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Shimizu Y, Ohshimo S, Saeki N, Oue K, Sasaki U, Imamura S, Kamio H, Imado E, Sadamori T, Tsutsumi YM, Shime N. New acoustic monitoring system quantifying aspiration risk during monitored anaesthesia care. Sci Rep 2023; 13:20196. [PMID: 37980396 PMCID: PMC10657450 DOI: 10.1038/s41598-023-46561-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/02/2023] [Indexed: 11/20/2023] Open
Abstract
Respiratory monitoring is crucial during monitored anaesthesia care (MAC) to ensure patient safety. Patients undergoing procedures like gastrointestinal endoscopy and dental interventions under MAC have a heightened risk of aspiration. Despite the risks, no current system or device can evaluate aspiration risk. This study presents a novel acoustic monitoring system designed to detect fluid retention in the upper airway during MAC. We conducted a prospective observational study with 60 participants undergoing dental treatment under MAC. We utilized a prototype acoustic monitoring system to assess fluid retention in the upper airway by analysing inspiratory sounds. Water was introduced intraorally in participants to simulate fluid retention; artificial intelligence (AI) analysed respiratory sounds pre and post-injection. We also compared respiratory sounds pre-treatment and during coughing events. Coughing was observed in 14 patients during MAC, and 31 instances of apnoea were detected by capnography. However, 27 of these cases had breath sounds. Notably, with intraoral water injection, the Stridor Quantitative Value (STQV) significantly increased; furthermore, the STQV was substantially higher immediately post-coughing in patients who coughed during MAC. In summary, the innovative acoustic monitoring system using AI provides accurate evaluations of fluid retention in the upper airway, offering potential to mitigate aspiration risks during MAC.Clinical trial number: jRCTs 062220054.
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Affiliation(s)
- Yoshitaka Shimizu
- Department of Dental Anesthesiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, 734-8553, Japan.
| | - Shinichiro Ohshimo
- Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Noboru Saeki
- Department of Anesthesiology and Critical Care, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Kana Oue
- Department of Dental Anesthesiology, Division of Oral & Maxillofacial Surgery and Oral Medicine, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8553, Japan
| | - Utaka Sasaki
- Department of Dental Anesthesiology, Division of Oral & Maxillofacial Surgery and Oral Medicine, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8553, Japan
| | - Serika Imamura
- Department of Dental Anesthesiology, Division of Oral & Maxillofacial Surgery and Oral Medicine, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8553, Japan
| | - Hisanobu Kamio
- Department of Dental Anesthesiology, Division of Oral & Maxillofacial Surgery and Oral Medicine, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8553, Japan
| | - Eiji Imado
- Department of Dental Anesthesiology, Division of Oral & Maxillofacial Surgery and Oral Medicine, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8553, Japan
| | - Takuma Sadamori
- Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Yasuo M Tsutsumi
- Department of Anesthesiology and Critical Care, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Nobuaki Shime
- Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
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Honda J, Murakawa M, Inoue S. Effect of averaging time and respiratory pause time on the measurement of acoustic respiration rate monitoring. JA Clin Rep 2023; 9:61. [PMID: 37773551 PMCID: PMC10541352 DOI: 10.1186/s40981-023-00654-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/18/2023] [Accepted: 09/24/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND Acoustic respiration rate (RRa) monitoring is a method of continuously measuring respiratory rate using a signal from an acoustic transducer placed over the airway. The purpose of the present study is to examine how the averaging time and respiratory pause time settings of an RRa monitor affect the detection time of sudden respiratory rate changes. METHODS A total of 40 healthy adult volunteers were included in the study. First, we measured the apnea detection time (apnea test) by dividing them into two groups (N = 20 each), one with a respiratory pause time setting of 20 s and the other with 40 s. Each group performed two apnea tests with an averaging time setting of 10 and 30 s. Next, we measured the tachypnea detection time (tachypnea test) for half of the subjects (N = 20) with two averaging time settings of 10 and 30 s. For each test, three measurements were taken, and the average of the three measurements was recorded. RESULTS There was no significant difference in the apnea detection time between the averaging time set at 10 and 30 s regardless of whether the respiratory pause time was set at 20 or 40 s. However, the apnea detection time was significantly shorter with the respiratory pause time of 20 s than 40 s, regardless of whether the averaging time was set at 10 or 30 s (p < 0.001). The tachypnea detection time was shorter with the averaging time of 10 s than 30 s (p < 0.001). Furthermore, the apnea detection time and tachypnea detection time were much longer than the actual settings. CONCLUSIONS The results of the current study show that in the measurement of RRa, the apnea detection time is more affected by the respiratory pause time setting than the averaging time setting; however, the tachypnea detection time is significantly affected by the averaging time setting.
