Summary Background Current staging methods do not accurately predict the risk of disease recurrence and benefit of adjuvant chemotherapy for patients who have had surgery for stage II colon cancer. ...We postulated that expression patterns of multiple microRNAs (miRNAs) could, if combined into a single model, improve postoperative risk stratification and prediction of chemotherapy benefit for these patients. Method Using miRNA microarrays, we analysed 40 paired stage II colon cancer tumours and adjacent normal mucosa tissues, and identified 35 miRNAs that were differentially expressed between tumours and normal tissue. Using paraffin-embedded specimens from a further 138 patients with stage II colon cancer, we confirmed differential expression of these miRNAs using qRT-PCR. We then built a six-miRNA-based classifier using the LASSO Cox regression model, based on the association between the expression of every miRNA and the duration of individual patients' disease-free survival. We validated the prognostic and predictive accuracy of this classifier in both the internal testing group of 138 patients, and an external independent group of 460 patients. Findings Using the LASSO model, we built a classifier based on the six miRNAs: miR-21-5p, miR-20a-5p, miR-103a-3p, miR-106b-5p, miR-143-5p, and miR-215. Using this tool, we were able to classify patients between those at high risk of disease progression (high-risk group), and those at low risk of disease progression (low-risk group). Disease-free survival was significantly different between these groups in every set of patients. In the initial training group of patients, 5-year disease-free survival was 89% (95% CI 77·3–94·4) for the low-risk group, and 60% (46·3–71·0) for the high-risk group (hazard ratio HR 4·24, 95% CI 2·13–8·47; p<0·0001). In the internal testing set of patients, 5-year disease-free survival was 85% (95% CI 74·3–91·8) for the low-risk group, and 57% (42·8–68·5) for the high-risk group (HR 3·63, 1·86–7·01; p<0·0001), and in the independent validation set of patients, was 85% (79·6–89·0) for the low-risk group and 54% (46·4–61·1) for the high-risk group (HR 3·70, 2·56–5·35; p<0·0001). The six-miRNA-based classifier was an independent prognostic factor for, and had better prognostic value than, clinicopathological risk factors and mismatch repair status. In an ad-hoc analysis, the patients in the high-risk group were found to have a favourable response to adjuvant chemotherapy (HR 1·69, 1·17–2·45; p=0·0054). We developed two nomograms for clinical use that integrated the six-miRNA-based classifier and four clinicopathological risk factors to predict which patients might benefit from adjuvant chemotherapy after surgery for stage II colon cancer. Conclusion Our six-miRNA-based classifier is a reliable prognostic and predictive tool for disease recurrence in patients with stage II colon cancer, and might be able to predict which patients benefit from adjuvant chemotherapy. It might facilitate patient counselling and individualise management of patients with this disease. Funding Natural Science Foundation of China.
Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial ...intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19.
We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists.
1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796–0·828) to 0·846 (0·815–0·852; p<0·0001) on internal testing and 0·731 (0·712–0·738) to 0·792 (0·780–0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755–0·786) to 0·805 (0·800–0·820; p<0·0001) on internal testing and 0·707 (0·695–0·729) to 0·752 (0·739–0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001).
In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19.
Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.
Avian influenza A H6N1 virus is one of the most common viruses isolated from wild and domestic avian species, but human infection with this virus has not been previously reported. We report the ...clinical presentation, contact, and environmental investigations of a patient infected with this virus, and assess the origin and genetic characteristics of the isolated virus.
A 20-year-old woman with an influenza-like illness presented to a hospital with shortness of breath in May, 2013. An unsubtyped influenza A virus was isolated from her throat-swab specimen and was transferred to the Taiwan Centres for Disease Control (CDC) for identification. The medical records were reviewed to assess the clinical presentation. We did a contact and environmental investigation and collected clinical specimens from the case and symptomatic contacts to test for influenza virus. The genomic sequences of the isolated virus were determined and characterised.
The unsubtyped influenza A virus was identified as the H6N1 subtype, based on sequences of the genes encoding haemagglutinin and neuraminidase. The source of infection was not established. Sequence analyses showed that this human isolate was highly homologous to chicken H6N1 viruses in Taiwan and had been generated through interclade reassortment. Notably, the virus had a G228S substitution in the haemagglutinin protein that might increase its affinity for the human α2-6 linked sialic acid receptor.
This is the first report of human infection with a wild avian influenza A H6N1 virus. A unique clade of H6N1 viruses with a G228S substitution of haemagglutinin have circulated persistently in poultry in Taiwan. These viruses continue to evolve and accumulate changes, increasing the potential risk of human-to-human transmission. Our report highlights the continuous need for preparedness for a pandemic of unpredictable and complex avian influenza.
Taiwan Centres for Disease Control.
