SRSF3 Knockdown Inhibits Lipopolysaccharide-Induced Inflammatory Response in Macrophages.

Serine/arginine-rich splicing factor 3 (SRSF3), the smallest member of the SR protein family, serves multiple roles in RNA processing, including splicing, translation, and stability. Recent studies have shown that SRSF3 is implicated in several inflammatory diseases. However, its impact on macrophage inflammation remains unclear. Herein, we determined the expression of SRSF3 in inflammatory macrophages and found that the level of SRSF3 was increased in macrophages within atherosclerotic plaques, as well as in RAW-264.7 macrophages stimulated by lipopolysaccharides. Moreover, the downregulation of SRSF3 suppressed the levels of inflammatory cytokines by deactivating the nuclear factor κB (NFκB) pathway. Furthermore, the alternative splicing of myeloid differentiation protein 2 (MD2), a co-receptor of toll-like receptor 4 (TLR4), is regulated by SRSF3. The depletion of SRSF3 increased the level of the shorter MD2B splicing variants, which contributed to inflammatory inhibition in macrophages. In conclusion, our findings imply that SRSF3 regulates lipopolysaccharide-stimulated inflammation, in part by controlling the alternative splicing of MD2 mRNA in macrophages.

Serine/Arginine-Rich Splicing Factor 7 Knockdown Inhibits Aerobic Glycolysis and Growth in HepG2 Cells by Regulating PKM2 Expression.

Serine/arginine-rich splicing factors (SRSFs), part of the serine/arginine-rich (SR) protein family, play a crucial role in precursor RNA splicing. Abnormal expression of SRSFs in tumors can disrupt normal RNA splicing, contributing to tumor progression. Notably, SRSF7 has been found to be upregulated in hepatocellular carcinoma (HCC), yet its specific role and molecular mechanisms in HCC pathogenesis are not fully understood. We investigated the expression and prognostic significance of SRSF7 in HCC using bioinformatics database analysis. In HepG2 cells, the expressions of SRSF7 and glycolytic enzymes were analyzed using qRT-PCR, and Western blot. Glucose uptake and lactate production were quantified using relevant reagent kits. Additionally, cell proliferation, clonogenicity, invasion, and apoptosis were evaluated using MTS assay, clonal formation assay, Transwell assay, and mitochondrial membrane potential assay, respectively. This study demonstrated significant overexpression of SRSF7 in HCC tissue, correlating with poor prognosis. Knockdown of SRSF7 in HepG2 cells resulted in inhibited proliferation, clonogenicity, and invasion, while apoptosis was enhanced. This knockdown also decreased glucose uptake and lactate production, along with a reduction in the expression of glucose transporter 1 (GLUT1) and lactate dehydrogenase A (LDHA). Furthermore, SRSF7 downregulation increased the pyruvate kinase muscle 1 (PKM1)/PKM2 ratio. The glycolytic boost due to PKM2 overexpression partially counteracted the effects of SRSF7 silencing on HepG2 cell growth. The knockdown of SRSF7 impairs aerobic glycolysis and growth in HepG2 cells by downregulating PKM2 expression.

Molecular Contrastive Pretraining with Collaborative Featurizations.

Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery. Recently, prosperous progress has been made in molecular pretraining with different molecular featurizations, including 1D SMILES strings, 2D graphs, and 3D geometries. However, the role of molecular featurizations with their corresponding neural architectures in molecular pretraining remains largely unexamined. In this paper, through two case studies─chirality classification and aromatic ring counting─we first demonstrate that different featurization techniques convey chemical information differently. In light of this observation, we propose a simple and effective MOlecular pretraining framework with COllaborative featurizations (MOCO). MOCO comprehensively leverages multiple featurizations that complement each other and outperforms existing state-of-the-art models that solely rely on one or two featurizations on a wide range of molecular property prediction tasks.

Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design.

