Potential Association of Gut Microbial Metabolism and Circulating mRNA Based on Multiomics Sequencing Analysis in Fetal Growth Restriction.

Objective: Fetal growth restriction (FGR) is a significant contributor to negative pregnancy and postnatal developmental outcomes. Currently, the exact pathological mechanism of FGR remains unknown. This study aims to utilize multiomics sequencing technology to investigate potential relationships among mRNA, gut microbiota, and metabolism in order to establish a theoretical foundation for diagnosing and understanding the molecular mechanisms underlying FGR. Methods: In this study, 11 healthy pregnant women and nine pregnant women with FGR were divided into Control group and FGR group based on the health status. Umbilical cord blood, maternal serum, feces, and placental tissue samples were collected during delivery. RNA sequencing, 16S rRNA sequencing, and metabolomics methods were applied to analyze changes in umbilical cord blood circulating mRNA, fecal microbiota, and metabolites. RT-qPCR, ELISA, or western blot were used to detect the expression of top 5 differential circulating mRNA in neonatal cord blood, maternal serum, or placental tissue samples. Correlation between differential circulating mRNA, microbiota, and metabolites was analyzed by the Spearman coefficient. Results: The top 5 mRNA genes in FGR were altered with the downregulation of TRIM34, DEFA3, DEFA1B, DEFA1, and QPC, and the upregulation of CHPT1, SMOX, FAM83A, GDF15, and NAPG in newborn umbilical cord blood, maternal serum, and placental tissue. The abundance of Bacteroides, Akkermansia, Eubacterium_coprostanoligenes_group, Phascolarctobacterium, Parasutterella, Odoribacter, Lachnospiraceae_UCG_010, and Dielma were significantly enriched in the FGR group. Metabolites such as aspartic acid, methionine, alanine, L-tryptophan, 3-methyl-2-oxovalerate, and ketoleucine showed notable functional alterations. Spearman correlation analysis indicated that metabolites like methionine and alanine, microbiota (Tyzzerella), and circulating mRNA (TRIM34, SMOX, FAM83A, NAPG) might play a role as mediators in the communication between the gut and circulatory system interaction in FGR. Conclusion: Metabolites (METHIONINE, alanine) as well as microbiota (Tyzzerella) and circulating mRNA (TRIM34, SMOX, FAM83A, NAPG) were possible mediators that communicated the interaction between the gut and circulatory systems in FGR.

Combustion Characteristics of Vat-Photopolymerized Fuels with a Spiral Star Port for Hybrid Propulsion.

Despite hybrid rocket motors offering distinct advantages over solid or liquid rocket motors, their low regression rate and insufficient combustion efficiency remain significantly unimproved. This study focuses on the effects of the helix lead on the regression rate distribution and combustion efficiency of vat-polymerized fuel grains with a spiral star port for a hybrid rocket. Both experimental and numerical investigations were conducted to study the combustion characteristics and regression rate distribution of three-dimensional (3D) print grains. Spiral star grains with varying helix leads of 60, 90, and 120 mm were fabricated using light-curing 3D printing technology. A 3D simulation model was developed to obtain the temperature distribution, species mass distribution, and combustion efficiency. Furthermore, firing tests were performed on a two-dimensional radial hybrid combustion test stand to measure the regression rate. Digital image processing of computed tomography images was used to determine the regression rate. Simulation results indicated that the spiral star grain port helps to improve the combustion efficiency compared with those seen with round tube and straight star port grains. With an increase in the axial distance, the flame zone gradually shrinks, and the smaller the helix lead, the faster the shrinkage. At a mass flow rate of 1.50 g/s for oxygen, the regression rate of the spiral star grains is significantly higher than that of the straight star grain and the conventional round tubular grains, and the regression rate gradually increases with a decrease in the helix lead. This finding is expected to solve the problem of the low regression rate of solid fuels with spiral star pore-shaped grains prepared by the light-curing 3D printing method.

Furin-Catalyzed Enhanced Magnetic Resonance Imaging Probe for Differential Diagnosis of Malignant Breast Cancers.

