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Herein, we describe a chemoenzymatic and diversity-oriented approach for the first syntheses of octasaccharide repeating units of the capsular polysaccharides of Haemophilus parasuisserovar 15 and serovar 5. The synthetic method features efficient enzymatic assembly of sialyl galactose or N-acetyl-galactosamine building blocks, highly stereoselective chemical construction of α-type H-phosphonate, and the β-stereospecific 1,3-glycosylation reaction of a rare sugar donor.

Because of water-surface uncertainty, green lasers used in airborne bathymetric LiDAR cannot accurately detect the water surface, as they reflect near-water surface penetration (NWSP) into the water column. The existence of the NWSP of a green laser in water is not beneficial for high-accuracy depth measurements; however, it can be useful for ocean–land interface (OLI) extraction. In this study, novel ocean–land discrimination and interface extraction methods based on the NWSPs of green lasers are proposed. First, the NWSPs of different green laser channels are calculated and averaged to obtain a robust NWSP for each laser footprint. Second, a novel NWSP-based ocean–land discriminator is proposed based on the different characteristics of the NWSPs in land and ocean areas. Third, density-based spatial clustering of applications with noise algorithm is used to identify and correct the misclassified points of the NWSP-based ocean–land discriminator, and the OLIs are formed by connecting the land boundary points. Finally, the novel NWSP method is verified using a raw ABL data set collected via Optech coastal zone mapping and imaging LiDAR. The OLIs derived by waveform clustering are used as reference to evaluate the performance of the proposed NWSP method. OLI extraction based on the proposed NWSP method and the traditional point elevation method can reach accuracies of 1.9 and 4.8 m, respectively, in the research area. The proposed NWSP method provides a novel and convenient means for OLI extraction using the NWSPs of green lasers.

As a promising high-energy-density cathode material for lithium-ion batteries, the application of FeF3is significantly impeded by its sluggish kinetics. In this study, we propose a straightforward method to fabricate quasi-two-dimensional (quasi-2D) oxygen-doped FeF3nanosheets through the thermal decomposition and liquid-phase exfoliation of commercial FeF3·3H2O. During the thermal decomposition process, some fluorine atoms are substituted by oxygen atoms, resulting in enhanced electrical conductivity and a reduction in the dissociation energy of Fe–F bonds. In addition, the quasi-2D geometrical feature reduces the diffusion length of Li ions. Consequently, this free-standing electrode delivers a high specific capacity (∼600 mAh g–1at a low current density of 0.03 A g–1), a stable cycling performance over 200 cycles at a current density of 0.3 A g–1(with a capacity retention of 82%), and excellent rate capability (350 mAh g–1at 2 A g–1). A more profound insight into the kinetics of this electrode reveals that Li ions preferentially diffuse along the thickness of the non-layered nanosheets, significantly contributing to the outstanding rate performance. Thus, this study tackles the kinetic challenges of FeF3in terms of the electronic and geometrical structures, offering valuable insights for the development of high-performance electrode materials based on the conversion reaction mechanism.

Spinel LiMn2O4cathode possesses open three-dimensional ion transport channels, high safety, and abundant resources, making it a promising candidate for high-power lithium-ion batteries. However, spinel LiMn2O4inevitably faces serious challenges of manganese dissolution and irreversible phase transitions during the electrochemical process, leading to rapid capacity decay and limiting its rate performance. Herein, we report a simple two-step solid-state sintering method to realize Li+and PO43–codoping synergy in spinel LiMn2O4. It is found that PO43–doping not only expands the lattice parameters of the material, enhancing its charge-transfer kinetics but also increases the relative content of active Mn3+, improving the specific capacity of spinel LiMn2O4. Meanwhile, Li+doping effectively enhances the stability of the spinel structure, and the irreversible phase transition from cubic phase to tetragonal phase could be effectively suppressed in spinel LiMn2O4. Consequently, benefiting from the synergistic effect of Li+and PO43–codoping, the optimally codoped spinel LiMn2O4electrode delivers high specific discharge capacities of 116 and 102 mA h g–1at 1 and 20C, respectively, and achieves 78.1% capacity retention after 1000 cycles at 5C. This strategy provides an effective modification approach to developing advanced electrode materials for lithium-ion batteries.

