Deoxyribonucleic Acid Molecules Research Articles

Investigation of the dynamical structures of double-chain deoxyribonucleic acid model in biological sciences

The present research investigates the double-chain deoxyribonucleic acid model, which is important for the transfer and retention of genetic material in biological domains. This model is composed of two lengthy uniformly elastic filaments, that stand in for a pair of polynucleotide chains of the deoxyribonucleic acid molecule joined by hydrogen bonds among the bottom combination, demonstrating the hydrogen bonds formed within the chain’s base pairs. The modified extended Fan sub equation method effectively used to explain the exact travelling wave solutions for the double-chain deoxyribonucleic acid model. Compared to the earlier, now in use methods, the previously described modified extended Fan sub equation method provide more innovative, comprehensive solutions and are relatively straightforward to implement. This method transforms a non-linear partial differential equation into an ODE by using a travelling wave transformation. Additionally, the study yields both single and mixed non-degenerate Jacobi elliptic function type solutions. The complexiton, kink wave, dark or anti-bell, V, anti-Z and singular wave shapes soliton solutions are a few of the creative solutions that have been constructed utilizing modified extended Fan sub equation method that can offer details on the transversal and longitudinal moves inside the DNA helix by freely chosen parameters. Solitons propagate at a consistent rate and retain their original shape. They are widely used in nonlinear models and can be found everywhere in nature. To help in understanding the physical significance of the double-chain deoxyribonucleic acid model, several solutions are shown with graphics in the form of contour, 2D and 3D graphs using computer software Mathematica 13.2. All of the requisite constraint factors that are required for the completed solutions to exist appear to be met. Therefore, our method of strengthening symbolic computations offers a powerful and effective mathematical tool for resolving various moderate nonlinear wave problems. The findings demonstrate the system’s potentially very rich precise wave forms with biological significance. The fundamentals of double-chain deoxyribonucleic acid model diffusion and processing are demonstrated by this work, which marks a substantial development in our knowledge of double-chain deoxyribonucleic acid model movements.

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.

Physics and small-scale dosimetry of -emitters for targeted radionuclide therapy: The case of .

Monte Carlo simulations have been considered for a long time the gold standard for dose calculations in conventional radiotherapy and are currently being applied for the same purpose in innovative radiotherapy techniques such as targeted radionuclide therapy (TRT). We present in this work a benchmarking study of the latest version of the Transport d'Ions Lourds Dans l'Aqua & Vivo (TILDA-V) Monte Carlo track structure code, highlighting its capabilities for describing the full slowing down of -particles in water and the energy deposited in cells by -emitters in the context ofTRT. We performed radiation transport simulations of -particles (10keV -100MeV ) in water with TILDA-Vand the Particle and Heavy Ion Transport code System (PHITS) version 3.33. We compared the predictions of each code in terms of track parameters (stopping power, range and radial dose profiles) and cellular S-values of the promising radionuclide astatine-211 ( ). Additional comparisons were made with available data in theliterature. The stopping power, range and radial dose profiles of -particles computed with TILDA-Vwere in excellent agreement with other calculations and available data. Overall, minor differences with PHITS were ascribed to phase effects, that is, related to the use of interaction cross sectionscomputed for water vapor or liquid water. However, important discrepancies were observed in the radial dose profiles of monoenergetic -particles, for which PHITS results showed a large underestimation of the absorbed dose compared to other codes and experimental data. The cellular S-values of computed with TILDA-V agreed within 4% with the values predicted by PHITS andMIRDcell. The validation of the TILDA-Vcode presented in this work opens the possibility to use it as an accurate simulation tool for investigating the interaction of -particles in biological media down to the nanometer scale in the context of medical research. The code may help nuclear medicine physicians in their choice of -emitters for TRT. Further research will focus on the application of TILDA-Vfor quantifying radioinduced damage on the deoxyribonucleic acid (DNA) molecule.

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.

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.