Molecular magnetic resonance imaging (mMRI) of biomarkers is essential for accurate cancer detection in precision medicine. However, the current clinically used contrast agents provide structural magnetic resonance imaging (sMRI) information only and rarely provide mMRI information. Here, a tumor-specific furin-catalyzed nanoprobe (NP) was reported for differential diagnosis of malignant breast cancers (BCs) in vivo. This NP with a compact structure of Fe3O4@Gd-DOTA NPs (FFG NPs) contains an "always-on" T2-weighted MR signal provided by the magnetic Fe3O4 core and a furin-catalyzed enhanced T1-weighted MR signal provided by the Gd-DOTA moiety. The FFG NPs were found to produce an activated T1 signal in the presence of furin catalysis and an "always-on" T2 signal, providing mMRI and sMRI information simultaneously. Ratiometric mMRI:sMRI intensity can be used for differential diagnosis of malignant BCs MDA-MB-231 and MCF-7, where the furin levels relatively differ. The proposed probe not only provides structural imaging but also enables real-time molecular differential visualization of BC through enzymatic activities of cancer tissues.

Accurate Excitation Energies of Point Defects from Fast Particle-Particle Random Phase Approximation Calculations.

We present an efficient particle-particle random phase approximation (ppRPA) approach that predicts accurate excitation energies of point defects, including the nitrogen-vacancy (NV-) and silicon-vacancy (SiV0) centers in diamond and the divacancy center (VV0) in 4H silicon carbide, with errors of ±0.2 eV compared with experimental values. Starting from the (N + 2)-electron ground state calculated with density functional theory (DFT), the ppRPA excitation energies of the N-electron system are calculated as the differences between the two-electron removal energies of the (N + 2)-electron system. We demonstrate that the ppRPA excitation energies converge rapidly with a few hundred canonical active-space orbitals. We also show that active-space ppRPA has weak DFT starting-point dependence and is significantly cheaper than the corresponding ground-state DFT calculation. This work establishes ppRPA as an accurate and low-cost tool for investigating excited-state properties of point defects and opens up new opportunities for applications of ppRPA to periodic bulk materials.

Development of a machine learning finite-range nonlocal density functional.

Kohn-Sham density functional theory has been the most popular method in electronic structure calculations. To fulfill the increasing accuracy requirements, new approximate functionals are needed to address key issues in existing approximations. It is well known that nonlocal components are crucial. Current nonlocal functionals mostly require orbital dependence such as in Hartree-Fock exchange and many-body perturbation correlation energy, which, however, leads to higher computational costs. Deviating from this pathway, we describe functional nonlocality in a new approach. By partitioning the total density to atom-centered local densities, a many-body expansion is proposed. This many-body expansion can be truncated at one-body contributions, if a base functional is used and an energy correction is approximated. The contribution from each atom-centered local density is a single finite-range nonlocal functional that is universal for all atoms. We then use machine learning to develop this universal atom-centered functional. Parameters in this functional are determined by fitting to data that are produced by high-level theories. Extensive tests on several different test sets, which include reaction energies, reaction barrier heights, and non-covalent interaction energies, show that the new functional, with only the density as the basic variable, can produce results comparable to the best-performing double-hybrid functionals, (for example, for the thermochemistry test set selected from the GMTKN55 database, BLYP based machine learning functional gives a weighted total mean absolute deviations of 3.33 kcal/mol, while DSD-BLYP-D3(BJ) gives 3.28 kcal/mol) with a lower computational cost. This opens a new pathway to nonlocal functional development and applications.

Tandem repeats of highly bioluminescent NanoLuc are refolded noncanonically by the Hsp70 machinery.

Chaperones are a large family of proteins crucial for maintaining cellular protein homeostasis. One such chaperone is the 70 kDa heat shock protein (Hsp70), which plays a crucial role in protein (re)folding, stability, functionality, and translocation. While the key events in the Hsp70 chaperone cycle are well established, a relatively small number of distinct substrates were repetitively investigated. This is despite Hsp70 engaging with a plethora of cellular proteins of various structural properties and folding pathways. Here we analyzed novel Hsp70 substrates, based on tandem repeats of NanoLuc (Nluc), a small and highly bioluminescent protein with unique structural characteristics. In previous mechanical unfolding and refolding studies, we have identified interesting misfolding propensities of these Nluc-based tandem repeats. In this study, we further investigate these properties through in vitro bulk experiments. Similar to monomeric Nluc, engineered Nluc dyads and triads proved to be highly bioluminescent. Using the bioluminescence signal as the proxy for their structural integrity, we determined that heat-denatured Nluc dyads and triads can be efficiently refolded by the E. coli Hsp70 chaperone system, which comprises DnaK, DnaJ, and GrpE. In contrast to previous studies with other substrates, we observed that Nluc repeats can be efficiently refolded by DnaK and DnaJ, even in the absence of GrpE co-chaperone. Taken together, our study offers a new powerful substrate for chaperone research and raises intriguing questions about the Hsp70 mechanisms, particularly in the context of structurally diverse proteins.

