Intestinal health of squab pigeons responded to parental dietary protein levels during breeding period.

The objective of this study was to determine the effects of dietary crude protein (CP) levels on intestinal antioxidant status, tight junction proteins expression, and amino acids transporters levels in squabs. A total of 180 pairs of White King parent pigeons approximately 10 mo old were randomly assigned to 5 groups with 6 replications of 6 pairs of parental pigeons each, and were fed with 14, 15, 16, 17, and 18% CP diets for 46 d, respectively. Dietary increasing CP levels increased final body weight (linear and quadratic, P < 0.05), serum urea nitrogen (linear, P<0.05) and triglyceride levels (quadratic, P < 0.05), and reduced kidney relative weight (quadratic, P < 0.05) in squabs. Final body weight of squabs in the 18% CP diet group was higher than that of the 14, 15, and 16% CP diet groups (P < 0.05) but was similar to that of the 17% CP diet group (P > 0.05). Increasing dietary CP levels reduced intestinal malondialdehyde contents (linear and quadratic, P < 0.05) and jejunal total superoxide dismutase (T-SOD) activity (linear, P < 0.05), and enhanced (linear and quadratic, P<0.05) ileal catalase and T-SOD activities in squabs, and these effects were more prominent in the 17% CP diet group. Graded CP levels up-regulated the mRNA expression of intestinal zonula occludens 1 (linear, P < 0.05), solute carrier family 7 members 9 (linear, P < 0.05) and claudin 1 (CLDN1, linear and quadratic, P < 0.05), ileal CLDN3 and solute carrier family 6 members 14 (linear, P < 0.05) but lowered jejunal solute carrier family 6 member 14 (quadratic, P<0.05) mRNA expression in squabs. The effects of dietary CP levels on intestinal tight junction proteins expression were more apparent when its supplemental levels were 18%. These results suggested that increasing parental dietary CP levels ranged from 14 to 18% during breeding period improved growth and intestinal function of squabs, with its recommended level being 17%.

Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis.

BACKGROUND: Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). PURPOSE: Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. METHODS: The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. RESULTS: The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. CONCLUSIONS: 18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.

Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer.

BACKGROUND: To investigate the association between Kirsten rat sarcoma viral oncogene homolog (KRAS) / neuroblastoma rat sarcoma viral oncogene homolog (NRAS) /v-raf murine sarcoma viral oncogene homolog B (BRAF) mutations and the tumor habitat-derived radiomic features obtained during pretreatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in patients with colorectal cancer (CRC). METHODS: We retrospectively enrolled 62 patients with CRC who had undergone 18F-FDG PET/computed tomography from January 2017 to July 2022 before the initiation of therapy. The patients were randomly split into training and validation cohorts with a ratio of 6:4. The whole tumor region radiomic features, habitat-derived radiomic features, and metabolic parameters were extracted from 18F-FDG PET images. After reducing the feature dimension and selecting meaningful features, we constructed a hierarchical model of KRAS/NRAS/BRAF mutations by using the support vector machine. The convergence of the model was evaluated by using learning curve, and its performance was assessed based on the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive exPlanation was used to interpret the contributions of various features to predictions of the model. RESULTS: The model constructed by using habitat-derived radiomic features had adequate predictive power with respect to KRAS/NRAS/BRAF mutations, with an AUC of 0.759 (95% CI: 0.585-0.909) on the training cohort and that of 0.701 (95% CI: 0.468-0.916) on the validation cohort. The model exhibited good convergence, suitable calibration, and clinical application value. The results of the SHapley Additive explanation showed that the peritumoral habitat and a high_metabolism habitat had the greatest impact on predictions of the model. No meaningful whole tumor region radiomic features or metabolic parameters were retained during feature selection. CONCLUSION: The habitat-derived radiomic features were found to be helpful in stratifying the status of KRAS/NRAS/BRAF in CRC patients. The approach proposed here has significant implications for adjuvant treatment decisions in patients with CRC, and needs to be further validated on a larger prospective cohort.

Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment 18F-FDG PET/CT Using Deep Learning.

