Conditional Generative Models for Simulation of EMG During Naturalistic Movements.

Numerical models of electromyography (EMG) signals have provided a huge contribution to our fundamental understanding of human neurophysiology and remain a central pillar of motor neuroscience and the development of human-machine interfaces. However, while modern biophysical simulations based on finite element methods (FEMs) are highly accurate, they are extremely computationally expensive and thus are generally limited to modeling static systems such as isometrically contracting limbs. As a solution to this problem, we propose to use a conditional generative model to mimic the output of an advanced numerical model. To this end, we present BioMime, a conditional generative neural network trained adversarially to generate motor unit (MU) activation potential waveforms under a wide variety of volume conductor parameters. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy. Consequently, the computational load is dramatically reduced, which allows the rapid simulation of EMG signals during truly dynamic and naturalistic movements.

A Particle Swarm Optimised Independence Estimator for Blind Source Separation of Neurophysiological Time Series.

The decomposition of neurophysiological recordings into their constituent neural sources is of major importance to a diverse range of neuroscientific fields and neuroengineering applications. The advent of high density electrode probes and arrays has driven a major need for novel semi-automated and automated blind source separation methodologies that take advantage of the increased spatial resolution and coverage these new devices offer. Independent component analysis (ICA) offers a principled theoretical framework for such algorithms, but implementation inefficiencies often drive poor performance in practice, particularly for sparse sources. Here we observe that the use of a single non-linear optimization function to identify spiking sources with ICA often has a detrimental effect that precludes the recovery and correct separation of all spiking sources in the signal. We go on to propose a projection-pursuit ICA algorithm designed specifically for spiking sources, which uses a particle swarm methodology to adaptively traverse a polynomial family of non-linearities approximating the asymmetric cumulants of the sources. We robustly prove state-of-the-art decomposition performance on recordings from high density intramuscular probes and demonstrate how the particle swarm quickly finds optimal contrast non-linearities across a range of neurophysiological datasets.

A Community-Guided Approach to Bronchiolitis: A Needs Assessment and Illness Perception Study.

The factors influencing caregivers' understanding of pediatric respiratory diseases, such as bronchiolitis, can guide patient care and the acceptability of treatment methods within the healthcare system. This study aims to identify illness perceptions and perform a needs assessment among caregivers of children diagnosed with respiratory diseases. This is a prospective, cross-sectional, questionnaire-driven study of a representative sample of caregivers whose children had an acute respiratory illness. The telephone-administered questionnaire was comprised of (1) demographic items; (2) illness perception questionnaire-revised (IPQ-R); and (3) items about personal barriers, the latter 2 of which employed a 5-point Likert response. Cronbach's alpha (α) was used to measure the internal consistency reliability for each item within the IPQ-R. The Pearson 2-tailed correlation coefficient was used to correlate questionnaire items. We included 75 caregivers whose children have been diagnosed with bronchiolitis (51%), reactive airway disease (RAD) (35%), asthma (33%), and wheezing (44%). We found no significance between the child's diagnosis and the site of recruitment. The most important components of the illness perception were illness coherence (α=0.849), psychological attributions (α=0.903), and barriers to diagnosis (α=0.633). Understanding caregivers' perceptions of respiratory diseases will lead to better treatment acceptance. We must clarify the terms used to define bronchiolitis from viral-induced wheezing, RAD, and the first asthma episode in older infants. Identifying caregivers' gaps in knowledge will help establish a cohesive approach to personalized treatment of respiratory diseases in children and their diagnosis.

NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement.

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, remain either limited in accuracy and conditions or are computationally heavy to apply. Here, we provide a computational platform to enable future work to overcome these limitations by presenting NeuroMotion, an open-source simulator that can modularly test a variety of approaches to the full-spectrum synthesis of EMG signals during voluntary movements. We demonstrate NeuroMotion using three sample modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, like the afore-estimated muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. We first show how MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regressing joint angles. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. In this way, NeuroMotion was able to generate full-spectrum EMG for the first use-case of human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this modular platform will enable validation of generative EMG models, complement experimental approaches and empower neuromechanical research.

