Accuracy of Two Continuous Glucose Monitoring Devices During Aerobic and High-Intensity Interval Training in Individuals with Type 1 Diabetes.

BBACKGROUND: This study aimed to evaluate the accuracy of Dexcom G6 (DG6) and FreeStyle Libre-2 (FSL2) during aerobic training and HIIT in individuals with type 1 diabetes (T1D). METHODS: Twenty-six males (mean age 29.3 ± 6.3 years and mean duration of diabetes 14.9 ± 6.1 years) participated in this study. Interstitial glucose levels were measured using DG6 and FSL2, while plasma glucose levels were measured every 10 min using YSI 2500 as the reference for glucose measurements in this study. The measurements began 20 min before the start of exercise and continued for 20 min after exercise. Seven measurements were taken for each subject and exercise. RESULTS: Both DG6 and FSL2 devices showed significant differences compared to YSI glucose data for both aerobic and HIIT exercises. Continuous glucose monitoring (CGM) devices exhibited superior performance during HIIT than aerobic training, with DG6 showing a mean absolute relative difference (MARD) of 14.03% versus 31.98%, respectively. In the comparison between the two devices, FSL2 demonstrated significantly higher effectiveness in aerobic training, yet its performance was inferior to DG6 during HIIT. According to the 40/40 criteria, both sensors performed similarly, with marks over 93% for all ranges and both exercises, and above 99% for HIIT and in the >180 mg/dL range, which is in accordance with FDA guidelines. CONCLUSIONS: The findings suggest that the accuracy of DG6 and FSL2 deteriorates during and immediately after exercise, but remains acceptable for both devices during HIIT. However, accuracy is compromised with DG6 during aerobic exercise. This study is the first to compare the accuracy of two CGMs, DG6 and FSL2, during two exercise modalities, using plasma glucose YSI measurements as the gold standard for comparisons.

A model of subcutaneous pramlintide pharmacokinetics and its effect on gastric emptying: Proof-of-concept based on populational data.

Pramlintide, an amylin analog, has been coming up as an agent in type 1 diabetes dual-hormone therapies (insulin/pramlintide). Since pramlintide slows down gastric emptying, it allows for easing glucose control and reducing the burden of meal announcements. Pre-clinical in silico evaluations are a key step in the development of any closed-loop strategy. However, mathematical models are needed, and pramlintide models in the literature are scarce. This work proposes a proof-of-concept pramlintide model, describing its subcutaneous pharmacokinetics (PK) and its effect on gastric emptying (PD). The model is validated with published populational (clinical) data. The model development is divided into three stages: intravenous PK, subcutaneous PK, and PD modeling. In each stage, a set of model structures are proposed, and their performance is assessed using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). In order to evaluate the modulation of the rate of gastric emptying, a literature meal model was used. The final pramlintide model comprises four compartments and a function that modulates gastric emptying depending on plasma pramlintide. Results show an appropriate fit for the data. Some aspects are left as open questions due to the lack of specific data (e.g., the influence of meal composition on the pramlintide effect). Moreover, further validation with individual data is necessary to propose a virtual cohort of patients.

Modeling the effect of glucagon on endogenous glucose production in type 1 diabetes: On the role of glucagon receptor dynamics.

This paper validates a glucoregulatory model including glucagon receptors dynamics in the description of endogenous glucose production (EGP). A set of models from literature are selected for a head-to-head comparison in order to evaluate the role of glucagon receptors. Each EGP model is incorporated into an existing glucoregulatory model and validated using a set of clinical data, where both insulin and glucagon are administered. The parameters of each EGP model are identified in the same optimization problem, minimizing the root mean square error (RMSE) between the simulation and the clinical data. The results show that the RMSE for the proposed receptors-based EGP model was lower when compared to each of the considered models (Receptors approach: 7.13±1.71 mg/dl vs. 7.76±1.45 mg/dl (p=0.066), 8.45±1.38 mg/dl (p=0.011) and 8.99±1.62 mg/dl (p=0.007)). This raises the possibility of considering glucagon receptors dynamics in type 1 diabetes simulators.

