Advancements in hydrogel design for articular cartilage regeneration: A comprehensive review.

This review paper explores the cutting-edge advancements in hydrogel design for articular cartilage regeneration (CR). Articular cartilage (AC) defects are a common occurrence worldwide that can lead to joint breakdown at a later stage of the disease, necessitating immediate intervention to prevent progressive degeneration of cartilage. Decades of research into the biomedical applications of hydrogels have revealed their tremendous potential, particularly in soft tissue engineering, including CR. Hydrogels are highly tunable and can be designed to meet the key criteria needed for a template in CR. This paper aims to identify those criteria, including the hydrogel components, mechanical properties, biodegradability, structural design, and integration capability with the adjacent native tissue and delves into the benefits that CR can obtain through appropriate design. Stratified-structural hydrogels that emulate the native cartilage structure, as well as the impact of environmental stimuli on the regeneration outcome, have also been discussed. By examining recent advances and emerging techniques, this paper offers valuable insights into developing effective hydrogel-based therapies for AC repair.

Sequence Design for RNA-RNA Interactions.

The design of RNA sequences with desired structural properties presents a challenging computational problem with promising applications in biotechnology and biomedicine. Most regulatory RNAs function by forming RNA-RNA interactions, e.g., in order to regulate mRNA expression. It is therefore natural to consider problems where a sequence is designed to form a desired RNA-RNA interaction and switch between structures upon binding. This contribution demonstrates the use of the Infrared framework to design interacting sequences. Specifically, we consider the regulation of the rpoS mRNA by the sRNA DsrA and design artificial 5 ' UTRs that place a downstream protein coding gene under control of DsrA. The design process is explained step by step in a Jupyter notebook, accompanied by Python code. The text discusses setting up design constraints for sampling sequences in Infrared, computing quality measures, constructing a suitable cost function, as well as the optimization procedure. We show that not only thermodynamic but also kinetic folding features can be relevant. Kinetics of interaction formation can be estimated efficiently using the RRIkinDP tool, and the chapter explains how to include kinetic folding features from RRIkinDP directly in the cost function. The protocol implemented in our Jupyter notebook can easily be extended to consider additional requirements or adapted to novel design scenarios.

Sequence Design Using RNAstructure.

RNA is present in all domains of life. It was once thought to be solely involved in protein expression, but recent advances have revealed its crucial role in catalysis and gene regulation through noncoding RNA. With a growing interest in exploring RNAs with specific structures, there is an increasing focus on designing RNA structures for in vivo and in vitro experimentation and for therapeutics. The development of RNA secondary structure prediction methods has also spurred the growth of RNA design software. However, there are challenges to designing RNA sequences that meet secondary structure requirements. One major challenge is that the secondary structure design problem is likely NP-hard, making it computationally intensive. Another issue is that objective functions need to consider the folding ensemble of RNA molecules to avoid off target structures. In this chapter, we provide protocols for two software tools from the RNAstructure package: "Design" for structured RNA sequence design and "orega" for unstructured RNA sequence design.

Machine Learning for RNA Design: LEARNA.

Machine learning algorithms, and in particular deep learning approaches, have recently garnered attention in the field of molecular biology due to remarkable results. In this chapter, we describe machine learning approaches specifically developed for the design of RNAs, with a focus on the learna_tools Python package, a collection of automated deep reinforcement learning algorithms for secondary structure-based RNA design. We explain the basic concepts of reinforcement learning and its extension, automated reinforcement learning, and outline how these concepts can be successfully applied to the design of RNAs. The chapter is structured to guide through the usage of the different programs with explicit examples, highlighting particular applications of the individual tools.

RNA Design Using incaRNAfbinv Demonstrated with the Identification of Functional RNA Motifs in Hepatitis Delta Virus.

Computational RNA design was introduced in the 1990s by Vienna's RNAinverse, which is a simple inverse RNA folding solver. Further developments and contemporary RNA design techniques, in addition to improved efficiency, offer more precise control over the designed sequences. incaRNAfbinv (incaRNAtion with RNA fragment-based inverse) is one such extension that builds upon RNAinverse and includes coarse-graining manipulations. The idea is that an RNA secondary structure can be decomposed to fragments of RNA motifs, and that a significant number of known natural RNA motifs exhibit a remarkable preservation in particular locations in a variety of genomes. This is taken into consideration by the ability of the user to select motifs that are known to be functional for a precise design, whilst the algorithm is more adaptable on other motifs. The latest version, incaRNAfbinv 2.0, is a free-to-use web-server which deploys the above methodology of fragment-based design. Its control over the decomposed RNA secondary structure motifs includes, among other advanced features, the insertion of constraints in a flexible manner. The resultant RNA designed sequences are ranked by their proximity to classical RNA design. Features and capabilities of incaRNAfbinv 2.0 are hereby illustrated with an example taken from hepatitis delta virus (HDV). The web-server is demonstrated in assisting to locate a known RNA motif that is responsible for HDV-3 RNA editing in more HDV genotypes than thought of before. This shows that computational RNA design by using inverse RNA folding is also a valuable strategy for locating functional RNA motifs in genomic data, in addition to artificially designing synthetic RNAs.

