METHODS: In this paper, we propose a novel approach to distinguish colonic polyps by integrating several techniques, including a modified deep residual network, principal component analysis and AdaBoost ensemble learning. A powerful deep residual network architecture, ResNet-50, was investigated to reduce the computational time by altering its architecture. To keep the interference to a minimum, median filter, image thresholding, contrast enhancement, and normalisation techniques were exploited on the endoscopic images to train the classification model. Three publicly available datasets, i.e., Kvasir, ETIS-LaribPolypDB, and CVC-ClinicDB, were merged to train the model, which included images with and without polyps.
RESULTS: The proposed approach trained with a combination of three datasets achieved Matthews Correlation Coefficient (MCC) of 0.9819 with accuracy, sensitivity, precision, and specificity of 99.10%, 98.82%, 99.37%, and 99.38%, respectively.
CONCLUSIONS: These results show that our method could repeatedly classify endoscopic images automatically and could be used to effectively develop computer-aided diagnostic tools for early CRC detection.
METHODS: We used the 2003-2013 Taiwanese National Health Insurance Research Database to identify RA patients who started any RA-related medical therapy from 2008 to 2012. Those who initiated etanercept or adalimumab therapy during 2008-2012 were selected as the TNFi group and those who never received biologic disease-modifying anti-rheumatic drug therapy were identified as the comparison group after excluding the patients who had a history of TB or human immunodeficiency virus infection/acquired immune deficiency syndrome. We used propensity score matching (1:6) for age, sex, and the year of the drug index date to re-select the TNFi group and the non-TNFi controls. After adjusting for potential confounders, hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated to examine the 1-year TB risk in the TNFi group compared with the non-TNFi controls. Subgroup analyses according to the year of treatment initiation and specific TNFi therapy were conducted to assess the trend of 1-year TB risk in TNFi users from 2008 to 2012.
RESULTS: This study identified 5,349 TNFi-treated RA patients and 32,064 matched non-TNFi-treated controls. The 1-year incidence rates of TB were 1,513 per 105 years among the TNFi group and 235 per 105 years among the non-TNFi controls (incidence rate ratio, 6.44; 95% CI, 4.69-8.33). After adjusting for age, gender, disease duration, comoridities, history of TB, and concomitant medications, TNFi users had an increased 1-year TB risk (HR, 7.19; 95% CI, 4.18-12.34) compared with the non-TNFi-treated controls. The 1-year TB risk in TNFi users increased from 2008 to 2011 and deceased in 2012 when the Food and Drug Administration in Taiwan announced the Risk Management Plan for patients scheduled to receive TNFi therapy.
CONCLUSION: This study showed that the 1-year TB risk in RA patients starting TNFi therapy was significantly higher than that in non-TNFi controls in Taiwan from 2008 to 2012.
METHODS: From January 2005 to December 2014, we conducted a nationwide case-control study, using Taiwan's National Health Insurance Research Database. Obstetric complications and perinatal outcomes in SLE patients were compared with those without SLE.
RESULTS: 2059 SLE offspring and 8236 age-matched, maternal healthy controls were enrolled. We found increased obstetric and perinatal complications in SLE population compared with healthy controls. SLE patients exhibited increased risk of preeclampsia/eclampsia (8.98% vs.1.98%, odds ratio [OR]: 3.87, 95% confidence interval [95% CI]: 3.08-4.87, p<0.0001). Their offspring tended to have lower Apgar scores (<7) at both 1 min (10.7% vs. 2.58%, p<0.0001) and 5 min (4.25% vs. 1.17%, p<0.0001), as well as higher rates of intrauterine growth restriction (IUGR, 9.91% vs. 4.12%, OR: 2.24, 95% CI: 1.85-2.71, p<0.0001), preterm birth (23.70% vs 7.56%, OR: 3.00, 95% CI: 2.61-3.45, p<0.0001), and stillbirth (4.23% vs. 0.87%, OR: 3.59, 95% CI: 2.54-5.06, p<0.0001). The risks of preterm birth and stillbirth were markedly increased in SLE patients with concomitant preeclampsia/eclampsia or IUGR. Preterm birth of SLE patients was 1~4 gestational weeks earlier than that of healthy controls and the peak occurrence of stillbirth in SLE population was at 20~30 gestational weeks.
CONCLUSIONS: Asian SLE patients exhibited increased risks of maternal complications and adverse birth outcomes. Frequent antenatal visits before 20 gestational weeks are recommended in high-risk SLE patients.
METHODS: After systematic screening, raw 16S rRNA gene sequences were obtained from ten case-control studies totaling 1703 subjects (969 PD, 734 non-PD controls; seven predominantly Caucasian and three predominantly non-Caucasian cohorts). Quality-filtered gene sequences were analyzed using a phylogenetic placement approach, which precludes the need for the sequences to be sourced from similar regions in the 16S rRNA gene, thus allowing a direct comparison between studies. Differences in microbiome composition and correlations with clinical variables were analyzed using multivariate statistics.
RESULTS: Study and geography accounted for the largest variations in gut microbiome composition. Microbiome composition was more similar for subjects from the same study than those from different studies with the same disease status. Microbiome composition significantly differed between Caucasian and non-Caucasian populations. After accounting for study differences, microbiome composition was significantly different in PD vs. controls (albeit with a marginal effect size), with several distinctive features including increased abundances of Megasphaera and Akkermansia, and reduced Roseburia. Several bacterial genera correlated with PD motor severity, motor response complications and cognitive function.
CONCLUSION: Consistent microbial features in PD merit further investigation. The large variations in microbiome findings of PD patients underscore the need for greater harmonization of future research, and personalized approaches in designing microbial-directed therapeutics.