Changes in the Treatment Strategy Following Intracoronary Pressure Wire in a Contemporaneous Real-Life Cohort of Patients With Intermediate Coronary Stenosis. Results From a Nationwide Registry.

BACKGROUND: Intracoronary pressure wire is useful to guide revascularization in patients with coronary artery disease. AIMS: To evaluate changes in diagnosis (coronary artery disease extent), treatment strategy and clinical results after intracoronary pressure wire study in real-life patients with intermediate coronary artery stenosis. METHODS: Observational, prospective and multicenter registry of patients in whom pressure wire was performed. The extent of coronary artery disease and the treatment strategy based on clinical and angiographic criteria were recorded before and after intracoronary pressure wire guidance. 12-month incidence of MACE (cardiovascular death, non-fatal myocardial infarction or new revascularization of the target lesion) was assessed. RESULTS: 1414 patients with 1781 lesions were included. Complications related to the procedure were reported in 42 patients (3.0 %). The extent of coronary artery disease changed in 771 patients (54.5 %). There was a change in treatment strategy in 779 patients (55.1 %) (18.0 % if medical treatment; 68.8 % if PCI; 58.9 % if surgery (p < 0.001 for PCI vs medical treatment; p = 0.041 for PCI vs CABG; p < 0.001 for medical treatment vs CABG)). In patients with PCI as the initial strategy, the change in strategy was associated with a lower rate of MACE (4.6 % vs 8.2 %, p = 0.034). CONCLUSIONS: The use of intracoronary pressure wire was safe and led to the reclassification of the extent of coronary disease and change in the treatment strategy in more than half of the cases, especially in patients with PCI as initial treatment.

Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions.

Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model's prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min.

Internal model control based module for the elimination of meal and exercise announcements in hybrid artificial pancreas systems.

BACKGROUND AND OBJECTIVES: Hybrid artificial pancreas systems outperform current insulin pump therapies in blood glucose regulation in type 1 diabetes. However, subjects still have to inform the system about meals intake and exercise to achieve reasonable control. These patient announcements may result in overburden and compromise controller performance if not provided timely and accurately. Here, a hybrid artificial pancreas is extended with an add-on module that releases subjects from meals and exercise announcements. METHODS: The add-on module consists of an internal-model controller that generates a "virtual" control action to compensate for disturbances. This "virtual" action is converted into insulin delivery, rescue carbohydrates suggestions, or insulin-on-board limitations, depending on a switching logic based on glucose measurements and predictions. The controller parameters are tuned by optimization and then related to standard parameters from the open-loop therapy. This module is implemented in a hybrid artificial pancreas system proposed by our research group for validation. This hybrid system extended with the add-on module is compared with the hybrid controller with carbohydrate counting errors (hybrid) and the hybrid controller with an alternative unannounced meal compensation module based on a meal detection algorithm (meal detector). The validation used the educational version of the UVa/Padova simulator to simulate the three controllers under two scenarios: one with only meals and another with meals and exercise. The exercise was modeled as a temporal increase of the insulin sensitivity resulting in the glucose drop usually related to an aerobic exercise. RESULTS: For the scenario with only meals, the three controllers achieved similar time in range (proposed: 85.1 [77.9,88.1]%, hybrid: 84.0 [75.9,86.4]%, meal detector: 81.9 [79.3,83.8]%, median [interquartile range]) with low time in moderate hypoglycemia. Under the scenario with meals and exercise, the proposed module reduces 4.61% the time in hypoglycemia achieved with the other controllers, suggesting an acceptable amount of rescues (27.2 [23.7, 31.0] g). CONCLUSIONS: The proposed add-on module achieved promising results: it outperformed the meal-detector-based controller, even achieving a postprandial performance as good as the hybrid controller (with carbohydrate counting errors). Also, the rescue suggestion feature of the module mitigated exercise-induced hypoglycemia with admissible rescue amounts.

