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Nosocomial infections, also known as health care-associated infections (HAIs) are infections that appear in patients under medical care in hospitals or other health care facilities and were absent at the time of admission. From 5 to 7 thousand cases of HAIs are registered in Ukraine every year, but according to experts, the actual number is 900 thousand cases per year. Intensive care unit (ICU) represents a prominent tool in HAIs control and quality assurance in many developed countries. In this respect, the US Center for Disease Control and Prevention (CDC) recommends monitoring HAI as a leading tool for the reduction of HAIs. Therefore, the aim of this research was to investigate microbiological profile of nosocomial infections in ICU of public hospitals in Lviv, Ukraine. Material and methods. 105 clinical materials from 50 patients in two public hospitals in Lviv (Lviv Regional clinical hospital and Military Medical Clinical Center of the Western Region) were analyzed during July-December 2018. 114 strains of pathogens were isolated. HAIs was defined as an infection that appeared in patients later than 48 hours after admission to the hospital. Urinary tract infections (UTI), blood stream infection (BI), respiratory tract infection (RTI) and surgical site infections (SSI) were diagnosed using the criteria defined by the CDC and clinical protocols. Causative bacterial strains were isolated and identified using standard methods and chromogenic media CHROMID*S.aureusElite, CHROMID*VRE, CHROMID*MRSASMART, CHROMID*CARBASMART, CHROMID* ESBL, CHROMID*OXA-48, CHROMID*Candida (Biomerieux) and biochemical identification kitsMIKRO-LA-TESTNEFERMtest24, ENTEROtest24, CANDIDAtest21, STAPHYtest24, STREPTOtest24 (ErbaLacema, Czech Republic). Antibiotic sensitivity test was done using Kirby-Bauer disc diffusion technique. (Ethical Committee or Institutional Animal Care and Use Committee Approval Danylo Halytsky Lviv National Medical University: 25/06/2018 № 6). HAIs structure was as follows: Staphylococcus lentus (22%), Eschrichia coli (22%), Klebsiella pneumoniae subsp.ozaenae (9.8%), Pseudomonas aeruginosa (43.9%), Klebsiella pneumoniae subsp.pneumonia (8.8%), Srreptococcus spp. (7.0%), Burkholderia pseudomallei (5.3%), Staphylococcus haemolyticus (3.5), Staphylococcus aureus (3.5%). There were several types of HAIs which are rarely found, such as Staphylococcus lentus, Staphylococcus simulans, Staphylococcus lugdunensis, Aerococcus viridans, Pragia fontium, Aeromonasichthiosmia, Raoultella terrigena, Macrococcus caseolyticus. Conclusions. In the era of antibiotics, HAIs are still uncontrollable. It is assessed that over 90.0% of HAIs are not recognized as epidemics. It is important to remember this fact when attempting to design prevention or control strategy to reduce HAIs either at WHO level, or at the level of local health care unit.

Objective. Gradient-based optimization using algorithmic derivatives can be a useful technique to improve engineering designs with respect to a computer-implemented objective function. Likewise, uncertainty quantification through computer simulations can be carried out by means of derivatives of the computer simulation. However, the effectiveness of these techniques depends on how 'well-linearizable' the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications. Approach. This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques. Main results. The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. However, the observed high density and magnitude of jumps was likely to preclude most meaningful uses of the derivatives. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path, and could be reduced in magnitude by a 'fuzzy voxels' approach. The investigated jumps in the MC function arose from local changes in the control flow that affected the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem. Significance. Besides the technical challenges of merely applying AD to existing software projects, the MC and MBIR codes must be adapted to compute smoother functions. For the MBIR code, we presented one possible approach for this while for the MC code, this will be subject to further research. For the tracking subprocedure, further research on surrogate models is necessary., (Creative Commons Attribution license.)

Objective. Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. This study introduces a novel metric for proton therapy quality control based on uncertainties in range verification of individual spots. Approach. We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an anthropomorphic phantom based on secondary charged particle detection in a silicon pixel telescope designed for proton computed tomography. The subsequently predicted Bragg peak positions, along with their uncertainties, are compared to the treatment plan, rejecting spots which are predicted to be outside the 95% confidence interval. The such-produced spot rejection rate presents a metric for the quality of the treatment fraction. Main results. The introduced spot rejection rate metric is shown to be well-defined for range predictors with well-calibrated uncertainties. Using this method, treatment errors in the form of lateral shifts can be detected down to 1 mm after around 1400 treated spots with spot intensities of 1 × 10 7 protons. The range verification model used in this metric predicts the Bragg peak depth to a mean absolute error of 1.107 ± 0.015 mm. Significance. Uncertainty-aware machine learning has potential applications in proton therapy quality control. This work presents the foundation for future developments in this area., (Creative Commons Attribution license.)