Highly Responsive Gate-Controlled p-GaN/AlGaN/GaN Ultraviolet Photodetectors with a High-Transmittance Indium Tin Oxide Gate.

This work presents highly responsive gate-controlled p-GaN/AlGaN/GaN ultraviolet photodetectors (UVPDs) on Si substrates with a high-transmittance ITO gate. The two-dimensional electron gas (2DEG) in the quantum well of the polarized AlGaN/GaN heterojunction was efficiently depleted by the p-GaN gate, leading to a high photo-to-dark current ratio (PDCR) of 3.2 × 105. The quantum wells of the p-GaN/AlGaN and AlGaN/GaN heterojunctions can trap the holes and electrons excited by the UV illumination, thus efficiently triggering a photovoltaic effect and photoconductive effect, separately. Furthermore, the prepared photodetectors allow flexible adjustment of the static bias point, making it adaptable to different environments. Compared to traditional thin-film semi-transparent Ni/Au gates, indium tin oxide (ITO) exhibits higher transmittance. Under 355 nm illumination, the photodetector exhibited a super-high responsivity exceeding 3.5 × 104 A/W, and it could even exceed 106 A/W under 300 nm illumination. The well-designed UVPD combines both the advantages of the high-transmittance ITO gate and the structure of the commercialized p-GaN/AlGaN/GaN high-electron-mobility transistors (HEMTs), which opens a new possibility of fabricating large-scale, low-cost, and high-performance UVPDs in the future.

Biosensors based on potent miniprotein binder for sensitive testing of SARS-CoV­2 variants of concern.

The miniprotein binder TRI2-2 was employed as an antibody alternative to build a single antibody-coupled TRI2-2 based gold nanoparticle-based lateral flow immunoassay (AT-GLFIA) biosensor. The biosensor provides high specificity and affinity binding between TRI2-2 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) spike antigen receptor binding domain (S-RBD). It also enables rapid testing of wild-type (WT), B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), P.1 (Gamma), and B.1.1.529 (Omicron) SARS-CoV-2 S-RBD and is at least ~ 16-fold more sensitive than conventional antibody pair-based GLFIA (AP-GLFIA). Besides, we developed a wireless micro-electrochemical assay (WMECA) biosensor based on the TRI2-2, which demonstrates an excellent VOCs testing capability at the pg mL-1 level. Overall, our results demonstrate that integrating miniprotein binders into conventional immunoassay systems is a promising design for improving the testing capabilities of such systems without hard-to-obtain antibody pair, complex reporter design, laborious signal amplification strategies, or specific instrumentation.

Enhancement of Deep Neural Network Recognition on MPSoC with Single Event Upset.

This paper introduces a new finding regarding single event upsets (SEUs) in configuration memory, and their potential impact on enhancing the performance of deep neural networks (DNNs) on the multiprocessor system on chip (MPSoC) platform. Traditionally, SEUs are considered to have negative effects on electronic systems or designs, but the current study demonstrates that they can also have positive contributions to the DNN on the MPSoC. The assertion that SEUs can have positive contributions to electronic system design was supported by conducting fault injections through dynamic reconfiguration on DNNs implemented on a 16nm FinFET technology Zynq UltraScale+ MPSoC. The results of the current study were highly significant, indicating that an SEU in configuration memory could result in an impressive 8.72% enhancement in DNN recognition on the MPSoC. One possible cause is that SEU in the configuration memory leads to slight changes in weight or bias values, resulting in improved activation levels of neurons and enhanced final recognition accuracy. This discovery offers a flexible and effective solution for boosting DNN performance on the MPSoC platform.

Copper(II)/DBU Relay Catalyzed Annulation of α-Carbonyl-γ-alkynyl Sulfoxonium Ylides for Accessing N-Sulfonamido 2H-Isoindoles.

