Gene Vectors Research Articles

Stable Expression of Coagulation Factors by RPS6 Promoter

To screen better promoters and provide more powerful tools for basic research and gene therapy of hemophilia. Bioinformatics methods were used to analyze the promoters expressing housekeeping genes with high abundance, so as to select potential candidate promoters. The GFP reporter gene vector was constructed, and the packaging efficiency of the novel promoter was investigated with EF1 α promoter as control, and the transcription and activities of the reporter gene were investigated too. The activity of the candidate promoter was investigated by loading F9 gene. The most potential RPS6 promoter was obtained by screening. There was no difference in lentiviral packaging between EF1 α-LV and RPS6-LV, and their virus titer were consistent. In 293T cells, the transduction efficiency and mean fluorescence intensity of RPS6pro-LV and EF1 αpro-LV were proportional to the lentiviral dose. The transfection efficiency of both promoters in different types of cells was in the following order: 293T>HEL>MSC; Compared with EF1 αpro-LV, RPS6pro-LV could obtain a higher fluorescence intensity in MSC cells, and RPS6pro-LV was more stable in long-term cultured HEL cells infected with two lentiviruses respectively. The results of RT-qPCR, Western blot and FIX activity (FIX∶C) detection of K562 cell culture supernatant showed that FIX expression in the EF1 α-F9 and RPS6-F9 groups was higher than that in the unloaded control group, and there was no significant difference in FIX expression between the EF1 α-F9 and RPS6-F9 groups. After screening and optimization, a promoter was obtained, which can be widely used for exogenous gene expression. The high stability and viability of the promoter were confirmed by long-term culture and active gene expression, which providing a powerful tool for basic research and clinical gene therapy of hemophilia.

Objective Space-Based Population Generation to Accelerate Evolutionary Algorithms for Large-Scale Many-Objective Optimization

The generation and updating of solutions, e.g., crossover and mutation, of many existing evolutionary algorithms directly operate on decision variables. The operators are very time consuming for large-scale and many-objective optimization problems. Different from them, this work proposes an objective space-based population generation method to obtain new individuals in the objective space and then map them to decision variable space and synthesize new solutions. It introduces three new objective vector generation methods and uses a linear mapping method to tightly connect objective space and decision one to jointly determine new-generation solutions. A loop can be formed directly between two spaces, which can generate new solutions faster and use more feedback information in the objective space. In order to demonstrate the performance of the proposed algorithm, this work performs a series of empirical experiments involving both large-scale decision variables and many objectives. Compared with the state-of-the-art traditional and large-scale algorithms, the proposed method exceeds or at least reaches its peers’ best level in overall performance while achieving great saving in execution time.

Reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy

The particle swarm optimization (PSO) algorithm has been one of the most effective methods for solving various engineering optimization problems. Most existing PSO variants frequently use fixed operators, the adoption of a fixed operator learning mode may restrict the intelligence level of each particle, thus reducing the performance of PSO in solving optimization issues with complicated fitness landscapes. To address single goal real-parameter numerical optimization while overcoming the above shortcoming, this paper proposes a reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy (NRLPSO). In NRLPSO, a dynamic oscillation inertial weight (DOW) strategy that provides particles with dynamic adjustment ability in different situations is designed. To resolve the operator selection conundrum of exploration and exploitation, a reinforcement learning-based velocity vector generation (VRL) strategy is developed. At each iteration, particles select the velocity update model based on reinforcement learning, and VRL helps to thoroughly search the problem space. A velocity updating mechanism based on cosine similarity (VCS) is applied to control the velocity learning mode to determine more promising solutions. Furthermore, to alleviate the problem of premature convergence, a local update strategy with neighborhood differential mutation (NDM) is employed to increase the diversity of the algorithm. To verify the efficiency of the proposed algorithm, the CEC2017 and CEC2022 test suites are implemented, and nine classic or state-of-the-art PSO variants are comprehensively tested. The experimental results show that NRLPSO outperforms the popular PSO variants in terms of convergence speed and accuracy. Since NRLPSO utilizes the DE mutations, it is compared with the representative LSHADE variant algorithm - LSHADE_SPACMA. Although LSHADE_SPACMA is better than NRLPSO concerning algorithm stability and convergence accuracy, we will refine our work in the future to enhance the performance in all aspects.

Extended-Spectrum β-Lactamases (ESBL) Producing Bacteria in Animals.

