Publications by authors named "D Kaplun"

Background: There has been a notable increase in interest in the transcriptional regulator Kaiso, which has been linked to the regulation of clonal hematopoiesis, myelodysplastic syndrome, and tumorigenesis. Nevertheless, there are no consistent data on the binding sites of Kaiso in vivo in the genome. Previous ChIP-seq analyses for Kaiso contradicted the accumulated data of Kaiso binding sites obtained in vitro.

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Pulmonary hypertension (PH) is a syndrome complex that accompanies a number of diseases of different etiologies, associated with basic mechanisms of structural and functional changes of the pulmonary circulation vessels and revealed pressure increasing in the pulmonary artery. The structural changes in the pulmonary circulation vessels are the main limiting factor determining the prognosis of patients with PH. Thickening and irreversible deposition of collagen in the pulmonary artery branches walls leads to rapid disease progression and a therapy effectiveness decreasing.

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Impedimetric biosensors represent a powerful and promising tool for studying and monitoring biological processes associated with proteins and can contribute to the development of new approaches in the diagnosis and treatment of diseases. The basic principles, analytical methods, and applications of hybrid impedimetric biosensors for express protein detection in biological fluids are described. The advantages of this type of biosensors, such as simplicity and speed of operation, sensitivity and selectivity of analysis, cost-effectiveness, and an ability to be integrated into hybrid microfluidic systems, are demonstrated.

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The aim of the study was to identify the features of diagnosis and treatment of sphenoid sinus diseases in patients over 60 years old. Clinical manifestations, results of endoscopic and radiological examination methods, and surgical intervention tactics were compared between elderly patients and younger patients (aged 18-59 years) who sought treatment for sphenoiditis in I.P.

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Article Synopsis
  • Agriculture is increasingly leveraging data through artificial intelligence (AI) and the Internet-of-Things (IoT) to improve practices like rainfall prediction and fruit health monitoring.
  • The proposed intelligent agriculture management system incorporates various sensors and uses advanced AI models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) layers, for accurate predictions.
  • The entire system operates in the cloud and can be accessed via a dedicated application, enhancing its usability in agricultural settings.
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