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Garelli F, Fushimi E, Rosales N, Arambarri D, Mendoza L, Serafini MC, Moscoso-Vásquez M, Stasi M, Duette P, García-Arabehety J, Giunta JN, De Battista H, Sánchez-Peña R, Grosembacher L. First Outpatient Clinical Trial of a Full Closed-Loop Artificial Pancreas System in South America. J Diabetes Sci Technol 2022:19322968221096162. [PMID: 35549733 DOI: 10.1177/19322968221096162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The first two studies of an artificial pancreas (AP) system carried out in Latin America took place in 2016 (phase 1) and 2017 (phase 2). They evaluated a hybrid algorithm from the University of Virginia (UVA) and the automatic regulation of glucose (ARG) algorithm in an inpatient setting using an AP platform developed by the UVA. The ARG algorithm does not require carbohydrate (CHO) counting and does not deliver meal priming insulin boluses. Here, the first outpatient trial of the ARG algorithm using an own AP platform and doubling the duration of previous phases is presented. METHOD Phase 3 involved the evaluation of the ARG algorithm in five adult participants (n = 5) during 72 hours of closed-loop (CL) and 72 hours of open-loop (OL) control in an outpatient setting. This trial was performed with an own AP and remote monitoring platform developed from open-source resources, called InsuMate. The meals tested ranged its CHO content from 38 to 120 g and included challenging meals like pasta. Also, the participants performed mild exercise (3-5 km walks) daily. The clinical trial is registered in ClinicalTrials.gov with identifier: NCT04793165. RESULTS The ARG algorithm showed an improvement in the time in hyperglycemia (52.2% [16.3%] OL vs 48.0% [15.4%] CL), time in range (46.9% [15.6%] OL vs 50.9% [14.4%] CL), and mean glucose (188.9 [25.5] mg/dl OL vs 186.2 [24.7] mg/dl CL) compared with the OL therapy. No severe hyperglycemia or hypoglycemia episodes occurred during the trial. The InsuMate platform achieved an average of more than 95% of the time in CL. CONCLUSION The results obtained demonstrated the feasibility of outpatient full CL regulation of glucose levels involving the ARG algorithm and the InsuMate platform.
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Affiliation(s)
- Fabricio Garelli
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Emilia Fushimi
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Nicolás Rosales
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Delfina Arambarri
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
| | - Leandro Mendoza
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
| | - María Cecilia Serafini
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires, Buenos Aires, Argentina
| | - Marcela Moscoso-Vásquez
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
| | | | | | | | | | - Hernán De Battista
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Ricardo Sánchez-Peña
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
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García-Violini D, Sánchez-Peña R, Moscoso-Vásquez M, Garelli F. Non-pharmaceutical intervention to reduce COVID-19 impact in Argentina. ISA Trans 2022; 124:225-235. [PMID: 34175123 PMCID: PMC8214935 DOI: 10.1016/j.isatra.2021.06.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 06/17/2021] [Accepted: 06/17/2021] [Indexed: 06/13/2023]
Abstract
This work is focused on the multilevel control of the population confinement in the city of Buenos Aires and its surroundings due to the pandemic generated by the COVID-19 outbreak. The model used here is known as SEIRD and two objectives are sought: a time-varying identification of the infection rate and the inclusion of a controller. A control differential equation has been added to regulate the transitions between confinement and normal life, according to five different levels. The plasma treatment from recovered patients has also been considered in the control algorithm. Using the proposed strategy the ICU occupancy is reduced, and as a consequence, the number of deaths is also decreased.
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Affiliation(s)
- Demián García-Violini
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Roque Sáenz Peña 352, B1876BXD, Bernal, Buenos Aires, Argentina
| | - Ricardo Sánchez-Peña
- Centro de Sistemas y Control, Instituto Tecnológico de Buenos Aires, Av. Eduardo Madero 399, C1106, CABA, Argentina; CONICET, Argentina.