Determining the viability of a new drug molecule is a time- and resource-intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for target proteins by combining deep reinforcement learning with real-time 3D molecular docking using AutoDock Vina, thereby simultaneously creating chemical novelty while constraining molecules for shape and molecular compatibility with target active sites. Moreover, through the use of various types of reward functions, we have introduced novelty in generative tasks for new molecules such as chemical similarity to a target ligand, molecules grown from known protein bound fragments, and creation of molecules that enforce interactions with target residues in the protein active site. The iMiner algorithm is embedded in a composite workflow that filters out Pan-assay interference compounds, Lipinski rule violations, uncommon structures in medicinal chemistry, and poor synthetic accessibility with options for cross-validation against other docking scoring functions and automation of a molecular dynamics simulation to measure pose stability. We also allow users to define a set of rules for the structures they would like to exclude during the training process and postfiltering steps. Because our approach relies only on the structure of the target protein, iMiner can be easily adapted for the future development of other inhibitors or small molecule therapeutics of any target protein.

Coupled thermo-mechanical interaction on a multi-layered skin tissue with temperature-dependent physical properties irradiated by a pulse laser.

A detailed understanding of the coupled thermo-mechanical interaction on the biological tissue irradiated by a pulse laser is essential for the existed therapeutic methods constructed on the photo-thermal effect, which will contribute to the design, characterization and optimization of strategies for delivering better treatment. The aim of present work is to explore the coupled thermo-mechanical behavior of a multi-layered skin tissue with temperature-dependent physical properties under the pulsed laser irradiation. A layered theoretical model involved variable physical parameters with temperature has been proposed firstly according to the generalized theory of thermo-elasticity with dual-phase lag mechanism. The numerical method based on an explicit finite difference scheme is then employed to predict the temporal and spatial distributions of the temperature, thermal deformation and stresses experienced to a short-pulse laser irradiation. On this basis, the effect of variable thermal and mechanical physical parameters of skin tissue on the coupled thermo-mechanical behavior and relative thermal damage has been evaluated.

DeePMD-kit v2: A software package for deep potential models.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

RIS-Aided Proactive Mobile Network Downlink Interference Suppression: A Deep Reinforcement Learning Approach.

A proactive mobile network (PMN) is a novel architecture enabling extremely low-latency communication. This architecture employs an open-loop transmission mode that prohibits all real-time control feedback processes and employs virtual cell technology to allocate resources non-exclusively to users. However, such a design also results in significant potential user interference and worsens the communication's reliability. In this paper, we propose introducing multi-reconfigurable intelligent surface (RIS) technology into the downlink process of the PMN to increase the network's capacity against interference. Since the PMN environment is complex and time varying and accurate channel state information cannot be acquired in real time, it is challenging to manage RISs to service the PMN effectively. We begin by formulating an optimization problem for RIS phase shifts and reflection coefficients. Furthermore, motivated by recent developments in deep reinforcement learning (DRL), we propose an asynchronous advantage actor-critic (A3C)-based method for solving the problem by appropriately designing the action space, state space, and reward function. Simulation results indicate that deploying RISs within a region can significantly facilitate interference suppression. The proposed A3C-based scheme can achieve a higher capacity than baseline schemes and approach the upper limit as the number of RISs increases.

Bio-thermal response and thermal damage in biological tissues with non-equilibrium effect and temperature-dependent properties induced by pulse-laser irradiation.

Comprehension of thermal behavior underlying the living biological tissues helps successful applications of current heat therapies. The present work is to explore the heat transport properties of irradiated tissue during tis thermal treatment, in which the local thermal non-equilibrium effect as well as temperature-dependent properties arose from complicated anatomical structure, is considered. Based on the generalized dual-phase lag (GDPL) model, a non-linear governing equation of tissue temperature with variable thermal physical properties is proposed. The effective procedure constructed on an explicit finite difference scheme is then developed to predict numerically the thermal response and thermal damage irradiated by a pulse laser as a therapeutic heat source. The parametric study on variable thermal physical parameters including the phase lag times, heat conductivity, specific heat capacity and blood perfusion rate has been performed to evaluate their influence on temperature distribution in time and space. On this basis, the thermal damage with different laser variables such as laser intensity and exposure time are further analyzed.

[Research progress on the effect of iron oxide nanoparticles in macrophage polarization].

