Synthetic Versions Research Articles

519. Antibodies Produced by Children with Kawasaki Disease (KD) Consistently Recognize Antigen in KD Tissues from the US and Japan

Abstract Background We reported that synthetic versions of IgA antibodies present in inflamed arterial tissue from a patient with fatal KD (JID 2004;190:856-65) and from circulating IgA/IgG plasmablasts from 9/11 children with acute KD (JID 2020;222:158-68) identified antigen within intracytoplasmic inclusion bodies (ICI) in ciliated bronchial epithelial cells and macrophages in inflamed tissues of fatal KD cases. We performed additional studies to extend these findings. Figure 1. KD antigen in bronchus of a Japanese child with KD. Antigen (brown) detected by IHC using MAb KD7-1D3 resides in intracytoplasmic inclusion bodies (arrows) in bronchial epithelial cells. Methods Monoclonal antibodies (MAbs) were produced from circulating plasmablasts from children with acute KD by cloning their immunoglobulin sequences into rabbit heavy chain and human light chain expression vectors and transfecting 293F cells. Immunohistochemistry (IHC) was performed on formalin-fixed, paraffin-embedded (FFPE) tissues from acute KD fatalities from the US and Japan.Figure 2.KD antigen in bronchus of a US child with KD.Antigen (brown) detected by IHC using MAb KD7-1D3 resides in intracytoplasmic inclusion bodies (arrows) in bronchial epithelial cells. Results To date, we have produced ∼100 MAbs from circulating plasmablasts from 12 patients with acute KD. We tested selected MAbs for reactivity with the KD peptide we reported in JID 2023; 228:412-21 (78 tested, with 34 (46%) positive) and/or tested them by IHC on KD tissues (62 tested, with 37 (60%) positive). At least one MAb from each of the 12 patients was positive by IHC, reacted with KD peptide, or both. IHC revealed antigen(s) in ICI in bronchial epithelium in all 10 US and 10 Japanese KD cases who died within one month after fever onset. Antigen was absent in infant controls who died of RSV, influenza, or congenital heart disease.Figure 3.Bronchus from a child with RSV infection showing absence of staining.IHC using MAb KD7-1D3 shows negative results. Conclusion The consistent detection of KD antigen(s) in fatal KD cases from the US and Japan targeted by MAbs from children with acute KD strongly supports the importance of the antigen(s) in KD pathogenesis. The presence of antigen(s) in bronchial epithelium strongly suggests that they derive from a single novel respiratory virus common to KD patients that can travel in macrophages to damage the coronary arteries. Because fatal cases are rare and FFPE is the tissue type available, the exact identification of the antigen(s) has been very difficult, although a peptide epitope/mimotope has been identified. Identifying the antigens targeted by KD MAbs currently offers the best opportunity for elucidating the etiologic agent of KD and should be a major focus of future KD research.Figure 4.Bronchus from a child with complex congenital heart disease showing absence of staining. IHC using MAb KD7-1D3 shows negative results. Disclosures All Authors: No reported disclosures

Generating synthetic identifiers to support development and evaluation of data linkage methods

We aimed to develop a framework for generating synthetic identifier datasets to support development and evaluation of data linkage methods. We evaluated whether replicating associations between attributes and identifiers improved the utility of the synthetic data for assessing linkage error. We determined the steps required to generate synthetic identifiers that replicate the properties of real-world data collection. We generated synthetic versions of a large United Kingdom cohort study (the Avon Longitudinal Study of Parents and Children), according to the quality and completeness of identifiers recorded over several waves of the cohort. We evaluated the utility of the synthetic identifier data in terms of assessing linkage quality (false matches and missed matches). Comparing data from two collection points in ALSPAC, we found within-person disagreement in identifiers (differences in recording due to both natural change and non-valid entries) in 18% of surnames and 12% of forenames. Rates of disagreement varied by maternal age and ethnic group. Synthetic data provided accurate estimates of linkage quality metrics compared with the original data (within 0.13-0.55% for missed matches and 0.00-0.04% for false matches). Incorporating associations between identifier errors and maternal age/ethnicity improved synthetic data utility. Replicating dependencies between attribute values (e.g. ethnicity), values of identifiers (e.g. name), identifier disagreements (e.g. missing values, errors or changes over time), and their patterns and distribution structure enables generation of realistic synthetic data that can be used for robust evaluation of linkage methods. Our framework provides a novel and generalisable mechanism for developing and benchmarking record linkage algorithms.

