Wireless Mouth Motion Recognition System Based on EEG-EMG Sensors for Severe Speech Impairments.

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)-electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids.

Aberrant N-glycosylation is a therapeutic target in carriers of a common and highly pleiotropic mutation in the manganese transporter ZIP8.

The treatment of defective glycosylation in clinical practice has been limited to patients with rare and severe phenotypes associated with congenital disorders of glycosylation (CDG). Carried by approximately 5% of the human population, the discovery of the highly pleiotropic, missense mutation in a manganese transporter ZIP8 has exposed under-appreciated roles for Mn homeostasis and aberrant Mn-dependent glycosyltransferases activity leading to defective N-glycosylation in complex human diseases. Here, we test the hypothesis that aberrant N-glycosylation contributes to disease pathogenesis of ZIP8 A391T-associated Crohn's disease. Analysis of N-glycan branching in intestinal biopsies demonstrates perturbation in active Crohn's disease and a genotype-dependent effect characterized by increased truncated N-glycans. A mouse model of ZIP8 391-Thr recapitulates the intestinal glycophenotype of patients carrying mutations in ZIP8. Borrowing from therapeutic strategies employed in the treatment of patients with CDGs, oral monosaccharide therapy with N-acetylglucosamine ameliorates the epithelial N-glycan defect, bile acid dyshomeostasis, intestinal permeability, and susceptibility to chemical-induced colitis in a mouse model of ZIP8 391-Thr. Together, these data support ZIP8 391-Thr alters N-glycosylation to contribute to disease pathogenesis, challenging the clinical paradigm that CDGs are limited to patients with rare diseases. Critically, the defect in glycosylation can be targeted with monosaccharide supplementation, providing an opportunity for genotype-driven, personalized medicine.

Metabolomics and Lipidomics Analyses Aid Model Classification of Type 2 Diabetes in Non-Human Primates.

Type 2 diabetes (T2D) is a global public health issue characterized by excess weight, abdominal obesity, dyslipidemia, hyperglycemia, and a progressive increase in insulin resistance. Human population studies of T2D development and its effects on systemic metabolism are confounded by many factors that cannot be controlled, complicating the interpretation of results and the identification of early biomarkers. Aged, sedentary, and overweight/obese non-human primates (NHPs) are one of the best animal models to mimic spontaneous T2D development in humans. We sought to identify and distinguish a set of plasma and/or fecal metabolite biomarkers, that have earlier disease onset predictability, and that could be evaluated for their predictability in subsequent T2D studies in human cohorts. In this study, a single plasma and fecal sample was collected from each animal in a colony of 57 healthy and dysmetabolic NHPs and analyzed for metabolomics and lipidomics. The samples were comprehensively analyzed using untargeted and targeted LC/MS/MS. The changes in each animal's disease phenotype were monitored using IVGTT, HbA1c, and other clinical metrics, and correlated with their metabolic profile. The plasma and fecal lipids, as well as bile acid profiles, from Healthy, Dysmetabolic (Dys), and Diabetic (Dia) animals were compared. Following univariate and multivariate analyses, including adjustments for weight, age, and sex, several plasma lipid species were identified to be significantly different between these animal groups. Medium and long-chain plasma phosphatidylcholines (PCs) ranked highest at distinguishing Healthy from Dys animals, whereas plasma triglycerides (TG) primarily distinguished Dia from Dys animals. Random Forest (RF) analysis of fecal bile acids showed a reduction in the secondary bile acid glycoconjugate, GCDCA, in diseased animals (AUC 0.76[0.64, 0.89]). Moreover, metagenomics results revealed several bacterial species, belonging to the genera Roseburia, Ruminococcus, Clostridium, and Streptococcus, to be both significantly enriched in non-healthy animals and associated with secondary bile acid levels. In summary, our results highlight the detection of several elevated circulating plasma PCs and microbial species associated with fecal secondary bile acids in NHP dysmetabolic states. The lipids and metabolites we have identified may help researchers to differentiate individual NHPs more precisely between dysmetabolic and overtly diabetic states. This could help assign animals to study groups that are more likely to respond to potential therapies where a difference in efficacy might be anticipated between early vs. advanced disease.

