Recent Advances in the Modification and Improvement of Bioprosthetic Heart Valves.

Valvular heart disease (VHD) has become a burden and a growing public health problem in humans, causing significant morbidity and mortality worldwide. An increasing number of patients with severe VHD need to undergo heart valve replacement surgery, and artificial heart valves are in high demand. However, allogeneic valves from donors are lacking and cannot meet clinical practice needs. A mechanical heart valve can activate the coagulation pathway after contact with blood after implantation in the cardiovascular system, leading to thrombosis. Therefore, bioprosthetic heart valves (BHVs) are still a promising way to solve this problem. However, there are still challenges in the use of BHVs. For example, their longevity is still unsatisfactory due to the defects, such as thrombosis, structural valve degeneration, calcification, insufficient re-endothelialization, and the inflammatory response. Therefore, strategies and methods are needed to effectively improve the biocompatibility and longevity of BHVs. This review describes the recent research advances in BHVs and strategies to improve their biocompatibility and longevity.

Predictive temperature control design for reaction calorimeter based on mechanism parameter model.

Isothermal control is the most basic and crucial function in the principle of a reaction calorimeter system and affects the speed and validity of the calorimetric experiment. However, the complex and uncertain working conditions in different reaction processes pose a challenge to the adaptability of temperature control algorithms. Aiming at the problem, a heat transfer model of the system is first established for temperature control design. From the simulation results, a prediction model based on equivalent mechanism parameters is determined for the control. Then, an integrated model predictive control (MPC) strategy is presented. To reduce the influence on the temperature control caused by the mismatch of the prediction model, a set of online parameter identification and adjustment methods is proposed. Simulations of the MPC control were implemented to analyze the control's performance. Experiments were also carried out to verify the advantages of the proposed strategy over the proportional-integral-derivative algorithm and demonstrate the role and efficiency of online identification. This control strategy can be applied to other laboratory-scale instruments with tank reactors.

A millikelvin precision temperature control system designed for a low cost, portable and variable temperature blackbody from 298.15 to 693.15 K.

Millikelvin precision portable variable temperature blackbody from 298.15 to 693.15 K is very important in the on-site calibration of infrared measuring instruments. The stability of the blackbody temperature directly affects the calibration quality. However, the temperature measurement and control system is the key component to guarantee the stability of the blackbody temperature. In this article, a measurement and control system of the low-cost and portable blackbody was designed and verified. A Pt100 platinum resistance thermometer was employed to measure the temperature of the blackbody radiation source. Based on the round-robin structure and current reversing technology, the precision of the temperature measurement achieved a sub-millikelvin level. To overcome the drawbacks that traditional proportional integral derivative (PID) controller would lead to large overshoot and long adjustment time during the temperature control of the large thermal inertia blackbody, a feedforward and segmented PID controller was introduced to improve the dynamic performance of the blackbody radiation source. The experimental results showed that the precision of the temperature measurement at 0.5 Hz was better than 0.5 mK and the temperature stability within 10 min was better than 3 mK in the temperature range from 298.15 to 693.15 K. Hence, the millikelvin precision measurement and control system has a strong prospect for practical application in high-performance blackbody development.

Epigallocatechin-3-Gallate Inhibits Atrial Fibrosis and Reduces the Occurrence and Maintenance of Atrial Fibrillation and its Possible Mechanisms.

BACKGROUND: Atrial fibrosis is one of the main causes of the onset and recurrence of atrial fibrillation (AF), for which there is no effective treatment. The aim of this study was to investigate the effect and mechanism of epigallocatechin-3-gallate (EGCG) on AF in rats. METHODS: The rat model of AF was established by rapid pacing induction after angiotensin-II (Ang-II) induced atrial fibrosis to verify the relationship between atrial fibrosis and the AF. The expression levels of TGF-ß/Smad3 pathway molecules and lysyl oxidase (LOX) in AF were detected. Subsequently, EGCG was used to intervene Ang-II-induced atrial fibrosis to explore the role of EGCG in the treatment of AF and its inhibitory mechanism on fibrosis. It was further verified that EGCG inhibited the production of collagen and the expression of LOX through the TGF-ß/Smad3 pathway at the cellular level. RESULTS: The results showed that the induction rate and maintenance time of AF in rats increased with the increase of the degree of atrial fibrosis. Meanwhile, the expressions of Col I, Col III, molecules related to TGF-ß/Smad3 pathway, and LOX increased significantly in the atrial tissues of rats in the Ang-II induced group. EGCG could reduce the occurrence and maintenance time of AF by inhibiting the degree of Ang-induced rat atrial fibrosis. Cell experiments confirmed that EGCG could reduce the synthesis of collagen and the expression of LOX in cardiac fibroblast induced by Ang-II. The possible mechanism is to down-regulate the expression of genes and proteins related to the TGF-ß/Smad3 pathway. CONCLUSION: EGCG could downregulate the expression levels of collagen and LOX by inhibiting the TGF-ß/Smad3 signaling pathway, alleviating Ang-II-induced atrial fibrosis, which in turn inhibited the occurrence and curtailed the duration of AF.

