Evaluation of a mental health first aid educational intervention for nursing professors

Background: A lack of mental health awareness and crisis intervention among nursing professors may contribute to students not receiving needed mental health support. This pilot QI project aimed to assess the impact of a Mental Health First Aid (MHFA)/Mental Health Matters (MHM) training program. Project objectives included: 1. A 10% increase in WCC nursing faculty confidence in identifying and intervening during a nursing student health crisis 2. A 5% increase in nursing students seeking assistance from WCC nursing faculty for referral to WCC personal counseling (PC) services 3. A 5% interest of the remaining WCC faculty in participation in the piloted program as a college-wide intervention Methods: After attending a faculty in-service regarding MHFA/MHM, a sample of nursing professors (n = 5) were administered the Mental Health Literacy Scale (MHLS) pre- and post-MHFA certification. A presentation of the MHM referral program was then conducted with the student body. After project implementation, preliminary results were shared with the remaining college-wide faculty, and interest was polled. Results: The Wilcoxen Signed-Rank Test was statistically significant for two MHLS categories. The Mann-Whitney U identified an increase (8.3%) in the MHLS mean scores. The Vargha and Delaney (A) effect size measures determined the clinical significance of the MHLS (question 18, very small, and question 25, no difference). Conclusions: Due to increased nursing faculty confidence, increased student referrals, and significant college-wide faculty interest, it is recommended that WCC support the implementation of this pilot QI project, college-wide. IRB Approval: IRB approval through full/expedited/exempt review

Physical Activity Assessments for Obesity Management in Primary Care

Obesity is an epidemic and chronic disease that affects health and quality of life. Physical activity is a modification encouraged in obesity care due to its benefits which reach beyond weight loss in obesity management and influence multiple body and organ systems. The purpose of the proposed quasi-experimental, evidence-based improvement project is to explore if routinely assessing for physical activity in the primary care setting using a standardized screening tool can address physical inactivity. The anticipated number of participants utilizing G*Power analysis software is a minimum of 15 and will be comprised of individuals from St. Louis, MO. The evidence-based practice intervention is the implementation of a standardized physical activity assessment screening questionnaire in the primary care setting. The primary outcome of physical activity level will be measured using the International Physical Activity Questionnaire- Short Form. Utilizing a tool to identify physical inactivity has the potential to impact the knowledge of providers regarding the current trend in physical activity/inactivity and assist them in the identification, tailored management, communication, and improvement of clinical outcomes related to the difficult phenomenon of obesity

Improving PRN pain medication reevaluation compliance : a quality improvement project

The International Association for the Study of Pain (IASP, 2020) defines pain as an unpleasant sensory and emotional experience related to actual or potential tissue damage, and it is one of the leading causes of emergency department visits globally (Cisewski & Motov, 2019). Chronic pain significantly impacts the adult population and is one of the common reasons adults seek medical care in 2021 (Rikard et al., 2023). An estimated 20.9% (51.6 million) of United States adults experience chronic pain, and around 6.9% of the population (17.1 million) suffer from high-impact chronic pain (Rikard et al., 2023). This prevalence highlights the critical importance of effective pain management in the healthcare system, as chronic pain affects not only the physical well-being of patients but also their emotional and mental health. Nurses, with their pivotal roles, ensure that patients receive the necessary pain medication, as their responsibilities include assessing pain, administering medication, evaluating, and documenting its effectiveness (Van Cleave et al., 2021). Regulatory bodies such as the Joint Commission (TJC, 2017) have established pain assessment and management standards for all JC-accredited healthcare organizations, ensuring that pain is adequately addressed, including providing education resources and programs to its staff. These standards necessitate hospitals to identify pain assessment and management efforts and formulate and oversee performance improvement activities (TJC, 2017). The Veterans Health Administration (VHA) has implemented policies for safe and effective pain management. However, compliance with pain level reevaluation for pro re nata (PRN) medications at VHA medical centers is challenged by policy variations, lack of standardization, and barcode medication administration (BCMA) system limitations

An initial assessement of moral attitude dynamic model for defusing culture war tensions