Nighttime scene understanding with label transfer scene parser

Semantic segmentation plays a crucial role in traffic scene understanding, especially in nighttime conditions. This paper tackles the task of semantic segmentation in nighttime scenes. The largest challenge of this task is the lack of annotated nighttime images to train a deep learning-based scene parser. The existing annotated datasets are abundant in daytime conditions but scarce in nighttime due to the high cost. Thus, we propose a novel Label Transfer Scene Parser (LTSP) framework for nighttime scene semantic segmentation by leveraging daytime annotation transfer. Our framework performs segmentation in the dark without training on real nighttime annotated data. In particular, we propose translating daytime images to nighttime conditions to obtain more data with annotation in an efficient way. In addition, we utilize the pseudo-labels inferred from unlabeled nighttime scenes to further train the scene parser. The novelty of our work is the ability to perform nighttime segmentation via daytime annotated labels and nighttime synthetic versions of the same set of images. The extensive experiments demonstrate the improvement and efficiency of our scene parser over the state-of-the-art methods with a similar semi-supervised approach on the benchmark of Nighttime Driving Test dataset. Notably, our proposed method utilizes only one-tenth of the amount of labeled and unlabeled data in comparison with the previous methods. Code is available at https://github.com/danhntd/Label_Transfer_Scene_Parser.git.

Purification and Identification of Novel Dipeptidyl Peptidase IV Inhibitory Peptides Derived from Bighead Carp (Hypophthalmichthys nobilis).

Dipeptidyl peptidase IV (DPP-IV) inhibitors are widely used in treating type 2 diabetes due to their ability to lower blood glucose levels. However, synthetic versions often lead to gastrointestinal side effects. This study explores DPP-IV inhibitory properties in peptides from bighead carp skin. Collagen was prepared, hydrolyzed into collagen peptides, and then fractionated for DPP-IV inhibitory activity examination. The most effective fractions were identified, and their peptide sequences were determined. Molecular docking analysis identified nine peptides with potential inhibitory activity, four of which (VYP, FVA, PPGF, PGLVG) were synthesized and tested in vitro. PPGF exhibited the highest potency with an IC50 of 4.63 nM, competitively binding to key DPP-IV sites, including ARG125, VAL711, TYR666, and TYR662. Other peptides showed varying effectiveness, with IC50 values of 398.87 nM (VYP), 402.02 nM (FVA), and 110.20 nM (PGLVG). These findings highlight bighead carp skin peptides as potent DPP-IV inhibitors with hypoglycemic potential, suggesting a novel avenue for diabetes management using natural peptides. Moreover, this research underscores the utilization of bighead carp by-products, contributing to environmental sustainability.

Well-Defined Synthetic Copolymers with Pendant Aldehydes Form Biocompatible Strain-Stiffening Hydrogels and Enable Competitive Ligand Displacement.

