Helminth Parasite Prevalence in the Endangered Ethiopian Wolf (Canis simensis) in Web Valley, Bale Mountains National Park, Ethiopia.

Ethiopian wolves (EWs), Canis simensis, are the rarest canids in the world and Africa's most endangered carnivore, found in only six isolated habitat fragments in the highlands of Ethiopia. Previous reports on the prevalence of parasites in the EW in Bale Mountains National Park (BMNP) are limited, with little information on their helminth fauna. This study seeks to understand the prevalence of helminth parasites in the EW in BMNP, Ethiopia. In this study, fecal samples were collected from 43 EWs in Web Valley (WV), BMNP, from June to October 2020, and the presence of helminth eggs was assessed using fecal sedimentation and centrifugal floatation methods with microscopy. Forty-two out of 43 fecal samples from wolves (98%) contained eggs from two taxonomic groups of helminths. Eggs from Capillaria spp. and Trichuris vulpis were found most frequently, followed by Toxocara canis, Diphyllobothrium spp., Toxascaris leonina, and Capillaria aerophila. One EW (2%) was recorded for harboring the cestode Moniezia expansa. About 9 of the 43 EWs (21%) presented monospecific infection: 9 EWs (21%) harbored 2 parasite species, 9 EWs (21%) hosted 3 parasite species, 11 EWs (26%) had infection involving 4 parasite species, 2 EWs (5%) were infected with 5 parasite species, 1 EW (2%) presented 6 parasite species, 1 EW (2%) harbored 7 parasite species, and 1 EW (2%) was diagnosed without parasite species. Concurrent helminth infection was highly associated with female EW. Megeti 3 was associated with a low level of concurrent helminth infection. The prevalence of helminth parasites found in wolves in the study area suggests that the environment is highly contaminated with intestinal parasites. Regular control of parasite transmission in EW, domestic dogs, and humans in and around BMNP, public education, and further parasite epidemiological studies must be conducted.

Molecular Characterization of Echinococcus canadensis (Cestoda, Taeniidae) in Wolf from the Kirov Region.

This work presents the results of studying the molecular characteristics of parasitic tapeworms Echinococcus canadensis. The helminths were discovered during the autopsy of a wolf (Canis lupus Linnaeus, 1758) killed by hunters in the Kirov oblast in 2021. A molecular phylogenetic study was performed by analyzing the sequence of a fragment of the first subunit of the mitochondrial cytochrome oxidase gene (CoxI). It was found that the detected echinococci belong to the G10 genotype of E. canadensis, which is common in wolves in the northern territories of the Holarctic. We discovered four positions at which the substitutions characteristic only of this genotype are revealed. A substitution at one of the positions that is characteristic exclusively for the representatives of the G10 genotype found in Russia and Finland was also discovered.

An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.

A Grey Wolf Optimizer Algorithm for Multi-Objective Cumulative Capacitated Vehicle Routing Problem Considering Operation Time.

In humanitarian aid scenarios, the model of cumulative capacitated vehicle routing problem can be used in vehicle scheduling, aiming at delivering materials to recipients as quickly as possible, thus minimizing their wait time. Traditional approaches focus on this metric, but practical implementations must also consider factors such as driver labor intensity and the capacity for on-site decision-making. To evaluate driver workload, the operation times of relief vehicles are typically used, and multi-objective modeling is employed to facilitate on-site decision-making. This paper introduces a multi-objective cumulative capacitated vehicle routing problem considering operation time (MO-CCVRP-OT). Our model is bi-objective, aiming to minimize both the cumulative wait time of disaster-affected areas and the extra expenditures incurred by the excess operation time of rescue vehicles. Based on the traditional grey wolf optimizer algorithm, this paper proposes a dynamic grey wolf optimizer algorithm with floating 2-opt (DGWO-F2OPT), which combines real number encoding with an equal-division random key and ROV rules for decoding; in addition, a dynamic non-dominated solution set update strategy is introduced. To solve MO-CCVRP-OT efficiently and increase the algorithm's convergence speed, a multi-objective improved floating 2-opt (F2OPT) local search strategy is proposed. The utopia optimum solution of DGWO-F2OPT has an average value of two fitness values that is 6.22% lower than that of DGWO-2OPT. DGWO-F2OPT's average fitness value in the algorithm comparison trials is 16.49% less than that of NS-2OPT. In the model comparison studies, MO-CCVRP-OT is 18.72% closer to the utopian point in Euclidean distance than CVRP-OT.

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization.

