Progress of medicinal plants and their active metabolites in ischemia-reperfusion injury of stroke: a novel therapeutic strategy based on regulation of crosstalk between mitophagy and ferroptosis.

The death of cells can occur through various pathways, including apoptosis, necroptosis, mitophagy, pyroptosis, endoplasmic reticulum stress, oxidative stress, ferroptosis, cuproptosis, and disulfide-driven necrosis. Increasing evidence suggests that mitophagy and ferroptosis play crucial regulatory roles in the development of stroke. In recent years, the incidence of stroke has been gradually increasing, posing a significant threat to human health. Hemorrhagic stroke accounts for only 15% of all strokes, while ischemic stroke is the predominant type, representing 85% of all stroke cases. Ischemic stroke refers to a clinical syndrome characterized by local ischemic-hypoxic necrosis of brain tissue due to various cerebrovascular disorders, leading to rapid onset of corresponding neurological deficits. Currently, specific therapeutic approaches targeting the pathophysiological mechanisms of ischemic brain tissue injury mainly include intravenous thrombolysis and endovascular intervention. Despite some clinical efficacy, these approaches inevitably lead to ischemia-reperfusion injury. Therefore, exploration of treatment options for ischemic stroke remains a challenging task. In light of this background, advancements in targeted therapy for cerebrovascular diseases through mitophagy and ferroptosis offer a new direction for the treatment of such diseases. In this review, we summarize the progress of mitophagy and ferroptosis in regulating ischemia-reperfusion injury in stroke and emphasize their potential molecular mechanisms in the pathogenesis. Importantly, we systematically elucidate the role of medicinal plants and their active metabolites in targeting mitophagy and ferroptosis in ischemia-reperfusion injury in stroke, providing new insights and perspectives for the clinical development of therapeutic drugs for these diseases.

Amino-Acid-Encoded Bioinspired Supramolecular Self-Assembly of Multimorphological Nanocarriers.

Supramolecular self-assembly has emerged as an efficient tool to construct well-organized nanostructures for biomedical applications by small organic molecules. However, the physicochemical properties of self-assembled nanoarchitectures are greatly influenced by their morphologies, mechanical properties, and working mechanisms, making it challenging to design and screen ideal building blocks. Herein, using a biocompatible firefly-sourced click reaction between the cyano group of 2-cyano-benzothiazole (CBT) and the 1,2-aminothiol group of cysteine (Cys), an amino-acid-encoded supramolecular self-assembly platform Cys(SEt)-X-CBT (X represents any amino acid) is developed to incorporate both covalent and noncovalent interactions for building diverse morphologies of nanostructures with bioinspired response mechanism, providing a convenient and rapid strategy to construct site-specific nanocarriers for drug delivery, cell imaging, and enzyme encapsulation. Additionally, it is worth noting that the biodegradation of Cys(SEt)-X-CBT generated nanocarriers can be easily tracked via bioluminescence imaging. By caging either the thiol or amino groups in Cys with other stimulus-responsive sites and modifying X with probes or drugs, a variety of multi-morphological and multifunctional nanomedicines can be readily prepared for a wide range of biomedical applications.

Advances in chemical removal and degradation technologies for microplastics in the aquatic environment: A review.

In recent years, global attention has been extensively focused on the water pollution and health risks caused by microplastics(MPs), thereby making the treatment of microplastics a key area of research. Chemical removal and degradation present effective approaches to addressing this issue. Consequently, this review summarizes the latest research advancements in the chemical removal and degradation of microplastics in water, comparing the treatment efficacy and advantages and disadvantages of various removal/degradation techniques. It elucidates the chemical mechanisms underlying the removal/degradation of microplastics and identifies the primary influencing factors during the treatment process. A systematic analysis of the performance of microplastic treatment technologies is conducted, examining the impact of microplastic characteristics, operational conditions, and other parameters on the effectiveness of microplastic treatment.

Mechanochemical Synthesis of Cuprous Complexes for X-ray Scintillation and Imaging.

