Digital synthesis of free-form multimaterial structures for realization of arbitrary programmed mechanical responses.

SignificanceCreating structures to realize function-oriented mechanical responses is desired for many applications. Yet, the use of a single material phase and heuristics-based designs may fail to attain specific target behaviors. Here, through a deterministic algorithmic procedure, multiple materials with dissimilar properties are intelligently synthesized into composite structures to achieve arbitrary prescribed responses. Created structures possess unconventional geometry and seamless integration of multiple materials. Despite geometric complexity and varied material phases, these structures are fabricated by multimaterial manufacturing, and tested to demonstrate that wide-ranging nonlinear responses are physically and accurately realized. Upon heteroassembly, resulting structures provide architectures that exhibit highly complex yet navigable responses. The proposed strategy can benefit the design of function-oriented nonlinear mechanical devices, such as actuators and energy absorbers.

Fault Diagnosis of Rolling Bearing Based on a Priority Elimination Method.

Aiming at the fault diagnosis accuracy of rolling bearings is not high enough, and unknown faults cannot be correctly identified. A priority elimination (PE) method is proposed in this paper. First, the priority diagnosis sequence of faults was determined by comparing the ratios of the inter-class distance to the intra-class distance for all faults. Then, the model training and fault diagnosis were carried out in order of the priority sequence, and the samples of the fault that had been identified were eliminated from the data set until all faults were diagnosed. For the diagnosis model, the stacked sparse auto-encoder network (SSAE) was selected to extract the features of the vibration signal. The extreme gradient boosting algorithm (XGBoost) was chosen to identify the fault type. Finally, the method was tested and verified by experimental data and compared with classical algorithms. Research results indicate the following: (1) with the addition of PE based on SSAE-XGBoost, the fault diagnosis accuracy can be improved from 96.3% to 99.27%, which is higher than other methods; (2) for the test set with the samples of unknown faults, the diagnosis accuracy of SSAE-XGBoost with PE can reach 92.34%, which is nearly 6% higher than that without PE and is also obviously higher than other classical fault diagnosis methods with or without PE. The PE method can not only improve the diagnosis accuracy of faults but also identify unknown faults, which provides a new method and way for fault diagnosis.

Low-Power Photodetectors Based on PVA-Modified Reduced Graphene Oxide Hybrid Solutions.

Photodetectors based on reduced graphene oxide (rGO) have attracted much attention owing to their simple and low-cost fabrication process. However, the aggregation and defects of rGO flakes still limit the performance of rGO photodetectors. Controlling the composition of rGO has become a vital factor for its prospective applications. For example, the interconnection between rGO and polymers for modified morphologies of rGO films leads to an enhanced performance of devices. In this work, a practical approach to engineer surface uniformity and enhance the performance of a photodetector by modifying the rGO film with hydrophilic polymers poly(vinyl alcohol) (PVA) is reported. Compared with the rGO photodetector, the on/off ratio for the PVA/rGO photodetector shows 3.5 times improvement, and the detectivity shows 53% enhancement even when the photodetector is operated at a low bias of 0.3 V. This study provides an effective route to realize PVA/rGO photodetectors with a low-power operation which shows promising opportunities for the future development of green systems.

An on-line processing strategy for head movement interferences removal of dynamic brain electrical impedance tomography based on wavelet decomposition.