Comparison of Artificial Intelligence Control Strategies for a Peristaltically Pumped Low-Pressure Driven Membrane Process.

Peristaltic pumping is used in membrane applications where high and sterile sealing is required. However, control is difficult due to the pulsating pump characteristics and the time-varying properties of the system. In this work, three artificial intelligence control strategies (artificial neural networks (ANN), fuzzy logic expert systems, and fuzzy-integrated local models) were used to regulate transmembrane pressure and crossflow velocity in a microfiltration system under high fouling conditions. A pilot plant was used to obtain the necessary data to identify the AI models and to test the controllers. Humic acid was employed as a foulant, and cleaning-in-place with NaOH was used to restore the membrane state. Several starting operating points were studied and setpoint changes were performed to study the plant dynamics under different control strategies. The results showed that the control approaches were able to control the membrane system, but significant differences in the dynamics were observed. The ANN control was able to achieve the specifications but showed poor dynamics. Expert control was fast but showed problems in different working areas. Local models required less data than ANN, achieving high accuracy and robustness. Therefore, the technique to be used will depend on the available information and the application dynamics requirements.

Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework.

Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. However, large intra-patient variability poses big challenges to glucose prediction. In previous works by the authors, clustering and local modeling techniques with seasonal stochastic models proved to be efficient, allowing for good glucose prediction accuracy for long prediction horizons. Continuous glucose monitoring (CGM) data were partitioned into fixed-length postprandial time subseries and clustered with Fuzzy C-Means to collect similar behaviors, enforcing seasonality at each cluster after subseries concatenation. Then, seasonal stochastic models were identified for each cluster and local predictions were integrated into a global prediction. However, free-living conditions do not support the fixed-length partition of CGM data since daily events duration is variable. In this work, a new algorithm is provided to overcome this constraint, allowing better coping with patient's variability under variable-length time-stamped daily events in supervision and control applications. Besides predicted glucose, two real-time indices are additionally provided-a crispness index, indicating good representation of current glucose behavior by a single model, and a normality index, allowing for the detection of an abnormal glucose behavior (unusual according to registered historical data). The framework is tested in a proof-of-concept in silico study with ten patients over four month training data and two independent two month validation datasets, with and without abnormal behaviors, from the distribution version of the UVA/Padova simulator extended with diverse sources of intra-patient variability.

Impact of diabetes in patients waiting for invasive cardiac procedures during COVID-19 pandemic.

BACKGROUND: During COVID-19 pandemic, elective invasive cardiac procedures (ICP) have been frequently cancelled or postponed. Consequences may be more evident in patients with diabetes. OBJECTIVES: The objective was to identify the peculiarities of patients with DM among those in whom ICP were cancelled or postponed due to the COVID-19 pandemic, as well as to identify subgroups in which the influence of DM has higher impact on the clinical outcome. METHODS: We included 2,158 patients in whom an elective ICP was cancelled or postponed during COVID-19 pandemic in 37 hospitals in Spain. Among them, 700 (32.4%) were diabetics. Patients with and without diabetes were compared. RESULTS: Patients with diabetes were older and had a higher prevalence of other cardiovascular risk factors, previous cardiovascular history and co-morbidities. Diabetics had a higher mortality (3.0% vs. 1.0%; p = 0.001) and cardiovascular mortality (1.9% vs. 0.4%; p = 0.001). Differences were especially important in patients with valvular heart disease (mortality 6.9% vs 1.7% [p < 0.001] and cardiovascular mortality 4.9% vs 0.9% [p = 0.002] in patients with and without diabetes, respectively). In the multivariable analysis, diabetes remained as an independent risk factor both for overall and cardiovascular mortality. No significant interaction was found with other clinical variables. CONCLUSION: Among patients in whom an elective invasive cardiac procedure is cancelled or postponed during COVID-19 pandemic, mortality and cardiovascular mortality is higher in patients with diabetes, irrespectively on other clinical conditions. These procedures should not be cancelled in patients with diabetes.

