Melatonin suppresses atherosclerosis by ferroptosis inhibition via activating NRF2 pathway.

Melatonin (MLT), a conserved small indole compound, exhibits anti-inflammatory and antioxidant properties, contributing to its cardioprotective effects. Lipoprotein-associated phospholipase A2 (Lp-PLA2) is associated with atherosclerosis disease risk, and is known as an atherosclerosis risk biomarker. This study aimed to investigate the impact of MLT on Lp-PLA2 expression in the atherosclerotic process and explore the underlying mechanisms involved. In vivo, ApoE-/- mice were fed a high-fat diet, with or without MLT administration, after which the plaque area and collagen content were assessed. Macrophages were pretreated with MLT combined with ox-LDL, and the levels of ferroptosis-related proteins, NRF2 activation, mitochondrial function, and oxidative stress were measured. MLT administration significantly attenuated atherosclerotic plaque progression, as evidenced by decreased plaque area and increased collagen. Compared with those in the high-fat diet (HD) group, the levels of glutathione peroxidase 4 (GPX4) and SLC7A11 (xCT, a cystine/glutamate transporter) in atherosclerotic root macrophages were significantly increased in the MLT group. In vitro, MLT activated the nuclear factor-E2-related Factor 2 (NRF2)/SLC7A11/GPX4 signaling pathway, enhancing antioxidant capacity while reducing lipid peroxidation and suppressing Lp-PLA2 expression in macrophages. Moreover, MLT reversed ox-LDL-induced ferroptosis, through the use of ferrostatin-1 (a ferroptosis inhibitor) and/or erastin (a ferroptosis activator). Furthermore, the protective effects of MLT on Lp-PLA2 expression, antioxidant capacity, lipid peroxidation, and ferroptosis were decreased in ML385 (a specific NRF2 inhibitor)-treated macrophages and in AAV-sh-NRF2 treated ApoE-/- mice. MLT suppresses Lp-PLA2 expression and atherosclerosis processes by inhibiting macrophage ferroptosis and partially activating the NRF2 pathway.

High-Valence Cu Induced by Photoelectric Reconstruction for Dynamically Stable Oxygen Evolution Sites.

Oxygen vacancies are generally considered to play a crucial role in the oxygen evolution reaction (OER). However, the generation of active sites created by oxygen vacancies is inevitably restricted by their condensation and elimination reactions. To overcome this limitation, here, we demonstrate a novel photoelectric reconstruction strategy to incorporate atomically dispersed Cu into ultrathin (about 2-3 molecular) amorphous oxyhydroxide (a-CuM, M = Co, Ni, Fe, or Zn), facilitating deprotonation of the reconstructed oxyhydroxide to generate high-valence Cu. The in situ XAFS results and first-principles calculations reveal that Cu atoms are stabilized at high valence during the OER process due to Jahn-Teller distortion, resulting in para-type double oxygen vacancies as dynamically stable catalytic sites. The optimal a-CuCo catalyst exhibits a record-high mass activity of 3404.7 A g-1 at an overpotential of 300 mV, superior to the benchmarking hydroxide and oxide catalysts. The developed photoelectric reconstruction strategy opens up a new pathway to construct in situ stable oxygen vacancies by high-valence Cu single sites, which extends the design rules for creating dynamically stable active sites.

Generation of streptocarbazoles with cytotoxicities by pathway engineering and insights into their biosynthesis.

Streptocarbazoles are a class of indolocarbazole (ICZ) compounds produced by Streptomyces strains that feature unique cyclic N-glycosidic linkages between the 1,3-carbon atoms of the glycosyl moiety and the two indole nitrogen atoms. Although several streptocarbazole compounds display effective cytotoxic activity, their biosynthesis remains unclear. Herein, through the inactivation of the aminotransferase gene spcI in the staurosporine biosynthetic gene cluster spc followed by heterologous expression, two new streptocarbazole derivatives (1 and 3) and three known ICZs (2, 4, and 5) were generated. Their structures were determined by a combination of spectroscopic methods, circular dichroism measurements, and single-crystal X-ray diffraction. Compounds 1-4 displayed moderate cytotoxicity against HCT-116 cell line, and compounds 3 and 4 were effective against Huh 7 cell line. Double-gene knockout experiments allowed us to propose a biosynthetic pathway for streptocarbazole productions. Furthermore, by overexpression of the involving key enzymes, the production of streptocarbazoles 1 and 3 were improved by approximately 1.5-2.5 fold. IMPORTANCE: Indolocarbazoles (ICZs) are a group of antitumor agents, with several analogs used in clinical trials. Therefore, the identification of novel ICZ compounds is important for drug discovery. Streptocarbazoles harbor unique N-glycosidic linkages (N13-C1' and N12-C3'), distinguishing them from the representative ICZ compound staurosporine; however, their biosynthesis remains unclear. In this study, two new streptocarbazoles (1 and 3) with cytotoxic activities were obtained by manipulating the staurosporine biosynthetic gene cluster spc followed by heterologous expression. The biosynthetic pathway of streptocarbazoles was proposed, and their productions were improved through the overexpression of the key enzymes involved. This study enriches the structural diversity of ICZ compounds and would facilitate the discovery of new streptocarbazoles via synthetic biological strategies.

Metformin Therapeutic Targets for Aortic Aneurysms: A Mendelian Randomization and Colocalization Study.

Background: Identifying effective pharmacological interventions to prevent the progressive enlargement and rupture of aortic aneurysms (AAs) is critical. Previous studies have suggested links between metformin use and a decreased incidence of AAs. In this study, we employed Mendelian randomization (MR) to investigate causal effects of metformin's targets on AA risk and to explore the underlying mechanisms underlying these effects. Methods: To examine the relationship between metformin use and AA risk, we implemented both two-sample MR and multivariable MR analyses. Utilizing genetic instrumental variables, we retrieved cis-expression quantitative trait loci (cis-eQTL) data for potential targets of metformin from the Expression Quantitative Trait Loci Genetics Consortium (eQTLGen) Consortium and Genotype-Tissue Expression (GTEx) project. Colocalization analysis was employed to ascertain the probability of shared causal genetic variants between single nucleotide polymorphisms (SNPs) associated with eQTLs and AA. Results: Our findings reveal that metformin use reduces AA risk, exhibiting a protective effect with an odds ratio (OR) of 4.88 × 10 - 3 (95% confidence interval [CI]: 7.30 × 10 - 5 -0.33, p = 0.01). Furthermore, the protective effect of type 2 diabetes on AA risk appears to be driven by metformin use ( OR MVMR = 1.34 × 10 - 4 , 95% CI: 3.97 × 10 - 8 -0.45, p = 0.03). Significant Mendelian randomization (MR) results were observed for the expression of two metformin-related genes in the bloodstream: NADH:ubiquinone oxidoreductase subunit A6 (NDUFA6) and cytochrome b5 type B (CYB5B), across two independent datasets ( OR CYB5B = 1.35, 95% CI: 1.20-1.51, p = 2.41 × 10 - 7 ; OR NDUFA6 = 1.12; 95% CI: 1.07-1.17, p = 1.69 × 10 - 6 ). The MR analysis of tissue-specific expression also demonstrated a positive correlation between increased NDUFA6 expression and heightened AA risk. Lastly, NDUFA6 exhibited evidence of colocalization with AA. Conclusions: Our study suggests that metformin may play a significant role in lowering the risk of AA. This protective effect could potentially be linked to the mitigation of mitochondrial and immune dysfunction. Overall, NDUFA6 has emerged as a potential mechanism through which metformin intervention may confer AA protection.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning.

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

Clinicopathologic assessment of secondary syphilis avoids misdiagnosis: results of comprehensive evaluation of 114 cases.

Introduction: Secondary syphilis is well-known for its protean cutaneous manifestations and therefore very easy to be misdiagnosed. Aim: The current study was to observe the frequency of histopathological features characterizing secondary syphilis, and summarize the diseases most likely to be misdiagnosed. Material and methods: In this study a total of 129 pathological specimens from 114 patients with biopsy-proven secondary syphilis were retrospectively analysed and categorized according to clinicopathologic characteristics. The frequency of histopathological features characterizing secondary syphilis were analysed by comparison with clinical features. Results: We found that in a single sample there is at least one feature or at most 13 features exist concurrently, and most demonstrated between 5 and 9 diagnostic features. Plasma cells (97.6% overall vs. 94.0% ≤ 6 features), endothelial swelling (86.8% vs. 74.0%), epidermis hyperplasia (73.6% vs. 62.0%) especially irregular acanthosis, lymphocytes infiltration (71.3% vs. 52.0%) and interstitial patterns (69% vs. 72.0%) were the most common findings in all cases as well as in cases with ≤ 6 features. Granulomatous inflammation is an uncommon histopathologic pattern in secondary syphilis (12.4%). The rash morphologies of our biopsies mainly manifesting as macules and maculopapules were more likely to have 6 or fewer features, which were not only easily misdiagnosed for pityriasis rosea, tinea and erythema multiforme, but also mostly taken from the trunk and genitalia. Atypical morphologies can be combined with plasma cell infiltration and T. pallidum immunohistochemical stain to confirm the diagnosis. Conclusions: In this study plasma cells from superficial and deep perivascular distribution to nodular infiltration were a crucial clue for diagnosis of secondary syphilis.

Radiomics Signature of Epicardial Adipose Tissue for Predicting Postoperative Atrial Fibrillation after Off-Pump Coronary Artery Bypass Surgery.

Background: Postoperative new atrial fibrillation (POAF) is a commonly observed complication after off-pump coronary artery bypass surgery (OPCABG), and models based on radiomics features of epicardial adipose tissue (EAT) on non-enhanced computer tomography (CT) to predict the occurrence of POAF after OPCABG remains unclear. This study aims to establish and validate models based on radiomics signature to predict POAF after OPCABG. Methods: Clinical characteristics, radiomics signature and features of non-enhanced CT images of 96 patients who underwent OPCABG were collected. The participants were divided into a training and a validation cohort randomly, with a ratio of 7:3. Clinical characteristics and EAT CT features with statistical significance in the multivariate logistic regression analysis were utilized to build the clinical model. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify significant radiomics features to establish the radiomics model. The combined model was constructed by integrating the clinical and radiomics models. Results: The area under the curve (AUC) of the clinical model in the training and validation cohorts were 0.761 (95% CI: 0.634-0.888) and 0.797 (95% CI: 0.587-1.000), respectively. The radiomics model showed better discrimination ability than the clinical model, with AUC of 0.884 (95% CI: 0.806-0.961) and 0.891 (95% CI: 0.772-1.000) respectively for the training and the validation cohort. The combined model performed best and exhibited the best predictive ability among the three models, with AUC of 0.922 (95% CI: 0.853-0.990) in the training cohort and 0.913 (95% CI: 0.798-1.000) in the validation cohort. The calibration curve demonstrated strong concordance between the predicted and actual observations in both cohorts. Furthermore, the Hosmer-Lemeshow test yielded p value of 0.241 and 0.277 for the training and validation cohorts, respectively, indicating satisfactory calibration. Conclusions: The superior performance of the combined model suggests that integrating of clinical characteristics, radiomics signature and features on non-enhanced CT images of EAT may enhance the accuracy of predicting POAF after OPCABG.

Demographic and clinical feature disparity between progress and non-progress patients with vitiligo after COVID-19 vaccination: A cross-sectional study.

There have been several case reports regarding newly developed vitiligo following the coronavirus disease 19 (COVID-19) vaccination. However, the relationship between COVID-19 vaccine and vitiligo progression remains unclear. To explore the relationship between COVID-19 vaccine and vitiligo progression and its potential influencing factors, A cross-sectional study was conducted on 90 patients with vitiligo who received inactivated COVID-19 vaccination. Detailed information covering demographic characteristics (age and sex), vitiligo clinical features (disease subtypes, duration, stage and comorbidities) and disease activity was collected through an electronic questionnaire. Ninety patients with vitiligo included 44.4% males, with an average age of 38.1 years (standard deviation, SD = 15.0). Patients were divided into progress group (29, 32.2%) and normal group (61, 67.8%) based on whether they experienced vitiligo progression after inactivated COVID-19 vaccination. 41.3% of patients in the progress group experienced vitiligo progression within 1 week after vaccination, and disease progression mainly occurred after the first dose inoculation (20, 69.0%). Logistic regression revealed that patients aged <45 years (odds ratio (OR) was 0.87, 95% confidence interval (CI): 0.34-2.22) and male patients (OR = 0.84, 95% CI: 0.34-2.05) had lower risk for vitiligo progression, while patients with segmental vitiligo (SV) subtype (OR = 1.68, 95% CI: 0.53-5.33), with <5 years disease duration (OR = 1.32, 95% CI: 0.51-3.47) had higher risk for vitiligo progression after COVID-19 vaccination, but without statistical significance. Over 30% patients experienced vitiligo progression after inactivated COVID-19 vaccination, and female patients, elder age, shorter disease duration and SV subtype are potential risk factors for vitiligo progression.

Enhanced Antitumor Efficacy of Novel Biomimetic Platelet Membrane-Coated Tetrandrine Nanoparticles in Nonsmall Cell Lung Cancer.

Nonsmall cell lung cancer (NSCLC) remains one of the leading causes of cancer-related death worldwide, posing a serious threat to global health. Tetrandrine (Tet) is a small molecule in traditional Chinese medicine with proven primary efficacy against multiple cancers. Although previous studies have demonstrated the potential anticancer effects of Tet on NSCLC, its poor water solubility has limited its further clinical application. Herein, a novel nanoparticle-based drug delivery system, platelet membrane (PLTM)-coated Tet-loaded polycaprolactone-b-poly(ethylene glycol)-b-polycaprolactone nanoparticles (PTeNPs), is proposed to increase the potency of Tet against NSCLC. First, tetrandrine nanoparticles (TeNPs) are created using an emulsion solvent evaporation method, and biomimetic nanoparticles (PTeNPs) are prepared by coating the nanoparticles with PLTMs. When coated with PLTMs, PTeNPs are considerably less phagocytized by macrophages than Tet and TeNPs. In addition, compared with Tet and TeNPs, PTeNPs can significantly inhibit the growth and invasion of NSCLC both in vitro and in vivo. With reliable biosafety, this drug delivery system provides a new method of sustained release and efficient anticancer effects against NSCLC, facilitating the incorporation of Tet in modern nanotechnology.

Tree-Invent: A Novel Multipurpose Molecular Generative Model Constrained with a Topological Tree.

De novo molecular design plays an important role in drug discovery. Here, a novel generative model, Tree-Invent, was proposed to integrate topological constraints in the generation of a molecular graph. In this model, a molecular graph is represented as a topological tree in which a ring system, a nonring atom, and a chemical bond are regarded as the ring node, single node, and edge, respectively. The molecule generation is driven by three independent submodels for carrying out operations of node addition, ring generation, and node connection. One unique feature of the generative model is that the topological tree structure can be specified as a constraint for structure generation, which provides more precise control of structure generation. Combined with reinforcement learning, the Tree-Invent model could efficiently explore targeted chemical space. Moreover, the Tree-Invent model is flexible enough to be used in versatile molecule design settings such as scaffold decoration, scaffold hopping, and linker generation.

BMP9 functions as a negative regulator in the myogenic differentiation of primary mouse myoblasts.

BMP9, a member of the TGF-ß superfamily, reveals the great translational promise for it has been shown to have the strong effect of osteogenic activity in vitro and in vivo. However, the implantation of certain BMPs (bone morphogenetic proteins) into muscular tissues induces ectopic bone formation. BMPs induce osteoblastic differentiation in skeletal muscle, suggesting that myogenic stem cells, such as myoblasts, are the potential progenitors of osteoblasts during heterotopic bone differentiation. Here, we investigate the role of BMP9 during primary mouse myoblasts differentiation. We found BMP9 enhanced cell proliferation and reduced myogenic differentiation of primary mouse myoblasts. In addition, adenovirus-mediated overexpression of BMP9 delayed muscle regeneration after BaCl2-induced injury. ALK1 knockdown reversed the inhibition of myoblast differentiation induced by BMP9. Our data indicate that BMP9 inhibits myogenic differentiation in primary mouse myoblasts and delays skeletal muscle regeneration after injury.

PbS QD-Coated Si Micro-Hole Array/Graphene vdW Schottky Near-Infrared Photodiode for PPG Heart Rate Measurement.

Near-infrared (NIR) photodetectors (PDs) have attracted much attention for use in noninvasive medical diagnosis and treatments. In particular, self-filtered NIR PDs are in high demand for a wide range of biomedical applications due to their ability for wavelength discrimination. In this work, we designed and then fabricated a Si micro-hole array/Graphene (Si MHA/Gr) van der Waals (vdW) Schottky NIR photodiode using a PbS quantum dot (QD) coating. The device exhibited a unique self-filtered NIR response with a responsivity of 0.7 A/W at -1 V and a response speed of 61 µs, which is higher than that seen without PbS QD coating and even in most previous Si/Gr Schottky photodiodes. The light trapping of the Si MHA and the PbS QD coating could be attributed to the high responsivity of the vdW photodiode. Furthermore, the presented NIR photodiode could also be integrated in photoplethysmography (PPG) for real-time heart rate (HR) monitoring. The extracted HR was in good accord with the values measured with the patient monitor-determined by analyzing the Fourier transform of the stable and reliable fingertip PPG waveform-suggesting its potential for practical applications.

3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates.

