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Pediatric Research Nov 2022Gas in scattering media absorption spectroscopy (GASMAS) is a novel optical technology employing near-infrared light. It has a potential use in the medical setting as a... (Review)
Review
Gas in scattering media absorption spectroscopy (GASMAS) is a novel optical technology employing near-infrared light. It has a potential use in the medical setting as a monitoring and diagnostic tool by detecting molecular oxygen within gas pockets and thus may be a useful adjunct in respiratory monitoring. GASMAS has potential advantages over other monitoring devices currently used in clinical practice. It is a non-invasive, continuous, non-ionising technology and provides unique information about molecular oxygen content inside the lungs. GASMAS may have a future role in optimising respiratory management of neonates in different clinical scenarios such as monitoring cardiorespiratory transition in the delivery room, assessing surfactant deficiency, and optimising endotracheal tube positioning. This article aims to summarise current evidence exploring GASMAS application in a neonate, discuss possible clinical benefits, and compare with other devices that are currently used in neonatal care. IMPACT: This article presents a novel optical technique to measure lung oxygen concentrations that may have important clinical uses. This review summarises the current literature investigating the concept of optical lung oxygen measurement. Information from this review can guide researchers in future studies.
Topics: Infant, Newborn; Humans; Spectrum Analysis; Oxygen; Gases; Monitoring, Physiologic; Respiratory Rate
PubMed: 35606473
DOI: 10.1038/s41390-022-02110-y -
Scientific Reports Dec 2020In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for...
In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.
Topics: Biometry; Brain; Correlation of Data; Electroencephalography; Eye; Humans; Occipital Lobe; Recurrence; Spectrum Analysis
PubMed: 33277526
DOI: 10.1038/s41598-020-77903-4 -
Analytica Chimica Acta Apr 2022Multi-gas detection represents a suitable solution in many applications, such as environmental and atmospheric monitoring, chemical reaction and industrial process... (Review)
Review
Multi-gas detection represents a suitable solution in many applications, such as environmental and atmospheric monitoring, chemical reaction and industrial process control, safety and security, oil&gas and biomedicine. Among optical techniques, Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) has been demonstrated to be a leading-edge technology for addressing multi-gas detection, thanks to the modularity, ruggedness, portability and real time operation of the QEPAS sensors. The detection module consists in a spectrophone, mounted in a vacuum-tight cell and detecting sound waves generated via photoacoustic excitation within the gas sample. As a result, the sound detection is wavelength-independent and the volume of the absorption cell is basically determined by the spectrophone dimensions, typically in the order of few cubic centimeters. In this review paper, the implementation of the QEPAS technique for multi-gas detection will be discussed for three main areas of applications: i) multi-gas trace sensing by exploiting non-interfering absorption features; ii) multi-gas detection dealing with overlapping absorption bands; iii) multi-gas detection in fluctuating backgrounds. The fundamental role of the analysis and statistical tools will be also discussed in detail in relation with the specific applications. This overview on QEPAS technique, highlighting merits and drawbacks, aims at providing ready-to-use guidelines for multi-gas detection in a wide range of applications and operating conditions.
Topics: Photoacoustic Techniques; Quartz; Spectrum Analysis
PubMed: 35341511
DOI: 10.1016/j.aca.2021.338894 -
The Journal of Physical Chemistry. A Feb 2022Machine learning has become more and more popular in computational chemistry, as well as in the important field of spectroscopy. In this concise review, we walk the... (Review)
Review
Machine learning has become more and more popular in computational chemistry, as well as in the important field of spectroscopy. In this concise review, we walk the reader through a short summary of machine learning algorithms and a comprehensive discussion on the connection between machine learning methods and vibrational spectroscopy, particularly for the case of infrared and Raman spectroscopy. We also briefly discuss state-of-the-art molecular representations which serve as meaningful inputs for machine learning to predict vibrational spectra. In addition, this review provides an overview of the transferability and best practices of machine learning in the prediction of vibrational spectra as well as possible future research directions.
Topics: Algorithms; Machine Learning; Spectrum Analysis, Raman; Vibration
PubMed: 35133168
DOI: 10.1021/acs.jpca.1c10417 -
Microbiology Spectrum Dec 2022The rapid and accurate identification of the causing agents during bacterial infections would greatly improve pathogen transmission, prevention, patient care, and...
