Comparison of lipooligosaccharide biosynthesis genes of Campylobacter jejuni strains with varying abilities to colonize the chicken gut and to invade Caco-2 cells

Campylobacter jejuni strains develop a high variability of lipooligosaccharide (LOS) structures on the cell surface based on variations in the genetic content of the LOS biosynthesis locus. While the importance of these variations for ganglioside mimicry as a critical factor in the triggering of Guillain–Barré syndrome has already been shown, little work has been done on the investigation of LOS structures and their function in the pathogenesis of gastrointestinal disease. In this study, the presence of several LOS genes in 40 C. jejuni strains with different abilities to colonize the chicken gut and to invade Caco-2 cells was investigated by PCR. Two genes, cgtB and wlaN, encoding putative β-1,3-galactosyltransferases were detected in most strongly invasive strains and rarely in non-invasive strains. A homopolymeric tract within the wlaN gene resulted in an intact gene product only in strongly invasive strains. The specific function of these genes during LOS biosynthesis is still unknown. cgtB and wlaN gene products are suggested to be involved in development of the colonization and invasion ability of C. jejuni. After a classification of the complete LOS loci, an association between a particular LOS class and colonization and invasion ability of the C. jejuni strain could not be detected. Lack of the pglB gene involved in protein glycosylation in one strain could be responsible for the weak colonization and invasion ability of this strain. There is some evidence that different genetic characteristics were responsible for strong or weak colonization and the invasion ability of C. jejuni strains.

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Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network

Optics & Laser Technology ◽

10.1016/j.optlastec.2020.106861 ◽

2021 ◽

Vol 137 ◽

pp. 106861

Author(s):

Deepa Joshi ◽

Ankit Butola ◽

Sheetal Raosaheb Kanade ◽

Dilip K. Prasad ◽

S.V. Amitha Mithra ◽

...

Keyword(s):

Neural Network ◽

Optical Coherence Tomography ◽

Deep Neural Network ◽

Optical Coherence ◽

Label Free ◽

Non Invasive ◽

Rice Seeds

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Genetic characteristics and taxonomic classification of Fimic Anthrosols in China

Geoderma ◽

10.1016/s0016-7061(03)00073-9 ◽

2003 ◽

Vol 115 (1-2) ◽

pp. 31-44 ◽

Cited By ~ 6

Author(s):

Min Zhang ◽

Li Ma ◽

Wenqing Li ◽

Baocheng Chen ◽

Jiwen Jia

Keyword(s):

Taxonomic Classification ◽

Genetic Characteristics

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P–331 The #Enzian classification: A comprehensive non-invasive and surgical description system for endometriosis

Human Reproduction ◽

10.1093/humrep/deab130.330 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

J Keckstein ◽

H Gernot

Keyword(s):

Classification System ◽

Working Group ◽

Complex Disease ◽

Diagnostic Methods ◽

Anatomical Location ◽

Ovarian Endometriosis ◽

Deep Endometriosis ◽

Non Invasive ◽

Complete Mapping

Abstract Study question Is there a classification for a complete mapping of endometriosis, including anatomical location, size of the lesions, and degree of involvement that can be used with both, diagnostics and surgery? Summary answer #Enzian classification improves in both, non-invasive diagnostic methods and surgical therapy for endometriosis as a universally usable classification system for all aspects of the disease. What is known already The most commonly used r-ASRM classification has certain limitations due to its incomplete description of DE, the complexity of the classification, and lack of reproducibility. In contrast, the Enzian classification, which has been implemented in the last decade, has proved to be the most suitable for the description of DE. However, since it does not include peritoneal and ovarian lesions and lacks a description of tubo-ovarian adhesions, it has not gained full acceptance. A combination of classification with different systems such as r-ASRM, EFI score and Enzian, may complicate classification of the disease due to overlaps and time-consuming documentation. Study design, size, duration The result is a consensus of a panel of renowned clinicians (working group), gynaecological surgeons and sonographers with extensive expertise in diagnosis and therapy of endometriosis. A first draft was written in 2019 by a joint effort of the first and last author and sent to all working group members. Taking all comments into account, a revised draft was then sent to all coauthors and repeated until a consensus was reached (9 revisions). Participants/materials, setting, methods Criteria used to invite the experts to participate in this consensus process included their having significant peer-reviewed publications in the field of diagnosis and management of endometriosis. Main results and the role of chance Our current proposal is the first of its kind to universally describe superficial and deep endometriosis, ovarian endometriosis, adenomyosis and adhesions by using a classification system that can be applied by gynaecologists, surgeons, sonographers and radiologists following the same principles. The correlation between preoperative and surgical staging, on the basis of the Enzian scheme, allows for consistent and clear classification of endometriosis, especially DE. Endometriosis can be mapped completely with one single classification system enabling the use of one common language. Limitations, reasons for caution This classification system is anatomically logical and should be easy to use. Further studies are ongoing and are needed to provide proof for the applicability, reproducibility and accuracy of the #Enzian classification for the description of endometriosis. Wider implications of the findings: #Enzian classification now enabled better coverage of various endometriosis localizations. The possibility of using this system preoperatively as well as postoperatively within the framework of diagnostics offers clinicians a significant improvement in the care of patients with such a complex disease. Trial registration number Not applicable

