Dataset Information

Inguinal lymph node sample collected by minimally invasive sampler helps to accurately diagnose ASF in dead pigs without necropsy.

ABSTRACT: African swine fever (ASF) is a highly contagious hemorrhagic and transboundary animal disease, and it threatens global food security. A full necropsy to harvest the sample matrices for diagnosis in the farm may lead to contamination of the premises and directly threaten to the herds. In the present study, we compared the ASFV loads of the common samples that can be collected without necropsy. The unmatched nasal, throat, rectal samples were randomly taken using cotton swabs, and inguinal lymph node samples were collected by the minimally invasive samplers from the dead pigs of an ASF field outbreak farm. The ASFV loads of the samples were detected by qPCR and the results suggested that the overall ASFV nucleic acids levels of inguinal lymph node samples were higher than the swabs. What's more, sets of matched nasal swabs, rectal swabs, throat swabs, inguinal lymph nodes, serums, spleens and lungs samples were collected from 15 dead ASFV naturally infected pigs. Similarly, the results showed that inguinal lymph node samples, together with serum, spleen and lungs samples, contained more ASFV nucleic acids than the swabs. Our findings demonstrated that the inguinal lymph node collected by minimally invasive sampler is an ideal tissue for diagnosing ASFV infection in dead pigs without necropsy.

SUBMITTER: Li X

PROVIDER: S-EPMC9554536 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Inguinal lymph node sample collected by minimally invasive sampler helps to accurately diagnose ASF in dead pigs without necropsy.

Li Xiaowen X Li Yang Y Fan Mingyu M Fan Shiran S Gao Wenchao W Ren Jing J Liu Qingyuan Q Li Jingtao J Wu Weisheng W Li Junxian J Yu Qiannan Q Wang Xinglong X Yan Zhichun Z

Frontiers in veterinary science 20220928

African swine fever (ASF) is a highly contagious hemorrhagic and transboundary animal disease, and it threatens global food security. A full necropsy to harvest the sample matrices for diagnosis in the farm may lead to contamination of the premises and directly threaten to the herds. In the present study, we compared the ASFV loads of the common samples that can be collected without necropsy. The unmatched nasal, throat, rectal samples were randomly taken using cotton swabs, and inguinal lymph n ...[more]

PMID: 36246330

Similar Datasets

Project description:ObjectiveDiagnoses using imaging-based measures alone offer the hope of improving the accuracy of clinical diagnosis, thereby reducing the costs associated with incorrect treatments. Previous attempts to use brain imaging for diagnosis, however, have had only limited success in diagnosing patients who are independent of the samples used to derive the diagnostic algorithms. We aimed to develop a classification algorithm that can accurately diagnose chronic, well-characterized neuropsychiatric illness in single individuals, given the availability of sufficiently precise delineations of brain regions across several neural systems in anatomical MR images of the brain.MethodsWe have developed an automated method to diagnose individuals as having one of various neuropsychiatric illnesses using only anatomical MRI scans. The method employs a semi-supervised learning algorithm that discovers natural groupings of brains based on the spatial patterns of variation in the morphology of the cerebral cortex and other brain regions. We used split-half and leave-one-out cross-validation analyses in large MRI datasets to assess the reproducibility and diagnostic accuracy of those groupings.ResultsIn MRI datasets from persons with Attention-Deficit/Hyperactivity Disorder, Schizophrenia, Tourette Syndrome, Bipolar Disorder, or persons at high or low familial risk for Major Depressive Disorder, our method discriminated with high specificity and nearly perfect sensitivity the brains of persons who had one specific neuropsychiatric disorder from the brains of healthy participants and the brains of persons who had a different neuropsychiatric disorder.ConclusionsAlthough the classification algorithm presupposes the availability of precisely delineated brain regions, our findings suggest that patterns of morphological variation across brain surfaces, extracted from MRI scans alone, can successfully diagnose the presence of chronic neuropsychiatric disorders. Extensions of these methods are likely to provide biomarkers that will aid in identifying biological subtypes of those disorders, predicting disease course, and individualizing treatments for a wide range of neuropsychiatric illnesses.

