Deoxyribonucleic Acid Sequence Research Articles

An Intelligent Cryptographic Approach for Preserving the Privacy and Security of Smart Home IoT Applications

Background:: Today, computer networks are everywhere, and we utilize the Internet to access our home network. IoT networks connect home appliances and provide remote instructions. Access to any tool over an uncertain network attracts assaults. User authentication might be password- or biometric-based. Data security across a secure network like the Internet is difficult when authenticating a device. Hashing is used for validation and confidentiality in several encryption and decryption schemes. Classic cryptographic security methods require a lot of memory, processing power, and power. They cannot work with low-resource IoT devices. Method:: Automatic Device-to-Device communiqué opens up new applications, yet network machines and devices have limited resources. A remote-access home device authentication mechanism is proposed in this research. A new, lightweight encryption approach based on Deoxyribonucleic- Acid (DNA) sequences is developed to make IoT device connections easy and secure. Home network and appliance controller devices use authentication tools. DNA sequences are random therefore we utilized them to create a secure secret key. Results:: Efficiency and strength are advantages of the proposed method. Our method prevents replay, server spoofing, and man-in-the-middle attacks. The suggested method protects network users and devices. Conclusion:: Meanwhile, we model the system and find that the network's delay, throughput, and energy consumption don't degrade considerably.

Epigenetic and Coping Mechanisms of Stress in Affective Disorders: A Scoping Review

This review aims to explore the intricate relationship among epigenetic mechanisms, stress, and affective disorders, focusing on how early life experiences and coping mechanisms contribute to susceptibility to mood disorders. Epigenetic factors play a crucial role in regulating gene expression without altering the DNA (deoxyribonucleic acid) sequence, and recent research has revealed associations between epigenetic changes and maladaptive responses to stress or psychiatric disorders. A scoping review of 33 studies employing the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses—Statement) guidelines investigates the role of stress-induced epigenetic mechanisms and coping strategies in affective disorder occurrence, development, and progression. The analysis encompasses various stress factors, including childhood trauma, work-related stress, and dietary deficiencies, alongside epigenetic changes, such as DNA methylation and altered gene expression. Findings indicate that specific stress-related genes frequently exhibit epigenetic changes associated with affective disorders. Moreover, the review examines coping mechanisms in patients with bipolar disorder and major depressive disorder, revealing mixed associations between coping strategies and symptom severity. While active coping is correlated with better outcomes, emotion-focused coping may exacerbate depressive or manic episodes. Overall, this review underscores the complex interplay among genetic predisposition, environmental stressors, coping mechanisms, and affective disorders. Understanding these interactions is essential for developing targeted interventions and personalized treatment strategies for individuals with mood disorders. However, further research is needed to elucidate specific genomic loci involved in affective disorders and the clinical implications of coping strategies in therapeutic settings.

Nonalcoholic Fatty Liver Disease: Changes in Gut Microbiota and Blood Lipids.

The global prevalence of nonalcoholic fatty liver disease (NAFLD) is 25%. This study aimed to explore differences in the gut microbial community and blood lipids between normal livers and those affected by NAFLD using 16S ribosomal deoxyribonucleic acid sequencing. Gut microbiome profiles of 40 NAFLD and 20 non-NAFLD controls were analyzed. Information about four blood lipids and 13 other clinical features was collected. Patients were divided into three groups by ultrasound and FibroScan, those with a normal liver, mild FL (FL1), and moderate-to-severe FL (FL2). FL1 and FL2 patients were divided into two groups, those with either hyperlipidemia or non-hyperlipidemia based on their blood lipids. Potential keystone species within the groups were identified using univariate analysis and a specificity-occupancy plot. Significant difference in biochemical parameters ion NAFLD patients and healthy individuals were identified by detrended correspondence analysis and canonical correspondence analysis. Decreased gut bacterial diversity was found in patients with NAFLD. Firmicutes/Bacteroidetes decreased as NAFLD progressed. Faecalibacterium and Ruminococcus 2 were the most representative fatty-related bacteria. Glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count were selected as the most significant biochemical indexes. Calculation of areas under the curve identified two microbiomes combined with the three biochemical indexes that identified normal liver and FL2 very well but performed poorly in diagnosing FL1. Faecalibacterium and Ruminococcus 2, combined with glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count distinguished NAFLD. We speculate that regulating the health of gut microbiota may release NAFLD, in addition to providing new targets for clinicians to treat NAFLD.

