Quantum State Tomography via Nonconvex Riemannian Gradient Descent.

The recovery of an unknown density matrix of large size requires huge computational resources. State-of-the-art performance has recently been achieved with the factored gradient descent (FGD) algorithm and its variants since they are able to mitigate the dimensionality barrier by utilizing some of the underlying structures of the density matrix. Despite the theoretical guarantee of a linear convergence rate, convergence in practical scenarios is still slow because the contracting factor of the FGD algorithms depends on the condition number κ of the ground truth state. Consequently, the total number of iterations needed to achieve the estimation error ϵ can be as large as O(sqrt[κ]ln(1/ϵ)). In this Letter, we derive a quantum state tomography scheme that improves the dependence on κ to the logarithmic scale. Thus, our algorithm can achieve the approximation error ϵ in O(ln(1/κϵ)) steps. The improvement comes from the application of nonconvex Riemannian gradient descent (RGD). The contracting factor in our approach is thus a universal constant that is independent of the given state. Our theoretical results of extremely fast convergence and nearly optimal error bounds are corroborated by the numerical results.

Minimum-monitor-unit optimization via a stochastic coordinate descent method.

Objective. Deliverable proton spots are subject to the minimum monitor-unit (MMU) constraint. The MMU optimization problem with relatively large MMU threshold remains mathematically challenging due to its strong nonconvexity. However, the MMU optimization is fundamental to proton radiotherapy (RT), including efficient IMPT and proton arc delivery (ARC). This work aims to develop a new optimization algorithm that is effective in solving the MMU problem.Approach.Our new algorithm is primarily based on stochastic coordinate decent (SCD) method. It involves three major steps: first to decouple the determination of active sets for dose-volume-histogram (DVH) planning constraints from the MMU problem via iterative convex relaxation method; second to handle the nonconvexity of the MMU constraint via SCD to localize the index set of nonzero spots; third to solve convex subproblems projected to this convex set of nonzero spots via projected gradient descent method.Main results.Our new method SCD is validated and compared with alternating direction method of multipliers (ADMM) for IMPT and ARC. The results suggest SCD had better plan quality than ADMM, e.g. the improvement of conformal index (CI) from 0.56 to 0.69 during IMPT, and from 0.28 to 0.80 during ARC for the lung case. Moreover, SCD successfully handled the nonconvexity from large MMU threshold that ADMM failed to handle, in the sense that (1) the plan quality from ARC was worse than IMPT (e.g. CI was 0.28 with IMPT and 0.56 with ARC for the lung case), when ADMM was used; (2) in contrast, with SCD, ARC achieved better plan quality than IMPT (e.g. CI was 0.69 with IMPT and 0.80 with ARC for the lung case), which is compatible with more optimization degrees of freedom from ARC compared to IMPT.Significance. To the best of our knowledge, our new MMU optimization method via SCD can effectively handle the nonconvexity from large MMU threshold that none of the current methods can solve. Therefore, we have developed a unique MMU optimization algorithm via SCD that can be used for efficient IMPT, proton ARC, and other particle RT applications where large MMU threshold is desirable (e.g. for the delivery of high dose rates or/and a large number of spots).

A spectral interior CT by a framelet-based reconstruction algorithm.

Reducing radiation dose is an important goal in medical computed tomography (CT), for which interior tomography is an effective approach. There have been interior reconstruction algorithms for monochromatic CT, but in reality, X-ray sources are polychromatic. Using a polychromatic acquisition model and motivated by framelet-based image processing algorithms, in this paper, we propose an interior reconstruction algorithm to obtain an image with spectral information assuming only one scan with a current energy-integrating detector. This algorithm is a new nonlinear iterative method by minimizing a special functional under a polychromatic acquisition model for X-ray CT, where the attenuation coefficients are energy-dependent. Experimental results validate that our algorithm can effectively reduce the beam-hardening artifacts and metal artifacts. It also produces color overlays which are useful in tumor identification and quantification.

Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction.

