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Integrated sensing and delivery of oxygen for next- generation smart wound dressings

Microsystems & Nanoengineering 2020年第1期6卷 991-1006页

作者： Manuel Ochoa Rahim Rahimi Jiawei Zhou Hongjie Jiang Chang Keun Yoon Dinesh Maddipatla Binu Baby Narakathu Vaibhav Jain Mark Michael Oscai Thaddeus Joseph Morken Rebeca Hannah Oliveira Gonzalo L.Campana Oscar W.Cummings Michael A.Zieger Rajiv Sood Massood Z.Atashbar Babak Ziaie School of Electrical and Computer Engineering Purdue UniversityWest Lafayette 47907 INUSA Birck Nanotechnololgy Center 1205W State StreetWest Lafayette 47907 INUSA School of Material Science Engineering Purdue UniversityWest Lafayette 47907 INUSA Electrical and Computer Engineering Department Western Michigan University4601 Campus Drive B-236Kalamazoo 49008 MIUSA School of Mechanical Engineering Purdue UniversityWest Lafayette 47907 INUSA Indiana University School of Medicine 980 West Walnut StreetBuilding R3 Room C634Indianapolis 46202 INUSA Department of Pathology and Laboratory Medicine Indiana University School of MedicineIU Health Pathology Laboratory350W 11th StreetRoom 4054IndianapolisIN 46202USA Weldon School of Biomedical Engineering Purdue UniversityWest Lafayette 47907 INUSA Present address:Shenzhen MSU-BIT University ShenzhenChina

Chronic wounds affect over 6.5 million Americans and are notoriously difficult to *** oxygenation of the wound bed is one of the most critical and treatable wound management factors,but existing oxygenation systems do not enable concurrent measurement and delivery of oxygen in a convenient wearable ***,we developed a low-cost alternative for continuous O 2 delivery and sensing comprising of an inexpensive,paper-based,biocompatible,flexible platform for locally generating and measuring oxygen in a wound *** platform takes advantage of recent developments in the fabrication of flexible microsystems including the incorporation of paper as a substrate and the use of a scalable manufacturing technology,inkjet ***,we demonstrate the functionality of the oxygenation patch,capable of increasing oxygen concentration in a gel substrate by 13%(5ppm)in *** platform is able to sense oxygen in a range of 5–26 *** vivo studies demonstrate the biocompatibility of the patch and its ability to double or triple the oxygen level in the wound bed to clinically relevant levels.

关键词： patch smart printing

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Clinical characteristics of hospitalized children with community-acquired pneumonia and respiratory infections: Using machine learning approaches to support pathogen prediction at admission

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Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 2023年第4期56卷 772-781页

作者： Tu-Hsuan Chang Yun-Chung Liu Siang-Rong Lin Pei-Hsin Chiu Chia-Ching Chou Luan-Yin Chang Fei-Pei Lai Department of Pediatrics Chi Mei Medical Center Tainan City Taiwan. Department of Pediatrics National Taiwan University Hospital College of Medicine National Taiwan University Taipei City Taiwan. Institute of Applied Mechanics National Taiwan University Taipei City Taiwan. Institute of Applied Mechanics National Taiwan University Taipei City Taiwan. Electronic address: ccchou@iam.ntu.edu.tw. Department of Pediatrics National Taiwan University Hospital College of Medicine National Taiwan University Taipei City Taiwan. Electronic address: lychang@ntu.edu.tw. Graduate Institute of Biomedical Electronics and Bioinformatics Taipei City National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei City Taiwan Department of Electrical Engineering National Taiwan University Taipei City Taiwan.