| | - Marcela Moscoso-Vásquez
- Centro de Sistemas y Control, Instituto Tecnológico de Buenos Aires, Av. Eduardo Madero 399, C1106, CABA, Argentina; CONICET, Argentina
| | - Fabricio Garelli
- Group of Control Applications, LEICI, Universidad Nacional de La Plata, Calle 48 y 116, CC 91 (1900), La Plata, Buenos Aires, Argentina; CONICET, Argentina
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Garelli F, Rosales N, Fushimi E, Arambarri D, Mendoza L, De Battista H, Sánchez-Peña R, García Arabehety J, Distefano S, Barcala C, Giunta J, Las Heras M, Martinez Mateu C, Prieto M, San Román E, Krochik G, Grosembacher L. Remote Glucose Monitoring Platform for Multiple Simultaneous Patients at Coronavirus Disease 2019 Intensive Care Units: Case Report Including Adults and Children. Diabetes Technol Ther 2021; 23:471-473. [PMID: 33337261 DOI: 10.1089/dia.2020.0556] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Fabricio Garelli
- Group of Control Applications, LEICI, Universidad Nacional de La Plata (UNLP), La Plata, Argentina
- CONICET, Argentina
| | - Nicolás Rosales
- Group of Control Applications, LEICI, Universidad Nacional de La Plata (UNLP), La Plata, Argentina
- CONICET, Argentina
| | - Emilia Fushimi
- Group of Control Applications, LEICI, Universidad Nacional de La Plata (UNLP), La Plata, Argentina
- CONICET, Argentina
| | - Delfina Arambarri
- Group of Control Applications, LEICI, Universidad Nacional de La Plata (UNLP), La Plata, Argentina
- CONICET, Argentina
| | - Leandro Mendoza
- Group of Control Applications, LEICI, Universidad Nacional de La Plata (UNLP), La Plata, Argentina
| | - Hernán De Battista
- Group of Control Applications, LEICI, Universidad Nacional de La Plata (UNLP), La Plata, Argentina
- CONICET, Argentina
| | - Ricardo Sánchez-Peña
- CONICET, Argentina
- Centro de Sistemas y Control, Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
| | | | | | | | - Javier Giunta
- Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | | | | | - Mariana Prieto
- Hospital de Pediatría J. P. Garrahan, Buenos Aires, Argentina
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Bertone-Cueto NI, Makarova J, Mosqueira A, García-Violini D, Sánchez-Peña R, Herreras O, Belluscio M, Piriz J. Volume-Conducted Origin of the Field Potential at the Lateral Habenula. Front Syst Neurosci 2020; 13:78. [PMID: 31998083 PMCID: PMC6961596 DOI: 10.3389/fnsys.2019.00078] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 12/02/2019] [Indexed: 01/30/2023] Open
Abstract
Field potentials (FPs) are easily reached signals that provide information about the brain's processing. However, FP should be interpreted cautiously since their biophysical bases are complex. The lateral habenula (LHb) is a brain structure involved in the encoding of aversive motivational values. Previous work indicates that the activity of the LHb is relevant for hippocampal-dependent learning. Moreover, it has been proposed that the interaction of the LHb with the hippocampal network is evidenced by the synchronization of LHb and hippocampal FPs during theta rhythm. However, the origin of the habenular FP has not been analyzed. Hence, its validity as a measurement of LHb activity has not been proven. In this work, we used electrophysiological recordings in anesthetized rats and feed-forward modeling to investigate biophysical basis of the FP recorded in the LHb. Our results indicate that the FP in the LHb during theta rhythm is a volume-conducted signal from the hippocampus. This result highlight that FPs must be thoroughly analyzed before its biological interpretation and argues against the use of the habenular FP signal as a readout of the activity of the LHb.
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Affiliation(s)
- Nicolas Iván Bertone-Cueto
- Grupo de Neurociencia de Sistemas, Instituto de Fisiología y Biofísica “Houssay” (IFIBIO “Houssay”), Universidad de Buenos Aires, CONICET, Buenos Aires, Argentina
| | | | - Alejo Mosqueira
- Grupo de Neurociencia de Sistemas, Instituto de Fisiología y Biofísica “Houssay” (IFIBIO “Houssay”), Universidad de Buenos Aires, CONICET, Buenos Aires, Argentina
| | | | | | | | - Mariano Belluscio
- Grupo de Neurociencia de Sistemas, Instituto de Fisiología y Biofísica “Houssay” (IFIBIO “Houssay”), Universidad de Buenos Aires, CONICET, Buenos Aires, Argentina
| | - Joaquin Piriz
- Grupo de Neurociencia de Sistemas, Instituto de Fisiología y Biofísica “Houssay” (IFIBIO “Houssay”), Universidad de Buenos Aires, CONICET, Buenos Aires, Argentina
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Abstract
BACKGROUND Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses-the so-called automatic regulation of glucose (ARG)-was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. METHOD An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. RESULTS The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fast-absorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). CONCLUSION In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.