Animals have been identified as potential reservoirs and vectors of resistance genes, with studies showing that Gram-negative bacteria can acquire resistance through the horizontal transmission of resistance genes on plasmids. It is important to understand the distribution of antimicrobial-resistant bacteria and their drug-resistant genes in animals. Previous review articles mostly focused on a single bacterium or a single animal. Our objective is to compile all ESBL-producing bacteria isolated from various animals in recent years and provide a comprehensive viewpoint. Using a thorough PubMed literature search spanning from 1 January 2020 to 30 June 2022, studies exploring extended-spectrum beta-lactamase (ESBL) producing bacteria in animals were included. ESBL-producing bacteria are present in animals from various countries around the world. The most common sources of these bacteria were farm animals, and the most frequently isolated bacteria were Escherichia coli and Klebsiella pneumoniae. The most detected ESBL genes were blaTEM, blaSHV, and blaCTX-M. The presence of ESBL-producing bacteria in animals highlights the importance of the One Health approach to address the issue of antibiotic resistance. Further research is needed to better understand the epidemiology and mechanisms of the spread of ESBL-producing bacteria in animal populations and their potential impact on human and animal health.

Optical solitons of the perturbed nonlinear Schrödinger equation using Lie symmetry method

This paper presents an analytical treatment of the perturbed nonlinear Schrödinger equation (P-NLSE) using the Lie symmetry method. The NLSE is a fundamental equation in nonlinear optics and describes the propagation of optical pulses in a nonlinear medium. In the presence of perturbations, the solution to the NLSE becomes more complicated and requires more sophisticated mathematical techniques to analyze. The Lie symmetry method is a powerful tool for finding exact solutions to nonlinear differential equations and has been applied successfully to a wide range of problems in physics and engineering. In this paper, we use the Lie symmetry method to obtain exact analytical solutions for the P-NLSE and study their properties. Our results show that the perturbations can have a significant effect on the behavior of the optical pulses, leading to phenomena such as soliton fission and dispersive shock waves. The Lie symmetry method is used to derive three generators for the Lie algebra, and two reductions are examined through combinations of vector generators. The Improved generalized Riccati equation and Nucci reduction method are then employed to obtain exact solutions, including bright soliton, multiple singular soliton, and exponential solutions.

English

Wireless capsule endoscopy (WCE) is a non-invasive wireless imaging technology that gained wider popularity. The main drawback of WCE is that it produces a massive amount of images that healthcare professionals should analyze, which is time-consuming. Many researchers have suggested machine learning and image-processing methods for classifying gastrointestinal tract disorders. Data augmentation and classical image processing techniques are integrated with the adjustable pre-trained deep convolutional neural network (DCNN) to categorize diseases in the digestive tract from WCE images. This study develops an Intelligent Wireless Endoscopic Image Classification using Gannet Optimization Algorithm with Deep Learning (IWEIC-GOADL) model. The IWEIC-GOADL technique mainly examines the WCE images for classification purposes. As a preprocessing step, the presented IWEIC-GOADL technique executes the Gabor filtering (GF) method for the noise removal process. In addition, the presented IWEIC-GOADL technique employs a deconvolution VGG19 (DeVGG19) model for feature vector generation, and its hyperparameter tuning process takes place by the GOA. Finally, the IWEIC-GODL technique applies the deep belief network (DBN) model for WCE image classification purposes. A wide range of simulations was performed on a benchmark dataset to demonstrate the better performance of the IWEIC-GODL technique. The stimulation outcome stated the improvements of the IWEIC-GODL algorithm over other recent techniques.

3D bioprinting of gene delivery scaffolds with controlled release

Localized gene delivery via engineered scaffolds offers spatiotemporal control of the gene vector release. Here, we explored the capability of digital light processing based bioprinting to fabricate 3D scaffolds in hydrogels for controlled gene delivery. We demonstrated the compatibility of the method with three representative hydrogel biomaterials for gene delivery. We further investigated the highly tunable release profile with these scaffolds by creating and combining distinct release mechanisms of diffusion and ion exchange. The efficacy of gene delivery of these scaffolds was validated in vitro using 293T cells. Results from this work could potentially facilitate the development of synergistic and personalized gene therapies.

