Urinary volatile organic compounds in prostate cancer biopsy pathologic risk stratification using logistic regression and multivariate analysis models.

Prostate cancer (PCa) is the second leading cause of cancer-related death in American men after lung cancer. The current PCa diagnostic method, the serum prostate-specific antigen (PSA) test, is not specific, thus, alternatives are needed to avoid unnecessary biopsies and over-diagnosis of clinically insignificant PCa. To explore the application of metabolomics in such effort, urine samples were collected from 386 male adults aged 44-93 years, including 247 patients with biopsy-proven PCa and 139 with biopsy-proven negative results. The PCa-positive group was further subdivided into two groups: low-grade (ISUP Grade Group = 1; n = 139) and intermediate/high-grade (ISUP Grade Group ≥ 2; n = 108). Volatile organic compounds (VOCs) in urine were extracted by stir bar sorptive extraction (SBSE) and analyzed using thermal desorption with gas chromatography and mass spectrometry (GC-MS). We used machine learning tools to develop and evaluate models for PCa diagnosis and prognosis. In total, 22,538 VOCs were identified in the urine samples. With regularized logistic regression, our model for PCa diagnosis yielded an area under the curve (AUC) of 0.99 and 0.88 for the training and testing sets respectively. Furthermore, the model for differentiating between low-grade and intermediate/high-grade PCa yielded an average AUC of 0.78 based on a repeated test-sample approach for cross-validation. These novel methods using urinary VOCs and logistic regression were developed to fill gaps in PCa screening and assessment of PCa grades prior to biopsy. Our study findings provide a promising alternative or adjunct to current PCa screening and diagnostic methods to better target patients for biopsy and mitigate the challenges associated with over-diagnosis and over-treatment of PCa.

Urinary fatty acid biomarkers for prostate cancer detection.

The lack of accuracy in the current prostate specific antigen (PSA) test for prostate cancer (PCa) screening causes around 60-75% of unnecessary prostate biopsies. Therefore, alternative diagnostic methods that have better accuracy and can prevent over-diagnosis of PCa are needed. Researchers have examined various potential biomarkers for PCa, and of those fatty acids (FAs) markers have received special attention due to their role in cancer metabolomics. It has been noted that PCa metabolism prefers FAs over glucose substrates for continued rapid proliferation. Hence, we proposed using a urinary FAs based model as a non-invasive alternative for PCa detection. Urine samples collected from 334 biopsy-designated PCa positive and 232 biopsy-designated PCa negative subjects were analyzed for FAs and lipid related compounds by stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The dataset was split into the training (70%) and testing (30%) sets to develop and validate logit models and repeated for 100 runs of random data partitioning. Over the 100 runs, we confirmed the stability of the models and obtained optimal tuning parameters for developing the final FA based model. A PSA model using the values of the patients' PSA test results was constructed with the same cohort for the purpose of comparing the performances of the FA model against PSA test. The FA final model selected 20 FAs and rendered an AUC of 0.71 (95% CI = 0.67-0.75, sensitivity = 0.48, and specificity = 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46-0.66, sensitivity = 0.44, and specificity = 0.71). The study supports the potential use of urinary FAs as a stable and non-invasive alternative test for PCa diagnosis.

Investigating the effects of storage conditions on urinary volatilomes for their reliability in disease diagnosis.

