http://repository.usmf.md:80/handle/20.500.12710/589 2026-04-10T13:38:16Z 2026-04-10T13:38:16Z Iapăscurtă, Victor Cîvîrjic, Ivan Șandru, Serghei http://repository.usmf.md:80/handle/20.500.12710/29926 2025-01-03T12:26:51Z 2024-01-01T00:00:00Z

Title: Monitoring the fetus O2 delivery during regional obstetric anesthesia using computational medicine approach: a proof-of-concept project based on system dynamics modeling Authors: Iapăscurtă, Victor; Cîvîrjic, Ivan; Șandru, Serghei Abstract: Introduction: Although there are numerous methods available for monitoring the patient/parturient's condition during anesthesia, the options for monitoring the fetus's condition are significantly more restricted. Of major importance in this context is the value of oxygen delivery to the fetal tissues (DfetusO2), which, in turn, depends on several factors, including the method of anesthesia used. Material and Methods: Based on the current knowledge of the physiology of the maternal and fetal body, as well as the interaction between them, it is possible to create a system dynamics model that would evolve in real time depending on several factors, including anesthetic ones, which would allow the estimation of DfetusO2 continuously. System dynamics modeling tools (e.g., NetLogo SDM and others) can be used for this. Results: Using the NetLogo programming environment, a system dynamics model was created, which consists of three subsystems: (a) the maternal system (primarily, elements that determine oxygen transport), (b) the fetoplacental system, and (c) the fetal system (with emphasis on the elements that determine DfetusO2). The DfetusO2 value is influenced by the dynamics of the physiological parameters, which are the foundation of the three subsystems and can be monitored using traditional methods. Modifying specific parameters within each subsystem directly impacts DfetusO2, the central element of the model's graphical interface. In this way, DfetusO2 can continuously monitor oxygen delivery to fetal tissues. The demo version of the created model includes several scenarios: (a) state of anesthesia, (b) maternal pathology (e.g., anemia, heart failure, etc.), and (c) fetoplacental pathology (e.g., abruptio placentae). Conclusions: The created model allows the modeling of physiological events in the described framework with the continuous estimation of DfetusO2, which, in turn, could fill the gap in monitoring the state of the fetus during the perianesthetic period. The next step in the current research would be to evaluate the model's accuracy under clinical conditions.

2024-01-01T00:00:00Z Iapăscurtă, Victor Cîvîrjic, Ivan Șandru, Serghei http://repository.usmf.md:80/handle/20.500.12710/29925 2026-01-20T08:34:01Z 2024-01-01T00:00:00Z

Title: Some anesthesiology considerations in diabetic foot management: present status and future outlook Authors: Iapăscurtă, Victor; Cîvîrjic, Ivan; Șandru, Serghei Abstract: Introduction: Roughly 20% of surgical patients experience diabetes, a substantial risk factor that contributes to adverse outcomes after surgery, including mortality, both infectious and non-infectious complications, and extended hospital stays. Diabetic foot is a situation that may necessitate specialized anesthesia techniques. Material and Methods: Through an extensive examination of PubMed sources, we have determined the current status of the issue and have established research paths, which involve the utilization of contemporary information technologies to address the problem in individuals with diabetic foot. Results: A total of 78 papers addressing the topic of diabetic foot and anesthesia over the past decade were identified. Similarly, a total of 129 papers specifically related to machine learning and artificial intelligence technologies were discovered independently. Furthermore, regional anesthesia (RA) has been extensively documented to have advantages in promoting the restoration of function. Nevertheless, there are legitimate concerns regarding the elevated incidence of complications linked to regional anesthesia in patients with diabetes. An area of interest pertains to the length of time that the anesthetic block lasts due to neuropathic alterations in these individuals. The research project seeks to utilize machine learning techniques to evaluate the risk of RA in patients with diabetic foot and predict the duration of the blockage using clinical data such as blood glucose levels, skin condition (particularly at the foot level, assessed through photo images), and other relevant factors. This information is intended for utilization in the decision-making process, facilitated by a software application. Conclusions: The issue of diabetic foot is a present concern, and the application of contemporary technologies utilizing machine learning/artificial intelligence can enhance the decision-making process for anesthetic management in this patient population.

2024-01-01T00:00:00Z Cernei, Natalia http://repository.usmf.md:80/handle/20.500.12710/25257 2023-09-26T10:20:05Z 2023-01-01T00:00:00Z

Title: Valoarea diagnostică și prognostică a markerilor de hemocitometrie la pacienții cu Covid-19 Authors: Cernei, Natalia Abstract: Currently, there are several international guidelines that clearly describe that infection with the SARS-CoV-2 virus presents abnormalities of the hemocytometric indices, especially in severe forms. The prognostic values of these biomarkers reflect the intensity of the hyperinflammatory response, mediated by cytokines, and are strongly associated with a poor outcome of SARS-CoV-2 infection. In severe forms of COVID-19, leukocytes show lymphocytopenia, decrease in the number of monocytes and eosinophils, significant increase in neutrophils and neutrophil/lymphocyte ratio. These simple and feasible parameters can be used to predict and early identify patients with severe and critical forms of COVID-19. Leukocytosis, neutrophilia, lymphocytopenia, thrombocytopenia and the neutrophil/lymphocyte ratio were identified as independent factors for the poor clinical outcome in this category of patients and can be called, with certainty, true prognostic markers of severity, mortality, with the need to admit patients to the intensive care unit. The estimation of hematological laboratory parameters at hospitalization, capable of differentiating severe cases from non-severe cases, cases with high or low risk of mortality, allows awareness, supervision and timely treatment of patients with early improvement of the clinical outcomes. However, the interpretation of these indicators in the surveillance of patients with COVID-19 must take into account the administration of drug remedies (corticosteroids) and the occurrence of bacterial co-infection that could interfere with the result. The assessment of the accuracy of hemocytometric biomarkers needs to be determined in further research worldwide – more relevant studies with more accurate design, more rigorous execution and larger sample size.

2023-01-01T00:00:00Z Iapăscurtă, V. Ghereg, V. http://repository.usmf.md:80/handle/20.500.12710/23620 2023-01-30T13:56:07Z 1998-01-01T00:00:00Z

Title: Alterarea transportului şl utilizării oxigenului - axa conceptului modern al patogeniei şi tratamentului stării de şoc (revistă a literaturii) Authors: Iapăscurtă, V.; Ghereg, V.

1998-01-01T00:00:00Z