Precision cancer monitoring using a novel, fully integrated, microfluidic array partitioning digital PCR platform.

A novel digital PCR (dPCR) platform combining off-the-shelf reagents, a micro-molded plastic microfluidic consumable with a fully integrated single dPCR instrument was developed to address the needs for routine clinical diagnostics. This new platform offers a simplified workflow that enables: rapid time-to-answer; low potential for cross contamination; minimal sample waste; all within a single integrated instrument. Here we showcase the capability of this fully integrated platform to detect and quantify non-small cell lung carcinoma (NSCLC) rare genetic mutants (EGFR T790M) with precision cell-free DNA (cfDNA) standards. Next, we validated the platform with an established chronic myeloid leukemia (CML) fusion gene (BCR-ABL1) assay down to 0.01% mutant allele frequency to highlight the platform's utility for precision cancer monitoring. Thirdly, using a juvenile myelomonocytic leukemia (JMML) patient-specific assay we demonstrate the ability to precisely track an individual cancer patient's response to therapy and show the patient's achievement of complete molecular remission. These three applications highlight the flexibility and utility of this novel fully integrated dPCR platform that has the potential to transform personalized medicine for cancer recurrence monitoring

Circulating methylation level of HTR2A is associated with inflammation and disease activity in rheumatoid arthritis

ObjectivesHTR2A is previously identified as a susceptibility gene for rheumatoid arthritis (RA). In this study, we performed the association analysis between DNA methylation of HTR2A with RA within peripheral blood samples.MethodsWe enrolled peripheral blood samples from 235 patients with RA, 30 osteoarthritis (OA) patients, and 30 healthy controls. The DNA methylation levels of about 218 bp from chr13: 46898190 to chr13: 46897973 (GRCh38/hg38) around HTR2A cg15692052 from patients were analyzed by targeted methylation sequencing.ResultsWe measured methylation status for 7 CpGs in the promoter region of HTR2A and obseved overall methylation status are signficantly increased in RA compared with normal inviduals (FDR= 9.05 x 10-5). The average cg15692052 methylation levels (methylation score) showed a positive correlation with CRP (r=0.15, P=0.023). Compared with the OA group or HC group, the proportion of haplotypes CCCCCCC (FDR=0.02 and 2.81 x 10-6) is signficantly increased while TTTTTCC (FDR =0.01) and TTTTTTT(FDR =6.92 x 10-3) are significantly decreased in RA. We find methylation haplotypes combining with RF and CCP could signficantly enhance the performance of the diagnosing RA and its comorbidities (hypertension, interstitial lung disease, and osteoporosis), especially in interstitial lung disease.ConclusionsIn our study, we found signficant hypermethylation of promoter region of HTR2A which indicates the potential clinical diagnostic role in rheumatoid arthritis

A comprehensive review of Tripterygium wilfordii hook. f. in the treatment of rheumatic and autoimmune diseases: Bioactive compounds, mechanisms of action, and future directions

Rheumatic and autoimmune diseases are a group of immune system-related disorders wherein the immune system mistakenly attacks and damages the body’s tissues and organs. This excessive immune response leads to inflammation, tissue damage, and functional impairment. Therapeutic approaches typically involve medications that regulate immune responses, reduce inflammation, alleviate symptoms, and target specific damaged organs. Tripterygium wilfordii Hook. f., a traditional Chinese medicinal plant, has been widely studied in recent years for its application in the treatment of autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis. Numerous studies have shown that preparations of Tripterygium wilfordii have anti-inflammatory, immunomodulatory, and immunosuppressive effects, which effectively improve the symptoms and quality of life of patients with autoimmune diseases, whereas the active metabolites of T. wilfordii have been demonstrated to inhibit immune cell activation, regulate the production of inflammatory factors, and modulate the immune system. However, although these effects contribute to reductions in inflammatory responses and the suppression of autoimmune reactions, as well as minimize tissue and organ damage, the underlying mechanisms of action require further investigation. Moreover, despite the efficacy of T. wilfordii in the treatment of autoimmune diseases, its toxicity and side effects, including its potential hepatotoxicity and nephrotoxicity, warrant a thorough assessment. Furthermore, to maximize the therapeutic benefits of this plant in the treatment of autoimmune diseases and enable more patients to utilize these benefits, efforts should be made to strengthen the regulation and standardized use of T. wilfordii

The Enhancing Effects of the Light Chain on Heavy Chain Secretion in Split Delivery of Factor VIII Gene

Coagulation factor VIII (FVIII) is secreted as a heterodimer consisting of a heavy chain (HC) and a light chain (LC), which can be expressed independently and reassociate with recovery of biological activity. Because of the size limitation of adeno-associated virus (AAV) vectors, a strategy for delivering the HC and LC separately has been developed. However, the FVIII HC is secreted 10–100-fold less efficiently than the LC. In this study, we demonstrated that the F309S mutation and enhanced B-domain glycosylations alone are not sufficient to improve FVIII HC secretion, which suggested a role of the FVIII LC in regulating HC secretion. To characterize this role of the FVIII LC, we compared FVIII HC secretion with and without the LC via post-translational protein trans-splicing. As demonstrated in vitro, ligation of the LC to the HC significantly increased HC secretion. Such HC secretion increases were also confirmed in vivo by hydrodynamic injection of FVIII intein plasmids into hemophilia A mice. Moreover, similar enhancement of HC secretion can also be observed when the LC is supplied in trans, which is probably due to the spontaneous association of the HC and the LC in the secretion pathway. In sum, enhancing the secretion of the FVIII HC polypeptide may require the proper association of the FVIII LC polypeptide in cis or in trans. These results may be helpful in designing new strategies to improve FVIII gene delivery

