A marine bacteria-inspired electrochemical regulation for continuous uranium extraction from seawater and salt lake brine.

Oceans and salt lakes contain vast amounts of uranium. Uranium recovery from natural water not only copes with radioactive pollution in water but also can sustain the fuel supply for nuclear power. The adsorption-assisted electrochemical processes offer a promising route for efficient uranium extraction. However, competitive hydrogen evolution greatly reduces the extraction capacity and the stability of electrode materials with electrocatalytic activity. In this study, we got inspiration from the biomineralisation of marine bacteria under high salinity and biomimetically regulated the electrochemical process to avoid the undesired deposition of metal hydroxides. The uranium uptake capacity can be increased by more than 20% without extra energy input. In natural seawater, the designed membrane electrode exhibits an impressive extraction capacity of 48.04 mg-U per g-COF within 21 days (2.29 mg-U per g-COF per day). Furthermore, in salt lake brine with much higher salinity, the membrane can extract as much uranium as 75.72 mg-U per g-COF after 32 days (2.37 mg-U per g-COF per day). This study provides a general basis for the performance optimisation of uranium capture electrodes, which is beneficial for sustainable access to nuclear energy sources from natural water systems.

Neuron-Inspired Nanofluidic Biosensors for Highly Sensitive and Selective Imidacloprid Detection.

Pesticides have caused concerns about food safety due to their residual effects in vegetables and fruits. Imidacloprid, as the frequently used neonicotinoid pesticide, could harm cardiovascular and respiratory function and cause reproductive toxicity in humans. Therefore, reliable methods for portable, selective, and rapid detection are desirable to develop. Herein, we report a neuron-inspired nanofluidic biosensor based on a tyrosine-modified artificial nanochannel for sensitively detecting imidacloprid. The functional tyrosine is modified on the outer surface of porous anodic aluminum oxide to rapidly capture imidacloprid through π-π interactions and hydrogen bonds. The integrated nanofluidic biosensor has a wide concentration range from 10-8 to 10-4 g/mL with an ultralow detection limit of 6.28 × 10-9 g/mL, which outperforms the state-of-the-art sensors. This work provides a new perspective on detecting imidacloprid residues as well as other hazardous pesticide residues in environmental and food samples.

Efficient Solar-osmotic Power Generation from Bioinspired Anti-fouling 2D WS2 Composite Membranes.

Nanofluidic reverse electrodialysis provides an attractive way to harvest osmotic energy. However, most attention was paid to monotonous membrane structure optimization to promote selective ion transport, while the role of external fields and relevant mechanisms are rarely explored. Here, we demonstrate a Kevlar-toughened tungsten disulfide (WS2 ) composite membrane with bioinspired serosa-mimetic structures as an efficient osmotic energy generator coupling light. As a result, the output power could be up to 16.43 W m-2 under irradiation, outperforming traditional two-dimensional (2D) membranes. Both the experiment and simulation uncover that the generated photothermal and photoelectronic effects could synergistically promote the confined ion transport process. In addition, this membrane also possesses great anti-fouling properties, endowing its practical application. This work paves new avenues for sustainable power generation by coupling solar energy.

MAEPI and CIR: New Metrics for Robust Evaluation of the Prediction Performance of AI-Based IOL Formulas.

Purpose: To develop a class of new metrics for evaluating the performance of intraocular lens power calculation formulas robust to issues that can arise with AI-based methods. Methods: The dataset consists of surgical information and biometry measurements of 6893 eyes of 5016 cataract patients who received Alcon SN60WF lenses at University of Michigan's Kellogg Eye Center. We designed two types of new metrics: the MAEPI (Mean Absolute Error in Prediction of Intraocular Lens [IOL]) and the CIR (Correct IOL Rate) and compared the new metrics with traditional metrics including the mean absolute error (MAE), median absolute error, and standard deviation. We evaluated the new metrics with simulation analysis, machine learning (ML) methods, as well as existing IOL formulas (Barrett Universal II, Haigis, Hoffer Q, Holladay 1, PearlDGS, and SRK/T). Results: Results of traditional metrics did not accurately reflect the performance of overfitted ML formulas. By contrast, MAEPI and CIR discriminated between accurate and inaccurate formulas. The standard IOL formulas received low MAEPI and high CIR, which were consistent with the results of the traditional metrics. Conclusions: MAEPI and CIR provide a more accurate reflection of the real-life performance of AI-based IOL formula than traditional metrics. They should be computed in conjunction with conventional metrics when evaluating the performance of new and existing IOL formulas. Translational Relevance: The proposed new metrics would help cataract patients avoid the risks caused by inaccurate AI-based formulas, whose true performance cannot be determined by traditional metrics.