Layered oxide cathodes of sodium-ion batteries (SIBs) are considered promising candidates due to their fascinating high capacity, good cyclability, and environmental friendliness. However, the air sensitivity of layered SIB cathodes causes high electrode manufacturing costs and performance deterioration, hampering their practical application. Herein, a commercial O3-type layered Na(Ni1/3Fe1/3Mn1/3)O2(NNFM) material is adopted to investigate the air corrosive problem and the suppression strategy. We reveal that once the layered material comes in contact with ambient air, cations migrate from transition metal (TM) layers to sodium layers at the near surface, although Na+and TM ions show quite different ion radii. Experimental results and theoretical calculations show that more Ni/Na disorder occurs in the air-exposed O3-NNFM materials, owing to a lower Ni migration energy barrier. The cation mixing results in detrimental structural distortion, along with the formation of residual alkali species on the surface, leading to high impedance for Na+diffusion during charge/discharge. To tackle this problem, an ultrathin and uniform hydrophobic molecular layer of perfluorodecyl trimethoxysilane is assembled on the O3-NNFM surface, which significantly suppresses unfavorable chemistry and structure degradation during air storage. The in-depth understanding of the structural degradation mechanism and suppression strategy presented in this work can facilitate high-energy cathode manufacturing from the perspective of future practical implementation and commercialization.

Forsythia suspensa, belonging to the deciduous shrubs of the Luteaceae family, a traditional Chinese medicine, has effects of alleviating swelling, clearing heat, detoxification and promoting blood circulation. The leaves of F. suspensacontain multiple chemical components and have a long history of use in folk medicines and health foods. The purpose of this study was to explore the effects of forsythin extract from F. suspensaleaves on intestinal microbiota and short-chain fatty acid (SCFA) content in rats with obesity induced by a high-fat diet. Forsythin extract in F. suspensaleaves increased the abundance of the intestinal microbiota, ameliorated intestinal microbiota disorders and inhibited the increase in total SCFA content in the intestinal tract in rats with obesity induced by a high-fat diet. These results suggested that forsythin extract in F. suspensaleaves may slow the development of obesity induced by a high-fat diet; thus, its active components and efficacy are worthy of further study.

Federated Learning (FL) in Consumer Internet of Things (CIoT) encounters significant privacy security threats when collaborative training Machine Learning models using data distributed among numerous smart CIoT devices. This paper’s objective is to evaluate the potential privacy risks associated with FL within CIoT context, specifically through the lens of passive malicious users. We propose an evaluation scheme (MIGAN) for privacy leakage, which incorporates various privacy inference attacks. First, this paper analyzes the characteristics of privacy inference attacks in FL and summarizes privacy security threats to guide the design of the MIGAN evaluation scheme. Then, to fully utilize the privacy information in FL, this paper proposes faster model inversion and white-box membership inference. Integrating these two modules into the MIGAN framework facilitates fast and high-quality privacy data reconstruction. Finally, this paper constructs the evaluation function to measure the correlation between compromised privacy data and users’ training data. Experiments conducted on the MNIST and CelebA datasets reveal that MIGAN’s data privacy reconstruction outperforms comparable methods. Notably, MIGAN quantifies the likelihood of potential privacy leakage at 14.7% for MNIST and 8.3% for CelebA in FL with 30 clients. These results validates the practicality and relevance of MIGAN in evaluating privacy leakage risks.

Edge computing extends computational capabilities to the physical edge of the world, providing faster and more efficient responses to consumer devices such as smart home appliances, smartphones, and wearable devices. However, it raises a critical question: How can we efficiently manage time-sensitive and computationally intensive tasks to meet device performance needs? To address this, we propose a computational cost-driven strategy for computation offloading based on reinforcement learning for consumer devices. Firstly, we convert the problem of minimizing computational costs, including latency and energy consumption, into maximizing the cumulative rewards problem of consumer devices. Second, we design a task completion time estimation method to ensure a favorable user experience. Third, by comprehensively considering the status of the network, computational demands, and task completion times, we employ reinforcement learning techniques to determine the optimal task offload strategy for consumer devices by maximizing cumulative rewards. This ensures efficient task completion with lower computational costs. The simulation results demonstrate that our method achieves the highest rewards in different scenarios. Our method reduces the task discard ratio by 25% compared to the PPO_KLP-based offload strategy.

