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Abstract Borayflo Haemostatic matrix is a new absorbable hemostatic gelatin matrix which can be used for intraoperative assisted hemostasis. In a prospective, multicenter, non-inferiority, randomized controlled trial, a total of 354 subjects were recruited from the departments of hepatobiliary surgery, obstetrics and gynaecology and orthopaedics of 4 hospitals and randomly allocated to test group (Borayflo Haemostatic matrix) or control group (Surgiflo Haemostatic matrix) in a 1:1 ratio. In the modified intention-to-treat population, 163 (93.14%) of 175 subjects in the test group versus 167 (94.89%) of 176 subjects in the control group successfully achieved hemostasis within 5 min (P > 0.05). Non-inferiority for effective rates of hemostasis at 5 min to Surgiflo Haemostatic matrix was shown in the study (treatment difference: -1.74% [95%CI, -6.70–3.22%] for modified intention-to-treat population). In terms of efficacy and safety, the new hemostatic gelatin matrix (Borayflo Haemostatic matrix) is equivalent to Surgiflo Haemostatic matrix. There was no significant difference in the incidence of AEs or SAEs between the test and control groups(P > 0.05). In addition, Borayflo Haemostatic matrix is a domestically produced haemostatic gelatin product, an advantage that will reduce the cost of surgical haemostasis for Chinese patients.

Abstract Previous work has demonstrated similarities and differences between aerial and terrestrial image viewing. Aerial scene categorization, a pivotal visual processing task for gathering geoinformation, heavily depends on rotation-invariant information. Aerial image-centered research has revealed effects of low-level features on performance of various aerial image interpretation tasks. However, there are fewer studies of viewing behavior for aerial scene categorization and of higher-level factors that might influence that categorization. In this paper, experienced subjects’ eye movements were recorded while they were asked to categorize aerial scenes. A typical viewing center bias was observed. Eye movement patterns varied among categories. We explored the relationship of nine image statistics to observers’ eye movements. Results showed that if the images were less homogeneous, and/or if they contained fewer or no salient diagnostic objects, viewing behavior became more exploratory. Higher- and object-level image statistics were predictive at both the image and scene category levels. Scanpaths were generally organized and small differences in scanpath randomness could be roughly captured by critical object saliency. Participants tended to fixate on critical objects. Image statistics included in this study showed rotational invariance. The results supported our hypothesis that the availability of diagnostic objects strongly influences eye movements in this task. In addition, this study provides supporting evidence for Loschky et al.’s (Journal of Vision, 15(6), 11, 2015) speculation that aerial scenes are categorized on the basis of image parts and individual objects. The findings were discussed in relation to theories of scene perception and their implications for automation development.

Chemoresistance is a major cause of treatment failure in many cancers. However, the life cycle of cancer cells as they respond to and survive environmental and therapeutic stress is understudied. In this study, we utilized a microfluidic device to induce the development of doxorubicin-resistant (DOXR) cells from triple negative breast cancer (TNBC) cells within 11 days by generating gradients of DOX and medium. In vivo chemoresistant xenograft models, an unbiased genome-wide transcriptome analysis, and a patient data/tissue analysis all showed that chemoresistance arose from failed epigenetic control of the nuclear protein-1 (NUPR1)/histone deacetylase 11 (HDAC11) axis, and high NUPR1 expression correlated with poor clinical outcomes. These results suggest that the chip can rapidly induce resistant cells that increase tumor heterogeneity and chemoresistance, highlighting the need for further studies on the epigenetic control of the NUPR1/HDAC11 axis in TNBC.