Unraveling the chicken T cell repertoire with enhanced genome annotation.

T cell receptor (TCR) repertoire sequencing has emerged as a powerful tool for understanding the diversity and functionality of T cells within the host immune system. Yet, the chicken TCR repertoire remains poorly understood due to incomplete genome annotation of the TCR loci, despite the importance of chickens in agriculture and as an immunological model. Here, we addressed this critical issue by employing 5' rapid amplification of complementary DNA ends (5'RACE) TCR repertoire sequencing with molecular barcoding of complementary DNA (cDNA) molecules. Simultaneously, we enhanced the genome annotation of TCR Variable (V), Diversity (D, only present in β and δ loci) and Joining (J) genes in the chicken genome. To enhance the efficiency of TCR annotations, we developed VJ-gene-finder, an algorithm designed to extract VJ gene candidates from deoxyribonucleic acid (DNA) sequences. Using this tool, we achieved a comprehensive annotation of all known chicken TCR loci, including the α/δ locus on chromosome 27. Evolutionary analysis revealed that each locus evolved separately by duplication of long homology units. To define the baseline TCR diversity in healthy chickens and to demonstrate the feasibility of the approach, we characterized the splenic α/β/γ/δ TCR repertoire. Analysis of the repertoires revealed preferential usage of specific V and J combinations in all chains, while the overall features were characteristic of unbiased repertoires. We observed moderate levels of shared complementarity-determining region 3 (CDR3) clonotypes among individual birds within the α and γ chain repertoires, including the most frequently occurring clonotypes. However, the β and δ repertoires were predominantly unique to each bird. Taken together, our TCR repertoire analysis allowed us to decipher the composition, diversity, and functionality of T cells in chickens. This work not only represents a significant step towards understanding avian T cell biology, but will also shed light on host-pathogen interactions, vaccine development, and the evolutionary history of avian immunology.

Classifying promoters by interpreting the hidden information of DNA sequences for disease prediction in clinical laboratories using Gaussian decision boundary estimation

A promoter is a brief stretch of DNA (100–1,000 bp) where RNA polymerase starts to transcribe a gene. A DNA (Deoxyribonucleic Acid) base pair is a fundamental unit of DNA structure and represents the pairing of two complementary nucleotide bases within the DNA double helix. The four DNA nucleotide bases are adenine (A), thymine (T), cytosine (C), and guanine (G). DNA base pairs are the building blocks of the DNA molecule, and their complementary pairing is central to the storage and transmission of genetic information in all living organisms. Normally, a promoter is found at the 5′ end of the transcription initiation site or immediately upstream. Numerous human disorders, particularly diabetes, cancer, and Huntington’s disease, have been shown to have DNA promoter as their root cause. The scientific community has long been interested in learning crucial information about protein-coding genes. Finding the promoters is therefore the first step in finding genes in DNA sequences. The scientific world has always been attracted by the effort to glean crucial knowledge about protein-coding genes. Consequently, identifying promoters has emerged as an intriguing challenge that has caught the interest of numerous researchers in the field of bioinformatics. We proposed Gaussian Decision Boundary Estimation in machine learning models to detect transcription start sites (promoters) in the DNA sequences of a common bacteria, Escherichia coli. The best features are identified through a score-based function to select relevant nucleotides that are directly responsible for promoter recognition, in order maximise the models’ performance. The Gaussian Decision Boundary Estimation based support-vector-machine model is trained with these features and finds the best hyperplane that separates the data into different classes. Throughout this study, promoter regions could be identified with high accuracy 99.9% which is better compared to other state of art algorithms. The comparison of machine learning classification models is another major emphasis of this paper in order to identify the model that most accurately predicts DNA sequence promoters. It provides analysis for further biological research as well as precision medicine.

MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network.

Glioblastoma (GBM) is aggressive and malignant. The methylation status of the -methylguanine-DNA methyltransferase (MGMT) promoter in GBM tissue is considered an important biomarker for developing the most effective treatment plan. Although the standard method for assessing the MGMT promoter methylation status is via bisulfite modification and deoxyribonucleic acid (DNA) sequencing of biopsy or surgical specimens, a secondary automated method based on medical imaging may improve the efficiency and accuracy of those tests. We propose a deep vision graph neural network (ViG) using multiparametric magnetic resonance imaging (MRI) to predict the MGMT promoter methylation status noninvasively. Our model was compared to the RSNA radiogenomic classification winners. The dataset includes 583 usable patient cases. Combinations of MRI sequences were compared. Our multi-sequence fusion strategy was compared with those using single MR sequences. Our best model [Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted pre-contrast (T1w), T2-weighted (T2)] outperformed the winning models with a test area under the curve (AUC) of 0.628, an accuracy of 0.632, a precision of 0.646, a recall of 0.677, a specificity of 0.581, and an F1 score of 0.661. Compared to the winning models with single MR sequences, our ViG utilizing fused-MRI showed a significant improvement statistically in AUC scores, which are FLAIR (), T1w (), T1wCE (), and T2 (). Our model is superior to challenge champions. A graph representation of the medical images enabled good handling of complexity and irregularity. Our work provides an automatic secondary check pipeline to ensure the correctness of MGMT methylation status prediction.

DNA marker utilization for the sustainable production of trehalose

Trehalose is a type of sugar that is known by its stability and resilience towards acid and low temperature. Furthermore, trehalose has numerous health benefits and has been used by several industries, including food, cosmetics, and pharmaceuticals. Even though trehalose could be easily produced using trehalose synthase (TreS) enzyme, a sustainable production of trehalose is still a problem. Our work aims to develop an approach to identify a novel trehalose synthase enzyme from various organisms, especially thermophilic bacteria, by implementing a deoxyribonucleic acid (DNA) marker technique. We first collected protein and DNA sequences from public biological databases and subsequently conducted sequence analysis. We then designed degenerate primers based on the conserved regions identified from the sequence analysis. The designed primers were subjected to primer characterization using Oligo Calc software. The primers were further validated via in-silico PCR amplification. In general, our designed primers possess the properties to work optimally. In addition, agarose gel electrophoresis that the primers successfully amplified nucleotides encoding TreS enzyme from all samples. Our findings may serve as a basis to discover the TreS enzyme variants which possess superior attributes, allowing the sustainable production of trehalose.

A pilot study to investigate the clinically predictive values of copy number variations detected by next-generation sequencing of cell-free deoxyribonucleic acid in spent culture media

