High-throughput Techniques Research Articles

Direct production of low-oxygen-concentration titanium from molten titanium

Titanium (Ti) is an attractive material, abundant in nature and possessing superior mechanical and chemical properties. However, its widespread use is significantly hampered by the strong affinity between titanium and oxygen (O), resulting in elevated manufacturing costs during the refining, melting, and casting processes. The current work introduces a high-throughput technique that effectively reduces the oxygen content in molten titanium to a level suitable for structural material applications (1000 mass ppm, equivalent to 0.1 mass%). This technique aspires to streamline the mass production of titanium by seamlessly integrating the refining, melting, and casting processes. The developed method leverages the high affinity of rare-earth metals, such as yttrium (Y), for oxygen. This method utilizes the formation reaction of their oxyhalides (YOF) to directly remove oxygen from liquid titanium, resulting in titanium with a significantly reduced oxygen content of 200 mass ppm. This technique enables the direct conversion of titanium oxide feeds into low-oxygen titanium without requiring conversion into intermediate compounds. Additionally, this process offers a pathway for the upgrade recycling of high-oxygen-content titanium scrap directly into low-oxygen titanium. Consequently, this technology holds the potential to dramatically lower titanium production costs, thereby facilitating its more widespread utilization.

Forest-to-Cropland Conversion Reshapes Microbial Hierarchical Interactions and Degrades Ecosystem Multifunctionality at a National Scale.

Conversion from natural lands to cropland, primarily driven by agricultural expansion, could significantly alter soil microbiome worldwide; however, influences of forest-to-cropland conversion on microbial hierarchical interactions and ecosystem multifunctionality have not been fully understood. Here, we examined the effects of forest-to-cropland conversion on intratrophic and cross-trophic microbial interactions and soil ecosystem multifunctionality and further disclosed their underlying drivers at a national scale, using Illumina sequencing combined with high-throughput quantitative PCR techniques. The forest-to-cropland conversion significantly changed the structure of soil microbiome (including prokaryotic, fungal, and protistan communities) while it did not affect its alpha diversity. Both intrakingdom and interkingdom microbial networks revealed that the intratrophic and cross-trophic microbial interaction patterns generally tended to be more modular to resist environmental disturbance introduced from forest-to-cropland conversion, but this was insufficient for the cross-trophic interactions to maintain stability; hence, the protistan predation behaviors were still disturbed under such conversion. Moreover, key soil microbial clusters were declined during the forest-to-cropland conversion mainly because of the increased soil total phosphorus level, and this drove a great degradation of the ecosystem multifunctionality (by 207%) in cropland soils. Overall, these findings comprehensively implied the negative effects of forest-to-cropland conversion on the agroecosystem, from microbial hierarchical interactions to ecosystem multifunctionality.

Abstract B014: Unveiling the future: Exploring cutting-edge technologies for synthetic lethality discovery

Abstract Synthetic lethality discovery is an emerging field of research that aims to reshape the landscape of cancer treatment, offering new hope and possibilities for patients faced with challenging diagnoses. In this article, we explored the cutting-edge technologies shaping the future of synthetic lathality research. Artificial intelligence and machine learning are not just buzzwords but indispensable tools that are shaping the field of precision medicine and personalized cancer treatment. By leveraging the power of computational tools, scientists can accelerate the identification of synthetic lethal interactions and pave the way for the development of more effective targeted cancer therapies. Through case studies, researchers can uncover the functional genomics of complex diseases and identify novel drug targets for cancer therapy. The integration of multi-omics approaches not only enhances the depth of analysis but also offers a more nuanced understanding of the molecular mechanisms driving synthetic lethal interactions, paving the way to unlock novel therapeutics and personalized treatment strategies. In addition, the integration of high-throughput screening techniques, such as CRISPR-Cas9 gene editing, opens up new avenues for identifying new drug targets and therapeutic strategies, ultimately paving a new era where personalized and targeted therapies redefine the way we approach cancer care, ushering in a novel era of precision and efficacy in medicine. Citation Format: Peter Oloche David. Unveiling the future: Exploring cutting-edge technologies for synthetic lethality discovery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B014.

