High Detection Research Articles

Electrochemical high sensitive detection of hydrogen peroxide based on platinum-palladium nano-enzyme modified microelectrode

Hydrogen peroxide (H2O2) is widely distributed in living organisms and the environment, and its concentration is closely associated with human health. Therefore, the development of an efficient and sensitive detection method for H2O2 is imperative. In this study, a PtPd nano-enzyme composite was electrodeposited onto a platinum wire microelectrode (ME) using a one-step electrochemical deposition method to fabricate an H2O2 electrochemical sensor (PtPd/ME) with exceptional performance. The successful modification of PtPd on the electrode surface was confirmed by scanning electron microscopy (SEM) and X-ray energy dispersive spectroscopy (EDS). Chronoamperometry (i-t) was employed to investigate various technical parameters including sensitivity, detection limit, detection range, reproducibility, and anti-interference capability. The results demonstrate that the sensor exhibits a sensitivity of 11.94 mA M-1 cm-2 with a detection limit of 0.034 μM and a wide linear range from 31.25 μM to 4.15 mM while also demonstrating excellent anti-interference properties. These superior performances can be attributed to the strong catalytic activity towards H2O2 provided by PtPd nanoparticles as well as the fast electron transfer rate facilitated by microelectrodes. Furthermore, this sensor effectively enables real-time monitoring of H2O2 released by ascorbic acid-stimulated vascular endothelial cells and demonstrates its potential for detecting H2O2 in contact lens care solutions. These findings provide valuable insights for food and drug testing, and demonstrate great potential for monitoring key pathological processes within living cells.

Mitigating Gradient-Based Data Poisoning Attacks on Machine Learning Models: A Statistical Detection Method

Objectives: This research paper aims to develop a novel method for identifying gradient-based data poisoning attacks on industrial applications like autonomous vehicles and intelligent healthcare systems relying on machine learning and deep learning techniques. These algorithms performs well only if they are trained on good quality dataset. However, the ML models are prone to data poisoning attacks, targeting the training dataset, manipulate its input samples such that the machine learning algorithm gets confused and produces wrong predictions. The current detection techniques are effective to detect known attacks and lack generalized detection to unknown attacks. To address this issue, this paper aims to integrate security elements within the machine learning framework, guaranteeing effective identification and mitigation of known and unknown threats and achieve generalized detection. Methods: ML Filter, a unique attack detection approach integrates ML-Filter Detection Algorithm and the Statistical Perturbation Bounds Identification Algorithm to determine the given dataset is poisoned or not. DBSCAN algorithm is used to divide the dataset into several smaller subsets and perform algorithmic analysis for detection. The performance of the proposed method is evaluated in terms of True positive rate and significance test accuracy. Findings: The probability distribution differences between original and poisoned datasets vary with change in perturbation size rather than the datasets and ML models use for application. This finding lead to determine the perturbation bounds using statistical pairwise distance metrics and corresponding significance tests computed on the results. ML Filter demonstrates a high detection rate of 99.63% for known attacks and achieves a generalized detection accuracy of 98% for unknown attacks. Novelty: A secured ML architecture and a unique statistical detection approach ML-Filter, effectively detect data poisoning attacks, demonstrating significant advancements in detecting both known and unknown threats in industrial applications utilizing machine learning and deep learning algorithms. Keywords: Privacy and security, Adversarial machine learning, Secured ML Architecture, ML-Filter, Statistical Perturbation Bounds Identification Algorithm

Poly3‐Hexylthiophene Titanium Dioxide (P3HT‐TiO2) Composite Sensor for High Response Detection of Ammonia at Room Temperature

