Technology In Biomedical Research Research Articles

Ethische Aspekte von Blockchain-Technologien in der biomedizinischen Forschung

Abstract Definition of the problem Biomedical research based on big data offers immense benefits. Large multisite research that integrates large amounts of personal health data, especially genomic and genetic data, might contribute to a more personalized medicine. This type of research requires the transfer and storage of highly sensitive data, which raises the question of how to protect data subjects against data harm, such as privacy breach, disempowerment, disenfranchisement, and exploitation. As a result, there is a trade-off between reaping the benefits of big-data-based biomedical research and protecting data subjects’ right to informational privacy. Arguments Blockchain technologies are often discussed as a technical fix for the abovementioned trade-off due to their specific features, namely data provenance, decentralization, immutability, and access and governance system. However, implementing blockchain technologies in biomedical research also raises questions regarding consent, legal frameworks, and workflow integration. Hence, accompanying measures, which I call enablers, are necessary to unleash the potential of blockchain technologies. These enablers are innovative models of consent, data ownership models, and regulatory models. Conclusion Blockchain technologies as a technical fix alone is insufficient to resolve the aforementioned trade-off. Combining this technical fix with the enablers outlined above might be the best way to perform biomedical research based on big data and at the same time protect the informational privacy of data subjects.

Methyl β-Cyclodextrin-sperm-mediated gene editing (MBCD-SMGE): a simple and efficient method for targeted mutant mouse production

BackgroundGenerating targeted mutant mice is a crucial technology in biomedical research. This study focuses on optimizing the CRISPR/Cas9 system uptake into sperm cells using the methyl β-cyclodextrin-sperm-mediated gene transfer (MBCD-SMGT) technique to generate targeted mutant blastocysts and mice efficiently. Additionally, the present study elucidates the roles of cholesterol and reactive oxygen species (ROS) in the exogenous DNA uptake by sperm.ResultsIn this study, B6D2F1 mouse sperm were incubated in the c-TYH medium with different concentrations of MBCD (0, 0.75, 1, and 2 mM) in the presence of 20 ng/µl pCAG-eCas9-GFP-U6-gRNA (pgRNA-Cas9) for 30 min. Functional parameters, extracellular ROS, and the copy numbers of internalized plasmid per sperm cell were evaluated. Subsequently, in vitro fertilization (IVF) was performed and fertilization rate, early embryonic development, and transfection rate were assessed. Finally, our study investigated the potential of the MBCD-SMGT technique in combination with the CRISPR-Cas9 system, referred to as MBCD-SMGE (MBCD-sperm-mediated gene editing), for generating targeted mutant blastocysts and mice. Results indicated that cholesterol removal from the sperm membrane using MBCD resulted in a premature acrosomal reaction, an increase in extracellular ROS levels, and a dose-dependent influence on the copy numbers of the internalized plasmids per sperm cell. Moreover, the MBCD-SMGT technique led to a larger population of transfected motile sperm and a higher production rate of GFP-positive blastocysts. Additionally, the current study validated the targeted indel in blastocyst and mouse derived from MBCD-SMGE technique.ConclusionOverall, this study highlights the significant potential of the MBCD-SMGE technique for generating targeted mutant mice. It holds enormous promise for modeling human diseases and improving desirable traits in animals.Graphical

Functional proteomics based on protein microarray technology for biomedical research.

This chapter traces a route through Proteomics from its origins to the present day. The different proteomics applications are discussed with a focus on microarray technology. Analytical microarrays, functional microarrays and reverse phase microarrays and their different applications are discussed. Several studies are mentioned where the great versatility of this approach is shown. Finally, the advantages and future challenges of microarray technology are outlined.

