Cataract and glaucoma detection based on Transfer Learning using MobileNet.

A serious eye condition called cataracts can cause blindness. Early and accurate cataract detection is the most effective method for reducing risk and averting blindness. The optic nerve head is harmed by the neurodegenerative condition known as glaucoma. Machine learning and deep learning systems for glaucoma and cataract detection have recently received much attention in research. The automatic detection of these diseases also depends on deep learning transfer learning platforms like VeggNet, ResNet, and MobilNet. The authors proposed MobileNetV1 and MobileNetV2 based on an optimized architecture building lightweight deep neural networks using depth-wise separable convolutions. The experiments used publicly available data sets with both cataract & normal and glaucoma & normal images, and the results showed that the proposed model had the highest accuracy compared to the other models.

Dry Knee Arthroscopy With Carbon Dioxide Insufflation for Anterior Cruciate Ligament Reconstruction.

Anterior cruciate ligament (ACL) injuries are commonly treated through orthopaedic surgery, with traditional procedures relying on arthroscopy using fluid as the medium. However, dry arthroscopy has emerged as a potentially advantageous alternative technique. This method allows the knee joint to remain dry, reducing the risk of fluid leakage and enabling a more precise surgical visualization, resulting in shorter operation times and fewer complications. Recent research has highlighted the benefits of carbon dioxide (CO2) insufflation during ACL reconstruction, which can decrease pain and discomfort during early recovery. This article introduces a technique for performing ACL reconstruction that eliminates the need for arthroscopic fluid for visualization or instrumentation. Based on CO2 insufflation, this technique shows promise as a viable alternative to traditional fluid distention methods.

Deep learning-based electricity theft prediction in non-smart grid environments.

In developing countries, smart grids are nonexistent, and electricity theft significantly hampers power supply. This research introduces a lightweight deep-learning model using monthly customer readings as input data. By employing careful direct and indirect feature engineering techniques, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), UMAP (Uniform Manifold Approximation and Projection), and resampling methods such as Random-Under-Sampler (RUS), Synthetic Minority Over-sampling Technique (SMOTE), and Random-Over-Sampler (ROS), an effective solution is proposed. Previous studies indicate that models achieve high precision, recall, and F1 score for the non-theft (0) class, but perform poorly, even achieving 0 %, for the theft (1) class. Through parameter tuning and employing Random-Over-Sampler (ROS), significant improvements in accuracy, precision (89 %), recall (94 %), and F1 score (91 %) for the theft (1) class are achieved. The results demonstrate that the proposed model outperforms existing methods, showcasing its efficacy in detecting electricity theft in non-smart grid environments.

Maternal arsenic exposure modifies associations between arsenic, folate and arsenic metabolism gene variants, and spina bifida risk: A case‒control study in Bangladesh.

BACKGROUND: Spina bifida is a type of neural tube defect (NTD); NTDs are developmental malformations of the spinal cord that result from failure of neural tube closure during embryogenesis and are likely caused by interactions between genetic and environmental factors. Arsenic induces NTDs in animal models, and studies demonstrate that mice with genetic defects related to folate metabolism are more susceptible to arsenic's effects. We sought to determine whether 25 single-nucleotide polymorphisms (SNPs) in genes involved in folate and arsenic metabolism modified the associations between maternal arsenic exposure and risk of spina bifida (a common NTD) among a hospital-based case-control study population in Bangladesh. METHODS: We used data from 262 mothers and 220 infants who participated in a case‒control study at the National Institutes of Neurosciences & Hospital and Dhaka Shishu Hospital in Dhaka, Bangladesh. Neurosurgeons assessed infants using physical examinations, review of imaging, and we collected histories using questionnaires. We assessed arsenic from mothers' toenails using inductively coupled plasma mass spectrometry (ICP-MS), and we genotyped participants using the Illumina Global Screening Array v1.0. We chose candidate genes and SNPs through a review of the literature. We assessed SNP-environment interactions using interaction terms and stratified models, and we assessed gene-environment interactions using interaction sequence/SNP-set kernel association tests (iSKAT). RESULTS: The median toenail arsenic concentration was 0.42 µg/g (interquartile range [IQR]: 0.27-0.86) among mothers of cases and 0.47 µg/g (IQR: 0.30-0.97) among mothers of controls. We found an two SNPs in the infants' AS3MT gene (rs11191454 and rs7085104) and one SNP in mothers' DNMT1 gene (rs2228611) were associated with increased odds of spina bifida in the setting of high arsenic exposure (rs11191454, OR 3.01, 95% CI: 1.28-7.09; rs7085104, OR 2.33, 95% CI: 1.20-4.and rs2228611, OR 2.11, 95% CI: 1.11-4.01), along with significant SNP-arsenic interactions. iSKAT analyses revealed significant interactions between mothers' toenail concentrations and infants' AS3MT and MTR genes (p = 0.02), and mothers' CBS gene (p = 0.05). CONCLUSIONS: Our results support the hypothesis that arsenic increases spina bifida risk via interactions with folate and arsenic metabolic pathways and suggests that individuals in the population who have certain genetic polymorphisms in genes involved with arsenic and folate metabolism may be more susceptible than others to the arsenic teratogenicity.

