Influenza A virus surveillance in domestic pigs in Kazakhstan 2018-2021

ABSTRACT: This study described the results of a surveillance program monitoring circulation of influenza A viruses among domestic pigs (Sus domesticus) in Kazakhstan during 2018-2021. PCR data derived from 2,513 samples (nasopharyngeal swabs) collected from swine on large pig complexes and peasant farms located in different regions of Kazakhstan revealed that about 5% of samples were positive for influenza A virus RNA. This result suggested low levels of influenza A virus circulation in Kazakhstan. Subtyping of a set of samples revealed that the main strains circulating in 2018-2019 were A/H1N1 and A/H3N2.Surveillance conducted in 2020-2021 identified only A/H1N1 viruses in swine. The PCR data were confirmed by isolation of six strains: five influenza A/H1N1 viruses and one A/H3N2 virus

Stalinist Terror in the South Caucasus

ISSN:1867-932

Arterial Hypertension and Associated Risk Factors in Kazakhstan: An Analysis of Blood Pressure Screening Results from May Measurement Month 2021–2023

Abstract Introduction May Measurement Month (MMM) is a global campaign with the aim to improve awareness of arterial hypertension (AH). Kazakhstan participated in the campaign in 2021, 2022 and 2023. Methods During the cross-sectional 2021–2023 MMM surveys, volunteer adults (≥ 18 years) from cities in Kazakhstan had their blood pressure (BP) measured three times in a seated position, and received a questionnaire on their demographics, lifestyle and medical history. In those not receiving antihypertensive treatment, AH was defined as a mean systolic and/or diastolic BP ≥ 140/90 mmHg. Results A total of 8231 individuals took part in the survey, with 1805 participants in 2021, 2410 participants in 2022 and 4016 participants in 2023. The prevalence of AH was estimated to be 37% in 2021 and 45% in 2022 and 2023. Of those identified as having AH, 51–70% were aware that they had the condition. Among those who were aware that they had AH, 68–91% were receiving antihypertensive therapy. However, 70–82% of treated participants were only receiving one to two drugs. BP was controlled to < 140/90 mmHg in 43–50% of treated participants and to < 130/80 mmHg in 15–16%. Conclusion The 2021, 2022 and 2023 MMM campaigns showed that high proportion of AH, a low level of AH awareness and inadequate BP control in Kazakhstan. Programs are needed to increase awareness of the risks of high BP and to improve the diagnosis and effective treatment of AH

ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset

&lt;p&gt;ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations.&nbsp;&lt;/p&gt; &lt;p&gt;ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge.&nbsp;The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations,&nbsp;depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion&nbsp;with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD.&nbsp;&lt;/p&gt; &lt;p&gt;Zip file has 2 main folders: 1. dataset_final_phase&nbsp;, 2. dataset_phase_1&lt;/p&gt; &lt;p&gt;Structure of dataset_final_phase:&lt;/p&gt; &lt;p&gt;2 folders:&lt;/p&gt; &lt;p&gt;1. test cases stenosis with 300 images with annotations&lt;/p&gt; &lt;p&gt;2. test case segmentation with 300 images with annotations&lt;/p&gt; &lt;p&gt;Structure of&nbsp;dataset_phase_1:&lt;/p&gt; &lt;p&gt;1. segmentation_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images and annotations folder, where 200 XCA images are provided.&nbsp;&lt;/p&gt; &lt;p&gt;2. stenosis_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images and annotations&nbsp;folder, where 200 XCA images are provided.&nbsp;&lt;/p&gt; &lt;p&gt;&nbsp;&lt;/p&gt; &lt;p&gt;The corresponding Dataset Article will be provided later.&nbsp;&lt;/p&gt; &lt;p&gt;[1]&nbsp;Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions&lt;/p&gt

ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset

&lt;p&gt;ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations.&nbsp;&lt;/p&gt; &lt;p&gt;ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge.&nbsp;The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations,&nbsp;depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion&nbsp;with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD.&nbsp;&lt;/p&gt; &lt;p&gt;The dataset structure is as follows: top-level directories "syntax" and "stenosis" contain files for the two dataset objectives, namely: i) vessel branch classification according to the SYNTAX methodology; and ii) stenosis detection. Inside both directories, there are 3 subsets of the dataset, such as "train", "val", and "test". Inside each of those folders, there are 2 lower-level directories - "images", and "annotations". Inside the "images" folder there are images in ".png" format, extracted from DICOM recordings. The "annotations" folders contain single ".JSON" files, which are named in correspondence to the objective, i.e. "train.JSON", "val.JSON", and "test.JSON".&lt;/p&gt; &lt;p&gt;The structure of ".JSON" contains three top-level fields: "images", "categories", and "annotations". The "images" field contains the unique "id" of the image in the dataset, its "width" and "height" in pixels, and the "file_name" sub-field, which contains specific information about the image. The "categories" field contains a unique "id" from 1 to 26, and a "name", relating it to the SYNTAX descriptions. The "annotations" field contains a unique "id" of the annotation, "image_id" value, relating it to the specific image from the "images" field, and a "category_id" relating it to the specific category from the "categories" field. The "segmentation" sub-field contains coordinates of mask edge points in "XYXY" format. Bounding box coordinates are given in the "bbox" field in the "XYWH" format, where the first 2 values represent the x and y coordinates of the left-most and top-most points in the segmentation mask. The height and width of the bounding box are determined by the difference between the right-most and bottom-most points and the first two values. Finally, the "area" field provides the total area of the bounding box, calculated as the area of a rectangle.&lt;/p&gt; &lt;p&gt;&nbsp;&lt;/p&gt; &lt;p&gt;The corresponding Dataset Article will be provided later.&nbsp;&lt;/p&gt; &lt;p&gt;[1]&nbsp;Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions&lt;/p&gt

ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1

ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD. Zip file has 2 main folders: 1. segmentation_dataset , 2. stenosis_dataset 1. segmentation_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images folder, where 200 XCA images are provided. 2. stenosis_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images folder, where 200 XCA images are provided. The corresponding Dataset Article will be provided later. [1] Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definition

ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset

&lt;p&gt;ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations.&nbsp;&lt;/p&gt; &lt;p&gt;ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge.&nbsp;The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations,&nbsp;depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion&nbsp;with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD.&nbsp;&lt;/p&gt; &lt;p&gt;Zip file has 2 main folders: 1. dataset_final_phase&nbsp;, 2. dataset_phase_1&lt;/p&gt; &lt;p&gt;Structure of dataset_final_phase:&lt;/p&gt; &lt;p&gt;2 folders:&lt;/p&gt; &lt;p&gt;1. test cases stenosis with 300 images with annotations&lt;/p&gt; &lt;p&gt;2. test case segmentation with 300 images with annotations&lt;/p&gt; &lt;p&gt;Structure of&nbsp;dataset_phase_1:&lt;/p&gt; &lt;p&gt;1. segmentation_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images and annotations folder, where 200 XCA images are provided.&nbsp;&lt;/p&gt; &lt;p&gt;2. stenosis_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images and annotations&nbsp;folder, where 200 XCA images are provided.&nbsp;&lt;/p&gt; &lt;p&gt;&nbsp;&lt;/p&gt; &lt;p&gt;The corresponding Dataset Article will be provided later.&nbsp;&lt;/p&gt; &lt;p&gt;[1]&nbsp;Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions&lt;/p&gt