Comparison of biopsy under-sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies

This study aimed to estimate the rates of biopsy undersampling and progression for four prostate cancer (PCa) active surveillance (AS) cohorts within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) consortium. We used a hidden Markov model (HMM) to estimate factors that define PCa dynamics for men on AS including biopsy under-sampling and progression that are implied by longitudinal data in four large cohorts included in the GAP3 database. The HMM was subsequently used as the basis for a simulation model to evaluate the biopsy strategies previously proposed for each of these cohor

Nano-Subsidence-Assisted Precise Integration of Patterned Two-Dimensional Materials for High-Performance Photodetector Arrays

The spatially precise integration of arrays of micropatterned two-dimensional (2D) crystals onto three-dimensionally structured Si/SiO2 substrates represents an attractive, low-cost system-on-chip strategy toward the realization of extended functions in silicon microelectronics. However, the reliable integration of such atomically thin arrays on planar patterned surfaces has proven challenging due to their poor adhesion to underlying substrates, as ruled by weak van der Waals interactions. Here, we report on an integration method utilizing the flexibility of the atomically thin crystals and their physical subsidence in liquids, which enables the reliable fabrication of the micropatterned 2D materials/Si arrays. Our photodiode devices display peak sensitivity as high as 0.35 A/W and external quantum efficiency (EQE) of ∼90%. The nano-subsidence technique represents a viable path to on-chip integration of 2D crystals onto silicon for advanced microelectronics

Comparison of biopsy under‐sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies

This study aimed to estimate the rates of biopsy undersampling and progression for four prostate cancer (PCa) active surveillance (AS) cohorts within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) consortium. We used a hidden Markov model (HMM) to estimate factors that define PCa dynamics for men on AS including biopsy under-sampling and progression that are implied by longitudinal data in four large cohorts included in the GAP3 database. The HMM was subsequently used as the basis for a simulation model to evaluate the biopsy strategies previously proposed for each of these cohor

Additional file 1 of Risks of digestive diseases in long COVID: evidence from a population-based cohort study

Additional file 1: Figure S1. Directed Acyclic Graphs (DAG) for covariate selection. Figure S2. Flow chart of eligible participants’ selection. Figure S3. Distribution of follow-up time in the contemporary cohort (A) and the historical cohort (B). Figure S4. Hazard ratio of digestive outcomes in COVID-19 group and the contemporary comparison by severity of COVID-19. Table S1. Respiratory support treatments definition. Table S2. Outcome ascertainment. Table S3. The numbers (percentages) of participants with missing covariates. Table S4. Baseline characteristics of COVID-19 group and contemporary comparisons before weighting. Table S5. Hazard ratio of digestive outcomes in COVID-19 group and the contemporary comparison at different follow-up times. Table S6. Baseline characteristics of COVID-19, contemporary comparisons by severity of COVID-19 before weighting. Table S7. Baseline characteristics of COVID-19, contemporary comparisons by severity of COVID-19 after weighting. Table S8. Baseline characteristics of COVID-19 group and contemporary comparisons by status of SARS-CoV reinfection before weighting. Table S9. Baseline characteristics of COVID-19 group and contemporary comparisons by severity of SARS-CoV reinfection after weighting. Table S10. Hazard ratio of digestive outcomes in the reinfected group, single SARS-CoV-2 infection group, and non-infected comparisons. Table S11. Hazard ratio of digestive outcomes in reinfected group and single SARS-CoV-2 infection group in head-to-head comparison. Table S12. Baseline characteristics of COVID-19 group and contemporary comparisons in the sensitive analysis restricting to the period before vaccination was available before weighting. Table S13. Baseline characteristics of COVID-19 group and contemporary comparisons in the sensitive analysis restricting to the period before vaccination was available after weighting. Table S14. Hazard ratio of digestive outcomes in COVID-19 group and contemporary and historical comparisons in subgroups in the sensitive analysis restricting to the period before vaccination was available. Table S15. Hazard ratio of digestive outcomes in COVID-19 group compared to the contemporary and historical comparisons by pooling estimates across all five imputed datasets. Table S16. Hazard ratio of digestive outcomes compared with contemporary and historical comparisons in subgroups. Table S17. Hazard ratio of digestive outcomes in COVID-19 group, the contemporary and historical comparison by sex. Table S18. Baseline characteristics of COVID-19 group and historical comparisons before weighting. Table S19. Baseline characteristics of COVID-19 group and historical comparisons after weighting. Table S20. Baseline characteristics of COVID-19 group and historical comparisons by severity of COVID-19 before weighting. Table S21. Baseline characteristics of COVID-19 group and historical comparisons by severity of COVID-19 after weighting. Table S22. Baseline characteristics of COVID-19 group and historical comparisons in the sensitive analysis restricting to the period before vaccination was available before weighting. Table S23. Baseline characteristics of COVID-19 group and historical comparisons in the sensitive analysis restricting to the period before vaccination was available after weighting. Table S24. Hazard ratio of digestive outcomes in COVID-19 group and the historical comparison by severity of COVID-19