Macrophages are important immune effector cells with significant plasticity and heterogeneity in the body immune system, and play an important role in normal physiological conditions and in the process of inflammation. It has been found that macrophage polarization involves a variety of cytokines and is a key link in immune regulation. Targeting macrophages by nanoparticles has a certain impact on the occurrence and development of a variety of diseases. Due to its characteristics, iron oxide nanoparticles have been used as the medium and carrier for cancer diagnosis and treatment, making full use of the special microenvironment of tumors to actively or passively aggregate drugs in tumor tissues, which has a good application prospect. However, the specific regulatory mechanism of reprogramming macrophages using iron oxide nanoparticles remains to be further explored. In this paper, the classification, polarization effect and metabolic mechanism of macrophages were firstly described. Secondly, the application of iron oxide nanoparticles and the induction of macrophage reprogramming were reviewed. Finally, the research prospect and difficulties and challenges of iron oxide nanoparticles were discussed to provide basic data and theoretical support for further research on the mechanism of the polarization effect of nanoparticles on macrophages.

Research Progress of Nanomaterials Acting on NK Cells in Tumor Immunotherapy and Imaging.

The prognosis for cancer patients has declined dramatically in recent years due to the challenges in treating malignant tumors. Tumor immunotherapy, which includes immune target inhibition and chimeric antigen receptor cell treatment, is currently evolving quickly. Among them, natural killer (NK) cells are gradually becoming another preferred cell immunotherapy after T cell immunotherapy due to their unique killing effects in innate and adaptive immunity. NK cell therapy has shown encouraging outcomes in clinical studies; however, there are still some problems, including limited efficacy in solid tumors, inadequate NK cell penetration, and expensive treatment expenses. Noteworthy benefits of nanomaterials include their chemical specificity, biocompatibility, and ease of manufacturing; these make them promising instruments for enhancing NK cell anti-tumor immune responses. Nanomaterials can promote NK cell homing and infiltration, participate in NK cell modification and non-invasive cell tracking and imaging modes, and greatly increase the effectiveness of NK cell immunotherapy. The introduction of NK cell-based immunotherapy research and a more detailed discussion of nanomaterial research in NK cell-based immunotherapy and molecular imaging will be the main topics of this review.

Interaction of phosphorus and GA3 improved oilseed flax grain yield and phosphorus-utilization efficiency.

Introduction: Phosphorus nutrition and hormone concentration both affect crop yield formation. Ascertaining the interaction of phosphorus and GA3 has a synergistic effect on the grain yield and phosphorus utilization efficiency of oilseed flax in dryland. It is extremely important for improving grain yield and phosphorus utilization efficiency. Methods: A field experiment was conducted in 2019 and 2020 at the Dingxi Oil Crops Test Station to investigated the effects of phosphorus, gibberellin (GA3), and their interaction on the grain yield and phosphorus-utilization efficiency of oilseed flax plants. Phosphorus fertilizer was applied at three levels (0, 67.5, 135 kg P2O5·ha-1) and GA3 was also sprayed at three concentrations (0, 15, and 30 mg·L-1). Results: The results showed that application of 67.5 kg P2O5·ha-1 reduced leaves acid phosphatase (ACPase) activity, but increased phosphorus accumulation throughout the growth period, the 1000-kernel weight (TKW), and the number of grains per capsule. Spraying GA3 significantly increased the leaves ACPase activity, phosphorus accumulation after anthesis and its contribution to grain, phosphorus-utilization efficiency, the number of capsules per plant, and TKW. The phosphorus accumulation at the anthesis, kernel, and maturity stages under the treatment of fertilizing 67.5 kg P2O5·ha-1 and spraying 30 mg·L-1 GA3 were increased by 56.06%, 73.51%, and 62.17%, respectively, compared with the control (no phosphorus, no GA3). And the phosphorus accumulation after anthesis and its contribution to grain also increased. 67.5 kg P2O5·ha-1 combined with 30 mg·L-1 GA3 and 135 kg P2O5·ha-1 combined with 15 mg·L-1 GA3 both significantly increased grain yield of oilseed flax, reaching 1696 kg·ha-1 and 1716 kg·ha-1 across two years, respectively. And there was no significant difference between them. However, the former treatment significant increased the apparent utilization rate, agronomic utilization rate, and partial productivity of phosphorus. The interaction between phosphorus and GA3 was significant for grain yield. Conclusion: Therefore, the application of 67.5 kg P2O5·ha-1 in combination with 30 mg·L-1 GA3 is an effective fertilization approach for enhancing oilseed flax growth and grain yield in the experiment region and other similar areas.