Dynamic hydrogels are attractive platforms for tissue engineering and regenerative medicine due to their ability to mimic key extracellular matrix (ECM) mechanical properties like strain-stiffening and stress relaxation while enabling enhanced processing characteristics like injectability, 3D printing, and self-healing. Systems based on imine-type dynamic covalent chemistry (DCvC) have become increasingly popular. However, most reported polymers comprising aldehyde groups are based on either end-group-modified synthetic or side-chain-modified natural polymers; synthetic versions of side-chain-modified polymers are noticeably absent. To facilitate access to new classes of dynamic hydrogels, we report the straightforward synthesis of a water-soluble copolymer with a tunable fraction of pendant aldehyde groups (12-64%) using controlled radical polymerization and their formation into hydrogel biomaterials with dynamic cross-links. We found the polymer synthesis to be well-controlled with the determined reactivity ratios consistent with a blocky gradient microarchitecture. Subsequently, we observed fast gelation kinetics with imine-type cross-linking. We were able to vary hydrogel stiffness from ≈2 to 20 kPa, tune the onset of strain-stiffening toward a biologically relevant regime (σc ≈ 10 Pa), and demonstrate cytocompatibility using human dermal fibroblasts. Moreover, to begin to mimic the dynamic biochemical nature of the native ECM, we highlight the potential for temporal modulation of ligands in our system to demonstrate ligand displacement along the copolymer backbone via competitive binding. The combination of highly tunable composition, stiffness, and strain-stiffening, in conjunction with spatiotemporal control of functionality, positions these cytocompatible copolymers as a powerful platform for the rational design of next-generation synthetic biomaterials.

Estimating orientation in natural scenes: A spiking neural network model of the insect central complex.

The central complex of insects contains cells, organised as a ring attractor, that encode head direction. The 'bump' of activity in the ring can be updated by idiothetic cues and external sensory information. Plasticity at the synapses between these cells and the ring neurons, that are responsible for bringing sensory information into the central complex, has been proposed to form a mapping between visual cues and the heading estimate which allows for more accurate tracking of the current heading, than if only idiothetic information were used. In Drosophila, ring neurons have well characterised non-linear receptive fields. In this work we produce synthetic versions of these visual receptive fields using a combination of excitatory inputs and mutual inhibition between ring neurons. We use these receptive fields to bring visual information into a spiking neural network model of the insect central complex based on the recently published Drosophila connectome. Previous modelling work has focused on how this circuit functions as a ring attractor using the same type of simple visual cues commonly used experimentally. While we initially test the model on these simple stimuli, we then go on to apply the model to complex natural scenes containing multiple conflicting cues. We show that this simple visual filtering provided by the ring neurons is sufficient to form a mapping between heading and visual features and maintain the heading estimate in the absence of angular velocity input. The network is successful at tracking heading even when presented with videos of natural scenes containing conflicting information from environmental changes and translation of the camera.

Deep-learning-based design of synthetic orthologs of SH3 signaling domains

Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can learn specialized functional constraints that control fitness in specific biological contexts. Here, we examine the ability of generative models to produce synthetic versions of Src-homology 3 (SH3) domains that mediate signaling in the Sho1 osmotic stress response pathway of yeast. We show that a variational autoencoder (VAE) model produces artificial sequences that experimentally recapitulate the function of natural SH3 domains. More generally, the model organizes all fungal SH3 domains such that locality in the model latent space (but not simply locality in sequence space) enriches the design of synthetic orthologs and exposes non-obvious amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of generative models to design ortholog-like functions invivo opens new avenues for engineering protein function in specific cellular contexts and environments.

Evaluation of synthetic electronic health records: A systematic review and experimental assessment

Recent studies have shown how synthetic data generation methods can be applied to electronic health records (EHRs) to obtain synthetic versions that do not violate privacy rules. This growing body of research has resulted in the emergence of numerous methods for evaluating the quality of generated data, with new publications often introducing novel evaluation methods. This work presents a detailed review of synthetic EHRs, focusing on the various evaluation methods used to assess the quality of the generated EHRs. We discuss the existing evaluation methods, offering insights into their use as well as providing an interpretation of the evaluation metrics from the perspectives of achieving fidelity, utility and privacy. Furthermore, we highlight the key factors influencing the selection of evaluation methods, such as the type of data (e.g., categorical, continuous, or discrete) and the mode of application (e.g., patient level, cohort level, and feature level). To assess the effectiveness of current evaluation measures, we conduct a series of experiments to shed light on the potential limitations of these measures. The findings from these experiments reveal notable shortcomings, including the need for meticulous application of methods to the data to reduce inconsistent evaluations, the qualitative nature of some assessments subject to individual judgment, the need for clinical validations, and the absence of techniques to evaluate temporal dependencies within the data. This highlights the need to place greater emphasis on evaluation measures, their application, and the development of comprehensive evaluation frameworks as it is crucial for advancing progress in this field.