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Affiliation(s)
- Jun Honda
- Department of Anesthesiology, Fukushima Medical University Hospital, 1 Hikarigaoka, Fukushima City, Fukushima, 960-1295, Japan.
| | - Masahiro Murakawa
- Department of Anesthesiology, Iwase General Hospital, 20, Kitamachi, Sukagawa City, Fukushima, 962-8503, Japan
| | - Satoki Inoue
- Department of Anesthesiology, Fukushima Medical University Hospital, 1 Hikarigaoka, Fukushima City, Fukushima, 960-1295, Japan
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Coleman J, Ginsburg AS, Macharia W, Ochieng R, Chomba D, Zhou G, Dunsmuir D, Xu S, Ansermino JM. Evaluation of Sibel’s Advanced Neonatal Epidermal (ANNE) wireless continuous physiological monitor in Nairobi, Kenya. PLoS One 2022; 17:e0267026. [PMID: 35771801 PMCID: PMC9246120 DOI: 10.1371/journal.pone.0267026] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 03/31/2022] [Indexed: 11/19/2022] Open
Abstract
Background Neonatal multiparameter continuous physiological monitoring (MCPM) technologies assist with early detection of preventable and treatable causes of neonatal mortality. Evaluating accuracy of novel MCPM technologies is critical for their appropriate use and adoption. Methods We prospectively compared the accuracy of Sibel’s Advanced Neonatal Epidermal (ANNE) technology with Masimo’s Rad-97 pulse CO-oximeter with capnography and Spengler’s Tempo Easy reference technologies during four evaluation rounds. We compared accuracy of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and skin temperature using Bland-Altman plots and root-mean-square deviation analyses (RMSD). Sibel’s ANNE algorithms were optimized between each round. We created Clarke error grids with zones of 20% to aid with clinical interpretation of HR and RR results. Results Between November 2019 and August 2020 we collected 320 hours of data from 84 neonates. In the final round, Sibel’s ANNE technology demonstrated a normalized bias of 0% for HR and 3.1% for RR, and a non-normalized bias of -0.3% for SpO2 and 0.2°C for temperature. The normalized spread between 95% upper and lower limits-of-agreement (LOA) was 4.7% for HR and 29.3% for RR. RMSD for SpO2 was 1.9% and 1.5°C for temperature. Agreement between Sibel’s ANNE technology and the reference technologies met the a priori-defined thresholds for 95% spread of LOA and RMSD. Clarke error grids showed that all HR and RR observations were within a 20% difference. Conclusion Our findings suggest acceptable agreement between Sibel’s ANNE and reference technologies. Clinical effectiveness, feasibility, usability, acceptability, and cost-effectiveness investigations are necessary for large-scale implementation.
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Affiliation(s)
- Jesse Coleman
- Evaluation of Technologies for Neonates in Africa (ETNA), Nairobi, Kenya
- * E-mail:
| | | | | | | | - Dorothy Chomba
- Department of Pediatrics, Aga Khan University, Nairobi, Kenya
| | - Guohai Zhou
- Center for Clinical Investigation, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Dustin Dunsmuir
- Anesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Shuai Xu
- Querrey Simpson Institute for Bioelectronics, Department of Biomedical Engineering, McCormick School of Engineering, Department of Dermatology & Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Evanston, Illinois, United States of America
| | - J. Mark Ansermino
- Anesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada
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Wang D, Macharia WM, Ochieng R, Chomba D, Hadida YS, Karasik R, Dunsmuir D, Coleman J, Zhou G, Ginsburg AS, Ansermino JM. Evaluation of a contactless neonatal physiological monitor in Nairobi, Kenya. Arch Dis Child 2022; 107:558-564. [PMID: 34740876 PMCID: PMC9125375 DOI: 10.1136/archdischild-2021-322344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Indexed: 11/04/2022]
Abstract
BACKGROUND Globally, 2.5 million neonates died in 2018, accounting for 46% of under-5 deaths. Multiparameter continuous physiological monitoring (MCPM) of neonates allows for early detection and treatment of life-threatening health problems. However, neonatal monitoring technology is largely unavailable in low-resource settings. METHODS In four evaluation rounds, we prospectively compared the accuracy of the EarlySense under-mattress device to the Masimo Rad-97 pulse CO-oximeter with capnography reference device for heart rate (HR) and respiratory rate (RR) measurements in neonates in Kenya. EarlySense algorithm optimisations were made between evaluation rounds. In each evaluation round, we compared 200 randomly selected epochs of data using Bland-Altman plots and generated Clarke error grids with zones of 20% to aid in clinical interpretation. RESULTS Between 9 July 2019 and 8 January 2020, we collected 280 hours of MCPM data from 76 enrolled neonates. At the final evaluation round, the EarlySense MCPM device demonstrated a bias of -0.8 beats/minute for HR and 1.6 breaths/minute for RR, and normalised spread between the 95% upper and lower limits of agreement of 6.2% for HR and 27.3% for RR. Agreement between the two MCPM devices met the a priori-defined threshold of 30%. The Clarke error grids showed that all observations for HR and 197/200 for RR were within a 20% difference. CONCLUSION Our research indicates that there is acceptable agreement between the EarlySense and Masimo MCPM devices in the context of large within-subject variability; however, further studies establishing cost-effectiveness and clinical effectiveness are needed before large-scale implementation of the EarlySense MCPM device in neonates. TRIAL REGISTRATION NUMBER NCT03920761.
