A Transformable and Self-oxygenated Smart Probe for Enhanced Tumor Sonodynamic Therapy.

Sonodynamic therapy (SDT) is emerging as a promising modality for cancer treatment. However, improving the tumor bioavailability and anti-hypoxia capability of sonosensitizers faces a big challenge. In this work, we present a tumor microenvironment (TME)-mediated nanomorphology transformation and oxygen (O2) self-production strategy to enhance the sonodynamic therapeutic efficacy of tumors. A smart probe Ce6-Leu@Mn2+ that consists of a glutathione (GSH) and leucine amino peptidase (LAP) dual-responsive unit, a 2-cyanobenzothiazole (CBT) group, and a Mn2+-chelated Ce6 as sonosensitizer for tumor SDT was synthesized, and its SDT potential for liver tumor HepG2 in living mice was systematically studied. It was found that the probes could self-assemble into large nanoparticles in physiological condition and spontaneously transformed into small particles under the dual stimulation of GSH and LAP in TME resulting in enhanced tumor accumulation and deep penetration. More notably, Ce6-Leu@Mn2+ could convert endogenous hydrogen peroxide to O2, thereby alleviating the hypoxia and achieving effective SDT against hypoxic tumors under the excitation of ultrasound. We thus believe this smart TME-responsive probe may provide a noninvasive and efficient means for malignant tumor treatment. STATEMENT OF SIGNIFICANCE: Sonodynamic therapy (SDT) is emerging as a promising therapeutic modality for cancer treatment. However, how to improve the tumor bioavailability and anti-hypoxia capability of sonosensitizers remains a huge challenge. Herein, we rationally developed a theranostic probe Ce6-Leu@Mn2+ that can transform into small-size nanoparticles from initial large particles under the dual stimulation of LAP and GSH in tumor microenvironment (TME) resulting in enhanced tumor accumulation, deep tissue penetration as well as remarkable O2 self-production for enhanced sonodynamic therapy of human liver HepG2 tumor in living mice. This smart TME-responsive probe may provide a noninvasive and efficient means for hypoxic tumor treatment.

Construction of a Mode-Combination Hamiltonian under the Grid-Based Representation for the Quantum Dynamics of OH + HO2 → O2 + H2O.

In this work, a systematic construction framework on a mode-combination Hamiltonian operator of a typical polyatomic reaction, OH + HO2 → O2 + H2O, is developed. First, a set of Jacobi coordinates are employed to construct the kinetic energy operator (KEO) through the polyspherical approach ( Phys. Rep. 2009, 484, 169). Second, due to the multiconfigurational electronic structure of this system, a non-adiabatic potential energy surface (PES) is constructed where the first singlet and triplet states are involved with spin-orbital coupling. To improve the training database, the training set of random energy data was optimized through a popular iterative optimization approach with extensive trajectories. Here, we propose an automatic trajectory method, instead of the classical trajectory on a crude PES, where the gradients are directly computed by the present ab initio calculations. Third, on the basis of the training set, the potential function is directly constructed in the canonical polyadic decomposition (CPD) form ( J. Chem. Theory Comput. 2021, 17, 2702-2713) which is helpful in propagating the nuclear wave function under the grid-based representation. To do this, the Gaussian process regression (GPR) approach for building the CPD form, called the CPD-GPR method ( J. Phys. Chem. Lett. 2022, 13, 11128-11135) is adopted where we further revise CPD-GPR by introducing the mode-combination (mc) scheme leading to the present CPD-mc-GPR approach. Constructing the full-dimension non-adiabatic Hamiltonian operator with mode combination, as test calculations, the nuclear wave function is propagated to preliminarily compute the reactive probability of OH + HO2 → O2 + H2O where the reactants are prepared in vibrational ground states and in the first triplet electronic state.

Direct Canonical-Polyadic-Decomposition of the Potential Energy Surface from Discrete Data by Decoupled Gaussian Process Regression.

A Gaussian process regression (GPR) approach for directly constructing the canonical polyadic decomposition (CPD) of a multidimensional potential energy surface (PES) by discrete training energies is proposed and denoted by CPD-GPR. The present CPD-GPR method requires the kernel function in a product of a series of one-dimensional functions. To test CPD-GPR, the reactive probabilities of H + H2 as a function of kinetics energy are performed. Comparing the dynamics results computed by the CPD-GPR PES with those by the original PES, a good agreement between these results can be clearly found. Discussions on the previous algorithms for building the decomposed form are also given. We further show that the CPD-GPR method might be the general algorithm for building the decomposed form. However, further development is needed to reduce the CPD rank. Therefore, the present CPD-GPR method might be helpful to inspire ideas for developing new tools in building decomposed potential functions.