Generating synthetic identifiers to support development and evaluation of data linkage methods.

Careful development and evaluation of data linkage methods is limited by researcher access to personal identifiers. One solution is to generate synthetic identifiers, which do not pose equivalent privacy concerns, but can form a 'gold-standard' linkage algorithm training dataset. Such data could help inform choices about appropriate linkage strategies in different settings. We aimed to develop and demonstrate a framework for generating synthetic identifier datasets to support development and evaluation of data linkage methods. We evaluated whether replicating associations between attributes and identifiers improved the utility of the synthetic data for assessing linkage error. We determined the steps required to generate synthetic identifiers that replicate the properties of real-world data collection. We then generated synthetic versions of a large UK cohort study (the Avon Longitudinal Study of Parents and Children; ALSPAC), according to the quality and completeness of identifiers recorded over several waves of the cohort. We evaluated the utility of the synthetic identifier data in terms of assessing linkage quality (false matches and missed matches). Comparing data from two collection points in ALSPAC, we found within-person disagreement in identifiers (differences in recording due to both natural change and non-valid entries) in 18% of surnames and 12% of forenames. Rates of disagreement varied by maternal age and ethnic group. Synthetic data provided accurate estimates of linkage quality metrics compared with the original data (within 0.13-0.55% for missed matches and 0.00-0.04% for false matches). Incorporating associations between identifier errors and maternal age/ethnicity improved synthetic data utility. We show that replicating dependencies between attribute values (e.g. ethnicity), values of identifiers (e.g. name), identifier disagreements (e.g. missing values, errors or changes over time), and their patterns and distribution structure enables generation of realistic synthetic data that can be used for robust evaluation of linkage methods.

Using Chemistry To Recreate the Complexity of the Extracellular Matrix: Guidelines for Supramolecular Hydrogel-Cell Interactions.

Hydrogels have emerged as a promising class of extracellular matrix (ECM)-mimicking materials in regenerative medicine. Here, we briefly describe current state-of-the-art of ECM-mimicking hydrogels, ranging from natural to hybrid to completely synthetic versions, giving the prelude to the importance of supramolecular interactions to make true ECM mimics. The potential of supramolecular interactions to create ECM mimics for cell culture is illustrated through a focus on two different supramolecular hydrogel systems, both developed in our laboratories. We use some recent, significant findings to present important design principles underlying the cell-material interaction. To achieve cell spreading, we propose that slow molecular dynamics (monomer exchange within fibers) is crucial to ensure the robust incorporation of cell adhesion ligands within supramolecular fibers. Slow bulk dynamics (stress-relaxation─fiber rearrangements, τ1/2 ≈ 1000 s) is required to achieve cell spreading in soft gels (<1 kPa), while gel stiffness overrules dynamics in stiffer gels. Importantly, this resonates with the findings of others which specialize in different material types: cell spreading is impaired in case substrate relaxation occurs faster than clutch binding and focal adhesion lifetime. We conclude with discussing considerations and limitations of the supramolecular approach as well as provide a forward thinking perspective to further understand supramolecular hydrogel-cell interactions. Future work may utilize the presented guidelines underlying cell-material interactions to not only arrive at the next generation of ECM-mimicking hydrogels but also advance other fields, such as bioelectronics, opening up new opportunities for innovative applications.

Androgens (dehydroepiandrosterone or testosterone) for women undergoing assisted reproduction.

Practitioners in the field of assisted reproductive technology (ART) continually seek alternative or adjunct treatments to improve ART outcomes. This Cochrane review investigates the adjunct use of synthetic versions of two naturally produced hormones, dehydroepiandrosterone (DHEA) and testosterone (T), in assisted reproduction. Steroid hormones are proposed to increase conception rates by positively affecting follicular response to gonadotrophin stimulation. This may lead to a greater oocyte yield and, subsequently, an increased chance of pregnancy. To assess the effectiveness and safety of DHEA and T as pre- or co-treatments in infertile women undergoing assisted reproduction. We searched the following electronic databases up to 8 January 2024: the Gynaecology and Fertility Group (CGF) Specialised Register, CENTRAL, MEDLINE, Embase, PsycINFO, and trial registries for ongoing trials. We also searched citation indexes, Web of Science, PubMed, and OpenGrey. We searched the reference lists of relevant studies and contacted experts in the field for any additional trials. There were no language restrictions. Randomised controlled trials (RCTs) comparing DHEA or T as an adjunct treatment to any other active intervention, placebo, or no treatment in women undergoing assisted reproduction. Two review authors independently selected studies, extracted relevant data, and assessed risk of bias. We pooled data from studies using fixed-effect models. We calculated odds ratios (ORs) for each dichotomous outcome. Analyses were stratified by type of treatment. We assessed the certainty of evidence for the main findings using GRADE methods. We included 29 RCTs. There were 1599 women in the intervention group and 1469 in the control group. Apart from three trials, the trial participants were women identified as 'poor responders' to standard in vitro fertilisation (IVF) protocols. The included trials compared either T or DHEA treatment with placebo or no treatment. Pre-treatment with DHEA versus placebo/no treatment: DHEA likely results in little to no difference in live birth/ongoing pregnancy rates (OR 1.30, 95% confidence interval (CI) 0.95 to 1.76; I² = 16%, 9 RCTs, N = 1433, moderate certainty evidence). This suggests that in women with a 12% chance of live birth/ongoing pregnancy with placebo or no treatment, the live birth/ongoing pregnancy rate in women using DHEA will be between 12% and 20%. DHEA likely does not decrease miscarriage rates (OR 0.85, 95% CI 0.53 to 1.37; I² = 0%, 10 RCTs, N =1601, moderate certainty evidence). DHEA likely results in little to no difference in clinical pregnancy rates (OR 1.18, 95% CI 0.93 to 1.49; I² = 0%, 13 RCTs, N = 1886, moderate certainty evidence). This suggests that in women with a 17% chance of clinical pregnancy with placebo or no treatment, the clinical pregnancy rate in women using DHEA will be between 16% and 24%. We are very uncertain about the effect of DHEA on multiple pregnancy (OR 3.05, 95% CI 0.47 to 19.66; 7 RCTs, N = 463, very low certainty evidence). Pre-treatment with T versus placebo/no treatment: T likely improves live birth rates (OR 2.53, 95% CI 1.61 to 3.99; I² = 0%, 8 RCTs, N = 716, moderate certainty evidence). This suggests that in women with a 10% chance of live birth with placebo or no treatment, the live birth rate in women using T will be between 15% and 30%. T likely does not decrease miscarriage rates (OR 1.63, 95% CI 0.76 to 3.51; I² = 0%, 9 RCTs, N = 755, moderate certainty evidence). T likely increases clinical pregnancy rates (OR 2.17, 95% CI 1.54 to 3.06; I² = 0%, 13 RCTs, N = 1152, moderate certainty evidence). This suggests that in women with a 12% chance of clinical pregnancy with placebo or no treatment, the clinical pregnancy rate in women using T will be between 17% and 29%. We are very uncertain about the effect of T on multiple pregnancy (OR 2.56, 95% CI 0.59 to 11.20; 5 RCTs, N = 449, very low certainty evidence). We are uncertain about the effect of T versus oestradiol or T versus oestradiol + oral contraceptive pills. The certainty of the evidence was moderate to very low, the main limitations being lack of blinding in the included trials, inadequate reporting of study methods, and low event and sample sizes in the trials. Data on adverse events were sparse; any reported events were minor. Pre-treatment with T likely improves, and pre-treatment with DHEA likely results in little to no difference, in live birth and clinical pregnancy rates in women undergoing IVF who have been identified as poor responders. DHEA and T probably do not decrease miscarriage rates in women under IVF treatment. The effects of DHEA and T on multiple pregnancy are uncertain. Data regarding adverse events were very limited; any reported events were minor. Research is needed to identify the optimal duration of treatment with T. Future studies should include data collection on adverse events and multiple pregnancy.

Transformer-powered surrogates close the ICF simulation-experiment gap with extremely limited data

Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.

Investigation of Deepfake Voice Detection Using Speech Pause Patterns: Algorithm Development and Validation

Background The digital era has witnessed an escalating dependence on digital platforms for news and information, coupled with the advent of “deepfake” technology. Deepfakes, leveraging deep learning models on extensive data sets of voice recordings and images, pose substantial threats to media authenticity, potentially leading to unethical misuse such as impersonation and the dissemination of false information. Objective To counteract this challenge, this study aims to introduce the concept of innate biological processes to discern between authentic human voices and cloned voices. We propose that the presence or absence of certain perceptual features, such as pauses in speech, can effectively distinguish between cloned and authentic audio. Methods A total of 49 adult participants representing diverse ethnic backgrounds and accents were recruited. Each participant contributed voice samples for the training of up to 3 distinct voice cloning text-to-speech models and 3 control paragraphs. Subsequently, the cloning models generated synthetic versions of the control paragraphs, resulting in a data set consisting of up to 9 cloned audio samples and 3 control samples per participant. We analyzed the speech pauses caused by biological actions such as respiration, swallowing, and cognitive processes. Five audio features corresponding to speech pause profiles were calculated. Differences between authentic and cloned audio for these features were assessed, and 5 classical machine learning algorithms were implemented using these features to create a prediction model. The generalization capability of the optimal model was evaluated through testing on unseen data, incorporating a model-naive generator, a model-naive paragraph, and model-naive participants. Results Cloned audio exhibited significantly increased time between pauses (P&lt;.001), decreased variation in speech segment length (P=.003), increased overall proportion of time speaking (P=.04), and decreased rates of micro- and macropauses in speech (both P=.01). Five machine learning models were implemented using these features, with the AdaBoost model demonstrating the highest performance, achieving a 5-fold cross-validation balanced accuracy of 0.81 (SD 0.05). Other models included support vector machine (balanced accuracy 0.79, SD 0.03), random forest (balanced accuracy 0.78, SD 0.04), logistic regression, and decision tree (balanced accuracies 0.76, SD 0.10 and 0.72, SD 0.06). When evaluating the optimal AdaBoost model, it achieved an overall test accuracy of 0.79 when predicting unseen data. Conclusions The incorporation of perceptual, biological features into machine learning models demonstrates promising results in distinguishing between authentic human voices and cloned audio.

High-throughput evaluation of genetic variants with prime editing sensor libraries.

Tumor genomes often harbor a complex spectrum of single nucleotide alterations and chromosomal rearrangements that can perturb protein function. Prime editing has been applied to install and evaluate genetic variants, but previous approaches have been limited by the variable efficiency of prime editing guide RNAs. Here we present a high-throughput prime editing sensor strategy that couples prime editing guide RNAs with synthetic versions of their cognate target sites to quantitatively assess the functional impact of endogenous genetic variants. We screen over 1,000 endogenous cancer-associated variants of TP53-the most frequently mutated gene in cancer-to identify alleles that impact p53 function in mechanistically diverse ways. We find that certain endogenous TP53 variants, particularly those in the p53 oligomerization domain, display opposite phenotypes in exogenous overexpression systems. Our results emphasize the physiological importance of gene dosage in shaping native protein stoichiometry and protein-protein interactions, and establish a framework for studying genetic variants in their endogenous sequence context at scale.

Toxicity and Risk of Biopesticides to Insect Pollinators in Urban and Agricultural Landscapes

Pollinators play important roles in providing pollination services, maintaining biodiversity, and boosting crop production. Even though pollinators are essential to the environment and agriculture, their decline has been noted across multiple studies in the recent past. Both natural and anthropogenic factors have contributed to their decline. Much of the focus has been placed on climate change, habitat loss, pests and pathogens, and synthetic pesticides, but relatively little is known about the effects of biopesticides. Biopesticides are biological control agents derived from living organisms and are classified into three groups: microbial, biochemical, and plant-incorporated protectant-based products. Biopesticides are formulated similarly to their synthetic counterparts and are readily available and used within urban and agricultural settings by pest management experts and household residents. The general public and much scientific literature support the prevailing idea that biopesticides are environmentally safe and pollinator friendly in comparison with synthetic versions. However, such generalizations are based on studies with a few key pollinator species and may not be relevant to several other species that provide crop pollination services. Studies focused on native pollinators have shown that some biopesticides have lethal and sublethal effects. Because each biopesticide exhibits varying effects across pollinator species, it could be dangerous to generalize their non-toxicity across taxa and environmental settings. In this article, recent research in this direction is discussed.

Improving Breast Tumor Multi-Classification from High-Resolution Histological Images with the Integration of Feature Space Data Augmentation

To support pathologists in breast tumor diagnosis, deep learning plays a crucial role in the development of histological whole slide image (WSI) classification methods. However, automatic classification is challenging due to the high-resolution data and the scarcity of representative training data. To tackle these limitations, we propose a deep learning-based breast tumor gigapixel histological image multi-classifier integrated with a high-resolution data augmentation model to process the entire slide by exploring its local and global information and generating its different synthetic versions. The key idea is to perform the classification and augmentation in feature latent space, reducing the computational cost while preserving the class label of the input. We adopt a deep learning-based multi-classification method and evaluate the contribution given by a conditional generative adversarial network-based data augmentation model on the classifier’s performance for three tumor classes in the BRIGHT Challenge dataset. The proposed method has allowed us to achieve an average F1 equal to 69.5, considering only the WSI dataset of the Challenge. The results are comparable to those obtained by the Challenge winning method (71.6), also trained on the annotated tumor region dataset of the Challenge.

PiRNA hsa_piR_019949 promotes chondrocyte anabolic metabolism by inhibiting the expression of lncRNA NEAT1

BackgroundOsteoarthritis is a prevalent degenerative joint condition typically found in individuals who are aged 50 years or older. In this study, the focus is on PIWI-interacting RNA (piRNA), which belongs to a category of small non-coding RNAs. These piRNAs play a role in the regulation of gene expression and the preservation of genomic stability. The main objective of this research is to examine the expression of a specific piRNA called hsa_piR_019949 in individuals with osteoarthritis, to understand its impact on chondrocyte metabolism within this condition.MethodsWe analyzed piRNA expression in osteoarthritis cartilage using the GEO database. To understand the impact of inflammatory factors on piRNA expression in chondrocytes, we conducted RT-qPCR experiments. We also investigated the effect of piRNA hsa_piR_019949 on chondrocyte proliferation using CCK-8 and clone formation assays. Furthermore, we assessed the influence of piRNA hsa_piR_019949 on chondrocyte apoptosis by conducting flow cytometry analysis. Additionally, we examined the differences in cartilage matrix composition through safranine O staining and explored the downstream regulatory mechanisms of piRNA using transcriptome sequencing. Lentiviral transfection of NEAT1 and NLRP3 was performed to regulate the metabolism of chondrocytes.ResultsUsing RNA sequencing technology, we compared the gene expression profiles of 5 patients with osteoarthritis to 3 normal controls. We found a gene called hsa_piR_019949 that showed differential expression between the two groups. Specifically, hsa_piR_019949 was downregulated in chondrocytes when stimulated by IL-1β, an inflammatory molecule. In further investigations, we discovered that overexpression of hsa_piR_019949 in vitro led to increased proliferation and synthesis of the extracellular matrix in chondrocytes, which are cells responsible for cartilage formation. Conversely, suppressing hsa_piR_019949 expression resulted in increased apoptosis (cell death) and degradation of the extracellular matrix in chondrocytes. Additionally, we found that the NOD-like receptor signaling pathway is linked to the low expression of hsa_piR_019949 in a specific chondrocyte cell line called C28/I2. Furthermore, we observed that hsa_piR_019949 can inhibit the expression of a long non-coding RNA called NEAT1 in chondrocytes. We hypothesize that NEAT1 may serve as a downstream target gene regulated by hsa_piR_019949, potentially influencing chondrocyte metabolism and function in the context of osteoarthritis.ConclusionsPiRNA hsa_piR_019949 has shown potential in promoting the proliferation of chondrocytes and facilitating the synthesis of extracellular matrix in individuals with osteoarthritis. This is achieved by inhibiting the expression of a long non-coding RNA called NEAT1. The implication is that by using hsa_piR_019949 mimics, which are synthetic versions of the piRNA, as a therapeutic approach, it may be possible to effectively treat osteoarthritis.