Objective(s): Recently, studies on microRNAs (miRNAs) and their targets and related genes have provided new therapeutic opportunities for controlling intervertebral disc degeneration (IDD). We aimed to investigate the effects of miR-148a-3p overexpression on IDD progression.Materials and Methods: This study used microRNA microarrays to analyze key regulators of IDD. Q-PCR was used to verify the IL-1β-induced down-regulation of miR-148a-3p expression both in nucleus pulposus (NP) tissues of IDD patients and in degenerated NP cells (NPCs) of rats. Rat NPC micromass cultures and ex vivo intervertebral disc (IVD) culture models were established, and histological staining was performed to verify the effect of miR-148a-3p on the general morphology and proteoglycan and collagen contents of IVDs. In addition, q-PCR and western blotting analyses were performed to examine the expression of ECM molecules and matrix-degrading enzymes. A luciferase reporter assay was used to confirm the target genes of miR-148a-3p.Results: Our data revealed that miR-148a-3p was down-regulated in IDD. Overexpression of miR-148a-3p had no effect on ACAN or COL2A1 gene expression but decreased MMP3, MMP13, and ADAMTS5 gene expression. The matrix deposited by miR-148a-3p-overexpressing rat NPCs contained high levels of proteoglycans and collagen. The ex vivo experiments verified that agomiR-148a-3p alleviated the NPC matrix degradation induced by IL-1β. A luciferase reporter assay confirmed that miR-148a-3p directly targeted ADAMTS5 and MMP13.Conclusion: We proved that miR-148a-3p can attenuate ECM loss and protect NP function by inhibiting matrix-degrading enzymes.

Acquiring high-quality hyperspectral imagery with high spatial and spectral resolution plays an important role in remote sensing. Due to the limited capacity of sensors, providing high spatial and spectral resolution is still a challenging issue. Spectral super-resolution (SSR) increases the spectral dimensionality of multispectral images to achieve resolution enhancement. In this article, we propose a spectral resolution enhancement method based on the generative adversarial network framework without introducing additional spectral responses prior. In order to adaptively rescale informative features for capturing interdependencies in the spectral and spatial dimensions, a spatial spectral feature attention module is introduced. The proposed method jointly exploits spatio-spectral distribution in the hyperspectral manifold to increase spectral resolution while maintaining spatial content consistency. Experiments are conducted on both synthetic Landsat 8 and Sentinel-2 radiance data and real coregistered advanced land image and Hyperion (MS and HS) images, which indicates the superiority of the proposed method compared to other state-of-the-art methods.

IntroductionThe phenotypic screening of drugs against Balamuthia mandrillaris, a neuropathogenic amoeba, involves two simultaneous phases: an initial step to test amoebicidal activity followed by an assay for cytotoxicity to host cells. The emergence of three-dimensional (3D) cell cultures has provided a more physiologically relevant model than traditional 2D cell culture for studying the pathogenicity of B. mandrillaris. However, the measurement of ATP, a critical indicator of cell viability, is complicated by the overgrowth of B. mandrillaris in coculture with host cells during drug screening, making it challenging to differentiate between amoebicidal activity and drug toxicity to human cells.MethodsTo address this limitation, we introduce a novel assay that utilizes three-dimensional hanging spheroid plates (3DHSPs) to evaluate both activities simultaneously on a single platform.Results and discussionOur study showed that the incubation of neurospheroids with clinically isolated B. mandrillaris trophozoites resulted in a loss of neurospheroid integrity, while the ATP levels in the neurospheroids decreased over time, indicating decreased host cell viability. Conversely, ATP levels in isolated trophozoites increased, indicating active parasite metabolism. Our findings suggest that the 3DHSP-based assay can serve as an endpoint for the phenotypic screening of drugs against B. mandrillaris, providing a more efficient and accurate approach for evaluating both parasite cytotoxicity and viability.

The axle bridge plays a crucial role in the bogie of low-floor light rail vehicles, impacting operational efficiency and fuel economy. To minimize the total cost of the structure and turning of axle bridges, an optimization model of structural and turning parameters was built, with the fatigue life, maximum stress, maximum deformation, and maximum main cutting force as constraints. Through orthogonal experiments and multivariate variance analysis, the key design variables which have a significant impact on optimization objectives and constraints (performance responses) were identified. Then the optimal Latin hypercube design and finite element simulation was used to build a Radial Basis Function (RBF) model to approximate the implicit relationship between design variables and performance responses. Finally, a multi-island genetic algorithm was applied to solve the integrated optimization model, resulting in an 8.457% and 1.1% reduction in total cost compared with the original parameters and parameters of sequential optimization, proving the effectiveness of the proposed method.