AbstractGas sensors for ammonia (NH3) play a crucial role in the maintenance of air quality, preservation of the atmosphere, and evaluation of human health. However, the endeavor to attain a high level of sensitivity in detecting NH3 under ambient conditions remains a challenging task. In this work, poly(3‐hexylthiophene)s and titanium dioxide (P3HT‐TiO2) heterostructure NH3 gas‐sensitive material was prepared, and by drop‐casting the material onto a substrate with Au interdigital electrodes, a sensor for high‐response detection of NH3 at room temperature was constructed. The designed gas sensor exhibited a high response signal of 114.61 % toward 15 ppm NH3 at room temperature (25 °C) with good reproducibility, strong long‐term stability, and excellent selectivity. Moreover, by analyzing the response law of the sensor under different humidity conditions, it has been noted that both excessively high and excessively low levels of humidity adversely affect the sensor's response. The remarkable increase in NH3 sensing properties is attributed to the synergistic enhancement effect of P−N heterojunction formed between P3HT and TiO2. This NH3 gas sensor exhibits tremendous potential for use due to its straightforward structure and outstanding ammonia sensing performance.

Ultrahigh Responsivity In2O3 UVA Photodetector through Modulation of Trimethylindium Flow Rate

Oxygen vacancies (Vo) can significantly degrade the electrical properties of indium oxide (In2O3) thin films, thus limiting their application in the field of ultraviolet detection. In this work, the Vo is effectively suppressed by adjusting the Trimethylindium (TMIn) flow rate (fTMIn). In addition, with the reduction of the fTMIn, the background carrier concentration and the roughness of the film decrease gradually. And a smooth In2O3 thin film with roughness of 0.44 nm is obtained when the fTMIn is 5 sccm. The MSM photodetectors (PDs) are constructed based on In2O3 thin films with different fTMIn to investigate the opto-electric characteristics of the films. The dark current of the PDs is significantly reduced by five orders from 100 mA to 0.28 μA with the reduction of the fTMIn from 50 sccm to 5 sccm. In addition, the photo response capacity of PDs is dramatically enhanced. The photo-to-dark current ratio (PDCR) increases from 0 to 2589. Finally, the PD with the fTMIn of 5 sccm possesses a record-high responsivity of 2.53 × 103 AW−1, a high detectivity of 5.43 × 107 Jones and a high EQE of 9383 × 100%. Our work provides an important reference for the fabrication of high-sensitivity UV PDs.

Nuclear medicine procedure guideline for sentinel lymph node localization

The authors present a procedure guideline for scintigraphic detection of sentinel lymph nodes in malignant melanoma, in breast cancer, in penile and vulva tumors, in head and neck cancer, and in prostate carcinoma. Important goals of sentinel lymph node scintigraphy comprise reduction of the extent of surgery, lower postoperative morbidity and optimization of histopathological examination focussing on relevant lymph nodes. Sentinel lymph node scintigraphy itself does notdiagnose tumorous lymph node involvement and is not indicated when lymph node or distant metastases have been definitely diagnosed before sentinel lymph node scintigraphy. Procedures are compiled with the aim to reliably localise sentinel lymph nodes with a high detection rate typically in early tumour stages. New aspects in this guideline are new radiopharmaceuticals such as tilmanocept and Tc-99m-PSMA and SPECT/CT allowing an easier anatomical orientation. Initial dynamic lymphoscintigraphy in breast cancer is of little significance nowadays. Radiation exposure is low so that pregnancy is not a contraindication for sentinel lymph node scintigraphy. A one-day protocol should preferentially be used. Even with high volumes of scintigraphic sentinel lymph node procedures surgeons, theatre staff and pathologists receive a radiation exposure < 1 mSv/year so that they do not require occupational radiation surveillance. Aspects of quality control were included (scintigraphy, quality control of gamma probe, 6 h SLN course for surgeons, certified breast centers, medical surveillance center).

