The heat shock factor 20-HSF4-cellulose synthase A2 module regulates heat stress tolerance in maize.

Temperature shapes the geographical distribution and behavior of plants. Understanding the regulatory mechanisms underlying the plant heat stress response is important for developing climate-resilient crops, including maize (Zea mays). To identify transcription factors (TFs) that may contribute to the maize heat stress response, we generated a dataset of short- and long-term transcriptome changes following a heat treatment time course in the inbred line B73. Co-expression network analysis highlighted several TFs, including the class B2a heat shock factor (HSF) ZmHSF20. Zmhsf20 mutant seedlings exhibited enhanced tolerance to heat stress. Furthermore, DNA affinity purification sequencing and Cleavage Under Targets and Tagmentation assays demonstrated that ZmHSF20 binds to the promoters of Cellulose synthase A2 (ZmCesA2) and three class A Hsf genes, including ZmHsf4, repressing their transcription. We showed that ZmCesA2 and ZmHSF4 promote the heat stress response, with ZmHSF4 directly activating ZmCesA2 transcription. In agreement with the transcriptome analysis, ZmHSF20 inhibited cellulose accumulation and repressed the expression of cell wall-related genes. Importantly, the Zmhsf20 Zmhsf4 double mutant exhibited decreased thermotolerance, placing ZmHsf4 downstream of ZmHsf20. We proposed an expanded model of the heat stress response in maize, whereby ZmHSF20 lowers seedling heat tolerance by repressing ZmHsf4 and ZmCesA2, thus balancing seedling growth and defense.

Identification, characterization and expression analysis of circRNA encoded by SARS-CoV-1 and SARS-CoV-2.

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA.

TREM2 on microglia cell surface binds to and forms functional binary complexes with heparan sulfate modified with 6-O-sulfation and iduronic acid.

The triggering receptor expressed on myeloid cells-2 (TREM2), a pivotal innate immune receptor, orchestrates functions such as inflammatory responses, phagocytosis, cell survival, and neuroprotection. TREM2 variants R47H and R62H have been associated with Alzheimer's disease, yet the underlying mechanisms remain elusive. Our previous research established that TREM2 binds to heparan sulfate (HS) and variants R47H and R62H exhibit reduced affinity for HS. Building upon this groundwork, our current study delves into the interplay between TREM2 and HS and its impact on microglial function. We confirm TREM2's binding to cell surface HS and demonstrate that TREM2 interacts with HS, forming HS-TREM2 binary complexes on microglia cell surfaces. Employing various biochemical techniques, including surface plasmon resonance, low molecular weight HS microarray screening, and serial HS mutant cell surface binding assays, we demonstrate TREM2's robust affinity for HS, and the effective binding requires a minimum HS size of approximately 10 saccharide units. Notably, TREM2 selectively binds specific HS structures, with 6-O-sulfation and, to a lesser extent, the iduronic acid residue playing crucial roles. N-sulfation and 2-O-sulfation are dispensable for this interaction. Furthermore, we reveal that 6-O-sulfation is essential for HS-TREM2 ternary complex formation on the microglial cell surface, and HS and its 6-O-sulfation are necessary for TREM2-mediated ApoE3 uptake in microglia. By delineating the interaction between HS and TREM2 on the microglial cell surface and demonstrating its role in facilitating TREM2-mediated ApoE uptake by microglia, our findings provide valuable insights that can inform targeted interventions for modulating microglial functions in Alzheimer's disease.

Modeling biomarker variability in joint analysis of longitudinal and time-to-event data.

The role of visit-to-visit variability of a biomarker in predicting related disease has been recognized in medical science. Existing measures of biological variability are criticized for being entangled with random variability resulted from measurement error or being unreliable due to limited measurements per individual. In this article, we propose a new measure to quantify the biological variability of a biomarker by evaluating the fluctuation of each individual-specific trajectory behind longitudinal measurements. Given a mixed-effects model for longitudinal data with the mean function over time specified by cubic splines, our proposed variability measure can be mathematically expressed as a quadratic form of random effects. A Cox model is assumed for time-to-event data by incorporating the defined variability as well as the current level of the underlying longitudinal trajectory as covariates, which, together with the longitudinal model, constitutes the joint modeling framework in this article. Asymptotic properties of maximum likelihood estimators are established for the present joint model. Estimation is implemented via an Expectation-Maximization (EM) algorithm with fully exponential Laplace approximation used in E-step to reduce the computation burden due to the increase of the random effects dimension. Simulation studies are conducted to reveal the advantage of the proposed method over the two-stage method, as well as a simpler joint modeling approach which does not take into account biomarker variability. Finally, we apply our model to investigate the effect of systolic blood pressure variability on cardiovascular events in the Medical Research Council elderly trial, which is also the motivating example for this article.

