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Universities are increasingly evaluated on the basis of their outputs. These are often converted to simple and contested rankings with substantial implications for recruitment, income, and perceived prestige. Such evaluation usually relies on a single data source to define the set of outputs for a university. However, few studies have explored differences across data sources and their implications for metrics and rankings at the institutional scale. We address this gap by performing detailed bibliographic comparisons between Web of Science (WoS), Scopus, and Microsoft Academic (MSA) at the institutional level and supplement this with a manual analysis of 15 universities. We further construct two simple rankings based on citation count and open access status. Our results show that there are significant differences across databases. These differences contribute to drastic changes in rank positions of universities, which are most prevalent for non-English-speaking universities and those outside the top positions in international university rankings. Overall, MSA has greater coverage than Scopus and WoS, but with less complete affiliation metadata. We suggest that robust evaluation measures need to consider the effect of choice of data sources and recommend an approach where data from multiple sources is integrated to provide a more robust data set.

BackgroundThe World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality. ObjectiveThe aim of this study was to develop a prediction system using lifestyle data, environmental factors, and patient symptoms for the early detection of AECOPD in the upcoming 7 days. MethodsThis prospective study was performed at National Taiwan University Hospital. Patients with COPD that did not have a pacemaker and were not pregnant were invited for enrollment. Data on lifestyle, temperature, humidity, and fine particulate matter were collected using wearable devices (Fitbit Versa), a home air quality–sensing device (EDIMAX Airbox), and a smartphone app. AECOPD episodes were evaluated via standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model. ResultsThe continuous real-time monitoring of lifestyle and indoor environment factors was implemented by integrating home air quality–sensing devices, a smartphone app, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean 4-month follow-up period, resulting in the detection of 25 AECOPD episodes. For 7-day AECOPD prediction, the proposed AECOPD predictive model achieved an accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. Receiver operating characteristic curve analysis showed that the area under the curve of the model in predicting AECOPD was greater than 0.9. The most important variables in the model were daily steps walked, stairs climbed, and daily distance moved. ConclusionsUsing wearable devices, home air quality–sensing devices, a smartphone app, and supervised prediction algorithms, we achieved excellent power to predict whether a patient would experience AECOPD within the upcoming 7 days. The AECOPD prediction system provided an effective way to collect lifestyle and environmental data, and yielded reliable predictions of future AECOPD events. Compared with previous studies, we have comprehensively improved the performance of the AECOPD prediction model by adding objective lifestyle and environmental data. This model could yield more accurate prediction results for COPD patients than using only questionnaire data.

Abstract Background Although tumor cells undergoing epithelial–mesenchymal transition (EMT) typically exhibit spindle morphology in experimental models, such histomorphological evidence of EMT has predominantly been observed in rare primary spindle carcinomas. The characteristics and transcriptional regulators of spontaneous EMT in genetically unperturbed non-spindled carcinomas remain underexplored. Methods We used primary culture combined with RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq), and in situ RNA-seq to explore the characteristics and transcription factors (TFs) associated with potential spontaneous EMT in non-spindled breast carcinoma. Results Our primary culture revealed carcinoma cells expressing diverse epithelial–mesenchymal traits, consistent with epithelial–mesenchymal plasticity. Importantly, carcinoma cells undergoing spontaneous EMT did not necessarily exhibit spindle morphology, even when undergoing complete EMT. EMT was a favored process, whereas mesenchymal–epithelial transition appeared to be crucial for secondary tumor growth. Through scRNA-seq, we identified TFs that were sequentially and significantly upregulated as carcinoma cells progressed through the EMT process, which correlated with increasing VIM expression. Once upregulated, the TFs remained active throughout the EMT process. ZEB1 was a key initiator and sustainer of EMT, as indicated by its earliest significant upregulation in the EMT process, its exact correlation with VIM expression, and the reversal of EMT and downregulation of EMT-upregulated TFs upon ZEB1 knockdown. The correlation between ZEB1 and vimentin expression in triple-negative breast cancer and metaplastic breast carcinoma tumor cohorts further highlighted its role. The immediate upregulation of ZEB2 following that of ZEB1, along with the observation that the knockdown of ZEB1 or ZEB2 downregulates both ZEB1 and ZEB2 concomitant with the reversal of EMT, suggests their functional cooperation in EMT. This finding, together with that of a lack of correlation of SNAI1, SNAI2, and TWIST1 expression with the mesenchymal phenotype, indicated EMT-TFs have a context-dependent role in EMT. Upregulation of EMT-related gene signatures during EMT correlated with poor patient outcomes, highlighting the biological importance of the model. Elevated EMT gene signatures and increased ZEB1 and ZEB2 expression in vimentin-positive compared to vimentin-negative carcinoma cells within the corresponding primary tumor tissue confirmed ZEB1 and ZEB2 as intrinsic, instead of microenvironmentally-induced, EMT regulators, and vimentin as an in vivo indicator of EMT. Conclusions Our findings provide insights into the characteristics and transcriptional regulators of spontaneous EMT in primary non-spindled carcinoma.

* Context.--RNA sequencing study has demonstrated that human epidermal growth factor receptor 2 (HER2) RNA levels influence anti-HER2 therapeutic efficacy. However, in situ HER2 RNA expression (isHRE), which evaluates HER2 RNA expression in tissue, has remained unclear in breast cancers (BCs) of various HER2 immunohistochemistry (IHC)/in situ hybridization (ISH) categories. Objective.--To correlate isHRE with all HER2 IHC/ fluorescence ISH (FISH) categories in BC. Design.--Formalin-fixed, paraffin-embedded tissue sections from 259 BCs, covering all IHC/FISH categories, were analyzed for isHRE by RNAscope. Results.--We validated HER2 RNAscope scoring as a semiquantitative method to evaluate isHRE and demonstrated significantly higher RNAscope scores in IHC 3+ than in IHC 2+ cases, and in IHC 2+ than in IHC 0/1 + cases. Among the 5 IHC 2+/FISH groups, group 1 (G1) cases had the highest scores. The scores in G3 cases were higher than those in G2, but not significantly different from those in G4 and G5. G4 cases had significantly higher scores than those in G2. Higher HER2 copy numbers and HER2:CEP 17 (centromere 17) copy number ratios were significantly correlated with higher isHRE in G1 cases, but not in G2 to G5 cases. RNAscope scores were significantly lower in HER2-negative (IHC 0) than in HER2-low (IHC 2+/FISH- and IHC 1+) BCs but were not different between IHC 0 and 1+ BCs when analyzed separately. Conclusions.--We demonstrate the HER2 RNA expression status among BCs of various HER2 IHC/FISH categories in tissue. Such information may be relevant for anti-HER2 treatment decisions considering the role of HER2 RNA expression in predicting anti-HER2 therapeutic efficacy., Approximately 15% of invasive breast cancers (BCs) are human epidermal growth factor receptor 2 (HER2) positive, defined as HER2 gene amplification or protein overexpression, (1) and such tumors are sensitive [...]