Comparison of DNA-Gold Nanoparticle Conjugation Methods: Application in Lateral Flow Nucleic Acid Biosensors.

Lateral flow nucleic acid biosensors (LFNABs) have attracted extensive attention due to their rapid turnaround time, low cost, and results that are visible to the naked eye. One of the key steps to develop LFNABs is to prepare DNA-gold nanoparticle (DNA-AuNP) conjugates, which affect the sensitivity of LFNABs significantly. To date, various conjugation methods-including the salt-aging method, microwave-assisted dry heating method, freeze-thaw method, low-pH method, and butanol dehydration method-have been reported to prepare DNA-AuNP conjugates. In this study, we conducted a comparative analysis of the analytical performances of LFNABs prepared with the above five conjugation methods, and we found that the butanol dehydration method gave the lowest detection limit. After systematic optimization, the LFNAB prepared with the butanol dehydration method had a detection limit of 5 pM for single-strand DNA, which is 100 times lower than that of the salt-aging method. The as-prepared LFNAB was applied to detect miRNA-21 in human serum, with satisfactory results. The butanol dehydration method thus offers a rapid conjugation approach to prepare DNA-AuNP conjugates for LFNABs, and it can also be extended to other types of DNA biosensors and biomedical applications.

Disease Quantification of Liver Lymphoma in CT Images without Lesion Segmentation.

AIM: This study aimed to automatically implement liver disease quantification (DQ) in lymphoma using CT images without lesion segmentation. BACKGROUND: Computed Tomography (CT) imaging manifestations of liver lymphoma include diffuse infiltration, blurred boundaries, vascular drift signs, and multiple lesions, making liver lymphoma segmentation extremely challenging. METHODS: The method includes two steps: liver recognition and liver disease quantification. We use the transfer learning technique to recognize the diseased livers automatically and delineate the livers manually using the CAVASS software. When the liver is recognized, liver disease quantification is performed using the disease map model. We test our method in 10 patients with liver lymphoma. A random grouping cross-validation strategy is used to evaluate the quantification accuracy of the manual and automatic methods, with reference to the ground truth. RESULTS: We split the 10 subjects into two groups based on lesion size. The average accuracy for the total lesion burden (TLB) quantification is 91.76%±0.093 for the group with large lesions and 95.57%±0.032 for the group with small lesions using the manual organ (MO) method. An accuracy of 85.44%±0.146 for the group with larger lesions and 81.94%±0.206 for the small lesion group is obtained using the automatic organ (AO) method, with reference to the ground truth. CONCLUSION: Our DQ-MO and DQ-AO methods show good performance for varied lymphoma morphologies, from homogeneous to heterogeneous, and from single to multiple lesions in one subject. Our method can also be extended to CT images of other organs in the abdomen for disease quantification, such as Kidney, Spleen and Gallbladder.