Modern Biomedical Research Research Articles

Better Safe than Sorry - Implementing Reliable Health Data Anonymization.

Modern biomedical research is increasingly data-driven. To create the required big datasets, health data needs to be shared or reused, which often leads to privacy challenges. Data anonymization is an important protection method where data is transformed such that privacy guarantees can be provided according to formal models. For applications in practice, anonymization methods need to be integrated into scalable and reliable tools. In this work, we tackle the problem of achieving reliability. Privacy models often involve mathematical definitions using real numbers which are typically approximated using floating-point numbers when implemented as software. We study the effect on the privacy guarantees provided and present a reliable computing framework based on fractional and interval arithmetic for improving the reliability of implementations. Extensive evaluations demonstrate that reliable data anonymization is practical and that it can be achieved with minor impacts on executions times and data utility.

Near-Infrared Multipurpose Lanthanide-Imaging Nanoprobes.

Optical imaging plays a growing role in modern biomedical research and clinical applications due to its high sensitivity, superb spatiotemporal resolution and minimal hazards. Lanthanide-doped nanoparticles (LDNPs), as a classical category of luminescent materials, exhibit promising photostability, near-infrared (NIR)-excited frequency up-/down-converting capabilities, emission fine-tuning and multispectral features, which have greatly promoted the endeavors of deeper and clearer diagnostics in complex living conditions. This review focuses on the recent advances of LDNP-based multipurpose imaging studies using upconversion, downshifting, lifetime, photoacoustic and multimodal nanoprobes in the NIR (650-1000 nm) and the second near-infrared window (NIR-II, 1000-1700 nm). The principle and design of various functional, activatable, multiplexing or multimodal lanthanide-imaging nanoprobes (LINPs) as well as representative biophotonic applications are summarized in detail. In addition, the future perspectives and challenges for facilitating LINPs to clinical translations are discussed.

Machine Learning in Cardiology-Ensuring Clinical Impact Lives Up to the Hype.

Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning's ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML's growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.

Reclaiming Warburg: using developmental biology to gain insight into human metabolic diseases.

Developmental biologists have frequently pushed the frontiers of modern biomedical research. From the discovery and characterization of novel signal transduction pathways to exploring the molecular underpinnings of genetic inheritance, transcription, the cell cycle, cell death and stem cell biology, studies of metazoan development have historically opened new fields of study and consistently revealed previously unforeseen avenues of clinical therapies. From this perspective, it is not surprising that our community is now an integral part of the current renaissance in metabolic research. Amidst the global rise in metabolic syndrome, the discovery of novel signaling roles for metabolites, and the increasing links between altered metabolism and many human diseases, we as developmental biologists can contribute skills and expertise that are uniquely suited for investigating the mechanisms underpinning human metabolic health and disease. Here, we summarize the opportunities and challenges that our community faces, and discuss how developmental biologists can make unique and valuable contributions to the field of metabolism and physiology.

Generation of Transgenic Rats using a Lentiviral Vector Approach.

Transgenic animal models are fundamentally important for modern biomedical research. The incorporation of foreign genes into early mouse or rat embryos is an invaluable tool for gene function analysis in living organisms. The standard transgenesis method is based on microinjecting foreign DNA fragments into a pronucleus of a fertilized oocyte. This technique is widely used in mice but remains relatively inefficient and technically demanding in other animal species. The transgene can also be introduced into one-cell-stage embryos via lentiviral infection, providing an effective alternative to standard pronuclear injections, especially in species or strains with a more challenging embryo structure. In this approach, a suspension that contains lentiviral vectors is injected into the perivitelline space of a fertilized rat embryo, which is technically less demanding and has a higher success rate. Lentiviral vectors were shown to efficiently incorporate the transgene into the genome to determine the generation of stable transgenic lines. Despite some limitations (e.g., Biosafety Level 2 requirements, DNA fragment size limits), lentiviral transgenesis is a rapid and efficient transgenesis method. Additionally, using female rats that are mated with a fertile male strain with a different dominant fur color is presented as an alternative to generate pseudopregnant foster mothers.

Two Birds with One Stone: NFAT1-MDM2 Dual Inhibitors for Cancer Therapy.

