Nevin Manimala

Mov Disord. 2022 Nov 25. doi: 10.1002/mds.29277. Online ahead of print.

ABSTRACT

BACKGROUND: Optic neuropathy is a near ubiquitous feature of Friedreich’s ataxia (FRDA). Previous studies have examined varying aspects of the anterior and posterior visual pathways but none so far have comprehensively evaluated the heterogeneity of degeneration across different areas of the retina, changes to the macula layers and combined these with volumetric MRI studies of the visual cortex and frataxin level.

METHODS: We investigated 62 genetically confirmed FRDA patients using an integrated approach as part of an observational cohort study. We included measurement of frataxin protein levels, clinical evaluation of visual and neurological function, optical coherence tomography to determine retinal nerve fibre layer thickness and macular layer volume and volumetric brain MRI.

RESULTS: We demonstrate that frataxin level correlates with peripapillary retinal nerve fibre layer thickness and that retinal sectors differ in their degree of degeneration. We also shown that retinal nerve fibre layer is thinner in FRDA patients than controls and that this thinning is influenced by the AAO and GAA1. Furthermore we show that the ganglion cell and inner plexiform layers are affected in FRDA. Our MRI data indicate that there are borderline correlations between retinal layers and areas of the cortex involved in visual processing.

CONCLUSION: Our study demonstrates the uneven distribution of the axonopathy in the retinal nerve fibre layer and highlight the relative sparing of the papillomacular bundle and temporal sectors. We show that thinning of the retinal nerve fibre layer is associated with frataxin levels, supporting the use the two biomarkers in future clinical trials design. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

PMID:36433650 | DOI:10.1002/mds.29277

Adv Mater. 2022 Nov 25:e2209386. doi: 10.1002/adma.202209386. Online ahead of print.

ABSTRACT

Dual-atom catalysts (DACs) have become an emerging platform to provide more flexible active sites for electrocatalytic reactions with multi-electron/proton transfer, such as CO₂ reduction reaction (CRR). However, the introduction of asymmetric dual-atom sites causes complexity in structure, leaving an incomprehensive understanding of inter-metal interaction and catalytic mechanism. Taking NiCu DACs as an example, herein, we propose a more rational structural model, and investigate the distance-dependent inter-metal interaction by combining theoretical simulations and experiments, including density functional theory computation, aberration-corrected transmission electron microscopy, synchrotron-based X-ray absorption fine structure, and Monte Carlo experiments. A distance threshold around 5.3 Å between adjacent Ni-N₄ and Cu-N₄ moieties is revealed to trigger effective electronic regulation and boost CRR performance on both selectivity and activity. A universal macro-descriptor rigorously correlating the inter-metal distance and intrinsic material features (e.g., metal loading, thickness) is established to guide the rational design and synthesis of advanced DACs. This study highlights the significance of identifying the inter-metal interaction in DACs, and helps bridge the gap between theoretical study and experimental synthesis of atomically dispersed catalysts with highly correlated active sites. This article is protected by copyright. All rights reserved.

PMID:36433641 | DOI:10.1002/adma.202209386

Stat Med. 2022 Nov 25. doi: 10.1002/sim.9613. Online ahead of print.

ABSTRACT

This paper introduces a nonparametric regression approach for univariate and multivariate skewed responses using Bayesian additive regression trees (BART). Existing BART methods use ensembles of decision trees to model a mean function, and have become popular recently due to their high prediction accuracy and ease of use. The usual assumption of a univariate Gaussian error distribution, however, is restrictive in many biomedical applications. Motivated by an oral health study, we provide a useful extension of BART, the skewBART model, to address this problem. We then extend skewBART to allow for multivariate responses, with information shared across the decision trees associated with different responses within the same subject. The methodology accommodates within-subject association, and allows varying skewness parameters for the varying multivariate responses. We illustrate the benefits of our multivariate skewBART proposal over existing alternatives via simulation studies and application to the oral health dataset with bivariate highly skewed responses. Our methodology is implementable via the R package skewBART, available on GitHub.

PMID:36433639 | DOI:10.1002/sim.9613

Stat Med. 2022 Nov 25. doi: 10.1002/sim.9610. Online ahead of print.

