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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

Salmonella sp. specific gene fragment 3335471 was used to establish a PCR-based assay for rapid detection of Salmonella sp. To establish the PCR assay, internal amplification control (IAC) was constructed using the composite primer method; the optimal concentration was determined to be 365 copies/μL. Storage of freeze-dried PCR solutions at –20°C for 3 months had no effect on detection results. Tests of artificially contaminated milk showed that Salmonella choleraesuis could be detected in 8 h when inoculated at 7 colony forming units (CFU)/10 mL. Next, a quantitative real-time PCR (RT-PCR) for detection of Salmonella sp. was developed. The assays showed high specificity for 13 Salmonella strains and 16 non-Salmonella strains. The optimal concentration of the IAC was 425 copies/μL for RT-PCR. Using S. choleraesuis as the target strain, the detection sensitivity of genome was 1.23 fg/μL and the limit of detection was 1–4 CFU/10 mL in artificially contaminated milk with 6 h of non-selective enrichment. This study established highly efficient, sensitive PCR and RT-PCR systems, providing effective approaches for the rapid detection of Salmonella sp. in the food industry.

High-performance gradient coils are always needed in magnetic resonance imaging (MRI) systems. However, conventional target-field methods for coil design have drawbacks such as extra magnetic deviation caused by a wire connection. In this work, a crescent-shaped structure is proposed to design bi-planar transverse gradient coils for a 50mT MRI system, in which the shape of each loop is determined by only three positional parameters, making it convenient to optimize. Smooth arcs with gradual-changing radii replace the extra connecting lines brought by conventional methods. An optimization problem aiming at high coil efficiency was established, and the optimal design was compared with the calculation result of a conventional target-field method. It is found that the efficiency of X and Y gradient coils is respectively 97.37 μT/m/A and 99.87 μT/m/A, higher than 89.32 μT/m/A and 91.57 μT/m/A of conventional target-field approach. Finally, the optimal gradient coils were constructed, and the effectiveness of the simulation was validated.

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

Radiotherapy is widely used in the clinical treatment of cancer patients and it may be used alone or in combination with surgery or chemotherapy to inhibit tumor development. However, radiotherapy may at times not kill all cancer cells completely, as certain cells may develop radioresistance that counteracts the effects of radiation. The emergence of radioresistance is associated with the genetic background and epigenetic regulation of cells. p53 is an important tumor suppressor gene that is expressed at low levels in cells. However, when cells are subjected to stress-induced stimulation, the expression level of p53 increases, thereby preventing genomic disruption. This mechanism has important roles in maintaining cell stability and inhibiting carcinogenesis. However, mutation and deletion destroy the anticancer function of p53 and may induce carcinogenesis. In tumor radiotherapy, the status of p53 expression in cancer cells has a close relationship with radiotherapeutic efficacy. Therefore, understanding how p53 expression affects the cellular response to radiation is of great significance for solving the problem of radioresistance and improving radiotherapeutic outcomes. For the present review, the literature was searched for studies published between 1979 and 2021 using the PubMed database (https://pubmed.ncbi.nlm.nih.gov/) with the following key words: Wild-type p53, mutant-type p53, long non-coding RNA, microRNA, gene mutation, radioresistance and radiosensitivity. From the relevant studies retrieved, the association between different p53 mutants and cellular radiosensitivity, as well as the molecular mechanisms of p53 affecting the radiosensitivity of cells, were summarized. The aim of the present study was to provide useful information for understanding and resolving radioresistance, to help clinical researchers develop more accurate treatment strategies and to improve radiotherapeutic outcomes for cancer patients with p53 mutations.

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.

Logistic regression has been widely used in artificial intelligence and machine learning due to its deep theoretical basis and good practical performance. Its training process aims to solve a large-scale optimization problem characterized by a likelihood function, where the gradient descent approach is the most commonly used. However, when the data size is large, it is very time-consuming because it computes the gradient using all the training data in every iteration. Though this difficulty can be solved by random sampling, the appropriate sampled examples size is difficult to be predetermined and the obtained could be not robust. To overcome this deficiency, we propose a novel algorithm for fast training logistic regression via adaptive sampling. The proposed method decomposes the problem of gradient estimation into several subproblems according to its dimension; then, each subproblem is solved independently by adaptive sampling. Each element of the gradient estimation is obtained by successively sampling a fixed volume training example multiple times until it satisfies its stopping criteria. The final estimation is combined with the results of all the subproblems. It is proved that the obtained gradient estimation is a robust estimation, and it could keep the objective function value decreasing in the iterative calculation. Compared with the representative algorithms using random sampling, the experimental results show that this algorithm obtains comparable classification performance with much less training time.