BACKGROUND:Acute respiratory infections (ARIs) are common in children. We developed machine learning models to predict pediatric ARI pathogens at admission. METHODS:We included hospitalized children with respiratory infections between 2010 and 2018. Clinical features were collected within 24 h of admission to construct models. The outcome of interest was the prediction of 6 common respiratory pathogens, including adenovirus, influenza virus types A and B, parainfluenza virus (PIV), respiratory syncytial virus (RSV), and Mycoplasma pneumoniae (MP). Model performance was estimated using area under the receiver operating characteristic curve (AUROC). Feature importance was measured using Shapley Additive exPlanation (SHAP) values. RESULTS:A total of 12,694 admissions were included. Models trained with 9 features (age, event pattern, fever, C-reactive protein, white blood cell count, platelet count, lymphocyte ratio, peak temperature, peak heart rate) achieved the best performance (AUROC: MP 0.87, 95% CI 0.83-0.90; RSV 0.84, 95% CI 0.82-0.86; adenovirus 0.81, 95% CI 0.77-0.84; influenza A 0.77, 95% CI 0.73-0.80; influenza B 0.70, 95% CI 0.65-0.75; PIV 0.73, 95% CI 0.69-0.77). Age was the most important feature to predict MP, RSV and PIV infections. Event patterns were useful for influenza virus prediction, and C-reactive protein had the highest SHAP value for adenovirus infections. CONCLUSION:We demonstrate how artificial intelligence can assist clinicians identify potential pathogens associated with pediatric ARIs upon admission. Our models provide explainable results that could help optimize the use of diagnostic testing. Integrating our models into clinical workflows may lead to improved patient outcomes and reduce unnecessary medical costs.

关键词： Children Community-acquired pneumonia Machine learning Pathogens prediction Respiratory infections

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Parietal cortex is recruited by frontal and cingulate areas to support action monitoring and updating during stopping

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NeuroImage 2025年 315卷 121288页

作者： Jung Uk Kang Layth Mattar José Vergara Victoria E Gobo Hernan G Rey Sarah R Heilbronner Andrew J Watrous Benjamin Y Hayden Sameer A Sheth Eleonora Bartoli Department of Neurosurgery Baylor College of Medicine Houston TX USA Present address: Department of Behavioral Science The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Neurosurgery Baylor College of Medicine Houston TX USA. Department of Neuroscience Baylor College of Medicine Houston TX USA. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Department of Electrical & Computer Engineering Rice University Houston TX USA. Department of Neuroscience Baylor College of Medicine Houston TX USA Department of Electrical & Computer Engineering Rice University Houston TX USA Menninger Department of Psychiatry and Behavioral Sciences Baylor College of Medicine Houston TX USA. Department of Electrical & Computer Engineering Rice University Houston TX USA. Electronic address: bartoli@bcm.edu.

Recent evidence indicates that the intraparietal sulcus (IPS) may play a causal role in action stopping, potentially representing a novel neuromodulation target for inhibitory control dysfunctions. Here, we leverage intracranial recordings in human subjects to establish the timing and directionality of information flow between IPS and prefrontal and cingulate regions during action stopping. Prior to successful inhibition, information flows primarily from the inferior frontal gyrus (IFG), a critical inhibitory control node, to IPS. In contrast, during stopping errors the communication between IPS and IFG is lacking, and IPS is engaged by posterior cingulate cortex, an area outside of the classical inhibition network and typically associated with default mode. Anterior cingulate and orbitofrontal cortex also display performance-dependent connectivity with IPS. Our functional connectivity results provide direct electrophysiological evidence that IPS is recruited by frontal and anterior cingulate areas to support action plan monitoring and updating, and by posterior cingulate during control failures.

关键词： functional connectivity inhibitory control intracranial recording parietal cortex

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Denoising of high-resolution 3D UTE-MR angiogram data using lightweight and efficient convolutional neural networks

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Magnetic resonance imaging 2025年 121卷 110426页

作者： Abel Worku Tessema Dagnachew Tessema Ambaye HyungJoon Cho Department of Biomedical Engineering Ulsan National Institute of Science and Technology Ulsan Republic of Korea School of Biomedical Engineering Jimma Institute of Technology Jimma University Jimma Ethiopia. Department of Electrical & Computer Engineering Hawassa University Hawassa Ethiopia. Department of Biomedical Engineering Ulsan National Institute of Science and Technology Ulsan Republic of Korea. Electronic address: hjcho@unist.ac.kr.

High-resolution magnetic resonance angiography (~ 50 μm MRA) data plays a critical role in the accurate diagnosis of various vascular disorders. However, it is very challenging to acquire, and it is susceptible to artifacts and noise which limits its ability to visualize smaller blood vessels and necessitates substantial noise reduction measures. Among many techniques, the BM4D filter is a state-of-the-art denoising technique but comes with high computational cost, particularly for high-resolution 3D MRA data. In this research, five different optimized convolutional neural networks were utilized to denoise contrast-enhanced UTE-MRA data using a supervised learning approach. Since noise-free MRA data is challenging to acquire, the denoised image using BM4D filter was used as ground truth and this research mainly focused on reducing computational cost and inference time for denoising high-resolution UTE-MRA data. All five models were able to generate nearly similar denoised data compared to the ground truth with different computational footprints. Among all, the nested-UNet model generated almost similar images with the ground truth and achieved SSIM, PSNR, and MSE of 0.998, 46.12, and 3.38e-5 with 3× faster inference time than the BM4D filter. In addition, most optimized models like UNet and attention-UNet models generated nearly similar images with nested-UNet but 8.8× and 7.1× faster than the BM4D filter. In conclusion, using highly optimized networks, we have shown the possibility of denoising high-resolution UTE-MRA data with significantly shorter inference time, even with limited datasets from animal models. This can potentially make high-resolution 3D UTE-MRA data to be less computationally burdensome.

关键词： BM4D Contrast-enhanced UTE-MRA Convolutional neural network Image denoising Supervised learning

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Effects of strain compensation on electron mobilities in InAs quantum wells grown on InP(001)

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Physical Review Materials 2025年第5期9卷 054607-054607页

作者： C. P. Dempsey J. T. Dong I. Villar Rodriguez Y. Gul S. Chatterjee M. Pendharkar S. N. Holmes M. Pepper C. J. Palmstrøm Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA Materials Department University of California Santa Barbara Santa Barbara California 93106 USA London Centre for Nanotechnology University College London 17-19 Gordon Street London WC1H 0AH United Kingdom Present address: Department of Materials Science and Engineering Stanford University Stanford CA 94305. Department of Electronic and Electrical Engineering University College London Torrington Place London WC1E 7JE United Kingdom

InAs quantum wells (QWs) grown on InP substrates are interesting for their applications in devices with high spin-orbit coupling and their potential role in creating topologically nontrivial hybrid heterostructures. These QWs rely on InGaAs cladding layers and InAlAs barrier layers to confine electrons within a thin InAs well. The highest mobility QWs are limited by interfacial roughness scattering and alloy disorder scattering in the cladding and barrier layers. Increasing QW thickness has been shown to reduce the effect of both of these scattering mechanisms. However, for current state-of-the-art devices with As-based cladding and barrier layers, the critical thickness is limited to ≤7 nm. In this paper, we demonstrate the use of strain compensation techniques in the InxGa1−xAs cladding layers, grown on In0.81Al0.19As barrier layers, to extend the critical thickness well beyond this limit. We induce tensile strain in the InGaAs cladding layers by reducing the In concentration from In0.81Ga0.19As to In0.70Ga0.30As and we observe changes in both the critical thickness of the well and the maximum achievable mobility. The peak electron mobility at 2 K is 1.16×106cm2/Vs, with a carrier density of 4.2×1011cm−2. Additionally, we study the quantum lifetime and Rashba spin splitting in the highest mobility device as these parameters are critical to determine if these structures can be used in topologically nontrivial devices.

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Impact of smoking cessation on metabolic parameters and renal function in patients with and without diabetes mellitus

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Revista clinica espanola 2024年第1期225卷 16-22页

作者： C-P Fu P-K Fu M-C Lu Y-Y Liao J-S Wang Department of Medicine School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Division of Endocrinology and Metabolism Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Medicine Chung Shan Medical University Taichung Taiwan. Smoking Cessation Treatment and Management Center Taichung Veterans General Hospital Taichung Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan. Smoking Cessation Treatment and Management Center Taichung Veterans General Hospital Taichung Taiwan. Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan Diabetes Center Taichung Veterans General Hospital Taichung Taiwan. Electronic address: jswang@vghtc.gov.tw.

