Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models.

Fine-tuning pretrained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing parameter-efficient fine-tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is nontrivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark data sets across distinct downstream tasks. Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated convergence speed by a maximum of 1034% and an average of 362%, the training efficiency is also improved by approximately 2 times. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.

The evolution of anionic nanoclusters at the electrode interface in water-in-salt electrolytes.

Water-in-salt electrolytes (WiSEs) have attracted extensive attention as promising alternatives to organic electrolytes. The limited electrochemical stability windows (ESWs) of aqueous electrolytes are significantly widened by WiSEs. However, the actual ESWs are lower than predicted as the interphase with WiSEs is not as stable as the solid electrolyte interphase (SEI) in conventional lithium-ion batteries. Therefore, identifying the interface state in WiSEs is vital to understanding their electrochemical behavior. Here, the structure of the lithium bis(trifluoromethane sulfonyl)imide (LiTFSI) electrolyte near the interface of the carbon electrode (Ketjen black) was evaluated by experimental methods (neutron diffraction, Raman, and nuclear magnetic resonance spectroscopy) and molecular dynamics (MD) simulations. The results reveal that the introduction of carbon electrodes increases the size of the anionic nanoclusters and enhances the microphase separation at the interface. The MD simulations show that cation-π interactions are responsible for the evolution of anionic nanoclusters at the electrode interface. Moreover, lower charge transfer resistance is achieved at carbon-based electrodes due to the specific interface state. Our findings provide a strategy for introducing cation-π interactions between electrodes and electrolytes to improve the electrochemical performance.

Dynamic assessment of coronary artery during different cardiac cycle in patients with coronary artery disease using coronary CT angiography.

INTRODUCTION: To evaluate the effect of the cardiac cycle for the coronary artery opening and coronary stenosis at the plaque to determine the phase of measuring maximum diameters required for coronary artery disease (CAD). METHODS: This retrospective study assessed data for 208 consecutive patients who underwent coronary computed tomography angiography (CTA). The cross-sectional area and diameters of the opening of the left main coronary artery (LM), left anterior descending branch (LAD), left circumflex branch (LCX) and right coronary artery (RCA), the stenosis rate of involved vessels were measured in 10 cardiac cycles. And all their dynamic changes were estimated by the linear mixed model. The relationship between stenosis rate and opening orifice were analyzed by monofactorial variance. RESULTS: The opening parameters and stenosis rate of the four main coronary arteries varied within the cardiac cycle (p < .05). The maximum opening area occurred at the 45%-55% phase; The range of stenosis rate varied approximately 11%-14% and the maximum stenosis rate was at the 65% phase. The degree of vascular stenosis for LM, LAD and LCX were not associated with their corresponding opening diameters, but were positively intercorrelation with each other. CONCLUSION: For patients with CAD, the maximum coronary artery stenosis rate were at 65% phase and the maximum value of coronary artery opening were at 45%-55% phase, which were chosen for the appropriate measurement and evaluation by CTA.

A random batch Ewald method for charged particles in the isothermal-isobaric ensemble.

We develop an accurate, highly efficient, and scalable random batch Ewald (RBE) method to conduct molecular dynamics simulations in the isothermal-isobaric ensemble (the NPT ensemble) for charged particles in a periodic box. After discretizing the Langevin equations of motion derived using suitable Lagrangians, the RBE method builds the mini-batch strategy into the Fourier space in the Ewald summation for the pressure and forces such that the computational cost is reduced to O(N) per time step. We implement the method in the Large-scale Atomic/Molecular Massively Parallel Simulator package and report accurate simulation results for both dynamical quantities and statistics for equilibrium for typical systems including all-atom bulk water and a semi-isotropic membrane system. Numerical simulations on massive supercomputing cluster are also performed to show promising central processing unit efficiency of the RBE.

Superscalability of the random batch Ewald method.

Coulomb interaction, following an inverse-square force-law, quantifies the amount of force between two stationary and electrically charged particles. The long-range nature of Coulomb interactions poses a major challenge to molecular dynamics simulations, which are major tools for problems at the nano-/micro-scale. Various algorithms are developed to calculate the pairwise Coulomb interactions to a linear scale, but poor scalability limits the size of simulated systems. Here, we use an efficient molecular dynamics algorithm with the random batch Ewald method on all-atom systems where the complete Fourier components in the Coulomb interaction are replaced by randomly selected mini-batches. By simulating the N-body systems up to 108 particles using 10 000 central processing unit cores, we show that this algorithm furnishes O(N) complexity, almost perfect scalability, and an order of magnitude faster computational speed when compared to the existing state-of-the-art algorithms. Further examinations of our algorithm on distinct systems, including pure water, a micro-phase separated electrolyte, and a protein solution, demonstrate that the spatiotemporal information on all time and length scales investigated and thermodynamic quantities derived from our algorithm are in perfect agreement with those obtained from the existing algorithms. Therefore, our algorithm provides a promising solution on scalability of computing the Coulomb interaction. It is particularly useful and cost-effective to simulate ultra-large systems, which is either impossible or very costly to conduct using existing algorithms, and thus will be beneficial to a broad range of problems at nano-/micro-scales.

How BamA recruits OMP substrates via poly-POTRAs domain.

Almost all the outer membrane proteins (OMPs) fold into an invariant ß-barrel fold via the polypeptide-transport-associated (POTRA) motif and ß-barrel assembly machinery (BAM). However, whether and how poly-POTRAs interact with OMPs remain largely unknown. Here, we have characterized the structures of Haemophilus influenzae poly-POTRAs via X-ray crystallography, small angle X-ray scattering, and molecular dynamics simulation. Unexpectedly, crystal packing reveals a putative OMP travel pathway spiraled by the conserved α2-ß2 edges in poly-POTRAs. Supportively, the structure-based mutations targeting the OMP binding sites significantly disrupt OMP biogenesis, resulting in severe cell growth defects. Another notable feature in H. influenzae POTRA structures is flexibility. As characterized by ELISA assays, poly-POTRAs could recruit OMP substrates in a step-wise manner. More importantly, the restriction of POTRA-POTRA linkage and flexibility significantly impairs the BamA function and causes cell growth defect. Altogether, these results suggest that the ß-strand augmentations and intrinsic flexibility are important factors for BamA-OMP recruitment.-Ma, X., Wang, Q., Li, Y., Tan, P., Wu, H., Wang, P., Dong, X., Hong, L., Meng, G. How BamA recruits OMP substrates via poly-POTRAs domain.

Decoupling between the translation and rotation of water in the proximity of a protein molecule.

The interaction between water and biomacromolecules is of fundamental interest in biophysics, biochemistry and physical chemistry. By combining neutron scattering and molecular dynamics simulations on a perdeuterated protein at a series of hydration levels, we demonstrated that the translational motion of water is slowed down more significantly than its rotation, when water molecules approach the protein molecule. Further analysis of the simulation trajectories reveals that the observed decoupling results from the fact that the translational motion of water is more correlated over space and more retarded by the charged/polar residues and spatial confinement on the protein surface, than the rotation. Moreover, around the stable protein residues (with smaller atomic fluctuations), water exhibits more decoupled dynamics, indicating a connection between the observed translation-rotation decoupling in hydration water and the local stability of the protein molecule.

LncRNA-ANRIL inhibits cell senescence of vascular smooth muscle cells by regulating miR-181a/Sirt1.

BACKGROUND: Cardiovascular disease is one of the major threats to human life and health, and vascular aging is an important cause of its occurrence. Antisense non-coding RNA in the INK4 locus (ANRIL) is a kind of long non-coding RNA (lncRNA) that plays important roles in cell senescence. However, the role and mechanism of ANRIL in senescence of vascular smooth muscle cells (VSMCs) are unclear. METHODS: Cell viability and cell cycle were evaluated using an MTT assay and flow cytometry analysis, respectively. Senescence-associated (SA)-ß-galactosidase (gal) staining was used to determine cell senescence. Dual luciferase reporter assays were conducted to confirm the binding of ANRIL and miR-181a, as well as miR-181a and Sirt1. The expression of ANRIL, miR-181a, and Sirt1 was determined using qRT-PCR and protein levels of SA-ß-gal and p53-p21 pathway-related proteins were evaluated by Western blotting. RESULTS: ANRIL and Sirt1 were down-regulated, whereas miR-181a was up-regulated in aging VSMCs. In young and aging VSMCs, over-expression of ANRIL could down-regulate miR-181a and up-regulate Sirt1. MTT and SA-ß-gal staining assays showed that over-expression of ANRIL and inhibition of miR-181a promoted cell viability and inhibited VSMC senescence. The dual-luciferase reporter assay determined that miR-181a directly targets ANRIL and the 3'-UTR of Sirt1. Furthermore, over-expression of ANRIL inhibited cell cycle arrest and the p53-p21 pathway. CONCLUSION: ANRIL promotes cell viability and inhibits senescence in VSMCs, possibly by regulating miR-181a/Sirt1, and alleviating cell cycle arrest by inhibiting the p53-p21 pathway. This study provides novel insights for the role of ANRIL in the development of cell senescence.

Gradual Crossover from Subdiffusion to Normal Diffusion: A Many-Body Effect in Protein Surface Water.

Dynamics of hydration water is essential for the function of biomacromolecules. Previous studies have demonstrated that water molecules exhibit subdiffusion on the surface of biomacromolecules; yet the microscopic mechanism remains vague. Here, by performing neutron scattering, molecular dynamics simulations, and analytic modeling on hydrated perdeuterated protein powders, we found water molecules jump randomly between trapping sites on protein surfaces, whose waiting times obey a broad distribution, resulting in subdiffusion. Moreover, the subdiffusive exponent gradually increases with observation time towards normal diffusion due to a many-body volume-exclusion effect.

Lymphoma and inflammation in the orbit: Diagnostic performance with diffusion-weighted imaging and dynamic contrast-enhanced MRI.

PURPOSE: To investigate the diagnostic performance of diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), and the combination of both in the differential diagnosis of lymphoma and inflammation in the orbit. MATERIALS AND METHODS: This retrospective study was approved by the Institutional Review Board and the informed consent requirement was waived. A total of 53 patients underwent preoperative 3T MRI. Parameters of DWI and DCE MRI were evaluated in these 30 patients with orbital lymphoma and 23 patients with orbital inflammation. The diagnostic performance was evaluated using receiver operating characteristic curve analysis. RESULTS: Apparent diffusion coefficient (ADC) values and parameters derived from DCE MRI of orbital lymphoma and orbital inflammation differed significantly (ADC, Tmax , contrast index [CI], enhancement ratio [ER], and washout ratio [WR]: P < 0.001, P = 0.008, P < 0.001, P < 0.001, and P = 0.005 for reviewer 1, respectively; P < 0.001, P = 0.004, P < 0.001, P < 0.001, and P < 0.001 for reviewer 2, respectively). Sensitivity, specificity, and accuracy values of DWI were 76.67%, 100%, and 86.79% for reviewer 1; 70%, 95.65%, and 81.13% for reviewer 2, respectively. The combination of both were 90%, 86.96%, and 88.68% for reviewer 1; 93.33%, 78.26%, and 86.79% for reviewer 2, respectively. The combination of both was significantly superior to DWI for differentiation of orbital lymphoma from orbital inflammation (P = 0.016 for reviewer 1; P = 0.001 for reviewer 2). CONCLUSION: The combination of DWI and DCE MRI can improve diagnostic performance in differentiating lymphoma from inflammation in the orbit compared with DWI alone. LEVEL OF EVIDENCE: 3 J. MAGN. RESON. IMAGING 2017;45:1438-1445.

The PI3K/Akt/mTOR pathway regulates the replicative senescence of human VSMCs.

Replicative senescence of vascular smooth muscle cells (VSMCs) contributes to aging as well as age-related cardiovascular diseases. Rapamycin can delay the onset of aging-related diseases via inhibition of the mammalian target of rapamycin (mTOR), but its role in vascular aging remains elusive. This study investigated the involvement of mTOR signaling in replicative senescence of VSMCs. Replicative senescence was induced by the extended passages of human VSMCs. Aging-related cell morphology was observed. The aging-related proteins and enzyme activity, and oxidative stress were measured. Significant increase in SA-ß-gal activity and protein expression, p53 and p16 protein expression, proliferation index (PI), malondialdehyde (MDA) concentration, superoxide dismutase (SOD) and glutathione peroxidase (GPX) activity, and significant decrease in telomerase activity was observed in aging VSMCs compared to young cells. Significant activation of PI3K/Akt/mTOR signaling was observed in aging cells but not young cells. Pretreatment of VSMCs with PI3K inhibitor blocked while PI3K activator increased the changes of the above replicative senescence-related parameters in VSMCs. Rapamycin and silencing of mTOR expression inhibited replicative senescence in VSMCs through decreasing the level of p-mTOR Ser2448, p-mTOR Thr2446, and S6K1 phosphorylation. This study for the first time demonstrated that the PI3K/Akt/mTOR/S6K1 signal pathway plays an important role in regulating replicative senescence of human VSMCs.

Adiponectin attenuates the osteoblastic differentiation of vascular smooth muscle cells through the AMPK/mTOR pathway.

Vascular calcification is common in patients with peripheral artery diseases and coronary artery diseases. The osteoblastic differentiation of vascular smooth muscle cells (VSMCs) contributes significantly to vascular calcification. Adiponectin has been demonstrated to exert a protective effect in osteoblastic differentiation of VSMCs through regulating mTOR activity. However, the upstream and downstream signaling molecules of adiponectin-regulated mTOR signaling have not been identified in VSMCs with osteoblastic differentiation. In this study, the VSMC differentiation model was established by beta-glycerophosphate (ß-GP) induction. The mineralization was identified by Alizarin Red S staining. Protein expression and phosphorylation were detected by Western blot or immunofluorescence. Adiponectin attenuated osteoblastic differentiation and mineralization of ß-GP-treated VSMCs. Adiponectin inhibited osteoblastic differentiation of VSMCs through increasing the level of p-AMPKα. Pretreatment of VSMCs with AMPK inhibitor blocked while AMPK activator enhanced the effect of adiponectin on osteoblastic differentiation of VSMCs. Adiponectin upregulated TSC2 expression and downregulated mTOR and S6K1 phosphorylation in ß-GP-treated VSMCs. Adiponectin treatment significantly attenuates the osteoblastic differentiation and calcification of VSMCs through modulation of AMPK-TSC2-mTOR-S6K1 signal pathway.

Exenatide can inhibit calcification of human VSMCs through the NF-kappaB/RANKL signaling pathway.