Multilayer Multiconfiguration Time-Dependent Hartree Study on the Mode-/Bond-Specific Quantum Dynamics of Water Dissociation on Cu(111).

In this work, full-dimensional (9D) quantum dynamics calculations on mode-/bond-specific surface scattering of a water molecule on a copper (111) rigid surface are performed through the multilayer multiconfiguration time-dependent Hartree (ML-MCTDH) method. To easily perform the ML-MCTDH calculations on such a triatomic molecule-surface system, we first choose specific Jacobi coordinates as a set of coordinates of water. Next, to efficiently perform the 9D ML-MCTDH wavepacket propagation, the potential energy surface is transferred to a canonical polyadic decomposition form with the aid of a Monte Carlo-based method. Excitation-specific dissociation probabilities of H2O on Cu(111) are computed, and mode-/bond-specific dynamics are demonstrated by comparison with a probability curve computed for a water molecule in the ground state. The dependence of the dissociation probability of the initial state of H2O is studied, and it is found that the excitation-specific dissociation probabilities can be divided into three groups. We find that the vibrationally excited states enhance the dissociation reactivity of H2O, while the rotationally excited states hardly influence it.

Estimating Preferred Alkane Carbon Numbers of Nonionic Surfactants in Normalized Hydrophilic-Lipophilic Deviation Theory from Dissipative Particle Dynamics Modeling.

The preferred alkane carbon number (PACN) in the normalized hydrophilic-lipophilic deviation (HLDN) theory is a numerical parameter and a transferable scale to characterize the amphiphilicity of surfactants, which is usually measured experimentally using the fish diagram or phase inversion temperature (PIT) methods, and the experimental measurement can only be applied to existing surfactants. Here, for the first time, we propose a procedure to estimate the PACN of CiEj nonionic surfactants directly from dissipative particle dynamics (DPD) simulation. The procedure leverages the method of moment concept to quantitatively evaluate the bending tendency of nonionic surfactant monolayers by calculating the torque density. Seven nonionic surfactants, CiEj (C6E2, C6E3, C8E3, C8E4, C10E4, C12E4, and C12E5), with known PACNs are modeled. Two surfactants, C10E4 and C6E2, were first selected to train and test the interaction parameters, and the relationship between interaction parameters and torque density was mapped for the C10E4-octane-water system using the artificial neural network (ANN) fitting approach to derive the interaction parameters giving zero torque density, then the interaction parameters were tested in the C6E2-dodecane-water system to get the final tuned interaction parameters for PACN estimation. With this procedure, we reproduce the PACN values and their trend of seven nonionic surfactants with reasonable accuracy, which opens the door for quantitative comparison of surfactant amphiphilicity and surfactant classification in silico using the PACN as a transferrable scale.

Correlation analysis between rim enhancement features of contrast-enhanced ultrasound and lymph node metastasis in breast cancer.

OBJECTIVE: To explore the correlation between rim enhancement features of contrast-enhanced ultrasound and lymphatic metastasis, and to provide theoretical support for clinical treatment of breast cancer. METHODS: 387 breast cancer patients (748 axillary lymph nodes in total) treated in our hospital from January 2017 to January 2020 were selected and analyzed by contrast-enhanced ultrasound. Pathological examination showed that 540 axillary lymph nodes showed metastasis whereas 208 axillary lymph nodes did not show metastasis. Univariate analysis and Logistic stepwise regression were used to analyze the correlation between rim enhancement features of contrast-enhanced ultrasound and axillary lymph node metastasis of breast cancer. RESULTS: Peripheral halo, peripheral convergence, rim enhancement, enhancement mode, enhancement amplitude, enhancement sequence, expansion after enhancement, peak intensity, time to peak, area under curve, thrombolysis in myocardial infarction, perfusion sequence, aspect ratio, and maximum cortical thickness were all related to lymph node metastasis of breast cancer by univariate analysis, and the difference was statistically significant (P < 0.05). Multivariate analysis showed that enhancement mode, enhancement amplitude, extension after enhancement, maximum cortical thickness, peak intensity and time to peak were all related to lymph node metastasis of breast cancer. CONCLUSION: Rim enhancement features of contrast-enhanced ultrasound of breast cancer are related to lymph node metastasis, which will provide a guidance for clinical treatment of breast cancer.

The relationship between the carotid atherosclerosis ultrasound parameters and the cardiac and endothelial functions of coronary heart disease patients.