Speckle‐Insensitive Fractal Superconducting Nanowire Single‐Photon Detector Coupled with Multimode Optical Fiber

AbstractSpeckles are ubiquitous phenomena associated with multi‐spatial‐mode optics, which may reduce detection efficiency and induce modal noise if the photoresponse of the detector is polarization‐dependent. Thus far, they limit the performance of superconducting nanowire single‐photon detectors (SNSPDs) coupled with multimode optical fibers (MMF). To solve this problem, here, it is shown that SNSPDs patterned into the fractal geometry is insensitive to speckles and generates minimal modal noise that would otherwise be induced by the polarization‐dependent local device efficiency of the meander and spiral SNSPDs. Using this advantageous property of the fractal SNSPDs, system detection efficiency at the wavelength of 1530 nm and 42‐ps timing jitter is demonstrated when we couple a fractal SNSPD with 50‐micrometer‐core, step‐index MMF. This work not only demonstrates a scheme to achieving high system detection efficiency for MMF‐coupled SNSPDs that can be used in many applications, but also provides insight on how engineered nanostructures of photodetectors can reduce modal noise when detecting light from multiple spatial modes.

Empowering software reliability: Leveraging efficient fault detection and removal efficiency

Advancements in science and technology have led to the widespread use of computer systems in various applications, emphasizing the importance of software reliability. Software failures can have severe consequences, making thorough testing crucial. Software reliability growth models (SRGMs) play a significant role in enhancing reliability by predicting improvement over time. This article introduces a comprehensive approach to software reliability that incorporates a dynamic fault detection rate, along with fault removal efficiency. The fault detection rate measures the rate at which faults are identified during testing, reflecting the effectiveness of the testing process. By incorporating this dynamic component, the model provides a more accurate estimation of software reliability and enables adaptive testing strategies and resource allocation. Achieving a high fault detection rate is desirable, but organizations must consider the cost implications and strike a balance between reliability and time-to-market constraints. This article extends the analysis to calculate the optimal release time and optimal warranty period that minimize development costs, subject to the desired reliability. By considering these factors, development teams can make informed decisions regarding the timing of software release and the duration of the warranty period, optimizing both reliability and cost.

UAV imagery-based railroad station building inspection using hybrid learning architecture

The apparent defects of railroad station building such as the broken glass, missing screw, and rust corrosion need to be inspected regularly and timely to ensure the safety of passenger. Unmanned aerial vehicle (UAV) imagery-based inspections have the potential to revolutionize current manual visual inspections by providing a better overhead view and mitigating safety concerns. This paper proposes a hybrid learning architecture called YOLOS (you only look once station scene) to simultaneously detect and segment station building surface defects of UAV images. First, a novel squeeze-and-excitation (SE) attention block is integrated into the detection branch to adaptively learn the weights of the feature channels, thereby promoting the network to pay attention to the critical deep features of the objects. And then, a new semantic segmentation branch parallel to the detection branch is designed and assembled in YOLOS for pixel-level defect recognition. Finally, extensive experiments on railroad station dataset established with drone imagery prove the effectiveness and robustness of the SE-based network on station surface defect detection. This method can quickly convert UAV imagery into useful information with a high detection rate.

Resurgence of common respiratory viruses in patients with community-acquired pneumonia (CAP)—A prospective multicenter study

BackgroundCommunity-acquired pneumonia (CAP) is a major global cause of death and hospitalization. Bacteria or community-acquired viruses (CARVs) cause CAP. COVID-19 associated restrictions effectively reduced the circulation of CARVs. ObjectivesThe aim of this study was to analyze the proportion of CARVs in adult patients with CAP from mid-2020 to mid-2023. Specifically, we aimed to compare the rate of influenza virus, SARS-CoV-2, and RSV detections in patients aged 18–59 years and ≥60 years. Study designWe analyze the proportion of 21 community-acquired respiratory viruses (CARVs) and three atypical bacteria (Bordetella pertussis, Legionella pneumophila, and Mycoplasma pneumoniae) in nasopharyngeal swab samples using molecular multiplex methods within the prospective, multicentre, multinational study of the German study Group CAPNETZ. We used stringent inclusion criteria throughout the study. ResultsWe identified CARVs in 364/1,388 (26.2 %) patients. In detail, we detected SARS-CoV-2 in 210/1,388 (15.1 %), rhino-/enterovirus in 64/1,388 (4.6 %), influenza virus in 23/1,388 (1.6 %) and RSV in 17/1,388 (1.2 %) of all patients. We detected RSV and influenza more frequently in patients ≥60 years, especially in 22/23 compared to the previous season. None of the atypical bacteria were detected. ConclusionsBeginning in 2023, we demonstrate a re-emergence of CARVs in CAP patients. Effective vaccines or specific antiviral therapies for more than two thirds of the detected viral infections are currently available. High detection rates of vaccine-preventable viruses in older age groups support targeted vaccination campaigns.