Percutaneous Mitral Valve Repair: Outcome Improvement with Operator Experience and a Second-Generation Device.

BACKGROUND AND AIM: Recent randomized data comparing percutaneous mitral valve repair (PMVR) versus optimal medical treatment in patients with functional MR (FMR) seemed to highlight the importance of the learning curve not only for procedural outcomes but also for patient selection. The aim of the study was to compare a contemporary series of patients undergoing PMVR using a second-generation Mitraclip device (Mitraclip NT) with previous cohorts treated with a first-generation system. METHODS: This multicenter study collected individual data from 18 centers between 2012 and 2017. The cohort was divided into three groups according to the use of the first-generation Mitraclip during the first (control-1) or second half (control-2) or the Mitraclip NT system. RESULTS: A total of 545 consecutive patients were included in the study. Among all, 182 (33.3%), 183 (33.3%), and 180 (33.3%) patients underwent mitral repair in the control-1, control-2, and NT cohorts, respectively. Procedural success was achieved in 93.3% of patients without differences between groups. Major adverse events did not statistically differ among groups, but there was a higher rate of pericardial effusion in the control-1 group (4.3%, 0.6%, and 2.6%, respectively; p = 0.025). The composite endpoint of death, surgery, and admission for congestive heart failure (CHF) at 12 months was lower in the NT group (23.5% in control-1, 22.5% in control-2, and 8.3% in the NT group; p = 0.032). CONCLUSIONS: The present paper shows that contemporary clinical outcomes of patients undergoing PMVR with the Mitraclip system have improved over time.

How often should ring pessaries be removed or changed in women with advanced POP? A prospective observational study.

INTRODUCTION AND HYPOTHESIS: This study aimed to evaluate the efficacy and safety of ring pessaries under continuous use for > 2 years. Our starting hypothesis was that their use without periodic removal, cleaning or replacement for between 24 to 48 months after insertion is safe and effective. METHODS: This was a prospective observational and descriptive study. One hundred one women who successfully completed the 24 first months of continuous use of a ring pessary were included and monitored for another 24 months. The objectives were to establish the percentage of patients maintaining its use 48 months after insertion, the reasons for discontinuation and the adverse events. Another purpose of this study was to determine the timing of replacement of the vaginal pessary in long-term users. RESULTS: Of the women, 92.1% (93/101) had successful pessary use, and it was discontinued by three patients (2.9%, 3/101); 76.2% (77/101) of the women continued pessary use after the end of the study, and in 16 (15.8%, 16/101) patients, after pessary removal, the prolapse disappeared and did not recur. Forty-five women (48.4%, 45/93) presented some adverse events that required temporary pessary removal. The most common one was an increase in vaginal discharge (73.3%, 33/45). In four women (8.9%, 4/45), the ring pessary was detected embedded in the vaginal epithelium. CONCLUSIONS: Continuous use of a ring pessary can be recommended for 2 years in hysterectomized women and for 4 years in non-hysterectomized women if there are no complications.

A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods.

Continuous Glucose Monitoring (CGM) has been a springboard of new diabetes management technologies such as integrated sensor-pump systems, the artificial pancreas, and more recently, smart pens. It also allows patients to make better informed decisions compared to a few measurements per day from a glucometer. However, CGM accuracy is reportedly affected during exercise periods, which can impact the effectiveness of CGM-based treatments. In this review, several studies that used CGM during exercise periods are scrutinized. An extensive literature review of clinical trials including exercise and CGM in type 1 diabetes was conducted. The gathered data were critically analysed, especially the Mean Absolute Relative Difference (MARD), as the main metric of glucose accuracy. Most papers did not provide accuracy metrics that differentiated between exercise and rest (non-exercise) periods, which hindered comparative data analysis. Nevertheless, the statistic results confirmed that CGM during exercise periods is less accurate.

