Machine learning for the meta-analyses of microbial pathogens' volatile signatures

Meta-Analysis Informed Functional Connectomes Representations for Depression Identification

BACKGROUND

Meta-analyses in neuroimaging have gained popularity. However, their clinical utility remains uncertain. Convergent masks, containing repeated clusters from publications, are often focal and small, and voxel-wise features can lead to the curse of dimensionality, limiting discriminative ability in clinical diagnosis.

PURPOSE

To develop a functional connectome representation (FCR) by integrating meta-analytic neuroimaging data and to evaluate its performance in identifying depression.

STUDY TYPE

Retrospective.

SUBJECTS

The principal data set included 151 patients with depression (male/female, 72/79) and 105 healthy controls (male/female, 48/57). An external test data set comprised 109 patients (male/female, 44/65) and 54 healthy controls (male/female, 15/39).

FIELD STRENGTH/SEQUENCE

3.0 T T1-weighted imaging, resting-state functional MRI with echo-planar sequence.

ASSESSMENT

We performed the community detection algorithm and principal component analysis to develop the FCR. The model's performance based on the FCR was evaluated in terms of accuracy, specificity, and sensitivity. Effect sizes (Cohen's d) for FCR components were calculated between patients and healthy controls. Model robustness was assessed by analyzing the association between accuracy and the degree of shuffled features in the permutation test.

STATISTICAL TESTS

Chi-squared test, two-sample t-test, effect sizes (Cohen's d), permutation tests for accuracy validation, and correlation analysis. Significance was determined at p < 0.05.

RESULTS

Effect sizes (Cohen's d) for each of the 39 principal components to quantify the magnitude of differences between depressed patients and healthy controls, ranged from d = -0.22 to d = 0.84. The FCR-based diagnostic model achieved an accuracy of 89.42% (principal data set) and 83.35% (external data set). Permutation tests (n = 1000) indicated that the model's accuracy was significantly higher than chance level. A significant negative correlation was observed between random noise and accuracy (r = -0.093).

DATA CONCLUSION

The FCR effectively discriminates between depressed patients and healthy controls, exhibiting strong diagnostic performance, generalization, and robustness, supporting its potential utility in clinical depression identification.

EVIDENCE LEVEL

Level 3.

TECHNICAL EFFICACY

Stage 2.

Machine Learning in FTIR Spectrum for the Identification of Antibiotic Resistance: A Demonstration with Different Species of Microorganisms

Recent studies introduced the importance of using machine learning algorithms in research focused on the identification of antibiotic resistance. In this study, we highlight the importance of building solid machine learning foundations to differentiate antimicrobial resistance among microorganisms. Using advanced machine learning algorithms, we established a methodology capable of analyzing the FTIR structural profile of the samples of Streptococcus pyogenes and Streptococcus mutans (Gram-positive), as well as Escherichia coli and Klebsiella pneumoniae (Gram-negative), demonstrating cross-sectional applicability in this focus on different microorganisms. The analysis focuses on specific biomolecules-Carbohydrates, Fatty Acids, and Proteins-in FTIR spectra, providing a multidimensional database that transcends microbial variability. The results highlight the ability of the method to consistently identify resistance patterns, regardless of the Gram classification of the bacteria and the species involved, reinforcing the premise that the structural characteristics identified are universal among the microorganisms tested. By validating this approach in four distinct species, our study proves the versatility and precision of the methodology used, in addition to bringing support to the development of an innovative protocol for the rapid and safe identification of antimicrobial resistance. This advance is crucial for optimizing treatment strategies and avoiding the spread of resistance. This emphasizes the relevance of specialized machine learning bases in effectively differentiating between resistance profiles in Gram-negative and Gram-positive bacteria to be implemented in the identification of antibiotic resistance. The obtained result has a high potential to be applied to clinical procedures.

