Fast Technique Research Articles

Development of a rapid detection method for molecular beacons LAMP for Vibrio parahaemolyticus in food safety

A bacteria called Vibrio parahaemolyticus can result in food poisoning; therefore, Finding Vibrio parahaemolyticus in food quickly is essential to reduce food poisoning. A MB-LAMP fast detection technique was created in this work to identify Vibrio parahaemolyticus DNA in food products with 102 CFU/mL of sensitivity and absence of cross-reactivity with four additional bacteria. For 20 hypothetical instances, the MB-LAMP system was consistent with the commercial Vibrio parahaemolyticus qPCR kit, and the former reduced the experimental time by about 35 min compared with the latter.

Model-based iterative reconstruction for direct imaging with point spread function encoded echo planar MRI

BackgroundEcho planar imaging (EPI) is a fast measurement technique commonly used in magnetic resonance imaging (MRI), but is highly sensitive to measurement non-idealities in reconstruction. Point spread function (PSF)-encoded EPI is a multi-shot strategy which alleviates distortion, but acquisition of encodings suitable for direct distortion-free imaging prolongs scan time. In this work, a model-based iterative reconstruction (MBIR) framework is introduced for direct imaging with PSF-EPI to improve image quality and acceleration potential. MethodsAn MBIR platform was developed for accelerated PSF-EPI. The reconstruction utilizes a subspace representation, is regularized to promote local low-rankedness (LLR), and uses variable splitting for efficient iteration. Comparisons were made against standard reconstructions from prospectively accelerated PSF-EPI data and with retrospective subsampling. Exploring aggressive partial Fourier acceleration of the PSF-encoding dimension, additional comparisons were made against an extension of Homodyne to direct PSF-EPI in numerical experiments. A neuroradiologists' assessment was completed comparing images reconstructed with MBIR from retrospectively truncated data directly against images obtained with standard reconstructions from non-truncated datasets. ResultsImage quality results were consistently superior for MBIR relative to standard and Homodyne reconstructions. As the MBIR signal model and reconstruction allow for arbitrary sampling of the PSF space, random sampling of the PSF-encoding dimension was also demonstrated, with quantitative assessments indicating best performance achieved through nonuniform PSF sampling combined with partial Fourier. With retrospective subsampling, MBIR reconstructs high-quality images from sub-minute scan datasets. MBIR was shown to be superior in a neuroradiologists' assessment with respect to three of five performance criteria, with equivalence for the remaining two. ConclusionsA novel image reconstruction framework is introduced for direct imaging with PSF-EPI, enabling arbitrary PSF space sampling and reconstruction of diagnostic-quality images from highly accelerated PSF-encoded EPI data.

A fast uncertainty quantification methodology and sampling technique for joint probability distribution of the Arrhenius rate expression: a case study applied to H2/CO kinetic mechanism

This work proposes a fast, novel, mathematically robust, and elegant unconstrained Method of Uncertainty Quantification (MUQ) for the temperature-dependent Arrhenius rate constant using Cholesky Decomposition (CD) of the covariance matrix of the Arrhenius parameters. The Arrhenius parameters of a reaction are treated as normally distributed correlated random variables. The MUQ method lends itself to an approach for Sampling of Arrhenius Curves (SAC), which automatically ensures that the generated samples are consistent with the distribution of Arrhenius parameters. The Method of Uncertainty Quantification and Sampling of Arrhenius Curves (MUQ-SAC) is used to train a quadratic polynomial response surface (PRS). Three important classes of Arrhenius curves possible within the SAC method are identified. From the total set of targets, a small subset of targets is first used to study the combinations of different classes of Arrhenius curves and their proportions within the design matrix, which is used to train PRS. Based on this understanding, response surfaces are generated for 64 ignition delay targets (Tig), which comprises of a wide range of pressure (P: 1--32.7 atm), temperature (T: 916-DIFadd-2869 K), and equivalence ratio (ϕ: 0.5--6.11), as well as for 74 laminar flame speed targets (Fls), spanning P: 1--25 atm, T: 285--600 K, and ϕ: 0.6--5. The H 2 /CO sub-mechanism of FFCM1.0 is chosen for the case study in which 22 reactions (66 Arrhenius parameters) are considered active parameters. The quality of the generated PRS is measured using the relative Maximum Residual Error (MRE). The quality of PRS is also checked against the search iterations encountered during mechanism optimisation. Accurate PRS could be generated over the search iterations, validating that the SAC method is able to span the entire uncertainty domain while training the PRS. The implementation of the MUQ-SAC method is made available at https://github.com/KrunalPanchal1995/MUQ-SAC.git.

Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

This study provides insights into the challenges involved in predicting the Reid vapor pressure (RVP) of gasoline-oxygenate blends (GOB), which is an important indicator of fuel quality and compliance with environmental and performance standards. Given the enormous variety of gasoline compositions and ratios available, there is a significant demand for a fast, straightforward, and cost-effective technique to predict RVP without relying on costly instruments or complicated spectral measurements that involve numerous input variables. A comparative performance analysis has been performed for different regression modelling strategies for predicting RVP in GOB, which is valuable for researchers and practitioners in the petroleum industry for saving time and money. Parametric and nonparametric approaches were compared using partial least squares regression (PLSR), nonlinear regression (NLR), and nonparametric regression (NPR) models. Locally weighted scatterplot smoothing (LOWESS) approach was applied to the NPR model. The gasoline’s physical characteristics (distillation curves and density) formed the basis for the analysis of these models’ performances. Acceptable error metrics have been reached for root mean square error of calibration and prediction (RMSEC and RMSEP) values, for the PLSR, NLR, and NPR models, which are 4.790, 6.235, 4.739, 6.149, 3.968, and 6.029, respectively, which are close for those reported in literature. The NPR model eliminates parametric constraints and allows for a different kind of data structure to emerge. The established models here demonstrate a sound ability to overcome barriers by omitting the use of inconvenient spectral measurements to save expense and simplify data calibration, making them a promising approach for RVP detection of GOB. This finding aids in the development of more accurate RVP prediction models and contributes to the optimization of fuel formulations.

Light-Triggered Disassembly of Molecular Motor-based Supramolecular Polymers Revealed by High-Speed AFM.

Photoresponsive supramolecular polymers have a major potential for applications in responsive materials that are externally triggered by light with spatio-temporal control of their polymerisation state. While changes in macroscopic properties revealed the adaptive nature of these materials, it remains challenging to capture the dynamic depolymerisation process at the molecular level, which requires fast observation techniques combined with in situ irradiation. By implementing in situ UV illumination into a High-Speed Atomic Force Microscope (HS-AFM) setup, we have been able to capture the disassembly of a light-driven molecular motor-based supramolecular polymer. The real-time visualisation of the light-triggered disassembly process not only reveals cooperative depolymerisation, it also shows that this process continues after illumination is halted. Combining the data with cryo-electron microscopy and spectroscopy approaches, we obtain a molecular-level description of the motor-based polymer dynamics reminiscent of actin chain-end depolymerisation. Our detailed understanding of supramolecular depolymerisation will drive the development of future responsive polymer systems.

Fast reconstruction of boiler numerical physical field based on proper orthogonal decomposition and conditional deep convolutional generative adversarial networks

ABSTRACT Obtaining real-time physical field data inside the boiler furnace is crucial for combustion diagnostics and optimization. In this work, we propose a fast reconstruction technique for the physical fields in a tangentially-fired coal boiler. The method combines the Proper Orthogonal Decomposition (POD) and Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) based on the boiler physical field obtained by computational fluid dynamics (CFD). Firstly, CFD simulations are performed using data from the boiler Distributed Control System (DCS). After verifying the CFD results, we expanded the simulation to 105 operational condition cases sample sets. Then, the physical fields of the sample set undergo POD to determine the appropriate number of POD bases, thereby ensuring the accuracy of the reconstruction. Finally, the cDCGAN method is employed to establish the correlation between the POD bases and the operational conditions of the boiler. Results show that the POD-cDCGAN method achieves rapid and accurate reconstruction of temperature and velocity fields, with mean relative error (MRE) below 1.5% and root relative squared error (RRSE) below 5.7%. The reconstruction time is reduced to 10.57s, significantly faster than direct CFD methods. The POD-cDCGAN method offers an efficient approach for online operational diagnosis and digital twin construction of boiler equipment.