CircSI-SSL: circRNA-binding site identification based on self-supervised learning.

MOTIVATION: In recent years, circular RNAs (circRNAs), the particular form of RNA with a closed-loop structure, have attracted widespread attention due to their physiological significance (they can directly bind proteins), leading to the development of numerous protein site identification algorithms. Unfortunately, these studies are supervised and require the vast majority of labeled samples in training to produce superior performance. But the acquisition of sample labels requires a large number of biological experiments and is difficult to obtain. RESULTS: To resolve this matter that a great deal of tags need to be trained in the circRNA-binding site prediction task, a self-supervised learning binding site identification algorithm named CircSI-SSL is proposed in this article. According to the survey, this is unprecedented in the research field. Specifically, CircSI-SSL initially combines multiple feature coding schemes and employs RNA_Transformer for cross-view sequence prediction (self-supervised task) to learn mutual information from the multi-view data, and then fine-tuning with only a few sample labels. Comprehensive experiments on six widely used circRNA datasets indicate that our CircSI-SSL algorithm achieves excellent performance in comparison to previous algorithms, even in the extreme case where the ratio of training data to test data is 1:9. In addition, the transplantation experiment of six linRNA datasets without network modification and hyperparameter adjustment shows that CircSI-SSL has good scalability. In summary, the prediction algorithm based on self-supervised learning proposed in this article is expected to replace previous supervised algorithms and has more extensive application value. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/cc646201081/CircSI-SSL.

AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding.

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.

New cyclophilin D inhibitor rescues mitochondrial and cognitive function in Alzheimer's disease.

Mitochondrial dysfunction is an early pathological feature of Alzheimer disease and plays a crucial role in the development and progression of Alzheimer's disease. Strategies to rescue mitochondrial function and cognition remain to be explored. Cyclophilin D (CypD), the peptidylprolyl isomerase F (PPIase), is a key component in opening the mitochondrial membrane permeability transition pore, leading to mitochondrial dysfunction and cell death. Blocking membrane permeability transition pore opening by inhibiting CypD activity is a promising therapeutic approach for Alzheimer's disease. However, there is currently no effective CypD inhibitor for Alzheimer's disease, with previous candidates demonstrating high toxicity, poor ability to cross the blood-brain barrier, compromised biocompatibility and low selectivity. Here, we report a new class of non-toxic and biocompatible CypD inhibitor, ebselen, using a conventional PPIase assay to screen a library of ∼2000 FDA-approved drugs with crystallographic analysis of the CypD-ebselen crystal structure (PDB code: 8EJX). More importantly, we assessed the effects of genetic and pharmacological blockade of CypD on Alzheimer's disease mitochondrial and glycolytic bioenergetics in Alzheimer's disease-derived mitochondrial cybrid cells, an ex vivo human sporadic Alzheimer's disease mitochondrial model, and on synaptic function, inflammatory response and learning and memory in Alzheimer's disease mouse models. Inhibition of CypD by ebselen protects against sporadic Alzheimer's disease- and amyloid-ß-induced mitochondrial and glycolytic perturbation, synaptic and cognitive dysfunction, together with suppressing neuroinflammation in the brain of Alzheimer's disease mouse models, which is linked to CypD-related membrane permeability transition pore formation. Thus, CypD inhibitors have the potential to slow the progression of neurodegenerative diseases, including Alzheimer's disease, by boosting mitochondrial bioenergetics and improving synaptic and cognitive function.

Circadian control of heparan sulfate levels times phagocytosis of amyloid beta aggregates.