Consequences of canceling elective invasive cardiac procedures during Covid-19 outbreak.

BACKGROUND: During COVID-19 pandemic in Spain, elective procedures were canceled or postponed, mainly due to health care systems overwhelming. OBJECTIVE: The objective of this study was to evaluate the consequences of interrupting invasive procedures in patients with chronic cardiac diseases due to the COVID-19 outbreak in Spain. METHODS: The study population is comprised of 2,158 patients that were pending on elective cardiac invasive procedures in 37 hospitals in Spain on the 14th of March 2020, when a state of alarm and subsequent lockdown was declared in Spain due to the COVID-19 pandemic. These patients were followed-up until April 31th. RESULTS: Out of the 2,158 patients, 36 (1.7%) died. Mortality was significantly higher in patients pending on structural procedures (4.5% vs. 0.8%, respectively; p < .001), in those >80 year-old (5.1% vs. 0.7%, p < .001), and in presence of diabetes (2.7% vs. 0.9%, p = .001), hypertension (2.0% vs. 0.6%, p = .014), hypercholesterolemia (2.0% vs. 0.9%, p = .026) [Correction added on December 23, 2020, after first online publication: as per Dr. Moreno's request changes in p-values were made after original publication in Abstract.], chronic renal failure (6.0% vs. 1.2%, p < .001), NYHA > II (3.8% vs. 1.2%, p = .001), and CCS > II (4.2% vs. 1.4%, p = .013), whereas was it was significantly lower in smokers (0.5% vs. 1.9%, p = .013). Multivariable analysis identified age > 80, diabetes, renal failure and CCS > II as independent predictors for mortality. CONCLUSION: Mortality at 45 days during COVID-19 outbreak in patients with chronic cardiovascular diseases included in a waiting list due to cancellation of invasive elective procedures was 1.7%. Some clinical characteristics may be of help in patient selection for being promptly treated when similar situations happen in the future.

Characteristics and Outcomes in Patients Presenting With COVID-19 and ST-Segment Elevation Myocardial Infarction.

There is limited information regarding clinical characteristics and outcomes of patients with SARS-CoV-2 (COVID-19) disease presenting with ST-segment elevation myocardial infarction (STEMI). In this multicenter retrospective study, we reviewed charts of patients admitted with symptomatic COVID-19 infection and STEMI to a total of 4 hospitals spanning Italy, Lithuania, Spain and Iraq from February 1, 2020 to April 15, 2020. A total of 78 patients were included in this study, 49 (63%) of whom were men, with a median age of 65 [58, 71] years, and high comorbidity burden. During hospitalization, 8 (10%) developed acute respiratory distress syndrome, and 14 (18%) required mechanical ventilation. 19 (24%) patients were treated with primary Percutaneous Coronary Intervention (PCI) and 59 (76%) were treated with fibrinolytic therapy. 13 (17%) patients required cardiac resuscitation, and 9 (11%) died. For the 19 patients treated with primary PCI, 8 (42%) required intubation and 8 (42%) required cardiac resuscitation; stent thrombosis occurred in 4 patients (21%). A total of 5 patients (26%) died during hospitalization. 50 (85%) of the 59 patients initially treated with fibrinolytic therapy had successful fibrinolysis. The median time to reperfusion was 27 minutes [20, 34]. Hemorrhagic stroke occurred in 5 patients (9%). Six patients (10%) required invasive mechanical ventilation; 5 (9%) required cardiac resuscitation, and 4 (7%) died. In conclusion, this is the largest case series to-date of COVID-19 positive patients presenting with STEMI and spans 4 countries. We found a high rate of stent thrombosis, indicating a possible need to adapt STEMI management for COVID-19 patients.

Is it safe and effective to maintain the vaginal pessary without removing it for 2 consecutive years?