To investigate the positive predictive value and false positive risk of copy number variations (CNV's) detected in cell free deoxyribonucleic acid (DNA) from spent culture media for nonviable or aneuploid embryos. Diagnostic/prognostic accuracy study. Patients aged 35 and younger with an indication for IVF-ICSI and elective single frozen embryo transfer at a single, private IVF center. Embryo selection was performed according to the conventional grading, blinded to noninvasive preimplantation genetic testing for aneuploidy (niPGT-A) results. After clinical outcomes were established, spent culture media samples were analyzed. Prognostic accuracy of CNVs according to niPGT-A results to predict nonviability or clinical aneuploidy. One hundred twenty patients completed the study. Interpretations of next-generation sequencing (NGS) profiles were as follows: 7.5% (n = 9) failed quality control; 62.5% (n = 75) no CNVs detected; and 30% (n = 36) abnormal copy number detected. Stratification of abnormal NGS profiles was as follows: 15% (n = 18) whole chromosome and 15% (n = 18) uncertain reproductive potential. An intermediate CNV was evident in 27.8% (n = 5) of the whole chromosome abnormalities. The negative predictive value for samples with no detected abnormality was 57.3% (43/75). Whole chromosome abnormality was associated with a positive predictive value of 94.4% (17/18), lower sustained implantation rate (5.6%, 1/18), and higher relative risk (RR) for nonviability compared with no detected abnormalities (RR 2.21, 95% CI: 1.66-2.94). No other CNVs were associated with significant differences in the sustained implantation or RRs for nonviability. Unequal sex chromosome proportions suggested that maternal contamination was not uncommon. A secondary descriptive analysis of 705 supernumerary embryos revealed proportions of NGS profile interpretations similar to the transferred cohort. Significant median absolute pairwise differences between certain subcategories of CNV abnormalities were apparent. Whole chromosome abnormalities were associated with a high positive predictive value and significant RR for nonviability. Embryos associated with other CNVs had sustained implantation rates similar to those with no abnormalities detected. Further studies are required to validate the clinical applicability of niPGT-A. clinicaltrials.gov (NCT04732013).

Next-Generation Sequencing (NGS): Platforms and Applications.

The demand for accurate, faster, and inexpensive sequencing of deoxyribonucleic acid (DNA) is increasing and is driving the emergence of next-generation sequencing (NGS) technologies. NGS can provide useful insights to help researchers and clinicians to develop the right treatment options. NGS has wide applications in novel fields in biology and medicine. These technologies are of great aid to decode mysteries of life, to improve the quality of crops to detect the pathogens, and also useful in improving life qualities. Thousands to millions of molecules can be sequenced simultaneously in parallel using various NGS methods. NGS can identify and characterize the microbial species more comprehensively than culture-based methods. Recently, the NGS approach has been used for oral microbial analysis.

GMean—a semi-supervised GRU and K-mean model for predicting the TF binding site

The transcription factor binding site is a deoxyribonucleic acid sequence that binds to transcription factors. Transcription factors are proteins that regulate the transcription gene. Abnormal turnover of transcription factors can lead to uncontrolled cell growth. Therefore, discovering the relationships between transcription factors and deoxyribonucleic acid sequences is an important component of bioinformatics research. Numerous deep learning and machine learning language models have been developed to accomplish these tasks. Our goal in this work is to propose a GMean model for predicting unlabelled deoxyribonucleic acid sequences. The GMean model is a hybrid model with a combination of gated recurrent unit and K-mean clustering. The GMean model is developed in three phases. The labelled and unlabelled data are processed based on k-mers and tokenization. The labelled data is used for training. The unlabelled data are used for testing and prediction. The experimental data consists of deoxyribonucleic acid experimental of GM12878, K562 and HepG2. The experimental results show that GMean is feasible and effective in predicting deoxyribonucleic acid sequences, as the highest accuracy is 91.85% in predicting K562 and HepG2. This is followed by the prediction of the sequence between GM12878 and K562 with an accuracy of 89.13%. The lowest accuracy is the prediction of the sequence between HepG2 and GM12828, which is 88.80%.

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.

Iterative Soft Decoding Algorithm for DNA Storage Using Quality Score and Redecoding.