RATIONALE AND OBJECTIVES: To develop an end-to-end deep learning (DL) model for non-invasively predicting non-small cell lung cancer (NSCLC) pathological subtypes based on 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) images, and to explore the potential value of DL technology. MATERIALS AND METHODS: Preoperative 18F-FDG PET/CT images of 189 patients with NSCLC were retrospectively collected. The whole cohort was randomly divided into a training cohort, a validation cohort, and an internal/extended test cohort at the ratio of 6:2:2 after preprocessing the images. In the training and validation cohorts, seven DL models-Shufflenet, VGG16, Googlenet, Inception v3, Resnet50, Densenet201, and Mobilenet v2-were trained and optimized. The generalization ability and clinical utility of the optimal model were evaluated in the internal and extended test cohorts. Moreover, Spearman's correlation analysis was used to evaluate the correlation between DL features and traditional radiological features such as tumor size and maximum standardized uptake values (SUVmax). RESULTS: Some DL features were significantly correlated with SUVmax and tumor size (P < 0.05). The Mobilenet v2 model achieved the best performance during the model development and validation phases. In the internal test group (area under the receiver operating characteristic curve [AUC]: 0.744, area under the precision-recall curve [AP]: 0.759) and extended test group (AUC: 0.767, AP: 0.768), the Mobilenet v2 model showed good generalization ability and reproducibility. Meanwhile, the decision curve analysis revealed that patients can benefit from the decisions made based on the Mobilenet v2 model. CONCLUSION: DL models offer great potential for classifying NSCLC pathological subtypes. Specifically, the Mobilenet v2 model performs well at end-to-end non-invasive pathological subtype stratification of NSCLC.

Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study.

Background: Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs. Methods: Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of the area under the curve (AUC). Findings: The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84-0.92; DFS, p < 0.05) and Center 2 (AUC, 0.91; 95% CI: 0.85-0.97; DFS, p < 0.05). The clinical factors of vascular invasion, perineural invasion, and tumor grade were associated with LNM (p < 0.05). The combination nomogram showed better prediction capability for LNM (AUC, 0.93; 95% CI: 0.89-0.96). Notably, our model maintained a satisfactory predictive ability for tumors at the 2-cm threshold, demonstrating its effectiveness across different tumor sizes in Center 1 (≤2 cm: AUC, 0.90 and >2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91). Interpretation: Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies. Funding: This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).

Clinical application of Al18F-NOTA-FAPI PET/CT in diagnosis and TNM staging of pancreatic adenocarcinoma, compared to 18F-FDG.

PURPOSE: This study aimed to investigate the ability of Al18F-NOTA-FAPI PET/CT to diagnose pancreatic carcinoma and tumor-associated inflammation with the comparison of 18F-FDG PET/CT. METHODS: Prospective analysis of Al18F-NOTA-FAPI PET/CT and 18F-FDG PET/CT scans of 31 patients from 05/2021 to 05/2022 were analyzed. Al18F-NOTA-FAPI imaging was performed in patients who had Ce-CT and FDG PET/CT and the diagnosis was still unclear. Follow-up histopathology or radiographic examination confirmed the findings. Radiotracer uptake, diagnostic performance, and TNM (tumor-node-metastasis) classifications were compared. RESULTS: A total of 31 patients with pancreatic carcinoma (all were adenocarcinoma) underwent Al18F-NOTA-FAPI-04 PET/CT, including 20 male and 11 female patients, with a mean age of 58.2 ± 8.5 years. FAPI-04 PET/CT imaging showed a higher value of SUVmax-15min/30min/60min, SUVmean-15min/30min/60min, TBR1, and TBR2 in pancreatic carcinoma than FDG (all P < 0.01). The mean level of Al18F-NOTA FAPI-04 uptake values of the pancreatic ductal adenocarcinoma was higher than that of pancreatitis in both SUVmax-30min (P < 0.01), SUVmean-30min (P < 0.05), SUVmax-60min (P < 0.01), and SUVmean-60min (P < 0.01). The FAPI △SUVmax-1, △SUVmax-2, and △SUVmean-2 uptake values of pancreatic carcinoma were higher than tumor-associated inflammation (all P < 0.01). TNM staging of 16/31 patients changed after Al18F-NOTA FAPI-04 PET/CT examination with all upstaging changes. CONCLUSION: Al18F-NOTA-FAPI-04 PET/CT at 15 and 30 min also demonstrated an equivalent detection ability of pancreatic lesion to 18F-FDG PET/CT. Delayed-phase Al18F-NOTA-FAPI-04 PET/CT can help differentiate pancreatic carcinoma and tumor-associated inflammation. Al18F-NOTA FAPI-04 PET/CT also performed better than FDG PET/CT in TNM staging. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2100051406. Registered 23 September 2021, https://www.chictr.org.cn/showproj.html?proj=133033.

Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss.

Aim: To explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss. Methods: Data on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords "loss of smell" and "loss of taste" were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases. Results: From 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P < 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100; the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021; 21 November 2021; 23 May 2021; and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021; 29 August 2021; 20 June 2021; and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19. Conclusion: Google Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1-3 weeks in advance.

A nomogram model based on MRI and radiomic features developed and validated for the evaluation of lymph node metastasis in patients with rectal cancer.

PURPOSE: The aim of this study was to develop and validate a nomogram model to evaluate lymph node metastasis (LNM) in patients with rectal cancer (RC). METHODS: A total of 162 patients with RC were included in the study. The MRI reported model, the Radscore model, and the Complex model were constructed using the logistics regression (LR) algorithm. The DeLong test and decision curve analysis (DCA) were used to compare the prediction performance and clinical utility of these models. The nomogram model was constructed to visualize the prediction results of the best model. Model performance was evaluated in the training and validation groups, and the calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the calibration. RESULT: All three models constructed by the LR algorithm were good at identifying LNM. The DeLong test and the DCA results showed that the Complex model outperformed the MRI reported model and the Radscore model in relation to their predictive performance and clinical utility. The nomogram of the Complex model had an area under the curve (AUC) of 0.902 (95% confidence interval (CI) 0.848-0.957) in the training group and an AUC of 0.891 (95% CI 0.799-0.983) in the validation group. Meanwhile, the nomogram showed good calibration. CONCLUSION: The nomogram model constructed based on T2WI radiomics and MRI reported had good diagnostic efficacies for LNM in patients with RC, and provided a new auxiliary method for accurate and individualized clinical management.

A clinical study on relationship between visualization of cardiac fibroblast activation protein activity by Al18F-NOTA-FAPI-04 positron emission tomography and cardiovascular disease.

Objective: FAP plays a vital role in myocardial injury and fibrosis. Although initially used to study imaging of primary and metastatic tumors, the use of FAPI tracers has recently been studied in cardiac remodeling after myocardial infarction. The study aimed to investigate the application of FAPI PET/CT imaging in human myocardial fibrosis and its relationship with clinical factors. Materials and methods: Retrospective analysis of FAPI PET/CT scans of twenty-one oncological patients from 05/2021 to 03/2022 with visual uptake of FAPI in the myocardium were applying the American Heart Association 17-segment model of the left ventricle. The patients' general data, echocardiography, and laboratory examination results were collected, and the correlation between PET imaging data and the above data was analyzed. Linear regression models, Kendall's TaU-B test, the Spearman test, and the Mann-Whitney U test were used for the statistical analysis. Results: 21 patients (60.1 ± 9.4 years; 17 men) were evaluated with an overall mean LVEF of 59.3 ± 5.4%. The calcific plaque burden of LAD, LCX, and RCA are 14 (66.7%), 12 (57.1%), and 9 (42.9%). High left ventricular SUVmax correlated with BMI (P < 0.05) and blood glucose level (P < 0.05), and TBR correlated with age (P < 0.05). A strong correlation was demonstrated between SUVmean and CTnImax (r = 0.711, P < 0.01). Negative correlation of SUVmean and LVEF (r = -0.61, P < 0.01), SUVmax and LVEF (r = -0.65, P < 0.01) were found. ROC curve for predicting calcified plaques by myocardial FAPI uptake (SUVmean) in LAD, LCX, and RCA territory showed AUCs were 0.786, 0.759, and 0.769. Conclusion: FAPI PET/CT scans might be used as a new potential method to evaluate cardiac fibrosis to help patients' management further. FAPI PET imaging can reflect the process of myocardial fibrosis. High FAPI uptakes correlate with cardiovascular risk factors and the distribution of coronary plaques.