Framework for Radiation Oncology Department-wide Evaluation and Implementation of Commercial Artificial Intelligence Autocontouring.

PURPOSE: Artificial intelligence (AI)-based autocontouring in radiation oncology has potential benefits such as standardization and time savings. However, commercial AI solutions require careful evaluation before clinical integration. We developed a multidimensional evaluation method to test pretrained AI-based automated contouring solutions across a network of clinics. METHODS AND MATERIALS: Curated data included 121 patient planning computed tomography (CT) scans with a total of 859 clinically approved contours used for treatment from 4 clinics. Regions of interest (ROIs) were generated with 3 commercial AI-based automated contouring software solutions (AI1, AI2, AI3) spanning the following disease sites: brain, head and neck (H&N), thorax, abdomen, and pelvis. Quantitative agreement between AI-generated and clinical contours was measured by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Qualitative assessment was performed by multiple experts scoring blinded AI-contours using a Likert scale. Workflow and usability surveying was also conducted. RESULTS: AI1, AI2, and AI3 contours had high quantitative agreement in 27.8%, 32.8%, and 34.1% of cases (DSC >0.9), performing well in pelvis (median DSC = 0.86/0.88/0.91) and thorax (median DSC = 0.91/0.89/0.91). All 3 solutions had low quantitative agreement in 7.4%, 8.8%, and 6.1% of cases (DSC <0.5), performing worse in brain (median DSC = 0.65/0.78/0.75) and H&N (median DSC = 0.76/0.80/0.81). Qualitatively, AI1 and AI2 contours were acceptable (rated 1-2) with at most minor edits in 70.7% and 74.6% of ROIs (2906 ratings), higher for abdomen (AI1: 79.2%) and thorax (AI2: 90.2%), and lower for H&N (29.0/35.6%). An end-user survey showed strong user preference for full automation and mixed preferences for accuracy versus total number of structures generated. CONCLUSIONS: Our evaluation method provided a comprehensive analysis of both quantitative and qualitative measures of commercially available pretrained AI autocontouring algorithms. The evaluation framework served as a roadmap for clinical integration that aligned with user workflow preference.

Integrating Artificial Intelligence and Machine Learning Into Cancer Clinical Trials.

The practice of oncology requires analyzing and synthesizing abundant data. From the patient's workup to determine eligibility to the therapies received to the post-treatment surveillance, practitioners must constantly juggle, evaluate, and weigh decision-making based on their best understanding of information at hand. These complex, multifactorial decisions have a tremendous opportunity to benefit from data-driven machine learning (ML) methods to drive opportunities in artificial intelligence (AI). Within the past 5 years, we have seen AI move from simply a promising opportunity to being used in prospective trials. Here, we review recent efforts of AI in clinical trials that have moved the needle towards improved prediction of actionable outcomes, such as predicting acute care visits, short term mortality, and pathologic extranodal extension. We then pause and reflect on how these AI models ask a different question than traditional statistics models that readers may be more familiar with; how then should readers conceptualize and interpret AI models that they are not as familiar with. We end with what we believe are promising future opportunities for AI in oncology, with an eye towards allowing the data to inform us through unsupervised learning and generative models, rather than asking AI to perform specific functions.

Very early life microbiome and metabolome correlates with primary vaccination variability in children.

IMPORTANCE: We show that simultaneous study of stool and nasopharyngeal microbiome reveals divergent timing and patterns of maturation, suggesting that local mucosal factors may influence microbiome composition in the gut and respiratory system. Antibiotic exposure in early life as occurs commonly, may have an adverse effect on vaccine responsiveness. Abundance of gut and/or nasopharyngeal bacteria with the machinery to produce lipopolysaccharide-a toll-like receptor 4 agonist-may positively affect future vaccine protection, potentially by acting as a natural adjuvant. The increased levels of serum phenylpyruvic acid in infants with lower vaccine-induced antibody levels suggest an increased abundance of hydrogen peroxide, leading to more oxidative stress in low vaccine-responding infants.

Glucocerebrosidase activity and lipid levels are related to protein pathologies in Parkinson's disease.