Machine learning-enabled colorimetric sensors for foodborne pathogen detection

In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One application of growing interest is sensing foodborne pathogens. There are unique considerations for sensing in the food industry, including food sample destruction, specificity amidst a complex food matrix, and high sensitivity requirements. Incorporating novel technology, such as nanotechnology, microfluidics, and smartphone app development, into colorimetric sensing methodology can enhance sensor performance. Nonetheless, there remain challenges to integrating sensors with existing food safety infrastructure. Recently, increasingly advanced machine learning techniques have been employed to facilitate nondestructive, multiplex detection for feasible assimilation of sensors into the food industry. With its ability to analyze and make predictions from highly complex data, machine learning holds potential for advanced yet practical colorimetric sensing of foodborne pathogens. This article summarizes recent developments and hurdles of machine learning-enabled colorimetric foodborne pathogen sensing. These advancements underscore the potential of interdisciplinary, cutting-edge technology in providing safer and more efficient food systems.

The Impact of Artificial Intelligence on Microbial Diagnosis

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

Machine learning for microbiologists

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

Evaluating algorithms of decision tree, support vector machine and regression for anode side catalyst data in proton exchange membrane water electrolysis

Nowadays, due to the various type of problems stemmed from using chemical compounds and fossil fuels which have widely influence on whole environment including acid rain, polar ice melting and etc., number of researches have been leading on replacing the nonrenewable energy sources with renewable ones in order to produce clean fuels. Among these, hydrogen emerges as a quintessential clean fuel, garnering substantial attention for its potential to be synthesized from the electric power generated by renewable sources like nuclear and solar energies. This is achieved through the employment of a proton exchange membrane water electrolysis (PEMWE) system, widely recognized as one of the most proficient and economically viable technologies for effecting the separation of H2O into H+ and OH-. In this study, the important affecting parameters on the anode side of catalyst in PEMWE and analyzed them by machine-learning (ML) algorithms through developing a data science (DS) procedure were discussed. Various machine learning models were subjected to comparison, wherein the Decision Tree models, specifically those configured with maximum depths of 3 and 4, emerged as the optimal choices, attaining a perfect 100% accuracy across both Dataset 1 and Dataset 2. Moreover, notable enhancements in accuracy values were observed for the Support Vector Machine (SVM) model, registering increments from 0.79 to 0.82 for Dataset 1 and 2, respectively. In stark contrast, the remaining models experienced a decrement in their accuracy scores. This phenomenon underscores the pivotal role played by the data generation process in rendering the models more faithful to real-world scenarios.

Fisher ratio feature selection by manual peak area calculations on comprehensive two-dimensional gas chromatography data using standard mixtures with variable composition, storage, and interferences

Comprehensive two-dimensional gas chromatography (GC×GC) is becoming increasingly more common for non-targeted characterization of complex volatile mixtures. The information gained with higher peak capacity and sensitivity provides additional sample composition information when one-dimensional GC is not adequate. GC×GC generates complex multivariate data sets when using non-targeted analysis to discover analytes. Fisher ratio (FR) analysis is applied to discern class markers, limiting complex GC×GC profiles to the most discriminating compounds between classes. While many approaches for feature selection using FR analysis exist, FR can be calculated relatively easily directly on peak areas after any native software has performed peak detection. This study evaluated the success rates of manual FR calculation and comparison to a critical F-value for samples analyzed by GC×GC with defined concentration differences. Long-term storage of samples and other spiked interferences were also investigated to examine their impact on analyzing mixtures using this FR feature selection strategy. Success rates were generally high with mostly 90-100% success rates and some instances of percentages between 80 and 90%. There were rare cases of false positives present and a low occurrence of false negatives. When errors were made in the selection of a compound, it was typically due to chromatographic artifacts present in chromatograms and not from the FR approach itself. This work provides foundational experimental data on the use of manual FR calculations for feature selection from GC×GC data.

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.