Deep Metric Learning With Locality Sensitive Mining for Self-Correcting Source Separation of Neural Spiking Signals.

Automated source separation algorithms have become a central tool in neuroengineering and neuroscience, where they are used to decompose neurophysiological signal into its constituent spiking sources. However, in noisy or highly multivariate recordings these decomposition techniques often make a large number of errors. Such mistakes degrade online human-machine interfacing methods and require costly post-hoc manual cleaning in the offline setting. In this article we propose an automated error correction methodology using a deep metric learning (DML) framework, generating embedding spaces in which spiking events can be both identified and assigned to their respective sources. Furthermore, we investigate the relative ability of different DML techniques to preserve the intraclass semantic structure needed to identify incorrect class labels in neurophysiological time series. Motivated by this analysis, we propose locality sensitive mining, an easily implemented sampling-based augmentation to typical DML losses which substantially improves the local semantic structure of the embedding space. We demonstrate the utility of this method to generate embedding spaces which can be used to automatically identify incorrectly labeled spiking events with high accuracy.

Changes in Bronchiolitis Characteristics During the COVID-19 Pandemic: A Description of Pediatric Emergency Department Visits in a Community Hospital, 2019-2021.

A retrospective, cross-sectional study of children with bronchiolitis aged 1 to 24 months during an ED visit between 2019 and 2021 was performed. Chi-square or Kruskal-Wallis was used to compare groups. The gamma coefficient was used to measure the association of variables through time. Bronchiolitis cases decreased by 75% from 2019 to 2020 and rose back to prepandemic levels by 2021. Radiographs (gamma -0.443), steroids (gamma -0.298), and bronchodilators (gamma -0.414) decreased during the study period (P < .001). Laboratory studies (gamma 0.032), viral testing (gamma 0.097), antibiotic use (gamma -0.069), and respiratory support (gamma 0.166) were unchanged. The decrease in steroids and bronchodilators was related to a clinical pathway that discouraged their use. Respiratory support remained unchanged. The COVID-19 pandemic (2019-2021) seems to have had little effect on the severity or resource utilization associated with bronchiolitis but may have unraveled a potential bronchiolitis phenotype that may have been more prominent during the pandemic.

A myoelectric digital twin for fast and realistic modelling in deep learning.

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.

Answers to common questions about COVID-19 vaccines in children with cancer.

BACKGROUND: The SARS-CoV-2 outbreak in 2020 evolved into a global pandemic, and COVID-19 vaccines became rapidly available, including for pediatric patients. However, questions emerged that challenged vaccine acceptance and use. We aimed to answer these questions and give recommendations applicable for use in pediatric patients with cancer by healthcare professionals and the public. METHODS: A 12-member global COVID-19 Vaccine in Pediatric Oncology Working Group made up of physicians and nurses from all world regions met weekly from March to July 2021. We used a modified Delphi method to select the top questions. The Working Group, in four-member subgroups, answered assigned questions by providing brief recommendations, followed by a discussion of the rationale for each answer. All Working Group members voted on each recommendation using a scale of 1 to 10, 10 being complete agreement. A "pass" recommendation corresponded to an agreement ≥7.5. RESULTS: We selected 15 questions from 173 suggested questions. Based on existing published information, we generated answers for each question as recommendations. The overall average agreement for the 24 recommendations was 9.5 (95% CI 9.4-9.6). CONCLUSION: Top COVID-19 vaccine-related questions could be answered using available information. Reports on COVID-19 vaccination and related topics have been published at record speed, aided by available technology and the priority imposed by the pandemic; however, all efforts were made to incorporate emerging information throughout our project. Recommendations will be periodically updated on a dedicated website.

CD24 Expression Dampens the Basal Antiviral State in Human Neuroblastoma Cells and Enhances Permissivity to Zika Virus Infection.

