Development and verification of a 10K liquid chip for Hainan black goat based on genotyping by pinpoint sequencing of liquid captured targets.

BACKGROUND: China has thousands years of goat breeding and abundant goat genetic resources. Additionally, the Hainan black goat is one of the high-quality local goat breeds in China. In order to conserve the germplasm resources of the Hainan black goat, facilitate its genetic improvement and further protect the genetic diversity of goats, it is urgent to develop a single nucleotide polymorphism (SNP) chip for Hainan black goat. RESULTS: In this study, we aimed to design a 10K liquid chip for Hainan black goat based on genotyping by pinpoint sequencing of liquid captured targets (cGPS). A total of 45,588 candidate SNP sites were obtained, 10,677 of which representative SNP sites were selected to design probes, which finally covered 9,993 intervals and formed a 10K cGPS liquid chip for Hainan black goat. To verify the 10K cGPS liquid chip, some southern Chinese goat breeds and a sheep breed with similar phenotype to the Hainan black goat were selected. A total of 104 samples were used to verify the clustering ability of the 10K cGPS liquid chip for Hainan black goat. The results showed that the detection rate of sites was 97.34% -99.93%. 84.5% of SNP sites were polymorphic. The heterozygosity rate was 3.08%-36.80%. The depth of more than 99.4% sites was above 10X. The repetition rate was 99.66%-99.82%. The average consistency between cGPS liquid chip results and resequencing results was 85.58%. In addition, the phylogenetic tree clustering analysis verified that the SNP sites on the chip had better clustering ability. CONCLUSION: These results indicate that we have successfully realized the development and verification of the 10K cGPS liquid chip for Hainan black goat, which provides a useful tool for the genome analysis of Hainan black goat. Moreover, the 10K cGPS liquid chip is conducive to the research and protection of Hainan black goat germplasm resources and lays a solid foundation for its subsequent breeding work.

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat (Triticum aestivum L.).

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

A 50-gene biomarker identifies estrogen receptor-modulating chemicals in a microarray compendium.

High throughput transcriptomics (HTTr) profiling has the potential to rapidly and comprehensively identify molecular targets of environmental chemicals that can be linked to adverse outcomes. We describe here the construction and characterization of a 50-gene expression biomarker designed to identify estrogen receptor (ER) active chemicals in HTTr datasets. Using microarray comparisons, the genes in the biomarker were identified as those that exhibited consistent directional changes when ER was activated (4 ER agonists; 4 ESR1 gene constitutively active mutants) and opposite directional changes when ER was suppressed (4 antagonist treatments; 4 ESR1 knockdown experiments). The biomarker was evaluated as a predictive tool using the Running Fisher algorithm by comparison to annotated gene expression microarray datasets including those evaluating the transcriptional effects of hormones and chemicals in MCF-7 cells. Depending on the reference dataset used, the biomarker had a predictive accuracy for activation of up to 96%. To demonstrate applicability for HTTr data analysis, the biomarker was used to identify ER activators in a set of 15 chemicals that are considered potential bisphenol A (BPA) alternatives examined at up to 10 concentrations in MCF-7 cells and analyzed by full-genome TempO-Seq. Using benchmark dose (BMD) modeling, the biomarker genes stratified the ER potency of BPA alternatives consistent with previous studies. These results demonstrate that the ER biomarker can be used to accurately identify ER activators in transcript profile data derived from MCF-7 cells.

Comprehensive fluorescence profiles of contamination-prone foods applied to the design of microcontact-printed in situ functional oligonucleotide sensors.

With both foodborne illness and food spoilage detrimentally impacting human health and the economy, there is growing interest in the development of in situ sensors that offer real-time monitoring of food quality within enclosed food packages. While oligonucleotide-based fluorescent sensors have illustrated significant promise, the development of such on-food sensors requires consideration towards sensing-relevant fluorescence properties of target food products-information that has not yet been reported. To address this need, comprehensive fluorescence profiles for various contamination-prone food products are established in this study across several wavelengths and timepoints. The intensity of these food backgrounds is further contextualized to biomolecule-mediated sensing using overlaid fluorescent oligonucleotide arrays, which offer perspective towards the viability of distinct wavelengths and fluorophores for in situ food monitoring. Results show that biosensing in the Cyanine3 range is optimal for all tested foods, with the Cyanine5 range offering comparable performance with meat products specifically. Moreover, recognizing that mass fabrication of on-food sensors requires rapid and simple deposition of sensing agents onto packaging substrates, RNA-cleaving fluorescent nucleic acid probes are successfully deposited via microcontact printing for the first time. Direct incorporation onto food packaging yields cost-effective sensors with performance comparable to ones produced using conventional deposition strategies.

Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC v1.0 BeadChip microarrays.

DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC v1.0 arrays. We conducted a comprehensive assessment of the EPIC v1.0 array probe reliability using 69 blood DNA samples, each measured twice, generated by the Alzheimer's Disease Neuroimaging Initiative study. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliability information for probes on the EPIC v1.0 array, will serve as a valuable resource for future DNAm studies.

Bioinformatics-based analysis of potential therapeutic target genes for polycystic ovary syndrome.

The purpose of this study was to screen differentially expressed genes in PCOS using gene chip data and investigate the biological functions of these DEGs in PCOS. Additionally, the study aimed to analyze the potential clinical significance of these genes using clinical data. In this study, we first screened the DEGs related to PCOS by using the gene chip data (GSE5090) from GEO database. Target gene prediction software was used to predict the target genes for these DEGs, and their functional enrichment was analysed. Subsequently, the STRING online tool and Cytoscape software were utilized to identify key genes by constructing protein-protein interaction networks (PPI). In the analysis of the GSE5090 dataset, seventeen differentially expressed genes (DEGs) were identified. Functional enrichment analysis revealed that these DEGs are predominantly associated with biological functions related to polycystic ovary syndrome (PCOS). Moreover, the tissue-specific expression analysis highlighted immune system markers, with a notable difference observed in 18 of these markers, accounting for 20.5% of the total. By constructing PPI networks and key gene regulatory networks, a total of three genes (RPL13, LEP, and ANXA1) were identified as key genes. In addition, the column-line graphical model performed well in predicting the risk of PCOS. Using ROC curves, the model proved to be effective in diagnosis. This study represents the first application of a bioinformatics approach to identify and confirm high expression levels of RPL13, LEP, and ANXA1 in patients with Polycystic Ovary Syndrome (PCOS). These key genes-RPL13, LEP, and ANXA1-may present viable targets for therapeutic interventions in PCOS, underscoring their potential clinical importance.

Theoretical Studies of DNA Microarray Present Potential Molecular and Cellular Interconnectivity of Signaling Pathways in Immune System Dysregulation.

Autoimmunity is defined as the inability to regulate immunological activities in the body, especially in response to external triggers, leading to the attack of the tissues and organs of the host. Outcomes include the onset of autoimmune diseases whose effects are primarily due to dysregulated immune responses. In past years, there have been cases that show an increased susceptibility to other autoimmune disorders in patients who are already experiencing the same type of disease. Research in this field has started analyzing the potential molecular and cellular causes of this interconnectedness, bearing in mind the possibility of advancing drugs and therapies for the treatment of autoimmunity. With that, this study aimed to determine the correlation of four autoimmune diseases, which are type 1 diabetes (T1D), psoriasis (PSR), systemic sclerosis (SSc), and systemic lupus erythematosus (SLE), by identifying highly preserved co-expressed genes among datasets using WGCNA. Functional annotation was then employed to characterize these sets of genes based on their systemic relationship as a whole to elucidate the biological processes, cellular components, and molecular functions of the pathways they are involved in. Lastly, drug repurposing analysis was performed to screen candidate drugs for repositioning that could regulate the abnormal expression of genes among the diseases. A total of thirteen modules were obtained from the analysis, the majority of which were associated with transcriptional, post-transcriptional, and post-translational modification processes. Also, the evaluation based on KEGG suggested the possible role of TH17 differentiation in the simultaneous onset of the four diseases. Furthermore, clomiphene was the top drug candidate for regulating overexpressed hub genes; meanwhile, prilocaine was the top drug for regulating under-expressed hub genes. This study was geared towards utilizing transcriptomics approaches for the assessment of microarray data, which is different from the use of traditional genomic analyses. Such a research design for investigating correlations among autoimmune diseases may be the first of its kind.

Single nucleotide polymorphism (SNP) chromosomal microarray as a diagnostic tool for mucinous tubular and spindle cell carcinoma: A validation study.