Assessing the immunogenicity risk of salmon calcitonin peptide impurities using in silico and in vitro methods.

Advances in synthetic peptide synthesis have enabled rapid and cost-effective peptide drug manufacturing. For this reason, peptide drugs that were first produced using recombinant DNA (rDNA) technology are now being produced using solid- and liquid-phase peptide synthesis. While peptide synthesis has some advantages over rDNA expression methods, new peptide-related impurities that differ from the active pharmaceutical ingredient (API) may be generated during synthesis. These impurity byproducts of the original peptide sequence feature amino acid insertions, deletions, and side-chain modifications that may alter the immunogenicity risk profile of the drug product. Impurities resulting from synthesis have become the special focus of regulatory review and approval for human use, as outlined in the FDA's Center for Drug Evaluation and Research guidance document, "ANDAs for Certain Highly Purified Synthetic Peptide Drug Products That Refer to Listed Drugs of rDNA Origin," published in 2021. This case study illustrates how in silico and in vitro methods can be applied to assess the immunogenicity risk of impurities that may be present in synthetic generic versions of the salmon calcitonin (SCT) drug product. Sponsors of generic drug abbreviated new drug applications (ANDAs) should consider careful control of these impurities (for example, keeping the concentration of the immunogenic impurities below the cut-off recommended by FDA regulators). Twenty example SCT impurities were analyzed using in silico tools and assessed as having slightly more or less immunogenic risk potential relative to the SCT API peptide. Class II human leukocyte antigen (HLA)-binding assays provided independent confirmation that a 9-mer sequence present in the C-terminus of SCT binds promiscuously to multiple HLA DR alleles, while T-cell assays confirmed the expected T-cell responses to SCT and selected impurities. In silico analysis combined with in vitro assays that directly compare the API to each individual impurity peptide may be a useful approach for assessing the potential immunogenic risk posed by peptide impurities that are present in generic drug products.

A spotlight on global collaboration in the Sc2.0 yeast consortium

The Synthetic Yeast Genome Project (Sc2.0) is an international collaboration that aims to create and optimize synthetic versions of each Saccharomyces cerevisiae chromosome, with the ultimate goal of assembling a yeast organism with a synthetic with design features facilitating applications in synthetic biology and engineering projects. The consortium research groups are global, and, here, we highlight the work of the China-based Sc2.0 researchers and their thoughts on the future of Sc2.0 and synthetic biology.

Colloidal robotics.

Robots have components that work together to accomplish a task. Colloids are particles, usually less than 100 µm, that are small enough that they do not settle out of solution. Colloidal robots are particles capable of functions such as sensing, computation, communication, locomotion and energy management that are all controlled by the particle itself. Their design and synthesis is an emerging area of interdisciplinary research drawing from materials science, colloid science, self-assembly, robophysics and control theory. Many colloidal robot systems approach synthetic versions of biological cells in autonomy and may find ultimate utility in bringing these specialized functions to previously inaccessible locations. This Perspective examines the emerging literature and highlights certain design principles and strategies towards the realization of colloidal robots.