e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature.

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space.

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords 'A61P, compound, structure' in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97-98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.

The Impact of Valve Surgery on the Safety and Cardiac Function of Patients with Valvular Heart Disease Complicated by Pulmonary Arterial Hypertension.

Objective: This study evaluates the effects of valve surgery on safety and cardiac function in patients with valvular heart disease complicated by pulmonary arterial hypertension (PAH), focusing on postoperative outcomes influenced by age, heart function grade, and PAH severity. Methods: A retrospective analysis was conducted on 307 valve surgery patients from April 2017 to April 2022. The cohort had a mean age of 57.6 years, with 56.9% males, and was stratified by NYHA functional class II-IV. Outcomes assessed included mortality, complication rates, left ventricular ejection fraction (LVEF), and pulmonary artery systolic pressure (PASP), with statistical analysis performed using t-tests and chi-square tests for continuous and categorical data, respectively. Results: Postoperative outcomes varied significantly with age, NYHA class, and PASP grade. Patients aged ≤60 exhibited an average PASP reduction of 44.46% in the male group and 44.44% in the female group and an LVEF improvement of 5.28% in the male group and 5.80% in the female group. However, these patients showed a higher risk of postoperative complications, such as renal failure, arrhythmia, low cardiac output syndrome, respiratory insufficiency, (23.31%), and a higher mortality rate (13.53%)(P < .05). Higher NYHA classes correlated with increased postoperative risks of complications and mortality rates, and elevated PASP grades were associated with larger improvements in PASP and LVEF but also higher postoperative risks. Conclusion: Valve surgery in valvular heart disease with PAH is influenced by patient age, functional status, and PAH severity. Despite advances in surgical techniques, there remains a notable gap in understanding the nuanced interplay between these conditions and the variable outcomes of valve surgery. This study addresses this research gap, offering comprehensive insights into how age, heart function, and PAH severity influence postoperative outcomes. These findings are crucial for clinicians, providing a more informed basis for tailored treatment strategies, and ultimately enhancing patient care in this complex clinical scenario.Healthcare providers should consider the age-specific benefits and risks of valve surgery in patients with valvular heart disease and pulmonary arterial hypertension. Tailored decision-making, particularly for those aged ≤60, higher NYHA classes, or severe PAH, is essential for optimizing individual outcomes.

Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors.

The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand-protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculations. Although the FMO method has demonstrated its reliability in calculating the gas phase potential energy, the binding of proteins and ligands also involves other contributing energy terms, such as solvent effects, the 'deformation energy' of a ligand's bioactive conformations, and entropy. Particularly in cases involving ionized fragments, the calculation of solvation free energy becomes particularly crucial. We conducted an evaluation of some previously reported implicit solvent methods on the same data set to assess their potential for improving the performance of the FMO method. Herein, we develop a new QM-based binding free energy calculation method called FMOScore, which enhances the performance of the FMO method. The FMOScore method incorporates linear fitting of various terms, including gas-phase potential energy, deformation energy, and solvation free energy. Compared to other widely used traditional prediction methods such as FEP+, MM/PBSA, MM/GBSA, and Autodock vina, FMOScore showed good performance in prediction accuracies. By constructing a retrospective case study, it was observed that incorporating calculations for solvation free energy and deformation energy can further enhance the precision of FMO predictions for binding affinity. Furthermore, using FMOScore-guided lead optimization against Src homology-2-containing protein tyrosine phosphatase 2 (SHP-2), we discovered a novel and potent allosteric SHP-2 inhibitor (compound 8).

VRPharmer: bringing virtual reality into pharmacophore-based virtual screening with interactive exploration and realistic visualization.

SUMMARY: Current pharmacophore-based virtual screening (VS) software has limited interactive capabilities and less intuitive screening processes. In this study, a novel tool named VRPharmer is proposed to perform the entire VS workflow in VR environments. VRPharmer enables users to interactively perceive computation processes and immersively observe molecular structures. Besides a typical screening mode (OPT mode), VRPharmer provides a unique interactive screening mode (SCORE mode) for freely exploring the optimal binding poses. Pharmacophore models are editable to study the impact of each feature and further refine the screening results. Moreover, molecular rendering algorithms are improved for precise representations. AVAILABILITY AND IMPLEMENTATION: VRPharmer is open-source software under the MIT license. The released version is available at https://github.com/VRPharmer/VRPharmer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Recognition of Micro-Motion Jamming Based on Complex-Valued Convolutional Neural Network.

