Early Detection Of Anomalies Research Articles

Zika virus infection in pregnant women and their children: A review

Zika virus (ZIKV) is an arthropod-borne virus (arbovirus) transmitted primarily by Aedes mosquitoes. ZIKV can be transmitted to humans by non-vector borne mechanisms such as sexual intercourse, maternal-foetal transmission or blood transfusion. In 2015, ZIKV emerged in the Americas, and spread to 87 countries and territories with autochthonous transmission, distributed across four of the six WHO regions. Most ZIKV infections in pregnancy are asymptomatic, but mother to child transmission of the virus can occur in 20 to 30% of cases and cause severe foetal and child defects. Children exposed to ZIKV while in utero might develop a pattern of structural anomalies and functional disabilities secondary to central nervous system damage, known as congenital Zika syndrome, and whose most common clinical feature is microcephaly. Normocephalic children born to mothers with ZIKV infection in pregnancy, and with no observable Zika-associated birth defects, may also present with later neurodevelopmental delay or post-natal microcephaly. Screening and detection of ZIKV infection in pregnancy is essential, because most women with ZIKV infection are asymptomatic and clinical manifestations are non-specific. However, the diagnosis of ZIKV infection poses multiple challenges due to limited resources and scarce laboratory capabilities in most affected areas, the narrow window of time that the virus persists in the bloodstream, the large proportion of asymptomatic infections, and the cross-reactivity with other flaviviruses such as Dengue virus (DENV). Molecular methods (RT-PCR) are the most reliable tool to confirm ZIKV infection, as serodiagnosis requires confirmation with neutralization tests in case of inconclusive or positive serology results. Prenatal ultrasound assessment is essential for monitoring foetal development and early detection of possible severe anomalies. A mid- and long-term follow-up of children exposed to ZIKV while in utero is necessary to promptly detect clinical manifestations of possible neurological impairment.Tweetable abstract: Zika virus infection during pregnancy is a cause of pregnancy loss and disability in children. Protection against mosquito bites, access to sexual and reproductive health services, prompt screening and detection of ZIKV infection in pregnancy, and prenatal ultrasound monitoring are key control strategies whilst a vaccine is not available.

Morphology and Morphometry of Foetal Corpus Callosum Using MRI – A Retrospective Study

Introduction: Corpus callosum (CC) is the largest commissural white fibres interconnecting cerebral hemispheres. The corpus callosum is responsible for interhemispheric transfer of information which is essential for cognitive function. The foetal corpus callosum serves as sensitive indicator for normal brain development and maturation. As the corpus callosum is a part of the highest order latest maturing mental network of the brain, its measurements are important to assess normal brain development and to locate structural changes. A comprehensive evaluation of normal human foetal corpus callosal development is essential to detect and understand the congenital anomalies of the brain. Thus, the prenatal diagnosis of partial or complete agenesis of the corpus callosum is important for predicting the normal development of the foetus. Foetal neural anomalies that are suspected on prenatal ultrasonography (USG) can be detected in early stage using foetal MRI. This imaging technique is highly useful for detailed visualization of normal neural development. Certain conditions like colpocephaly and widening of interhemispheric fissure can be clearly visualized using foetal MRI when compared to prenatal ultrasonography. Aim and objective: Was to establish the normal reference values for the measurement of foetal corpus callosum. The length and thickness of the foetal CC was measured corresponding to gestational age (GA) between 18-36weeks. Materials and methods: A retrospective MRI study was carried out in Radiology department of Sri Ramachandra Hospital. The study was conducted on 50 pregnant women with GA of 18-32 weeks and morphology of foetal corpus callosum was measured using MRI. The corpus callosum was visualized in a mid-sagittal plane as an anechoic structure, delimited by two echogenic lines superiorly by sulcus of the corpus callosum and inferiorly by the septum pellucidum. The length of corpus callosum was measured from the anterior most aspect of genu to the posterior most aspect of the splenium and the width of individual parts were measured and correlated with gestational age. The values obtained from the study were statistically calculated using regression coefficient method. Results: In the present study following parameters were observed such as length and width of diverse parts of Corpus callosum. The length of foetal CC ranged from 25.96 to 47.2 mm in 18 to 32 weeks of gestational age. The range of width of rostrum, genu, body and splenium were 1.2 to 2.2 mm, 1.2-2.8mm, 1.3-3.1mm and 1.36-3.2mm respectively. Conclusion: The periodic development of nervous system can be calculated more effectively with the morphometric measurement of foetal CC and its correlation with BPD. It is considered to be accurate than using BPD measurement of head circumference in USG. Hence, with the normative data of foetal CC measurements correlated with gestational age would give us accurate details of neuronal growth rather than measuring biparietal diameter (BPD) alone using USG. This knowledge will be highly helpful for the gynaecologists to predict the abnormal development of the foetus and it is advised to include foetal CC parameters as a one of the tools for early detection of CNS anomalies.

