High-throughput Generation Research Articles

Integrating OMICS-based platforms and analytical tools for diagnosis and management of pancreatic cancer: a review.

Cancer remains the second leading cause of death worldwide, surpassed only by cardiovascular disease. From the different types of cancer, pancreatic cancer (PaC) has one of the lowest survival rates, with a survival rate of about 20% after the first year of diagnosis and about 8% after 5 years. The lack of highly sensitive and specific biomarkers, together with the absence of symptoms in the early stages, determines a late diagnosis, which is associated with a decrease in the effectiveness of medical intervention, regardless of its nature - surgery and/or chemotherapy. This review provides an updated overview of recent studies combining multi-OMICs approaches (e.g., proteomics, metabolomics) with analytical tools, highlighting the synergy between high-throughput molecular data generation and precise analytical tools such as LC-MS, GC-MS and MALDI-TOF MS. This combination significantly improves the detection, quantification and identification of biomolecules in complex biological systems and represents the latest advances in understanding PaC management and the search for effective diagnostic tools. Large-scale data analysis coupled with bioinformatics tools enables the identification of specific genetic mutations, gene expression patterns, pathways, networks, protein modifications and metabolic signatures associated with PaC pathogenesis, progression and treatment response through the integration of multi-OMICs data.

Facile and versatile PDMS-glass capillary double emulsion formation device coupled with rapid purification toward microfluidic giant liposome generation

The exceptional ability of liposomes to mimic a cellular lipid membrane makes them invaluable tools in biomembrane studies and bottom-up synthetic biology. Microfluidics provides a promising toolkit for creating giant liposomes in a controlled manner. Nevertheless, challenges associated with the microfluidic formation of double emulsions, as precursors to giant liposomes, limit the full exploration of this potential. In this study, we propose a PDMS-glass capillary hybrid device as a facile and versatile tool for the formation of double emulsions which not only eliminates the need for selective surface treatment, a well-known problem with PDMS formation chips, but also provides fabrication simplicity and reusability compared to the glass-capillary formation chips. These advantages make the presented device a versatile tool for forming double emulsions with varying sizes (spanning two orders of magnitude in diameter), shell thickness, number of compartments, and choice of solvents. We achieved robust thin shell double emulsion formation by operating the hybrid chip in double dripping mode without performing hydrophilic/phobic treatment a priori. In addition, as an alternative to the conventional, time-consuming density-based separation method, a tandem separation chip is developed to deliver double emulsions free of any oil droplet contamination in a continuous and rapid manner without any need for operator handling. The applicability of the device was demonstrated by forming giant liposomes using the solvent extraction method. This easy-to-replicate, flexible, and reliable microfluidic platform for the formation and separation of double emulsion templates paves the way for the high-throughput microfluidic generation of giant liposomes and synthetic cells, opening exciting avenues for biomimetic research.The presented giant liposome assembly line features a novel treatment-free hybrid chip for double emulsion formation coupled with a high throughput separation chip for sample purification.

Clinical and Genomic Phenotype of Brain Metastasis in Nasopharyngeal Carcinoma.

Brain metastasis in nasopharyngeal carcinoma is a rare but poor prognosis clinical problem. This study aims to investigate the clinical characteristics and identify the genomic profiling of nasopharyngeal carcinoma brain metastasis. Patients with a diagnosis of nasopharyngeal carcinoma who visited at the Fifth Affiliated Hospital of Sun Yat-sen University since January 2013 to December 2023 were retrospectively collected. Clinical data of patients diagnosed with nasopharyngeal carcinoma brain metastasis were extracted. Paraffin blocks of NPC brain metastases were acquired for immunohistochemistry and genetic testing. High-throughput second generation sequencing was performed for genomic analysis. The mutation landscape was further analyzed. Of the 2378 NPC patients from our database, only six were clinically diagnosed with nasopharyngeal carcinoma brain metastasis. Three were pathologically diagnosed with nasopharyngeal carcinoma brain metastasis. The time interval from the first diagnosis of nasopharyngeal carcinoma to brain metastasis was 15-56 months. The common sites of brain metastasis were frontal lobe and cerebellum, and could be single or multiple, cystic or solid lesions. The OS ranged from 7 to 48 months. Single nucleotide variants were found in 32 genes, such as PTEN, TP53, NFKBIA, KMT2C, and NOTCH1. Copy number variation occurred in five genes, including PTEN, CCDN1, FGF19, FGF3 and FGF4. PTEN and fibroblast growth factors might be involved in the molecular regulation of brain metastasis in nasopharyngeal carcinoma.