Project description:UnlabelledBackgroundTranscriptomes are powerful resources, providing a window on the expressed portion of the genome that can be generated rapidly and at low cost for virtually any organism. However, because many genes have tissue-specific expression patterns, developing a complete transcriptome usually requires a 'discovery pool' of individuals to be sacrificed in order to harvest mRNA from as many different types of tissue as possible. This hinders transcriptome development in large, charismatic and endangered species, many of which stand the most to gain from such approaches. To circumvent this problem in a model pinniped species, we 454 sequenced cDNA from testis, heart, spleen, intestine, kidney and lung tissues obtained from nine adult male Antarctic fur seals (Arctocephalus gazella) that died of natural causes at Bird Island, South Georgia.ResultsAfter applying stringent quality control criteria based on length and annotation, we obtained 12,397 contigs which, in combination with 454 data previously obtained from skin, gave a total of 23,096 unique contigs. Homology was found to 77.0% of dog (Canis lupus familiaris) transcripts, suggesting that the combined assembly represents a substantial proportion of this species' transcriptome. Moreover, only 0.5% of transcripts revealed sequence similarity to bacteria, implying minimal contamination, and the percentage of transcripts involved in cell death was low at 2.6%. Transcripts with immune-related annotations were almost five-fold enriched relative to skin and represented 13.2% of all spleen-specific contigs. By reference to the dog, we also identified transcripts revealing homology to five class I, ten class II and three class III genes of the Major Histocompatibility Complex and derived the putative genomic distribution of 17,121 contigs, 2,119 in silico mined microsatellites and 9,382 single nucleotide polymorphisms.ConclusionsOur findings suggest that transcriptome development based on samples collected post mortem may greatly facilitate genomic studies, not only of marine mammals but also more generally of species that are of conservation concern.

Project description:BackgroundSimulation-based learning with virtual patients is a highly effective method that could potentially be further enhanced by including reflection phases. The effectiveness of reflection phases for learning to diagnose has mainly been demonstrated for problem-centered instruction with text-based cases, not for simulation-based learning. To close this research gap, we conducted a study on learning history-taking using virtual patients. In this study, we examined the added benefit of including reflection phases on learning to diagnose accurately, the associations between knowledge and learning, and the diagnostic process.MethodsA sample of N = 121 medical students completed a three-group experiment with a control group and pre- and posttests. The pretest consisted of a conceptual and strategic knowledge test and virtual patients to be diagnosed. In the learning phase, two intervention groups worked with virtual patients and completed different types of reflection phases, while the control group learned with virtual patients but without reflection phases. The posttest again involved virtual patients. For all virtual patients, diagnostic accuracy was assessed as the primary outcome. Current hypotheses were tracked during reflection phases and in simulation-based learning to measure diagnostic process.ResultsRegarding the added benefit of reflection phases, an ANCOVA controlling for pretest performance found no difference in diagnostic accuracy at posttest between the three conditions, F(2, 114) = 0.93, p = .398. Concerning knowledge and learning, both pretest conceptual knowledge and strategic knowledge were not associated with learning to diagnose accurately through reflection phases. Learners' diagnostic process improved during simulation-based learning and the reflection phases.ConclusionsReflection phases did not have an added benefit for learning to diagnose accurately in virtual patients. This finding indicates that reflection phases may not be as effective in simulation-based learning as in problem-centered instruction with text-based cases and can be explained with two contextual differences. First, information processing in simulation-based learning uses the verbal channel and the visual channel, while text-based learning only draws on the verbal channel. Second, in simulation-based learning, serial cue cases are used to gather information step-wise, whereas, in text-based learning, whole cases are used that present all data at once.