[EMBARGOED UNTIL 08/01/2025] This dissertation introduces Moral Attitude Dynamic Model (MADM) for defusing conflicts tensions by incorporating the key constructs of Moral Foundation Theory into The Contingency Theory of Accommodation (Contingency Theory). MADM advances Contingency Theory by incorporating both the key conceptual constructs and methodological approaches of Moral Foundation Theory. Consequently, it allows for real-time tracking and analysis of target public's attitudes towards a certain issue as it allows for the employment of computational methods. Therefore, this model could effectively inform conflicts management and political polarization. Accordingly, MADM extends the application of Contingency Theory in the context of social controversies with a long history. Moreover, this study applies MADM on defusing the tensions of two culture issues (GM food and Abortion) as an initial assessment. Concretely, issue related tweets were scraped through academic API provided by Twitter, and issue supporters' and opponents' tweets were identified by supervised machine learning classifiers for each issue respectively. Distributed Dictionary Representation, a Natural Language Processing tool was adopted for quantify supporters' and opponents' moral stances along the enhanced contingency continuum, the key construct of MADM, and multilevel linear modeling was adopted to investigate the quantified moral stances. Accordingly, pro-GM food and pro-abortion messages were constructed based on the moral stance diagnosis results and an experiment was conducted for message and model effectiveness evaluation. The experiment results show the potential of MADM as an effective tool for conflict management. The moral reframed pro-abortion message constructed based on MADM significantly decreases the participants' anger and the affective polarization. In sum, the results suggest that MADM could be a great tool for monitoring the public's opinion, developing quick responses, and evaluating the intervention. In other words, MADM could be a great tool for mitigating polarization and for real-time conflict management in the ever-changing environment.Includes bibliographical references

Applications of deep learning in protein structure prediction : from complexes to intrinsically disordered proteins

[EMBARGOED UNTIL 05/01/2025] Proteins are essential biomolecules that play crucial roles in various biological processes within organisms. Their complex interactions and structures are fundamental to understanding cellular mechanisms and developing therapeutic strategies. However, traditional experimental methods like X-ray crystallography and NMR spectroscopy, despite their accuracy, are costly and time-consuming. This has prompted the exploration of computational models, particularly deep learning techniques, to predict protein structures, especially of complexes, more efficiently. This dissertation presents the development of DNCON2_Inter, DeepComplex, and Disformer. DNCON2_Inter predicts inter-chain contacts in homooligomers using deep convolutional neural networks, leveraging monomeric multiple sequence alignments (MSAs) and co-evolutionary features to enhance prediction accuracy. Using the predicted inter-chain contacts as distant restraints, quaternary structures can be produced. High precision of the inter-chain contacts leads to better quality models of the complexes. DeepComplex, a web server, extends this approach to predict inter-chain contacts and reconstruct quaternary structures of both homodimers and heterodimers. Finally, Disformer, was proposed in the prediction of intrinsically disordered proteins (IDP) which only gain structural and functional importance upon interaction with other molecules. Disformer employs a transformer-based dual graph approach combining Graph Attention Networks (GAT) and Graph Convolutional Neural Networks (GCN). It excels in predicting intrinsically disordered regions (IDRs) by leveraging both sequence-based and structure-based features for a comprehensive graph-based binary node classification. These contributions collectively enhance the predictive capabilities of protein structural analysis, providing new insights into protein interactions and disorder. This research not only advances our understanding of protein dynamics but also paves the way for future developments in the prediction and analysis of protein complexes and IDPs.Includes bibliographical references

The lived experience of bilateral risk-reducing mastectomy and impact on body image in young women with increased lifetime hereditary breast cancer risk

[EMBARGOED UNTIL 05/01/2025] Previvor is a term applied to a person with an identified, elevated lifetime cancer risk but who lacks a cancer diagnosis. Management strategies can be undertaken to decrease the risk of developing certain cancers. For women with a pathogenic variant that increases the predisposition for a breast cancer diagnosis, a bilateral risk-reducing mastectomy (BRRM) is the most effective cancer prevention strategy. Currently, there is a dearth of literature examining BRRM and its effects on young women. This study sought to understand the lived experience of BRRM, along with its impact on body image, in young female previvors in the first 12 months following surgery. Two qualitative methods, descriptive phenomenology and photo-elicitation, were utilized to describe the lived experience of BRRM and body image. Narrative data served as the primary data source, augmented by participant-provided visual data. A sample of 13 women were interviewed. Eight themes were found to describe how young previvors process an increased lifetime breast cancer risk, select BRRM and reconstruction methods, and express the effect of BRRM on body image. Findings provide a rich description of risk-reduction and body image outcomes in young previvors. Results from this project will be used to design future research for improving the physical and psychosocial health of this unique population.Includes bibliographical references