OBJECTIVE: Improve the reconstructed image with fast and multiclass dictionaries learning when magnetic resonance imaging is accelerated by undersampling the k-space data. METHODS: A fast orthogonal dictionary learning method is introduced into magnetic resonance image reconstruction to provide adaptive sparse representation of images. To enhance the sparsity, image is divided into classified patches according to the same geometrical direction and dictionary is trained within each class. A new sparse reconstruction model with the multiclass dictionaries is proposed and solved using a fast alternating direction method of multipliers. RESULTS: Experiments on phantom and brain imaging data with acceleration factor up to 10 and various undersampling patterns are conducted. The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods. CONCLUSION: Artifacts are better suppressed and image edges are better preserved than the compared methods. Besides, the computation of the proposed approach is much faster than the typical K-SVD dictionary learning method in magnetic resonance image reconstruction. SIGNIFICANCE: The proposed method can be exploited in undersampled magnetic resonance imaging to reduce data acquisition time and reconstruct images with better image quality.

A framelet-based iterative maximum-likelihood reconstruction algorithm for spectral CT.

Standard computed tomography (CT) cannot reproduce spectral information of an object. Hardware solutions include dual-energy CT which scans the object twice in different x-ray energy levels, and energy-discriminative detectors which can separate lower and higher energy levels from a single x-ray scan. In this paper, we propose a software solution and give an iterative algorithm that reconstructs an image with spectral information from just one scan with a standard energy-integrating detector. The spectral information obtained can be used to produce color CT images, spectral curves of the attenuation coefficient µ(r, E)at points inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. Our software solution requires no change on hardware of a CT machine. With the Shepp-Logan phantom, we have found that although the photoelectric and Compton components were not perfectly reconstructed, their composite effect was very accurately reconstructed as compared to the ground truth and the dual-energy CT counterpart. This means that our proposed method has an intrinsic benefit in beam hardening correction and metal artifact reduction. The algorithm is based on a nonlinear polychromatic acquisition model for x-ray CT. The key technique is a sparse representation of iterations in a framelet system. Convergence of the algorithm is studied. This is believed to be the first application of framelet imaging tools to a nonlinear inverse problem.

Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction.

This paper explores robust recovery of a superposition of R distinct complex exponential functions with or without damping factors from a few random Gaussian projections. We assume that the signal of interest is of 2N - 1 dimensions and R < 2N - 1. This framework covers a large class of signals arising from real applications in biology, automation, imaging science, etc. To reconstruct such a signal, our algorithm is to seek a low-rank Hankel matrix of the signal by minimizing its nuclear norm subject to the consistency on the sampled data. Our theoretical results show that a robust recovery is possible as long as the number of projections exceeds O(Rln2N). No incoherence or separation condition is required in our proof. Our method can be applied to spectral compressed sensing where the signal of interest is a superposition of R complex sinusoids. Compared to existing results, our result here does not need any separation condition on the frequencies, while achieving better or comparable bounds on the number of measurements. Furthermore, our method provides theoretical guidance on how many samples are required in the state-of-the-art non-uniform sampling in NMR spectroscopy. The performance of our algorithm is further demonstrated by numerical experiments.

Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.

Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model that bridges analysis and synthesis models by introducing a penalty term on the distance of frame coefficients to the range of the analysis operator. In this paper, we study the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and propose a new efficient numerical algorithm to solve the optimization problem. By tuning the balancing parameter, the new model achieves solutions of three models. It is found that the balanced model has a comparable performance with the analysis model. Besides, both of them achieve better results than the synthesis model no matter what value the balancing parameter is. Experiment shows that our proposed numerical algorithm constrained split augmented Lagrangian shrinkage algorithm for balanced model (C-SALSA-B) converges faster than previously proposed algorithms accelerated proximal algorithm (APG) and alternating directional method of multipliers for balanced model (ADMM-B).

Accelerated NMR spectroscopy with low-rank reconstruction.

