Optimization of EWOC principle in BLRM design for phase 1 oncology trials.

Bayesian logistic regression model (BLRM) is widely used to guide dose escalation decisions in phase 1 oncology trials. An important feature of BLRM design is the appealing safety performance due to its escalation with overdose control (EWOC). However, some recent literature indicates that BLRM with EWOC may have a relatively low probability to find the maximum tolerated dose (MTD) compared to some other dose escalation designs. This work discusses this design problem and proposes a practical solution to improve the performance of BLRM design. Specifically, we suggest increasing the EWOC cutoff from routine value 0.25 to a value between 0.3 and 0.4, which will increase the chance of finding the correct MTD with minimal compromise to overdosing risk. Our comparative simulation studies indicate that BLRM with an increased EWOC cutoff has comparable operating characteristics on the correct MTD selection and over-toxicity control as other dose escalation designs (BOIN, mTPI, keyboard, etc.). Moreover, we compare the methodology and operating characteristics of BLRM designs with various decision rules that allow more flexible overdosing control. A case study of dose escalation in a recent phase 1 oncology trial is provided to show how BLRM with optimal EWOC cutoff operates well in practice.

Association between F2-isoprostanes and self-reported stressors in pregnant americans of African and European ancestry.

Background: Poor birth outcomes such as preterm birth/delivery disproportionately affect African Americans compared to White individuals. Reasons for this disparity are likely multifactorial, and include prenatal psychosocial stressors, and attendant increased lipid peroxidation; however, empirical data linking psychosocial stressors during pregnancy to oxidative status are limited. Methods: We used established scales to measure five psychosocial stressors. Maternal adverse childhood experiences, financial stress, social support, anxiety, and depression were measured among 50 African American and White pregnant women enrolled in the Stress and Health in Pregnancy cohort. Liquid chromatography-tandem mass spectrometry was used to measure biomarkers of oxidative stress (four urinary F2-isoprostane isomers), to estimate oxidative status. Linear regression models were used to evaluate associations between psychosocial stressors, prenatal oxidative status and preterm birth. Results: After adjusting for maternal obesity, gestational diabetes, and cigarette smoking, African American women with higher oxidative status were more likely to report higher maternal adverse childhood experience scores (ß = 0.16, se = 1.07, p-value = 0.024) and depression scores (ß = 0.05, se = 0.02, p = 0.014). Higher oxidative status was also associated with lower gestational age at birth (ß = -0.13, se = 0.06, p = 0.04) in this population. These associations were not apparent in Whites. However, none of the cross-product terms for race/ethnicity and social stressors reached statistical significance (p > 0.05). Conclusion: While the small sample size limits inference, our novel data suggest that psychosocial stressors may contribute significantly to oxidative stress during pregnancy, and preterm birth or delivery African Americans. If replicated in larger studies, these findings would support oxidative stress reduction using established dietary or pharmacological approaches present a potential avenue to mitigate adverse effects of psychosocial stressors on birth outcomes.

Immortalised chronic myeloid leukemia (CML) derived mesenchymal stromal cells (MSCs) line retains the immunomodulatory and chemoprotective properties of CML patient-derived MSCs.

Despite the success of Tyrosine kinase inhibitors (TKIs) in treating chronic myeloid leukemia (CML), leukemic stem cells (LSCs) persist, contributing to relapse and resistance. CML Mesenchymal Stromal Cells (MSCs) help in LSC maintenance and protection from TKIs. However, the limited passage and self-differentiation abilities of primary CML MSCs hinder extensive research. To overcome this, we generated and characterized an immortalised CML patient-derived MSC (iCML MSC) line and assessed its role in LSC maintenance. We also compared the immunophenotype and differentiation potential between primary CML MSCs at diagnosis, post-treatment, and with normal bone marrow MSCs. Notably, CML MSCs exhibited enhanced chondrogenic differentiation potential compared to normal MSCs. The iCML MSC line retained the trilineage differentiation potential and was genetically stable, enabling long-term investigations. Functional studies demonstrated that iCML MSCs protected CML CD34+ cells from imatinib-induced apoptosis, recapitulating the bone marrow microenvironment-mediated resistance observed in patients. iCML MSC-conditioned media enabled CML CD34+ and AML blast cells to proliferate rapidly, with no impact on healthy donor CD34+ cells. Gene expression profiling revealed dysregulated genes associated with calcium metabolism in CML CD34+ cells cocultured with iCML MSCs, providing insights into potential therapeutic targets. Further, cytokine profiling revealed that the primary CML MSC lines abundantly secreted 25 cytokines involved in immune regulation, supporting the hypothesis that CML MSCs create an immune modulatory microenvironment that promotes growth and protects against TKIs. Our study establishes the utility of iCML MSCs as a valuable model to investigate leukemic-stromal interactions and study candidate genes involved in mediating TKI resistance in CML LSCs.

Time-Frequency Fragment Selection for Disease Detection from Imbalanced Phonocardiogram Data.

Cardiovascular disease (CVD) has become the most concerning disease worldwide. A Phonocardiogram (PCG), the graphical representation of heart sound, is a non-invasive method that helps to detect CVD by analyzing its characteristics. Several machine learning (ML) approaches have been proposed in the last decade to assist practitioners in interpreting this disease accurately. However, the ML-based method requires a considerable amount of PCG data with a balance between data categories for unbiased performance. Moreover, PCG data in the literature is scarce, and the available database has a strong imbalance between the normal and abnormal categories. This data imbalance causes outcomes to be severely biased towards classes with greater samples. This work proposes a variable-hop fragment selection method with a pre-trained CNN model to counter the issues of data scarcity and imbalance. The proposed framework improves 7.12% of unweighted average recall (UAR) value for assessing an imbalanced PCG dataset compared to the state-of-the-art method and reports an overall UAR of 92.46% on the PhysioNet/CinC Challenge 2016 dataset. The improved performance signifies the clinical relevance of the work providing reliable assistance for heart auscultation and has the potential to screen for heart pathologies in data constraint applications.

Scalable graphene sensor array for real-time toxins monitoring in flowing water.

Risk management for drinking water often requires continuous monitoring of various toxins in flowing water. While they can be readily integrated with existing water infrastructure, two-dimensional (2D) electronic sensors often suffer from device-to-device variations due to the lack of an effective strategy for identifying faulty devices from preselected uniform devices based on electronic properties alone, resulting in sensor inaccuracy and thus slowing down their real-world applications. Here, we report the combination of wet transfer, impedance and noise measurements, and machine learning to facilitate the scalable nanofabrication of graphene-based field-effect transistor (GFET) sensor arrays and the efficient identification of faulty devices. Our sensors were able to perform real-time detection of heavy-metal ions (lead and mercury) and E. coli bacteria simultaneously in flowing tap water. This study offers a reliable quality control protocol to increase the potential of electronic sensors for monitoring pollutants in flowing water.

Critical review on uranium and arsenic content and their chemical mobilization in groundwater: A case study of the Malwa region Punjab, India.

The presence of pollutants like uranium and arsenic in the groundwater can have a terrible impact on people's health (both radiologically and toxicologically) and their economic conditions. Their infiltration into groundwater can occur through geochemical reactions, natural mineral deposits, mining and ore processing. Governments and scientists are working to address these issues, and significant progress has been achieved, but it's challenging to deal with and mitigate without adequately understanding the different chemical processes and the mobilization mechanism of these hazardous chemicals. Most of the articles and reviews have focused on the particular form of contaminants and specific sources of pollution, such as fertilizers. However, no literature report exists explaining why particular forms appear and the possible basis of their chemical origins. Hence, in this review, we tried to answer the various questions by devising a hypothetical model and chemical schematic flowcharts for the chemical mobilization of arsenic and uranium in groundwater. An effort has been made to explain how chemical seepage and excessive groundwater use resulted in the change in aquifers' chemistry, as evidenced by their physicochemical parameters and heavy metal analysis. Many technological advancements have taken place to mitigate these issues. Still, in low-middle-income countries, especially in the Malwa region of Punjab, also known as Punjab's cancer belt, paying a high amount for installing and maintaining these technologies is an unviable option. In addition to working to improve people's access to sanitary facilities and clean water to drink, the policy-level intervention would focus on increasing community awareness and continued research on developing better and more economical technologies. Our designed model/chemical flowcharts will help policymakers and researchers better understand the problems and alleviate their effects. Moreover, these models can be utilized in other parts of the globe where similar questions exist. This article emphasises the value of understanding the intricate issue of groundwater management through a multidisciplinary and interdepartmental approach.

Spin-Controlled Helical Quantum Sieve Chiral Spectrometer.

This article reports on a molecular-spin-sensitive-antenna (MSSA) that is based on stacked layers of organically functionalized graphene on a fibrous helical cellulose network for carrying out spatiotemporal identification of chiral enantiomers. The MSSA structures combine three complementary features: (i) chiral separation via a helical quantum sieve for chiral trapping, (ii) chiral recognition by a synthetically implanted spin-sensitive center in a graphitic lattice; and (iii) chiral selectivity by a chirality-induced-spin mechanism that polarizes the local electronic band-structure in graphene through chiral-activated Rashba spin-orbit interaction field. Combining the MSSA structures with decision-making principles based on neuromorphic artificial intelligence shows fast, portable, and wearable spectrometry for the detection and classification of pure and a mixture of chiral molecules, such as butanol (S and R), limonene (S and R), and xylene isomers, with 95-98% accuracy. These results can have a broad impact where the MSSA approach is central as a precautionary risk assessment against potential hazards impacting human health and the environment due to chiral molecules; furthermore, it acts as a dynamic monitoring tool of all parts of the chiral molecule life cycles.

Variable selection in nonlinear function-on-scalar regression.

We develop a new method for variable selection in a nonlinear additive function-on-scalar regression (FOSR) model. Existing methods for variable selection in FOSR have focused on the linear effects of scalar predictors, which can be a restrictive assumption in the presence of multiple continuously measured covariates. We propose a computationally efficient approach for variable selection in existing linear FOSR using functional principal component scores of the functional response and extend this framework to a nonlinear additive function-on-scalar model. The proposed method provides a unified and flexible framework for variable selection in FOSR, allowing nonlinear effects of the covariates. Numerical analysis using simulation study illustrates the advantages of the proposed method over existing variable selection methods in FOSR even when the underlying covariate effects are all linear. The proposed procedure is demonstrated on accelerometer data from the 2003-2004 cohorts of the National Health and Nutrition Examination Survey (NHANES) in understanding the association between diurnal patterns of physical activity and demographic, lifestyle, and health characteristics of the participants.

Adaptive Bayesian variable clustering via structural learning of breast cancer data.

The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize a large number of clusters) as prior on the number of clusters. The posterior distributions of the parameters are not in explicit form and we use a reversible jump Markov chain Monte Carlo based technique is used to simulate the parameters from the posteriors. The end products of the proposed method are estimated cluster configuration of the proteins (variables) along with the number of clusters. The Bayesian method is flexible enough to cluster the proteins as well as estimate the number of clusters. The performance of the proposed method has been substantiated with extensive simulation studies and one protein expression data with a hereditary disposition in breast cancer where the proteins are coming from different pathways.

Gate-Controlled Chiral Recognition and Spin Assessment with All-Electric Hybrid Quantum Wire-Based Transistors.

The search for inexpensive and all-electric tunable methods for portable and fast recognition and discrimination between various chiral enantiomers, mainly those found in the gas phase, has been one of the most challenging tasks in the field of analytical chemistry. The current article reports on a chiral sensitive electric architecture (CSEA) of a helical polyaniline (PANI)@carbon nanotube (CNT) hybrid quantum-wire based field effect transistor (FET) platform. The CSEA architecture exhibits gate-controlled-channel-chirality modulation for the selective distinction of Limonene (S(+)/R(-)) at ≈12 V intervals. Typical gate-modulated selectivity of S(+)-Limonene and R(-)-Limonene using two opposite helically turned hybrids, namely as, S-PANI@CNT and R-PANI@CNT are 6.5 and 2.8, respectively. Theoretical analysis and modelling relates the gas-phase chiral quantum probe with spin-channel modulation in CNT by Rashba spin-orbit interaction. This is achieved by applied gate voltage, CNT's unique curved surface, adsorbed chiral adatom induced scattering center on the curved graphitic lattice and helicoid field from a synthetically prepared helical PANI@CNT hybrid interface.

Maternal Mediterranean Diet Adherence and Its Associations with Maternal Prenatal Stressors and Child Growth.

Background: Psychosocial and physiologic stressors, such as depression and obesity, during pregnancy can have negative consequences, such as increased systemic inflammation, contributing to chronic disease for both mothers and their unborn children. These conditions disproportionately affect racial/ethnic minorities. The effects of recommended dietary patterns in mitigating the effects of these stressors remain understudied. Objectives: We aimed to evaluate the relations between maternal Mediterranean diet adherence (MDA) and maternal and offspring outcomes during the first decade of life in African Americans, Hispanics, and Whites. Methods: This study included 929 mother-child dyads from the NEST (Newborn Epigenetics STudy), a prospective cohort study. FFQs were used to estimate MDA in pregnant women. Weight and height were measured in children between birth and age 8 y. Multivariable linear regression models were used to examine associations between maternal MDA, inflammatory cytokines, and pregnancy and postnatal outcomes. Results: More than 55% of White women reported high MDA during the periconceptional period compared with 22% of Hispanic and 18% of African American women (P < 0.05). Higher MDA was associated with lower likelihood of depressive mood (ß = -0.45; 95% CI: -0.90, -0.18; P = 0.02) and prepregnancy obesity (ß = -0.29; 95% CI: -0.57, -0.0002; P = 0.05). Higher MDA was also associated with lower body size at birth, which was maintained to ages 3-5 and 6-8 y-this association was most apparent in White children (3-5 y: ß = -2.9, P = 0.02; 6-8 y: ß = -3.99, P = 0.01). Conclusions: If replicated in larger studies, our data suggest that MDA provides a potent avenue by which effects of prenatal stressors on maternal and fetal outcomes can be mitigated to reduce ethnic disparities in childhood obesity.