Micro-motion jamming is a new jamming method to inverse synthetic aperture radar (ISAR) in recent years. Compared with traditional jamming methods, it is more flexible and controllable, and is a great threat to ISAR. The prerequisite of taking relevant anti-jamming measures is to recognize the patterns of micro-motion jamming. In this paper, a method of micro-motion jamming pattern recognition based on complex-valued convolutional neural network (CV-CNN) is proposed. The micro-motion jamming echo signals are serialized and input to the network, and the result of recognition is output. Compared with real-valued convolutional neural network (RV-CNN), it can be found that the proposed method has a higher recognition accuracy rate. Additionally, the recognition accuracy rate is analyzed with different signal-to-noise ratio (SNR) and number of training samples. Simulation results prove the effectiveness of the proposed recognition method.

Bamboo-like Vortex Chains Confined in Canals with Suppressed Superconductivity and Standing Waves of Quasiparticles.

The vortex core can be regarded as a nanoscale confined system for quasiparticles in a type-II superconductor. It is very interesting to investigate the interplay between the vortex core and other microscopic quantum confined systems. We observe band-like canals with the width of about 15 nm on the surface of KCa2(Fe1-xNix)4As4F2 (x = 0.05) by scanning tunneling microscopy. Some canals suppress superconductivity and confine parallel standing waves due to the quasiparticle interference. Upon magnetic fields being applied, some elongated vortices are formed within canals showing bamboo-like one-dimensional vortex chains. Interestingly, the confined vortex cores are elongated roughly along the perpendicular direction of canals, and the local density of states at positive and negative energies shows an in-phase oscillation at zero field; but, it becomes out-of-phase crossing the vortex cores. Our work reveals a new type of vortex patterns in confined canals and its interplay with the quasiparticle interference.

Site-2 Protease Slr1821 Regulates Carbon/Nitrogen Homeostasis during Ammonium Stress Acclimation in Cyanobacterium Synechocystis sp. PCC 6803.

Excess ammonium imposes toxicity and stress response in cyanobacteria. How cyanobacteria acclimate to NH4+ stress is so far poorly understood. Here, Synechocystis sp. PCC6803 S2P homolog Slr1821 was identified as the essential regulator through physiological characterization and transcriptomic analysis of its knockout mutant. The proper expression of 60% and 67% of the NH4+ activated and repressed genes, respectively, were actually Slr1821-dependent since they were abolished or reversed in ∆slr1821. Synechocystis 6803 suppressed nitrogen uptake and assimilation, ammonium integration and mobilization of other nitrogen sources upon NH4+ stress. Opposite regulation on genes for assimilation of nitrogen and carbon, such as repression of nitrogen regulatory protein PII, PII interactive protein PirC and activation of carbon acquisition regulator RcbR, demonstrated that Synechocystis 6803 coordinated regulation to maintain carbon/nitrogen homeostasis under increasing nitrogen, while functional Slr1821 was indispensable for most of this coordinated regulation. Additionally, slr1821 knockout disrupted the proper response of regulators and transporters in the ammonium-specific stimulon, and resulted in defective photosynthesis as well as compromised translational and transcriptional machinery. These results provide new insight into the coordinated regulation of nutritional fluctuation and the functional characterization of S2Ps. They also provide new targets for bioengineering cyanobacteria in bioremediation and improving ammonium tolerance in crop plants.

Preferred Spin Excitations in the Bilayer Iron-Based Superconductor CaK(Fe_{0.96}Ni_{0.04})_{4}As_{4} with Spin-Vortex Crystal Order.