The tumor suppressor p53 is believed to be the mostly studied molecule in modern biomedical research. Although p53 interacts with hundreds of molecules to exert its biological functions, there are only a few modulators regulating its expression and function, with murine double minute 2 (MDM2) playing a key role in this regard. MDM2 also contributes to malignant transformation and cancer development through p53-dependent and -independent mechanisms. There is an increasing interest in developing MDM2 inhibitors for cancer prevention and therapy. We recently demonstrated that the nuclear factor of activated T cells 1 (NFAT1) activates MDM2 expression. NFAT1 regulates several cellular functions in cancer cells, such as cell proliferation, migration, invasion, angiogenesis, and drug resistance. Both NFAT isoforms and MDM2 are activated and overexpressed in several cancer subtypes. In addition, a positive correlation exists between NFAT1 and MDM2 in tumor tissues. Our recent clinical study has demonstrated that high expression levels of NFAT1 and MDM2 are independent predictors of a poor prognosis in patients with hepatocellular carcinoma. Thus, inhibition of the NFAT1-MDM2 pathway appears to be a novel potential therapeutic strategy for cancer. In this review, we summarize the potential oncogenic roles of MDM2 and NFAT1 in cancer cells and discuss the efforts of discovery and the development of several newly identified MDM2 and NFAT1 inhibitors, focusing on their potent in vitro and in vivo anticancer activities. This review also highlights strategies and future directions, including the need to focus on the development of more specific and effective NFAT1-MDM2 dual inhibitors for cancer therapy.

Ultrasensitive 3D Printed Immunoarrays for Protein Detection Down to Single Cell Levels

Accurate, sensitive measurements of proteins are crucial for modern biomedical research, with applications in basic research, medical diagnostics, therapy guidance, and drug development. Fast, selective, low cost detection of multiple proteins is important for public health and a key component of future personalized medicine. There are >50,000 gene product proteins (omitting post-translational modifications and IgGs) in human plasma, and large subsets in serum, urine, and saliva. However, techniques such as LC-MS/MS, immunoassays and protein counting can reliably detect <2000 of these proteins and are quite expensive. This paper addresses the challenge to develop low cost, automated 3D-printed immunoarrays for liquid protein samples and cells with LODs <0.1 fg/mL. We have developed prototype multiplexed microfluidic immunoarrays with sensitivity better than protein counting applicable to plasma, serum, saliva, urine, cell lysates and cell cultures. Protein detection at the single cell level was also achieved.Unique detection features including nanostructured sensing, array design, and protocol optimization provide large decreases in LODs and greatly enhanced sensitivities. Our prototypes are based on chemiluminescence (CL) and amperometric detection. Our CL array achieved LODs of 0.1 fg/mL for membrane protein DSG3, as well as VEGF-C and VEGF-D in conditioned media of cell cultures. This LOD is 0.8 zeptomolar, or ~100 molecules of DSG3 in 90 mL and results from 30 trillion capture antibodies (Ab1) in array detection cylinders using an ultrabright femto-luminol CL reagent activated by H2O2 and poly-(horseradish peroxidase) (poly-HRP, 400 HRPs/molecule). Femto-luminol combined with poly-HRP provides very high CL signal amplification and S/N using the 400 HPR polymer.Proteins are also efficiently captured in detection cylinders with 30 trillion antibodies, combined with the ultrabright CL reagent and rigorous protocol optimization to prove unprecedented LODs. Microprocessor automation can achieve fast multiplexed assays with these immunoarrays at <$0.50/protein. We are also exploring ultra-high sensitivity amperometric detection in these systems with similar sensitivity enhancement principles.

The Efficiency of Immunoprecipitation of microRNA/Ago2 Complexes from Human Blood Plasma Is Protocol Dependent

The study of extracellular miRNA is one of the most dynamic areas of modern biomedical research. Oftentimes, there is a need to isolate miRNAs associated with a particular carrier, for example, a ribonucleoprotein complex. The most thoroughly investigated protein component of these complexes is Ago2. Complexes are commonly isolated by immunoprecipitation with specific antibodies. Here we compare three methods for immunoprecipitating Ago2/microRNA complexes from blood plasma. In the first protocol, anti-Ago2-antibodies are added to the plasma following protein A-sepharose. In second protocol, anti-Ago2-antibodies are bound to sepharose from the very beginning, and then mixed with plasma. The third protocol differs from the second in that sepharose is blocked by non-specific antibodies at the final stage. To compare the efficiency of these protocols, the levels of miR-16-5p, miR-21-5p, and miR-144-3p were analyzed after precipitation with anti-Ago2 antibodies and control antibodies. For miR-16-5p all protocols were efficient, for miR-21-5p only the second technique yielded results, while for miR-144-3p none of the protocols resulted in extraction. Thus, we conclude that different protocols for immunoprecipitation of microRNA/Ago2 complexes favor different miRNAs.