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Affiliation(s)
- Dee Wang
- Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada
| | | | | | - Dorothy Chomba
- Department of Pediatrics, Aga Khan University, Nairobi, Kenya
| | | | | | - Dustin Dunsmuir
- Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Jesse Coleman
- Centre for International Child Health, Vancouver, British Columbia, Canada
| | - Guohai Zhou
- Center for Clinical Investigation, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Amy Sarah Ginsburg
- Clinical Trials Center, University of Washington, Seattle, Washington, USA
| | - J Mark Ansermino
- Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada
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Liu L, Ji Y, Gao Y, Li T, Xu W. A Data-Driven Adaptive Emotion Recognition Model for College Students Using an Improved Multifeature Deep Neural Network Technology. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1343358. [PMID: 35665293 PMCID: PMC9162810 DOI: 10.1155/2022/1343358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 11/17/2022]
Abstract
With the increasing pressure on college students in terms of study, work, emotion, and life, the emotional changes of college students are becoming more and more obvious. For college student management workers, if they can accurately grasp the emotional state of each college student in all aspects of the whole process, it will be of great help to student management work. The traditional way to understand students' emotions at a certain stage is mostly through chats, questionnaires, and other methods. However, data collection in this way is time-consuming and labor-intensive, and the authenticity of the collected data cannot be guaranteed because students will lie out of impatience or unwillingness to reveal their true emotions. In order to explore an accurate and efficient emotion recognition method for college students, more objective physiological data are used for emotion recognition research. Since emotion is generated by the central nervous system of the human brain, EEG signals directly reflect the electrophysiological activity of the brain. Therefore, in the field of emotion recognition based on physiological signals, EEG signals are favored due to their ability to intuitively respond to emotions. Therefore, a deep neural network (DNN) is used to classify the collected emotional EEG data and obtain the emotional state of college students according to the classification results. Considering that different features can represent different information of the original data, in order to express the original EEG data information as comprehensively as possible, various features of the EEG are first extracted. Second, feature fusion is performed on multiple features using the autosklearn model integration technique. Third, the fused features are input to the DNN, resulting in the final classification result. The experimental results show that the method has certain advantages in public datasets, and the accuracy of emotion recognition exceeds 88%. This proves the used emotion recognition is feasible to be applied in real life.
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Affiliation(s)
- Li Liu
- Jiangsu Vocational College of Information Technology, Wuxi, Jiangsu 214153, China
- Jiangsu Key Laboratory of Media Design and Software Technology (Jiangnan University), Wuxi 214122, China
| | - Yunfeng Ji
- Jiangsu Vocational College of Information Technology, Wuxi, Jiangsu 214153, China
| | - Yun Gao
- Jiangsu Vocational College of Information Technology, Wuxi, Jiangsu 214153, China
| | - Tao Li
- Jiangsu Vocational College of Information Technology, Wuxi, Jiangsu 214153, China
| | - Wei Xu
- Jiangsu Vocational College of Information Technology, Wuxi, Jiangsu 214153, China
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Rossol SL, Yang JK, Toney-Noland C, Bergin J, Basavaraju C, Kumar P, Lee HC. Non-Contact Video-Based Neonatal Respiratory Monitoring. CHILDREN-BASEL 2020; 7:children7100171. [PMID: 33036226 PMCID: PMC7600716 DOI: 10.3390/children7100171] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/01/2020] [Accepted: 10/03/2020] [Indexed: 12/25/2022]
Abstract
Respiratory rate (RR) has been shown to be a reliable predictor of cardio-pulmonary deterioration, but standard RR monitoring methods in the neonatal intensive care units (NICU) with contact leads have been related to iatrogenic complications. Video-based monitoring is a potential non-contact system that could improve patient care. This iterative design study developed a novel algorithm that produced RR from footage analyzed from stable NICU patients in open cribs with corrected gestational ages ranging from 33 to 40 weeks. The final algorithm used a proprietary technique of micromotion and stationarity detection (MSD) to model background noise to be able to amplify and record respiratory motions. We found significant correlation—r equals 0.948 (p value of 0.001)—between MSD and the current hospital standard, electrocardiogram impedance pneumography. Our video-based system showed a bias of negative 1.3 breaths and root mean square error of 6.36 breaths per minute compared to standard continuous monitoring. Further work is needed to evaluate the ability of video-based monitors to observe clinical changes in a larger population of patients over extended periods of time.
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Affiliation(s)
- Scott L. Rossol
- Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (J.K.Y.); (C.T.-N.); (J.B.); (H.C.L.)
- Correspondence:
| | - Jeffrey K. Yang
- Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (J.K.Y.); (C.T.-N.); (J.B.); (H.C.L.)
| | - Caroline Toney-Noland
- Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (J.K.Y.); (C.T.-N.); (J.B.); (H.C.L.)
| | - Janine Bergin
- Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (J.K.Y.); (C.T.-N.); (J.B.); (H.C.L.)
| | | | - Pavan Kumar
- CocoonCam, Sunnyvale, CA 94089, USA; (C.B.); (P.K.)
| | - Henry C. Lee
- Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (J.K.Y.); (C.T.-N.); (J.B.); (H.C.L.)
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Eisenberg ME, Levin R. Response to letter to the editor. J Clin Monit Comput 2019; 34:183-184. [PMID: 31845138 DOI: 10.1007/s10877-019-00407-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 07/22/2019] [Indexed: 11/25/2022]
Affiliation(s)
| | - Raz Levin
- Medtronic, 7 HaMarpe St, 97774, Jerusalem, Israel.
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Barbosa Pereira C, Dohmeier H, Kunczik J, Hochhausen N, Tolba R, Czaplik M. Contactless monitoring of heart and respiratory rate in anesthetized pigs using infrared thermography. PLoS One 2019; 14:e0224747. [PMID: 31693688 PMCID: PMC6834247 DOI: 10.1371/journal.pone.0224747] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 10/21/2019] [Indexed: 01/16/2023] Open
Abstract
Pig experiments have played an important role in medical breakthroughs during the last century. In fact, pigs are one of the major animal species used in translational research, surgical models and procedural training due to their anatomical and physiological similarities to humans. To ensure high bioethical standards in animal trials, new directives have been implemented, among others, to refine the procedures and minimize animals' stress and pain. This paper presents a contactless motion-based approach for monitoring cardiorespiratory signals (heart rate and respiratory rate) in anesthetized pigs using infrared thermography. Heart rate monitoring is estimated by measuring the vibrations (precordial motion) of the chest caused by the heartbeat. Respiratory rate, in turn, is computed by measuring the mechanical chest movements that accompany the respiratory cycle. To test the feasibility of this approach, thermal videos of 17 anesthetized pigs were acquired and analyzed. A high agreement between infrared thermography and a gold standard (electrocardiography and capnography-derived respiratory rate) was achieved. The mean absolute error averaged 3.43 ± 3.05 bpm and 0.27 ± 0.48 breaths/min for heart rate and respiratory rate, respectively. In sum, infrared thermography is capable of assessing cardiorespiratory signals in pigs. Future work should be conducted to evaluate infared thermography capability of capturing information for long term monitoring of research animals in a diverse set of facilities.
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Affiliation(s)
- Carina Barbosa Pereira
- Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, Aachen, NRW, Germany
- * E-mail:
| | - Henriette Dohmeier
- Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, Aachen, NRW, Germany
| | - Janosch Kunczik
- Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, Aachen, NRW, Germany
| | - Nadine Hochhausen
- Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, Aachen, NRW, Germany
| | - René Tolba
- Institute for Laboratory Animal Science and Experimental Surgery, Faculty of Medicine, RWTH Aachen University, Aachen, NRW, Germany
| | - Michael Czaplik
- Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, Aachen, NRW, Germany
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Letter to the editor. J Clin Monit Comput 2019; 34:181-182. [PMID: 31392654 DOI: 10.1007/s10877-019-00363-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 07/16/2019] [Indexed: 10/26/2022]
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