Abstract Background Most high-throughput screening (HTS) systems studying the cytotoxic effect of chimeric antigen receptor (CAR) T cells on tumor cells rely on two-dimensional cell culture that does not recapitulate the tumor microenvironment (TME). Tumor spheroids, however, can recapitulate the TME and have been used for cytotoxicity assays of CAR T cells. But a major obstacle to the use of tumor spheroids for cytotoxicity assays is the difficulty in separating unbound CAR T and dead tumor cells from spheroids. Here, we present a three-dimensional hanging spheroid plate (3DHSP), which facilitates the formation of spheroids and the separation of unbound and dead cells from spheroids during cytotoxicity assays. Results The 3DHSP is a 24-well plate, with each well composed of a hanging dripper, spheroid wells, and waste wells. In the dripper, a tumor spheroid was formed and mixed with CAR T cells. In the 3DHSP, droplets containing the spheroids were deposited into the spheroid separation well, where unbound and dead T and tumor cells were separated from the spheroid through a gap into the waste well by tilting the 3DHSP by more than 20°. Human epidermal growth factor receptor 2 (HER2)-positive tumor cells (BT474 and SKOV3) formed spheroids of approximately 300–350 μm in diameter after 2 days in the 3DHSP. The cytotoxic effects of T cells engineered to express CAR recognizing HER2 (HER2-CAR T cells) on these spheroids were directly measured by optical imaging, without the use of live/dead fluorescent staining of the cells. Our results suggest that the 3DHSP could be incorporated into a HTS system to screen for CARs that enable T cells to kill spheroids formed from a specific tumor type with high efficacy or for spheroids consisting of tumor types that can be killed efficiently by T cells bearing a specific CAR. Conclusions The results suggest that the 3DHSP could be incorporated into a HTS system for the cytotoxic effects of CAR T cells on tumor spheroids. Graphical Abstract

Objective The aim of this study was to compare the radiological features of intravertebral clefts (IVC) between Kümmell disease (KD) and acute osteoporotic vertebral compression fracture (OVCF). Materials and Methods This is a retrospective study. A total of 79 patients with IVC from January 2014 to December 2018 were included in this study. There were 22 men and 57 women, with an average of 73.5 years. Based on the exact time interval from injury to treatment and the pathological examination results, the patients were divided into KD group (44 patients) and acute OVCF group (35 patients). The two groups were compared by the margin sclerosis of IVC, vertebra and pedicle ossification, stress fracture of the spinous process, paravertebral callus, the shape of IVC, cleft in the adjacent disc, and flatness of IVC's margin from plain radiographs and computed tomography (CT). The two groups were compared by the IVC content, double‐line sign, and signal of fracture vertebral from their magnetic resonance imaging (MRI). Results There were no significant differences in sex, age, and fracture distribution between the KD group and the acute OVCF group. IVC was present in both the KD group and the acute OVCF group. Six radiological features were only present in the KD group, including sclerosis of the cleft margin (95.5%, 42/44), ossification of the fractured vertebrae (100%, 44/44), ossification of the pedicle (31.8%, 14/44), double‐line sign (27.3%, 12/44), stress fracture of the spinous process (13.6%, 6/44), and even formation of paravertebral callus (18.2%, 8/44). Although there were statistical differences in the other four radiological features of content of IVC (P = 0.02), cleft sign in adjacent intervertebral disc (P

Abstract Background Extremity liposarcoma represents 25% of extremity soft tissue sarcoma and has a better prognosis than liposarcoma occurring in other anatomic sites. The purpose of this study was to develop two nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of patients with extremity liposarcoma. Methods A total of 2170 patients diagnosed with primary extremity liposarcoma between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were performed to explore the independent prognostic factors and establish two nomograms. The area under the curve (AUC), C-index, calibration curve, decision curve analysis (DCA), Kaplan-Meier analysis, and subgroup analyses were used to evaluate the nomograms. Results Six variables were identified as independent prognostic factors for both OS and CSS. In the training cohort, the AUCs of the OS nomogram were 0.842, 0.841, and 0.823 for predicting 3-, 5-, and 8-year OS, respectively, while the AUCs of the CSS nomogram were 0.889, 0.884, and 0.859 for predicting 3-, 5-, and 8-year CSS, respectively. Calibration plots and DCA revealed that the nomogram had a satisfactory ability to predict OS and CSS. The above results were also observed in the validation cohort. In addition, the C-indices of both nomograms were significantly higher than those of all independent prognostic factors in both the training and validation cohorts. Stratification of the patients into high- and low-risk groups highlighted the differences in prognosis between the two groups in the training and validation cohorts. Conclusion Age, sex, tumor size, grade, M stage, and surgery status were confirmed as independent prognostic variables for both OS and CSS in extremity liposarcoma patients. Two nomograms based on the above variables were established to provide more accurate individual survival predictions for extremity liposarcoma patients and to help physicians make appropriate clinical decisions.