Botnet Attack Detection in IoT Devices using Ensemble Classifiers with Reduced Feature Space

The Internet of Things (IoT) is an advancing important technology offers multiple perks, such as webcams, baby monitors, room temperature controllers, smart security cameras and intelligent home automations resulting in the creation of intelligent settings that greatly simplify daily living. However, there are cybersecurity dangers associated with IoT devices due to their lack of protection. For example, Internet of Things botnets have become a major risk. IoT has been a boon for attackers to perform malicious attacks like information theft, DDoS, sending junk data to disrupt networks. IoT devices face serious security issues, from having default weak and common passwords, and a lack of security, rarely and poorly monitored, to having open access to management systems, always connected to the internet. In this paper, we used the N-BaIoT dataset which includes datasets of 9 IoT devices infected with 2 Bot viruses Mirai and Bashlite, where each botnet has 5 sub-attacks and the benign datasets of 9 devices. An analysis with the N-BaIoT dataset which initially had 115 features were reduced to 35 features by using manual reduction and further reduced to single feature in 5-time instances equivalent to 5 features using heat map. We then classified the sub-attacks of 2 botnets and benign of 9 IoT devices by using 7 Machine Learning based classifiers in the Weka tool and Python and compared our results with the manually reduced 35 Features and Heat map based 5 features. Performance metrics like correctly classified, incorrectly classified instances and time taken to build the model were evaluated to verify the proposed work. We found out that over 3 ensemble machine learning classifiers performed extremely well with 99 % accuracies for all devices. In order to verify the logic of our work we tried implementing our proposed model in a different dataset with 3 ensemble classifiers and were able to achieve high detection rates.

Using Deep Learning Algorithm in Security Informatics

The utilization of deep learning algorithms in security informatics has revolutionized cybersecurity, offering advanced solutions for threat detection and mitigation. This paper presents findings from research exploring the efficacy of deep learning in various security domains, including anomaly detection, malware detection, phishing detection, and threat intelligence analysis. Results demonstrate high detection rates and accuracy, with anomaly detection achieving a remarkable 98.5% detection rate and malware detection showcasing a classification accuracy of 99.2%. Phishing detection also yielded promising results with a detection accuracy of 95.8%. These findings underscore the potential of deep learning in enhancing security defenses. However, challenges such as interpretability and robustness remain, necessitating further research and development. By addressing these challenges and prioritizing robust security measures, organizations can leverage deep learning to create more effective and trustworthy security solutions, thereby mitigating cyber threats and safeguarding digital assets.

Realizing Weak Light Detection in a 2D Dielectric Switching Hybrid Perovskite Incorporating Dimethylamine

Abstract2D organic–inorganic hybrid perovskites (OIHPs) are regarded as potential candidates for the advancement of multifunctional phase‐transition materials due to their ability to integrate flexible functionality and superior electronic features from both organic and inorganic components. However, the development of 2D OIHPs with dielectric switches and weak light detection capability that are crucial for high‐resolution sensing and imaging, remains challenge. Herein, a novel 2D OIHP BDADMAPb2Br7 (1, BDA = 1,4‐butanediamine and DMA = dimethylamine) is presented, featuring a 2D Dion–Jacobson (DJ) bilayered structure incorporated with secondary amine. Upon heating and cooling cycles, 1 demonstrates long‐term reversible dielectric switching properties at 328 K. Meanwhile, 1 also shows compelling semiconductor characteristics, including a low dark current of 2.87 × 10−11 A (10 V), a large switching ratio of 4 × 103, and a high detectivity of 1.3 × 1012 Jones. In addition, the photodetector based on 1 exhibits an excellent weak light detection ability of 340 nW cm−2 at 405 nm. Such a 2D OIHP demonstrates a novel platform for creating multifunctional materials and significantly promotes the development of integrated devices.