A copper(II)/DBU relay catalyzed annulation of α-carbonyl-γ-alkynyl sulfoxonium ylides as a new class of sulfoxonium ylide reagents with sulfonyl hydrazides is reported, enabling intramolecular oxygen migration to produce a series of N-sulfonamido 2H-isoindoles with good yields. The present annulation proceeded readily by combining the Cu(II)-catalyzed 6-endo-dig oxo-cyclization with the DBU-catalyzed isochromene skeletal rearrangement, resulting in the formation of multiple new chemical bonds.

Linear Scaling Calculations of Excitation Energies with Active-Space Particle-Particle Random-Phase Approximation.

We developed an efficient active-space particle-particle random-phase approximation (ppRPA) approach to calculate accurate charge-neutral excitation energies of molecular systems. The active-space ppRPA approach constrains both indexes in particle and hole pairs in the ppRPA matrix, which only selects frontier orbitals with dominant contributions to low-lying excitation energies. It employs the truncation in both orbital indexes in the particle-particle and the hole-hole spaces. The resulting matrix, whose eigenvalues are excitation energies, has a dimension that is independent of the size of the systems. The computational effort for the excitation energy calculation, therefore, scales linearly with system size and is negligible compared with the ground-state calculation of the (N - 2)-electron system, where N is the electron number of the molecule. With the active space consisting of 30 occupied and 30 virtual orbitals, the active-space ppRPA approach predicts the excitation energies of valence, charge-transfer, Rydberg, double, and diradical excitations with the mean absolute errors (MAEs) smaller than 0.03 eV compared with the full-space ppRPA results. As a side product, we also applied the active-space ppRPA approach in the renormalized singles (RS) T-matrix approach. Combining the non-interacting pair approximation that approximates the contribution to the self-energy outside the active space, the active-space GRSTRS@PBE approach predicts accurate absolute and relative core-level binding energies with the MAEs around 1.58 and 0.3 eV, respectively. The developed linear scaling calculation of excitation energies is promising for applications to large and complex systems.

SENP5 deteriorates traumatic brain injury via SUMO2-dependent suppression of E2F1 SUMOylation.

Traumatic brain injury (TBI) represents a main public health concern during the past decade, attracting considerable interest because of its rising prevalence, wide-ranging risk factors and lifelong familial and societal influence. SUMO2 can conjugate to substrates upon various cellular stresses. Nevertheless, whether and how SUMO2-specific proteases partake in TBI is less understood. The aim of this study is to dissect the effects of SUMO-specific peptidase 5 (SENP5) on accentuating TBI in rats in an effort to unveil its underlying mechanism. SENP5 is overexpressed in hippocampal tissues of TBI rats, and inhibition of SENP5 reduces neurological function scores, decreases brain water content, inhibits apoptosis in hippocampal tissues, and attenuates brain injury caused in rats. Moreover, SENP5 inhibits the SUMOylation level of E2F transcription factor 1 (E2F1) and increases the protein expression of E2F1. Silencing of E2F1 blocks the p53 signaling pathway. Overexpression of E2F1 partially reverses the protective effect of sh-SENP5 on TBI in rats. These findings reveal an essential role of SENP5 and the SUMOylation status of E2F1 in the TBI development.

Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.

Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fields. Currently, molecular mechanical (MM) force fields are mainly utilized in MD simulations because of their low computational cost. Quantum mechanical (QM) calculation has high accuracy, but it is exceedingly time consuming for protein simulations. Machine learning (ML) provides the capability for generating accurate potential at the QM level without increasing much computational effort for specific systems that can be studied at the QM level. However, the construction of general machine learned force fields, needed for broad applications and large and complex systems, is still challenging. Here, general and transferable neural network (NN) force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins by training NN models on 27 fragments partitioned from the residue-based systematic molecular fragmentation (rSMF) method. The NN for each fragment is based on atom types and uses new input features that are similar to MM inputs, including bonds, angles, dihedrals, and non-bonded terms, which enhance the compatibility of CHARMM-NN to MM MD and enable the implementation of CHARMM-NN force fields in different MD programs. While the main part of the energy of the protein is based on rSMF and NN, the nonbonded interactions between the fragments and with water are taken from the CHARMM force field through mechanical embedding. The validations of the method for dipeptides on geometric data, relative potential energies, and structural reorganization energies demonstrate that the CHARMM-NN local minima on the potential energy surface are very accurate approximations to QM, showing the success of CHARMM-NN for bonded interactions. However, the MD simulations on peptides and proteins indicate that more accurate methods to represent protein-water interactions in fragments and non-bonded interactions between fragments should be considered in the future improvement of CHARMM-NN, which can increase the accuracy of approximation beyond the current mechanical embedding QM/MM level.