Project description:Background and aimsComputed tomography (CT) scan is frequently used to detect hepatocellular carcinoma (HCC) in routine clinical practice. The aim of this study is to develop a deep-learning AI system to improve the diagnostic accuracy of HCC by analysing liver CT imaging data.MethodsWe developed a deep-learning AI system by training on CT images from 7512 patients at Henan Provincial Peoples' Hospital. Its performance was validated on one internal test set (Henan Provincial Peoples' Hospital, n = 385) and one external test set (Henan Provincial Cancer Hospital, n = 556). The area under the receiver-operating characteristic curve (AUROC) was used as the primary classification metric. Accuracy, sensitivity, specificity, precision, negative predictive value and F1 metric were used to measure the performance of AI systems and radiologists.ResultsAI system achieved high performance in identifying HCC patients, with AUROC of 0.887 (95% CI 0.855-0.919) on the internal test set and 0.883 (95% CI 0.855-0.911) on the external test set. For internal test set, accuracy was 81.0% (76.8-84.8%), sensitivity was 78.4% (72.4-83.7%), specificity was 84.4% (78.0-89.6%) and F1 (harmonic average of precision and recall rate) was 0.824. For external test set, accuracy was 81.3% (77.8-84.5%), sensitivity was 89.4% (85.0-92.8%), specificity was 74.0% (68.5-78.9%) and F1 was 0.819. Compared with radiologists, AI system achieved comparable accuracy and F1 metric on internal test set (0.853 versus 0.818, P = 0.107; 0.863 vs. 0.824, P = 0.082) and external test set (0.805 vs. 0.793, P = 0.663; 0.810 vs. 0.814, P = 0.866). The predicted HCC risk scores by AI system in HCC patients with multiple tumours and high fibrosis stage were higher than those with solitary tumour and low fibrosis stage (tumour number: 0.197 vs. 0.138, P = 0.006; fibrosis stage: 0.183 vs. 0.127, P < 0.001). Radiologists' review showed that the accuracy of saliency heatmaps predicted by algorithms was 92.1% (95% CI: 89.2-95.0%).ConclusionsAI system achieved high performance in the detection of HCC compared with a group of specialised radiologists. Further investigation by prospective clinical trials was necessitated to verify this model.

Project description:Background & aimsClinically significant portal hypertension (CSPH) is a landmark in the natural history of cirrhosis, influencing clinical decisions in patients with hepatocellular carcinoma (HCC). Previous small series suggested that splanchnic volume measurements may predict portal hypertension. We aimed to evaluate whether volumetry obtained by standard multidetector computerised tomography (MDCT) can predict CSPH in patients with HCC.MethodsWe included 175 patients with HCC, referred for hepatic venous pressure gradient (HVPG) evaluation, in whom contemporary MDCT was available. Liver volume, spleen volume (SV) and liver segmental volume ratio (LSVR: volume of the segments I-III/volume of the segments IV-VIII) were calculated semi-automatically from MDCT. Other non-invasive tests (NITs) were also employed.ResultsVolume parameters could be measured in almost 100% of cases with an excellent inter-observer agreement (intraclass correlation coefficient >0.950). SV and LSVR were independently associated with CSPH (HVPG ≥10 mmHg) and did not interact with aetiology. The volume Index (VI), calculated as the product of SV and LSVR, predicted CSPH (AUC 0.83; 95% CI 0.77-0.89). Similar results were observed in an external cohort (n = 23) (AUC 0.87; 95% CI 0.69-1.00). Setting a sensitivity and specificity of 98%, VI could have avoided 35.9% of HVPG measurements. The accuracy of VI was similar to that of other NITs. VI also accurately predicted HVPG greater than 12, 14, 16 and 18 mmHg (AUC 0.81 [95% CI 0.74-0.88], 0.84 [95% CI 0.77-0.91], 0.85 [95% CI 0.77-0.92] and 0.87 [95% CI 0.79-0.94], respectively).ConclusionsQuantification of liver and spleen volumes by MDCT is a simple, accurate and reliable method of CSPH estimation in patients with compensated cirrhosis and HCC.Impact and implicationsAn increase in portal pressure strongly impacts outcomes after surgery in patients with early hepatocellular carcinoma (HCC). Direct measurement through hepatic vein catheterization remains the reference standard for portal pressure assessment, but its invasiveness limits its application. Therefore, we evaluated the ability of CT scan-based liver and spleen volume measurements to predict portal hypertension in patients with HCC. Our results indicate that the newly described index, based on quantification of liver and spleen volume, accurately predicts portal hypertension. These results suggest that a single imaging test may be used to diagnose and stage HCC, while providing an accurate estimation of portal hypertension, thus helping to stratify surgical risks.

Dataset Information

Inguinal lymph node sample collected by minimally invasive sampler helps to accurately diagnose ASF in dead pigs without necropsy.

Publications

Inguinal lymph node sample collected by minimally invasive sampler helps to accurately diagnose ASF in dead pigs without necropsy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets