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Development and Characterization of Injectable, Cell-Encapsulated Chitosan-Genipin Hydrogels
Full text linkOver 150,000 patients undergo lower extremity amputation every year in the United States, most commonly caused by complications due to diabetes mellitus, peripheral vascular disease, and trauma. Diseased or damaged tissues that are unable to naturally repair themselves must either be fully removed or replaced, otherwise the injuries can lead to further complications such as infection or death. Tissue resection and amputation, as forms of removing damaged tissue, are not favorable to patients as they can cause pain, reduce mobility, and negatively impact quality of life. However, replacing lost or damaged tissues with donor tissues carries the risks of tissue rejection, the need for lifelong immunosuppressive medications post-operation, and critical shortages of donor tissues. Therefore, there is a need for alternative treatment solutions to repair or replace lost or damaged tissues. One major research area is the combination of biomaterial scaffolds and stem cells to develop and restore new, functional tissues. This research aims to produce an injectable, in-situ crosslinking hydrogel capable of encapsulating stem cells for use in tissue regeneration or wound repair applications by utilizing the beneficial antibacterial and biocompatible properties of chitosan and the effective, non-toxic crosslinker genipin to produce an effective, proactive wound dressing. It was hypothesized that combining the absorptive, antibacterial, pH regulating effect, and 3-dimensional (3D) structure of the chitosan-genipin hydrogels with stem cells would more effectively promote tissue regeneration than either component individually. Herein, a thermally-driven, injectable chitosan-genipin hydrogel capable of in-situ crosslinking at 37 °C and cell encapsulation was developed and characterized. Material characterization studies presented that the in-situ crosslinking hydrogels had a gelation time of 150 minutes, an average elasticity of 449 Pa, and an average fluid uptake of 793% over 14 days. In vitro characterization studies presented that the in-situ crosslinking hydrogels were biocompatible with keratinocytes over 7 days averaging between 80 – 157% viability compared to control cells, and that stem cells remained viable when encapsulated within the in-situ-crosslinking hydrogels over 14 days, averaging \u3e80% cell viability, and their migration rate out of the hydrogel was significantly reduced compared to non-encapsulated cells. Two in vivo studies were performed to evaluate the wound healing efficacy of the in-situ crosslinking chitosan-genipin hydrogels and cell-encapsulated chitosan-genipin hydrogels. The in-situ-crosslinking chitosan-genipin hydrogel was able to provide a stable matrix for the attachment and growth of new bone tissue and retain stem cells at the defect site, while encapsulated stem cells promoted chondrogenesis. In a full-thickness wound model, the in-situ-crosslinking chitosan-genipin hydrogels accelerated wound closure compared to control treatments, and both in-situ-crosslinking hydrogels and slow-frozen chitosan-genipin hydrogel sheets reduced inflammation and improved re-epithelialization of the injury site. These promising results confirm that the chitosan-genipin in-situ-crosslinking hydrogels are an excellent platform for promoting tissue regeneration and provide a viable matrix for stem cell encapsulation and delivery to an injury site
The Impact of Irrational Beliefs on Dysfunctional Decision-Making in B2B Salespeople
Full text linkThe complexity of the contemporary business-to-business (B2B) sales landscape requires salespeople to respond faster, be more knowledgeable, and add more value to buyer interactions than ever before. As such, B2B salespeople must carefully consider the impact of their decisions since they have the potential to directly impact organizational revenue and bottom-line outcomes. The present research utilizes rational-emotive behavior theory to examine judgment and decision-making in B2B salespeople. Research questions are presented and tested with a sample of 306 B2B salespeople using structural equation modeling. The results of the analysis reveal that irrational beliefs lead to dysfunctional emotions, and in turn, dysfunctional decision-making behaviors in B2B salespeople. In doing so, the present research highlights the indispensable role cognition plays in impacting emotions, and the principal role emotions play in impacting decision-making
Comparing Proliferation Rates and Collagen Production of Cells Treated with CuHARS