Research on distributed data mining system and algorithm based on multi-agent

Data mining means extracting hidden, previous unknown knowledge and rules with potential value to decision from mass data in database. Association rule mining is a main researching area of data mining area, which is widely used in practice. With the development of network technology and the improvement of level of IT application, distributed database is commonly used. Distributed data mining is mining overall knowledge which is useful for management and decision from database distributed in geography. It has become an important issue in data mining analysis. Distributed data mining can achieve a mining task with computers in different site on the internet. It can not only improve the mining efficiency, reduce the transmitting amount of network data, but is also good for security and privacy of data. Based on related theories and current research situation of data mining and distributed data mining, this thesis will focus on analysis on the structure of distributed mining system and distributed association rule mining algorithm. This thesis first raises a structure of distributed data mining system which is base on multi-agent. It adopts star network topology, and realize distributed saving mass data mining with multi-agent. Based on raised distributed data mining system, this these brings about a new distributed association rule mining algorithm?RK-tree algorithm. RK-tree algorithm is based on the basic theory of twice knowledge combination. Each sub-site point first mines local frequency itemset from local database, then send the mined local frequency itemset to the main site point. The main site point combines those local frequency itemset and get overall candidate frequency itemset, and send the obtained overall candidate frequency itemset to each sub-site point. Each sub-site point count the supporting rate of those overall candidate frequency itemset and sent it back to the main site point. At last, the main site point combines the results sent by sub-site point and gets the overall frequency itemset and overall association rule. This algorithm just needs three times communication between the main and sub-site points, which greatly reduces the amount and times of communication, and improves the efficiency of selection. What's more, each sub-site point can fully use existing good centralized association rule mining algorithm to realize local association rule mining, which can enable them to obtain better local data mining efficiency, as well as reduce the workload. This algorithm is simple and easy to realize. The last part of this thesis is the conclusion of the analysis, as well as the direction of further research

Automated Classification and Cluster Visualization of Genotypes Derived from High Resolution Melt Curves

<div><p>Introduction</p><p>High Resolution Melting (HRM) following PCR has been used to identify DNA genotypes. Fluorescent dyes bounded to double strand DNA lose their fluorescence with increasing temperature, yielding different signatures for different genotypes. Recent software tools have been made available to aid in the distinction of different genotypes, but they are not fully automated, used only for research purposes, or require some level of interaction or confirmation from an analyst.</p><p>Materials and Methods</p><p>We describe a fully automated machine learning software algorithm that classifies unknown genotypes. Dynamic melt curves are transformed to multidimensional clusters of points whereby a training set is used to establish the distribution of genotype clusters. Subsequently, probabilistic and statistical methods were used to classify the genotypes of unknown DNA samples on 4 different assays (40 <i>VKORC1</i>, <i>CYP2C9*2</i>, <i>CYP2C9*3</i> samples in triplicate, and 49 <i>MTHFR c</i>.<i>665C>T</i> samples in triplicate) run on the Roche LC480. Melt curves of each of the triplicates were genotyped separately.</p><p>Results</p><p>Automated genotyping called 100% of <i>VKORC1</i>, <i>CYP2C9*3</i> and MTHFR c.665C>T samples correctly. 97.5% of <i>CYP2C9*2</i> melt curves were genotyped correctly with the remaining 2.5% given a no call due to the inability to decipher 3 melt curves in close proximity as either homozygous mutant or wild-type with greater than 99.5% posterior probability.</p><p>Conclusions</p><p>We demonstrate the ability to fully automate DNA genotyping from HRM curves systematically and accurately without requiring any user interpretation or interaction with the data. Visualization of genotype clusters and quantification of the expected misclassification rate is also available to provide feedback to assay scientists and engineers as changes are made to the assay or instrument.</p></div

Separation of <i>MTHFR C</i>.<i>665C>T</i> genotypes using data from different temperature ranges.

<p>Left: 54 to 87C (probe and amplicon), middle: 54 to 74C (probe only) and right: 74 to 87C (amplicon only). Top: normalized derivative curves, middle: separation of genotype clusters in 2D. Bottom row shows the expected probability cross table via Monte Carlo simulation of 3D spherical coordinates.</p

Automated genotyping procedure.

<p>A. Fluorescence (<i>F</i>) versus temperature (<i>T</i>). B.–<i>dF/dT</i> versus <i>T</i>. C. Temperature shifted–<i>dF/dT</i>. D. Normalized–<i>dF/dT</i> curves with training set genotype averages (black lines). E. A 3D point represents each curve correlated against each average curve. F. Points transformed to spherical coordinates. G. Genotype likelihood table H. 2D projection of correlation parameters for visualization.</p