Predominance of fetal malformations among pregnant women: A multi-centric observational study

Objective: To observe the predominance of fetal anomalies in pregnant women in a multi-centric setting. Methods: This prospective observational study included 20225 pregnant women who came for antenatal care in University Hospital and fetal medicine units from 2016 to 2019. Fetal anatomical scanning was done for all participants. Results: One hundred eighty-three cases had fetal congenital anomalies, yielding a prevalence of around 0.9%. Third of cases had positive consanguinity, this increased in cases of skeletal and thoracic anomalies. The presence of past history of anomalies was evident in 8.2% mostly with skeletal and heart anomalies. History of drug intake was only verified in 1.6% of cases. Sixty-three women out of 183 (34.4%) were diagnosed to have anomalies in fetal nervous system. Conclusion: Prenatal diagnosis are recommended for early detection of congenital anomalies and counselling.

A threshold-based, real-time analysis in early detection of endpoint anomalies using SIEM expertise

IT functions in organisations produce enormous quantities of data, logs and events. In the current IT world, handling a large amount of data is a challenging task and it is the responsibility of network administrators to transmit and store the data securely. If any data is disclosed or tampered with by an attacker, either locally or remotely, then the impact can be very high. To overcome this, firewall and intrusion detection system (IDS) and intrusion prevention system (IPS) solutions are used to verify all packets on the network.

The Setup and Application of Reference Material in Sequencing-Based Noninvasive Prenatal Testing

Introduction: The sequencing-based noninvasive prenatal testing (NIPT) has been successfully integrated into clinical practice and facilitated the early detection of fetal chromosomal anomalies. However, a comprehensive reference material to evaluate and quality control NIPT services from different NIPT providers remains unavailable. Methods: In this study, we established a set of NIPT reference material consisting of 192 simulated samples. Most of the potential factors influencing the accuracy of NIPT, such as fetal fraction, mosaicism, and interfering substances, were included in the reference material. We compared the performance of chromosomal abnormalities detection on 3 widely used sequencers (NextSeq 500, BGISEQ-500, and Ion Proton) based on the reference material. Results: All 3 sequencers provided highly accurate and reliable results to samples with ≥3.5% fetal fractions and high percentage of mosaicism. Conclusions: The established reference material can serve as a universal standard quality control for the current and new-coming NIPT providers based on various sequencers.

Real-time fault detection and process control based on multi-channel sensor data fusion

Sensor signals acquired in industrial equipment contain rich information which can be analyzed to facilitate effective monitoring of equipment, early detection of system anomalies, quick diagnosis of fault root causes, and intelligent system design and control. In many mechatronic systems, multiple signals are acquired by different sensor channels (i.e., multi-channel data) which can be represented by high-order arrays (tensorial data). The multi-channel data has a high-dimensional and complex cross-correlation structure. It is crucial to develop a method that considers the interrelationships between different sensor channels. This paper proposes a new equipment monitoring approach based on uncorrelated multilinear discriminant analysis that can effectively model the multi-channel data to achieve a superior monitoring and fault diagnosis performance compared to other competing methods. The proposed method is applied directly to the high-dimensional tensorial data. Features are extracted and combined with multivariate control charts to achieve real-time fault detection of equipment. The effectiveness of the proposed method in quick detection of equipment faults is demonstrated with both the simulation and a real-world case study.

