Mucoepidermoid carcinoma of the lung with hemoptysis as initial symptom: A case report.

BACKGROUND: Mucoepidermoid carcinoma of the lung is a rare malignant tumor, accounting for 0.1%-0.2% of all lung malignancies. It is a primary salivary gland tumor of the lung. Surgical resection is the primary treatment for pulmonary mucoepidermoid carcinoma, for which there has been no standardized treatment strategy. This article reports a case of a young woman with pulmonary mucoepidermoid carcinoma with hemoptysis as the first symptom. CASE SUMMARY: A 24-year-old female patient presented with "4 d of hemoptysis" as the chief complaint. She had no special history and denied any smoking or drinking history. Physical examination revealed that the vital signs were stable and scattered small wet rales were heard in the left lung. After admission, the lung tumor markers were checked, and no abnormalities were found. After completing the bronchoscopy, a spherical lesion was observed at the main bronchus 1.5 cm away from the protubercle, with obvious pulsation and little blood seepage on the surface, and histopathological biopsy results showed acute and chronic inflammation. She was transferred to the Department of Thoracic Surgery for surgical treatment on the 16th day after admission. After exclusion of surgical conjunctures, the patient underwent resection of the tumor in the left main bronchus with single-pore video-assisted thoracic surgery on the 19th day after admission. The postoperative histopathological biopsy results showed mucoepidermoid carcinoma of the lung. The patient and her family refused to complete genetic testing and she was discharged from the hospital on the 8th day after surgery. During the follow-up period, the patient experienced shortness of breath after feeling active and had no special discomfort. CONCLUSION: We have documented a case of moderately differentiated mucoepidermoid lung cancer with hemoptysis as the first symptom to improve clinicians' understanding of the disease and provide a new dimension of thinking for its future diagnosis and treatment.

Recurrence network analysis of the synchronous EEG time series in normal and epileptic brains.

We sought to analyze the dynamic properties of brain electrical activity from healthy volunteers and epilepsy patients using recurrence networks. Phase-space trajectories of synchronous electroencephalogram signals were obtained through embedding dimension in phase-space reconstruction based on the distance set of space points. The recurrence matrix calculated from phase-space trajectories was identified with the adjacency matrix of a complex network. Then, we applied measures to characterize the complex network to this recurrence network. A detailed analysis revealed the following: (1) The recurrence networks of normal brains exhibited a sparser connectivity and smaller clustering coefficient compared with that of epileptic brains; (2) the small-world property existed in both normal and epileptic brains consistent with the previous empirical studies of structural and functional brain networks; and (3) the assortative property of the recurrence network was found by computing the assortative coefficients; their values increased from normal to epileptic brain which accurately suggested the difference of the states. These universal and non-universal characteristics of recurrence networks might help clearly understand the underlying neurodynamics of the brain and provide an efficient tool for clinical diagnosis.

Synchronization-based approach for detecting functional activation of brain.

In this paper, we investigate a synchronization-based, data-driven clustering approach for the analysis of functional magnetic resonance imaging (fMRI) data, and specifically for detecting functional activation from fMRI data. We first define a new measure of similarity between all pairs of data points (i.e., time series of voxels) integrating both complete phase synchronization and amplitude correlation. These pairwise similarities are taken as the coupling between a set of Kuramoto oscillators, which in turn evolve according to a nearest-neighbor rule. As the network evolves, similar data points naturally synchronize with each other, and distinct clusters will emerge. The clustering behavior of the interaction network of the coupled oscillators, therefore, mirrors the clustering property of the original multiple time series. The clustered regions whose cross-correlation coefficients are much greater than other regions are considered as the functionally activated brain regions. The analysis of fMRI data in auditory and visual areas shows that the recognized brain functional activations are in complete correspondence with those from the general linear model of statistical parametric mapping, but with a significantly lower time complexity. We further compare our results with those from traditional K-means approach, and find that our new clustering approach can distinguish between different response patterns more accurately and efficiently than the K-means approach, and therefore more suitable in detecting functional activation from event-related experimental fMRI data.

Scale invariance of human electroencephalogram signals in sleep.

In this paper, we investigate the dynamical properties of electroencephalogram (EEG) signals of humans in sleep. By using a modified random walk method, we demonstrate that scale-invariance is embedded in EEG signals after a detrending procedure is applied. Furthermore, we study the dynamical evolution of the probability density function (PDF) of the detrended EEG signals by nonextensive statistical modeling. It displays a scale-independent property, which is markedly different from the usual scale-dependent PDF evolution and cannot be described by the Fokker-Planck equation.

Self-organized Boolean game on networks.

A model of a Boolean game with only one free parameter p that denotes the strength of local interaction is proposed wherein each agent acts according to the information obtained from his neighbors in the network, and those in the minority are rewarded. The simulation results indicate that the dynamic of the system is sensitive to network topology, whereby the network of larger degree variance, i.e., the system of greater information heterogeneity, leads to less system profit. The system can self-organize to a stable state and perform better than the random choice game, although only the local information is available to the agents. In addition, in heterogeneity networks, the agents with more information gain more than those with less information for a wide extent of interaction strength p.

[The study of phosphorus in seawater cultured pearls using FTIR].

Seawater cultured pearl was studied by high resolution fourier transform infrared spectroscopy (FTIR) both before and after having been heated at 160 centigrade degree. The results of FTIR indicate that the main material in both samples is aragonite (CaCO3). But compared with the spectra of the seawater cultured pearl, there are some changes that happen in that of the heated sample. Some other peaks arise in the latter. And the intensities of these peaks increase with the heating time. Differential Thermal Analysis (DTA) curve of the seawater cultured pearl shows that seawater cultured pearl has no change between 25-160 degrees C except absorbed water evaporating from the sample. All these evidences prove that besides aragonite, there exists something else in the seawater cultured pearl. This component was determined to be pahuite by FTIR spectroscopic analysis. X-Ray Diffraction (XRD) analysis of the same sample came to the same conclusion.