Ultra-Fast Portable and Wearable Sensing Design for Continuous and Wide-Spectrum Molecular Analysis and Diagnostics.

The design and characterization of spatiotemporal nano-/micro-structural arrangement that enable real-time and wide-spectrum molecular analysis is reported and demonestrated in new horizons of biomedical applications, such as wearable-spectrometry, ultra-fast and onsite biopsy-decision-making for intraoperative surgical oncology, chiral-drug identification, etc. The spatiotemporal sesning arrangement is achieved by scalable, binder-free, functionalized hybrid spin-sensitive (<↑| or <↓|) graphene-ink printed sensing layers on free-standing films made of porous, fibrous, and naturally helical cellulose networks in hierarchically stacked geometrical configuration (HSGC). The HSGC operates according to a time-space-resolved architecture that modulate the mass-transfer rate for separation, eluation and detection of each individual compound within a mixture of the like, hereby providing a mass spectrogram. The HSGC could be used for a wide range of applictions, including fast and real-time spectrogram generator of volatile organic compounds during liquid-biopsy, without the need of any immunochemistry-staining and complex power-hungry cryogenic machines; and wearable spectrometry that provide spectral signature of molecular profiles emiited from skin in the course of various dietry conditions.

Inference in Functional Linear Quantile Regression.

In this paper, we study statistical inference in functional quantile regression for scalar response and a functional covariate. Specifically, we consider a functional linear quantile regression model where the effect of the covariate on the quantile of the response is modeled through the inner product between the functional covariate and an unknown smooth regression parameter function that varies with the level of quantile. The objective is to test that the regression parameter is constant across several quantile levels of interest. The parameter function is estimated by combining ideas from functional principal component analysis and quantile regression. An adjusted Wald testing procedure is proposed for this hypothesis of interest, and its chi-square asymptotic null distribution is derived. The testing procedure is investigated numerically in simulations involving sparse and noisy functional covariates and in a capital bike share data application. The proposed approach is easy to implement and the R code is published online at https://github.com/xylimeng/fQR-testing.

Stretchable and Highly Permeable Nanofibrous Sensors for Detecting Complex Human Body Motion.

Wearable strain sensors have been attracting special attention in the detection of human posture and activity, as well as for the assessment of physical rehabilitation and kinematics. However, it is a challenge to fabricate stretchable and comfortable-to-wear permeable strain sensors that can provide highly accurate and continuous motion recording while exerting minimal constraints and maintaining low interference with the body. Herein, covalently grafting nanofibrous polyaniline (PANI) onto stretchable elastomer nanomeshes is reported to obtain a freestanding ultrathin (varying from 300 to 10 000 nm) strain sensor that has high gas permeability (10-33 mg h-1 ). The sensor demonstrates a low weight and can be directly laminated onto the dynamic human skin for long periods of time. The sensor, which produces an intimate connection with solid or living objects, has a stable performance with excellent sustainability, linearity, durability, and low hysteresis. It exibits excellent performance for continuous interrogation of complex movements, mimicking muscle activities, and resembling brain activity. This includes a very precise discrimination of bending and twisting stimuli at different angles (1-180°) and speeds (3-18 rpm) and very low exertion of counter-interference. These results imply the utility of this appraoch for advanced developments of robotic e-skins or e-muscles.

A Dinickel Catalyzed Cyclopropanation without the Formation of a Metal Carbene Intermediate.

(NDI)Ni2 catalysts (NDI=naphthyridine-diimine) promote cyclopropanation reactions of 1,3-dienes using (Me3 Si)CHN2 . Mechanistic studies reveal that a metal carbene intermediate is not part of the catalytic cycle. The (NDI)Ni2 (CHSiMe3 ) complex was independently synthesized and found to be unreactive toward dienes. Based on DFT models, we propose an alternative mechanism that begins with a Ni2 -mediated coupling of (Me3 Si)CHN2 and the diene. N2 extrusion followed by radical C-C bond formation generates the cyclopropane product. This model reproduces the experimentally observed regioselectivity and diastereoselectivity of the reaction.