A non-invasive, low-cost and easy-to-operate underwater terrain monitoring method

A fast, reliable, through water and non-intrusive bed-profile measurement technique that is economical and easy to use in a wide range of laboratory experiments involving sediment transport is proposed. A consumer-grade 3-D range sensing technology which by its low cost, easy operation and relatively good accuracy is becoming an attractive alternative to expensive 3-D laser scanners. The Microsoft XBOX 360 kinect sensor 2.0 is a low-cost and highly popular depth camera, which can be used to scan 3D surface terrain of dynamic objects and has the potential to scan terrain below and above water. Through the software matching of kinect 2.0, a data storage and analysis system are formed, which makes it has the ability to obtain underwater terrain data. In addition, the correction model of water refraction error is established, which can eliminate the measurement error caused by water refraction. Up to four different water conditions, kinect 2.0 is tested. Finally, the influences of water depth, suspend load volume concentration and moving flow velocity on the measurement accuracy of kinect 2.0 are explored, and the compensation methods of measurement error caused by the above factors are given. The results of this paper can provide reference for underwater topographic survey.

Biogenic amines detection in meat and meat products: the mechanisms, applications, and future trends

Biogenic amine is one of the main categories of hazardous compounds in meat and meat products, making its detection methods vital for the assurance of edible safety. In this sense, many biogenic amine detection techniques such as chromatographic, electrophoretic, and electrochemical methods have been developed, which play an irreplaceable role in ensuring the safety of meat and meat products. Due to the increasing demand for fast and on-site detection techniques, visual detection methods have been gradually developed compared with non-visual methods such as chromatography and electrophoresis. Herein, we comprehensively review the mechanism and the latest progress of these biogenic amine detection methods; besides, we put forward the prospects for the future development of biogenic amine detection techniques, with a view to providing support for the establishment of more accurate and efficient detection, prevention and control strategies of biogenic amines.

The Modified Ultrasound-Assisted Method: A Study of the Correlation between Magnetic Resonance Imaging and the Ultrasound-Assisted Evaluation of the Critical Shoulder Angle.

An increased or decreased critical shoulder angle (CSA) is a known risk factor for osteoarthritis, lesions, and re-ruptures in the rotator cuff. A CSA greater than 35° correlates with degenerative rotator cuff tears, while a CSA of less than 30° correlates with osteoarthritis in the glenohumeral joint. The diagnostic gold standard for its determination is X-ray or MRI. The primary objective of this research was to assess the viability of utilizing sonography imaging as a diagnostic tool to determine the modified critical shoulder angle (mCSA). This study aimed to investigate the feasibility and effectiveness of sonographic techniques in accurately diagnosing CSA compared to MRI. A cohort study was carried out (level of evidence 3). The CSA (MRI) and the mCSA (ultrasound) were assessed retrospectively by two independent board-certified investigators in 109 patients with shoulder pain by MRI and musculoskeletal sonography. The CSA in the MRI dataset was determined using routine protocols and then compared to the values assessed using the modified sonography-assisted method (mCSA). Both results were analyzed with linear regression to determine a possible correlation. All investigations were performed by a DEGUM (German Society for Medical Ultrasound)-certified specialist in musculoskeletal sonography. A total of 112 patients were included in this study, namely 40 female patients and 72 male patients with a mean age of 54.7 years at the time of the investigation. The mean CSA in MRI was 31.5° ± 3.899, and the mCSA in sonography was 30.1° ± 4.753. The inter- and intraobserver reliability for the CSA was factual with values of 0.993 and 0.967. The inter- and intraobserver reliability for mCSA was factual as well, with values of 0.989 and 0.948. The ANOVA analysis did not reveal a significant difference between the CSA and the mCSA values, and linear regression determined the R2 value to be 0.358 with p < 0.05. Diagnosing the mCSA using sonography is a safe and valid method. No statistically significant differences between the results in MRI and sonography could be seen. Although this is a retrospective, single-center study including only Caucasian mid-Europeans, and with the known limitations of ultrasound imaging, it nevertheless shows that sonography can be used as a simple, cheap, and fast technique to assess a modified CSA, which shows very good correlation with the standard CSA without losing the diagnostic quality.