The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer.

Purpose: Machine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features. Methods: One hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters-four clinical factors, four laboratory indicators, and 91 [18F]F-FDG PET/CT radiomic features-were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The retained minimum optimal feature subset was input into ten machine learning to construct a classifier for distinguishing between LUAD and LUSC. Univariate and multivariate analyses were used to identify the independent risk factors of the NSCLC subtype and constructed the Clinical model. Finally, the area under the receiver operating characteristic curve (AUC) values, sensitivity, specificity, and accuracy (ACC) was used to validate the machine learning model with the best performance effect and Clinical model in the validation group, and the DeLong test was used to compare the model performance. Results: Boruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators, and nine PEF/CT radiomic features. The Random Forest (RF) model and Support Vector Machine (SVM) model in the training group showed the best performance. Gender (P=0.018) and smoking status (P=0.011) construct the Clinical model. In the validation group, the SVM model (AUC: 0.876, ACC: 0.800) and RF model (AUC: 0.863, ACC: 0.800) performed well, while Clinical model (AUC:0.712, ACC: 0.686) performed moderately. There was no significant difference between the RF and Clinical models, but the SVM model was significantly better than the Clinical model. Conclusions: The proposed SVM and RF models successfully identified LUAD and LUSC. The results indicate that the proposed model is an accurate and noninvasive predictive tool that can assist clinical decision-making, especially for patients who cannot have biopsies or where a biopsy fails.

Prediction model based on 18F-FDG PET/CT radiomic features and clinical factors of EGFR mutations in lung adenocarcinoma.

The aim of this study was to build a prediction model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma. A retrospective analysis was performed on 88 patients with lung adenocarcinoma. All patients underwent an 18F-FDG PET/CT scan and genetic testing of EGFR before the treatment. In the training set, the radiomic features and clinical factors were screened out, and model-1 based on CT radiomic features, model-2 based on PET radiomic features, model-3 based on clinical factors, and model-4 based on radiomic features combined with clinical factors were established, respectively. The performance of the prediction model was assessed by area under the receiver operating characteristic (ROC) curve (AUC). The DeLong test was used to compare the performance of the models to screen out the optimal model, and then built the nomogram of the optimal model. The effect and clinical utility of the nomogram was verified in the validation cohort. In our analysis, model-4 was superior to the other prediction models in identifying EGFR mutations. The AUC was 0.864 (95% CI: 0.777-0.950), with a sensitivity of 0.714 and a specificity of 0.784. The nomogram of model-4 was established. In the validation cohort, the concordance index (C-index) value of the calibration curve of the nomogram model was 0.778 (95%CI: 0.585-0.970), and the nomogram had a good clinical utility. We demonstrated that the model based on 18F-FDG PET/CT radiomic features combined with clinical factors could predict EGFR mutations in lung adenocarcinoma, which was expected to be an important supplement to molecular diagnosis.

18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma.