Circadian Gene Selection for Time-to-event Phenotype by Integrating CNV and RNAseq Data.

BACKGROUND: The endogenous circadian clock, which controls daily rhythms in the expression of at least half of the mammalian genome, has a major influence on cell physiology. Consequently, disruption of the circadian system is associated with wide range of diseases including cancer. While several circadian clock genes have been associated with cancer progression, little is known about the survival when two or more platforms are considered together. Our goal was to determine if survival outcomes are associated with circadian clock function. To accomplish this goal, we developed a Bayesian hierarchical survival model coupled with the global local shrinkage prior and applied this model to available RNASeq and Copy Number Variation data to select significant circadian genes associates with cancer progression. RESULTS: Using a Bayesian shrinkage approach with the Bayesian accelerated failure time (AFT) model we showed the circadian clock associated gene DEC1 is positively correlated to survival outcome in breast cancer patients. The R package circgene implementing the methodology is available at https://github.com/MAITYA02/circgene. CONCLUSIONS: The proposed Bayesian hierarchical model is the first shrinkage prior based model in its kind which integrates two omics platforms to identify the significant circadian gene for cancer survival.

Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes.

In this paper, we propose an innovative method for jointly analyzing survival data and longitudinally measured continuous and ordinal data. We use a random effects accelerated failure time model for survival outcomes, a linear mixed model for continuous longitudinal outcomes and a proportional odds mixed model for ordinal longitudinal outcomes, where these outcome processes are linked through a set of association parameters. A primary objective of this study is to examine the effects of association parameters on the estimators of joint models. The model parameters are estimated by the method of maximum likelihood. The finite-sample properties of the estimators are studied using Monte Carlo simulations. The empirical study suggests that the degree of association among the outcome processes influences the bias, efficiency, and coverage probability of the estimators. Our proposed joint model estimators are approximately unbiased and produce smaller mean squared errors as compared to the estimators obtained from separate models. This work is motivated by a large multicenter study, referred to as the Genetic and Inflammatory Markers of Sepsis (GenIMS) study. We apply our proposed method to the GenIMS data analysis.

Bayesian structural equation modeling in multiple omics data with application to circadian genes.

MOTIVATION: It is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions, which might be dormant in a single-source analysis. Moreover, different studies have justified the more powerful analyses of multi-platform data. Toward this, in this study, we consider the circadian genes' omics profile, such as copy number changes and RNA-sequence data along with their survival response. We develop a Bayesian structural equation modeling coupled with linear regressions and log normal accelerated failure-time regression to integrate the information between these two platforms to predict the survival of the subjects. We place conjugate priors on the regression parameters and derive the Gibbs sampler using the conditional distributions of them. RESULTS: Our extensive simulation study shows that the integrative model provides a better fit to the data than its closest competitor. The analyses of glioblastoma cancer data and the breast cancer data from TCGA, the largest genomics and transcriptomics database, support our findings. AVAILABILITY AND IMPLEMENTATION: The developed method is wrapped in R package available at https://github.com/MAITYA02/semmcmc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Robust kernel association testing (RobKAT).

Testing the association between single-nucleotide polymorphism (SNP) effects and a response is often carried out through kernel machine methods based on least squares, such as the sequence kernel association test (SKAT). However, these least-squares procedures are designed for a normally distributed conditional response, which may not apply. Other robust procedures such as the quantile regression kernel machine (QRKM) restrict the choice of the loss function and only allow inference on conditional quantiles. We propose a general and robust kernel association test with a flexible choice of the loss function, no distributional assumptions, and has SKAT and QRKM as special cases. We evaluate our proposed robust association test (RobKAT) across various data distributions through a simulation study. When errors are normally distributed, RobKAT controls type I error and shows comparable power with SKAT. In all other distributional settings investigated, our robust test has similar or greater power than SKAT. Finally, we apply our robust testing method to data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) clinical trial to detect associations between selected genes including the major histocompatibility complex (MHC) region on chromosome six and neurotropic herpesvirus antibody levels in schizophrenia patients. RobKAT detected significant association with four SNP sets (HST1H2BJ, MHC, POM12L2, and SLC17A1), three of which were undetected by SKAT.

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data.

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated "The Cancer Proteome Atlas" (TCPA), which contains reverse-phase protein arrays-based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.