Simulated Annealing for RNA Design with SIMARD.

Ribonucleic acid (RNA) design is the inverse of RNA folding. RNA folding aims to identify the most likely secondary structure into which a given strand of nucleotides will fold. RNA design algorithms, on the other hand, attempt to design a strand of nucleotides that will fold into a specified secondary structure. Despite the apparent NP-hard nature of RNA design, promising results can be achieved when formulated as a combinatorial optimization problem and approached with simple heuristics. The main focus of this paper is to describe an RNA design algorithm based on simulated annealing. Additionally, noteworthy features and results will be presented herein.

Datasets for Benchmarking RNA Design Algorithms.

RNA molecules play vital roles in many biological processes, such as gene regulation or protein synthesis. The adoption of a specific secondary and tertiary structure by RNA is essential to perform these diverse functions, making RNA a popular tool in bioengineering therapeutics. The field of RNA design responds to the need to develop novel RNA molecules that possess specific functional attributes. In recent years, computational tools for predicting RNA sequences with desired folding characteristics have improved and expanded. However, there is still a lack of well-defined and standardized datasets to assess these programs. Here, we present a large dataset of internal and multibranched loops extracted from PDB-deposited RNA structures that encompass a wide spectrum of design difficulties. Furthermore, we conducted benchmarking tests of widely utilized open-source RNA design algorithms employing this dataset.

The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.

Nucleic acid tests (NATs) are considered as gold standard in molecular diagnosis. To meet the demand for onsite, point-of-care, specific and sensitive, trace and genotype detection of pathogens and pathogenic variants, various types of NATs have been developed since the discovery of PCR. As alternatives to traditional NATs (e.g., PCR), isothermal nucleic acid amplification techniques (INAATs) such as LAMP, RPA, SDA, HDR, NASBA, and HCA were invented gradually. PCR and most of these techniques highly depend on efficient and optimal primer and probe design to deliver accurate and specific results. This chapter starts with a discussion of traditional NATs and INAATs in concert with the description of computational tools available to aid the process of primer/probe design for NATs and INAATs. Besides briefly covering nanoparticles-assisted NATs, a more comprehensive presentation is given on the role CRISPR-based technologies have played in molecular diagnosis. Here we provide examples of a few groundbreaking CRISPR assays that have been developed to counter epidemics and pandemics and outline CRISPR biology, highlighting the role of CRISPR guide RNA and its design in any successful CRISPR-based application. In this respect, we tabularize computational tools that are available to aid the design of guide RNAs in CRISPR-based applications. In the second part of our chapter, we discuss machine learning (ML)- and deep learning (DL)-based computational approaches that facilitate the design of efficient primer and probe for NATs/INAATs and guide RNAs for CRISPR-based applications. Given the role of microRNA (miRNAs) as potential future biomarkers of disease diagnosis, we have also discussed ML/DL-based computational approaches for miRNA-target predictions. Our chapter presents the evolution of nucleic acid-based diagnosis techniques from PCR and INAATs to more advanced CRISPR/Cas-based methodologies in concert with the evolution of deep learning (DL)- and machine learning (ml)-based computational tools in the most relevant application domains.

Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation.

The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This report focuses on the application of computational molecular filters, applied either pre- or post-screening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns.

MolPredictX: A Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models.

MolPredictX is a free-access web tool in which it is possible to analyze the prediction of biological activity of chemical molecules. MolPredictX has been available online to the general public for just over a year and has now gone through its first update. We also developed its version for android, being the first free app capable of predicting biological activities. MolPredictX is available for free at https://www.molpredictX.ufpb.br/ , and its mobile application version can be obtained from Google Play.

PFAS-Biomolecule Interactions: Case Study Using Asclepios Nodes and Automated Workflows in KNIME for Drug Discovery and Toxicology.

