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Background: For individuals who wish to reduce their cannabis use without formal help, there are a variety of self-help tools available. Although some are proven to be effective in reducing cannabis use, effect sizes are typically small. More insight into predictors of successful reduction of use among individuals who frequently use cannabis and desire to reduce/quit could help identify factors that contribute to successful cannabis use moderation., Methods: We analyzed data taken from a randomized controlled trial comparing the effectiveness of the digital cannabis intervention ICan to four online modules of educational information on cannabis. For the current study, we included 253 participants. Success was defined as reducing the grams of cannabis used in the past 7 days at baseline by at least 50 % at 6-month follow-up. To train and evaluate the machine learning models we used a nested k-fold cross-validation procedure., Results: The results show that the two models applied had comparable low AUROC values of .61 (Random Forest) and .57 (Logistic Regression). Not identifying oneself as a cannabis user, not using tobacco products, high levels of depressive symptoms, high levels of psychological distress and high initial cannabis use values were the relatively most important predictors for success, although overall the associations were not strong., Conclusions: Our study found only modest prediction accuracy when using machine learning models to predict success among individuals who use cannabis and desire to reduce/quit and show interest in digital self-help tools., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2024. Published by Elsevier B.V.)

Aim: The aim of this study was to compare the peak extensor torque (primary outcome), sensory-motor capacity and Lysholm questionnaire score up to the fourth postoperative month post-primary anterior cruciate ligament reconstruction (ACLR) with bone-patellar-tendon-bone (BPTB) graft using either ipsilateral or contralateral technique in young sportsmen., Methods: We performed a randomized controlled trial from 2018 to 2019. The same preoperative protocol, surgical technique and postoperative protocol were used in both groups. Eighty-eight young adult male patients were randomized, and 44 of each group completed the trial., Results: After the follow up analysis, the contralateral receptor and donor limb demonstrated a statistically significant increase in peak extension torque compared with the ipsilateral operated limb (effect size (ES) = 0.99, power = 0.99, P < 0.0001; ES = 0.46, power = 0.56, P < 0.04). The symmetry between limbs was compared between groups. In the contralateral group, there was a significant decrease in the recipient limb (after surgery), compared with the donor limb (before surgery) (ES = 0.87, power = 0.99, P < 0.0001). In the ipsilateral group, the operated limb demonstrated a significant decrease in peak extension torque compared with the non-operated limb after surgery (ES = 1.88, power = 0.99, P < 0.0001). The comparison between groups in sensory-motor capacity and Lysholm score demonstrated a non-significant group effect postoperatively., Conclusion: The use of contralateral BPTB is more effective in increasing peak extension torque when compared with ipsilateral technique in young sportsmen. Furthermore, patients achieved greater symmetry (side to side) in quadriceps muscle performance after the 4-month follow up with a contralateral graft., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Injury to the medial compartment of the knee is the most common ligament injury to this joint. The medial approach must consider the presence of associated anteromedial instability. Untreated injury of these instabilities can result in failure of the other reconstructed ligaments. As treatment is usually associated with other ligaments, it is relevant that the technique could save grafts and synthetic material. This article aims to describe a technique for the treatment of anteromedial instabilities through semimembranosus tendon tenodesis in a more anterior and distal position, promoting the tensioning of the posteromedial structures., (© 2021 by the Arthroscopy Association of North America. Published by Elsevier.)

