scholarly journals Development Of Novel Multiplex PCR For Rapid Diagnosis Of Coinfected Hemo-Parasites In Cattle

2021 ◽  
Author(s):  
Pankaj Kumar ◽  
Abhay Kumar ◽  
Kamal Sarma ◽  
Paresh Sharma ◽  
Rashmi Rekha Kumari ◽  
...  
Keyword(s):  
Multiplex Pcr ◽  
Large Scale ◽  
False Negative ◽  
Parasitic Infection ◽  
Epidemiological Studies ◽  
Kappa Coefficient ◽  
Detection Methods ◽  
Specific Gene ◽  
Rrna Gene ◽  
Babesia Bigemina

A novel, rapid and specific multiplex polymerase chain reaction has been developed for the diagnosis of hemo-parasitic infection in bovine blood by three of the most common hemo-parasites. The reported method relied on the detection of the three different bovine hemoparasites isolated from red blood cells (RBCs) of cattle by conventional Giemsa stained blood smear (GSBS) and confirmed by multiplex PCR. The designed multiplex primer sets can amplify 205, 313 and 422 bp fragments of apocytochrome b, sporozoite and macroschizont 2 (spm2) and 16S rRNA gene for Babesia bigemina, Theileria annulata and Anaplasma marginale, respectively. This multiplex PCR was sensitive with the ability to detect the presence of 150 ng of genomic DNA. The primers used in this multiplex PCR also showed highly specific amplification of specific gene fragments of each respective parasite DNA without the presence of non-specific and non-target PCR products. This multiplex PCR system was used to diagnose GSBS confirmed blood samples (N=12) found infected or co-infected with hemoparasites. A comparison of the two detection methods revealed that 58.33% of specimens showed concordant diagnoses with both techniques. The specificity, positive predictive value and kappa coefficient of agreement was highest for diagnosis of B. bigemina and lowest for A. marginale. The overall Kappa coefficient for diagnosis based on GSBS for multiple pathogen compared to multiplex PCR was 0.56 slightly behind the threshold of 0.6 of agreement. Therefore, confirmation should always be made based on PCR to rule out false positive due to differences in subjective observations, stain particles and false negative due to low level of parasitaemia. The simplicity and rapidity of this specific multiplex PCR method make it suitable for large-scale epidemiological studies and for follow-up of drug treatments.

scholarly journals Development of Novel Multiplex PCR for Diagnosis of Co-infected Hemo-parasites in Cattle

2021 ◽  
Author(s):  
Pankaj Kumar ◽  
Abhay Kumar ◽  
Kamal Sarma ◽  
Paresh Sharma ◽  
Rashmi Rekha Kumari ◽  
...  
Keyword(s):  
Multiplex Pcr ◽  
Large Scale ◽  
Parasitic Infection ◽  
Epidemiological Studies ◽  
Kappa Coefficient ◽  
Detection Methods ◽  
Diagnostic Process ◽  
Specific Gene ◽  
Rrna Gene ◽  
Babesia Bigemina

Background: A novel, rapid and specific multiplex polymerase chain reaction was developed to diagnose hemo-parasitic infection in bovine blood co-infected with three of the most common hemo-parasites. Methods: The diagnostic process relied on the detection of the three different bovine hemoparasites isolated from red blood cells (RBCs) of cattle (N=30) by conventional Giemsa stained blood smear (GSBS) and confirmed by multiplex PCR. The multiplex PCR system was used to diagnose GSBS positive blood samples (N=12) found infected or co-infected with hemoparasites. The designed multiplex primer sets was attempted to amplify 205, 313 and 422 bp fragments of apocytochrome b, sporozoite and macroschizont 2 (spm2) and 16S rRNA gene for Babesia bigemina, Theileria annulata and Anaplasma marginale, respectively. Result: This multiplex PCR was sensitive with the ability to detect the presence of 150 ng of genomic DNA. The primers used in this multiplex PCR also showed highly specific amplification of specific gene fragments of each respective parasite. Comparing the two detection methods revealed that 58.33% of specimens showed concordant diagnoses with both techniques. The specificity, positive predictive value and kappa coefficient of the agreement was highest for diagnosis of B. bigemina and lowest for A. marginale. The overall Kappa coefficient for diagnosis based on GSBS for multiple pathogens compared to multiplex PCR was 0.56, slightly behind the threshold of 0.6 of agreement. Therefore, confirmation should always be based on PCR to rule out false positives due to differences in subjective observations, stain particles and false negatives due to low parasitemia. The simplicity and rapidity of this specific multiplex PCR method make it suitable for large-scale epidemiological studies and follow-up of drug treatments.

scholarly journals Intestinal Protozoan Infections Shape Fecal Bacterial Microbiota in Children from Guinea-Bissau.

2020 ◽  
Author(s):  
Sebastian von Huth ◽  
Louise B. Thingholm ◽  
Poul-Erik Kofoed ◽  
Corinna Bang ◽  
Malte C. Rühlemann ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Giardia Lamblia ◽  
Large Scale ◽  
Intestinal Parasites ◽  
Parasitic Infection ◽  
Parasitic Infections ◽  
Rrna Gene ◽  
Bacterial Phyla ◽  
Guinea Bissau ◽  
Bacterial Microbiota

Abstract Background: Intestinal parasitic infections, caused by helminths and protozoa, are globally distributed and major causes of worldwide morbidity. The gut microbiota may modulate parasite virulence and host response upon infection. The complex interplay between parasites and the gut microbiota is poorly understood, partly due to sampling difficulties in remote areas with high parasite prevalence and burden. Results: In a large study of children in Bissau, Guinea-Bissau, we found high prevalence of intestinal parasites, including hookworms, Entamoeba spp. and Giardia lamblia. By high-quality sequencing of the 16S rRNA gene of fecal samples stored on filter paper from a total of 1,204 children, we demonstrate that the bacterial microbiota is more or less unaltered by helminth infections, whereas it is shaped by the presence of both pathogenic and non-pathogenic protozoa, including Entamoeba spp. and Giardia lamblia. Within-sample (alpha) diversity remains largely unaffected, whereas overall community composition (beta diversity) is significantly affected by infection with both nonpathogenic Entamoeba coli (R2=0.0131, P= 0.0001) and Endolimax nana (R2=0.00902, P= 0.0001), and by pathogenic Entamoeba histolytica (R2=0.0164, P= 0.0001) and Giardia lamblia (R2=0.00676, P= 0.0001). Heavy infection load with multiple parasite species induces more pronounced shifts in microbiota community than mild ones. A total of 31 bacterial genera across all four major gut bacterial phyla associates with protozoan infection, including increased abundance of Prevotella, Campylobacter and two Clostridium clades (IV and WIVb), and decreased abundance of Collinsella (associated with irritable bowel syndrome), Lactobacillus, Ruminococcus, Veillonella and one Clostridium clade (XVIII). Further, we demonstrate that filter papers are usable for storage of samples at room temperature in large-scale studies where immediate freezing is not possible with minor alterations of microbiota composition. Conclusion: In the present largest-to-date study, we demonstrate that the fecal bacterial microbiota is shaped by intestinal parasitic infection, with most pronounced associations for protozoan species. Our results provide insights into the interplay between the microbiota and intestinal parasites, which are useful to understand infection outcome and design further studies aimed at optimizing treatment strategies.

scholarly journals SmokeNet: Satellite Smoke Scene Detection Using Convolutional Neural Network with Spatial and Channel-Wise Attention

2019 ◽  
Vol 11 (14) ◽  
pp. 1702 ◽  
Author(s):  
Rui Ba ◽  
Chen Chen ◽  
Jing Yuan ◽  
Weiguo Song ◽  
Siuming Lo
Keyword(s):  
Neural Network ◽  
Satellite Imagery ◽  
Large Scale ◽  
State Of The Art ◽  
Kappa Coefficient ◽  
Feature Representation ◽  
Detection Methods ◽  
Smoke Detection ◽  
Training Images ◽  
Moderate Resolution Imaging Spectroradiometer

A variety of environmental analysis applications have been advanced by the use of satellite remote sensing. Smoke detection based on satellite imagery is imperative for wildfire detection and monitoring. However, the commonly used smoke detection methods mainly focus on smoke discrimination from a few specific classes, which reduces their applicability in different regions of various classes. To this end, in this paper, we present a new large-scale satellite imagery smoke detection benchmark based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, namely USTC_SmokeRS, consisting of 6225 satellite images from six classes (i.e., cloud, dust, haze, land, seaside, and smoke) and covering various areas/regions over the world. To build a baseline for smoke detection in satellite imagery, we evaluate several state-of-the-art deep learning-based image classification models. Moreover, we propose a new convolution neural network (CNN) model, SmokeNet, which incorporates spatial and channel-wise attention in CNN to enhance feature representation for scene classification. The experimental results of our method using different proportions (16%, 32%, 48%, and 64%) of training images reveal that our model outperforms other approaches with higher accuracy and Kappa coefficient. Specifically, the proposed SmokeNet model trained with 64% training images achieves the best accuracy of 92.75% and Kappa coefficient of 0.9130. The model trained with 16% training images can also improve the classification accuracy and Kappa coefficient by at least 4.99% and 0.06, respectively, over the state-of-the-art models.

scholarly journals Selecting Clustering Algorithms for IBD Mapping

2021 ◽  
Author(s):  
Ruhollah Shemirani ◽  
Gillian M Belbin ◽  
Keith Burghardt ◽  
Kristina Lerman ◽  
Christy L Avery ◽  
...  
Keyword(s):  
Statistical Power ◽  
Large Scale ◽  
Clustering Algorithms ◽  
False Negative ◽  
Chromosome 1 ◽  
Detection Methods ◽  
Scalable Clustering ◽  
Markov Clustering ◽  
Cluster Properties ◽  
Greedy Methods

Background: Groups of distantly related individuals who share a short segment of their genome identical-by-descent (IBD) can provide insights about rare traits and diseases in massive biobanks via a process called IBD mapping. Clustering algorithms play an important role in finding these groups. We set out to analyze the fitness of commonly used, fast and scalable clustering algorithms for IBD mapping applications. We designed a realistic benchmark for local IBD graphs and utilized it to compare clustering algorithms in terms of statistical power. We also investigated the effectiveness of common clustering metrics as replacements for statistical power. Results: We simulated 3.4 million clusters across 850 experiments with varying cluster counts, false-positive, and false-negative rates. Infomap and Markov Clustering (MCL) community detection methods have high statistical power in most of the graphs, compared to greedy methods such as Louvain and Leiden. We demonstrate that standard clustering metrics, such as modularity, cannot predict statistical power of algorithms in IBD mapping applications, though they can help with simulating realistic benchmarks. We extend our findings to real datasets by analyzing 3 populations in the Population Architecture using Genomics and Epidemiology (PAGE) Study with ~51,000 members and 2 million shared segments on Chromosome 1, resulting in the extraction of ~39 million local IBD clusters across three different populations in PAGE. We used cluster properties derived in PAGE to increase the accuracy of our simulations and comparison. Conclusions: Markov Clustering produces a 30% increase in statistical power compared to the current state-of-art approach, while reducing runtime by 3 orders of magnitude; making it computationally tractable in modern large-scale genetic datasets. We provide an efficient implementation to enable clustering at scale for IBD mapping and poplation-based linkage for various populations and scenarios.

EVALUATION OF THE ISOLATION AND DETECTION METHODS FOR SALMONELLA SPP. FROM EGG SHELL CONTAMINATION USING MULTIPLEX PCR.

2015 ◽  
Vol 14 (1) ◽  
pp. 282-288
Author(s):  
Israa Adnan Ibraheam Al-Baghdady ◽  
Ashwak Bassim Jassim ◽  
Zainab Khudher Ahmed
Keyword(s):  
Multiplex Pcr ◽  
Detection Methods ◽  
Salmonella Spp ◽  
Egg Shell

Use of gene probes for the detection of quiescent enteric bacteria in marine and fresh waters

1995 ◽  
Vol 31 (5-6) ◽  
pp. 291-298
Author(s):  
Sally A. Anderson ◽  
Gillian D. Lewis ◽  
Michael N. Pearson
Keyword(s):  
Membrane Filtration ◽  
Enteric Bacteria ◽  
Probe Method ◽  
Detection Methods ◽  
23S Rrna ◽  
Specific Gene ◽  
Gene Probe ◽  
Selective Media ◽  
Selective Enrichment ◽  
E Coli

Specific gene probe detection methods that utilise a non-selective culturing step were tested for the ability to recognise the presence of quiescent enteric bacteria (Escherichia coli and Enterococcus faecalis ) within illuminated freshwater and seawater microcosms. An E. coli specific uidA gene probe and a 23S rRNA oligonucleotide probe for Enterococci were compared with recoveries using membrane filtration and incubation on selective media (mTEC and mE respectively). From these microcosm experiments a greater initial detection (from 4 hours to 1 day) of E. coli and Ent. faecalis using gene probe methods was observed. Additionally, a comparison of E. coli direct viable counts (DVC) in sunlight exposed microcosms with recoveries by selective media and gene probe methods revealed a large number of viable non-culturable cells. This suggests that enumeration of E. coli by a gene probe method is limited by the replication of the bacteria during the initial non-selective enrichment step. The detection of stressed Ent. faecalis by the oligonucleotide gene probe method was significantly greater than recovery on selective mE agar, indicating an Enterococci non-growth phase.

The emerging roles of extracellular vesicles in ovarian cancer

2020 ◽  
Vol 21 ◽  
Author(s):  
Yin-xue Wang ◽  
Yi-xiang Wang ◽  
Yi-ke Li ◽  
Shi-yan Tu ◽  
Yi-qing Wang
Keyword(s):  
Ovarian Cancer ◽  
Extracellular Vesicles ◽  
Large Scale ◽  
Current Treatment ◽  
Detection Methods ◽  
Drug Resistant ◽  
Apoptotic Bodies ◽  
Biological Mechanisms ◽  
Gynecological Malignancy ◽  
Resistance To Chemotherapy

: Ovarian cancer (OC) is one of the deadliest gynecological malignancy. Epithelial ovarian cancer (EOC) is its most common form. OC has both a poor prognosis and a high mortality rate due to the difficulties of early diagnosis, the limitation of current treatment and resistance to chemotherapy. Extracellular vesicles is a heterogeneous group of cellderived submicron vesicles which can be detected in body fluids, and it can be classified into three main types including exosomes, micro-vesicles, and apoptotic bodies. Cancer cells can produce more EVs than healthy cells. Moreover, the contents of these EVs have been found distinct from each other. It has been considered that EVs shedding from tumor cells may be implicated in clinical applications. Such as a tool for tumor diagnosis, prognosis and potential treatment of certain cancers. In this review, we provide a brief description of EVs in diagnosis, prognosis, treatment, drug-resistant of OC. Cancer-related EVs show powerful influences on tumors by various biological mechanisms. However, the contents mentioned above remain in the laboratory stage and there is a lack of large-scale clinical trials, and the maturity of the purification and detection methods is a constraint. In addition, amplification of oncogenes on ecDNA is remarkably prevalent in cancer, it may be possible that ecDNA can be encapsulated in EVs and thus detected by us. In summary, much more research on EVs needs to be perform to reveal breakthroughs in OC and to accelerate the process of its application on clinic.

scholarly journals Insectivorous songbirds as early indicators of future defoliation by spruce budworm

2021 ◽  
Author(s):  
Marion Germain ◽  
Daniel Kneeshaw ◽  
Louis De Grandpré ◽  
Mélanie Desrochers ◽  
Patrick M. A. James ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Spruce Budworm ◽  
Detection Methods ◽  
Maximum Probability ◽  
Point Count ◽  
Probability Of Presence ◽  
Time Lagged ◽  
One Year ◽  
Local Sampling

Abstract Context Although the spatiotemporal dynamics of spruce budworm outbreaks have been intensively studied, forecasting outbreaks remains challenging. During outbreaks, budworm-linked warblers (Tennessee, Cape May, and bay-breasted warbler) show a strong positive response to increases in spruce budworm, but little is known about the relative timing of these responses. Objectives We hypothesized that these warblers could be used as sentinels of future defoliation of budworm host trees. We examined the timing and magnitude of the relationships between defoliation by spruce budworm and changes in the probability of presence of warblers to determine whether they responded to budworm infestation before local defoliation being observed by standard detection methods. Methods We modelled this relationship using large-scale point count surveys of songbirds and maps of cumulative time-lagged defoliation over multiple spatial scales (2–30 km radius around sampling points) in Quebec, Canada. Results All three warbler species responded positively to defoliation at each spatial scale considered, but the timing of their response differed. Maximum probability of presence of Tennessee and Cape May warbler coincided with observations of local defoliation, or provided a one year warning, making them of little use to guide early interventions. In contrast, the probability of presence of bay-breasted warbler consistently increased 3–4 years before defoliation was detectable. Conclusions Early detection is a critical step in the management of spruce budworm outbreaks and rapid increases in the probability of presence of bay-breasted warbler could be used to identify future epicenters and target ground-based local sampling of spruce budworm.

scholarly journals Lack of an association between gallstone disease and bilirubin levels with risk of colorectal cancer: a Mendelian randomisation analysis

2021 ◽  
Author(s):  
Richard Culliford ◽  
Alex J. Cornish ◽  
Philip J. Law ◽  
Susan M. Farrington ◽  
Kimmo Palin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Large Scale ◽  
Causal Effect ◽  
Gallstone Disease ◽  
Epidemiological Studies ◽  
Mendelian Randomisation ◽  
Linear Relationships ◽  
Potential Risk Factors ◽  
The Relationship ◽  
Circulating Levels

Abstract Background Epidemiological studies of the relationship between gallstone disease and circulating levels of bilirubin with risk of developing colorectal cancer (CRC) have been inconsistent. To address possible confounding and reverse causation, we examine the relationship between these potential risk factors and CRC using Mendelian randomisation (MR). Methods We used two-sample MR to examine the relationship between genetic liability to gallstone disease and circulating levels of bilirubin with CRC in 26,397 patients and 41,481 controls. We calculated the odds ratio per genetically predicted SD unit increase in log bilirubin levels (ORSD) for CRC and tested for a non-zero causal effect of gallstones on CRC. Sensitivity analysis was applied to identify violations of estimator assumptions. Results No association between either gallstone disease (P value = 0.60) or circulating levels of bilirubin (ORSD = 1.00, 95% confidence interval (CI) = 0.96–1.03, P value = 0.90) with CRC was shown. Conclusions Despite the large scale of this study, we found no evidence for a causal relationship between either circulating levels of bilirubin or gallstone disease with risk of developing CRC. While the magnitude of effect suggested by some observational studies can confidently be excluded, we cannot exclude the possibility of smaller effect sizes and non-linear relationships.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

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