Array Datasets Research Articles

Chemistry-informed machine learning: Using chemical property features to improve gas classification performance

Chemical recognition using machine learning based on detection by gas sensors relies on the accuracy and sensitivity of the sensors at capturing the key features of target classes. In some cases, however, the electronic signal transduced from the detection of analytes does not completely represent the key attributes, resulting in inaccurate classification results when trained from signal data alone. To overcome this shortcoming, we propose a novel “chemistry-informed” machine learning framework composed of two modules. From available sensor response data, Module 1 identifies and predicts the chemical properties of the analytes that give rise to the sensitivity and selectivity of the sensors, and Module 2 performs final classifications using the dataset concatenating predicted chemical properties and raw sensor responses. To evaluate the performance and generalizability of our methodology, we conducted experiments with three gas sensor array datasets for gas detection. In all the cases, the performance of gas species classification was improved when the raw features were combined with the predicted chemical property features. The main contribution of our framework is that it bridges the gap between the gas sensor signals and the target analytes, thereby improving classification performance beyond that of models trained exclusively on sensor response data.

Analyzing Gas Data Using Deep Learning and 2-D Gramian Angular Fields

The notion of employing deep learning (DL) for gas classification has kindled a revolution that has improved both data collection measures and classification performance. Yet, the current literature, with its vast contributions, has the potential in enhancing the current state of the art by employing both DL and novel visualization methods to boost classification performance and speed. Therefore, this article presents a dual classification system for high-performance gas classification: on 1-D time series data and 2-D Gramian Angular Field (GAF) data. For the GAF case study, 1-D data are converted into 2-D counterparts by means of normalization, segmentation, averaging, and color coding. The gas sensor array (GSA) dataset is used for evaluating the implemented AlexNet model for classifying 2-D GAF data and an improved version of GasNet for 1-D time-based data. Using a cloud-based architecture, the two models are evaluated and benchmarked with the state of the art. Evaluation results of the modified GasNet model on time series data signify the state-of-the-art accuracy of 96.5%, while AlexNet achieved 81.0% test accuracy of GAF classification with near real-time performance on edge computing platforms.

The repertoire of copy number alteration signatures in human cancer.

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

Identification and characterization of noncoding RNAs-associated competing endogenous RNA networks in major depressive disorder.

Major depressive disorder (MDD) is a common and serious mental illness. Many novel genes in MDD have been characterized by high-throughput methods such as microarrays or sequencing. Recently, noncoding RNAs (ncRNAs) were suggested to be involved in the complicated environmental-genetic regulatory network of MDD occurrence; however, the interplay among RNA species, including protein-coding RNAs and ncRNAs, in MDD remains unclear. To investigate the RNA expression datasets downloaded from a public database and construct a network based on differentially expressed long noncoding RNA (lncRNAs), microRNAs (miRNAs), and mRNAs between MDD and controls. Gene expression data were searched in NCBI Gene Expression Omnibus using the search term "major depressive disorder." Six array datasets from humans were related to the search term: GSE19738, GSE32280, GSE38206, GSE52790, GSE76826, and GSE81152. These datasets were processed for initial assessment and subjected to quality control and differential expression analysis. Differentially expressed lncRNAs, miRNAs, and mRNAs were determined, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed, and protein-protein interaction network was generated. The results were analyzed for their association with MDD. After analysis, 3 miRNAs, 12 lncRNAs, and 33 mRNAs were identified in the competing endogenous RNA network. Two of these miRNAs were earlier shown to be involved in psychiatric disorders, and differentially expressed mRNAs were found to be highly enriched in pathways related to neurogenesis and neuroplasticity as per Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The expression of hub gene fatty acid 2-hydroxylase was enriched, and the encoded protein was found to be involved in myelin formation, indicating that neurological development and signal transduction are involved in MDD pathogenesis. The present study presents candidate ncRNAs involved in the neurogenesis and neuroplasticity pathways related to MDD.