BACKGROUND: Cancer detection presents challenges regarding invasiveness, cost, and reliability. As a result, exploring alternative diagnostic methods holds significant clinical importance. Urinary metabolomic profiling has emerged as a promising avenue; however, its application for cancer diagnosis may be influenced by sample preparation or storage conditions. OBJECTIVE: This study aimed to assess the impact of sample storage and processing conditions on urinary volatile organic compounds (VOCs) profiles and establish a robust standard operating procedure (SOP) for such diagnostic applications. METHODS: Five key variables were investigated: storage temperatures, durations, freeze-thaw cycles, sample collection conditions, and sample amounts. The analysis of VOCs involved stir bar sorptive extraction coupled with thermal desorption-gas chromatography/mass spectrometry (SBSE-TD-GC-MS), with compound identification facilitated by the National Institute of Standards and Technology Library (NIST). Extensive statistical analysis, including combined scatterplot and response surface (CSRS) plots, partial least squares-discriminant analysis (PLS-DA), and probability density function plots (PDFs), were employed to study the effects of the factors. RESULTS: Our findings revealed that urine storage duration, sample amount, temperature, and fasting/non-fasting sample collection did not significantly impact urinary metabolite profiles. This suggests flexibility in urine sample collection conditions, enabling individuals to contribute samples under varying circumstances. However, the influence of freeze-thaw cycles was evident, as VOC profiles exhibited distinct clustering patterns based on the number of cycles. This emphasizes the effect of freeze-thaw cycles on the integrity of urinary profiles. CONCLUSIONS: The developed SOP integrating SBSE-TD-GC-MS and statistical analyses can serve as a valuable tool for analyzing urinary organic compounds with minimal preparation and sensitive detection. The findings also support that urinary VOCs for cancer screening and diagnosis could be a feasible alternative offering a robust, non-invasive, and sensitive approach for cancer screening.

Broadband electromagnetic wave absorption using pure carbon aerogel by synergistically modulating propagation path and carbonization degree.

Carbon materials were widely used as electromagnetic (EM) wave absorption due to their advantages of light weight, environmental resistance and high electrical conductivity. However, conventional means were typically available by combining carbon and other materials to achieve effective absorption. Herein, a novel strategy using pure carbon aerogel with oriented structure was reported to enhance the EM wave absorption by synergistically modulating the wave propagation path and carbonization degree. The aerogel contained proposed modified carbon nanofibers (MCNF) derived from bacterial cellulose (BC), and core-shell carbon nanofibers @ reduced oxide graphene (CNF@RGO). The oriented structure was induced by the temperature field, which manifests anisotropic EM constitutive parameters (εx ≠ Îµz) at different directions of incident wave. The carbonization degree was adjusted by varying the carbonization temperature. At the carbonization temperature of 700 °C, the maximum reflection loss and effective absorption bandwidth reached -53.94 dB and 7.14 GHz, respectively, enabling the aerogel to outperform its previous counterparts. To clarify the EM wave mode-of-action in conjunction, physical models of the aerogel were established in addition to finite element simulation and theoretical analysis. Notably, the aerogel with a density of 3.6 mg/cm3 featured ultra-light weight, superhydrophobicity, superior compressibility, and thermal insulation. Our work offers an efficient strategy for designing broadband and multifunctional EM wave absorption materials (EWAMs), promising great potentials in complex stealth equipment.

Multifunctional microwave absorption materials of multiscale cobalt sulfide/diatoms co-doped carbon aerogel.

Microwave absorption materials (MAMs) have attracted much attention for their potential applications in stealth technology and prevention of electromagnetic pollution problems. Multifunctional MAMs are highly demanded because they can be applied in harsh environments. Hence, based on multiscale manipulation of atomic engineering, nanostructure and microstructure, a multiscale hollow cobalt sulfide/diatoms co-doped carbon aerogel was preparedthrough the physical crosslinking of divalent ions, unidirectional freezing, kirkendall effect, and heteroatomic doping. The aerogel with a low density of 13.1 mg/mm3 has a unique "lamellar-pillar" network structure due to the growth of ice crystals during the preparation process. With the assistance of thiourea, the doping of N, S atoms and the construction of hollow cobalt sulfide are accomplished simultaneously. The ingenious integration facilitates the synergistic effect of conductive loss, defect polarization, interfacial polarization, and multiple scattering. The multiscale hollow cobalt sulfide/diatoms co-doped carbon aerogel had a maximum reflection loss of -51.96 dB and an effective absorption bandwidth of 6.4 GHz, which is higher than that of other reported MAMs. It is further verified through finite element simulation and experiments that the aerogel has an excellent microwave absorption properties. In addition, the aerogel has excellent thermal insulation and flame retardant properties. Therefore, the development of this aerogel can help to use MAMs in complex applications.