This article proposes a global sensitivity analysis method of a wireless power transfer (WPT) system in domino structure. The sensitivity of system characteristics to capacitor deviations is studied to identify sensitive parameters that cause significant uncertainty in the output. The effect of capacitor deviations on output current, power transfer efficiency, and power factor is comprehensively investigated. Interactions between capacitors are considered, and the main effect index S and the total effect index ST are calculated by Sobol’ method. Its results indicate that the output current, power factor, and efficiency are slightly affected by the deviation of the capacitor in series with the receiving coil. Interaction between capacitors has a significant effect on the output current and power factor. Finally, the experimental setups of the three-coil and four-coil WPT systems are built. The experimental results show that when the resonant capacitor on the receiving side is varied within ±10%, the fluctuation of the output current is less than 1.5%, the maximum variation in efficiency is less than 2%, and the power factor remains almost above 0.9. The validity of the proposed global sensitivity analysis method is verified.

Intraductal papillary neoplasm of the bile duct (IPNB) is a heterogeneous disease similar to intraductal papillary mucinous neoplasm of the pancreas. These lesions have been recognized as one of the three major precancerous lesions in the biliary tract since 2010. In 2018, Japanese and Korean pathologists reached a consensus, classifying IPNBs into type l and type 2 IPNBs. IPNBs are more prevalent in male patients in East Asia and are closely related to diseases such as cholelithiasis and schistosomiasis. From a molecular genetic perspective, IPNBs exhibit early genetic variations, and different molecular pathways may be involved in the tumorigenesis of type 1 and type 2 IPNBs. The histological subtypes of IPNBs include gastric, intestinal, pancreaticobiliary, or oncocytic subtypes, but type 1 IPNBs typically exhibit more regular and well-organized histological features than type 2 IPNBs and are more commonly found in the intrahepatic bile ducts with abundant mucin. Due to the rarity of these lesions and the absence of specific clinical and laboratory features, imaging is crucial for the preoperative diagnosis of IPNB, with local bile duct dilation and growth along the bile ducts being the main imaging features. Surgical resection remains the optimal treatment for IPNBs, but negative bile duct margins and the removal of lymph nodes in the hepatic hilum significantly improve the postoperative survival rates for patients with IPNBs.

Existing studies have primarily focused on the resources and technologies related to renewable energy. However, it is essential to investigate the influence of economic, social, and governmental actions on the spatiotemporal changes in renewable energy. This paper utilizes spatial autocorrelation analysis, fixed effects modeling, and Geographical and Time Weighted Regression (GTWR) to analyze the phased characteristics and spatial differences of renewable energy electricity, based on time series and spatial layout data of renewable energy power by province and power source type in China. Additionally, it incorporates relevant economic, demographic, and investment data to identify the driving factors behind the spatiotemporal transformations of photovoltaic (PV) and wind power, as well as the main forces promoting sustainable growth. The study reveals the presence of spatial agglomeration in China’s renewable energy power generation. Moreover, a positive correlation is observed between investments in environmental protection (EPI), state-owned electric power (SPI), scientific and technological advancements (STI), and renewable energy electricity. Technological progress has reduced the dependence of renewable energy on the natural environment, facilitating its promotion and widespread adoption. These findings provide empirical support and policy guidance for the sustainable and rapid growth of China’s renewable energy industry.

Dietary omega-3 polyunsaturated fatty acids (ω-3 PUFAs) can be classified into animal- and plant-derived ω-3 PUFAs. Patients with type 2 diabetes (T2DM) are frequently accompanied by dyslipidemia, which is closely related to the high-density lipoprotein (HDL-C) subfractions change. This study aimed to determine the effects of different sources ω-3 PUFAs on glucolipid metabolism and lipoprotein subfractions in T2DM with dyslipidemia. Ninety T2DM patients with dyslipidemia were randomly assigned to take 3 g/day fish oil (FO, containing eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)), 3 g/day perilla oil (PO, containing α-linolenic acid (ALA)), or 3 g/day blend oil (BO, containing EPA, DHA and ALA) for 3 months. 90 patients completed the intervention. There was a significant reduction of glycated hemoglobin (HbA1c) in all the groups. The triglycerides (TG) in the FO group were significantly different with a group × time interaction (P= 0.043), which was higher compared with the other two groups. The serum small HDL-C subfractions in the PO group was higher and the serum large HDL-C subfractions in the PO group was lower than those in the BO and FO groups. Plant-derived ω-3 PUFAs are more effective at controlling blood glucose than animal-derived ω-3 PUFAs. However, animal-derived ω-3 PUFAs have a significant lowering effect on TG compared with plant-derived ω-3 PUFAs. Particularly, large HDL-C subfractions after animal-derived ω-3 PUFAs intake were higher than plant-derived ω-3 PUFAs intake; while small HDL-C subfractions were lower. Both the animal- and plant-derived ω-3 PUFAs have practical value in improving glucose and lipids metabolism in T2DM patients with dyslipidemia.

