Early and swift identification of fungal-infection using infrared spectroscopy.

Fungal pathogens pose significant threats to agricultural crops and food products, leading to economic losses, compromised food quality, and health hazards. Early detection is crucial for effective control and treatment. This study explores Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy for rapid fungal detection in bread. Using a machine learning algorithm (Random Forest), FTIR-ATR accurately distinguished between pure and infected bread samples, achieving 86% overall accuracy and 84% accuracy in identifying specific fungi like Rhizopus and Aspergillus on the first day of infection. These findings highlight FTIR-ATR's potential for early fungal infection detection, promising improved food quality and reduced economic losses through timely intervention.

Significant reduction of the culturing time required for bacterial identification and antibiotic susceptibility determination by infrared spectroscopy.

Rapid testing of bacteria for antibiotic susceptibility is essential for effective treatment and curbing the emergence of multidrug-resistant bacteria. The misuse of antibiotics, coupled with the time-consuming classical testing methods, intensifies the threat of antibiotic resistance, a major global health concern. In this study, employing infrared spectroscopy-based machine learning techniques, we significantly shortened the time required for susceptibility testing to 10 hours, a significant improvement from the 24 hours in our previous studies as well as the conventional methods that typically take at least 48 hours. This remarkable reduction in turnaround time (from 48 hours to 10 hours), achieved by minimizing the culturing period, offers a game-changing advantage for clinical applications. Our study involves a dataset comprising 400 bacterial samples (200 E. coli, 100 Klebsiella pneumoniae, and 100 Pseudomonas aeruginosa) with an impressive 96% accuracy in the taxonomic classification at the species level and up to 82% accuracy in bacterial susceptibility to various antibiotics.

Infrared spectroscopy-based machine learning algorithms for rapid detection of Klebsiella pneumoniae isolated directly from patients' urine and determining its susceptibility to antibiotics.

Among the most prevalent and detrimental bacteria causing urinary tract infections (UTIs) is Klebsiella (K.) pneumoniae. A rapid determination of its antibiotic susceptibility can enhance patient treatment and mitigate the spread of resistant strains. In this study, we assessed the viability of using infrared spectroscopy-based machine learning as a rapid and precise approach for detecting K. pneumoniae bacteria and determining its susceptibility to various antibiotics directly from a patient's urine sample. In this study, 2333 bacterial samples, including 636 K. pneumoniae were investigated using infrared micro-spectroscopy. The obtained spectra (27996spectra) were analyzed with XGBoost classifier, achieving a success rate exceeding 95 % for identifying K. pneumoniae. Moreover, this method allows for the simultaneous determination of K. pneumoniae susceptibility to various antibiotics with sensitivities ranging between 74 % and 81 % within approximately 40 min after receiving the patient's urine sample.

Quick Detection of Proteus and Pseudomonas in Patients' Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning.

Bacterial resistance to antibiotics is a primary global healthcare concern as it hampers the effectiveness of commonly used antibiotics used to treat infectious diseases. The development of bacterial resistance continues to escalate over time. Rapid identification of the infecting bacterium and determination of its antibiotic susceptibility are crucial for optimal treatment and can save lives in many cases. Classical methods for determining bacterial susceptibility take at least 48 h, leading physicians to resort to empirical antibiotic treatment based on their experience. This random and excessive use of antibiotics is one of the most significant drivers of the development of multidrug-resistant (MDR) bacteria, posing a severe threat to global healthcare. To address these challenges, considerable efforts are underway to reduce the testing time of taxonomic classification of the infecting bacterium at the species level and its antibiotic susceptibility determination. Infrared spectroscopy is considered a rapid and reliable method for detecting minor molecular changes in cells. Thus, the main goal of this study was the use of infrared spectroscopy to shorten the identification and the susceptibility testing time of Proteus mirabilis and Pseudomonas aeruginosa from 48 h to approximately 40 min, directly from patients' urine samples. It was possible to identify the Proteus mirabilis and Pseudomonas aeruginosa species with 99% accuracy and, simultaneously, to determine their susceptibility to different antibiotics with an accuracy exceeding 80%.

Detection of extended-spectrum ß-lactamase-producing bacteria isolated directly from urine by infrared spectroscopy and machine learning.

