R-MFE-TCN: A correlation prediction model between body surface and tumor during respiratory movement.

BACKGROUND: 2D CT image-guided radiofrequency ablation (RFA) is an exciting minimally invasive treatment that can destroy liver tumors without removing them. However, CT images can only provide limited static information, and the tumor will move with the patient's respiratory movement. Therefore, how to accurately locate tumors under free conditions is an urgent problem to be solved at present. PURPOSE: The purpose of this study is to propose a respiratory correlation prediction model for mixed reality surgical assistance system, Riemannian and Multivariate Feature Enhanced Temporal Convolutional Network (R-MFE-TCN), and to achieve accurate respiratory correlation prediction. METHODS: The model adopts a respiration-oriented Riemannian information enhancement strategy to expand the diversity of the dataset. A new Multivariate Feature Enhancement module (MFE) is proposed to retain respiratory data information, so that the network can fully explore the correlation of internal and external data information, the dual-channel is used to retain multivariate respiratory feature, and the Multi-headed Self-attention obtains respiratory peak-to-valley value periodic information. This information significantly improves the prediction performance of the network. At the same time, the PSO algorithm is used for hyperparameter optimization. In the experiment, a total of seven patients' internal and external respiratory motion trajectories were obtained from the dataset, and the first six patients were selected as the training set. The respiratory signal collection frequency was 21 Hz. RESULTS: A large number of experiments on the dataset prove the good performance of this method, which improves the prediction accuracy while also having strong robustness. This method can reduce the delay deviation under long window prediction and achieve good performance. In the case of 400 ms, the average RMSE and MAE are 0.0453  and 0.0361 mm, respectively, which is better than other research methods. CONCLUSION: The R-MFE-TCN can be extended to respiratory correlation prediction in different clinical situations, meeting the accuracy requirements for respiratory delay prediction in surgical assistance.

Metabolites and metabolic pathway analysis of sulfadimidine in carp (Cyprinus carpio) based on UHPLC-Q-orbitrap HRMS.

Sulfadimidine (SM2) is an N-substituted derivative of p-aminobenzenesulfonyl structure. This study aimed to analyze the metabolism of SM2 in carp (Cyprinus carpio). The carps were fed with SM2 at a dose of 200 mg/(kg · bw) and then killed. The blood, muscle, liver, kidney, gill, other guts, and carp aquaculture water samples were collected. The UHPLC-Q-Exactive Plus Orbitrap-MS was adopted for determining the metabolites of SM2 in the aforementioned samples. Twelve metabolites, which were divided into metabolites in vivo and metabolites in vitro, were identified using Compound Discoverer software. The metabolic pathways in vivo of SM2 in carp included acetylation, hydroxylation, glucoside conjugation, glycine conjugation, carboxylation, glucuronide conjugation, reduction, and methylation. The metabolic pathways in vitro included oxidation and acetylation. This study clarified the metabolites and metabolic pathways of SM2 in carp and provided a reference for further pharmacodynamic evaluation and use in aquaculture.

Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma.

Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interest and extracting prominent radiomic features. An external validation of the model was performed with the DWI data of 20 patients with IMCC who were subsequently included in the study. The area under the receiver operating curve (AUC), accuracy (ACC), precision (PRE), sensitivity (REC), and F1 score were used to evaluate the diagnostic performance of the model. Following the process of feature selection, a total of nine features were retained, with skewness being the most crucial radiomic feature demonstrating the highest diagnostic performance, followed by Gray Level Co-occurrence Matrix lmc1 (glcm-lmc1) and kurtosis, whose diagnostic performances were slightly inferior to skewness. Skewness and kurtosis showed a negative correlation with the pathological grading of IMCC, while glcm-lmc1 exhibited a positive correlation with the IMCC pathological grade. Compared with the other three models, the SVM radiomic model had the best diagnostic performance with an AUC of 0.957, an accuracy of 88.2%, a sensitivity of 85.7%, a precision of 85.7%, and an F1 score of 85.7% in the training set, as well as an AUC of 0.829, an accuracy of 76.5%, a sensitivity of 71.4%, a precision of 71.4%, and an F1 score of 71.4% in the external validation set. The DWI-based radiomic model proved to be efficacious in predicting the pathological grade of IMCC. The model with the SVM classifier algorithm had the best prediction efficiency and robustness. Consequently, this SVM-based model can be further explored as an option for a non-invasive preoperative prediction method in clinical practice.

Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes.

OBJECTIVE: To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. MATERIALS AND METHODS: A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. RESULTS: The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. CONCLUSION: The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.

Variation and characterization of prometryn in oysters (Crassostrea gigas) after seawater exposure.

Prometryn (PRO) is frequently detected in shellfish of international trade among triazine herbicides because of its wide application in agriculture and aquaculture worldwide. Nevertheless, the variations of PRO remain unclear in aquatic organisms, which affect the accuracy of their food safety risk assessment. In the present study, the tissue-specific accumulation, biotransformation, and potential metabolic pathway of PRO were reported in oyster species Crassostrea gigas for the first time. The experiments were conducted through semi-static seawater exposure with low and high concentrations of PRO (at nominal concentrations of 10 and 100 µg/L) via daily renewal over 22 days, followed by 16 days of depuration in clean seawater. The characterization of prometryn in oysters was then evaluated through the bioaccumulation behavior, elimination pathway and metabolic transformation, comparing with other organisms. The digestive gland and gonad were found to be the main target organs during uptake. In addition, the highest bioconcentration factor of 67.4 ± 4.1 was observed when exposed to low concentration. The level of PRO in oyster tissues rapidly decreased within 1 day during depuration, with an elimination rate of >90 % for the gill. Moreover, four metabolites of PRO were identified in oyster samples of exposed groups, including HP, DDIHP, DIP, and DIHP, in which HP was the major metabolite. Considering the mass percentage of hydroxylated metabolites higher than 90 % in oyster samples, PRO poses a larger threat to aquatic organisms than rat. Finally, the biotransformation pathway of PRO in C. gigas was proposed, the major metabolic process of which was hydroxylation along with N-dealkylation. Meanwhile, the newly discovered biotransformation of PRO in oyster indicates the importance of monitoring environmental levels of PRO in cultured shellfish, to prevent possible ecotoxicological effects as well as to ensure the safety of aquatic products.

Classification of metastatic hepatic carcinoma and hepatocellular carcinoma lesions using contrast-enhanced CT based on EI-CNNet.

BACKGROUND: For hepatocellular carcinoma and metastatic hepatic carcinoma, imaging is one of the main diagnostic methods. In clinical practice, diagnosis mainly relied on experienced imaging physicians, which was inefficient and cannot met the demand for rapid and accurate diagnosis. Therefore, how to efficiently and accurately classify the two types of liver cancer based on imaging is an urgent problem to be solved at present. PURPOSE: The purpose of this study was to use the deep learning classification model to help radiologists classify the single metastatic hepatic carcinoma and hepatocellular carcinoma based on the enhanced features of enhanced CT (Computer Tomography) portal phase images of the liver site. METHODS: In this retrospective study, 52 patients with metastatic hepatic carcinoma and 50 patients with hepatocellular carcinoma were among the patients who underwent preoperative enhanced CT examinations from 2017-2020. A total of 565 CT slices from these patients were used to train and validate the classification network (EI-CNNet, training/validation: 452/113). First, the EI block was used to extract edge information from CT slices to enrich fine-grained information and classify them. Then, ROC (Receiver Operating Characteristic) curve was used to evaluate the performance, accuracy, and recall of the EI-CNNet. Finally, the classification results of EI-CNNet were compared with popular classification models. RESULTS: By utilizing 80% data for model training and 20% data for model validation, the average accuracy of this experiment was 98.2% ± 0.62 (mean ± standard deviation (SD)), the recall rate was 97.23% ± 2.77, the precision rate was 98.02% ± 2.07, the network parameters were 11.83 MB, and the validation time was 9.83 s/sample. The classification accuracy was improved by 20.98% compared to the base CNN network and the validation time was 10.38 s/sample. Compared with other classification networks, the InceptionV3 network showed improved classification results, but the number of parameters was increased and the validation time was 33 s/sample, and the classification accuracy was improved by 6.51% using this method. CONCLUSION: EI-CNNet demonstrated promised diagnostic performance and has potential to reduce the workload of radiologists and may help distinguish whether the tumor is primary or metastatic in time; otherwise, it may be missed or misjudged.