Ever since deoxyribonucleic acid (DNA) was considered as a next-generation data-storage medium, lots of research efforts have been made to correct errors occurred during the synthesis, storage, and sequencing processes using error correcting codes (ECCs). Previous works on recovering the data from the sequenced DNA pool with errors have utilized hard decoding algorithms based on a majority decision rule. To improve the correction capability of ECCs and robustness of the DNA storage system, we propose a new iterative soft decoding algorithm, where soft information is obtained from FASTQ files and channel statistics. In particular, we propose a new formula for log-likelihood ratio (LLR) calculation using quality scores (Q-scores) and a redecoding method which may be suitable for the error correction and detection in the DNA sequencing area. Based on the widely adopted encoding scheme of the fountain code structure proposed by Erlich et al., we use three different sets of sequenced data to show consistency for the performance evaluation. The proposed soft decoding algorithm gives 2.3% ∼ 7.0% improvement of the reading number reduction compared to the state-of-the-art decoding method and it is shown that it can deal with erroneous sequenced oligo reads with insertion and deletion errors.

Genetic polymorphism and the link to stress-induced metabolic dysfunction

Background: The impact of stress on health is well-established and it continues to influence modern life. While some individuals may manage stress well, others may experience detrimental impacts on their physical and emotional health. As stress has been related to several illnesses, including obesity, diabetes, cardiovascular diseases, and more, there is growing concern about stress-induced metabolic dysfunction. Main body: Here we review recent literature, elucidating the role of genetic polymorphisms in modulating an individual's reactivity to stress and susceptibility to stress-related metabolic disorders. This article discusses the genetic components, in particular the polymorphisms, that may predispose individuals toward metabolic dysregulation brought on by stress. Deoxyribonucleic acid (DNA) sequence differences that affect more than 1% of the population are known as genetic polymorphisms. Changes from genetic polymorphisms result in different gene expression, protein function, and other outcomes. Genetic polymorphisms in key stress-regulating genes can influence an individual's susceptibility to metabolic disturbances in response to chronic stress. Conclusion: The hypothalamic pituitary adrenal (HPA) axis and related molecular pathways play a pivotal role in mediating the effects of stress on metabolism. Understanding the genetic basis of stress-induced metabolic dysfunction has implications for personalized medicine and preventive strategies. By identifying individuals with genetic susceptibility to metabolic disturbances, targeted interventions can be designed to mitigate the adverse effects of stress on metabolic health. Furthermore, lifestyle modifications, stress management techniques, and tailored dietary interventions may be employed to promote overall well-being and prevent stress-induced metabolic disorders.

Enhancer Recognition: A Transformer Encoder-Based Method with WGAN-GP for Data Augmentation.

Enhancers are located upstream or downstream of key deoxyribonucleic acid (DNA) sequences in genes and can adjust the transcription activity of neighboring genes. Identifying enhancers and determining their functions are important for understanding gene regulatory networks and expression regulatory mechanisms. However, traditional enhancer recognition relies on manual feature engineering, which is time-consuming and labor-intensive, making it difficult to perform large-scale recognition analysis. In addition, if the original dataset is too small, there is a risk of overfitting. In recent years, emerging methods, such as deep learning, have provided new insights for enhancing identification. However, these methods also present certain challenges. Deep learning models typically require a large amount of high-quality data, and data acquisition demands considerable time and resources. To address these challenges, in this paper, we propose a data-augmentation method based on generative adversarial networks to solve the problem of small datasets. Moreover, we used regularization methods such as weight decay to improve the generalizability of the model and alleviate overfitting. The Transformer encoder was used as the main component to capture the complex relationships and dependencies in enhancer sequences. The encoding layer was designed based on the principle of k-mers to preserve more information from the original DNA sequence. Compared with existing methods, the proposed approach made significant progress in enhancing the accuracy and strength of enhancer identification and prediction, demonstrating the effectiveness of the proposed method. This paper provides valuable insights for enhancer analysis and is of great significance for understanding gene regulatory mechanisms and studying disease correlations.

