MSRB2 Ameliorates H2O2-induced Chondrocyte Oxidative Stress and Suppresses Apoptosis in Osteoarthritis.

BACKGROUND: Chondrocyte oxidative stress and apoptosis are critical factors contributing to the pathogenesis of osteoarthritis (OA). Methionine sulfoxide reductase B2 (MSRB2) is a mitochondrial protein that protects cells from oxidative stress and is involved in apoptosis. This study aimed to investigated the expression of MSRB2 in articular cartilage tissues and elucidated its effect on H2O2-stimulated chondrocytes. METHODS: Human chondrocytes were cultured in Dulbecco's modified Eagle's medium (DMEM)/F12. MSRB2 overexpression in chondrocytes was achieved by transfecting with an MSRB2 overexpression plasmid. Western blot, quantitative RT-PCR, Immunofluorescence staining, and TUNEL assay were employed in this study. RESULTS: MSRB2 expression was found to be reduced in OA patients. Furthermore, overexpression of MSRB2 in H2O2-induced chondrocytes mitigated apoptosis and enhanced cell viability. Elevated MSRB2 expression diminished chondrocyte ROS contents, decreased cytochrome C (Cyc) in the cytoplasm, and regulated mitochondrial membrane potential to maintain mitochondrial homeostasis. Interestingly, knockdown of charged multivesicular body protein 5 (CHMP5) led to a decreased inMSRB2 expression in chondrocytes. Additionally, protein levels of CHMP5 and MSRB2 were reduced in H2O2-stimulated chondrocytes, and silencing CHMP5 reduced MSRB2 expression. Knockdown of CHMP5 increased cleaved caspase-3 expression in H2O2-induced chondrocytes and elevated TUNEL-positive chondrocytes. CONCLUSION: MSRB2 decreased in OA, and overexpression of MSRB2 alleviated oxidative stress and apoptosis of chondrocyte.

Strengthening the Synergy between Oxygen Vacancies in Electrocatalysts for Efficient Glycerol Electrooxidation.

Defect-engineered bimetallic oxides exhibit high potential for the electrolysis of small organic molecules. However, the ambiguity in the relationship between the defect density and electrocatalytic performance makes it challenging to control the final products of multi-step multi-electron reactions in such electrocatalytic systems. In this study, controllable kinetics reduction is used to maximize the oxygen vacancy density of a Cu─Co oxide nanosheet (CuCo2O4 NS), which is used to catalyze the glycerol electrooxidation reaction (GOR). The CuCo2O4-x NS with the highest oxygen-vacancy density (CuCo2O4-x-2) oxidizes C3 molecules to C1 molecules with selectivity of almost 100% and a Faradaic efficiency of ≈99%, showing the best oxidation performance among all the modified catalysts. Systems with multiple oxygen vacancies in close proximity to each other synergistically facilitate the cleavage of C─C bonds. Density functional theory calculations confirm the ability of closely spaced oxygen vacancies to facilitate charge transfer between the catalyst and several key glycolic-acid (GCA) intermediates of the GOR process, thereby facilitating the decomposition of C2 intermediates to C1 molecules. This study reveals qualitatively in tuning the density of oxygen vacancies for altering the reaction pathway of GOR by the synergistic effects of spatial proximity of high-density oxygen vacancies.

CHMP5 attenuates osteoarthritis via inhibiting chondrocyte apoptosis and extracellular matrix degradation: involvement of NF-κB pathway.