Ray tracing intraocular lens calculation performance improved by AI-powered postoperative lens position prediction.

AIMS: To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves cataract surgery refraction prediction performance of a commonly used ray tracing power calculation suite (OKULIX). METHODS AND ANALYSIS: A dataset of 4357 eyes of 4357 patients with cataract was gathered at the Kellogg Eye Center, University of Michigan. A previously developed machine learning (ML)-based method was used to predict the postoperative ACD based on preoperative biometry measured with the Lenstar LS900 optical biometer. Refraction predictions were computed with standard OKULIX postoperative ACD predictions and ML-based predictions of postoperative ACD. The performance of the ray tracing approach with and without ML-based ACD prediction was evaluated using mean absolute error (MAE) and median absolute error (MedAE) in refraction prediction as metrics. RESULTS: Replacing the standard OKULIX postoperative ACD with the ML-predicted ACD resulted in statistically significant reductions in both MAE (1.7% after zeroing mean error) and MedAE (2.1% after zeroing mean error). ML-predicted ACD substantially improved performance in eyes with short and long axial lengths (p<0.01). CONCLUSIONS: Using an ML-powered postoperative ACD prediction method improves the prediction accuracy of the OKULIX ray tracing suite by a clinically small but statistically significant amount, with the greatest effect seen in long eyes.

Evaluation of the Nallasamy formula: a stacking ensemble machine learning method for refraction prediction in cataract surgery.

AIMS: To develop a new intraocular lens power selection method with improved accuracy for general cataract patients receiving Alcon SN60WF lenses. METHODS AND ANALYSIS: A total of 5016 patients (6893 eyes) who underwent cataract surgery at University of Michigan's Kellogg Eye Center and received the Alcon SN60WF lens were included in the study. A machine learning-based method was developed using a training dataset of 4013 patients (5890 eyes), and evaluated on a testing dataset of 1003 patients (1003 eyes). The performance of our method was compared with that of Barrett Universal II, Emmetropia Verifying Optical (EVO), Haigis, Hoffer Q, Holladay 1, PearlDGS and SRK/T. RESULTS: Mean absolute error (MAE) of the Nallasamy formula in the testing dataset was 0.312 Dioptres and the median absolute error (MedAE) was 0.242 D. Performance of existing methods were as follows: Barrett Universal II MAE=0.328 D, MedAE=0.256 D; EVO MAE=0.322 D, MedAE=0.251 D; Haigis MAE=0.363 D, MedAE=0.289 D; Hoffer Q MAE=0.404 D, MedAE=0.331 D; Holladay 1 MAE=0.371 D, MedAE=0.298 D; PearlDGS MAE=0.329 D, MedAE=0.258 D; SRK/T MAE=0.376 D, MedAE=0.300 D. The Nallasamy formula performed significantly better than seven existing methods based on the paired Wilcoxon test with Bonferroni correction (p<0.05). CONCLUSIONS: The Nallasamy formula (available at https://lenscalc.com/) outperformed the seven other formulas studied on overall MAE, MedAE, and percentage of eyes within 0.5 D of prediction. Clinical significance may be primarily at the population level.

A Neural Network Model for Color Element Data Analysis for Urban Spatial Environment.

In this paper, a CNN model for color element data analysis of the urban spatial environment is constructed through an in-depth study of color element data analysis. This paper investigates a high-order structure formed by a few nodes; it proposes a motif-based graph autoencoder MODEL, combining redefined first- and second-order similarities and perfectly integrating motif structure and autoencoder. The algorithm first proposes an efficient graph transformation method to add the influence of central nodes. It then offers a primary awareness mechanism to aggregate the information of noncentral neighbors. Cen GCN_D and Cen GCN_E outperform the latest algorithms in node classification, link prediction, node clustering, and network visualization. As the number of network layers increases, the advantages of these two variants become progressively more prominent. This paper uses a support vector machine to implement classification validation based on CNN. The experimental results show that when 450 images are randomly selected as training data, the classification accuracy obtained by using the features of different CNN output layers is distributed between 91.4% and 95.2%. When the training set of the experiment reaches more than 300, the accuracy can exceed 90%, and the experimental results corresponding to different training sets a more stable trend. Finally, the trained classifier model is obtained in this thesis, which achieves the purpose of fast classification prediction based on CNN for color element data analysis of urban spatial environments.

