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BACKGROUND: We compared the BACTEC Peds Plus (Becton Dickinson, USA) and BacT/Alert PF (bioMerieux, France) pediatric blood culture bottles in the context of recovery and time to detection (TTD) of bacteria and fungi from pediatric patients. METHODS: Blood samples were collected for culture from pediatric patients who were hospitalized during 2010 at a university hospital. BACTEC Peds Plus and BacT/Alert PF bottles were placed in the BACTEC FX and BacT/Alert 3D blood culture system, respectively, and tested for 5 days. Bottles flagged by instruments as positive were removed from the instruments and the TTDs were recorded. RESULTS: A total of 5,018 sets (1 set, 1 BACTEC Peds Plus and 1 BacT/Alert PF) were evaluated. Overall, the recovery proportions for BACTEC Peds Plus and BacT/Alert PF bottles were 57% (134/195) and 69% (112/195), respectively. There was a significant difference between the 0.38% contamination rate in BacT/Alert PF bottles and the 0.16% contamination rate in BACTEC Peds Plus bottles (P=0.035). The average TTD for all microorganisms was significantly decreased for the BACTEC Peds Plus bottles (P=0.021), but was increased for Candida parapsilosis compared to the results for the BacT/Alert PF bottles (P=0.028). CONCLUSION: We conclude that the rate of detection and contamination is higher when BacT/Alert PF bottles are used than when BACTEC Peds Plus bottles are used for pediatric blood culture. The BACTEC Peds Plus bottles detect nearly all enrolled microorganisms significantly faster than do the BacT/Alert PF bottles.
Bacteria ; Candida ; Fungi ; Humans

BACKGROUND: Rotaviruses are the primary cause of severe acute gastroenteritis in infants and young children worldwide. We evaluated the performance of the new GENEDIA Rotavirus Ag Rapid test (Greencross Medical Science, Korea) immunochromatographic assay (ICA) for detecting human rotavirus in stool specimens, in comparison with ELISA and PCR assays. METHODS: One hundred rotavirus-positive stool samples and 150 rotavirus-negative stool samples, confirmed by ELISA and PCR tests, were analysed using the GENEDIA Rotavirus Ag rapid test. The positive agreement (sensitivity), negative agreement (specificity), and total agreement rates of the ICA compared to ELISA and PCR were determined. To assess the analytical performance of the ICA, we tested its detection limit, reproducibility, and cross-reactivity. RESULTS: The positive, negative, and total agreement rates of the ICA were 99%, 100%, and 99.6%, respectively, when compared with the results confirmed by ELISA and PCR. The total turnaround time of the ICA was less than 20 minutes. The lower limit of detection of the ICA for rotavirus was 1.33x10(3) TCID50/mL, which was similar to that of ELISA but higher than that of PCR. No cross-reactivity was detected for 11 viruses and 19 bacteria. CONCLUSIONS: The GENEDIA Rotavirus Ag rapid test was easy to perform and provided rapid results, which showed high agreement with those obtained using ELISA and PCR. This test appears to be a useful tool for the diagnosis of rotavirus infection.
Bacteria ; Child ; Diagnosis ; Enzyme-Linked Immunosorbent Assay ; Gastroenteritis ; Humans ; Immunochromatography* ; Infant ; Limit of Detection ; Polymerase Chain Reaction ; Rotavirus Infections ; Rotavirus*

PURPOSE: Parathyroid adenoma detection with dual-phase (99m)Tc-sestamibi (MIBI) scintigraphy depends on differential MIBI washout from thyroid. However, autoimmune thyroid disease (AITD) may cause MIBI to be retained in the thyroid gland and reduce parathyroid detection.We evaluated the impact of AITD on MIBI thyroid retention and additional benefit of SPECT/CT in these patients.METHODS: Dual phase planar MIBI and SPECT/CT was performed on 82 patients. SPECT/CTwas performed immediatelyafter delayed planar scan. Thyroid density (Hounsfield unit, CT-HU) and size were measured on CT component of SPECT/CT. MIBI uptake in early scans and retention in delayed scans were visually graded and correlated with clinical factors and CT findings. Finally, planar and SPECT/CT findings were compared for parathyroid lesion visualization according to thyroid MIBI retention.RESULTS: In early scan, multivariate analysis showed only thyroid size predicted early uptake. In delayed scan, multivariate analysis showed higher visual grade in early scan, lower CTHU or AITD were significant predictors for delayed thyroid parenchymal retention. Overall, ten more parathyroid lesions were visualized on SPECT/CT compared to planar scans (57 vs. 47, p = 0.002). SPECT/CT was especially more useful in patients with thyroidal MIBI retention, as eight out of the ten additional lesions detected were found in patients with thyroid MIBI retention.CONCLUSION: AITD is an important factor for MIBI thyroid parenchymal retention on delayed scans, and may impede parathyroid lesion detection. Patients with MIBI retention in the thyroid parenchyma on delayed scans are likely to benefit from an additional SPECT/CT.
Humans ; Multivariate Analysis ; Parathyroid Neoplasms ; Radionuclide Imaging ; Thyroid Diseases ; Thyroid Gland