Plasmonic Metasurfaces for Superposition of Profile-Tunable Tightly Focused Vector Beams and Generation of the Structured Light

Vector beams (VBs) and their superposition have found important applications in versatile fields such as optical communications, super-resolution microscopy and quantum information, and metasurfaces have enabled the miniaturization and integration of the optical systems manipulating the vector beams, providing potential applications to subwavelength regimes. In this work, we propose a metasurface to realize the superposition of profile-tunable tightly focused VBs, with the novel structured light fields generated. The metasurface is composed of two sets of orthogonal-nanoslit pairs arranged on the inner and outer rings. By realizing the chiral conversion of circularly polarized light with the slit-pairs which act as half-wave plates, and by creating helical phase profiles of optical vortices with the geometrical phase of rotational nano-slit pairs, two focused Bessel VBs are formed. By finely varying the diameters of two sets of rings, the doughnuts of the two Bessel VBs of different orders are tuned to be of the same size, and the superposition of the two VBs is realized. The theoretical analyses of the superimposed fields were presented, the FDTD simulations were performed to optimize the designed metasurfaces, and the experimental measurements were carried out to validate feasibility of the metasurface. The novel and interesting characteristics of the superposed fields different from those of the conventional VBs were demonstrated. This work will be of significance for classical and quantum applications of VBs in various fields.

Generation of a Triple-Shuttling Vector and the Application in Plant Plus-Strand RNA Virus Infectious cDNA Clone Construction.

Infectious cloning of plant viruses is a powerful tool for studying the reverse genetic manipulation of viral genes in virus-host plant interactions, contributing to a deeper understanding of the life history and pathogenesis of viruses. Yet, most of the infectious clones of RNA virus constructed in E. coli are unstable and toxic. Therefore, we modified the binary vector pCass4-Rz and constructed the ternary shuttle vector pCA4Y. The pCA4Y vector has a higher copy number in the E. coli than the conventional pCB301 vector, can obtain a high concentration of plasmid, and is economical and practical, so it is suitable for the construction of plant virus infectious clones in basic laboratories. The constructed vector can be directly extracted from yeast and transformed into Agrobacterium tumefaciens to avoid toxicity in E. coli. Taking advantage of the pCA4Y vector, we established a detailed large and multiple DNA HR-based cloning method in yeast using endogenous recombinase. We successfully constructed the Agrobacterium-based infectious cDNA clone of ReMV. This study provides a new choice for the construction of infectious viral clones.

Android malware category detection using a novel feature vector-based machine learning model

Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones. The researchers have conducted numerous studies on the detection of Android malware, but the majority of the works are based on the detection of generic Android malware. The detection based on malware categories will provide more insights about the malicious patterns of the malware. Therefore, this paper presents a detection solution for different Android malware categories, including adware, banking, SMS malware, and riskware. In this paper, a novel Huffman encoding-based feature vector generation technique is proposed. The experiments have proved that this novel approach significantly improves the efficiency of the detection model. This method makes use of system call frequencies as features to extract malware’s dynamic behavior patterns. The proposed model was evaluated using machine learning and deep learning methods. The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%.

Multifunctional Mesoporous Silica-Coated Gold Nanorods Mediate Mild Photothermal Heating-Enhanced Gene/Immunotherapy for Colorectal Cancer.

Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths in the world. It is urgent to search for safe and effective therapies to address the CRC crisis. The siRNA-based RNA interference targeted silencing of PD-L1 has extensive potential in CRC treatment but is limited by the lack of efficient delivery vectors. In this work, the novel cytosine-phosphate-guanine oligodeoxynucleotides (CpG ODNs)/siPD-L1 co-delivery vectors AuNRs@MS/CpG ODN@PEG-bPEI (ASCP) were successfully prepared by two-step surface modification of CpG ODNs-loading and polyethylene glycol-branched polyethyleneimine-coating around mesoporous silica-coated gold nanorods. ASCP promoted dendritic cells (DCs) maturation by delivering CpG ODNs, exhibiting excellent biosafety. Next, mild photothermal therapy (MPTT) mediated by ASCP killed tumor cells and released tumor-associated antigens, further promoting DC maturation. Furthermore, ASCP exhibited mild photothermal heating-enhanced performance as gene vectors, resulting in an increased PD-L1 gene silencing effect. Enhanced DCs maturity and enhanced PD-L1 gene silencing significantly promoted the anti-tumor immune response. Finally, the combination of MPTT and mild photothermal heating-enhanced gene/immunotherapy effectively killed MC38 cells, leading to strong inhibition of CRC. Overall, this work provided new insights into the design of mild photothermal/gene/immune synergies for tumor therapy and may contribute to translational nanomedicine for CRC treatment.

Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis.