The Gut Microbiota of Pregnant Rats Alleviates Fetal Growth Restriction by Inhibiting the TLR9/MyD88 Pathway.

Fetal growth restriction (FGR) is a prevalent obstetric condition. This study aimed to investigate the role of Toll-like receptor 9 (TLR9) in regulating the inflammatory response and gut microbiota structure in FGR. An FGR animal model was established in rats, and ODN1668 and hydroxychloroquine (HCQ) were administered. Changes in gut microbiota structure were assessed using 16S rRNA sequencing, and fecal microbiota transplantation (FMT) was conducted. HTR-8/Svneo cells were treated with ODN1668 and HCQ to evaluate cell growth. Histopathological analysis was performed, and relative factor levels were measured. The results showed that FGR rats exhibited elevated levels of TLR9 and myeloid differentiating primary response gene 88 (MyD88). In vitro experiments demonstrated that TLR9 inhibited trophoblast cell proliferation and invasion. TLR9 upregulated lipopolysaccharide (LPS), LPS-binding protein (LBP), interleukin (IL)-1ß and tumor necrosis factor (TNF)-α while downregulating IL-10. TLR9 activated the TARF3-TBK1-IRF3 signaling pathway. In vivo experiments showed HCQ reduced inflammation in FGR rats, and the relative cytokine expression followed a similar trend to that observed in vitro. TLR9 stimulated neutrophil activation. HCQ in FGR rats resulted in changes in the abundance of Eubacterium_coprostanoligenes_group at the family level and the abundance of Eubacterium_coprostanoligenes_group and Bacteroides at the genus level. TLR9 and associated inflammatory factors were correlated with Bacteroides, Prevotella, Streptococcus, and Prevotellaceae_Ga6A1_group. FMT from FGR rats interfered with the therapeutic effects of HCQ. In conclusion, our findings suggest that TLR9 regulates the inflammatory response and gut microbiota structure in FGR, providing new insights into the pathogenesis of FGR and suggesting potential therapeutic interventions.

Efficient Computation of the Electrostatic Component of Solvation Free Energy via a Two-Point Padé Approximation.

We develop an efficient method to compute the electrostatic component of the solvation free energy via the two-point Padé approximation. The Padé approximant uses four parameters to describe the electrostatic free energy change of the solvation process, which could be readily determined from four thermodynamic properties obtained in two simulations, namely, the first- and second-order free energy gradients of any two states. Therefore, instead of sampling at multiple intermediate states, only two states, e.g., electrostatically fully solvated and desolvated, are needed to determine the Padé approximant and compute the corresponding free energy contribution. Applications to several model systems, including both neutral and charged species, show that the method can accurately produce electrostatic solvation free energy. The method would be very useful to save computational cost in applications in which accurate but expensive energy functions like quantum mechanics are used.

Investigating the Failure Mechanism of p-GaN Gate HEMTs under High Power Stress with a Transparent ITO Gate.

The channel temperature distribution and breakdown points are difficult to monitor for the traditional p-GaN gate HEMTs under high power stress, because the metal gate blocks the light. To solve this problem, we processed p-GaN gate HEMTs with transparent indium tin oxide (ITO) as the gate terminal and successfully captured the information mentioned above, utilizing ultraviolet reflectivity thermal imaging equipment. The fabricated ITO-gated HEMTs exhibited a saturation drain current of 276 mA/mm and an on-resistance of 16.6 Ω·mm. During the test, the heat was found to concentrate in the vicinity of the gate field in the access area, under the stress of VGS = 6 V and VDS = 10/20/30 V. After 691 s high power stress, the device failed, and a hot spot appeared on the p-GaN. After failure, luminescence was observed on the sidewall of the p-GaN while positively biasing the gate, revealing the side wall is the weakest spot under high power stress. The findings of this study provide a powerful tool for reliability analysis and also point to a way for improving the reliability of the p-GaN gate HEMTs in the future.