The immune-metabolic crosstalk between CD3+C1q+TAM and CD8+T cells associated with relapse-free survival in HCC.

Introduction: Although multiple targeted treatments have appeared, hepatocellular carcinoma (HCC) is still one of the most common causes of cancer-related deaths. The immunosuppressive tumor microenvironment (TME) is a critical factor in the oncogenesis and progression of HCC. The emerging scRNA-seq makes it possible to explore the TME at a high resolution. This study was designed to reveal the immune-metabolic crosstalk between immune cells in HCC and provide novel strategies to regulate immunosuppressive TME. Method: In this study, we performed scRNA-seq on paired tumor and peri-tumor tissues of HCC. The composition and differentiation trajectory of the immune populations in TME were portrayed. Cellphone DB was utilized to calculate interactions between the identified clusters. Besides, flow cytometry, RT-PCR and seahorse experiments were implemented to explore potential metabolic and epigenetic mechanisms of the inter-cellular interaction. Result: A total of 19 immune cell clusters were identified and 7 were found closely related to HCC prognosis. Besides, differentiation trajectories of T cells were also presented. Moreover, a new population, CD3+C1q+ tumor-associated macrophages (TAM) were identified and found significantly interacted with CD8+ CCL4+T cells. Compared to the peri-tumor tissue, their interaction was attenuated in tumor. Additionally, the dynamic presence of this newly found cluster was also verified in the peripheral blood of patients with sepsis. Furthermore, we found that CD3+C1q+TAM affected T cell immunity through C1q signaling-induced metabolic and epigenetic reprogramming, thereby potentially affecting tumor prognosis. Conclusion: Our study revealed the interaction between CD3+C1q+TAM and CD8+ CCL4+T cells and may provide implications for tackling the immunosuppressive TME in HCC.

Reaction Analysis and Process Optimization with Online Infrared Data Based on Kinetic Modeling and Partial Least Squares Quantitation.

In-situ Fourier transform infrared (FT-IR) spectroscopy has been recognized as an important technology for online monitoring of chemical reactions. However, analysis of the real-time IR data for identification and quantification of uncertain reactants or intermediates is often ambiguous and difficult. Here, we propose an analysis algorithm based on reaction kinetic modeling and the chemometric method of partial least squares (PLS) to comprehensively and quantitatively study reaction processes. Concentration profiles and apparent kinetic parameters can be simultaneously calculated from the spectral data, without the demand of complicated analysis on characteristic absorbance peaks or tedious sampling efforts for multivariate modeling. Paal-Knorr reactions and glyoxylic acid synthesis reactions were selected as typical reactions to validate the algorithm. A lack of fit of the Paal-Knorr reaction spectra was less than 2.5% at various conditions, and the absolute errors between the predicted values and HPLC measurement of glyoxylic acid synthesis were less than 6% during the reaction process. Moreover, the reaction kinetic models extracted from FT-IR data were used to simulate reaction processes and optimize the conditions in order to maximize product yields, which proved that this analysis method could be used for process optimization.

AMTB, a TRPM8 antagonist, suppresses growth and metastasis of osteosarcoma through repressing the TGFß signaling pathway.

Since its first identification in prostate cancers and prostate tissues, transient receptor potential melastatin-subfamily member 8 (TRPM8) is subsequently found to be overexpressed in a wide range of cancers and is shown to be implicated in tumorigenesis and tumor progression. Here, we used N-(3-aminopropyl)-2-[(3-methylphenyl) methoxy] -N-(2-thienylmethyl) benzamide hydrochloride (AMTB), a specific TRPM8 antagonist, to explore its antitumoral effect on osteosarcoma. We find that AMTB suppresses osteosarcoma cell proliferation, metastasis and induces cellular apoptosis. Xenograft model in nude mice experiments also define that AMTB can increase the sensitivity of tumor cells to cisplatin, the cytotoxic chemotherapeutic regimens in treating osteosarcoma. Molecularly, AMTB specifically antagonizes TRPM8 which is upregulated in osteosarcoma and its expression level in osteosarcoma tissues is negatively related to patients' prognosis. Finally, RNA sequencing analysis was performed to explore the mechanism underlying the antitumoral effect of AMTB on osteosarcoma cells and the results prove that AMTB suppresses the Transforming Growth Factor ß (TGFß) signaling pathway. Our study provides evidence that TRPM8 could be a potential therapeutic target and AMTB can suppress growth and metastasis of osteosarcoma cells through repressing the TGFß signaling pathway and increase the sensitivity of tumor cells to cisplatin.

Topology optimization design of three-dimensional multi-material and multi-body structure based on irregular cellular hybrid cellular automata method.