From the nose to the lungs: the intricate journey of airborne pathogens amidst commensal bacteria

The recent COVID-19 pandemic has dramatically brought the pitfalls of airborne pathogens to the attention of the scientific community. Not only viruses but also bacteria and fungi may exploit air transmission to colonize and infect potential hosts and be the cause of significant morbidity and mortality in susceptible populations. The efforts to decipher the mechanisms of pathogenicity of airborne microbes have brought to light the delicate equilibrium that governs the homeostasis of mucosal membranes. The microorganisms already thriving in the permissive environment of the respiratory tract represent a critical component of this equilibrium and a potent barrier to infection by means of direct competition with airborne pathogens or indirectly via modulation of the immune response. Moving down the respiratory tract, physicochemical and biological constraints promote site-specific expansion of microbes that engage in cross talk with the local immune system to maintain homeostasis and promote protection. In this review, we critically assess the site-specific microbial communities that an airborne pathogen encounters in its hypothetical travel along the respiratory tract and discuss the changes in the composition and function of the microbiome in airborne diseases by taking fungal and SARS-CoV-2 infections as examples. Finally, we discuss how technological and bioinformatics advancements may turn microbiome analysis into a valuable tool in the hands of clinicians to predict the risk of disease onset, the clinical course, and the response to treatment of individual patients in the direction of personalized medicine implementation.

Metagenomics and artificial intelligence in the context of human health

Human microbiome is ubiquitous, dynamic, and site-specific consortia of microbial communities. The pathogenic nature of microorganisms within human tissues has led to an increase in microbial studies. Characterization of genera, like Streptococcus, Cutibacterium, Staphylococcus, Bifidobacterium, Lactococcus and Lactobacillus through culture-dependent and culture-independent techniques has been reported. However, due to the unique environment within human tissues, it is difficult to culture these microorganisms making their molecular studies strenuous. MGs offer a gateway to explore and characterize hidden microbial communities through a culture-independent mode by direct DNA isolation. By function and sequence-based MGs, Scientists can explore the mechanistic details of numerous microbes and their interaction with the niche. Since the data generated from MGs studies is highly complex and multi-dimensional, it requires accurate analytical tools to evaluate and interpret the data. Artificial intelligence (AI) provides the luxury to automatically learn the data dimensionality and ease its complexity that makes the disease diagnosis and disease response easy, accurate and timely. This review provides insight into the human microbiota and its exploration and expansion through MG studies. The review elucidates the significance of MGs in studying the changing microbiota during disease conditions besides highlighting the role of AI in computational analysis of MG data.

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications.

Bacterial volatile compound-based tools for crop management and quality

Bacteria produce a huge diversity of metabolites, many of which mediate ecological relations. Among these, volatile compounds cause broad-range effects at low doses and, therefore, may be exploited for plant defence strategies and agricultural production, but such applications are still in their early development. Here, we review the latest technologies involving the use of bacterial volatile compounds for phytosanitary inspection, biological control, plant growth promotion, and crop quality. We highlight a variety of effects with a potential applicative interest, based on either live biocontrol and/or biostimulant agents, or the isolated metabolites responsible for the interaction with hosts or competitors. Future agricultural technologies may benefit from the development of new analytical tools to understand bacterial interactions with the environment.

Measurement of Volatile Compounds for Real-Time Analysis of Soil Microbial Metabolic Response to Simulated Snowmelt