Accelerated multi-dimensional NMR spectroscopy is a prerequisite for high-throughput applications, studying short-lived molecular systems and monitoring chemical reactions in real time. Non-uniform sampling is a common approach to reduce the measurement time. Here, a new method for high-quality spectra reconstruction from non-uniformly sampled data is introduced, which is based on recent developments in the field of signal processing theory and uses the so far unexploited general property of the NMR signal, its low rank. Using experimental and simulated data, we demonstrate that the low-rank reconstruction is a viable alternative to the current state-of-the-art technique compressed sensing. In particular, the low-rank approach is good in preserving of low-intensity broad peaks, and thus increases the effective sensitivity in the reconstructed spectra.

Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study.

Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over one breathing cycle and the fourth dimension is actually breathing phase instead of time. In many clinical applications, it is desirable to obtain true 4DCBCT with the fourth dimension being time, i.e., each constituent CBCT image corresponds to an instantaneous projection. Theoretically it is impossible to reconstruct a CBCT image from a single projection. However, if all the constituent CBCT images of a 4DCBCT scan share a lot of redundant information, it might be possible to make a good reconstruction of these images by exploring their sparsity and coherence/redundancy. Though these CBCT images are not completely time resolved, they can exploit both local and global temporal coherence of the patient anatomy automatically and contain much more temporal variation information of the patient geometry than the conventional 4DCBCT. We propose in this work a computational model and algorithms for the reconstruction of this type of semi-time-resolved CBCT, called cine-CBCT, based on low rank approximation that can utilize the underlying temporal coherence both locally and globally, such as slow variation, periodicity or repetition, in those cine-CBCT images.

Effects of Jianpi Jiedu Recipe on reversion of P-glycoprotein-mediated multidrug resistance through COX-2 pathway in colorectal cancer.

OBJECTIVE: To evaluate the underlying mechanism of Jianpi Jiedu Recipe (, JJR) in the reversion of multidrug resistance concerning colorectal cancer in vitro and in vivo. METHODS: Mice were treated orally with JJR at a daily 4.25 g/(kg·day) or injected with vinblastine (VCR) 2.5 mg/(kg·day) for 3 weeks after having been inoculated with HCT8/V cells; tumor tissues were assayed by hematoxylin and eosin staining. Firstly, the effects of JJR on the expression of cyclooxygenase-2 (COX-2) were tested by real-time polymerase chain reaction (PCR) technique and COX-2 gene silenced by siRNA. Secondly, the variation of intracellular concentration of oxaliplatin (L-OHP) was evaluated by the inductively coupled plasma mass spectroscopy (ICPMS) in HCT8/V and its COX-2 siRNA cells; the concentration of JJR combined with chemotherapeutic drugs and the reverse effect of multidrug resistance (MDR) in HCT8/V cells was evaluated by the MTT assay. Thirdly, real-time quantitative PCR and Western blot analysis were used to detect the multidrug resistance gene 1 (MDR1) mRNA and P-gp expression. RESULTS: JJR had an inhibitory effect on the growth of tumors in vivo, and it, in combination with chemotherapeutic drugs, could reverse the drug-resistance of HCT8/V cells and increase the sensitivity of HCT8/V cells to VCR, DDP, 5-Fu, and THP. ICP-MS results showed that JJR could increase the concentration of drugs in HCT8/V cells (P<0.01). Furthermore, it was shown that JJR could reverse drug resistance of colorectal cancer cells by decreasing MDR1 expression and P-gp level via downregulation of COX-2, which has been represented as one of the major mechanisms that contributes to the MDR phenotype (P<0.01). CONCLUSION: JJR reversed multidrug resistance and enhanced the sensitivity to chemotherapy, which could be attributed to the down-regulation of COX-2 in MDR1/P-gp-mediated MDR colorectal cancer after chemotherapy.

Low-rank regularization for learning gene expression programs.

Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets.

Assessment of left ventricular dyssynchrony and cardiac function in patients with different pacing modes using real-time three-dimensional echocardiography: Comparison with tissue Doppler imaging.