Gravitational waves from domain walls in Pulsar Timing Array datasets

We present a model-independent search for a gravitational wave background from cosmic domain walls (DWs) in the NANOGrav 12.5 years dataset and International PTA Data Release 2. DWs that annihilate at temperatures ∼ 20–50 MeV with tensions ∼ (40–100 TeV)3 provide as good a fit to both datasets as the astrophysical background from supermassive black hole mergers. DWs may decay into the Standard Model (SM) or a dark sector. In the latter case we predict an abundance Δ N eff of dark radiation well within the reach of upcoming CMB surveys. Complementary signatures at colliders and laboratories can arise if couplings to the SM are present. As an example, we discuss heavy axion scenarios, where DW annihilation may interestingly be induced by QCD confinement.

Fast rank-based normalization of miRNA qPCR arrays using support vector regression

One of the first important steps in elucidating the function of microRNAs (miRNAs) is expression profiling. Many methods including low-density qPCR arrays are increasingly used to profile the expression of miRNAs. Normalization techniques are necessary due to certain biases in profiling approaches, and the techniques can significantly affect the accuracy of miRNA quantification. Most normalization methods for continous expression data have been developed for mRNA microarrays and new and modified methods should be used for miRNA studies in general and RT-qPCR miRNA arrays in particular. Previously, cyclic normalization using support vector regression has been successfully applied to mRNA arrays. Here, a new method based on support vector regression is introduced for miRNA normalization and the cyclic nature of algorithm in cyclic spline normalization has also been modified. It was shown that by creating a baseline array, it is possible to remove the cyclic nature of the normalization to achieve faster normalization, with no loss of accuracy. To assess how much the mentioned normalization method reduces technical error, mean square error (MSE) in two real miRNA qPCR array datasets and a simulated dataset before and after normalizations was robustly modelled and compared. Our method was also systematically compared with the most commonly used methods for normalization of qPCR miRNA arrays. The new method showed lower MSE values corresponding to other common methods of miRNA normalization.

Hsa-miR-662 as a New Prognostic Biomarker in Patients with Breast Cancer; In-silico and Experimental Study

Background & Objective: Breast cancer (BC) is a complex genetic disease that has an average annual incidence of one million people and is the second leading cause of death among women in the world. Therefore, a better understanding of tumor biology and the determination of biomarkers for early diagnosis of disease is essential. MicroRNAs and long non-coding RNAs are novel gene regulators that play key roles in tumor initiation and progression. The current study was performed to assess the biomarker potential. This study performed a combination of in-silico and experimental investigations of altered miRNAs in BC to assess the use of miRNAs as novel biomarkers for early detection and prognosis prediction of patients with BC.
 Materials & Methods: We searched the miRNA expression patterns of BC from three expression arrays (GSE58606, GSE38867, and GSE40525) from the Gene Expression Omnibus (GEO) database to recognize differentially expressed miRNAs (DEMs) between BC tissues and normal adjacent samples. Using “Limma” package’s Quantile Normalization function and INMEX bioinformatic tool, hub DEMs were identified. MiRNAs targeted genes were found and visualized via the miRWalk and miRTargetLink databases and their Enrichment analysis was performed for identified genes. Due to more validation of DEGs, GSE70951, an independent expression array dataset, was analyzed. By merging DEMs and DEGs, miRNA-mRNA network was constructed. After elucidation of hub miRNAs, the capacity of detected miRNAs to differentiate BC from adjacent controls was estimated by Kaplan-Meier analysis. Furthermore, RT-qPCR on 100 BC samples and 100 adjacent non-tumor tissues was performed to validate the in-silico results.
 Results: According to our study, in BC samples, miR-662 was differentially downregulated in comparison with normal adjacent tissues.
 Conclusion: Altogether, miR-662 can be considered as a viable target for BC diagnostics and treatment.