The rapid and accurate identification of the causing agents during bacterial infections would greatly improve pathogen transmission, prevention, patient care, and medical treatments in clinical settings. Although many conventional and molecular methods have been proven to be efficient and reliable, some of them suffer technical biases and limitations that require the development and application of novel and advanced techniques. Recently, due to its cost affordability, noninvasiveness, and label-free feature, Raman spectroscopy (RS) is emerging as a potential technique for fast bacterial detection. However, the method is still hampered by many technical issues, such as low signal intensity, poor reproducibility, and standard data set insufficiency, among others. Thus, it should be cautiously claimed that Raman spectroscopy could provide practical applications in real-world settings. In order to evaluate the implementation potentials of Raman spectroscopy in the identification of bacterial pathogens, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, which showed that a convolutional neural network (CNN) deep learning algorithm achieved the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. In summary, the SERS technique holds a promising potential for fast bacterial pathogen identification in clinical laboratories with the integration of machine learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples. In this study, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, the results of which showed that the convolutional neural network (CNN) deep learning algorithm could achieve the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. Taken together, we concluded that the SERS technique held a promising potential for fast bacterial pathogen diagnosis in clinical laboratories with the integration of deep learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples.
Topics: Humans; Spectrum Analysis, Raman; Deep Learning; Reproducibility of Results; Bacteria; Bacterial Infections
PubMed: 36314973
DOI: 10.1128/spectrum.02580-22 -
Experimental and Molecular Pathology Apr 2022Pathological gross examination of breast carcinoma samples is sometimes laborious. A tissue pre-mapping method could indicate neoplastic areas to the pathologist and...
Pathological gross examination of breast carcinoma samples is sometimes laborious. A tissue pre-mapping method could indicate neoplastic areas to the pathologist and enable focused sampling. Differential Mobility Spectrometry (DMS) is a rapid and affordable technology for complex gas mixture analysis. We present an automated tissue laser analysis system for imaging approaches (iATLAS), which utilizes a computer-controlled laser evaporator unit coupled with a DMS gas analyzer. The system is demonstrated in the classification of porcine tissue samples and three human breast carcinomas. Tissue samples from eighteen landrace pigs were classified with the system based on a pre-designed matrix (spatial resolution 1-3 mm). The smoke samples were analyzed with DMS, and tissue classification was performed with several machine learning approaches. Porcine skeletal muscle (n = 1030), adipose tissue (n = 1329), normal breast tissue (n = 258), bone (n = 680), and liver (n = 264) were identified with 86% cross-validation (CV) accuracy with a convolutional neural network (CNN) model. Further, a panel tissue that comprised all five tissue types was applied as an independent validation dataset. In this test, 82% classification accuracy with CNN was achieved. An analogous procedure was applied to demonstrate the feasibility of iATLAS in breast cancer imaging according to 1) macroscopically and 2) microscopically annotated data with 10-fold CV and SVM (radial kernel). We reached a classification accuracy of 94%, specificity of 94%, and sensitivity of 93% with the macroscopically annotated data from three breast cancer specimens. The microscopic annotation was applicable to two specimens. For the first specimen, the classification accuracy was 84% (specificity 88% and sensitivity 77%). For the second, the classification accuracy was 72% (specificity 88% and sensitivity 24%). This study presents a promising method for automated tissue imaging in an animal model and lays foundation for breast cancer imaging.
Topics: Animals; Breast; Breast Neoplasms; Female; Humans; Ion Mobility Spectrometry; Lasers; Spectrum Analysis; Swine
PubMed: 35337806
DOI: 10.1016/j.yexmp.2022.104759 -
Biosensors May 2023Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational... (Review)
Review
Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.
Topics: Humans; Spectrum Analysis, Raman; Spectrophotometry, Infrared; Neoplasms; Chemistry, Physical
PubMed: 37232918
DOI: 10.3390/bios13050557 -
EBioMedicine Jul 2021In this article of EBioMedicine, Santosh Renuse and colleagues show the relevance of combining immunoaffinity capture with targeted mass spectrometry measurement to...
In this article of EBioMedicine, Santosh Renuse and colleagues show the relevance of combining immunoaffinity capture with targeted mass spectrometry measurement to detect SARS-CoV-2 nucleocapsid proteins in nasopharyngeal swab samples. The COVID-19 pandemic has confirmed the need to improve the toolbox available to diagnose respiratory infections. Rapid, reliable, and highly specific detection is essential if we are to mount immediate preventive and therapeutic responses. This report stands out from previous studies as it implements immunocapture along with robust validation for a large cohort of subjects. The results presented show that mass spectrometry is definitively at a crossroads for large-scale clinical applications.
Topics: COVID-19; Humans; Mass Spectrometry; Pandemics; SARS-CoV-2; Spectrum Analysis
PubMed: 34252697
DOI: 10.1016/j.ebiom.2021.103480 -
Blood Advances Feb 2024
Topics: Spectrum Analysis, Raman; T-Lymphocytes
PubMed: 38349671
DOI: 10.1182/bloodadvances.2023012129 -
Molecules (Basel, Switzerland) Dec 2022Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to...
Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms [...].
Topics: Molecular Docking Simulation; Thermodynamics; Proteins; Spectrum Analysis; Binding Sites; Protein Binding; Spectrometry, Fluorescence
PubMed: 36500497
DOI: 10.3390/molecules27238405