Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods.

BACKGROUND: Periodontitis is a chronic inflammatory condition triggered by immune system malfunction. Mitochondrial extracellular vesicles (MitoEVs) are a group of highly heterogeneous extracellular vesicles (EVs) enriched in mitochondrial fractions. The objective of this research was to examine the correlation between MitoEVs and the immune microenvironment of periodontitis. METHODS: Data from MitoCarta 3.0, GeneCards, and GEO databases were utilized to identify differentially expressed MitoEV-related genes (MERGs) and conduct functional enrichment and pathway analyses. The random forest and LASSO algorithms were employed to identify hub MERGs. Infiltration levels of immune cells in periodontitis and healthy groups were estimated using the CIBERSORT algorithm, and phenotypic subgroups of periodontitis based on hub MERG expression levels were explored using a consensus clustering method. RESULTS: A total of 44 differentially expressed MERGs were identified. The random forest and LASSO algorithms identified 9 hub MERGs (BCL2L11, GLDC, CYP24A1, COQ2, MTPAP, NIPSNAP3A, FAM162A, MYO19, and NDUFS1). ROC curve analysis showed that the hub gene and logistic regression model presented excellent diagnostic and discriminating abilities. Immune infiltration and consensus clustering analysis indicated that hub MERGs were highly correlated with various types of immune cells, and there were significant differences in immune cells and hub MERGs among different periodontitis subtypes. CONCLUSION: The periodontitis classification model based on MERGs shows excellent performance and can offer novel perspectives into the pathogenesis of periodontitis. The high correlation between MERGs and various immune cells and the significant differences between immune cells and MERGs in different periodontitis subtypes can clarify the regulatory roles of MitoEVs in the immune microenvironment of periodontitis. Future research should focus on elucidating the functional mechanisms of hub MERGs and exploring potential therapeutic interventions based on these findings.

Pheochromocytoma crisis with refractory Acute Respiratory Distress Syndrome (ARDS), Takotsubo syndrome, emergency adrenalectomy, and need for Extracorporeal Membrane Oxygenation (ECMO) in a previously undiagnosed and asymptomatic patient, due to the use of metoclopramide.

BACKGROUND: Pheochromocytoma (PCC) crisis is a rare life-threatening endocrine emergency. The diagnosis and treatment of PCC crisis, with acute respiratory distress syndrome (ARDS) as the first manifestation, is highly challenging, and traditional PCC management strategies are no longer suitable for these patients. CASE PRESENTATION: A 46-year-old female patient was admitted to the Intensive Care Unit (ICU) following sudden-onset acute respiratory distress and subsequent initiation of mechanical ventilation via endotracheal intubation. She was initially suspected of having a PCC crisis through the bedside critical care ultrasonic examination protocol. The computed tomography examination revealed a left adrenal neoplasm of 6.5cm × 5.9cm. The plasma-free metanephrine level was 100 times higher than the reference value. These findings were compatible with her PCC diagnosis. Alpha-blockers and fluid intake were started immediately. The endotracheal intubation was removed on the 11th day after admission to the ICU. The patient progressed to severe ARDS again, and invasive ventilation and continuous renal replacement therapy were needed. Despite aggressive therapy, her condition deteriorated. Therefore, she underwent veno-arterial extracorporeal membrane oxygenation (VA-ECMO)-assisted emergency adrenalectomy after multidisciplinary discussion. Postoperatively, the patient was supported by VA-ECMO for 7days. She was discharged from the hospital on day 30 after tumor resection. CONCLUSIONS: This case highlighted the challenges in diagnosing and managing ARDS associated with PCC crisis. The traditional preoperative preparation protocol and optimal operation timing for patients with PCC are not suitable for patients with PCC crisis. Patients with life-threatening PCC crisis may benefit from early tumor removal, and VA-ECMO could maintain hemodynamic stability during and after surgery.

Exploring the impact of fungal spores from agricultural environments on the mice lung microbiome and metabolic profile.

