High-throughput Research Research Articles

High-throughput calculation for superconductivity of sodalite-like clathrate ternary hydrides MXH12 at high pressure

Inspired by the experimental synthesis of the first theoretically predicted clathrate hydride CaH6 with high Tc of 215 K at 172 GPa, we designed a series of Pm-3m phase ternary hydrides MXH12 and performed high-throughput calculations. Ten compounds were discovered, among which YHfH12, YThH12, ScZrH12, and LaThH12 show good superconductivity with critical temperatures above 150 K. LaThH12 and YCeH12 can be dynamically stable at a minimum of 100 GPa. Aiming at a comprehensive description of the H24 cage that dominates the superconductivities of MXH12, we proposed a variable AE, which is defined as the average ELF value at the center of all the H–H bonds in the cage-like hydrides. Combining the AE with the contribution of the H element to the total DOS at the Fermi level, we constructed a simple liner model (called the AE model) by fitting the data of hydrides MXH12. The mean absolute error is 11.7 K suggesting a strong linear correlation between AE and Tc. Furthermore, the AE model can be generalized to predict the superconductivity of other cage-like hydrides such as LaH10 (or YH10) with H32 cage, YH9 (or CeH9) with H29 cage, and more complex cage-like hydrides such as La4H23 and LaBeH8. The AE model could estimate the Tc of clathrate hydrides with small computational cost and provide reliable reference for further high-throughput calculations and experimental research.

A Chromosome-Level Genome Assembly of the Reef Stonefish (Synanceia verrucosa) Provides Novel Insights into Stonustoxin (sntx) Genes.

Reef stonefish (Synanceia verrucosa) is one of the most venomous fishes, but its biomedical study has been restricted to molecular cloning and purification of its toxins, instead of high-throughput genetic research on related toxin genes. In this study, we constructed a chromosome-level haplotypic genome assembly for the reef stonefish. The genome was assembled into 24 pseudo-chromosomes, and the length totaled 689.74 Mb, reaching a contig N50 of 11.97 Mb and containing 97.8% of complete BUSCOs. A total of 24,050 protein-coding genes were annotated, of which metalloproteinases, C-type lectins, and stonustoxins (sntx) were the most abundant putative toxin genes. Multitissue transcriptomic and venom proteomic data showed that sntx genes, especially those clustered within a 50-kb region on the chromosome 2, had higher transcription levels than other types of toxins as well as those sntx genes scatteringly distributed on other chromosomes. Further comparative genomic analysis predicted an expansion of sntx-like genes in the Percomorpha lineage including nonvenomous fishes, but Scorpaenoidei species experienced extra independent sntx duplication events, marking the clear-cut origin of authentic toxic stonustoxins. In summary, this high-quality genome assembly and related comparative analysis of toxin genes highlight valuable genetic differences for potential involvement in the evolution of venoms among Scorpaeniformes fishes.

Applied Molecular-Based Quality Control of Biobanked Samples for Multi-Omics Approach.

Biobanks are vital for high-throughput translational research, but the rapid development of novel molecular techniques, especially in omics assays, poses challenges to traditional practices and recommendations. In our study, we used biospecimens from oncological patients in Polish clinics and collaborated with the Indivumed Group. For serum/plasma samples, we monitored hemolysis, controlled RNA extraction, assessed cDNA library quality and quantity, and verified NGS raw data. Tissue samples underwent pathologic evaluation to confirm histology and determine tumor content. Molecular quality control measures included evaluating the RNA integrity number, assessing cDNA library quality and quantity, and analyzing NGS raw data. Our study yielded the creation of distinct workflows for conducting preanalytical quality control of serum/plasma and fresh-frozen tissue samples. These workflows offer customization options to suit the capabilities of different biobanking entities. In order to ensure the appropriateness of biospecimens for advanced research applications, we introduced molecular-based quality control methods that align with the demands of high-throughput assays. The novelty of proposed workflows, rooted in innovative molecular techniques, lies in the integration of these QC methods into a comprehensive schema specifically designed for high-throughput research applications.