Cancer is a deadly disease caused by various biochemical abnormalities and genetic diseases. Colon and lung cancer have developed as two major causes of disability and death in human beings. The histopathological detection of these malignancies is a vital element in determining the optimal solution. Timely and initial diagnosis of the sickness on either front diminishes the possibility of death. Deep learning (DL) and machine learning (ML) methods are used to hasten such cancer recognition, allowing the research community to examine more patients in a much shorter period and at a less cost. This study introduces a marine predator's algorithm with deep learning as a lung and colon cancer classification (MPADL-LC3) technique. The presented MPADL-LC3 technique aims to properly discriminate different types of lung and colon cancer on histopathological images. To accomplish this, the MPADL-LC3 technique employs CLAHE-based contrast enhancement as a pre-processing step. In addition, the MPADL-LC3 technique applies MobileNet to derive feature vector generation. Meanwhile, the MPADL-LC3 technique employs MPA as a hyperparameter optimizer. Furthermore, deep belief networks (DBN) can be applied for lung and color classification. The simulation values of the MPADL-LC3 technique were examined on benchmark datasets. The comparison study highlighted the enhanced outcomes of the MPADL-LC3 system in terms of different measures.

Recent progress in polymeric gene vectors: Delivery mechanisms, molecular designs, and applications.

Gene therapy and gene delivery have drawn extensive attention in recent years especially when the COVID-19 mRNA vaccines were developed to prevent severe symptoms caused by the corona virus. Delivering genes, such as DNA and RNA into cells, is the crucial step for successful gene therapy and remains a bottleneck. To address this issue, vehicles (vectors) that can load and deliver genes into cells are developed, including viral and non-viral vectors. Although viral gene vectors have considerable transfection efficiency and lipid-based gene vectors become popular since the application of COVID-19 vaccines, their potential issues including immunologic and biological safety concerns limited their applications. Alternatively, polymeric gene vectors are safer, cheaper, and more versatile compared to viral and lipid-based vectors. In recent years, various polymeric gene vectors with well-designed molecules were developed, achieving either high transfection efficiency or showing advantages in certain applications. In this review, we summarize the recent progress in polymeric gene vectors including the transfection mechanisms, molecular designs, and biomedical applications. Commercially available polymeric gene vectors/reagents are also introduced. Researchers in this field have never stopped seeking safe and efficient polymeric gene vectors via rational molecular designs and biomedical evaluations. The achievements in recent years have significantly accelerated the progress of polymeric gene vectors toward clinical applications.

Dopamine-mimetic-coated polyamidoamine-functionalized Fe3O4 nanoparticles for safe and efficient gene delivery

Fe3O4 nanoparticles (NPs) are widely used in the construction of drug and gene delivery vectors because of their particular physicochemical properties. Surface modification can not only reduce the cytotoxicity of Fe3O4, but also further improve the biocompatibility and delivery efficiency. In this work, firstly, polydopamine (PDA)-coated Fe3O4 NPs (named Fe3O4@PDA) were prepared by using the self-polymerization characteristics of dopamine in alkaline environment. Then, polyamidoamine (PAMAM) was modified by the Michael addition reaction to prepare water-soluble core—shell magnetic NPs of Fe3O4@PDA@PAMAM, and its potential as gene vector was further evaluated. The results revealed that Fe3O4@PDA@PAMAM had the ability to condense and protect DNA, and showed lower cytotoxicity, higher cell uptake and transfection efficiency than those of PAMAM. It has the potential to become a magnetic targeted gene vector for further study.

A weight vector generation method based on normal distribution for preference-based multi-objective optimization

A weight vector generation method based on normal distribution for preference-based multi-objective optimization

The advent of a pan-collagenous CLOVIS POINT for pathotropic targeting and cancer gene therapy, a retrospective.

The 'Clovis Point'-an enabling prehistoric gain-of-function in stone-age tool technologies which empowered the Paleoindian-Americans to hunt, to strike-deep, and to kill designated target megafauna more efficiently-was created biochemically by molecular-genetic bio-engineering. This Biomedical "Clovis Point" was crafted by adapting a broad-spectrum Pan-Collagen Binding Domain (Pan-Coll/CBD) found within the immature pre-pro-peptide segment of Von Willebrand Factor into a constructive series of advanced medical applications. Developed experimentally, preclinically, and clinically into a cutting-edge Biotechnology Platform, the Clovis Point is suitable for 1) solid-state binding of growth factors on collagenous scaffolds for improved orthopedic wound healing, 2) promoting regeneration of injured/diseased tissues; and 3) autologous stem cell capture, expansion, and gene-based therapies. Subsequent adaptations of the high-affinity Pan-Coll/CBD (exposed-collagen-seeking/surveillance function) for intravenous administration in humans, enabled the physiological delivery, aka Pathotropic Targeting to diseased tissues via the modified envelopes of gene vectors; enabling 4) precision tumor-targeting for cancer gene therapy and 5) adoptive/localized immunotherapies, demonstrating improved long-term survival value-thus pioneering a proximal and accessible cell cycle control point for cancer management-empowering modern medical oncologists to address persistent problems of chemotherapy resistance, recurrence, and occult progression of metastatic disease. Recent engineering adaptations have advanced the clinical utility to include the targeted delivery of small molecule APIs: including taxanes, mAbs, and RNA-based therapeutics.

