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Detection of circulating tumor cells (CTCs) could be widely used for early diagnosis and real-time monitoring of tumor progression in liquid biopsy samples. Compared with normal cells, tumor cells exhibit relatively strong negative surface charges due to the high rate of glycolysis. In this study, a cationic fluorescence "turn-on" aggregation-induced emission (AIE) nanoprobe based on gold nanorods (GNRs) was designed and tested to detect tumor cells specifically. In brief, tetraphenylethene (TPE), an AIE dye, was conjugated to the cationic polymer polyethylenimine (PEI) yielding TPEI. TPEI-PEG-SH was obtained by further functionalizing TPEI with a thiol group. TPEI-PEG-SH was grafted to the surface of GNRs, yielding the cationic AIE nanoprobe, named as GNRs-PEG-TPEI. The nanoprobe was characterized to have a uniform particle size of 172 nm, a strong positive surface charge (+54.87 mV), and a surface modification load of ∼40%. The

Support vector machine classification algorithm has been deeply studied in the field of intelligent information processing with its solid theoretical foundation and excellent classification performance, and it has been widely used in real life, such as face recognition, text classification, sentiment analysis, and spam filtering, etc. The time complexity of the classic support vector machine algorithm is proportional to the square of the data size, and this poses a serious challenge to the calculability, effectiveness and efficiency of the support vector machine algorithm for massive data. In this paper, a large-scale support vector machine classification algorithm based on the granulation mechanism is proposed from the granulation and fusion of data information. A data granulation mechanism is firstly constructed using binary tree and classification label information, as well as the core sample set is obtained by combining with instance selection method, and then the core sample set and the remaining samples construct multiple weak support vector classifiers; finally draw on the idea of granularity fusion to generate a strong classifier. The experimental results on the standard data set verify the effectiveness and efficiency of the proposed algorithm, and it provides new research ideas and processing methods for processing massive data.

Pulmonary fibrosis (PF) is a serious and progressive lung disease which is possibly life-threatening. It causes lung scarring and affects lung functions including epithelial cell injury, massive recruitment of immune cells and abnormal accumulation of extracellular matrix (ECM). There is currently no cure for PF. Treatment for PF is aimed at slowing the course of the disease and relieving symptoms. Pirfenidone (PFD) and nintedanib (NDNB) are currently the only two FDA-approved oral medicines to slow down the progress of idiopathic pulmonary fibrosis, a specific type of PF. Novel drug delivery systems and therapies have been developed to improve the prognosis of the disease, as well as reduce or minimize the toxicities during drug treatment. The drug delivery routes for these therapies are various including oral, intravenous, nasal, inhalant, intratracheal and transdermal; although this is dependent on specific treatment mechanisms. In addition, researchers have also expanded current animal models that could not fully restore the clinicopathology, and developed a series of in vitro models such as organoids to study the pathogenesis and treatment of PF. This review describes recent advances on pathogenesis exploration, classifies and specifies the progress of drug delivery systems by their delivery routes, as well as an overview on the in vitro and in vivo models for PF research.

Owing to the absence of hypotheses of the underlying distributions of the data and the strong generation ability, the k -nearest neighbor (kNN) classification algorithm is widely used to face recognition, text classification, emotional analysis, and other fields. However, kNN needs to compute the similarity between the unlabeled instance and all the training instances during the prediction process; it is difficult to deal with large-scale data. To overcome this difficulty, an increasing number of acceleration algorithms based on data partition are proposed. However, they lack theoretical analysis about the effect of data partition on classification performance. This paper has made a theoretical analysis of the effect using empirical risk minimization and proposed a large-scale k -nearest neighbor classification algorithm based on neighbor relationship preservation. The process of searching the nearest neighbors is converted to a constrained optimization problem. Then, it gives the estimation of the difference on the objective function value under the optimal solution with data partition and without data partition. According to the obtained estimation, minimizing the similarity of the instances in the different divided subsets can largely reduce the effect of data partition. The minibatch k -means clustering algorithm is chosen to perform data partition for its effectiveness and efficiency. Finally, the nearest neighbors of the test instance are continuously searched from the set generated by successively merging the candidate subsets until they do not change anymore, where the candidate subsets are selected based on the similarity between the test instance and cluster centers. Experiment results on public datasets show that the proposed algorithm can largely keep the same nearest neighbors and no significant difference in classification accuracy as the original kNN classification algorithm and better results than two state-of-the-art algorithms.