Technological development of multidimensional liquid chromatography-mass spectrometry in proteome research

Liquid chromatography-mass spectrometry (LC-MS) is the method of choice for high-throughput proteomic research. Limited by the peak capacity, the separation performance of conventional single-dimensional LC hampers the development of proteomics. Combining different separation modes orthogonally, multidimensional liquid chromatography (MDLC) with high peak capacity was developed to address this challenge. MDLC has evolved rapidly since its establishment, and the progress of proteomics has been greatly facilitated by the advent of novel MDLC-MS-based methods. In this paper, we will review the advances of MDLC-MS-based methodologies and technologies in proteomics studies, from different perspectives including novel application scenarios and proteomic targets, automation, miniaturization, and the improvement of the classic methods in recent years. In addition, attempts regarding new MDLC-MS models are also mentioned together with the outlook of MDLC-MS-based proteomics methods.

Evaluating Retention Index Score Assumptions to Refine GC-MS Metabolite Identification.

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in gas chromatography-mass spectrometry (GC-MS) still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RIs were matched to library RI with a generous window of ±35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive (TP), true negative, or unknown. Metabolites with at least 30 TP identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, and so on.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical metabolite-specific distributions improved the TP and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimize the effort to complete manual verification.

ICatcher+: Robust and Automated Annotation of Infants' and Young Children's Gaze Behavior From Videos Collected in Laboratory, Field, and Online Studies.

Technological advances in psychological research have enabled large-scale studies of human behavior and streamlined pipelines for automatic processing of data. However, studies of infants and children have not fully reaped these benefits because the behaviors of interest, such as gaze duration and direction, still have to be extracted from video through a laborious process of manual annotation, even when these data are collected online. Recent advances in computer vision raise the possibility of automated annotation of these video data. In this article, we built on a system for automatic gaze annotation in young children, iCatcher, by engineering improvements and then training and testing the system (referred to hereafter as iCatcher+) on three data sets with substantial video and participant variability (214 videos collected in U.S. lab and field sites, 143 videos collected in Senegal field sites, and 265 videos collected via webcams in homes; participant age range = 4 months-3.5 years). When trained on each of these data sets, iCatcher+ performed with near human-level accuracy on held-out videos on distinguishing "LEFT" versus "RIGHT" and "ON" versus "OFF" looking behavior across all data sets. This high performance was achieved at the level of individual frames, experimental trials, and study videos; held across participant demographics (e.g., age, race/ethnicity), participant behavior (e.g., movement, head position), and video characteristics (e.g., luminance); and generalized to a fourth, entirely held-out online data set. We close by discussing next steps required to fully automate the life cycle of online infant and child behavioral studies, representing a key step toward enabling robust and high-throughput developmental research.

Organs-on-chips technologies – A guide from disease models to opportunities for drug development

Current in-vitro 2D cultures and animal models present severe limitations in recapitulating human physiopathology with striking discrepancies in estimating drug efficacy and side effects when compared to human trials. For these reasons, microphysiological systems, organ-on-chip and multiorgans microdevices attracted considerable attention as novel tools for high-throughput and high-content research to achieve an improved understanding of diseases and to accelerate the drug development process towards more precise and eventually personalized standards. This review takes the form of a guide on this fast-growing field, providing useful introduction to major themes and indications for further readings.We start analyzing Organs-on-chips (OOC) technologies for testing the major drug administration routes: (1) oral/rectal route by intestine-on-a-chip, (2) inhalation by lung-on-a-chip, (3) transdermal by skin-on-a-chip and (4) intravenous through vascularization models, considering how drugs penetrate in the bloodstream and are conveyed to their targets. Then, we focus on OOC models for (other) specific organs and diseases: (1) neurodegenerative diseases with brain models and blood brain barriers, (2) tumor models including their vascularization, organoids/spheroids, engineering and screening of antitumor drugs, (3) liver/kidney on chips and multiorgan models for gastrointestinal diseases and metabolic assessment of drugs and (4) biomechanical systems recapitulating heart, muscles and bones structures and related diseases. Successively, we discuss technologies and materials for organ on chips, analyzing (1) microfluidic tools for organs-on-chips, (2) sensor integration for real-time monitoring, (3) materials and (4) cell lines for organs on chips. (Nano)delivery approaches for therapeutics and their on chip assessment are also described. Finally, we conclude with a critical discussion on current significance/relevance, trends, limitations, challenges and future prospects in terms of revolutionary impact on biomedical research, preclinical models and drug development.