Cuprous complex scintillators show promise for X-ray detection with abundant raw materials, diverse luminescent mechanisms, and adjustable structures. However, their synthesis typically requires a significant amount of organic solvents, which conflict with green chemistry principles. Herein, we present the synthesis of two high-performance cuprous complex scintillators using a simple mechanochemical method for the first time, namely [CuI(PPh3)2R] (R = 4-phenylpyridine hydroiodide (PH, Cu-1) and 4-(4-bromophenyl)pyridine hydroiodide (PH-Br, Cu-2). Both materials demonstrated remarkable scintillation performances, exhibiting radioluminescence (RL) intensities 1.52 times (Cu-1) and 2.52 times (Cu-2) greater than those of Bi4Ge3O12 (BGO), respectively. Compared to Cu-1, the enhanced RL performance of Cu-2 can be ascribed to its elevated quantum yield of 51.54%, significantly surpassing that of Cu-1 at 37.75%. This excellent luminescent performance is derived from the introduction of PH-Br, providing a more diverse array of intermolecular interactions that effectively constrain molecular vibration and rotation, further suppressing the nonradiative transition process. Furthermore, Cu-2 powder can be prepared into scintillator film with excellent X-ray imaging capabilities. This work establishes a pathway for the rapid, eco-friendly, and cost-effective synthesis of high-performance cuprous complex scintillators.

Machine Learning Prediction of Molecular Binding Profiles on Metal-Porphyrin via Spectroscopic Descriptors.

The study of molecular adsorption is crucial for understanding various chemical processes. Spectroscopy offers a convenient and non-invasive way of probing structures of adsorbed states and can be used for real-time observation of molecular binding profiles, including both structural and energetic information. However, deciphering atomic structures from spectral information using the first-principles approach is computationally expensive and time-consuming because of the sophistication of recording spectra, chemical structures, and their relationship. Here, we demonstrate the feasibility of a data-driven machine learning approach for predicting binding energy and structural information directly from vibrational spectra of the adsorbate by using CO adsorption on iron porphyrin as an example. Our trained machine learning model is not only interpretable but also readily transferred to similar metal-nitrogen-carbon systems with comparable accuracy. This work shows the potential of using structure-encoded spectroscopic descriptors in machine learning models for the study of adsorbed states of molecules on transition metal complexes.

Solid-state atomic hydrogen as a broad-spectrum RONS scavenger for accelerated diabetic wound healing.

Hydrogen therapy shows great promise as a versatile treatment method for diseases associated with the overexpression of reactive oxygen and nitrogen species (RONS). However, developing an advanced hydrogen therapy platform that integrates controllable hydrogen release, efficient RONS elimination, and biodegradability remains a giant technical challenge. In this study, we demonstrate for the first time that the tungsten bronze phase H0.53WO3 (HWO) is an exceptionally ideal hydrogen carrier, with salient features including temperature-dependent highly-reductive atomic hydrogen release and broad-spectrum RONS scavenging capability distinct from that of molecular hydrogen. Moreover, its unique pH-responsive biodegradability ensures post-therapeutic clearance at pathological sites. Treatment with HWO of diabetic wounds in an animal model indicates that the solid-state atomic H promotes vascular formation by activating M2-type macrophage polarization and anti-inflammatory cytokine production, resulting in acceleration of chronic wound healing. Our findings significantly expand the basic categories of hydrogen therapeutic materials and pave the way for investigating more physical forms of hydrogen species as efficient RONS scavengers for clinical disease treatment.

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics.

The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.

Catalytic Structure Design by AI Generating with Spectroscopic Descriptors.

