Congenital granular cell epulis: 24 new cases with more differences than similarities to granular cell tumor.

Congenital granular cell epulis (CGCE) is a rare tumor of gingiva that is exclusive to newborns, has marked female predominance, and is rarely associated with other abnormalities. Although benign in behavior, CGCE can be lethal by obstruction of respiration and/or deglutition and can require a multidisciplinary team of specialist at birth for survival of an otherwise normal infant. Histologically, CGCE resembles granular cell tumor (GCT), but unlike GCT, which is Schwannian-derived, derivation of CGCE remains an enigma, largely because of its low prevalence. This study presents 24 new cases of CGCE, the largest series since the original description 150 years ago and permits detailed study of homogeneity of cases diagnosed as CGCE as well as detailed comparisons of CGCE with GCT by clinical, morphological, immunohistochemical, and ultrastructural studies. The data show homogeneity within the CGCE cases, more differences than similarities between CGCE and GCT, and no immunohistochemical staining for common placental proteins/hormones in CGCE. The findings support a primitive mesenchymal cell origin, and a progressive degenerative process in CGCE, rather than neoplasia. Prenatal detection of this lesion is important to facilitate adequate preparations for support of these infants during labor and delivery.

Semiparametric methods for survival data with measurement error under additive hazards cure rate models.

It is well established that measurement error has drastically negative impact on data analysis. It can not only bias parameter estimates but may also cause loss of power for testing relationship between variables. Although survival analysis of error-contaminated data has attracted extensive interest, relatively little attention has been paid to dealing with survival data with error-contaminated covariates when the underlying population is characterized by a cured fraction. In this paper, we consider this problem for which lifetimes of the non-cured individuals are featured by the additive hazards model and the measurement error process is described by an additive model. Unlike estimating the relative risk in the proportional hazards model, the additive hazards model allows us to estimate the absolute risk difference associated with the covariates. To allow the model flexibility, we incorporate time-dependent covariates in the model. We develop estimation methods for the two scenarios, without or with measurement error. The proposed methods are evaluated from both the theoretical view point and the numerical perspectives. Furthermore, a real-life data application is presented to illustrate the utility of the methodology.

A support vector machine-based cure rate model for interval censored data.

The mixture cure rate model is the most commonly used cure rate model in the literature. In the context of mixture cure rate model, the standard approach to model the effect of covariates on the cured or uncured probability is to use a logistic function. This readily implies that the boundary classifying the cured and uncured subjects is linear. In this article, we propose a new mixture cure rate model based on interval censored data that uses the support vector machine to model the effect of covariates on the uncured or the cured probability (i.e. on the incidence part of the model). Our proposed model inherits the features of the support vector machine and provides flexibility to capture classification boundaries that are nonlinear and more complex. The latency part is modeled by a proportional hazards structure with an unspecified baseline hazard function. We develop an estimation procedure based on the expectation maximization algorithm to estimate the cured/uncured probability and the latency model parameters. Our simulation study results show that the proposed model performs better in capturing complex classification boundaries when compared to both logistic regression-based and spline regression-based mixture cure rate models. We also show that our model's ability to capture complex classification boundaries improve the estimation results corresponding to the latency part of the model. For illustrative purpose, we present our analysis by applying the proposed methodology to the NASA's Hypobaric Decompression Sickness Database.

Target fortification of breast milk: how often should milk analysis be done?

Target fortification (TFO) reduces natural macronutrient variation in breast milk (BM). Daily BM analysis for TFO increases neonatal intensive care unit work load by 10-15 min/patient/day and may not be feasible in all nurseries. The variation of macronutrient intake when BM analysis is done for various schedules was studied. In an observational study, we analyzed 21 subsequent samples of native 24-h BM batches, which had been prepared for 10 healthy infants (gestational age 26.1 ± 1.3 weeks, birth weight: 890 ± 210 g). Levels of protein and fat (validated near-infrared milk analyzer), as well as lactose (UPLC-MS/MS) generated the database for modelling TFO to meet recommendations of European Society for Paediatric Gastroenterology Hepatology and Nutrition. Intake of macronutrients and energy were calculated for different schedules of BM measurements for TFO (n = 1/week; n = 2/week; n = 3/week; n = 5/week; n = 7/week) and compared to native and fixed dose fortified BM. Day-to-day variation of macronutrients (protein 20%, carbohydrate 13%, fat 17%, energy 10%) decreased as the frequency of milk analysis increased and was almost zero for protein and carbohydrate with daily measurements. Measurements two/week led to mean macronutrient intake within a range of ± 5% of targeted levels. A reduced schedule for macronutrient measurement may increase the practical use of TFO. To what extent the day-to-day variation affects growth while mean intake is stable needs to be studied.