The Asclepios suite of KNIME nodes represents an innovative solution for conducting cheminformatics and computational chemistry tasks, specifically tailored for applications in drug discovery and computational toxicology. This suite has been developed using open-source and publicly accessible software. In this chapter, we introduce and explore the Asclepios suite through the lens of a case study. This case study revolves around investigating the interactions between per- and polyfluorinated alkyl substances (PFAS) and biomolecules, such as nuclear receptors. The objective is to characterize the potential toxicity of PFAS and gain insights into their chemical mode of action at the molecular level. The Asclepios KNIME nodes have been designed as versatile tools capable of addressing a wide range of computational toxicology challenges. Furthermore, they can be adapted and customized to accomodate the specific needs of individual users, spanning various domains such as nanoinformatics, biomedical research, and other related applications. This chapter provides an in-depth examination of the technical underpinnings and foundations of these tools. It is accompanied by a practical case study that demonstrates the utilization of Asclepios nodes in a computational toxicology investigation. This showcases the extendable functionalities that can be applied in diverse computational chemistry contexts. By the end of this chapter, we aim for readers to have a comprehensive understanding of the effectiveness of the Asclepios node functions. These functions hold significant potential for enhancing a wide spectrum of cheminformatics applications.

Toward Increasing the Credibility of RNA Design.

In the problem of RNA design, also known as inverse folding, RNA sequences are predicted that achieve the desired secondary structure at the lowest possible free energy and under certain constraints. The designed sequences have applications in synthetic biology and RNA-based nanotechnologies. There are also known cases of the successful use of inverse folding to discover previously unknown noncoding RNAs. Several computational methods have been dedicated to the problem of RNA design. They differ by algorithm and additional parameters, e.g., those determining the goal function in the sequence optimization process. Users can obtain many promising RNA sequences quite easily. The more difficult issue is to critically evaluate them and select the most favorable and reliable sequence that form1s the expected RNA structure. The latter problem is addressed in this paper. We propose an RNA design protocol extended to include sequence evaluation, for which a 3D structure is used. Experiments show that the accuracy of RNA design can be improved by adding a 3D structure prediction and analysis step.

Epidermal secretion-purified biosensing patch with hydrogel sebum filtering membrane and unidirectional flow microfluidic channels.

The development of biosensing electronics for real-time sweat analysis has attracted increasing research interest due to their promising applications for non-invasive health monitoring. However, one of the critical challenges lies in the sebum interference that largely limits the sensing reliability in practical scenarios. Herein, we report a flexible epidermal secretion-purified biosensing patch with a hydrogel filtering membrane that can effectively eliminate the impact of sebum and sebum-soluble substances. The as-prepared sebum filtering membranes feature a dual-layer sebum-resistant structure based on the poly(hydroxyethyl methacrylate) hydrogel functionalized with nano-brush structured poly(sulfobetaine) to eliminate interferences and provide self-cleaning capability. Furthermore, the unidirectional flow microfluidic channels design based on the Tesla valve was incorporated into the biosensing patch to prevent external sebum contamination and allow effective sweat refreshing for reliable sensing. By seamlessly combining these components, the epidermal secretion-purified biosensing patch enables continuous monitoring of sweat uric acid, pH, and sodium ions with significantly improved accuracy of up to 12 %. The proposed strategy for enhanced sweat sensing reliability without sebum interference shows desirable compatibility for different types of biosensors and would inspire the advances of flexible and wearable devices for non-invasive healthcare.

The influence of color vision deficiency on vessel visibility during colorectal endoscopic submucosal dissection and the potential advantage of red dichromatic imaging to achieve color vision barrier-free.

Objectives: Although color information is important in gastrointestinal endoscopy, there are limited studies on how endoscopic images are viewed by people with color vision deficiency. We aimed to investigate the differences in the visibility of blood vessels during endoscopic submucosal dissection (ESD) among people with different color vision characteristics and to examine the effect of red dichromatic imaging (RDI) on blood vessel visibility. Methods: Seventy-seven pairs of endoscopic images of white light imaging (WLI) and RDI of the same site were obtained during colorectal ESD. The original images were set as type C (WLI-C and RDI-C), a common color vision. These images were computationally converted to simulate images perceived by people with color vision deficiency protanope (Type P) or deutanope (Type D) and denoted as WLI-P and RDI-P or WLI-D and RDI-D. Blood vessels and background submucosa that needed to be identified during ESD were selected in each image, and the color differences between these two objects were measured using the color difference (ΔE 00) to assess the visibility of blood vessels. Results: ΔE 00 between a blood vessel and the submucosa was greater under RDI (RDI-C/P/D: 24.05 ± 0.64/22.85 ± 0.66/22.61 ± 0.64) than under WLI (WLI-C/P/D: 22.26 ± 0.60/5.19 ± 0.30/8.62 ± 0.42), regardless of color vision characteristics. This improvement was more pronounced in Type P and Type D and approached Type C in RDI. Conclusions: Color vision characteristics affect the visibility of blood vessels during ESD, and RDI improves blood vessel visibility regardless of color vision characteristics.