Metallofullerene nanoparticles circumvent tumor resistance to cisplatin by reactivating endocytosis.

Cisplatin is a chemotherapeutic drug commonly used in clinics. However, acquired resistance confines its application in chemotherapeutics. To overcome the acquired resistance to cisplatin, it is reasoned, based on our previous findings of mediation of cellular responses by [Gd@C(82)(OH)(22)](n) nanoparticles, that [Gd@C(82)(OH)(22)](n) may reverse tumor resistance to cisplatin by reactivating the impaired endocytosis of cisplatin-resistant human prostate cancer (CP-r) cells. Here we report that exposure of the CP-r PC-3-luc cells to cisplatin in the presence of nontoxic [Gd@C(82)(OH)(22)](n) not only decreased the number of surviving CP-r cells but also inhibited growth of the CP-r tumors in athymic nude mice as measured by both optical and MRI. Labeling the CP-r PC-3 cells with transferrin, an endocytotic marker, demonstrated that pretreatment of the CP-r PC-3-luc cells with [Gd@C(82)(OH)(22)](n) enhanced intracellular accumulation of cisplatin and formation of cisplatin-DNA adducts by restoring the defective endocytosis of the CP-r cancer cells. The results suggest that [Gd@C(82)(OH)(22)](n) nanoparticles overcome tumor resistance to cisplatin by increasing its intracellular accumulation through the mechanism of restoring defective endocytosis. The technology can be extended to other challenges related to multidrug resistance often found in cancer treatments.

Leak Proof PDBBind: A Reorganized Dataset of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction.

Many physics-based and machine-learned scoring functions (SFs) used to predict protein-ligand binding free energies have been trained on the PDBBind dataset. However, it is controversial as to whether new SFs are actually improving since the general, refined, and core datasets of PDBBind are cross-contaminated with proteins and ligands with high similarity, and hence they may not perform comparably well in binding prediction of new protein-ligand complexes. In this work we have carefully prepared a cleaned PDBBind data set of non-covalent binders that are split into training, validation, and test datasets to control for data leakage. The resulting leak-proof (LP)-PDBBind data is used to retrain four popular SFs: AutoDock vina, Random Forest (RF)-Score, InteractionGraphNet (IGN), and DeepDTA, to better test their capabilities when applied to new protein-ligand complexes. In particular we have formulated a new independent data set, BDB2020+, by matching high quality binding free energies from BindingDB with co-crystalized ligand-protein complexes from the PDB that have been deposited since 2020. Based on all the benchmark results, the retrained models using LP-PDBBind that rely on 3D information perform consistently among the best, with IGN especially being recommended for scoring and ranking applications for new protein-ligand systems.

DMFF: An Open-Source Automatic Differentiable Platform for Molecular Force Field Development and Molecular Dynamics Simulation.

In the simulation of molecular systems, the underlying force field (FF) model plays an extremely important role in determining the reliability of the simulation. However, the quality of the state-of-the-art molecular force fields is still unsatisfactory in many cases, and the FF parameterization process largely relies on human experience, which is not scalable. To address this issue, we introduce DMFF, an open-source molecular FF development platform based on an automatic differentiation technique. DMFF serves as a powerful tool for both top-down and bottom-up FF development. Using DMFF, both energies/forces and thermodynamic quantities such as ensemble averages and free energies can be evaluated in a differentiable way, realizing an automatic, yet highly flexible FF optimization workflow. DMFF also eases the evaluation of forces and virial tensors for complicated advanced FFs, helping the fast validation of new models in molecular dynamics simulation. DMFF has been released as an open-source package under the LGPL-3.0 license and is available at https://github.com/deepmodeling/DMFF.

Interferon-α and its effects on cancer cell apoptosis.