Simultaneous assessment of human genome and methylome data in a single experiment using limited deamination of methylated cytosine.

Multiomics require concerted recording of independent information, ideally from a single experiment. In this study, we introduce RIMS-seq2, a high-throughput technique to simultaneously sequence genomes and overlay methylation information while requiring only a small modification of the experimental protocol for high-throughput DNA sequencing to include a controlled deamination step. Importantly, the rate of deamination of 5-methylcytosine is negligible and thus does not interfere with standard DNA sequencing and data processing. Thus, RIMS-seq2 libraries from whole- or targeted-genome sequencing show the same germline variation calling accuracy and sensitivity compared with standard DNA-seq. Additionally, regional methylation levels provide an accurate map of the human methylome.

Correlation between physicochemical properties, flavor characteristics and microbial community structure in Dushan shrimp sour paste

Dushan shrimp sour paste (DSSP), a traditional Guizhou condiment, and its unique flavor is determined by the fermentation microbiota. However, the relationship between the microbiota structure and its flavor remains unclear. This study identified 116 volatile flavor compounds using electronic nose and headspace solid-phase microextraction-gas chromatography mass spectrometry (HS-SPME-GC–MS) techniques, of which 19 were considered as key flavor compounds, mainly consisting of 13 esters and 1 alcohol. High-throughput sequencing technique, the bacterial community structure of nine groups of DSSPs was determined. Further analysis revealed Vagococcus, Lactococcus, and Tepidimicrobium as key bacteria involved in flavor formation. This study contributes to our understanding of the relationship between bacterial communities and the flavor formation, and provides guidance for screening starter culture that enhance the flavor of DSSP in industrial production.

High-throughput screening unveils nitazoxanide as a potent PRRSV inhibitor by targeting NMRAL1

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) poses a major threat to the global swine industry, yet effective prevention and control measures remain elusive. This study unveils Nitazoxanide (NTZ) as a potent inhibitor of PRRSV both in vitro and in vivo. Through High-Throughput Screening techniques, 16 potential anti-PRRSV compounds are identified from a library comprising FDA-approved and pharmacopeial drugs. We show that NTZ displays strong efficacy in reducing PRRSV proliferation and transmission in a swine model, alleviating viremia and lung damage. Additionally, Tizoxanide (TIZ), the primary metabolite of NTZ, has been identified as a facilitator of NMRAL1 dimerization. This finding potentially sheds light on the underlying mechanism contributing to TIZ’s role in augmenting the sensitivity of the IFN-β pathway. These results indicate the promising potential of NTZ as a repurposed therapeutic agent for Porcine Reproductive and Respiratory Syndrome (PRRS). Additionally, they provide valuable insights into the antiviral mechanisms underlying NTZ’s effectiveness.

In situ thermal conductivity measurement revealing kinetics of thermochemical reactions

Utilizing thermochemical reactions for thermal energy storage and solar fuel production has been an emerging research topic. Thermal transport properties of the materials are an important parameter that can determine the kinetics and efficiency of thermochemical reactions. With the increasing number of new thermochemical materials (TCMs); however, there is a lack of reliable techniques to monitor the thermal transport property of the materials and their changes as a function of reactions in real time. In this work, we report the in situ monitoring of thermochemical reactions using modulated photothermal radiometry (MPR). The thermal conductivities of two TCMs, namely, calcium hydroxide (Ca(OH)2) and Ba0.15Sr0.85FeO3−δ (BSF1585), were measured as a function of temperature and time using the MPR technique. The measured thermal conductivities were correlated to the reaction. The work has two significant contributions to the research communities. First, it provides a non-invasive diagnostic tool for monitoring the thermal transport properties of TCMs that can potentially be a high-throughput measurement technique conducive to optimizing TCMs, reactors, and related thermal systems. Second, for TCMs that show observable changes in thermal transport properties, a correlation between the measured thermal conductivity and the conversion fraction of the reaction can be established for monitoring the reaction kinetics based on thermal characterization.