Zika virus (ZIKV) exhibits distinct selectivity for infection of various cells and tissues, but how host cellular factors modulate varying permissivity remains largely unknown. Previous studies showed that the neuroblastoma cell line SK-N-AS (expressing low levels of cellular protein CD24) was highly restricted for ZIKV infection, and that this restriction was relieved by ectopic expression of CD24. We tested the hypothesis that CD24 expression allowed ZIKV replication by suppression of the antiviral response. SK-N-AS cells expressing an empty vector (termed CD24-low cells) showed elevated basal levels of phosphorylated STAT1, IRF-1, IKKE, and NFκB. In response to exogenously added type I interferon (IFN-I), CD24-low cells had higher-level induction of antiviral genes and activity against two IFN-I-sensitive viruses (VSV and PIV5-P/V) compared to SK-N-AS cells with ectopic CD24 expression (termed CD24-high cells). Media-transfer experiments showed that the inherent antiviral state of CD24-low cells was not dependent on a secreted factor such as IFN-I. Transcriptomics analysis revealed that CD24 expression decreased expression of genes involved in intracellular antiviral pathways, including IFN-I, NFκB, and Ras. Our findings that CD24 expression in neuroblastoma cells represses intracellular antiviral pathways support the proposal that CD24 may represent a novel biomarker in cancer cells for susceptibility to oncolytic viruses.

Online Tracking of the Phase Difference Between Neural Drives to Antagonist Muscle Pairs in Essential Tremor Patients.

Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the phase of tremorogenic neural drive to muscles, providing a methodology for future tremor-suppression neuroprostheses.

Subsequent malignant neoplasms in the Childhood Cancer Survivor Study: Occurrence of cancer types in which human papillomavirus is an established etiologic risk factor.

BACKGROUND: Human papillomavirus (HPV)-associated subsequent malignant neoplasms (SMNHPV ) in childhood cancer survivors are poorly understood. METHODS: The cumulative risk of SMNHPV was assessed among 24,363 Childhood Cancer Survivor Study participants. Standardized incidence ratios (SIRs) and absolute excess risk were calculated using age-matched, sex-matched, and calendar year rates from the Surveillance, Epidemiology, and End Results program. Poisson regression models identified SMNHPV risk factors, evaluating relative SIRs (rSIR) and 95% confidence intervals (95% CIs). RESULTS: In total, 46 survivors developed an SMNHPV (median age, 31 years [range, 10-56 years]; median time from primary cancer, 21 years [range, 9-35 years]). SMNHPV sites included oropharynx (N = 44), anorectum (N = 6), uterine cervix (N = 2), and vulva (N = 2). The 33-year cumulative incidence was 0.3% (95% CI, 0.2%-0.4%), and the SIR was nearly 3-fold that of the general population (SIR, 2.86; 95% CI, 2.05-4.00). Female survivors were not at increased risk of cervical or vulvar cancers compared with the general population. All survivors had an elevated risk of oropharyngeal SMNHPV (males: SIR, 4.06; 95% CI, 2.37-6.97; females: SIR, 8.44; 95% CI 4.88-14.61) and anorectal SMNHPV (males: SIR, 13.56; 95% CI, 5.09-36.13; females: SIR, 9.15; 95% CI, 2.29-36.61). Males (vs females: rSIR, 1.99; 95% CI, 1.00-3.94); head, neck, and pelvic radiotherapy doses >3000 centigray (vs none: rSIR, 2.35; 95% CI, 1.11-4.97); and cisplatin-equivalent doses >400 mg/m2 (vs none: rSIR, 4.51; 95% CI, 1.78-11.43) were associated with increased SMNHPV SIRs in multivariable analysis. CONCLUSIONS: Childhood cancer survivors are at increased risk for SMN in sites susceptible to HPV-associated malignancies. Further research examining HPV in the etiology of SMN and the promotion of HPV vaccination and surveillance guidelines for SMNHPV in cancer survivors is warranted.

Deep Learning for Robust Decomposition of High-Density Surface EMG Signals.

Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trains. Once the separation matrix is blindly estimated on a signal interval, it is also possible to apply the same matrix to subsequent signal segments. Nonetheless, the trained separation matrices are sub-optimal in noisy conditions and require that incoming data undergo computationally expensive whitening. One unexplored alternative is to instead use the paired HD-sEMG signal and BSS output to train a model to predict MU activations within a supervised learning framework. A gated recurrent unit (GRU) network was trained to decompose both simulated and experimental unwhitened HD-sEMG signal using the output of the gCKC algorithm. The results on the experimental data were validated by comparison with the decomposition of concurrently recorded intramuscular EMG signals. The GRU network outperformed gCKC at low signal-to-noise ratios, proving superior performance in generalising to new data. Using 12 seconds of experimental data per recording, the GRU performed similarly to gCKC, at rates of agreement of 92.5% (84.5%-97.5%) and 94.9% (88.8%-100.0%) respectively for GRU and gCKC against matched intramuscular sources.

Corrigendum: Fertility Preservation for Child and Adolescent Cancer Patients in Asian Countries.

[This corrects the article DOI: 10.3389/fendo.2019.00655.].

Use of community forums to increase knowledge of HPV and cervical cancer in African American communities.

Cervical cancer adversely impacts African American communities. While disparities in incidence remain unclear, communities continue to use forums to increase cervical cancer education. The purpose of this paper is to examine the efficacy of using community forums to increase human papillomavirus vaccine (HPVV) and cervical cancer knowledge in African American communities. This study is a one-group pretest-posttest study design using a 17-item questionnaire to collect data from 412 participants in diverse communities. Our analyses revealed perceived knowledge increased significantly after the forums for African American participants. For African Americans, perceived knowledge prior to the forums was explained by gender, access to care, and trust in clinical trials. After the forum, perceived knowledge was associated with access to care and trust in vaccines. Participants who had health insurance reported higher perceived HPV and cervical cancer knowledge and greater trust in vaccines. This study found community forums that address the cultural and historical context of research mistreatment related to HPVV development and include diverse racial/ethnic representation of stakeholders may be a useful strategy to increase HPVV, and cervical cancer knowledge in African American communities.

Analysis of Retinal Segmentation Changes at High Altitude With and Without Acetazolamide.

Purpose: Our aim was to assess retinal venous diameter and segmented retinal layer thickness variation in acute systemic hypoxia with and without acetazolamide and to relate these changes to high altitude headache (HAH), as a proxy for intracerebral pathophysiology. Methods: A total of 20 subjects participated in a 4-day ascent to the Margherita Hut (4,559 m) on Monte Rosa in the Italian Alps. Each participant was randomized to either oral acetazolamide 250 mg twice daily or placebo. A combination of digital imaging and optical coherence tomography was used to measure retinal vessel diameter and retinal layer thickness. Clinically-assessed HAH was recorded. Results: A total of 18 participants had usable digital and OCT images, with 12 developing HAH. Significant thickening was seen only in the two inner layers of the retina, the retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL) at P = 0.012 and P = 0.010, respectively, independent of acetazolamide. There was a significant positive correlation between HAH and both retinal venous diameter (T = 4.953, P = 0.001) and retinal artery diameter (T = 2.865, P = 0.015), with both unaffected by acetazolamide (F = 0.439, P = 0.518). Conclusions: Retinal venous diameter correlates positively with HAH, adding further evidence for the proposed venous outflow limitation mechanism. The inner layers of the retina swelled disproportionately when compared to the outer layers under conditions of systemic hypoxia. Acetazolamide does not appear to influence altitudinal changes of retinal layers and vasculature.

Zika virus as an oncolytic treatment of human neuroblastoma cells requires CD24.

Neuroblastoma is the second most common childhood tumor. Survival is poor even with intensive therapy. In a search for therapies to neuroblastoma, we assessed the oncolytic potential of Zika virus. Zika virus is an emerging mosquito-borne pathogen unique among flaviviruses because of its association with congenital defects. Recent studies have shown that neuronal progenitor cells are likely the human target of Zika virus. Neuroblastoma has been shown to be responsive to infection. In this study, we show that neuroblastoma cells are widely permissive to Zika infection, revealing extensive cytopathic effects (CPE) and producing high titers of virus. However, a single cell line appeared poorly responsive to infection, producing undetectable levels of non-structural protein 1 (NS1), limited CPE, and low virus titers. A comparison of these poorly permissive cells to highly permissive neuroblastoma cells revealed a dramatic loss in the expression of the cell surface glycoprotein CD24 in poorly permissive cells. Complementation of CD24 expression in these cells led to the production of detectable levels of NS1 expression after infection with Zika, as well as dramatic increases in viral titers and CPE. Complementary studies using the Zika virus index strain and a north African isolate confirmed these phenotypes. These results suggest a possible role for CD24 in host cell specificity by Zika virus and offer a potential therapeutic target for its treatment. In addition, Zika viral therapy can serve as an adjunctive treatment for neuroblastoma by targeting tumor cells that can lead to recurrent disease and treatment failure.