Objective: To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA)., Methods: Several types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3-5 within 6 months., Results: The Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80-86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40-51) and 89% specificity (95%-CI 86-92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83-88) at 24 h after CA, with 62% sensitivity (95%-CI 57-67) and 84% specificity (95%-CI 79-88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives., Conclusions: Quantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data., Significance: Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

The diagnosis of rapid eye movement (REM) sleep behavior disorder (SBD) requires videopolysomnography detection of excessive electromyographic activity during REM sleep, which is time consuming and difficult. An easier, faster, reliable, and reproducible methodology is needed for its diagnosis. The isolated form of RBD represents an early manifestation of the synucleinopathies Parkinson disease and dementia with Lewy bodies. There is a need to find neuroprotective drugs capable of preventing parkinsonism and dementia onset in isolated RBD. Clonazepam and melatonin ameliorate the RBD symptoms, but therapeutic alternatives are needed when these medications fail or show produce side effects., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score (mRS) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on average 1090 patients in the training set and 273 patients in the test set, the fuzzy decision tree misclassified on average 77 (standard deviation of 7) patients compared to 83 (±7) using CART. The mean number of nodes (decision and leaf nodes) in the fuzzy decision tree was 11 (±2) compared to 26 (±1) for CART decision trees. With an average accuracy of 72% and much fewer nodes than CART, the developed evolutionary algorithm for fuzzy decision trees might be used to gain insights into the predictive value of patient characteristics and can contribute to the development of more accurate medical outcome prediction methods with improved clarity for practitioners and patients., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The pathogenic mutation p.Arg14del in the gene encoding Phospholamban (PLN) is known to cause cardiomyopathy and leads to increased risk of sudden cardiac death. Automatic tools might improve the detection of patients with this rare disease. Deep learning is currently the state-of-the-art in signal processing but requires large amounts of data to train the algorithms. In situations with relatively small amounts of data, like PLN, transfer learning may improve accuracy. We propose an ECG-based detection of the PLN mutation using transfer learning from a model originally trained for sex identification. The sex identification model was trained with 256,278 ECGs and subsequently finetuned for PLN detection (155 ECGs of patients with PLN) with two control groups: a balanced age/sex matched group and a randomly selected imbalanced population. The data was split in 10 folds and 20% of the training data was used for validation and early stopping. The models were evaluated with the area under the receiver operating characteristic curve (AUROC) of the testing data. We used gradient activation for explanation of the prediction models. The models trained with transfer learning outperformed the models trained from scratch for both the balanced (AUROC 0.87 vs AUROC 0.71) and imbalanced (AUROC 0.0.90 vs AUROC 0.65) population. The proposed approach was able to improve the accuracy of a rare disease detection model by transfer learning information from a non-manual annotated and abundant label with only limited data available., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocardiographic (ECG) features have been identified for diagnosis. Machine learning is a powerful tool used in ECG analysis and has shown to outperform cardiologists., Objectives: We aimed to develop machine learning and deep learning models to diagnose PLN p.Arg14del cardiomyopathy using ECGs and evaluate their accuracy compared to an expert cardiologist., Methods: We included 155 adult PLN mutation carriers and 155 age- and sex-matched control subjects. Twenty-one PLN mutation carriers (13.4%) were classified as symptomatic (symptoms of heart failure or malignant ventricular arrhythmias). The data set was split into training and testing sets using 4-fold cross-validation. Multiple models were developed to discriminate between PLN mutation carriers and control subjects. For comparison, expert cardiologists classified the same data set. The best performing models were validated using an external PLN p.Arg14del mutation carrier data set from Murcia, Spain (n = 50). We applied occlusion maps to visualize the most contributing ECG regions., Results: In terms of specificity, expert cardiologists (0.99) outperformed all models (range 0.53-0.81). In terms of accuracy and sensitivity, experts (0.28 and 0.64) were outperformed by all models (sensitivity range 0.65-0.81). T-wave morphology was most important for classification of PLN p.Arg14del carriers. External validation showed comparable results, with the best model outperforming experts., Conclusion: This study shows that machine learning can outperform experienced cardiologists in the diagnosis of PLN p.Arg14del cardiomyopathy and suggests that the shape of the T wave is of added importance to this diagnosis., (Copyright © 2020 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)