Snowmelt dynamics are a significant determinant of microbial metabolism in soil and regulate global biogeochemical cycles of carbon and nutrients by creating seasonal variations in soil redox and nutrient pools. With an increasing concern that climate change accelerates both snowmelt timing and rate, obtaining an accurate characterization of microbial response to snowmelt is important for understanding biogeochemical cycles intertwined with soil. However, observing microbial metabolism and its dynamics non-destructively remains a major challenge for systems such as soil. Microbial volatile compounds (mVCs) emitted from soil represent information-dense signatures and when assayed non-destructively using state-of-the-art instrumentation such as Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-TOF-MS) provide time resolved insights into the metabolism of active microbiomes. In this study, we used PTR-TOF-MS to investigate the metabolic trajectory of microbiomes from a subalpine forest soil, and their response to a simulated wet-up event akin to snowmelt. Using an information theory approach based on the partitioning of mutual information, we identified mVC metabolite pairs with robust interactions, including those that were non-linear and with time lags. The biological context for these mVC interactions was evaluated by projecting the connections onto the Kyoto Encyclopedia of Genes and Genomes (KEGG) network of known metabolic pathways. Simulated snowmelt resulted in a rapid increase in the production of trimethylamine (TMA) suggesting that anaerobic degradation of quaternary amine osmo/cryoprotectants, such as glycine betaine, may be important contributors to this resource pulse. Unique and synergistic connections between intermediates of methylotrophic pathways such as dimethylamine, formaldehyde and methanol were observed upon wet-up and indicate that the initial pulse of TMA was likely transformed into these intermediates by methylotrophs. Increases in ammonia oxidation signatures (transformation of hydroxylamine to nitrite) were observed in parallel, and while the relative role of nitrifiers or methylotrophs cannot be confirmed, the inferred connection to TMA oxidation suggests either a direct or indirect coupling between these processes. Overall, it appears that such mVC time-series from PTR-TOF-MS combined with causal inference represents an attractive approach to non-destructively observe soil microbial metabolism and its response to environmental perturbation.

Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks

Liquid crystal (LC)-based materials are promising platforms to develop rapid, miniaturised and low-cost gas sensor devices. In hybrid gel films containing LC droplets, characteristic optical texture variations are observed due to orientational transitions of LC molecules in the presence of distinct volatile organic compounds (VOC). Here, we investigate the use of deep convolutional neural networks (CNN) as pattern recognition systems to analyse optical textures dynamics in LC droplets exposed to a set of different VOCs. LC droplets responses to VOCs were video recorded under polarised optical microscopy (POM). CNNs were then used to extract features from the responses and, in separate tasks, to recognise and quantify the vapours exposed to the films. The impact of droplet diameter on the results was also analysed. With our classification models, we show that a single individual droplet can recognise 11 VOCs with small structural and functional differences (F1-score above 93%). The optical texture variation pattern of a droplet also reflects VOC concentration changes, as suggested by applying a regression model to acetone at 0.9-4.0% (v/v) (mean absolute errors below 0.25% (v/v)). The CNN-based methodology is thus a promising approach for VOC sensing using responses from individual LC-droplets.

Breath Biopsy and Discovery of Exclusive Volatile Organic Compounds for Diagnosis of Infectious Diseases

Exhaled breath contains thousand metabolites and volatile organic compounds (VOCs) that originated from both respiratory tract and internal organ systems and their microbiomes. Commensal and pathogenic bacteria and virus of microbiomes are capable of producing VOCs of different chemical classes, and some of them may serve as biomarkers for installation and progression of various common human diseases. Here we describe qualitative and quantitative methods for measuring VOC fingerprints generated by cellular and microbial metabolic and pathologic pathways. We describe different chemical classes of VOCs and their role in the host cell-microbial interactions and their impact on infection disease pathology. We also update on recent progress on VOC signatures emitted by isolated bacterial species and microbiomes, and VOCs identified in exhaled breath of patients with respiratory tract and gastrointestinal diseases, and inflammatory syndromes, including the acute respiratory distress syndrome and sepsis. The VOC curated databases and instrumentations have been developed through statistically robust breathomic research in large patient populations. Scientists have now the opportunity to find potential biomarkers for both triage and diagnosis of particular human disease.

