Composition of heavy metals in sediment, water, and fish of the Ganga and Yamuna Rivers in two major cities of India.

The rapid industrial development in the Indian capital region has led to significant waste generation, which, despite undergoing treatment prior to disposal, contributes substantially to water body contamination. Given the diverse nature of these wastes and their potential repercussions across the food chain, a study was conducted to evaluate heavy metal contamination levels in the Ganga and Yamuna Rivers of two major cities. Six heavy metals (Pb, Cd, Hg, Cu, Cr, and Zn) were analyzed in fish, water, and sediment samples by utilizing flame atomic absorption spectrophotometry (Avanta Σ) from March 2019 to February 2020. Results revealed distinct heavy metal distribution patterns, with Cr > Zn > Pb > Cu > Cd > Hg in the Ganga River and Zn > Cr > Pb > Cu > Cd > Hg in the Yamuna River for fish samples. Additionally, levels of Hg in Cyprinus carpio and Sperata oar from the Ganga River, and Pb, Cd, Hg, and Cr in Salmophasia bacaila and Mystus cavasius from the Yamuna River exceeded WHO/FAO permissible limits. In water samples, the predominant heavy metal sequences were Pb > Cu > Zn > Cr > Cd > Hg for the Ganga River and Cr > Zn > Pb > Cu > Cd > Hg for the Yamuna River, with Pb, Cr, Zn, and Cd surpassing WHO standards. Sediment analysis revealed varying heavy metal compositions, with Zn > Cr > Pb > Cu > Cd > Hg in the Ganga River and Cr > Zn > Pb > Cu > Cd > Hg in the Yamuna River. While drinking water and fish from the Ganga River were deemed safe for consumption, those from the Yamuna River were not. Given the toxic nature of heavy metals and their detrimental health impacts, regular monitoring and effective management strategies are imperative.

A review on heavy metal biosorption utilizing modified chitosan.

Heavy metal pollution in water bodies is a global concern. The prominent source of metal contamination in aqueous streams and groundwater is wastewater containing heavy metal ions. Elevated concentrations of heavy metals in water bodies can have a negative impact on water quality and public health. The most effective way to remove metal contaminants from drinking water is thought to be adsorption. A deacetylated derivative of chitin, chitosan, has a wide range of commercial uses since it is biocompatible, nontoxic, and biodegradable. Due to its exceptional adsorption behavior toward numerous hazardous heavy metals from aqueous solutions, chitosan and its modifications have drawn a lot of interest in recent years. Due to its remarkable adsorption behavior toward a range of dangerous heavy metals, chitosan is a possible agent for eliminating metals from aqueous solutions. The review has focused on the ideas of biosorption, its kinds, architectures, and characteristics, as well as using modified (physically and chemically modified) chitosan, blends, and composites to remove heavy metals from water. The main objective of the review is to describe the most important aspects of chitosan-based adsorbents that might be beneficial for enhancing the adsorption capabilities of modified chitosan and promoting the usage of this material in the removal of heavy metal pollutants.

Enhancing teeth segmentation using multifusion deep neural net in panoramic X-ray images.

BACKGROUND: Precise teeth segmentation from dental panoramic X-ray images is an important task in dental practice. However, several issues including poor image contrast, blurring borders of teeth, presence of jaw bones and other mouth elements, makes reading and examining such images a challenging and time-consuming task for dentists. Thus, developing a precise and automated segmentation technique is required. OBJECTIVE: This study aims to develop and test a novel multi-fusion deep neural net consisting of encoder-decoder architecture for automatic and accurate teeth region segmentation from panoramic X-ray images. METHODS: The encoder has two different streams based on CNN which include the conventional CNN stream and the Atrous net stream. Next, the fusion of features from these streams is done at each stage to encode the contextual rich information of teeth. A dual-type skip connection is then added between the encoder and decoder to minimise semantic information gaps. Last, the decoder comprises deconvolutional layers for reconstructing the segmented teeth map. RESULTS: The assessment of the proposed model is performed on two different dental datasets consisting of 1,500 and 1,000 panoramic X-ray images, respectively. The new model yields accuracy of 97.0% and 97.7%, intersection over union (IoU) score of 91.1% and 90.2%, and dice coefficient score (DCS) of 92.4% and 90.7% for datasets 1 and 2, respectively. CONCLUSION: Applying the proposed model to two datasets outperforms the recent state-of-the-art deep models with a relatively smaller number of parameters and higher accuracy, which demonstrates the potential of the new model to help dentists more accurately and efficiently diagnose dental diseases in future clinical practice.

