Flow-adaptive data validation scheme in PIV

The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.

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Estimation of PWC gradients over the Kanto Plain using GPS data: Validation and possible meteorological implications

Earth Planets and Space ◽

10.1186/bf03352304 ◽

2000 ◽

Vol 52 (11) ◽

pp. 907-912 ◽

Cited By ~ 7

Author(s):

Kazumasa Aonashi ◽

Yoshinori Shoji ◽

Ryu-ichi Ichikawa ◽

Hiroshi Hanado

Keyword(s):

Gps Data ◽

Data Validation

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VEGF as a potential molecular target in periodontitis: a meta-analysis and microarray data validation

Journal of Inflammation ◽

10.1186/s12950-021-00281-9 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Bo Ren ◽

Que Feng ◽

Shan He ◽

Yanfeng Li ◽

Jiadong Fan ◽

...

Keyword(s):

Growth Factor ◽

Clinical Sample ◽

Meta Analysis ◽

Enrichment Analysis ◽

Therapeutic Drug ◽

Data Validation ◽

Control Groups ◽

Vegf Expression ◽

Healthy Control

Abstract Background Anti-vascular endothelial growth factor (VEGF) has been used as a therapeutic drug for the treatment of some human diseases. However, no systematic evidence is performed for assessing the role of VEGF in periodontitis. We carried out a comprehensive analysis to explore the role of VEGF in patients with periodontitis. Methods Multiple databases were searched for eligible studies. The pooled standardized mean difference (SMD) and odds ratio (OR) with the corresponding 95% confidence interval (CI) were applied to evaluate the effect sizes. Clinical data validation from microarray analysis was used. Pathway and process enrichment analysis were also investigated. Results Finally, 16 studies were included in this analysis. Overall, there was a significantly higher level of VEGF expression in periodontitis than in healthy control groups (OR = 16.64, 95% CI = 6.01–46.06, P < 0.001; SMD = 2.25, 95% CI = 1.25–3.24, P < 0.001). Subgroup analysis of ethnicity showed that VEGF expression was still correlated with periodontitis in the Asian and European populations. No correlation was observed between VEGF expression and age, gender, and pathological type. A large clinical sample data (427 periodontitis patients and 136 healthy controls) further validated that VEGF expression was higher in periodontitis than in healthy control groups (P = 0.023). VEGF was involved in many functions such as blood vessel development, response to growth factor, cell proliferation, and cell adhesion. Conclusions High levels of VEGF were credible implications for the development of periodontitis. Anti-VEGF therapy may be valuable for the treatment of periodontitis in clinical management.

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