BACKGROUND: Osteoarthritis (OA), the most common joint disease, is linked with chondrocyte apoptosis and extracellular matrix (ECM) degradation. Charged multivesicular body protein 5 (CHMP5), a member of the multivesicular body, has been reported to serve as an anti-apoptotic protein to participate in leukemia development. However, the effects of CHMP5 on apoptosis and ECM degradation in OA remain unclear. METHODS: In this study, quantitative proteomics was performed to analyze differential proteins between normal and OA patient articular cartilages. The OA mouse model was constructed by the destabilization of the medial meniscus (DMM). In vitro, interleukin-1 beta (IL-1ß) was used to induce OA in human chondrocytes. CHMP5 overexpression and silencing vectors were created using an adenovirus system. The effects of CHMP5 on IL-1ß-induced chondrocyte apoptosis were investigated by CCK-8, flow cytometry, and western blot. The effects on ECM degradation were examined by western blot and immunofluorescence. The potential mechanism was explored by western blot and Co-IP assays. RESULTS: Downregulated CHMP5 was identified by proteomics in OA patient cartilages, which was verified in human and mouse articular cartilages. CHMP5 overexpression repressed cell apoptosis and ECM degradation in OA chondrocytes. However, silencing CHMP5 exacerbated OA chondrocyte apoptosis and ECM degradation. Furthermore, we found that the protective effect of CHMP5 against OA was involved in nuclear factor kappa B (NF-κB) signaling pathway. CONCLUSIONS: This study demonstrated that CHMP5 repressed IL-1ß-induced chondrocyte apoptosis and ECM degradation and blocked NF-κB activation. It was shown that CHMP5 might be a novel potential therapeutic target for OA in the future.

A spatiotemporal atlas of mouse liver homeostasis and regeneration.

The mechanism by which mammalian liver cell responses are coordinated during tissue homeostasis and perturbation is poorly understood, representing a major obstacle in our understanding of many diseases. This knowledge gap is caused by the difficulty involved with studying multiple cell types in different states and locations, particularly when these are transient. We have combined Stereo-seq (spatiotemporal enhanced resolution omics-sequencing) with single-cell transcriptomic profiling of 473,290 cells to generate a high-definition spatiotemporal atlas of mouse liver homeostasis and regeneration at the whole-lobe scale. Our integrative study dissects in detail the molecular gradients controlling liver cell function, systematically defining how gene networks are dynamically modulated through intercellular communication to promote regeneration. Among other important regulators, we identified the transcriptional cofactor TBL1XR1 as a rheostat linking inflammation to Wnt/ß-catenin signaling for facilitating hepatocyte proliferation. Our data and analytical pipelines lay the foundation for future high-definition tissue-scale atlases of organ physiology and malfunction.

Electron Accumulation Induced by Electron Injection-Incomplete Discharge on NiFe LDH for Enhanced Oxygen Evolution Reaction.

Optimizing the local electronic structure of electrocatalysts can effectively lower the energy barrier of electrochemical reactions, thus enhancing the electrocatalytic activity. However, the intrinsic contribution of the electronic effect is still experimentally unclear. In this work, the electron injection-incomplete discharge approach to achieve the electron accumulation (EA) degree on the nickel-iron layered double hydroxide (NiFe LDH) is proposed, to reveal the intrinsic contribution of EA toward oxygen evolution reaction (OER). Such NiFe LDH with EA effect results in only 262 mV overpotential to reach 50 mA cm-2, which is 51 mV-lower compared with pristine NiFe LDH (313 mV), and reduced Tafel slope of 54.8 mV dec-1 than NiFe LDH (107.5 mV dec-1). Spectroscopy characterizations combined with theoretical calculations confirm that the EA near concomitant Vo can induce a narrower energy gap and lower thermodynamic barrier to enhance OER performance. This study clarifies the mechanism of the EA effect on OER activity, providing a direct electronic structure modulation guideline for effective electrocatalyst design.

Antibody Nanotweezer Constructing Bivalent Transistor-Biomolecule Interface with Spatial Tolerance.

Establishing a multivalent interface between the biointerface of a living system and electronic device is vital to building intelligent bioelectronic systems. How to achieve multivalent binding with spatial tolerance at the nanoscale remains challenging. Here, we report an antibody nanotweezer that is a self-adaptive bivalent nanobody enabling strong and resilient binding between transistor and envelope proteins at biointerfaces. The antibody nanotweezer is constructed by a DNA framework, where the nanoscale patterning of nanobodies along with their local spatial adaptivity enables simultaneous recognition of target epitopes without binding stress. As such, effective binding affinity increases by 1 order of magnitude compared with monovalent antibody. The antibody nanotweezer operating on transistor offers enhanced signal transduction, which is implemented to detect clinical pathogens, showing ∼100% overall agreement with PCR results. This work provides a perspective of engineering multivalent interfaces between biosystems with solid-state devices, holding great potential for organoid intelligence on a chip.