Refined moderation analysis with binary outcomes in precision medicine research.

Moderation analysis for evaluating differential treatment effects serves as the bedrock of precision medicine, which is of growing interest in many fields. In the analysis of data with binary outcomes, we observe an interesting symmetry property concerning the ratio of odds ratios, which suggests that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and treatment variable in logistic regression models. We then obtain refined inference on moderating effects by rearranging data and combining two models into one via a generalized estimating equation approach. The improved efficiency is helpful in addressing the lack-of-power problem that is common in the search for important moderators. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.

Vibration training reducing falls in community-living older adults: a pilot randomized controlled trial.

BACKGROUND: Although vibration training has been applied in older adults, it remains unclear if it can reduce falls. AIMS: This pilot randomized-controlled trial aimed to test the effects of an 8-week vibration training program on reducing falls among community-dwelling adults. METHODS: Forty-eight older adults were randomized to two groups: training and control. The training group received three weekly training sessions over eight weeks while the control group maintained their normal lifestyle over the 8-week period. Immediately before (or baseline), following (post-training), and three months after (retest) the 8-week training course, a group of fall risk factors were assessed for all participants. Each participant was also exposed to an unexpected gait-slip on a treadmill during post-training and retest sessions. Their daily-living fall incidence was collected for 12 months after the baseline test. The slip fall was the primary outcome, prospective all-cause falls were the secondary outcome, and fall risk factors acted as the tertiary ones. RESULTS: The vibration training program significantly reduced the risk of slip-falls and improved all fall risk factors immediately after the training course. The training effect may be carried over for three months. The 8-week training program could also lower the number of falls between the baseline test and retest and reduce the recurrent faller rate across the 12 months after the baseline test. DISCUSSION: This study indicates that vibration training might have some effects on fall-related measures in older adults. CONCLUSIONS: An 8-week vibration training program could be effective to reduce falls in older adults. GOV REGISTRATION NUMBER: NCT02694666.

Refined moderation analysis with categorical outcomes in precision medicine.

Moderation analysis is an integral part of precision medicine research. Concerning moderation analysis with categorical outcomes, we start with an interesting observation, which shows that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and the treatment variable in logistic regression models. Hence two estimators of moderating effects can be obtained. We then established the joint asymptotic normality for the two estimators, on which basis refined inference can be made for moderation analysis. The improved precision is helpful in addressing the lack-of-power problem that is common in search of moderators. The above-mentioned results hold for both experimental and observational data. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.

In-situ construction of nitrogen-doped reduced graphene oxide@carbon nanofibers towards the synergetic enhancement of their microwave absorption properties via integrating point defects and structure engineering.

The aim of this work is to develop materials that can absorb microwave to meet the requirements of stealth technology and solve the problem of electromagnetic pollution. However, the challenge is having materials with high-efficient absorption properties at an ultralow filling rate and visualizing the microwave response. The strategy used in this work was to integrate point defect and microstructure in preparing materials, nitrogen-doped reduced graphene oxide@ carbon nanofibers with high-efficient microwave absorption and double-layered structure. Ethylenediamine (nitrogen source), was doped into the materials, resulting in the generation of the defects. The microwave absorption performance of the materials was affected by the degree of defects due to the dipole polarization loss and conductive loss. The optimal samples gained the maximum reflection loss of -54.7 dB and effective absorption bandwidth of 4.74 GHz at a filling rate of only 8 wt%. More significantly, the microwave absorbing mechanism was analyzed visually in the response field. Furthermore, the actual stealth effects were evaluated by the radar cross section reduction, and the value was 29.2 dBm2. The experimental results illustrated that nitrogen-doped reduced graphene oxide@ carbon nanofibers may be alternative materials with high microwave absorption performance at a low filling rate.

Regulated dielectric loss based on core-sheath carbon-carbon hierarchical nanofibers toward the high-performance microwave absorption.