To reduce the wear and damage of the chute caused by long-term impact of coke, a structure parameter optimization model was established in this paper, which takes the minimum impact force as the objective and the coke-conveying speed as the constraint. Furthermore, the ensemble of support vector regression (E-SVR) with different kernel functions was developed to replace the implicit relationship between the conveying speed, the impact force, and the structure parameters. Using the numerical examples, the effectiveness of the E-SVR model was verified. Finally, the optimal chute structure parameters were obtained by using the E-SVR model. After optimization, the maximum impact force was reduced by 17.07% and the maximum conveying speed was reduced by 6.59%, which still falls within the specified range. Therefore, the feasibility of the optimization results and the effectiveness of the E-SVR surrogate model were verified.

Cohesive non-swelling soil (CNS) cushion technology is widely used to solve swelling deformation problems in expansive soil areas. However, the swelling inhibition mechanism is still not fully understood. In this study, the inhibition effect on expansive soil using a CNS layer was studied by performing five types of laboratory model tests under unidirectional seepage. The results showed that CNS cushion technology produced a sound inhibition effect on the swelling characteristics of expansive soil. It was shown that the cations in the CNS layer moved downward and accumulated on the surface of solids and produced an electrical environment inside the expansive soil. In this process, the adsorbed hydrated cations participated in ion exchange with the expansive soil, leading to the modification effect on its swelling potential. Meanwhile, the adsorbed water membrane surrounding the expansive soil aggregates formed by the hydrated cations obstructed further adsorption of water molecules, which inhibited the swelling development of expansive soil. Therefore, the swelling inhibition mechanism can be attributed to three factors: (i) modification effect, (ii) electrical environment, and (iii) deadweight of the CNS layer. The combined contribution of modification effect and electrical environment can be considered as an electric charge effect, which mainly controls the swelling characteristics of expansive soil. Keywords: Cohesive non-swelling soil (CNS), Expansive soil, Unidirectional seepage, Electric charge effect, Swelling inhibition mechanism

Object tracking is one of the most important components in numerous applications of computer vision. Remote sensing videos provided by commercial satellites make it possible to extend this topic into the earth observation domain. In satellite videos, typical moving targets like vehicles and planes only cover a small area of pixels, and they could easily be confused with surrounding complex ground scenes. Similar objects nearby in satellite videos can hardly be differed by appearance details due to the resolution constraint. Thus, tracking drift caused by distractions is also a thorny problem. Facing challenges, traditional tracking methods such as correlation filters with hand-crafted visual features achieve unsatisfactory results in satellite videos. Methods based on deep neural networks have demonstrated their superiority in various ordinary visual tracking benchmarks, but their results on satellite videos remain unexplored. In this article, deep learning technologies are applied to object tracking in satellite videos for better performance. A simple regression network is used to combine a regression model with convolutional layers and a gradient descent algorithm. The regression network fully exploits the abundant background context to learn a robust tracker. Instead of handcrafted features, both appearance features and motion features, which are extracted by pretrained deep neural networks, are used for accurate object tracking. In cases when the tracker encounters ambiguous appearance information, the motion features could provide complementary and discriminative information to improve tracking performances. Experimental results on various satellite videos show that the proposed method achieves better tracking performance than other state-of-the-arts.

In this study, the radial basis function (RBF) which has good performance for nonlinear problem is introduced to approximate the implicit relationships between EDM parameters and performance responses for 304 steel. The fitting precision of RBF is compared with the second order polynomial response surface (PRS), support vector regression (SVR) and Kriging model (KRG) using the multiple correlation coefficient (R2) based cross validation error method. Then the RBF model is called to conduct multi-objective optimization using non-dominated sorting genetic algorithm II (NSGA-II) method. The energy consumption index unit energy consumption (UEC) and the air-pollution indices PM2.5 and PM10 are considered in proposed multi-objective optimization model. UEC is considered as the objective function to reduce the machining cost and the PM indices are termed as the constraints to protect the operators' health. The pulse current, time period and duty cycle are considered as the main factors affecting the EDM responses. According to the Pareto plots of multi-objective optimization model, conclusion can be drawn that SR and PM10 play significant roles in multi-optimization and PM2.5 has less influence on optimization results. The results of the present study reveal that using maximum material removal rate (MRR) and minimum UEC as objective and using surface roughness (SR), PM2.5 and PM10 as constraints can be an effective method to provide appropriate process parameters reference for EDM machining.