On the use of machine learning and data-transformation methods to predict hydration kinetics and strength of alkali-activated mine tailings-based binders

The escalating production of mine tailings (MT), a byproduct of the mining industry, constitutes significant environmental and health hazards, thereby requiring a cost-effective and sustainable solution for its disposal or reuse. This study proposes the use of MT as the primary ingredient (≥70%mass) in binders for construction applications, thereby ensuring their efficient upcycling as well as drastic reduction of environmental impacts associated with the use of ordinary Portland cement (OPC). The early-age hydration kinetics and compressive strength of MT-based binders are evaluated with an emphasis on elucidating the influence of alkali activation parameters and the amount of slag or cement that are used as minor constituents. This study reveals correlations between cumulative heat release and compressive strengths at different ages; these correlations can be leveraged to estimate the compressive strength based on hydration kinetics. Furthermore, this study presents a random forest (RF) model—in conjunction with fast Fourier and direct cosine transformation techniques to overcome the limitations associated with limited volume and diversity of the database—to enable high-fidelity predictions of time-dependent hydration kinetics and compressive strength of MT-based binders in relation to mixture design. Overall, this study demonstrates a sustainable approach to upcycle mine tailings as the primary component in low-carbon construction binders; and presents both analytical and machine learning-based approaches for accurate a priori predictions of hydration kinetics and compressive strength of these binders.

Dissecting the Interaction Fingerprints and Binding Affinity of BYL719 Analogs Targeting PI3Kα.

Phosphatidylinositol-3-kinase Alpha (PI3Kα) is a lipid kinase which regulates signaling pathways involved in cell proliferation. Dysregulation of these pathways promotes several human cancers, pushing for the development of anticancer drugs to target PI3Kα. One such medicinal chemistry campaign at Novartis led to the discovery of BYL719 (Piqray, Alpelicib), a PI3Kα inhibitor approved by the FDA in 2019 for treatment of HR+/HER2-advanced breast cancer with a PIK3CA mutation. Structure-based drug design played a key role in compound design and optimization throughout the discovery process. However, further characterization of potency drivers via structural dynamics and energetic analyses can be advantageous for ensuing PI3Kα programs. Here, our goal is to employ various in-silico techniques, including molecular simulations and machine learning, to characterize 14 ligands from the BYL719 analogs and predict their binding affinities. The structural insights from molecular simulations suggest that although the ligand-hinge interaction is the primary driver of ligand stability at the pocket, the R group positioning at C2 or C6 of pyridine/pyrimidine also plays a major role. Binding affinities predicted via thermodynamic integration (TI) are in good agreement with previously reported IC50s. Yet, computationally demanding techniques such as TI might not always be the most efficient approach for affinity prediction, as in our case study, fast high-throughput techniques were capable of classifying compounds as active or inactive, and one docking approach showed accuracy comparable to TI.

Fast and Lightweight Image Cryptosystem for IoMT Applications

This paper presents a fast and lightweight image encryption technique founded on a new concept of progressive and selective diffusion. We make use of modified chaotic systems like the sine-logistic system, sine-tangent system, and tent-logistic system to generate key blocks for diffusion. The encryption procedure comprises three stages: column diffusion, permutation, and row diffusion, which collectively contribute to the overall encryption mechanism. In the first stage, progressive and selective column substitution of the input image is conducted employing key-blocks D1 and D2, in a manner that each substituted column data is a function of previously diffused data. In the second stage, the column-substituted image goes through an adaptive permutation operation realized using an improved sin-logistic system. The last stage incorporates two distinct mappings based on key arrays and key blocks to ensure selective and progressive inter-row substitution/diffusion. The experimental results suggest that the proposed image system exhibits rapid performance as it reports an average encryption time of 0.03 seconds for an image of the size 256×256. Besides, the use of 1-D chaotic maps together with simple yet effective confusion and diffusion processes make it lightweight. Furthermore, it provides better security performance shown by various parameters like Information Entropy Analysis (IE), Correlation Coefficient analysis (CC), Histogram analysis, Differential Attacks Analysis (NPCR and UACI), key sensitivity assessment, and fidelity analysis. The suggested system has the capability to meet the cryptographic objectives as it reports average values of IE = 7.9974, CC =0.0035, NPCR =99.61%, UACI = 33.48%, and an enormously large key space compared to the state-of-art. The proposed technique offers enhanced data security, preserving patient privacy and preventing unauthorized access, while ensuring efficient encryption performance to assure the security and immediate transmission and storage of medical images. The better security performance together with computational efficiency and lightweight nature makes it an ideal candidate to be used in IoMT systems.

