Immunoglobulin repertoire sequencing and de novo sequencing - Powerful tools for identifying free light chains from patients with light chain cast nephropathy.

In patients with light chain cast nephropathy (LCCN), abundantly produced monoclonal immunoglobulin free light chains (FLCs) play a vital role in pathogenesis. Determining the precise sequences of patient-derived FLCs is therefore highly desirable. Although immunoglobulin repertoire sequencing (5' RACE-seq) has been proven to be sensitive enough to provide full-length V(D)J region (variable, diversity and joining genes) of FLCs using bone marrow samples, an invasive and bone marrow independent method is still in demand. Here a de novo sequencing workflow based on the bottom-up proteomics for patient-derived FLCs was established. PEAKS software was used for the de novo sequencing of peptides that were further assembled into full-length FLC sequences. This de novo protein sequencing method can obtain the full-length amino acid sequences of FLCs, and had been shown to be as reliable as 5' RACE-seq. The two LCCN sequences derived from above the two methods were identical, and they possessed more hydrophobic or nonpolar amino acids compared with the corresponding germline, which may be associated with the pathogenesis.

Exercise-Induced Pulmonary Hypertension in Long-Term Survivors of Congenital Diaphragmatic Hernia.

OBJECTIVE: To determine the prevalence of exercise-induced pulmonary hypertension (PH) among long-survivors of congenital diaphragmatic hernia repair. STUDY DESIGN: This is a single-center, retrospective cohort study of CDH survivors who underwent exercise stress echocardiography (ESE) at Boston Children's Hospital from January 2006 to June 2020. PH severity was assessed by echocardiogram at baseline and after exercise. Patients were categorized by right ventricular systolic pressure (RVSP) after exercise: Group 1 - no or mild PH; and Group 2 - moderate or severe PH (RVSP ≥ 60 mmHg or ≥ ½ systemic blood pressure). RESULTS: Eighty-four patients with CDH underwent 173 ESE with median age 8.1 (4.8 - 19.1) years at first ESE. Sixty-four patients were classified as Group 1, 11 as Group 2, and 9 had indeterminate RVSP with ESE. Moderate to severe PH after exercise was found in 8 (10%) patients with no or mild PH at rest. Exercise-induced PH was associated with larger CDH defect size, patch repair, use of ECMO, supplemental oxygen at discharge, and higher WHO functional class. Higher VE/VCO2 slope, lower peak oxygen saturation, and lower percent predicted FEV1, and FEV1/FVC ratio were associated with Group 2 classification. ESE changed management in 9/11 Group 2 patients. PH was confirmed in all 5 Group 2 patients undergoing cardiac catheterization after ESE. CONCLUSIONS: Among long-term CDH survivors, 10% had moderate-severe exercise-induced PH on ESE, indicating ongoing pulmonary vascular abnormalities. Further studies are needed to optimally define PH screening and treatment for patients with repaired CDH.

Risk factors for losing hepatitis B virus surface antibody in patients with HBV surface antigen negative/surface antibody positive serostatus receiving biologic disease-modifying anti-rheumatic drugs: a nested case-control study

Abstract Background: Hepatitis B virus (HBV) reactivation consequent to immunosuppressive therapy is an increasingly prevalent problem with serious clinical implications. Treatment with biologic agents conduces to the loss of protective antibody to HBV surface antigen (anti-HBs), which significantly increases the risk of HBV reactivation. Hence, we investigated the risk factors for losing anti-HBs in patients with rheumatic diseases and HBV surface antigen negative/anti-HBs positive (HBsAg-/anti-HBs+) serostatus during treatment with biologic disease-modifying anti-rheumatic drugs (DMARDs). Methods: Using a nested case-control design, we prospectively enrolled patients with rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis/psoriasis, or juvenile idiopathic arthritis, who were treated with biologic DMARDs at Changhua Christian Hospital, Taiwan, from January 2013 to June 2019 and had HBsAg-/anti-HBs+ serostatus; the analytic sample excluded all patients with HBsAg+ or anti-HBs- serostatus. Anti-HBs titers were monitored 6-monthly and cases were defined as anti-HBs < 10 mIU/ml during follow-up. Cases were matched one- to-all with controls with anti-HBs ≥ 10 mIU/ml on the same ascertainment date and equivalent durations of biologic DMARDs treatment (control patients could be resampled and could also become cases during follow-up). Between-group characteristics were compared and risk factors for anti-HBs loss were investigated by conditional logistic regression analyses. Results: Among 294 eligible patients, 23 cases were matched with 311 controls. The incidence of anti-HBs loss was ∼ 2.7%/person-year during biologic DMARDs treatment. Besides lower baseline anti-HBs titer (risk ratio 0.93, 95% CI 0.89-0.97), cases were significantly more likely than controls to have diabetes mellitus (risk ratio 4.76, 95% CI 1.48-15.30) and chronic kidney disease (risk ratio 14.00, 95% CI 2.22-88.23) in univariate analysis. Risk factors remaining significantly associated with anti-HBs loss in multivariate analysis were lower baseline anti-HBs titer (adjusted risk ratio 0.93, 95% CI 0.88-0.97) and chronic kidney disease (adjusted risk ratio 45.68, 95% CI 2.39-871.5). Conclusions: Besides lower baseline anti-HBs titer, chronic kidney disease also strongly predicts future anti-HBs negativity in patients with HBsAg-/anti-HBs+ serostatus who receive biologic DMARDs to treat rheumatic diseases. Patients with low anti-HBs titer (≤ 100 mIU/ml) and/or chronic kidney disease should be monitored during biologic DMARDs therapy, to enable timely prophylaxis to preempt potential HBV reactivation.