Microbial respiration in contrasting ocean provinces via high-frequency optode assays

Microbial respiration is a critical component of the marine carbon cycle, determining the proportion of fixed carbon that is subject to remineralization as opposed to being available for export to the ocean depths. Despite its importance, methodological constraints have led to an inadequate understanding of this process, especially in low-activity oligotrophic and mesopelagic regions. Here, we quantify respiration rates as low as 0.2 µmol O2 L-1 d-1 in contrasting ocean productivity provinces using oxygen optode sensors to identify size-fractionated respiration trends. In the low productivity region of the North Pacific Ocean at Station Papa, surface whole water microbial respiration was relatively stable at 1.2 µmol O2 L-1 d-1. Below the surface, there was a decoupling between respiration and bacterial production that coincided with increased phytodetritus and small phytoplankton. Size-fractionated analysis revealed that cells <5 µm were responsible for the majority of the respiration in the Pacific, both at the surface and below the mixed layer. At the North Atlantic Porcupine Abyssal Plain, surface whole water microbial respiration was higher (1.7 µmol O2 L-1 d-1) than in the Pacific and decreased by 3-fold below the euphotic zone. The Atlantic size-fraction contributions to total respiration shifted on the order of days during the evolution of a phytoplankton bloom with regular storm disturbances. The high-resolution optode method used in the Atlantic captured these significant shifts and is consistent with coinciding stain-based respiration methods and historical site estimates. This study highlights the dynamic nature of respiration across vertical, temporal, and size-fractionated factors, emphasizing the need for sensitive, high-throughput techniques to better understand ocean ecosystem metabolism.

Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding.

Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering analysis is a commonly used method in scRNA-seq data analysis, helping researchers identify cell types and uncover interactions between cells. However, the choice of a robust similarity metric in the clustering procedure is still an open challenge due to the complex underlying structures of the data and the inherent noise in data acquisition. Here, we propose a deep clustering method for scRNA-seq data called scRISE (scRNA-seq Iterative Smoothing and self-supervised discriminative Embedding model) to resolve this challenge. The model consists of two main modules: an iterative smoothing module based on graph autoencoders designed to denoise the data and refine the pairwise similarity in turn to gradually incorporate cell structural features and enrich the data information; and a self-supervised discriminative embedding module with adaptive similarity threshold for partitioning samples into correct clusters. Our approach has shown improved quality of data representation and clustering on seventeen scRNA-seq datasets against a number of state-of-the-art deep learning clustering methods. Furthermore, utilizing the scRISE method in biological analysis against the HNSCC dataset has unveiled 62 informative genes, highlighting their potential roles as therapeutic targets and biomarkers.

Comparative RNA-seq analysis of Arabidopsis thaliana response to AtPep1 and flg22, reveals the identification of PP2-B13 and ACLP1 as new members in pattern-triggered immunity.