Expeditious and eco-friendly synthesis of new multifunctionalized pyrrole derivatives and evaluation of their antioxidant property.

Diminution of oxidative stress-mediated diseases is an essential pharmaceutical objective in modern biomedical research. The present work stresses upon the efficient and eco-friendly synthesis of an array of novel diversely functionalized pyrrole derivatives which are found to be antioxidants with reactive oxygen species (ROS) shielding competency against the deleterious consequence of oxidative stress. The results of the investigation displayed the effect of structural modification of the pyrrole derivatives on their respective antioxidant properties to various ROS. Noteworthy, the pyrrole moiety bearing 4-hydroxycoumarin or 2-hydroxy-1,4-naphthoquinone as substituent showed outstanding defensive potency towards OH and O2- while, nitrogen atom linked with aliphatic side-chain in the pyrrole scaffold made a strong affirmative impression in DPPH scavenging assay. More interestingly, an influencing reducing power was observed in pyrrole derivatives carrying cyclohexane 1,3-dione as one of the substituents. To have a comprehensive acuteness into the antioxidant capacity of the synthesized pyrrole derivatives against Trolox as a standard antioxidant, a crucial approach was taken into account by calculating TEAC (Trolox Equivalent Antioxidant Capacity) in case of OH and DPPH scavenging activity.

Approach to The Selection of Significant Features in Solving Biomedical Problems of Binary Classification of Microarray Data

In modern biomedical research aimed at finding methods for early diagnosis of cancer, microarrays containing certain biological information about patients are used. Based on these data, patients are assigned to one of two classes, corresponding to the presence and absence of some diagnosis. When solving this problem, one of the steps that have a decisive influence on the quality of classification is the significant features selection. This paper proposes a criterion for the selection of significant features, based on the ledge-coefficient of correlation. The ledge-coefficient was previously used to estimate the degree of interrelation of numerical and binary features. For two sets of microarray data, comparative examples of their binary classification are presented using three feature selection algorithms, three dimensionality reduction methods, six classification models. The use of the ledge-criterion for feature selection made it possible to obtain a classification quality comparable to the results of using common methods of feature selection, such as t -test and U -test. For the data set of the peptide microarrays considered in the paper, the effectiveness of applying the projection method to latent structures had previously been identified. The use of this method in combination with the significant features’ selection using the ledge-criterion made it possible to obtain a higher classification quality measure.

TopoDB: a novel multifunctional management system for laboratory animal colonies.

Animal models are widely employed in basic research to test mechanistic hypotheses in a complex biological environment as well as to evaluate the therapeutic potential of candidate compounds in preclinical settings. Rodents, and in particular mice, represent the most common in vivo models for their small size, short lifespan and possibility to manipulate their genome. Over time, a typical laboratory will develop a substantial number of inbred strains and transgenic mouse lines, requiring a substantial effort, in both logistic and economic terms, to maintain an animal colony for research purposes and to safeguard the integrity of results. To meet this need, here we present TopoDB, a robust and extensible web-based platform for the rational management of laboratory animals. TopoDB allows an easy tracking of individual animals within the colony and breeding protocols as well as the convenient storage of both genetic and phenotypic data generated in the different experiments. Altogether, these features facilitate and enhance the design of in vivo research, thus reducing the number of necessary animals and the housing costs. In summary, TopoDB represents a novel valuable tool in modern biomedical research. Database URL: https://github.com/UCSF-MS-DCC/TopoDB

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.