Parkinson's disease (PD) and dementia with Lewy bodies (DLB) are progressive neurodegenerative diseases characterized by the accumulation of misfolded α-synuclein in the form of Lewy pathology. While most cases are sporadic, there are rare genetic mutations that cause disease and more common variants that increase incidence of disease. The most prominent genetic mutations for PD and DLB are in the GBA1 and LRRK2 genes. GBA1 mutations are associated with decreased glucocerebrosidase activity and lysosomal accumulation of its lipid substrates, glucosylceramide and glucosylsphingosine. Previous studies have shown a link between this enzyme and lipids even in sporadic PD. However, it is unclear how the protein pathologies of disease are related to enzyme activity and glycosphingolipid levels. To address this gap in knowledge, we examined quantitative protein pathology, glucocerebrosidase activity and lipid substrates in parallel from 4 regions of 91 brains with no neurological disease, idiopathic, GBA1-linked, or LRRK2-linked PD and DLB. We find that several biomarkers are altered with respect to mutation and progression to dementia. We found mild association of glucocerebrosidase activity with disease, but a strong association of glucosylsphingosine with α-synuclein pathology, irrespective of genetic mutation. This association suggests that Lewy pathology precipitates changes in lipid levels related to progression to dementia.

Modeling Brain Dynamics During Virtual Reality-Based Emergency Response Learning Under Stress.

BACKGROUND: Stress affects learning during training, and virtual reality (VR) based training systems that manipulate stress can improve retention and retrieval performance for firefighters. Brain imaging using functional Near Infrared Spectroscopy (fNIRS) can facilitate development of VR-based adaptive training systems that can continuously assess the trainee's states of learning and cognition. OBJECTIVE: The aim of this study was to model the neural dynamics associated with learning and retrieval under stress in a VR-based emergency response training exercise. METHODS: Forty firefighters underwent an emergency shutdown training in VR and were randomly assigned to either a control or a stress group. The stress group experienced stressors including smoke, fire, and explosions during the familiarization and training phase. Both groups underwent a stress memory retrieval and no-stress memory retrieval condition. Participant's performance scores, fNIRS-based neural activity, and functional connectivity between the prefrontal cortex (PFC) and motor regions were obtained for the training and retrieval phases. RESULTS: The performance scores indicate that the rate of learning was slower in the stress group compared to the control group, but both groups performed similarly during each retrieval condition. Compared to the control group, the stress group exhibited suppressed PFC activation. However, they showed stronger connectivity within the PFC regions during the training and between PFC and motor regions during the retrieval phases. DISCUSSION: While stress impaired performance during training, adoption of stress-adaptive neural strategies (i.e., stronger brain connectivity) were associated with comparable performance between the stress and the control groups during the retrieval phase.

Predicted Inferior Outcomes for Lung SBRT With Treatment Planning Systems That Fail Independent Phantom-Based Audits.

PURPOSE: More than 15% of radiation therapy clinics fail external audits with anthropomorphic phantoms conducted by Imaging and Radiation Oncology Core-Houston (IROC-H) while passing other industry-standard quality assurance (QA) tests. We seek to evaluate the predicted effect of such failed plans on outcomes for patients treated with stereotactic body radiation therapy (SBRT) for lung tumors. METHODS AND MATERIALS: We conducted a retrospective study of 55 patients treated with SBRT for lung tumors with a prescription biologically equivalent dose (BED)10 ≥100 Gy using a treatment planning system (TPS) that passed IROC-H phantom audits. Sample linear accelerator beam models with introduced errors were commissioned by varying the multileaf collimator leaf-tip offset parameter (ie, dosimetric leaf gap) over the range ±1.0 mm relative to the validated model. These models mimic TPS that pass internal QA measures but fail IROC-H tests. Patient plans were recalculated on sample beam models. The predicted tumor control probability (TCP) and normal tissue complication probability (NTCP) were calculated based on published dose-response models. RESULTS: A leaf-tip offset value of -1.0 mm decreased the fraction of plans receiving a planning treatment volume of BED10 ≥100 Gy from 95% to 27%. This translated to a significant decrease in 2-year TCP of 4.8% (95% CI: 2.0%-5.5%) with a decrease in TCP up to 21%. Conversely, a leaf-tip offset of +1.0 mm resulted in 36% of patients exceeding previously met organs at risk (OAR) constraints, including 2 instances of spinal cord and brachial plexus overdoses and a small increase in chest wall NTCP of 0.7%, (95% CI: 0.5%-0.8%). CONCLUSIONS: Simulated treatment plans with modest MLC leaf offsets result in lung SBRT plans that significantly underdose tumor or exceed OAR constraints. These dosimetric endpoints translate to significant detriments in TCP. These simulated plans mimic planning systems that pass internal QA measures but fail independent phantom-based tests, underscoring the need for enhanced quality assurance including external audits of TPS.