PURPOSE: This article analyzes the image heterogeneity of clear cell renal cell carcinoma (ccRCC) based on positron emission tomography (PET) and positron emission tomography-computed tomography (PET/CT) texture parameters, and provides a new objective quantitative parameter for predicting pathological Fuhrman nuclear grading before surgery. METHODS: A retrospective analysis was performed on preoperative PET/CT images of 49 patients whose surgical pathology was ccRCC, 27 of whom were low grade (Fuhrman I/II) and 22 of whom were high grade (Fuhrman III/IV). Radiological parameters and standard uptake value (SUV) indicators on PET and computed tomography (CT) images were extracted by using the LIFEx software package. The discriminative ability of each texture parameter was evaluated through receiver operating curve (ROC). Binary logistic regression analysis was used to screen the texture parameters with distinguishing and diagnostic capabilities and whose area under curve (AUC) > 0.5. DeLong's test was used to compare the AUCs of PET texture parameter model and PET/CT texture parameter model with traditional maximum standardized uptake value (SUVmax) model and the ratio of tumor SUVmax to liver SUVmean (SUL)model. In addition, the models with the larger AUCs among the SUV models and texture models were prospectively internally verified. RESULTS: In the ROC curve analysis, the AUCs of SUVmax model, SUL model, PET texture parameter model, and PET/CT texture parameter model were 0.803, 0.819, 0.873, and 0.926, respectively. The prediction ability of PET texture parameter model or PET/CT texture parameter model was significantly better than SUVmax model (P = 0.017, P = 0.02), but it was not better than SUL model (P = 0.269, P = 0.053). In the prospective validation cohort, both the SUL model and the PET/CT texture parameter model had good predictive ability, and the AUCs of them were 0.727 and 0.792, respectively. CONCLUSION: PET and PET/CT texture parameter models can improve the prediction ability of ccRCC Fuhrman nuclear grade; SUL model may be the more accurate and easiest way to predict ccRCC Fuhrman nuclear grade.

Prevalence of Hepatitis B Virus Infection in Shenzhen, China, 2015-2018.

China has nearly 10% of the general HBV carrier population in the world; this infection is the most common cause of chronic liver disease. Understanding HBV epidemiology is essential for future infection control, evaluation, and treatment. This study determined the prevalence of HBV infection in Shenzhen by serological testing and analysis in 282,166 HBV screening cases for the following: HBcAb, indicative of previous HBV infection; HBsAg, indicative of chronic (current) infection; HBsAb, indicative of immunity from vaccination; and 34,368 HBV etiological screening cases for HBV-DNA, indicative of virus carriage, in which 1,204 cases were genotyped and mutation analyzed for drug-resistance evaluation. Shenzhen was a highly endemic area of HBV throughout the study period (prevalence 9.69%). HBV infections were almost entirely in the 20 and older age groups with a male-to-female ratio of 1.16:1 which is approximately the same as the male-to-female ratio of the general population in China. However, only 71.25% of the general population retained HBV immune protection. Genotype B and C were identified as the most common agents; recombinant B/C and B/D also existed; some cases, however, could not be genotyped. NAs resistant mutation occurrence patterns were multitudinous; single mutation patterns of rtM204I/V and rtL180M occurrences accounted for majority, followed by the combinational mutation pattern L180M + M204I/V. Drug-resistance was prevalent, mainly occurring in the cross resistance patterns LAM + LdT and LAM + LdT + ETV, and significantly more critical in males. These results demonstrate that all people free from HBV infection should obtain injections of the vaccine or booster shots, and conventional virologic detection in a clinical laboratory center should incorporate genotype and mutation alongside the serological factors for etiology and develop better classification methods, such as sequencing.

Precisely Controllable Core-Shell Ag@Carbon Dots Nanoparticles: Application to in Situ Super-Sensitive Monitoring of Catalytic Reactions.

Carbon dots (CDs) have attracted extensive interest owing to their unparalleled physical and chemical characteristics. CDs based nanocomposites have also drawn increasing attention because the combination of different characteristics could offer additional brilliant properties (such as photocatalysis and Raman scattering). In this work, we developed a fast, facile, and controllable method for fabricating core-shell Ag@CDs nanoparticles (NPs) based on the ability of CDs to directly reduce Ag+ to Ag NPs without an external photoirradiation process or additional reductants. The as-prepared Ag@CDs NPs caused efficient CDs fluorescence quenching, and the typical bands of carbon species were obtained in the Raman spectrum of CDs. In addition, we found that the Ag@CDs NPs could be utilized as an efficient surface-enhanced Raman scattering (SERS) substrate, showing a discernible detection concentration as low as 10-8 M by using p-aminothiophenol (PATP) as the probe molecules. The as-prepared Ag@CDs NPs used as the SERS substrate also exhibited excellent peroxidase-like catalytic activity for in situ super-sensitive monitoring of the oxidation of 3,3',5,5'-tetramethylbenzidine by H2O2, a plasmon-enhanced driven photocatalytic reaction of p-nitrothiophenol (PNTP) dimerizing into 4,4'-dimercaptoazobenzene, and catalytic driven reduction of PNTP to PATP in the presence of NaBH4 in real time. Moreover, the determination of H2O2 with a significantly lower discernible detection concentration was obtained. This work demonstrated that the hybrid nanostructures not only exhibited unique SERS properties but also showed excellent catalytic activities, especially as an ultrasensitive SERS substrate for monitoring heterogeneous catalytic reactions in real time. This would make it possible to not only obtain the information about catalytic molecular changes but also conduct quantitative and qualitative analysis, and widen the application of CDs in SERS and catalytic reactions.

