In-vitro model for assessing glucose diffusion through skin

Ullah, Sana; Hamade, Fadi; Bubniene, U.; Engblom, Johan; Ramanavičius, Arūnas; Ruzgas, Tautgirdas

doi:10.1016/j.bios.2018.03.039

Cited by 28 publications

(20 citation statements)

References 22 publications

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“…This limits the choice of naturally available nutrients to peptides and lipids, restricting access to urea, ammonia, vitamins or sugars (Scharschmidt and Fischbach 2013 ). However, the latter will at least partially be compensated for by diffusion of glucose from the cell culture medium into the dermal and epidermal layers of the model (Khalil et al 2006 ; Ullah et al 2018 ). This provides some selectional advantage for the more invasive P. oleovorans as even a thin stratum corneum layer constitutes a considerable barrier for passive diffusion (Ullah et al 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Microbially competent 3D skin: a test system that reveals insight into host–microbe interactions and their potential toxicological impact

et al. 2020

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The skin`s microbiome is predominantly commensalic, harbouring a metabolic potential far exceeding that of its host. While there is clear evidence that bacteria-dependent metabolism of pollutants modulates the toxicity for the host there is still a lack of models for investigating causality of microbiome-associated pathophysiology or toxicity. We now report on a biologically characterised microbial–skin tissue co-culture that allows studying microbe–host interactions for extended periods of time in situ. The system is based on a commercially available 3D skin model. In a proof-of-concept, this model was colonised with single and mixed cultures of two selected skin commensals. Two different methods were used to quantify the bacteria on the surface of the skin models. While Micrococcus luteus established a stable microbial–skin tissue co-culture, Pseudomonas oleovorans maintained slow continuous growth over the 8-day cultivation period. A detailed skin transcriptome analysis showed bacterial colonisation leading to up to 3318 significant changes. Additionally, FACS, ELISA and Western blot analyses were carried out to analyse secretion of cytokines and growth factors. Changes found in colonised skin varied depending on the bacterial species used and comprised immunomodulatory functions, such as secretion of IL-1α/β, Il-6, antimicrobial peptides and increased gene transcription of IL-10 and TLR2. The colonisation also influenced the secretion of growth factors such as VFGFA and FGF2. Notably, many of these changes have already previously been associated with the presence of skin commensals. Concomitantly, the model gained first insights on the microbiome’s influence on skin xenobiotic metabolism (i.e., CYP1A1, CYP1B1 and CYP2D6) and olfactory receptor expression. The system provides urgently needed experimental access for assessing the toxicological impact of microbiome-associated xenobiotic metabolism in situ.

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Section: Discussionmentioning

confidence: 99%

Microbially competent 3D skin: a test system that reveals insight into host–microbe interactions and their potential toxicological impact

et al. 2020

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“…All commercial CGM devices are labeled as minimally invasive since they require either a microneedle or a small capsule to be inserted in the subcutis, and they represent an important innovation because they allow reducing the burden of performing multiple daily invasive self-monitoring tests of BG concentrations. Of note, in recent years, there has been a great effort in investigating noninvasive glucose monitoring technologies (see [3][4][5][6] for reviews on the topic). Noninvasive CGM devices represent a further step in reducing the burden related to the daily management of T1D, but unfortunately, they are all still prototypes.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only

Prendin

Favero

Vettoretti

et al. 2021

Sensors

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In type 1 diabetes management, the availability of algorithms capable of accurately forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could enable proactive therapeutic actions, e.g., the consumption of carbohydrates to mitigate, or even avoid, an impending critical event. The only input of this kind of algorithm is often continuous glucose monitoring (CGM) sensor data, because other signals (such as injected insulin, ingested carbs, and physical activity) are frequently unavailable. Several predictive algorithms fed by CGM data only have been proposed in the literature, but they were assessed using datasets originated by different experimental protocols, making a comparison of their relative merits difficult. The aim of the present work was to perform a head-to-head comparison of thirty different linear and nonlinear predictive algorithms using the same dataset, given by 124 CGM traces collected over 10 days with the newest Dexcom G6 sensor available on the market and considering a 30-min prediction horizon. We considered the state-of-the art methods, investigating, in particular, linear black-box methods (autoregressive; autoregressive moving-average; and autoregressive integrated moving-average, ARIMA) and nonlinear machine-learning methods (support vector regression, SVR; regression random forest; feed-forward neural network, fNN; and long short-term memory neural network). For each method, the prediction accuracy and hypoglycemia detection capabilities were assessed using either population or individualized model parameters. As far as prediction accuracy is concerned, the results show that the best linear algorithm (individualized ARIMA) provides accuracy comparable to that of the best nonlinear algorithm (individualized fNN), with root mean square errors of 22.15 and 21.52 mg/dL, respectively. As far as hypoglycemia detection is concerned, the best linear algorithm (individualized ARIMA) provided precision = 64%, recall = 82%, and one false alarm/day, comparable to the best nonlinear technique (population SVR): precision = 63%, recall = 69%, and 0.5 false alarms/day. In general, the head-to-head comparison of the thirty algorithms fed by CGM data only made using a wide dataset shows that individualized linear models are more effective than population ones, while no significant advantages seem to emerge when employing nonlinear methodologies.

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“…Biocompatibility-related aspects of conducting polymers are very important, because some biosensors and biofuel cells have recently been implanted in the body [151,152] or attached to the skin [153] of the patient. Therefore, the biocompatibility of these devices is a very important issue; otherwise, non-biocompatible structures can cause inflammation and/or other serious disorders [154,155].…”

Section: Biocompatibility-related Aspects Of Some Conducting Polymersmentioning

confidence: 99%

Conducting Polymers in the Design of Biosensors and Biofuel Cells

2020

Self Cite

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Fast and sensitive determination of biologically active compounds is very important in biomedical diagnostics, the food and beverage industry, and environmental analysis. In this review, the most promising directions in analytical application of conducting polymers (CPs) are outlined. Up to now polyaniline, polypyrrole, polythiophene, and poly(3,4-ethylenedioxythiophene) are the most frequently used CPs in the design of sensors and biosensors; therefore, in this review, main attention is paid to these conducting polymers. The most popular polymerization methods applied for the formation of conducting polymer layers are discussed. The applicability of polypyrrole-based functional layers in the design of electrochemical biosensors and biofuel cells is highlighted. Some signal transduction mechanisms in CP-based sensors and biosensors are discussed. Biocompatibility-related aspects of some conducting polymers are overviewed and some insights into the application of CP-based coatings for the design of implantable sensors and biofuel cells are addressed. New trends and perspectives in the development of sensors based on CPs and their composites with other materials are discussed.

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In-vitro model for assessing glucose diffusion through skin

Cited by 28 publications

References 22 publications

Microbially competent 3D skin: a test system that reveals insight into host–microbe interactions and their potential toxicological impact

Microbially competent 3D skin: a test system that reveals insight into host–microbe interactions and their potential toxicological impact

Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only

Conducting Polymers in the Design of Biosensors and Biofuel Cells

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