As a response to stealth technology and electromagnetic pollution, microwave absorbing materials have attracted the attention of many research scholars. However, achieving effective absorption with a low filling level is still a challenge in the harsh environment. Here, an emerging carbon-carbon composite fiber with a core-sheath structure is cleverly tailored for high-performance microwave absorber by tuning the dielectric loss. Reasonable engineering heterogeneous interfaces and conductive paths give rise to a synergistic effect of the impedance matching, conductive loss, polarization loss and multiple scattering. The obtained CR-800 achieves the maximum reflection loss of -51.91 dB, effective absorbing bandwidth of 4.82 GHz, and radar cross section (RCS) reduction value of 41.5 dBm2. Furthermore, the composites own superior environmental adaptation with stable absorbing properties in the harsh environment benefited from great environmental resistance of carbon materials. Given this, the core-sheath carbon-carbon composite fibers are expected to be a candidate for radar stealth technology and electromagnetic pollution.

Multiple moderator meta-analysis using the R-package Meta-CART.

In meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other's effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-package metacart, which provides user-friendly functions to conduct meta-CART analyses in R. This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples.

A telephone-based education and support intervention for Rural Breast Cancer Survivors: a randomized controlled trial comparing two implementation strategies in rural Florida.

PURPOSE: To compare two implementation telephone-based strategies of an evidence-based educational and support intervention to Rural Breast Cancer Survivors (RBCS) in which education was delivered early or after the support component. METHODS: Florida RBCS participated in a 12-month randomized clinical trial (RCT) with two arms: Early Education and Support (EE-S) and Support and Delayed Education (S-DE). Arms differed in the timing of 6 support and 3 education sessions. Main outcome was quality of life (QOL, SF-36 physical and mental composite scores [PCS, MCS]). Secondary outcomes were depressive symptoms (Centers for Epidemiologic Studies Depression Scale, CES-D), mood (Profile of Mood States, POMS), and social support (Medical Outcomes Study Social Support Survey, MOS-SSS). Outcomes were analyzed longitudinally using repeated measures models fitted with linear mixed methods. RESULTS: Of 432 RBCS (mean 25.6 months from diagnosis), about 48% were 65+, 73% married/partnered, and 28% with ≤high school education. There were no differences between EE-S and S-DE in demographics or outcomes at baseline (mean (standard deviation): SF-36 PCS, 44.88 (10.6) vs. 45.08 (10.6); MCS, 49.45 (11.1) vs. 48.1 (11.9); CES-D, 10.11 (9.8) vs. 10.86 (10.5); POMS-SF, 23.95 (38.6) vs. 26.35 (38.8); MOS-SSS, 79.2 (21.2) vs. 78.66 (21.2)) or over time. One exception was slightly worse mean scores at month 9 in MCS (Cohen's d, - 0.22; 95% CI, - 0.38, - 0.06) and POMS (Cohen's d, 0.23; 95% CI, 0.07, 0.39) for EE-S vs. S-DE. CONCLUSIONS: The implementation strategies were equivalent. IMPLICATIONS FOR CANCER SURVIVORS: Enhancing support may be considered before delivering not-in-person interventions to RBCS.

Variable selection in joint frailty models of recurrent and terminal events.

Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, for example, death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the "minimum approximated information criterion" method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.

Facile Synthesis of Cobalt-Doped Porous Composites with Amorphous Carbon/Zn Shell for High-Performance Microwave Absorption.

A facile method for the preparation of microwave absorbers with low density, high microwave absorptivity, and broad band is of paramount importance to the progress in practical application. Herein, commonly-used metal organic frameworks (MOFs) prepared just by mechanical stirring in methanol at room temperature were chosen as sacrificial templates to synthesize porous carbon composites with tunable dielectric and magnetic properties. With the replacement of Co atoms on the surface of zeolitic imidazolate framework-67 (ZIF-67) by Zn atoms, a Co-doped porous carbon composite with a low-dielectric amorphous carbon/Zn shell was constructed after annealing, leading to excellent impedance matching condition. Consequently, the as-obtained composite (Co/C@C-800) shows marvelous microwave absorption properties with an absorption capacity of -43.97 dB and a corresponding effective absorption bandwidth of 4.1 GHz, far exceeding that of the traditional porous carbon and composites directly derived from ZIF-67. The results provide a convenient way to modify MOFs for enhanced microwave absorption materials from the synergy of dielectric and magnetic losses.