Corrigendum: DWI Combined With Hepatobiliary Phase Enhanced Imaging Can Better Differentiate Cholangiocarcinoma From Atypical Liver Abscesses.

[This corrects the article DOI: 10.3389/fonc.2022.723089.].

DWI Combined With Hepatobiliary-Phase Enhanced Imaging Can Better Differentiate Cholangiocarcinoma From Atypical Liver Abscesses.

Objective: To investigate the value of diffusion-weighted imaging (DWI) combined with the hepatobiliary phase (HBP) Gd-BOPTA enhancement in differentiating intrahepatic mass-forming cholangiocarcinoma (IMCC) from atypical liver abscess. Materials and Methods: A retrospective analysis was performed on 43 patients with IMCCs (IMCC group) and 25 patients with atypical liver abscesses (liver abscess group). The DWI signal, the absolute value of the contrast noise ratio (│CNR│) at the HBP, and visibility were analyzed. Results: A relatively high DWI signal and a relatively high peripheral signal were presented in 29 patients (67.5%) in the IMCC group, and a relatively high DWI signal was displayed in 15 patients (60.0%) in the atypical abscess group with a relatively high peripheral signal in only one (6.7%) patient and a relatively high central signal in 14 (93.3%, 14/15). A significant (P<0.001) difference existed in the pattern of signal between the two groups of patients. On T2WI, IMCC was mainly manifested by homogeneous signal (53.5%), whereas atypical liver abscesses were mainly manifested by heterogeneous signal and relatively high central signal (32%, and 64%), with a significant difference (P<0.001) in T2WI imaging presentation between the two groups. On the HBP imaging, there was a statistically significant difference in peripheral │CNR│ (P< 0.001) and visibility between two groups. The sensitivity of the HBP imaging was significantly (P=0.002) higher than that of DWI. The sensitivity and accuracy of DWI combined with enhanced HBP imaging were significantly (P=0.002 and P<0.001) higher than those of either HBP imaging or DWI alone. Conclusion: Intrahepatic mass-forming cholangiocarcinoma and atypical liver abscesses exhibit different imaging signals, and combination of DWI and hepatobiliary-phase enhanced imaging has higher sensitivity and accuracy than either technique in differentiating intrahepatic mass-forming cholangiocarcinoma from atypical liver abscesses.

Values of MRI Imaging Presentations in the Hepatobiliary Phase, DWI and T2WI Sequences in Predicting Pathological Grades of Intrahepatic Mass-Forming Cholangiocarcinoma.

Objective: To retrospectively investigate the value of various MRI image menifestations in the hepatobiliary phase (HBP), DWI and T2WI sequences in predicting the pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC). Materials and Methods: Forty-three patients of IMCCs confirmed by pathology were enrolled including 25 cases in well- or moderately-differentiated group and 18 cases in poorly-differentiated group. All patients underwent DWI, T2WI and HBP scan. The Chi square test was used to compare the differences in the general information. Logistic regression analysis was used to analyze the risk factors in predicting the pathological grade of IMCCs. Results: The maximal diameter of the IMCC lesion was < 3 cm in 11 patients, between 3 cm and 6 cm in 15, and > 6 cm in 17. Sixteen cases had intrahepatic metastasis, including 5 in the well- or moderately-differentiated group and 11 in the poorly-differentiated group. Seventeen (39.5%) patients presented with target signs in the DWI sequence, including 9 in the well- or moderately-differentiated group and 8 in the poorly-differentiated group. Twenty (46.5%) patients presented with target signs in the T2WI sequence, including 8 in the well- or moderately-differentiated group and 12 in the poorly-differentiated group. Nineteen cases (54.3%) had a complete hypointense signal ring, including 13 in the well- or moderately-differentiated group and 6 in the poorly-differentiated group. Sixteen (45.7%) cases had an incomplete hypointense signal ring, including 5 in the well- or moderately-differentiated group and 11 in the poorly-differentiated group. The lesion size, intrahepatic metastasis, T2WI signal, and integrity of a hypointense signal ring in HBP were statistically significantly different between two gourps. T2WI signal, presence or non-presence of intrahepatic metastasis, and integrity of hypointense signal ring were the independent influencing factors for pathological grade of IMCC. Conclusion: Target sign in T2WI sequence, presence of intrahepatic metastasis and an incomplete hypointense-signal ring in HBP are more likely to be present in poorly-differentiated IMCCs.