Capturing and monitoring global differences in untreated and treated end-stage kidney disease, kidney replacement therapy modality, and outcomes.

A large gap between the number of people with end-stage kidney disease (ESKD) who received kidney replacement therapy (KRT) and those who needed it has been recently identified, and it is estimated that approximately one-half to three-quarters of all people with ESKD in the world may have died prematurely because they could not receive KRT. This estimate is aligned with a previous report that estimated that >3 million people in the world died each year because they could not access KRT. This review discusses the reasons for the differences in treated and untreated ESKD and KRT modalities and outcomes and presents strategies to close the global KRT gap by establishing robust health information systems to guide resource allocation to areas of need, inform KRT service planning, enable policy development, and monitor KRT health outcomes.

A Bayesian multi-risks survival (MRS) model in the presence of double censorings.

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.

Maximum likelihood estimation for stochastic volatility in mean models with heavy-tailed distributions.

In this article, we introduce a likelihood-based estimation method for the stochastic volatility in mean (SVM) model with scale mixtures of normal (SMN) distributions (Abanto-Valle et al., 2012). Our estimation method is based on the fact that the powerful hidden Markov model (HMM) machinery can be applied in order to evaluate an arbitrarily accurate approximation of the likelihood of an SVM model with SMN distributions. The method is based on the proposal of Langrock et al. (2012) and makes explicit the useful link between HMMs and SVM models with SMN distributions. Likelihood-based estimation of the parameters of stochastic volatility models in general, and SVM models with SMN distributions in particular, is usually regarded as challenging as the likelihood is a high-dimensional multiple integral. However, the HMM approximation, which is very easy to implement, makes numerical maximum of the likelihood feasible and leads to simple formulae for forecast distributions, for computing appropriately defined residuals, and for decoding, i.e., estimating the volatility of the process.

Bayesian analysis of stochastic volatility-in-mean model with leverage and asymmetrically heavy-tailed error using generalized hyperbolic skew Student's t-distribution.

A stochastic volatility-in-mean model with correlated errors using the generalized hyperbolic skew Student-t (GHST) distribution provides a robust alternative to the parameter estimation for daily stock returns in the absence of normality. An efficient Markov chain Monte Carlo (MCMC) sampling algorithm is developed for parameter estimation. The deviance information, the Bayesian predictive information and the log-predictive score criterion are used to assess the fit of the proposed model. The proposed method is applied to an analysis of the daily stock return data from the Standard & Poor's 500 index (S&P 500). The empirical results reveal that the stochastic volatility-in-mean model with correlated errors and GH-ST distribution leads to a significant improvement in the goodness-of-fit for the S&P 500 index returns dataset over the usual normal model.

Bayesian path specific frailty models for multi-state survival data with applications.

Multi-state models can be viewed as generalizations of both the standard and competing risks models for survival data. Models for multi-state data have been the theme of many recent published works. Motivated by bone marrow transplant data, we propose a Bayesian model using the gap times between two successive events in a path of events experienced by a subject. Path specific frailties are introduced to capture the dependence structure of the gap times in the paths with two or more states. Under improper prior distributions for the parameters, we establish propriety of the posterior distribution. An efficient Gibbs sampling algorithm is developed for drawing samples from the posterior distribution. An extensive simulation study is carried out to examine the empirical performance of the proposed approach. A bone marrow transplant data set is analyzed in detail to further demonstrate the proposed methodology.