Exposure to particulate matter (PM) from agricultural environments has been extensively reported to cause respiratory health concerns in both animals and agricultural workers. Furthermore, PM from agricultural environments, containing fungal spores, has emerged as a significant threat to public health and the environment. Despite its potential toxicity, the impact of fungal spores present in PM from agricultural environments on the lung microbiome and metabolic profile is not well understood. To address this gap in knowledge, we developed a mice model of immunodeficiency using cyclophosphamide and subsequently exposed the mice to fungal spores via the trachea. By utilizing metabolomics techniques and 16 S rRNA sequencing, we conducted a comprehensive investigation into the alterations in the lung microbiome and metabolic profile of mice exposed to fungal spores. Our study uncovered significant modifications in both the lung microbiome and metabolic profile post-exposure to fungal spores. Additionally, fungal spore exposure elicited noticeable changes in α and ß diversity, with these microorganisms being closely associated with inflammatory factors. Employing non-targeted metabolomics analysis via GC-TOF-MS, a total of 215 metabolites were identified, among which 42 exhibited significant differences. These metabolites are linked to various metabolic pathways, with amino sugar and nucleotide sugar metabolism, as well as galactose metabolism, standing out as the most notable pathways. Cysteine and methionine metabolism, along with glycine, serine and threonine metabolism, emerged as particularly crucial pathways. Moreover, these metabolites demonstrated a strong correlation with inflammatory factors and exhibited significant associations with microbial production. Overall, our findings suggest that disruptions to the microbiome and metabolome may hold substantial relevance in the mechanism underlying fungal spore-induced lung damage in mice.

Sustainable production of genistin from glycerol by constructing and optimizing Escherichia coli.

Genistin is one of the bioactive isoflavone glucosides found in legumes, which have great nutraceutical and pharmaceutical significance. The market available isoflavones are currently produced by direct plant extraction. However, its low abundance in plant and structural complexity hinders access to this phytopharmaceutical via plant extraction or chemical synthesis. Here, the E. coli cell factory for sustainable production of genistin from glycerol was constructed. First, we rebuilt the precursor genistein biosynthesis pathway in E. coli, and its titer was then increased by 668% by identifying rate-limiting steps and applying an artificial protein scaffold system. Then de novo production of genistin from glycerol was achieved by functional screening and introduction of glycosyl-transferases, UDP-glucose pathway and specific genistin efflux pumps, and 48.1 mg/L of genistin was obtained. A further engineered E. coli strain equipped with an improved malonyl-CoA pathway, alternative glycerol-utilization pathways, acetyl-CoA carboxylase (ACC), and CRISPR interference (CRISPRi) mediated regulation produced up to 137.8 mg/L of genistin in shake flask cultures. Finally, 202.7 mg/L genistin was achieved through fed-batch fermentation in a 3-L bioreactor. This study represents the de novo genistin production from glycerol for the first time and will lay the foundation for low-cost microbial production of glucoside isoflavones. In addition, the multiphase workflow may provide a reference for engineering the biosynthetic pathways in other microbial hosts as well, for green manufacturing of complex natural products.

Neuroprotective potency of a soy whey fermented by Cordyceps militaris SN-18 against hydrogen peroxide-induced oxidative injury in PC12 cells.

PURPOSE: Soy whey is a byproduct generated from the processing of several soybean products. Its valorization has continued to attract significant research interest in recent times due to the nutritional and bioactive potency of its chemical composition. Herein, the neuroprotective potency of a soy whey fermented by Cordyceps militaris SN-18 against hydrogen peroxide (H2O2)-induced oxidative injury in PC12 cells was investigated. METHODS: The phenolic compositions were analyzed by high-performance liquid chromatography. Antioxidant activities were assessed by ABTS•+ scavenging assay, DPPH radical scavenging assay, reducing power assay, and ferric reducing antioxidant power assay. The neuroprotective effects of fermented soy whey (FSW) were investigated based on the oxidative injury model in PC12 cells. RESULTS: FSW possessed higher total phenolic content and antioxidant activities compared with unfermented soy whey (UFSW) and that most of the isoflavone glycosides were hydrolyzed into their corresponding aglycones during fermentation. The extract from FSW exhibited a greater protective effect on PC12 cells against oxidative injury by promoting cell proliferation, restoring cell morphology, inhibiting lactic dehydrogenase leakage, reducing reactive oxygen species levels, and enhancing antioxidant enzyme activities compared with that from UFSW. Additionally, cell apoptosis was significantly inhibited by FSW through down-regulation of caspase-3, caspase-9, and Bax and up-regulation of Bcl-2 and Bcl-xL. S-phase cell arrest was attenuated by FSW through increasing cyclin A, CDK1 and CDK2, and decreasing p21 protein. CONCLUSION: Fermentation with C. militaris SN-18 could significantly improve the bioactivity of soy whey by enhancing the ability of nerve cells to resist oxidative damage.