Elucidation of the orientation of selected drugs with 2-hydroxylpropyl-ß-cyclodextrin using 2D-NMR spectroscopy and molecular modeling.

This project aims to study the nature of interaction and orientation of selected drugs such as dexamethorphan HBr (DXM), diphenhydramine HCl (DPH), and lidocaine HCl (LDC) inclusion complexes with hydroxyl-propyl ß-cyclodextrin (HP-ß-CD) using 1HNMR spectroscopy, 2D-NMR ROESY and molecular-modeling techniques. Freeze-drying technique was used to formulate the inclusion complexes between DXM, DPH and LDC with HP-ß-CD (1:1 M ratio) in solid state. Inclusion complex formation was initially characterized by Fourier transform-infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. Further characterization of inclusion complexes to determine the interaction of DXM, DPH and LDC with HP-ß-CD was performed using the 1HNMR spectroscopy, 2D-NMR ROESY and molecular modeling techniques. Inclusion complexes of DXM, DPH and LDC with HP-ß-CD were successfully prepared using the freeze-drying technique. Preliminary studies with FT-IR, DSC, XRD and SEM indicated the formation of inclusion complexes of DXM, DPH and LDC with HP-ß-CD at 1:1 M ratio. 1HNMR study showed a change in proton chemical shift upon complexation. 2D-NMR ROESY (two-dimensional) spectroscopy gave an insight into the spatial arrangement between the host and guest atoms. 2D-ROESY experiments further predicted the direction of orientation of guest molecules, indicating the probability that amino moieties of DXM, DPH and LDC are inside the hydrophobic HP-ß-CD cavity. Cross-peaks of inclusion complexes demonstrated intermolecular nuclear Overhauser effects (NOE) between the amino protons in DXM, DPH and LDC and H-atoms of HP-ß-CD. Molecular modeling studies further confirmed the NMR data, providing a structural basis of the individual complex formations. Microsecond time-level molecular dynamics and metadynamics simulations indicate much stronger binding of DXM to HP-ß-CD and more dynamic behavior for DPH and LDC. In particular, LDC can exhibit multiple binding modes, and even spent some time (∼1-2%) out of the carrier, proving the dynamic nature of the complex. To conclude, 2D-NMR and molecular dynamic simulations elucidate the formation of inclusion complexes and intermolecular interactions of DXM, DPH and LDC with HP-ß-CD.

Formulation and Evaluation of Antibacterial Creams and Gels Containing Metal Ions for Topical Application.

Background. Skin infections occur commonly and often present therapeutic challenges to practitioners due to the growing concerns regarding multidrug-resistant bacterial, viral, and fungal strains. The antimicrobial properties of zinc sulfate and copper sulfate are well known and have been investigated for many years. However, the synergistic activity between these two metal ions as antimicrobial ingredients has not been evaluated in topical formulations. Objective. The aims of the present study were to (1) formulate topical creams and gels containing zinc and copper alone or in combination and (2) evaluate the in vitro antibacterial activity of these metal ions in the formulations. Method. Formulation of the gels and creams was followed by evaluating their organoleptic characteristics, physicochemical properties, and in vitro antibacterial activity against Escherichia coli and Staphylococcus aureus. Results. Zinc sulfate and copper sulfate had a strong synergistic antibacterial activity in the creams and gels. The minimum effective concentration was found to be 3 w/w% for both active ingredients against the two tested microorganisms. Conclusions. This study evaluated and confirmed the synergistic in vitro antibacterial effect of copper sulfate and zinc sulfate in a cream and two gels.