Resistant bacteria have become one of the leading health threats in the last decades. Extended-spectrum ß-lactamase (ESBL) producing bacteria, including Escherichia (E.) coli and Klebsiella (K.) pneumoniae (the most frequent ones), are a significant class out of all resistant infecting bacteria. Due to the widespread and ongoing development of ESBL-producing (ESBL+) resistant bacteria, many routinely used antibiotics are no longer effective against them. However, an early and reliable ESBL+ bacteria detection method will improve the efficiency of treatment and limit their spread. In this work, we investigated the capability of infrared (IR) spectroscopy based machine learning tools [principal component analysis (PCA) and Random Forest (RF) classifier] for the rapid detection of ESBL+ bacteria isolated directly from patients' urine. For that, we examined 1881 E. coli samples (416 ESBL+ and 1465 ESBL-) and 609 K. pneumoniae samples (237 ESBL+ and 372 ESBL-). All samples were isolated directly from the urine of midstream patients. This study revealed that within 40 min of receiving the patient urine it is possible to determine the infecting bacterium as E. coli or K. pneumoniae with 95% success rate while it was possible to determine the ESBL+E. coli and ESBL+K. pneumoniae with 83% and 78% accuracy rates, respectively.

Instant detection of extended-spectrum ß-lactamase-producing bacteria from the urine of patients using infrared spectroscopy combined with machine learning.

Antibiotics are considered the most effective treatment against bacterial infections. However, most bacteria have already developed resistance to a broad spectrum of commonly used antibiotics, mainly due to their uncontrolled use. Extended-spectrum beta-lactamase (ESBL)-producing bacteria are an essential class of multidrug-resistant (MDR) bacteria. It is of extreme urgency to develop a method that can detect ESBL-producing bacteria rapidly for the effective treatment of patients with bacterial infectious diseases. Fourier transform infrared (FTIR) microscopy is a sensitive method that can rapidly detect cellular molecular changes. In this study, we examined the potential of FTIR spectroscopy-based machine learning algorithms for the rapid detection of ESBL-producing bacteria obtained directly from a patient's urine. Using 591 ESBL-producing and 1658 non-ESBL-producing samples of Escherichia coli (E. coli) and Klebsiella pneumoniae, our results show that the FTIR spectroscopy-based machine learning approach can identify ESBL-producing bacteria within 40 minutes from receiving a patient's urine sample, with a success rate of 80%.

Rapid determination of Proteus mirabilis susceptibility to antibiotics using infrared spectroscopy in tandem with random forest.

Bacterial infections cause serious illnesses that are treated with antibiotics. Currently used methods for detecting bacterial antibiotic susceptibility consume 48-72 h, leading to overuse of antibiotics. Thus, many bacterial species have acquired resistance to a broad range of available antibiotics. There is an urgent need to develop efficient methods for rapid determination of bacterial susceptibility to antibiotics. The combination of machine learning and Fourier-transform infrared (FTIR) spectroscopy has generated a promising diagnostic approach in medicine and biology. Our main goal is to examine the potential of FTIR spectroscopy to determine the susceptibility of urinary tract infection-Proteus mirabilis to a specific range of antibiotics, within about 20 min after 24 h culture and identification. We measured the infrared spectra of 489 different P. mirabilis isolates and used random forest to analyze this spectral database. A classification success rate of ~84% was achieved in differentiating between the resistant and sensitive isolates based on their susceptibility to ceftazidime, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, ciprofloxacin, gentamicin, and sulfamethoxazole antibiotics in a time span of 24 h instead of 48 h.

Fast identification and susceptibility determination of E. coli isolated directly from patients' urine using infrared-spectroscopy and machine learning.

For effective treatment, it is crucial to identify the infecting bacterium at the species level and to determine its antimicrobial susceptibility. This is especially true now, when numerous bacteria have developed multidrug resistance to most commonly used antibiotics. Currently used methods need âˆ¼ 48 h to identify a bacterium and determine its susceptibility to specific antibiotics. This study reports the potential of using infrared spectroscopy with machine learning algorithms to identify E. coli isolated directly from patients' urine while simultaneously determining its susceptibility to antibiotics within âˆ¼ 40 min after receiving the patient's urine sample. For this goal, 1,765 E. coli isolates purified directly from urine samples were collected from patients with urinary tract infections (UTIs). After collection, the samples were tested by infrared microscopy and analyzed by machine learning. We achieved success rates of âˆ¼ 96% in isolate level identification and âˆ¼ 84% in susceptibility determination.

Culture-independent susceptibility determination of E. coli isolated directly from patients' urine using FTIR and machine-learning.

One of the most common human bacterial infections is the urinary tract infection (UTI). The main cause of UTI is Escherichia (E.) coli bacteria (∼75%). Because most of the bacteria are resistant to many antibiotics as a result of their indiscriminate overuse, it is extremely important, for effective treatment, to identify the infecting bacteria and to determine, as quickly as possible, their susceptibility to antibiotics. Classical methods require at least 48 hours for determining bacterial susceptibility. In this study, 1798 E. coli isolates from different UTIs were isolated directly from patients' urine, measured by Fourier transform infrared (FTIR) microscopy and analyzed by machine learning algorithms for simultaneous identification and susceptibility determination within 40 minutes since receiving the urine samples. Our results show that it is possible to identify the bacteria at the species level with an accuracy of ∼95% and to determine their susceptibility to different antibiotics with an accuracy ranging from 75% to 83%.