Retrospective Analysis of the Value of Enhanced CT Radiomics Analysis in the Differential Diagnosis Between Pancreatic Cancer and Chronic Pancreatitis.

PURPOSE: To investigate the feasibility of enhanced computed tomography (CT) radiomics analysis to differentiate between pancreatic cancer (PC) and chronic pancreatitis. METHODS AND MATERIALS: The CT images of 151 PCs and 24 chronic pancreatitis were retrospectively analyzed in the three-dimensional regions of interest on arterial phase (AP) and venous phase (VP) and segmented by MITK software. A multivariable logistic regression model was established based on the selected radiomics features. The radiomics score was calculated, and the nomogram was established. The discrimination of each model was analyzed by the receiver operating characteristic curve (ROC). Decision curve analysis (DCA) was used to evaluate clinical utility. The precision recall curve (PRC) was used to evaluate whether the model is affected by data imbalance. The Delong test was adopted to compare the diagnostic efficiency of each model. RESULTS: Significant differences were observed in the distribution of gender (P = 0.034), carbohydrate antigen 19-9 (P < 0.001), and carcinoembryonic antigen (P < 0.001) in patients with PC and chronic pancreatitis. The area under the ROC curve (AUC) value of AP multivariate regression model, VP multivariate regression model, AP combined with VP features model (Radiomics), clinical feature model, and radiomics combined with clinical feature model (COMB) was 0.905, 0.941, 0.941, 0.822, and 0.980, respectively. The sensitivity and specificity of the COMB model were 0.947 and 0.917, respectively. The results of DCA showed that the COMB model exhibited net clinical benefits and PRC shows that COMB model have good precision and recall (sensitivity). CONCLUSION: The COMB model could be a potential tool to distinguish PC from chronic pancreatitis and aid in clinical decisions.

Dynamic Imaging Changes of COVID-19 Pneumonia at Different Stages.

INTRODUCTION: To investigate the Computed Tomography (CT) imaging characteristics and dynamic changes of COVID-19 pneumonia at different stages. METHODS: Forty-six patients infected with COVID-19 who had chest CT scans were enrolled, and CT scans were performed 4-6 times with an interval of 2-5 days. RESULTS: At the early stage (n=25), ground glass opacity was presented in 11 patients (11/25 or 44.0 %) and ground glass opacity mixed with consolidation in 13 (13/25 or 52.0 %) in the lung CT images. At the progressive stage (n=38), ground glass opacity was presented in only one patient (1/38 or 2.6 %) and ground glass opacity mixed with consolidation in 33 (33/38 or 86.8 %). In the early improvement stage (n=38), the imaging presentation was ground glass opacity alone in three patients (3/38 or 7.9 %) and ground glass opacity mixed with consolidation in 34 (34/38 or 89.5 %). In the late improvement (absorption) stage (n=33), the primary imaging presentation was ground glass presentation in eight patients (8/33 or 24.2 %) and ground glass opacity mixed with consolidation in 23 (23/33 or 69.7 %). The lesion reached the peak at 4-16 days after disease onset, and 26 (26/38 or 68.4 %) patients reached the disease peak within ten days. Starting from 6 to 20 days after onset, the disease began to be improved, with 30 (30/38 or 78.9 %) patients being improved within 15 days. CONCLUSION: COVID-19 pneumonia will progress to the peak stage at a mediate time of seven days and enter the improvement stage at twelve days. Computed tomography imaging of the pulmonary lesion has a common pattern from disease onset to improvement and recovery and provides important information for evaluation of the disease course and treatment effect.

