Introduction: This retrospective study was conducted to perform an external validation of the in vitro fertilisation (IVF) predict model developed by Scott Nelson et al in an Asian population.
Materials And Methods: All IVF cycles registered in the study centre from January 2005 to December 2010 were included. Observed and predicted values of at least 1 live birth per cycle were compared by discrimination, calibration. Hosmer-Lemeshow test was used to assess the goodness-of-fit of the model calibration and Brier score was used to assess overall model performance.
Results: Among 634 IVF cycles, rate of at least 1 live birth was 30.6%. Causes of infertility were unexplained in 35.5% cases. Fifty-seven percent of women came for their first IVF treatment. First IVF cycle showed significantly higher success in comparison to subsequent cycles. The odds ratio of successful live birth was worse in women with endometriosis. Observed outcome was found to be more than the prediction of the model. The area under the curve (AUC) in this study was found to be 0.65 that was close to that of Nelson model (0.6335) done in internal validation. Brier score (average prediction error) of model was 0.2. Chi square goodness-of-fit test indicated that there was difference between the predicted and observed value (x² =18.28, df = 8, P = 0.019). Overall statistical findings indicated that the accuracy of the prediction model fitted poorly with the study population.
Conclusion: Ovarian reserve, treatment centre and racial effect on predictability cannot be excluded. So it is important to make a good prediction model by considering the additional factors before using the model widely.
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Transl Oncol
January 2025
Johns Hopkins Greenberg Bladder Cancer Institute, Brady Urological Institute, Johns Hopkins University, Baltimore, MD, USA. Electronic address:
Bladder cancer (BLCA) genomic profiling has identified molecular subtypes with distinct clinical characteristics and variable sensitivities to frontline therapy. BLCAs can be categorized into luminal or basal subtypes based on their gene expression. We comprehensively characterized nine human BLCA cell lines (UC3, UC6, UC9, UC13, UC14, T24, SCaBER, RT4V6 and RT112) into molecular subtypes using orthotopic xenograft models.
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January 2025
Division of Vector Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado.
Plague is a rare, potentially fatal flea-borne zoonosis endemic in the western United States. A previous model described interannual variation in human cases based on temperature and lagged precipitation. We recreated this model in northeastern Arizona (1960-1997) to evaluate its capacity to predict recent cases (1998-2022).
View Article and Find Full Text PDFJCO Clin Cancer Inform
January 2025
SimBioSys Inc, Chicago, IL.
Purpose: Perfusion modeling presents significant opportunities for imaging biomarker development in breast cancer but has historically been held back by the need for data beyond the clinical standard of care (SoC) and uncertainty in the interpretability of results. We aimed to design a perfusion model applicable to breast cancer SoC dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series with results stable to low temporal resolution imaging, comparable with published results using full-resolution DCE-MRI, and correlative with orthogonal imaging modalities indicative of biophysical markers.
Methods: Subsampled high-temporal-resolution DCE-MRI series were run through our perfusion model and resulting fits were compared for consistency.
This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons.
View Article and Find Full Text PDFPLoS One
January 2025
Academy of Fine Arts, Jiangsu Second Normal University, Nanjing, China.
Urban waterfront areas, which are essential natural resources and highly perceived public areas in cities, play a crucial role in enhancing urban environment. This study integrates deep learning with human perception data sourced from street view images to study the relationship between visual landscape features and human perception of urban waterfront areas, employing linear regression and random forest models to predict human perception along urban coastal roads. Based on aesthetic and distinctiveness perception, urban coastal roads in Xiamen were classified into four types with different emphasis and priorities for improvement.
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