Reconstructing the Photoacoustic Image with High Quality using the Deep Neural Network Model

The range of diagnostic equipment has been widened and improved by the quick development of biomedical research technologies. The creation of multifunctional instruments that become essential for biomedical operations has been discovered by several research organizations to be made possible by optical imaging, acoustic image analysis, and magnetic resonance imaging. One of the most crucial tools is hyperspectral photoacoustic (PA) imaging, which combines optical and ultrasonic technology. In this study, the reconstruction of the PA pictures employs a new deployment of deep learning methods. This enabled us to train and evaluate our deep-learning approach under several imaging situations in addition to firmly establishing the contextual information. This study presents an optimization approach that blends multispectral optical acoustic imaging with detailed transfer learning-based diagnostic imaging. The particle swarm-convolutional neural network (PS-CNN) technique aims to reconstruct and categorize the presence of cancer using ultrasonic pictures. In image processing, the technique of bilateral filtration (BF) is commonly employed to remove noise. Additionally, the biological images are separated using portable LED Net frameworks. It is also possible to employ a feature extraction technique with the PS optimization methodology. Last but not least, biological images employ a CNN model to assign suitable classification. Using a standard dataset, the PS-CNN technology’s efficacy is confirmed, and testing findings revealed that it performs superior to other methods.

Top-Down Mass Spectrometry of Synthetic Single Guide Ribonucleic Acids Enabled by Facile Sample Clean-Up.

In recent years, CRISPR-Cas9 genome editing has become an important technology in biomedical research and has demonstrated tremendous therapeutic potential. With Cas9 endonuclease, the use of single guide ribonucleic acids (sgRNAs) allows for sequence-specific cutting on target double-stranded deoxyribonucleic acids. Therefore, the design and quality of sgRNAs can greatly affect the efficiency and specificity of genome editing. Mass spectrometry (MS) has been a powerful tool to detect molecular features and sequence a variety of biomolecules; however, as the sizes of oligonucleotides get larger, it becomes more challenging to desalt samples and achieve high-quality intact spectra with effective fragmentation. Here, we develop a simple but effective online column-based clean-up method (reversed-phase column in a size exclusion mode) that removes formulation salts and metal adducts from larger oligonucleotides upon entering the mass spectrometer in a consistent manner. Using the top-down approach without any nuclease digestion, we characterized and sequenced 100-nucleotide-long sgRNAs by higher-energy collision dissociation (HCD), collision-induced dissociation (CID), ultraviolet photodissociation (UVPD), and activated electron photodetachment (a-EPD). In a single 10 min liquid chromatography-tandem MS (LC-MS/MS) run, CID yielded the best sequence coverage, of 67%. When adding complementary UVPD and a-EPD runs, we achieved 80% overall sequence coverage and 100% cleavages for the variable sequence, the first 20 nucleotides from the 5' end. This LC-MS/MS platform provides a facile top-down workflow to analyze and sequence larger chemically modified oligonucleotides with no sample treatment.

Gene Editing in Mouse Zygotes Using the CRISPR/Cas9 System.

Engineering of the mouse germline is a key technology in biomedical research for studying the function of genes in health and disease. Since the first knockout mouse was described in 1989, gene targeting was based on recombination of vector encoded sequences in mouse embryonic stem cell lines and their introduction into preimplantation embryos to obtain germline chimeric mice. This approach has been replaced in 2013 by the application of the RNA-guided CRISPR/Cas9 nuclease system, which is introduced into zygotes and directly creates targeted modifications in the mouse genome. Upon the introduction of Cas9 nuclease and guide RNAs into one-cell embryos, sequence-specific double-strand breaks are created that are highly recombinogenic and processed by DNA repair enzymes. Gene editing commonly refers to the diversity of DSB repair products that include imprecise deletions or precise sequence modifications copied from repair template molecules. Since gene editing can now be easily applied directly in mouse zygotes, it has rapidly become the standard procedure for generating genetically engineered mice. This article covers the design of guide RNAs, knockout and knockin alleles, options for donor delivery, preparation of reagents, microinjection or electroporation of zygotes, and the genotyping of pups derived from gene editing projects.

Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen

The fast advancement of biomedical research technology has expanded and enhanced the spectrum of diagnostic instruments. Various research groups have found optical imaging, ultrasonic imaging, and magnetic resonance imaging to create multifunctional devices that are critical for biomedical activities. Multispectral photoacoustic imaging that integrates the ideas of optical and ultrasonic technologies is one of the most essential instruments. At the same time, early cancer identification is becoming increasingly important in order to minimize fatality. Deep learning (DL) techniques have recently advanced to the point where they can be used to diagnose and classify cancer using biological images. This paper describes a hybrid optimization method that combines in-depth transfer learning-based cancer detection with multispectral photoacoustic imaging. The goal of the PS-ACO-RNN approach is to use ultrasound images to detect and classify the presence of cancer. Bilateral filtration (BF) is often used as a noise removal approach in image processing. In addition, lightweight LEDNet models are used to separate the biological images. A feature extractor with particle swarm with ant colony optimization (PS-ACO) paradigm can also be used. Finally, biological images assign appropriate class labels using a recurrent neural network (RNN) model. The effectiveness of the PS-ACO-RNN technique is verified using a benchmark database, and test results show that the PS-ACO-RNN approach works better than current approaches.

A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks.

Despite Artificial Intelligence (AI) being a leading technology in biomedical research, real-life implementation of AI-based Computer-Aided Diagnosis (CAD) tools into the clinical setting is still remote due to unstandardized practices during development. However, few or no attempts have been made to propose a reproducible CAD development workflow for 3D MRI data. In this paper, we present the development of an easily reproducible and reliable CAD tool using the Clinica and MONAI frameworks that were developed to introduce standardized practices in medical imaging. A Deep Learning (DL) algorithm was trained to detect frontotemporal dementia (FTD) on data from the NIFD database to ensure reproducibility. The DL model yielded 0.80 accuracy (95% confidence intervals: 0.64, 0.91), 1 sensitivity, 0.6 specificity, 0.83 F1-score, and 0.86 AUC, achieving a comparable performance with other FTD classification approaches. Explainable AI methods were applied to understand AI behavior and to identify regions of the images where the DL model misbehaves. Attention maps highlighted that its decision was driven by hallmarking brain areas for FTD and helped us to understand how to improve FTD detection. The proposed standardized methodology could be useful for benchmark comparison in FTD classification. AI-based CAD tools should be developed with the goal of standardizing pipelines, as varying pre-processing and training methods, along with the absence of model behavior explanations, negatively impact regulators’ attitudes towards CAD. The adoption of common best practices for neuroimaging data analysis is a step toward fast evaluation of efficacy and safety of CAD and may accelerate the adoption of AI products in the healthcare system.

PHASE 0 CLINICAL TRIAL: HOPE THROUGH RESEARCH

This paper Development of a drug molecule has always been a long and an exclusive process. From preclinical to clinical testing, it takes almost 10-15 years with an expense of almost US$2 Billion. It has been observed that serious challenges faced by researchers resulting in highest rate of failure in development of new drug molecules in clinical research. Seeing these grievances, the US FDA in 2004 introduced a “Critical Path “document highlighting the major decline of the drug development process despite having new advances and technology in biomedical research and the majors to improve the quality and effectiveness of overall drug discovery and development process. Thus, in 2006 a new guidance was issued on Exploratory IND studies by US FDA which is called phase 0 clinical study. It uses the concept of micro dosing. Minimum 30-40% of drugs fail in phase 1 trial due to unfavorable pk and pd. Micro-dosing is a new toolkit used in the drug development process and includes low or minute intake of molecules hardly to show any pharmacological effect when administered by participants and thus, helps in evaluating pk profiles. It shows no therapeutic effect, so chances of adverse reactions are also low. It acts as a bridge between preclinical and clinical study and can be beneficial in reducing extensive use of animal testing. Human screening is conducted to enroll few healthy participants for minimal no. of 7 days for the conduction of phase 0 trial. With recent technology, research and development and advancement, micro dosing has become more and more revolutionary and is emerging as the dormant challenge to be enormously used in more and more drug development process and make a shift to the process in a positive direction. The First emerging development of phase 0 trial can be witnessed in successful oncology trial in patients with progressive malignancies. Results proved to show a good and a well-tolerated bioavailability, and a desirable pharmacokinetics and biochemical data and hence this evidence can be used further to guide the development process and improve the drug development scenario.

Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images.

The rapid development of technologies in biomedical research has enriched and broadened the range of medical equipment. Magnetic resonance imaging, ultrasonic imaging, and optical imaging have been discovered by diverse research communities to design multimodal systems, which is essential for biomedical applications. One of the important tools is photoacoustic multimodal imaging (PAMI) which combines the concepts of optics and ultrasonic systems. At the same time, earlier detection of breast cancer becomes essential to reduce mortality. The recent advancements of deep learning (DL) models enable detection and classification the breast cancer using biomedical images. This article introduces a novel social engineering optimization with deep transfer learning-based breast cancer detection and classification (SEODTL-BDC) model using PAI. The intention of the SEODTL-BDC technique is to detect and categorize the presence of breast cancer using ultrasound images. Primarily, bilateral filtering (BF) is applied as an image preprocessing technique to remove noise. Besides, a lightweight LEDNet model is employed for the segmentation of biomedical images. In addition, residual network (ResNet-18) model can be utilized as a feature extractor. Finally, SEO with recurrent neural network (RNN) model, named SEO-RNN classifier, is applied to allot proper class labels to the biomedical images. The performance validation of the SEODTL-BDC technique is carried out using benchmark dataset and the experimental outcomes pointed out the supremacy of the SEODTL-BDC approach over the existing methods.

A combined bioinformatics and LC-MS-based approach for the development and benchmarking of a comprehensive database of Lymnaea CNS proteins.

Applications of key technologies in biomedical research, such as qRT-PCR or LC-MS-based proteomics, are generating large biological (-omics) datasets which are useful for the identification and quantification of biomarkers in any research area of interest. Genome, transcriptome and proteome databases are already available for a number of model organisms including vertebrates and invertebrates. However, there is insufficient information available for protein sequences of certain invertebrates, such as the great pond snail Lymnaea stagnalis, a model organism that has been used highly successfully in elucidating evolutionarily conserved mechanisms of memory function and dysfunction. Here, we used a bioinformatics approach to designing and benchmarking a comprehensive central nervous system (CNS) proteomics database (LymCNS-PDB) for the identification of proteins from the CNS of Lymnaea by LC-MS-based proteomics. LymCNS-PDB was created by using the Trinity TransDecoder bioinformatics tool to translate amino acid sequences from mRNA transcript assemblies obtained from a published Lymnaea transcriptomics database. The blast-style MMSeq2 software was used to match all translated sequences to UniProtKB sequences for molluscan proteins, including those from Lymnaea and other molluscs. LymCNS-PDB contains 9628 identified matched proteins that were benchmarked by performing LC-MS-based proteomics analysis with proteins isolated from the Lymnaea CNS. MS/MS analysis using the LymCNS-PDB database led to the identification of 3810 proteins. Only 982 proteins were identified by using a non-specific molluscan database. LymCNS-PDB provides a valuable tool that will enable us to perform quantitative proteomics analysis of protein interactomes involved in several CNS functions in Lymnaea, including learning and memory and age-related memory decline.

In Vitro Oxygen Glucose Deprivation Model of Ischemic Stroke: A Proteomics-Driven Systems Biological Perspective.