Multi-Mode Surface Wave Tomography of a Water-Rich Layer of the Jizhong Depression Using Beamforming at a Dense Array

Urban structure imaging using noise-based techniques has rapidly developed in recent years. Given the complexity of the cross-correlation function in high-frequency signals, here, the beamforming (BF) method was used to analyze one data set taken from a dense array in the Jizhong Depression and obtain multi-mode dispersion curves. Multi-mode surface waves improved inversion stability, reduced non-uniqueness, and yielded a one-dimensional shear wave (S-wave) velocity model. Interpolation yielded a high-resolution three-dimensional (3D) S-wave velocity model for the study area. The model shows that velocity gradually changed in the horizontal direction and greatly increased in the vertical direction, which is largely consistent with changes in the sedimentary environment related to the continuous subsidence of the Jizhong Depression since the Quaternary. A low-velocity anomaly at a depth of ~300–400 m was revealed and determined to be caused by either a deep-buried ancient river course or low-lying area. This study demonstrates the potential of the BF method for processing dense array data sets of urban exploration. The high-resolution 3D S-wave velocity model provides a new reference for studying the Quaternary structure of the Jizhong Depression, as well as groundwater resources, urban infrastructure, and underground spaces.

Exploring Realistic Nanohertz Gravitational-wave Backgrounds

Hundreds of millions of supermassive black hole binaries are expected to contribute to the gravitational-wave signal in the nanohertz frequency band. Their signal is often approximated either as an isotropic Gaussian stochastic background with a power-law spectrum or as an individual source corresponding to the brightest binary. In reality, the signal is best described as a combination of a stochastic background and a few of the brightest binaries modeled individually. We present a method that uses this approach to efficiently create realistic pulsar timing array data sets using synthetic catalogs of binaries based on the Illustris cosmological hydrodynamic simulation. We explore three different properties of such realistic backgrounds that could help distinguish them from those formed in the early universe: (i) their characteristic strain spectrum, (ii) their statistical isotropy, and (iii) the variance of their spatial correlations. We also investigate how the presence of confusion noise from a stochastic background affects detection prospects of individual binaries. We calculate signal-to-noise ratios of the brightest binaries in different realizations for a simulated pulsar timing array based on the NANOGrav 12.5 yr data set extended to a time span of 15 yr. We find that ∼6% of the realizations produce systems with signal-to-noise ratios larger than 5, suggesting that individual systems might soon be detected (the fraction increases to ∼41% at 20 yr). These can be taken as a pessimistic prediction for the upcoming NANOGrav 15 yr data set, since it does not include the effect of potentially improved timing solutions and newly added pulsars.

Molecular signatures of intrarenal complement receptors C3AR1 and C5AR1 correlate with renal outcome in human lupus nephritis

ObjectiveLupus nephritis is one of the most common and serious complications of systemic lupus erythematosus (SLE). Lupus nephritis is a major cause of kidney failure in patients with SLE, attributed...

A novel hybrid dimension reduction and deep learning-based classification for neuromuscular disorder

Correct classification of neuromuscular disorders is essential to provide accurate diagnosis. Presently, gene microarray technology is a widely accepted technology to monitor the expression level of a large number of genes simultaneously. The gene microarray data are a high dimensional data, which usually contains small samples having a large number of genes. Therefore, dimension reduction is a crucial task for correct classification of diseases. Dimension reduction eliminates those genes which are less expressive and enhances the efficiency of the classification model. In the present paper, we developed a novel hybrid dimension reduction method and a deep learning-based classification model for neuromuscular disorders. The hybrid dimension reduction method is deployed in three phase: in the first phase, the expressive genes are selected using F test method, and the mutual information method and the best one among them are selected for further processing. In second phase, the gene selected by the best model is further transformed to low dimension by PCA. In third phase, the deep learning-based classification model is deployed. For experimentation, two diseased and multi-diseased micro array data sets, which is publicly available, is used. The best accuracy by 50-100-50-25-13 deep learning architecture with hybrid dimension reduction, where 100 genes select by F test and PCA with 50 principal components is 89% for NMD data set. The best accuracy by 50-100-2 deep learning architecture with hybrid dimension reduction, where 100 genes select by F test and PCA with 50 principal components is 97% for FSHD data set. The proposed hybrid method gives better classification accuracy result and reduces the search space and time complexity as well for both two diseased and multi-diseased micro array data sets.