Full text linkCartilage is the padding in joints that protects the bones and aids in motion. Problems with the cartilage in the knee can be caused by mechanical damage or diseases like osteoarthritis. Chondrocytes make cartilage. The objective of this study is to determine the doubling time of chondrocytes per passage. I also want to determine the base amount of collagen-II created by chondrocytes as they age. I also want to determine how long it takes CuHARS to break down in different cell medias. Chondrocytes, human dermal fibroblasts, and CRL 2303 cells were grown and cultured for over a year. During that time, media was collected for a collagen assay, and the cells were imaged mid-passage to determine the doubling time. Also, CuHARS were mixed with cell media to determine the time required for the material to break down in the media. Cell growth rates were inconsistent. CRLs make less collagen as they age. Results on collagen production by HDFs were inconclusive. Treated chondrocytes may make more collagen than untreated chondrocytes, but more testing is necessary. Ultimately, much more experimentation is necessary. CRL 2303 cells appear to make less collagen as they age. Human dermal fibroblasts need much more experimentation. Treated chondrocytes initially make more collagen than untreated ones
Machine Learning-Driven Process Analysis and Optimization in Solid-State Welding and Fusion-Based Additive Manufacturing
Full text linkIn the modern era of advanced manufacturing, optimizing process parameters is pivotal in ensuring the quality and reliability of sophisticated component fabrication. This study presents a novel, data-driven approach to parameter optimization in two cutting-edge manufacturing techniques: Friction Stir Welding (FSW) and Laser Powder Bed Fusion (LPBF). By leveraging machine learning methodologies, this research addresses the critical challenge of efficiently determining optimal process parameters, a task traditionally relying on time-consuming and resource-intensive trial-and-error methods. This study will lead to a robust data-driven framework for process analysis of more advanced manufacturing techniques like the Additive Friction Stir Deposition (AFSD) process. Friction Stir Welding (FSW) is renowned for its solid-state deformation mechanism and high joining strength. The process is controlled by adjusting the process parameters, including rotational speed, translational speed, and axial force. However, the lack of proper settings and optimization of these parameters can result in a defective weld joint and unexpected failure. The traditional trial-and-error-based experimental and computational approaches for finding the optimal combination of parameters are expensive, timeconsuming, and intricate. Machine Learning (ML), a data analysis technique, can be a useful alternative to optimize the process parameters in the FSW process. The tool rotational speed, translational speed, and axial force in FSW can be categorized as the input parameters or feature variables in the ML dataset. On the other hand, a performance parameter, such as the ultimate tensile strength of the welded joint, can be set as the output parameter or the target variable in the dataset. This study presents an ML modeling technique to predict the ultimate tensile strength for an FSW welded joint. A dataset is developed by collecting data for the three features and a target variable from experimental procedures. The skill of the trained ML model is estimated using the k-fold cross-validation procedure. The ML modeling findings for several supervised algorithms are evaluated based on the mean absolute error, root means squared error, relative absolute error, and root relative squared error. The sensitivity analysis is conducted by finding the global correlation coefficient. The ML model is validated by comparing its results with the experimental data and showing a good agreement between the ML model prediction and experimental results. Laser Powder Bed Fusion is an additive manufacturing process that uses a highpower laser to selectively melt and fuse metal powder particles, creating complex threedimensional objects layer by layer. The ML framework developed for the FSW process is further enhanced and applied to the LPBF process. This study presents a cost-effective and high-precision Machine Learning (ML) method for predicting the melt-pool geometry and optimizing the process parameters in the laser powder-bed fusion (LPBF) process with Ti- 6Al-4V alloy. Unlike many ML models, the presented method incorporates five key features, including three process parameters (laser power, scanning speed, and spot size) and two material parameters (layer thickness and powder porosity). The target variables are the melt-pool width and depth, which collectively define the melt-pool geometry and give insight into the melt-pool dynamics in LPBF. The dataset integrates information from an extensive literature survey, computational fluid dynamics (CFD) modeling, and laser melting experiments. Multiple ML regression methods are assessed to determine the best model to predict the melt-pool geometry. Ten-fold cross-validation is applied to evaluate the model performance using five evaluation metrics. Several data preprocessing, augmentation, and feature engineering techniques are performed