PURPOSE: This study aimed to discover the relationship between the carotid atherosclerosis ultrasound parameters and the cardiac and endothelial functions of coronary heart disease patients. METHODS: 150 patients with coronary artery disease were divided into a single-branch group (one coronary artery with stenosis > 50%), a double-branch group (two coronary arteries with stenosis > 50%), and a multi-branch group (multiple coronary arteries with stenosis > 50%) based on the severity of each patient's coronary stenosis. Meanwhile, 50 healthy volunteers who were admitted to the hospital for routine health checks were recruited as the control group. This study tested the ultrasound parameters of carotid artery atherosclerosis among all the subjects in each group [common carotid artery sclerosis (ß), carotid artery compliance (AC), elastic coefficient (Ep), pulse wave conduction velocity (PWVß)], including the left ventricular end diastolic inner diameter (LVEDD), left ventricular ejection fraction (LVEF) and endothelial function parameters [endothelin-1 (ET-1), von Willebrand factor (vWF), and nitric oxide (NO)]. RESULTS: The study found that the ß, AC, Ep, PWVß, LVEDD, LVESD, ET-1, and vWF levels of patients with coronary artery disease were all higher than the corresponding levels in the control group (P < 0.05). The values increased as the number of coronary artery branches with stenosis increased (P < 0.05). The LVEF and NO of the patients with coronary artery disease were lower than they were in the control group (P < 0.05). The LVEF and NO decreased as the coronary artery branches with stenosis increased (P < 0.05). The correlation analysis indicated that the ultrasound parameter of carotid atherosclerosis has a significant positive relation with the LVEDD, LVESD, ET-1, and vWF levels (P < 0.05) and a negative relation with the LVEF and NO levels (P < 0.05). CONCLUSION: The ultrasound parameter of carotid atherosclerosis, cardiac function, and endothelial function can be used for the early diagnosis of coronary heart disease.

Revisiting the Gaussian process regression for fitting high-dimensional potential energy surface and its application to the OH + HO2→ O2 + H2O reaction.

In this work, Gaussian process regression (GPR) for fitting a high-dimensional potential energy surface (PES) is revisited and implemented to construct the PES of OH + HO2 → O2 + H2O. Using mixed kernel function and optimized distribution of the training database, only ∼3 × 103 energy points are needed to approach convergence, which implies the power of GPR in saving lots of computational cost. Moreover, the convergence of the GPR PES is inspected, leading to discussions on the advantages of the GPR fitting approach. By the segmented strategy [Meng et al., J. Chem. Phys. 144, 154312 (2016)], a GPR PES with a fitting error of ∼21 meV is constructed using ∼4600 energy points at the CCSD(T)-F12a/aug-cc-pVTZ level. The rate coefficients are then computed through the ring-polymer molecular dynamics (RPMD) method. An agreement between the present RPMD calculations and the previous observations is found, implying the accuracy of the present calculations. Moreover, the unusual feature of the Arrhenius curve is interpreted by a coupled harmonic oscillator model [Q. Meng, J. Phys. Chem. A 122, 8320 (2018)] together with a simple kinetics model.

Neural-network potential energy surface with small database and high precision: A benchmark of the H + H2 system.

To deeply understand the neural-network (NN) fitting procedure in constructing a potential energy surface (PES) in a wide energy range with a rather small database, based on the existing BKMP2 PES of H + H2, the relationship between NN function features and the size of the database is studied using the multiconfiguration time-dependent Hartree method for quantum dynamics calculations. First, employing 3843, 3843, 2024, and 1448 energy points, four independent NN-PESs are constructed to discuss the relationship among the size of the database, NN functional structure, and fitting accuracy. Dynamics calculations on these different NN PESs give similar reactive probabilities, which indicate that one has to balance the number of energy points for NN training and the number of neurons in the NN function. To explain this problem and try to resolve it, a quantitative model between the data volume and network scale is proposed. Then, this model is discussed and verified through 14 NN PESs fitted using 3843 energy points and various NN functional forms.

A Novel Reprocessable and Recyclable Acrylonitrile-Butadiene Rubber Based on Dynamic Oxime-Carbamate Bond.

The covalent cross-linked rubber has outstanding mechanical properties and chemical resistance, making it possible for a wide range of applications. Towards efforts to resource waste and environmental pollution, rubber recycling is a concerning problem. However, it is a big challenge to endow the most widely used commercial rubber systems with recyclability. In this paper, a novel reprocessable and recyclable acrylonitrile-butadiene rubber (NBR) is developed by introducing oxime-carbamate bonds into the raw NBR. Amidoxime NBR is prepared by a nucleophilic addition reaction of hydroxylamine hydrochloride and raw NBR, and then cross-linked amidoxime NBR using different amounts of toluene diisocynate (TDI). Results show that the obtained material exhibits good reprocessable property; the repairing efficiency exceeds 90% after two remoldings. In addition, it also has better mechanical properties: A tensile strength reaching a maximum value of 4.85 MPa when TDI cross-linker is 15.36 wt%, which is superior to vulcanized NBR (3.18 MPa).