Mucinous tubular and spindle cell carcinoma (MTSCC) shows significant overlap with papillary renal cell carcinoma (PRCC), and harbor recurrent copy-number alterations (CNA). We evaluated 16 RCC with features suggestive of MTSCC using chromosomal microarrays. The cohort was comprised of 8 females and males, each, with an age range of 33-79 years (median, 59), and a tumor size range of 3.4-15.5 cm (median, 5.0). Half the tumors were high-grade (8/16, 50%) with features such as necrosis, marked cytologic atypia, and sarcomatoid differentiation, and 5/16 (31%) were high stage (≥pT3a). Three (of 16, 19%) cases had a predominant (>95%) spindle cell component, whereas 5/16 (31%) were composed of a predominant (>95%) epithelial component. Most cases (12/16, 75%) exhibited a myxoid background and/or extravasated mucin, at least focally. Twelve (of 16, 75%) cases demonstrated CNA diagnostic of MTSCC (losses of chromosomes 1, 4, 6, 8, 9, 13, 14, 15, and 22). In addition, 2 high-grade tumors showed loss of CDKN2A/B, and gain of 1q, respectively, both of which are associated with aggressive behavior. Three (of 16, 19%) cases, demonstrated nonspecific CNA, and did not meet diagnostic criteria for established RCC subtypes. One (of 16, 6%) low-grade epithelial predominant tumor (biopsy) demonstrated characteristic gains of 7, 17, and loss of Y, diagnostic of PRCC. MTSCC can be a morphologically heterogenous tumor. Our study validates the detection of characteristic chromosomal CNA for diagnostic use that may be useful in challenging cases with unusual spindle cell or epithelial predominant features, as well as in high-grade tumors.

CyanoStrainChip: A Novel DNA Microarray Tool for High-Throughput Detection of Environmental Cyanobacteria at the Strain Level.

Detecting cyanobacteria in environments is an important concern due to their crucial roles in ecosystems, and they can form blooms with the potential to harm humans and nonhuman entities. However, the most widely used methods for high-throughput detection of environmental cyanobacteria, such as 16S rRNA sequencing, typically provide above-species-level resolution, thereby disregarding intraspecific variation. To address this, we developed a novel DNA microarray tool, termed the CyanoStrainChip, that enables strain-level comprehensive profiling of environmental cyanobacteria. The CyanoStrainChip was designed to target 1277 strains; nearly all major groups of cyanobacteria are included by implementing 43,666 genome-wide, strain-specific probes. It demonstrated strong specificity by in vitro mock community experiments. The high correlation (Pearson's R > 0.97) between probe fluorescence intensities and the corresponding DNA amounts (ranging from 1-100 ng) indicated excellent quantitative capability. Consistent cyanobacterial profiles of field samples were observed by both the CyanoStrainChip and next-generation sequencing methods. Furthermore, CyanoStrainChip analysis of surface water samples in Lake Chaohu uncovered a high intraspecific variation of abundance change within the genus Microcystis between different severity levels of cyanobacterial blooms, highlighting two toxic Microcystis strains that are of critical concern for Lake Chaohu harmful blooms suppression. Overall, these results suggest a potential for CyanoStrainChip as a valuable tool for cyanobacterial ecological research and harmful bloom monitoring to supplement existing techniques.

Capturing of extracellular vesicles derived from single cells of Escherichia coli.

Bacteria secrete extracellular vesicles (EVs), also referred to as bacterial membrane vesicles, which carry, among other compounds, lipids, nucleic acids and virulence factors. Recent studies highlight the role of EVs in the emergence of antibiotic resistance, e.g. as carrier and absorbent particles of the drug to protect the cells, or as a pathway to disseminate resistance elements. In this study, we are interested in characterizing the secretion of EVs at the single bacterial level to ultimately understand how cells respond to antibiotic treatment. We introduce a microfluidic device that enables culture of single bacterial cells and capture of EVs secreted from these individuals. The device incorporates parallel, narrow winding channels to trap single rod-shaped E. coli cells at their entrances. The daughter cells are immediately removed by continuous flow on the open side of the trap, so that the trap contains always only a single cell. Cells grew in these traps over 24 h with a doubling time of 25 minutes. Under antibiotic treatment, the doubling time did not change, but we observed small changes in the cell length of the trapped cells (decrease from 4.0 µm to 3.6 µm for 0 and 250 ng mL-1 polymyxin B, respectively), and cells stopped growing within hours, depending on the drug concentration. Compared to bulk culture, the results indicate a higher susceptibility of on-chip-cultured cells (250 ng mL-1vs. >500 ng mL-1 in bulk), which may be caused, among other reasons, by the space limitation in the cell trap and shear forces. During the culture, EVs secreted by the trapped cells entered the winding channel. We developed a procedure to selectively coat these channels with poly-L-lysine resulting in a positively charged surface, which enabled electrostatic capture of negatively charged EVs. Subsequently, the immobilized EVs were stained with a lipophilic dye and detected by fluorescence microscopy. Our findings confirm large variations of EV secretion among individual bacteria and indicate a relative high rate of EV secretion under antibiotic treatment. The proposed method can be extended to the detection of other secreted substances of interest and may facilitate the elucidation of unknown heterogeneities in bacteria.