BACKGROUND: Head movement interferences are a common problem during prolonged dynamic brain electrical impedance tomography (EIT) clinical monitoring. Head movement interferences mainly originate from body movements of patients and nursing procedures performed by medical staff, etc. These body movements will lead to variation in boundary voltage signals, which affects image reconstruction. METHODS: This study employed a data preprocessing method based on wavelet decomposition to inhibit head movement interferences in brain EIT data. Mixed Gaussian models were applied to describe the distribution characteristics of brain EIT data. We identified head movement signal through the differences in distribution characteristics of corresponding wavelet decomposition coefficients between head movement artifacts and normal signals, and then managed the contaminated data with improved on-line wavelet processing methods. RESULTS: To validate the efficacy of the method, simulated signal experiments and human data experiments were performed. In the simulation experiment, the simulated movement artifact was significantly reduced and data quality was improved with indicators' increase in PRD and correlation coefficient. Human data experiments demonstrated that this method effectively suppressed head movement in signals and reduce artifacts resulting from head movement artifacts in images. CONCLUSION: In this paper, we proposed an on-line strategy to manage the head movement interferences from the brain EIT data based on the distribution characteristics of wavelet coefficients. Our strategy is capable of reducing the movement interference in the data and improving the reconstructed images. This work would improve the clinical practicability of brain EIT and contribute to its further promotion.

Optimal combination of electrodes and conductive gels for brain electrical impedance tomography.

BACKGROUND: Electrical impedance tomography (EIT) is an emerging imaging technology that has been used to monitor brain injury and detect acute stroke. The time and frequency properties of electrode-skin contact impedance are important for brain EIT because brain EIT measurement is performed over a long period when used to monitor brain injury, and is carried out across a wide range of frequencies when used to detect stroke. To our knowledge, no study has simultaneously investigated the time and frequency properties of both electrode and conductive gel for brain EIT. METHODS: In this study, the contact impedance of 16 combinations consisting of 4 kinds of clinical electrode and five types of commonly used conductive gel was measured on ten volunteers' scalp for a period of 1 h at frequencies from 100 Hz to 1 MHz using the two-electrode method. And then the performance of each combination was systematically evaluated in terms of the magnitude of contact impedance, and changes in contact impedance with time and frequency. RESULTS: Results showed that combination of Ag+/Ag+Cl- powder electrode and low viscosity conductive gel performed best overall (Ten 20® in this study); it had a relatively low magnitude of contact impedance and superior performance regarding contact impedance with time (p < 0.05) and frequency (p < 0.05). CONCLUSIONS: Experimental results indicates that the combination of Ag+/Ag+Cl- powder electrode and low viscosity conductive gel may be the best choice for brain EIT.

Rapid and sensitive detection of ketamine in blood using novel fluorescence genosensor.

In recent years, drug abuse has been considered as a most challenging social problem that aroused public attention. Ketamine has increased in unregulated use as a 'recreational drug' in teenagers. However, there is no suitable and maneuverable detection method for ketamine in situ at the moment. Fluorescence sensor technique, with predominant recognition and simple operation, is a good potential application in drug detection. Here, we first reported a highly sensitive and selective fluorescence genosensor for rapid detection of ketamine based on DNA-templated silver nanoclusters (DNA-AgNCs) probes, in which the DNA sequence could specially recognize ketamine with high affinity. Parameters affecting detection efficiency were investigated and optimized. Under optimum conditions, the as-prepared genosensor can allow for the determination of ketamine in the concentration range of 0.0001-20 µg/mL with two linear equations: one is y = 2.84x-7.139 (R2 = 0.987) for 0.0001-0.1 µg/mL, and the other is y = 1.87x-0.091 (R2 = 0.962) for 0.1-20 µg/mL, and the estimated detection limit of ketamine is 0.06 ng/mL. Moreover, the feasibility of this proposed method was also demonstrated by analyzing forensic blood samples. Compared with official gas chromatography/mass spectrometry (GC/MS), this fluorescence genosensor is simple, rapid, and accurate for quantitative determination of ketamine in blood for pharmaceutical and forensic analysis. Overall, it is the first report on a fluorescence genosensor for detecting ketamine directly in blood. This research may provide a new insight for the analyst to band fluorescence genosensor technology together with drug monitoring in the battle against drug abuse and forensic examination. Graphical abstract High selectively detection of ketamine using a novel fluorescence genosensor based on DNA-AgNCs probe.

Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography.

