Silicon dioxide thin film mediated single cell nucleic acid isolation.

A limited amount of DNA extracted from single cells, and the development of single cell diagnostics make it necessary to create a new highly effective method for the single cells nucleic acids isolation. In this paper, we propose the DNA isolation method from biomaterials with limited DNA quantity in sample, and from samples with degradable DNA based on the use of solid-phase adsorbent silicon dioxide nanofilm deposited on the inner surface of PCR tube

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented

Schematic principles of A) SDTF synthesis on inner surface PCR tube by ion beam deposition method, B) PCR tubes holder and samples configuration for silicon dioxide IBD.

<p>Schematic principles of A) SDTF synthesis on inner surface PCR tube by ion beam deposition method, B) PCR tubes holder and samples configuration for silicon dioxide IBD.</p

SDTF chemical structure and plastic (polypropylene) sample with functional covering scheme.

<p>A) The result of ion etching of thin films layers of Ag(indicator)/SiO₂(silicon dioxide)/C(polypropylene)-substrate by Auger electron spectroscopy thin films ion profiling PCR tube inner surface sample. The spectrum indicates the chemical elements surface concentration by results of which is observed the absence of silicon dioxide on the PCR tube inner surface. B) Ag(indicator)/SiO₂(silicon dioxide)/C(polypropylene)-substrate surface concentration on inner surface of PCR tube after AES Ion profiling of thin films. The spectrum indicates an increase of the silicon and oxygen peaks. C) Auger electron spectroscopy depth profile of SDTF on graphite substrate. D) The dashes line show the research area of PCR tube inner surface.</p

Phase analysis, element distribution mapping of plastic (polypropylene) sample with functional covering and real-time polymerase chain reaction.

<p>A) Raman spectrum of two samples of inner surface PCR tubes with amorphous SDTF prepared by the IBD method under the same conditions. B) Auger electron spectroscopy element distribution mapping. Scale bar is 200 µm. The spectrum color scheme shows distribution of: carbon (C) – upper left scan sector, oxygen (O) – upper right scan sector, silicon (Si) – lower left scan sector and secondary electron image (lower right scan sector). C) Amplification curves obtained using 1– approach, which involves the introduction of a single cell (oocyte) in a tube without silicon dioxide nanocovering; 2– method implied the introduction of a single cell (oocyte) in a tube with silicon dioxide nanocovering, followed by a cell lysis and washing of tubes from impurities. It gave uniform positive results in the course of PCR, which confirms the efficient extraction of DNA from single cells.</p