Generalized mean residual life models for case-cohort and nested case-control studies

Identification of novel phenolic inhibitors from traditional Chinese medicine against toxic α-synuclein aggregation via regulating phase separation

Parkinson's disease (PD), a neurodegenerative disorder without cure, is characterized by the pathological aggregation of α-synuclein (α-Syn) in Lewy bodies. Classic deposition pathway and condensation pathway contribute to α-Syn aggregation, and liquid-liquid phase separation is the driving force for condensate formation, which subsequently undergo liquid-solid phase separation to form toxic fibrils. Traditional Chinese Medicine (TCM) has a long history in treating neurodegenerative disease; herein, we identified chemicals from herbs that inhibit α-Syn aggregation. We screened commonly prescribed TCMs for PD from the CNKI database and registered patents, 13 chemicals were identified in the TCMSP databases as candidate inhibitors, among which three phenols, forsythoside B (FTSB), echinacoside (ECH), and 4-hydroxyindole (C4-OH) efficiently inhibit α-Syn aggregation. Moreover, FTSB and ECH increase α-Syn fluidity within condensates, inhibit α-Syn transition into amyloid fibrils and reduce fibril-induced toxicity in SH-SY5Y cells. Importantly, they disaggregated preformed α-Syn amyloid fibrils. Notably, in an α-Syn overexpressing NL5901 C. elegans PD model, either FTSB or ECH treatment significantly extended the lifespan and improved the PD-like movement disorders, both in the preventive and therapeutic treatment approaches, by reducing toxic α-Syn inclusion formation and improving the fluidity of α-Syn. Together, we offer new therapeutic candidates targeting phase separation-associated aggregation for PD.

Mortality, and life expectancy in Epilepsy and Status epilepticus-current trends and future aspects

Patients with epilepsy carry a risk of premature death which is on average two to three times higher than in the general population. The risk of death is not homogenously distributed over all ages, etiologies, and epilepsy syndromes. People with drug resistant seizures carry the highest risk of death compared to those who are seizure free, whose risk is similar as in the general population. Most of the increased risk is directly related to the cause of epilepsy itself. Sudden unexplained death in epilepsy patients (SUDEP) is the most important cause of epilepsy-related deaths especially in the young and middle-aged groups. Population based studies with long-term follow up demonstrated that the first years after diagnosis carry the highest risk of death, while in the later years the mortality decreases. Improved seizure control and being exposed to a specialized comprehensive care centre may help to reduce the risk of death in patients with epilepsy. The mortality of status epilepticus is substantially increased with case fatality rates between 4.6% and 39%, depending on its cause and duration, and the age of the population studied. The epidemiological data on overall and cause specific mortality as well as their determinants and risk factors are critically reviewed and methodological issues pertinent to the studies on mortality of epilepsy and Status epilepticus are discussed.

Generalized mean residual life models for survival data with missing censoring indicators

The mean residual life (MRL) function is an important and attractive alternative to the hazard function for characterizing the distribution of a time-to-event variable. In this article, we study the modeling and inference of a family of generalized MRL models for right-censored survival data with censoring indicators missing at random. To estimate the model parameters, augmented inverse probability weighted estimating equation approaches are developed, in which the non-missingness probability and the conditional probability of an uncensored observation are estimated by parametric methods or nonparametric kernel smoothing techniques. Asymptotic properties of the proposed estimators are established and finite sample performance is evaluated by extensive simulation studies. An application to brain cancer data is presented to illustrate the proposed methods.

Partially linear single-index generalized mean residual life models

Mean residual life (MRL) function defines the remaining life expectancy of a subject who has survived to a time point and is an important alternative to the hazard function for characterizing the distribution of a time-to-event variable. Existing MRL models primarily focus on studying the association between risk factors and disease risks using linear model specifications in multiplicative or additive scale. When risk factors have complex correlation structures, nonlinear effects, or interactions, the prefixed linearity assumption may be insufficient to capture the relationship. Single-index modeling framework offers flexibility in reducing dimensionality and modeling nonlinear effects. In this article, we propose a class of partially linear single-index generalized MRL models, the regression component of which consists of both a semiparametric single-index part and a linear regression part. Regression spline technique is employed to approximate the nonparametric single-index function, and parameters are estimated using an iterative algorithm. Double-robust estimators are also proposed to protect against the misspecification of censoring distribution or MRL models. A further contribution of this article is a nonparametric test proposed to formally evaluate the linearity of the single-index function. Asymptotic properties of the estimators are established, and the finite-sample performance is evaluated through extensive numerical simulations. The proposed models and inference approaches are demonstrated by a New York University Langone Health (NYULH) COVID-19 dataset.