The Potency of Vitamin C in Tomato Plant for the Result of Genetically Modified Lanceolate Gene Through Agrobacterium Tumefaciens Using CRISPR-CAS 9

Vitamin C is the important part in the formation of protein in the body, one of which is obtained from tomatoes (Solanum lycopersicum L.). Due to the needed of vitamin C, a way is needed to get vitamin C from tomatoes which are of higher value. Tomato genetically modified using the lanceolate gene through Agrobacterium tumefaciens using CRISPR-CAS9 is a promising solution for the food world. The literature study method is carried out by reviewing the theory and the results of previous research qualitatively. The results is that genetically engineered tomatoes have the advantage of high vitamin content by using plant improvement and modification. The bacterium Agrobacterium tumefaciens functions as a vector and insertion site for recombinant genes and CRISPR-CAS9 technology is used to modify the target genome because it has a CAS9 protein containing two homologous domains resulting in a new trait. Tomatoes with lanceolate leaves will have fruit with high vitamin content because the results of photosynthesis are focused on fruit development. Suggestions for conducting in-depth research to ensure safety in the food quality and health. The goals of the research were to 1) determine the potency of tomato rich in vitamin C using genetic engineering of Agrobacterium tumefaciens, 2) structure and function of the Lanceolate gene in plants, 3) application of CRISPR-CAS 9 in genetic engineering, and 4) advantages and disadvantages of genetically modified tomato. God gave humans the ability to manage His creation. So that humans can glorifying God through research.

Adaptive Aquila Optimizer with Explainable Artificial Intelligence-Enabled Cancer Diagnosis on Medical Imaging.

Explainable Artificial Intelligence (XAI) is a branch of AI that mainly focuses on developing systems that provide understandable and clear explanations for their decisions. In the context of cancer diagnoses on medical imaging, an XAI technology uses advanced image analysis methods like deep learning (DL) to make a diagnosis and analyze medical images, as well as provide a clear explanation for how it arrived at its diagnoses. This includes highlighting specific areas of the image that the system recognized as indicative of cancer while also providing data on the fundamental AI algorithm and decision-making process used. The objective of XAI is to provide patients and doctors with a better understanding of the system's decision-making process and to increase transparency and trust in the diagnosis method. Therefore, this study develops an Adaptive Aquila Optimizer with Explainable Artificial Intelligence Enabled Cancer Diagnosis (AAOXAI-CD) technique on Medical Imaging. The proposed AAOXAI-CD technique intends to accomplish the effectual colorectal and osteosarcoma cancer classification process. To achieve this, the AAOXAI-CD technique initially employs the Faster SqueezeNet model for feature vector generation. As well, the hyperparameter tuning of the Faster SqueezeNet model takes place with the use of the AAO algorithm. For cancer classification, the majority weighted voting ensemble model with three DL classifiers, namely recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM). Furthermore, the AAOXAI-CD technique combines the XAI approach LIME for better understanding and explainability of the black-box method for accurate cancer detection. The simulation evaluation of the AAOXAI-CD methodology can be tested on medical cancer imaging databases, and the outcomes ensured the auspicious outcome of the AAOXAI-CD methodology than other current approaches.

Carbon Dots with Guanidinium and Amino Acid Functional Groups for Targeted Small Interfering RNA Delivery toward Tumor Gene Therapy.

Small interfering RNA (siRNA)-based gene therapy represents a promising strategy for tumor treatment. Novel gene vectors that can achieve targeted delivery of siRNA to the tumor cells without causing any side effects are urgently needed. To this end, the large amino acid mimicking carbon dots with guanidinium functionalization (LAAM GUA-CDs) are designed and synthesized by choosing arginine and dopamine hydrochloride as precursors. LAAM GUA-CDs can load siRNA through the multiple hydrogen bonds between their guanidinium groups and phosphate groups in siRNA. Meanwhile, the amino acid groups at the edges of LAAM GUA-CDs endow them the capacity to target tumors. After loading siBcl-2 as a therapeutic agent, LAAM GUA-CDs/siBcl-2 has a high tumor inhibition rate of up to 68%, which is twice more than that of commercial Lipofectamine 2000. Furthermore, LAAM GUA-CDs do not cause side effect during antitumor treatment owing to their high tumor-targeting ability, thus providing a versatile strategy for tumor-targeted siRNA delivery and cancer therapy.

A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ML) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system's localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user's instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers' suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS.