Nanocomposite Multimodal Sensor Array Integrated with Auxetic Structure for an Intelligent Biometrics System.

A multimodal sensor array, combining pressure and proximity sensing, has attracted considerable interest due to its importance in ubiquitous monitoring of cardiopulmonary health- and sleep-related biometrics. However, the sensitivity and dynamic range of prevalent sensors are often insufficient to detect subtle body signals. This study introduces a novel capacitive nanocomposite proximity-pressure sensor (NPPS) for detecting multiple human biometrics. NPPS consists of a carbon nanotube-paper composite (CPC) electrode and a percolating multiwalled carbon nanotube (MWCNT) foam enclosed in a MWCNT-coated auxetic frame. The fractured fibers in the CPC electrode intensify an electric field, enabling highly sensitive detection of proximity and pressure. When pressure is applied to the sensor, the synergic effect of MWCNT foam and auxetic deformation amplifies the sensitivity. The simple and mass-producible fabrication protocol allows for building an array of highly sensitive sensors to monitor human presence, sleep posture, and vital signs, including ballistocardiography (BCG). With the aid of a machine learning algorithm, the sensor array accurately detects blood pressure (BP) without intervention. This advancement holds promise for unrestricted vital sign monitoring during sleep or driving.

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity.

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.

Isolation and Quantification of Methylated Cell-Free DNA in Plasma on an Integrated Microfluidic System.

Methylated cell-free DNA (cfDNA) has been deemed a promising biomarker for ovarian cancer (OvCa) prognosis and therapy selection. However, exploring the methylation profiles of tumor suppressor genes in cfDNA remains a challenge due to their extremely low concentrations and complicated protocols, as well as methodological constraints. In this study, an integrated microfluidic system was developed to automatically (1) capture methylated cfDNA in plasma by magnetic beads coated with the methyl-CpG-binding domain and (2) quantify the methylation level of tumor suppressor genes by on-chip quantitative polymerase chain reaction (qPCR). For capturing methylated cfDNA from a very small amount of plasma, samples along with beads were mixed in a new micromixer to enhance the capture rate. With a high capture rate (72%) and a limit of quantification of 0.1 pg/µL (3 orders of magnitude lower than that of the benchtop method), the compact system could detect the methylated cfDNA from only 20 µL of plasma sample in 2 h. Furthermore, the dynamic range, from 0.1 to 2000 pg/µL of methylated cfDNA, spans the physiological range in plasma, signifying that this device has great potential for personalized medicine in OvCa.

Estimations of the joint distribution of failure time and failure type with dependent truncation.

In biomedical studies involving survival data, the observation of failure times is sometimes accompanied by a variable which describes the type of failure event (Kalbeisch and Prentice, 2002). This paper considers two specific challenges which are encountered in the joint analysis of failure time and failure type. First, because the observation of failure times is subject to left truncation, the sampling bias extends to the failure type which is associated with the failure time. An analytical challenge is to deal with such sampling bias. Second, in case that the joint distribution of failure time and failure type is allowed to have a temporal trend, it is of interest to estimate the joint distribution of failure time and failure type nonparametrically. This paper develops statistical approaches to address these two analytical challenges on the basis of prevalent survival data. The proposed approaches are examined through simulation studies and illustrated by using a real data set.

Methods for multivariate recurrent event data with measurement error and informative censoring.

In multivariate recurrent event data regression, observation of recurrent events is usually terminated by other events that are associated with the recurrent event processes, resulting in informative censoring. Additionally, some covariates could be measured with errors. In some applications, an instrumental variable is observed in a subsample, namely a calibration sample, which can be applied for bias correction. In this article, we develop two non-parametric correction approaches to simultaneously correct for the informative censoring and measurement errors in the analysis of multivariate recurrent event data. A shared frailty model is adopted to characterize the informative censoring and dependence among different types of recurrent events. To adjust for measurement errors, a non-parametric correction method using the calibration sample only is proposed. In the second approach, the information from the whole cohort is incorporated by the generalized method of moments. The proposed methods do not require the Poisson-type assumption for the multivariate recurrent event process and the distributional assumption for the frailty. Moreover, we do not need to impose any distributional assumption on the underlying covariates and measurement error. Both methods perform well, but the second approach improves efficiency. The proposed methods are applied to the Nutritional Prevention of Cancer trial to assess the effect of selenium treatment on the recurrences of basal cell carcinoma and squamous cell carcinoma.

Lidocaine Skin Patch (Lidopat® 5%) Is Effective in the Treatment of Traumatic Rib Fractures: A Prospective Double-Blinded and Vehicle-Controlled Study.

OBJECTIVE: The purpose of this study was to determine the efficacy of the Lidopat(®) 5% skin patch in relieving rib fracture pain. SUBJECTS AND METHODS: From June 2009 to May 2011, 44 trauma patients with isolated rib fractures were enrolled in this study and randomized in a double-blind method into 2 groups. The experimental group (group E: 27 patients) used a Lidopat(®) 5% skin patch at the trauma site and took an oral analgesic drug for pain relief. The placebo group (group P: 17 patients) used a placebo vehicle patch and an oral analgesic drug. RESULTS: The mean age, weight and hospital stay of patients were 56.8 ± 13.8 years, 67.4 ± 12.6 kg and 6.34 ± 1.3 days, respectively. In the first 4 days, there were no significant differences in pain scores between the groups (p > 0.05). After the 5th day, the average pain score was significantly less in group E (mean 1.5) than in group P (mean 3.10; p < 0.05). There was no significant difference in the number of fractured ribs between groups (p = 0.904). The use of meperidine and the length of hospital stay (6.0 vs. 6.9 days) were both significantly less in group E (p = 0.043 and 0.009, respectively). CONCLUSION: In this study, the use of the Lidopat(®) 5% skin patch in patients with isolated rib fractures alleviated pain and shortened the hospital stay, and a lower dose of pain-relieving medication was used.

