North London College

Immunohistochemistry (IHC) and Immunofluorescence (IF) are the two most commonly employed methods of cell staining, which aims to enhance visibility of cellular components for medical research and diagnosis. Immunofluorescence which uses fluorescently labeled antibodies to detect specific proteins is preferred over immunohistochemistry for multiplexing and colocalization which analyze protein distributions due to its transparent nature. However access is often barred due to high costs of reagents and equipment, slide quality degradation overtime, and technical limitations such as autofluorescence, and background staining from non-specific binding or cross reactivity antibodies. To address the aforementioned issue, we propose a novel machine learning-based immunofluorescence staining approach utilizing Generative Adversarial Network (GAN). The model intakes a digital pathology image of IHC and converts it to an IF image through feature maps. The proposed model achieved state-of-art performance with PSNR value of 30.67 and SSIM value of 0.8992. Additionally, the qualitative experimental results demonstrate the efficacy of the proposed method in enhancing generating high-quality IF stained images.

Autism Spectrum Disorder (ASD) is a neurological and developmental disorder that has effects on how people behave and interact. It is a lifelong condition, and those born with it show symptoms typically during one’s early childhood. Over the past two decades, the cases and prevalence of ASD have been on a steady increase, with those born in the United States in 2012 being around 4 times more likely to have ASD compared to those born 20 years before. The treatment of ASD is shown to be more effective at earlier ages, which is why being able to perform an early diagnosis is crucial for those born with the condition. However, most of the current tests for child ASD diagnosis rely on the analysis of behavioral patterns by other people. This becomes more of an issue the younger the child gets, because at the youngest ages, there are not many clear actions or traits that a baby can physically show, making early subjective judgment vague and unreliable. To address this problem, I proposed an ASD screening framework that utilizes geometric patterns from retinal blood vessel scans. The framework contains two modules, the vessel segmentation and the ASD prediction network. The segmentation network processes the retinal images and generates a vessel segmentation map, which is then used for the ASD prediction network for a prediction of the severity. Through experimental results, it is demonstrated that the proposed approach is feasible by achieving an accuracy of 94.2% on a public dataset.

Oral medications are prevalent within modern-day society because of their convenience for patients and their cost-effectiveness. However, challenges such as poor solubility and gastrointestinal degradation decrease their effectiveness. Recently, advancements in nanotechnology have improved the efficiency of drug delivery, with specialized nanocarriers being developed and synthesized for different functions. This review is concentrated on chitosan nanoparticles (CSNPs), which are derived from chitosan, a polysaccharide that is biodegradable and offers a promising solution for overcoming certain challenges associated with drug delivery. CSNPs have been synthesized using a variety of methods such as emulsion cross-linking, ionic gelation, and biosynthesis and more novel methods such as incorporation of a magnetic core. These methods have shown improvements in particle stability, CSNP solubility, and targeted drug delivery, however, challenges like aggregation remain, and must be addressed to optimize the performance of CSNPs. Overall, CSNPs offer a promising advancement in the usage of nanoparticles in medicine with important implications for improving oral drug delivery systems and creating innovative solutions within healthcare.

Drug-drug interactions can cause adverse effects and impact patient safety. Traditionally, the prediction of DDIs has been labor intensive. However, these approaches have certain limitations in terms of scalability, convergence of different drug combinations, and capturing complex interactions. The potential combinations increase as the number of approved drugs increases; this results in the need for efficient methods. This research aims to utilize artificial intelligence and machine learning techniques to develop a method that accurately detects potential DDIs. The proposed idea allows for greater accuracy and automation in predicting DDI, reducing the time and cost required. The proposed method takes two drug formulas as input and employs a Drug Feature Extractor to extract their features. These features are then represented in feature maps, which are used when calculating a similarity score between input drugs and other drugs in the data set, and to predict potential DDIs. The model combines a drug similarity calculator and DDI predictor, enabling the system to process data in a “human-like” method; aiding in predicting interactions for newly developed drugs. The proposed model achieved state-of-art performance with an accuracy of 88.9%. The results demonstrate the efficacy of the proposed method in predicting potential drug interactions.