An Aggregation-Induced Fluorescence Probe for Detection H2S and Its Application in Cell Imaging.

Monitoring hydrogen sulfide (H2S) in living organisms is very important because H2S acts as a regulator in many physiological and pathological processes. Upregulation of endogenous H2S concentration has been shown to be closely related to the occurrence and development of tumors, atherosclerosis, neurodegenerative diseases and diabetes. Herin, a novel fluorescent probe HND with aggregation-induced emission was designed. Impressively, HND exhibited a high selectivity, fast response (1 min) and low detection limit (0.61 μM) for H2S in PBS buffer (10 mM, pH = 7.42). Moreover, the reaction mechanism between HND and H2S was conducted by Job's plot, HR-MS, and DFT. In particular, HND was successfully employed to detect H2S in HeLa cells.

ZnO/g-C3N4 photocatalyst activated by low-pressure ultraviolet for restoring the SWASV signals: A fast pretreatment method for electrochemically detecting Cd2+ and Pb2+ in soil extracts

Soil organic matter (SOM) significantly impacts the detection accuracy of Cd2+ and Pb2+ using square wave anodic stripping voltammetry (SWASV) due to the complexation of SOM to heavy metal ions (HMIs), thereby attenuating SWASV signals. This study explored an effective pretreatment method that combined low-pressure ultraviolet (LPUV) photolysis with the ZnO/g-C3N4 photocatalyst, activating the photocatalyst to generate highly oxidative •OH radicals and O2•− radicals, which effectively disrupted this complexation, consequently restoring the electroactivity of HMIs and achieving high-fidelity SWASV signals. The parameters of the LPUV-ZnO/g-C3N4 photocatalytic system were meticulously optimized, including the pH of photolysis, duration of photolysis, g-C3N4 mass fraction, and concentration of the photocatalyst. Furthermore, the ZnO/g-C3N4 photocatalyst was thoroughly characterized, with an in-depth investigation on the synergistic interaction between ZnO and g-C3N4 and the mechanisms contributing to the restoration of SWASV signals. This synergistic interaction effectively separated charge carriers and reduced charge transfer resistance, enabling photogenerated electrons (e−) from the conduction band of g-C3N4 to be quickly transferred to the conduction band of ZnO, preventing the recombination of e− and hole (h+) and generating more radicals to disrupt complexation and restore the SWASV signals. Finally, the analysis of HMIs in real soil extracts using the proposed pretreatment method demonstrated high detection accuracy of 94.9% for Cd2+ and 99.8% for Pb2+, which validated the feasibility and effectiveness of the proposed method in environmental applications.

Real-time biomolecule detection using GMR chip-based sensor with green-synthesized Fe3O4/rGO nanocomposites as magnetic labels