Analysis of Cataract Surgery Instrument Identification Performance of Convolutional and Recurrent Neural Network Ensembles Leveraging BigCat.

Purpose: To develop a method for accurate automated real-time identification of instruments in cataract surgery videos. Methods: Cataract surgery videos were collected at University of Michigan's Kellogg Eye Center between 2020 and 2021. Videos were annotated for the presence of instruments to aid in the development, validation, and testing of machine learning (ML) models for multiclass, multilabel instrument identification. Results: A new cataract surgery database, BigCat, was assembled, containing 190 videos with over 3.9 million annotated frames, the largest reported cataract surgery annotation database to date. Using a dense convolutional neural network (CNN) and a recursive averaging method, we were able to achieve a test F1 score of 0.9528 and test area under the receiver operator characteristic curve of 0.9985 for surgical instrument identification. These prove to be state-of-the-art results compared to previous works, while also only using a fraction of the model parameters of the previous architectures. Conclusions: Accurate automated surgical instrument identification is possible with lightweight CNNs and large datasets. Increasingly complex model architecture is not necessary to retain a well-performing model. Recurrent neural network architectures add additional complexity to a model and are unnecessary to attain state-of-the-art performance. Translational Relevance: Instrument identification in the operative field can be used for further applications such as evaluating surgical trainee skill level and developing early warning detection systems for use during surgery.

AI-powered effective lens position prediction improves the accuracy of existing lens formulas.

AIMS: To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves the refraction prediction performance of existing intraocular lens (IOL) calculation formulas. METHODS: A dataset of 4806 patients with cataract was gathered at the Kellogg Eye Center, University of Michigan, and split into a training set (80% of patients, 5761 eyes) and a testing set (20% of patients, 961 eyes). A previously developed ML-based method was used to predict the postoperative ACD based on preoperative biometry. This ML-based postoperative ACD was integrated into new effective lens position (ELP) predictions using regression models to rescale the ML output for each of four existing formulas (Haigis, Hoffer Q, Holladay and SRK/T). The performance of the formulas with ML-modified ELP was compared using a testing dataset. Performance was measured by the mean absolute error (MAE) in refraction prediction. RESULTS: When the ELP was replaced with a linear combination of the original ELP and the ML-predicted ELP, the MAEs±SD (in Diopters) in the testing set were: 0.356±0.329 for Haigis, 0.352±0.319 for Hoffer Q, 0.371±0.336 for Holladay, and 0.361±0.331 for SRK/T which were significantly lower (p<0.05) than those of the original formulas: 0.373±0.328 for Haigis, 0.408±0.337 for Hoffer Q, 0.384±0.341 for Holladay and 0.394±0.351 for SRK/T. CONCLUSION: Using a more accurately predicted postoperative ACD significantly improves the prediction accuracy of four existing IOL power formulas.

Gender differences in refraction prediction error of five formulas for cataract surgery.

OBJECTIVES: To evaluate gender differences in optical biometry measurements and lens power calculations. METHODS: Eight thousand four hundred thirty-one eyes of five thousand five hundred nineteen patients who underwent cataract surgery at University of Michigan's Kellogg Eye Center were included in this retrospective study. Data including age, gender, optical biometry, postoperative refraction, implanted intraocular lens (IOL) power, and IOL formula refraction predictions were gathered and/or calculated utilizing the Sight Outcomes Research Collaborative (SOURCE) database and analyzed. RESULTS: There was a statistical difference between every optical biometry measure between genders. Despite lens constant optimization, mean signed prediction errors (SPEs) of modern IOL formulas differed significantly between genders, with predictions skewed more hyperopic for males and myopic for females for all 5 of the modern IOL formulas tested. Optimization of lens constants by gender significantly decreased prediction error for 2 of the 5 modern IOL formulas tested. CONCLUSIONS: Gender was found to be an independent predictor of refraction prediction error for all 5 formulas studied. Optimization of lens constants by gender can decrease refraction prediction error for certain modern IOL formulas.