Differences in Clinical and Imaging Features between Asymptomatic and Symptomatic COVID-19 Patients.

Objectives: The clinical and imaging features of asymptomatic carriers of severe acute respiratory syndrome coronavirus 2 and symptomatic COVID-19 patients. Methods: The clinical and chest computed tomography imaging data of 47 asymptomatic carriers and 36 symptomatic COVID-19 patients were derived. All patients underwent 4-6 CT scans over a period of 2-5 days. Results: The bulk of asymptomatic carriers who developed symptoms and most of the COVID-19 patients were older than 18 years of age with a decreased lymphocyte count, abnormal hepatic and renal function, and increased D-dimer and C-reactive protein. In the early stage, the pulmonary lesion involved mostly 1-2 lobes at the peripheral area in asymptomatic carriers but more than three lobes at both the central and peripheral areas in COVID-19 patients. In the progression stage, the lesion of asymptomatic carriers extended from the peripheral to the central area, and no significant difference was found in the lesion range compared with the symptomatic control group. In early improvement stage, the lesion was rapidly absorbed, and lesions were located primarily at the peripheral area in asymptomatic carriers; contrastingly, lesions were primarily located at both the central and peripheral areas in symptomatic patients. Asymptomatic carriers reflected a significantly shorter duration from disease onset to peak progression stage compared with the symptomatic. Conclusions: Asymptomatic carriers are a potential source of transmission and may become symptomatic COVID-19 patients despite indicating less severe pulmonary damage, earlier improvement, and better prognosis. Early isolation and intervention can eliminate such carriers as potential sources of transmission and improve their prognosis.

Simultaneous determination of triazine herbicides and their metabolites in shellfish by HPLC-MS/MS combined with Q/E-Orbitrap HRMS.

Triazine herbicides are used extensively in agriculture and aquaculture worldwide because of their broad effectiveness in weed control. However, after they are discharged into the sea, they seriously contaminate aquatic ecosystems and threaten aquatic organisms, especially shellfish. Currently, there are no established methods for the detection and confirmation of triazine herbicides and their metabolites in biological matrixes. Hence, the food safety of aquatic products cannot be accurately evaluated, which creates a technical barrier against international aquatic product trade. In this study, for the first time, a method was developed for the analysis and confirmation of seven triazine herbicides and 13 metabolites in shellfish, based on alkaline acetonitrile extraction and neutral Al2O3 cartridge purification coupled with internal standard calibration. Specifically, quantitative and qualitative analysis was conducted using high-performance liquid chromatography-triple quadrupole mass spectrometry (HPLC-MS/MS), and accurate identification was carried out by quadrupole orbitrap high-resolution mass spectrometry (Q/E Orbitrap HRMS). The results showed that target analytes demonstrated good linearity within the corresponding range (R2 > 0.995). The limit of detection and limit of quantitation of the proposed method were 0.1 and 0.3 µg/kg, respectively. The average recoveries of analytes were between 70.0% and 120% when spiked at three levels with blank oyster (Crassostrea gigas) as the matrix, and the relative standard deviations (RSDs) were all less than 12% (n=6). The proposed method was successfully applied for the detection of triazine herbicide residues in oyster samples during actual breeding, and the presence of DIP, HP, DEHA, and other metabolites in positive samples was confirmed by Q/E Orbitrap HRMS. This method exhibits high accuracy, high sensitivity, and good reproducibility. It has promising application prospects in the field of hazard analysis and the positive identification of aquatic products.

Differentiating Between an Atypical Hepatic Abscess and Tumor Metastasis Using Magnetic Resonance Imaging and Hepatobiliary Phase Imaging.