Cancer is a leading cause of death worldwide, with an increasing mortality rate over the past years. The early detection of cancer contributes to early diagnosis and subsequent treatment. How to detect early cancer has become one of the hot research directions of cancer. Tumor biomarkers, biochemical parameters for reflecting cancer occurrence and progression have caused much attention in cancer early detection. Due to high sensitivity, convenience and low cost, biosensors have been largely developed to detect tumor biomarkers. This review describes the application of various biosensors in detecting tumor markers. Firstly, several typical tumor makers, such as neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), prostate-specific antigen (PSA), squamous cell carcinoma antigen (SCCA), carbohydrate, antigen19-9 (CA19-9) and tumor suppressor p53 (TP53), which may be helpful for early cancer detection in the clinic, are briefly described. Then, various biosensors, mainly focusing on electrochemical biosensors, optical biosensors, photoelectrochemical biosensors, piezoelectric biosensors and aptamer sensors, are discussed. Specifically, the operation principles of biosensors, nanomaterials used in biosensors and the application of biosensors in tumor marker detection have been comprehensively reviewed and provided. Lastly, the challenges and prospects for developing effective biosensors for early cancer diagnosis are discussed.

Objective: This study aimed to conduct a systematic review of literature comparing the clinical effectiveness and safety between anterior reconstruction (AR) and posterior osteotomy (PO) in the treatment of Kümmell's disease with neurological deficits. Methods: We systematically reviewed the literature in PubMed, EMBASE, Cochrane Database of Systematic Reviews, and the Web of Science for “spin*,” “surg*,” “Kümmell's disease,” “Kummell's disease,” “Kummell disease,” “vertebral osteonecrosis,” “vertebral pseudarthrosis,” “intravertebral vacuum cleft,” “delayed vertebral collapse,” and “compression fracture nonunion”. Quality was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation method. Results: A total of 10 publications involving 268 Kümmell's disease patients with neurological deficits were included in this review, with 7 studies of low- or very low-quality. There were 37.7% and 62.3% of patients receiving AR and PO, respectively. For clinical outcomes, AR group showed no significant differences in pain, neurological dysfunction, and imaging outcome improvements compared with patients who underwent PO. However, the incidence of implant-related complications including loose screw, screw fracture, screw disconnection, and plate dislodgment, was higher in AR group compared with PO group (21.6% vs. 14.3%). As another major complication, AR group more often required a second surgery. Conclusion: This systematic review demonstrated that both AR and PO could improve pain, neurological dysfunction and imaging outcomes. However, serious comorbidities, multilevel corpectomies and/or severe osteoporosis highly required PO. Design discrepancies were found in the current studies, further higher-quality studies are warranted. Level of evidence: Level III, therapeutic study. Keywords: Kümmell's disease, Neurological deficits, Anterior reconstruction, Posterior osteotomy, Postoperative outcomes, Systematic review

Due to the limited capability for information processing, humans only choose a small amount of input data received from visual field to better understand their environment. The selection of visual input implies the nonuniform distribution of visual attention, which is influenced by environmental visual stimuli and endogenous subject interest. Traditional saliency models do not differentiate individuals, exploring the common trend in attention deployment. This paper investigates individual nuance and association in both saccadic movements and attention distribution, and then discusses how individuality plays a role in predicting attention with low-level and deep features, respectively. It turns out that individual differences indeed exist and can be better discriminated by deep features. In conclusion, individuality not only contributes to improving the accuracy of attention prediction models but also gives us a hint about some interesting viewing behavior that stands out from the crowd pattern.

With the growing popularity of location-based social networks (LBSNs), time-specific POI recommendation has become important in recent years, which provides more accurate recommendation services for users in specific spatio–temporal contexts. In this paper, we propose a spatio–temporal distance metric embedding model (ST-DME) for time–specific recommendation, which exploits both temporal and geo-sequential property of a check-in to effectively model users’ time-specific preferences. Specifically, we divide timestamps of user’ check-ins into different time slots and adopt Euclidean distance rather than inner product of latent vectors to measure users’ preferences for POIs in a given time slot. We also apply a transition coefficient based on users’ most recent check-ins to incorporate geo-sequential influence in users’ check-in behaviors. A weighted pairwise loss with a hard sampling strategy is adopted to optimize latent vectors of users, POIs, and time slots in a metric space. Extensive experiments are conducted to demonstrate the effectiveness of our proposed method and results show that ST-DME outperforms state-of-the-art algorithms for time-specific POI recommendation on two public LBSNs data sets.