BACKGROUND: Electrode disconnection is a common occurrence during long-term monitoring of brain electrical impedance tomography (EIT) in clinical settings. The data acquisition system suffers remarkable data loss which results in image reconstruction failure. The aim of this study was to: (1) detect disconnected electrodes and (2) account for invalid data. METHODS: Weighted correlation coefficient for each electrode was calculated based on the measurement differences between well-connected and disconnected electrodes. Disconnected electrodes were identified by filtering out abnormal coefficients with discrete wavelet transforms. Further, previously valid measurements were utilized to establish grey model. The invalid frames after electrode disconnection were substituted with the data estimated by grey model. The proposed approach was evaluated on resistor phantom and with eight patients in clinical settings. RESULTS: The proposed method was able to detect 1 or 2 disconnected electrodes with an accuracy of 100%; to detect 3 and 4 disconnected electrodes with accuracy of 92 and 84% respectively. The time cost of electrode detection was within 0.018 s. Further, the proposed method was capable to compensate at least 60 subsequent frames of data and restore the normal image reconstruction within 0.4 s and with a mean relative error smaller than 0.01%. CONCLUSIONS: In this paper, we proposed a two-step approach to detect multiple disconnected electrodes and to compensate the invalid frames of data after disconnection. Our method is capable of detecting more disconnected electrodes with higher accuracy compared to methods proposed in previous studies. Further, our method provides estimations during the faulty measurement period until the medical staff reconnects the electrodes. This work would improve the clinical practicability of dynamic brain EIT and contribute to its further promotion.

In Vivo Bioimpedance Spectroscopy Characterization of Healthy, Hemorrhagic and Ischemic Rabbit Brain within 10 Hz-1 MHz.

Acute stroke is a serious cerebrovascular disease and has been the second leading cause of death worldwide. Conventional diagnostic modalities for stroke, such as CT and MRI, may not be available in emergency settings. Hence, it is imperative to develop a portable tool to diagnose stroke in a timely manner. Since there are differences in impedance spectra between normal, hemorrhagic and ischemic brain tissues, multi-frequency electrical impedance tomography (MFEIT) shows great promise in detecting stroke. Measuring the impedance spectra of healthy, hemorrhagic and ischemic brain in vivo is crucial to the success of MFEIT. To our knowledge, no research has established hemorrhagic and ischemic brain models in the same animal and comprehensively measured the in vivo impedance spectra of healthy, hemorrhagic and ischemic brain within 10 Hz-1 MHz. In this study, the intracerebral hemorrhage and ischemic models were established in rabbits, and then the impedance spectra of healthy, hemorrhagic and ischemic brain were measured in vivo and compared. The results demonstrated that the impedance spectra differed significantly between healthy and stroke-affected brain (i.e., hemorrhagic or ischemic brain). Moreover, the rate of change in brain impedance following hemorrhagic and ischemic stroke with regard to frequency was distinct. These findings further validate the feasibility of using MFEIT to detect stroke and differentiate stroke types, and provide data supporting for future research.

Analytical realization of complex thermal meta-devices.

Fourier's law dictates that heat flows from warm to cold. Nevertheless, devices can be tailored to cloak obstacles or even reverse the heat flow. Mathematical transformation yields closed-form equations for graded, highly anisotropic thermal metamaterial distributions needed for obtaining such functionalities. For simple geometries, devices can be realized by regular conductor distributions; however, for complex geometries, physical realizations have so far been challenging, and sub-optimal solutions have been obtained by expensive numerical approaches. Here we suggest a straightforward and highly efficient analytical de-homogenization approach that uses optimal multi-rank laminates to provide closed-form solutions for any imaginable thermal manipulation device. We create thermal cloaks, rotators, and concentrators in complex domains with close-to-optimal performance and esthetic elegance. The devices are fabricated using metal 3D printing, and their omnidirectional thermal functionalities are investigated numerically and validated experimentally. The analytical approach enables next-generation free-form thermal meta-devices with efficient synthesis, near-optimal performance, and concise patterns.