Performance Analysis of Electrochemical Detection Platform for DNA Hybridization Using TGN-Based Nanobiosensor

Trilayer graphene nanoribbon (TGN) has great potential in related biomedical applications such as drug and gene delivery and deoxyribonucleic acid (DNA) sensing. The performance of a biosensor based on a liquid gate TGN field effect transistor (Lg-TGNFET) to different DNA cancentrations and adsorption effect on the sensing parameters are analytically investigated in this research. Also, the energy band structure, density of states, carrier concentration, electrical conductance and caused electrical response on TGN as a detection element are studied considering the spin–orbit coupling effect, in which significant current change of the TGN-based biosensor is observed after exposure to DNA different values. DNA concentration as a function of gate voltage is assumed and sensing factor is defined. The results of this research confirm that the current of the biosensor is decreased and Vg-min is obviously left-shifted by increasing value of DNA concentration, suggesting that DNA molecules n-dopes the TGN film. In order to verify the accuracy of the TGN-based biosensor, it is compared with recent analytical and experimental reports on DNA biosensors. The proposed biosensor with high specificity and sensitivity exposes higher current compared to that of monolayer graphene counterpart for analogous ambient conditions.

Application of gene editing technology to DNA digital data storage

While the archival digital storage industry is approaching its physical limits, demand is increasing significantly, so alternatives are emerging. The modern world is in dire need of durable, scalable and economical alternative storage media. Deoxyribonucleic acid (DNA), a promising storage medium, offers superior information durability, capacity and energy consumption, making it a promising candidate for long-term data storage. However, the design and realization of DNA digital data storage face many problems, but gene editing technology, as a technology that makes modifications to genes directly from the molecular level, provides a breakthrough in solving these problems. In this paper, I show some methods for designing DNA digital data storage based on gene editing technology. The method utilizes gene editing technology to modify DNA molecules to improve their storage capacity and stability. At the same time, this paper also introduces the application cases of gene editing technology in DNA bio storage devices and looks forward to its future development.

A RGB image encryption technique using chaotic maps of fractional variable-order based on DNA encoding

This paper presents a controller-based synchronization scheme in a leader-follower architecture for chaotic maps of fractional variable-order. The proposed scheme demonstrates successful synchronization of three different chaotic maps through simulation results. The implementation on Arduino UNO boards is used to showcase the relatively low algorithm complexity of the proposed scheme. Additionally, the paper explores a potential application of the proposed scheme in an RGB image encryption system, leveraging the chaotic behavior of the system for pseudo-random sequence generation. The encoding scheme utilizes the complementary property of Deoxyribonucleic acid (DNA) molecule bases. The presented encryption scheme employs the variable-order of the fractional derivative as a key for improved safety. This enhancement improves the methodology resistance against brute-force attacks while maintaining resilience against other cryptoanalysis techniques.

Gelatin composite gel particles comprised of in-situ formed zinc oxide and silver nanoparticles with enhanced antibacterial activities via enzymatic degradation

As a globally prevalent pathogen, Staphylococcus aureus (S. aureus) poses significant challenges to disease prevention and treatment within the community. Despite the extensively established potent antibacterial activity of metal or metal oxide nanoparticles (NPs), their clinical application is impeded primarily by their unpredictable cytotoxicity. Herein, we synthesized antibacterial composite gel particles based on in-situ formed silver and zinc oxide (Ag and ZnO) NPs as antibacterial agents and gelatin for targeted drug release against S. aureus via enzymatic degradation. The as-synthesized composite gel particles exhibited enhanced antibacterial activity against S. aureus compared to other pathogenic bacteria, which could mainly be attributed to the targeted release of payloads facilitated by enzymatic degradation triggered by gelatinases in S. aureus and the well-distributed NPs in the gel network. The silver and zinc oxide NPs as well as the released ions could cause the disruption of cell membranes, bind with the deoxyribonucleic acid molecules, and release reactive oxygen species inside the cell, resulting in the bacterial cell lysis and the deoxyribonucleic acid fragmentation. The in vivo experiments showed that the Caenorhabditis elegans treated with S. aureus and Shigella flexneri were rescued by composite gel particles, evidenced by the prolonged lifespan. The Ag/ZnO/Gelatin gel particles exhibited the most potent antibacterial activity and effective protection from infection among all. This study demonstrated the selective antibacterial activities of composite gel particles on S. aureus via enzymatic degradation and highlights the importance of preparing antimicrobial agents in gelatin networks to proceed targeted release against bacteria.

