No abstract

This article is the series of methodology of clinical prediction model construction (total 16 sections of this methodology series). The first section mainly introduces the concept, current application status, construction methods and processes, classification of clinical prediction models, and the necessary conditions for conducting such researches and the problems currently faced. The second episode of these series mainly concentrates on the screening method in multivariate regression analysis. The third section mainly introduces the construction method of prediction models based on Logistic regression and Nomogram drawing. The fourth episode mainly concentrates on Cox proportional hazards regression model and Nomogram drawing. The fifth Section of the series mainly introduces the calculation method of C-Statistics in the logistic regression model. The sixth section mainly introduces two common calculation methods for C-Index in Cox regression based on R. The seventh section focuses on the principle and calculation methods of Net Reclassification Index (NRI) using R. The eighth section focuses on the principle and calculation methods of IDI (Integrated Discrimination Index) using R. The ninth section continues to explore the evaluation method of clinical utility after predictive model construction: Decision Curve Analysis. The tenth section is a supplement to the previous section and mainly introduces the Decision Curve Analysis of survival outcome data. The eleventh section mainly discusses the external validation method of Logistic regression model. The twelfth mainly discusses the in-depth evaluation of Cox regression model based on R, including calculating the concordance index of discrimination (C-index) in the validation data set and drawing the calibration curve. The thirteenth section mainly introduces how to deal with the survival data outcome using competitive risk model with R. The fourteenth section mainly introduces how to draw the nomogram of the competitive risk model with R. The fifteenth section of the series mainly discusses the identification of outliers and the interpolation of missing values. The sixteenth section of the series mainly introduced the Zhou et al. Clinical prediction models with R

In Gram-negative bacterial pathogens, specialized chaperones bind to secreted effector proteins and maintain them in a partially unfolded form competent for translocation by type III secretion systems/injectisomes. How diverse sets of effector-chaperone complexes are recognized by injectisomes is unclear. Here we describe a new mechanism of effector-chaperone recognition by the Chlamydia injectisome, a unique and ancestral line of these evolutionarily conserved secretion systems. By yeast two-hybrid analysis we identified networks of Chlamydia-specific proteins that interacted with the basal structure of the injectisome, including two hubs of protein-protein interactions that linked known secreted effector proteins to CdsQ, the putative cytoplasmic C-ring component of the secretion apparatus. One of these protein-interaction hubs is defined by Ct260/Mcsc (Multiple cargo secretion chaperone). Mcsc binds to and stabilizes at least two secreted hydrophobic proteins, Cap1 and Ct618, that localize to the membrane of the pathogenic vacuole (“inclusion”). The resulting complexes bind to CdsQ, suggesting that in Chlamydia, the C-ring of the injectisome mediates the recognition of a subset of inclusion membrane proteins in complex with their chaperone. The selective recognition of inclusion membrane proteins by chaperones may provide a mechanism to co-ordinate the translocation of subsets of inclusion membrane proteins at different stages in infection.

Transfusion-dependent β-thalassemia (TDT) and sickle cell disease (SCD) are the most common inherited hematologic disorders, affecting approximately 60,000 and 300,000 patients worldwide, respectively. Current therapies, including red blood cell (RBC) transfusion and iron chelation in TDT and transfusion, pain management, and hydroxyurea in SCD, help to manage the disorders but do not address the underlying cause. Drug therapies, such as crizanlizumab and luspatercept, have also helped to reduce the need for transfusion in TDT patients and the incidence of vaso-occlusive episodes in SCD patients. Allogeneic bone marrow transplantation may be a curative option, but finding an appropriate donor is difficult. An association has been observed between elevated levels of fetal hemoglobin and improved morbidity and mortality in these patients. Downregulating BCL11A, a transcription factor that blocks fetal hemoglobin in erythroid cells, may help to increase fetal hemoglobin levels and improve outcomes. Using the CRISPR-Cas9 gene-editing technique, CTX001, an investigational drug, was infused in 2 patients. This article describes the results of infusing CTX001 in 1 patient with TDT and another with SCD.