Alzheimer's Disease (AD) is a neuroinflammatory disease characterized partly by the inability to clear, and subsequent build-up, of amyloid-beta (Aß). AD has a bi-directional relationship with circadian disruption (CD) with sleep disturbances starting years before disease onset. However, the molecular mechanism underlying the relationship of CD and AD has not been elucidated. Myeloid-based phagocytosis, a key component in the metabolism of Aß, is circadianly-regulated, presenting a potential link between CD and AD. In this work, we revealed that the phagocytosis of Aß42 undergoes a daily circadian oscillation. We found the circadian timing of global heparan sulfate proteoglycan (HSPG) biosynthesis was the molecular timer for the clock-controlled phagocytosis of Aß and that both HSPG binding and aggregation may play a role in this oscillation. These data highlight that circadian regulation in immune cells may play a role in the intricate relationship between the circadian clock and AD.

A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation.

BACKGROUND: Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA. RESULTS: CircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs. CONCLUSIONS: This efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server ( http://server.malab.cn/CircDA ) is provided, and the code is open-sourced ( https://github.com/nmt315320/CircDA.git ) for the convenience of algorithm improvement.

CircRNA identification and feature interpretability analysis.

BACKGROUND: Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation. RESULTS: We developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification. CONCLUSIONS: CircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git .

Inhalable Polymeric Microparticles for Phage and Photothermal Synergistic Therapy of Methicillin-Resistant Staphylococcus aureus Pneumonia.

Acute methicillin-resistant Staphylococcus aureus (MRSA) pneumonia is a common and serious lung infection with high morbidity and mortality rates. Due to the increasing antibiotic resistance, toxicity, and pathogenicity of MRSA, there is an urgent need to explore effective antibacterial strategies. In this study, we developed a dry powder inhalable formulation which is composed of porous microspheres prepared from poly(lactic-co-glycolic acid) (PLGA), internally loaded with indocyanine green (ICG)-modified, heat-resistant phages that we screened for their high efficacy against MRSA. This formulation can deliver therapeutic doses of ICG-modified active phages to the deep lung tissue infection sites, avoiding rapid clearance by alveolar macrophages. Combined with the synergistic treatment of phage therapy and photothermal therapy, the formulation demonstrates potent bactericidal effects in acute MRSA pneumonia. With its long-term stability at room temperature and inhalable characteristics, this formulation has the potential to be a promising drug for the clinical treatment of MRSA pneumonia.

Ubiquitin-specific peptidase 14 maintains estrogen receptor α stability via its deubiquitination activity in endometrial cancer.

USP14 deubiquitinates ERα to maintain its stability in ECEndometrial cancer (EC) is one of the common gynecological malignancies of which the incidence has been rising for decades. It is considered that continuously unopposed estrogen exposure is the main risk factor for EC initiation. Thus, exploring the modulation of estrogen/estrogen receptor α (ERα) signaling pathway in EC would be helpful to well understand the mechanism of EC development and find the potential target for EC therapy. Ubiquitin-specific peptidase 14 (USP14), a member of the proteasome-associated deubiquitinating enzyme family, plays a crucial role in a series of tumors. However, the function of USP14 in EC is still elusive. Here, our results have demonstrated that USP14 is highly expressed in EC tissues compared with that in normal endometrial tissues, and higher expression of USP14 is positively correlated with poor prognosis. Moreover, USP14 maintains ERα stability through its deubiquitination activity. Our results further demonstrate that USP14 depletion decreases the expression of ERα-regulated genes in EC-derived cell lines. Moreover, knockdown of USP14 or USP14-specific inhibitor treatment significantly suppresses cell growth and migration in EC cell lines or in mice. We further provide the evidence to show that the effect of USP14 on EC cell growth, if not all, at least is partially related to ERα pathway. Our study provides new sights for USP14 to be a potential therapeutic target for the treatment of EC, especially for EC patients with fertility preservation needs.

Application of Vitreoscilla Hemoglobin as a Green and Efficient Biocatalyst for the Synthesis of Benzoxazoles in Water.

We report an efficient and eco-friendly method for the Vitreoscilla hemoglobin (VHb)-catalyzed synthesis of benzoxazoles in water at room temperature. tert-Butyl hydroperoxide and 2,2,6,6-tetramethyl-1-piperidinyloxy were used as oxidant and radical scavenger, respectively. A total of 27 functionally diverse benzoxazoles were prepared in moderate to high yields (62 %-94 %) by the annulation reaction of phenols with amines in the presence of VHb in 1 h. Thus, this method is highly viable for practical applications. This work broadens the application of hemoglobin to organic synthesis.

Molecular Mechanism of Adenovirus Late Protein L4-100K Chaperones the Trimerization of Hexon.