The availability of a rapid, low-cost, and easy-to-use device to monitor the bovine serum albumin (BSA) level in human blood and urine is vital in early disease detection. This work reports a biosensor system based on a giant magnetoresistance (GMR) chip as a transducer integrated with Fe3O4/rGO magnetic labels for BSA protein detection. The commercial chip AAL024 is utilized as the GMR sensor, combined with an Arduino microcontroller (AM) and a basic differential amplifier to acquire the digital output voltage. The Fe3O4/rGO nanocomposites were synthesized by a hybrid process based on the green synthesis route utilizing plant extracts. The nanocomposites exhibit superparamagnetic behavior, affecting the magnetic label detection quality. In investigating sensor performance, Fe3O4/rGO nanocomposites can generate sufficient stray fields under a 4 Oe of bias magnetic field (HB). A larger net stray field is produced by a higher concentration of magnetic labels, which suggests that there are more Fe3O4 nanoparticles attached to the sensor surface. Further, the effectiveness of the biosensor was evaluated by detecting the BSA protein. As a result, a high sensitivity of 22.98 mV/(mg/mL) and a low limit of detection (LOD) of 0.3 mg/mL were obtained in detecting Fe3O4/rGO(5:1)/BSA with a voltage signal that can be collected in 30 s. Besides its high sensitivity, low LOD, and fast detection, the sensor exhibits remarkable reproducibility and stability, as evidenced by the relative signal deviation (RSD) range of 1.1 – 5.6%. These findings demonstrate that the GMR chip-based sensor integrated with Fe3O4/rGO magnetic labels provides a simple biosensor system with real-time electronic readout.

Evaluating models for estimating introduction rates of alien species from discovery records

AbstractAimReducing the rate of alien species introductions is a major conservation aim. However, accurately quantifying the rate at which species are introduced into new regions remains a challenge due to the confounding effect of observation efforts on discovery records. Despite the recognition of this issue, most analyses are still based on raw discovery records, leading to biased inferences. In this study, we evaluate different models for estimating introduction rates, including new models that use auxiliary data on observation effort, and identify their strengths and weaknesses.InnovationWe compare four models: (1) a naïve model which assumes perfect detection; (2) a model proposed by Solow and Costello (the S&amp;C model); (3) constant detection model: a modified version of the S&amp;C model with constant detection probabilities and (4) a novel sampling proxy model: a model that uses external data on observation effort. We simulate discovery records of varying lengths, introduction rates and temporal patterns of detection probabilities to explore scenarios under which these models accurately estimate underlying introduction rates. (5) We also include code to perform a model based on Belmaker using independent data on the number of native species.Main conclusionWe found that the length of the discovery records and the annual number of recorded species play a crucial role in the performance of all models. Under simulated scenarios of high detection, the naïve model is usually the best‐performing model, but it falls short when detection is low. Moreover, we find that in simulations which most likely mimic most real‐world cases (i.e. non‐monotonic probability of detection), incorporating external data on observation effort using the sampling proxy model, substantially improve estimates. This highlights the importance of considering observation effort when estimating introduction rates of alien species. To facilitate the use of these models, we provide a decision workflow and a dedicated R package (‘alien’).

Improved magnetic-based apparatus enables non-destructive and high sensitive steel-bar corrosion detection

Steel-bar corrosion can not only induce corrosion cracking, but also cause the bearing capacity decline of concrete structures. This contribution reported an improved magnetic-based steel bar corrosion detection apparatus with simple structure and high accuracy. Accelerated corrosion tests on steel rebar were carried out to calibrate the accuracy of the magnetic-based corrosion detection apparatus. A parameter of sensitivity coefficient, k, was proposed to evaluate the corrosion detection sensitivity of the apparatus. An equivalent numerical model was established based on multi-physical simulation to quantify the corrosion degrees and explore the impacts of the frame internal structural parameters. Results show that k is stable and changes linearly with the diameter of steel rods tested. Increasing frame width and foot length can increase k, and adding extra calibration magnetic circuits leads to reduction in k. Overall, the modified magnetic-based apparatus provides a simple, reliable, accurate and non-destructive method for steel corrosion detection.