A wireless fluorescent sensing device for on-site closed-loop detection of hydrazine levels in the environment

Given the extensive need for the detection of hydrazine (N2H4) in the biomedical and chemical-pharmaceutical sectors, there is a necessity to devise a fast, sensitive, specific, and portable technique for precisely quantifying hydrazine at environmental levels. In our work, an "OFF-ON" type fluorescent probe namely 2-(4-(10-(naphthalen-2-yl)anthracen-9-yl)phenyl)isoindole-1,3-dione (NAP), which was inspired by the "Gabriel" reaction, was synthesized. The NAP fluorescent cellulose film successfully achieved the detection of hydrazine vapor with a LOD = 0.658 ppm. Compared to previous qualitative methods for detecting hydrazine, this study successfully achieved quantitative identification of hydrazine at low concentrations. In addition, a portable sensor device based on NAP cellulose film was successfully integrated, enabling ultra-sensitive, wireless, remote, and real-time detection of N2H4 vapor. It was determined that the probe (NAP) exhibited excellent detection performance when applied to various environmental samples including distilled water, tap water, creek water, soil and plants. This study introduces a potentially effective approach for detecting hydrazine in real-world settings.

Fast MRI Techniques for Abdominal Imaging Simplified: Part 1—Making Sense of It All

Fast MRI Techniques for Abdominal Imaging Simplified: Part 1—Making Sense of It All

Fast MRI Techniques for Abdominal Imaging Simplified: Part 1—Making Sense of It All

Motion is a major limiting factor in abdominal MRI. Scan time is related to MR signal acquisition and k-space filling. K-space is the raw data matrix that stores digitized MR signal, which is Fourier transformed into an image. So rapid signal acquisition, reduced k-space filling, and partial Fourier techniques can reduce scan time. This article aims to provide a simplified explanation of the physics behind various techniques for fast abdominal MRI.

Comparison of Optical Trocar Access Versus Veress Needle Insertion Technique for Peritoneal Entry in Laparoscopic Donor Nephrectomy

Nearly half of all complications in laparoscopic surgery occur at the intraperitoneal access. We compared direct optical entry (DOE) with Veress needle entry (VNE) in terms of abdominal entry time and entry-associated complications in patients who underwent laparoscopic donor nephrectomy (LDN). Between June 2010 and July 2023, data from 813 patients who underwent LDN were analyzed. Age, male-to-female ratio, American Society of Anesthesiologists (ASA) score, body mass index, operation side, previous abdominal surgery, abdominal entry technique, abdominal entry time, entry-associated complications, conversion to different abdominal entry techniques, and conversion to open surgery were evaluated. DOE and VNE were performed on 433 and 281 patients, respectively. Age (48.89 ± 12.11 vs 47.59 ± 11.34 years), male-to-female ratio (44.8% vs 40.9% males and 55.2% vs 59.1% females), ASA score (2 vs 2 median ASA score), body mass index (26.72 ± 6.11 vs 27.07 ± 5.83 kg/m2), and operation side (63.0% vs 61.3% left nephrectomy) were not statistically significant differences between the DOE and VNE groups (P > .05). However, abdominal entry time was significantly reduced in the DOE group compared with the VNE group (26.8 ± 20.8 vs 49.5 ± 34.1 seconds, P = .007, respectively). Entry-associated complications were observed in 8 (1.12%) patients. No major (grades 3-5) complications were observed. There were no statistically significant differences in overall (0.6% vs 1.7%, P = .291), grade 1, and grade 2 complication rates between the DOE and VNE groups (0.4% vs 1.4%, P = .366; 0.2% vs 0.3%, P = .714, respectively). DOE and VNE are not foolproof. DOE is a safe, feasible, and faster technique in patients who underwent LDN.

Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis

Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on the economy and social life of any society. Plant disease identification is a big challenge for agriculture production, for which we need a fast and accurate technique to identify plant disease. With the recent advancement in deep learning, we can develop a robust and accurate system. This research investigated the use of deep learning for accurate and fast tomato plant disease identification. In this research, we have used individual and merged datasets of tomato plants with 10 diseases (including healthy plants). The main aim of this work is to check the accuracy of the existing convolutional neural network models such as Visual Geometry Group, Residual Net, and DenseNet on tomato plant disease detection and then design a custom deep neural network model to give the best accuracy in case of the tomato plant. We have trained and tested our models with datasets containing over 18,000 and 25,000 images with 10 classes. We achieved over 99% accuracy with our custom model. This high accuracy was achieved with less training time and lower computational cost compared to other CNNs. This research demonstrates the potential of deep learning for efficient and accurate tomato plant disease detection, which can benefit farmers and contribute to improved agricultural production. The custom model’s efficient performance makes it promising for practical implementation in real-world agricultural settings.

