Objective: To establish the method for determination of titanium dioxide in the air of workplace by inductivehy coupled plasma optical emission spectrometry (ICP-OES) . Methods: The titanium dioxide was collected by filter membrane and then digested by microwave digestion apparatus in the mixed solvents (HNO₃∶HF∶H₂O=4∶1∶1) , dilutedto 25 ml and detected by ICP-OES. Results: The sampling efficiency was higher than 95%; the linearity of ICP-OES was good at the range of 10-500 μg/ml, the minimum quantitation concentration was 0.72 mg/m³ (as collecting 150 L air sample) , the maximum quantitation concentration was 21.7 mg/m³ (as collecting 960 L air sample) , the recovery was ranged from 99.0%-102.0%, the RSD of intra- and inter-batch precision were 0.5%-3.2% and 1.7%-3.5%, respectively. Conclusion The sampling method and determination method meet the requirements of guide for establishing occupational health standards-part 4: determinatin methods of air chemicals in workplace (GBZ/T 210.4-2008) , and areapplys to the collection and determination of TiO₂ in the air of workplace.

Objective:To study the role of a novel brain-derived peptide hypoxic-ischemic brain damage associated peptide (HIBDAP) in regulating pyroptosis of oxygen-glucose deprived (OGD) microglia.Methods:The sequence of HIBDAP was coupled with the sequence of cell-penetrating peptide transactivator of transcription (TAT) to form TAT-HIBDAP. Fluorescein isothiocyanate (FITC) labeled TAT-HIBDAP was added to microglia cells and observed under fluorescence microscope. Microglia cells were treated with different concentrations of TAT-HIBDAP (1, 5, 10, 20 μmol/L) and then OGD process. Cell pyroptosis was analyzed using lactate dehydrogenase (LDH) assay. The concentration of TAT-HIBDAP with the most prominent inhibiting effects was determined and selected for subsequent experiments. The pyroptosis morphology of the control group, the OGD group and the HIBDAP group (5 μmol/L TAT-HIBDAP+OGD) was observed using transmission electron microscope. The mRNA and protein expression of NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasomes were examined using real-time quantitative PCR and Western Blot analysis.Results:Fluorescence microscope showed FITC-labeled TAT-HIBDAP could successfully enter microglia cells. Compared with the OGD group, low concentrations of TAT-HIBDAP (1, 5, 10 μmol/L) could significantly reduce microglia pyroptosis and the concentration of 5 μmol/L showed the most prominent effects. Compared with the control group, OGD group showed typical pyroptosis morphology and HIBDAP group showed significantly improved morphology. The mRNA and protein expression of NLRP3 inflammasomes in the OGD group were significantly higher than the control group and also the HIBDAP group.Conclusions:The novel brain-derived peptide HIBDAP may reduce the expression of NLRP3 inflammasomes and inhibit the pyroptosis of OGD microglia.

Objective:To characterize the antimicrobial resistance, resistance machanism and population genetics of Salmonella( S.) Derby in China, preliminarily reveal the population genetic characteristics of S. Derby in China, discover possible transmission patterns or potential transmission pathways, and provide certain reference for strengthening S. disease monitoring and developing prevention and control strategies. Methods:A total of 201 strains of S. Derby from different areas in China were used for the susceptible tests to 16 antibiotics and whole-genome sequencing. Finally, combined with the genome sequences of 134 strains of S. Derby from public databases, 335 strains of S. Derby were used for resistance genotype analysis and multi-locus sequence typing (MLST), and a phylogenetic tree based on the core genome single nucleotide polymorphisms was constructed for evolutionary analysis. Results:The results showed that 201 strains of S. Derby showed resistance to 16 kinds of antibiotics at different levels. The overall resistance rate was 97.51%. The resistance rates to antibiotics varied in S. Derby from different sources (human, animal, and food), the differences were significant (all P<0.05). A total of 38 resistance genes were carried by 335 strains of S. Derby, of which, fosfomycin gene fosA7 was found in all the strains (100.00%) and aminoglycoside genes aac(6')-Iaa accounted for 99.70%. The consistency of resistance genes and phenotypes varied with antibiotics. Except aminoglycosides and chloramphenicol, the consistencies of resistance genes and phenotypes for other antibiotics were high. MLST showed that 334 strains of S. Derby belonged to ST40. Phylogenetic trees indicated the risk for cross-infection between animal and human, food and human, and the possibility of long-distance interprovincial transmission of the bacteria by animal, to which further epidemiological studies are needed. Conclusions:The drug resistance of S. Derby is serious in China and the risk for cross-transmission between human and animal or food exists. It is necessary to establish and strengthen the comprehensive surveillance and risk assessment to prevent the spread of antibiotic resistant strains or elements through animal, food and human chains.