The aim of this study was to evaluate the left ventricular mechanical dyssynchrony (LVMD) and left ventricular dysfunction of patients in AAI, DDD and VVI pacing modes using real-time three-dimensional echocardiography (RT3DE) and tissue Doppler imaging (TDI). The results from the RT3DE and TDI were subsequently compared. Twenty patients with sick sinus syndrome (SSS) who had undergone the implantation of a dual-chamber pacemaker were enrolled in this study and the pacemakers were programmed to AAI, DDD and VVI modes, sequentially. The RT3DE and TDI parameters were obtained following pacing for 24 h in each mode. With RT3DE, we measured the systolic dyssynchrony indices, including Tmsv16-SD%, Tmsv12-SD%, Tmsv6-SD%, Tmsv16-Dif%, Tmsv12-Dif% and Tmsv6-Dif%, left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV) and left ventricular ejection fraction (LVEF), respectively. With TDI, we measured the standard deviation and the maximal difference in time from the QRS onset to the peak systolic velocity for 12 left ventricular myocardial segments, i.e. Ts-SD and Ts-Dif, respectively. The results showed that the Tmsv16-SD% and Ts-SD in the AAI mode were significantly lower than those in the DDD and VVI modes (P<0.05); however, there were no significant differences between the DDD and VVI modes (P>0.05). The LVEF in the AAI, DDD and VVI modes was 63.1±8.9, 58.6±11.2 and 57.9±7.6%, respectively (P>0.05). There were negative correlations between the LVEF and Tmsv16-SD% (r, -0.651; P<0.001) and Ts-SD (r, -0.649; P<0.0001). A moderate correlation (r, 0.698; P<0.0001) was observed between Tmsv16-SD% and Ts-SD. The concordance rate between Tmsv16-SD% and Ts-SD for detecting LVMD was 76%. This study showed that DDD and VVI pacing modes induced significant LVMD and a reduction in LVEF, unlike the AAI pacing mode. RT3DE and TDI were capable of objectively evaluating LVMD; however, each method had certain faults. At present, there is a lack of a uniform standard for assessing LVMD; therefore, the use of a variety of techniques and indices is necessary in order to comprehensively evaluate LVMD in patients with different cardiac pacing modes.

Zuo Jin Wan, a Traditional Chinese Herbal Formula, Reverses P-gp-Mediated MDR In Vitro and In Vivo.

Zuo Jin Wan (ZJW), a typical traditional Chinese medicine (TCM) formula, has been identified to have anticancer activity in recent studies. In this study, we determined the underlying mechanism of ZJW in the reversal effect of multidrug resistance on colorectal cancer in vitro and in vivo. Our results showed that ZJW significantly enhanced the sensitivity of chemotherapeutic drugs in HCT116/L-OHP, SGC7901/DDP, and Bel/Fu MDR cells. Moreover, combination of chemotherapy with ZJW could reverse the drug resistance of HCT116/L-OHP cells, increase the sensitivity of HCT116/L-OHP cells to L-OHP, DDP, 5-Fu, and MMC in vitro, and inhibit the tumor growth in the colorectal MDR cancer xenograft model. ICP-MS results showed that ZJW could increase the concentration of chemotherapeutic drugs in HCT116/L-OHP cells in a dose-dependent manner. Furthermore, we showed that ZJW could reverse drug resistance of colorectal cancer cells by decreasing P-gp level in vitro and in vivo, which has been represented as one of the major mechanisms that contribute to the MDR phenotype. Our study has provided the first direct evidence that ZJW plays an important role in reversing multidrug resistance of human colorectal cancer and may be considered as a useful target for cancer therapy.

Role of poly(ADP-ribose) glycohydrolase in the regulation of cell fate in response to benzo(a)pyrene.