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection.

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

Automated detection of fatal cerebral haemorrhage in postmortem CT data.

During the last years, the detection of different causes of death based on postmortem imaging findings became more and more relevant. Especially postmortem computed tomography (PMCT) as a non-invasive, relatively cheap, and fast technique is progressively used as an important imaging tool for supporting autopsies. Additionally, previous works showed that deep learning applications yielded robust results for in vivo medical imaging interpretation. In this work, we propose a pipeline to identify fatal cerebral haemorrhage on three-dimensional PMCT data. We retrospectively selected 81 PMCT cases from the database of our institute, whereby 36 cases suffered from a fatal cerebral haemorrhage as confirmed by autopsy. The remaining 45 cases were considered as neurologically healthy. Based on these datasets, six machine learning classifiers (k-nearest neighbour, Gaussian naive Bayes, logistic regression, decision tree, linear discriminant analysis, and support vector machine) were executed and two deep learning models, namely a convolutional neural network (CNN) and a densely connected convolutional network (DenseNet), were trained. For all algorithms, 80% of the data was randomly selected for training and 20% for validation purposes and a five-fold cross-validation was executed. The best-performing classification algorithm for fatal cerebral haemorrhage was the artificial neural network CNN, which resulted in an accuracy of 0.94 for all folds. In the future, artificial neural network algorithms may be applied by forensic pathologists as a helpful computer-assisted diagnostics tool supporting PMCT-based evaluation of cause of death.

Enterobacter cloacae from urinary tract infections: frequency, protein analysis, and antimicrobial resistance

The genus Enterobacter belongs to the ESKAPE group, which includes Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. This group is characterized by the development of resistance to various antibiotics. In recent years, Enterobacter cloacae (E. cloacae) has emerged as a clinically important pathogen responsible for a wide range of healthcare-associated illnesses. Identifying Enterobacter species can be challenging due to their similar phenotypic characteristics. The emergence of multidrug-resistant E. cloacae is also a significant problem in healthcare settings. Therefore, our study aimed to identify and differentiate E. cloacae using Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) as a fast and precise proteomic analytical technique. We also tested hospital-acquired E. cloacae isolates that produce Extended-spectrum beta-lactamases (ESBL) against commonly used antibiotics for treating urinary tract infections (UTIs). We used a total of 189 E. cloacae isolates from 2300 urine samples of patients with UTIs in our investigation. We employed culturing techniques, as well as the BD Phoenix™ automated identification system (Becton, Dickinson) and Analytical Profile Index (API) system for the biochemical identification of E. cloacae isolates. We used the MALDI Biotyper (MBT) device for peptide mass fingerprinting analysis of all isolates. We utilized the single peak intensities and Principal Component Analysis (PCA) created by MBT Compass software to discriminate and cluster the E. cloacae isolates. Additionally, we evaluated the sensitivity and resistance of ESBL-E. cloacae isolates using the Kirby Bauer method. Out of the 189 E. cloacae isolates, the BD Phoenix system correctly identified 180 (95.24%) isolates, while the API system correctly identified 165 (87.30%) isolates. However, the MBT accurately identified 185 (98.95%) isolates with a score of 2.00 or higher. PCA positively discriminated the identified E. cloacae isolates into one group, and prominent peaks were noticed between 4230 mass-to-charge ratio (m/z) and 8500 m/z. The ESBL-E. cloacae isolates exhibited a higher degree of resistance to ampicillin, amoxicillin-clavulanate, cephalothin, cefuroxime, and cefoxitin. Several isolates were susceptible to carbapenems (meropenem, imipenem, and ertapenem); however, potential future resistance against carbapenems should be taken into consideration. In conclusion, MALDI-TOF MS is a powerful and precise technology that can be routinely used to recognize and differentiate various pathogens in clinical samples. Additionally, the growing antimicrobial resistance of this bacterium may pose a significant risk to human health.