In this research, a high-throughput RNA sequencing-based transcriptome analysis technique (RNA-Seq) was used to evaluate differentially expressed genes (DEGs) in the wild type Arabidopsis seedlings in response to AtPep1, a well-known peptide representing an endogenous damage-associated molecular pattern (DAMP), and flg22, a well-known microbe-associated molecular pattern (MAMP). We compared and dissected the global transcriptional landscape of Arabidopsis thaliana in response to AtPep1 and flg22 and could identify shared and unique DEGs in response to these elicitors. We found that while a remarkable number of flg22 up-regulated genes were also induced by AtPep1, 256 genes were exclusively up-regulated in response to flg22, and 328 were exclusively up-regulated in response to AtPep1. Furthermore, among down-regulated DEGs upon flg22 treatment, 107 genes were exclusively down-regulated by flg22 treatment, while 411 genes were exclusively down-regulated by AtPep1. We found a number of hitherto overlooked genes to be induced upon treatment with either flg22 or with AtPep1, indicating their possible involvement general pathways in innate immunity. Here, we characterized two of them, namely PP2-B13 and ACLP1. pp2-b13 and aclp1 mutants showed increased susceptibility to infection by the virulent pathogen Pseudomonas syringae DC3000 and its mutant Pst DC3000 hrcC (lacking the type III secretion system), as evidenced by increased proliferation of the two pathogens in planta. Further, we present evidence that the aclp1 mutant is deficient in ethylene production upon flg22 treatment, while the pp2-b13 mutant is deficient in the production of reactive oxygen species (ROS). The results from this research provide new information for a better understanding of the immune system in Arabidopsis.

High-throughput composition screening of high-entropy rare-earth monosilicates for superior CMAS corrosion resistance up to 1873 K

Superior calcium-magnesium-aluminum-silicate (CMAS) corrosion resistance is the key to the applications of high-entropy rare-earth monosilicates (HEREMs) as environmental barrier coatings (EBCs) for next-generation gas turbine engines. Here, we report a high-throughput composition screening strategy to achieve superior CMAS corrosion resistance in HEREMs for the first time. Specifically, we first fabricate ten kinds of (Ho0.25Yb0.25Lu0.25X0.25)2SiO5 (X = Sc, Tm, Er, Y, Dy, Gd, Eu, Sm, Nd, and La) HEREM samples using a high-throughput pressure-less sintering technique, and subsequently screen their CMAS corrosion resistance at 1673 K via a high-throughput testing apparatus. Among all the as-fabricated samples, the HEREM-Er samples are found to have the best CMAS corrosion resistance, even up to 1873 K, which outperforms the EBC materials that have been reported. Such superior CMAS corrosion resistance of the HEREM-Er samples is primarily attributed to the formation of a dense apatite barrier layer, which results from its decreased melting point, as confirmed by first-principles calculations and molecular dynamic simulations. Our work provides a guide for designing exceptional anti-CMAS corrosion high-entropy rare-earth silicates.

Metabolomics analysis reveals characteristic metabolites in different levels of oxaliplatin-induced neurotoxicity.

Oxaliplatin (L-OHP), a third-generation platinum-based anti-tumor drug, finds widespread application in the first-line treatment of metastatic colorectal cancer. Despite its efficacy, the drug's usage is curtailed by a litany of side effects, with L-OHP-induced peripheral neuropathy (OIPN) being the most debilitating. This condition can be classified into varying degrees of severity. Employing serum metabolomics, a high-sensitivity, high-throughput technique, holds promise as a method to identify biomarkers for clinical assessment and monitoring of OIPN patients across different severity levels. In our study, we analyzed serum metabolites in patients with different OIPN levels using ultra-performance liquid chromatography-high resolution mass spectrometry. By employing statistical analyses and pathway enrichment studies, we aimed to identify potential biomarkers and metabolic pathways. Our findings characterized the serum metabolic profiles of patients with varying OIPN levels. Notably, pathway analysis revealed a significant correlation with lipid metabolism, amino acid metabolism, and energy metabolism. Multivariate statistical analysis and receiver operator characteristic curve evaluation pointed to anhalamine and glycochenodeoxycholic acid as potential biomarkers for OIPN C and A, which suggest that serum metabolomics may serve as a potent tool for exploring the metabolic status of patients suffering from diverse diseases and for discovering novel biomarkers.

Theranostic Potential of Bacteriophages against Oral Squamous Cell Carcinoma.

Oral Squamous Cell Carcinoma (OSCC) is a widespread and challenging disease that accounts for 94% of cancers of the oral cavity worldwide. Bacteriophages (phages) have shown promise as a potential theranostic agent for the treatment of OSCC. It may offer advantages in overcoming the challenges of conventional methods. Modern high-throughput pyrosequencing techniques confirm the presence of specific bacterial strains associated with OSCC. Bio-panning and filamentous phages facilitate visualization of the peptide on surfaces and show high affinity in OSCC cells. The peptide has the potential to bind integrin (αvβ6), aid in diagnosis, and inhibit the proliferation of OSCC cells. Mimotopes of tumor-associated antigens show cytotoxic and immune responses against cancer cells. Biomarker-based approaches such as transferrin enable early OSCC diagnosis. A modified temperate phage introduces CRISPR-Cas3 to target antimicrobial-resistant bacteria associated with OSCC. The research findings highlight the evolving field of phage diagnostics and therapy and represent a new avenue for non-invasive, targeted approaches to the detection and treatment of OSCC. However, extensive clinical research is required to validate the efficacy of phages in innovative cancer theranostic strategies.

Who inhabits the built environment? A microbiological point of view on the principal bacteria colonizing our urban areas.

Modern lifestyle greatly influences human well-being. Indeed, nowadays people are centered in the cities and this trend is growing with the ever-increasing population. The main habitat for modern humans is defined as the built environment (BE). The modulation of life quality in the BE is primarily mediated by a biodiversity of microbes. They derive from different sources, such as soil, water, air, pets, and humans. Humans are the main source and vector of bacterial diversity in the BE leaving a characteristic microbial fingerprint on the surfaces and spaces. This review, focusing on articles published from the early 2000s, delves into bacterial populations present in indoor and outdoor urban environments, exploring the characteristics of primary bacterial niches in the BE and their native habitats. It elucidates bacterial interconnections within this context and among themselves, shedding light on pathways for adaptation and survival across diverse environmental conditions. Given the limitations of culture-based methods, emphasis is placed on culture-independent approaches, particularly high-throughput techniques to elucidate the genetic and -omic features of BE bacteria. By elucidating these microbiota profiles, the review aims to contribute to understanding the implications for human health and the assessment of urban environmental quality in modern cities.

Binary ZnY porous materials through integrated high-throughput approach

Porous materials, characterized by their important applications in catalysis, energy storage, and conversion, are predominantly derived from binary alloy systems due to the compositional complexity and the limited availability of phase diagrams for multi-component alloys. In this work, supergravity-induced solidification, a high-throughput approach, is employed to construct ternary MgZnY combinatorial libraries in a near-equilibrium state. This method utilizes the differences in densities and melting points among metal phases, where denser phases with higher melting points solidify and migrate towards the bottom, while lighter phases precipitate successively as the temperature decreases, resulting in samples with compositional and structural gradients along the centrifugal direction. Subsequently, a high-throughput vapor phase dealloying technique is developed based on the above bulk gradient samples, selectively volatilizing elements with high saturated vapor pressures and leaving behind residual elements that evolve into porous structures. Through the integrated high-throughput approach, we have successfully identified multiple precursor alloys capable of constructing intermetallic nanoporous Zn2Y1 (atomic percentage, at.%) with diverse morphologies. Furthermore, we have meticulously fabricated several alloys using the aforementioned high-throughput techniques. These alloys demonstrate consistent nanoporous compositions, underscoring the efficacy of our high-throughput approaches. This integrated high-throughput method significantly simplifies the exploration and development of porous materials, offering an innovative route to exploit the capabilities of multi-component alloy systems in a broad range of applications.

Brownification increases the abundance of microorganisms related to carbon and nitrogen cycling in shallow lakes

Brownification in aquatic ecosystems under global change has attracted attention. The composition and quantity of dissolved organic matter transported from various land use types to lakes differ significantly, causing varying ecological effects of lake brownification by region. Bacterial communities make a significant contribution to the material cycle of ecosystems and are sensitive to environmental changes. In this study, a series of mesocosm systems were used to simulate forest lakes and urban lakes with different degrees of brownification, and a high-throughput amplicon sequencing technique was used to explore the changes in the composition, structure, and function of bacterial communities in shallow lakes undergoing brownification. Principal coordinate analysis (PCoA) and Jensen‒Shannon distance typing analysis both indicated significant differences in bacterial communities between forest lakes and urban lakes. The α diversity of bacterial communities in urban lakes increased with the degree of brownification. However, whether forest lakes or urban lakes, brownification increased the abundance of carbon cycling-related bacterial phyla (Proteobacteria, Poribacteria, and Chloroflexi) and nitrogen cycling-related bacterial genera (Microbacteriaceae, Limnohabitans, Comamonadaceae, Bacillus, and Rhizobiales_Incertae_Sedis). Additionally, the carbon and nitrogen cycling functions of bacterial communities in forest lakes are dominant, while those in urban lakes are dominated by functions related to light. Our study has preliminarily revealed that lake brownification promotes the growth of carbon and nitrogen cycling microorganisms, providing a new paradigm for understanding the response of lake ecosystems in different catchment areas to environmental changes and the carbon and nitrogen cycling processes in shallow lake ecosystems.

Effects of Different Farming Modes on Salmo trutta fario Growth and Intestinal Microbial Community.

The gut microbiota plays a pivotal role in upholding intestinal health, fostering intestinal development, fortifying organisms against pathogen intrusion, regulating nutrient absorption, and managing the body's lipid metabolism. However, the influence of different cultivation modes on the growth indices and intestinal microbes of Salmo trutta fario remains underexplored. In this study, we employed high-throughput sequencing and bioinformatics techniques to scrutinize the intestinal microbiota in three farming modes: traditional pond aquaculture (TPA), recirculating aquaculture (RA), and flow-through aquaculture (FTA). We aimed to assess the impact of different farming methods on the water environment and Salmo trutta fario's growth performance. Our findings revealed that the final weight and weight gain rate in the FTA model surpassed those in the other two. Substantial disparities were observed in the composition, relative abundance, and diversity of Salmo trutta fario gut microbiota under different aquaculture modes. Notably, the dominant genera of Salmo trutta fario gut microbiota varied across farming modes: for instance, in the FTA model, the most prevalent genera were SC-I-84 (7.34%), Subgroup_6 (9.93%), and UTCFX1 (6.71%), while, under RA farming, they were Bacteroidetes_vadinHA17 (10.61%), MBNT15 (7.09%), and Anaeromyxoactor (6.62%). In the TPA model, dominant genera in the gut microbiota included Anaeromyxobacter (8.72%), Bacteroidetes_vadinHA17 (8.30%), and Geobacter (12.54%). From a comparative standpoint, the genus-level composition of the gut microbiota in the RA and TPA models exhibited relative similarity. The gut microbiota in the FTA model showcased the most intricate functional diversity, while TPA farming displayed a more intricate interaction pattern with the gut microbiota. Transparency, pH, dissolved oxygen, conductivity, total dissolved solids, and temperature emerged as pivotal factors influencing Salmo trutta fario gut microbiota under diverse farming conditions. These research findings offer valuable scientific insights for fostering healthy aquaculture practices and disease prevention and control measures for Salmo trutta fario, holding substantial significance for the sustainable development of the cold-water fish industry in the Qinghai-Tibet Plateau.

High-throughput single-microbe RNA sequencing reveals adaptive state heterogeneity and host-phage activity associations in human gut microbiome.

Microbial communities such as those residing in the human gut are highly diverse and complex, and many with important implications in health and diseases. The effects and functions of these microbial communities are determined not only by their species compositions and diversities but also by the dynamic intra- and inter-cellular states at the transcriptional level. Powerful and scalable technologies capable of acquiring single-microbe-resolution RNA sequencing information in order to achieve comprehensive understanding of complex microbial communities together with their hosts is therefore utterly needed. Here we report the development and utilization of a droplet-based smRNA-seq (single-microbe RNA sequencing) method capable of identifying large species varieties in human samples, which we name smRandom-seq2. Together with a triple-module computational pipeline designed for the bacteria and bacteriophage sequencing data by smRandom-seq2 in four human gut samples, we established a single-cell level bacterial transcriptional landscape of human gut microbiome, which included 29,742 single microbes and 329 unique species. Distinct adaptive responses states among species in Prevotella and Roseburia genus and intrinsic adaptive strategy heterogeneity in Phascolarctobacterium succinatutens were uncovered. Additionally, we identified hundreds of novel host-phage transcriptional activity associations in the human gut microbiome. Our results indicated the smRandom-seq2 is a high-throughput and high-resolution smRNA-seq technique that is highly adaptable to complex microbial communities in real-word situations and promises new perspectives in the understanding of human microbiomes.

Post-stroke cognitive impairment: exploring molecular mechanisms and omics biomarkers for early identification and intervention

Post-stroke cognitive impairment (PSCI) is a major stroke consequence that has a severe impact on patients’ quality of life and survival rate. For this reason, it is especially crucial to identify and intervene early in high-risk groups during the acute phase of stroke. Currently, there are no reliable and efficient techniques for the early diagnosis, appropriate evaluation, or prognostication of PSCI. Instead, plenty of biomarkers in stroke patients have progressively been linked to cognitive impairment in recent years. High-throughput omics techniques that generate large amounts of data and process it to a high quality have been used to screen and identify biomarkers of PSCI in order to investigate the molecular mechanisms of the disease. These techniques include metabolomics, which explores dynamic changes in the organism, gut microbiomics, which studies host–microbe interactions, genomics, which elucidates deeper disease mechanisms, transcriptomics and proteomics, which describe gene expression and regulation. We looked through electronic databases like PubMed, the Cochrane Library, Embase, Web of Science, and common databases for each omics to find biomarkers that might be connected to the pathophysiology of PSCI. As all, we found 34 studies: 14 in the field of metabolomics, 5 in the field of gut microbiomics, 5 in the field of genomics, 4 in the field of transcriptomics, and 7 in the field of proteomics. We discovered that neuroinflammation, oxidative stress, and atherosclerosis may be the primary causes of PSCI development, and that metabolomics may play a role in the molecular mechanisms of PSCI. In this study, we summarized the existing issues across omics technologies and discuss the latest discoveries of PSCI biomarkers in the context of omics, with the goal of investigating the molecular causes of post-stroke cognitive impairment. We also discuss the potential therapeutic utility of omics platforms for PSCI mechanisms, diagnosis, and intervention in order to promote the area’s advancement towards precision PSCI treatment.

High-Throughput Multitarget Molecular Detection in an Automatic Light-Addressable Photoelectrochemical Sensing Platform.

Successively emerged high-throughput multitarget molecular detection methods bring significant development tides in chemical, biological, and environmental fields. However, several persistent challenges of intricate sample preparation, expensive instruments, and tedious and skilled operations still need to be further addressed. Here, we propose an automatic light-addressable photoelectrochemical (ALA-PEC) sensing platform for sensitive and selective detection of multitarget molecules. With Au nanoparticle-decorated TiO2 nanotube photonic crystals (Au-TiO2 NTPCs) as a photoelectrode and 8 kinds of antibiotics as target molecules, the ALA-PEC sensing system implements automatic detection of multimolecules in a short time with high sensitivity and good selectivity. Random samples with different amounts of antibiotics have been well-distinguished in the ALA-PEC system, and both the chemical components and concentrations have been well-illustrated in a pattern recognition model. It is worth noting that 8 samples are not the limit of the ALA-PEC sensing platform, which can be easily expanded to more complex detection arrays based on practical needs. The emerging ALA-PEC sensing platform provides a new solution for rapid screening and detection of multitarget and high-throughput substances and potentially brings the automatic, portable, sensitive, high-throughput, and cost-effective detection technique to an entire new realm.