Developing molecular generative models for directly generating 3D conformation has recently become a hot research area. Here, an autoencoder based generative model was proposed for molecular conformation generation. A unique feature of our method is that the graph information embedded relative coordinate (GIE-RC), satisfying translation and rotation invariance, was proposed as a novel way for encoding molecular three-dimensional structure. Compared with commonly used Cartesian coordinate and internal coordinate, GIE-RC is less sensitive on errors when decoding latent variables to 3D coordinates. By using this method, a complex 3D generation task can be turned into a graph node feature generation problem. Examples were shown that the GIE-RC based autoencoder model can be used for both ligand and peptide conformation generation. Additionally, this model was used as an efficient conformation sampling method to augment conformation data needed in the construction of neural network-based force field.

The Role of Taraxacum mongolicum in a Puccinellia tenuiflora Community under Saline-Alkali Stress.

Coexisting salt and alkaline stresses seriously threaten plant survival. Most studies have focused on halophytes; however, knowledge on how plants defend against saline-alkali stress is limited. This study investigated the role of Taraxacum mongolicum in a Puccinellia tenuiflora community under environmental saline-alkali stress to analyse the response of elements and metabolites in T. mongolicum, using P. tenuiflora as a control. The results show that the macroelements Ca and Mg are significantly accumulated in the aboveground parts (particularly in the stem) of T. mongolicum. Microelements B and Mo are also accumulated in T. mongolicum. Microelement B can adjust the transformation of sugars, and Mo contributes to the improvement in nitrogen metabolism. Furthermore, the metabolomic results demonstrate that T. mongolicum leads to decreased sugar accumulation and increased amounts of amino acids and organic acids to help plants resist saline-alkali stress. The resource allocation of carbon (sugar) and nitrogen (amino acids) results in the accumulation of only a few phenolic metabolites (i.e., petunidin, chlorogenic acid, and quercetin-3-O-rhamnoside) in T. mongolicum. These phenolic metabolites help to scavenge excess reactive oxygen species. Our study primarily helps in understanding the contribution of T. mongolicum in P. tenuiflora communities on coping with saline-alkali stress.

A source-sink model explains the difference in the metabolic mechanism of mechanical damage to young and senescing leaves in Catharanthus roseus.

BACKGROUND: Mechanical damage is an unavoidable threat to the growth and survival of plants. Although a wound to senescing (lower) leaves improves plant vitality, a wound to younger (upper) leaves often causes damage to or death of the whole plant. Source-sink models are often used to explain how plants respond to biotic or abiotic stresses. In this study, a source-sink model was used to explain the difference in the metabolic mechanism of mechanical damage to young and senescing leaves of Catharanthus roseus. RESULTS: In our study, GC-MS and LC-QTOF-MS metabolomics techniques were used to explore the differences in source-sink allocation and metabolic regulation in different organs of Catharanthus roseus after mechanical damage to the upper/lower leaves (WUL/WLL). Compared with that of the control group, the energy supplies of the WUL and WLL groups were increased and delivered to the secondary metabolic pathway through the TCA cycle. The two treatment groups adopted different secondary metabolic response strategies. The WLL group increased the input to the defense response after damage by increasing the accumulation of phenolics. A source-sink model was applied to the defensive responses to local (damaged leaves) and systemic (whole plant) damage. In the WUL group, the number of sinks increased due to damage to young leaves, and the tolerance response was emphasized. CONCLUSION: The accumulation of primary and secondary metabolites was significantly different between the two mechanical damage treatments. Catharanthus roseus uses different trade-offs between tolerance (repair) and defense to respond to mechanical damage. Repairing damage and chemical defenses are thought to be more energetically expensive than growth development, confirming the trade-offs and allocation of resources seen in this source-sink model.

Improved production of 2'-fucosyllactose in engineered Saccharomyces cerevisiae expressing a putative α-1, 2-fucosyltransferase from Bacillus cereus.