Digital Twins (DTs) can generate digital replicas for mobile networks (MNs) that accurately reflect the state of MN. Machine learning (ML) models trained in DT for MN (DTMN) virtual environments can be more robustly implemented in MN. This can avoid the training difficulties and runtime errors caused by MN instability and multiple failures. However, when using data from various devices in the MN system, DTs must prioritize data privacy. Federated learning (FL) enables the construction of models without data leaving devices to protect DTMN data privacy. Nevertheless, FL’s privacy protection needs further improvement for it only guarantees device-level data ownership but ignores that models may retain private information from data. Therefore, this paper focuses on data forgetting in privacy protection, and proposes a novel FL-based unlearning framework (FedME2), which contains MEval and MErase modules. Guided by memory evaluation information from MEval and employing MErase’s multi-loss training approach, FedME2 gets accurate data forgetting in DTMN. In four DTMN virtual environments, FedME2 achieves an average data forgetting rate of approximately 75% for global models under FL and kept the influence on global models’ accuracy below 4%. FedME2 has better data forgetting and improves DTMN data privacy protection while guaranteeing model accuracy.

In recent years, researches on improving high-voltage performance of lithium-ion batteries incorporating LiNi0.5Co0.2Mn0.3O2(NCM523) and artificial graphite (AG) have been widely reported. However, limited attentions have been paid to understand the effects and influence mechanisms of charge and discharge rates and charge limit currents on cyclability of NCM523/AG cells. Herein, a ∼1.9 Ah NCM523/AG pouch cell is employed, whose electrochemical and structural evolutions after 800 cycles at various rates are comprehensively investigated. We find that cycling performances are strongly influenced by charge rate, followed by limit current and discharge rate. The cell charged at a high rate and cell charged until reaching a low limit current both exhibit low capacity retentions compared to the cell discharged at a high rate. Possible failure reasons are analyzed by advanced characterizations. Results reveal that NCM523 cathodes of the cells deteriorated early experience severe transition metal dissolution, lattice distortion, and partial phase transformation. Meanwhile, the deposited transition metals on AG anodes catalyze the electrolyte consumption, lithium plating and active area loss. Finally, these side reactions notably increase cell impedance and electrochemical polarization. Undoubtedly, these findings clearly outline the challenges and optimization direction for high-rate NCM523/AG cells.

Solid-state lithium batteries (SSBs) have been widely researched as next-generation energy storage technologies due to their high energy density and high safety. However, lithium dendrite growth through the solid electrolyte usually results from the catastrophic interface contact between the solid electrolyte and lithium metal. Herein, a gradient nitrogen-doping strategy by nitrogen plasma is introduced to modify the surface and subsurface of the garnet electrolyte, which not only etches the surface impurities (e.g., Li2CO3) but also generates an in situformed Li3N-rich interphase between the solid electrolyte and lithium anode. As a result, the Li/LLZTON-3/Li cells show a low interfacial resistance (3.50 Ω cm2) with a critical current density of about 0.65 mA cm–2at room temperature and 1.60 mA cm–2at 60 °C, as well as a stable cycling life for over 1300 h at 0.4 mA cm–2at room temperature. A hybrid solid-state full cell paired with a LiFePO4cathode exhibits excellent cycling durability and rate performance at room temperature. These results demonstrate a rational strategy to enable lithium utilization in SSBs.

Flexible wearable pressure sensors have received increasing attention as the potential application of flexible wearable devices in human health monitoring and artificial intelligence. However, the complex and expensive process of the conductive filler has limited its practical production and application on a large scale to a certain extent. This study presents a kind of piezoresistive sensor by sinking nonwoven fabrics (NWFs) into tungsten disulfide (WS2) and Ti3C2TxMXene solutions. With the advantages of a simple production process and practicality, it is conducive to the realization of large-scale production. The assembled flexible pressure sensor exhibits high sensitivity (45.81 kPa–1), wide detection range (0–410 kPa), fast response/recovery time (18/36 ms), and excellent stability and long-term durability (up to 5000 test cycles). Because of the high elastic modulus of MXene and the synergistic effect between WS2and MXene, the detection range and sensitivity of the piezoresistive pressure sensor are greatly improved, realizing the stable detection of human motion status in all directions. Meanwhile, its high sensitivity at low pressure allows the sensor to accurately detect weak signals such as weak airflow and wrist pulses. In addition, combining the sensor with deep-learning makes it easy to recognize human respiratory patterns with high accuracy, demonstrating its potential impact in the fields of ergonomics and low-cost flexible electronics.