Long-read sequencing unveils novel somatic variants and methylation patterns in the genetic information system of early lung cancer.

Lung cancer poses a global health challenge, necessitating advanced diagnostics for improved outcomes. Intensive efforts are ongoing to pinpoint early detection biomarkers, such as genomic variations and DNA methylation, to elevate diagnostic precision. We conducted long-read sequencing on cancerous and adjacent non-cancerous tissues from a patient with lung adenocarcinoma. We identified somatic structural variations (SVs) specific to lung cancer by integrating data from various SV calling methods and differentially methylated regions (DMRs) that were distinct between these two tissue samples, revealing a unique methylation pattern associated with lung cancer. This study discovered over 40,000 somatic SVs and over 180,000 DMRs linked to lung cancer. We identified approximately 700 genes of significant relevance through comprehensive analysis, including genes intricately associated with many lung cancers, such as NOTCH1, SMOC2, CSMD2, and others. Furthermore, we observed that somatic SVs and DMRs were substantially enriched in several pathways, such as axon guidance signaling pathways, which suggests a comprehensive multi-omics impact on lung cancer progression across various biological investigation levels. These datasets can potentially serve as biomarkers for early lung cancer detection and may hold significant value in clinical diagnosis and treatment applications.

Ultrasimple size encoded microfluidic chip for rapid simultaneous multiplex detection of DNA sequences.

Simultaneous multiplexed analysis can provide comprehensive information for disease diagnosis. However, the current multiplex methods rely on sophisticated barcode technology, which hinders its wider application. In this study, an ultrasimple size encoding method is proposed for multiplex detection using a wedge-shaped microfluidic chip. Driving by negative pressure, microparticles are naturally arranged in distinct stripes based on their sizes within the chip. This size encoding method demonstrates a high level of precision, allowing for accuracy in distinguishing 3-5 sizes of microparticles with a remarkable accuracy rate of up to 99%, even the microparticles with a size difference as small as 0.5 µm. The entire size encoding process is completed in less than 5 min, making it ultrasimple, reliable, and easy to operate. To evaluate the function of this size encoding microfluidic chip, three commonly co-infectious viruses' nucleic acid sequences (including complementary DNA sequences of HIV and HCV, and DNA sequence of HBV) are employed for multiplex detection. Results indicate that all three DNA sequences can be sensitively detected without any cross-interference. This size-encoding microfluidic chip-based multiplex detection method is simple, rapid, and high-resolution, its successful application in serum samples renders it highly promising for potential clinical promotion.

A rapid and low-cost platform for detection of bacterial based on microchamber PCR microfluidic chip.

Polymerase chain reaction (PCR) has been considered as the gold standard for detecting nucleic acids. The simple PCR system is of great significance for medical applications in remote areas, especially for the developing countries. Herein, we proposed a low-cost self-assembled platform for microchamber PCR. The working principle is rotating the chamber PCR microfluidic chip between two heaters with fixed temperature to solve the problem of low temperature variation rate. The system consists of two temperature controllers, a screw slide rail, a chamber array microfluidic chip and a self-built software. Such a system can be constructed at a cost of about US$60. The micro chamber PCR can be finished by rotating the microfluidic chip between two heaters with fixed temperature. Results demonstrated that the sensitivity of the temperature controller is 0.1℃. The relative error of the duration for the microfluidic chip was 0.02 s. Finally, we successfully finished amplification of the target gene of Porphyromonas gingivalis in the chamber PCR microfluidic chip within 35 min and on-site detection of its PCR products by fluorescence. The chip consisted of 3200 cylindrical chambers. The volume of reagent in each volume is as low as 0.628 nL. This work provides an effective method to reduce the amplification time required for micro chamber PCR.