Rectal cancer is one of the most common gastrointestinal malignancies in China. Accurate and reasonable assessment of the preoperative staging of rectal cancer can significantly enhance treatment outcomes and improve patient prognosis. Magnetic resonance imaging is the technique of choice for local staging of rectal cancer and has significant advantages in the diagnosis of rectal primary tumors (T) and peri-intestinal lymph nodes (N). In this review paper, the research ideas and progress of traditional radiomics and deep learning methods for preoperative TN staging prediction of rectal cancer were reviewed around multimodal magnetic resonance images, with the aim of providing new ideas for realizing fully automated TN staging algorithms for rectal cancer.

Magnetic resonance imaging (MRI) is an important medical imaging method, whose major limitation is its long scan time due to the imaging mechanism, increasing patients' cost and waiting time for the examination. Currently, parallel imaging (PI) and compress sensing (CS) together with other reconstruction technologies have been proposed to accelerate image acquisition. However, the image quality of PI and CS depends on the image reconstruction algorithms, which is far from satisfying in respect to both the image quality and the reconstruction speed. In recent years, image reconstruction based on generative adversarial network (GAN) has become a research hotspot in the field of magnetic resonance imaging because of its excellent performance. In this review, we summarized the recent development of application of GAN in MRI reconstruction in both single- and multi-modality acceleration, hoping to provide a useful reference for interested researchers. In addition, we analyzed the characteristics and limitations of existing technologies and forecasted some development trends in this field.
Humans ; Acceleration ; Algorithms ; Magnetic Resonance Imaging ; Technology

α-Amino acid ester acyltransferase (Aet) catalyzes the L-alanyl-L-glutamine forming reaction from L-alaine methylester hydrochloride and L-glutamine. In this study, the recombinant Escherichia coli saet-QC01 was used to express the α-amino acid acyltransferase, and its expression conditions were optimized. The recombinant protein was separated and purified by Ni-NTA affinity chromatography, and its enzymatic properties and catalytic applications were studied. The induction conditions suitable for enzyme production optimized were as follows: The temperature was 20 ℃, the induction stage (OD₆₀₀=2.0-2.5), IPTG concentration was 0.6 mmol/L, induction time was 12 h. The optimal reaction conditions of α-amino acid acyltransferase were 27 ℃, pH 8.5, it was most stable between pH 7.0 and 8.0 and relatively stable in an acidic environment, and low concentration of Co²⁺ or EDTA could promote the enzyme activity. Under optimal reaction conditions, 600 mmol/L of L-alaine methylester hydrochloride and 480 mmol/L of L-glutamine, the yield of L-alanyl-L-glutamine reached 78.2 g/L and productivity of 1.955 g/L/min, the conversion rate reached 75.0%. α-Amino acid ester acyltransferase has excellent acid-basei resistance, high catalytic efficiency. These characteristics suggest its application prospects in the industrial production.