Optical properties study of high-throughput horizontal composition gradient FeSe1-xTex thin film based on ellipsometry

The high-throughput superconductivity research model can effectively accelerate the material synthesis and physical characterization process. It can rapidly form a database of materials and reveal the hidden key physical laws, which provides significant assistance to the research of superconducting materials, especially the exploration of superconductivity mechanism. In this work, FeSe1-xTex thin film with spread composition was grown by combined laser molecular beam epitaxy, the third generation combined thin film preparation technique. The film thickness and surface morphology were obtained by atomic force microscopy, which showed that the sample surface was relatively smooth. In addition, we measured the distribution of critical elements and c-axis lattice constants within the different microzones along the direction of composition variation on the surface of the film, by X-ray photoelectron spectroscopy and X-ray diffraction, respectively, which verified that the film was prepared in accordance with a continuous composition gradient. Based on this, nine microzones of the film were tested using the ellipsometer equipped with a focused light spot. The dispersion model of the FeSe1-xTex layer was established using the Drude-Lorentz oscillator, and the optical constants of the film with horizontal composition gradient were obtained by inversion and fitting of the data within each microzone. The results indicate that the FeSe1-xTex thin film shows an obvious composition-dependent optical property. Our work reports the optical constants of FeSe1-xTex composition-spread thin film by using a nondestructive and accurate method, spectroscopic ellipsometry, which enriches the database of this material. This work may provide a reference for establishing the correlation between the optical and superconducting properties of FeSe-based superconductors.

A method of cotton root segmentation based on edge devices.

The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root.

Technical development and feasibility of a reusable vest to integrate cardiovascular magnetic resonance with electrocardiographic imaging

BackgroundElectrocardiographic imaging (ECGI) generates electrophysiological (EP) biomarkers while cardiovascular magnetic resonance (CMR) imaging provides data about myocardial structure, function and tissue substrate. Combining this information in one examination is desirable but requires an affordable, reusable, and high-throughput solution. We therefore developed the CMR-ECGI vest and carried out this technical development study to assess its feasibility and repeatability in vivo. MethodsCMR was prospectively performed at 3T on participants after collecting surface potentials using the locally designed and fabricated 256-lead ECGI vest. Epicardial maps were reconstructed to generate local EP parameters such as activation time (AT), repolarization time (RT) and activation recovery intervals (ARI). 20 intra- and inter-observer and 8 scan re-scan repeatability tests. Results77 participants were recruited: 27 young healthy volunteers (HV, 38.9 ± 8.5 years, 35% male) and 50 older persons (77.0 ± 0.1 years, 52% male). CMR-ECGI was achieved in all participants using the same reusable, washable vest without complications. Intra- and inter-observer variability was low (correlation coefficients [rs] across unipolar electrograms = 0.99 and 0.98 respectively) and scan re-scan repeatability was high (rs between 0.81 and 0.93). Compared to young HV, older persons had significantly longer RT (296.8 vs 289.3 ms, p = 0.002), ARI (249.8 vs 235.1 ms, p = 0.002) and local gradients of AT, RT and ARI (0.40 vs 0.34 ms/mm, p = 0,01; 0.92 vs 0.77 ms/mm, p = 0.03; and 1.12 vs 0.92 ms/mm, p = 0.01 respectively). ConclusionOur high-throughput CMR-ECGI solution is feasible and shows good reproducibility in younger and older participants. This new technology is now scalable for high throughput research to provide novel insights into arrhythmogenesis and potentially pave the way for more personalised risk stratification. Clinical trial registration: Title: Multimorbidity Life-Course Approach to Myocardial Health—A Cardiac Sub-Study of the MRC National Survey of Health and Development (NSHD) (MyoFit46). National Clinical Trials (NCT) number: NCT05455125. URL: https://clinicaltrials.gov/ct2/show/NCT05455125?term=MyoFit&draw=2&rank=1

Automated electrolyte formulation and coin cell assembly for high-throughput lithium-ion battery research

We present ODACell, an automated electrolyte formulation and coin cell assembly system for accelerated battery research.