Prevention and treatment of HPV-related cancer through a mRNA vaccine expressing APC-targeting antigen.

Persistent human papillomavirus (HPV) infection is associated with multiple malignancies. Developing therapeutic vaccines to eliminate HPV-infected and malignant cells holds significant value. In this study, we introduced a lipid nanoparticle encapsulated mRNA vaccine expressing tHA-mE7-mE6. Mutations were introduced into E6 and E7 of HPV to eliminate their tumourigenicity. A truncated influenza haemagglutinin protein (tHA), which binds to the CD209 receptor on the surface of dendritic cells (DCs), was fused with mE7-mE6 in order to allow efficient uptake of antigen by antigen presenting cells. The tHA-mE7-mE6 (mRNA) showed higher therapeutic efficacy than mE7-mE6 (mRNA) in an E6 and E7+ tumour model. The treatment resulted in complete tumour regression and prevented tumour formation. Strong CD8+ T-cell immune response was induced, contributing to preventing and curing of E6 and E7+ tumour. Antigen-specific CD8+ T were found in spleens, peripheral blood and in tumours. In addition, the tumour infiltration of DC and NK cells were increased post therapy. In conclusion, this study described a therapeutic mRNA vaccine inducing strong anti-tumour immunity in peripheral and in tumour microenvironment, holding promising potential to treat HPV-induced cancer and to prevent cancer recurrence.

The CDK4/6 inhibitor Palbociclib synergizes with ATRA to induce differentiation in AML.

Differentiation therapy based on ATRA almost cured acute promyelocytic leukemia (APL). However, it is disappointing that ATRA is not effective against other acute myeloid leukemia (AML) subtypes. Developing new and effective anti-AML therapies that promote leukemia differentiation is necessary. The CDK4/6-cyclin D pathway is a key initiator of the G1/S phase transition, which determines cell fate. Herein, we investigated whether the CDK4/6 inhibitor palbociclib would synergize with ATRA to promote leukemia differentiation in vitro and in vivo. Our findings revealed that CDK4/6-cyclin D pathway genes were aberrantly expressed in AML, and we observed that palbociclib sensitized AML cells to ATRA-induced morphologic, biochemical, and functional changes indicative of myeloid differentiation. The combination of palbociclib and ATRA attenuated AML cell expansion in vivo. These enhanced differentiation effects may be associated with the regulation of transcription factors, including RARα, E2F1, and STAT1. Overall, our findings demonstrate that CDK4/6 inhibition sensitizes AML cells to ATRA and could guide the development of novel therapeutic strategies for AML patients.

Spatially and Temporally Resolved Dynamic Response of Co-Based Composite Interface during the Oxygen Evolution Reaction.

Interfacial interaction dictates the overall catalytic performance and catalytic behavior rules of the composite catalyst. However, understanding of interfacial active sites at the microscopic scale is still limited. Importantly, identifying the dynamic action mechanism of the "real" active site at the interface necessitates nanoscale, high spatial-time-resolved complementary-operando techniques. In this work, a Co3O4 homojunction with a well-defined interface effect is developed as a model system to explore the spatial-correlation dynamic response of the interface toward oxygen evolution reaction. Quasi in situ scanning transmission electron microscopy-electron energy-loss spectroscopy with high spatial resolution visually confirms the size characteristics of the interface effect in the spatial dimension, showing that the activation of active sites originates from strong interfacial electron interactions at a scale of 3 nm. Multiple time-resolved operando spectroscopy techniques explicitly capture dynamic changes in the adsorption behavior for key reaction intermediates. Combined with density functional theory calculations, we reveal that the dynamic adjustment of multiple adsorption configurations of intermediates by highly activated active sites at the interface facilitates the O-O coupling and *OOH deprotonation processes. The dual dynamic regulation mechanism accelerates the kinetics of oxygen evolution and serves as a pivotal factor in promoting the oxygen evolution activity of the composite structure. The resulting composite catalyst (Co-B@Co3O4/Co3O4 NSs) exhibits an approximately 70-fold turnover frequency and 20-fold mass activity than the monomer structure (Co3O4 NSs) and leads to significant activity (η10 ∼257 mV). The visual complementary analysis of multimodal operando/in situ techniques provides us with a powerful platform to advance our fundamental understanding of interfacial structure-activity relationships in composite structured catalysts.