A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture.

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Statistical and cross-sectional analysis of factors effecting the prevalence of pressure ulcer in hospital admitted stroke patients.

Every year, millions of people around the world are disabled by stroke, it is well recognized that complications aftera stroke extend hospital stays and pressure ulcers, a stroke consequence, which can be prevented by educating the caregiver. The primary focus of this research is not only to investigate the prevalence of pressure ulcers (PU) among stroke patients, but this study also introduced a variety of factors which influence the formation of PU, such as restricted mobility, gender, duration of stroke, hypertension, diabetes, hygiene, type of mattress, malnutrition, awareness, etc. In addition, this research provides a comparative and statistical analysis, a cause of the catastrophic disabilities influenced by a variety of factors. Moreover, the proposed research also provides a room for the pertinent treatment of stroke patient to curtail the formation of pressure ulcer. In this research, a total of 120 stroke patients were initially included to monitor the frequency of pressure ulcers at incipient stage. Out of the total patients, the number of patients with ischemic stroke were 78.5 % while 8.3 % were of haemorrhagic type. In the results, the demographic characteristics and the factors which influence the formation of PU of the patients were examined with their cross-sectional impact on each other through comparative and statistical analysis. It was discovered that among all the stroke patients, 8.3 % were found with a PUs and the most frequent localization was sacrum and no new PU was observed for the participants under the observation.

Arsenic modifies the effect of folic acid in spina bifida prevention, a large hospital-based case-control study in Bangladesh.

BACKGROUND: Spina bifida, a developmental malformation of the spinal cord, is associated with high rates of mortality and disability. Although folic acid-based preventive strategies have been successful in reducing rates of spina bifida, some areas continue to be at higher risk because of chemical exposures. Bangladesh has high arsenic exposures through contaminated drinking water and high rates of spina bifida. This study examines the relationships between mother's arsenic exposure, folic acid, and spina bifida risk in Bangladesh. METHODS: We conducted a hospital-based case-control study at the National Institute of Neurosciences & Hospital (NINS&H) in Dhaka, Bangladesh, between December 2016 and December 2022. Cases were infants under age one year with spina bifida and further classified by a neurosurgeon and imaging. Controls were drawn from children seen at NINS&H and nearby Dhaka Shishu Hospital. Mothers reported folic acid use during pregnancy, and we assessed folate status with serum assays. Arsenic exposure was estimated in drinking water using graphite furnace atomic absorption spectrophotometry (GF-AAS) and in toenails using inductively coupled plasma mass spectrometry (ICP-MS). We used logistic regression to examine the associations between arsenic and spina bifida. We used stratified models to examine the associations between folic acid and spina bifida at different levels of arsenic exposure. RESULTS: We evaluated data from 294 cases of spina bifida and 163 controls. We did not find a main effect of mother's arsenic exposure on spina bifida risk. However, in stratified analyses, folic acid use was associated with lower odds of spina bifida (adjusted odds ratio [OR]: 0.50, 95% confidence interval [CI]: 0.25-1.00, p = 0.05) among women with toenail arsenic concentrations below the median value of 0.46 µg/g, and no association was seen among mothers with toenail arsenic concentrations higher than 0.46 µg/g (adjusted OR: 1.09, 95% CI: 0.52-2.29, p = 0.82). CONCLUSIONS: Mother's arsenic exposure modified the protective association of folic acid with spina bifida. Increased surveillance and additional preventive strategies, such as folic acid fortification and reduction of arsenic, are needed in areas of high arsenic exposure.

