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Effect of roasting in electric oven on oil quality and residue from Cucurbita maxima (Marina di Chioggia) and Cucurbita pepo (Calabaza Mercado Verde) seeds from Morocco
No full textThis study aimed to assess the impact of the roasting process, conducted in an electric oven, on the oil quality and residue derived from two pumpkin seed species, namely Cucurbita maxima (C. maxima) and Cucurbita pepo (C. pepo), cultivated in Morocco. The seeds underwent roasting at temperatures of 60, 90, 120, and 150 °C for 45 min. The cold press extracted oils were characterize in terms of fatty acids, phytosterols, tocopherols and pigment content meanwhile phenolic content and antioxidant activity were evaluated in the residues. The heat treatment did not significantly affect fatty acid content; however, it affected sterols, particularly β-sitosterol, which experienced an 8 % decrease in C. maxima. Instead, total sterols significantly increased in C. pepo from 153.84 to 181.71 mg/100g. Moreover, the heat treatment influenced the tocopherol contents, revealing a substantial increase in both species. Phenolic content was significantly affected in C. pepo whereas the variation in C. maxima was statistically nonsignificant. Antioxidant activity exhibited fluctuations during the heat treatment, resulting in an overall increase in the oils. The roasting process influences the composition of bioactive compounds and antioxidant activity in pumpkin seed oil. These findings contribute to a deeper exploration of the functional properties of pumpkin seed products
Isolation and Characterization of Bacterial Contaminants from Bone Marrow-Derived Mesenchymal Stem Cell Cultures
No full textThis study aimed to assess and manage bacterial contamination in multiple batches of mesenchymal stem cell (MSC) cultures derived from rabbit bone marrow. Routine visual inspection and microscopic examination were employed for the detection of the contaminated cultures. The contaminated cultures were inoculated on Nutrient agar and multiple isolated colonies were subjected to Gram staining and biochemical characterization. Further, molecular identification of the bacterial isolates was performed using polymerase chain reaction. The determination of antibiotic susceptibility patterns was conducted using the Kirby-Bauer disc diffusion method. Among the 351 mesenchymal stem cell culture (SCC) flasks monitored, only 1.42% were found to be contaminated. Based on the phenotypic and biochemical characterization, the major bacterial contaminants were identified as Staphylococcus aureus, Bacillus spp., and Escherichia coli infiltrating during various stages of cell processing. Antibiotic susceptibility patterns revealed varying responses among isolates, crucial for effective antimicrobial strategies and maintaining aseptic conditions in SCCs. The study emphasizes the importance of regular monitoring to maintain sterile environments, validate culture quality, and uphold safety standards. The findings indicate the need to establish stringent quality control measures, crucial for the successful translation of MSC research into clinical applications. The research advocates for continuous monitoring, adherence to SOPs, and further investigations into preventive strategies for ensuring the safety and efficacy of MSC-based therapies
Excessive Checking in Obsessive-Compulsive Disorder: Neurochemical Correlates Revealed by 7T Magnetic Resonance Spectroscopy
No full textBackground
Compulsive checking, a common symptom of obsessive-compulsive disorder (OCD), has been difficult to capture experimentally. Therefore, determination of its neural basis remains challenging despite some evidence suggesting that it is linked to dysfunction of cingulostriatal systems. This study introduces a novel experimental paradigm to measure excessive checking and its neurochemical correlates.
Methods
Thirty-one patients with OCD and 29 healthy volunteers performed a decision-making task requiring them to decide whether 2 perceptually similar visual representations were the same or different under a high-uncertainty condition without feedback. Both groups underwent 7T magnetic resonance spectroscopy scans on the same day. Correlations between out-of-scanner experimental measures of checking and the glutamate/GABA (gamma-aminobutyric acid) ratio in the anterior cingulate cortex, supplementary motor area, and occipital cortex were assessed. Their relationship with subjective ratings of doubt, anxiety, and confidence was also investigated.
Results
Patients with OCD exhibited excessive and dysfunctional checking, which was significantly correlated with changes in the glutamate/GABA ratio within the anterior cingulate cortex. No behavioral/neurochemical relationships were evident for either the supplementary motor area or occipital cortex. The excessive checking observed in patients was negatively correlated with their confidence levels and positively related to doubt, anxiety, and compulsivity traits.