Predicting factors and top gene identification for survival data of breast cancer

<abstract> <p>For high-throughput research with biological data-sets generated sequentially or by transcriptional micro-arrays, proteomics or other means, analytic techniques that address their high dimensional aspects remain desirable. The computation part basically predicts the tendency towards mortality due to breast cancer (BC) by using several classification methods, i.e., Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Decision Tree (DT), and compared the models' performances. We proceed with the RF method since it provides better results than any other underlying models based on accuracy. We have also demonstrated some traditional and competing risk models, illustrated the models with real data analysis, depicted their curves' natures and also compared their fits using prediction error curves and the concordance index. Furthermore, two different survival splitting rules are used by using separate Random Survival Forest (RSF) methods and also constructing the ranking of risk factors due to breast cancer. The results show that high-level grade and diameter are the most important predictors for mortality progression in the presence of competing events of death, and lymph nodes, age and angiography are other vital criteria for this purpose. We have also implemented RSF backward selection criteria, which enables top gene selection related to mortality progression due to breast cancer. This method identifies c-MYB, CDCA7, NUSAP1, BIRC5, ANGPTL4, JAG1, IL6ST, and remaining genes that are mainly responsible for mortality progression due to breast cancer. In this work, <italic>R</italic> software is used to obtain and evaluate the results.</p> </abstract>

Method validation and clinical application for the quantification of lopinavir, efavirenz, and ritonavir in breast milk using liquid chromatography tandem mass spectrometry.

A liquid chromatography tandem mass spectrometry (LC-MS/MS) method has been validated for quantification of three antiretroviral drugs (efavirenz [EFV], lopinavir [LPV], and ritonavir [RTV]) from human breast milk. The samples were extracted employing protein precipitation method using methanol as precipitating agent. The supernatant was evaporated and reconstituted before injecting into the chromatograph and separated on a biphenyl column. Calibration curves for the three tested antiretroviral drugs were linear (r ≥ 0.999) over the range examined. The inter- and intra-day coefficients of variation (CV) were ≤15% for efavirenz, lopinavir, and ritonavir. Mean recovery ranged from 96% to 105% and no major matrix effects were observed. This validated LC-MS/MS method was efficiently applied to determine EFV, LPV, and RTV concentrations in breast milk from Human Immunodeficiency Virus (HIV)-positive breastfeeding mothers. This assay requires a simple sample processing method with a short run time, making it well suited for high-throughput routine clinical or research purposes.

Advances in the application of 3D tumor models in precision oncology and drug screening.

Traditional tumor models cannot perfectly simulate the real state of tumors in vivo, resulting in the termination of many clinical trials. 3D tumor models’ technology provides new in vitro models that bridge the gap between in vitro and in vivo findings, and organoids maintain the properties of the original tissue over a long period of culture, which enables extensive research in this area. In addition, they can be used as a substitute for animal and in vitro models, and organoids can be established from patients’ normal and malignant tissues, with unique advantages in clinical drug development and in guiding individualized therapies. 3D tumor models also provide a promising platform for high-throughput research, drug and toxicity testing, disease modeling, and regenerative medicine. This report summarizes the 3D tumor model, including evidence regarding the 3D tumor cell culture model, 3D tumor slice model, and organoid culture model. In addition, it provides evidence regarding the application of 3D tumor organoid models in precision oncology and drug screening. The aim of this report is to elucidate the value of 3D tumor models in cancer research and provide a preclinical reference for the precise treatment of cancer patients.

Looking beyond the cytogenetics in haematological malignancies: decoding the role of tandem repeats in DNA repair genes.

In cancer research, one of the most significant findings was to characterize the DNA repair deficiency as carcinogenic. Amongst the various repair mechanisms, mismatch repair (MMR) and direct reversal of DNA damage systems are designated as multilevel safeguards in the human genome. Defects in these elevate the rate of mutations and results in dire consequences like cancer. Of the several molecular signatures in human genome, tandem repeats (TRs) appear at various frequencies in the exonic, intronic, and regulatory regions of the DNA. Hypervariability among these repeats in the coding and non-coding regions of the genes is well characterized for solid tumours, but its significance in haematologic malignancies remains to be explored. The purpose of our study was to elucidate the role of nucleotide repeat instability in the coding and non-coding regions of 10 different repair genes in myeloid and lymphoid cell lines compared to the control samples. We selected MMR deficient extensively studied microsatellite instable colorectal cancer (HCT116), and MMR proficient breast cancer (MCF-7) cells along with underemphasized haematologic cancer cell lines to decipher the hypermutability of tandem repeats. A statistically significant TR variation was observed for MSH2 and MSH6 genes in 4 and 3 of the 6 cell lines respectively. KG1 (AML) and Daudi (Burkitt's lymphoma) were found to have compromised DNA repair competency with highly unstable nucleotide repeats. Taken together, the results suggest that mutable TRs in intronic and non-intronic regions of repair genes in blood cancer might have a tumorigenic role. Since this is a pilot study on cell lines, high throughput research in large cohorts can be undertaken to reveal novel diagnostic markers for unexplained blood cancer patients with normal karyotypes or otherwise with karyotypic defects.

Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning.

For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers' mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.

Identification, conservation, and expression of tiered pharmacogenes in zebrafish.

The number of adverse drug events in the United States is critically high, with annual rates exceeding 1 million cases over the last nine years. One cause of adverse drug events is the underlying genetic variation that can alter drug responses. Pharmacogenomics is a growing field that seeks to better understand the relationship between a patient’s genetics and drug efficacy. Currently, pharmacogenomics relies largely on human trials, as there is not a well-developed animal model for studying preventative measures and alternative treatments. Here, we analyzed pharmacogene expression at two developmental time points in zebrafish to demonstrate the potential of using this model organism for high-throughput pharmacogenomics research. We found that 76% of tiered human pharmacogenes have a zebrafish ortholog, and of these, many have highly conserved amino acid sequences. Additional gene ontology analysis was used to classify pharmacogenes and identify candidate pathways for future modeling in zebrafish. As precision medicine burgeons, adopting a high-throughput in vivo model such as the zebrafish could greatly increase our understanding of the molecular pathology underlying adverse drug events.

Characterization of human nasal organoids from chronic rhinosinusitis patients.

ABSTRACTPatient-derived organoids grown in three-dimensional cultures provide an excellent platform for phenotypic high-throughput screening and drug-response research. Organoid technology has been applied to study stem cell biology and various human pathologies. This study investigates the characteristics and cellular morphology of organoids derived from primary human nasal epithelial cells (HNECs) of chronic rhinosinusitis (CRS) patients. Nasal organoids were cultured up to 20 days and morphological, cell composition and functional parameters were measured by immunofluorescence, RT-qPCR, western blot and FACS analysis. The results showed that nasal organoids expressed the stem cell marker leucine-rich repeat-containing G-protein-coupled receptor 5 (LGR5), and markers for apical junction genes, goblet cells and ciliated cells. Moreover, we were able to regrow and expand the nasal organoids well after freezing and thawing. This study provides an effective and feasible method for development of human nasal organoids, suitable for the phenotypic high-throughput screening and drug response research.

Structural Activity Relationship based Medicinal Perspectives of Pyrimidine Derivatives as Anti-Alzheimer's Agents: A Comprehensive Review.

Pyrimidine is an aromatic and heterocyclic organic compound containing a 6-membered ring consisting of four carbon and two nitrogen atoms on alternative positions. Pyrimidine scaffolds described their existence in the medicinal chemist's cause for their synthesizing practicability and nonpoisonous nature. However, the reason behind neurological disorders is still an open challenge for scientific research and development organizations. Efficacy voids are widespread before researchers, despite high throughput research in the field of anti-Alzheimer's drugs.Researchers have constantly investigated all the probabilities for restraining the unwanted adverse effects of the anti-Alzheimer's agents and are focusing more extensively to rehabilitate neurological disorders. The scientific literature on drug development has been an aspiration to medicinal chemists and other researchers to facilitate further research. Therefore, this review emphasizes the structure-activity relationship (SAR) based approach and the pharmacological advancements of pyrimidine moiety in the new era of therapeutics as anti-Alzheimer's agents.

High-throughput fabrication of TiO2 nanotube arrays by 4-electrode bipolar electrochemistry

Size tunable TiO2 nanotube arrays (TNAs) are promising materials in biomedical applications and photocatalysis. Rapidly screening an optimal dimension of TNAs under various application conditions is challengeable, for which a high-throughput TNA platform is significantly important. Herein, 4-electrode bipolar electrochemistry (4EBPE) was proposed to implement a controllable fabrication of gradient TNAs. Theoretical calculations showed that the shift of cathode/anode boundary is the principal cause of uncontrollable overpotential distribution over the bipolar electrode (BPE) and undesirable gradient of the TNAs for normal bipolar anodization. The 4EBPE could improve the anodization process of gradient TNAs through an extra imposed overpotential on the BPE. The dimension of TNAs fabricated by 4EBPE is linearly dependent on the position over the electrode, and the gradient ranges of TNAs can be freely controlled through the electrochemical parameters. The gradient TNAs fabricated by the 4EBPE are promising platforms for high-throughput research.