The radiofrequency (RF) receive coil is a direct probe for magnetic resonance imaging (MRI), and its performance determines the quality of MRI results. The RF coil employed for low-field MRI has a low working frequency, which makes its characteristic different from the RF coil exploited for conventional clinic MRI and may result in a different optimum RF coil configuration. To design and optimize a head RF receive coil for a very-low-field (50.4 mT) MRI system, we investigated the relationship between the structure and performance of the RF coil. Specifically, we focused on a quadrature RF coil consisting of a saddle coil and a modified Helmholtz coil wound around the surface of an elliptical cylinder. First, we evaluated the efficiency and RF magnetic field inhomogeneity of one-loop RF coil and determined the optimum dimension for saddle coil and modified Helmholtz RF coil. Then, we further studied the performance of the optimum-dimension RF coil from the perspective of the number of RF coil loops and revealed that the number of loops of RF coil for very-low-field MRI was a remarkable feature influencing the alternative current (AC) resistance of the RF coil and therefore make the SNR increase first and then decrease with the number of RF coil loops. We finally obtained the optimum number of loops for the saddle coil, modified Helmholtz coil, and fabricated a quadrature RF coil. The performance of the quadrature coil was evaluated through CuSO

Very-low field magnetic resonance imaging (VLF-MRI, B0 0.1T) has an essential application in medical imaging diagnosis because of its light weight and low cost. For single-channel RF coil VLF-MRI system, a planar spiral LC-resonator placed on the surface of samples was designed to improve the local SNR. First, an equivalent circuit model was established to evaluate the boosting effects on radiofrequency (RF) magnetic field and SNR. Second, the relationship between the resonant capacitance and the transmission coefficient was deduced according to the circuit model, and the appropriate resonant capacitance was obtained. Then, the influence of the diameter and the number of turns of the LC-resonator on the SNR is considered, and the structure of the LC-resonator was optimized to maximize the SNR. Finally, a phantom MRI experiment was carried out with our home-built 54.6 mT MRI system to compare the SNR of the experiment with the calculation, the SNR enhancement trend of the two was consistent. Additional experiments were conducted using orange and chicken leg to demonstrate the SNR enhancement abilities of the LC-resonator. The enhancement of SNR reached up to 1.8-fold and 2.2-fold depending on the distance between the sample and LC-resonator. For comparison, we conducted imaging experiments on surface receiving coil with the same parameters, and the results show that the SNR of the LC resonator is comparable to that of the surface coil. The reported LC-resonator provide a low-cost local enhancement method for VLF-MRI.

Ferromagnetic structures, particularly the anti-eddy plate, in a bi-planar permanent-magnet-type low-field (0.05 T) magnetic resonance imaging (MRI) brain scanner can distort the gradient field in the target region. This study aims to provide a new gradient coil design method that reduces ferromagnetic influences on gradient field linearity. Thus, a simplified model of electromagnetic (EM) structures of the permanent-magnet-type MRI scanners was established. By using precise analytical proof, the anti-eddy plate was reduced to a homogeneous magnetic plate. The overall effects of the EM structures, which can be represented by bi-planar magnetic plates, were evaluated. In sequence, the image magnetic dipole was first introduced to show the effects of anti-eddy plates were added to the conventional equivalent magnetic dipole (EMD) approach. A novel equivalent image magnetic dipole (EIMD) method was proposed to build the gradient coil pattern. The effect of ferromagnetic materials was predicted throughout the gradient coil design phase using the proposed method, and a high-linear gradient field was generated under real working conditions. The computational and experimental results showed that the gradient coil was linear when ferromagnetic structures were present. The effectiveness of the proposed method was demonstrated by comparing T1-weighted images of the conventional method to those of the proposed method. The proposed method reduced image distortion caused by nearby EM structures in bi-planar permanent-magnet-type low-field MRI systems and provided an effective and concise solution for gradient coil designs.