Deep learning enabled materials design and characterization

[EMBARGOED UNTIL 05/01/2025] In this dissertation, deep learning methodologies are applied to innovative computational approaches for the development and characterization of materials in the material science field. The first part of the research focuses on a physics-informed machine learning workflow for virtual experimentation in the 3D printing of thermoplastics, where traditional methods are limited by complex chemical reactions and extensive design possibilities. Utilizing a dataset of 62 formulations and 216 Stress-Strain curves, this method employs dimension reduction and a novel machine learning model with physics-informed descriptors to simulate over 100,000 virtual experiment sets in under one minute, significantly enhancing the speed of material discovery. The second part improves the analysis of characterization data, specifically X-ray diffraction (XRD) patterns, by using Transformer-based models that surpass previous CNN-based models in training speed and accuracy. This segment introduces a novel data augmentation technique that simulates experimental errors and uses interpretability analysis to show how the model captures long-distance interactions between XRD peaks. It also explores the potential of transfer learning from XRD to Fourier-transform infrared spectroscopy (FTIR) data, broadening the model's applicability and improving the efficiency and accuracy of material characterization. Overall, this dissertation demonstrates how deep learning can revolutionize material science research, providing faster, more accurate tools for material development and analysis.Includes bibliographical references

Replacing titanium dioxide in food products with a natural alternative made from acid whey and millet

[EMBARGOED UNTIL 05/01/2025] The increasing interest in replacing synthetic food additives with healthier, more sustainable alternatives has directed considerable attention toward titanium dioxide (TiO2), a widely used whitening agent in the food industry. This study explores the replacement of TiO2 in food products with a natural alternative made from acid whey and millet, addressing the health and environmental concerns associated with TiO2. Acid whey (AW) is a byproduct of Greek yogurt production, and it presents environmental disposal challenges because of its high mineral content and high biological oxygen demand. Simultaneously, millet, known for its high nutritional value and climate resiliency, has yet to be fully utilized in food processing. This research investigates using millet (Barnyard and Little millet) as a neutralizer and encapsulating agent for acid whey to facilitate its spray drying. Through optimization experiments, two formulations of acid whey millet (AWM) powder, Barnyard millet acid whey (BAW) and Little millet acid whey (LAW), were developed, both exhibiting excellent physio functional properties, water solubility, and particle sizes ranging from 1.18 [plus or minus] 0.02 to 1.42 [plus or minus] 0.14 [mu] m. Millet addition to AW also improved AWM powders' glass transition temperature, shape, morphology, and antioxidant activity. Chemical analysis demonstrated high lactose, minerals, and antioxidant levels in AWM powder, highlighting its potential as a functional food ingredient. A comparative analysis with TiO2 revealed comparable lightness and whiteness index in AWM, alongside superior nutritional and functional attributes. AWM proved effective as a TiO2 substitute in confectionery coatings and iron-fortified extrudates without compromising quality or safety. The study also addresses scaling up this solution from laboratory to pilot plant trials, discussing encountered challenges and insights. This research significantly contributes to discussions on sustainable food additives, offering practical solutions for safer, more environmentally conscious food practices by leveraging acid whey and millet's synergistic potential as a natural food whitening agent.Includes bibliographical references

Judicial Decision-Making in Intimate Partner Violence Cases: A Scoping Review and Critical Analysis

Family courts have increasingly made decisions in intimate partner violence (IPV) cases that extend or restrict protections for certain groups of survivors, impacting both the individuals and their family systems. Understanding how judicial decisions are made in cases involving IPV is critical to addressing systemic issues and advocating for fair, predictable outcomes for underrepresented populations. This scoping review paper identifies scholarships that inform us on how court decisions are made for cases involving IPV in the literature. Scholarships in the literature are analyzed based on the framework of intersectionality – a lens that many IPV scholars have suggested to be appropriate in revealing how structural forces reinforce social inequalities – and American Legal Realism – a legal theory that provides a useful theoretical perspective in analyzing judicial decision-makings. The analysis addresses knowledge gaps in the literature and ends by suggesting directions for future research including, an integration of the two frameworks to better understand court decisions of IPV cases.This is an original manuscript of an article published by Taylor & Francis in Family Transitions on Jan 25, 2025, available at: https://doi.org/10.1080/28375300.2025.245377