Cellulose nanocrystals (CNCs), castor oil (CO), and recycled poly(ethylene terephthalate) (rPET), were used to add value to renewable raw materials and to a recycled polymer produced worldwide, producing mats composed of fibers on the nano- and submicrometric (ultrathin) scales through a sustainable process. Bio-based electrospun mats composed of aligned (rotary collector) and nonaligned (static collector) nanofibers/ultrathin fibers were produced from the electrospinning of solutions prepared from rPET (mixed with CO, CNCs, or CNCs/CO). The contact angle results showed that the CNC mat surfaces composed of nonaligned fibers were hydrophilic, and in contrast, these surfaces were hydrophobic when composed of aligned fibers. Among the mats composed of nonaligned fibers, PET/CO/CNC exhibited storage and Young's moduli approximately eleven and ten times, respectively, better than those of neat rPET. The PET/CO/CNC mat showed both modulus and tensile strength values higher than those of PET/CNC, when characterized in the preferential direction of fiber alignment. Electrospun mats were obtained from environmentally sound raw materials with diversified properties, which were modulated by the type of collector used, as well as whether CO and CNC were mixed with rPET, and have the potential for use in applications such as membrane separation processes and biomedical applications., Competing Interests: Declaration of competing interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Deep learning (DL) has shown promising results in improving atrial fibrillation (AF) detection algorithms. However, these models are often criticized because of their "black box" nature., Aim: To develop a morphology based DL model to discriminate AF from sinus rhythm (SR), and to visualize which parts of the ECG are used by the model to derive to the right classification., Methods: We pre-processed raw data of 1469 ECGs in AF or SR, of patients with a history AF. Input data was generated by normalizing all single cycles (SC) of one ECG lead to SC-ECG samples by 1) centralizing the R wave or 2) scaling from R-to- R wave. Different DL models were trained by splitting the data in a training, validation and test set. By using a DL based heat mapping technique we visualized those areas of the ECG used by the classifier to come to the correct classification., Results: The DL model with the best performance was a feedforward neural network trained by SC-ECG samples on a R-to-R wave basis of lead II, resulting in an accuracy of 0.96 and F1-score of 0.94. The onset of the QRS complex proved to be the most relevant area for the model to discriminate AF from SR., Conclusion: The morphology based DL model developed in this study was able to discriminate AF from SR with a very high accuracy. DL model visualization may help clinicians gain insights into which (unrecognized) ECG features are most sensitive to discriminate AF from SR., Competing Interests: Declaration of competing interest Baalman, Schroevers, Oakley, Van der Stuijt, Ramos, Lopes, Bleijendaal and Marquering report no relationships that could be constructed as conflicts of interest. Brouwer reports compensation for services from Boston Scientific. Knops reports consulting fees, research grants and honoraria for Boston Scientific, consulting fees research grants with Medtronic and Abbott. De Groot reports research grants from the Dutch Organization for Scientific Research, Abbott, AtriCure, Boston Scientific, Medtronic, consulting fees for Articure, Bayer, Diachii Sankyo, J&J, Novartis and Sewie., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Kraft lignin (KL) and castor oil (CO) were used as polyols in the synthesis of bio-based polyurethanes (PUs) in the absence of both solvents and catalysts at room temperature with simultaneous film formation. KL was purified (PKL), and both KL and PKL were fully characterized. CO was mixed with different percentages of PKL (0%, 10%, 30%, and 50%), as well as with polymeric methyl phenyl diisocyanate. After degassing, the reaction mixture was stirred; when the medium viscosity was suitable for spreading, it was poured onto a glass plate, and the thickness was adjusted using an extender. The storage modulus (E', 25 °C) and tensile strength of the lignopolyurethane films (LignoPU COPKL ) were higher than those of the control film (PU CO ). LignoPU COPKL30 and LignoPU COPKL50 did not break under the conditions that the other films broke under. It was noted phase segregation (rigid and flexible domains) for LignoPU COPKL30 and LignoPU COPKL50 , and the glass transition temperature (T g ) of the flexible domains (96.2 °C and 52.3 °C, respectively) was higher than that of PU CO (8.4 °C). The formed films were also characterized by scanning electron microscopy, thermogravimetric analysis, X-ray diffraction, contact angles, and swelling tests. To our knowledge, the approach of this study is unprecedented., (Copyright © 2018. Published by Elsevier B.V.)

Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarkers (old age, pre-stroke mRS, NIHSS, occlusion location, ASPECTS, among others) have an important role in treatment selection and prognosis. Radiological biomarkers require expert annotation and are subject to inter-observer variability. Recently, Deep Learning has been introduced to reproduce these radiological image biomarkers. Instead of reproducing these biomarkers, in this work, we investigated Deep Learning techniques for building models to directly predict good reperfusion after endovascular treatment (EVT) and good functional outcome using CT angiography images. These models do not require image annotation and are fast to compute. We compare the Deep Learning models to Machine Learning models using traditional radiological image biomarkers. We explored Residual Neural Network (ResNet) architectures, adapted them with Structured Receptive Fields (RFNN) and auto-encoders (AE) for network weight initialization. We further included model visualization techniques to provide insight into the network's decision-making process. We applied the methods on the MR CLEAN Registry dataset with 1301 patients. The Deep Learning models outperformed the models using traditional radiological image biomarkers in three out of four cross-validation folds for functional outcome (average AUC of 0.71) and for all folds for reperfusion (average AUC of 0.65). Model visualization showed that the arteries were relevant features for functional outcome prediction. The best results were obtained for the ResNet models with RFNN. Auto-encoder initialization often improved the results. We concluded that, in our dataset, automated image analysis with Deep Learning methods outperforms radiological image biomarkers for stroke outcome prediction and has the potential to improve treatment selection., (Copyright © 2019. Published by Elsevier Ltd.)

Background: The amount of blood detected on brain computed tomography scan is frequently used in prediction models for delayed cerebral ischemia (DCI) in patients with aneurysmal subarachnoid hemorrhage (aSAH). These models, which include coarse grading scales to assess the amount of blood, have only moderate predictive value. Therefore, we aimed to develop a predictive model for DCI including automatically quantified total blood volume (TBV)., Methods: We included patients from a prospective aSAH registry. TBV was assessed with an automatic hemorrhage quantification algorithm. The outcome measure was clinical deterioration due to DCI. Clinical and radiologic variables were included in a logistic regression model. The final model was selected by bootstrapped backward selection and internally validated by assessing the optimism-corrected R 2 value, c-statistic, and calibration plot. The c-statistic of the TBV model was compared with models that used the (modified) Fisher scale instead., Results: We included 369 patients. After backward selection, only TBV was included in the final model. The internally validated R 2 value was 6%, and the c-statistic was 0.64. The c-statistic of the TBV model was higher than both the Fisher scale model (0.56; P < 0.001) and the modified Fisher scale model (0.58; P < 0.05)., Conclusions: In our registry, only TBV independently predicted DCI. TBV discriminated better than the (modified) Fisher scale, but still had only moderate value for predicting DCI. Our findings suggest that other factors need to be identified to achieve better accuracy for predicting DCI., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Aquatic ecotoxicity assays used to assess ecological risk assume that organisms living in a contaminated habitat are forcedly exposed to the contamination. This assumption neglects the ability of organisms to detect and avoid contamination by moving towards less disturbed habitats, as long as connectivity exists. In fluvial systems, many environmental parameters vary spatially and thus condition organisms' habitat selection. We assessed the preference of zebra fish (Danio rerio) when exposed to water samples from two western Ecuadorian rivers with apparently distinct disturbance levels: Pescadillo River (highly disturbed) and Oro River (moderately disturbed). Using a non-forced exposure system in which water samples from each river were arranged according to their spatial sequence in the field and connected to allow individuals to move freely among samples, we assayed habitat selection by D. rerio to assess environmental disturbance in the two rivers. Fish exposed to Pescadillo River samples preferred downstream samples near the