Bluetooth low energy (BLE)-based indoor localization has attracted increasing interests for its low-cost, low-power consumption, and ubiquitous availability in mobile devices. In this paper, a novel denoising autoencoder-based BLE indoor localization (DABIL) method is proposed to provide high-performance 3-D positioning in large indoor places. A deep learning model, called denoising autoencoder, is adopted to extract robust fingerprint patterns from received signal strength indicator measurements, and a fingerprint database is constructed with reference locations in 3-D space, rather than traditional 2-D plane. Field experiments show that 3-D space fingerprinting can effectively increase positioning accuracy, and DABIL performs the best in terms of both horizontal accuracy and vertical accuracy, comparing with a traditional fingerprinting method and a deep learning-based method. Moreover, it can achieve stable performance with incomplete beacon measurements due to unpredictable BLE beacon lost.

Scanpaths are composed of fixations and saccades. Viewing trends reflected by scanpaths play an important role in scientific studies like saccadic model evaluation and real-life applications like artistic design. Several scanpath synthesis methods have been proposed to obtain a scanpath that is representative of the group viewing trend. But most of them either target a specific category of viewing materials like webpages or leave out some useful information like gaze duration. Our previous work defined the representative scanpath as the barycenter of a group of scanpaths, which actually shows the averaged shape of multiple scanpaths. In this paper, we extend our previous framework to take gaze duration into account, obtaining representative scanpaths that describe not only attention distribution and shift but also attention span. The extended framework consists of three steps: Eye-gaze data preprocessing, scanpath aggregation and gaze duration analysis. Experiments demonstrate that the framework can well serve the purpose of mining viewing patterns and “barycenter” based representative scanpaths can better characterize the pattern.

Repeated exposure to cocaine was previously found to cause sensitized behavioral responses and structural remodeling on medium spiny neurons of the nucleus accumbens (NAc) and caudate putamen (CPu). Rac1 has emerged as a key integrator of environmental cues that regulates dendritic cytoskeletons. In this study, we investigated the role of Rac1 in cocaine-induced dendritic and behavioral plasticity in the CPu. We found that Rac1 activation was reduced in the NAc but increased in the CPu following repeated cocaine treatment. Inhibition of Rac1 activity by a Rac1-specific inhibitor NSC23766, overexpression of a dominant negative mutant of Rac1 (T17N-Rac1) or local knockout of Rac1 attenuated the cocaine-induced increase in dendrites and spine density in the CPu, whereas overexpression of a constitutively active Rac1 exert the opposite effect. Moreover, NSC23766 reversed the increased number of asymmetric spine synapses in the CPu following chronic cocaine exposure. Downregulation of Rac1 activity likewise attenuates behavioral reward responses to cocaine exposure, with activation of Rac1 producing the opposite effect. Thus, Rac1 signaling is differentially regulated in the NAc and CPu after repeated cocaine treatment, and induction of Rac1 activation in the CPu is important for cocaine exposure-induced dendritic remodeling and behavioral plasticity.

Influenza A viruses are often present in environmental and clinical samples at concentrations below the limit of detection (LOD) of molecular diagnostics. Here we report an integrated microfluidic preconcentration and nucleic amplification system (μFPNAS) which enables both preconcentration of influenza A virus H1N1 (H1N1) and amplification of its viral RNA, thereby lowering LOD for H1N1. H1N1 virus particles were first magnetically preconcentrated using magnetic nanoparticles conjugated with an antibody specific for the virus. Their isolated RNA was amplified to cDNA through thermocycling in a trapezoidal chamber of the μFPNAS. A detection limit as low as 100 TCID50 (50% tissue culture infective dose) in saliva can be obtained within 2 hours. These results suggest that the LOD of molecular diagnostics for virus can be lowered by systematically combining immunomagnetic separation and reverse transcriptase-polymerase chain reaction (RT-PCR) in one microfluidic device.