Tailored fluorescent polyionic nanoclays for enhanced sensing applications

[EMBARGOED UNTIL 05/01/2025] Skin-interfaced wearable electronics capable of real-time monitoring of vital biophysiological signals have gained significant attention. However, traditional wearable devices often face challenges such as the use of costly materials, intricate fabrication processes, and poor stability under mechanical stress and prolonged wear. Moreover, the limited breathability of substrates can compromise comfort and cause inflammation over long-term use. My research primarily addresses these challenges by innovating materials, adapting fabrication technologies, and modifying devices to enhance breathability, stretchability, affordability, and other unique characteristics essential for on-skin wearables. The dissertation starts from the development of cost-effective, eco-friendly, and breathable wearable electronics. Direct writing technique to apply conductive graphite patterns onto cellulose paper, facilitating the development of on-skin electronics (chapter 1). Then laser-scribed molybdenum dioxide (LSM) with high electrical conductivity, biocompatibility, chemical stability, and MRI compatibility.is developed to achieve mask-free, high-resolution, and large-scale fabrication of highly conductive materials on flexible substrates and Janus devices capable of monitoring both bodily and environmental signals (Chapter 2) is also obtained. Using phase-separation technology, we develop porous composites of silver nanowires (Ag NWs) with an ultralow percolation threshold. These composites enable strain-resilient near-field communication (NFC), facilitating wireless powering and data transmission for both skin-interfaced and implantable bioelectronics (Chapter 3). Last work is focus on the engineering cellulose nanofiber interfaces (CNFI) on porous substrates to achieve surface flatness for high-quality bioelectronics printing and to construct mechanical heterogeneity for strain-resilient bioelectronics. Additionally, CNFI for microfluidic channel (MFC) is also constructed for continuous and real-time collection, transportation, and discharge of sweat (Chapter 4).We demonstrate the integration of fluorescent species into onium ion-functionalized polyionic nanoclays (PINCs) via covalent modification methods, facilitating the creation of solvent-responsive materials with sensing capabilities. By co-incorporating functional targets such as 3-aminopropyl or 3-mercaptopropyl groups, subsequent conjugation with reactive fluorophores enhances quantum yields and enables solvent-responsive properties. For instance, dansyl chloride and acrylodan conjugation generates solvent-responsive fluorescent PINCs capable of sensing the local environment (polarity, acidity) and engaging in guest-host interactions. Dansyl-labelled nanoclay exhibits a [approx]7-fold increase in quantum yield compared to free probe, while maintaining a consistent pKa. This work lays a foundation for engineered nanosheets suitable for imaging, theranostics, and signal-amplified sensing applications. In parallel, we successfully incorporate pyrene probes, known for their structured emission and prolonged excited state lifetimes, into imidazolium-functionalized PINCs via covalent modification with 3-mercaptopropyl groups, followed by thiolene click chemistry. Pyrene functionalization levels range from 0.1 percent to 15 percent of total silane groups on the clay surface. Our investigation reveals composition-dependent selective excimer and monomer formation in ethanolic solutions, with increased pyrene modification correlating with enhanced excimer formation, indicating uniform surface modification. Pyrene-conjugated PINCs (Py-PINCs) demonstrate promising capabilities in detecting explosives such as TNT, RDX, and PETN, with efficient quenching of excimer emission observed, particularly with TNT. The Stern-Volmer quenching constant for TNT is 15600[plus or minus]134 M-1. Additionally, Py-PINCs exhibit sensitivity to halide ions, with higher quenching efficiency observed for iodide compared to bromide, facilitated by enhanced electrostatic interactions with the positively charged PINC surface. Solid-state Py-PINCs serve as oxygen sensors, displaying fluorescence quenching upon exposure to oxygen gas, with a linear response to increasing oxygen pressure and a Stern-Volmer quenching constant of 5.29[plus or minus]0.07 bar-1. This research establishes a foundation for advanced sensing technologies applicable in security, environmental monitoring, and biomedical research. Furthermore, we report the synthesis of magnesium phyllo(organo)silicate nanosheets with a 2:1 phyllosilicate structure, characterized by a negative surface charge from covalently attached carboxylate and sulfonate groups, forming polyanionic nanoclays. Pyrene probes are successfully integrated into these nanoclays via covalent modification with 3-mercaptopropyl groups, enabling subsequent conjugation through thiol-ene click chemistry. These pyrene-tagged polyanionic nanoclays exhibit a superquenching effect towards lead ions (Pb2+), with Stern-Volmer quenching constants of (8.4 [plus or minus] 0.4) x 107 M-1 and (5.7 [plus or minus] 0.3) x 107 M-1 for pyrene-tagged carboxylate and sulfonate polyanionic nanoclays, respectively. This phenomenon is attributed to the high anionic charge density on the surface, which attracts Pb2+ ions through electrostatic interactions. Selective and efficient quenching is observed for Pb2+ compared to other metal ions, significantly greater than neutral and anionic free pyrene probes and pyrene-tagged polycationic nanoclays. This research provides a framework for the development of next-generation engineered nanosheets tailored for advanced sensing technologies, offering selective and efficient quenching compared to other metal ions and nanoclay compositions.Includes bibliographical references