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ABSTRACT Objective Diabetes mellitus is associated with significant morbidity and mortality, and education is known to play a key role in managing this disease. This study addresses the effects of a structured education program (SEP) on self-care in subjects with type 1 diabetes mellitus (T1DM). The aim was to evaluate the effect of a SEP on glycemic control, knowledge, and skills associated with diabetes care in subjects with T1DM. Subjects and methods A total of 47 adults with T1DM were followed up for 20 months (32 participated in the SEP and 15 served as a control group). The SEP consisted of workshops, individualized care, 24-hour distant support, and a questionnaire assessing knowledge of diabetes care. Glycosylated hemoglobin (HbA1c) levels were measured before and after the SEP implementation. Results Compared with pre-SEP levels, the mean HbA1c levels decreased by approximately 20% (21 mmol/mol) at 1 year, with a further 11% reduction (10 mmol/mol) observed 8 months later (p < 0.001). Knowledge about diabetes care increased by 37% between the pre-SEP and post-SEP questionnaires (p < 0.005). Conclusion Relevant improvements occurred after SEP activities. The sustained decrease in HbA1c levels and the overall increase in knowledge and confidence regarding diabetes care reinforce the importance, necessity, and positive outcomes of a SEP intervention in T1DM.

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Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV) reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecomposition of HRV parameter covariance matrices was used to create an input for a support vector machine (SVM)-based classifier. We analyzed clinical data from 12 patients (9 female; 3 male; age 34.5 ± 7.5 years), involving 34 seizures and a total of 55.2 h of interictal electrocardiogram (ECG) recordings. Data from 123.6 h of ECG recordings from healthy subjects were used to test false positive rate per hour (FP/h) in a completely independent data set. Our methodological approach allowed the detection of impending seizures from 5 min to just before the onset of a clinical/electrical seizure with a sensitivity of 94.1%. The FP rate was 0.49 h-1 in the recordings from patients with epilepsy and 0.19 h-1 in the recordings from healthy subjects. Our results suggest that it is feasible to use the dynamics of HRV parameters for the early detection and, potentially, the prediction of epileptic seizures.

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OBJECTIVE: Diabetes mellitus is associated with significant morbidity and mortality, and education is known to play a key role in managing this disease. This study addresses the effects of a structured education program (SEP) on self-care in subjects with type 1 diabetes mellitus (T1DM). The aim was to evaluate the effect of a SEP on glycemic control, knowledge, and skills associated with diabetes care in subjects with T1DM. SUBJECTS AND METHODS: A total of 47 adults with T1DM were followed up for 20 months (32 participated in the SEP and 15 served as a control group). The SEP consisted of workshops, individualized care, 24-hour distant support, and a questionnaire assessing knowledge of diabetes care. Glycosylated hemoglobin (HbA1c) levels were measured before and after the SEP implementation. RESULTS: Compared with pre-SEP levels, the mean HbA1c levels decreased by approximately 20% (21 mmol/mol) at 1 year, with a further 11% reduction (10 mmol/mol) observed 8 months later (p < 0.001). Knowledge about diabetes care increased by 37% between the pre-SEP and post-SEP questionnaires (p < 0.005). CONCLUSION: Relevant improvements occurred after SEP activities. The sustained decrease in HbA1c levels and the overall increase in knowledge and confidence regarding diabetes care reinforce the importance, necessity, and positive outcomes of a SEP intervention in T1DM.

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Electrochemotherapy is a combination of high electric field and anticancer drugs. The treatment basis is electroporation or electropermeabilization of the cell membrane. Electroporation is a threshold phenomenon and, for efficient treatment, an adequate local distribution of electric field within the treated tissue is important. When this local electric field is not enough, there is a regrown tumor cell; however, if it is stronger than necessary, permanent damage to the tissue occurs. In the treatment of dogs, electrochemotherapy is not yet an established treatment for mast cell tumor in veterinary medicine, although there are studies showing evidence of its effectiveness. In this study, we examined electrochemotherapy of dog mast cell tumor with numerical simulation of local electric field distribution. The experimental result was used to validate the numerical models. The effect of tumor position and tissue thickness (tumor in different parts of dog body) was investigated using plate electrodes. Our results demonstrated that the electrochemotherapy is efficient and flexible, and even when the tumor extends into the subcutis, the treatment with plate electrode eliminated the tumor cells. This result suggests that electrochemotherapy is a suitable method to treat mast cell tumors in dog.