Poly(ADP-ribosyl)ation is a crucial regulator of cell fate in response to genotoxic stress. Poly(ADP-ribosyl)ation plays important roles in multiple cellular processes, including DNA repair, chromosomal stability, chromatin function, apoptosis, and transcriptional regulation. Poly(ADP-ribose) (PAR) degradation is carried out mainly by poly(ADP-ribose) glycohydrolase (PARG) enzymes. Benzo(a)pyrene (BaP) is a known human carcinogen. Previous studies in our laboratory demonstrated that exposure to BaP caused a concentration-dependent DNA damage in human bronchial epithelial (16HBE) cells. The role of PARG in the regulation of DNA damage induced by BaP is still unclear. To gain insight into the function of PARG and PAR in response to BaP, we used lentiviral gene silencing to generate 16HBE cell lines with stably suppressed PARG, and determined parameters of cell death and cell cycle following BaP exposure. We found that PARG was partially dependent on PAR synthesis, PARG depletion led to PAR accumulation. BaP-induced cell death was regulated by PARG, the absence of which was beneficial for undamaged cells. Our results further suggested that PARG probably has influence on ATM/p53 pathway and metabolic activation of BaP. Experimental evidences provided from this study suggest significant preventive properties of PAR accumulation in the toxicity caused by BaP.

Framelet-based blind motion deblurring from a single image.

How to recover a clear image from a single motion-blurred image has long been a challenging open problem in digital imaging. In this paper, we focus on how to recover a motion-blurred image due to camera shake. A regularization-based approach is proposed to remove motion blurring from the image by regularizing the sparsity of both the original image and the motion-blur kernel under tight wavelet frame systems. Furthermore, an adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problem. The experiments on both synthesized images and real images show that our algorithm can effectively remove complex motion blurring from natural images without requiring any prior information of the motion-blur kernel.

Anti-tumor activity and apoptosis-regulation mechanisms of bufalin in various cancers: new hope for cancer patients.

The induction of apoptosis in target cells is a key mechanism for most anti-tumor therapies. Bufalin is a cardiotonic steroid that has the potential to induce differentiation and apoptosis of tumor cells. Research on bufalin has so far mainly involved leukemia, prostate cancer, gastric cancer and liver cancer, and has been confined to in vitro studies. The bufadienolides bufalin and cinobufagin have been shown to induce apoptosis in a wide spectrum of cancer cell. The present article reviews the anticancer effects of bufalin. It induces apoptosis of lung cancer cells via the PI3K/Akt pathway and also suppressed the proliferation of human non-small cell lung cancer A549 cell line in a time and dose dependent manner. Bufalin, bufotalin and gamabufotalin, key bufadienolides, significantly sensitize human breast cancer cells with differing ER-alpha status to apoptosis induction by the TNF-related apoptosis-inducing ligand (TRAIL). In addition, bufadienolides induce prostate cancer cell apoptosis more significantly than that in breast epithelial cell lines. Similar effects have been observed with hepatocellular carcinoma (HCC) but the detailed molecular mechanisms of inducing apoptosis in this case are still unclear. Bufalin exerts profound effects on leukemia therapy in vitro. Results of multiple studies indicate that bufalin has marked anti-tumor activities through its ability to induce apoptosis. Large-scale randomized, double-blind, placebo or positive drug parallel controlled studies are now required to confirm the efficacy and apoptosis-inducing potential of bufalin in various cancers in the cliniucal setting.

Robust principal component analysis-based four-dimensional computed tomography.

The purpose of this paper for four-dimensional (4D) computed tomography (CT) is threefold. (1) A new spatiotemporal model is presented from the matrix perspective with the row dimension in space and the column dimension in time, namely the robust PCA (principal component analysis)-based 4D CT model. That is, instead of viewing the 4D object as a temporal collection of three-dimensional (3D) images and looking for local coherence in time or space independently, we perceive it as a mixture of low-rank matrix and sparse matrix to explore the maximum temporal coherence of the spatial structure among phases. Here the low-rank matrix corresponds to the 'background' or reference state, which is stationary over time or similar in structure; the sparse matrix stands for the 'motion' or time-varying component, e.g., heart motion in cardiac imaging, which is often either approximately sparse itself or can be sparsified in the proper basis. Besides 4D CT, this robust PCA-based 4D CT model should be applicable in other imaging problems for motion reduction or/and change detection with the least amount of data, such as multi-energy CT, cardiac MRI, and hyperspectral imaging. (2) A dynamic strategy for data acquisition, i.e. a temporally spiral scheme, is proposed that can potentially maintain similar reconstruction accuracy with far fewer projections of the data. The key point of this dynamic scheme is to reduce the total number of measurements, and hence the radiation dose, by acquiring complementary data in different phases while reducing redundant measurements of the common background structure. (3) An accurate, efficient, yet simple-to-implement algorithm based on the split Bregman method is developed for solving the model problem with sparse representation in tight frames.

[Evaluation on left ventricular systolic synchronicity and cardiac function in patients with permanent cardiac pacing by real-time three-dimensional echocardiography].

OBJECTIVE: To determine the feasibility on the left ventricular systolic synchronism and cardiac function evaluation in patients with permanent cardiac pacing by real-time three-dimensional echocardiography. METHODS: Fifteen patients with sick sinus syndrome post dual-chamber pacemaker implantation were enrolled in this study. Pacemakers were programmed to AAI, DDD, and VVI respectively. After pacing for 5 minutes in each mode, participants were examined with real-time three-dimensional echocardiography. Images in different pacing modes were obtained and analyzed by the off-line Qlab 4.2 software. Parameters including global and 17-segmental volume-time curves (VTCs), dispersion of time to minimal regional volume for 16, 12, and 6 left ventricular segments (Tmsv16-s, Tmsv12-s, Tmsv6-s), and maximal difference of time to minimal regional volume for l6, 12 and 6 left ventricular segments (Tmsv16-dif, Tmsv12-dif, Tmsv6-dif), end diastolic volume (EDV), end systolic volume (ESV), left ventricular ejection fraction (LVEF) were measured respectively. Parameters of peak filling rate (PFR), regional end diastolic volume (rEDV), regional end systolic volume (rESV), and regional ejection fraction (rEF) were also calculated. RESULTS: Left ventricular systolic synchronism as reflected by VTCs, Tmsv16-s, Tmsv12-s, Tmsv6-s, Tmsv16-dif, Tmsv12-dif and Tmsv6-dif as well as parameters reflecting ventricular function, i.e., LVEF, PFR were significantly better in AAI mode than in DDD and VVI models (all P < 0.05). All above indexes were similar between DDD and VVI models (all P > 0.05). rEFs of left inferior wall in base, septum in base and apex were significantly lower in DDD and VVI models compared that in AAI mode (P < 0.05). CONCLUSION: Real-time three-dimensional echocardiography can objectively and accurately evaluate left ventricular systolic synchronism and cardiac function in patients with permanent cardiac pacing and AAI mode is superior to DDD and VVI models.

[Histo-morphological changes of eustachian tube of guinea pigs with effusion after being treated by pulmonary surfactant].

OBJECTIVE: To study the effect of pulmonary surfactant on otitis media with effusion in guinea pigs to find a new way to manage otitis media with effusion. METHODS: Nonviable heat-killed pneumococci (HKP) solution was inoculated into the middle ear cavity in guinea pigs via a transeardrum approach to set up a model of otitis media with effusion in guinea pigs. Seven days after being injected with pulmonary surfactant (PS) by transeardrum approach, ABR threshold and histomorphological changes of eustachian tube mucosa of guinea pigs were examined by light microscopy and scanning microscopy. RESULTS: Five days following inoculation of HKB serous effusion were present in the middle ear cavity of guinea pigs, but disappearance of light cone. Response (mean +/- s) threshold raised from (14.0 +/- 3.1) dB to (45.0 +/- 5.7) dB. The eustachian tube mucosa was thickened and lined eosin-stained structureless matter over mucosa, while cilia of eustachian tube mucosa irregularly arranged. Seven days after being treated by PS, serous effusion of tympanum was reduced or disappeared, and response threshold decreased from (45.0 +/- 5.7) dB to (23.5 +/- 6.3) dB. There was significantly difference between them (P < 0.001). Eustachian tube mucosa was thinned, Cilia of eustachian tube mucosa regularly arranged to the nasopharynx. CONCLUSION: Pulmonary surfactant plays a important role in otitis media with effusion of guinea pigs.