BACKGROUD AND OBJECTIVES:As patients with diabetes are at a significantly higher risk of cardiovascular diseases than those without diabetes, it is important to gain a clinical understanding of the differential effects of smoking cessation on several risk factors between patients with and without diabetes. MATERIALS AND METHODS:Patients who participated in a smoking cessation program received an assessment of the outcomes of interest. The outcomes were changes in metabolic parameters and renal function from baseline to 6-month follow-up after the smoking cessation program. RESULT:A total of 1954 patients joined the smoking cessation program, and 1381 patients were in the smoking cessation failure (SCF) group and 573 were in the smoking cessation success (SCS) group. The decrease in HbA1c after smoking cessation was only observed in patients with diabetes. Smoking cessation was also associated with a significant decrease in LDL cholesterol in patients with diabetes. In terms of renal function, smoking cessation was associated with an improvement in eGFR, and the trend was similar in patients with and without diabetes. CONCLUSION:Successful smoking cessation was associated with improvement in renal function. Moreover, it was associated with improvements in HbA1c and LDL cholesterol in patients with diabetes, despite significant weight gain.

关键词： Body weight Chronic kidney disease Dejar de fumar Diabetes mellitus Enfermedad renal crónica Glycated hemoglobin Hemoglobina glucosilada Peso corporal Smoking cessation

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The Nanostructures Of Amorphous Silicas

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Microscopy and Microanalysis 2020年第S2期6卷 14-15页

作者： Linn W. Hobbs Xianglong Yuan L. C. Qin Vinay Pulim Alexander Coventry Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge MA 02139-4307 present address: Fundamental Research Laboratory NEC Corporation Tsukuba Ibaraki 305 Japan Laboratory for Computer Science Massachusetts Institute of Technology Cambridge MA02139-4307

Silicon dioxide is an important catalyst material, a mainstay insulator in microelectronics, and a widely distributed terrestrial and marine skeletal mineral. Geologically, it is found in one of a large number of polymorphic crystalline states, but can also be rendered “amorphous” by rapid cooling from the melt through a glass transition, depositing from a vapor or from solution (in radiolaria skeletons), oxidizing silicon, or irradiating with electrons, ions or neutrons. While the structures of the crystalline polymorphs are well documented, the structure of even the exhaustively studied vitreous silica remains largely enigmatic. Diffraction provides average information about short-range order—which appears to comprise [SiO4] tetrahedral units in all but a high-pressure crystalline polymorph—but is relatively insensitive to alternative medium-range arrangements of these structural units. One sensitive, but little understood, indicator is the position and shape of the first sharp diffraction peak (FSDP).

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Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation

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Journal of affective disorders 2023年 343卷 86-95页

作者： Cheng-Ta Li Chi-Sheng Chen Chih-Ming Cheng Chung-Ping Chen Jen-Ping Chen Mu-Hong Chen Ya-Mei Bai Shih-Jen Tsai Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan Division of Psychiatry School of Medicine National Yang-Ming Chiao-Tung University Taipei Taiwan Institute of Brain Science and Brain Research Center School of Medicine National Yang-Ming Chiao-Tung University Taipei Taiwan Institute of Cognitive Neuroscience National Central University Jhongli Taiwan. Electronic address: ctil2@vghtpe.gov.tw. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan. Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan. Division of Psychiatry School of Medicine National Yang-Ming Chiao-Tung University Taipei Taiwan. Institute of Brain Science and Brain Research Center School of Medicine National Yang-Ming Chiao-Tung University Taipei Taiwan.

BACKGROUND:10-Hz repetitive transcranial magnetic stimulation(rTMS) and intermittent theta-burst stimulation(iTBS) over left prefrontal cortex are FDA-approved, effective options for treatment-resistant depression (TRD). Optimal prediction models for iTBS and rTMS remain elusive. Therefore, our primary objective was to compare prediction accuracy between classification by frontal theta activity alone and machine learning(ML) models by linear and non-linear frontal signals. The second objective was to study an optimal ML model for predicting responses to rTMS and iTBS. METHODS:Two rTMS and iTBS datasets (n = 163) were used: one randomized controlled trial dataset (RCTD; n = 96) and one outpatient dataset (OPD; n = 67). Frontal theta and non-linear EEG features that reflect trend, stability, and complexity were extracted. Pretreatment frontal EEG and ML algorithms, including classical support vector machine(SVM), random forest(RF), XGBoost, and CatBoost, were analyzed. Responses were defined as ≥50 % depression improvement after treatment. Response rates between those with and without pretreatment prediction in another independent outpatient cohort (n = 208) were compared. RESULTS:Prediction accuracy using combined EEG features by SVM was better than frontal theta by logistic regression. The accuracy for OPD patients significantly dropped using the RCTD-trained SVM model. Modern ML models, especially RF (rTMS = 83.3 %, iTBS = 88.9 %, p-value(ACC > NIR) < 0.05 for iTBS), performed significantly above chance and had higher accuracy than SVM using both selected features (p < 0.05, FDR corrected for multiple comparisons) or all EEG features. Response rates among those receiving prediction before treatment were significantly higher than those without prediction (p = 0.035). CONCLUSION:The first study combining linear and non-linear EEG features could accurately predict responses to left PFC iTBS. The bootstraps-based ML model (i.e., RF) had the best predictive accuracy for

