The generalized order statistics arising from three populations with the lower truncated proportional hazard rate models and its application to the sensitivity to the early disease stage.

In this paper, we present some results to make inference about the parameters of lower truncated proportional hazard rate models with the same baseline distributions based on three independent generalized order statistics samples. Then, especially by considering the results of the diagnostic tests for the non-diseased, early-diseased stage and fully diseased populations, we make inference about sensitivity to the early disease stage parameter. The maximum likelihood estimator, a generalized pivotal estimator and some Bayes estimators are obtained for different structures of prior distributions. The percentile bootstrap confidence interval, a generalized pivotal confidence interval and some Bayesian credible intervals are also presented. A Monte Carlo simulation study is used to evaluate the performances of the obtained point estimators and confidence/credible intervals and two competitors. We use two real datasets to illustrate the proposed methods.

Empirical likelihood confidence interval for sensitivity to the early disease stage.

Disease status can naturally be classified into three or more ordinal stages rather than just being binary stages. Many works have been done for the estimation and inference procedure regarding three ordinal disease stages, which are non-disease, early disease, and full disease stages. The early disease stage can be very important for therapeutic intervention and prevention potentiality. As a diagnostic measure, sensitivity to the early disease stage is often used. In this article, we propose confidence intervals for the sensitivity to early disease stage based on given target specificity for non-disease stage and target sensitivity to full disease stage using both empirical likelihood (EL) and adjusted EL procedures. We compare the performance of the proposed EL confidence intervals with other procedures in our simulation study. The proposed procedures are further applied to Alzheimer's Disease Neuroimaging Initiative data set.

Copper-Catalyzed Direct Allenylation of Inactive Cyclic Ethers.

We report here a direct allenylation reaction of inactive cyclic ethers. The reaction proceeds through a copper-catalyzed 1,4-difunctionalization of 1,3-enynes, with cyano group installed at the allenes simultaneously. This methodology shows a broad functional group compatibility to 1,3-enynes. Diversified allene-modified cyclic ether derivatives were synthesized with high regioselectivity under mild conditions.

Systematic identification of dysregulated lncRNAs associated with platinum-based chemotherapy response across 11 cancer types.

Aberrant expression of long non-coding RNAs (lncRNAs) leads to the development of chemoresistance by regulating a series of biological processes, which is one of the major obstacles in the cancer treatment. This study aimed to identify some key lncRNAs that are associated with platinum-based chemoresistance in multiple cancers. Regulating the expression levels of these lncRNAs can enhance the sensitivity of patients to chemotherapy drugs and improve the therapeutic effect of cancer. By systematically analyzing 648 samples regarding platinum drug response from the Cancer Genome Atlas (TCGA), we have identified 32 dysregulated lncRNAs across 11 cancer types that could affect platinum-based chemotherapy response, of which 78.125% (25/32) were significantly down-regulated in drug-resistant samples. Drug response prediction model that had been constructed based on the expression pattern of these dysregulated lncRNAs could accurately predict the chemotherapy response of tumor patients, and the area under the curve (AUC) was between 0.8034 and 0.9984. In particular, all of these dysregulated lncRNAs that we identified were cancer-specific. They were significantly associated with the survival of tumor patients and could serve as cancer-specific biomarkers for prognosis. In conclusion, this study will contribute to improving the drug resistance of tumor patients during chemotherapy, and it is of real significance for selecting effective chemotherapy drugs and achieving precision medicine.

Rhodium-Catalyzed Pauson-Khand-Type Cyclization of 1,5-Allene-Alkynes: A Chirality Transfer Strategy for Optically Active Bicyclic Ketones.

An efficient chirality transfer in the [RhCl(CO)2 ]2 -catalyzed [2+2+1] cyclization of optically active axially chiral 1,3-disubstituted allenynes with CO to access optically active bicyclopentenone compounds has been developed. The distal C=C bond of allenes reacted with the alknye unit and CO to afford [4.3.0]-bicyclic products with high ee values under mild reaction conditions with an excellent selectivity.

Penalized Empirical Likelihood for the Sparse Cox Regression Model.

The current penalized regression methods for selecting predictor variables and estimating the associated regression coefficients in the sparse Cox model are mainly based on partial likelihood. In this paper, a bias-corrected empirical likelihood method is proposed for the sparse Cox model in conjunction with appropriate penalty functions when the dimensionality of data is high. Theoretical properties of the resulting estimator for the large sample are proved. Simulation studies suggest that penalized empirical likelihood works better than partial likelihood in terms of selecting correct predictors without introducing more model errors. The well-known primary biliary cirrhosis data set is used to illustrate the proposed penalized empirical likelihood method.

Weighted Estimating Equations for Additive Hazards Models with Missing Covariates.

This paper presents simple weighted and fully augmented weighted estimators for the additive hazards model with missing covariates when they are missing at random. The additive hazards model estimates the difference in hazards and has an intuitive biological interpretation. The proposed weighted estimators for the additive hazards model use incomplete data nonparametrically and have close-form expressions. We show that they are consistent and asymptotically normal, and are more efficient than the simple weighted estimator which only uses the complete data. We illustrate their finite-sample performance through simulation studies and an application to study the progression from mild cognitive impairment to dementia using data from the Alzheimer's Disease Neuroimaging Initiative as well as an application to the mouse leukemia study.

Nanotechnology as a new strategy for the diagnosis and treatment of gliomas.

Glioma is the most common malignant tumor of the central nervous system (CNS), and is characterized by high aggressiveness and a high recurrence rate. Currently, the main treatments for gliomas include surgical resection, temozolomide chemotherapy and radiotherapy. However, the prognosis of glioma patients after active standardized treatment is still poor, especially for glioblastoma (GBM); the median survival is still only 14.6 months, and the 5-year survival rate is only 4-5%. The current challenges in glioma treatment include difficulty in complete surgical resection, poor blood‒brain barrier (BBB) drug permeability, therapeutic resistance, and difficulty in tumor-specific targeting. In recent years, the rapid development of nanotechnology has provided new directions for diagnosing and treating gliomas. Nanoparticles (NPs) are characterized by excellent surface tunability, precise targeting, excellent biocompatibility, and high safety. In addition, NPs can be used for gene therapy, photodynamic therapy, and antiangiogenic therapy and can be combined with biomaterials for thermotherapy. In recent decades, breakthroughs in diagnosing and treating gliomas have been made with various functional NPs, and NPs are expected to become a new strategy for glioma diagnosis and treatment. In this paper, we review the main obstacles in the treatment of glioma and discuss the potential and challenges of the latest nanotechnology in the diagnosis and treatment of glioma.

Novel empirical likelihood inference for the mean difference with right-censored data.

This paper focuses on comparing two means and finding a confidence interval for the difference of two means with right-censored data using the empirical likelihood method combined with the independent and identically distributed random functions representation. In the literature, some early researchers proposed empirical link-based confidence intervals for the mean difference based on right-censored data using the synthetic data approach. However, their empirical log-likelihood ratio statistic has a scaled chi-squared distribution. To avoid the estimation of the scale parameter in constructing confidence intervals, we propose an empirical likelihood method based on the independent and identically distributed representation of Kaplan-Meier weights involved in the empirical likelihood ratio. We obtain the standard chi-squared distribution. We also apply the adjusted empirical likelihood to improve coverage accuracy for small samples. In addition, we investigate a new empirical likelihood method, the mean empirical likelihood, within the framework of our study. The performances of all the empirical likelihood methods are compared via extensive simulations. The proposed empirical likelihood-based confidence interval has better coverage accuracy than those from existing methods. Finally, our findings are illustrated with a real data set.

Effects of Vibration Training on Cognition and Quality of Life in Individuals With Multiple Sclerosis.