Characterization of mechanical properties of soft tissues using sub-microscale tensile testing and 3D-Printed sample holder.

Obtaining the mechanical properties of soft tissues is critical in many medical fields, such as regenerative medicine and surgical simulation training. Although various tissue-characterization methods have been developed, such as AFM, indentation, and elastography, there remain some limitations on their accuracy and validity for measuring small and fragile soft tissues. This paper presents a tensile testing technique to measure the mechanical properties of soft tissues directly and accurately. Tensile testing was chosen as the primary method because of its simple procedure and ability to derive mechanical properties without requiring many assumptions or complicated models. However, tensile testing on soft tissues presents challenges related to gripping the tissue sample without affecting its inherent properties, applying minuscule forces to the sample, and measuring the cross-section area and strain of the sample. To solve these issues, this study presents a sub-micro scale tensile testing system that uses a flexure mechanism and a novel 3D-printed sample holder for gripping the tissue samples. The system also measures tested samples' cross-section area and strain using two high-resolution cameras. The system was validated by testing standard materials and used to characterize the elastic modulus, yield stress, and yield strain of lung tissue slices from six different mice. The results from the validation tests showed a less than 2.5% error for elastic modulus values measured using the tensile tester. At the same time, results from the mice lung tissue measurements revealed qualitative findings that closely matched those seen in the literature and displayed low coefficient of variation values, demonstrating the high repeatability of the system.

Techniques for characterizing mechanical properties of soft tissues.

The characterization of soft tissues remains a vital need for various bioengineering and medical fields. Developing areas such as regenerative medicine, robot-aided surgery, and surgical simulations all require accurate knowledge about the mechanical properties of soft tissues to replicate their mechanics. Mechanical properties can be characterized through several different characterization techniques such as atomic force microscopy, compression testing, and tensile testing. However, many of these methods contain considerable differences in ability to accurately characterize the mechanical properties of soft tissues. As a result of these variations, there are often discrepancies in the reported values for numerous studies. This paper reviews common characterization methods that have been applied to obtain the mechanical properties of soft tissues and highlights their advantages as well as disadvantages. The limitations, accuracies, repeatability, in-vivo testing capability, and types of properties measurable for each method are also discussed.

Evaluation of the TruSight Oncology 500 Assay for Routine Clinical Testing of Tumor Mutational Burden and Clinical Utility for Predicting Response to Pembrolizumab.

Pembrolizumab is approved for treating patients with unresectable or metastatic solid tumors with high tumor mutational burden (TMB), as assessed by the Food and Drug Administration-approved companion diagnostic FoundationOneCDx, after progression on prior treatment. To expand TMB assessment for enriching response to pembrolizumab, TMB measurement from TruSight Oncology 500 (TSO500) was evaluated in archival pan-tumor samples from 294 patients enrolled in eight pembrolizumab monotherapy studies. TSO500 is a panel-based next-generation sequencing assay with broad availability, quick turnaround time, and a standardized bioinformatics pipeline. TSO500 TMB was evaluated for correlation and concordance with two reference methods: FoundationOneCDx and whole-exome sequencing. The TSO500 cut-off for TMB-high was selected based on the receiver-operating characteristic curve analysis against each reference method's cut-off for TMB-high. Clinical utility of the selected TSO500 cut-off (10 mutations/Mb) was assessed by calculating the sensitivity, specificity, positive and negative predictive values, and objective response rate enrichment. There was high correlation and concordance of TSO500 TMB with both reference methods. TSO500 TMB was associated significantly with the best overall response, and the selected cut-off had comparable clinical utility with respective cut-offs for the reference methods in predicting response to pembrolizumab. As a commercial assay with global kit distribution complete with an off-the-shelf software package, TSO500 may provide additional access to immunotherapy for patients with tumors with TMB ≥10 mutations/Mb.