OBJECTIVE: To identify diffusion-weighted imaging (DWI) patterns and conspicuity discrepancies on hepatobiliary phase imaging (HBPI) to distinguish atypical hepatic abscesses from hepatic metastases. MATERIALS AND METHODS: This retrospective study recruited 31 patients with 43 atypical hepatic abscesses and 32 patients with 35 hepatic metastases who underwent gadobenate dimeglumine-enhanced magnetic resonance imaging. All lesions were confirmed by pathological or clinical diagnosis. For the qualitative and quantitative analyses, the signal intensity, DWI pattern, apparent diffusion coefficient, degree of perilesional edema, perilesional hyperemia, perilesional signal on HBPI, conspicuity, size discrepancy between sequences, contrast-to-noise ratio, signal-to-noise ratio, and relative enhancement ratio on dynamic phases were independently assessed by two radiologists. Significant findings for differentiating the two groups were identified via univariate and multivariate analyses with a nomogram for predicting atypical hepatic abscesses. The interobserver agreement was also analyzed for each variable. RESULTS: The multivariate analysis revealed that the conspicuity discrepancy (odds ratio [OR] 34.78, 95% confidence interval [CI] 2.09-579.47, p = 0.013) and non-peripheral high signal intensity (SI) rim on DWI (OR 67.46, 95% CI 2.64, 1723.20, p = 0.011) were significant independent factors for predicting atypical hepatic abscesses. They were also shown to be high predictor points on the nomogram. When any of the set criteria were satisfied, 97.7% of atypical hepatic abscesses were correctly identified, with a specificity of 65.7%. When both criteria were combined, the specificity was up to 100%, with a sensitivity of 44.9%. CONCLUSION: Conspicuity discrepancy and a non-peripheral high SI rim on DWI are reliable and meaningful features that can distinguish atypical hepatic abscesses from hepatic metastases.

Semicarbazide Accumulation, Distribution and Chemical Forms in Scallop (Chlamys farreri) after Seawater Exposure.

Semicarbazide is a newly recognized marine pollutant and has the potential to threaten marine shellfish, the ecological equilibrium and human health. In this study, we examined the accumulation, distribution, and chemical forms of semicarbazide in scallop tissues after exposure to 10, 100, and 1000 µg/L for 30 d at 10 °C. We found a positive correlation between semicarbazide residues in the scallops and the exposure concentration (p < 0.01). Semicarbazide existed primarily in free form in all tissues while bound semicarbazide ranged from 12.1 to 32.7% and was tissue-dependent. The time for semicarbazide to reach steady-state enrichment was 25 days and the highest levels were found in the disgestive gland, followed by gills while levels in gonads and mantle were similar and were lowest in adductor muscle. The bioconcentration factor (BCF) of semicarbazide at low exposure concentrations was higher than that at high exposure concentrations. These results indicated that the scallop can uptake semicarbazide from seawater and this affects the quality and safety of these types of products when used as a food source.

Clinical and imaging features of pediatric COVID-19.

BACKGROUND: Pediatric COVID-19 is relatively mild and may vary from that in adults. This study was to investigate the epidemic, clinical, and imaging features of pediatric COVID-19 pneumonia for early diagnosis and treatment. METHODS: Forty-one children infected with COVID-19 were analyzed in the epidemic, clinical and imaging data. RESULTS: Among 30 children with mild COVID-19, seven had no symptoms, fifteen had low or mediate fever, and eight presented with cough, nasal congestion, diarrhea, headache, or fatigue. Among eleven children with moderate COVID-19, nine presented with low or mediate fever, accompanied with cough and runny nose, and two had no symptoms. Significantly (P < 0.05) more children had a greater rate of cough in moderate than in mild COVID-19. Thirty children with mild COVID-19 were negative in pulmonary CT imaging, whereas eleven children with moderate COVID-19 had pulmonary lesions, including ground glass opacity in ten (90.9%), patches of high density in six (54.5%), consolidation in three (27.3%), and enlarged bronchovascular bundles in seven (63.6%). The lesions were distributed along the bronchus in five patients (45.5%). The lymph nodes were enlarged in the pulmonary hilum in two patients (18.2%). The lesions were presented in the right upper lobe in two patients (18.1%), right middle lobe in one (9.1%), right lower lobe in six (54.5%), left upper lobe in five (45.5%), and left lower lobe in eight (72.7%). CONCLUSIONS: Children with COVID-19 have mild or moderate clinical and imaging presentations. A better understanding of the clinical and CT imaging helps ascertaining those with negative nucleic acid and reducing misdiagnosis rate for those with atypical and concealed symptoms.