Oxygen glucose deprivation (OGD) of brain cells is the commonest in vitro model of ischemic stroke that is used extensively for basic and preclinical stroke research. Protein mass spectrometry is one of the most promising and rapidly evolving technologies in biomedical research. A systems-level understanding of cell-type-specific responses to oxygen and glucose deprivation without systemic influence is a prerequisite to delineate the response of the neurovascular unit following ischemic stroke. In this systematic review, we summarize the proteomics studies done on different OGD models. These studies have followed an expression or interaction proteomics approach. They have been primarily used to understand the cellular pathophysiology of ischemia-reperfusion injury or to assess the efficacy of interventions as potential treatment options. We compile the limitations of OGD model and downstream proteomics experiment. We further show that despite having limitations, several proteins shortlisted as altered in in vitro OGD-proteomics studies showed comparable regulation in ischemic stroke patients. This showcases the translational potential of this approach for therapeutic target and biomarker discovery. We next discuss the approaches that can be adopted for cell-type-specific validation of OGD-proteomics results in the future. Finally, we briefly present the research questions that can be addressed by OGD-proteomics studies using emerging techniques of protein mass spectrometry. We have also created a web resource compiling information from OGD-proteomics studies to facilitate data sharing for community usage. This review intends to encourage preclinical stroke community to adopt a hypothesis-free proteomics approach to understand cell-type-specific responses following ischemic stroke.

Evolution of Artificial Intelligence-Powered Technologies in Biomedical Research and Healthcare.

Artificial intelligence represents a powerful computational tool to analyze large amounts of data with and without supervision. Such powerful techniques have significantly impacted the advancement of multiple sectors from social media to data security, the automotive industry, gaming, finances, and healthcare. Only recently, however, artificial intelligence has been making significant strides in healthcare and biomedical research. Despite such advancements and the expectation of potential breakthroughs arising from implementing artificial intelligence in these fields, there is limited knowledge transfer and training for most healthcare professionals on using these computational techniques. While there is a wide array of publications on artificial intelligence applied to scientific research, most are too technical (not aimed at the non-initiated), too broad (with an overwhelming amount of data), or too focused on a particular application. This chapter presents an overview of artificial intelligence and its derivatives, giving a historical perspective, a succinct technical explanation of the underlying basis, and some examples of its applications. It finishes with a brief discussion on the challenges for implementing AI to be fully accepted in the scientific community.

Genome-Editing Technologies in Biomedical Research: The Regulatory Conditions for the Development

Significant progress has been made in the development of genetic technologies in recent decades. Currently, high-performance sequencing and, most importantly, genome editing technologies are widely used and available for laboratories in Russia. Existing technologies are not without drawbacks that significantly hinder further development, in addition, all the necessary reagents and materials, as well as equipment, are produced exclusively abroad. The review highlights the international experience of using genome editing technologies for the treatment and prevention of genetic diseases, vector-borne and viral infections, it offers recommendations for the development of this area in the Russian Federation. Attention is drawn to the legal and ethical regulation, mainly at the level of basic principles. The conclusion is formulated on the need for accelerated adaptation of basic ethical and legal principles for genome editing activities in scientific biomedical activities.

Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies.

High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. Supplementary data are available at Bioinformatics online.

3D Cell Technology in Biomedical Research

Traditional two dimensional cell culture has enabled great strides in biomedicine but needs to be improved to be able to keep up with the demands of modern biomedical research. 2D monolayer culture cannot replicate tissue responses and needs to be supplemented with extensive animal research. Growing cells in three dimensional scaffolds provides a more functional model for biomedical research than traditional monolayer culture. Depending on the needs and the complexity of the model there are several ways that 3D models can be initiated. Simple spheroids can be grown in low adherence plates and in hanging drops while larger spheroids and co-cultured ones need to be grown in systems with greater support such as hydro gels. The system that offers the greatest flexibility is the magnetic levitation approach. In the paper we offer a brief resume to various 3D methods and their characteristics to ease the choice of methods for implementing 3D cell culture techniques.

CSI NGS Portal: An Online Platform for Automated NGS Data Analysis and Sharing.