BACKGROUND: Arterial calcification is an important pathological change of diabetic vascular complication. Osteoblastic differentiation of vascular smooth muscle cells (VSMCs) plays an important cytopathologic role in arterial calcification. The glucagon-like peptide-1 receptor agonists (GLP-1RA), a novel type of antidiabetic drugs, exert cardioprotective effects through the GLP-1 receptor (GLP-1R). However, the question of whether or not GLP-1RA regulates osteoblastic differentiation and calcification of VSMCs has not been answered, and the associated molecular mechanisms have not been examined. METHODS: Calcifying VSMCs (CVSMCs) were isolated from cultured human arterial smooth muscle cells through limiting dilution and cloning. The extent of matrix mineralization was measured by Alizarin Red S staining. Protein expression and phosphorylation were detected by Western blot. Gene expression of receptor activator of nuclear factor-κB ligand (RANKL) was silenced by small interference RNA (siRNA). RESULTS: Exenatide, an agonist of GLP-1 receptor, attenuated ß-glycerol phosphate (ß-GP) induced osteoblastic differentiation and calcification of human CVSMCs in a dose- and time-dependent manner. RANKL siRNA also inhibited osteoblastic differentiation and calcification. Exenatide decreased the expression of RANKL in a dose-dependent manner. 1,25 vitD3 (an activator of RANKL) upregulated, whereas BAY11-7082 (an inhibitor of NF-κB) downregulated RANKL, alkaline phosphatase (ALP), osteocalcin (OC), and core binding factor α1 (Runx2) protein levels and reduced mineralization in human CVSMCs. Exenatide decreased p-NF-κB and increased p-AMPKα levels in human CVSMCs 48 h after treatment. Significant decrease in p-NF-κB (p-Ser(276), p-Ser(536)) level was observed in cells treated with exenatide or exenatide + BAY11-7082. CONCLUSION: GLP-1RA exenatide can inhibit human VSMCs calcification through NF-κB/RANKL signaling.

Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning.

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.

PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.

Protein language models (PLMs) play a dominant role in protein representation learning. Most existing PLMs regard proteins as sequences of 20 natural amino acids. The problem with this representation method is that it simply divides the protein sequence into sequences of individual amino acids, ignoring the fact that certain residues often occur together. Therefore, it is inappropriate to view amino acids as isolated tokens. Instead, the PLMs should recognize the frequently occurring combinations of amino acids as a single token. In this study, we use the byte-pair-encoding algorithm and unigram to construct advanced residue vocabularies for protein sequence tokenization, and we have shown that PLMs pre-trained using these advanced vocabularies exhibit superior performance on downstream tasks when compared to those trained with simple vocabularies. Furthermore, we introduce PETA, a comprehensive benchmark for systematically evaluating PLMs. We find that vocabularies comprising 50 and 200 elements achieve optimal performance. Our code, model weights, and datasets are available at https://github.com/ginnm/ProteinPretraining . SCIENTIFIC CONTRIBUTION: This study introduces advanced protein sequence tokenization analysis, leveraging the byte-pair-encoding algorithm and unigram. By recognizing frequently occurring combinations of amino acids as single tokens, our proposed method enhances the performance of PLMs on downstream tasks. Additionally, we present PETA, a new comprehensive benchmark for the systematic evaluation of PLMs, demonstrating that vocabularies of 50 and 200 elements offer optimal performance.

Effects of rehabilitation management on lifestyle and quality of life of patients with coronary heart disease after percutaneous coronary intervention based on behavior change theory.

BACKGROUND: The aim of this study was to assess the effects of rehabilitation management on the lifestyle and quality of life of patients with coronary heart disease (CHD) after percutaneous coronary intervention (PCI) based on the behavior change theory. METHODS: A total of 222 CHD patients admitted from January 2019 to April 2021 were randomly divided into research and control groups (N.=111). Control group was administered with routine postoperative nursing management, while research group received behavior changing nursing intervention. The quality-of-life scores, blood pressure and blood lipid levels before and after surgery, as well as length of hospitalization, treatment duration, symptom relief time, incidence rate of complications and nursing satisfaction were compared. RESULTS: After surgery, the length of hospitalization, treatment duration and symptom relief time were shorter, and the incidence rate of complications, levels of total cholesterol (TC), triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP) and diastolic blood pressure (DBP) were lower in research group than those in control group, while research group was better in physical function, social function, physiological functioning, physical pain, mental health, emotional functioning, vitality and overall health scores, satisfaction and high-density lipoprotein cholesterol (HDL-C) level than control group (P<0.05). CONCLUSIONS: The application of behavior change theory in the cardiac rehabilitation management of patients with CHD after PCI can improve the lifestyle and quality of life.

Discontinuous low temperature stress and plant growth regulators during the germination period promote roots growth in alfalfa (Medicago sativa L.).