Background: Multiple sclerosis (MS) detrimentally affects cognition and quality of life (QOL). Interventions that can improve cognitive deficit and QOL in individuals with MS are desired. This pilot study investigated the possible effects of vibration training on improving cognition and QOL in individuals with MS. Methods: Eighteen adults with MS were randomized into 2 groups: training and control. The training group underwent 6 weeks of vibration training, and the control group maintained their normal lifestyle throughout the study. In both groups, before and after the training course, the disability status was evaluated by the Patient Determined Disease Steps scale and the Multiple Sclerosis Functional Composite (MSFC), cognitive function was assessed by the Behavior Rating Inventory of Executive Function-Adults (BRIEF-A) and the Buschke Selective Reminding Test (SRT), and QOL was gauged by the 36-item Short Form Health Survey (SF-36). Results: The training was well accepted by the participants, and no major adverse event was reported. All participants finished the entire protocol. Compared with the control group, the training group showed greater improvements in MSFC score, Metacognition Index score of the BRIEF, SRT score, and physical domain score of the SF-36. Conclusions: These results suggest that vibration training could be an effective alternative training paradigm to enhance cognition and QOL in individuals with MS, and they provide an encouraging base to conduct a large-scale clinical trial.

The doubling time analysis for modified infectious disease Richards model with applications to COVID-19 pandemic.

In the absence of reliable information about transmission mechanisms for emerging infectious diseases, simple phenomenological models could provide a starting point to assess the potential outcomes of unfolding public health emergencies, particularly when the epidemiological characteristics of the disease are poorly understood or subject to substantial uncertainty. In this study, we employ the modified Richards model to analyze the growth of an epidemic in terms of 1) the number of times cumulative cases double until the epidemic peaks and 2) the rate at which the intervals between consecutive doubling times increase during the early ascending stage of the outbreak. Our theoretical analysis of doubling times is combined with rigorous numerical simulations and uncertainty quantification using synthetic and real data for COVID-19 pandemic. The doubling-time approach allows to employ early epidemic data to differentiate between the most dangerous threats, which double in size many times over the intervals that are nearly invariant, and the least transmissible diseases, which double in size only a few times with doubling periods rapidly growing.

Designing a typhoid environmental surveillance study: A simulation model for optimum sampling site allocation.

Environmental surveillance can be used for monitoring enteric disease in a population by detecting pathogens, shed by infected people, in sewage. Detection of pathogens depends on many factors: infection rates and shedding in the population, pathogen fate in the sewerage network, and also sampling sites, sample size, and assay sensitivity. This complexity makes the design of sampling strategies challenging, which creates a need for mathematical modeling to guide decision making. In the present study, a model was developed to simulate pathogen shedding, pathogen transport and fate in the sewerage network, sewage sampling, and detection of the pathogen. The simulation study used Salmonella enterica serovar Typhi (S. Typhi) as the target pathogen and two wards in Kolkata, India as the study area. Five different sampling strategies were evaluated for their sensitivity of detecting S. Typhi, by sampling unit: sewage pumping station, shared toilet, adjacent multiple shared toilets (primary sampling unit), pumping station + shared toilets, pumping station + primary sampling units. Sampling strategies were studied in eight scenarios with different geographic clustering of risk, pathogen loss (decay, leakage), and sensitivity of detection assays. A novel adaptive sampling site allocation method was designed, that updates the locations of sampling sites based on their performance. We then demonstrated how the simulation model can be used to predict the performance of environmental surveillance and how it is improved by optimizing the allocation of sampling sites. The results are summarized as a decision tree to guide the sampling strategy based on disease incidence, geographic distribution of risk, pathogen loss, and the sensitivity of the detection assay. The adaptive sampling site allocation method consistently outperformed alternatives with fixed site locations in most scenarios. In some cases, the optimum allocation method increased the median sensitivity from 45% to 90% within 20 updates.

EMPIRICAL LIKELIHOOD INFERENCE FOR THE COX MODEL WITH TIME-DEPENDENT COEFFICIENTS VIA LOCAL PARTIAL LIKELIHOOD.

The Cox model with time-dependent coefficients has been studied by a number of authors recently. In this paper, we develop empirical likelihood (EL) pointwise confidence regions for the time-dependent regression coefficients via local partial likelihood smoothing. The EL simultaneous confidence bands for a linear combination of the coefficients are also derived based on the strong approximation methods. The empirical likelihood ratio is formulated through the local partial log-likelihood for the regression coefficient functions. Our numerical studies indicate that the EL pointwise/simultaneous confidence regions/bands have satisfactory finite sample performances. Compared with the confidence regions derived directly based on the asymptotic normal distribution of the local constant estimator, the EL confidence regions are overall tighter and can better capture the curvature of the underlying regression coefficient functions. Two data sets, the gastric cancer data and the Mayo Clinic primary biliary cirrhosis data, are analyzed using the proposed method.

Identification of cancer prognosis-associated lncRNAs based on the miRNA-TF co-regulatory motifs and dosage sensitivity.

Long non-coding RNAs (lncRNAs) have been shown to be vital players in a majority of physiological and pathological processes, including tumorigenesis and tumor progression. The aim of this study was to identify lncRNAs that can serve as biomarkers for cancer prognosis. Based on dosage sensitivity, we utilized the biological features of known cancer-related lncRNAs, and identified microRNA and transcription factor (miRNA-TF) co-regulatory motifs in an effort to establish a holistic analysis framework and predict new cancer prognosis-associated lncRNAs. We found that lncRNAs with low dosage sensitivity regulated by more than 3 types of co-regulatory motifs were more likely to be associated with cancer. By the use of the integrative analysis of 3035 tumor samples across 9 types of cancer, a total of 33 cancer prognosis-associated lncRNAs were identified. Additionally, on the basis of the miRNA-TF co-regulatory network, we also predicted potential small molecule drugs such as Glucocorticoid and Ginsenoside Rh2 for treating KIRC by targeting miRNA. This study explains the causes of abnormalities in the genome from a new perspective, and provides new clues for cancer diagnosis and prognosis, and research for anti-cancer drugs.

Diphenyl-Diselenide-Mediated Domino Claisen-Type Rearrangement/Cyclization of Propargylic Aryl Ethers: Synthesis of Naphthofuran-2-carboxaldehyde Derivatives.

A diphenyl-diselenide-mediated Claisen-type rearrangement/cyclization of propargylic aryl ethers under metal-free conditions is developed, affording various naphthofuran-2-carboxaldehydes in moderate to excellent yield. The broad substrate scope and excellent functional group compatibility suggest that it can be a straightforward and powerful method to access naphthofuran-2-carboxaldehydes in a highly regioselective manner. Moreover, this reaction can be scaled up to the gram scale.

DNA co-methylation analysis of lincRNAs across nine cancer types reveals novel potential epigenetic biomarkers in cancer.

Aim: The potential functions and prognostic value of lincRNAs with co-methylation events are explored in 9 cancer types. Materials & methods: Here, we evaluated the co-methylation events in promoter and gene-body regions between two lincRNAs across 9 cancer types by constructing a systematic biological framework. Results: The co-methylation events in both promoter and gene-body regions tended to be highly cancer specific. Patient samples could be separated by tumor and normal types according to the eigengenes of universal co-methylation clusters. Functional enrichment results revealed the lincRNAs that brought promoter and gene-body co-methylation events that affected cancer progress through participating in different pathways and could serve as potential prognostic biomarkers. Conclusion: The study provides new insight into the epigenetic regulation in cancer and leads to a potential new direction for epigenetic biomarker discovery.

Identification of cancer-related potential biomarkers based on lncRNA-pseudogene-mRNA competitive networks.

Accumulating evidence indicates that mRNAs and noncoding RNAs act as competitive endogenous RNAs (ceRNAs) and play a key role in tumorigenesis. However, the complex competitive relationship among genes remains unknown. In the present study, the long noncoding RNAs (lncRNAs), pseudogenes and mRNAs that compete with common microRNAs are defined as lncRNA-pseudogene-mRNA competitive triples. We find that some candidate ceRNAs, modules and triples are associated with cancers and can significantly divide patients into high-risk and low-risk groups; thus, they may serve as potential cancer biomarkers. In sum, the present study systematically analyzes the association between competitive triples and cancer, which provides a reference for a deeper understanding of cancer progression.