Infra-red spectroscopy combined with machine learning algorithms enables early determination of Pseudomonas aeruginosa's susceptibility to antibiotics.

Pseudomonas (P.) aeruginosa is a bacterium responsible for severe infections that have become a real concern in hospital environments. Nosocomial infections caused by P. aeruginosa are often hard to treat because of its intrinsic resistance and remarkable ability to acquire further resistance mechanisms to multiple groups of antimicrobial agents. Thus, rapid determination of the susceptibility of P. aeruginosa isolates to antibiotics is crucial for effective treatment. The current methods used for susceptibility determination are time-consuming; hence the importance of developing a new method. Fourier-transform infra-red (FTIR) spectroscopy is known as a rapid and sensitive diagnostic tool, with the ability to detect minor abnormal molecular changes including those associated with the development of antibiotic- resistant bacteria. The main goal of this study is to evaluate the potential of FTIR spectroscopy together with machine learning algorithms, to determine the susceptibility of P. aeruginosa to different antibiotics in a time span of ∼20 min after the first culture. For this goal, 590 isolates of P. aeruginosa, obtained from different infection sites of various patients, were measured by FTIR spectroscopy and analyzed by machine learning algorithms. We have successfully determined the susceptibility of P. aeruginosa to various antibiotics with an accuracy of 82-90%.

Determination of Klebsiella pneumoniae Susceptibility to Antibiotics Using Infrared Microscopy.

Klebsiella pneumoniae (K. pneumoniae) is one of the most aggressive multidrug-resistant bacteria associated with human infections, resulting in high mortality and morbidity. We obtained 1190 K. pneumoniae isolates from different patients with urinary tract infections. The isolates were measured to determine their susceptibility regarding nine specific antibiotics. This study's primary goal is to evaluate the potential of infrared spectroscopy in tandem with machine learning to assess the susceptibility of K. pneumoniae within approximately 20 min following the first culture. Our results confirm that it was possible to classify the isolates into sensitive and resistant with a success rate higher than 80% for the tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful method for a K. pneumoniae susceptibility test.

Rapid detection of Klebsiella pneumoniae producing extended spectrum ß lactamase enzymes by infrared microspectroscopy and machine learning algorithms.

Antimicrobial drugs have played an indispensable role in decreasing morbidity and mortality associated with infectious diseases. However, the resistance of bacteria to a broad spectrum of commonly-used antibiotics has grown to the point of being a global health-care problem. One of the most important classes of multi-drug resistant bacteria is Extended Spectrum Beta-Lactamase-producing (ESBL+) bacteria. This increase in bacterial resistance to antibiotics is mainly due to the long time (about 48 h) that it takes to obtain lab results of detecting ESBL-producing bacteria. Thus, rapid detection of ESBL+ bacteria is highly important for efficient treatment of bacterial infections. In this study, we evaluated the potential of infrared microspectroscopy in tandem with machine learning algorithms for rapid detection of ESBL-producing Klebsiella pneumoniae (K. pneumoniae) obtained from samples of patients with urinary tract infections. 285 ESBL+ and 365 ESBL-K. pneumoniae samples, gathered from cultured colonies, were examined. Our results show that it is possible to determine that K. pneumoniae is ESBL+ with ∼89% accuracy, ∼88% sensitivity and ∼89% specificity, in a time span of ∼20 minutes following the initial culture.

Differentiation of Pectobacterium and Dickeya spp. phytopathogens using infrared spectroscopy and machine learning analysis.

Pectobacterium and Dickeya spp. are soft rot Pectobacteriaceae that cause aggressive diseases on agricultural crops leading to substantial economic losses. The accurate, rapid and low-cost detection of these pathogenic bacteria are very important for controlling their spread, reducing the consequent financial loss and for producing uninfected potato seed tubers for future generations. Currently used methods for the identification of these bacterial pathogens at the strain level are based mainly on molecular techniques, which are expensive. We used an alternative method, infrared spectroscopy, to measure 24 strains of five species of Pectobacterium and Dickeya. Measurements were then analyzed using machine learning methods to differentiate among them at the genus, species and strain levels. Our results show that it is possible to differentiate among different bacterial pathogens with a success rate of ~99% at the genus and species levels and with a success rate of over 94% at the strain level.