The role of SARS-CoV-2 ORF7a in autophagy flux disruption: implications for viral infection and pathogenesis.

Although alterations in the autophagy-lysosome pathway have been observed in the SARS-CoV-2 infection and invasion process since the outbreak of the coronavirus disease in 2019, the in-depth mechanism of autophagic and lysosomal reprogramming by SARS-CoV-2 has yet to be well identified. Our recent study unveiled a pivotal role played by the open reading frame 7a (ORF7a) protein in the SARS-CoV-2 genome, particularly in the modulation of macroautophagy/autophagy flux and function during viral infection and pathogenesis. Our study elucidated the underlying molecular mechanisms by which SARS-CoV-2 ORF7a intercepts autophagic flux, evades host autophagy-lysosome degradation, and accelerates viral infection and progeny germination. Furthermore, our study highlights that ORF7a can be a therapeutic target, and glecaprevir may hold potential as a drug against SARS-CoV-2 by targeting ORF7a. The key observations revealed in this study also contribute to a growing understanding of the function of SARS-CoV-2 ORF7a and the mechanisms underlying COVID-2019 treatment.

Structure Design and Heat Transfer Performance Analysis of a Novel Composite Phase Change Active Cooling Channel Wall for Hypersonic Aircraft.

Efficient and stable heat dissipation structure is crucial for improving the convective heat transfer performance of thermal protection systems (TPSs) for hypersonic aircraft. However, the heat dissipation wall of the current TPS is limited by a single material and structure, inefficiently dissipating the large amount of accumulated heat generated during the high-speed maneuvering flight of hypersonic aircraft. Here, a convection cooling channel structure of TPS is proposed, which is an innovative multi-level structure inspired by the natural honeycomb. An active cooling channel (PCM-HC) is designed by using a variable-density topology optimization method and filled with phase change material (PCM). Numerical simulations are used to investigate the thermal performance of the PCM-HC wall, focusing on the influence of PCM properties, structural geometric parameters, and PCM types on heat transfer characteristics. The results demonstrate that the honeycomb-like convection cooling channel wall, combined with PCM latent heat of phase change, exhibits superior heat dissipation capability. With a heat flux input of 50 kW/m2, the maximum temperature on the inner wall of PCM-HC is reduced by 12 K to 20 K. Different PCMs have opposing effects on heat transfer performance due to their distinct thermophysical properties. This work can provide a theoretical basis for the design of high-efficiency cooling channel, improving the heat dissipation performance in the TPS of hypersonic aircraft.

A preliminary study on the application of electrical impedance tomography based on cerebral perfusion monitoring to intracranial pressure changes.

Background: In intracranial pathologic conditions of intracranial pressure (ICP) disturbance or hemodynamic instability, maintaining appropriate ICP may reduce the risk of ischemic brain injury. The change of ICP is often accompanied by the change of intracranial blood status. As a non-invasive functional imaging technique, the sensitivity of electrical impedance tomography (EIT) to cerebral hemodynamic changes has been preliminarily confirmed. However, no team has conducted a feasibility study on the dynamic detection of ICP by EIT technology from the perspective of non-invasive whole-brain blood perfusion monitoring. In this study, human brain EIT image sequence was obtained by in vivo measurement, from which a variety of indicators that can reflect the tidal changes of the whole brain impedance were extracted, in order to establish a new method for non-invasive monitoring of ICP changes from the level of cerebral blood perfusion monitoring. Methods: Valsalva maneuver (VM) was used to temporarily change the cerebral blood perfusion status of volunteers. The electrical impedance information of the brain during this process was continuously monitored by EIT device and real-time imaging was performed, and the hemodynamic indexes of bilateral middle cerebral arteries were monitored by transcranial Doppler (TCD). The changes in monitoring information obtained by the two techniques were compared and observed. Results: The EIT imaging results indicated that the image sequence showed obvious tidal changes with the heart beating. Perfusion indicators of vascular pulsation obtained from EIT images decreased significantly during the stabilization phase of the intervention (PAC: 242.94 ± 100.83, p < 0.01); perfusion index which reflects vascular resistance increased significantly in the stable stage of intervention (PDT: 79.72 ± 18.23, p < 0.001). After the intervention, the parameters gradually returned to the baseline level before compression. The changes of EIT indexes in the whole process are consistent with the changes of middle cerebral artery velocity related indexes shown in TCD results. Conclusion: The EIT image combined with the blood perfusion index proposed in this paper can reflect the decrease of cerebral blood flow under the condition of increased ICP in real time and intuitively. With the advantages of high time resolution and high sensitivity, EIT provides a new idea for non-invasive bedside measurement of ICP.

Federated learning with hyper-network-a case study on whole slide image analysis.

Federated learning(FL) is a new kind of Artificial Intelligence(AI) aimed at data privacy preservation that builds on decentralizing the training data for the deep learning model. This new technique of data security and privacy sheds light on many critical domains with highly sensitive data, including medical image analysis. Developing a strong, scalable, and precise deep learning model has proven to count on a variety of high-quality data from different centers. However, data holders may not willing to share their data considering the restriction of privacy. In this paper, we approach this challenge with a federated learning paradigm. Specifically, we present a case study on the whole slide image classification problem. At each local client center, a multiple-instance learning classifier is developed to conduct whole slide image classification. We introduce a privacy-preserving federated learning framework based on hyper-network to update the global model. Hyper-network is deployed at the global center that produces the weights of the local network conditioned on its input. In this way, hyper-networks can simultaneously learn a family of the local client networks. Instead of communicating raw data with the local client, only model parameters injected with noise are transferred between the local client and the global model. By using a large scale of whole slide images with only slide-level labels, we mensurated our way on two different whole slide image classification problems. The results demonstrate that our proposed federated learning model based on hyper-network can effectively leverage multi-center data to develop a more accurate model which can be used to classify a whole slide image. Its improvements in terms of over the isolated local centers and the commonly used federated averaging baseline are significant. Code will be available.

Algorithmic encoding of adaptive responses in temperature-sensing multimaterial architectures.

We envision programmable matters that can alter their physical properties in desirable manners based on user input or autonomous sensing. This vision motivates the pursuit of mechanical metamaterials that interact with the environment in a programmable fashion. However, this has not been systematically achieved for soft metamaterials because of the highly nonlinear deformation and underdevelopment of rational design strategies. Here, we use computational morphogenesis and multimaterial polymer 3D printing to systematically create soft metamaterials with arbitrarily programmable temperature-switchable nonlinear mechanical responses under large deformations. This is made possible by harnessing the distinct glass transition temperatures of different polymers, which, when optimally synthesized, produce local and giant stiffness changes in a controllable manner. Featuring complex geometries, the generated structures and metamaterials exhibit fundamentally different yet programmable nonlinear force-displacement relations and deformation patterns as temperature varies. The rational design and fabrication establish an objective-oriented synthesis of metamaterials with freely tunable thermally adaptive behaviors. This imbues structures and materials with environment-aware intelligence.

Dynamics of multipartite quantum steering for different types of decoherence channels.

Multipartite quantum steering, a unique resource for asymmetric quantum network information tasks, is very fragile to the inevitable decoherence, which makes it useless for practical purposes. It is thus of importance to understand how it decays in the presence of noise channels. We study the dynamic behaviors of genuine tripartite steering, reduced bipartite steering, and collective steering of a generalized three-qubit W state when only one qubit interacts independently with the amplitude damping channel (ADC), phase damping channel (PDC) or depolarizing channel (DC). Our results provide the region of decoherence strength and state parameters that each type of steering can survive. The results show that these steering correlations decay the slowest in PDC and some non-maximally entangled states more robust than the maximally entangled ones. Unlike entanglement and Bell nonlocality, the thresholds of decoherence strength that reduced bipartite steering and collective steering can survive depend on the steering direction. In addition, we find that not only one party can be steered by a group system, but also two parties can be steered by a single system. There is a trade-off between the monogamy relation involving one steered party and two steered parties. Our work provides comprehensive information about the effect of decoherence on multipartite quantum steering, which will help to realize quantum information processing tasks in the presence of noise environments.