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), a prototypic halogenated aromatic hydrocarbon (HAH), is known as one of the most potent toxicants. At least a part of its toxic effects appears to be derived from its ability to induce TNF-alpha production. However, the signaling pathway of TCDD that leads to TNF-alpha expression has not been elucidated. In this study, we investigated the signaling mechanism of TCDD-induced TNF-alpha expression in PMA-differentiated THP-1 macrophages. TCDD induced both mRNA and protein expression of TNF-alpha in a dose- and time-dependent manner. Alpha-Naphthoflavone (NF), an aryl hydrocarbon receptor (AhR) inhibitor, prevented the TCDD-induced expression of TNF-alpha at both mRNA and protein levels. Genistein, a protein tyrosine kinase (PTK) inhibitor, and PD153035, an EGFR inhibitor, also blocked the increase of TNF-alpha expression by TCDD, indicating the role of EGFR in TCDD-induced TNF-alpha expression. On the other hand, PP2, a c-Src specific inhibitor, did not affect TCDD-induced TNF-alpha expression. EGFR phosphorylation was detected as early as 5 min after TCDD treatment. TCDD-induced EGFR activation was AhR-dependent since co-treatment with alpha-NF prevented it. ERK was found to be a downstream effector of EGFR activation in the signaling pathway leading to TNF-alpha production after TCDD stimulation. Activation of ERK was observed from 30 min after TCDD treatment. PD98059, an inhibitor of the MEK-ERK pathway, completely prevented the TNF-alpha mRNA and protein expression induced by TCDD, whereas inhibitors of JNK and p38 MAPK had no effect. PD153035, an EGFR inhibitor, as well as alpha-NF significantly reduced ERK phosphorylation, suggesting that ERK activation by TCDD was mediated by both EGFR and AhR. These results indicate that TNF-alpha production by TCDD in differentiated THP-1 macrophages is AhR-dependent and involves activation of EGFR and ERK, but not c-Src, JNK, nor p38 MAPK. A signaling pathway is proposed where TCDD induces sequential activation of AhR, EGFR and ERK, leading to the increased expression of TNF-alpha.
Animals ; Benzoflavones/pharmacology ; Cell Differentiation ; Cell Line, Tumor ; Enzyme Activation ; Genistein/pharmacology ; Hazardous Substances/*toxicity ; Humans ; MAP Kinase Signaling System/drug effects/physiology ; Macrophages/*metabolism ; Mice ; Phosphorylation ; Pyrimidines/pharmacology ; Quinazolines/pharmacology ; RNA, Messenger/metabolism ; Receptor, Epidermal Growth Factor/antagonists & inhibitors/metabolism ; Receptors, Aryl Hydrocarbon/antagonists & inhibitors ; Signal Transduction ; Tetrachlorodibenzodioxin/*toxicity ; Tumor Necrosis Factor-alpha/*biosynthesis ; src-Family Kinases/antagonists & inhibitors/metabolism

Background: Although patients with chronic obstructive pulmonary disease (COPD) experience high morbidity and mortality worldwide, few biomarkers are available for COPD.Here, we analyzed potential biomarkers for the diagnosis of COPD by using word embedding. Methods: To determine which biomarkers are likely to be associated with COPD, we selected respiratory disease-related biomarkers. Degrees of similarity between the 26 selected biomarkers and COPD were measured by word embedding. And we infer the similarity with COPD through the word embedding model trained in the large-capacity medical corpus, and search for biomarkers with high similarity among them. We used Word2Vec, Canonical Correlation Analysis, and Global Vector for word embedding. We evaluated the associations of selected biomarkers with COPD parameters in a cohort of patients with COPD. Results: Cytokeratin 19 fragment (Cyfra 21-1) was selected because of its high similarity and its significant correlation with the COPD phenotype. Serum Cyfra 21-1 levels were determined in patients with COPD and controls (4.3 ± 5.9 vs. 3.9 ± 3.6 ng/mL, P = 0.611). The emphysema index was significantly correlated with the serum Cyfra 21-1 level (correlation coefficient = 0.219,P = 0.015). Conclusion Word embedding may be used for the discovery of biomarkers for COPD and Cyfra 21-1 may be used as a biomarker for emphysema. Additional studies are needed to validate Cyfra 21-1 as a biomarker for COPD.

Background: Although patients with chronic obstructive pulmonary disease (COPD) experience high morbidity and mortality worldwide, few biomarkers are available for COPD.Here, we analyzed potential biomarkers for the diagnosis of COPD by using word embedding. Methods: To determine which biomarkers are likely to be associated with COPD, we selected respiratory disease-related biomarkers. Degrees of similarity between the 26 selected biomarkers and COPD were measured by word embedding. And we infer the similarity with COPD through the word embedding model trained in the large-capacity medical corpus, and search for biomarkers with high similarity among them. We used Word2Vec, Canonical Correlation Analysis, and Global Vector for word embedding. We evaluated the associations of selected biomarkers with COPD parameters in a cohort of patients with COPD. Results: Cytokeratin 19 fragment (Cyfra 21-1) was selected because of its high similarity and its significant correlation with the COPD phenotype. Serum Cyfra 21-1 levels were determined in patients with COPD and controls (4.3 ± 5.9 vs. 3.9 ± 3.6 ng/mL, P = 0.611). The emphysema index was significantly correlated with the serum Cyfra 21-1 level (correlation coefficient = 0.219,P = 0.015). Conclusion Word embedding may be used for the discovery of biomarkers for COPD and Cyfra 21-1 may be used as a biomarker for emphysema. Additional studies are needed to validate Cyfra 21-1 as a biomarker for COPD.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.