Generative artificial intelligence has depicted a beautiful blueprint for on-demand design in chemical research. However, the few successful chemical generations have only been able to implement a few special property values because most chemical descriptors are mathematically discrete or discontinuously adjustable. Herein, we use spectroscopic descriptors with machine learning to establish a quantitative spectral structure-property relationship for adsorbed molecules on metal monatomic catalysts. Besides catalytic properties such as adsorption energy and charge transfer, the complete spatial relative coordinates of the adsorbed molecule were successfully inverted. The spectroscopic descriptors and prediction models are generalized, allowing them to be transferred to several different systems. Due to the continuous tunability of the spectroscopic descriptors, the design of catalytic structures with continuous adsorption states generated by AI in the catalytic process has been achieved. This work paves the way for using spectroscopy to enable real-time monitoring of the catalytic process and continuous customization of catalytic performance, which will lead to profound changes in catalytic research.

City-scale Vehicle Trajectory Data from Traffic Camera Videos.

Vehicle trajectory data underpins various applications in intelligent transportation systems, such as traffic surveillance, traffic prediction, and traffic control. Traditional vehicle trajectory datasets, recorded by GPS devices or single cameras, are often biased towards specific vehicles (e.g., taxis) or incomplete (typically < 1 km), limiting their reliability for downstream applications. With the widespread deployment of traffic cameras across the city road network, we have the opportunity to capture all vehicles passing by. By collecting city-scale traffic camera video data, we apply a trajectory recovery framework that identifies vehicles across all cameras and reconstructs their paths in between. Leveraging this approach, we are the first to release a comprehensive vehicle trajectory dataset that covers almost full-amount of city vehicle trajectories, with approximately 5 million trajectories recovered from over 3000 traffic cameras in two metropolises. To assess the quality and quantity of this dataset, we evaluate the recovery methods, visualize specific cases, and compare the results with external road speed and flow statistics. The results demonstrate the consistency and reliability of the released trajectories. This dataset holds great promise for research in areas such as unveiling traffic dynamics, traffic network resilience assessment, and traffic network planning.

Epsilon-MnO2 simply prepared by redox precipitation as an efficient catalyst for ciprofloxacin degradation by activating peroxymonosulfate.

Four kinds of manganese oxides were successfully prepared by hydrothermal and redox precipitation methods, and the obtained oxides were used for CIP removal from water by activating PMS. The microstructure and surface properties of four oxides were systematically characterized. The results showed that ε-MnO2 prepared by the redox precipitation method had large surface area, low crystallinity, high surface Mn(III)/Mn(Ⅳ) ratio and the highest activation efficiency for PMS, that is, when the concentration of PMS was 0.6 g/L, 0.2 g/L ε-MnO2 could degrade 93% of CIP within 30 min. Multiple active oxygen species, such as sulfate radical, hydroxyl radical and singlet oxygen, were found in CIP degradation, among which sulfate radical was the most important one. The degradation reaction mainly occurred on the surface of the catalyst, and the surface hydroxyl group played an important role in the degradation. The catalyst could be regenerated in situ through the redox reaction between Mn4+ and Mn3+. The ε-MnO2 had the advantages of simple preparation, good stability and excellent performance, which provided the potential for developing new green antibiotic removal technology.

Leveraging Interlayer Interaction in M-N-C Catalysts for Enhanced Activity in Oxygen Reduction Reactions.

Atomically dispersed metal-nitrogen-carbon (M-N-C) materials are deemed promising catalysts for the oxygen reduction reaction (ORR) in fuel cells. Yet the multilayer nature of M-N-C has been largely neglected in computational analysis. To bridge the gap, we conducted a first-principles investigation using bilayer M-N-C models (TMNx/G-TMNy/G, TM = Mn, Fe, Co, Ni, Cu, G = graphene, x, y = 3 or 4), where the TMs on the top serves as the active center. While in-plane TMN4 at the bottom has a minimal impact on the ORR, out-of-plane TMN3 substantially influences the adsorption free energy of OH through a strong interlayer bonding interaction. By leveraging interlayer interactions, we appreciably lowered the overpotential of selected TMN4 (TM = Co, Ni, Cu) and achieved a minimum of 0.40 V on CoN4/G-CuN3/G. Constant potential calculations revealed weak dependence of OH binding energy on external voltage and obtained results comparable to constant charge calculation. This study provided new physical insight into modulating naturally occurring multilayer M-N-C catalysts.

