Early detection of premalignant changes in cell cultures using light-induced fluorescence spectroscopy

An Improved Penalty-Based Excited-State Variational Monte Carlo Approach with Deep-Learning Ansatzes

We introduce several improvements to the penalty-based variational quantum Monte Carlo (VMC) algorithm for computing electronic excited states of Entwistle et al. [Nat. Commun. 14, 274 (2023)] and demonstrate that the accuracy of the updated method is competitive with other available excited-state VMC approaches. A theoretical comparison of the computational aspects of these algorithms is presented, where several benefits of the penalty-based method are identified. Our main contributions include an automatic mechanism for tuning the scale of the penalty terms, an updated form of the overlap penalty with proven convergence properties, and a new term that penalizes the spin of the wave function, enabling the selective computation of states with a given spin. With these improvements, along with the use of the latest self-attention-based ansatz, the penalty-based method achieves a mean absolute error below 1 kcal/mol for the vertical excitation energies of a set of 26 atoms and molecules, without relying on variance matching schemes. Considering excited states along the dissociation of the carbon dimer, the accuracy of the penalty-based method is on par with that of natural-excited-state (NES) VMC, while also providing results for additional sections of the potential energy surface, which were inaccessible with the NES method. Additionally, the accuracy of the penalty-based method is improved for a conical intersection of ethylene, with the predicted angle of the intersection agreeing well with both NES-VMC and multireference configuration interaction.

Detection of extended-spectrum β-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 β-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.

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 ∼ 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 ∼ 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 ∼ 96% in isolate level identification and ∼ 84% in susceptibility determination.

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%.

Early diagnosis of Alzheimer's disease using infrared spectroscopy of isolated blood samples followed by multivariate analyses

Alzheimer's disease (AD) is the most common cause of dementia, particularly in the elderly. The disease is characterized by cognitive decline that typically starts with insidious memory loss and progresses relentlessly to produce global impairment of all higher cortical functions. Due to better living conditions and health facilities in developed countries, which result in higher overall life spans, these countries report upward trends of AD among their populations. There are, however, no specific diagnostic tests for AD and clinical diagnosis is especially difficult in the earliest stages of the disease. Early diagnosis of AD is frequently subjective and is determined by physicians (generally neurologists, geriatricians, and psychiatrists) depending on their experience. Diagnosing AD requires both medical history and mental status testing. Having trouble with memory does not mean you have AD. AD has no current cure, but treatments for symptoms are available and research continues. In this study, we investigated the potential of infrared microscopy to differentiate between AD patients and controls, using Fourier transform infrared (FTIR) spectroscopy of isolated blood components. FTIR is known as a quick, safe, and minimally invasive method to investigate biological samples. For this goal, we measured infrared spectra from white blood cells (WBCs) and plasma taken from AD patients and controls, with the consent of the patients or their guardians. Applying multivariate analysis, principal component analysis (PCA) followed by linear discriminant analysis (LDA), it was possible to differentiate among the different types of mild, moderate, and severe AD, and the controls, with 85% accuracy when using the WBC spectra and about 77% when using the plasma spectra. When only the moderate and severe stages were included, an 83% accuracy was obtained using the WBC spectra and about 89% when using the plasma spectra.

Using Infrared Spectroscopy and Multivariate Analysis to Detect Antibiotics' Resistant Escherichia coli Bacteria

Bacterial pathogens are one of the primary causes of human morbidity worldwide. Historically, antibiotics have been highly effective against most bacterial pathogens; however, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global health-care problem. Early and rapid determination of bacterial susceptibility to antibiotics has become essential in many clinical settings and, sometimes, can save lives. Currently classical procedures require at least 48 h for determining bacterial susceptibility, which can constitute a life-threatening delay for effective treatment. Infrared (IR) microscopy is a rapid and inexpensive technique, which has been used successfully for the detection and identification of various biological samples; nonetheless, its true potential in routine clinical diagnosis has not yet been established. In this study, we evaluated the potential of this technique for rapid identification of bacterial susceptibility to specific antibiotics based on the IR spectra of the bacteria. IR spectroscopy was conducted on bacterial colonies, obtained after 24 h culture from patients' samples. An IR microscope was utilized, and a computational classification method was developed to analyze the IR spectra by novel pattern-recognition and statistical tools, to determine E. coli susceptibility within a few minutes to different antibiotics, gentamicin, ceftazidime, nitrofurantoin, nalidixic acid, ofloxacin. Our results show that it was possible to classify the tested bacteria into sensitive and resistant types, with success rates as high as 85% for a number of examined antibiotics. These promising results open the potential of this technique for faster determination of bacterial susceptibility to certain antibiotics.

Detection of antibiotic resistant Escherichia Coli bacteria using infrared microscopy and advanced multivariate analysis

DeterminingE. colibacteria susceptibility by analyzing their FTIR spectra using multivariate analysis.

A novel approach for the detection of early gastric cancer: fluorescence spectroscopy of gastric juice

OBJECTIVE

This study aimed to investigate the efficacy of fluorescence spectroscopy of gastric juice for early gastric cancer (EGC) screening.

METHODS

Gastric juice was collected from 101 participants who underwent endoscopy in the Outpatient Endoscopy Center of Peking University Third Hospital. The participants were divided into three groups: the normal mucosa or chronic non-atrophic gastritis (NM-CNAG) group (n = 35), advanced gastric cancer (AGC) group (n = 33) and EGC group (n = 33). Fluorescence spectroscopic analysis was performed in all the gastric juice samples and the maximum fluorescence intensity of the first peak (P1 FI) was measured.

RESULTS

The mean fluorescence intensity of P1 FI of gastric juice in AGC (92.1 ± 10.7) and EGC (90.8 ± 12.0) groups was significantly higher than that in the NM-CNAG group (55.7 ± 7.5) (AGC vs NM-CNAG, P = 0.006 and EGC vs NM-CNAG, P = 0.015, respectively). The areas under the receiver operating characteristic curves for the detection of AGC and EGC were 0.681 (95% confidence interval [CI] 0.553-0.810, P = 0.010) and 0.655 (95% CI 0.522-0.787, P = 0.028). With the P1 FI of ≥47.7, the sensitivity, specificity and accuracy for detecting EGC were 69.7%, 57.1% and 63.2%, respectively.

CONCLUSIONS

The enhancement of P1 FI of gastric juice occurs at the early stage of gastric cancer. Fluorescence spectroscopy of gastric juice may be used as a novel screening tool for the early detection of gastric cancer.

Spectral signatures of colonic malignancies in the mid-infrared region: from basic research to clinical applicability

The process of carcinogenesis in the colon progresses through several overlapping stages, making the evaluation process challenging, as well as subjective. Owing to the complexity of colonic tissues and the search for a technique that is rapid and foolproof for precise grading and evaluation of biopsies, many spectroscopic techniques have been evaluated in the past few decades for their efficiency and clinical compatibility. Fourier-transform infrared spectroscopy, being quantitative and objective, has the capacity for automation and relevance to cancer diagnosis. This article highlights investigations on the application of Fourier-transform infrared spectroscopy (particularly microscopy) in colon cancer diagnosis and parallel developments in data analysis techniques for the characterization of spectral signatures of malignant tissues in the colon.