De novo copy number variations in candidate genomic regions in patients of severe autism spectrum disorder in Vietnam.

Autism spectrum disorder (ASD) is a developmental disorder with a prevalence of around 1% children worldwide and characterized by patient behaviour (communication, social interaction, and personal development). Data on the efficacy of diagnostic tests using copy number variations (CNVs) in candidate genes in ASD is currently around 10% but it is overrepresented by patients of Caucasian background. We report here that the diagnostic success of de novo candidate CNVs in Vietnamese ASD patients is around 6%. We recruited one hundred trios (both parents and a child) where the child was clinically diagnosed with ASD while the parents were not affected. We performed genetic screening to exclude RETT syndrome and Fragile X syndrome and performed genome-wide DNA microarray (aCGH) on all probands and their parents to analyse for de novo CNVs. We detected 1708 non-redundant CNVs in 100 patients and 118 (7%) of them were de novo. Using the filter for known CNVs from the Simons Foundation Autism Research Initiative (SFARI) database, we identified six CNVs (one gain and five loss CNVs) in six patients (3 males and 3 females). Notably, 3 of our patients had a deletion involving the SHANK3 gene-which is the highest compared to previous reports. This is the first report of candidate CNVs in ASD patients from Vietnam and provides the framework for building a CNV based test as the first tier screening for clinical management.

Full-Combinatorial Concentration Gradient Array with 3D Micro/Nanofluidics for Antibiotic Susceptibility Testing.

Recent advancements in micro/nanofluidics have facilitated on-chip microscopy of cellular responses in a high-throughput and controlled microenvironment with the desired physicochemical properties. Despite its potential benefits to combination drug discovery, generating a complete combinatorial set of concentration gradients for multiple reagents in an array format remains challenging. The main reason is limited layouts of conventional micro/nanofluidic systems based on two-dimensional channel networks. In this paper, we present a device with three-dimensional (3D) interconnection of micro/nanochannels capable of generating a complete combinatorial set of concentration gradients for two reagents. The device was readily fabricated by laminating a pair of multilayered monolithic films containing a Christmas tree-like mixer, a cell culture chamber array, and through-holes, all within each single film. We assessed the reliable generation of a full-combinatorial concentration gradient array and validated it by using numerical analysis. We applied the proposed device to test the antibiotic susceptibility of bacterial cells in a convenient one-step manner. Furthermore, we explored the potential of the device to accommodate the arrayed complete combinatorial set for two or more drugs, while extending the capabilities of our laminated object manufacturing method for realizing 3D micro/nanofluidic systems.

High-throughput DNA synthesis for data storage.

With the explosion of digital world, the dramatically increasing data volume is expected to reach 175 ZB (1 ZB = 1012 GB) in 2025. Storing such huge global data would consume tons of resources. Fortunately, it has been found that the deoxyribonucleic acid (DNA) molecule is the most compact and durable information storage medium in the world so far. Its high coding density and long-term preservation properties make itself one of the best data storage carriers for the future. High-throughput DNA synthesis is a key technology for "DNA data storage", which encodes binary data stream (0/1) into quaternary long DNA sequences consisting of four bases (A/G/C/T). In this review, the workflow of DNA data storage and the basic methods of artificial DNA synthesis technology are outlined first. Then, the technical characteristics of different synthesis methods and the state-of-the-art of representative commercial companies, with a primary focus on silicon chip microarray-based synthesis and novel enzymatic DNA synthesis are presented. Finally, the recent status of DNA storage and new opportunities for future development in the field of high-throughput, large-scale DNA synthesis technology are summarized.

Pharmacogenomic allele coverage of genome-wide genotyping arrays: a comparative analysis.

The use of genome-wide genotyping arrays in pharmacogenomics (PGx) research and clinical implementation applications is increasing but it is unclear which arrays are best suited for these applications. Here, we conduct a comparative coverage analysis of PGx alleles included on genome-wide genotyping arrays, with an emphasis on alleles in genes with PGx-based prescribing guidelines. Genomic manifest files for seven arrays including the Axiom Precision Medicine Diversity Array (PMDA), Axiom PMDA Plus, Axiom PangenomiX, Axiom PangenomiX Plus, Infinium Global Screening Array, Infinium Global Diversity Array (GDA) and Infinium GDA with enhanced PGx (GDA-PGx) Array, were evaluated for coverage of 523 star alleles across 19 pharmacogenes included in prescribing guidelines developed by the Clinical Pharmacogenetic Implementation Consortium and Dutch Pharmacogenomics Working Group. Specific attention was given to coverage of the Association of Molecular Pathology's Tier 1 and Tier 2 allele sets for CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, NUDT15, TPMT and VKORC1 . Coverage of the examined PGx alleles was highest for the Infinium GDA-PGx (88%), Axiom PangenomiX Plus (77%), Axiom PangenomiX (72%) and Axiom PMDA Plus (70%). Three arrays (Infinium GDA-PGx, Axiom PangenomiX Plus and Axiom PMDA Plus) fully covered the Tier 1 alleles and the Axiom PangenomiX array provided full coverage of Tier 2 alleles. In conclusion, PGx allele coverage varied by gene and array. A superior array for all PGx applications was not identified. Future comparative analyses of genotype data produced by these arrays are needed to determine the robustness of the reported coverage estimates.

Biporous silica nanostructure-induced nanovortex in microfluidics for nucleic acid enrichment, isolation, and PCR-free detection.

Efficient pathogen enrichment and nucleic acid isolation are critical for accurate and sensitive diagnosis of infectious diseases, especially those with low pathogen levels. Our study introduces a biporous silica nanofilms-embedded sample preparation chip for pathogen and nucleic acid enrichment/isolation. This chip features unique biporous nanostructures comprising large and small pore layers. Computational simulations confirm that these nanostructures enhance the surface area and promote the formation of nanovortex, resulting in improved capture efficiency. Notably, the chip demonstrates a 100-fold lower limit of detection compared to conventional methods used for nucleic acid detection. Clinical validations using patient samples corroborate the superior sensitivity of the chip when combined with the luminescence resonance energy transfer assay. The enhanced sample preparation efficiency of the chip, along with the facile and straightforward synthesis of the biporous nanostructures, offers a promising solution for polymer chain reaction-free detection of nucleic acids.

Dynamic selection of high-affinity aptamers using a magnetically activated continuous deflection microfluidic chip.

To accelerate the discovery of high-affinity aptamers, a magnetically activated continuous deflection (MACD) chip was designed. The MACD chip could achieve dynamic selection in a continuous flow, which meant that the binding and separation were carried out consecutively. Dynamic selection could make selection efficient. Low-affinity sequences could be eluted in time and high-affinity sequences could be enriched via dynamic selection. The stringency of the conditions could be further increased by lowering the target concentration in the dynamic selection. Finally, a C.al3 aptamer with high-affinity and high-specificity for Candida albicans (C. albicans) was obtained through six rounds of selection. Its dissociation constant (Kd) was 7.9 nM. This demonstrated that dynamic selection using a MACD chip was an effective method for high-affinity aptamer selection.

Population characteristic exploitation-based multi-orientation multi-objective gene selection for microarray data classification.

Gene selection is a process of selecting discriminative genes from microarray data that helps to diagnose and classify cancer samples effectively. Swarm intelligence evolution-based gene selection algorithms can never circumvent the problem that the population is prone to local optima in the process of gene selection. To tackle this challenge, previous research has focused primarily on two aspects: mitigating premature convergence to local optima and escaping from local optima. In contrast to these strategies, this paper introduces a novel perspective by adopting reverse thinking, where the issue of local optima is seen as an opportunity rather than an obstacle. Building on this foundation, we propose MOMOGS-PCE, a novel gene selection approach that effectively exploits the advantageous characteristics of populations trapped in local optima to uncover global optimal solutions. Specifically, MOMOGS-PCE employs a novel population initialization strategy, which involves the initialization of multiple populations that explore diverse orientations to foster distinct population characteristics. The subsequent step involved the utilization of an enhanced NSGA-II algorithm to amplify the advantageous characteristics exhibited by the population. Finally, a novel exchange strategy is proposed to facilitate the transfer of characteristics between populations that have reached near maturity in evolution, thereby promoting further population evolution and enhancing the search for more optimal gene subsets. The experimental results demonstrated that MOMOGS-PCE exhibited significant advantages in comprehensive indicators compared with six competitive multi-objective gene selection algorithms. It is confirmed that the "reverse-thinking" approach not only avoids local optima but also leverages it to uncover superior gene subsets for cancer diagnosis.