OBJECTIVES: Chronic suppurative otitis media (CSOM) and middle ear cholesteatoma (MEC) are the 2 most common chronic middle ear diseases. In the process of diagnosis and treatment, the 2 diseases are prone to misdiagnosis and missed diagnosis due to their similar clinical manifestations. High resolution computed tomography (HRCT) can clearly display the fine anatomical structure of the temporal bone, accurately reflect the middle ear lesions and the extent of the lesions, and has advantages in the differential diagnosis of chronic middle ear diseases. This study aims to develop a deep learning model for automatic information extraction and classification diagnosis of chronic middle ear diseases based on temporal bone HRCT image data to improve the classification and diagnosis efficiency of chronic middle ear diseases in clinical practice and reduce the occurrence of missed diagnosis and misdiagnosis. METHODS: The clinical records and temporal bone HRCT imaging data for patients with chronic middle ear diseases hospitalized in the Department of Otorhinolaryngology, Xiangya Hospital from January 2018 to October 2020 were retrospectively collected. The patient's medical records were independently reviewed by 2 experienced otorhinolaryngologist and the final diagnosis was reached a consensus. A total of 499 patients (998 ears) were enrolled in this study. The 998 ears were divided into 3 groups: an MEC group (108 ears), a CSOM group (622 ears), and a normal group (268 ears). The Gaussian noise with different variances was used to amplify the samples of the dataset to offset the imbalance in the number of samples between groups. The sample size of the amplified experimental dataset was 1 806 ears. In the study, 75% (1 355) samples were randomly selected for training, 10% (180) samples for validation, and the remaining 15% (271) samples for testing and evaluating the model performance. The overall design for the model was a serial structure, and the deep learning model with 3 different functions was set up. The first model was the regional recommendation network algorithm, which searched the middle ear image from the whole HRCT image, and then cut and saved the image. The second model was image contrast convolutional neural network (CNN) based on twin network structure, which searched the images matching the key layers of HRCT images from the cut images, and constructed 3D data blocks. The third model was based on 3D-CNN operation, which was used for the final classification and diagnosis of the 3D data block construction, and gave the final prediction probability. RESULTS: The special level search network based on twin network structure showed an average AUC of 0.939 on 10 special levels. The overall accuracy of the classification network based on 3D-CNN was 96.5%, the overall recall rate was 96.4%, and the average AUC under the 3 classifications was 0.983. The recall rates of CSOM cases and MEC cases were 93.7% and 97.4%, respectively. In the subsequent comparison experiments, the average accuracy of some classical CNN was 79.3%, and the average recall rate was 87.6%. The precision rate and the recall rate of the deep learning network constructed in this study were about 17.2% and 8.8% higher than those of the common CNN. CONCLUSIONS The deep learning network model proposed in this study can automatically extract 3D data blocks containing middle ear features from the HRCT image data of patients' temporal bone, which can reduce the overall size of the data while preserve the relationship between corresponding images, and further use 3D-CNN for classification and diagnosis of CSOM and MEC. The design of this model is well fitting to the continuous characteristics of HRCT data, and the experimental results show high precision and adaptability, which is better than the current common CNN methods.
Algorithms ; Ear Diseases ; Humans ; Neural Networks, Computer ; Retrospective Studies ; Tomography, X-Ray Computed/methods*

Accurate segmentation of ground glass nodule (GGN) is important in clinical. But it is a tough work to segment the GGN, as the GGN in the computed tomography images show blur boundary, irregular shape, and uneven intensity. This paper aims to segment GGN by proposing a fully convolutional residual network, i.e., residual network based on atrous spatial pyramid pooling structure and attention mechanism (ResAANet). The network uses atrous spatial pyramid pooling (ASPP) structure to expand the feature map receptive field and extract more sufficient features, and utilizes attention mechanism, residual connection, long skip connection to fully retain sensitive features, which is extracted by the convolutional layer. First, we employ 565 GGN provided by Shanghai Chest Hospital to train and validate ResAANet, so as to obtain a stable model. Then, two groups of data selected from clinical examinations (84 GGN) and lung image database consortium (LIDC) dataset (145 GGN) were employed to validate and evaluate the performance of the proposed method. Finally, we apply the best threshold method to remove false positive regions and obtain optimized results. The average dice similarity coefficient (DSC) of the proposed algorithm on the clinical dataset and LIDC dataset reached 83.46%, 83.26% respectively, the average Jaccard index (IoU) reached 72.39%, 71.56% respectively, and the speed of segmentation reached 0.1 seconds per image. Comparing with other reported methods, our new method could segment GGN accurately, quickly and robustly. It could provide doctors with important information such as nodule size or density, which assist doctors in subsequent diagnosis and treatment.
Algorithms ; China ; Disease Progression ; Humans ; Multiple Pulmonary Nodules ; Neural Networks, Computer ; Tomography, X-Ray Computed/methods*

In the USA, there were about 1 ‍806 ‍590 new cancer cases in 2020, and 606 520 cancer deaths are expected to have occurred in 2021. Lung cancer has become the leading cause of death from cancer in both men and women (Siegel et al., 2020). Clinical studies show that the five-year survival rate of lung cancer patients after early diagnosis and treatment intervention can reach 80%, compared with that of patients having advanced lung cancer. Thus, the early diagnosis of lung cancer is a key factor to reduce mortality.
Male ; Humans ; Female ; Tomography, X-Ray Computed/methods* ; Algorithms ; Lung Neoplasms/pathology* ; Cluster Analysis