Anomaly detection in multivariate streaming PMU data using density estimation technique in wide area monitoring system

In recent years, the advancement in technology and the availability of a large number of sensors has led to a huge amount of real-time streaming data.The real-time data measured by Phasor Measurement Units (PMU), are of streaming data type and their analysis can identify the status of the power system failure which leads to system instability.Hence online monitoring of PMU data is extremely important which includes preprocessing, filtering, and analysis before taking any corrective actions in the power system.One of the crucial analyses that can be performed over PMU data is the identification of anomalies that arise due to several factors. Early detection of anomalies in the PMU streaming data is highly useful though in real-time it is difficult to detect anomalies consistently.This paper proposes a novel framework for detecting anomalies in multivariate streaming PMU data based on the Density Estimation technique. The main novelty of this paper is that the proposed model is suitable for any kind of anomaly detection in multivariate streaming PMU data which is not addressed in literatures. The proposed system implements Principal Component Analysis in streaming real-time data to select the relevant features. The K-Means algorithm is performed over the selected feature vectors to find the number of clusters in the streaming PMU data. The Gaussian Mixture Model (GMM) is developed to represent the normal PMU data of the system. The minimum and maximum value of the probability of the training samples being generated by the model is selected as a minimum and maximum threshold. The samples with probability less than the minimum threshold or greater than the maximum threshold are considered as anomalies. The main advantage of this proposed model is that it can perform anomaly detection in multivariate streaming PMU data even online within the specified window. The proposed system can identify real time anomalies of multivariate dataset even by offline training model of GMM. The proposed model is developed in MATLAB 2018b version and is tested in streaming PMU data both for offline and online datasets. The performance of the proposed model is validated using performance metrics and the results show that F1 score is 0.95 for 2 features, 0.91 for 3 features, and 0.99 for 8, 12 and 16 features and 1 for 10 features in offline mode. The performance evaluation of testing data in online mode for 2, 3, 8, 10, 12 and 16 features are obtained which proves that the system is adapting to the streaming PMU data with reduced false positive rate and F1 score of 1 for 8, 10, 12 and 16 features. The experimental results prove that the proposed framework for anomaly detection in streaming data is more effective and accurate in identifying the anomalies in real time PMU streamed dataset.

Autocorrelation Integrated Gaussian Based Anomaly Detection using Sensory Data in Industrial Manufacturing

In industrial processes, early detection of anomalies is crucial for reducing process failures, meeting the quality assurance (QA) requirements, and lowering raw material wastage. Therefore, anomaly detection algorithms should identify an anomaly in a timely manner, and hence, allows immediate corrective actions to be applied. In this context, this paper proposes a low-complexity algorithm for detecting anomalies in industrial steelmaking furnaces operation. The algorithm utilizes the vibration measurements collected from several built-in sensors to compute the temporal correlation using the autocorrelation function (ACF). Furthermore, the proposed model parameters are tuned by solving multi-objective optimization using a genetic algorithm (GA). The proposed algorithm is tested using a practical dataset provided by an industrial steelmaking plant. The obtained results show that the proposed algorithm outperforms the support vector machine (SVM) and random forest (RF) algorithms in most key performance measures with the advantage of a substantial decrease in training and execution times.

Early warning signals of failures in building management systems

In the context of sensor data generated by Building Management Systems (BMS), early warning signals are still an unexplored topic. The early detection of anomalies can help preventing malfunctions of key parts of a heating, cooling and air conditioning (HVAC) system that may lead to a range of BMS problems, from important energy waste to fatal errors in the worst case. We analyse early warning signals in BMS sensor data for early failure detection. In this paper, the studied failure is a malfunction of one specific Air Handling Unit (AHU) control system that causes temperature spikes of up to 30 degrees Celsius due to overreaction of the heating and cooling valves in response to an anomalous temperature change caused by the pre-heat coil in winter period in a specific area of a manufacturing facility. For such purpose, variance, lag-1 autocorrelation function (ACF1), power spectrum (PS) and variational autoencoder (VAE) techniques are applied to both univariate and multivariate scenarios. The univariate scenario considers the application of these techniques to the control variable only (the one that displays the failure), whereas the multivariate analysis considers the variables affecting the control variable for the same purpose. Results show that anomalies can be detected up to 32 hours prior to failure, which gives sufficient time to BMS engineers to prevent a failure and therefore, an proactive approach to BMS failures is adopted instead of a reactive one.

Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing

The early and robust detection of anomalies occurring in discrete manufacturing processes allows operators to prevent harm, e.g. defects in production machinery or products. While current approaches for data-driven anomaly detection provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in products. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated, and compared based on a real industrial metal forming dataset.

A FINITE ELEMENT METHOD FOR NEUTRON NOISE ANALYSIS IN HEXAGONAL REACTORS

The early detection of anomalies through the analysis of the neutron noise recorded by incore and ex-core instrumentation gives the possibility to take proper actions before such problems lead to safety concerns or impact plant availability. The study of the neutron fluctuations permits to detect and differentiate anomalies depending on their type and possibly to characterize and localize such anomalies. This method is non-intrusive and does not require any external perturbation of the system. To effectively use the neutron noise for reactor diagnostics it is essential to accurately model the effects of the anomalies on the neutron field. This paper deals with the development and validation of a neutron noise simulator for reactors with different geometries. The neutron noise is obtained by solving the frequency-domain two-group neutron diffusion equation in the first order approximation. In order to solve this partial differential equation a code based on a high order finite element method is developed. The novelty of this simulator resides on the possibility of dealing with rectangular meshes in any kind of geometry, thus allowing for complex domains and any location of the perturbation. The finite element method also permits automatic refinements in the cell size (h-adaptability) and in its polynomial degree (p-adaptability) that lead to a fast convergence. In order to show the possibilities of the neutron noise simulator developed a perturbation in a hexagonal two-dimensional reactor is investigated in this paper.

Cranial ultrasonographic screening findings among healthy neonates and their association with neurodevelopmental outcomes

To analyze the findings of cranial ultrasonographic screening in asymptomatic neonates and to assess the association between abnormal results and neurodevelopment. We retrospectively reviewed the cranial ultrasonographic screening results of healthy neonates born between 35 and 42 weeks gestation at our hospital from October 2011 to October 2018. In total, 11,681 neonates underwent cranial ultrasonographic screening during the study period, and 9666 (82.7%) had normal results. Of 2015 neonates with abnormal findings, 294 had more than two abnormalities. The most common minor findings were subependymal cysts (8.99%), choroid plexus cysts (2.43%), lenticulostriate vasculopathy (2.34%), frontal horn cysts (1.80%), and enlarged cisterna magna (1.04%). Then, 33 (0.28%) neonates had major abnormalities, including cerebral hemorrhage, periventricular heterotopia, focal cortical dysplasia, anomalies of the corpus callosum, and vascular malformation. Of 1334 neonates who underwent serial clinical evaluations, 76 (5.69%) had neurodevelopmental disorders, including developmental delay, attention-deficit/hyperactivity disorder, and autistic spectrum disorder. The incidence rate of intracranial anomalies in healthy neonates was 17.3%, and about 5.69% had neurodevelopmental disorders. Cranial ultrasonographic screening has its own value in helping early detection of intracranial anomalies in healthy neonates, some of which have prognostic implications.

Neurocranium thickness mapping in early childhood

The neurocranium changes rapidly in early childhood to accommodate the growing brain. Developmental disorders and environmental factors such as sleep position may lead to abnormal neurocranial maturation. Therefore, it is important to understand how this structure develops, in order to provide a baseline for early detection of anomalies. However, its anatomy has not yet been well studied in early childhood due to the lack of available imaging databases. In hospitals, CT is typically used to image the neurocranium when a pathology is suspected, but the presence of ionizing radiation makes it harder to construct databases of healthy subjects. In this study, instead, we use a dataset of MRI data from healthy normal children in the age range of 6 months to 36 months to study the development of the neurocranium. After extracting its outline from the MRI data, we used a conformal geometry-based analysis pipeline to detect local thickness growth throughout this age span. These changes will help us understand cranial bone development with respect to the brain, as well as detect abnormal variations, which will in turn inform better treatment strategies for implicated disorders.