Our previous studies have highlighted the importance of ezrin in esophageal squamous cell carcinoma (ESCC). Here our objective was to explore the clinical significance of ezrin-interacting proteins, ...which would provide a theoretical basis for understanding the function of ezrin and potential therapeutic targets for ESCC. We employed affinity purification and mass spectrometry to identify PDIA3, CNPY2 and STMN1 as potential ezrin-interacting proteins. Confocal microscopy and co-immunoprecipitation analysis further confirmed the colocalization and interaction of ezrin with PDIA3, CNPY2 and STMN1. Tissue microarray (TMA) data of ESCC samples ( n = 263) showed that the 5-year overall survival (OS) and disease-free survival (DFS) were significantly lower for the CNPY2 (OS: P = .003; DFS: P = .011) and STMN1 (OS: P = .010; DFS: P = .002) high expression groups compared with the low expression groups. By contrast, overexpression of PDIA3 was significantly correlated with favorable survival (OS: P < .001; DFS: P = .001). Cox regression demonstrated the prognostic value of PDIA3, CNPY2 and STMN1 in ESCC. Furthermore, decision tree analysis revealed that the resulting classifier of both ezrin and its interacting proteins could be used to better predict OS and DFS of patients with ESCC. In conclusion, a signature of ezrin-interacting proteins accurately predicts ESCC patient survival or tumor recurrence.
Abstract Introduction Basic fibroblast growth factor (bFGF) plays differential effects on the proliferation, differentiation, and extracellular matrix turnover in various tissues. However, limited ...information is known about the effect of bFGF on dental pulp cells. The purposes of this study were to investigate whether bFGF influences the cell differentiation and extracellular matrix turnover of human dental pulp cells (HDPCs) and the related gene and protein expression as well as the role of the mitogen-activated protein kinase (MEK)/extracellular-signal regulated kinase (ERK) signaling pathway. The expression of fibroblast growth factor receptors (FGFRs) in HDPCs was also studied. Methods The expression of FGFR1 and FGFR2 in HDPCs was investigated by reverse-transcription polymerase chain reaction. HDPCs were treated with different concentrations of bFGF. Cell proliferation was evaluated using the 3-(4,5-dimethyl-thiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay. Cell differentiation was evaluated using alkaline phosphatase (ALP) staining. Changes in messenger expression of cyclin B1 and tissue inhibitor of metalloproteinase (TIMP) 1 were determined by reverse-transcription polymerase chain reaction. Changes in protein expression of cdc2, TIMP-1, TIMP-2, and collagen I were determined by Western blotting. U0126 was used to clarify the role of MEK/ERK signaling. Results HDPCs expressed both FGFR1 and FGFR2. Cell viability was stimulated by 50–250 ng/mL bFGF. The expression and enzyme activities of ALP were inhibited by 10–500 ng/mL bFGF. At similar concentrations, bFGF stimulates cdc2, cyclin B1, and TIMP-1 messenger RNA and protein expression. bFGF showed little effect on TIMP-2 and partly inhibited collagen I expression of pulp cells. U0126 (a MEK/ERK inhibitor) attenuated the bFGF-induced increase of cyclin B1, cdc2, and TIMP-1. Conclusions bFGF may be involved in pulpal repair and regeneration by activation of FGFRs to regulate cell growth; stimulate cdc2, cyclin B1, and TIMP-1 expression; and inhibit ALP. These events are partly associated with MEK/ERK signaling.
Additional intervention and medical treatment of complications may follow the primary treatment of a ureteral stone. We investigated the cost of the treatment of ureteral stone(s) within 45 days ...after initial intervention by means of retrospective analysis of the National Health Insurance Research Database of Taiwan. All patients of ages ≥20 years diagnosed with ureteral stone(s)( International Classification of Diseases, Ninth Revision, Clinical Modification/ICD-9-CM: 592.1) from January 2001 to December 2011 were enrolled. We included a comorbidity code only if the diagnosis appeared in at least 2 separate claims in a patient’s record. Treatment modalities (code) included extracorporeal shock-wave lithotripsy (SWL; 98.51), ureteroscopic lithotripsy (URSL; 56.31), percutaneous nephrolithotripsy (PNL; 55.04), (open) ureterolithotomy (56.20), and laparoscopy (ie, laparoscopic ureterolithotomy; 54.21). There were 28 513 patients with ureteral stones (13 848 men and 14 665 women) in the randomized sample of 1 million patients. The mean cost was 526.4 ± 724.1 United States Dollar (USD). The costs of treatment were significantly increased in patients with comorbidities. The costs of treatment among each primary treatment modalities were 1212.2 ± 627.3, 1146.7 ± 816.8, 2507.4 ± 1333.5, 1533.3 ± 1137.1, 2566.4 ± 2594.3, and 209.8 ± 473.2 USD in the SWL, URSL, PNL, (open) ureterolithotomy, laparoscopy (laparoscopic ureterolithotomy), and conservative treatment group, respectively. In conclusion, URSL was more cost-effective than SWL and PNL as a primary treatment modality for ureteral stone(s) when the possible additional costs within 45 days after the initial operation were included in the calculation.
PDF
