Objective To develop an up-converting phosphor technology based lateral flow assay ( UPT-LF) to detect ricin toxin ( RT) quickly, accurately and quantitatively.Methods Ricin-monoclonal antibodies were prepared and their affinity was evaluated before four types of monoclonal antibodies with the highest titer were applied to couple with the up-converting phosphor nano-particles ( UCP-NPs) as the bio-conjugate and disperse on the analysis membrane as the test line, respectively.Following systematic optimization to establish the RT-UPT-LF strip, the sensitivity, precision, quantita-tive ability and specificity of RT-UPT-LF were evaluated.Results The detection could be accomplished within 15 min and the detection limit of the RT-UPT-LF assay could reach 0.5 ng/ml within the quantitative detection range of 0.5-1000 ng/ml.Other non-specific toxins at a concentration of 1000 ng/ml did not cause any non-specific reactions.Conclusion The developed RT-UPT-LF strip provides a new means for on-site quantitative detection of ricin toxin.

BACKGROUND:The phenomenon of osteoinduction by biomaterials has been proven in animal experiments. OBJECTIVE:To investigate whether the ability of a biomaterial to initiate bone formation in ectopic implantation sites improves the performance of osteoinductive biomaterial as a scaffold for tissue-engineered bone. METHODS:We compared ectopic bone formation by combining autologous adipose-derived stromal cells with an osteoinductive and a nonosteoinductive biphasic calcium phosphate ceramic to create a tissue engineering construction in the muscle of dogs. Al implants were implanted in the back muscle of 10 adult dogs for 8 weeks and 12 weeks, including osteoinductive biphasic calcium phosphate ceramic+adipose-derived stromal cells (osteoinductive complex group), osteoinductive biphasic calcium phosphate ceramic (osteoinductive broup), nonosteoinductive biphasic calcium phosphate ceramic+adipose-derived stromal cells (nonosteoinductive complex group), and nonosteoinductive biphasic calcium phosphate ceramic (nonosteoinductive group). Micro-CT analysis and histomorphometry were performed to evaluate and quantify ectopic bone formation. RESULTS AND CONCLUSION:Ectopic bone formation was visible in the osteoinductive complex group and osteoinductive group, and the former group was superior to the latter one in quality of new bone (P<0.05). However, there was no ectopic bone formation in the other two groups. Micro-CT results were consistent with the histomorphological detection. These findings indicate that osteoinductive biphasic calcium phosphate ceramic, as a kind of bone tissue engineering scaffold material, has a better osteogenic capacity, while adipose-derived mesenchymal stem cells serve as seed cells to promote the ectopic bone formation.

Objective To develop an up-converting phosphor technology based lateral flow (UPT-LF) assay for rapid detection of Salmonella paratyphi A, S.paratyphi B, Escherichia coli O157 ∶H7 and Vibrio parahaemolyticus. Methods With up-converting phosphor nano-particles ( UCP-NPs ) as the bio-marker, four double-antibody-sandwich mode based UPT-LF strips for detecting the above mentioned four pathogens were prepared respectively and their sensitivi-ty, accuracy, linearity and specificity were evaluated .Furthermore, the feasibility of detecting bacteria in food samples was evaluated by different food samples artificially contaminated with less than 10 CFU target pathogens .Results The sensitivi-ty of UPT-LF assays for four pathogens was 105 ~106 CFU/ml with excellent specificity .The four strips had a good linear response with the linear fitting coefficient of determination (r) for each target pathogen ranging from 0.985 to 0.996.The positive rate of detecting pathogens from samples was acceptable .Conclusion The four developed UPT-LF strips provide a new choice for rapid , specific and sensitive and quantitative detection of S.paratyphi A , S.paratyphi B, E.coli O157∶H7 and V.parahemolyticus.

The petroleum ether fraction of ethanol extracts from Boenninghausenia sessilicarpawas isolated by combination of several chromatographic methods including silica gel, ODS, and Sephadex LH-20 column chromatography, and finally purified by preparative HPLC. The structures of the isolated compounds were identified based on the spectral data. As a result, 15 coumarin compounds were isolated and identified from the petroleum ether extraction. Their structures were determined as osthenon (1), murrangatin (2), 3-(1,1-dimethylallyl)-8-hydroxy-7-methoxycoumarin (3), xanthotoxin (4), isopimpinellin (5), chalepensin (6), isodemethylfuropinarine (7), imperatorin (8), phellopterin (9), heraclenol (10), byakangelicin (11), neobyakangelicol (12), chalepin (13), luvangetin (14), 3-(1, 1-dimethylallyl)-xanthyletin (15). Among them, compounds 1 - 3, 6 - 10 and 14 -15 were firstly isolated from B. sessilicarpa.

OBJECTIVETo develop an up-converting phosphor technology based lateral flow (UPT-LF) assay for rapid and quantitative detection of Yersinia pestis, Bacillus anthracis spore and Brucella spp.and make the comparison with BioThreat Alert (BTA) test strips (Tetracore Inc., USA).

METHODSUsing up-converting phosphor nano-particles (UCP-NPs) as the bio-marker, three double-antibody-sandwich model based UPT-LF strips including Plague-UPT-LF, Anthrax-UPT-LF, Brucella-UPT-LF were prepared and its sensitivity, accuracy, linearity and specificity were determined by detecting 10(10), 10(9), 10(8), 10(7), 10(6), 10(5) and 0 CFU/ml series of concentrations of Y.pestis, B.anthracis, Brucella standards and other 27 kinds of 10(9) CFU/ml series of contrations of bacteria strains.Furthermore, the speed, sensitivity and accuracy of bacteria standards and simulated sample detection were compared between UPT-LF and BTA system.

RESULTSThe detection limit of Plague-UPT-LF, Anthrax-UPT-LF and Brucella-LF was 10(5) CFU/ml. The CV of series of bacteria concentrations was ≤ 15%, and the r between lg (T/C-cut-off) and lg (concentration) was 0.996,0.998 and 0.999 (F values were 1 647.57, 743.51 and 1 822.17. All the P values were <0.001), respectively. The specificity of Plague-UPT-LF and Brucella-LF were excellent, while that of Anthrax-UPT-LF was a little bit regretful because of non-specific reaction with two isolates of B. subtilis and one B.cereus. On-site evaluation showed the detection time of UPT-LF for all Y.pestis, B.anthracis spore and Brucella spp.was 33, 36 and 37 min, while BTA was 115, 115 and 111 min, which revealed the higher detection speed and sensitivity of UPT-LF comparing with BTA. The negative rate of two methods for blank standard was both 5/5, the sensitivity of UPT-LF for Y.pestis,B.anthracis spore and Brucella spp. was all 10(5) CFU/ml, then BTA was 10(6), 10(6) and 10(5) CFU/ml, respectively. The detection rate of UPT-LF for all three bacteria analog positive samples was 16/16, while BTA for B.anthracis was 7/16 only.

CONCLUSIONThe good performance including rapidness, simplicity and high sensitivity will bring the bright future of UPT-LF to be broadly used on-site as first response to bio-terrorism.

Bacillus anthracis ; Brucella ; Immunochromatography ; Plague ; Sensitivity and Specificity ; Spores, Bacterial ; Yersinia pestis

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.