Interferon (IFN)-α is a cytokine that exhibits a wide range of biological activities and is used in various cancer treatments. It regulates numerous genes that serve roles in antiviral, antiproliferative and proapoptotic activities. For decades, one of the main aspects of clinical oncology has been the development of anticancer therapeutics that promote the effective elimination of cancer cells via apoptosis. However, the updated available information concerning IFN-α-induced cancer cell apoptosis needs to be assembled, so as to provide an improved theoretical reference for the basic scientific research and clinical treatment of malignant tumors. Therefore, the present review focuses on the potential effects of IFN-α in inducing cancer cell apoptosis. The biological characteristics of IFN-α, the apoptotic signaling pathways and molecular mechanisms of apoptosis caused by IFN-α are discussed in different types of cancer cells. The present review provided a comprehensive understanding of the effects of IFN-α on cancer cell apoptosis, which will aid in developing more efficient strategies to effectively control the progression of certain cancers.

Water and Nitrogen Coupling Increased the Water-Nitrogen Use Efficiency of Oilseed Flax.

Increasing water shortages and environmental pollution from excess chemical nitrogen fertilizer use necessitate the development of irrigation-nitrogen conservation technology in oilseed flax production. Therefore, a two-year split-plot design experiment (2017-2018) was conducted with three types of irrigation (I) levels (no irrigation (I0), irrigation of 1200 m3 ha-1 (I1200), and 1800 m3 ha-1 (I1800)) as the main plot and three nitrogen (N) application rates (0 (N0), 60 (N60) and 120 (N120) kg N ha-1) as the subplot in Northwest China to determine the effects of irrigation and N rates on oilseed flax grain yield, yield components, water-use efficiency (WUE), and N partial factor productivity (NPFP). The results show that I1800 optimized the farmland water storage and water storage efficiency (WSE), which gave rise to greater above-ground biomass. Under I1800, the effective capsule (EC) number increased significantly with increasing irrigation amounts, which increased significantly with increasing nitrogen application rate (0-120 kg ha-1). Both irrigation and nitrogen indirectly affect GY by affecting EC; the highest grain yield was observed at the I1800N60 treatment, which increased by 69.04% and 22.80% in 2017 and 2018 compared with the I0N0 treatment, respectively. As a result, both irrigation and N affect grain yield by affecting soil water status, improving above-ground biomass, and finally affecting yield components. In addition, I1800N60 also obtained a higher WUE and the highest NPFP due to a higher grain yield and a lower N application rate. Hence, our study recommends that irrigation with 1800 m3 ha-1 coupled with 60 kg N ha-1 could be a promising strategy for synergistically improving oilseed flax WUE, grain yield and yield components within this semi-arid region.

Crop yield and water use efficiency in response to long-term diversified crop rotations.

Crop production and water productivity may be impacted by diverse crop rotation and management practices. A field study was conducted from 2017-2020 in the Loess Plateau to evaluate the effects of crop rotation sequences on pre-planting and post-harvest soil water storage (SWS), annualized crop yield, water use, and water productivity. Crops in rotation included oil flax (Linum usitatissimum L.) (F), wheat (Triticum aestivum L.) (W), potato (Solanum tuberosum L.) (P). Twelve 4-year-cycle crop rotation treatments, along with a continuous oil flax treatment as a baseline, were included. The results showed that the average soil water content under the 0-150 cm soil layer in all treatments was increased after one rotation cycle, and the PWFW treatment achieved the highest SWC (17.1%). The average soil water storage (winter fallow season) and evapotranspiration (ETa) (growing season) under different crop rotation sequences were lower than those under continuous oil flax cropping. The ETa of FFFF increased by 28.9, 2.7, 15.3, and 28.4%, compared to average crop rotations in 2017, 2018, 2019, and 2020, respectively. Crop rotation had a significant effect on average annual yield and water use efficiency (WUE), which varied by year and rotation sequence. The crop rotations with the highest grain yield of oil flax were FFWP (2017), WFWP (2018),WPFF (2019) and FWPF (2020); the grain yield of wheat was highest when the two pre-crops (previously cultivated crops) were F-F, and potato yield was highest when the two pre-crops were W-F (except 2018). On average, the WUE of oil flax was 8.6, 38.7, 22.7, and 42.1% lower with FFFF than other diversity crop rotations in 2017, 2018, 2019, and 2020. We found that the WUE was not the largest when the grain yield of oil flax and wheat was highest. The treatments with maximum grain yield and WUE were not consistent. Our findings also revealed that wheat-potato-oil flax or potato-wheat-oil flax rotation could increase oil flax grain yields while wheat-oil flax-potato-oil flax markedly improved oil flax WUE.