to improve the accuracy of the models. Results show that the “Extra Trees Regression” and “Gaussian Process Regression” models yield the least errors for predicting melt-pool width and depth, respectively. The ML modeling results are compared with the experimental and CFD modeling results to validate the proposed ML models. The Sensitivity analysis also determines the most influential parameter affecting the melt-pool geometry. The processing parameters are optimized using an iterative grid search method employing the trained ML models. The presented ML framework offers computational speed and simplicity, which can be implemented in other additive manufacturing techniques to comprehend the critical traits. This comprehensive study demonstrates the efficacy of ML techniques in optimizing process parameters for both FSW and LPBF, offering computational speed and simplicity. The presented ML framework has the potential for broader implementation across various additive manufacturing techniques, including additive friction-stir deposition, elucidating critical traits in the process, and enabling process parameter optimization
Using Multiple Regression Analysis to Determine the Strength of Certain Factors on Student Absenteeism
Full text linkDuring the 2015-16 academic year, approximately 16% of the student population—exceeding 7 million students—were absent from school for 15 days or more. The escalation in chronic absenteeism is influenced by various factors, including poor health conditions, nonstandard work schedules of parents, socioeconomic disadvantages, changes in household compositions, frequent residential relocation, and substantial family responsibilities. Previous research on student absenteeism has examined the negative impacts that chronic absenteeism has had on students in diverse communities such as racial minorities, students with disabilities, and English Language Learners communities. We utilized information obtained from the U.S. Department of Education’s Civil Rights Data Collection to conduct multiple regression analysis, aiming to investigate the correlations between chronic absenteeism and factors such as poverty rates, healthcare accessibility, teacher salaries, and gross domestic product, including all U.S. states along with the District of Columbia. Our findings indicate that in certain states, there is a strong correlation between the factors used in this project
A Closer Look at the Structure of Uniquely Pancyclic Graphs
Full text linkIn this paper we will consider uniquely pancyclic graphs which are described as graphs on n vertices which have a unique cycle of size k for each integer k from 3 to n. To date, there are only seven known graphs of this type. To narrow down what a uniquely pancyclic graph could look like in order to aid the search in finding new ones, the focus of this paper is on the different ways the three and four cycles for these graphs can occur
Attachment Styles as Moderators of Loneliness in Remote Work
Full text linkThis dissertation investigates the role of attachment styles in moderating the relationship between remote work and loneliness. Drawing on attachment theory, which posits that early life experiences shape attachment styles and influence social interactions, the study examines whether attachment styles (anxious, avoidant, and secure) moderate the association between remote work and feelings of loneliness. The research targets 394 remote workers, using a quantitative, cross-sectional survey design with proportional stratified sampling to reflect the distribution of hybrid and fully remote work arrangements in the U.S. workforce. The Extent of RemoteWork measure, the Three-Item Loneliness Scale, and the Adult Attachment Scale assess remote work frequency, loneliness, and attachment styles. Hierarchical multiple regression analyses were used to test the hypotheses. Results indicated a positive relationship between the extent of remote work and loneliness, but attachment styles did not significantly moderate this relationship. These findings suggest that attachment styles did not play a significant role in moderating the relationship between remote work and loneliness in this sample. This research highlights the need for further investigation into individual differences in remote work experiences, with potential implications for organizational strategies aimed at supporting employee well-being in remote-work settings
Techno-Economic Analysis of Waste Plastic Gasification