Clustered Regularly Interspaced Short Palindromic Repeats- a new era in Genomic technology and its applications in Theranostics

Clustered Regularly Interspaced Short Palindromic Repeats is termed as CRISPR. Bacteria having "spacer" sequences of deoxyribonucleic acid between the repeats of genomic sequences that resemble genomic sequences in viruses were found to have repetitive DNA sequences known as CRISPR.The CRISPR technology in genetic engineering has revolutionalized the field of medicine in treating various genetically linked diseases that are difficult to treat such as Lebers Congenital amaurosis, in which there is bi-allelic deletion caused by mutation in mitochondrial DNA MT-ND4(NADH ubiquinone oxidoreductase chain4) 1178G are treated by intravitreous injection of AAV2-ND4 produced by CRISPR-Cas9 genomic editing technology.CRISPR-Cas9 genomic editing is used to remove the expression of receptor Enhancer Protein-6 (Reep6 p.Leu135Pro ) gene that causes retinitis pigmentosa. Open angle Glaucoma caused by mutations in myocilin (MYOR) gene was effectively removed out by CRISPR-Cas9 editing technology. Mutation in KRT-12 gene that caused Meesman epithelial corneal dystrophy ( MECD) was alleviated by Cas9/sgRNA injection into the stroma of cornea. CRISPR-Cas9 genomic sequencing used in the treatment of Haemophilia -B , an inherited disease caused by mutation of factor IX gene Y371D was successfully modified by CRISPR-

Key dependent information confidentiality scheme based on deoxyribonucleic acid (DNA) and circular shifting

In this era of advanced information technology, the exploration and development of novel mechanisms to ensure information confidentiality have consistently captivated the attention of upcoming researchers. In this article, we present a pioneering approach that combines DNA sequencing with a four-dimensional (4D) hyperchaotic map to bolster the security of digital information. Our primary focus is on the design of a robust and secure scheme for encrypting color images, leveraging DNA cryptography and hyperchaos. By extracting three distinct DNA sequences, we generate encryption keys through the integration of DNA computing and 4D hyperchaotic maps. Notably, these keys are intricately linked to the plaintext and vary with any alterations in the input. Consequently, the proposed encryption method stands resilient against an array of potential cryptographic attacks. To gauge the algorithm's security, we subject it to rigorous standard statistical analysis. Our findings underscore the efficiency and robustness of the proposed framework, establishing its potential for facilitating secure communication.

Bioresource collection of blood components from pregnant women to identify fetal genetic features, as well as to search for genetic markers of gestational complications

Aim. To create collection of plasma and leukocyte blood samples from pregnant women to improve methods of non-invasive prenatal testing, as well as to search for deoxyribonucleic acid markers and study the molecular mechanisms of gestational complications.Material and methods. The collection creation process uses original standard operating procedures. The biomaterial for storage was plasma and leukocytes from the blood of pregnant women.Results. As of July 2023, the collection contains samples of blood plasma and buffy coat suspension from 5814 pregnant women. For 5692 women, whole- genome sequencing of extracellular deoxyribonucleic acid from blood plasma was performed on an Ion GeneStudio S5 system (Thermo Fisher Scientific Inc, USA). Data on cytogenetic examination of fetal material after invasive prenatal diagnosis were collected from 522 women. In 241 cases, samples were obtained from pregnant women with fetuses with a normal karyotype and in 282 cases from pregnant women with fetuses with chromosomal abnormalities. After pregnancy ended with childbirth, data on the course and outcomes of pregnancy were collected from 590 women. In 212 cases, pregnancy was complicated by great obstetrical syndromes. Based on the collection, a previously developed bioinformatic algorithm for non-invasive prenatal analysis was tested on a DNBSEQ-G50 sequencer (MGI, China). The obtained specificity corresponded to the expected one and was >99,9%. The sensitivity of the method is 100%.Conclusion. The created collection, as well as the clinical and genomic data associated with it, is a valuable resource for basic and applied research.