Efficient catalyst-free N2 fixation by water radical cations under ambient conditions.

The growth and sustainable development of humanity is heavily dependent upon molecular nitrogen (N2) fixation. Herein we discover ambient catalyst-free disproportionation of N2 by water plasma which occurs via the distinctive HONH-HNOH+• intermediate to yield economically valuable nitroxyl (HNO) and hydroxylamine (NH2OH) products. Calculations suggest that the reaction is prompted by the coordination of electronically excited N2 with water dimer radical cation, (H2O)2+•, in its two-center-three-electron configuration. The reaction products are collected in a 76-needle array discharge reactor with product yields of 1.14 µg cm-2 h-1 for NH2OH and 0.37 µg cm-2 h-1 for HNO. Potential applications of these compounds are demonstrated to make ammonia (for NH2OH), as well as to chemically react and convert cysteine, and serve as a neuroprotective agent (for HNO). The conversion of N2 into HNO and NH2OH by water plasma could offer great profitability and reduction of polluting emissions, thus giving an entirely look and perspectives to the problem of green N2 fixation.

A Dynamics-Learning Multirate Estimation Approach for the Feeding Condition Perception of Complex Industry Processes.

In this study, we propose a dynamics-learning multirate estimation approach to perceive the quality-related indices (QRIs) of the feeding solution of a unit process. A quality-related index for estimation is an intermediate technical indicator between a unit process and a proceeding unit process; hence, the estimation problem is formulated as a two-stage estimation problem utilizing the production data of both unit processes. Dynamics-learning bidirectional long short-term memory (BiLSTM) with different inputs for the forward and backward layers is proposed to manage the input data from the different unit processes. In the dynamics-learning BiLSTM, a cycle control gate is added in the memory cell to learn the dynamics of the QRIs, thereby enabling a high-rate estimation under multirate conditions. A Bayesian estimation model is then combined with the dynamics-learning BiLSTM model to manage the process delay. Ablation and comparative experiments are conducted to evaluate the feasibility and effectiveness of the proposed estimation approach. The experimental results illustrate the performance and high-rate estimation ability of the proposed approach.

Error-Triggered Adaptive Sparse Identification for Predictive Control and Its Application to Multiple Operating Conditions Processes.

With the digital transformation of process manufacturing, identifying the system model from process data and then applying to predictive control has become the most dominant approach in process control. However, the controlled plant often operates under changing operating conditions. What is more, there are often unknown operating conditions such as first appearance operating conditions, which make traditional predictive control methods based on identified model difficult to adapt to changing operating conditions. Moreover, the control accuracy is low during operating condition switching. To solve these problems, this article proposes an error-triggered adaptive sparse identification for predictive control (ETASI4PC) method. Specifically, an initial model is established based on sparse identification. Then, a prediction error-triggered mechanism is proposed to monitor operating condition changes in real time. Next, the previously identified model is updated with the fewest modifications by identifying parameter change, structural change, and combination of changes in the dynamical equations, thus achieving precise control to multiple operating conditions. Considering the problem of low control accuracy during the operating condition switching, a novel elastic feedback correction strategy is proposed to significantly improve the control accuracy in the transition period and ensure accurate control under full operating conditions. To verify the superiority of the proposed method, a numerical simulation case and a continuous stirred tank reactor (CSTR) case are designed. Compared with some state-of-the-art methods, the proposed method can rapidly adapt to frequent changes in operating conditions, and it can achieve real-time control effects even for unknown operating conditions such as first appearance operating conditions.

A novel human single-domain antibody-drug conjugate targeting CEACAM5 exhibits potent in vitro and in vivo antitumor activity.