[Rapid Detection of Adenovirus in Fecal Samples by Capillary Electrophoresis-laser Induced Fluorescence and Microchip Capillary Electrophoresis-laser Induced Fluorescence].

OBJECTIVE: To establish a rapid and sensitive method based on polymerase chain reaction (PCR) combined with capillary electrophoresis-laser induced fluorescence (CE-LIF) and microchip capillary electrophoresis-laser induced fluorescence (MCE-LIF) for detecting adenoviruses in fecal samples. METHODS: The DNA of adenovirus in fecal samples were extracted by the commercial kits and the conserved region of hexon gene was selected as the target gene and amplified by PCR reaction. After labeling highly sensitive nucleic acid fluorescent dye SYBR Gold and SYBR Orange respectively, PCR amplification products were separated by CE and MCE under the optimized condition and detected by LIF detector. RESULTS: PCR amplification products could be detected within 9 min by CE-LIF and 6 min by MCE-LIF under the optimized separation condition. The sequenced PCR product showed good specificity in comparison with the prototype sequences from NCBI. The intraday and inter-day relative standard deviation (RSD) of the size (bp) of the target DNA was in the range of 1.14%-1.34% and 1.27%- 2.76%, respectively, for CE-LIF, and 1.18%-1.48% and 2.85%-4.06%, respectively, for MCE-LIF. The detection limits was 2.33 x 10(2) copies/mL for CE-LIF and 2.33 x 10(3) copies/mL for MCE-LIF. The two proposed methods were applied to detect fecal samples, both showing high accuracy. CONCLUSION: The two proposed methods of PCR-CE-LIF and PCR-MCE-LIF can detect adenovirus in fecal samples rapidly, sensitively and specifically.

Zirconium phenylphosphonate-anchored methyltrioxorhenium as novel heterogeneous catalyst for epoxidation of cyclohexene.

Epoxidation of olefins to epoxides is widely recognized as an important unit process in the manufacture of fine chemicals and intermediates. Developing an environmentally benign heterogeneous catalytic system for olefin epoxidation with high activity and selectivity is still a challenge in this research field. Herein, we report our attempts to synthesize novel zirconium phenylphosphonate-anchored methyltrioxorhenium (MTO/ZrPP) heterogeneous catalysts by a conventional impregnation method and evaluate their catalytic performance for epoxidation of cyclohexene using urea-hydrogen peroxide adduct (UHP) as oxidant without the addition of base ligands. The MTO/ZrPP catalyst samples are characterized by powder X-ray diffraction (XRD), Fourier transform infrared (FT-IR), inductively coupled plasma emission spectrometry (ICP-ES), high resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), and solid-state (1)H magic-angle spinning nuclear magnetic resonance ((1)H MAS NMR) techniques. Meanwhile, the density functional theory (DFT) calculation is carried out to further understand the structure feature and interactions of the MTO/ZrPP catalyst. It is revealed that MTO is anchored on support surface by the favored hydrogen-bonding interaction between two oxo ligands of MTO and two H atoms from the adjacent phenyls of ZrPP. MTO/ZrPP catalyst displays excellent catalytic activity for cyclohexene epoxidation. Moreover, only cyclohexene oxide production can be obtained under the employed reaction conditions.