Spin-orbit coupling (SOC) is a key to understand the magnetically driven superconductivity in iron-based superconductors, where both local and itinerant electrons are present and the orbital angular momentum is not completely quenched. Here, we report a neutron scattering study on the bilayer compound CaK(Fe_{0.96}Ni_{0.04})_{4}As_{4} with superconductivity coexisting with a noncollinear spin-vortex crystal magnetic order that preserves the tetragonal symmetry of the Fe-Fe plane. In the superconducting state, two spin resonance modes with odd and even L symmetries due to the bilayer coupling are found similar to the undoped compound CaKFe_{4}As_{4} but at lower energies. Polarization analysis reveals that the odd mode is c-axis polarized, and the low-energy spin anisotropy can persist to the paramagnetic phase at high temperature, which closely resembles other systems with in-plane collinear and c-axis biaxial magnetic orders. These results provide the missing piece of the puzzle on the SOC effect in iron-pnictide superconductors, and also establish a common picture of c-axis preferred magnetic excitations below T_{c} regardless of the details of magnetic pattern or lattice symmetry.

MiR155 Relieves Acute Heart Transplantation in Mice by Modulating Th1/Th17 Immune Response.

Heart transplantation is an effective method for the treatment of end-stage heart disease. Therefore, this article aimed to establish a stable and effective mouse abdominal heart transplantation model. MiR155 alleviates the acute heart transplantation response by regulating Th1 / Th17 immune cytokines. This paper used the control method of randomly selecting samples to classify 30 healthy mice that met the conditions. First, C57BL / 6 mice were used as recipients, and Balb / c mouse hearts were used as donors to establish mouse hearts as a transplantation acute reaction model. A chronic rejection model of mouse heart transplantation was established by C57BL / 6 mice as recipients and Bm12 mouse hearts as donors. The survival time of the two groups of transplanted hearts was carefully recorded. The results of the study showed that in the heart transplantation acute/chronic rejection model, the average survival time of the donor's heart in the allograft group was (7.5 ± 0.37) / (63.4 ± 4.37) days, which was the same compared with the two groups. Therefore, in-depth analysis of the experimental control results and conclusions from the experimental results of the mice, this study can better respond to the pathological changes of acute/chronic rejection and reach the standard of model establish.

MicroRNA-29b reduces myocardial ischemia-reperfusion injury in rats via down-regulating PTEN and activating the Akt/eNOS signaling pathway.

Reperfusion may cause injuries to the myocardium in ischemia situation, which is called ischemia/reperfusion (I/R) injury. The study aimed to explore the roles of microRNA-29b (miR-29b) in myocardial I/R injury. Myocardial I/R injury rat model was established. Differentially expressed miRNAs between the model rats and the sham-operated rats were analyzed. miR-29b expression in myocardial tissues was measured. Gain-of-function of miR-29b was performed, and then the morphological changes, infarct size, myocardial function, oxidative stress, and the cell apoptosis in myocardial tissues were detected. The target relation between miR-29b and PTEN was detected through bio-information prediction and dual luciferase reporter gene assay. Activation of Akt/eNOS signaling was detected. H9C2 cells were subjected to hypoxia/reoxygenation treatment to perform in vitro experiments. I/R rats presented severe inflammatory infiltration, increased infarct size and cell apoptosis, increased oxidative stress and decreased myocardial function. miR-29b was downregulated in I/R rats, and up-regulation of miR-29b reversed the above changes. miR-29b directly bound to PTEN, and overexpression of miR-29b reduced PTEN expression level and increased the protein levels of p-Akt/Akt and p-eNOS/eNOS. In vivo results were confirmed in in vitro experiments. This study provided evidence that miR-29b could alleviate the myocardial I/R injury in vivo and in vitro by inhibiting PTEN expression and activating the Akt/eNOS signaling pathway.

A fast protein binding site comparison algorithm for proteome-wide protein function prediction and drug repurposing.

The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.

ePharmer: An integrated and graphical software for pharmacophore-based virtual screening.

Pharmacophore-based virtual screening (VS) has emerged as an efficient computer-aided drug design technique when appraising multiple ligands with similar structures or targets with unknown crystal structures. Current pharmacophore modeling and analysis software suffers from inadequate integration of mainstream methods and insufficient user-friendly program interface. In this study, we propose a stand-alone, integrated, graphical software for pharmacophore-based VS, termed ePharmer. Both ligand-based and structure-based pharmacophore generation methods were integrated into a compact architecture. Fine-grained modules were carefully organized into the computing, integration, and visualization layers. Graphical design covered the global user interface and specific user operations including editing, evaluation, and task management. Metabolites prediction analysis with the chosen VS result is provided for preselection of wet experiments. Moreover, the underlying computing units largely adopted the preliminary work of our research team. The presented software is currently in client use and will be released for both professional and nonexpert users. Experimental results verified the favorable computing capability, user convenience, and case performance of the proposed software.

