No abstract

The presented compact algorithm for recognizing handwritten digits of the MNIST database, created on the LogNNet reservoir neural network, reaches the recognition accuracy of 82%. The algorithm was tested on a low-memory Arduino board with 2 Kb static RAM low-power microcontroller. The dependences of the accuracy and time of image recognition on the number of neurons in the reservoir have been investigated. The memory allocation demonstrates that the algorithm stores all the necessary information in RAM without using additional data storage, and operates with original images without preliminary processing. The simple structure of the algorithm, with appropriate training, can be adapted for wide practical application, for example, for creating mobile biosensors for early diagnosis of adverse events in medicine. The study results are important for the implementation of artificial intelligence on peripheral constrained IoT devices and for edge computing.

Artificial intelligence (AI) has recently become an object of interest for specialists from various fields of science and technology, including healthcare professionals. Significantly increased funding for the development of AI models confirms this fact. Advances in machine learning (ML), availability of large data sets, and increasing processing power of computers promote the implementation of AI in many areas of human activity. Being a type of AI, machine learning allows automatic development of mathematical models using large data sets. These models can be used to address multiple problems, such as prediction of various events in obstetrics and neonatology. Further integration of artificial intelligence in perinatology will facilitate the development of this important area in the future. This review covers the main aspects of artificial intelligence and machine learning, their possible application in healthcare, potential limitations and problems, as well as outlooks in the context of AI integration into perinatal medicine. Key words: artificial intelligence, cardiotocography, neonatal asphyxia, fetal congenital abnormalities, fetal hypoxia, machine learning, neural networks, prediction, prognosis, perinatal risk, prenatal diagnosis

Objective: to review domestic and foreign literature on the issue of machine learning methods applied in medical information systems (MIS), to analyze the accuracy and efficiency of the technologies under study, their advantages and disadvantages, the possibilities of implementation in clinical practice.Material and methods. The literature search was performed in the PubMed/MEDLINE databases covering the period from 2000 to 2020 (using groups of keyphrases: "machine learning", "laboratory data", "clinical events", "prediction diseases"), CyberLeninka ("machine learning", "laboratory data", "clinical events", "prediction diseases" Russian keyphrases combinations) and Papers With Code ("clinical events", "prediction diseases", "electronic health record"). After reviewing the full text of 30 literature sources that met the selection criteria, the 19 most relevant articles were selected.Results. An analysis of sources that describe the application of artificial intelligence techniques to obtain predictive analytics, taking into account information about patients, such as demographic, anamnestic, and laboratory data, the data of instrumental studies, information about existing and former diseases available in MIS, was performed. The existing ways of predicting adverse medical outcomes using machine learning methods were considered. Information about the significance of the used laboratory data for constructing high-precision predictive mathematical models is presented.Conclusion. Implementation of machine learning algorithms in MIS seems to be a promising tool for effective prediction of adverse medical events for wide application in real clinical practice. It corresponds to the global trend in the development of personalized medicine based on the calculation of individual risk. There is an increase in the activity of research in the field of predicting noncommunicable diseases using artificial intelligence technologies.

Hydrogen sulfide is produced endogenously by a variety of enzymes involved in cysteine metabolism. Clinical data indicate that endogenous levels of hydrogen sulfide are diminished in various forms of cardiovascular diseases. The aim of the current study was to investigate the effects of hydrogen sulfide supplementation on cardiac function during reperfusion in a clinically relevant experimental model of cardiopulmonary bypass. Twelve anesthetized dogs underwent hypothermic cardiopulmonary bypass. After 60 minutes of hypothermic cardiac arrest, reperfusion was started after application of either saline vehicle (control, n = 6), or the sodium sulfide infusion (1 mg/kg/hour, n = 6). Biventricular hemodynamic variables were measured by combined pressure-volume-conductance catheters. Coronary and pulmonary blood flow, vasodilator responses to acetylcholine and sodiumnitroprusside and pulmonary function were also determined. Administration of sodium sulfide led to a significantly better recovery of left and right ventricular systolic function (P < 0.05) after 60 minutes of reperfusion. Coronary blood flow was also significantly higher in the sodium sulfide-treated group (P < 0.05). Sodium sulfide treatment improved coronary blood flow, and preserved the acetylcholine-induced increases in coronary and pulmonary blood (P < 0.05). Myocardial ATP levels were markedly improved in the sulfide-treated group. Thus, supplementation of sulfide improves the recovery of myocardial and endothelial function and energetic status after hypothermic cardiac arrest during cardiopulmonary bypass. These beneficial effects occurred without any detectable adverse hemodynamic or cardiovascular effects of sulfide at the dose used in the current study. The aim of the current study was to test potential cytoprotective and anti-inflammatory effects of the novel biological mediator hydrogen sulfide in murine models. Murine J774 macrophages were grown in culture and exposed to cytotoxic concentrations of nitrosoglutathione, or peroxynitrite (a reactive species formed from the reaction of nitric oxide and superoxide). Pretreatment of the cells with sodium sulfide (60-300 µM) reduced the loss of cell viability elicited by the nitric oxide donor compound (3 mM) or by peroxynitrite (3 mM), as measured by the MTT method. Sodium sulfide did not affect cell viability in the concentration range tested. In mice subjected to bacterial lipopolysaccharide (LPS, 5 mg/kg i.p.), treatment of the animals with sodium sulfide (0.2 mg/kg/hour for 4 hours, administered in Alzet minipumps) reduced the LPSinduced increase in plasma IL-1β and TNFα levels. These responses were attenuated when animals were pretreated with the heme oxygenase inhibitor tin-protoporphyrin IX (6 mg/kg). The current results point to the cytoprotective and anti-inflammatory effects of hydrogen sulfide, in cells exposed to nitrosative stress, and in animals subjected to endotoxemia. Introduction It has been previously shown that the two forms of acute cholecystitis, acute acalculous cholecystiti...