Moire profilometry through simultaneous dual fringe projection for accurate phase demodulation: a comparative study.

In optical metrology, fringe projection and moire techniques have been widely used to measure the topography of objects. We can combine the advantages of the two techniques by applying a configuration of simultaneous dual projection in the fringe projection technique, which generates a superimposed fringe pattern containing a moire pattern that is phase modulated according to the topography. In this work, we present an analytic and comparative study of three methods to demodulate the phase of the moire pattern: the spatial, spatial-temporal, and temporal methods. Those methods consist of two steps: first, the moire pattern is extracted from the superimposed fringe pattern; next, the phase of the moire pattern is demodulated. The analytical results show that the resulting phase map has double phase sensitivity compared to that of the classical fringe projection technique. Experimental and numeric results prove the feasibility of this technique.

Rhabdomyolysis in COVID-19 Patients: A Retrospective Observational Study.

Background The coronavirus disease 2019 (COVID-19) pandemic has caused significant morbidity and mortality worldwide. Knowledge about the pathophysiology of the disease and its effect on multiple systems is growing. Kidney injury has been a topic of focus, and rhabdomyolysis is suspected to be one of the contributing mechanisms. However, information on rhabdomyolysis in patients affected by COVID-19 is limited. We aim to describe the incidence, clinical characteristics, and outcomes of patients hospitalized with COVID-19 who developed rhabdomyolysis. Materials and methods A retrospective observational cohort consisted of patients who were admitted and had an outcome between March 16 to May 27, 2020, inclusive of those dates at a single center in the Bronx, New York City. All consecutive inpatients with lab-confirmed COVID-19 were identified. Patients with peak total creatine kinase (CK) over 1,000 U/L were reviewed; 140 patients were included in the study. The main outcomes during hospitalization were new-onset renal replacement therapy and in-hospital mortality. Results The median age was 68 years (range: 21-93); 64% were males. The most common comorbidities were hypertension (73%), diabetes mellitus (47%), and chronic kidney disease (24%). Median CK on admission was 1,323 U/L (interquartile range [IQR]: 775 - 2,848). Median CK on discharge among survivors was 852 (IQR: 170 - 1,788). Median creatinine on admission was 1.78 mg/dL (IQR: 1.23 - 3.06). During hospitalization, 49 patients (35%) received invasive mechanical ventilation, 24 patients (17.1%) were treated with renal replacement therapy (RRT), and 66 (47.1%) died. Conclusions Rhabdomyolysis was a common finding among hospitalized patients with COVID-19 in our hospital in the Bronx. The incidence of new-onset renal replacement therapy and in-hospital mortality is higher in patients who develop rhabdomyolysis. McMahon score, rather than isolated creatine kinase levels, was a statistically significant predictor of new-onset RRT. Clinicians should maintain a high level of suspicion for rhabdomyolysis in COVID-19 patients throughout their admission and use validated scores like McMahon score to devise their treatment plan accordingly.

National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation.

PURPOSE: Artificial intelligence (AI) is about to touch every aspect of radiation therapy, from consultation to treatment planning, quality assurance, therapy delivery, and outcomes modeling. There is an urgent need to train radiation oncologists and medical physicists in data science to help shepherd AI solutions into clinical practice. Poorly trained personnel may do more harm than good when attempting to apply rapidly developing and complex technologies. As the amount of AI research expands in our field, the radiation oncology community needs to discuss how to educate future generations in this area. METHODS AND MATERIALS: The National Cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019) was the first of 2 data science workshops in radiation oncology hosted by the NCI in 2019. During this workshop, the Training and Education Working Group was formed by volunteers among the invited attendees. Its members represent radiation oncology, medical physics, radiology, computer science, industry, and the NCI. RESULTS: In this perspective article written by members of the Training and Education Working Group, we provide and discuss action points relevant for future trainees interested in radiation oncology AI: (1) creating AI awareness and responsible conduct; (2) implementing a practical didactic curriculum; (3) creating a publicly available database of training resources; and (4) accelerating learning and funding opportunities. CONCLUSION: Together, these action points can facilitate the translation of AI into clinical practice.

Genomics models in radiotherapy: From mechanistic to machine learning.

Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.