Selenoprotein H mediates low selenium-related cognitive decline through impaired oligodendrocyte myelination with disrupted hippocampal lipid metabolism in female mice.

Low selenium levels are closely associated with reduced cognitive performance and lipid dysregulation, yet the mechanism of action remains unclear. The physiological function of selenium is primarily mediated by selenoproteins. Selenoprotein H (SELENOH), as one of the selenium-containing proteins, has an unelucidated role in regulating cognitive status and lipid metabolism. In this study, we established a Selenoh gene knockout (HKO) mouse model to investigate whether Selenoh mediates the impact of selenium on cognitive function. We found that HKO mice showed a significant decline in cognition compared with the wild-type (HWT) littermates, and were not affected by deficient or excessive selenium, while no differences in anxiety and depression behavior were observed. HKO mice showed reduced myelin basic protein expression in hippocampal oligodendrocytes, with decreased glycolipid levels and increased phospholipid and sphingolipid levels in the hippocampus. Furthermore, the high-fat diet (HFD) exerted no effect on cognition and limited impact on the gene profile in the hippocampus of HKO mice. Compared with those of HWT mice, the myelination pathways in the hippocampus of HKO mice were downregulated as revealed by RNA-seq, which was further confirmed by the reduced expression levels of myelin-related proteins. Finally, HKO increased the expression of hippocampal fatty acid transporter (FATP) 4, and HFD increased the FATP4 expression in HWT mice but not in HKO mice. In summary, our study demonstrated that HKO induced cognitive decline by impairing myelination in oligodendrocytes with disrupted hippocampal lipid metabolism, which provided a novel viewpoint on the selenoprotein-mediated neurodegenerative diseases of selenium.

Phenotyping of single plant cells on a microfluidic cytometry platform with fluorescent, mechanical, and electrical modules.

Compared to animal cells, phenotypic characterization of single plant cells on microfluidic platforms is still rare. In this work, we collated population statistics on the morphological, biochemical, physical and electrical properties of Arabidopsis protoplasts under different external and internal conditions, using progressively improved microfluidic platforms. First, we analyzed the different effects of three phytohormones (auxin, cytokinin and gibberellin) on the primary cell wall (PCW) regeneration process using a microfluidic flow cytometry platform equipped with a single-channel fluorescence sensor. Second, we correlated the intracellular reactive oxygen species (ROS) level induced by heavy metal stress with the concurrent PCW regeneration process by using a dual-channel fluorescence sensor. Third, by integrating contraction channels, we were able to effectively discriminate variations in cell size while monitoring the intensity of intracellular ROS signaling. Fourth, by combining an electrical impedance electrode with the contraction channel, we analyzed the differences in electrical and mechanical properties of wild-type and mutant plant cells before and after primary cell wall regeneration. Overall, our work demonstrates the feasibility and sensitivity of microfluidic flow cytometry in high-throughput phenotyping of plant cells and provides a reference for assessing metabolic and physiological indicators of individual plant cells in multiple dimensions.

Fe(III)-Triggered Radical Arylation of Arene Moieties from Cyclopropanols to Construct Dibenzocyclohepta/octanones: Synthesis of N-Acetylcolchinol-O-methyl ether.

Tricyclic 6-7-6 and 6-8-6 carbon ring systems are present in numerous biologically active natural molecules. However, simple and efficient synthetic approaches to these scaffolds remain challenging. Herein, we report a versatile strategy for constructing these ring systems via Fe(NO3)3-triggered radical arylation of arenes starting from cyclopropanols. This synthetic utility has been demonstrated in the synthesis of the natural product N-acetylcolchinol-O-methyl ether.

Liensinine inhibits IL-1ß-stimulated inflammatory response in chondrocytes and attenuates papain-induced osteoarthritis in rats.

Osteoarthritis (OA) is a joint disease caused by inflammation of cartilage and synovial tissue. Suppressing the process of inflammatory reaction and the generation of oxidative stress is an effective strategy to alleviate the progression of OA. Liensinine is one of the main components of lotus seeds, which has anti-hypertensive and anti-arrhythmia activities. In this study, we aimed to determine the anti-inflammatory effect of liensinine in an OA. Here, we found that liensinine significantly inhibited the inflammatory response of SW1353 cells and primary chondrocytes by inhibiting the release of inflammatory cytokines and oxidative stress. Moreover, we showed that liensinine was able to inhibit the activation of the NF-κB signaling pathway in IL-1ß-induced SW1353 cells. Lastly, we found that liensinine significantly ameliorated cartilage damage and inflammatory response in papain-induced rats. Our study demonstrated a significant protective effect of liensinine against OA, which might be by inhibiting the activation of the NF-κB signaling pathway, and provide a new insight for the treatment of OA using liensinine.

Machine learning assisted microfluidics dual fluorescence flow cytometry for detecting bladder tumor cells based on morphological characteristic parameters.

BACKGROUND: Bladder cancer (BC) is the most common malignant tumor and has become a major public health problem, leading the causes of death worldwide. The detection of BC cells is of great significance for clinical diagnosis and disease treatment. Urinary cytology based liquid biopsy remains high specificity for early diagnosis of BC, however, it still requires microscopy examination which heavily relies on manual operations. It is imperative to investigate the potential of automated and indiscriminate cell differentiation technology to enhance the sensitivity and efficiency of urine cytology. RESULTS: Here, we developed a machine learning algorithm empowered dual-fluorescence flow cytometry platform (µ-FCM) for urinary cytology analysis. A phenotype characteristic parameter (CP) which correlated with the size of the cell and nucleus was defined to achieve the differentiation of the BC cells and uroepithelial cells with high throughput and high accuracy. Based on CP analysis, SV-HUC-1 cells were almost differentiated from EJ cells and effectively reduced the overlap with 5637 cells. To further differentiate SV-HUC-1 cells and 5637 cells, support vector machine (SVM) machine learning algorithm was optimized to assist data analysis with the highest accuracies of 84.7 % for cell differentiation including the specificity of 91.0 % and the sensitivity of 75.0 %. Furthermore, the false positive rate (FPR) compensation enabled the detection rates of rare BC cells predicted by the well-trained SVM model were close to the true proportions with the recognition error in 0.4 % for the tumor cells. SIGNIFICANCE: As a proof of concept, the developed µ-FCM system successfully demonstrates the capacity to identify the distribution of exfoliated cells in real urine samples. This system underscores the significance of integrating AI with microfluidics to perform high-throughput phenotyping of exfoliated cells, offering a pathway toward scalable, efficient, and automatic microfluidic systems in the fields of both biosensing and in vitro diagnosis of BC.

A strategy to reconstitute immunity without GVHD via adoptive allogeneic Tscm therapy.

Introduction: Adoption of allogeneic T cells directly supplements the number of T cells and rapidly induces T-cell immunity, which has good efficacy for treating some tumors and immunodeficiency diseases. However, poor adoptive T-cell engraftment and graft-versus-host disease (GVHD) limit the application of these methods. Alloreactive T-cell clones were eliminated from the donor T-cell repertoire, and the remaining T-cell clones were prepared as Tscm for T-cell adoptive treatment to reconstruct recipient T-cell immunity without GVHD. Methods: The subjects in this study included three different strains of mice. Lymphocytes from mice (C57BL/6) were used as the donor T-cell repertoire, from which the Tscm allo-reactive T cell clone was depleted (ATD-Tscm). This was confirmed by showing that the Tscm was not responsive to the alloantigen of the recipient (BALB/c). To prepare ATD-Tscm cells, we used recipient lymphocytes as a simulator, and coculture of mouse and recipient lymphocytes was carried out for 7 days. Sorting of non-proliferative cells ensured that the prepared Tscm cells were nonresponsive. The sorted lymphocytes underwent further expansion by treatment with TWS119 and cytokines for an additional 10 days, after which the number of ATD-Tscm cells increased. The prepared Tscm cells were transferred into recipient mice to observe immune reconstitution and GVHD incidence. Results: Our protocol began with the use of 1×107 donor lymphocytes and resulted in 1 ×107 ATD-Tscm cells after 17 days of preparation. The prepared ATD-Tscm cells exhibited a nonresponse upon restimulation of the recipient lymphocytes. Importantly, the prepared ATD-Tscm cells were able to bind long and reconstitute other T-cell subsets in vivo, effectively recognizing and answering the "foreign" antigen without causing GVHD after they were transferred into the recipients. Discussion: Our strategy was succeeded to prepare ATD-Tscm cells from the donor T-cell repertoire. The prepared ATD-Tscm cells were able to reconstitute the immune system and prevent GVHD after transferred to the recipients. This study provides a good reference for generating ATD-Tscm for T-cell adoptive immunotherapy.

Efficient and robust phase unwrapping method based on SFNet.

Phase unwrapping is a crucial step in obtaining the final physical information in the field of optical metrology. Although good at dealing with phase with discontinuity and noise, most deep learning-based spatial phase unwrapping methods suffer from the complex model and unsatisfactory performance, partially due to simple noise type for training datasets and limited interpretability. This paper proposes a highly efficient and robust spatial phase unwrapping method based on an improved SegFormer network, SFNet. The SFNet structure uses a hierarchical encoder without positional encoding and a decoder based on a lightweight fully connected multilayer perceptron. The proposed method utilizes the self-attention mechanism of the Transformer to better capture the global relationship of phase changes and reduce errors in the phase unwrapping process. It has a lower parameter count, speeding up the phase unwrapping. The network is trained on a simulated dataset containing various types of noise and phase discontinuity. This paper compares the proposed method with several state-of-the-art deep learning-based and traditional methods in terms of important evaluation indices, such as RMSE and PFS, highlighting its structural stability, robustness to noise, and generalization.

Endoplasmic reticulum-resident selenoproteins and their roles in glucose and lipid metabolic disorders.

Glucose and lipid metabolic disorders (GLMDs), such as diabetes, dyslipidemia, metabolic syndrome, nonalcoholic fatty liver disease, and obesity, are significant public health issues that negatively impact human health. The endoplasmic reticulum (ER) plays a crucial role at the cellular level for lipid and sterol biosynthesis, intracellular calcium storage, and protein post-translational modifications. Imbalance and dysfunction of the ER can affect glucose and lipid metabolism. As an essential trace element, selenium contributes to various human physiological functions mainly through 25 types of selenoproteins (SELENOs). At least 10 SELENOs, with experimental and/or computational evidence, are predominantly found on the ER membrane or within its lumen. Two iodothyronine deiodinases (DIOs), DIO1 and DIO2, regulate the thyroid hormone deiodination in the thyroid and some external thyroid tissues, influencing glucose and lipid metabolism. Most of the other eight members maintain redox homeostasis in the ER. Especially, SELENOF, SELENOM, and SELENOS are involved in unfolded protein responses; SELENOI catalyzes phosphatidylethanolamine synthesis; SELENOK, SELENON, and SELENOT participate in calcium homeostasis regulation; and the biological significance of thioredoxin reductase 3 in the ER remains unexplored despite its established function in the thioredoxin system. This review examines recent research advances regarding ER SELENOs in GLMDs and aims to provide insights on ER-related pathology through SELENOs regulation.

Vitamin D3 exacerbates steatosis while calcipotriol inhibits inflammation in non-alcoholic fatty liver disease in Sod1 knockout mice: a comparative study of two forms of vitamin D.

The role of vitamin D (VD) in non-alcoholic fatty liver disease (NAFLD) remains controversial, possibly due to the differential effects of various forms of VD. In our study, Sod1 gene knockout (SKO) mice were utilized as lean NAFLD models, which were administered 15 000 IU VD3 per kg diet, or intraperitoneally injected with the active VD analog calcipotriol for 12 weeks. We found that VD3 exacerbated hepatic steatosis in SKO mice, with an increase in the levels of Cd36, Fatp2, Dgat2, and CEBPA. However, calcipotriol exerted no significant effect on hepatic steatosis. Calcipotriol inhibited the expression of Il-1a, Il-1b, Il-6, Adgre1, and TNF, with a reduction of NFκB phosphorylation in SKO mice. No effect was observed by either VD3 or calcipotriol on hepatocyte injury and hepatic fibrosis. Co-immunofluorescence stains of CD68, a liver macrophage marker, and VDR showed that calcipotriol reduced CD68 positive cells, and increased the colocalization of VDR with CD68. However, VD3 elevated hepatocyte VDR expression, with no substantial effect on the colocalization of VDR with CD68. Finally, we found that VD3 increased the levels of serum 25(OH)D3 and 24,25(OH)2D3, whereas calcipotriol decreased both. Both VD3 and calcipotriol did not disturb serum calcium and phosphate levels. In summary, our study found that VD3 accentuated hepatic steatosis, while calcipotriol diminished inflammation levels in SKO mice, and the difference might stem from their distinct cellular selectivity in activating VDR. This study provides a reference for the application of VD in the treatment of lean NAFLD.

Revealing Prdx4 as a potential diagnostic and therapeutic target for acute pancreatitis based on machine learning analysis.

Acute pancreatitis (AP) is a common systemic inflammatory disease resulting from the activation of trypsinogen by various incentives in ICU. The annual incidence rate is approximately 30 out of 100,000. Some patients may progress to severe acute pancreatitis, with a mortality rate of up to 40%. Therefore, the goal of this article is to explore the key genes for effective diagnosis and treatment of AP. The analysis data for this study were merged from two GEO datasets. 1357 DEGs were used for functional enrichment and cMAP analysis, aiming to reveal the pathogenic genes and potential mechanisms of AP, as well as potential drugs for treating AP. Importantly, the study used LASSO and SVM-RFE machine learning to screen the most likely AP occurrence biomarker for Prdx4 among numerous candidate genes. A receiver operating characteristic of Prdx4 was used to estimate the incidence of AP. The ssGSEA algorithm was employed to investigate immune cell infiltration in AP. The biomarker Prdx4 gene exhibited significant associations with a majority of immune cells and was identified as being expressed in NKT cells, macrophages, granulocytes, and B cells based on single-cell transcriptome data. Finally, we found an increase in Prdx4 expression in the pancreatic tissue of AP mice through immunohistochemistry. After treatment with recombinant Prdx4, the pathological damage to the pancreatic tissue of AP mice was relieved. In conclusion, our study identified Prdx4 as a potential AP hub gene, providing a new target for treatment.

PM2.5 induces a senescent state in mouse AT2 cells.

PM2.5 is known to induce lung injury, but its toxic effects on lung regenerative machinery and the underlying mechanisms remain unknown. In this study, primary mouse alveolar type 2 (AT2) cells, considered stem cells in the gas-exchange barrier, were sorted using fluorescence-activated cell sorting. By developing microfluidic technology with constricted microchannels, we observed that both passage time and impedance opacities of mouse AT2 cells were reduced after PM2.5, indicating that PM2.5 induced a more deformable mechanical property and a higher membrane permeability. In vitro organoid cultures of primary mouse AT2 cells indicated that PM2.5 is able to impair the proliferative potential and self-renewal capacity of AT2 cells but does not affect AT1 differentiation. Furthermore, cell senescence biomarkers, p53 and γ-H2A.X at protein levels, P16ink4a and P21 at mRNA levels were increased in primary mouse AT2 cells after PM2.5 stimulations as shown by immunofluorescent staining and quantitative PCR analysis. Using several advanced single-cell technologies, this study sheds light on new mechanisms of the cytotoxic effects of atmospheric fine particulate matter on lung stem cell behavior.

Convolutional Neural Network-Driven Impedance Flow Cytometry for Accurate Bacterial Differentiation.

Impedance flow cytometry (IFC) has been demonstrated to be an efficient tool for label-free bacterial investigation to obtain the electrical properties in real time. However, the accurate differentiation of different species of bacteria by IFC technology remains a challenge owing to the insignificant differences in data. Here, we developed a convolutional neural networks (ConvNet) deep learning approach to enhance the accuracy and efficiency of the IFC toward distinguishing various species of bacteria. First, more than 1 million sets of impedance data (comprising 42 characteristic features for each set) of various groups of bacteria were trained by the ConvNet model. To improve the efficiency for data analysis, the Spearman correlation coefficient and the mean decrease accuracy of the random forest algorithm were introduced to eliminate feature interaction and extract the opacity of impedance related to the bacterial wall and membrane structure as the predominant features in bacterial differentiation. Moreover, the 25 optimized features were selected with differentiation accuracies of >96% for three groups of bacteria (bacilli, cocci, and vibrio) and >95% for two species of bacilli (Escherichia coli and Salmonella enteritidis), compared to machine learning algorithms (complex tree, linear discriminant, and K-nearest neighbor algorithms) with a maximum accuracy of 76.4%. Furthermore, bacterial differentiation was achieved on spiked samples of different species with different mixing ratios. The proposed ConvNet deep learning-assisted data analysis method of IFC exhibits advantages in analyzing a huge number of data sets with capacity for extracting predominant features within multicomponent information and will bring about progress and advances in the fields of both biosensing and data analysis.

U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting.

In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination platform as an image capturing system to obtain photographs of laboratory cultures. Consequently, a dataset was introduced consisting of 390 photos of agar plate cultures, which included 8 microorganisms. Secondly, we implemented a new algorithm for image preprocessing based on light intensity correction, which facilitated clearer differentiation between colony and media areas. Thirdly, a U2-Net was used to predict the probability distribution of the edge of the Petri dish in images to locate region of interest (ROI), and then threshold segmentation was applied to separate it. This U2-Net achieved an F1 score of 99.5% and a mean absolute error (MAE) of 0.0033 on the validation set. Then, another U2-Net was used to separate the colony region within the ROI. This U2-Net achieved an F1 score of 96.5% and an MAE of 0.005 on the validation set. After that, the colony area was segmented into multiple components containing single or adhesive colonies. Finally, the colony components (CC) were innovatively rotated and the image crops were resized as the input (with 14,921 image crops in the training set and 4281 image crops in the validation set) for the ResNet50 network to automatically count the number of colonies. Our method achieved an overall recovery of 97.82% for colony counting and exhibited excellent performance in adhesion classification. To the best of our knowledge, the proposed "light intensity correction-based image preprocessing→U2-Net segmentation for Petri dish edge→U2-Net segmentation for colony region→ResNet50-based counting" scheme represents a new attempt and demonstrates a high degree of automation and accuracy in recognizing and counting single-colony and multi-colony targets.

Physiological tolerance of black locust (Robinia pseudoacacia L.) and changes of rhizospheric bacterial communities in response to Cd and Pb in the contaminated soil.

Woody plants possess great potential for phytoremediation of heavy metal-contaminated soil. A pot trial was conducted to study growth, physiological response, and Cd and Pb uptake and distribution in black locust (Robinia pseudoacacia L.), as well as the rhizosphere bacterial communities in Cd and Pb co-contaminated soil. The results showed that R. pseudoacacia L. had strong physiological regulation ability in response to Cd and Pb stress in contaminated soil. The total chlorophyll, malondialdehyde (MDA), soluble protein, and sulfhydryl contents, as well as antioxidant enzymes (superoxide dismutase, peroxidase, catalase) activities in R. pseudoacacia L. leaves under the 40 mg·kg-1 Cd and 1000 mg·kg-1 Pb co-contaminated soil were slightly altered. Cd uptake in R. pseudoacacia L. roots and stems increased, while the Pb content in the shoots of R. pseudoacacia L. under the combined Cd and Pb treatments decreased in relative to that in the single Pb treatments. The bacterial α-diversity indices (e.g., Sobs, Shannon, Simpson, Ace, and Chao) of R. pseudoacacia L. rhizosphere soil under Cd and Pb stress were changed slightly relative to the CK treatment. However, Cd and Pb stress could significantly (p < 0.05) alter the rhizosphere soil microbial communities. According to heat map and LEfSe (Linear discriminant analysis Effect Size) analysis, Bacillus, Sphingomonas, Terrabacter, Roseiflexaceae, Paenibacillus, and Myxococcaceae at the genus level were notably (p < 0.05) accumulated in the Cd- and/or Pb-contaminated soil. Furthermore, the MDA content was notably (p < 0.05) negatively correlated with the relative abundances of Isosphaeraceae, Gaiellales, and Gemmatimonas. The total biomass of R. pseudoacacia L. was positively (p < 0.05) correlated with the relative abundances of Xanthobacteraceae and Vicinamibacreraceae. Network analysis showed that Cd and Pb combined stress might enhance the modularization of bacterial networks in the R. pseudoacacia L. rhizosphere soil. Thus, the assembly of the soil bacterial communities in R. pseudoacacia L. rhizosphere may improve the tolerance of plants in response to Cd and/or Pb stress.

Pinch-off droplet generator using microscale gigahertz acoustics.

The generation and dispensing of microdroplets is a vital process in various fields such as biomedicine, medical diagnosis and chemistry. However, most methods still require the structures of nozzles or microchannels to assist droplet generation, which leads to limitations on system flexibility and restrictions on the size range of the generated droplets. In this paper, we propose a nozzle-free acoustic-based method for generating droplets using a gigahertz (GHz) bulk acoustic wave (BAW). Unlike most of the acoustofluidic approaches, the proposed method produces the droplet by pinching-off the liquid column generated by the acoustic body force at the oil-water interface. Benefitting from the focused acoustic energy and small footprint of the device, four orders of magnitude (ranging from 2 µm to 1800 µm) of droplet size could be produced by controlling the working time and power of the device. We also demonstrated cell encapsulation in the droplet and a high cell viability was achieved. The proposed acoustic-based droplet generation method exhibits capacity for generating droplets with a wide size range, versatility toward different viscosities, as well as biocompatibility for handling viable samples, which shows potential in miniaturization and scalability.

Rational design of multivalent biosensor surfaces to enhance viral particle capture.

Viral particles bind to receptors through multivalent protein interactions. Such high avidity interactions on sensor surfaces are less studied. In this work, three polyelectrolytes that can form biosensing surfaces with different interfacial characteristics in probe density and spatial arrangement were designed. Quartz crystal microbalance, interferometry and atomic force microscopy were used to study their surface density and binding behaviors with proteins and virus particles. A multivalent adsorption kinetic model was developed to estimate the number of bonds from the viral particles bound to the polyelectrolyte surfaces. Experimental results show that the heterogeneous 3D surface with jagged forest-like structure enhances the virus capture ability by maximizing the multivalent interactions. As a proof of concept, specific coronavirus detection was achieved in spiked swab samples. These results indicate the importance of both probe density and their spatial arrangement on the sensing performance, which could be used as a guideline for rational biosensing surface design.

Tunable nanochannel resistive pulse sensing device using a novel multi-module self-assembly.

Nanochannel-based resistive pulse sensing (nano-RPS) system is widely used for the high-sensitive measurement and characterization of nanoscale biological particles and biomolecules due to its high surface to volume ratio. However, the geometric dimensions and surface properties of nanochannel are usually fixed, which limit the detections within particular ranges or types of nanoparticles. In order to improve the flexibility of nano-RPS system, it is of great significance to develop nanochannels with tunable dimensions and surface properties. In this work, we proposed a novel multi-module self-assembly (MS) strategy which allows to shrink the geometric dimensions and tune surface properties of the nanochannels simultaneously. The MS-tuned nano-RPS device exhibits an enhanced signal-to-noise ratio (SNR) for nanoparticle detections after shrunk the geometric dimensions by MS strategy. Meanwhile, by tuning the surface charge, an enhanced resolution for viral particles detection was achieved with the MS-tuned nano-RPS devices by analyzing the variation of pulse width due the tuned surface charge. The proposed MS strategy is versatile for various types of surface materials and can be potentially applied for nanoscale surface reconfiguration in various nanofluidic devices.

Optical imaging technology realizes early tumor diagnosis by detecting angiogenesis.

The occurrence and development of blood vessels play a key role in different stages of tumor growth, while current imaging techniques are difficult to detect early tumor angiogenesis because of their low sensitivity. Therefore, this article introduces high-sensitivity optical imaging technology to achieve early tumor diagnosis by detecting tumor angiogenesis. Liver and pancreatic tumor models in nude mice were respectively established to represent tumors with a rich or poor blood supply. The two optical imaging methods, in vivo confocal fluorescence imaging and photoacoustic imaging, were used to detect tumor angiogenesis at different stages. Finally, the changes in blood vessels were verified by immunostaining. Both autoluminescence imaging and pathological staining confirmed that these two tumor models were successfully established. In vivo confocal fluorescence imaging found that the early tumor blood vessel structure had obvious characteristics: disorder, tortuous deformation, thin diameter, which were significantly different from the normal tissues. Photoacoustic imaging could effectively identify blood vessels inside early tumors, which were small and disordered and might be used as one of the predictors of early tumor development. CD31 immunostaining was used to evaluate the vascular status of tumors at different stages and under different blood supply conditions. The vascular structures observed under the microscope in the two tumor models were consistent with the results observed by optical imaging methods. The optical imaging methods could monitor the characteristics of angiogenesis in the rich or poor blood supply tumors, especially the early diagnosis of tumors.

Improved thermostability and robustness of L-arabinose isomerase by C-terminal elongation and its application in rare sugar production.

Rare sugar was defined as a sugar that occurs in very small quantities in nature. Among them, l-ribose and d-tagatose were of high added value and useful as pharmaceutical intermediate for anti-HBV drugs or low calorie sweetener in food industry. Bio-production of the two rare sugar from biomass waste has not been investigated. Hence, development of a feasible and efficient co-production method was of practical usage. However, lack of suitable biocatalyst has become a bottleneck. By sequence alignment and analysis, a C-terminal α-helix from l-arabinose isomerase (L-AI) family was selected as a tool for protein engineering. This α-helix was ligated to C-terminal of Lactobacillus fermentum L-AI (LFAI) and significantly enhanced its thermostability and robustness for both l-arabinose and galactose catalysis. The mutant LFAI-C4 enzyme was immobilized by alginate and antimicrobial peptide poly-l-lysine, and was used to convert pretreated corncob acid hydrolysate (PCAH) into l-ribulose and d-tagatose in the presence of boric acid. In addition, we identified and immobilized a novel thermostable mannose-6-phosphate isomerase from Bacillus subtilis (BsMPI-2) which was efficient in catalyzing retaining l-ribulose into l-ribose and showing no activity on d-tagatose. The dual immobilized enzymes (LFAI-C4 and BsMPI-2) system co-produced 191.9 g/L l-ribose and 80.1 g/L d-tagatose, respectively. Showing a total yield of 46.6% from l-arabinose to l-ribose, which was the highest among reported. The dual immobilized enzymes system preserved 82% activity after 40 batches reaction, showing excellent potentials for industrial use. This study presents a promising alternative for rare sugar production from low-value raw material and showed satisfied conversion rate, product concentration, and operation stability.

Functional Characterization and Phenotyping of Protoplasts on a Microfluidics-Based Flow Cytometry.

A better understanding of the phenotypic heterogeneity of protoplasts requires a comprehensive analysis of the morphological and metabolic characteristics of many individual cells. In this study, we developed a microfluidic flow cytometry with fluorescence sensor for functional characterization and phenotyping of protoplasts to allow an unbiased assessment of the influence of environmental factors at the single cell level. First, based on the measurement of intracellular homeostasis of reactive oxygen species (ROS) with a DCFH-DA dye, the effects of various external stress factors such as H2O2, temperature, ultraviolet (UV) light, and cadmium ions on intracellular ROS accumulation in Arabidopsis mesophyll protoplasts were quantitatively investigated. Second, a faster and stronger oxidative burst was observed in Petunia protoplasts isolated from white petals than in those isolated from purple petals, demonstrating the photoprotective role of anthocyanins. Third, using mutants with different endogenous auxin, we demonstrated the beneficial effect of auxin during the process of primary cell wall regeneration. Moreover, UV-B irradiation has a similar accelerating effect by increasing the intracellular auxin level, as shown by double fluorescence channels. In summary, our work has revealed previously underappreciated phenotypic variability within a protoplast population and demonstrated the advantages of a microfluidic flow cytometry for assessing the in vivo dynamics of plant metabolic and physiological indices at the single-cell level.