Proton penetration mechanism and selective hydrogen isotope separation through two-dimensional biphenylene.

Hydrogen isotope separation is of prime significance in various scientific and industrial applications. Nevertheless, the existing technologies are often expensive and energy demanding. Two-dimensional carbon materials are regarded as promising candidates for cost-effective separation of different hydrogen isotopes. Herein, based on theoretical calculations, we have systematically investigated the proton penetration mechanism and the associated isotope separation behavior through two-dimensional biphenylene, a novel graphene allotrope. The unique non-uniform rings with different sizes in the biphenylene layer resemble the topological defects of graphene, serving as proton transmission channels. We found that a proton can readily pass through biphenylene with a low energy barrier in some specific patterns. Furthermore, large kinetic isotope effect ratios for proton-deuteron (13.58) and proton-triton (53.10) were observed in an aqueous environment. We thus conclude that biphenylene would be a potential carbon material used for hydrogen isotope separation. This subtle exploitation of the natural structural specificity of biphenylene provides new insight into the search for materials for hydrogen isotope separation.

Bone marrow mesenchymal stem cells-derived exosomes mediated delivery of tetramethylpyrazine attenuate cerebral ischemic injury.

OBJECTIVES: Tetramethylpyrazine (TEP) can protect the brain from ischemic damage, but it has defects such as short half-life, fast absorption, wide distribution, and rapid elimination, which limits its application. Exosomes (Exos) have the property of loading drugs and transporting signal substances. Here, we elucidated the effect of TEP-loaded bone marrow mesenchymal stem cell (BMSC)-derived Exos (Exo-TEP) on cerebral ischemic injury. MATERIALS AND METHODS: The Exos were extracted by ultracentrifugation and TEP was loaded into the Exos by electroporation. Oxygen-glucose deprivation (OGD) induced-primary cortical neurons and middle cerebral artery occlusion (MCAO)-induced mouse models were used to determine the effect of Exo-TEP on cerebral ischemic injury in vitro and in vivo. RESULTS: Exo-TEP exhibited a stable and sustained release pattern compared to free TEP. Exo-TEP treatment was more significant in improving OGD-mediated decrease in cell activity, as well as a elevation in apoptosis and ROS production in cortical neurons. In comparison with Exo and free TEP treatment, Exo-TEP treatment significantly improved pathological changes, shrunk cerebral infarction volume, as well reduced neurological deficit scores and neuronal apoptosis, and oxidative stress. CONCLUSIONS: Exo-TEP was superior to free TEP in improving cerebral ischemic injury by reducing neuronal apoptosis and oxidative stress.

Strategies for enhancing performance of perovskite bismuth ferrite photocatalysts (BiFeO3): A comprehensive review.

Organic pollutants pose a significant threat to water safety, and their degradation is of paramount importance. Photocatalytic technology has emerged as a promising approach for environmental remediation, and Bismuth ferrite (BiFeO3) has been shown to exhibit remarkable potential for photocatalytic degradation of water pollutants, with its excellent crystal structure properties and visible light photocatalytic activity. This review presents an overview of the crystal properties and photocatalytic mechanism of perovskite bismuth ferrite (BiFeO3), as well as a summary of various strategies for enhancing its efficiency in photocatalytic degradation of organic pollutants. These strategies include pure phase preparation, microscopic modulation, composite modification of BiFeO3, and the integration of Fenton-like reactions and external field-assisted methods to improve its photocatalytic performance. The review emphasizes the impact of each strategy on photocatalytic enhancement. By providing comprehensive strategies for improving the efficiency of BiFeO3 photocatalysis, this review inspires new insights for efficient degradation of organic pollutants using BiFeO3 photocatalysis and contributes to the development of photocatalysis in environmental remediation.

Manipulation of sub-terahertz waves using digital coding metasurfaces based on liquid crystals.