AI-Powered Effective Lens Position Prediction Improves the Accuracy of Existing Lens Formulas.

AIMS: To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves the refraction prediction performance of existing intraocular lens (IOL) calculation formulas. METHODS: A dataset of 4806 cataract patients were gathered at the Kellogg Eye Center, University of Michigan, and split into a training set (80% of patients, 5761 eyes) and a testing set (20% of patients, 961 eyes). A previously developed ML-based method was used to predict the postoperative ACD based on preoperative biometry. This ML-based postoperative ACD was integrated into new effective lens position (ELP) predictions using regression models to rescale the ML output for each of four existing formulas (Haigis, Hoffer Q, Holladay, and SRK/T). The performance of the formulas with ML-modified ELP was compared using a testing dataset. Performance was measured by the mean absolute error (MAE) in refraction prediction. RESULTS: When the ELP was replaced with a linear combination of the original ELP and the ML-predicted ELP, the MAEs ± SD (in Diopters) in the testing set were: 0.356 ± 0.329 for Haigis, 0.352 ± 0.319 for Hoffer Q, 0.371 ± 0.336 for Holladay, and 0.361 ± 0.331 for SRK/T which were significantly lower than those of the original formulas: 0.373 ± 0.328 for Haigis, 0.408 ± 0.337 for Hoffer Q, 0.384 ± 0.341 for Holladay, and 0.394 ± 0.351 for SRK/T. CONCLUSION: Using a more accurately predicted postoperative ACD significantly improves the prediction accuracy of four existing IOL power formulas.

Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography.

Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are affected in patients with DR. However, most studies analyzed the thickness by calculating summary statistics from retinal thickness maps of the macula region. This study aims to apply a density function-based statistical framework to the thickness data obtained through OCT, and to compare the predictive power of various retinal layers to assess the severity of DR. We used a prototype data set of 107 subjects which are comprised of 38 non-proliferative DR (NPDR), 28 without DR (NoDR), and 41 controls. Based on the thickness profiles, we constructed novel features which capture the variation in the distribution of the pixel-wise retinal layer thicknesses from OCT. We quantified the predictive power of each of the retinal layers to distinguish between all three pairwise comparisons of the severity in DR (NoDR vs NPDR, controls vs NPDR, and controls vs NoDR). When applied to this preliminary DR data set, our density-based method demonstrated better predictive results compared with simple summary statistics. Furthermore, our results indicate considerable differences in retinal layer structuring based on the severity of DR. We found that: (a) the outer plexiform layer is the most discriminative layer for classifying NoDR vs NPDR; (b) the outer plexiform, inner nuclear and ganglion cell layers are the strongest biomarkers for discriminating controls from NPDR; and (c) the inner nuclear layer distinguishes best between controls and NoDR.

Gradient Boosting Decision Tree Algorithm for the Prediction of Postoperative Intraocular Lens Position in Cataract Surgery.

Purpose: To develop a method for predicting postoperative anterior chamber depth (ACD) in cataract surgery patients based on preoperative biometry, demographics, and intraocular lens (IOL) power. Methods: Patients who underwent cataract surgery and had both preoperative and postoperative biometry measurements were included. Patient demographics and IOL power were collected from the Sight Outcomes Research Collaborative (SOURCE) database. A gradient-boosting decision tree model was developed to predict the postoperative ACD. The mean absolute error (MAE) and median absolute error (MedAE) were used as evaluation metrics. The performance of the proposed method was compared with five existing formulas. Results: In total, 847 patients were assigned randomly in a 4:1 ratio to a training/validation set (678 patients) and a testing set (169 patients). Using preoperative biometry and patient sex as predictors, the presented method achieved an MAE of 0.106 ± 0.098 (SD) on the testing set, and a MedAE of 0.082. MAE was significantly lower than that of the five existing methods (P < 0.01). When keratometry was excluded, our method attained an MAE of 0.123 ± 0.109, and a MedAE of 0.093. When IOL power was used as an additional predictor, our method achieved an MAE of 0.105 ± 0.091 and a MedAE of 0.080. Conclusions: The presented machine learning method achieved greater accuracy than previously reported methods for the prediction of postoperative ACD. Translational Relevance: Increasing accuracy of postoperative ACD prediction with the presented algorithm has the potential to improve refractive outcomes in cataract surgery.