Nowadays, wearable electronic devices are developing rapidly with the internet of things and human–computer interactions. However, there are problems such as low power, short power supply time, and difficulty in charging, leading to a limited range of practical applications. In this paper, a composite hydrogel composed of polyacrylamide, hydroxypropyl methylcellulose, and MXene (Ti3C2Tx) nanosheets was developed, which formed a stable double-chain structure by hydrogen bonding. The configuration endows the hydrogel with excellent properties, such as high strength, strong stretchability, excellent electrical conductivity, and high strain sensitivity. Based on these characteristics, a flexible multifunctional triboelectric nanogenerator (PHM–TENG) was prepared using the hydrogel as a functional electrode. The nanogenerator can collect biomechanical energy and convert it to 183 V with a maximum power density of 78.3 mW/m2. It is worth noting that PHM–TENG can be applied as a green power source for driving miniature electronics. Also, it can be used as an auto-powered strain sensor that distinguishes letters, enabling monitoring under small strain conditions. This work is anticipated to provide an avenue for the development of new intelligent systems for handwriting recognition.

Postprandial metabolism plays major roles in many pathological conditions. The n-6/n-3 polyunsaturated fatty acid (PUFA) ratio is closely related to various physiological disorders. This study aimed to investigate the effects of high fat meals with different n-6/n-3 PUFA ratios on postprandial metabolism in normal control (NC) and hypertriglyceridemia (HTG) rats. The postprandial response of triglyceride (TG) in HTG groups was higher than that in NC groups after different n-6/n-3 PUFA ratio meals. The HTG groups showed higher postprandial total cholesterol (TC) responses than NC groups after 1:1 and 20:1 ratio meals. The 5:1 n-6/n-3 PUFA ratio elicited lower postprandial responses of tumor necrosis factor α (TNF-α) than 1:1 and 10:1 ratios in HTG groups. The postprandial malondialdehyde (MDA) response was lower after a 5:1 n-6/n-3 PUFA ratio meal than 1:1 and 20:1 ratio meals in HTG groups. The 1:1 ratio resulted in a lower postprandial reactive oxygen species (ROS) level than 5:1 and 10:1 n-6/n-3 PUFA ratios in NC groups. The results showed that a low n-6/n-3 PUFA ratio improved postprandial dysmetabolism induced by a high fat meal in NC and HTG rats. A high n-6/n-3 PUFA ratio increased the difference in postprandial metabolism between NC and HTG rats.

Flexible strain sensors based on self-adhesive, high-tensile, super-sensitive conductive hydrogels have promising application in human–computer interaction and motion monitoring. Traditional strain sensors have difficulty in balancing mechanical strength, detection function, and sensitivity, which brings challenges to their practical applications. In this work, the double network hydrogel composed of polyacrylamide (PAM) and sodium alginate (SA) was prepared, and MXene and sucrose were used as conductive materials and network reinforcing materials, respectively. Sucrose can effectively enhance the mechanical performance of the hydrogels and improve the ability to withstand harsh conditions. The hydrogel strain sensor has excellent tensile properties (strain >2500%), high sensitivity with a gauge factor of 3.76 at 1400% strain, reliable repeatability, self-adhesion, and anti-freezing ability. Highly sensitive hydrogels can be assembled into motion detection sensors that can distinguish between various strong or subtle movements of the human body, such as joint flexion and throat vibration. In addition, the sensor can be applied in handwriting recognition of English letters by using the fully convolutional network (FCN) algorithm and achieved the high accuracy of 98.1% for handwriting recognition. The as-prepared hydrogel strain sensor has broad prospect in motion detection and human–machine interaction, which provides great potential application of flexible wearable devices.