Landfill leachates contain highly concentrated pollutants, and their uncontrolled discharge poses significant risks to the public health and environment. This study validated a pilot-scale two-stage aerobic biochemical and reagent method for the pre-treatment of moderately old landfill leachate at a pilot-scale treatment plant with a capacity of 400 L per day. The kinetic curves of key pollutants were described using two-factor dimensionless exponential equations, allowing for predicting maximally achievable treatment effects during the first aerobic biochemical stage. The optimal duration of aerobic biochemical pre-treatment was determined based on the concept of limiting pollutant content and minimizing operating costs for the two-stage process. Reagent-based leachate pre-treatment using the modified Fenton method was verified in a batch reactor. Optimal concentrations and dosages of reagent solutions, including polyacrylamide, aluminium sulphate, ferrous sulphate, and hydrogen peroxide, were found to comply with Ukrainian regulations for wastewater discharge into sewerage networks. Key pollution indicators of pre-treated leachate, such as NH 4 + -N (13.4-15.5 mg × L -1 ), TKN (25.7-30.2 mg × L -1 ), BOD tot (71.8-76.9 mg × L -1 ), and COD (390-459 mg × L -1 ), meet the required standards., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Background: Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and in situ treatment validation in proton therapy. The pCT system of the Bergen pCT collaboration is able to handle very high particle intensities by means of track reconstruction. However, incorrectly reconstructed and secondary tracks degrade the image quality. We have investigated whether a convolutional neural network (CNN)-based filter is able to improve the image quality., Material and Methods: The CNN was trained by simulation and reconstruction of tens of millions of proton and helium tracks. The CNN filter was then compared to simple energy loss threshold methods using the Area Under the Receiver Operating Characteristics curve (AUROC), and by comparing the image quality and Water Equivalent Path Length (WEPL) error of proton and helium radiographs filtered with the same methods., Results: The CNN method led to a considerable improvement of the AUROC, from 74.3% to 97.5% with protons and from 94.2% to 99.5% with helium. The CNN filtering reduced the WEPL error in the helium radiograph from 1.03 mm to 0.93 mm while no improvement was seen in the CNN filtered pRads., Conclusion: The CNN improved the filtering of proton and helium tracks. Only in the helium radiograph did this lead to improved image quality.

Radiation therapy using protons and heavier ions is a fast-growing therapeutic option for cancer patients. A clinical system for particle imaging in particle therapy would enable online patient position verification, estimation of the dose deposition through range monitoring and a reduction of uncertainties in the calculation of the relative stopping power of the patient. Several prototype imaging modalities offer radiography and computed tomography using protons and heavy ions. A Digital Tracking Calorimeter (DTC), currently under development, has been proposed as one such detector. In the DTC 43 longitudinal layers of laterally stacked ALPIDE CMOS monolithic active pixel sensor chips are able to reconstruct a large number of simultaneously recorded proton tracks. In this study, we explored the capability of the DTC for helium imaging which offers favorable spatial resolution over proton imaging. Helium ions exhibit a larger cross section for inelastic nuclear interactions, increasing the number of produced secondaries in the imaged object and in the detector itself. To that end, a filtering process able to remove a large fraction of the secondaries was identified, and the track reconstruction process was adapted for helium ions. By filtering on the energy loss along the tracks, on the incoming angle and on the particle ranges, 97.5% of the secondaries were removed. After passing through 16 cm water, 50.0% of the primary helium ions survived; after the proposed filtering 42.4% of the primaries remained; finally after subsequent image reconstruction 31% of the primaries remained. Helium track reconstruction leads to more track matching errors compared to protons due to the increased available focus strength of the helium beam. In a head phantom radiograph, the Water Equivalent Path Length error envelope was 1.0 mm for helium and 1.1 mm for protons. This accuracy is expected to be sufficient for helium imaging for pre-treatment verification purposes.

Introduction: Healthcare-associated infections (HCAIs) are a global health problem, and the problem of HCAIs in Ukraine remains poorly understood because of problems with the registration system., Objective: To analyze the official data of the number of registered HCAIs in Ukraine for the period 2009-2019, compare them with the available data in scientific publications., Material and Methods: Statistical analysis of information from the "Laboratory Centers of the Ministry of Health of Ukraine" kindly provided by the State Institution "Public Health Center of the Ministry of Health of Ukraine"., Results: In 2019, 2,611 cases of HCAIs were registered, the lowest annual number of registered HCAIs in the last twelve years. The maximum number of HCAIs in 2011 was 7,448. An average of 5,089±756 cases of HCAIs has been registered annually. By age structure, the average for 2009-2019 was 78.0±5.8 % for adults and 22.0 % for children (0-17 y.o.). In 2019 13.8% of registered potential HCAIs agents were identified as MDR, and 80.0-87.0% belongs to the group of 12-17, the most common pathogens., Conclusions: The estimated minimum number of HCAIs in Ukraine was expected to be about 1 million per year. Official statistics on registered cases of HCAIs in Ukraine do not reflect reality, so the registration system and investigation of HCAIs in Ukraine needs to be reformed., Competing Interests: The authors have no conflict of interest., (© National Institute of Public Health – National Institute of Hygiene.)