Hybrid chaos-based image encryption algorithm using Chebyshev chaotic map with deoxyribonucleic acid sequence and its performance evaluation

<span lang="EN-US">The media content shared on the internet has increased tremendously nowadays. The streaming service has major role in contributing to internet traffic all over the world. As the major content shared are in the form of images and rapid increase in computing power a better and complex encryption standard is needed to protect this data from being leaked to unauthorized person. Our proposed system makes use of chaotic maps, deoxyribonucleic acid (DNA) coding and ribonucleic acid (RNA) coding technique to encrypt the image. As videos are nothing but collection of images played at the rate of minimum 30 frames/images per second, this methodology can also be used to encrypt videos. The complexity and dynamic nature of chaotic systems makes decryption of <span>content by unauthorized personal difficult. The hybrid usage of chaotic systems along with DNA and RNA sequencing improves the encryption efficiency of the algorithm and also makes it possible to decrypt the images at the same time without consuming too much</span> of computation power.</span>

Coupling Single Domain Antibodies to Catalytic Hairpin Assemblies for Homogeneous Assays

AbstractImmunoassays are widely used in various fields, including biomedical research, clinical diagnostics, and environmental monitoring. Single domain antibodies (sdAbs) provide small, tailorable, recognition elements that have been integrated into immunoassays for detecting a myriad of targets. Deoxyribonucleic acid (DNA) circuits are synthetic molecular devices composed of DNA strands that can perform logical or computational operations. They have a range of applications, including biosensing, diagnostics, and drug delivery. Recently, an immunoassay method was reported that used catalytic hairpin assemblies (CHA) with antibodies for homogeneous protein detection. The CHA process uses DNA hairpins that interact in the presence of a catalytic DNA sequence. This paper presents a new strategy to couple the recognition of sdAbs with CHA circuits using genetic fusions of sdAbs with rhizavidin (rz), a dimeric biotin binding protein. A pair of sdAb‐rz constructs is each functionalized with a biotinylated DNA sequence that represents half of the catalyst. When both sdAbs bind to the target protein, a signal is generated through the CHA circuit. The split catalyst approach amplifies signals through a DNA circuit without washing steps. Furthermore, this method distinctively utilizes programmable DNA circuits, which are highly modular and can accommodate new targets without disrupting the assay.

Molecular Characteristics and the Effect of Mutations in Different Sites of the rplD Gene Among Clinical Isolates of Azithromycin Resistance Neisseria gonorrhoeae in Eastern China

Background: The emergence of resistance to azithromycin complicates the treatment of Neisseria gonorrhoeae, the causative agent of gonorrhea. Objectives: The main objective of this study was to investigate the impact of mutations in different sites of the rplD gene on azithromycin resistance (AZM-R) and the molecular characteristics of N. gonorrhoeae. This study aimed to provide evidence for drug resistance and transmission. Methods: A total of 37 isolates of N. gonorrhoeae were collected within January 2018 to December 2020. These isolates were obtained from urine, semen, or vaginal secretions of different patients. Azithromycin resistance was assessed, and genes associated with AZM-R, namely rplD, and rplV, were analyzed using polymerase chain reaction (PCR) and deoxyribonucleic acid (DNA) sequencing. All clinical isolates were characterized through multi-locus sequence typing (MLST). Results: The study comprised 21 azithromycin-resistant N. gonorrhoeae isolates, with two of them demonstrating high resistance, indicated by a minimum inhibitory concentration (MIC) greater than 256 µg/mL. Additionally, 16 sensitive isolates were included in the study. Ten isolates were observed to have rplD point mutations, including mutations such as G70D, G70S, G68D, and A43T. No mutations were detected in rplV. The rate of point mutations in rplD was significantly different between the azithromycin-sensitive (AZM-S) group and the AZM-R group (P < 0.05). Among the 37 isolates studied, 12 distinct MLST types were identified and further grouped into four different MLST goeBURST groups. The two isolates with high-level AZM-R were ST1901 and ST1588, respectively. Conclusions: The clinical isolates of N. gonorrhoeae from Wenzhou, Eastern China, exhibit significant genetic diversity and a relatively high prevalence of AZM-R. Mutations in the rplD gene were identified, which reduced susceptibility to macrolides and were significantly associated with increased AZM-R.