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Article ; Online: Zio® XT Patches in Pediatrics During the COVID-19 Pandemic: Comparisons Between In-Person and Mail-Home Application.

Khan, Mohammad S / Dawson, Angela Y / Snyder, Christopher

2023

Abstract: The Zio® (Zio) XT Patch is a 14-day continuous ambulatory ECG monitor. During the Covid-19 pandemic, Zios were mailed directly to patients for self-application. The purpose of this study was to compare the percent artifact, a marker for quality, of in- ... ...

Abstract	The Zio® (Zio) XT Patch is a 14-day continuous ambulatory ECG monitor. During the Covid-19 pandemic, Zios were mailed directly to patients for self-application. The purpose of this study was to compare the percent artifact, a marker for quality, of in-clinic (IC) to mail-home (MH) applications in a pediatric population. A single-center, IRB-approved study of patients 0- < 21 years of age with Zios was studied for wear and artifact time filtered out based on iRhythm's proprietary algorithm. In total, 284 Zios were randomly selected and analyzed for total wear time and artifact. Of these, 149 were IC prior to 12/31/2019 and 135 MH patches prescribed after 1/1/2020. No significant difference was found for percent artifact between the IC (7.8%) and MH (8.3%) group. Average IC wear-time was 127 h compared to MH at 99 h (p = 0.02). In conclusion, application of Zio patches outside of the pediatric cardiology clinic offers equivalent artifact, a marker of quality, as those applied in clinic and should be consideration as a viable alternative.
Language	English
Publishing date	2023-12-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	800857-7
ISSN	1432-1971 ; 0172-0643
ISSN (online)	1432-1971
ISSN	0172-0643
DOI	10.1007/s00246-023-03354-z
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Article ; Online: Minimizing Device-Device Interactions Using Bipolar Pacemaker Leads in a Pediatric Patient.

Khan, Mohammad S / Hoyt, Walter / Snyder, Christopher

Pediatric cardiology

2022 Volume 43, Issue 4, Page(s) 923–925

Abstract: Phrenic nerve injury can lead to a disruption of the autonomic nervous system (ANS) resulting in episodes of bradycardic arrest. Implanted diaphragmatic pacing has been used to overcome phrenic nerve paralysis, but these do not change the ANS. Therefore, ...

Abstract	Phrenic nerve injury can lead to a disruption of the autonomic nervous system (ANS) resulting in episodes of bradycardic arrest. Implanted diaphragmatic pacing has been used to overcome phrenic nerve paralysis, but these do not change the ANS. Therefore, patients with phrenic nerve paralysis may require the implantation of a permanent cardiac pacemaker to overcome bradycardic episodes. Having two electronic devices in the same patient may lead to device-device interaction (DDI). This can result in over-sensing leading to lack of pacing of either device. We present the case of a 17-year-old pediatric male with phrenic nerve injury who required implantation of both diaphragm and cardiac pacemaker. Intra-procedural interrogation of the cardiac pacemaker demonstrated DDI in unipolar mode, but not in bipolar. Thus, we demonstrated the safe utilization of multiple implantable electronic devices in the pediatric patient without device-device interaction.
MeSH term(s)	Adolescent ; Bradycardia ; Child ; Diaphragm/innervation ; Humans ; Male ; Pacemaker, Artificial ; Paralysis ; Phrenic Nerve
Language	English
Publishing date	2022-01-13
Publishing country	United States
Document type	Case Reports ; Journal Article
ZDB-ID	800857-7
ISSN	1432-1971 ; 0172-0643
ISSN (online)	1432-1971
ISSN	0172-0643
DOI	10.1007/s00246-022-02816-0
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Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Proceedings of the North American Society of Head and Neck Pathology, Baltimore, MD, March 17, 2021: The Mistakes I Made When I Stepped Out of My Neck of the Woods.

Khan, Mohammad S / Malhotra, Ajay / Prasad, Manju L

Head and neck pathology

2021 Volume 15, Issue 1, Page(s) 113–119

Abstract: Rapidly growing, symptomatic, non-hematological, malignant neck masses are unusual in young adults. We report a case of a 34-year-old African American male with sickle cell trait who presented with a large left supraclavicular/cervical mass comprising of ...

Abstract	Rapidly growing, symptomatic, non-hematological, malignant neck masses are unusual in young adults. We report a case of a 34-year-old African American male with sickle cell trait who presented with a large left supraclavicular/cervical mass comprising of poorly differentiated malignant epithelial cells consistent with metastatic carcinoma of unknown origin. Upon immunohistochemistry, the tumor showed loss of INI1 (BAF47) and retained PAX-8 expression. After extensive clinical and radiological work-up the primary tumor was found to be a 2.6 cm renal medullary carcinoma. This case highlights the role of multidisciplinary approach to the diagnosis of a neck mass and to understanding that certain genetically-defined tumors can occur at and metastasize to any site.
MeSH term(s)	Adult ; Carcinoma, Medullary/pathology ; Humans ; Kidney Neoplasms/pathology ; Lymphatic Metastasis/pathology ; Male ; Neck/pathology ; Sickle Cell Trait
Language	English
Publishing date	2021-03-15
Publishing country	United States
Document type	Case Reports ; Journal Article
ZDB-ID	2407834-7
ISSN	1936-0568 ; 1936-055X
ISSN (online)	1936-0568
ISSN	1936-055X
DOI	10.1007/s12105-021-01296-7
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Article ; Online: Breath biomarkers of insulin resistance in pre-diabetic Hispanic adolescents with obesity.

Khan, Mohammad S / Cuda, Suzanne / Karere, Genesio M / Cox, Laura A / Bishop, Andrew C

Scientific reports

2022 Volume 12, Issue 1, Page(s) 339

Abstract: Insulin resistance (IR) affects a quarter of the world's adult population and is a major factor in the pathogenesis of cardio-metabolic disease. In this pilot study, we implemented a non-invasive breathomics approach, combined with random forest machine ... ...

Abstract	Insulin resistance (IR) affects a quarter of the world's adult population and is a major factor in the pathogenesis of cardio-metabolic disease. In this pilot study, we implemented a non-invasive breathomics approach, combined with random forest machine learning, to investigate metabolic markers from obese pre-diabetic Hispanic adolescents as indicators of abnormal metabolic regulation. Using the ReCIVA breathalyzer device for breath collection, we have identified a signature of 10 breath metabolites (breath-IR model), which correlates with Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) (R = 0.95, p < 0.001). A strong correlation was also observed between the breath-IR model and the blood glycemic profile (fasting insulin R = 0.91, p < 0.001 and fasting glucose R = 0.80, p < 0.001). Among tentatively identified metabolites, limonene, undecane, and 2,7-dimethyl-undecane, significantly cluster individuals based on HOMA-IR (p = 0.003, p = 0.002, and p < 0.001, respectively). Our breath-IR model differentiates between adolescents with and without IR with an AUC-ROC curve of 0.87, after cross-validation. Identification of a breath signature indicative of IR shows utility of exhaled breath metabolomics for assessing systemic metabolic dysregulation. A simple and non-invasive breath-based test has potential as a diagnostic tool for monitoring IR progression, allowing for earlier detection of IR and implementation of early interventions to prevent onset of type 2 diabetes mellitus.
MeSH term(s)	Adolescent ; Age Factors ; Biomarkers/metabolism ; Breath Tests ; Cross-Sectional Studies ; Feasibility Studies ; Female ; Health Status ; Hispanic or Latino ; Humans ; Insulin Resistance/ethnology ; Machine Learning ; Male ; Metabolome ; Metabolomics ; Pediatric Obesity/diagnosis ; Pediatric Obesity/ethnology ; Pediatric Obesity/metabolism ; Pediatric Obesity/physiopathology ; Pilot Projects ; Prediabetic State/diagnosis ; Prediabetic State/ethnology ; Prediabetic State/metabolism ; Prediabetic State/physiopathology ; Predictive Value of Tests ; Race Factors ; Texas/epidemiology ; Volatile Organic Compounds/metabolism
Chemical Substances	Biomarkers ; Volatile Organic Compounds
Language	English
Publishing date	2022-01-10
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-04072-3
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Book ; Online: F-RouND

Paranjothi, Anirudh / Atiquzzaman, Mohammed / Khan, Mohammad S.

Fog-based Rogue Nodes Detection in Vehicular Ad hoc Networks

2021

Abstract: Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The rogue nodes in VANETs broadcast malicious information leading to potential hazards, including the collision of vehicles. Previous researchers ... ...

Abstract	Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The rogue nodes in VANETs broadcast malicious information leading to potential hazards, including the collision of vehicles. Previous researchers used either cryptography, trust values, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose fog-based rogue nodes detection (F-RouND), a fog computing scheme, which dynamically creates a fog utilizing the on-board units (OBUs) of all vehicles in the region for rogue nodes detection. The novelty of F-RouND lies in providing low processing delays and FPR at high vehicle densities. The performance of our F-RouND framework was carried out with simulations using OMNET++ and SUMO simulators. Results show that F-RouND ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at high vehicle densities compared to existing rogue nodes detection schemes.
Keywords	Computer Science - Networking and Internet Architecture
Subject code	000
Publishing date	2021-02-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: HVD-LSTM based recognition of epileptic seizures and normal human activity.

Khan, Pritam / Khan, Yasin / Kumar, Sudhir / Khan, Mohammad S / Gandomi, Amir H

Computers in biology and medicine

2021 Volume 136, Page(s) 104684

Abstract: In this paper, we detect the occurrence of epileptic seizures in patients as well as activities namely stand, walk, and exercise in healthy persons, leveraging EEG (electroencephalogram) signals. Using Hilbert vibration decomposition (HVD) on non-linear ... ...

Abstract	In this paper, we detect the occurrence of epileptic seizures in patients as well as activities namely stand, walk, and exercise in healthy persons, leveraging EEG (electroencephalogram) signals. Using Hilbert vibration decomposition (HVD) on non-linear and non-stationary EEG signal, we obtain multiple monocomponents varying in terms of amplitude and frequency. After decomposition, we extract features from the monocomponent matrix of the EEG signals. The instantaneous amplitude of the HVD monocomponents varies because of the motion artifacts present in EEG signals. Hence, the acquired statistical features from the instantaneous amplitude help in identifying the epileptic seizures and the normal human activities. The features selected by correlation-based Q-score are classified using an LSTM (Long Short Term Memory) based deep learning model in which the feature-based weight update maximizes the classification accuracy. For epilepsy diagnosis using the Bonn dataset and activity recognition leveraging our Sensor Networks Research Lab (SNRL) data, we achieve testing classification accuracies of 96.00% and 83.30% respectively through our proposed method.
MeSH term(s)	Epilepsy/diagnosis ; Human Activities ; Humans ; Seizures ; Vibration ; Walking
Language	English
Publishing date	2021-07-27
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2021.104684
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Zs.A 653: Show issues

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Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Book ; Online: Survey on Congestion Detection and Control in Connected Vehicles

Paranjothi, Anirudh / Khan, Mohammad S. / Zeadally, Sherali

2020

Abstract: The dynamic nature of vehicular ad hoc network (VANET) induced by frequent topology changes and node mobility, imposes critical challenges for vehicular communications. Aggravated by the high volume of information dissemination among vehicles over ... ...