DDAP: docking domain affinity and biosynthetic pathway prediction tool for type I polyketide synthases.

SUMMARY: DDAP is a tool for predicting the biosynthetic pathways of the products of type I modular polyketide synthase (PKS) with the focus on providing a more accurate prediction of the ordering of proteins and substrates in the pathway. In this study, the module docking domain (DD) affinity prediction performance on a hold-out testing dataset reached 0.88 as measured by the area under the receiver operating characteristic (ROC) curve (AUC); the Mean Reciprocal Ranking (MRR) of pathway prediction reached 0.67. DDAP has advantages compared to previous informatics tools in several aspects: (i) it does not rely on large databases, making it a high efficiency tool, (ii) the predicted DD affinity is represented by a probability (0-1), which is more intuitive than raw scores, (iii) its performance is competitive compared to the current popular rule-based algorithm. DDAP is so far the first machine learning based algorithm for type I PKS DD affinity and pathway prediction. We also established the first database of type I modular PKSs, featuring a comprehensive annotation of available docking domains information in bacterial biosynthetic pathways. AVAILABILITY AND IMPLEMENTATION: The DDAP database is available at https://tylii.github.io/ddap. The prediction algorithm DDAP is freely available on GitHub (https://github.com/tylii/ddap) and released under the MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Network Propagation Predicts Drug Synergy in Cancers.

Combination therapies are commonly used to treat patients with complex diseases that respond poorly to single-agent therapies. In vitro high-throughput drug screening is a standard method for preclinical prioritization of synergistic drug combinations, but it can be impractical for large drug sets. Computational methods are thus being actively explored; however, most published methods were built on a limited size of cancer cell lines or drugs, and it remains a challenge to predict synergism at a large scale where the diversity within the data escalates the difficulty of prediction. Here, we present a state-of-the-field synergy prediction algorithm, which ranked first in all subchallenges in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. The model was built and evaluated using the largest drug combination screening dataset at the time of the competition, consisting of approximately 11,500 experimentally tested synergy scores of 118 drugs in 85 cancer cell lines. We developed a novel feature extraction strategy by integrating the cross-cell and cross-drug information with a novel network propagation method and then assembled the information in monotherapy and simulated molecular data to predict drug synergy. This represents a significant conceptual advancement of synergy prediction, using extracted features in the form of simulated posttreatment molecular profiles when only the pretreatment molecular profile is available. Our cross-tissue synergism prediction algorithm achieves promising accuracy comparable with the correlation between experimental replicates and can be applied to other cancer cell lines and drugs to guide therapeutic choices.Significance: This study presents a novel network propagation-based method that predicts anticancer drug synergy to the accuracy of experimental replicates, which establishes a state-of-the-field method as benchmarked by the pharmacogenomics research community involving models generated by 160 teams. Cancer Res; 78(18); 5446-57. ©2018 AACR.

Prioritizing predictive biomarkers for gene essentiality in cancer cells with mRNA expression data and DNA copy number profile.

Motivation: Finding driver genes that are responsible for the aberrant proliferation rate of cancer cells is informative for both cancer research and the development of targeted drugs. The established experimental and computational methods are labor-intensive. To make algorithms feasible in real clinical settings, methods that can predict driver genes using less experimental data are urgently needed. Results: We designed an effective feature selection method and used Support Vector Machines (SVM) to predict the essentiality of the potential driver genes in cancer cell lines with only 10 genes as features. The accuracy of our predictions was the highest in the Broad-DREAM Gene Essentiality Prediction Challenge. We also found a set of genes whose essentiality could be predicted much more accurately than others, which we called Accurately Predicted (AP) genes. Our method can serve as a new way of assessing the essentiality of genes in cancer cells. Availability and implementation: The raw data that support the findings of this study are available at Synapse. https://www.synapse.org/#! Synapse: syn2384331/wiki/62825. Source code is available at GitHub. https://github.com/GuanLab/DREAM-Gene-Essentiality-Challenge. Supplementary information: Supplementary data are available at Bioinformatics online.