Effects of Drying Methods on the Content, Structural Isomers, and Composition of Astaxanthin in Antarctic Krill.

Antarctic krill (Euphausia superba) is one of the important bioresources in Antarctic waters, containing many bioactives (e.g., astaxanthin), which have a highly potential value for commercial exploitation. In this study, the effects of processing methods on the content, structural isomers, and composition of astaxanthins (free astaxanthin and astaxanthin esters) were studied. Three drying methods, comprising freeze-drying, microwave drying, and hot-air drying, were used. Free astaxanthin (Ast), astaxanthin monoesters (AM), and astaxanthin diesters (AD) in boiled krill (control) and dried krill were extracted and analyzed using high-resolution mass spectrometry with ultraviolet detection. After the three processes, total astaxanthin loss ranged from 8.6 to 64.9%, and the AM and AD contents ranged from 78.3 to 16.6 and 168.7 to 90.5 µg/g, respectively. Compared to other kinds of astaxanthin esters, astaxanthin esters, which linked to eicosapentaenoic acid and docosahexaenoic acid, as well as the Ast, were more easily degraded, and AM was more susceptible to degradation than AD. All-E-astaxanthin easily transformed to the 13Z-astaxanthin than to the 9Z-astaxanthin during the drying process, but the proportions of optical isomers changed due to drying by no more than 5%. The results suggested that freeze-drying, low-power microwave drying (≤1 kW), and low-temperature hot-air drying (≤60 °C) are optimal drying methods for ensuring the quality of krill products.

Detection of Polycyclic Aromatic Hydrocarbons in Water Samples by Annular Platform-Supported Ionic Liquid-Based Headspace Liquid-Phase Microextraction.

In this paper, a new method of annular platform-supported headspace liquid-phase microextraction (LPME) was designed using ionic liquid as an extraction solvent, wherein extraction stability and efficiency were improved by adding an annular platform inside the extraction bottle. The ionic liquid 1-silicyl-3-benzylimidazolehexafluorophosphate was first synthesized and proved to be an excellent extraction solvent. Coupled with liquid chromatography, the proposed method was employed to analysis of polycyclic aromatic hydrocarbons (PAHs) in water and optimized in aspects of extraction temperature, extraction solvent volume, extraction time, pH, stirring rate, and salt effect of solution. The results indicated that this method showed good linearity (R 2 > 0.995) within 0.5 µg·L-1 to 1000 µg·L-1 for PAHs. The method was more suitable for extraction of volatile PAHs, with recoveries from 65.0% to 102% and quantification limits from 0.01 to 0.05 µg·L-1. It has been successfully applied for detection of PAHs in seawater samples.

Core brain networks interactions and cognitive control in internet gaming disorder individuals in late adolescence/early adulthood.

Regardless of whether it is conceptualized as a behavioral addiction or an impulse-control disorder, internet gaming disorder (IGD) has been speculated to be associated with impaired cognitive control. Efficient cognitive behavior involves the coordinated activity of large-scale brain networks, however, whether the interactions among these networks during resting state modulated cognitive control behavior in IGD adolescents remain unclear. Twenty-eight IGD adolescents and twenty-five age-, gender-, and education-matched healthy controls participated in our study. Stroop color-word task was conducted to evaluate the cognitive control deficits in IGD adolescents. Functional connectivity and Granger Causal Analysis were employed to investigate the functional and effective connections within and between the salience, central executive, and default mode networks. Meanwhile, diffusion tensor imaging was used to assess the structural integrity of abnormal network connections. The abnormal functional connectivity within central executive networks and effective connectivity within salience network in IGD adolescents were detected. Moreover, the inefficient interactions between these two brain networks were observed. In addition, we identified reduced fractional anisotropy in salience network, right central executive network tracts, and between-network (the anterior cingulate cortex-right dorsolateral prefrontal cortex tracts) pathways in IGD individuals. Notably, we observed a significant correlation between the effective and structural connection from salience network to central executive network and the number of errors during incongruent condition in Stroop task in both IGD and control subjects. Our results suggested that impaired cognitive control in IGD adolescents is likely to be mediated through the abnormal interactions and structural connection between intrinsic large-scale brain networks.