Morphological Effects in SnO2 Chemiresistors for Ethanol Detection: A Review in Terms of Central Performances and Outliers

SnO₂ is one of the most studied materials in gas sensing and is often used as a benchmark for other metal oxide-based gas sensors. To optimize its structural and functional features, the fine tuning of the morphology in nanoparticles, nanowires, nanosheets and their eventual hierarchical organization has become an active field of research. In this paper, the different SnO₂ morphologies reported in literature in the last five years are systematically compared in terms of response amplitude through a statistical approach. To have a dataset as homogeneous as possible, which is necessary for a reliable comparison, the analysis is carried out on sensors based on pure SnO₂, focusing on ethanol detection in a dry air background as case study. Concerning the central performances of each morphology, results indicate that none clearly outperform the others, while a few individual materials emerge as remarkable outliers with respect to the whole dataset. The observed central performances and outliers may represent a suitable reference for future research activities in the field.

Novel biotechnological approaches for monitoring and immunization against resistant to antibiotics Escherichia coli and other pathogenic bacteria

The application of next-generation molecular, biochemical and immunological methods for developing new vaccines, antimicrobial compounds, probiotics and prebiotics for zoonotic infection control has been fundamental to the understanding and preservation of the symbiotic relationship between animals and humans. With increasing rates of antibiotic use, resistant bacterial infections have become more difficult to diagnose, treat, and eradicate, thereby elevating the importance of surveillance and prevention programs. Effective surveillance relies on the availability of rapid, cost-effective methods to monitor pathogenic bacterial isolates. In this opinion article, we summarize the results of some research program initiatives for the improvement of live vaccines against avian enterotoxigenic Escherichia coli using virulence factor gene deletion and engineered vaccine vectors based on probiotics. We also describe methods for the detection of pathogenic bacterial strains in eco-environmental headspace and aerosols, as well as samples of animal and human breath, based on the composition of volatile organic compounds and fatty acid methyl esters. We explain how the introduction of these low-cost biotechnologies and protocols will provide the opportunity to enhance co-operation between networks of resistance surveillance programs and integrated routine workflows of veterinary and clinical public health microbiology laboratories.

Sustainable plant polyesters as substrates for optical gas sensors

The fast and non-invasive detection of odors and volatile organic compounds (VOCs) by gas sensors and electronic noses is a growing field of interest, mostly due to a large scope of potential applications. Additional drivers for the expansion of the field include the development of alternative and sustainable sensing materials. The discovery that isolated cross-linked polymeric structures of suberin spontaneously self-assemble as a film inspired us to develop new sensing composite materials consisting of suberin and a liquid crystal (LC). Due to their stimuli-responsive and optically active nature, liquid crystals are interesting probes in gas sensing. Herein, we report the isolation and the chemical characterization of two suberin types (from cork and from potato peels) resorting to analyses of gas chromatography–mass spectrometry (GC-MS), solution nuclear magnetic resonance (NMR), and X-ray photoelectron spectroscopy (XPS). The collected data highlighted their compositional and structural differences. Cork suberin showed a higher proportion of longer aliphatic constituents and is more esterified than potato suberin. Accordingly, when casted it formed films with larger surface irregularities and a higher C/O ratio. When either type of suberin was combined with the liquid crystal 5CB, the ensuing hybrid materials showed distinctive morphological and sensing properties towards a set of 12 VOCs (comprising heptane, hexane, chloroform, toluene, dichlormethane, diethylether, ethyl acetate, acetonitrile, acetone, ethanol, methanol, and acetic acid). The optical responses generated by the materials are reversible and reproducible, showing stability for 3 weeks. The individual VOC-sensing responses of the two hybrid materials are discussed taking as basis the chemistry of each suberin type. A support vector machines (SVM) algorithm based on the features of the optical responses was implemented to assess the VOC identification ability of the materials, revealing that the two distinct suberin-based sensors complement each other, since they selectively identify distinct VOCs or VOC groups. It is expected that such new environmentally-friendly gas sensing materials derived from natural diversity can be combined in arrays to enlarge selectivity and sensing capacity.