关键词： Artificial intelligence Depression EEG Machine learning Repetitive transcranial magnetic stimulation theta burst stimulation

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Harmonized connectome resampling for variance in voxel sizes

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Magnetic resonance imaging 2025年 121卷 110424页

作者： Elyssa M McMaster Nancy R Newlin Gaurav Rudravaram Adam M Saunders Aravind R Krishnan Lucas W Remedios Michael E Kim Hanliang Xu Jongyeon Yoon Derek B Archer Kurt G Schilling François Rheault Laurie E Cutting Bennett A Landman Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA. Electronic address: elyssa.m.mcmaster@vanderbilt.edu. Department of Computer Science Vanderbilt University Nashville TN USA. Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA. Vanderbilt Memory and Alzheimer's Center Vanderbilt University School of Medicine Nashville TN USA. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville TN USA. Department of Computer Science Université de Sherbrooke Sherbrooke QC Canada. Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville TN USA Vanderbilt Kennedy Center Vanderbilt University Nashville TN USA Peabody College of Education Vanderbilt University Nashville TN USA. Department of Computer Science Vanderbilt University Nashville TN USA

Diffusion MRI (dMRI) fiber tractography presents exciting opportunities to deepen our knowledge of human brain connectivity and discover novel alterations in white matter. To date, there has been no comprehensive study characterizing the effect of diffusionweighted magnetic resonance imaging (dMRI) voxel resolution on the resulting connectome for high resolution subject data. We assessed the statistical significance of graph measures derived from dMRI data by comparing connectomes from the same scans across different resolutions with 44 subjects (32 female) from the Human Connectome Project - Young Adult dataset (HCP-YA) with scan/rescan data (88 scans). We explored 15 isotropic and anisotropic resolutions, generated tractography and connectomes, and compared graph measures between each resolution and its nearest larger and smaller resolutions. Nearly all pairwise comparisons yielded statistically significant differences in graph measures (p ≤ 0.05, Wilcoxon Sign-Rank Test). Upon up sampling the 14 down sampled resolutions in 0.5 mm increments, we observed mitigation of the spatial sampling effect on both the tractography and the connectome's complex graph measures. To investigate translational impact, we resampled 22 subjects from HCP-YA to the resolutions of two major national studies and up-sampled this data back to 1 mm isotropic with different interpolation methods. Similarity in results improved with higher resolution, even after initial down-sampling. To ensure robust tractography and connectomes, resample data to 1 mm isotropic resolution.

关键词： Connectomics Diffusion MRI Harmonization Spatial sampling Tractography

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Corrigendum to "A multimodal neuroimaging-based risk score for mild cognitive impairment" [NeuroImage: Clinical 45 (2025) 103719]

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NeuroImage. Clinical 2025年 45卷 103728页

作者： Elaheh Zendehrouh Mohammad S E Sendi Anees Abrol Ishaan Batta Reihaneh Hassanzadeh Vince D Calhoun Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States Department of Electrical and Computer Engineering at Georgia Institute of Technology Atlanta GA United States. Electronic address: ezendehrouh3@gatech.edu. Harvard Medical School and McLean Hospital Boston MA United States. Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States. Department of Electrical and Computer Engineering at Georgia Institute of Technology Atlanta GA United States. Department of Electrical and Computer Engineering at Georgia Institute of Technology Atlanta GA United States Departments of Psychology and Computer Science Georgia State University Atlanta GA United States. Electronic address: vcalhoun@gsu.edu.

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