Correction: Advancing automatic text summarization: Unleashing enhanced binary multi-objective grey wolf optimization with mutation.

[This corrects the article DOI: 10.1371/journal.pone.0304057.].

Prevalence of Obesity and its Effects in Patients With COVID-19: A Systematic Review and Meta-analysis.

Background: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease worldwide. Obesity has been proven to increase the susceptibility of an individual to infections, but the relationship between obesity and COVID-19 is still unclear. This study aimed to conduct a systematic review and meta-analysis of the prevalence of obesity and its effects in patients with COVID-19. Methods: Web of Science, PubMed and Embase were searched for English language studies up to May 22, 2020. We used a random or fixed-effects model to calculate pooled prevalence rates and odds ratio (OR) with 95% confidence intervals (CI). Results: Twelve studies with a total of 14 364 patients met the inclusion criteria. The pooled prevalence of obesity in patients with COVID-19 was 32.0% (95% CI, 26%-38%, P < .001). The prevalence of obesity in ICU COVID-19 patients were 37.0% (95% CI, 29%-46%, P < .001). Comparing between obese and non-obese patients, the meta-analysis showed that obesity was an important risk factor associated with COVID-19 patients needed for ICU care (OR: 1.36, 95% CI 1.22-1.52, P < .001). Conclusion: Obesity was highly prevalent (32.0%) in patients with COVID-19, especially in ICU patients (37.0%), and was an important risk factor for COVID-19 patients needed for ICU care.

Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach.

In this Paper solar desiccant air conditioning system integrated with cross flow Maisotsenko cycle (M-cycle) indirect evaporative cooler is used to investigate the performance of whole system in different range of parameters. Solar evacuated tube electric heater is used to supply the regeneration temperature to the desiccant wheel, whereas, Desiccant Wheel (DW) and M-cycle is used to handle latent load and sensible load separately. Major contribution of this research is to predict system level performance parameters of a Solar Assisted Desiccant Air Conditioning (Sol-DAC) system using Radial Basis Function Neural Network (RBF-NN) under real transient experimental inlet conditions. Nine parameters are mainly considered as input parameters to train the RBF-NN model, which are, supply Air temperature at the process side of desiccant wheel, supply air humidity ratio at process side of the desiccant wheel, outlet temperature from the desiccant wheel at process side, outlet humidity ratio from the desiccant wheel at process side, regeneration temperature at regeneration side of the DW, outlet temperature from the heat recovery wheel at process side, outlet humidity ratio out from the Heat Recovery Wheel (HRW) at process side, temperature before heat recovery wheel regeneration side of the system, humidity ratio before heat recovery wheel regeneration side of the system. Four parameters are considered as the output of the RBF-NN model, namely: output temperature, output humidity, Cooling Capacity (CC), and Coefficient of Performance (COP). The results of the RBF-NN model shows that the best Mean Squared Error (MSE) and Regression coefficient (R) for outlet temperature prediction are 0.00998279 and 0.99832 when regeneration temperature is 70 °C and inlet humidity at 18 g/kg. Best MSE and R for predication of outlet humidity are 0.0102932 and 0.99485 when the regeneration temperature is 70 °C and inlet humidity at 16 g/kg. Best MSE and R for predication of COP are 0.0106691 and 0.9981 when the regeneration temperature is 70 °C and inlet humidity 12 g/kg. Best MSE and R for predication of CC are 0.0144943 and 0.99711 when the regeneration temperature is 70 °C and inlet humidity 14 g/kg. Experimental and predicted performance parameters were in close agreement and showed minimal deviation. Investigations of predicted results revealed that trained RBF-NN model was capable of predicting the trend of output result under the varying input condition.

Cost Analysis of Bladder Catheterization After Pelvic Floor Surgery.

IMPORTANCE: Approximately 15-45% of female patients develop transient postoperative urinary retention (POUR) following pelvic reconstructive surgery. Catheter options for bladder drainage include transurethral indwelling catheter (TIC), intermittent self-catheterization (ISC), and suprapubic tube (SPT). Each strategy has risks and benefits; none have been shown to be clinically superior, and to date, no comprehensive comparative economic analysis has been published. OBJECTIVE: The objective of this study was to evaluate the cost of these different bladder catheterization strategies after transvaginal pelvic surgery. STUDY DESIGN: A Canadian universal single-payer (government funded) health system perspective was taken, and a decision tree model was constructed to evaluate the costs associated with each catheterization strategy over a 6-week horizon. Base-cases were set based on recently published clinical data of our institutions, 2 academic tertiary care centers, and based on systematic reviews and meta-analyses. Costs were established in consultation with process stakeholders, in addition to published values. RESULTS: The average cost calculated for management of transient POUR after outpatient pelvic reconstructive surgery was 150.69 CAD (median 154.86; interquartile range [IQR] 131.30-176.33) for TIC, 162.28 CAD (median 164.72; IQR 144.36-189.39) for ISC and 255.67 CAD (median 270.63; IQR 234.32-276.82) for SPT. In costing inpatient surgical data, the average cost calculated was 134.22 CAD (median 123.61; IQR 108.87-151.85) for TIC and 224.61 CAD (median 216.07; IQR 203.86-231.23) for SPT. CONCLUSION: TIC and ISC were found to be significantly less costly than SPT in managing transient POUR following transvaginal pelvic reconstructive surgery.

Advancing automatic text summarization: Unleashing enhanced binary multi-objective grey wolf optimization with mutation.

Automatic Text Summarization (ATS) is gaining popularity as there is a growing demand for a system capable of processing extensive textual content and delivering a concise, yet meaningful, relevant, and useful summary. Manual summarization is both expensive and time-consuming, making it impractical for humans to handle vast amounts of data. Consequently, the need for ATS systems has become evident. These systems encounter challenges such as ensuring comprehensive content coverage, determining the appropriate length of the summary, addressing redundancy, and maintaining coherence in the generated summary. Researchers are actively addressing these challenges by employing Natural Language Processing (NLP) techniques. While traditional methods exist for generating summaries, they often fall short of addressing multiple aspects simultaneously. To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. The performance of this enhanced algorithm is assessed by comparing it with state-of-the-art algorithms using the DUC2002 dataset. Experimental results demonstrate that the proposed algorithm significantly outperforms the compared approaches.

Extensively drug-resistant Salmonella Typhi leading to relapsed urinary tract infection: A case report.

Salmonella enterica serotype Typhi (S Typhi) associated urinary tract infections are exceedingly rare, accounting for less than 1% of cases. Such infections have known to occur in immune-compromised or individuals with urogenital structural abnormalities. With the emergence of extensively drug resistant S Typhi strains in Pakistan, the management of its various unique presentations poses therapeutic challenges. We report the first documented case of a 74 years old male patient presenting with relapsed urinary tract infection secondary to extensively drug resistant S Typhi.

DenseHillNet: a lightweight CNN for accurate classification of natural images.

The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fruits, land sliding, and medical images, there is a need for further research on the detection of natural images, particularly glaciers and mountains. To address the limitations of traditional CNNs, such as vanishing gradients and the need for many layers, the proposed work introduces a novel model called DenseHillNet. The model utilizes a DenseHillNet architecture, a type of CNN with densely connected layers, to accurately classify images as glaciers or mountains. The model contributes to the development of automation technologies in transportation and outdoor activities. The dataset used in this study comprises 3,096 images of each of the "glacier" and "mountain" categories. Rigorous methodology was employed for dataset preparation and model training, ensuring the validity of the results. A comparison with a previous work revealed that the proposed DenseHillNet model, trained on both glacier and mountain images, achieved higher accuracy (86%) compared to a CNN model that only utilized glacier images (72%). Researchers and graduate students are the audience of our article.

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging.

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.

The Importance of Neurosurgical Intervention and Surgical Timing for Management of Pediatric Patients with Myelomeningoceles in Bangladesh.

OBJECTIVE: Reports on the management and survival of children with myelomeningocele defects in Bangladesh are limited. This study describes the characteristics and outcomes of these children, focusing on the timing of surgical repair and factors affecting survival. METHODS: We enrolled patients with myelomeningoceles in a case-control study on arsenic exposure and spina bifida in Bangladesh. Cases were subsequently followed at regular intervals to assess survival. Demographic, clinical, and surgical characteristics were reviewed. Univariate tests identified factors affecting survival. RESULTS: Between 2016 and 2022, we enrolled 272 patients with myelomeningocele. Postnatal surgical repair was performed in 63% of cases. However, surgery within 5 days after birth was infrequent (<10%) due to delayed presentation, and there was a high rate (29%) of preoperative deaths. Surgical repair significantly improved patient survival (P < 0.0001). Older age at time of surgery was also associated with improved survival rates, which most likely represents that those who survived to older ages prior to surgery accommodated better with their lesions. Patients who presented with ruptured lesions had lower survival rates. CONCLUSIONS: Timely neurosurgical repair of myelomeningoceles in Bangladesh is hindered by late patient presentation, resulting in a high preoperative patient death rate. Neurosurgical intervention remains a significant predictor of survival. Increased access to neurosurgical care and education of families and non-neurosurgical providers on the need for timely surgical intervention are important for improving the survival of infants with myelomeningoceles.

Epipericardial Fat Necrosis and Covid-19.

Background: Epipericardial fat necrosis (EFN) is a rare and self-limiting cause of acute chest pain. We describe a case of EFN in a patient with a recent coronavirus disease (COVID-19). Case Presentation: A 55-year-old male presented with a sudden onset of left-sided pleuritic chest pain for the past two days. The patient was diaphoretic, tachypneic, and tachycardic. Acute coronary syndrome was ruled out. A computed tomography (CT) pulmonary angiogram revealed an ovoid encapsulated fatty mass surrounded by dense appearing tissue. Patient symptoms improved remarkably with a short course of non-steroidal anti-inflammatory drugs (NSAIDs). Discussion: EFN typically presents with a sudden onset of excruciating chest pain. Misdiagnosis, under-diagnosis, and mismanagement are unavoidable. EFN is incidentally diagnosed on CT scan. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects visceral adipose tissue and appears to increase the risk of EFN by promoting inflammatory cytokine production and death of adipocytes. Conclusion: EFN is a rare cause of acute chest pain. SARS-CoV-2 is likely to induce EFN. This rare clinical entity should be considered in the differential of acute chest pain especially in patients with active or recent COVID-19. LEARNING POINTS: Epipericardial fat necrosis (EFN) is a rare cause of acute pleuritic chest pain that is often misdiagnosed and mismanaged.SARS-CoV-2 can possibly increase the risk of EFN and this entity should be considered in the differential of chest pain, especially in patients with active or recent coronavirus disease (COVID-19).Clinician awareness of EFN and its potential association with COVID-19, can reduce unnecessary testing and emotional distress.

Genome-wide analysis of spina bifida risk variants in a case-control study from Bangladesh.

BACKGROUND: Human studies of genetic risk factors for neural tube defects, severe birth defects associated with long-term health consequences in surviving children, have predominantly been restricted to a subset of candidate genes in specific biological pathways including folate metabolism. METHODS: In this study, we investigated the association of genetic variants spanning the genome with risk of spina bifida (i.e., myelomeningocele and meningocele) in a subset of families enrolled from December 2016 through December 2022 in a case-control study in Bangladesh, a population often underrepresented in genetic studies. Saliva DNA samples were analyzed using the Illumina Global Screening Array. We performed genetic association analyses to compare allele frequencies between 112 case and 121 control children, 272 mothers, and 128 trios. RESULTS: In the transmission disequilibrium test analyses with trios only, we identified three novel exonic spina bifida risk loci, including rs140199800 (SULT1C2, p = 1.9 × 10-7), rs45580033 (ASB2, p = 4.2 × 10-10), and rs75426652 (LHPP, p = 7.2 × 10-14), after adjusting for multiple hypothesis testing. Association analyses comparing cases and controls, as well as models that included their mothers, did not identify genome-wide significant variants. CONCLUSIONS: This study identified three novel single nucleotide polymorphisms involved in biological pathways not previously associated with neural tube defects. The study warrants replication in larger groups to validate findings and to inform targeted prevention strategies.

Arsenic modifies the effect of folic acid in spina bifida prevention, a large hospital-based case-control study in Bangladesh.

Background: Spina bifida, a developmental malformation of the spinal cord, is associated with high rates of mortality and disability. Although folic acid-based preventive strategies have been successful in reducing rates of spina bifida, some areas continue to be at higher risk because of chemical exposures. Bangladesh has high arsenic exposures through contaminated drinking water and high rates of spina bifida. Methods: We conducted a hospital-based case-control study at the National Institute of Neurosciences & Hospital (NINS&H) in Dhaka, Bangladesh, between December 2016 and December 2022. Cases were infants under age one year with spina bifida and further classified using data from observations by neurosurgeons and available imaging. Controls were drawn from children who presented to NINS&H or Dhaka Shishu Hospital (DSH) during the same study period. Mothers reported folic acid use during pregnancy, and we assessed folate status with serum assays. Arsenic exposure was estimated in drinking water using graphite furnace atomic absorption spectrophotometry (GF-AAS) and in toenails using inductively coupled plasma mass spectrometry (ICP-MS). Results: We evaluated data from 294 cases of spina bifida and 163 controls. We did not find a main effect of mother's arsenic exposure on spina bifida risk. However, in stratified analyses, folic acid use was associated with lower odds of spina bifida (adjusted odds ratio [OR]: 0.50, 95% confidence interval [CI]: 0.25-1.00, p = 0.05) among women with toenail arsenic concentrations below the median, and no association was seen among mothers with toenail arsenic concentrations higher than median (adjusted OR: 1.09, 95% CI: 0.52-2.29, p = 0.82). Conclusions: Mother's arsenic exposure modified the protective association of folic acid with spina bifida. Increased surveillance and additional preventive strategies, such as folic acid fortification and reduction of arsenic, are needed in areas of high arsenic exposure.

Association of Wilms Tumor with Crossed Fused Renal Ectopia in Children: A Case Report.

Introduction: Wilms tumor is the most common renal malignancy in children. The occurrence of Wilms tumor with various congenital genitourinary anomalies has been reported, particularly in horseshoe kidneys, hypospadias, disorder of sexual development, and double collecting system. However, Wilms tumor with crossed renal ectopia is a rare finding. Case Description: We are reporting a case report of Wilms tumor in a 3-year-old girl who presented with a huge left flank mass with cross-fused renal ectopia. After the initial workup and triphasic computed tomography scan of the chest, abdomen, and pelvis for confirmation of diagnosis and metastasis, the patient underwent image-guided tissue biopsy, followed by neoadjuvant chemotherapy, left radical nephrectomy with separation of fused right ectopic renal moiety, and adjuvant chemoradiation. Practical Implication: This report shows an association of Wilms tumor with cross-fused renal ectopia, a rare combination. Unusual findings on imaging with unilateral flank mass should be considered as part of the differential diagnosis for this rare finding. Adjuvant chemotherapy and modern imaging helped delineate the anatomy and ease the surgery for safe resection, improving the overall outcome.