Metal organic framework derived tubular-like indium trioxide to construct photoelectrochemical sensor for detecting carcinoembryonic antigen

Photoelectrochemical (PEC) sensor realizes high sensitivity detection of biomolecules and chemical substances through the combination of photoelectric conversion and chemical reaction, which has important significance in biomedicine and environmental monitoring. In this work, we designed a flexible PEC sensor based on In2O3-CdS hierarchical heterojunction for detecting carcinoembryonic antigen (CEA) in human serum. In2O3-CdS hierarchical heterojunction exhibited outstanding photoelectrochemical performance, which lays the foundation for a highly sensitive detection process. The electron transfer process was impeded as a result of the antigen being recognized and captured by the specific antibody on the electrode surface, leading to the decline of the photocurrent signal. Based on this principle, the PEC sensor exhibited a good linear relationship range from 0.002 ng mL−1 to 20 ng mL−1 under the most suitable conditions. In addition, this PEC sensor showed better stability and reproducibility in real samples compared to the commercially available ELISA kit. This work provided a reliable method for early cancer screening and diagnosis, which exhibited great significance and broad prospect for improving cancer treatment effect and survival rate.

Ultrawideband sub-pascal sensitivity piezopolymer detectors

Piezoelectric detectors are integral part of modern ultrasound imaging systems. Their utility has also been extended beyond the established methodologies into the emerging realm of hybrid optoacoustic imaging. Conventional piezoceramic detectors, however, struggle to combine high detection sensitivity with ultrawide bandwidth, both considered critical for attaining optimal optoacoustic imaging performance. Our research, both theoretical and empirical, unveils that damped piezopolymer detectors fabricated from PVDF-TrFE are markedly capable of achieving a synergistic blend between broad bandwidth and superb sensitivity. Experimental evaluations reflected an average sensitivity of 15.5 µV/Pa within a 1–10 MHz band for a 120 µm thick detector and 6.4 µV/Pa within a 1–30 MHz band for a 20 µm thick detector, thus outperforming conventional piezoelectric analogues. The resultant noise equivalent pressure (NEPs) values were 0.3 Pa and 1.2 Pa for the 20 µm and 120 µm detectors, respectively. Our findings herald a significant stride towards enhancing the efficacy of ultrawideband ultrasound and optoacoustic imaging systems.

The role of aztreonam in rational antibacterial therapy of resistant nosocomial gram-negative infections. The new life for a well-known β-lactam

Background. The situation regarding the prevalence of resistant pathogens, types of β-lactamase products, and, accordingly, the justification of rational approaches to antibacterial therapy in Ukraine remains insufficiently studied. Objective: based on the determination of the etiological structure of the causative agents of hospital infections in the Kharkiv region and the state of their resistance due to the production of β-lactamases, to propose rational approaches to antibacterial therapy and assess the feasibility of using aztreonam as a potentially effective mean of treating infections caused by resistant bacteria. Materials and methods. In 251 patients, samples of biomaterials were tested by the polymerase chain reaction for the presence of the following gram-negative pathogens: P.aeruginosa, Enterobacter, E.coli, K.pneumoniae, Proteus spp. and A.baumanii. A molecular genetic study was also conducted to identify certain types of β-lactamases. Results. In the structure of hospital pathogens, the absolute majority of gram-negative bacteria belong to the enterobacteria family. When determining the profile of β-lactamases, we found that in 59.38 % of cases, E.coli was able to produce metallo-β-lactamases, which makes the therapy with carbapenems or ceftazidime-avibactam impossible. An alternative in such cases is a combination of aztreonam and ceftazidime-avibactam. When the causative agent K.pneumoniae is detected, in some cases there may also be a need for combined therapy with the use of aztreonam. It was found that Enterobacter spp. in most cases is sensitive to carbapenems. A.baumanii is completely insensitive to carbapenems and requires alternative approaches to antibacterial therapy, including aztreonam, ceftazidime-avibactam, and polymyxins. P.aeruginosa was found only as part of polymicrobial associations, so it was impossible to analyze its β-lactamase production profile. Conclusions. The results of the conducted study demonstrate an extremely high detection of gram-negative antibiotic-resistant pathogens in patients of the intensive care units in the Kharkiv region. Microorganisms such as E.coli and A.baumanii have the most unfavorable profile of antibiotic resistance, which is due to the high frequency of production of serine carbapenemases and metallo-β-lactamases, whose presence makes the effective use of carbapenems impossible.