Precision medicine: Subgroup identification in longitudinal trajectories.

In clinical studies, the treatment effect may be heterogeneous among patients. It is of interest to identify subpopulations which benefit most from the treatment, regardless of the treatment's overall performance. In this study, we are interested in subgroup identification in longitudinal studies when nonlinear trajectory patterns are present. Under such a situation, evaluation of the treatment effect entails comparing longitudinal trajectories while subgroup identification requires a further evaluation of differential treatment effects among subgroups induced by moderators. To this end, we propose a tree-structured subgroup identification method, termed "interaction tree for longitudinal trajectories", which combines mixed effects models with regression splines to model the nonlinear progression patterns among repeated measures. Extensive simulation studies are conducted to evaluate its performance and an application to an alcohol addiction pharmacogenetic trial is presented.

Bootstrap confidence intervals for the optimal cutoff point to bisect estimated probabilities from logistic regression.

To classify estimated probabilities from a logistic regression model into two groups (e.g., yes or no, disease or no disease), the optimal cutoff point or threshold is crucial. While various methods have been proposed for estimating such a threshold, statistical inference is not generally available. To tackle this issue, we put forward several bootstrap based methods, including the conventional nonparametric bootstrap standard errors and the quantile interval. Special emphasis is placed on a more precise bagging estimator of the optimal cutoff point, for which a confidence interval can be obtained via the recently proposed infinitesimal jackknife method. We investigate the empirical performance of the proposed methods by simulation and illustrate their use via the analysis of a fertility data set concerning seminal quality prediction.

Adaptation to repeated gait-slip perturbations among individuals with multiple sclerosis.

BACKGROUND: Perturbation training, built upon motor adaptation and learning, has been increasingly used as a fall prevention paradigm in older adults. This training paradigm involves repeated externally-induced perturbations (like slips) to facilitate the error-driven learning of necessary motor skills for preventing falls. It remains unknown if people with multiple sclerosis can adapt to large-scale slip perturbations, which impedes the application of perturbation training in persons with multiple sclerosis. This study explored whether people with multiple sclerosis can adapt to large-scale repeated gait-slips. METHODS: Thirteen individuals with multiple sclerosis (the mean ± standard deviation of the Patient Determined Disability Steps: 2.27 ± 1.42) were exposed unexpectedly to a block of five repeated standard slips while walking on a treadmill. The outcome (fall or recovery) for each slip, as our primary outcome measure, was determined. A battery of secondary variables, including dynamic gait stability and gait parameters, were also calculated. Both primary and secondary variables were compared across trials. RESULTS: Our participants showed a rapidly reduced slip-fall rate (from 92.3% on the first slip to 30.8% on the fifth, p < 0.001). They mainly adopted proactive, assisted by reactive, strategies to improve dynamic gait stability, thus reducing the risk of slip-falls. The proactive adjustments, including shortened step, reduced foot landing angle, and flexed knee, shifted the center of mass anteriorly to be closer to the base of support. Such changes in center of mass position improved dynamic gait stability before the slip. Dynamic gait stability after the slip was also improved across trials, as a reactive strategy. CONCLUSION: With practice, people with multiple sclerosis can adapt to large-scale, high-speed, gait-slips and acquire necessary skills against falls. Such skills primarily involve proactive strategy which is assisted by reactive strategy. The proactive strategy would shift the body's center of mass closer to the base of support, improving dynamic gait stability and reducing falls. Our findings could provide a theoretical foundation for deploying perturbation training to prevent falls in people with multiple sclerosis.