(1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and accurately predict treatment responses. (2) Methods: We utilized our highly sensitive cell-free DNA (cfDNA) detection system, in combination with a machine learning algorithm, to provide a platform for improved diagnosis and prognosis of HCC. (3) Results: cfDNA, specifically alpha-fetoprotein (AFP) expression in captured cfDNA, demonstrated the highest accuracy for diagnosing malignancies among the serum/plasma biomarkers used in this study, including AFP, aspartate aminotransferase, alanine aminotransferase, albumin, alkaline phosphatase, and bilirubin. The diagnostic/prognostic capability of cfDNA was further improved by establishing a cfDNA score (cfDHCC), which integrated the total plasma cfDNA levels and cfAFP-DNA expression into a single score using machine learning algorithms. (4) Conclusion: The cfDHCC score demonstrated significantly improved accuracy in determining the pathological features of HCC and predicting patients’ survival outcomes compared to the other biomarkers. The results presented herein reveal that our cfDNA capture/analysis platform is a promising approach to effectively utilize cfDNA as a biomarker for the diagnosis and prognosis of HCC.
The development of minimally invasive tests for cancer diagnosis and prognosis will aid in the research of new treatments and improve survival rates. Liquid biopsies seek to derive actionable information from tumor material found in routine blood samples. The relative scarcity of tumor material in this complex mixture makes isolating and detecting cancerous material such as proteins, circulating tumor DNA, exosomes, and whole circulating tumor cells a challenge for device engineers. This review describes the chemistry and applications of branched and hyperbranched to improve the performance of liquid biopsy devices. These polymers can improve the performance of a liquid biopsy through several mechanisms. For example, polymers designed to increase the affinity of capture enhance device sensitivity. On the other hand, polymers designed to increase binding avidity or repel nonspecific adsorption enhance device specificity. Branched and hyperbranched polymers can also be used to amplify the signal from small amounts of detected material. The further development of hyperbranched polymers in liquid biopsy applications will enhance device capabilities and help these critical technologies reach the oncology clinic where they are sorely needed.
In this study, we proposed a new method of detecting abnormalities by analyzing power generation data of photovoltaic (PV) systems installed in renewable energy housing support project sites. The study site is north of Gakbuk-myeon, Cheongdo-gun, Gyeongsangbuk-do, Korea, where 63 PV systems have been installed and operated. Based on the system design and surrounding environment, the 63 PV systems were clustered into 6 groups using the K-means clustering method, which is an unsupervised machine learning algorithm. The power production data from the PV systems in each group were analyzed and set as abnormal values if they deviated from the range of ±2.58 times the standard deviation from the mean (assuming a normal distribution and 99% confidence interval). As a result, several abnormalities were detected in the PV systems in November 2020. The cause of the abnormalities was confirmed through site investigation. The proposed method is expected to accelerate the diagnosis of PV systems in renewable energy housing support project sites.
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