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Mac-2 Binding Protein Glycosylation Isomer: Emerging Non-Invasive Serum Marker for Liver Fibrosis

Annals of Laboratory Medicine ◽

10.3343/alm.2018.38.4.289 ◽

2018 ◽

Vol 38 (4) ◽

pp. 289-290

Author(s):

Sun Young Cho ◽

Mina Hur

Keyword(s):

Liver Fibrosis ◽

Binding Protein ◽

Serum Marker ◽

Protein Glycosylation ◽

Non Invasive

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Portal hypertension and its management in children

Archives of Disease in Childhood ◽

10.1136/archdischild-2015-310022 ◽

2017 ◽

Vol 103 (2) ◽

pp. 186-191 ◽

Cited By ~ 9

Author(s):

Tassos Grammatikopoulos ◽

Patrick James McKiernan ◽

Anil Dhawan

Keyword(s):

Portal Hypertension ◽

Liver Disease ◽

Gastrointestinal Bleeding ◽

Surgical Procedures ◽

Vascular Occlusion ◽

Portal System ◽

Mortality And Morbidity ◽

Non Invasive ◽

Intravascular Pressure

Portal hypertension (PHT), defined as raised intravascular pressure in the portal system, is a complication of chronic liver disease or liver vascular occlusion. Advances in our ability to diagnose and monitor the condition but also predict the risk of gastrointestinal bleeding have enabled us to optimise the management of children with PHT either at a surveillance or at a postbleeding stage. A consensus among paediatric centres in the classification of varices can be beneficial in streamlining future paediatric studies. New invasive (endoscopic and surgical procedures) and non-invasive (pharmacotherapy) techniques are currently used enabling clinicians to reduce mortality and morbidity in children with PHT.

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The Application of Deep Learning for the Classification of Internal Human Cardiac Anatomy

2018 Design of Medical Devices Conference ◽

10.1115/dmd2018-6887 ◽

2018 ◽

Author(s):

Erik Gaasedelen ◽

Alex Deakyne ◽

Paul Iaizzo

Keyword(s):

Deep Learning ◽

Surgical Procedures ◽

Electrical Conduction ◽

Human Heart ◽

Cardiac Anatomy ◽

Non Invasive ◽

Spatial Environment ◽

The Creation

The applications of sensing and localization are becoming more sophisticated in many invasive and non-invasive surgical procedures and there is great interest to apply them to the human heart. Ideally, such tools could be indispensable for allowing physicians to spatially understand relative tissue morphologies and their associated electrical conduction. Yet today there remains a steep divide between the creation of spatial environment models and the contextual understandings of adjacent features. To begin to address this, we explore the problem of anatomical perception by applying deep learning to the identification of internal cardiac anatomy images.

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Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

10.21203/rs.3.rs-66836/v2 ◽

2021 ◽

Author(s):

Ali Mohammad Alqudah ◽

Shoroq Qazan ◽

Ihssan S. Masad

Keyword(s):

Artificial Intelligence ◽

Input Image ◽

Image Size ◽

X Ray ◽

Softmax Classifier ◽

Non Invasive ◽

Efficient Detection ◽

X Ray Imaging ◽

Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

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Modern knowledge on pathogenesis, diagnosis and treatment of helicobacter infection

Experimental and Clinical Gastroenterology ◽

10.31146/1682-8658-ecg-193-9-15-26 ◽

2021 ◽

pp. 15-26

Author(s):

H. Yu. Kiselev ◽

C. L. Gorlenko ◽

Ya. A. El-Taravi ◽

E. E. Porubayeva ◽

E. V. Budanova

Keyword(s):

Drug Resistance ◽

Risk Factor ◽

Peptic Ulcer ◽

World Wide ◽

International Agency ◽

Proper Treatment ◽

Non Invasive ◽

Modern Knowledge ◽

H Pylori

Since its discovery, H. pylori infection is known as one of the risk factor for the development of gastritis, peptic ulcer, GIT tumors and numerous other diseases such as psoriasis. Infection caused by H. pylori is posed as the top oncogene in the risk of the development of gastrocarcinoma (First class oncogene by Classification of International Agency for Research of Cancer). That is why the elaboration of fast and accurate methods of diagnosis (non-invasive methods especially) and proper treatment of Helicobacter infection is still very important. Throughout the time, knowledge about pathogenesis of Helicobacter infection have been expanded with the detection of adhesins, chemotaxins and multiple virulence factors related to invasion, adhesion and cytotoxicity of H. pylori. Invasive and non-invasive methods of diagnostics are currently being improved in effectiveness and accuracy. But still, due to different factors (e. g., dramatically increasing drug resistance), eradication of H. pylori remains big problem world-wide. Our review represents modern data on pathogenesis, diagnostics and treatment of Helicobacter infection.

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