Identification of Novel Phenotypes Correlated with CKD: A Phenotype-Wide Association Study.

Background: A comprehensive understanding of phenotypes related to CKD will facilitate the identification and management of CKD. We aimed to panoramically test and validate associations between multiple phenotypes and CKD using a phenotype-wide association study (PheWAS). Methods: 15,815 subjects from cross-sectional cohorts of the National Health and Nutrition Examination Survey (1999-2006) were randomly 50:50 split into training and testing sets. CKD was defined as eGFR < 60 mL/min/1.73m2. We performed logistic regression analyses between each of 985 phenotypes with CKD in the training set (false discovery rate < 1%) and validated in the testing set (false discovery rate < 1% ). Random forest (RF) model, Nagelkerke's Pseudo-R2, and the area under the receiver operating characteristic (AUROC) were used to validate the identified phenotypes. Results: We identified 18 phenotypes significantly related to CKD, among which retinol, red cell distribution width (RDW), and C-peptide were less researched. The top 5 identified phenotypes were blood urea nitrogen (BUN), homocysteine (HCY), retinol, parathyroid hormone (PTH), and osmolality in RF importance ranking. Besides, BUN, HCY, PTH, retinol, and uric acid were the most important phenotypes based on Pseudo-R2. AUROC of the RF model was 0.951 (full model) and 0.914 (top 5 phenotypes). Conclusion: Our study demonstrated associations between multiple phenotypes with CKD from a holistic view, including 3 novel phenotypes: retinol, RDW, and C-peptide. Our findings provided valid evidence for the identification of novel biomarkers for CKD.

Silicon or Calcium Doping Coordinates the Immunostimulatory Effects of Aluminum Oxyhydroxide Nanoadjuvants in Prophylactic Vaccines.

Aluminum salts still remain as the most popular adjuvants in marketed human prophylactic vaccines due to their capability to trigger humoral immune responses with a good safety record. However, insufficient induction of cellular immune responses limits their further applications. In this study, we prepare a library of silicon (Si)- or calcium (Ca)-doped aluminum oxyhydroxide (AlOOH) nanoadjuvants. They exhibit well-controlled physicochemical properties, and the dopants are homogeneously distributed in nanoadjuvants. By using Hepatitis B surface antigen (HBsAg) as the model antigen, doped AlOOH nanoadjuvants mediate higher antigen uptake and promote lysosome escape of HBsAg through lysosomal rupture induced by the dissolution of the dopant in the lysosomes in bone marrow-derived dendritic cells (BMDCs). Additionally, doped nanoadjuvants trigger higher antigen accumulation and immune cell activation in draining lymph nodes. In HBsAg and varicella-zoster virus glycoprotein E (gE) vaccination models, doped nanoadjuvants induce high IgG titer, activations of CD4+ and CD8+ T cells, cytotoxic T lymphocytes, and generations of effector memory T cells. Doping of aluminum salt-based adjuvants with biological safety profiles and immunostimulating capability is a potential strategy to mediate robust humoral and cellular immunity. It potentiates the applications of engineered adjuvants in the development of vaccines with coordinated immune responses.

BPLT+: a Bayesian-based personalized recommendation model for health care.

In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation (BPLT(+)) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate BPLT(+) model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed BPLT(+) model.