INTRODUCTION AND HYPOTHESIS: The study was aimed at evaluating the safety and efficacy of ring pessaries without support under continuous use without periodic removal or replacement for the treatment of advanced pelvic organ prolapse (POP) in women for 2 consecutive years. METHODS: This study was a prospective observational study. A total of 123 women were recruited in a tertiary hospital from January 2013 to January 2016. The primary objective was the percentage of patients maintaining the use of the pessary after 24 months. The secondary objectives were the reasons for discontinuation and the adverse events in patients with successful fittings. RESULTS: A total of 115 patients (93.5%) had a successful fitting. Four patients died of non-pessary-related causes during the study and, one patient dropped out the follow-up so that finally, 110 patients were included in the efficacy analysis. Pessary use was maintained by 91.8% of the women at the end of the study. The adverse events rate was low (27.0%). The two main factors of interruption in the pessary use were: age (OR 0.93; 95% CI 0.87-0.99) and history of urinary urge incontinence (OR 0.33; 95% CI 0.11-0.96]). CONCLUSIONS: A high success rate and low adverse events rate were achieved in patients with advanced-stage POP with continuous pessary use for 24 months, indicating that a ring pessary could also be used without periodic removal for at least the first 2 years. This practice could reduce the number of control visits.

Seasonal Local Models for Glucose Prediction in Type 1 Diabetes.

Linear empirical dynamic models have been widely used for blood glucose prediction and risks prevention in people with type 1 diabetes. More accurate blood glucose prediction models with longer prediction horizon (PH) are desirable to enable warnings to patients about imminent blood glucose changes with enough time to take corrective actions. In this study, a blood glucose prediction method is developed by integrating the predictions of a set of seasonal local models (each of them corresponding to different glucose profiles observed along historical data). In the modeling step, the number of sets and their corresponding glucose profiles characteristics are obtained by clustering techniques (Fuzzy C-Means). Then, Box-Jenkins methodology is used to identify a seasonal model for each set. Finally, blood glucose predictions of local models are integrated using different techniques. The proposed method is tested by using 18 60-h closed-loop experiments (including different exercise types and artificial pancreas strategies) and achieving mean absolute percentage error (MAPE) of 2.94%, 3.89%, 5.41%, 6.29% and 8.66% for 15-, 30-, 45-, 60-, and 90-min PHs, respectively.

Parallel Control of an Artificial Pancreas with Coordinated Insulin, Glucagon, and Rescue Carbohydrate Control Actions.

BACKGROUND: An artificial pancreas with insulin and glucagon delivery has the potential to reduce the risk of hypo- and hyperglycemia in people with type 1 diabetes. However, a maximum dose of glucagon of 1 mg/d is recommended, potentially still requiring rescue carbohydrates in some situations. This work presents a parallel control structure with intrinsic insulin, glucagon, and rescue carbohydrates coordination to overcome glucagon limitations when needed. METHODS: The coordinated controller that combines insulin, glucagon, and rescue carbohydrate suggestions (DH-CC-CHO) was compared with the insulin and glucagon delivery coordinated controller (DH-CC). The impact of carbohydrate quantization for practical delivery was also assessed. An in silico study using the UVA-Padova simulator, extended to include exercise and various sources of variability, was performed. RESULTS: DH-CC and DH-CC-CHO performed similarly with regard to mean glucose (126.25 [123.43; 130.73] vs 127.92 [123.99; 132.97] mg/dL, P = .088), time in range (93.04 [90.00; 95.92] vs 92.91 [90.05; 95.75]%, P = .508), time above 180 mg/dL (4.94 [2.72; 7.53] vs 4.99 [2.93; 7.24]%, P = .966), time below 70 mg/dL (0.61 [0.09; 1.75] vs 0.96 [0.23; 2.17]%, P = .1364), insulin delivery (43.50 [38.68; 51.75] vs 42.86 [38.58; 51.36] U/d, P = .383), and glucagon delivery (0.75 [0.40; 1.83] vs 0.76 [0.43; 0.99] mg/d, P = .407). Time below 54 mg/dL was different (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.16]%, P = .036), although non-clinically significant. This was due to the carbs quantization effect in a specific patient, as no statistical difference was found when carbs were not quantized (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.00]%, P = .265). CONCLUSIONS: The new strategy of automatic rescue carbohydrates suggestion in coordination with insulin and glucagon delivery to overcome constraints on daily glucagon delivery was successfully evaluated in an in silico proof of concept.