Assembly of the adenovirus capsid protein hexon depends on the assistance of the molecular chaperone L4-100K. However, the chaperone mechanisms remain unclear. In this study, we found that L4-100K was involved in the hexon translation process and could prevent hexon degradation by the proteasome in cotransfected human cells. Two nonadjacent domains, 84-133 and 656-697, at the N-terminal and C-terminal regions of human adenovirus type 5 L4-100K, respectively, were found to be crucial and cooperatively responsible for hexon trimer expression and assembly. These two chaperone-related domains were conserved in the sequence of L4-100K and in the function of hexon assembly across different adenovirus serotypes. Different degrees of cross-activity of hexon trimerization with different serotypes were detected in subgroups B, C, and D, which were proven to be controlled by the interaction between the C-terminal chaperone-related domain of L4-100K and hypervariable regions (HVR) of hexon. Additionally, HVR-chimeric hexon mutants were successfully assembled with the assistance of the 1-697 mutant. Structural analysis of 656-697 by nuclear magnetic resonance and structural prediction of L4-100K using Robetta showed that the two conserved domains are mainly composed of α-helices and are located on the surface of the highly folded core region. Our research provides a more complete understanding of hexon assembly and guidance for the development of hexon-chimeric adenovirus vectors that will be safer, smarter, and more efficient. IMPORTANCE Adenovirus vectors have been widely used in clinical trials of vaccines and gene therapy, although some deficiencies remain. Chimeric modification of the hexon was expected to improve the potency of preexisting immune evasion and targeting, but in many cases, viral packaging is prevented by the inability of the chimeric hexon to assemble correctly. So far, few studies have examined the mechanisms of hexon trimer assembly. Here, we show how the chaperone protein L4-100K contributes to the assembly of the adenovirus capsid protein hexon, and these data will provide a guide for novel adenovirus vector design and development, as we desired.

Learning representations for gene ontology terms by jointly encoding graph structure and textual node descriptors.

Measuring the semantic similarity between Gene Ontology (GO) terms is a fundamental step in numerous functional bioinformatics applications. To fully exploit the metadata of GO terms, word embedding-based methods have been proposed recently to map GO terms to low-dimensional feature vectors. However, these representation methods commonly overlook the key information hidden in the whole GO structure and the relationship between GO terms. In this paper, we propose a novel representation model for GO terms, named GT2Vec, which jointly considers the GO graph structure obtained by graph contrastive learning and the semantic description of GO terms based on BERT encoders. Our method is evaluated on a protein similarity task on a collection of benchmark datasets. The experimental results demonstrate the effectiveness of using a joint encoding graph structure and textual node descriptors to learn vector representations for GO terms.

DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction.

MOTIVATION: Accurate prediction of drug-target binding affinity (DTA) is crucial for drug discovery. The increase in the publication of large-scale DTA datasets enables the development of various computational methods for DTA prediction. Numerous deep learning-based methods have been proposed to predict affinities, some of which only utilize original sequence information or complex structures, but the effective combination of various information and protein-binding pockets have not been fully mined. Therefore, a new method that integrates available key information is urgently needed to predict DTA and accelerate the drug discovery process. RESULTS: In this study, we propose a novel deep learning-based predictor termed DataDTA to estimate the affinities of drug-target pairs. DataDTA utilizes descriptors of predicted pockets and sequences of proteins, as well as low-dimensional molecular features and SMILES strings of compounds as inputs. Specifically, the pockets were predicted from the three-dimensional structure of proteins and their descriptors were extracted as the partial input features for DTA prediction. The molecular representation of compounds based on algebraic graph features was collected to supplement the input information of targets. Furthermore, to ensure effective learning of multiscale interaction features, a dual-interaction aggregation neural network strategy was developed. DataDTA was compared with state-of-the-art methods on different datasets, and the results showed that DataDTA is a reliable prediction tool for affinities estimation. Specifically, the concordance index (CI) of DataDTA is 0.806 and the Pearson correlation coefficient (R) value is 0.814 on the test dataset, which is higher than other methods. AVAILABILITY AND IMPLEMENTATION: The codes and datasets of DataDTA are available at https://github.com/YanZhu06/DataDTA.

Observation of coupling interaction between surface plasmons and Tamm plasmons.

The optical effect analogous to electromagnetically induced transparency (EIT) in atomic systems has attracted broad attention in the field of photonics due to its promising applications in optical storage and integrated devices. Herein, we firstly report the experimental observation of the EIT-like effect generated from the coupling between surface plasmons (SPs) and Tamm plasmons (TPs) in a hybrid multilayer system at the near-infrared band. This multilayer system is composed of a nanofabricated silver grating on a silver/Bragg mirror with a SiO2 spacer. The experimental results show that a narrow reflection peak can appear in the wide reflection spectral dip due to the coupling between the SPs in the silver grating and TPs in the silver/Bragg mirror, which agree well with the finite-difference time-domain (FDTD) simulations. It is also found that the dip position of the EIT-like spectrum presents a redshift with the increase of the silver grating width. These results will provide a new way, to the best of our knowledge, for the generation of the EIT-like effect and light spectral manipulation in multilayer structures.

Guided wave resonance-based digital holographic microscopy for high-sensitivity monitoring of the refractive index.

Surface plasmon resonance holographic microscopy (SPRHM) has been employed to measure the refractive index but whose performance is generally limited by the metallic intrinsic loss. Herein we first, to our knowledge, utilize guided wave resonance (GWR) with low loss to realize the monitoring of the refractive index by integrating with digital holographic microscopy (DHM). By depositing a dielectric layer on a silver film, we observe a typical GWR in the dielectric layer with stronger field enhancement and higher sensitivity to the surrounding refractive index compared to the silver film-supported SPR, which agrees well with calculations. The innovative combination of the GWR and DHM contributes to the highly sensitive dynamic monitoring of the surrounding refractive index variation. Through the measurement with DHM, we found that the GWR presents an excellent sensitivity, which is 2.6 times higher than that of the SPR on the silver film. The results will pave a new pathway for digital holographic interferometry and its applications in environmental and biological detections.

Metabolomic characterization of COVID-19 survivors in Jilin province.

BACKGROUND: The COVID-19 pandemic has escalated into a severe global public health crisis, with persistent sequelae observed in some patients post-discharge. However, metabolomic characterization of the reconvalescent remains unclear. METHODS: In this study, serum and urine samples from COVID-19 survivors (n = 16) and healthy subjects (n = 16) underwent testing via the non-targeted metabolomics approach using UPLC-MS/MS. Univariate and multivariate statistical analyses were conducted to delineate the separation between the two sample groups and identify differentially expressed metabolites. By integrating random forest and cluster analysis, potential biomarkers were screened, and the differential metabolites were subsequently subjected to KEGG pathway enrichment analysis. RESULTS: Significant differences were observed in the serum and urine metabolic profiles between the two groups. In serum samples, 1187 metabolites were detected, with 874 identified as significant (457 up-regulated, 417 down-regulated); in urine samples, 960 metabolites were detected, with 39 deemed significant (12 up-regulated, 27 down-regulated). Eight potential biomarkers were identified, with KEGG analysis revealing significant enrichment in several metabolic pathways, including arginine biosynthesis. CONCLUSIONS: This study offers an overview of the metabolic profiles in serum and urine of COVID-19 survivors, providing a reference for post-discharge monitoring and the prognosis of COVID-19 patients.

Development of Fluorenyl-Based Copolyimines with Adjustable Mechanical Properties, Recyclability and Friction Resistance.

Dynamic polyimines are a class of fascinating dynamic polymers with recyclability and reparability owing to their reversible Schiff-base reactions. However, balancing the dynamic properties and mechanical strength of dynamic polyimines presents a major challenge due to the dissociative and associative nature of the imine bonds. Herein, we introduced bulky fluorene groups and polyether amine into the skeleton of polyimine networks to achieve a tradeoff in comprehensive properties. The resulting dynamic polyimines with fluorene groups (Cardo-DPIs) were successfully synthesized by combining the rigid diamine 9,9-bis(4-aminophenyl)fluorene and the flexible polyether amine, demonstrating a high tensile strength of 64.7 MPa. Additionally, Cardo-DPIs films with more content of rigid fluorene groups exhibited higher water resistance, glass transition temperature and wear-resisting ability. Moreover, the Cardo-DPIs films not only efficiently underwent thermal reshaping, but also exhibited excellent self-healing capabilities and chemical degradation in acidic solutions. Furthermore, the resulting films can achieve fully closed-loop recovery by free amine solution for 2 h at room temperature. This study broadens the scope of dynamic polyimine materials and promotes the balanced development of their functional and mechanical properties.