Vacuum-assisted laparostomy in severe abdominal trauma and urgent abdominal pathology with compartment syndrome, peritonitis and sepsis: Comparison with other options for multistage surgical treatment (systematic review and meta-analysis)

Background. The concept of multistage surgical treatment of patients has been established in surgery rather recently and therefore the discussions on the expediency of using a particular surgical technique in a specific situation still continue. Vacuum-assisted laparostomy is being widely implemented into clinical practice for the treatment of abdominal compartment syndrome, severe peritonitis and abdominal trauma, but the indications and advantages of this method are not clearly defined yet.The aim of the study. To conduct a systematic review and meta-analysis on the comparison of the effectiveness of vacuum-assisted laparostomy with various variants of relaparotomy and laparostomy without negative pressure therapy in  the  treatment of patients with urgent abdominal pathology and abdominal trauma complicated by widespread peritonitis, sepsis or compartment syndrome.Material and methods. A systematic literature search was conducted in accordance with the recommendations of “Preferred Reporting Items for Systematic Reviews and Meta-Analyses”. We carried out the analysis of non-randomized (since January 2007 until August 6, 2022) and randomized (without time limits for the start of the study and until August 6, 2022) studies from the electronic databases eLibrary, PubMed, Cochrane Library, Science Direct, Google Scholar Search, Mendeley.Results. Vacuum-assisted laparostomy causes statistically significant shortening of the time of treatment of patients in the ICU and in hospital and a decrease in  postoperative mortality compared to other variants of laparostomy without vacuum assistance.Conclusion. To obtain data of a higher level of evidence and higher grade of recommendations, it is necessary to further conduct systematic reviews and meta-analyses based on randomized clinical studies

Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery

Aim. To develop and evaluate the effectiveness of models for predicting mortality after coronary bypass surgery, obtained using machine learning analysis of preoperative data.Material and methods. As part of a cohort study, a retrospective prediction of in-hospital mortality after coronary artery bypass grafting (CABG) was performed in 2182 patients with stable coronary artery disease. Patients were divided into 2 following samples: learning (80%, n=1745) and training (20%, n=437). The initial ratio of surviving (n=2153) and deceased (n=29) patients in the total sample indicated a pronounced class imbalance, and therefore the resampling method was used in the training sample. Five machine learning (ML) algorithms were used to build predictive risk models: Logistic regression, Random Forrest, CatBoost, LightGBM, XGBoost. For each of these algorithms, cross-validation and hyperparameter search were performed on the training sample. As a result, five predictive models with the best parameters were obtained. The resulting predictive models were applied to the learning sample, after which their performance was compared in order to determine the most effective model.Results. Predictive models implemented on ensemble classifiers (CatBoost, LightGBM, XGBoost) showed better results compared to models based on logistic regression and random forest. The best quality metrics were obtained for CatBoost and LightGBM based models (Precision — 0,667, Recall — 0,333, F1-score — 0,444, ROC AUC — 0,666 for both models). There were following common high-ranking parameters for deciding on the outcome for both models: creatinine and blood glucose levels, left ventricular ejection fraction, age, critical stenosis (>70%) of carotid arteries and main lower limb arteries.Conclusion. Ensemble machine learning methods demonstrate higher predictive power compared to traditional methods such as logistic regression. The prognostic models obtained in the study for preoperative prediction of in-hospital mortality in patients referred for CABG can serve as a basis for developing systems to support medical decision-making in patients with coronary artery disease

Structure of low-silica glasses in the K2O-Nb2O5-SiO2 system

The nanostructure and nonlinear optical properties of high-niobium glasses in the (1 − x)KNbO3-xSiO2 system with an SiO2 content x = 0.05-0.35 have been studied by small-angle neutron scattering (SANS), electron microscopy (EM), and second-optical-harmonic generation (SHG). Vitreous samples are manufactured by the methods of fast melt cooling (pressing with metal plates and quenching between rotating rolls). Glasses with x 0.2 are homogeneous, but form a silica-enriched nanostructure after thermal treatments. At temperatures below ∼T g + 50°C, silica-enriched regions manifest a very slight tendency to grow, whereas, according to SANS and X-ray diffraction data, their chemical composition is observed to shift appreciably towards SiO2 with thermal treatment. The obtained data on an inhomogeneous structure allows us to clarify the complicated character of the previously revealed dependence T g (x). Nano-inhomogeneous transparent samples produce a weak SHG signal, which indicates the quasi-periodic modulation of the chemical composition and, correspondingly, polarizability, in the volume of glass. The nonlinear optical phase KNbO3 precipitates at later stages of crystallization, when a glass loses its transparency. In this case, the SHG signal is amplified by several orders of magnitude

Microfluorescence Analysis of Nanostructuring Inhomogeneity in Optical Fibers with Embedded Gallium Oxide Nanocrystals

A spectroscopic protocol is proposed to implement confocal microfluorescence imaging to the analysis of microinhomogeneity in the nanocrystallization of the core of fibers belonging to a new kind of broadband fiber amplifier based on glass with embedded nanocrystals. Nanocrystallization, crucial for achieving an adequate light emission efficiency of transition metal ions in these materials, has to be as homogeneous as possible in the fiber to assure optical amplification. This requirement calls for a sensitive method for monitoring nanostructuring in oxide glasses. Here we show that mapping microfluorescence excited at 633 nm by a He-Ne laser may give a useful tool in this regard, thanks to quasi-resonant excitation of coordination defects typical of germanosilicate materials, such as nonbridging oxygens and charged Ge-O-Ge sites, whose fluorescence are shown to undergo spectral modifications when nanocrystals form into the glass. The method has been positively checked on prototypes of optical fibers--preventively characterized by means of scanning electron microscopy and energy dispersive spectroscopy--fabricated from preforms of Ni-doped Li₂O-Na₂O-Sb₂O₃-Ga₂O₃-GeO₂-SiO₂ glass in silica cladding and subjected to heat treatment to activate gallium oxide nanocrystal growth. The method indeed enables not only the mapping of the crystallization degree but also the identification of drawing-induced defects in the fiber cladding

Crystallization and nonlinear optical properties of potassium niobium silicate glasses

The crystn. of the xK2O.xNb2O5.(1-2x)SiO2 (x = 0.167 - 0.250) glasses and glasses close in compn. to K2O.Nb2O5.4SiO2 at the K2O:Nb2O5 ratio ≠ 1 is investigated. In high-silica glasses, the metastable phase sepn. followed by the bulk multiphase crystn. are obsd. at temps. close to the glass transition point, Tg. The nanostructured transparent glasses that exhibit the optical second harmonic generation (SHG) effect are formed at the early stages of phase sepn. The surface crystn. of glasses with the pptn. of the KNbSi2O7 noncentrosym. phase occurs at higher temps