Impact Strength of Various Types of Acrylic Resin: An In Vitro Study.

AIM: The aim of this study is to evaluate and compare the impact strength of conventional acrylic resin, high-impact acrylic resin, high-impact acrylic resin reinforced with silver nanoparticles, and high-impact acrylic resin reinforced with a zirconium oxide powder. MATERIALS AND METHODS: A total of 60 samples were prepared of dimensions 60 mm length × 7 mm width × 4 mm thickness to test impact strength. Machined stainless steel dies of the same dimension were used to form molds for the fabrication of these samples. Of 60 samples, 15 samples were prepared each from conventional acrylic resin (Group A1), high-impact acrylic resin (Group A2), acrylic resin reinforced with silver nanoparticles (Group A3), and acrylic resin reinforced with zirconium oxide powder (Group A4). Izod-Charpy pendulum impact testing machine was used. RESULTS: The impact strength of group A1 was in the range of 2.83-3.30 kJ/m2 (M = 3.12 kJ/m2, SD = 0.16), group A2 was in range of 5.10-5.78 kJ/m2 (M = 5.51 kJ/m2, SD = 0.18), group A3 was in range 3.18-3.56 kJ/m2 (M = 3.37 kJ/m2, SD = 0.11), and group A4 was in range 7.18-7.78 kJ/m2 (M = 7.5 kJ/m2, SD = 0.18). Statistical analysis using one-way ANOVA and t-test revealed significant differences (p < 0.001). CONCLUSION: High-impact acrylic resin reinforced with zirconium oxide powder has the highest impact strength. CLINICAL SIGNIFICANCE: This research sheds light on the usefulness of novel filler materials in clinical prosthodontics.

Exploiting multimodal CNN architecture for automated teeth segmentation on dental panoramic X-ray images.

Panoramic X-ray images are the major source used in field of dental image segmentation. However, such images suffers from the disturbances like low contrast, presence of jaw bones, nose bones, spinal bone, and artifacts. Thus, to observe these images manually is a tedious task, requires expertise of dentist and is time consuming. Hence, there is need to develop an automated tool for teeth segmentation. Recently, few deep models have been developed for dental image segmentation. But, such models possess large number of training parameters, thus making the segmentation a very complex task. Also, these models are based only on conventional CNN and lacks in exploiting multimodal CNN features for dental image segmentation. Thus, to address these issues, a novel encoder-decoder model based on multimodal-feature extraction for automatic segmentation of teeth area is proposed. The encoder has three different CNN based architectures: conventional CNN, atrous-CNN, and separable CNN to encode rich contextual information. Whereas decoder contains a single stream of deconvolutional layers for segmentation. The proposed model is tested on 1500 panoramic X-ray images and uses very less parameters when compared to state-of-the-art methods. Besides this, the precision and recall are 95.01% and 94.06%, which out performs the state-of-the art methods.

Evaluation of symptoms along with depression, anxiety and stress levels of Sars-CoV-2 positive patients during the second wave in the city of Indore, Central India - a cross sectional study.