Single-Step Electrochemical Upcycling of PET: Waste to Value-Added Chemicals, Oral Presentation

Globally, an estimated 7.4 billion tons of plastic have accumulated, with projections suggesting this could escalate to 40 billion metric tons by 2050. Unfortunately, only about 10% of this plastic is recycled, due to the lack of sustainable and economical solutions [1]. Alarmingly, over 70% of plastic waste ends up in landfills or oceans where it gradually decomposes into small particles (average diameter less than 5 mm), often referred to as microplastics (MPs). This widespread presence of microplastics poses a significant hazard to global ecosystems and human health. Microplastics can be ingested by organisms through various pathways including consumption of contaminated food, inhalation of polluted air, and dermal contact [2].Polyethylene terephthalate (PET), is the second most abundant plastic produced worldwide due to its extensive use in consumer packaging. Conventional mechanical recycling of PET often leads to the loss of its desirable properties [3]. Therefore, there is significant interest in chemical recycling technologies capable of depolymerizing PET. Through chemical hydrolysis at high temperatures, PET can be broken down into its constituent monomers, terephthalic acid and ethylene glycol. Nonetheless, the subsequent chemical separation of these products, particularly ethylene glycol (EG), presents challenges due to its high boiling point, viscosity, and water solubility, necessitating energy-intensive distillation processes. In response to this challenge, electrochemical methods have gained popularity for oxidizing PET hydrolysates into valuable products such as formic acid and glycolate, thereby offering a more sustainable pathway for upcycling [4]. However, most studies on electrochemical upcycling of PET still utilize a two-stage process. Initially, PET is hydrolyzed in a potassium hydroxide KOH solution at temperatures exceeding 80°C. Subsequently hydrolysate is used as an electrolyte to produce formate at the anode while hydrogen is produced at the cathode.Botte's group is investigating electrochemical approaches to convert plastics into valuable chemicals [5]. An effective enhanced electrocatalytic hydrolysis process for recycling PET waste into value-added chemicals was developed. This novel approach integrates the hydrolysis of PET with the concurrent electrochemical oxidation of ethylene glycol employing transition metals as catalyst and using mid temperature conditions. The effect of different variables on the overall conversion and the product distribution were evaluated. Operating time, voltage and particle size were varied, and the end products were characterized by FTIR, SEM, NMR, and IC. Our studies indicate that the electrochemical approach enhanced PET hydrolysis in comparison to traditional chemical hydrolysis [6] along with the oxidation of ethylene glycol into formic acid/formate in a single process eliminating the need for separate hydrolysis. Optimization of this process could be a sustainable approach for upcycling of PET microplastic into valuable products.[1] Li, M. and S. Zhang, Tandem Chemical Depolymerization and Photoreforming of Waste PET Plastic to High-Value-Added Chemicals. ACS Catal. 2024. 14(5): p. 2949-2958,doi: 10.1021/acscatal.3c05535.[2] E. Bäckström, K. Odelius, and M. Hakkarainen, “Trash to Treasure: Microwave-Assisted Conversion of Polyethylene to Functional Chemicals,” Ind. Eng. Chem. Res., vol. 56, no. 50, pp. 14814–14821, Dec. 2017, doi: 10.1021/acs.iecr.7b04091.[3] R. K. Brizendine et al., “Particle Size Reduction of Poly(ethylene terephthalate) Increases the Rate of Enzymatic Depolymerization But Does Not Increase the Overall Conversion Extent,” ACS Sustain. Chem. Eng., vol. 10, no. 28, pp. 9131–9140, Jul. 2022, doi: 10.1021/acssuschemeng.2c01961.[4] Y. Li, L. Q. Lee, H. Zhao, Y. Zhao, P. Gao, and H. Li, “Alcohol–alkali hydrolysis for high-throughput PET waste electroreforming-assisted green hydrogen generation,” J. Mater. Chem. A, vol. 12, no. 4, pp. 2121–2128, Jan. 2024, doi: 10.1039/D3TA05522A.[5] G. G. Botte, “Processes for electrochemical up-cycling of plastics and systems thereof,” WO2021257972A1, Dec. 23, 2021 Accessed: Jul. 21, 2023. [Online]. Available: https://patents.google.com/patent/WO2021257972A1/en[6] F. Lu and G. G. Botte, “Understanding the Electrochemically Induced Conversion of Urea to Ammonia Using Nickel Based Catalysts,” Electrochimica Acta, vol. 246, pp. 564–571, Aug. 2017, doi: 10.1016/j.electacta.2017.06.055.

Spherical Shell Bioprinting to Produce Uniform Spheroids with Controlled Sizes.

Conventional cell spheroid production methods are largely manual, leading to variations in size and shape that compromise consistency and reliability for use in cell-based therapeutic applications. To enhance spheroid production, a spherical shell bioprinting system was implemented, enabling the high-throughput generation of uniform cell spheroids with precisely controlled sizes. The system encapsulates cells within thin alginate hydrogel shells formed through bioprinting and ion crosslinking reactions. Alginate-calcium ion crosslinking created alginate shells that contained gelatin-based bioinks with embedded cells, facilitating spontaneous cell aggregation within the shells and eliminating the need for plastic wells. By adjusting cell concentrations in the alginate-gelatin bioink, we achieved precise control over spheroid size, maintaining a sphericity above 0.94 and size deviations within ±10 µm. This method has been successfully applied to various cell types including cancer cells, fibroblasts, chondrocytes, and epithelial cells, demonstrating its versatility. This scalable approach enhances the reliability of cell therapy and drug screening, offering a robust platform for future biomedical applications.

Lightweight high-throughput true random number generator based on state switchable ring oscillator

True random number generators (TRNGs) perform an extremely critical role in cryptographic algorithms and security protocols, scientific simulation, industrial testing, privacy protection, and numerous other domains. Nevertheless, modern TRNGs have difficulty striking a reasonable balance between high throughput and low hardware consumption. In this paper, a novel lightweight high-throughput TRNG based on state switchable ring oscillators (SSROs) is proposed. Under the effect of flip-flops that are prone to entering the metastable region, the SSROs randomly switch between oscillatory and buffer states to create jitter and metastability. A feedback strategy is adopted to effectively eliminate the fixed point in the circuit, which further enhances the randomness of the structure. The proposed TRNG is implemented on Xilinx Artix-7 and Kintex-7 FPGAs, with support for automatic routing. It achieves a throughput of up to 400 Mbps while consuming only 16 LUTs and 13 DFFs, showing extremely high resource utilization efficiency. Experimental results show that the output random sequence passes the NIST SP800-22 test, the NIST SP800-90B test, and the AIS-31 test without any post-processing, exhibiting strong robustness against voltage and temperature variations as well as frequency injection attacks.

High-throughput Generation of Collagen Microbeads for 3D Cell Culture and Extracellular Vesicle Production.

Collagen type I, a fundamental component of natural extracellular matrices across species, is an attractive material for the development of tissue engineering constructs. Nevertheless, the poor mechanical properties and thermal instability hinder its use for specific construction of 3D cultures and 3D printing. In this study, we present a microfluidic high-throughput approach for producing high-quality and uniform collagen microbeads without introducing any chemical modification. We achieved rapid and uniform collagen droplet fabrication with sizes spanning from 50 μm to 1200 μm in a production rate of up to 10000 droplets per minute. The resulting collagen microbeads can serve as numerous microbioreactors which are suspended in the culture medium without precipitation and are ideal for 3D cell growth. We demonstrated excellent cell compatibility, facilitating cell attachment and proliferation, as well as promoting extracellular vesicle secretion from collagen microbeads. This technology is facile and versatile for high throughput 3D cell culture, heterogeneous tissue modeling, and extracellular vesicle production, which is essential in drug delivery and drug screening.

Atomic-scale investigation on the structures and mechanical properties of metastable grain boundaries in aluminum

Grain boundary (GB) structural transition in pure metals open up new possibilities for material microstructure design. By managing the distribution of ground-state and metastable GB structures, the beneficial effects of GBs on materials can be maximised. However, the application of GB transitions to develop high-performance aluminum is limited by the inadequate understanding of the metastable GB structure in aluminium and its associated plastic deformation mechanisms. In this work, we firstly used a high-throughput GB generation method to construct multiple GB structure for a given misorientation angle, and screen out the ground-state and metastable GB structures according to the energy principle. Six typical symmetrically tilted GBs were investigated, including Σ5(210), Σ17(410) and Σ17(530) GBs around <001> tilt axis, and Σ9(221), Σ11(332), Σ17(223) GBs around <110> axis. Then, molecular dynamics simulations were carried out to perform uniaxial tensile tests on the six GBs and their corresponding metastable GBs at different temperatures. The results show that the tensile strength of <001> metastable GBs differs slightly from the ground-state GB at various temperatures, but for <110> GBs, the strength of most metastable GBs is significantly stronger or weaker compared to that of ground-state GB at 10 K. An increase in temperature reduced this difference in tensile strength of <110> GBs. According to atomic analysis of the GB structural characteristics, it was found that stress and temperature-induced GB structural transition, GB strain localization, and the prevention of dislocation nucleation or propagation from the dissociated structure are the main causes of the differences in tensile responses between the ground-state and metastable GBs.

The Mutscore metapredictor: a new application of artificial intelligence in cardiogenetics

Abstract Introduction The rapid development of high-throughput next generation sequencing has shifted the limits of genetic knowledge from variant sequencing to variant interpretation. The current standards recommend that sequence variants are interpreted according to a comprehensive analysis including, among others, computational data. The two major types of in-silico predictive tools encompass those predicting the theoretical variant effect on splicing and algorithms predicting the potential damage of missense variants. However, overall for missense variants, prediction accuracy is often limited and results are sometimes inconsistent between software programs. Purpose We aimed at testing the predictive performance of a new predictive algorithm (Mutscore) for missense variants based on a machine learning approach, in our cohort of families referred for hereditary cardiac diseases. Methods We retrospectively reviewed DNA sequencing results from 230 families (from 01.07.2014 till 01.07.2023) addressed to our center for channelopathy or hereditary cardiomyopathy. Missense variants were identified and evaluated according to the current standards of the American College of Medical Genetics and Genomics. Variants were analysed with the commonly used in-silico tools CADD, Polyphen, Alpha-missense and Revel. We further applied the Mutscore, a new metapredictor integrating 16 existing predictive tools to data on variant topographical location. Results Among our 230 families, we detected 252 missense variants (class I-II 2.4%, class III 62.3%, class IV and V 15.9% and 19.4% respectively). We observed a significant positive correlation (r = 0.59, p=0.000) between the Mutscore and the interpretation of variants according to standards. The Mutscore had a better predictive performance than Polyphen (AUC 0.83 vs 0.67, p=0.007), Alpha-missense (AUC 0.87 vs 0.79, p=0.047) and CADD (AUC 0.87 vs 0.70, p=0.0001). Compared to Revel, the Mutscore had a comparable predictive performance (0.89 vs 0.87, p=NS), but a better sensitivity (75% vs 66%) at the maximum tolerated false positive rate of 10%. Figure 1. Focusing on variants of uncertain significance (VUS), we applied Mutscore cut-off values which were identified to reclassify variants in our previous study (1). Importantly, the Mutscore reclassified 45% of VUS into likely benign/pathogenic variants. Figure 2. Conclusions The Mutscore, through its metaprediction approach integrating data on variant topographical location, improves the accuracy of pathogenicity prediction as compared to three out of four algorithms commonly used in clinical practice, and seems to represent a valuable tool contributing to VUS disambiguation.

MAGIC matrices: freeform bioprinting materials to support complex and reproducible organoid morphogenesis.

Organoids are powerful models of tissue physiology, yet their applications remain limited due to their relatively simple morphology and high organoid-to-organoid structural variability. To address these limitations we developed a soft, composite yield-stress extracellular matrix that supports optimal organoid morphogenesis following freeform 3D bioprinting of cell slurries at tissue-like densities. The material is designed with two temperature regimes: at 4 °C it exhibits reversible yield-stress behavior to support long printing times without compromising cell viability. When transferred to cell culture at 37 °C, the material cross-links and exhibits similar viscoelasticity and plasticity to basement membrane extracts such as Matrigel. We first characterize the rheological properties of MAGIC matrices that optimize organoid morphogenesis, including low stiffness and high stress relaxation. Next, we combine this material with a custom piezoelectric printhead that allows more reproducible and robust self-organization from uniform and spatially organized tissue "seeds." We apply MAGIC matrix bioprinting for high-throughput generation of intestinal, mammary, vascular, salivary gland, and brain organoid arrays that are structurally similar to those grown in pure Matrigel, but exhibit dramatically improved homogeneity in organoid size, shape, maturation time, and efficiency of morphogenesis. The flexibility of this method and material enabled fabrication of fully 3D microphysiological systems, including perfusable organoid tubes that experience cyclic 3D strain in response to pressurization. Furthermore, the reproducibility of organoid structure increased the statistical power of a drug response assay by up to 8 orders-of-magnitude for a given number of comparisons. Combined, these advances lay the foundation for the efficient fabrication of complex tissue morphologies by canalizing their self-organization in both space and time.

High-throughput generation of monodisperse double emulsions via controllable symmetric splitting in Y-shaped microchannels

To achieve high-throughput generation of monodisperse double emulsions via controllable symmetric splitting, the splitting dynamics of double emulsions in Y-shaped microchannels are systematically investigated by the combination of experimental study and numerical simulation. Four distinct breakup flow patterns of double emulsions splitting are initially identified, namely non-breakup, once uneven breakup, twice uneven breakup and twice even breakup, and the underlying hydrodynamic mechanisms of each flow pattern are further elucidated through the flow field structures and pressure distributions. The results show that the upstream pressure created by the flow obstruction of continuous phase governs the stable and symmetric splitting of double emulsions in Y-shaped microchannels. Effects of the flowrates of continuous phase, the outer and inner diameters of double emulsions, the physical properties of all the inner, middle and outer fluids as well as the angle of Y-shaped microchannel on the flow pattern transition laws are systematically clarified, and the corresponding universal flow pattern diagrams for predicting the critical boundary for the transition of flow patterns are established. Based on controllable symmetric splitting of double emulsions, three-level splitting microfluidic networks are rationally designed to achieve high-throughput generation of monodisperse double emulsions as well as monodisperse microcapsules via template synthesis. The results in this study not only advance the deep understanding of the breakup dynamics of double emulsions in Y-shaped microchannels, but also offer valuable guidance for the rational design of microfluidic devices for mass production of monodisperse double emulsions, thus providing a significant contribution to the fields of micro-chemical engineering and droplet microfluidics.

Adaptive Data Sampling and Feature Optimization with LSH-IS and Pearson Correlation in Big Data Contexts

A huge amount of data is produced with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). Storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. Instance and Feature selection has been a key research area in data mining, which chooses a subset of relevant features for use in model building. This paper aims to provide an overview of instance and feature selection methods for big data mining. It first discusses the current challenges and difficulties faced when mining valuable information from big data.Instance and feature selection has become an effective approach due to enormous data which is continuously being produced in the field of research. It is difficult to process such large datasets by many systems. Though the traditional techniques are useful for large datasets, the numbers when in hundreds, thousands or millions face scaling problems. The proposed work focuses on, scalable instance and feature selection in big data environment. Locality-sensitive hashing instance selection (LSH-IS) is a two pass method used to find similar instances along with Pearson correlation coefficient for feature selection. Hash function family is used which is a general method of reducing the size of a set; this is achieved by re- indexing the elements into buckets. This process find similar instance in same bucket, hence instance can be reduced. The work aims at improving the performance of locality sensitive hashing by storing additional information of the instances and features assigned of each class in the bucket and also to improve accuracy of instance and feature selection algorithm.

Deep learning model for precise prediction and design of low-melting point phthalonitrile monomers

Phthalonitrile is a widely applied resin due to its outstanding high-temperature resistance and versatility. However, the high melting point of its monomer limits its range of applications. Due to the scarcity of data, traditional deep learning models face challenges in accurately predicting the properties of phthalonitrile monomers. In this work, we propose a novel deep learning model based on techniques such as molecular similarity weighting, pre-training parameter averaging, and hybrid neural networks to address the issue of data sparsity. Our model exhibits exceptionally high accuracy for predicting the melting point of phthalonitrile monomers, e.g., the mean absolute error on the experimental dataset is 15.3 °C. To explore novel low-melting monomers, we develop a high-throughput molecule generation software and use our model to filter the virtual molecules, and ultimately obtain candidates that meet low-melting and synthesizability requirements. Moreover, we have successfully synthesized one candidate molecule, and the high consistency between our predictions and experimental measurements again demonstrates our model’s accuracy. Meanwhile, molecule dynamic simulations are conducted to provide explanations for the reduction in melting point. This study not only provides guidance for designing phthalonitrile monomers, but also proposes an innovative approach for exploring of new materials under conditions of sparse data.

High-throughput true random number generator based on a dual-input oscillation circuit

The rapid development in the fields of information encryption, key generation, and algorithm initialization requires an increasing rate of true random numbers, making it necessary to design a fast true random number generator. In this work, a dual-mode conversion high throughput true random number generator (TRNG) based on a dual input oscillating XOR circuit (DIO-XOR) is proposed, which is designed using DIO-XOR, combined with a MUX selects the feedback mode of the whole circuit: self-feedback/mutual feedback. It is verified by automatic layout and routing on Xilinx Artix-7 and Kintex-7 FPGAs with a throughput rate of 650Mbps, which is achieved with low hardware overhead, and passes the entropy estimation test suite, NIST SP800-90B, as well as TESTU01 with high entropy, the sequences generated in the temperature-voltage fluctuation test pass the NIST SP800-22 test with good robustness.

Fast and Accurate Recognition of Perovskite Fluorescent Anti-counterfeiting Labels Based on Lightweight Convolutional Neural Networks.

Anti-counterfeiting technology has always been a key issue in the field of information security. Physical Unclonable Function (PUF) labels, which are random patterns produced by a stochastic process, emerge as an effective anti-counterfeiting strategy due to the inherent randomness of their physical patterns. In this study, we developed a high-throughput droplet array generation technique based on surface tension confinement to prepare perovskite crystal films with controllable shapes and sizes. We utilized the random distribution of perovskite nanocrystal particles to construct the PUF textures of the labels. Compared to other anti-counterfeiting labels, our labels not only possess fluorescent properties but also feature microscale dimensions (less than 5.3 × 10-2mm2), low cost (less than 3 × 10-4 USD), and high encoding capacity (1.7 × 101956), providing support for multilevel anti-counterfeiting protection. Additionally, we introduce an innovative PUF recognition method based on a Partial Convolutional Network (PaCoNet), effectively addressing the limitations of previous methods, in terms of recognition accuracy and speed. Experimental validation on a data set of perovskite nanocrystal films with up to 60 different macroscopic shapes and unique microscopic textures demonstrates that our method achieves a recognition accuracy of up to 99.65% and significantly reduces the recognition time per image to just 0.177 s, highlighting the potential application of these labels in the field of anti-counterfeiting.

Tailoring esophageal tumor spheroids on a chip with inverse opal scaffolds for drug screening

Esophageal cancer (EC) is characterized by high morbidity and mortality, and chemotherapy has become an indispensable means for comprehensive treatment. However, due to the limitation of the effective in vitro disease model, the development of chemotherapeutic agents still faces great challenges. In this paper, we present a novel tumor spheroid on a chip platform based on inverse opal hydrogel scaffolds to screen chemotherapeutic agents for EC treatment. With the microfluidic emulsion approach, the inverse opal hydrogel scaffolds were generated with tunable and organized pores, which could provide spatial confinement for cell growth. Thus, the suspended KYSE-70 cells could successfully form uniform cell spheroids on the inverse opal hydrogel scaffolds. It was demonstrated that the tumor cell spheroids could recapitulate 3D growth patterns in vivo and exhibited higher sensitivity to the chemotherapy agents compared with monolayer cells. Besides, by employing the scaffolds into a microfluidics to construct esophageal tumor on a chip, the device could realize high-throughput tumor cell spheroids generation and drug screening, indicating its promising role in chemotherapy drug development.

Generalized predictive analysis of reactions in paper devices via graph neural networks

Microfluidic technology facilitates high-throughput generation of time series data for biological and medical studies. Deep learning enables accurate, predictive analysis and proactive decision-making based on autonomous recognition of intricate pattern hidden in series. In this work, we first devised a paper-based microfluidic system for portable nucleic acid amplification test with economic energy consumption. Then, we employed Graph Neural Network (GNN), distinguished by its non-Euclidean data structure tailored for deep learning, with spatio-temporal attention mechanism to perform near-sensor predictive analysis of the on-chip reaction. Our findings demonstrated that the novel GNN model can provide accurate predictions of positive outcomes at the early stages of the reaction using less than one-third of the total reaction time. Then, the deep learning model trained by on-chip data was subsequently applied to more than 900 clinical plots. Generalization of the GNN model was successfully validated across different detection methods, diverse types of datasets and time series with variable length. Accuracy, sensitivity and specificity of the predictive approach were 96.5 %, 94.3 % and 99.0 % by utilizing the early half of reaction information. Finally, we compared the GNN model with various deep learning models. Despite differences in the prediction of negative samples among various models were minute, GNN obviously offered overall superior performance. This work ignites a cutting-edge application of deep learning in point-of-care and near-sensor tests. By harnessing the power of body area networks and edge/fog computing, our approach unlocks promising possibilities in diverse fields like healthcare and instrument science.

Evidence for the production of asexual resting cysts in a free-living species of Symbiodiniaceae (Dinophyceae)

Coral reef ecosystems are the most productive and biodiverse marine ecosystems, with their productivity levels highly dependent on the symbiotic dinoflagellates belonging to the family Symbiodiniaceae. As a unique life history strategy, resting cyst production is of great significance in the ecology of many dinoflagellate species, those HABs-causing species in particular, however, there has been no confirmative evidence for the resting cyst production in any species of the family Symbiodiniaceae. Based on morphological and life history observations of cultures in the laboratory and morpho-molecular detections of cysts from the marine sediments via fluorescence in situ hybridization (FISH), cyst photography, and subsequent singe-cyst PCR sequencing, here we provide evidences for the asexual production of resting cysts by Effrenium voratum, the free-living, red tide-forming, and the type species of the genus Effrenium in Symbiodiniaceae. The evidences from the marine sediments were obtained through a sequential detections: Firstly, E. voratum amplicon sequence variants (ASVs) were detected in the cyst assemblages that were concentrated with the sodium polytungstate (SPT) method from the sediments collected from different regions of China Seas by high-throughput next generation sequencing (NGS); Secondly, the presence of E. voratum in the sediments was detected by PCR using the species-specific primers for the DNA directly extracted from sediment; Thirdly, E. voratum cysts were confirmed by a combined approach of FISH using the species-specific probes, light microscopic (LM) photography of the FISH-positive cysts, and a subsequent single-cyst PCR sequencing for the FISH-positive and photographed cysts. The evidences from the laboratory-reared clonal cultures of E. voratum include that: 1) numerous cysts formed in the two clonal cultures and exhibited a spherical shape, a smooth surface, absence of ornaments, and a large red accumulation body; 2) cysts could maintain morphologically intact for a storage of two weeks to six months at 4 °C in darkness and of which 76–92 % successfully germinated through an internal development processes within a time period of 3–21 days after being transferred back to the normal culturing conditions; 3) two or four germlings were released from each cyst through the cryptopylic archeopyle in all cysts with continuous observations of germination processes; and 4) while neither sexual mating of gametes nor planozygote (cells with two longitudinal flagella) were observed, the haploidy of cysts was proven with flow cytometric measurements and direct LM measurements of fluorescence from cells stained with either propidium iodide (PI) or DAPI, which together suggest that the cysts were formed asexually. All evidences led to a conclusion that E. voratum is capable of producing asexual resting cysts, although its sexuality cannot be completely excluded, which guarantees a more intensive investigation. This work fills a gap in the knowledge about the life cycle, particularly the potential of resting cyst formation, of the species in Symbiodiniaceae, a group of dinoflagellates having unique life forms and vital significance in the ecology of coral reefs, and may provide novel insights into understanding the recovery mechanisms of coral reefs destructed by the global climate change and suggest various forms of resting cysts in the cyst assemblages of dinoflagellates observed in the field sediments, including HABs-causing species.

Development of a novel high-throughput culture system for hypoxic 3D hydrogel cell culture.

Animal models lack physiologic relevance to the human system which results in low clinical translation of results derived from animal testing. Besides spheroids or organoids, hydrogel-based 3D in vitro models are used to mimic the in vivo situation increasing the relevance while reducing animal testing. However, to establish hydrogel-based 3D models in applications such as drug development or personalized medicine, high-throughput culture systems are required. Furthermore, the integration of oxygen-reduced (hypoxic) conditions has become increasingly important to establish more physiologic culture models. Therefore, we developed a platform technology for the high-throughput generation of miniaturized hydrogels for 3D cell culture. The Oli-Up system is based on the shape of a well-plate and allows for the parallel culture of 48 hydrogel samples, each with a volume of 15µl. As a proof-of-concept, we established a 3D culture of gelatin-methacryloyl (GelMA)-encapsulated mesenchymal stem/stromal cells (MSCs). We used a hypoxia reporter cell line to establish a defined oxygen-reduced environment to precisely trigger cellular responses characteristicof hypoxia in MSCs. In detail, the expression of hypoxia response element (HRE) increased dependent on the oxygen concentration and cell density. Furthermore, MSCs displayed an altered glucose metabolism and increased VEGF secretion upon oxygen-reduction. In conclusion, the Oli-Up system is a platform technology for the high-throughput culture of hydrogel-based 3D models in a defined oxygen environment. As it is amenable for automation, it holds the potential for high-throughput screening applications such as drug development and testing in more physiologic 3D in vitro tissue models.

High Throughput Bioprinting Using Decellularized Adipose Tissue-Based Hydrogels for 3D Breast Cancer Modeling.

3D bioprinting allows rapid automated fabrication and can be applied for high throughput generation of biomimetic constructs for in vitro drug screening. Decellularized extracellular matrix (dECM) hydrogel is a popular biomaterial choice for tissue engineering and studying carcinogenesis as a tumor microenvironmental mimetic. This study proposes a method for high throughput bioprinting with decellularized adipose tissue (DAT) based hydrogels for 3D breast cancer modeling. A comparative analysis of decellularization protocol using detergent-based and detergent-free decellularization methods for caprine-origin adipose tissue is performed, and the efficacy of dECM hydrogel for 3D cancer modeling is assessed. Histological, biochemical, morphological, and biological characterization and analysis showcase the cytocompatibility of DAT hydrogel. The rheological property of DAT hydrogel and printing process optimization is assessed to select a bioprinting window to attain 3D breast cancer models. The bioprinted tissues are characterized for cellular viability and tumor cell-matrix interactions. Additionally, an approach for breast cancer modeling is shown by performing rapid high throughput bioprinting in a 96-well plate format, and in vitro drug screening using 5-fluorouracil is performed on 3D bioprinted microtumors. The results of this study suggest that high throughput bioprinting of cancer models can potentially have downstream clinical applications like multi-drug screening platforms and personalized disease models.