confluence zone with the Oro River. Fish exposed to Oro River samples preferred upstream waters. When exposed to samples from both rivers simultaneously, fish exhibited the same pattern of habitat selection by preferring the Oro River samples. Given that the rivers are connected, preference for the Oro River enabled us to predict a depression in fish populations in the Pescadillo River. Although these findings indicate higher disturbance levels in the Pescadillo River, none of the physical-chemical variables measured was significantly correlated with the preference pattern towards the Oro River. Non-linear spatial patterns of habitat preference suggest that other environmental parameters like urban or agricultural contaminants play an important role in the model organism's habitat selection in these rivers. The non-forced exposure system represents a habitat selection-based approach that can serve as a valuable tool to unravel the factors that dictate organisms' spatial distribution in connected ecosystems., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Although females with patellofemoral pain (PFP) show a decrease in hip and knee muscle strength, there is a lack of studies that associates this with postural stability. The purpose of this study was to assess the dynamic postural stability and muscle strength in the hips and knees of females with and without PFP, and to verify the association between the postural stability and the muscle strength in the PFP group., Methods: Two groups were tested: one with 25 PFP and one with 25 asymptomatic. Postural stability was evaluated during stepping up down tasks using a force platform to determine the center of pressure (COP) excursion and velocity. A handheld dynamometer was used to assess the muscles strength. The correlation analysis was conducted between the COP variables and the muscle strength., Results: The PFP group demonstrated greater total and medial-lateral COP displacement (8887.7±761.7 vs. 8129.4±691.9mm, P<0.001; 32.3±5.5 vs. 21.7±2.7mm, P<0.001) and a higher total of medial-lateral COP velocity (22.2±5.2 vs. 17.0±1.6 P=0.001). The PFP group showed weaknesses in all muscles (P<0.05), and there was a good positive correlation between the anterior-posterior displacement and the velocity of the extensor hip muscle (r=0.52, P<0.01; r=0.55, P<0.001)., Conclusions: Subjects with PFP have frontal dynamic postural stability deficit and show an association between hip extensor and sagittal plane stability., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Background: Chronic low back pain is one of the most common problematic health conditions worldwide and is highly associated with disability, quality of life, emotional changes, and work absenteeism. Graded activity programs, based on cognitive behavioral therapy, and exercises are common treatments for patients with low back pain. However, recent evidence has shown that there is no evidence to support graded activity for patients with chronic nonspecific low back pain., Aim: to compare the effectiveness of graded activity and physiotherapy in patients with chronic nonspecific low back pain., Methods: A total of 66 patients with chronic nonspecific low back pain were randomized to perform either graded activity (moderate intensity treadmill walking, brief education and strength exercises) or physiotherapy (strengthening, stretching and motor control). These patients received individual sessions twice a week for six weeks. The primary measures were intensity of pain (Pain Numerical Rating Scale) and disability (Rolland Morris Disability Questionnaire)., Results: After six weeks, significant improvements have been observed in all outcome measures of both groups, with a non-significant difference between the groups. For intensity of pain (mean difference = 0.1 points, 95% confidence interval [CI] = -1.1-1.3) and disability (mean difference = 0.8 points, 95% confidence interval [CI] = -2.6-4.2). No differences were found in the remaining outcomes., Conclusion: The results of this study suggest that graded activity and physiotherapy showed to be effective and have similar effects for patients with chronic nonspecific low back pain., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Bis (trifluoromethyl) sulfone, CF(3)SO(2)CF(3), was obtained as a byproduct in the synthesis of CF(3)SO(2)SCF(3). The compound was characterized by infrared and Raman spectroscopy as well quantum chemical calculations. Quantum mechanical calculations indicate the possible existence of two conformers symmetrically equivalent with C(2) symmetry. The preference