Due to high volumetric energy density, the major market share of cathode materials for lithium-ion batteries is still dominated by LiCoO2(LCO) at a 3C field. However, a number of challenges will be triggered if the charge voltage is increased from 4.2/4.3 to 4.6 V to further increase energy density, such as a violent interface reaction, Co dissolution, and release of lattice oxygen. Here, LCO is coated with the fast ionic conductor Li1.8Sc0.8Ti1.2(PO4)3(LSTP) to form LCO@LSTP, while a stable interface of LCO is in situconstructed by the decomposition of LSTP at the LSTP/LCO interface. As decomposition products of LSTP, Ti and Sc elements can be doped into LCO and thus reconstruct the interface from a layered structure to a spinel structure, which improves the stability of the interface. Moreover, Li3PO4from the decomposition of LSTP and remaining LSTP coating as a fast ionic conductor can improve Li+transport when compared with bare LCO, and thus boost the specific capacity to 185.3 mAh g–1at 1C. Benefited from the stable interface and fast ion conducting coating, the LCO@LSTP (1 wt %) cathode delivers a high capacity of 202.3 mAh g–1at the first cycle (0.5C, 3.0–4.6 V), and shows a higher capacity retention of 89.0% than LCO (50.9%) after 100 cycles. Furthermore, the change of the Fermi level obtained by using a kelvin probe force microscope (KPFM) and the oxygen band structure calculated by using density functional theory further illustrate that LSTP supports the performance of LCO. We anticipate that this study can improve the conversion efficiency of energy-storage devices.

Pulmonary epithelial barrier injury contributes to acute lung injury, accelerating exudate formation, and resulting alveolar edema. Heme oxygenase-1 (HO-1) plays an important role in ameliorating the pathological symptoms of acute lung injury (ALI). Using an ALI mouse model induced by LPS inhalation, the present study explored the potential molecular regulatory effect of hemin (a potent HO-1 inducer) against ALI epithelial damage. Lipopolysaccharide challenge triggered dysfunction of the alveolar epithelial barrier, impaired tight junctions, and disrupted alveoli fluid clearance, while these pathological changes were effectively reversed by hemin treatment. Furthermore, HO-1 elevation inhibited the activation of the nucleotide-binding domain-like receptor protein 3 (NLRP3) inflammasome and oxidative stress in alveolar epithelia, leading to the suppression of inflammatory responses and epithelial pyroptosis, as indicated by the decreased levels of NLRP3 and apoptosis-associated speck-like protein containing a CARD domain (ASC), repressed cleavage of caspase-1 and gasdermin D, and reduced expression levels of inflammatory cytokine IL-1β. In contrast, protoporphyrin IX zinc (II) (ZnPP, an HO-1 inhibitor) treatment had no protective effect on LPS inhalation-induced ALI in mice. In summary, HO-1 induction serves a critical role in maintaining airway epithelium homeostasis through the inhibition of NLRP3/ASC/caspase-1–mediated pyroptosis and inflammation in the occurrence of ALI.

Population aging is a global problem worldwide, and the discovery of antiaging drugs and knowledge of their potential molecular mechanisms are research hotspots in biomedical field. Tetrahydroxystilbene glucoside (TSG) is a natural component isolated from Heshouwu (Polygonum multiflorumThunb.). It has been widely used to treat various chronic diseases for its remarkable biological activities. In this study, we successfully established aging larval zebrafish by exposing larvae to 2 mM hydrogen peroxide (H2O2). Using this aging model, we assessed the antiaging effect of TSG with different concentrations (25–100 μg/mL). After being treated with H2O2, zebrafish showed the obvious aging-associated phenotypes characterized by higher senescence-associated β-galactosidase activity, significantly downregulated expression of sirtuin 1 (sirt1) and telomerase reverse transcriptase (tert), and upregulated serpine1mRNA level compared to the control group. TSG pretreatment delayed the aging process of oxidative stress-induced zebrafish, indicative of the reduced positive rate of senescence-associated β-galactosidase, improved swimming velocity, and stimulus-response capacity. Further studies proved that TSG could suppress reactive oxygen species production and enhance the activity of antioxidant enzymes superoxide dismutase and catalase. TSG also inhibited the H2O2-induced expressions of inflammation-related genes il-1β, il-6, cxcl-c1c, and il-8in aging zebrafish, but it did not affect apoptosis-related genes (bcl-2, bax, and caspase-3) of aging zebrafish. In conclusion, TSG can protect against aging by regulating the antioxidative genes and enzyme activity, as well as inflammation in larval zebrafish, providing insight into the application of TSG for clinical treatment of aging or aging-related diseases.