AIUM Practice Parameter for the Performance of Detailed Diagnostic Obstetric Ultrasound Examinations Between 12 Weeks 0 Days and 13 Weeks 6 Days.

AIUM Practice Parameter for the Performance of Detailed Diagnostic Obstetric Ultrasound Examinations Between 12 Weeks 0 Days and 13 Weeks 6 Days.

“Overcoming the Defect”:A Perinatal Review of Congenital Anomalies in 6984 Consecutive Deliveries in a Tertiary Care Center in Western Nepal

Introduction: Neonatal congenital anomalies are the 17th leading cause of global disease burden. Congenital anomalies in low-income countries are high due to prevalence of nutritional deficiencies, intrauterine infections, teratogenic exposure and unsupervised self medication. This study aimed to find various antenatal risk factors for birth defects and the delays in health seeking behaviors in cases of still births with birth defects. Methods: In this retrospective study the records of mothers and neonates born with congenital anomalies during the period of 30 months were reviewed. Consanguinity, intrauterine infections, presence of anemia, and history of drug intake were noted. In cases of stillborns with anomalies, mode of delivery, labor complications and the three delays leading to morbidities were also noted. Results: There were 65 congenital anomalies during the study period with an incidence of 9.3 per 1000 births. Out of them, 43 were live births and 22 were still births. The commonest anomaly amongst live births was of musculoskeletal system (n=11, 25.5%) and amongst still births was of nervous system (n=10, 45.4%). Consanguineous marriage was present in 30 (46.1%) parents and folic acid was taken by only 26(40%) of the mothers. In cases of still births with anomalies, most deliveries were vaginal (97%) without any maternal complications. Among the three delays, delay in deciding to seek care was observed in most cases (n=14, 63.6%).Conclusion: Factors like consanguinity and intake of folic acid are modifiable factors useful in preventing congenital anomalies. Encouraging early antenatal visits might help in early detection of anomalies.

Spectrum of Binocular Vision Anomalies in Keratoconus Subjects.

The purposes of this study were to measure the various parameters of binocular vision (BV) in patients with KC and to identify the factors that possibly influence them. This was a prospective, observational study of BV in patients with KC and with best-corrected visual acuity ≤0.4 logMAR in each eye. Age-matched, nonstrabismic normal patients served as controls. Binocular vision examination included motor assessment, accommodation parameters, and fusional vergence. Binocular vision parameters of 84 KC patients were compared with those of 71 normal controls. The KC group showed statistically significant differences between fellow eyes in refractive cylinder (0.44 ± 2.1 D, P = .03), best-corrected visual acuity (0.06 ± 0.09 logMAR, P = .001), and maximum keratometry (6.64 ± 8.2D, P = .005). The control group showed no significant difference in any refractive or keratometric parameter between fellow eyes. Sixty-six (78.6%) of 84 KC patients had various combinations of BV anomalies: 48.8% had impaired stereopsis, 44% had abnormal fusional vergence, and 39.3% had accommodative infacility. Among individual parameters, all except accommodative amplitudes and negative distance fusional vergence showed statistically significant impairment compared with controls (P < .001). Median (interquartile range) stereoacuity was 70 (50 to 550) arc sec in the KC group and 40 (30 to 50) arc sec in control (P < .0001). There was a statistically significant but weak correlation between stereoacuity and positive fusional vergence for near (P = .008; Spearman coefficient, -0.28) and weak but significant negative correlation between phoria status and negative fusional vergence for near (P = .03; Spearman coefficient, -0.24). A large proportion of KC patients have BV anomalies. Assessment of BV function should be included in the clinical examination of all KC patients.

A Study on Morphometric Dimensions of Human Fetal Spleen at Different Gestational Ages

Introduction:The spleen is the largest hemolymphatic organ of the human body that plays a significant role in Hematopoiesis and immune phagocytosis. Recent studies have emphasized the importance of spleen as hematopoietic organ in fetal period, role in synthesis of immunoglobulins &amp; defense of human body against infections. A thorough knowledge of splenic morphometry and dimensions is vital in comprehending the development of spleen in fetal period.Aim:The aim of the study is to study detailed morphometric analysis human fetal splenic specimens of various gestational ages and compare the findings with earlier studiesSubjects and Methods:The present study included 40 fetal cadaveric spleen and morphometric features i.e.,length, breadth, width and weight of spleen were measured.Results:The average weight of the fetal spleen was 1.98 gm and varied from 0.1 to 5.1 gm. The average length, breadth and width of spleen in prenatal group were 2.21 cm, 1.39 cm and 0.645 cm respectively. In the present study the average weight of the fetal spleen was 1.98 gm and varied from 0.1 to 5.1 gm. There was statistically significant increase in splenic weight till 33 weeks.Conclusion:Knowledge of spleen size at different gestational ages will help in early detection and diagnosis of splenic anomalies, identifying congenital malformations, early diagnosis and prompt treatment of intrauterine infections.

Secuencia de Prune Belly: a propósito de un caso

Antecedentes: La secuencia Prune-Belly es un trastorno genético que se caracteriza por una tríada que incluye deficiente musculatura de la pared abdominal, anomalías urogenitales y criptorquidia bilateral. Suele acompañarse de malformaciones asociadas. La aproximación quirúrgica debe reservarse para aquellos pacientes con complicaciones severas.Resumen del caso: Se presenta el caso de un neonato de sexo masculino con agenesia de la musculatura de la pared abdominal, displasia renal multiquística y criptorquidia bilateral, que es transferido de un hospital básico del oriente ecuatoriano y que durante su hospitalización se le sostuvo con un manejo conservador.Conclusiones: El abordaje de la secuencia de Prune-Belly, debe ser multidisciplinario por su frecuente asociación con otras malformaciones; el diagnóstico prenatal cumple un rol fundamental en la detección precoz de anomalías severas.

Morphometric study of the diaphragmatic surface of the liver in the human fetus.

This study aimed to examine age-specific reference intervals and growth dynamics of the best fit for liver dimensions on the diaphragmatic surface of the fetal liver. The research material consisted of 69 human fetuses of both sexes (32♂, 37♀) aged 18–30 weeks. Using methods of anatomical dissection, digital image analysis and statistics, a total of 10 measurements and 2 calculations were performed. No statistical significant differences between sexes were found (p>0.05). The parameters studied displayed growth models that followed natural logarithmic functions. The mean value of the transverse–to–vertical diameter ratio of the liver throughout the analyzed period was 0.71±0.11. The isthmic ratio decreased significantly from 0.81±0.12 in the 18–19th week to 0.62±0.06 in the 26–27th week, and then increased to 0.68±0.11 in the 28–30th week of fetal life (p<0.01). The morphometric parameters of the diaphragmatic surface of the liver present age-specific reference data. No sex differences are found. The transverse–to–vertical diameter ratio supports a proportionate growth of the fetal liver. Quantitative anatomy of the growing liver may be of relevance in both the ultrasound monitoring of the fetal development and the early detection of liver anomalies.

Intramuscular vascular malformation in triceps: A case report with literature review

Vascular anomalies comprise a widely heterogeneous group of tumors and malformations. The tendency of radiologists and pathologists to report the intramuscular vascular anomalies as hemangiomas may mislead the treating surgeon which affects the diagnosis and management plan. The International Society for the Study of Vascular Anomalies classification has helped clear the confusion created by interchangeable terminology like hemangioma for such lesions. The vascular tumors may involute with time and deserve a conservative trial, whereas the symptomatic vascular malformations will eventually need surgical intervention. Mass lesions with normal skin without any discoloration, venous engorgement, or inflammatory signs can be easily passed for a lipoma. The high index of suspicion and availability of ultrasound and color Doppler help in the early detection of vascular anomalies. In the case presented herein, the vascular malformation symptomatic for about two decades was excised successfully with a plane of dissection through surrounding normal triceps muscle. This case highlights that the correct classification of vascular anomalies cannot be overemphasized because it guides the management plan according to the type of lesion and helps in setting up a genuine database for scientific research.