Efficiently managing multiple process parameters is critical for achieving optimal performance in additive manufacturing. This study investigates the relationship between eight key parameters in fused deposition modeling (FDM) and their impact on responses like average surface roughness (Ra), tensile strength (TS), and flexural strength (FS) of carbon fiber-reinforced polyamide 12 (PA 12-CF) material. The study integrates response surface methodology (RSM), grey relational analysis (GRA), and grey wolf optimization (GWO) to achieve this goal. A total of 51 experiments were planned using a definitive screening design (DSD) based on response RSM. The printing process parameters, including layer thickness, infill density, and build orientation, significantly affect Ra, TS, and FS. GRA combines responses into a single measure, grey relational grade (GRG), and a regression model is developed. GWO is then employed to optimize GRG across parameters. Comparison with GRA-optimized parameters demonstrates GWO's ability to discover refined solutions, reducing average surface roughness to 4.63 µm and increasing tensile strength and flexural strength to 88.5 MPa and 103.12 MPa, respectively. Practical implications highlight the significance of GWO in industrial settings, where optimized parameters lead to reduced costs and improved product quality. This integrated approach offers a systematic methodology for optimizing FDM processes, ensuring robustness and efficiency in additive manufacturing applications.

Epigenetics.

Epigenetics is the study of heritable changes to the genome and gene expression patterns that are not caused by direct changes to the DNA sequence. Examples of these changes include posttranslational modifications to DNA-bound histone proteins, DNA methylation, and remodeling of nuclear architecture. Collectively, epigenetic changes provide a layer of regulation that affects transcriptional activity of genes while leaving DNA sequences unaltered. Sequence variants or mutations affecting enzymes responsible for modifying or sensing epigenetic marks have been identified in patients with congenital heart disease (CHD), and small-molecule inhibitors of epigenetic complexes have shown promise as therapies for adult heart diseases. Additionally, transgenic mice harboring mutations or deletions of genes encoding epigenetic enzymes recapitulate aspects of human cardiac disease. Taken together, these findings suggest that the evolving field of epigenetics will inform our understanding of congenital and adult cardiac disease and offer new therapeutic opportunities.

Advanced CKD detection through optimized metaheuristic modeling in healthcare informatics.

Data categorization is a top concern in medical data to predict and detect illnesses; thus, it is applied in modern healthcare informatics. In modern informatics, machine learning and deep learning models have enjoyed great attention for categorizing medical data and improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, and long-term execution duration, raise fundamental problems. This study presents a novel classification model employing metaheuristic methods to maximize efficient positives on Chronic Kidney Disease diagnosis. The medical data is initially massively pre-processed, where the data is purified with various mechanisms, including missing values resolution, data transformation, and the employment of normalization procedures. The focus of such processes is to leverage the handling of the missing values and prepare the data for deep analysis. We adopt the Binary Grey Wolf Optimization method, a reliable subset selection feature using metaheuristics. This operation is aimed at improving illness prediction accuracy. In the classification step, the model adopts the Extreme Learning Machine with hidden nodes through data optimization to predict the presence of CKD. The complete classifier evaluation employs established measures, including recall, specificity, kappa, F-score, and accuracy, in addition to the feature selection. Data related to the study show that the proposed approach records high levels of accuracy, which is better than the existing models.

Human Genetics of Ventricular Septal Defect.

Ventricular septal defects (VSDs) are recognized as one of the commonest congenital heart diseases (CHD), accounting for up to 40% of all cardiac malformations, and occur as isolated CHDs as well as together with other cardiac and extracardiac congenital malformations in individual patients and families. The genetic etiology of VSD is complex and extraordinarily heterogeneous. Chromosomal abnormalities such as aneuploidy and structural variations as well as rare point mutations in various genes have been reported to be associated with this cardiac defect. This includes both well-defined syndromes with known genetic cause (e.g., DiGeorge syndrome and Holt-Oram syndrome) and so far undefined syndromic forms characterized by unspecific symptoms. Mutations in genes encoding cardiac transcription factors (e.g., NKX2-5 and GATA4) and signaling molecules (e.g., CFC1) have been most frequently found in VSD cases. Moreover, new high-resolution methods such as comparative genomic hybridization enabled the discovery of a high number of different copy number variations, leading to gain or loss of chromosomal regions often containing multiple genes, in patients with VSD. In this chapter, we will describe the broad genetic heterogeneity observed in VSD patients considering recent advances in this field.

Human Genetics of Hypoplastic Left Heart Syndrome.

Hypoplastic left heart syndrome (HLHS) is a severe congenital cardiovascular malformation characterized by hypoplasia of the left ventricle, aorta, and other structures on the left side of the heart. The pathologic definition includes atresia or stenosis of both the aortic and mitral valves. Despite considerable progress in clinical and surgical management of HLHS, mortality and morbidity remain concerns. One barrier to progress in HLHS management is poor understanding of its cause. Several lines of evidence point to genetic origins of HLHS. First, some HLHS cases have been associated with cytogenetic abnormalities (e.g., Turner syndrome). Second, studies of family clustering of HLHS and related cardiovascular malformations have determined HLHS is heritable. Third, genomic regions that encode genes influencing the inheritance of HLHS have been identified. Taken together, these diverse studies provide strong evidence for genetic origins of HLHS and related cardiac phenotypes. However, using simple Mendelian inheritance models, identification of single genetic variants that "cause" HLHS has remained elusive, and in most cases, the genetic cause remains unknown. These results suggest that HLHS inheritance is complex rather than simple. The implication of this conclusion is that researchers must move beyond the expectation that a single disease-causing variant can be found. Utilization of complex models to analyze high-throughput genetic data requires careful consideration of study design.

Absence of SARS-CoV-2 in wildlife of northeastern Minnesota and Isle Royale National Park.

AIMS: We investigated the presence of SARS-CoV-2 in free-ranging wildlife populations in Northeastern Minnesota on the Grand Portage Indian Reservation and Isle Royale National Park. METHODS AND RESULTS: One hundred twenty nasal samples were collected from white-tailed deer, moose, grey wolves and black bears monitored for conservation efforts during 2022-2023. Samples were tested for viral RNA by RT-qPCR using the CDC N1/N2 primer set. Our data indicate that no wildlife samples were positive for SARS-CoV-2 RNA. CONCLUSIONS: Continued surveillance is therefore crucial to better understand the changing landscape of zoonotic SARS-CoV-2 in the Upper Midwest.

Warming alters cascading effects of a dominant arthropod predator on fungal community composition in the Arctic.

Rapid climate change in the Arctic is altering microbial structure and function, with important consequences for the global ecosystem. Emerging evidence suggests organisms in higher trophic levels may also influence microbial communities, but whether warming alters these effects is unclear. Wolf spiders are dominant Arctic predators whose densities are expected to increase with warming. These predators have temperature-dependent effects on decomposition via their consumption of fungal-feeding detritivores, suggesting they may indirectly affect the microbial structure as well. To address this, we used a fully factorial mesocosm experiment to test the effects of wolf spider density and warming on litter microbial structure in Arctic tundra. We deployed replicate litter bags at the surface and belowground in the organic soil profile and analyzed the litter for bacterial and fungal community structure, mass loss, and nutrient characteristics after 2 and 14 months. We found there were significant interactive effects of wolf spider density and warming on fungal but not bacterial communities. Specifically, higher wolf spider densities caused greater fungal diversity under ambient temperature but lower fungal diversity under warming at the soil surface. We also observed interactive treatment effects on fungal composition belowground. Wolf spider density influenced surface bacterial composition, but the effects did not change with warming. These findings suggest a widespread predator can have indirect, cascading effects on litter microbes and that effects on fungi specifically shift under future expected levels of warming. Overall, our study highlights that trophic interactions may play important, albeit overlooked, roles in driving microbial responses to warming in Arctic terrestrial ecosystems. IMPORTANCE: The Arctic contains nearly half of the global pool of soil organic carbon and is one of the fastest warming regions on the planet. Accelerated decomposition of soil organic carbon due to warming could cause positive feedbacks to climate change through increased greenhouse gas emissions; thus, changes in ecological dynamics in this region are of global relevance. Microbial structure is an important driver of decomposition and is affected by both abiotic and biotic conditions. Yet how activities of soil-dwelling organisms in higher trophic levels influence microbial structure and function is unclear. In this study, we demonstrate that predicted changes in abundances of a dominant predator and warming interactively affect the structure of litter-dwelling fungal communities in the Arctic. These findings suggest predators may have widespread, indirect cascading effects on microbial communities, which could influence ecosystem responses to future climate change.

Living with wolves: A worldwide systematic review of attitudes.

This systematic review of peer reviewed articles on attitudes towards gray wolves (Canis lupus), shows that attitudes are mainly measured either by mean values of attitudes or by proportional differences in attitudes. This may impact on how attitudes are perceived and interpreted across studies and areas. However, independent of method used, we found that people living in areas where wolves always have existed, are more negative towards wolves compared to people living in areas where there are no wolves, or where wolves have recovered after years of absence. People who express fear, or being directly affected by having wolves, such as farmers and hunters, report more negative attitudes compared to other groups of respondents. For wolf conservation we recommend politicians and management authorities to prepare local societies of the different consequences of living in wolf areas. We recommend using dialogues and conflict management methods to minimize the level of conflicts.

An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems.

As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the optimization problems in various engineering fields. In order to further improve the convergence speed, solution accuracy, and local minima escaping ability of the traditional GWO algorithm, this work proposes a multi-strategy fusion improved gray wolf optimization (IGWO) algorithm. First, the initial population is optimized using the lens imaging reverse learning algorithm for laying the foundation for global search. Second, a nonlinear control parameter convergence strategy based on cosine variation is proposed to coordinate the global exploration and local exploitation ability of the algorithm. Finally, inspired by the tunicate swarm algorithm (TSA) and the particle swarm algorithm (PSO), a nonlinear tuning strategy for the parameters, and a correction based on the individual historical optimal positions and the global optimal positions are added in the position update equations to speed up the convergence of the algorithm. The proposed algorithm is assessed using 23 benchmark test problems, 15 CEC2014 test problems, and 2 well-known constraint engineering problems. The results show that the proposed IGWO has a balanced E&P capability in coping with global optimization as analyzed by the Wilcoxon rank sum and Friedman tests, and has a clear advantage over other state-of-the-art algorithms.

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM.

Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.

Data-driven optimization for microgrid control under distributed energy resource variability.

The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid's real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

Aborted sudden cardiac death in a young patient with epilepsy and the Gorlin Goltz syndrome.

Epilepsy is one of the most common chronical neurological conditions affecting over 50 million people worldwide. In addition to the stigma and discrimination, individuals with epilepsy suffer from a nearly three-fold increased risk of premature death compared to the general population. Although these premature deaths occur due to multiple causes, sudden unexpected death in epilepsy (SUDEP) still challenges neurologists and clinicians dealing with individuals with epilepsy. Recently, an increased interest in cardiac outcomes related to acute seizures and chronic epilepsy resulted in the groundbreaking development of the "epileptic heart" concept, and sudden cardiac death in individuals with epilepsy, which is 4.5 times as frequent as SUDEP according to some observational data, has gained more attention. As we gather information and learn about possible comorbidities and consequences of seizures and/or chronic epilepsy, we present a clinical case of a young patient with an unusual association of epilepsy, the Gorlin Goltz syndrome, and a cardiac fibroma with Wolf-Parkinson-White (WPW), who had multiple aborted cardiac arrests. Diagnostic challenges and multiple possible causes of sudden cardiac death in this single patient report are discussed.

Transcriptomic and metabolomic profiles of Pirata subpiraticus in response to copper exposure.

Copper (Cu) contamination represents a persistent and significant form of heavy metal pollution in agricultural ecosystems, posing serious threats to organisms in current society. Spiders serve as crucial biological indicators for assessing the impact of heavy metals-induced toxicity. However, the specific molecular responses of spiders to Cu exposure and the mechanisms involved are not well understood. In our study, the wolf pond spiders, Pirata subpiraticus, were exposed to Cu for 21 d, resulting in a notable decline in survival rates compared with the control (n = 50, p < 0.05). We observed an increased expression of enzymes like glutathione peroxidase and superoxide dismutase (p < 0.05), signaling a strong oxidative stress response crucial for counteracting the harmful effects of reactive oxygen species. This response was corroborated by a rise in malondialdehyde levels (p < 0.05), a marker of lipid peroxidation and oxidative damage. Transcriptomic and metabolomic analyses revealed 2004 differentially expressed genes (DEGs) and 220 metabolites (DEMs). A significant number of these DEGs were involved in the glutathione biosynthetic process and antioxidant activity. A conjoint analysis revealed that under the Cu stress, several important enzymes and metabolites were altered (e.g., cathepsin A, legumain, and lysosomal acid lipase), affecting the activities of key biological processes and components, such as lysosome and insect hormone biosynthesis. Additionally, the protein interaction network analysis showed an up-regulation of processes like the apoptotic process, glutamate synthase activity, and peroxisome, suggesting that spiders activate cellular protective strategies to cope with stress and maintain homeostasis. This study not only deepens our understanding of spider biology in the context of environmental stress but also makes a significant contribution to the field of environmental stress biology.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning.

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958-2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000-2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Detection of 4p16.3 deletion and 11p15.5p15.4 gain in a boy by comparative genomic hybridization array: A case report.

BACKGROUND: Nonallelic homologous recombination (NAHR) of segmental duplications or low copy repeats (LCRs) result in DNA gain/loss and play an important role in the origin of genomic disorders. CASE SUMMARY: A 3-year- old boy was referred for genetic analysis. Comparative genomic hybridization array analysis revealed a loss of 3776 kb in the 4p16.3 chromosomal region and a gain of 3201 kb in the 11p15.5p15.4 chromosomal region. CONCLUSION: Genomic imbalances caused by NAHR in LCRs result in deletion and duplication syndromes.