INTRODUCTION: The consequences of the second wave hitting India have drastically laid a huge impact on the mental state of patients. The second wave had proven to be far more dangerous and hence the psychological evaluation needed to be conducted to know the scenario of patients suffering from SARS-CoV-2. OBJECTIVE: This study was undertaken to evaluate the symptoms of SARS-CoV-2 patients along with the existing depression, anxiety and stress levels amongst them. MATERIAL AND METHODS: An observational, cross-sectional questionnaire-based survey was conducted among 351 patients infected with SARS-CoV-2 during the second wave in Indore, Central India. The questionnaire consisted of questions pertaining to socio-demographic characteristics, clinical signs and symptoms. Evaluation of depression, anxiety and stress levels were done by use of 21 item Depression, Anxiety, Stress Scale (DASS-21). RESULTS: The most common symptom amongst patients was cough (42.2%) followed by fever (40.2%). Sixty-nine (19.6%) patients were asymptomatic. Depression score was found to have significant, positive weak correlation with age (ρ-0.124, p-0.020, p value <.05). No significant difference was observed between the depression, anxiety and stress score of males and females. Based on the scores assigned to the responses, patients who tested positive were belonging to normal category with no diagnosed depression, anxiety or stress. CONCLUSION: The present study showed fever, cough, headache, weakness, and chest pain as the common sign and symptoms of COVID-19 during the second wave. There was a prevalence of low levels of anxiety, stress and depression amongst patients in Radha Saomi Covid Care Centre, Indore during the second wave.

Water quality of springs and lakes in the Kumaon Lesser Himalayan Region of Uttarakhand, India.

The scarcity of drinking water has become a bitter reality in many countries. The gap between demand and supply of water has been increasing exponentially year by year. Deforestation, vigorous use of groundwater for agricultural practices, and pollution of our present water resources such as rivers, lakes, and wells are triggering the freshwater scarcity problem. Ninety percent of people in Uttarakhand depend on springs for their daily life activities. In such a case, the quality and quantity of spring water should be a prime topic to be focussed on. In the Kumaon region of Uttarakhand, spring water quality is good but there is an issue with its availability, especially in summer. This review paper details the studies that have been conducted on nutrient status, hardness, heavy metals, and the presence of microbiological diversity in spring water. It also uncovers information on some critical springs, geological settings of their aquifers, and the steps that have been adopted to rejuvenate the spring. Some other measures have been carried out by the government and local communities for springs' revival and their improvement in discharge rate, including the construction of percolation pits, contour trenches, check dams, and improvement of water resources. It has been observed among the analyzed sample that the Kumaon region is dominated by arsenic, cadmium, chromium, and lead, whereas aluminum, barium, cobalt, and manganese are more in the Garhwal region. Apart from springs, this review paper also reveals the physicochemical characteristics of the spring-fed rivers and lakes of the Kumaon region.

A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors.

Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle. A significant amount of electrical energy is consumed due to the large count of operational units worldwide; hence, even an enhancement in minute efficiency can direct considerable contributions within revenue saving, global electricity consumption, and other environmental facts. In order to improve the efficiency of induction motors, this research paper presents a novel contribution to maximizing the efficiency of induction motors. As such, a model of induction motor drive is taken, in which the proportional integral (PI) controller is tuned. The optimal tuning of gains of a PI controller such as proportional gain and integral gain is conducted. The tuning procedure in the controller is performed in such a condition that the efficiency of the induction motor should be maximum. Moreover, the optimization concept relies on the development of a new hybrid algorithm, the so-called Scrounger Strikes Levy-based dragonfly algorithm (SL-DA), that hybridizes the concept of dragonfly algorithm (DA) and group search optimization (GSO). The proposed algorithm is compared with particle swarm optimization (PSO) for verification. The analysis of efficiency, speed, torque, energy savings, and output power is validated, which confirms the superior performance of the suggested method over the comparative algorithms employed.

Frequency, Prevalence, Interaction, and Gender Differences of Six Psychosocial Factors in Patients Presenting to a Psychiatric Emergency Service.

INTRODUCTION: The psychiatric emergency service (PES) has become an increasingly utilized patient care approach over the past 50 years. Psychosocial factors play an important role in PES utilization and disposition of patients in this environment. PES utilization in our region has increased 450% in the past 18 years, while the population has changed <1%. Our objective was to determine the frequency and relationship between six psychosocial factors in our patients, in comparison to the general population. METHODS: We completed a retrospective chart review of 160 patients selected at random who utilized the PES during 2009-2010. We looked for historical presence of a broken family, abuse or neglect, substance abuse, legal problems, violence, or incomplete education. We also looked at the relationship of these factors to race and gender. RESULTS: 75% had three or more of the factors measured. In our population, substance abuse and broken family were most prevalent. Females had significantly more abuse or neglect as compared to males, while male subjects had significantly more substance abuse and legal issues than females. The presence of a broken family was strongly associated with abuse or neglect, while violence was associated with incomplete education and legal issues. The prevalence of these factors in our patients was higher than the general population. CONCLUSION: The average patient presenting to the PES has multiple major psychosocial problems at higher frequencies than the general population. Understanding the relationship between multiple psychosocial factors and increasing PES utilization can direct us towards addressing the problems causing the increase in PES presentation. A planned future prospective study will examine the incidence of these psychosocial factors in patients presenting to the PES as compared to the general population.

Variable Length Character N-Gram Embedding of Protein Sequences for Secondary Structure Prediction.

BACKGROUND: The prediction of a protein's secondary structure from its amino acid sequence is an essential step towards predicting its 3-D structure. The prediction performance improves by incorporating homologous multiple sequence alignment information. Since homologous details not available for all proteins. Therefore, it is necessary to predict the protein secondary structure from single sequences. OBJECTIVE AND METHODS: Protein secondary structure predicted from their primary sequences using n-gram word embedding and deep recurrent neural network. Protein secondary structure depends on local and long-range neighbor residues in primary sequences. In the proposed work, the local contextual information of amino acid residues captures variable-length character n-gram words. An embedding vector represents these variable-length character n-gram words. Further, the bidirectional long short-term memory (Bi-LSTM) model is used to capture the long-range contexts by extracting the past and future residues information in primary sequences. RESULTS: The proposed model evaluates on three public datasets ss.txt, RS126, and CASP9. The model shows the Q3 accuracy of 92.57%, 86.48%, and 89.66% for ss.txt, RS126, and CASP9. CONCLUSION: The proposed model performance compares with state-of-the-art methods available in the literature. After a comparative analysis, it observed that the proposed model performs better than state-of-the-art methods.

A review on traditional practice of tooth brushing in Ayurveda and its relevance in current era.

Ayurveda recommends sound and healthy life. It basically focuses on prevention rather than treatment. It includes a systematic lifestyle pattern which is composed of several principles and activities. Dinacharya (Daily routine) is one of the important principles. It includes various activities started from just before sunrise. All these activities are necessary for the maintenance of homeostasis of the body. Tooth brushing is one of the essential routines of Ayurveda daily practices. Traditionally it has been advocated with a soft twig of medicinal plants with or without herbal tooth powder. There are so many medicinal plants have been described in this context. Contradictory plants have also been described. Oral hygiene products are so much advanced and globalized nowadays. There are so many types of toothbrushes; dentifrices, dental floss, tongue scrapers, mouthwashes, oral irrigators, etc. are available along with specifications. Contemporary oral hygiene products esp. dentifrices are chemically rich and have their own side effects. No doubt these products have their own advantages like easy availability and adjustability according to current lifestyle. But their safety is questionable on long-term use. Herbal products have additional benefits over it that those are natural and easily adjustable and acceptable with body physiology.

Validity and reliability of intraoral conventional tracer and intraoral digital tracer in different positions for recording horizontal jaw relation in edentulous patients.

AIM: The purpose of the study is to evaluate and compare the horizontal mandibular positions recorded between intraoral conventional tracer and intraoral digital tracer in upright and supine position. MATERIALS AND METHODS: Twenty-four edentulous patients with well-formed ridge and adequate interarch distance space were selected. MATLAB software was assimilated with intraoral digital tracer and was utilized in the study for recording the horizontal movements of the mandible, i.e., gothic arch tracing by intraoral digital tracer and was observed on a laptop with the help of MATLAB Software. For each subject, multiple mandibular readings were recorded and analyzed through software, and similar readings were recorded with conventional intraoral tracers. The comparison between intraoral conventional tracer and intraoral digital tracer was done to assess the reliability. Moreover, the consistency of recording horizontal mandibular position was also compared between upright and supine position. RESULTS: The data were procured and utilized in comparison for different positions revealed statistically significant difference by using Student's Paired t-test. The test resulted in supine position better compared to upright position (P = 0.0001). The association between supine position with upright position was calculated using Fischer's exact test, and it was also found to be statistically significant (P = 0.002). The Pearson's Correlation analysis was performed to check the agreement between upright and supine position and very weak downhill correlation (r 2= -0.130) was observed between the two variables. CONCLUSION: On evaluation and comparison of horizontal mandibular position, it was found that the intraoral digital tracing technique is more valid compared to conventional intraoral tracer technique. It was also observed that the consistency of reproducibility in recording horizontal mandibular position in supine position is significantly higher than upright position.

A Survey on Medical Image Analysis in Capsule Endoscopy.

BACKGROUND AND OBJECTIVE: Capsule Endoscopy (CE) is a non-invasive, patient-friendly alternative to conventional endoscopy procedure. However, CE produces 6 to 8 hrs long video posing a tedious challenge to a gastroenterologist for abnormality detection. Major challenges to an expert are lengthy videos, need of constant concentration and subjectivity of the abnormality. To address these challenges along with high diagnostic accuracy, design and development of automated abnormality detection system is a must. Machine learning and computer vision techniques are devised to develop such automated systems. METHODS: Study presents a review of quality research papers published in IEEE, Scopus, and Science Direct database with search criteria as capsule endoscopy, engineering, and journal papers. The initial search retrieved 144 publications. After evaluating all articles, 62 publications pertaining to image analysis are selected. RESULTS: This paper presents a rigorous review comprising all the aspects of medical image analysis concerning capsule endoscopy namely video summarization and redundant image elimination, Image enhancement and interpretation, segmentation and region identification, Computer-aided abnormality detection in capsule endoscopy, Image and video compression. The study provides a comparative analysis of various approaches, experimental setup, performance, strengths, and limitations of the aspects stated above. CONCLUSIONS: The analyzed image analysis techniques for capsule endoscopy have not yet overcome all current challenges mainly due to lack of dataset and complex nature of the gastrointestinal tract.

Assessment of Cadmium Scavenging Potential of Canna indica L.

The aquatic plant, Canna indica L. (Indian shoot) of Cannaceae family was investigated to assess cadmium scavenging potential at 5, 10, 25, 50, 100 and 150 mg Cd L-1 exposers. The results showed that Canna has considerable potential of cadmium accumulation, which was up to 58.69 and 10.13 mg Cd kg-1 dry weight in root and shoot of Canna, respectively. The effects of different cadmium levels on biomass production of plant tissues were significantly (p = 0.05) showed negative relation due to cadmium toxicity. The root concentration factor was higher than the bioconcentration factor which indicated the lower translocation factor of Canna. Considering the high root concentration factor, average bioconcentration factor, rapid growth and optimum adaptive properties up to 100 mg Cd L-1 level, the Canna could be employed as an eco-friendly and efficient aquatic plant for cadmium scavenging. This study plays a potential role in remediation of cadmium contaminated wastewater.

Automated and effective content-based image retrieval for digital mammography.

Nowadays, huge number of mammograms has been generated in hospitals for the diagnosis of breast cancer. Content-based image retrieval (CBIR) can contribute more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms already annotated by diagnostic descriptions and treatment results. Since labels, artifacts, and pectoral muscles present in mammograms can bias the retrieval procedures, automated detection and exclusion of these image noise patterns and/or non-breast regions is an essential pre-processing step. In this study, an efficient and automated CBIR system of mammograms was developed and tested. First, the pre-processing steps including automatic labelling-artifact suppression, automatic pectoral muscle removal, and image enhancement using the adaptive median filter were applied. Next, pre-processed images were segmented using the co-occurrence thresholds based seeded region growing algorithm. Furthermore, a set of image features including shape, histogram based statistical, Gabor, wavelet, and Gray Level Co-occurrence Matrix (GLCM) features, was computed from the segmented region. In order to select the optimal features, a minimum redundancy maximum relevance (mRMR) feature selection method was then applied. Finally, similar images were retrieved using Euclidean distance similarity measure. The comparative experiments conducted with reference to benchmark mammographic images analysis society (MIAS) database confirmed the effectiveness of the proposed work concerning average precision of 72% and 61.30% for normal & abnormal classes of mammograms, respectively.

Evaluation of the Protection Provided by Hepatitis B Vaccination in India.

OBJECTIVE: In India, Hepatitis B vaccination is recommended at 6 wk except for hospital-deliveries. The authors examined protection afforded by the birth dose. METHODS: A case-control study was done. HBsAg and HBcAb were tested in 2671 children, 1 to 5 y and HBsAb was evaluated in a subset of 1413 children. Vaccination history was recorded. Cases were HBsAg carriers. In another analysis, children who got infected (HBsAg and/or HBcAb positive) were considered as cases. Exposed were the unvaccinated. In another analysis, exposed were those vaccinated without the birth dose. RESULTS: The odds ratio (OR) for HBsAg positivity with birth vaccination was 0.35 (95% CI 0.19-0.66); while with vaccination at 6 wk was 0.29 (95%CI 0.14-0.61), both compared to unvaccinated. Birth vaccination has no added protection when compared to the unvaccinated. Unvaccinated children in index study had HBsAg positivity of 4.38%. The number needed to treat (NNT) to prevent one case of HBsAg positivity was 32.6 (95% CI, 20.9 to 73.6). The odds of getting HBV infection was 0.42 (CI 0.25-0.68) with birth dose and 0.49 (CI 0.30-0.82) without the birth dose compared to the unvaccinated. Protective antibody (HBsAb) was present in about 70% of the vaccinated. In the unimmunised, in the first 2 y HBsAb protection was present in 40%. The odds ratio (OR) for HBsAb in the fully vaccinated between 4 and 5 y was 1.4 (95%CI 0.9-2.18) compared to the unvaccinated. CONCLUSIONS: The present study lends support to the pragmatic approach of the Government to vaccinate babies born at home starting at 6 wk.

A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection.

BACKGROUND AND OBJECTIVE: For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step. METHODS: To address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells. RESULTS: The proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches. CONCLUSIONS: The experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other segmentation approaches used for cancer detection.

Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests.

Mammogram classification is a crucial and challenging problem, because it helps in early diagnosis of breast cancer and supports radiologists in their decision to analyze similar mammograms out of a database by recognizing the classes of current mammograms. This paper proposes an effective method for classifying mammograms using random forests with wavelet based center-symmetric local binary pattern (WCS-LBP). To classify mammograms, multi-resolution CS-LBP texture characteristics from non-overlapping regions of the mammograms are captured. Further, we examine most relevant features using support vector machine-recursive feature elimination (SVM-RFE). Finally, we feed the selected features to decision trees and construct random forests which are an ensemble of random decision trees. Using wavelet based local CS-LBP features with random forest, we classify the test images into different categories having the maximum posterior probability. The proposed method shows the improved performance as compared with other variant features and state-of-art methods. The obtained performance measures are 97.3% accuracy, 97.3% precision, 97.2% recall, 97.2% F-measure and 94.1% Matthews correlation coefficient (MCC).

L-DOPA therapy interferes with urine catecholamine analysis in children with suspected neuroblastoma: a case series.

Neuroblastoma is the most common solid extracranial malignancy diagnosed in childhood. Clinical presentation is variable, and metastatic disease is common at diagnosis. Analyses of urinary catecholamines and their metabolites are commonly requested as a first-line investigation when clinical suspicion exists. Levodopa (L-Dopa) therapy is utilized as a treatment for a number of disorders in childhood, including Dopa-responsive dystonia. Neuroblastoma may mimic some of the clinical features of this disorder. L-Dopa can interfere with analysis of urinary catecholamines and their metabolites and complicate the interpretation of results. We present the cases of three children who were prescribed L-dopa at the time of analysis of urinary catecholamines and metabolites as a screen for neuroblastoma, but who did not have the disease. Comparison of their results with those from cases with true neuroblastoma reveal that it is impossible to reliably distinguish true neuroblastoma from L-Dopa therapy using these tests. We recommend that patients should be off L-dopa therapy, if possible when these tests are performed. These cases illustrate the importance of providing clinical details and drug history to the laboratory in order to avoid diagnostic confusion.