Simultaneous determination of tanshinol, protocatechuic aldehyde, protocatechuic acid, notoginsenoside R1, ginsenoside Rg1 and Rb1 in rat plasma by LC-MS/MS and its application.

Dantonic pill, consisting of Salviae miltiorrhize, Panax notoginseng and Borneol, is a widely used compound Chinese medicine for preventing and treating ischemic cardiovascular diseases in China. In the present study, an original and sensitive method for simultaneous determination of tanshinol (i.e. danshensu), protocatechuic aldehyde, protocatechuic acid, notoginsenoside R1, ginsenoside Rg1 and Rb1 in rat plasma by liquid chromatography-tandem mass spectrometry operated in positive/negative ion switching mode was established and validated. The lower limits of quantification for tanshinol, protocatechuic aldehyde, protocatechuic acid, notoginsenoside R1, ginsenoside Rg1 and Rb1 were 5, 0.5, 1, 0.5, 0.5 and 2 ng/mL, respectively. All of the calibration curves showed good linearity over the investigated concentration range (r > 0.99). Validation results demonstrated that the above compounds were accurately, precisely and robustly quantified in rat plasma. The method was successfully applied to characterize the pharmacokinetic profiles of all six compounds in rats following a single oral administration of Dantonic pill.

Modulatory effects of silibinin in various cell signaling pathways against liver disorders and cancer - A comprehensive review.

Silibinin, a natural flavanone, derived from the milk thistle plant (Silybum marianum), was illustrated for several medicinal uses such as liver-protective, anti-oxidant, anti-cancer, anti-inflammation and many other. However, silibinin has poor absorbance and bioavailability due to low water solubility, thereby limiting its clinical applications and therapeutic efficiency. To overcome this problem, the combination of silibinin with phosphatidylcholine (PC) as a formulation was used to enhance the solubility and bioavailability. The results indicated that silibinin-PC taken orally markedly enhanced bioavailability and therapeutic efficiency. In addition, a deeper understanding of the signaling pathways modulated by silibinin is important to realize its potential in developing targeted therapies against liver disorders and cancer. Silibinin has been shown to inhibit many cell signaling pathways in preclinical models, demonstrating promising effects against liver disorders and cancer through in vitro and in vivo studies. This review summarizes the pharmacokinetic properties, bioavailability, safety data, clinical activities and modulatory effects of silibinin in different cell signaling pathways against liver disorders and cancer.

5,7-Dihydroxy-4'-methoxyisoflavone induces apoptosis by inhibiting the ERK and Akt pathways in human osteosarcoma cells.

Phytoestrogens are known to prevent tumor progression by inhibiting proliferation and inducing apoptosis in cancer cells. In this study we determine the effect of 5,7-dihydroxy-4'-methoxyisoflavone, a phytoestrogen, on proliferation and apoptosis in the human osteosarcoma (OS) cell line U2OS. 5,7-Dihydroxy-4'-methoxyisoflavone dose-dependently inhibited proliferation in U2OS cells, which was accompanied by an increase of early apoptotic cells. However, 5,7-dihydroxy-4'-methoxyisoflavone had little effect on the growth and apoptosis of normal human skin fibroblast (HSF) cells. This may indicate that 5,7-dihydroxy-4'-methoxyisoflavone can selectively inhibit the proliferation of cancerous cells. Meanwhile, 5,7-dihydroxy-4'-methoxyisoflavone decreased the protein levels of phosphorylated ERK and Akt. Inactivation of these pathways was confirmed by upregulation of Bax expression and downregulation of Bcl-2 expression. Phosphorylated Akt protein levels were decreased in HSF cells only at a high concentration (80 µM) of 5,7-dihydroxy-4'-methoxyisoflavone. Together, we suggest that 5,7-dihydroxy-4'-methoxyisoflavone promotes cell death of human OS cells U2OS by induction of apoptosis, which is associated with the inhibition of ERK and Akt signaling. Thus, 5,7-dihydroxy-4'-methoxyisoflavone may have less toxicity compared to normal tissue and could be a potential therapy for OS.