Abstract	The dynamic nature of vehicular ad hoc network (VANET) induced by frequent topology changes and node mobility, imposes critical challenges for vehicular communications. Aggravated by the high volume of information dissemination among vehicles over limited bandwidth, the topological dynamics of VANET causes congestion in the communication channel, which is the primary cause of problems such as message drop, delay, and degraded quality of service. To mitigate these problems, congestion detection, and control techniques are needed to be incorporated in a vehicular network. Congestion control approaches can be either open-loop or closed loop based on pre-congestion or post congestion strategies. We present a general architecture of vehicular communication in urban and highway environment as well as a state-of-the-art survey of recent congestion detection and control techniques. We also identify the drawbacks of existing approaches and classify them according to different hierarchical schemes. Through an extensive literature review, we recommend solution approaches and future directions for handling congestion in vehicular communications.
Keywords	Computer Science - Networking and Internet Architecture
Subject code	000
Publishing date	2020-07-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Message Dissemination in Connected Vehicles

Paranjothi, Anirudh / Atiquzzaman, Mohammed / Khan, Mohammad S.

2020

Abstract: Advances in connected vehicles based on Vehicular Ad-hoc Networks (VANETs) in recent years have gained significant attention in Intelligent Transport Systems (ITS) in terms of disseminating messages in an efficient manner. VANET uses Dedicated Short ... ...

Abstract	Advances in connected vehicles based on Vehicular Ad-hoc Networks (VANETs) in recent years have gained significant attention in Intelligent Transport Systems (ITS) in terms of disseminating messages in an efficient manner. VANET uses Dedicated Short Range Communication (DSRC) for disseminating messages between vehicles and between infrastructures. Though DSRC based communications are viable, it is still challenging to disseminate messages in a timely manner when vehicles are not in the transmission range of each other. Furthermore, DSRC communication channels are heavily congested when the vehicle density increases on the road. To address these limitations, two emerging paradigms: 1) vehicular cloud computing and 2) vehicular fog computing are been adopted to disseminate message between the vehicles in a connected vehicular environment. Vehicular fog computing uses fog nodes for the dissemination of messages among vehicles. Any real-world object can be formed as a fog node by acquiring the properties such as 1) network connectivity, 2) computation, and 3) storage. In this book chapter, we highlight the significance of message dissemination in connected vehicles based on techniques like DSRC, vehicular cloud computing, and vehicular fog computing. Our objective is to help the readers better understand the fundamentals of connected vehicles and communication techniques while disseminating messages between vehicles and between infrastructures.
Keywords	Computer Science - Networking and Internet Architecture
Subject code	000
Publishing date	2020-09-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Protein-Protein interactive networks identified in bronchoalveolar lavage of severe compared to nonsevere asthma.

Hastie, Annette T / Bishop, Andrew C / Khan, Mohammad S / Bleecker, Eugene R / Castro, Mario / Denlinger, Loren C / Erzurum, Serpil C / Fahy, John V / Israel, Elliot / Levy, Bruce D / Mauger, David T / Meyers, Deborah A / Moore, Wendy C / Ortega, Victor E / Peters, Stephen P / Wenzel, Sally E / Steele, Chad H

Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology

2024 Volume 54, Issue 4, Page(s) 265–277

Abstract: Introduction: Previous bronchoalveolar lavage fluid (BALF) proteomic analysis has evaluated limited numbers of subjects for only a few proteins of interest, which may differ between asthma and normal controls. Our objective was to examine a more ... ...