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Affiliation(s)
- Emilia Fushimi
- Grupo de Control Aplicado (GCA), Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
- Emilia Fushimi. Instituto LEICI (Grupo de Control Aplicado), Depto. Electrotecnia, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP),, Calle 48 y116, La Plata 1900, Argentina.
| | - Patricio Colmegna
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
- University of Virginia (UVA), Center for Diabetes Technology, Charlottesville, VA, USA
- Universidad Nacional de Quilmes (UNQ), Argentina
| | - Hernán De Battista
- Grupo de Control Aplicado (GCA), Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
| | - Fabricio Garelli
- Grupo de Control Aplicado (GCA), Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
| | - Ricardo Sánchez-Peña
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
- Universidad Nacional de Quilmes (UNQ), Argentina
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Sánchez-Peña R, Colmegna P, Garelli F, De Battista H, García-Violini D, Moscoso-Vásquez M, Rosales N, Fushimi E, Campos-Náñez E, Breton M, Beruto V, Scibona P, Rodriguez C, Giunta J, Simonovich V, Belloso WH, Cherñavvsky D, Grosembacher L. Artificial Pancreas: Clinical Study in Latin America Without Premeal Insulin Boluses. J Diabetes Sci Technol 2018; 12:914-925. [PMID: 29998754 PMCID: PMC6134619 DOI: 10.1177/1932296818786488] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Emerging therapies such as closed-loop (CL) glucose control, also known as artificial pancreas (AP) systems, have shown significant improvement in type 1 diabetes mellitus (T1DM) management. However, demanding patient intervention is still required, particularly at meal times. To reduce treatment burden, the automatic regulation of glucose (ARG) algorithm mitigates postprandial glucose excursions without feedforward insulin boluses. This work assesses feasibility of this new strategy in a clinical trial. METHODS A 36-hour pilot study was performed on five T1DM subjects to validate the ARG algorithm. Subjects wore a subcutaneous continuous glucose monitor (CGM) and an insulin pump. Insulin delivery was solely commanded by the ARG algorithm, without premeal insulin boluses. This was the first clinical trial in Latin America to validate an AP controller. RESULTS For the total 36-hour period, results were as follows: average time of CGM readings in range 70-250 mg/dl: 88.6%, in range 70-180 mg/dl: 74.7%, <70 mg/dl: 5.8%, and <50 mg/dl: 0.8%. Results improved analyzing the final 15-hour period of this trial. In that case, the time spent in range was 70-250 mg/dl: 94.7%, in range 70-180 mg/dl: 82.6%, <70 mg/dl: 4.1%, and <50 mg/dl: 0.2%. During the last night the time spent in range was 70-250 mg/dl: 95%, in range 70-180 mg/dl: 87.7%, <70 mg/dl: 5.0%, and <50 mg/dl: 0.0%. No severe hypoglycemia occurred. No serious adverse events were reported. CONCLUSIONS The ARG algorithm was successfully validated in a pilot clinical trial, encouraging further tests with a larger number of patients and in outpatient settings.
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Affiliation(s)
- Ricardo Sánchez-Peña
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Ricardo Sánchez-Peña, PhD, National Scientific and Technical Research Council (CONICET), Instituto Tecnológico de Buenos Aires (ITBA), Av Madero 399, Buenos Aires, C1106ACD, Argentina.
| | - Patricio Colmegna
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- University of Virginia, Charlottesville, VA, USA
- Universidad Nacional de Quilmes, Bernal, Buenos Aires, Argentina
| | - Fabricio Garelli
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Universidad Nacional de La Plata, La Plata, Buenos Aires Argentina
| | - Hernán De Battista
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Universidad Nacional de La Plata, La Plata, Buenos Aires Argentina
| | - Demián García-Violini
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Marcela Moscoso-Vásquez
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Nicolás Rosales
- Universidad Nacional de La Plata, La Plata, Buenos Aires Argentina
| | - Emilia Fushimi
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Universidad Nacional de La Plata, La Plata, Buenos Aires Argentina
| | | | - Marc Breton
- University of Virginia, Charlottesville, VA, USA
| | - Valeria Beruto
- Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Paula Scibona
- Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | | | - Javier Giunta
- Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
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