Green Valley High School

Topic: Neurodegenerative Diseases Title: Lipid Metabolism Disruptions as Early Noninvasive Biomarkers of Alzheimer’s Disease: A Lipidomics Review Objective: To analyze how disruptions in lipid metabolism and cholesterol processing, which include changes in the levels of sphingolipids, ceramides, plasmalogens, and cholesteryl esters, help in early detection of Alzheimer’s disease, and to analyze whether these lipid alterations are reliable, noninvasive biomarkers for diagnosis and disease progression. Background: Alzheimer’s disease is increasingly linked to early disruptions in how the brain processes lipids and energy. Studies in metabolomics and lipidomics show that individuals with Alzheimer's often experience shifts in cholesterol-related lipids, fatty acids, and other metabolic pathways before noticeable symptoms develop. Because these changes can be detected in blood or cerebrospinal fluid, they raise the possibility of identifying the disease much earlier. However, lipid patterns widely vary between individuals, and the complexity of measuring so many lipids makes it difficult to determine which markers are the most reliable predictors of disease and age. Methods: To investigate whether cholesterol-related lipid biomarkers can aid in early diagnosis of Alzheimer’s and Parkinson’s disease, we conducted a structured literature review using Web of Science. We screened studies focused on lipidomics, metabolomics, and cholesterol-transport pathways. Articles analyzing blood, cerebrospinal fluid, and post-mortem brain tissue were included in the analysis. Across studies, we compared reported alterations in sphingolipids, ceramides, plasmalogens, cholesteryl esters, desmosterol, and ApoE/ABCA1 activity. We synthesized findings to evaluate which lipid signatures most consistently distinguished early neurodegeneration and predicted disease progression. Results: A total of 141 articles were extracted from Web of Science using targeted keywords, revealing that altered lipid metabolism and cholesterol transport are early indicators of Alzheimer's and Parkinson's disease. It was primarily identified using lipidomic, metabolic and multi-omics profiling. Findings included decreased sphingomyelins, cholesterol esters, desmosterol, plasmalogens, increased ceramides, and altered ApoE and ABCA1 activity Conclusions: Biomarkers of lipid metabolism and cholesterol transport are sufficient methods of indicating onset Alzheimer’s and Parkinson’s disease. Despite this method not being wildly used due to not being a conformities method, we can further use biomarkers to eventually benefit early diagnosis.

The global demand for cybersecurity professionals continues to outpace the available workforce, creating a critical gap that must be addressed through early education. This paper presents a project-based teaching aid designed for secondary education to inspire students to learn cybersecurity in secondary school classrooms and pursue careers in cybersecurity. The teaching aid employs the Secure Software Development Life Cycle (SSDLC) as a lens to examine cyberattacks and includes three hands-on laboratory exercises that illustrate common cyberattacks and their countermeasures. While this paper focuses on three specific labs for illustration, the lab set is designed to be modular and expandable, enabling the integration of additional cybersecurity topics and attack scenarios. The project leverages free and open-source technologies, ensuring accessibility and equity by enabling web-based remote desktop connections to the lab infrastructure, making it easily implementable by secondary school educators. With most existing hands-on curricula designed for collegiate-level instruction and requiring significant technical setup, this work fills a crucial gap by providing age-appropriate and more accessible lab materials for secondary cybersecurity education.

The rapid growth of orbital debris presents an escalating challenge to spacecraft safety and the long-term sustainability of low Earth orbit.While end-of-life and collision-avoidance measures can limit new debris generation, they do little to address the expanding population of inactive satellites, rocket bodies, and fragments already in orbit.To confront this issue, we must turn toward Active Debris Removal (ADR): mission-level interventions designed to capture, stabilize, and deorbit existing objects.This paper provides a comparative review of seven representative ADR methods, categorized into contact-based and non-contact systems.Each approach is evaluated through a set of parameters including capture mechanics, sensing and guidance architecture, control and stability requirements, power and propulsion demands, and mission scalability.The review integrates experimental and simulation data from major demonstrations in each category, notably Astroscale's ELSA-d mission and JAXA's electrodynamic tether program, while also drawing on smaller-scale studies that explore additional ADR techniques.Through this analysis, the paper investigates how operational and technical constraints influence the performance and applicability of current ADR technologies, providing a foundation for ongoing development toward more effective debris-removal solutions.

Purpose: This study investigates the adoption of electronic payment systems among Micro, Small, and Medium Enterprises (MSMEs) in Koronadal City, focusing on identifying the drivers and inhibitors influencing this shift toward a cashless economy. Study design/methodology/approach: The research employs a descriptive multivariate correlational design, surveying 351 registered MSMEs across various sectors in Koronadal City. Data were collected using a structured questionnaire validated for reliability and analyzed using multiple linear regression to determine significant predictors of electronic payment adoption. Findings: The results reveal a low adoption rate of electronic payment systems, with cash transactions remaining dominant. Key drivers include customer demand, internet accessibility, digital infrastructure, and government incentives, while perceived security concerns act as significant inhibitors. The regression model explains 68.4% of the variance in adoption levels, highlighting customer demand as the most significant predictor. Originality/value: This study provides critical insights into the unique challenges and opportunities faced by MSMEs in smaller cities transitioning to digital payments. The findings contribute to the discourse on financial inclusion and digital transformation, offering actionable recommendations for policymakers, financial institutions, and MSMEs to address barriers and promote adoption in regional contexts.

Accurately predicting credit card defaulting remains a significant challenge in the field of financial risk management even with the abundance and accessibility of modern-day technology. This paper focuses on leveraging machine learning techniques to increase accuracy regarding predicting individuals who default while addressing limitations from similar past studies. This iterated and improved model can be implemented as a back-end code for an application or cloud-based service, which can be used by low-income families to help mitigate and improve spending habits, hopefully resulting in declined defaulting across many households. Utilizing this Kaggle dataset 1 , this study uses the application of the XGBoost algorithm, optimized through extensive hyperparameter tuning and advanced feature engineering techniques such as increasing regularization to prevent overfitting and increased alpha and reg_lambda to penalize complex models. The implementation of XGBoost not only shattered the performance and quality of traditional credit card defaulting prediction models but also illustrated the capabilities and potential of machine learning in developing solutions that are both effective and equitable, providing critical support to financially vulnerable populations.

Purpose This study investigates the relationship between young professionals' perceptions of cyber security and their level of online shopping on e-commerce platforms in Koronadal City. Study design/methodology/approach A descriptive-correlational research design was employed to assess the socio-economic profile, cyber security concerns, and personal risk mitigation practices of 68 young professionals. Data were collected using a structured survey instrument and analyzed through frequency distribution, weighted mean, standard deviation, and simple linear regression. Findings The majority of respondents were aged 27 to 30, earned below PHP 10,000 monthly, and were primarily self-employed. Participants expressed high concerns regarding the security of personal and financial information, with data security being the highest concern (mean = 4.57). A significant inverse relationship was found between cybersecurity concerns and the level of e-commerce usage (β = -0.179, p = 0.046), indicating that greater concerns about cybersecurity led to lower online shopping frequency. Personal risk mitigation practices were moderate, with respondents frequently updating passwords and exercising caution with suspicious links, though the use of two-factor authentication remained inconsistent. Originality/value This study provides empirical insights into how cybersecurity concerns influence online shopping behavior among young professionals. The findings emphasize the need for e-commerce platforms to enhance security measures and transparency to improve consumer confidence. Additionally, the study underscores the importance of targeted cybersecurity education to encourage safer and more frequent e-commerce engagement.

p16INK4a is a critical tumor suppressor protein involved in regulating cell cycle progression. Mutations or structural changes in p16INK4a have been implicated in various cancers, making it an important therapeutic target. This study utilized computational approaches, including molecular docking and binding site prediction, to identify small-molecule inhibitors capable of binding and stabilizing mutated p16INK4a. We hypothesize that the docked ligands bind strongly to the p16INK4a protein and prevent aggregation. The protein structure was modeled using AlphaFold 3 and analyzed using AutoDock Vina for docking simulations. Based on the favorable binding energies, five potential inhibitors were identified. The pharmaceutical properties of these ligands were computed using the SwissADME web server. Based on this analysis, all the inhibitors demonstrated high gastrointestinal absorption and drug-likeness according to Lipinski’s rule but showed low blood-brain barrier permeability. The current work will be a foundation for designing p16INK4a-targeted therapies to inhibit cancer progression.