for the staggered form was studied using the total energy scheme and the natural bond orbital (NBO) partition scheme. Additionally, the total potential energy was deconvoluted using a sixfold decomposition in terms of a Fourier-type expansion, showing that the hyperconjugative effect was dominant in stabilizing the staggered conformer. Infrared and Raman spectra of CF(3)SO(2)CF(3) were obtained. Harmonic vibrational wavenumbers and a scaled force field were calculated, leading to a final root mean-square deviation of 7.8 cm(-1) when comparing experimental and calculated wavenumbers., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Context and Objective: Patellar tendinopathy is a common condition in sports. It may occur at any location of the patellar tendon, but the most commonly affected area is the inferior pole of the patella. Among various diagnostic tests, the one most used is palpation of the inferior pole of the patella. The aim of this study was to investigate the prevalence of pain complaints among individuals with pathological knee conditions and to evaluate palpation of the inferior pole of the patella as a diagnostic test for patellar tendinopathy., Methods: Palpation of the patellar tendon was performed on 318 individuals who presented with knee-related complaints. Palpation was performed with the individual in the supine position and the knee extended. The age, gender, physical activity and labor activity of each individual were recorded at the time the symptoms appeared; the diagnosis was also recorded., Results: Of the total number of individuals evaluated, 124 (39%) felt pain on palpation of the inferior pole of the patella. Of these, only 40 (32.3%) received a diagnosis of patellar tendinopathy. We did not observe any difference with respect to gender and age distribution. When evaluating daily physical activity levels, however, we observed that individuals with pain on palpation of the inferior pole of the patella experienced more intense physical activity., Conclusions: Palpation of the inferior pole of the patella is a diagnostic procedure with high sensitivity and moderate specificity for diagnosing patellar tendinopathy, especially among individuals who perform activities with high functional demands.

A bare graphite-polyurethane composite was evaluated as an alternative electrode in the determination of atenolol (ATN) in pharmaceutical formulations. Using a DPV procedure, a linear analytical curve was observed in the 4-100mumolL(-1) range with a LOD=3.16mumolL(-1), without need of surface renewing between successive runs, with recoveries between 95.5 and 108%. Interference of other antihypertensive drugs was observed, but not from the usual components of tablets. The results of the proposed method agreed with HPLC ones within 95% confidence level.

The preparation and electrochemical characterization of a carbon paste electrode modified with bis(N,N-piperidinedithiocarbamate)-mu-tris(N,N-piperidinedithiocarbamate)diruthenium(III) complex, alpha-[Ru2(Pip)5]Cl are described. The best voltammetric response was obtained for a 10% (m/m) alpha-[Ru2(Pip)5]Cl content in the paste, potassium acid phthalate solution pH 4.0 as supporting electrolyte and scan rate of 100 mV s-1. The analytical potentialities of the electrode have been evaluated using L-ascorbic acid (vitamin C) as a probe. A sensitive linear voltammetric response for L-ascorbic acid was obtained in the concentration range 4.50-113x10(-5) mol l-1 (7.92-200 mg l-1) with a slope of 1.12x10(4) microA mol-1, and a detection limit (3sigma/slope) of 7.00x10(-6) mol l-1 using cyclic voltammetry. The concentrations of L-ascorbic acid in pharmaceutical formulations (tablets and liquid) were determined using the modified electrode and compared with an iodimetric procedure with good agreement at the 95% confidence and relative error lower than 1%.

A biosensor modified with peroxidase from crude extract of zucchini (Cucurbita pepo) was developed for the determination of dopamine in pharmaceutical samples. This enzyme catalyses the oxidation of dopamine to dopaminequinone, in presence of hydrogen peroxide, which the electrochemical reduction can be followed at a peak potential of -0.02 V. The recovery of dopamine from the samples ranged from 94.8% to 106% and a rectilinear analytical curve for dopamine concentration from 5.0 x 10(-4) to 3.0 x 10(-3) mol L-1 (r=0.9982) was obtained. The detection limit was 2.6x10(-5) mol L-1 and the relative standard deviation was less than 1.2% for 7.9 x 10(-4) mol L-1 dopamine in 0.1 mol L-1 phosphate buffer solution at pH 6.0 (n=10).