Downregulation of GLUT3 impairs STYK1/NOK-mediated metabolic reprogramming and proliferation in NIH-3T3 cells.

Serine threonine tyrosine kinase 1 (STYK1)/novel oncogene with kinase domain (NOK) has been demonstrated to promote cell carcinogenesis and tumorigenesis, as well as to strengthen cellular aerobic glycolysis, which is considered to be a defining hallmark of cancer. As the carriers of glucose into cells, glucose transporters (GLUTs) are important participants in cellular glucose metabolism and even tumorigenesis. However, to the best of our knowledge, the role of GLUTs in biological events caused by STYK1/NOK has not yet been reported. The present study assessed GLUT3 as a key transporter, and glucose consumption and lactate production assays revealed that downregulation of GLUT3 impaired STYK1/NOK-induced augmented glucose uptake and lactate production, and RT-qPCR and western blotting confirmed that GLUT3 knockdown attenuated the STYK1/NOK-induced increase in the expression levels of key enzymes implicated in glycolysis. Furthermore, MTT and Transwell assays demonstrated that STYK1/NOK-triggered cell proliferation and migration were also markedly decreased following knockdown of GLUT3. To the best of our knowledge, the present study is the first to demonstrate that GLUT3 serves a prominent role in STYK1/NOK-driven aerobic glycolysis and cell proliferation characteristics. These findings may provide a clue for the investigation of the oncogenic activity of STYK1/NOK and for the identification of potential tumor therapy targets associated with GLUT3.

Gonadal development and biochemical composition of Chinese mitten crabs (Eriocheir sinensis) from four sources.

The gonadal development; edible tissue ratio; and proximate, fatty acid, and free amino acid composition were examined and compared among Eriocheir sinensis samples from four sources. The gonadosomatic index (GSI) of Heilongjiang (HLJ) crabs was significantly higher than that of the other crabs from September to October (P < 0.05). Of all the edible tissues of E. sinensis, HLJ crab had the highest linolenic acid (18:3n3, LNA) and arachidonic acid (20:4n6, ARA) contents (P < 0.05), while Qinghai (QH) crab had the highest eicosapentaenoic acid (20:5n3, EPA) content (P < 0.05). The highest docosahexaenoic acid (22:6n3, DHA) and DHA/EPA contents were observed in Shandong (SD) crabs (P < 0.05), and the highest linoleic acid (18:2n6, LA) content was found in Shanghai (SH) crabs (P < 0.05). In conclusion, E. sinensis inhabiting relatively low-temperature regions (northern China) can initiate gonadal development earlier, and the quality of E. sinensis living in paddies and lakes is better than that of crabs living in ponds.

Bioreducible Zinc(II)-Dipicolylamine Functionalized Hyaluronic Acid Mediates Safe siRNA Delivery and Effective Glioblastoma RNAi Therapy.

RNA interference (RNAi) is an emerging therapeutic modality for tumors. However, lack of a safe and efficient small interfering RNA (siRNA) delivery system limits its clinical application. Here, we report a bioreducible and less-cationic siRNA delivery carrier by conjugating Zn(II)-dipicolylamine complexes (Zn-DPA) onto hyaluronic acid (HA) via a redox-sensitive disulfide (-SS-) linker. Such polymer conjugates can formulate stable siRNA nanomedicines via coordination between zinc ions of DPA and the anionic phosphate of siRNA. After the conjugates are taken up by cells, intracellular reduction stimulus subsequently triggers the release of siRNAs and elucidates the desired RNAi effect. Our studies showed the formulated siRNA nanomedicines can be efficiently delivered into tumor cells/tissues and mediates less cytotoxicities both in vitro and in vivo. More importantly, when applied in a xenograft glioblastoma tumor model, this siRNA nanomedicine demonstrated significantly enhanced antitumor ability comparing to naked siRNA. This work demonstrates that such bioreducible Zn-DPA-functionalized HA conjugates without using cationic material as a siRNA carrier represents a promising direction for RNAi-based cancer therapy.