Suppressing interferences of EIT on synchronous recording EEG based on comb filter for seizure detection.

Background and objective: The purpose of this study was to eliminate the interferences of electrical impedance tomography (EIT) on synchronous recording electroencephalography (EEG) for seizure detection. Methods: The simulated EIT signal generated by COMSOL Multiphysics was superimposed on the clinical EEG signal obtained from the CHB-MIT Scalp EEG Database, and then the spectrum features of superimposed mixed signals were analyzed. According to the spectrum analysis, in addition to high-frequency interference at 51.2 kHz related to the drive current, there was also low-frequency interference caused by switching of electrode pairs, which were used to inject drive current. A low pass filter and a comb filter were used to suppress the high-frequency interference and low-frequency interference, respectively. Simulation results suggested the low-pass filter and comb filter working together effectively filtered out the interference of EIT on EEG in the process of synchronous monitoring. Results: As a result, the normal EEG and epileptic EEG could be recognized effectively. Pearson correlation analysis further confirmed the interference of EIT on EEG was effectively suppressed. Conclusions: This study provides a simple and effective interference suppression method for the synchronous monitoring of EIT and EEG, which could be served as a reference for the synchronous monitoring of EEG and other medical electromagnetic devices.

Protecting nonlocal quantum correlations in correlated squeezed generalized amplitude damping channel.

Nonlocal quantum correlations, such as quantum entanglement, quantum steering, and Bell nonlocality, are crucial resources for quantum information tasks. How to protect these quantum resources from decoherence is one of the most urgent problems to be solved. Here, we investigate the evolution of these correlations in the correlated squeezed generalized amplitude damping (SGAD) channel and propose a scheme to protect them with weak measurement (WM) and quantum measurement reversal (QMR). Compared with the results of the uncorrelated SGAD channel, we find that when [Formula: see text], correlation and squeezing effects can prolong the survival time of quantum entanglement, Bell nonlocality, and quantum steering by about 152 times, 207 times, and 10 times, respectively. In addition, local WM and QMR can effectively recover the disappeared nonlocal quantum correlations either in uncorrelated or completely correlated SGAD channels. Moreover, we find that these initial nonlocal quantum correlations could be drastically amplified under the correlated channel. And the steering direction can be flexibly manipulated either by changing the channel parameters or the strength of WM and QMR. These results not only make a step forward in suppressing decoherence and enhancing quantum correlation in noise channels, but also help to develop relevant practical applications.

Temperature dependence of dielectric properties of blood at 10 Hz-100 MHz.

The temperature dependence of the dielectric properties of blood is important for studying the biological effects of electromagnetic fields, electromagnetic protection, disease diagnosis, and treatment. However, owing to the limitations of measurement methods, there are still some uncertainties regarding the temperature characteristics of the dielectric properties of blood at low and medium frequencies. In this study, we designed a composite impedance measurement box with high heat transfer efficiency that allowed for a four/two-electrode measurement method. Four-electrode measurements were carried out at 10 Hz-1 MHz to overcome the influence of electrode polarization, and two-electrode measurements were carried out at 100 Hz-100 MHz to avoid the influence of distribution parameters, and the data was integrated to achieve dielectric measurements at 10 Hz-100 MHz. At the same time, the temperature of fresh blood from rabbits was controlled at 17-39°C in combination with a temperature-controlled water sink. The results showed that the temperature coefficient for the real part of the resistivity of blood remained constant from 10 Hz to 100 kHz (-2.42%/°C) and then gradually decreased to -0.26%/°C. The temperature coefficient of the imaginary part was positive and bimodal from 6.31 kHz to 100 MHz, with peaks of 5.22%/°C and 4.14%/°C at 126 kHz and 39.8 MHz, respectively. Finally, a third-order function model was developed to describe the dielectric spectra at these temperatures, in which the resistivity parameter in each dispersion zone decreased linearly with temperature and each characteristic frequency increased linearly with temperature. The model could estimate the dielectric properties at any frequency and temperature in this range, and the maximum error was less than 1.39%, thus laying the foundation for subsequent studies.