Conclusions
We conclude that experimental measures of excessive and dysfunctional checking in OCD, which have been linked to increased doubt, anxiety, and lack of confidence, are related to an imbalance between excitatory and inhibitory neural activity within the anterior cingulate cortex. This study adds to our understanding of the role of this region in OCD by providing a laboratory model of the possible development of compulsive checking
Utilisation Of AI In Nursing Management Within Hospital Settings
No full textThe advent of AI in nursing services represents a transformative step in health care technology, and is poised to elevate patient care quality and streamline nursing operations
Exploring single-use, multi-use dialysers for patient well-being
No full textDialysis treatment is a critical lifeline for patients with kidney failure. Patients need to decide the choice between single-use and multi-use dialysers. This plays a vital role in ensuring patient safety and treatment effectiveness
Profiling patterns in healthcare using an ensemble model framework to predict employee health risks
Get PDFIn the current evolution of the digital world, data has become the cornerstone of decision-making processes, shaping industries and societies alike. The exponential growth of data, commonly referred to as big data, has sparked a surge in interest in advanced analytics techniques to harness its potential. Among these techniques, big data analytics, particularly in healthcare, holds immense promise for understanding overall population health and predicting high-risk and high-cost individuals. This thesis delves into the realm of healthcare analytics in Malaysia, focusing on the analysis of extensive medical data to identify patterns and insights that can aid in the identification of high-risk and high-cost individuals. The objectives of this research are: first, to uncover and comprehend usage patterns within healthcare claims data, elucidating factors contributing to the identification of high-risk individuals; second, to propose an innovative ensemble stacking model approach; and third, to demonstrate the efficacy of this approach in enhancing predictive accuracy. The proposed ensemble stacking model integrates the Stacking technique with hybrid feature selection and feature engineering methodologies. By amalgamating multiple predictive models into a cohesive framework, the ensemble model offers superior predictive accuracy compared to traditional single-model approaches. Furthermore, the model's versatility enables its application across various classification tasks within the healthcare domain. Through empirical analysis, this research highlights the enhanced predictive accuracy and efficacy of the ensemble model framework. Notably, key features such as ICD Category, TotalRemainingAmt, and TotalAmtInsured emerge as significant contributors to determining an individual's risk profile based on their medical claim patterns and behaviours. By leveraging big data analytics and ensemble modelling techniques, this research contributes to the advancement of predictive analytics in healthcare, offering valuable insights for decision-makers and stakeholders in the industry
Optimization and discretization of dragonfly algorithm for solving continuous and discrete optimization problems
Get PDFOptimization is prevalent in almost all areas since a plethora of problems can be formulated as optimization problems. Hence, optimization algorithms, consisting of exact and heuristic methods, are crucial for a myriad of real-world applications. The exact methods tend to be computationally expensive and time-consuming. Hence, heuristic and metaheuristic algorithms are being increasingly favoured as they provide near-optimal solutions in a feasible amount of time. Swarm intelligence algorithms are metaheuristic algorithms inspired by the simple interactions of biological organisms in a population. Owing to their exploitation and exploration capabilities, swarm intelligence algorithms have a good performance in solving complex problems. The Dragonfly Algorithm (DA) is one which is inspired by the swarming behaviours of dragonflies during hunting and migrating in nature. It has been found to have a higher performance than multiple other swarm intelligence algorithms in various applications. However, despite having a good performance, it has certain limitations like a low exploitation phase. Furthermore, the original DA is only suitable for solving continuous optimization problems. Although there is a binary version of the algorithm, it cannot be directly used for solving discrete optimization problems like the Traveling Salesman Problem (TSP). Hence, in this project, the exploitation of the original DA is improved by using the stochastic hill climbing algorithm as a local search technique. The optimized continuous DA is applied for the training of Artificial Neural Networks (ANNs) so as to determine the optimal connection weights and biases for the network. The trained network is then applied to benchmark classification problems. Based on the experimental results, the optimized DA algorithm is a much better training algorithm for ANNs as compared to the usual gradient-descent backpropagation algorithm since the resultant ANNs trained by the optimized DA achieve higher accuracy. Moreover, the ANNs trained by the proposed optimized DA have a higher accuracy than those trained by the