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Real-time digital polymerase chain reaction (qdPCR) provides enhanced precision in the field of molecular diagnostics by integrating absolute quantification with process information. However, the optimal reaction conditions are traditionally determined through multiple iterative of experiments. Therefore, we proposed a novel approach to precisely determine the optimal reaction conditions for qdPCR using a standard process, employing real-time fluorescence monitoring within microwells. The temperature-sensitive fluorophore intensity presented the real temperature of each microwell. This enabled us to determine the optimal denaturation and annealing time for qdPCR based on the corresponding critical temperatures derived from the melting curves and amplification efficiency, respectively. To confirm this method, we developed an ultrathin laminated chip (UTL chip) and chose a target that need to be absolutely quantitative. The UTL chip was designed using a fluid‒solid‒thermal coupling simulation model and exhibited a faster thermal response than a commercial dPCR chip. By leveraging our precise determination of reaction conditions and utilizing the UTL chip, 40 cycles of amplification were achieved within 18 min. This was accomplished by precisely controlling the denaturation temperature at 2 s and the annealing temperature at 10 s. Furthermore, the absolutely quantitative of DNA showed good correlation (R2 > 0.999) with the concentration gradient detection using the optimal reaction conditions with the UTL chip for qdPCR. Our proposed method can significantly improve the accuracy and efficiency of determining qdPCR conditions, which holds great promise for application in molecular diagnostics. [Display omitted] [ABSTRACT FROM AUTHOR]

Sample digital technology is a powerful method for absolute quantification of target molecules such as nucleic acids and proteins. The excellent sample stability and mass production capability has enabled the development of microwell array-based sample digitizing methods. However, in current microwell array chips, samples are loaded by the liquid scraping method, which requires complex manual operation and results in a low filling rate and limited hole filling uniformity. Here, we perform sample loading of a through-hole array chip by a microfluidics-driven method and design a double independent S-shaped flow channels sandwiched through-hole array chip. Because of the capillary force and capillary burst pressure, the sample flowing in the channel can be trapped into through-holes, but cannot flow through the other side. Via air flow and displacement of the remaining sample in the channel, the sample can be partitioned consistently, with zero surplus sample residue in the channel. We evaluated the actual performance of the sample-loading process: the chip enables 99.10% filling rate of 18 500 through-holes, with a grayscale coefficient of variation value of 6.03% determined from fluorescence images. In performing digital polymerase chain reaction on chip, the chip demonstrates good performance for the absolute quantification of target DNA. The simple and robust design of our chip, with excellent filling rate and microsample uniformity, indicates potential for use in a variety of sample digitization applications. [ABSTRACT FROM AUTHOR]

The digital polymerase chain reaction (dPCR) is a new and developing nucleic acid detection technology with high sensitivity that can realize the absolute quantitative analysis of samples. In order to improve the accuracy of quantitative results, real-time digital PCR emphasizes the kinetic information during amplification to identify prominent abnormal data. However, it is challenging to use a unified standard to accurately classify the amplification curve of each well as negative and positive, due to the interference caused by various factors in the experiment. In this work, a normal distribution–based cycle threshold value self-correcting model (NCSM) was established, which focused on the feature of the cycle threshold values in amplification curves and conducted continuous detection and correction on the whole. The cycle threshold value distribution was closer to the ideal normal distribution to avoid the influence of interference. Thus, the model achieves a more accurate classification between positive and negative results. The corrective process was applied to plasmid samples and resulted in an accuracy improvement from 92 to 99%. The coefficient of variation was below 5% when considering the quantitation of a range between 100 and 10,000 copies. At the same time, by utilizing this model, the distribution of cycle threshold values at the endpoint can be predicted with fewer thermal cycles, which can reduce the cycling time by around 25% while maintaining a consistency of more than 98%. Therefore, using the NCSM can effectively enhance the quantitative accuracy and increase the detection efficiency based on the real-time dPCR platform. [ABSTRACT FROM AUTHOR]

Osteosarcoma is a highly malignant bone cancer which primarily occurs in children and young adults. Increasing evidence indicates that long noncoding RNAs (lncRNAs), which function as competing endogenous RNAs (ceRNAs) that sponge microRNAs (miRNAs) and messenger RNAs (mRNAs), play a pivotal role in the pathogenesis and progression of cancers. The regulatory mechanisms of lncRNA-mediated ceRNAs in osteosarcoma have not been fully elucidated. In this study, we identified differentially expressed lncRNAs, miRNAs, and mRNAs in osteosarcoma based on RNA microarray profiles in the Gene Expression Omnibus (GEO) database. A ceRNA network was constructed utilizing bioinformatic tools. Kaplan-Meier survival analysis showed that lncR-C3orf35 and HMGB1 were associated with poor prognosis of osteosarcoma patients. Furthermore, results of Gene Set Enrichment Analysis (GSEA) suggested that lncR-C3orf35 may be involved in cellular invasion, the Toll-like receptor signaling pathway, and immune cell infiltration in the tumor microenvironment. Further analysis showed that patients with osteosarcoma metastasis expressed higher levels of lncR-C3orf35 and HMGB1 compared to metastasis-free patients. Moreover, the metastasis-free survival rate of the high lncR-C3orf35/HMGB1 expression group was significantly lower than that of the low expression group. The ESTIMATE algorithm was used to calculate the immune score and stromal scores for each sample. High lncR-C3orf35 and HMGB1 levels were correlated with low immune scores. ImmuCellAI analysis revealed that a low proportion of macrophage infiltration was associated with high lncR-C3orf35 and HMGB1 expression. The differential expression of lncR-C3orf35, miR-142-3p, and HMGB1 was further verified by quantitative real-time PCR. This study indicates that lncR-C3orf35 could be considered as a novel potential biomarker and therapeutic target of osteosarcoma, which may contribute to a better understanding of ceRNA regulatory mechanisms. [ABSTRACT FROM AUTHOR]

For digital polymerase chain reaction (PCR), data classification is always a crucial task. The dynamic real-time amplification process information of each partition is always ignored in typical digital PCR analysis, which can easily lead to inaccurate outcomes. In this work, an integrated device that offers real-time chip-based digital PCR analysis was established. In addition, an enhanced process-based classification model (PAM) was built and trained. And then the device and the analytical model were employed in classification tasks for different concentrations of Epstein-Barr Virus (EBV) plasmid quantification assays. The results indicated that the real-time analysis device achieved a linearity of 0.97, the classification method was able to distinguish the false-positive curves, and the recognition error of positive wells was decreased by 64.4% compared with typical static analysis techniques when low concentrations of samples were tested. With these advantages, it is supposed that the real-time digital PCR analysis apparatus and the improved classification method can be employed to enhance the performance of digital PCR technology. • An integrated device supporting real-time digital polymerase chain reaction (RT-dPCR) was established. • An improved data classification method for RT-dPCR was proposed based on the amplification process of each subsample. • The improved classification method would lead to a more accurate result than traditional static analysis methods. [ABSTRACT FROM AUTHOR]