Abstract	Introduction: Previous bronchoalveolar lavage fluid (BALF) proteomic analysis has evaluated limited numbers of subjects for only a few proteins of interest, which may differ between asthma and normal controls. Our objective was to examine a more comprehensive inflammatory biomarker panel in quantitative proteomic analysis for a large asthma cohort to identify molecular phenotypes distinguishing severe from nonsevere asthma. Methods: Bronchoalveolar lavage fluid from 48 severe and 77 nonsevere adult asthma subjects were assessed for 75 inflammatory proteins, normalized to BALF total protein concentration. Validation of BALF differences was sought through equivalent protein analysis of autologous sputum. Subjects' data, stratified by asthma severity, were analysed by standard statistical tests, principal component analysis and 5 machine learning algorithms. Results: The severe group had lower lung function and greater health care utilization. Significantly increased BALF proteins for severe asthma compared to nonsevere asthma were fibroblast growth factor 2 (FGF2), TGFα, IL1Ra, IL2, IL4, CCL8, CCL13 and CXCL7 and significantly decreased were platelet-derived growth factor a-a dimer (PDGFaa), vascular endothelial growth factor (VEGF), interleukin 5 (IL5), CCL17, CCL22, CXCL9 and CXCL10. Four protein differences were replicated in sputum. FGF2, PDGFaa and CXCL7 were independently identified by 5 machine learning algorithms as the most important variables for discriminating severe and nonsevere asthma. Increased and decreased proteins identified for the severe cluster showed significant protein-protein interactions for chemokine and cytokine signalling, growth factor activity, and eosinophil and neutrophil chemotaxis differing between subjects with severe and nonsevere asthma. Conclusion: These inflammatory protein results confirm altered airway remodelling and cytokine/chemokine activity recruiting leukocytes into the airways of severe compared to nonsevere asthma as important processes even in stable status.
MeSH term(s)	Adult ; Humans ; Vascular Endothelial Growth Factor A ; Proteomics ; Fibroblast Growth Factor 2 ; Asthma ; Cytokines/metabolism ; Bronchoalveolar Lavage ; Chemokines ; Bronchoalveolar Lavage Fluid
Chemical Substances	Vascular Endothelial Growth Factor A ; Fibroblast Growth Factor 2 (103107-01-3) ; Cytokines ; Chemokines
Language	English
Publishing date	2024-01-22
Publishing country	England
Document type	Journal Article
ZDB-ID	645204-8
ISSN	1365-2222 ; 0954-7894 ; 0960-2178
ISSN (online)	1365-2222
ISSN	0954-7894 ; 0960-2178
DOI	10.1111/cea.14447
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Zs.A 767: Show issues

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Article: Type-2 Diabetes as a Risk Factor for Severe COVID-19 Infection

Norouzi, Mahnaz / Norouzi, Shaghayegh / Ruggiero, Alistaire / Khan, Mohammad S. / Myers, Stephen / Kavanagh, Kylie / Vemuri, Ravichandra

Microorganisms. 2021 June 03, v. 9, no. 6

2021

Abstract: The current outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), termed coronavirus disease 2019 (COVID-19), has generated a notable challenge for diabetic patients. Overall, people with diabetes have a higher risk of ... ...

Abstract	The current outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), termed coronavirus disease 2019 (COVID-19), has generated a notable challenge for diabetic patients. Overall, people with diabetes have a higher risk of developing different infectious diseases and demonstrate increased mortality. Type 2 diabetes mellitus (T2DM) is a significant risk factor for COVID-19 progression and its severity, poor prognosis, and increased mortality. How diabetes contributes to COVID-19 severity is unclear; however, it may be correlated with the effects of hyperglycemia on systemic inflammatory responses and immune system dysfunction. Using the envelope spike glycoprotein SARS-CoV-2, COVID-19 binds to angiotensin-converting enzyme 2 (ACE2) receptors, a key protein expressed in metabolic organs and tissues such as pancreatic islets. Therefore, it has been suggested that diabetic patients are more susceptible to severe SARS-CoV-2 infections, as glucose metabolism impairments complicate the pathophysiology of COVID-19 disease in these patients. In this review, we provide insight into the COVID-19 disease complications relevant to diabetes and try to focus on the present data and growing concepts surrounding SARS-CoV-2 infections in T2DM patients.
Keywords	COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; glucose ; glycoproteins ; hyperglycemia ; immune system ; metabolism ; mortality ; noninsulin-dependent diabetes mellitus ; pathophysiology ; people ; peptidyl-dipeptidase A ; prognosis ; risk factors
Language	English
Dates of publication	2021-0603
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2720891-6
ISSN	2076-2607
ISSN	2076-2607
DOI	10.3390/microorganisms9061211
Database	NAL-Catalogue (AGRICOLA)

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