original DA when the performance of the algorithms are compared both in terms of iteration number and computational cost. The ANNs trained by the optimized DA also achieve higher accuracy than those trained by some other swarm intelligence algorithms. The optimized continuous DA is also applied for channel estimation in an optical communication system to determine the optimal channel coefficients. Based on the experimental results, the optimized DA is found to provide significantly better results than the original DA. Moreover, DA is adapted to be suitable for discrete optimization problems by adapting the equations of DA and by using the method of swap operators to update the position of the search agents. The adapted discrete DA is applied to a small TSP problem and it is able to provide the optimal solution for the small TSP problem. However, it is found to have low effectiveness for large TSP problems. Hence, the adapted discrete DA is optimized by improving the low exploitation phase. The steepest-ascent hill climbing algorithm is used as a local search technique to improve the exploitation of the adapted discrete DA. The optimized discrete DA is then applied to a TSP problem modelling a package delivery system in the area of Kuala Lumpur and to benchmark TSP problems. Based on the experimental results, the proposed optimized discrete DA has a higher effectiveness than the adapted discrete DA when the performance of the algorithms is compared in terms of both iteration number and computation cost. It also has a higher effectiveness than some other swarm intelligence algorithms. Furthermore, it provides optimal solutions for several benchmark TSP problems
Object tracing from synthetic fluid spray through instance segmentation
Get PDFSince the advent of rapid transportation systems, combustion engines have been a significant technological advancement. These engines require fuel to be atomized for efficient combustion. Atomization involves breaking down liquid sprays into smaller ligaments and droplets, which is crucial for optimal combustion in liquid-fueled propulsion devices. However, accurately measuring and analyzing the size and distribution of objects in a spray can be challenging, especially near the nozzle, due to the complexity of the spray and limited digital detectors. To address this, a method using two region-based R-CNN models named Mask R-CNN and BMask R-CNN and two transformer-based models named PatchDCT and FastInst has been developed to detect and segment individual droplets from synthetic liquid spray images. This approach involved training the model on augmented images and testing it on both augmented and original images. A total of 5791 epochs for Mask R-CNN, 3000 epochs for BMask R-CNN, 20k epochs for PatchDCT and FastInst have been used. Initially, the object detection rate was not good due to the dense objects all over the images. A divide and conquer technique using cropping window extraction was employed with nine window sizes to reduce object density, resulting in a significant increase in object count. The total number of objects on the first test dataset increased from 517 to 1231 using (height, width/3) and 2887 using (height/3, width/3) window with Mask R-CNN. A similar increment happened with BMask R-CNN, PatchDCT and FastInst model testing. After filtering all of the crop windows, it is shown that the models are able to identify significantly more droplets while maintaining a good mask prediction when the width is decreased by three while maintaining the height as the fluid flow is horizontal. The resulting objects were then analyzed using a customized nearest neighbor algorithm to calculate their correspondence between frames and a BFS algorithm was used to trace their movement path. On the training dataset, Mask R-CNN achieved 74.93 mean average precision with 75% IoU, where BMask R-CNN has only 44.68, PatchDCT shows 81 and FastInst achieved 41.49. Along with the movement path, objects’ contact tracing and breakdown rate has also been calculated. The information obtained, including object size, distribution, and tracing, can be valuable for engineers designing fuel injectors, leading to improved performance and efficiency
Malaysian Carrageenophyte Kappapphycus species inhibit Lipopolysaccharides-Induced Neuroinflammation by suppressing the AKT/NFxB and ERK signaling pathways in BV2 Microglia AKT/NFkB and ERK signaling pathways in BV2 Microglia.
No full textNeuroinflammation is normally caused by the stimulation of microglia and astrocytes with the production of proinflammatory mediators such as inducible nitric oxide synthase (iNOS), nitric oxide (NO) and cyclooxygenase-2 (COX-2), as well as the generation of proinflammatory cytokines, including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6. Unregulated activities and overproduction of proinflammatory mediators and cytokines will cause neuronal death, which will trigger the development of neurodegenerative diseases. Kappaphycus alvarezii and Kappaphycus malesianus are red algae cultivated in Semporna, Sabah, East Malaysia. They are remarkable with their k-carrageenans which have been widely used as a thickening agent in food, cosmetics, and nutraceutical industry. Kappaphycus spp. have been reported with several pharmacological effects, including antioxidant, anti-viral and anti-cancer effects. However, limited research was focusing on their neurological effects.