An optimized total focusing method based on delay-multiply-and-sum for nondestructive testing

Total focusing method (TFM) attracts much interest because of high image resolution and large inspection coverage. However, the synthetic focusing approach based on delay-and-sum beamforming employs only the defect information contained in the dataset while ignoring the spatial information of the array signals, leading to limited imaging performance mixed with artifacts and noise. In addition, the signal-to-noise ratio (SNR) suffers due to single-element emission of full matrix capture. This work combines a modified delay-multiply-and-sum (DMAS) beamforming approach with conventional synthetic focusing in the TFM algorithm, to achieve optimization of TFM imaging performance. DMAS-based TFM is able to take full advantage of the defect and spatial information in the array dataset, and to generate new frequency components for better image reconstruction. As demonstrated on a series of comparative simulation and experimental results, the imaging results of the optimized TFM provide a considerable improvement in SNR. Better lateral spatial resolution is also achieved due to the increased number of equivalent transducer elements and second harmonic component. Therefore, this work provides a quite promising alternative solution for the post-processing of ultrasonic phased array with improved imaging performance.

Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies

BackgroundDNA methylation (DNAm)-based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy, the consistency of their performance remains to be determined. We therefore conduct a systematic evaluation across 101 different DNAm data preprocessing and normalization strategies and assess how each analytical strategy affects the consistency of 41 DNAm-based predictors.ResultsOur analyses are conducted in a large EPIC DNAm array dataset from the Jackson Heart Study (N = 2053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent consistency when appropriate data processing and normalization steps are implemented. Across all pairs of predictors, we find a moderate correlation in performance across analytical strategies (mean rho = 0.40, SD = 0.27), highlighting significant heterogeneity in performance across algorithms. Successful or unsuccessful removal of technical variation furthermore significantly impacts downstream phenotypic association analysis, such as all-cause mortality risk associations.ConclusionsWe show that DNAm-based algorithms are sensitive to technical variation. The right choice of data processing strategy is important to achieve reproducible estimates and improve prediction accuracy in downstream phenotypic association analyses. For each of the 41 DNAm predictors, we report its degree of consistency and provide the best performing analytical strategy as a guideline for the research community. As DNAm-based predictors become more and more widely used, our work helps improve their performance and standardize their implementation.

Anti-CXCL8 Autoantibody: A Potential Diagnostic Biomarker for Esophageal Squamous Cell Carcinoma.

Background and Objectives: Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies. Anti-tumor associated antigen autoantibodies (TAAbs) can be used as biomarkers for tumor detection. The aim of this study was to identify a reliable TAAb as the diagnostic marker for ESCC. Materials and Methods: The Cancer Genome Atlas (TCGA) database was used to screen candidate genes. The mRNA expression of the key gene was then verified by micro array dataset GSE44021 from the Gene Expression Omnibus (GEO) database and the diag nostic value of the corresponding autoantibody to the key gene in ESCC was detected by enzyme-linked im muno sorbent assay (ELISA). Results: CXCL8 was identified as the key gene. The dataset GSE44021 showed that CXCL8 mRNA expression was prominently over-expressed in ESCC tissues compared with normal tissues. ELISA results showed that the level of anti-CXCL8 autoantibody in ESCC patients was significantly higher than in normal controls and the receiver operating char ac teristic (ROC) curve indicated that anti-CXCL8 autoantibody could discriminate ESCC patients from normal controls, with the area under the ROC curve (AUC) for the verification cohort, and the validation cohort were 0.713 and 0.751, respectively. Conclusions: Our study illustrated that anti-CXCL8 autoantibody had good diagnostic value, and may become a candidate biomarker for ESCC.

Heritability of insidious uveitis in Appaloosa horses.

Equine recurrent uveitis (ERU) is a blinding ocular disorder among horses, and the Appaloosa horse breed is disproportionally affected by a chronic form of this intraocular inflammatory disease known as insidious uveitis. Strong breed predisposition and previous investigations suggest that there is a genetic component to the pathology of insidious uveitis among Appaloosa horses; however, no estimates of the heritability of the disease have previously been determined. This study aimed to characterize the genetic underpinning of the disease by estimating the heritability for insidious uveitis among Appaloosas. After combining two genotyping array datasets from the Illumina Equine SNP70 BeadChip and the Axiom Equine 670 K Genotyping Array, heritability was estimated for 59 affected and 83 unaffected horses using both restricted maximum likelihood (REML) and phenotype correlation - genotype correlation solvers from the linkage disequilibrium adjusted kinship software. Based on previous research, age and sex were used as covariates, and the locus responsible for the characteristic Appaloosa coat pattern (LP), previously associated with ERU risk, was included as a fixed effect ('top predictor'). Using prevalence values from 0.05 to 0.42, the heritability estimate for insidious uveitis ranged from 0.95 (SE=0.14) to 1.74 (SE=0.25) with LP contributing 0.16-0.33 to the estimate. This study suggests that insidious uveitis is highly heritable (REML 95% CI, h2 =0.68-1.0) and additional loci outside of LP are contributing to the genetic risk for insidious uveitis for Appaloosas. Once identified, these other genetic factors may lead to new disease mitigation efforts in veterinary care and breeding practices.