Causal estimation using semiparametric transformation models under prevalent sampling.

This article presents methods and inference for causal estimation in semiparametric transformation models for the prevalent survival data. Through the estimation of the transformation models and covariate distribution, we propose a few analytical procedures to estimate the causal survival function. As the data are observational, the unobserved potential outcome (survival time) may be associated with the treatment assignment, and therefore there may exist a systematic imbalance between the data observed from each treatment arm. Further, due to prevalent sampling, subjects are observed only if they have not experienced the failure event when data collection began, causing the prevalent sampling bias. We propose a unified approach, which simultaneously corrects the bias from the prevalent sampling and balances the systematic differences from the observational data. We illustrate in the simulation study that standard analysis without proper adjustment would result in biased causal inference. Large sample properties of the proposed estimation procedures are established by techniques of empirical processes and examined by simulation studies. The proposed methods are applied to the Surveillance, Epidemiology, and End Results (SEER) and Medicare-linked data for women diagnosed with breast cancer.

Combined estimating equation approaches for semiparametric transformation models with length-biased survival data.

Survival data are subject to length-biased sampling when the survival times are left-truncated and the underlying truncation time random variable is uniformly distributed. Substantial efficiency gains can be achieved by incorporating the information about the truncation time distribution in the estimation procedure [Wang (1989) Journal of the American Statistical Association 84, 742-748; Wang (1996) Biometrika 83, 343-354]. Under the semiparametric transformation models, the maximum likelihood method is expected to be fully efficient, yet it is difficult to implement because the full likelihood depends on the nonparametric component in a complicated way. Moreover, its asymptotic properties have not been established. In this article, we extend the martingale estimating equation approach [Chen et al. (2002) Biometrika 89, 659-668; Kim et al. (2013) Journal of the American Statistical Association 108, 217-227] and the pseudo-partial likelihood approach [Severini and Wong (1992) The Annals of Statistics 4, 1768-1802; Zucker (2005) Journal of the American Statistical Association 100, 1264-1277] for semiparametric transformation models with right-censored data to handle left-truncated and right-censored data. In the same spirit of the composite likelihood method [Huang and Qin (2012) Journal of the American Statistical Association 107, 946-957], we further construct another set of unbiased estimating equations by exploiting the special probability structure of length-biased sampling. Thus the number of estimating equations exceeds the number of parameters, and efficiency gains can be achieved by solving a simple combination of these estimating equations. The proposed methods are easy to implement as they do not require additional programming efforts. Moreover, they are shown to be consistent and asymptotically normally distributed. A data analysis of a dementia study illustrates the methods.

Methylation status of retinoic acid receptor beta2 promoter and global DNA in esophageal squamous cell carcinoma.

BACKGROUND: Focal hypermethylation in promoter regions of tumor suppressor genes against the background of global hypomethylation is a landmark of carcinogenesis. This study aimed to investigate the methylation status of retinoic acid receptor beta2 (RARß2) and long interspersed nuclear elements (LINE-1) in different stages of esophageal squamous cell carcinoma (ESCC). METHOD: The tumor and adjacent normal esophageal tissues from 125 male ESCC patients who underwent primary surgery were analyzed for the methylation status of RARß2 promoter and LINE-1 through methylation-specific polymerase chain reaction and pyrosequencing. RESULTS: RARß2 hypermethylation was detected in 20% of the tumor samples, but not in the normal counterparts. The methylation frequency of LINE-1 was significantly lower in the tumor than in the normal parts (median: 67.7% vs. 80%, P < 0.0005). Ninety-eight patients (78.4%) had both RARß2 hypermethylation and LINE-1 hypomethylation or either one. There was a trend toward higher risk of advanced T stage (P for trend = 0.05) or lymph node metastasis (P for trend = 0.02) when more adverse gene methylation profiles were present. CONCLUSION: Methylation status of RARß2 and LINE-1 was related to the development and possibly the severity of ESCC.

Antituberculosis treatment and hepatotoxicity in patients with chronic viral hepatitis.

BACKGROUND: Whether antituberculosis (anti-TB) treatment in patients with chronic viral hepatitis affects the incidence and onset time of drug-induced hepatotoxicity (DIH) is still controversial. The aim of this retrospective study was to find out whether chronic viral hepatitis affects the incidence and onset time of DIH. METHODS: All patients diagnosed with active TB and being treated at a tertiary referral hospital between 2002 and 2009 were identified from medical records, from which 553 patients were enrolled in the study. The incidence and onset of DIH in patients with and without chronic viral hepatitis (controls) were compared. RESULTS: The incidence of DIH was similar in patients with and without chronic hepatitis (8 % [32/392] vs. 7 % [11/161], P > 0.05). The incidence of transient liver function impairment (TLI) was significantly lower in controls than in chronic hepatitis patients (2 % [9/392] vs. 12 % [20/161], P < 0.001. The mean onset times of DIH in the control, hepatitis B virus (HBV), and hepatitis C virus (HCV) groups were not significantly different (40, 39, and 67 days, respectively, all P > 0.05). The mean onset times of TLI in the control, HBV, and HCV groups were significantly different (23, 48, and 68 days, respectively, all P < 0.05). CONCLUSIONS: Liver function impairment during anti-TB therapy in patients with chronic viral hepatitis was due to mostly TLI, with TLI occurring later than in controls. Chronic viral hepatitis had no significant effect on the incidence of DIH.