Friedel Oscillations of Vortex Bound States under Extreme Quantum Limit in KCa_{2}Fe_{4}As_{4}F_{2}.

We report the observation of discrete bound states with the energy levels deviating from the widely believed ratio of 1∶3∶5 in the vortices of an iron-based superconductor KCa_{2}Fe_{4}As_{4}F_{2} through scanning tunneling microscopy (STM). Meanwhile Friedel oscillations of vortex bound states are also observed for the first time in related vortices. By doing self-consistent calculations of Bogoliubov-de Gennes equations, we find that at extreme quantum limit, the superconducting order parameter exhibits a Friedel-like oscillation, which modifies the energy levels of the vortex bound states and explains why it deviates from the ratio of 1∶3∶5. The observed Friedel oscillations of the bound states can also be roughly interpreted by the theoretical calculations, however some features at high energies could not be explained. We attribute this discrepancy to the high energy bound states with the influence of nearby impurities. Our combined STM measurement and the self-consistent calculations illustrate a generalized feature of vortex bound states in type-II superconductors.

Extreme Suppression of Antiferromagnetic Order and Critical Scaling in a Two-Dimensional Random Quantum Magnet.

Sr_{2}CuTeO_{6} is a square-lattice Néel antiferromagnet with superexchange between first-neighbor S=1/2 Cu spins mediated by plaquette centered Te ions. Substituting Te by W, the affected impurity plaquettes have predominantly second-neighbor interactions, thus causing local magnetic frustration. Here we report a study of Sr_{2}CuTe_{1-x}W_{x}O_{6} using neutron diffraction and µSR techniques, showing that the Néel order vanishes already at x=0.025±0.005. We explain this extreme order suppression using a two-dimensional Heisenberg spin model, demonstrating that a W-type impurity induces a deformation of the order parameter that decays with distance as 1/r^{2} at temperature T=0. The associated logarithmic singularity leads to loss of order for any x>0. Order for small x>0 and T>0 is induced by weak interplane couplings. In the nonmagnetic phase of Sr_{2}CuTe_{1-x}W_{x}O_{6}, the µSR relaxation rate exhibits quantum critical scaling with a large dynamic exponent, z≈3, consistent with a random-singlet state.

Design, Synthesis, and Structure-Activity Relationship Study of Potent MAPK11 Inhibitors.

Huntington's disease (HD) is a rare single-gene neurodegenerative disease, which can only be treated symptomatically. Currently, there are no approved drugs for HD on the market. Studies have found that MAPK11 can serve as a potential therapeutic target for HD. Regrettably, no MAPK11 small molecule inhibitors have been approved at present. This paper presents three series of compounds that were designed and synthesized based on the structure of skepinone-L, a known MAPK14 inhibitor. Among the synthesized compounds, 13a and 13b, with IC50 values of 6.40 nM and 4.20 nM, respectively, displayed the best inhibitory activities against MAPK11. Furthermore, the structure-activity relationship (SAR) is discussed in detail, which is constructive in optimizing the MAPK11 inhibitors for better activity and effect against HD.

Characterization of iron walnut in different regions of China based on phytochemical composition.

Little is known about the phytochemical composition of iron walnuts. Differences in the geographical origin of iron walnuts associated with economic benefits should also be examined. In this study, the phytochemical composition (fatty acids, Vitamin E, total polyphenols and flavonoids, amino acids, and minerals) of iron walnuts in China was investigated. The results showed that there were significant differences (p < 0.05) in the phytochemical composition of iron walnut oils and flours from different regions. Positive (r > 0.5, p < 0.05) and negative (r < - 0.5, p < 0.05) correlations were found between amino acids/minerals and amino acids/oleic acid, with the highest correlation coefficient (r = 0.742, p < 0.05) between Cu and tyrosine. In addition, based on the 12 phytochemical fingerprints selected by random forest, a geographical-origin identification model for iron walnuts was established, with a corresponding correct classification rate of 96.6%. The top three phytochemical fingerprints for the geographical-origin identification of iron walnut were microelements, macroelements, and antioxidant composition, with contribution rates of 61.7%, 18.1%, and 9.9%, respectively.