Full text linkThe increasing generation of plastic waste and its environmental impact has driven interest in alternative disposal methods such as gasification. This study presents a techno-economic analysis of the gasification of waste plastics, specifically polypropylene (PP) and polyethylene (PE), to produce syngas. A two-stage process—thermal decomposition followed by steam reforming—was simulated using Aspen Plus. The simulation results were validated against experimental data available in the literature. The economic analysis was conducted by estimating the capital and operating costs of the gasification plant, considering factors such as feedstock costs, energy requirements, and syngas purification systems. Net Present Value Calculations were used to estimate the minimum selling price of products from the simulation. Two process configurations studies were considered. In the first configuration, PE and PP were gasified to produce syngas and later purified to obtain pure hydrogen. In the second configuration, the syngas produced was used to produce liquid fuels through Fischer Tropsch (FT) synthesis. A minimum selling price of 1.64 per gallon was estimated for hydrogen production and the FT product stream respectively. Further, the effects of the feed composition on hydrogen yield and the minimum selling price of hydrogen were examined. It was observed that the hydrogen yield increased and consequently, the minimum selling price per kilogram of hydrogen reduced as the mass fraction of PP was increased in the feed. From the cost economic analysis, it was also observed that the cost in acquiring and preparing PE and PP based feedstock contributes significantly (80% for H2 and 70% for FT liquid fuels) to the operating cost. The equipment cost also contributes significantly to the capital cost (58% for H2 and 70% for FT liquid fuels) and the total capital investment for both configurations. A combination of sourcing cheaper feed stock and retrofitting old equipment significantly impacts the Total Capital Investment (TCI) and this could make plastic gas waste economically viable. The synthesis of syngas from waste plastics not only offers a sustainable waste management solution but also provides a potential feedstock for downstream chemical processes
Micropatterning of Biologically Derived Surfaces with Functional Clay Nanotubes
Full text linkIn this study, we have generalized an assembly of 50 nm diameter clay nanotubes on larger 20-50 μm diameter biological microfibers such as human hair (protein), cotton (cellulose) and synthetic polyethylene terephthalate- PET. We developed a common set of parameters and nanoclay coating techniques on these naturally different microfibers. We exploited and optimized the electrostatic interaction of the components through strategic adjustments in the solvent pH, employing polycation treatment, as well as selectively modifying the halloysite nanotubes with silanes. For 5 mins, a 5 wt% aqueous dispersion of halloysite nanotubes were applied to get approximately 1-2 μm thick fiber coating after a pretreatment with 0.1 wt % of cationic polyethyleneimine – PEI to allow for a more stable coating. In our first study, we reported the optimized properties of a polycation treatment PEI for hair surface nanoclay coating, where the ideal molecular weight to use for PEI was found to be at 1300 at pH 11. At the optimized conditions, halloysite coverage was over 70 % and the color retention lasted for up to 6 shampoo washes. As an extension of this project, we also further investigated magnetite nanoparticles coating on human hair by stabilizing them with polyelectrolytes such as polystyrene sulfonate - PSS. The findings from this study showed that magnetite nanoparticles of 200 nm diameter could be deposited on human hair in dense arrays and may be used as effective carriers for drug loading and delivery. Similarly, we also investigated enhanced flame retardancy and antibacterial protection on cotton through the application of a halloysite nanoclay coating. Unbleached, unprocessed cotton was treated with multiple PEI/HNT bilayers and was burned to determine the extent of fire protection. Through our experiments we found out that simply adding two bilayers of PEI/HNT coating reduced cotton flammability. The two bilayers accounted for a total of 7 wt% of the tissue. Surprisingly, we also found out that adding increased nanoclay layers improved flame retardancy by only 6%. We were also able to load the clay nanotubes with color-enhancing dyes and antimicrobial chloramphenicol showing that we can construct an architectural coating with complex functionality. Finally, we attempted to study assembly of enzyme loaded HNTs on plastic microfibers for their biocatalytic degradation. We obtained preliminary data for National Science Foundation (NSF) proposal by loading enzymes into halloysite nanotubes and applying such HNT colloids for 5-10 μm diameter polyethylene terephthalate – PET microfibers from used COVID medical masks. We found excellent coating that could be exploited for enzyme delivery to microplastic and to initiate biocatalytic degradation. This may be done in future work if the project will be supported by NSF
Adherence to Mediterranean Diet Principles in Minority College Students
Full text linkEvery year, millions of young adults attend college. The Centers for Disease Control and Prevention (CDC) lists heart disease and diabetes as the top health concerns for minority Americans. African American and Hispanic/Latino communities could see a reduction in illness risk by promoting physical activity and healthy eating habits such as the Mediterranean Diet. Research indicates that young adults may benefit significantly from taking charge of their eating habits at the crucial point in their transition from high school to college. By determining the dietary patterns of minority college students, as related to the Mediterranean diet principles, this study intends to evaluate the foundation from which intervention studies may be developed. This research aims to determine the level of adherence to the Mediterranean diet principles in college students and to evaluate whether there is a difference in adherence between racial groups. As dietary patterns influence overall health and are a strategy to prevent chronic disease, specifically those prevalent in minority populations, findings could inform future health promotion and disease prevention interventions. A cross-sectional online survey design was used to study using enrolled U.S. college students aged 18-26 (63% women and 37% men). Adherence to Mediterranean diet principles was measured by the KIDMED 2.0 validated tool. The self-administered questionnaire included 38 items, including three demographic items, sixteen dietary questions about adherence to the Mediterranean diet, five physical activity questions, and two self-reported height and weight. The KIDMED 2.0 Questionnaire Score tool is used for dietary questions. It took approximately 15 minutes to complete the questionnaire. The sample was recruited using a network sampling strategy via email, flyers, announcements, and social media postings. Of the 206 participants who completed the initial online questionnaire, 68 were excluded for not meeting the study criteria, as they were not within the eligible age range. The remaining 138 participants were included in the analysis. The ages ranged from 18 to 26 years, and all were college students. Most participants were female (n=87, 63.0%) and White (n=55, 39.9%). Table 1 shows White, Hispanic-origin participants made up 10.9% (n= 15, 10.9%), African American/Blacks 27.5% (n=38), Black, Hispanic-origin 8.7% (n= 12), and the other minorities (Native American, Indian, Asian, Middle Eastern, Biracial, Multiracial). The calculated BMI from the self-reported height and weight resulted in two participants (2.2%) being categorized as underweight, 51 (37%) were categorized as normal, 49 (35.5%) were categorized as overweight, 24 (17.4 %) were categorized as obese I, and 3 (2.2%) were categorized as obese II. Of 138 participants, 130 (94.2%) completed the KIDMED 2.0 component of the questionnaire. The scores are categorized into three categories ≤3 poor diet quality, 4-7 average diet, and ≥8 good diet quality. Asian /Other minorities had the highest mean KIDMED 2.0 score, followed by Blacks and Blacks of Hispanic origin and Whites. Whites of Hispanic origin had the lowest mean score for the KIDMED 2.0. A one-way ANOVA was run to compare the scores between the races. A significant difference was found [F (4, 125) = 2.93, p = .024]. Post hoc comparisons using the Tukey HSD test indicated that the mean score for Whites of Hispanic origin was significantly different from the Asian /Other minorities’ race group. In the total sample, only 24.5% of the participants were categorized as having “good” diet quality in terms of the Mediterranean diet. In this sample, Blacks had the highest proportion (29.4%) in the highest quality category, followed by Asian/Other (23.5%). Whites (44.2%) had the highest proportion of participants in the lowest diet quality category, followed by Blacks (30.8%). Analysis of individual tool items as they relate to food categories consumed describes practical differences among racial groups that could enhance specificity of future education and studies. A one-way ANOVA was run to compare the mean BMIs between the race categories. No significant differences were found, [F (4, 124) = 1.27, p = 0.28]. A oneway ANOVA was run to compare the mean BMIs between KIDMED 2.0 categories. No significant differences were found, [F (2, 122) = 1.50, p = 0.23] A Pearson correlation was run to determine whether there was a relationship between BMI and KIDMED 2.0 scores, no significant relationship was found, r = -0.12, p = 0.17. This study demonstrated differences in the adherence rate to Mediterranean diet principles in college students using the KIDMED 2.0 tool, among racial groups. Overall, the majority of students had diets of poor-good quality diets with only approximately 30% reaching a quality level considered to promote health. Whites of Hispanic origin had the poorest adherence to the diet. Low adherence to the Mediterranean diet principles is contrary to preventing nutrition – related chronic disease in all races, and may more significantly affect racial minorities. The results of this study lay a foundation for planning future interventions by identifying specific food categories to target by racial groups