ABSTRACT

The need to model a cure fraction, the proportion of a cohort not susceptible to the event of interest, arises in a variety of contexts including tumor relapse in oncology. Existing methodology assumes that follow-up is long enough for all uncured subjects to have experienced the event of interest at the time of analysis, and researchers have demonstrated that fitting cure models without sufficient follow-up leads to bias. Few statistical methods exist to evaluate sufficient follow-up, and they can exhibit poor performance and lead users to falsely conclude sufficient follow-up, leading to bias, or to falsely claim insufficient follow-up, possibly leading to additional, costly data collection. We propose a new quantitative statistic (RECeUS) to evaluate whether cure models may be appropriate to apply to censored data. Specifically, we propose that the estimated proportion of censored uncured subjects in a study can be used to evaluate cure model appropriateness. We evaluated the performance of RECeUS against existing methods via simulation and with two data examples, and we observe that RECeUS displays superior performance. In simulated and real-world settings, RECeUS correctly identifies both situations in which data appear appropriate for cure modeling and when data seem inappropriate for fitting cure models.

PMID:36433635 | DOI:10.1002/sim.9610

Int J Biostat. 2022 Nov 28. doi: 10.1515/ijb-2022-0047. Online ahead of print.

ABSTRACT

With our increased ability to capture large data, causal inference has received renewed attention and is playing an ever-important role in biomedicine and economics. However, one major methodological hurdle is that existing methods rely on many unverifiable model assumptions. Thus robust modeling is a critically important approach complementary to sensitivity analysis, where it compares results under various model assumptions. The more robust a method is with respect to model assumptions, the more worthy it is. The doubly robust estimator (DRE) is a significant advance in this direction. However, in practice, many outcome measures are functionals of multiple distributions, and so are the associated estimands, which can only be estimated via U-statistics. Thus most existing DREs do not apply. This article proposes a broad class of highly robust U-statistic estimators (HREs), which use semiparametric specifications for both the propensity score and outcome models in constructing the U-statistic. Thus, the HRE is more robust than the existing DREs. We derive comprehensive asymptotic properties of the proposed estimators and perform extensive simulation studies to evaluate their finite sample performance and compare them with the corresponding parametric U-statistics and the naive estimators, which show significant advantages. Then we apply the method to analyze a clinical trial from the AIDS Clinical Trials Group.

PMID:36433631 | DOI:10.1515/ijb-2022-0047

Med Phys. 2022 Nov 25. doi: 10.1002/mp.16125. Online ahead of print.

ABSTRACT

Studies of skeletal metabolism using measurements of bone metabolic flux (K_i ) obtained with [¹⁸ F] sodium fluoride ([¹⁸ F]NaF) positron emission tomography (PET) scans have been used in clinical research for the last 30 years. The technique has proven useful as an imaging biomarker in trials of novel drug treatments for osteoporosis and investigating other metabolic bone diseases, including chronic kidney disease mineral and bone disorder. It has also been shown to be valuable in metastatic bone disease in breast cancer patients and may have potential in other cancer types, such as prostate cancer, to assess early bone fracture risk. However, these studies have usually required a 60-min dynamic PET scan and measurement of the arterial input function (AIF), making them difficult to translate into the clinic for diagnostic purposes. We have previously proposed a simplified method that estimates the K_i value at an imaging site from a short (4-min) static scan and venous blood samples. A key advantage of this method is that, by acquiring a series of static scans, values of K_i can be quickly measured at multiple sites using a single injection of the tracer. To date, the widespread use of [¹⁸ F]NaF PET has been limited by the need to measure the AIF required for the mathematical modeling of tracer kinetics to derive K_i and other kinetic parameters. In this report, we review different methods of measuring the AIF, including direct arterial sampling, the use of a semi-population input function (SP-AIF), and image-derived input function, the latter two requiring only two or three venous blood samples obtained between 30 and 60 min after injection. We provide an SP-AIF model and a spreadsheet for calculating K_i values using the static scan method that others can use to study bone metabolism in metabolic and metastatic bone diseases without requiring invasive arterial blood sampling. The method shortens scan times, simplifies procedures and reduces the cost of multi-center trials without losing accuracy or precision. This article is protected by copyright. All rights reserved.

PMID:36433629 | DOI:10.1002/mp.16125

Sensors (Basel). 2022 Nov 21;22(22):9014. doi: 10.3390/s22229014.

ABSTRACT

Line-of-sight (LOS) indoor optical wireless communications (OWC) enable a high data rate transmission while potentially suffering from optical channel obstructions. Additional LOS links using diversity techniques can tackle the received signal performance degradation, where channel gains often differ in multiple LOS channels. In this paper, a novel active transmitter detection scheme in spatial modulation (SM) is proposed to be incorporated with signal space diversity (SSD) technique to enable an increased OWC system throughput with an improved bit-error-rate (BER). This transmitter detection scheme is composed of a signal pre-distortion technique at the transmitter and a power-based statistical detection method at the receiver, which can address the problem of power-based transmitter detection in SM using carrierless amplitude and phase modulation waveforms with numerous signal levels. Experimental results show that, with the proposed transmitter detection scheme, SSD can be effectively provided with ~0.61 dB signal-to-noise-ratio (SNR) improvement. Additionally, an improved data rate ~7.5 Gbit/s is expected due to effective transmitter detection in SM. The SSD performances at different constellation rotation angles and under different channel gain distributions are also investigated, respectively. The proposed scheme provides a practical solution to implement power-based SM and thus aids the SSD realization for improving system performance.

PMID:36433606 | DOI:10.3390/s22229014

Sensors (Basel). 2022 Nov 21;22(22):8998. doi: 10.3390/s22228998.

ABSTRACT

OBJECTIVE: Quality of intraoperative teamwork may have a direct impact on patient outcomes. Heart rate variability (HRV) synchrony may be useful for objective assessment of team cohesion and good teamwork. The primary aim of this study was to investigate the feasibility of using HRV synchrony in surgical teams. Secondary aims were to investigate the association of HRV synchrony with length of procedure (LOP), complications, number of intraoperative glitches and length of stay (LOS). We also investigated the correlation between HRV synchrony and team familiarity, pre- and intraoperative stress levels (STAI questionnaire), NOTECHS score and experience of team members.

METHODS: Ear, nose and throat (ENT) and vascular surgeons (consultant and registrar team members) were recruited into the study. Baseline demographics including level of team members’ experience were gathered before each procedure. For each procedure, continuous electrocardiogram (ECG) recording was performed and questionnaires regarding pre- and intraoperative stress levels and non-technical skills (NOTECHS) scores were collected for each team member. An independent observer documented the time of each intraoperative glitch. Statistical analysis was conducted using stepwise multiple linear regression.

RESULTS: Four HRV synchrony metrics which may be markers of efficient surgical collaboration were identified from the data: 1. number of HRV synchronies per hour of procedure, 2. number of HRV synchrony trends per hour of procedure, 3. length of HRV synchrony trends per hour of procedure, 4. area under the HRV synchrony trend curve per hour of procedure. LOP was inversely correlated with number of HRV synchrony trends per hour of procedure (p < 0.0001), area under HRV synchrony trend curve per hour of procedure (p = 0.001), length of HRV synchrony trends per hour of procedure (p = 0.002) and number of HRV synchronies per hour of procedure (p < 0.0001). LOP was positively correlated with: FS (p = 0.043; R = 0.358) and intraoperative STAI score of the whole team (p = 0.007; R = 0.493).

CONCLUSIONS: HRV synchrony metrics within operating teams may be used as an objective marker to quantify surgical teamwork. We have shown that LOP is shorter when the intraoperative surgical teams’ HRV is more synchronised.

PMID:36433593 | DOI:10.3390/s22228998

Sensors (Basel). 2022 Nov 20;22(22):8988. doi: 10.3390/s22228988.

ABSTRACT

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application.

PMID:36433582 | DOI:10.3390/s22228988

J Med Virol. 2022 Nov 25. doi: 10.1002/jmv.28350. Online ahead of print.

ABSTRACT

Human Endogenous Retroviruses (HERVs) are viral sequences integrated into the human genome, resulting from the infection of human germ-line cells by ancient exogenous retroviruses. Despite losing their replication and retrotransposition abilities, HERVs appear to have been co-opted in human physiological functions while their aberrant expression is linked to human disease. The role of HERVs in multiple malignancies has been demonstrated, however, the extent to which HERV activation and expression participate in the development of cancer is not yet fully comprehended. In this review article, we discuss the presumed role of HERVs in carcinogenesis and their promising diagnostic and prognostic implications. Additionally, we explore recent data on the HERVs in cancer therapeutics, either through the manipulation of their expression, to induce anti-tumor innate immunity responses or as cancer immunotherapy targets. Finally, more precise and higher resolution high-throughput sequencing approaches will further elucidate HERV participation in human physiological and pathological processes. This article is protected by copyright. All rights reserved.

PMID:36428242 | DOI:10.1002/jmv.28350