Quantum Phases of SrCu_{2}(BO_{3})_{2} from High-Pressure Thermodynamics.

We report heat capacity measurements of SrCu_{2}(BO_{3})_{2} under high pressure along with simulations of relevant quantum spin models and map out the (P,T) phase diagram of the material. We find a first-order quantum phase transition between the low-pressure quantum dimer paramagnet and a phase with signatures of a plaquette-singlet state below T=2 K. At higher pressures, we observe a transition into a previously unknown antiferromagnetic state below 4 K. Our findings can be explained within the two-dimensional Shastry-Sutherland quantum spin model supplemented by weak interlayer couplings. The possibility to tune SrCu_{2}(BO_{3})_{2} between the plaquette-singlet and antiferromagnetic states opens opportunities for experimental tests of quantum field theories and lattice models involving fractionalized excitations, emergent symmetries, and gauge fluctuations.

Neutron Spin Resonance in a Quasi-Two-Dimensional Iron-Based Superconductor.

The neutron spin resonance is generally regarded as a key to understanding the magnetically mediated Cooper pairing in unconventional superconductors. Here, we report an inelastic neutron scattering study on the low-energy spin excitations in a quasi-two-dimensional iron-based superconductor KCa_{2}Fe_{4}As_{4}F_{2}. We have discovered a two-dimensional spin resonant mode with downward dispersions, a behavior closely resembling the low branch of the hourglass-type spin resonance in cuprates. While the resonant intensity is predominant by two broad incommensurate peaks near Q=(0.5,0.5) with a sharp energy peak at E_{R}=16 meV, the overall energy dispersion of the mode exceeds the measured maximum total gap Δ_{tot}=|Δ_{k}|+|Δ_{k+Q}|. These results deeply challenge the conventional understanding of the resonance modes as magnetic excitons regardless of underlining pairing symmetry schemes, and it also points out that when the iron-based superconductivity becomes very quasi-two-dimensional, the electronic behaviors are similar to those in cuprates.

LncRNA LOXL1-AS1 controls osteogenic and adipocytic differentiation of bone marrow mesenchymal stem cells in postmenopausal osteoporosis through regulating the miR-196a-5p/Hmga2 axis.

INTRODUCTION: Exploring molecular mechanisms of human bone marrow mesenchymal stem cells (hBMMSCs) differentiation, a crucial step for bone formation, is a new direction for treating postmenopausal osteoporosis. LncRNAs are involved in lots of biological processes including hBMMSCs differentiation. The present study aimed to explore the effect of LOXL1-AS1 on hBMMSCs differentiation. MATERIALS AND METHODS: We examined the expression levels of LOXL1-AS1, miR-196a-5p and Hmga2 in peripheral blood from postmenopausal osteoporosis patients by RT-qPCR, and detected their changes during the osteogenic differentiation of hBMMSCs by RT-qPCR. RT-qPCR and western blot measured the level of biomarkers of bone formation and osteogenic differentiation (osteopontin, OPN; Alkaline phosphatase, ALP; Runt-related transcription factor-2, Runx-2). The effects of LOXL1-AS1 on the osteogenic and adipocytic differentiation of hBMMSCs were, respectively, determined by ALP, ARS staining assays and oil red O staining assay. RESULTS: The abnormal high expression of LOXL1-AS1 was found in patients. LOXL1-AS1 expression showed a gradual decrease during the osteogenic differentiation of hBMMSCs. However, LOXL1-AS1 overexpression inhibited the hBMMSCs osteogenic differentiation but promoted adipocytic differentiation. Furthermore, LOXL1-AS1 was identified to be a sponge of miR-196a-5p and Hmga2 as a target gene of miR-196a-5p. Besides, LOXL1-AS1 sponged miR-196a-5p to mediate Hmga2 expression, which played contrary effects on regulating osteogenic and adipocytic differentiation of hBMMSCs. Moreover, LOXL1-AS1/miR-196a-5p/Hmga2 axis regulated hBMMSCs differentiation through controlling C/EBPß-mediated PPARγ expression. CONCLUSION: These findings facilitate understanding the molecular mechanism of hBMMSCs differentiation and bring up a novel sight for postmenopausal osteoporosis therapy.