AIE-active fluorescent polymeric nanoparticles about dextran derivative: preparation and bioimaging application

Aggregation-induced emission (AIE), as a special phenomenon of fluorescence, can elegantly overcome the fluorescence quenching caused by common fluorescent materials under high concentration conditions and has attracted interest of researchers in many fields. Particularly AIE-active polymer nanoparticles have been widely utilized in a modern biomedical research. In this work, we prepared a novel kind of AIE-active fluorescent polymeric nanoparticals (Dex-OH-CHO) through a facile esterification between a new hydrophobic AIE-active 1, 8-naphthalimide derivative and the hydrophilic dextran. The structure and optical properties of Dex-OH-CHO were characterized in detail by FTIR, 1H NMR, XPS, TEM and fluorescence spectra. The results showed that Dex-OH-CHO emitted light-blue fluorescence in aqueous solution with high fluorescent quantum yield (Φ = 24.43%, concentration is 20 μg/mL), low CMC (5 μg/mL), good photostability, high water solubility and well dispersivity. Moreover, good biocompatibility and ideal cell uptake made Dex-OH-CHO had a great application potential in biological imaging.

Residual Diploidy in Polyploid Tissues: A Cellular State with Enhanced Proliferative Capacity for Tissue Regeneration?

A major objective of modern biomedical research aims to promote tissue self-regeneration after injury, obviating the need for whole organ transplantation and avoiding mortality due to organ failure. Identifying the population of cells capable of regeneration, alongside understanding the molecular mechanisms that activate that population to re-enter the cell cycle, are two important steps to advancing the field of endogenous tissue regeneration toward the clinic. In recent years, an emerging trend has been observed, whereby polyploidy of relevant parenchymal cells, arising from alternative cell cycles as part of a normal developmental process, is linked to restricted proliferative capacity of those cells. An accompanying hypothesis, therefore, is that a residual subpopulation of diploid parenchymal cells retains proliferative competence and is the major driver for any detected postnatal cell turnover. In this perspective review, we examine the emerging literature on residual diploid parenchymal cells and the possible link of this population to endogenous tissue regeneration, in the context of both heart and liver. We speculate on additional cell types that may play a similar role in their respective tissues and discuss outstanding questions for the field.

Deep learning for drug-drug interaction extraction from the literature: a review.

Drug-drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been widely studied and emphasized in modern biomedical research. The previous rules-based and machine learning approaches rely on tedious feature engineering, which is labourious, time-consuming and unsatisfactory. With the development of deep learning technologies, this problem is alleviated by learning feature representations automatically. Here, we review the recent deep learning methods that have been applied to the extraction of DDIs from biomedical literature. We describe each method briefly and compare its performance in the DDI corpus systematically. Next, we summarize the advantages and disadvantages of these deep learning models for this task. Furthermore, we discuss some challenges and future perspectives of DDI extraction via deep learning methods. This review aims to serve as a useful guide for interested researchers to further advance bioinformatics algorithms for DDIs extraction from the literature.

Streamlining drug discovery assays for cardiovascular disease using zebrafish

ABSTRACTIntroduction: In the last decade, our armamentarium of cardiovascular drug therapy has expanded significantly. Using innovative functional genomics strategies such as genome editing by CRISPR/Cas9 as well as high-throughput assays to identify bioactive small chemical compounds has significantly facilitated elaboration of the underlying pathomechanism in various cardiovascular diseases. However, despite scientific progress approvals for cardiovascular drugs has stagnated significantly compared to other fields of drug discovery and therapy during the past years.Areas covered: In this review, the authors discuss the aspects and pitfalls during the early phase of cardiovascular drug discovery and describe the advantages of zebrafish as an in vivo organism to model human cardiovascular diseases (CVD) as well as an in vivo platform for high-throughput chemical compound screening. They also highlight the emerging, promising techniques of automated read-out systems during high-throughput screening (HTS) for the evaluation of important cardiac functional parameters in zebrafish with the potential to streamline CVD drug discovery.Expert opinion: The successful identification of novel drugs to treat CVD is a major challenge in modern biomedical and clinical research. In this context, the definition of the etiologic fundamentals of human cardiovascular diseases is the prerequisite for an efficient and straightforward drug discovery.

Screening and Transcriptional Analysis of Polyketide Synthases and Non-ribosomal Peptide Synthetases in Bacterial Strains From Krubera-Voronja Cave.