Adjustment of a numerical model for pore pressure generation during an earthquake.

This article proposes methodology for evaluating the accuracy of the pore pressure generation model devised by Byrne, as implemented in a commercial software program using a Mohr-Coulomb-type failure criterion and a Finn constitutive model. The different empirical formulas of liquefaction developed by Seed and Idriss are reviewed, as well as various constitutive models specified in the literature, emphasizing the selection of the Finn model for the liquefaction study. In the analysis a comparison is carried out using the factors of safety against liquefaction (FSLs) devised by Seed and Idriss and the adapted formula by Boulanger and Idriss. The analysis assumes a hypothesis to verify whether a soil element is liquefied. The results are then compared with those of a numerical model that simulates a soil column, the base of which is subjected to the same seismic inputs of varying magnitudes, Mw, and peak ground accelerations, Pga, to which the empirical model was subjected. Adjusted equations are provided on the based on that comparison to allow for the calibration of the Byrne equation using the (N1)60 value obtained via a standard penetration test (SPT), for the study of liquefaction problems in situations in which there are earthquakes of varying magnitudes.

Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration.

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are "Mets" (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only "Mets" is also viable for a more immediate implementation of this correction into market devices.

Insuficiencia cervical y prolapso de bolsa amniótica "en reloj de arena"en gestante pretérmino / Cervical Insufficiency and Prolapse of an "Hourglass" Amniotic Sac in Preterm Pregnant Women

RESUMEN Introducción: La insuficiencia cervical se concibe hoy día como un proceso biológico continuo con grados progresivos de insuficiencia. La expresión más extrema de la insuficiencia cervical se conoce como "bolsa en reloj de arena". La mayoría de los autores están de acuerdo en recomendar el reposo en cama y realizar cerclaje cervical de emergencia. Objetivo: Presentar el caso de una gestante en la que se logró la reversión del prolapso de las membranas amnióticas en "reloj de arena" con buenos resultados perinatales. Presentación de caso: Paciente de 31 años con antecedentes de parto anterior a las 36,5 semanas, que en los momentos actuales cursa una gestación de 20,6 semanas. Acude a la consulta por dolor en hipogastrio y al examen físico se comprueba que la bolsa amniótica protruye por el orificio cervical externo y presenta una dilatación cervical de 2 cm. Se aplica tratamiento medicamentoso y se reduce el prolapso de la bolsa. Conclusiones: Con el tratamiento conservador se logró una buena evolución de la gestación, con parto eutócico y buenos resultados perinatales(AU)

ABSTRACT Introduction: Cervical insufficiency is conceived today as a continuous biological process with progressive degrees of insufficiency. The most extreme expression of cervical insufficiency is known as "hourglass bag". Most authors agree to recommend bed rest and perform emergency cervical cerclage. Objective: To report the case of a pregnant woman in whom the prolapse reversal of the amniotic membranes in hourglass was achieved with good perinatal results. Case report: A 31-year-old patient who is currently pregnant at 20.6 weeks and has history of childbirth prior to 36.5 weeks. She comes to the consultation complaining of pain in hypogastrium. The physical examination shows that the amniotic sac protrudes through the external cervical orifice and there is 2cm cervical dilation. Drug treatment is applied and the prolapse of the sac is reduced. Conclusions: The conservative treatment achieved good evolution of the gestation, with eutócico childbirth and good perinatal results(AU)