Therefore, it is our interest to explore and compare the bioactivity of K. alvarezii and K. malesianus from the neuroprotective aspect. This study aimed to investigate the anti-neuroinflammatory activity in K. alvarezii and K. malesianus crude extracts (ethyl acetate, ethanol, and methanol extracts) on lipopolysaccharides (LPS)-induced BV2 microglial cell line and their underlying mechanisms. Cell viability assay (MTT assay) was used to examine the percentage of viable BV2 microglial cell line after treating with respective K. alvarezii and K. malesianus crude extracts. Nitric oxide inhibition assay (Griess assay)
was used to investigate the nitric oxide inhibitory activity of all crude extracts, while the crude extract with the highest nitric oxide inhibitory activity was used for western blot and ELISA analyses to investigate the proinflammatory mediators (iNOS and COX-2) and cytokines (TNF-⍺, IL-6 and IL-1β) expression in LPS-induced BV2 microglial cell line. The potential compound(s) in the extract with the highest promising anti-neuroinflammatory effect were identified using the liquid chromatography-mass spectrometry (LC-MS) analysis. Our results indicated that all extracts of both seaweeds had maintained cell viability above 80% at the concentration of 2.5 mg/mL, except for K.malesianus ethanol extract (69.7% ± 0.31 mg/mL). K. alvarezii ethyl acetate extract and
K. malesianus methanol extract indicated the highest nitric oxide inhibitory activity without showing significant cytotoxicity towards BV2 microglial cell line. Thus, K. alvarezii ethyl acetate and K. malesianus methanol extract was selected to further evaluate their proinflammatory mediators and proinflammatory cytokines expressions and signaling pathways. The results disclosed that K. malesianus methanol extract demonstrated higher inhibitory activity in the formation of proinflammatory mediators and cytokines via AKT/NF-kB and ERK pathways. According to the LC-MS analysis for K. malesianus methanol extract, three known bioactive compounds were identified with anti-neuroinflammatory activities, namely, 2,6-nonadien-1-ol, prosopinine and eplerenone. In conclusion, all K. alvarezii and K. malesianus extracts showed inhibitory activity on nitric oxide and proinflammatory cytokines production, which may contribute to the anti-neuroinflammatory activity. Moreover, western blot results explained that K.
alvarezii and K. malesianus extracts had contributed to anti-neuroinflammatory activity through inhibiting iNOS and COX-2 production via AKT/NF-kB, and ERK pathways. K. malesianus methanol extract is the most potent extract and may acts as a potent anti-neuroinflammatory therapy in the prevention of neurodegenerative disorders
Development of a multi-epitope Influenza A vaccine with self-adjuvanting Poly-Laticco-Glycolic Acid (PLGA) delivery
No full textInfluenza is a highly contagious respiratory disease led to 3-5 million severe cases worldwide, resulting in over 650,000 respiratory deaths annually. To date, the efficacy of the seasonal influenza vaccines were estimated to be only 29% against the circulating influenza B/Victoria and A(H1N1)pdm2009. This highlighted the need to develop a next generation influenza vaccine that could induce long-lasting and cross-reactive immunity in vaccinated individuals. Many strategies have been explored for the development of next-generation influenza vaccines and the multi-epitope peptide-based vaccine is one of the promising vaccine platforms as they are cost-effective, easily modifiable, and have minimal risk of reactogenicity. However, the multi-epitope peptide based vaccines often required to be incorporated with adjuvant to increase their immunogenicity. In this study, the immunogenicity of each of the ten peptides derived from literature mining was validated in BALBC/c mice by incorporating them in different peptide formulations containing adjuvant components such as Montanide ISA 51, complete Freund’s adjuvant (CFA) and incomplete Freund’s adjuvant (IFA) containing CpG ODN 1826. Prior to in vivo study, the ten peptides selected from literature mining were subjected to immunoinformatic analysis. Subsequently, immunization of mice with the respective peptide formulations showed that mice immunized with the mixture of ten peptides formulated in IFA containing CpG ODN 1826 elicited the strongest cell-mediated immune responses in the immunised mice than that observed in mice immunized with the mixture of ten peptides formulated in CFA, highlighting that the incorporation of CpG ODN 1826 produced a synergistic effect when formulated in IFA as an adjuvant. Importantly, a total of six immunogenic peptides (M2.1, M2.2, NP, P3, P5 and P6) were identified as they were shown to stimulate significant IFN-! production in immune cells as well as CD4+ and CD8+ T-cells. Recently, nanoparticles emerged as a potential delivery platform for protein or peptide based vaccines. As such, the FDA-approved poly (D, L-lactide-co-glycolic acid) (PLGA) was used to encapsulate the mixture of six immunogenic peptides to be evaluated as a nanovaccine and its immunogenicity was evaluated and compared to the mixture of six peptides formulated in IFA containing CpG ODN 1826 in BALB/c mice. Remarkably, mice immunized with PLGA NPs encapsulating six peptides elicited the strongest IgG response at 1,000,000 titres and it was ten-fold higher when compared to mice immunized with the mixture of six peptides formulated in IFA containing CpG ODN 1826. In addition, immunization of mice with PLGA NPs incapsulating the six peptides was shown to increase the expression of IFN-! significantly (2-3-fold higher) when compared with the group of mice administered with the mixture of six peptides formulated in IFA containing CpG ODN 1826. However, one of the peptides, P3, was found to induce a significant necrotic effect in the CD3+ T-cells present in murine splenocytes. Elimination of peptide 3 from the current nanovaccine formulation would be ideal in future studies. Overall, the study demonstrated the self-adjuvanting property of the PLGA nanoparticles as a delivery system without the need to include any toxic and costly conventional adjuvants in the multi-epitope peptide-based vaccine