Identification of novel bioactive compounds represents an important field in modern biomedical research. Microorganisms of the underexplored environments, such as deserts, hot springs, oceans, and caves are highly promising candidates for screening such metabolites. Screening for biosynthetic genes is the most effective strategy to characterize bioactivity in a certain environment. However, knowledge is either scant or non-existent about the expression of the biosynthetic genes encoding for various bioactive compounds in the microorganisms from the caves. The aim of the current study was to screen for the genes of polyketide synthases and non-ribosomal peptide synthetases in Krubera–Voronja Cave (43.4184 N 40.3083 E, Western Caucasus) bacterial isolates as well as to evaluate the expression of these genes under laboratory conditions. In total, 91 bacterial strains isolated from the cave were screened for the presence of polyketide synthase and non-ribosomal peptide synthetase genes. Phenotypically inactive strains were the main focus (the test group) of our study, while the strains with the identified antibacterial activity served as the control group. Our PCR-based screening clearly showed that the majority of the strains harbored at least one biosynthetic gene. Prediction of the putative products allowed us to identify bioactive compounds with antibacterial, anticancer, antifungal, anti-inflammatory, antimycoplasmic, antiviral, insecticidal, and thrombolytic activity. For most polyketide synthases and non-ribosomal peptide synthetases, putative products could not be predicted; they are unknown. Qualitative transcriptional analysis did not show substantial differences between the test group and the control group of the strains. One to four biosynthetic genes were constitutively expressed in all the tested strains, irrespective of the group. Quantitative transcriptional analysis of the constitutively expressed biosynthetic genes demonstrated that the expression of a particular gene could be affected by both the amount of the nutrients in the culture medium and the growth phase.

Pseudonymization for research data collection: is the juice worth the squeeze?

BackgroundThe collection of data and biospecimens which characterize patients and probands in-depth is a core element of modern biomedical research. Relevant data must be considered highly sensitive and it needs to be protected from unauthorized use and re-identification. In this context, laws, regulations, guidelines and best-practices often recommend or mandate pseudonymization, which means that directly identifying data of subjects (e.g. names and addresses) is stored separately from data which is primarily needed for scientific analyses.DiscussionWhen (authorized) re-identification of subjects is not an exceptional but a common procedure, e.g. due to longitudinal data collection, implementing pseudonymization can significantly increase the complexity of software solutions. For example, data stored in distributed databases, need to be dynamically combined with each other, which requires additional interfaces for communicating between the various subsystems. This increased complexity may lead to new attack vectors for intruders. Obviously, this is in contrast to the objective of improving data protection. What is lacking is a standardized process of evaluating and reporting risks, threats and countermeasures, which can be used to test whether integrating pseudonymization methods into data collection systems actually improves upon the degree of protection provided by system designs that simply follow common IT security best practices and implement fine-grained role-based access control models. To demonstrate that the methods used to describe systems employing pseudonymized data management are currently heterogeneous and ad-hoc, we examined the extent to which twelve recent studies address each of the six basic security properties defined by the International Organization for Standardization (ISO) standard 27,000. We show inconsistencies across the studies, with most of them failing to mention one or more security properties.ConclusionWe discuss the degree of privacy protection provided by implementing pseudonymization into research data collection processes. We conclude that (1) more research is needed on the interplay of pseudonymity, information security and data protection, (2) problem-specific guidelines for evaluating and reporting risks, threats and countermeasures should be developed and that (3) future work on pseudonymized research data collection should include the results of such structured and integrated analyses.

Oxygen and mammalian cell culture: are we repeating the experiment of Dr. Ox?

Mammalian cell culture represents a cornerstone of modern biomedical research. There is growing appreciation that the media conditions in which cells are cultured can profoundly influence the observed biology and reproducibility. Here, we consider a key but often ignored variable, oxygen, and review why being mindful of this environmental parameter is so important in the design and interpretation of cell culture studies.

Molecular recognition based rapid diagnosis of immunoglobulins via proteomic profiling of protein-nanoparticle complexes

Protein-nanoparticle (NP) interaction, which inevitably form protein corona (PC), has been the subject of much debate about its role in modern biomedical research. In this regard, PC associated with two different NPs viz., magneto-fluorescent (MF) and chitosan coated MF (CMF) NPs were thoroughly investigated, to analyze the effect of polymer coating on protein adsorption. Bradford assay, along with the spectroscopic and microscopic studies suggested increase in adsorbed protein quantity, though the results varied significantly on moving from bare to polymeric coating and in vitro to ex vivo conditions. Interestingly, polymer coated NPs showed increased protein adsorption and induce minimal changes in protein structural integrity under the same conditions. We predict that the changes in secondary structure of primary corona determine the overall signature of surface binding proteins in PC. Our findings suggested that rapid diagnosis of immunoglobulins is possible using the concept of protein corona formation ex vivo.