The integration of automation into production processes is emerging as a transformative force, impacting economic growth, labor market structures, and broader economic landscapes. Prettner's automation-augmented Solow Growth Model provides a novel theoretical framework for analyzing the role of automation capital in economic production, offering insights into its effects on both economic growth and income disparities between workers and business owners. This study investigates the impact of automation on economic growth and income inequality in South Korea, applying Prettner’s automation-augmented Solow Growth Model. By incorporating automation capital into the production function, this model helps elucidate the broader economic implications of automation. South Korea, with its advanced manufacturing sector and rapid technological adoption, serves as an ideal case for examining these dynamics. Using time series data from 2011 to 2019 and the Ordinary Least Squares (OLS) method, our analysis reveals a positive correlation between automation capital and economic growth, highlighting automation's role in driving South Korea’s economic progress. While the link between automation and income inequality is less clear, the study suggests that automation has the potential to significantly influence economic structures in South Korea. This emphasizes the need for further research to fully understand these relationships and inform policy decisions.

Abstract Sepsis remains a major global health issue in pediatric and adult populations, largely due to a lack of understanding of its complex pathophysiology. Despite its high mortality rate, there have been few advancements in sepsis-specific therapies over recent decades. The study aimed to investigate the potential benefits of a genome-wide transcriptomic approach to sepsis in pediatric and adult populations in reducing sepsis-related mortality and enhancing sepsis guidelines. The scoping review explored gene expression data pertinent to developing sepsis guidelines related to its definition, classification, disease severity, molecular biomarking, and benchmarking. A system-biology approach using transcriptomics was adopted to enhance the understanding of sepsis at the mRNA gene expression level. The study involved a search of the PubMed database for original research or systematic reviews that involved transcriptomic application in the context of clinical sepsis published over a ten-year period, from 2012-2022. Of the 14,048 studies retrieved, a full-text analysis was performed. Five main concepts emerged: case definition, classification, quantifying severity of sepsis, transcriptomic biomarkers, and benchmarking. Studies were categorized according to these five categories. The results showed evidence of a connection between the transcript and clinical sepsis, demonstrating that transcript-driven sepsis categorization is possible. Integrating transcriptomic data with clinical endpoints holds promise for more precise sepsis treatment. Although further exploration is needed, the methodology shows potential for disease modification.

This research investigated how recent issues surrounding refugees in Jeju had affected the residents’ cultural sensitivity. The responses of 109 Jeju residents were used for an analysis. An independent t-test was conducted to see the difference in cultural difference in two groups: the residents who view refugees as potential neighbors and those who don’t. Based on the result of Pearson’s correlation analysis, a multiple linear regression analysis was conducted to see how refugee-related variables explain the change in residents’ cultural sensitivity. The result revealed that people open to refugees have a higher level of cultural sensitivity, and women in Jeju fear refugees more so than men. Also, Korean identity is negatively correlated with cultural sensitivity. Most importantly, the findings of this research suggested that accepting refugees can foster Jeju residents’ cultural sensitivity, which is vital to the Jeju economy and its community.

Being mostly made up of highly permeable basalt and volcanic ash soil, Jeju Island’s lithosphere characterizes its streams to be dry, flowing only when precipitation is happening. Under this condition, this research was motivated to identify the need of conservation of underground water, which is taking up most of (84% of) Jeju’s water usage, and made an attempt to reduce the permeability of stream beds so that it can replace underground water and be used instead. To this end, this study suggested a simple method to make dry streams to carry water all-year-round by reducing permeability of stream floor. The experiment of permeability was performed on the porous basalt and compared it with that of same basalt with volcanic ash soil and Jumunjin sand layer added on top. The results showed a dramatic decrease in permeability of water when both volcanic ash soil and Jumunjin sand is were layered on top of porous basalt. Despite being gained in a controlled environment with a simple test, this result may provide a realistic and effective method of preserving Jeju Island’s underground water which ultimately is a method of resolving water related issues.