Leveraging the specificity of antibody to deliver cytotoxic agent into tumor, antibody-drug conjugates (ADCs) have become one of the hotspots in the development of anticancer therapies. Although significant progress has been achieved, there remain challenges to overcome, including limited penetration into solid tumors and potential immunogenicity. Fully human single-domain antibodies (UdAbs), with their small size and human nature, represent a promising approach for addressing these challenges. Carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) is a glycosylated cell surface protein that rarely expressed in normal adult tissues but overexpressed in diverse cancers, taking part in tumorigenesis, progression, and metastasis. In this study, we investigated the therapeutic potential of UdADC targeting CEACAM5. We performed biopanning in our library and obtained an antibody candidate B9, which bound potently and specifically to CEACAM5 protein (KD = 4.84 nM) and possessed excellent biophysical properties (low aggregation tendency, high homogeneity, and thermal stability). The conjugation of B9 with a potent cytotoxic agent, monomethyl auristatin E (MMAE), exhibited superior antitumor efficacy against CEACAM5-expressing human gastric cancer cell line MKN-45, human pancreatic carcinoma cell line BxPC-3 and human colorectal cancer cell line LS174T with IC50 values of 38.14, 25.60, and 101.4 nM, respectively. In BxPC-3 and MKN-45 xenograft mice, administration of UdADC B9-MMAE (5 mg/kg, i.v.) every 2 days for 4 times markedly inhibited the tumor growth without significant change in body weight. This study may have significant implications for the design of next-generation ADCs.

Strongly-Interacted NiSe2 /NiFe2 O4 Architectures Built Through Selective Atomic Migration as Catalysts for the Oxygen Evolution Reaction.

The interactions between the catalyst and support are widely used in many important catalytic reactions but the construction of strong interaction with definite microenvironments to understand the structure-activity relationship is still challenging. Here, strongly-interacted composites are prepared via selective exsolution of active NiSe2 from the host matrix of NiFe2 O4 (S-NiSe2 /NiFe2 O4 ) taking advantage of the differences of migration energy, in which the NiSe2 possessed both high dispersion and small size. The characteristics of spatially resolved scanning transmission X-ray microscopy (STXM) coupled with analytical Mössbauer spectra for the surface and bulk electronic structures unveiled that this strongly interacted composite triggered more charge transfers from the NiSe2 to the host of NiFe2 O4 while stabilizing the inherent atomic coordination of NiFe2 O4 . The obtained S-NiSe2 /NiFe2 O4 exhibits overpotentials of 290 mV at 10 mA cm-2 for oxygen evolution reaction (OER). This strategy is general and can be extended to other supported catalysts, providing a powerful tool for modulating the catalytic performance of strongly-interacted composites.

EaLDL: Element-Aware Lifelong Dictionary Learning for Multimode Process Monitoring.

With the rapid development of modern industry and the increasing prominence of artificial intelligence, data-driven process monitoring methods have gained significant popularity in industrial systems. Traditional static monitoring models struggle to represent the new modes that arise in industrial production processes due to changes in production environments and operating conditions. Retraining these models to address the changes often leads to high computational complexity. To address this issue, we propose a multimode process monitoring method based on element-aware lifelong dictionary learning (EaLDL). This method initially treats dictionary elements as fundamental units and measures the global importance of dictionary elements from the perspective of the multimode global learning process. Subsequently, to ensure that the dictionary can represent new modes without losing the representation capability of historical modes during the updating process, we construct a novel surrogate loss to impose constraints on the update of dictionary elements. This constraint enables the continuous updating of the dictionary learning (DL) method to accommodate new modes without compromising the representation of previous modes. Finally, to evaluate the effectiveness of the proposed method, we perform comprehensive experiments on numerical simulations as well as an industrial process. A comparison is made with several advanced process monitoring methods to assess its performance. Experimental results demonstrate that our proposed method achieves a favorable balance between learning new modes and retaining the memory of historical modes. Moreover, the proposed method exhibits insensitivity to initial points, delivering satisfactory results under various initial conditions.

π Bridge Engineering-Boosted Dual Enhancement of Type-I Photodynamic and Photothermal Performance for Mitochondria-Targeting Multimodal Phototheranostics of Tumor.

Designing mitochondria-targeting phototheranostic agents (PTAs), which can simultaneously possess exceptional and balanced type-I photodynamic therapy (PDT) and photothermal therapy (PTT) performance, still remains challenging. Herein, benzene, furan, and thiophene were utilized as π bridges to develop multifunctional PTAs. STB with thiophene as a π bridge, in particular, benefiting from stronger donor-accepter (D-A) interactions, reduced the singlet-triplet energy gap (ΔES1-T1), allowed more free intramolecular rotation, and exhibited outstanding near-infrared (NIR) emission, effective type-I reactive oxygen species (ROS) generation, and relatively high photothermal conversion efficiency (PCE) of 51.9%. In vitro and in vivo experiments demonstrated that positive-charged STB not only can actively target the mitochondria of tumor cells but also displayed strong antitumor effects and excellent in vivo imaging ability. This work subtly established a win-win strategy by π bridge engineering, breaking the barrier of making a balance between ROS generation and photothermal conversion, boosting a dual enhancement of PDT and PTT performance, and stimulating the development of multimodal imaging-guided precise cancer phototherapy.

Tensile Strain-Mediated Spinel Ferrites Enable Superior Oxygen Evolution Activity.

Exploring efficient strategies to overcome the performance constraints of oxygen evolution reaction (OER) electrocatalysts is vital for electrocatalytic applications such as H2O splitting, CO2 reduction, N2 reduction, etc. Herein, tunable, wide-range strain engineering of spinel oxides, such as NiFe2O4, is proposed to enhance the OER activity. The lattice strain is regulated by interfacial thermal mismatch during the bonding process between thermally expanding NiFe2O4 nanoparticles and the nonexpanding carbon fiber substrate. The tensile lattice strain causes energy bands to flatten near the Fermi level, lowering eg orbital occupancy, effectively increasing the number of electronic states near the Fermi level, and reducing the pseudoenergy gap. Consequently, the energy barrier of the rate-determining step for strained NiFe2O4 is reduced, achieving a low overpotential of 180 mV at 10 mA/cm2. A total water decomposition voltage range of 1.52-1.56 V at 10 mA/cm2 (without iR correction) was achieved in an asymmetric alkaline electrolytic cell with strained NiFe2O4 nanoparticles, and its robust stability was verified with a voltage retention of approximately 99.4% after 100 h. Furthermore, the current work demonstrates the universality of tuning OER performance with other spinel ferrite systems, including cobalt, manganese, and zinc ferrites.

A robust sparse identification method for nonlinear dynamic systems affected by non-stationary noise.

In the field of science and engineering, identifying the nonlinear dynamics of systems from data is a significant yet challenging task. In practice, the collected data are often contaminated by noise, which often severely reduce the accuracy of the identification results. To address the issue of inaccurate identification induced by non-stationary noise in data, this paper proposes a method called weighted ℓ1-regularized and insensitive loss function-based sparse identification of dynamics. Specifically, the robust identification problem is formulated using a sparse identification mathematical model that takes into account the presence of non-stationary noise in a quantitative manner. Then, a novel weighted ℓ1-regularized and insensitive loss function is proposed to account for the nature of non-stationary noise. Compared to traditional loss functions like least squares and least absolute deviation, the proposed method can mitigate the adverse effects of non-stationary noise and better promote the sparsity of results, thereby enhancing the accuracy of identification. Third, to overcome the non-smooth nature of the objective function induced by the inclusion of loss and regularization terms, a smooth approximation of the non-smooth objective function is presented, and the alternating direction multiplier method is utilized to develop an efficient optimization algorithm. Finally, the robustness of the proposed method is verified by extensive experiments under different types of nonlinear dynamical systems. Compared to some state-of-the-art methods, the proposed method achieves better identification accuracy.

Controlling the terminal layer atom of InTe for enhanced electrochemical oxygen evolution reaction and hydrogen evolution reaction performance.

Herein, we report the method of molecular-beam-epitaxial growth (MBE) for precisely regulating the terminal surface with different exposed atoms on indium telluride (InTe) and studied the electrocatalytic performances toward hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The improved performances result from the exposed In or Te atoms cluster, which affects the conductivity and active sites. This work provides insights into the comprehensive electrochemical attributes of layered indium chalcogenides and exhibits a new route for catalyst synthesis.