Revealing the Mechanism of the Bias Temperature Instability Effect of p-GaN Gate HEMTs by Time-Dependent Gate Breakdown Stress and Fast Sweeping Characterization.

The bias temperature instability (BTI) effect of p-GaN gate high-electron-mobility transistors (HEMTs) is a serious problem for reliability. To uncover the essential cause of this effect, in this paper, we precisely monitored the shifting process of the threshold voltage (VTH) of HEMTs under BTI stress by fast sweeping characterizations. The HEMTs without time-dependent gate breakdown (TDGB) stress featured a high VTH shift of 0.62 V. In contrast, the HEMT that underwent 424 s of TDGB stress clearly saw a limited VTH shift of 0.16 V. The mechanism is that the TDGB stress can induce a Schottky barrier lowering effect on the metal/p-GaN junction, thus boosting the hole injection from the gate metal to the p-GaN layer. This hole injection eventually improves the VTH stability by replenishing the holes lost under BTI stress. It is the first time that we experimentally proved that the BTI effect of p-GaN gate HEMTs was directly dominated by the gate Schottky barrier that impeded the hole supply to the p-GaN layer.

Unveiling the unprecedented catalytic capability of micro-sized Co-ZIF-L for the thermal decomposition of RDX by 2D-structure-induced mechanism reversal.

Developing MOF-based catalysts with superior catalytic properties for the thermal decomposition of cyclotrimethylenetrinitramine (RDX) is significant for the application of novel and efficient combustion catalysts oriented to RDX-based propellants with excellent combustion performance. Herein, micro-sized Co-ZIF-L with a star-like morphology (SL-Co-ZIF-L) was found to exhibit unprecedented catalytic capability for the decomposition of RDX, which can lower the decomposition temperature of RDX by 42.9 °C and boost the heat release by 50.8%, superior to that of all the ever-reported MOFs and even ZIF-67, which has similar chemical composition but a much smaller size. In-depth mechanism study from both experimental and theoretical views reveals that the weekly interacted 2D layered structure of SL-Co-ZIF-L could activate the exothermic C-N fission pathway for the decomposition of RDX in the condensed phase, thus reversing the commonly advantageous N-N fission pathway and promoting the decomposition process in the low-temperature stage. Our study reveals the unusually superior catalytic capability of micro-sized MOF catalysts and sheds light on the rational structure design of catalysts used in micromolecule transformation reactions, typically the thermal decomposition of energetic materials.

pH/thermal dual-responsive multifunctional drug delivery system for effective photoacoustic imaging-guided tumor chemo/photothermal therapy.

The development of a combination of chemo/photothermal therapy could overcome the limitations of single-modality therapy and enhance therapeutic efficacy. In this study, a pH/thermal dual-responsive multifunctional drug delivery system with dual-drug loading and enhanced chemo/photothermal therapy is developed based on polydopamine-coated mesoporous silica-gold nanorods (PDA-AuNRs@MSN). Nanoscale mesoporous silica-gold nanorods encapsulating doxorubicin (DOX) are designed as a core and then modified by polydopamine. The PDA shell not only conjugates with another anticancer bortezomib (Btz) to form pH-sensitive bond through boronic acid and catechol but also acts as a gatekeeper to control the release of doxorubicin and enhance the photothermal effect. Such a nanocarrier not only acts as a contrast agent for PA imaging but also serves as a therapeutic agent for enhanced chemo/photothermal therapy. The DOX and Btz could be released in an on-demand mode under near-infrared light irradiation and acid environment. The tumor size and location could be observed via PA imaging after intravenous injection into 4T1-bearing mice. Compared with AuNRs@MSN, PDA-AuNRs@MSN exhibit an increased near-infrared (NIR) absorption at 808 nm and an enhanced photothermal effect. The integrated D/B-PDA-AuNRs@MSN nanoparticles show higher cell apoptosis and enhanced tumor treatment efficacy in vitro and in vivo in comparison with single chemotherapy or photothermal therapy. Combined together, D/B-PDA-AuNRs@MSN show pH/thermal-responsive controlled-release and synergistic chemo/photothermal therapy for tumor.