A Trial-Based Cost-Effectiveness Analysis of Bevacizumab and Chemotherapy Versus Chemotherapy Alone for Advanced Nonsquamous Non-Small-Cell Lung Cancer in China.

BACKGROUND: The first-line bevacizumab plus chemotherapy resulted in a clinical efficacy for Chinese patients with advanced nonsquamous non-small-cell lung cancer (NSCLC). Some economic analyses have carried out various methods to evaluate the cost-effectiveness of bevacizumab as the first-line treatment for NSCLC in other countries. Our objective was to assess the cost-effectiveness of bevacizumab plus chemotherapy compared with chemotherapy alone for the first-line treatment of advanced nonsquamous NSCLC. METHODS: A Markov model was applied from the perspective of the Chinese health care system to assess cost-effectiveness. It was based on the clinical trial BEYOND that compared bevacizumab plus carboplatin/paclitaxel (B+CP) with placebo plus carboplatin and paclitaxel (PI+CP) for advanced nonsquamous NSCLC. Ten-year quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) were calculated. One-way sensitivity analysis and probabilistic sensitivity analyses (PSA) were performed. RESULTS: QALYs were 1.17 years in the B+CP group and 0.83 years in the PI+CP group, resulting in a difference of 0.34 years. The ICER was $130,937.09/QALY, which was far beyond the willing-to-pay threshold of $24,314/QALY. At a threshold of $130,584/QALY, addition of bevacizumab had a 50% probability of being cost-effective. CONCLUSIONS: Bevacizumab is not cost-effective when combined with chemotherapy for patients with advanced nonsquamous NSCLC based on the Chinese health care system, resulting in a less demand in the Chinese market.

Intracranial Nonskull-Based Chondrosarcoma Arising from the Sagittal Sinus: A Case Report and Review of the Literature.

BACKGROUND: Intracranial nonskull-based chondrosarcoma is a very rare malignant tumor. In fact, it is difficult to diagnose because of its atypical radiologic and morphologic features. We report a case of an intracranial parasagittal chondrosarcoma in the left parietal lobe accompanied with significant peritumoral edema (an extremely rare phenomenon for this tumor), with a review of the literature, to clarify aspects in the diagnosis of this rare tumor. METHODS: A literature search was using PubMed with specific key terms, inclusion criteria, and exclusion criteria. Selected case studies and case series were then compared and summarized them in Table 1. A 59-year-old male patient presented with a history of progressive right hemiparesis and loss of sensation for 45 days. RESULTS: Magnetic resonance imaging revealed a 2.6 × 2.4 × 2.8-cm mass arising from the superior sagittal sinus in his left parietal lobe. A left parietal parasagittal craniotomy was performed, and a macroscopically complete excision of the tumor was achieved. The patient recovered well postoperatively. Regular follow-up after 6 months revealed that he had almost recovered full strength to his right limbs, and there was no evidence of recurrence. Intracranial parasagittal chondrosarcoma in the parietal lobe is extremely rare. CONCLUSIONS: Although magnetic resonance imaging was performed, it is sometimes difficult to distinguish intracranial chondrosarcoma from meningioma and glioma, especially when the tumor arises from a nonskull base such as the meninges. Some cases could have significant peritumoral edema, although it is extremely rare. Histologic examination may aid in the diagnosis of this tumor. Neurosurgery is the most effective therapy for these tumors. Postoperative radiotherapy needs to be considered when the tumor is incompletely resected, has atypical histology, or is associated with significant peritumoral edema.