This paper presents a novel sub-terahertz liquid crystal (LC) phase shifter based on digital coding metasurfaces. The proposed structure consists of metal gratings and resonant structures. They are both immersed in LC. The metal gratings function as reflective surfaces for electromagnetic waves and electrodes for controlling the LC layer. The proposed structure changes the state of the phase shifter by switching the voltage on every grating. It allows the deflection of LC molecules within a subregion of the metasurface structure. Four switchable coding states of the phase shifter are obtained experimentally. The phase of the reflected wave varies by 0°, 102°, 166°, and 233° at 120 GHz. Due to the presence of the transverse control electric field, modulation speed is approximately doubled compared to the free relaxation state. This work provides a novel idea for wavefront modulation of phase.

MMP9-Associated Tumor Stem Cells, CCL1-Silenced Dendritic Cells, and Cytokine-Induced Killer Cells Have a Remarkable Therapeutic Efficacy for Acute Myeloid Leukemia by Activating T Cells.

Purpose: Dendritic cells (DC) are specialized antigen-presenting cells, and cytokine-induced killer (CIK) cells have a specific killing activity to a variety of tumors. However, the underlining mechanism and function of DC-CIK cells in acute myeloid leukemia (AML) remain largely elusive. Methods: Gene expression profiles of leukemia patients were obtained from TCGA, DC cell components were evaluated using the quanTIseq method, and cancer stem cell scores were estimated using machine learning methods. The transcriptomes were obtained in DC-CIK cells from normal and AML patients by high-throughput sequencing. Large differentially expressed mRNAs were verified by RT-qPCR assay, and MMP9 and CCL1 were selected for subsequent studies in vivo and in vitro experiments. Results: Significant positive correlations were found with DC versus cancer stem cells (p = 0.008) and the expression of MMP9 versus cancer stem cells (p = 0.018). MMP9 and CCL1 were found to be highly expressed in DC-CIK cells from AML patients. DC-CIK cells with MMP9 and CCL1 knockout alone had little effect on leukemia cells, while knockdown of MMP9 and CCL1 in DC-CIK cells increased cytotoxicity, suppressed proliferation, and induced apoptosis of leukemia cells. In addition, we proved that MMP9- and CCL1-silenced DC-CIK cells significantly elevated the CD3+CD4+ and CD3+CD8+ cells and lowered the CD4+PD-1+ and CD8+PD-1+ T cells. Meanwhile, blockage of MMP9 and CCL1 in DC-CIK cells dramatically increased IL-2 and IFN-γ, increased CD107aþ (LAMP-1) and granzyme B (GZMB), and downregulated PD-1, CTLA4, TIM3, and LAG3 T cells from AML patients and AML model mice. Furthermore, activated T cells in DC-CIK cells knocking down MMP9 and CCL1 also prevented proliferation and accelerated apoptosis of AML cells. Conclusion: Our findings demonstrated that blockage of MMP9 and CCL1 in DC-CIK cells could markedly enhance the therapeutic efficiency in AML via activating T cells.

Computation-based regulation of excitonic effects in donor-acceptor covalent organic frameworks for enhanced photocatalysis.

The strong excitonic effects widely exist in polymer-semiconductors and the large exciton binding energy (Eb) seriously limits their photocatalysis. Herein, density functional theory (DFT) calculations are conducted to assess band alignment and charge transfer feature of potential donor-acceptor (D-A) covalent organic frameworks (COFs), using 1,3,5-tris(4-aminophenyl)triazine (TAPT) or 1,3,5-tris(4-aminophenyl)benzene (TAPB) as acceptors and tereph-thaldehydes functionalized diverse groups as donors. Given the discernable D-A interaction strengths in the D-A pairs, their Eb can be systematically regulated with minimum Eb in TAPT-OMe. Guided by these results, the corresponding D-A COFs are synthesized, where TAPT-OMe-COF possesses the best activity in photocatalytic H2 production and the activity trend of other COFs is associated with that of calculated Eb for the D-A pairs. In addition, further alkyne cycloaddition for the imine linkage in the COFs greatly improves the stability and the resulting TAPT-OMe-alkyne-COF with a substantially smaller Eb exhibits ~20 times higher activity than the parent COF.

Water quality characteristics and evaluation of Qilian Mountain National Park section in Heihe River Basin based on water quality indices and 3D fluorescence technology.

The water quality of the Heihe River Basin affects the life quality and health of tens of thousands of residents along it. However, there are relatively few studies that evaluate its water quality. In this study, we used principal component analysis (PCA), an improved comprehensive water quality index (WQI), and three-dimensional (3D) fluorescence technology to identify pollutants and evaluate water quality at nine monitoring sites in the Qilian Mountain National Park in Heihe River Basin. PCA was applied to concentrate the water quality indices into nine items. The analysis shows that the water quality in the study area is mainly polluted by organic matter, nitrogen, and phosphorus. According to the revised WQI model, the water quality of the study area is from moderate to good, while the water quality of Qinghai section is worse than that of Gansu section. According to the 3D fluorescence spectrum analysis of the monitoring sites, the organic pollution of water comes from vegetation decay, animal feces, and some human activities. This study can not only provide support and basis for water environment protection and management in the Heihe River Basin, but also promote the healthy development of the water environment in the Qilian Mountains.

Distribution and risk assessment of microplastics in Liujiaxia Reservoir on the upper Yellow River.

Microplastics (MPSs) distribution in global freshwater systems is extensively reported, but the distribution of MPSs in reservoirs of the Yellow River has rarely been studied. To fill in this gap, we systematically investigated the distribution of MPSs in surface water and bank sediments gleaned from Liujiaxia Reservoir of the upper Yellow River for the first time and conducted an ecological risk assessment in succession in this work. The results showed that the main polymer types of MPSs in the surface water and bank sediments of Liujiaxia Reservoir were polyethylene terephthalate (PET), polystyrene (PS), and polypropylene (PP), and the abundance of MPSs in the reservoir surface water and bank sediments ranged from 4.48 to 12.09 item/L and 447.27 to 1543.80 item/kg (dry weight), respectively. Further physical morphology analysis of MPSs in the samples revealed that MPSs in the surface water and bank sediments of Liujiaxia Reservoir were predominantly fibrous with small particle sizes (<1 mm), and there was abundant color, mainly exhibiting transparent, black, and blue. The results of the MPS pollution load index (PLI>1) and the hazard ranking of MPSs (HZone = 10.20 for surface water and HZone = 14.06 for bank sediments) yielded a hazard class II for MPS pollution in Liujiaxia Reservoir, the combined pollution risk index (PRIZone = 17.05 for surface water and PRIZone = 31.25 for bank sediments) stated clearly the potential ecological risk in the Liujiaxia Reservoir. Briefly, this study supplemented and enriched the data on the distribution of MPSs in the reservoirs of the Yellow River basin, and provide a benchmark for future pollution control and management in the reservoir area.

Insight into the Mechanism for Catalytic Activity of the Oxygen/Hydrogen Evolution Reaction on a Dual-Site Catalyst.

The dual-site catalysts consisting of two adjacent single-atom sites on graphene have exhibited promising catalytic activity of the electrochemical oxygen/hydrogen evolution reaction (OER/HER). However, the electrochemical mechanisms of the OER/HER on dual-site catalysts have still been ambiguous. In this work, we employed density functional theory calculations to study the catalytic activity of the OER/HER with a O-O (H-H) direct coupling mechanism on dual-site catalysts. Specifically, these element steps should be classified into two categories: a step evolving proton-coupled electron transfer (PCET step) that needs to be driven by electrode potential and a step without PCET (non-PCET step) that occurs naturally under mild conditions. Our calculated results show that both the maximal free energy change (ΔGMax) contributed by the PCET step and the activity barrier (Ea) of the non-PCET step must be examined to evaluate the catalytic activity of the OER/HER on the dual site. Importantly, it is a basically inevitable negative relationship between ΔGMax and Ea, which would play a critical role in guiding the rational design of effective dual-site catalysts for electrochemical reactions.