BACKGROUND: 2'-fucosyllactose (2'-FL) is one of the most abundant oligosaccharides in human milk. It constitutes an authorized functional additive to improve infant nutrition and health in manufactured infant formulations. As a result, a cost-effective method for mass production of 2'-FL is highly desirable. RESULTS: A microbial cell factory for 2'-FL production was constructed in Saccharomyces cerevisiae by expressing a putative α-1, 2-fucosyltransferase from Bacillus cereus (FutBc) and enhancing the de novo GDP-L-fucose biosynthesis. When enabled lactose uptake, this system produced 2.54 g/L of 2'-FL with a batch flask cultivation using galactose as inducer and carbon source, representing a 1.8-fold increase compared with the commonly used α-1, 2-fucosyltransferase from Helicobacter pylori (FutC). The production of 2'-FL was further increased to 3.45 g/L by fortifying GDP-mannose synthesis. Further deleting gal80 enabled the engineered strain to produce 26.63 g/L of 2'-FL with a yield of 0.85 mol/mol from lactose with sucrose as a carbon source in a fed-batch fermentation. CONCLUSION: FutBc combined with the other reported engineering strategies holds great potential for developing commercial scale processes for economic 2'-FL production using a food-grade microbial cell factory.

De Novo Molecule Design Through the Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites.

De novo molecule design through the molecular generative model has gained increasing attention in recent years. Here, a novel generative model was proposed by integrating the three-dimensional (3D) structural information of the protein binding pocket into the conditional RNN (cRNN) model to control the generation of drug-like molecules. In this model, the composition of the protein binding pocket is effectively characterized through a coarse-grain strategy and the 3D information of the pocket can be represented by the sorted eigenvalues of the Coulomb matrix (EGCM) of the coarse-grained atoms composing the binding pocket. In current work, we used our EGCM method and a previously reported binding pocket descriptor, DeeplyTough, to train cRNN models and evaluated their performance. It has been shown that the model trained with the constraint of protein environment information has a clear tendency on generating compounds with higher similarity to the original X-ray-bound ligand than the normal RNN model and also better docking scores. Our results demonstrate the potential application of the controlled generative model for the targeted molecule generation and guided exploration on the drug-like chemical space.

Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method.

In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical mechanic methods. However, the development of NNPES is highly nontrivial. In particular, it has been troubling to construct a dataset that is as small as possible yet can cover the target chemical space. In this work, an ESOINN-DP method is developed, which has the enhanced self-organizing incremental neural network (ESOINN) and a newly proposed error indicator at its core. With ESOINN-DP, one can construct the NNPES with little human intervention, and this method ensures that the constructed reference dataset covers the target chemical space with minimum redundancy. The performance of the ESOINN-DP method has been well validated by developing neural network potential energy surfaces for water clusters, tripeptides, and by de-redundancy of a sub-dataset of the ANI-1 database. We believe that the ESOINN-DP method provides a novel idea for the construction of NNPES and, especially, the reference datasets, and it can be used for molecular dynamics (MD) simulations of various gas-phase and condensed-phase chemical systems.

Individual and combined hepatocytotoxicity of DDT and cadmium in vitro.

The organochlorine insecticide dichlorodiphenyltrichloroethane (DDT) and heavy metal cadmium (Cd) are widespread environmental pollutants. They are persistent in the environment and can accumulate in organisms. Although the individual toxicity of DDT and Cd has been well documented, their combined toxicity is still not clear. Since liver is their common target, in this study, the individual and combined toxicity of DDT and Cd in human liver carcinoma HepG2 and human normal liver THLE-3 cell lines were investigated. The results showed that DDT and Cd inhibited the viability of HepG2 and THLE-3 cells dose-dependently and altered lysosomal morphology and function. Intracellular reactive oxygen species and lipid peroxidation levels were induced by DDT and Cd treatment. The combined cytotoxicity of DDT and Cd was greater than their individual cytotoxicity, and the interaction between Cd and DDT was additive on the inhibition of cell viability and lysosomal function of HepG2 cells. The interaction was antagonistic on the inhibition of cell viability of THLE-3 cells. These results may facilitate the evaluation of the cumulative risk of pesticides and heavy metal residues in the environment.

Fragment-Based Ab Initio Molecular Dynamics Simulation for Combustion.

We develop a fragment-based ab initio molecular dynamics (FB-AIMD) method for efficient dynamics simulation of the combustion process. In this method, the intermolecular interactions are treated by a fragment-based many-body expansion in which three- or higher body interactions are neglected, while two-body interactions are computed if the distance between the two fragments is smaller than a cutoff value. The accuracy of the method was verified by comparing FB-AIMD calculated energies and atomic forces of several different systems with those obtained by standard full system quantum calculations. The computational cost of the FB-AIMD method scales linearly with the size of the system, and the calculation is easily parallelizable. The method is applied to methane combustion as a benchmark. Detailed reaction network of methane reaction is analyzed, and important reaction species are tracked in real time. The current result of methane simulation is in excellent agreement with known experimental findings and with prior theoretical studies.