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions.

The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.

A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification.

The shafting systems of hydropower units work as the core component for the conversion of water energy to electric energy and have been running for a long time in the hostile hydraulic-mechanical-electrical-coupled environment-their vibration faults are frequent. How to quickly and accurately identify vibration faults to improve the reliability of the unit is a key issue. This study proposes a novel shafting vibration fault identification framework, which is divided into three coordinated stages: nonlinear modeling, signal denoising, and holographic identification. A nonlinear dynamical model of bending-torsion coupling vibration induced by multiple excitation vibration sources of the shafting system is established in the first stage. The multi-stage signal denoising method combines Savitzky-Golay (SG) smoothing filtering, singular value decomposition (SVD), and variational mode decomposition (VMD). SG-SVD-VMD is used for the guide bearing the vibration signals in the second stage. Further, the holospectrum theory is innovatively introduced to obtain the holospectra of the simulated and measured signals, and the shafting vibration faults of the real unit are identified by comparing the holospectrum of the measured signal with the simulated signal. These results show that the shafting nonlinear model can effectively reflect the vibration characteristics of the coupled vibration source and reveal the influence and fault characteristics of each external excitation on the shafting vibration. The shafting vibration faults of operating units can be identified by analyzing the holospectra of the shafting simulation signals and measuring the noise reduction signals. Thus, this framework can guide the safe and stable operation of hydropower units.

Clinical characteristics and predictive value of lower CD4+T cell level in patients with moderate and severe COVID-19: a multicenter retrospective study.

BACKGROUND: In December 2019, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, Hubei, China. Moreover, it has become a global pandemic. This is of great value in describing the clinical symptoms of COVID-19 patients in detail and looking for markers which are significant to predict the prognosis of COVID-19 patients. METHODS: In this multicenter, retrospective study, 476 patients with COVID-19 were enrolled from a consecutive series. After screening, a total of 395 patients were included in this study. All-cause death was the primary endpoint. All patients were followed up from admission till discharge or death. RESULTS: The main symptoms observed in the study included fever on admission, cough, fatigue, and shortness of breath. The most common comorbidities were hypertension and diabetes mellitus. Patients with lower CD4+T cell level were older and more often male compared to those with higher CD4+T cell level. Reduced CD8+T cell level was an indicator of the severity of COVID-19. Both decreased CD4+T [HR:13.659; 95%CI: 3.235-57.671] and CD8+T [HR: 10.883; 95%CI: 3.277-36.145] cell levels were associated with in-hospital death in COVID-19 patients, but only the decrease of CD4+T cell level was an independent predictor of in-hospital death in COVID-19 patients. CONCLUSIONS: Reductions in lymphocytes and lymphocyte subsets were common in COVID-19 patients, especially in severe cases of COVID-19. It was the CD8+T cell level, not the CD4+T cell level, that reflected the severity of the patient's disease. Only reduced CD4+T cell level was independently associated with increased in-hospital death in COVID-19 patients. TRIAL REGISTRATION: Prognostic Factors of Patients With COVID-19, NCT04292964 . Registered 03 March 2020. Retrospectively registered.

On-the-Fly Fusion of Remotely-Sensed Big Data Using an Elastic Computing Paradigm with a Containerized Spark Engine on Kubernetes.

Remotely-sensed satellite image fusion is indispensable for the generation of long-term gap-free Earth observation data. While cloud computing (CC) provides the big picture for RS big data (RSBD), the fundamental question of the efficient fusion of RSBD on CC platforms has not yet been settled. To this end, we propose a lightweight cloud-native framework for the elastic processing of RSBD in this study. With the scaling mechanisms provided by both the Infrastructure as a Service (IaaS) and Platform as a Services (PaaS) of CC, the Spark-on-Kubernetes operator model running in the framework can enhance the efficiency of Spark-based algorithms without considering bottlenecks such as task latency caused by an unbalanced workload, and can ease the burden to tune the performance parameters for their parallel algorithms. Internally, we propose a task scheduling mechanism (TSM) to dynamically change the Spark executor pods' affinities to the computing hosts. The TSM learns the workload of a computing host. Learning from the ratio between the number of completed and failed tasks on a computing host, the TSM dispatches Spark executor pods to newer and less-overwhelmed computing hosts. In order to illustrate the advantage, we implement a parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) to enable the efficient fusion of big RS images with a Spark aggregation function. We construct an OpenStack cloud computing environment to test the usability of the framework. According to the experiments, TSM can improve the performance of the PESTARFM using only PaaS scaling to about 11.7%. When using both the IaaS and PaaS scaling, the maximum performance gain with the TSM can be even greater than 13.6%. The fusion of such big Sentinel and PlanetScope images requires less than 4 min in the experimental environment.

Neuroprotective Potency of Tofu Bio-Processed Using Actinomucor elegans against Hypoxic Injury Induced by Cobalt Chloride in PC12 Cells.

Fermented soybean products have attracted great attention due to their health benefits. In the present study, the hypoxia-injured PC12 cells induced by cobalt chloride (CoCl2) were used to evaluate the neuroprotective potency of tofu fermented by Actinomucor elegans (FT). Results indicated that FT exhibited higher phenolic content and antioxidant activity than tofu. Moreover, most soybean isoflavone glycosides were hydrolyzed into their corresponding aglycones during fermentation. FT demonstrated a significant protective effect on PC12 cells against hypoxic injury by maintaining cell viability, reducing lactic dehydrogenase leakage, and inhibiting oxidative stress. The cell apoptosis was significantly attenuated by the FT through down-regulation of caspase-3, caspases-8, caspase-9, and Bax, and up-regulation of Bcl-2 and Bcl-xL. S-phase cell arrest was significantly inhibited by the FT through increasing cyclin A and decreasing the p21 protein level. Furthermore, treatment with the FT activated autophagy, indicating that autophagy possibly acted as a survival mechanism against CoCl2-induced injury. Overall, FT offered a potential protective effect on nerve cells in vitro against hypoxic damage.

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model.

Accurate degradation tendency prediction (DTP) is vital for the secure operation of a pumped storage unit (PSU). However, the existing techniques and methodologies for DTP still face challenges, such as a lack of appropriate degradation indicators, insufficient accuracy, and poor capability to track the data fluctuation. In this paper, a hybrid model is proposed for the degradation tendency prediction of a PSU, which combines the integrated degradation index (IDI) construction and convolutional neural network-long short-term memory (CNN-LSTM). Firstly, the health model of a PSU is constructed with Gaussian process regression (GPR) and the condition parameters of active power, working head, and guide vane opening. Subsequently, for comprehensively quantifying the degradation level of PSU, an IDI is developed using entropy weight (EW) theory. Finally, combining the local feature extraction of the CNN with the time series representation of LSTM, the CNN-LSTM model is constructed to realize DTP. To validate the effectiveness of the proposed model, the monitoring data collected from a PSU in China is taken as case studies. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) obtained by the proposed model are 1.1588, 0.8994, 0.0918, and 0.9713, which can meet the engineering application requirements. The experimental results show that the proposed model outperforms other comparison models.

PARSUC: A Parallel Subsampling-Based Method for Clustering Remote Sensing Big Data.

Remote sensing big data (RSBD) is generally characterized by huge volumes, diversity, and high dimensionality. Mining hidden information from RSBD for different applications imposes significant computational challenges. Clustering is an important data mining technique widely used in processing and analyzing remote sensing imagery. However, conventional clustering algorithms are designed for relatively small datasets. When applied to problems with RSBD, they are, in general, too slow or inefficient for practical use. In this paper, we proposed a parallel subsampling-based clustering (PARSUC) method for improving the performance of RSBD clustering in terms of both efficiency and accuracy. PARSUC leverages a novel subsampling-based data partitioning (SubDP) method to realize three-step parallel clustering, effectively solving the notable performance bottleneck of the existing parallel clustering algorithms; that is, they must cope with numerous repeated calculations to get a reasonable result. Furthermore, we propose a centroid filtering algorithm (CFA) to eliminate subsampling errors and to guarantee the accuracy of the clustering results. PARSUC was implemented on a Hadoop platform by using the MapReduce parallel model. Experiments conducted on massive remote sensing imageries with different sizes showed that PARSUC (1) provided much better accuracy than conventional remote sensing clustering algorithms in handling larger image data; (2) achieved notable scalability with increased computing nodes added; and (3) spent much less time than the existing parallel clustering algorithm in handling RSBD.

Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy.

Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition.

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches.

Structure-based antigenic epitope and PEGylation improve the efficacy of staphylokinase.

Staphylokinase (Sak) holds promise for use in thrombolytic therapy for acute myocardial infarction. However, its immunogenicity is a major disadvantage under clinical conditions. PEGylation has become a sophisticated method to decrease that immunogenicity. In this report, according predicted epitope from the active center, five residues, including Gly79, Leu82, Lys84, Ala97, and Arg104 have been mutant as cysteine for mono PEGylation, respectively. According to the relative immunogenicity of Sak or its PEGylation derivatives, the amount of specific anti-Sak IgG antibodies elicited by PEGylation proteins, including C79G, C82L, C84K, C97A, and C104R in BALB/c mice decreased by approximately 15-75% each. PEGylated Sak derivatives showed a decrease of up to 75% in the immune reactivity in PEG-Sak-C104R. Thrombelastography experiments showed that two PEG-conjugated derivatives, PEG-Sak-C97A (Ly30, 68.14 ± 2.51%) and PEG-Sak-C104R (Ly30, 66.49 ± 5.97%), the LY30 of PEG-Sak-C97A, and PEG-Sak-C104R produced values very similar to those of wild-type Sak. The fibrin plate assays showed the bioactivity of PEG-Sak-C104R to exhibit the most activity approximately as much as urokinase (diameter of halo pattern, 18.6 ± 1.06 mm) and tPA (diameter of halo pattern, 17.2 ± 0.49 mm). The Sak PEGylation derivative PEG-Sak-C104R was also selected for further in vivo activity experimentation. The thrombolytic ability of PEG-Sak-C104R is a little lower than wild-type Sak, whereas, this PEGylated protein retained high activity suitable for thrombolytic therapy. Collectively, with the in vivo and in vitro experiments, the present study suggests that site mutant PEGylation, PEG-Sak-C104R, is a suitable type of PEGylation for clinical applications. Further optimization would help maintain the bioactivity and decrease the immunogenicity of staphylokinase.

Neuro-fuzzy modeling to predict physicochemical and microbiological parameters of partially dried cherry tomato during storage: effects on water activity, temperature and storage time.

In the study, osmotically dehydrated cherry tomatoes were partially dried to water activity between 0.746 and 0.868, vacuum-packed and stored at 4-30 °C for 60 days. Adaptive neuro-fuzzy inference system (ANFIS) was utilized to predict the physicochemical and microbiological parameters of these partially dried cherry tomatoes during storage. Satisfactory accuracies were obtained when ANFIS was used to predict the lycopene and total phenolic contents, color and microbial contamination. The coefficients of determination for all the ANFIS models were higher than 0.86 and showed better performance for prediction compared with models developed by response surface methodology. Through ANFIS modeling, the effects of storage conditions on the properties of partially dried cherry tomatoes were visualized. Generally, contents of lycopene and total phenolics decreased with the increase in water activity, temperature and storage time, while aerobic plate count and number of yeasts and molds increased at high water activities and temperatures. Overall, ANFIS approach can be used as an effective tool to study the quality decrease and microbial pollution of partially dried cherry tomatoes during storage, as well as identify the suitable preservation conditions.

Soluble expression, purification, and characterization of active recombinant human tissue plasminogen activator by auto-induction in E. coli.

BACKGROUND: Human tissue plasminogen activator (tPA) belongs to the serine protease family. It converts plasminogen into plasmin and is used clinically to treat thrombosis. Human tPA is composed of 527 amino acids residues and contains 17 disulfide bonds. Escherichia coli has been used only rarely for the efficient production of recombinant tPA. However, the functional expression of full-length tPA that contains multiple disulfide bonds on an industrial scale remains challenging. Here, we describe the soluble expression and characterization of full-length tPA by auto-induction in E. coli. RESULTS: We achieved optimal levels of gene expression, minimized negative effects related to the production of heterologous proteins, and optimized cytoplasmic yields. Three different E. coli strains, BL21 (DE3), Rosetta, and Origami 2, could express tPA using an auto-induction mechanism. In addition, similar yields of recombinant protein were produced at temperatures of 33, 35, and 37°C. The E. coli strain origami 2 could increase disulfide bond formation in cytoplasmic tPA and produce purified soluble recombinant protein (~0.9 mg/l medium). The full-length tPA was monomeric in solution, and fibrin plate assays confirmed that the recombinant tPA displayed serine protease activity. CONCLUSIONS: This is the first report that describes the heterologous expression of correctly folded active full-length tPA. This could provide valuable information for using prokaryotic auto-induction expression systems to produce tPA at industrial and pharmaceutical levels without in vitro refolding during the production step.

A two-dimensional coupled flow-mass transport model based on an improved unstructured finite volume algorithm.

A two-dimensional coupled water quality model is developed for modeling the flow-mass transport in shallow water. To simulate shallow flows on complex topography with wetting and drying, an unstructured grid, well-balanced, finite volume algorithm is proposed for numerical resolution of a modified formulation of two-dimensional shallow water equations. The slope-limited linear reconstruction method is used to achieve second-order accuracy in space. The algorithm adopts a HLLC-based integrated solver to compute the flow and mass transport fluxes simultaneously, and uses Hancock's predictor-corrector scheme for efficient time stepping as well as second-order temporal accuracy. The continuity and momentum equations are updated in both wet and dry cells. A new hybrid method, which can preserve the well-balanced property of the algorithm for simulations involving flooding and recession, is proposed for bed slope terms approximation. The effectiveness and robustness of the proposed algorithm are validated by the reasonable good agreement between numerical and reference results of several benchmark test cases. Results show that the proposed coupled flow-mass transport model can simulate complex flows and mass transport in shallow water.

Cleavage of pro-tumor necrosis factor alpha by ADAM metallopeptidase domain 17: a fluorescence-based protease assay cleaves its natural protein substrate.

A fluorescence-based Adam 17 activity assay that cleaves pro-tumor necrosis factor alpha (pro-TNFα) protein substrate has been developed. The key to the assay was site-specific labeling of a fluorescence dye to the N-terminal end of the substrate protein, which was achieved by the protein ligation method. The protease cleavage reaction was monitored by fluorescence polarization. This homogeneous assay allows reaction progress to be recorded kinetically in real time. The results were validated by gel electrophoresis and high-performance liquid chromatography. As expected, the reaction could be inhibited by an ADAM metallopeptidase domain 17 (Adam 17) active site inhibitor. Interestingly, the reaction rate of pro-TNFα cleavage by Adam 17 was also reduced by a small molecule binding to pro-TNFα protein, the substrate of the reaction. This small molecule, however, did not affect the activity of Adam 17 to its peptide substrate. These results demonstrate that this natural protein substrate-based fluorescent assay was able to identify the inhibitor binding to substrate protein in addition to that binding to the protease itself. Comparing this protein substrate with a short peptide substrate, the activity of Adam 17 showed different pH profiles. With pro-TNFα the optimal pH was approximately 7.4, whereas with the peptide substrate the optimal pH was higher than 9.0.

Formation of heat-induced cottonseed congossypin(7S) fibrils at pH 2.0.

BACKGROUND: Heat-induced protein aggregation is important for the texture of various food products. Many types of food proteins have been found to assemble into fibrillar structures under certain conditions. We studied fibril formation of cottonseed 7S storage protein upon heating (for 0-720 min) at 90°C and pH 2.0, investigated the conversion rate, and determined the extent of thermal aggregation. RESULTS: Thioflavin-T fluorescence and Congo-red analysis indicated the formation of amyloid-like fibrils upon heating. Centrifugal filtration indicated that the conversion was very low (<10%) until congossypin concentration up to 2 mg mL(-1), and the conversion increases with increasing heating time, but levels off after longer heating times. Dynamic light scattering and atomic force microscopy showed that the extent of thermal aggregation at pH 2.0, or contour length of the worm-like and fine-stranded aggregates, progressively increased with increasing heating time. Furthermore, reducing electrophoresis analyses indicated that progressive polypeptide hydrolysis occurred upon heating. Experiments indicate that congossypin can form heat-induced amyloid-like aggregates and the conversion of congossypin monomers into fibrils increased with heating time and protein concentration. CONCLUSION: The results would be of vital importance for the utilisation of cottonseed proteins to produce thermally induced fibrillar gels with excellent properties.