Genome-wide profiling of dysregulated piRNAs and their target genes implicated in oncogenicity of tongue squamous cell carcinoma

PIWI-interacting RNAs (piRNAs) are single-stranded, 23–36 nucleotide long RNAs that regulate gene expression in the germline but are also detected in some cancers. However, there are no reports yet on piRNA expression in tongue squamous cell carcinoma (TSCC), the most common oral cancer (80–90% percent of all oral cancers). We performed small RNA and whole transcriptome sequencing in H357 tongue cancer and HOK cells (GEO database accession numbers: GSE196674 and GSE196688). We also examined nine published sets of gene expression array data of TSCC tissues from the GEO database to decode piRNAs and their putative targets that may be involved in tumorigenesis. We identified a pool of 16,058 and 25,677 piRNAs in H357 and HOK, respectively, among which 406 are differentially expressed. We also found that 2094 protein-coding genes are differentially expressed in either TSCC tissues or cell lines. We performed target predictions for these piRNAs, pathway and disease function (DF) analyses, as well as qRT-PCR validation of piRNA-target pairs. These experiments revealed one up-regulated (FDFT1) and four down-regulated (OGA, BDH1, TAT, HYAL4) target genes that are enriched in 11 canonical pathways (CPs), with postulated roles in the initiation and progression of TSCC. Downregulation of piR-33422 is predicted to upregulate the FDFT1 gene, which encodes a mevalonate/cholesterol-pathway related farnesyl-diphosphate farnesyltransferase. The FDFT1 appears to be involved in the largest number of oncogenesis-related processes and is interacting with statins, which is a classical cancer drug. This study provides the first evidence of the piRNome of TSCC, which could be investigated further to decode piRNA-mediated gene regulations in malignancy and potential drug targets, such as FDFT1.

Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance

Genomic selection (GS) is a powerful tool for improving genetic gain in maize breeding. However, its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms. Although sequencing-based and array-based genotyping platforms have been used for GS, few studies have compared prediction performance among platforms. In this study, we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing (GBS), a 40K SNP array, and target sequence capture (TSC) using eight GS models. The GBS marker dataset yielded the highest predictabilities for all traits, followed by TSC and SNP array datasets. We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs, and BayesB, GBLUP, and RKHS performed well, while XGBoost performed poorly in most cases. We also selected significant SNP subsets using genome-wide association study (GWAS) analyses in three panels to predict hybrid performance. GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost, but depended heavily on the GWAS panel. We conclude that there is still room for optimization of the existing SNP array, and using genotyping by target sequencing (GBTS) techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.

Identification of key genes and pathways associated with diabetes of the exocrine pancreas.

This study aimed to identify potential essential genes and pathways in diabetes of the exocrine pancreas (DEP) and explore possible molecular mechanisms. The array dataset GSE76895 was downloaded from the Gene Expression Omnibus database. Pancreatic tissue samples from 20 Diabetes of the exocrine pancreas and 32 nondiabetic individuals were selected for analysis. GEO2R analyzed differentially expressed genes (DEGs) in the 2 groups. Gene ontology annotation, Kyoto Encyclopedia of Genes Genomes and Reactome pathway enrichment analyses and Gene Set Enrichment Analysis were performed in this study. Protein-protein interaction (PPI) networks were constructed using Cytoscape software, and core networks were identified using MCODE plugins. A total of 62 genes, including 59 up-regulated and 3 down-regulated genes, were differentially expressed in DEP samples compared with nondiabetic patients. PPI network with 53 nodes and 138 edges was established. HLA-DRA is identified as the central gene of the PPI network and maybe a marker gene for DEP. Furthermore, up-regulated DEGs are mainly enriched in pathways related to the immune system and infection. The results of this study suggest that HLA-DRA and immune system pathways may play essential roles in DEP.

Evaluating the prevalence of spurious correlations in pulsar timing array data sets

ABSTRACT Pulsar timing array collaborations have recently reported evidence for a noise process with a common spectrum among the millisecond pulsars in the arrays. The spectral properties of this common-noise process are consistent with expectations for an isotropic gravitational-wave background (GWB) from inspiralling supermassive black hole binaries. However, recent simulation analyses based on Parkes Pulsar Timing Array data indicate that such a detection may arise spuriously. In this paper, we use simulated pulsar timing array data sets to further test the robustness of the inference methods for spectral and spatial correlations from a GWB. Expanding on our previous results, we find strong support (Bayes factors exceeding 105) for the presence of a common-spectrum noise process in data sets where no common process is present, under a wide range of timing noise prescriptions per pulsar. We show that these results are highly sensitive to the choice of Bayesian priors on timing noise parameters, with priors that more closely match the injected distributions of timing noise parameters resulting in diminished support for a common-spectrum noise process. These results emphasize shortcomings in current methods for inferring the presence of a common-spectrum process, and imply that the detection of a common process is not a reliable precursor to detection of the GWB. Future searches for the nanohertz GWB should remain focused on detecting spatial correlations, and make use of more tailored specifications for a common-spectrum noise process.

Circulating Monocytes Act as a Common Trigger for the Calcification Paradox of Osteoporosis and Carotid Atherosclerosis via TGFB1-SP1 and TNFSF10-NFKB1 Axis.

BackgroundOsteoporosis often occurs with carotid atherosclerosis and causes contradictory calcification across tissue in the same patient, which is called the “calcification paradox”. Circulating monocytes may be responsible for this unbalanced ectopic calcification. Here, we aimed to show how CD14+ monocytes contribute to the pathophysiology of coexisting postmenopausal osteoporosis and carotid atherosclerosis.MethodsWe comprehensively analyzed osteoporosis data from the mRNA array dataset GSE56814 and the scRNA-seq dataset GSM4423510. Carotid atherosclerosis data were obtained from the GSE23746 mRNA dataset and GSM4705591 scRNA-seq dataset. First, osteoblast and vascular SMC lineages were annotated based on their functional expression using gene set enrichment analysis and AUCell scoring. Next, pseudotime analysis was applied to draw their differentiated trajectory and identify the key gene expression changes in crossroads. Then, ligand–receptor interactions between CD14+ monocytes and osteoblast and vascular smooth muscle cell (SMC) lineages were annotated with iTALK. Finally, we selected calcification paradox-related expression in circulating monocytes with LASSO analysis.ResultsFirst, we found a large proportion of delayed premature osteoblasts in osteoporosis and osteogenic SMCs in atherosclerosis. Second, CD14+ monocytes interacted with the intermediate cells of the premature osteoblast and osteogenic SMC lineage by delivering TGFB1 and TNFSF10. This interaction served as a trigger activating the transcription factors (TF) SP1 and NFKB1 to upregulate the inflammatory response and cell senescence and led to a retarded premature state in the osteoblast lineage and osteogenic transition in the SMC lineage. Then, 76.49% of common monocyte markers were upregulated in the circulating monocytes between the two diseases, which were related to chemotaxis and inflammatory responses. Finally, we identified 7 calcification paradox-related genes on circulating monocytes, which were upregulated in aging cells and downregulated in DNA repair cells, indicating that the aging monocytes contributed to the development of the two diseases.ConclusionsOur work provides a perspective for understanding the triggering roles of CD14+ monocytes in the development of the calcification paradox in osteoporosis- and atherosclerosis-related cells based on combined scRNA and mRNA data. This study provided us with an elucidation of the mechanisms underlying the calcification paradox and could help in developing preventive and therapeutic strategies.