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Conductivity measurements in suspensions of biological cells have been used since many years for electroporation effectiveness evaluation. However, conductivity modeling by means of instantaneous values of current and voltage during pulse application does not take into account the effects of the sample reactance and the dielectric dispersion of the medium. This can lead to misinterpretation in the electroporation analysis. The electrical modeling and characterization of electroporation using sinusoidal signal analysis at 10 kHz proposed in this paper allows us to avoid distortions due to reactive effects of the sample. A simple equation establishes the relation between suspension conductivity and membrane conductance. This model was used in experiments with suspensions of yeast cells and applied electric fields of up to 450 kV/m for 1 ms. The analysis using the proposed model resulted in membrane conductance values of up to 8000 S/m (2) and allowed estimating the distribution profile of conductance on the cell membrane.

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There are several models of decomposition of the electrocardiogram (ECG). Some of these models are intended to describe the ECG signal, and others are more specific to extract the relevant information relating to individual waveform which contributes to explain the P-QRS complex. The latter approach may be particularly suitable for a portion where a morphological analysis of the ECG is of particular interest, as the cardiac repolarization segment or T-wave. This study aims: to model and detect useful patterns in the evaluation of T wave morphology, which explains the different changes in ventricular repolarization during inhalation of Salbutamol.

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An intense electric field can be applied to increase the membrane conductance G(m) and consequently, the conductivity of cell suspension. This phenomenon is called electroporation. This mechanism is used in a wide range of medical applications, genetic engineering, and therapies. Conductivity measurements of cell suspensions were carried out during application of electric fields from 40 to 165 kV/m. Experimental results were analyzed with two electroporation models: the asymptotic electroporation model was used to estimate G(m) at the beginning and at the end of electric field pulse, and the extended Kinosita electroporation model to increase G(m) linearly in time. The maximum G(m) was 1-7 × 10(4) S/m(2), and the critical angle (when the G(m) is insignificant) was 50°-65°. In addition, the sensitivity of electroporated membrane conductance to extracellular and cytoplasmatic conductivity and cell radius has been studied. This study showed that external conductivity and cell radius are important parameters affecting the pore-opening phenomenon. However, if the cell radius is larger than 7 µm in low conductivity medium, the cell dimensions are not so important.

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Autonomic neuropathy (AN) is a common and serious complication of diabetes. Early detection is essential to enable appropriate interventional therapy. It has long been recognized that subjects with diabetic peripheral neuropathy (DPN) are at much greater risk of developing AN, but there is currently no simple screening tool to assess them. The aim of this study was to investigate pupil responsiveness in diabetic subjects with and without DPN using dynamic pupillometry. During the first test, one flash was administered and the pupil response recorded for 3 seconds. In the second test, twenty-five flashes at one-second intervals were administered and the pupil response recorded for 30 seconds. Several time related parameters were computed from the results. A total of 29 diabetic subjects (17 no DPN, 12 DPN) and 25 healthy volunteers took part in the study. In the first test, pupil-iris ratios in darkness, large deviation and plateau were significantly different between groups. Latency time from flash exposure to the start of constriction was significantly longer in diabetic subjects with DPN compared to healthy volunteers. There was no difference in latency times of largest deviation, plateau or duration of constriction between groups. In the second test, the pupil-iris ratios evaluated in the frame preceding the tenth and the twenty-fifth light flash were significantly greater in healthy volunteers than diabetic subjects with DPN. Latency time from the tenth and twenty-fifth flash exposure to the start of constriction was significantly shorter in healthy volunteers than in diabetic subjects with DPN.

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A new computational approach for electrical analysis especially designed for application in biological tissues is presented. It is based on the modelling of the electrical properties of the medium by means of lumped circuit elements, such as capacitance, conductance and current sources. The cell scale model is suitable for modelling the local anisotropy around cell membranes. It permits to obtain the electric potential, ionic concentrations and current densities around cells in time steps in an iterative process. The tissue scale model utilises volume-averaged values of conductivity and permittivity and models suitably the dispersive characteristic of biological tissues. It permits to obtain potential and current distributions in large volumes of tissue in the time or frequency domain. An example of analysis of skeletal muscle is presented aiming to demonstrate the features of the method.

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