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.

In this study, we developed a rapid and sensitive loop-mediated isothermal amplification (LAMP) assay for Salmonella detection targeting the gene62181533. The addition of manganous ions and calcein to the reaction system allowed the visualization of an apparent color change during the amplification reaction. The detection limit of LAMP using a six-primer combination was 1.057 copies/μl (standard plasmid), 4.1 CFU/ml (cell), and 7.68 fg/μl (genome). The LAMP assay was 100% specific based on 32 Salmonella strains and 25 non-Salmonella bacteria. In inoculated milk, the LAMP assay detected Salmonella at an initial inoculation concentration of 0.81 CFU/ml following 12 h of pre-enrichment in buffered peptone water. One hundred food samples (beef, chicken, and pork) were tested by the LAMP assay. The positive-sample ratios were 11/100 by LAMP, 11/100 by conventional PCR, and 11/100 by a culture-based method. The results revealed that the gene62181533-based LAMP assay is a rapid, specific, and sensitive method for the detection of Salmonella in foods.

The availability of very high spatial resolution (VHR) remote sensing imagery provides unique opportunities to exploit meaningful change information in detail with object-oriented image analysis. This study investigated land cover (LC) changes in Shahu Lake of Wuhan using multi-temporal VHR aerial images in the years 1978, 1981, 1989, 1995, 2003, and 2011. A multi-resolution segmentation algorithm and CART (classification and regression trees) classifier were employed to perform highly accurate LC classification of the individual images, while a post-classification comparison method was used to detect changes. The experiments demonstrated that significant changes in LC occurred along with the rapid urbanization during 1978–2011. The dominant changes that took place in the study area were lake and vegetation shrinking, replaced by high density buildings and roads. The total area of Shahu Lake decreased from ~7.64 km2 to ~3.60 km2 during the past 33 years, where 52.91% of its original area was lost. The presented results also indicated that urban expansion and inadequate legislative protection are the main factors in Shahu Lake’s shrinking. The object-oriented change detection schema presented in this manuscript enables us to better understand the specific spatial changes of Shahu Lake, which can be used to make reasonable decisions for lake protection and urban development.

The purpose of this study is to find new molecular targets for the detection of Salmonella. With the online BLAST Program, we compared homology of genomic sequences and specificity in GenBank among Salmonella serovars and non-Salmonella strains and found 98 Salmonella specific target sequences. We selected 33 target sequences of Gene ID from 3335000 to 3337003 for the specificity evaluation, and finally 8 specific fragments screened out, they are 3334138, 3335583, 3335471, 3335211, 3335068, 3336466, 3336736 and 3336998. Primer SC8 of gene 3335583 and SC9 of gene 3335471 were the best in specificity and sensitivity among these primers. The detection sensitivity of Primer SC9 was 1.23 fg/μl for DNA templates and 720 cfu/ml for whole cells, while primer SC8's was 12.3 fg/μl and 720 cfu/ml, respectively. Salmonella could be detected successfully by the PCR method developed in this study after 8 h enrichment when the milk samples were artificially contaminated by this organism at 7 cfu per 10 ml milk.

Salmonella enterica serotype Paratyphi B is a globally distributed human-specific pathogen causing paratyphoid fever. The aim of this study was to develop a rapid and reliable polymerase chain reaction (PCR) assay for its detection in food. The SPAB_01124 gene was found to be unique to S. Paratyphi B using comparative genomics. Primers for fragments of the SPAB_01124 gene and the Salmonella-specific invA gene were used in combination to establish a multiplex PCR assay that showed 100% specificity across 45 Salmonella strains (representing 34 serotypes) and 18 non-Salmonella strains. The detection limit was 2.2 CFU mL(-1) of S. Paratyphi B after 12-h enrichment in pure culture. It was shown that co-culture with S. Typhimurium or Escherichia coli up to concentrations of 3.6 × 10(5) CFU and 3.3 × 10(4) CFU, respectively, did not interfere with PCR detection of S. Paratyphi B. In artificially contaminated milk, the assay could detect as few as 62 CFU mL(-1) after 8 h of enrichment. In conclusion, comparative genomics was found to be an efficient approach to the mining of pathogen-specific target genes, and the PCR assay that was developed from this provided a rapid, specific, and sensitive method for detection of S. Paratyphi B.

S. Dublin has caused widespread concerns in cattle produce. Using a comparative genomic method, two specific targets like SeD_A1118 and SeD_A2283 for S. Dublin identification were firstly obtained. An efficient multiplex PCR for S. Dublin detection based on the two novel specific genes and invA was therefore developed.