analysis can be used to assess the pathogenicity of pathogenic fungi.     

Dynamic friction of polyester air-jet textured yarns

In this paper, friction of air-jet textured yarns is investigated. Using a friction measuring apparatus fabricated in-house, dynamic friction forces of the yarns under yarn-to-metal (YM) and yarn-to-yarn (YY) rubbing modes are measured. The influence of processing variables of air-jet texturing viz., overfeed, air pressure, dry/wet texturing and normal/core-and-effect texturing on dynamic friction is analysed. The results indicate that friction force increases with increasing rubbing speeds and yarn input tension. YM dynamic friction decreases initially and then starts to increase at higher overfeeds. YY dynamic friction increases with increasing overfeed. YM dynamic friction decreases with an increase in air pressure while an opposite trend is observed for YY friction. Wet textured yarns have higher friction than dry textured yarns. Core wetted core-and-effect textured yarns have higher friction than normal textured yarns.     

Predicting bending rigidity of woven fabrics using artificial neural networks

This paper reports an investigation on the predictability of bending property of woven fabrics from their constructional parameters using artificial neural network (ANN) approach. Number of cotton grey fabrics made of plain and satin weave designs were desized, scoured, and relaxed. The fabrics were then conditioned and tested for bending properties. Thread density in fabric, yarn linear density, twist in yarn, and weave design were accounted as input parameters for the model whereas bending rigidity in warp and weft directions of fabric formed the outputs. Gradient descent with momentum and an adaptive learning rate back-propagation was employed as learning algorithm to train the network. A sensitivity analysis was carried out to study the robustness of the model.     

Ginsenoside rich fraction of Panax ginseng C.A. Meyer improve feeding behavior following radiation-induced pica in rats

Panax ginseng is an indigenous medicinal herb and has traditionally been used among Asian population for relief of many human ailments. We investigated the prophylactic role of Korean P. ginseng extract (KG) against X-ray irradiation-induced emesis in an acute rat pica model. Rats were treated with KG (12.5, 25, 50 mg/kg orally at -48, -24 and 0 h) prior to X-ray irradiation (6 Gy), and intake of kaolin and normal food and body weight changes examined as an index of the acute emetic stimulus. Levels of serotonin in small intestine tissue were assessed and histopathology of gastric tissue, small intestine and colon examined specific staining. Pre-treatment with KG (12.5 and 25 mg/kg) reduced X-ray irradiation-induced kaolin intake at 24h. Normal food intake was improved in rats treated with 25 mg/kg KG. The anti-emetic effect of KG was further confirmed on the basis of serotonin release, histopathological findings. Our findings collectively indicate that KG protects against X-ray irradiation-induced acute pica to a moderate extent, leading to improved feeding behavior in rats.     

Predicting bending rigidity of woven fabrics by neuro-genetic hybrid modeling

The possibility of prediction of bending rigidity of cotton woven fabrics with the application of Neuro-genetic model has been explored. For this purpose, number of cotton grey fabrics meant for apparel end-use was desized, scoured, and relaxed. The fabrics were then conditioned and tested for bending properties. A feed-forward neural network model was first formed and trained with adaptive learning rate back-propagation with momentum. In the second model, a hybrid learning strategy was adopted. A genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. Later, a back-propagation was applied as a local search algorithm to achieve global optima. Results of hybrid neural network model were compared with that of back-propagation neural network model in terms of their prediction performance. Results show that the prediction by Neuro-genetic model is better in comparison with that of back-propagation neural model.