In high-cold regions, alfalfa is susceptible to cold damage during the seed germination. The effects of discontinuous low temperature stress and plant growth regulators (PGRs) on alfalfa were studied in response to the high day/night temperature differentials in the area. The experiments included seed germination, seedling cold tolerance and plant recovery. Variable temperatures (VT) of 0 °C/15 °C, 5 °C/20 °C and 10 °C/25 °C were set and seeds were soaked with alginate oligosaccharides (AOS), brassinolide (BR) and diethyl aminoethyl hexanoate (DA-6) during the germination period. Parameters such as seed germination and mean germination time (MGT), phenylalanine ammonia-lyase (PAL) activity and oligomeric proanthocyanidins (OPC) content of early seedlings, dry matter accumulation and root crown of the restored plants were analysed. The results showed that low variable-temperature (LVT) stress prolonged the MGT but had little inhibitory effect on germination percentage. Early seedlings adapted to LVT stress by regulating their own water and OPC content, PAL activity and other parameters. LVT induced early alfalfa seedlings to increase their underground biomass by shortening root length and increasing root diameter, and those that had accumulated more underground biomass had faster growth rates and higher total biomass when the ambient temperature rose. AOS also promoted an increase in root crown diameter and root dry weight. This research proved that LVT stress and AOS during the germination process can lead to better growth of alfalfa in high cold regions.

SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering.

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

Effects of irradiation behavior on physicochemical properties of glassy radioactive contaminated soil containing various contents of actinides nuclear waste.

The evaluation of radiation resistance of the treated radioactive contaminated soil is crucial. The irradiation behavior of simulated radioactive soil waste irradiated with 1.5 MeV Xe20+ ions at fluences of 1 × 1012-1 × 1015 ions/cm2 was studied. Before the irradiation experiment, all the samples were sintered by microwave. The results showed that microwave sintering may be used to treat radioactive contaminated soil. In addition, the irradiation experiment results show that when the Nd2O3 content was low (<20 wt.%), the irradiation has little effect on the sample. When the Nd2O3 content was higher, the Vickers hardness of the sample (25 wt.%) decreased by 7 % at a fluence of 1 × 1015 ions/cm2, which may be due to the high Nd2O3 content that destroyed the overall stability of the glass waste form. The low normalized leaching rate of the irradiated sample (LRNd, ∼10-6 g·m-2·d-1) also proved that it had good aqueous durability. Moreover, the radiation resistance of the sample was illustrated by studying the influence mechanism of 1.5 MeV Xe20+ irradiation on radioactive contaminated soil. This work can help to study the environmental pollution problems of radioactive contaminated soil containing various contents of actinide nuclear waste.

Icariin attenuates the calcification of vascular smooth muscle cells through ERα - p38MAPK pathway.

Objective: To investigate the relationship between icariin and the osteoblastic differentiation of vascular smooth muscle cells (VSMCs) and the signal pathway involved. Methods: We applied a universally accepted calcification model of VSMCs induced by ß glycerophosphate. Then the VSMCs calcification was observed by treatment with icariin and/or inhibitors of estrogen receptors (ERs) and p38-mitogen-activated protein kinase (MAPK) signaling. Results: Icariin inhibited osteoblastic differentiation and mineralization of VSMCs due to decreased ALP activity and Runx2 expression. Further study demonstrated that icariin exerted this suppression effect through activating p38-MAPK but not extracellular-regulated kinase, JNK or Akt. An inhibitor of p38-MAPK partially reversed the inhibitory effects of icariin on osteoblastic differentiation. Interestingly, treatment of VSMCs with an ER antagonist ICI182780 and a selective ERα receptor antagonist PPT attenuated icariin-mediated inhibition effect of VSMCs calcification, associated with suppression of p38-MAPK phosphorylation. Conclusions: Icariin inhibited the osteoblastic differentiation of VSMCs, and that the inhibitory effects were mediated by p38-MAPK pathways through ERα.