Highly stable silicon oxycarbide all-solid-state batteries enabled by machined learning accelerated screening of oxides and sulfides electrolytes.

Traditional trial-error approach severely limits and restricts rapid development of high-performance anode and electrolytes materials, searching huge parameters space of various anode-solid electrolyte interfaces in an effective and efficient way is the key issue. Here, a novel computational strategy combining machine learning and first-principles is proposed to achieve efficient high-throughput screening of oxides and sulfides electrolytes for highly stable silicon oxycarbide all-solid-state batteries. First-principles calculations demonstrate significant compact of material type and elemental doping on interfacial compatibility between silicon oxycarbide and various electrolytes. By proposing several novel descriptors including interfacial adhesion and formation energies of frozen system with low computation cost, the amounts of demanded trainings data are significantly reduced. Gradient-boosted regression tree model shows low mean absolute errors of 0.09 and high R2 value of 0.99 for the prediction of interface formation energy, demonstrating ultrahigh accuracy and reliability of the algorithm. The present work discovers a series of uninvestigated stable anode-solid electrolytes interfacial couples for further experimental preparation.

Selection of Fusion-Site Overhang Sets for High-Fidelity and High-Complexity Golden Gate Assembly.

Golden Gate Assembly depends on the accurate ligation of overhangs at fragment fusion sites to generate full-length products with all parts in the desired order. Traditionally, fusion-site sequences are selected by using validated sets of overhang sequences or by applying a handful of semi-empirical rules to guide overhang choice. While these approaches allow dependable assembly of 6-8 fragments in one pot, recent work has demonstrated that comprehensive measurement of ligase fidelity allows prediction of high-fidelity junction sets that enable much more complex assemblies of 12, 24, or even 36+ fragments in a single reaction that will join with high accuracy and efficiency. In this chapter, we outline the application of a set of online tools that apply these comprehensive datasets to the analysis of existing junction sets, the de novo selection of new high-fidelity overhang sets, the modification and expansion of existing sets, and the principles for dividing known sequences at an arbitrary number of high-fidelity breakpoints.

Modular DNA Construct Design for High-Throughput Golden Gate Assembly.

Golden Gate cloning enables the modular assembly of DNA parts into desired synthetic genetic constructs. The "one-pot" nature of Golden Gate reactions makes them particularly amenable to high-throughput automation, facilitating the generation of thousands of constructs in a massively parallel manner. One potential bottleneck in this process is the design of these constructs. There are multiple parameters that must be considered during the design of an assembly process, and the final design should also be checked and verified before implementation. Doing this by hand for large numbers of constructs is neither practical nor feasible and increases the likelihood of introducing potentially costly errors. In this chapter we describe a design workflow that utilizes bespoke computational tools to automate the key phases of the construct design process and perform sequence editing in batches.

Using ApE for In Silico Golden Gate Cloning.

Golden Gate cloning allows rapid and reliable assembly of multiple DNA fragments in a defined orientation. Golden Gate cloning requires careful design of the restriction fragment overhangs to minimize undesired products and to generate the desired junctions. The ApE (A plasmid Editor) software package can assist in silico design of input fragments or to generate expected assembly products.

Genetic Mouse Models of Lymphomas.

Mouse models are an indispensable tool in lymphoma research. Here, we focus on the utilization of genetically engineered mouse models as preclinical avatars in lymphoma research. We describe lymphoma-relevant alleles and allele combinations, as well as general considerations for model selection. We further illustrate concepts of gene targeting and model design and provide guidelines for breeding strategies and colony maintenance.

Transitioning the production of lipidic mesophase-based delivery systems from lab-scale to robust industrial manufacturing following a risk-based quality by design approach augmented by artificial intelligence.

Lipidic mesophase drug carriers have demonstrated the capacity to host and effectively deliver a wide range of active pharmaceutical ingredients, yet they have not been as extensively commercialized as other lipid-based products, such as liposomal delivery systems. Indeed, scientists are primarily focused on investigating the physics of these systems, especially in biological environments. Meanwhile, the production methods remain less advanced, and researchers are still uncertain about how the manufacturing process might affect the quality of formulations. Bringing these products to the market will require an industrial translation process. In this scenario, we have developed a robust strategy to produce lipidic mesophase-based drug delivery systems using a dual-syringe setup. We identified four critical process parameters in the newly developed method (dual-syringe method), in comparison to eight in the standard production method (gold standard), and we defined their optimal limits following a Quality by Design approach. The robustness and versatility of the proposed method were assessed experimentally by incorporating drugs with diverse physicochemical properties and augmented by machine learning which, by predicting the drug release from lipidic mesophases, reduces the formulation development time and costs.