Effect of mono-/divalent metal ions on the conductivity characteristics of DNA solutions transferring through a microfluidic channel

Interactions between deoxyribonucleic acid (DNA) and metal ions are vital for maintaining life functions, however, there are still unsolved questions about its mechanisms. It is of great practical significance to study these issues for medical chip design, drug development, health care, etc. In this investigation, the conductivity properties of λ-DNA solutions with mono-/divalent metal ions (Na+, K+, Mg2+, and Ca2+) are experimentally studied as they are electrically driven through a 5 μm microfluidic channel. Experimental data indicate that the conductivities of λ-DNA solutions with metal ions (M+/M2+) basically tend to reduce firstly and then increase as the voltage increases, of which the turning points varied with the metal ions. When the voltage surpasses turning points, the conductivity of λ-DNA-M+ solutions increases with the concentration of metal ions, while that of λ-DNA-M2+ solutions decrease. Moreover, the conductivity of λ-DNA-M2+ solutions is always smaller than that of λ-DNA-M+ solutions, and with high-concentration M2+, it is even smaller than that of the λ-DNA solution. The main reasons for the above findings could be attributed to the polarization of electrodes and different mechanisms of interactions between metal ions and λ-DNA molecules. This investigation is helpful for the precise manipulation of single DNA molecules in micro-/nanofluidic space and the design of new biomedical micro-/nanofluidic sensors.

On the interactions involving serine proteases obtained from Monacrosporium thaumasium (Ascomycota: Orbiliomycetes) and deoxyribonucleic acid (DNA): biological macromolecules in action

The use of force spectroscopy approaches performed with optical tweezers can be very useful in determining the binding modes and the physical chemistry of DNA interactions with ligands, from small drugs to proteins. Helminthophagous fungi, on the other hand, have important enzyme secretion mechanisms for various purposes, and the interactions between such enzymes and nucleic acids are very poorly studied. Therefore, the main goal of the present work was to investigate, at the molecular level, the mechanisms of interaction between fungal serine proteases and the double-stranded (ds) DNA molecule. Experimental assays performed with this single molecule technique consist in exposing different concentrations of the protease of this fungus to dsDNA until saturation while monitoring the changes on the mechanical properties of the macromolecular complexes formed, from where the physical chemistry of the interaction can be deduced. It was found that the protease binds strongly to the double-helix, forming aggregates and changing the persistence length of the DNA molecule. The present work thus allowed us to infer information at the molecular level on the pathogenicity of these proteins, an important class of biological macromolecules, when applied to a target specimen.

Cross-Server End-to-End Patient Key Agreement Protocol for DNA-Based U-Healthcare in the Internet of Living Things

(1) Background: Third-generation sequencing (TGS) technique directly sequences single deoxyribonucleic acid (DNA) molecules, enabling real-time sequencing and reducing sequencing time from a few days to a few hours. Sequencing devices can be miniaturized and DNA-reading sensors placed on the body to monitor human health and vital signs, building an “internet of living things” (IoLT) facilitating ubiquitous healthcare services. In many cases, patients may wish to directly connect to each other for purposes of sharing real-time sequencing data, medical status or trading genomic data, etc. (2) Problems: User registration for a specific service may be limited due to some reason. Registering for multiple redundant services would also result in wasted money and possible wasteful communication overhead. In addition, since medical data and health information are very sensitive, security and privacy issues in the network are of paramount importance. (3) Methods: In this article, I propose a cross-server end-to-end (CS-E2E) patient authenticated key agreement protocol for DNA-based healthcare services in IoLT networks. My work allows two patients to mutually authenticate each other through assistance of respective servers, so that they can establish a reliable shared session key for securing E2E communications. The design employs multiple cost-saving solutions and robust cryptographic primitives, including smart-card-based single sign-on, elliptic curve cryptography, biohash function, etc. (4) Results: My proposed protocol is proven to be secure against various attacks and to incur reasonable communication cost compared to its predecessor works. The protocol also provides the support for more security properties and better functionalities. (5) Conclusions: The E2E communications between the patients are properly protected using the proposed approach. This assures a secure and efficient cross-server patient conversation for multiple purposes of healthcare communication.

History of advances in genetic engineering of viruses before COVID-19 pandemic.

On December 31, 2019, the World Health Organization's China Country Office was alerted to cases of pneumonia of unknown cause detected in Wuhan City, Hubei Province of China. Due to the fact that to date, the question of the origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not been resolved yet, the author analyzed the main advances in the development of genetic engineering of viruses that took place before the onset of the COVID-19 pandemic. The first artificial genetically modified viruses could appear in nature in the mid-1950s. The technique of nucleic acid hybridization was developed by the end-1960s. In the late 1970s, a method called the "reverse genetics" emerged to synthesize ribonucleic acid and deoxyribonucleic acid molecules. In the early 1980-s, it became possible to combine the genes of different viruses and insert the genes of one virus into the genome of another virus. Since that time, the production of vector vaccines began. At present, by modern technologies one can assemble any virus based on the nucleotide sequence available in the virus database or designed by a computer as a virtual model. Scientists around the world are invited to answer the call of Neil Harrison and Jeffrey Sachs of Columbia University, for a thorough and independent investigation into the origin of SARS-CoV-2. Only a full understanding of the origin of the new virus can minimize the likelihood of a similar pandemic in the future.