Next-generation sequencing (NGS) has been a widely-used technology in biomedical research for understanding the role of molecular genetics of cells in health and disease. A variety of computational tools have been developed to analyse the vastly growing NGS data, which often require bioinformatics skills, tedious work and a significant amount of time. To facilitate data processing steps minding the gap between biologists and bioinformaticians, we developed CSI NGS Portal, an online platform which gathers established bioinformatics pipelines to provide fully automated NGS data analysis and sharing in a user-friendly website. The portal currently provides 16 standard pipelines for analysing data from DNA, RNA, smallRNA, ChIP, RIP, 4C, SHAPE, circRNA, eCLIP, Bisulfite and scRNA sequencing, and is flexible to expand with new pipelines. The users can upload raw data in FASTQ format and submit jobs in a few clicks, and the results will be self-accessible via the portal to view/download/share in real-time. The output can be readily used as the final report or as input for other tools depending on the pipeline. Overall, CSI NGS Portal helps researchers rapidly analyse their NGS data and share results with colleagues without the aid of a bioinformatician. The portal is freely available at: https://csibioinfo.nus.edu.sg/csingsportal.

The expanding regulatory mechanisms and cellular functions of circular RNAs.

Many protein-coding genes in higher eukaryotes can produce circular RNAs (circRNAs) through back-splicing of exons. CircRNAs differ from mRNAs in their production, structure and turnover and thereby have unique cellular functions and potential biomedical applications. In this Review, I discuss recent progress in our understanding of the biogenesis of circRNAs and the regulation of their abundance and of their biological functions, including in transcription and splicing, sequestering or scaffolding of macromolecules to interfere with microRNA activities or signalling pathways, and serving as templates for translation. I further discuss the emerging roles of circRNAs in regulating immune responses and cell proliferation, and the possibilities of applying circRNA technologies in biomedical research.

PNA-Based Dynamic Combinatorial Libraries (PDCL) and screening of lectins

Selections from dynamic combinatorial libraries (DCL) benefit from the dynamic nature of the library that can change constitution upon addition of a selection pressure, such as ligands binding to a protein. This technology has been predominantly used with small molecules interacting with each other through reversible covalent interaction. However, application of this technology in biomedical research and drug discovery has been limited by the reversibility of covalent exchange and the analytical deconvolution of small molecule fragments. Here we report a supramolecular approach based on the use of a constant short PNA tag to direct the combinatorial pairing of fragment. This PNA tag yields fast exchange kinetics, while still delivering the benefits of cooperativity, and provides favourable properties for analytical deconvolution by MALDI. A selection from >6,000 assemblies of glycans (mono-, di-, tri-saccharides) targeting AFL, a lectin from pathogenic fungus, yielded a 95 nM assembly, nearly three orders of magnitude better in affinity than the corresponding glycan alone (41 µM).

CAS9 is a genome mutator by directly disrupting DNA-PK dependent DNA repair pathway

With its high efficiency for site-specific genome editing and easy manipulation, the clustered regularly interspaced short palindromic repeats (CRISPR)/ CRISPR associated protein 9 (CAS9) system has become the most widely used gene editing technology in biomedical research. In addition, significant progress has been made for the clinical development of CRISPR/CAS9 based gene therapies of human diseases, several of which are entering clinical trials. Here we report that CAS9 protein can function as a genome mutator independent of any exogenous guide RNA (gRNA) in human cells, promoting genomic DNA double-stranded break (DSB) damage and genomic instability. CAS9 interacts with the KU86 subunit of the DNA-dependent protein kinase (DNA-PK) complex and disrupts the interaction between KU86 and its kinase subunit, leading to defective DNA-PK-dependent repair of DNA DSB damage via non-homologous end-joining (NHEJ) pathway. XCAS9 is a CAS9 variant with potentially higher fidelity and broader compatibility, and dCAS9 is a CAS9 variant without nuclease activity. We show that XCAS9 and dCAS9 also interact with KU86 and disrupt DNA DSB repair. Considering the critical roles of DNA-PK in maintaining genomic stability and the pleiotropic impact of DNA DSB damage responses on cellular proliferation and survival, our findings caution the interpretation of data involving CRISPR/CAS9-based gene editing and raise serious safety concerns of CRISPR/CAS9 system in clinical application.