In recent years, Hybrid Cellular Automata Method (HCAM) has been successfully applied to solve structural topology optimization problems. However, there was no report on HCAM research of three-dimensional composite structure composed of multiple materials and multiple bodies. Therefore, in this paper, three-dimensional non-cube cells of irregular size (such as tetrahedral cells with adaptive changes inside length) and Finite Element Method (FEM) are introduced to extend HCAM, which is better and more flexibly to fit complex geometric shapes. Furthermore, a better structure configuration of multi-material and multi-body structure is obtained. The typical example study showed that the proposed topology optimization method could effectively remove the redundant materials of multi-material and multi-body structure, and the optimized structure configuration could still meet the requirements of the original condition after geometric reconstructed. Thus it provided a reference for the intelligent design of other products.

Mechanism and kinetic study of Paal-Knorr reaction based on in-situ MIR monitoring.

An in-depth understanding of reaction processes is beneficial to the development and quality control of chemical products. In this work, the mechanism and kinetics of the Paal-Knorr reaction for pyrrole derivatives are thoroughly studied using in-situ Fourier transform infrared (FTIR) spectroscopy. The hemiacetal amine intermediate, reactants, and products were identified and quantified by the treatment of real-time infrared spectra via chemometrics method and two-dimensional correlation spectroscopy (2DCOS) technique. Based on the IR quantitative models, influences of operating conditions on reaction processes were investigated, and the reaction kinetic model was built with kinetic parameters of two rate-limiting reaction steps calculated. This approach of analysis on the in-situ FTIR data demonstrated the ability to extract useful information on reaction components, especially the intermediate spectrum, from the confounding real-time IR data. The in-situ FTIR monitoring combined with the IR analysis methods is proved as a powerful tool for revealing the reaction mechanism and kinetics.

GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions.

Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions.

Exploring Additional Valuable Information From Single-Cell RNA-Seq Data.

Single-cell RNA-seq (scRNA-seq) technologies are broadly applied to dissect the cellular heterogeneity and expression dynamics, providing unprecedented insights into single-cell biology. Most of the scRNA-seq studies mainly focused on the dissection of cell types/states, developmental trajectory, gene regulatory network, and alternative splicing. However, besides these routine analyses, many other valuable scRNA-seq investigations can be conducted. Here, we first review cell-to-cell communication exploration, RNA velocity inference, identification of large-scale copy number variations and single nucleotide changes, and chromatin accessibility prediction based on single-cell transcriptomics data. Next, we discuss the identification of novel genes/transcripts through transcriptome reconstruction approaches, as well as the profiling of long non-coding RNAs and circular RNAs. Additionally, we survey the integration of single-cell and bulk RNA-seq datasets for deconvoluting the cell composition of large-scale bulk samples and linking single-cell signatures to patient outcomes. These additional analyses could largely facilitate corresponding basic science and clinical applications.

Correction of nonlinearities in series structure-based resistance thermometry readouts.

Series structure-based resistance thermometry readouts offer several advantages for multi-point temperature measurements. However, because of the diversity of nonlinear error sources and differences among channels in such readouts, existing nonlinear error correction methods are ineffective. In view of this situation, a nonlinear error correction method based on error source analysis is proposed. The proposed method first determines the impacts of error sources by analyzing the circuit architecture. The contributions of the common-mode rejection ratio and the mismatch between positive and opposite exciting currents are then eliminated using resistance bridge calibrators. Finally, the residuals are fitted to various polynomial functions. The results of experiments show that correction based on the proposed method results in a maximum nonlinear readout error of 1.87 × 10-5, compared with 4.01 × 10-5 using the classical method. Thus, the proposed method of nonlinear error correction is effective for series structure-based resistance thermometry readout.

Systematic profiling of ACE2 expression in diverse physiological and pathological conditions for COVID-19/SARS-CoV-2.

Recent retrospective studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) revealed that the patients with common comorbidities of cancers and chronic diseases face significantly poorer clinical outcomes than those without. Since the expression profile of ACE2, a crucial cell entry receptor for SARS-CoV-2, could indicate the susceptibility to SARS-CoV-2 infection, here we systematically dissected ACE2 expression using large-scale multi-omics data from 30 organs/tissues, 33 cancer types and some common chronic diseases involving >28 000 samples. It was found that sex and age could be correlated with the susceptibility of SARS-CoV-2 infection for certain tissues. Strikingly, ACE2 was up-regulated in cervical squamous cell carcinoma and endocervical adenocarcinoma, colon adenocarcinoma, oesophageal carcinoma, kidney renal papillary cell carcinoma, lung adenocarcinoma and uterine corpus endometrial carcinoma compared to controls. Furthermore, the patients with common chronic diseases regarding angiocardiopathy, type 2 diabetes, liver, pneumonia and hypertension were also with higher ACE2 expression compared to related controls, which were validated using independent data sets. Collectively, our study may reveal a novel important mechanism that the patients with certain cancers and chronic diseases may express higher ACE2 expression compared to the individuals without diseases, which could lead to their higher susceptibility to multi-organ injury of SARS-CoV-2 infection.