Polymeric DNA Hydrogels and Their Applications in Drug Delivery for Cancer Therapy.

The biomolecule deoxyribonucleic acid (DNA), which acts as the carrier of genetic information, is also regarded as a block copolymer for the construction of biomaterials. DNA hydrogels, composed of three-dimensional networks of DNA chains, have received considerable attention as a promising biomaterial due to their good biocompatibility and biodegradability. DNA hydrogels with specific functions can be prepared via assembly of various functional sequences containing DNA modules. In recent years, DNA hydrogels have been widely used for drug delivery, particularly in cancer therapy. Benefiting from the sequence programmability and molecular recognition ability of DNA molecules, DNA hydrogels prepared using functional DNA modules can achieve efficient loading of anti-cancer drugs and integration of specific DNA sequences with cancer therapeutic effects, thus achieving targeted drug delivery and controlled drug release, which are conducive to cancer therapy. In this review, we summarized the assembly strategies for the preparation of DNA hydrogels on the basis of branched DNA modules, hybrid chain reaction (HCR)-synthesized DNA networks and rolling circle amplification (RCA)-produced DNA chains, respectively. The application of DNA hydrogels as drug delivery carriers in cancer therapy has been discussed. Finally, the future development directions of DNA hydrogels in cancer therapy are prospected.

Review on DNA Cryptography

Cryptography is the science that secures data and communication over the network by applying mathematics and logic to design strong encryption methods. In the modern era of e-business and e-commerce the protection of confidentiality, integrity and availability (CIA triad) of stored information as well as of transmitted data is very crucial. Deoxyribonucleic acid (DNA) is a genetic molecule consisting of two linked strands that wind around each other to form a double helical structure. The backbone of each strand is made of alternating deoxyribose sugar and phosphate groups. To each sugar one of four bases are attached i.e., adenine (A), cytosine (C), guanine (G) and thymine (T). DNA molecules, having the capacity to store, process and transmit information, inspires the idea of DNA cryptography. It is the rapid emerging unconventional techniques which combines the chemical characteristics of biological DNA sequences with classical cryptography to ensure non-vulnerable transmission of data. This innovative method is based on the notion of DNA computing. The methodologies of DNA cryptography are not coded mathematically; thus, it could be too secure to be cracked easily.

A new approach for DNA sequence analysis using multiwavelet transform (MWT)

Finding the nucleotide sequence within a DNA (deoxyribonucleic acid) molecule is the process of DNA sequencing. The DNA of every organism is made up of a specific pattern of nucleotides. Scientists can compare the DNA of different organisms by sequence to understand how organisms are connected. Three different forms of DNA sequencing - fragment analysis, Next Generation Sequencing (NGS), and Sanger sequencing - enable many new applications that increase our knowledge of genomes. It significantly impacts how well we understand how the genome works. Finding the hidden structural information of DNA is an important task. This paper introduces Multi-Wavelet Transformation (MWT) for analysis of the sequence of the DNA symbols. The symbolic DNA sequence has four indicator sequences. The MWT assigned symbols to numbers based on DNA structure. It can change each channel’s weighting to optimize energy distribution over MWT output. Analysis of synthetic and actual DNA sequences allowed the researchers to evaluate how well the proposed MWT works. The results show that the MWT has a higher energy dispersion than the regular WT and the bionic wavelet transform (BWT). Future DNA sequence analyses should be able to discover hidden structural features using this MWT approach.

Locus-Specific Detection of DNA Methylation: The Advance, Challenge, and Perspective of CRISPR-Cas Assisted Biosensors.

Deoxyribonucleic acid (DNA) methylation is one of the epigenetic characteristics that result in heritable and revisable phenotype changes but without sequence changes in DNA. Aberrant methylation occurring at a specific locuswasreported to be associated with cancers, insulin resistance, obesity, Alzheimer's disease, Parkinson's disease, etc. Therefore, locus-specific DNA methylation can serve as a valuable biomarker for disease diagnosis and therapy. Recently, Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas systems are applied to develop biosensors for DNA, ribonucleic acid, proteins, and small molecules detection. Because of their highly specific binding ability and signal amplification capacity, CRISPR-Cas assisted biosensor also serve as a potential tool for locus-specific detection of DNA methylation. In this perspective, based on the detection principle, a detailed classification and comprehensive discussion of recent works about the latest advances in locus-specific detection of DNA methylation using CRISPR-Cas systems are provided. Furthermore, current challenges and future perspectives of CRISPR-based locus-specific detection of DNA methylation are outlined.

Neural-like growing networks in the development of general intelligence. Neural-like growing networks (P. II)

This article is devoted to the development of general artificial intelligence (AGI) based on a new type of neural networks – “neural-like growing networks”. It consists of two parts. The first one was published in N4, 2022, and describes an artificial neural-like element (artificial neuron) in terms of its functionality, which is as close as possible to a biological neuron. An artificial neural-like element is the main element in building neural-like growing networks. The second part deals with the structures and functions of artificial and natural neural networks. The paper proposes a new approach for creating neural-like growing networks as a means of developing AGI that is as close as possible to the natural intelligence of a person. The intelligence of man and living organisms is formed by their nervous system. According to I.P. Pavlov's definition, the main mechanism of higher nervous activity is the reflex activity of the nervous system. In the nerve cell, the main storage of unconditioned reflexes is the deoxyribonucleic acid (DNA) molecule. The article describes ribosomal protein synthesis that contributes to the implementation of unconditioned reflexes and the formation of conditioned reflexes as the basis for learning biological objects. The first part of the work shows that the structure and functions of ribosomes almost completely coincide with the structure and functions of the Turing machine. Turing invented this machine to prove the fundamental (theoretical) possibility of constructing arbitrarily complex algorithms from extremely simple operations, and the operations themselves are performed automatically. Here arises a stunning analogy, nature created DNA and the ribosome to build complex algorithms for creating biological objects and their communication with each other and with the external environment, and the ribosomal protein synthesis is carried out by many ribosomes at the same time. It was concluded that the nerve cells of the brain are analog multi-machine complexes – ultra-fast molecular supercomputers with an unusually simple analog programming device.

Electrochemical Biosensors in the Diagnosis of Acute and Chronic Leukemias.

Until now, morphological assessment with an optical or electronic microscope, fluorescence in situ hybridization, DNA sequencing, flow cytometry, polymerase chain reactions, and immunohistochemistry have been employed for leukemia identification. Nevertheless, despite their numerous different vantages, it is difficult to recognize leukemic cells correctly. Recently, the electrochemical evaluation with a nano-sensing interface seems an attractive alternative. Electrochemical biosensors measure the modification in the electrical characteristics of the nano-sensing interface, which is modified by the contact between a biological recognition element and the analyte objective. The implementation of nanosensors is founded not on single nanomaterials but rather on compilating these components efficiently. Biosensors able to identify the molecules of deoxyribonucleic acid are defined as DNA biosensors. Our review aimed to evaluate the literature on the possible use of electrochemical biosensors for identifying hematological neoplasms such as acute promyelocytic leukemia, acute lymphoblastic leukemia, and chronic myeloid leukemia. In particular, we focus our attention on using DNA electrochemical biosensors to evaluate leukemias.

On the multiple explicit exact solutions to the double‐chain DNA dynamical system

The nonlinear dynamics of deoxyribonucleic acid (DNA) is the subject of this research. For an organism to flourish and reproduce, it needs the genetic information that is contained in its DNA. In the DNA molecule, the hydrogen bonds between the base pairs of the two polynucleotide chains are represented by an elastic membrane (or a series of linear springs) that connects the model's two long elastic homogeneous strands (or rods). Using two integration norms, namely, the new auxiliary equation method (NAEM) and the extended sinh‐Gordon equation expansion method (ShGEEM), we obtain a variety of nonlinear dynamical solitary wave structures in various shapes, including hyperbolic, trigonometric, and exponential function solutions, as well as some unique solitary wave solutions, including bell‐shaped, kink, singular, combined, and multiple solitons. Using the logarithmic transformation assistant, we examine a variety of wave structures and recover singular periodic wave solutions with unknown parameters, along with the validity constraints. According to the results, the governing model contains an unusually large number of theoretically exact solitary wave solutions. The physical characterization of some of the presented results can be visualized in 3D and 2D profiles by selecting the appropriate parameter values.