Microfluidics in Gas Sensing and Artificial Olfaction

Rapid, real-time, and non-invasive identification of volatile organic compounds (VOCs) and gases is an increasingly relevant field, with applications in areas such as healthcare, agriculture, or industry. Ideal characteristics of VOC and gas sensing devices used for artificial olfaction include portability and affordability, low power consumption, fast response, high selectivity, and sensitivity. Microfluidics meets all these requirements and allows for in situ operation and small sample amounts, providing many advantages compared to conventional methods using sophisticated apparatus such as gas chromatography and mass spectrometry. This review covers the work accomplished so far regarding microfluidic devices for gas sensing and artificial olfaction. Systems utilizing electrical and optical transduction, as well as several system designs engineered throughout the years are summarized, and future perspectives in the field are discussed.

Sniffing Out Urinary Tract Infection-Diagnosis Based on Volatile Organic Compounds and Smell Profile

Current available methods for the clinical diagnosis of urinary tract infection (UTI) rely on a urine dipstick test or culturing of pathogens. The dipstick test is rapid (available in 1-2 min), but has a low positive predictive value, while culturing is time-consuming and delays diagnosis (24-72 h between sample collection and pathogen identification). Due to this delay, broad-spectrum antibiotics are often prescribed immediately. The over-prescription of antibiotics should be limited, in order to prevent the development of antimicrobial resistance. As a result, there is a growing need for alternative diagnostic tools. This paper reviews applications of chemical-analysis instruments, such as gas chromatography-mass spectrometry (GC-MS), selected ion flow tube mass spectrometry (SIFT-MS), ion mobility spectrometry (IMS), field asymmetric ion mobility spectrometry (FAIMS) and electronic noses (eNoses) used for the diagnosis of UTI. These methods analyse volatile organic compounds (VOCs) that emanate from the headspace of collected urine samples to identify the bacterial pathogen and even determine the causative agent's resistance to different antibiotics. There is great potential for these technologies to gain wide-spread and routine use in clinical settings, since the analysis can be automated, and test results can be available within minutes after sample collection. This could significantly reduce the necessity to prescribe broad-spectrum antibiotics and allow the faster and more effective use of narrow-spectrum antibiotics.

Emerging coronavirus diseases and future perspectives

Coronavirus related infectious diseases seems to be biggest challenge of 21 century that have been constantly emerging and threating public health around the globe. Coronavirus disease-19 (COVID-19) that was detected as cause of respiratory tract infection in China by end the December 2019 impelled World Health Organization to declare in January 2020 public health emergency of international concern and consequently pandemic in March 2020. Over a past six months COVID-19 pandemic has wrapped up all continents except Antarctica. Scientists around the globe are finding way to tackle and reduce the ultimate risk and size of pandemic with lower morbidity and mortality rates. In this context, technologies such as sequencing, Crispr and artificial intelligence are playing vital role in diagnosis and management of infectious disease in contrast to conventional methods. Despite of this, there is a need to have rapid and early diagnostic tools and systems that recognize infectious disease in asymptotic condition. Here we provide an overview on the recent CoV outbreak and contribution of technologies with the emphasis on the future management for detection of such infectious diseases.

Detection of Group B Streptococcus in pregnancy by vaginal volatile organic compound analysis: a prospective exploratory study

Our objective was to assess whether volatile organic compound (VOC) analysis of vaginal swabs can detect maternal Group B Streptococcus (GBS) during pregnancy in a prospective exploratory study. Around 243 women attending a high-risk antenatal clinic at one university teaching hospital in the UK consented to take part and provide vaginal swabs throughout pregnancy. VOC analysis of vaginal swabs was undertaken and compared with the reference standard of GBS detected using enrichment culture method. The chemical components that emanated from the vaginal swabs were measured by gas chromatograph ion mobility spectrometry. This platform has both high sensitivity and good specificity to a range of chemical compounds. Our main outcome was to determine the sensitivity and specificity of VOC analysis for the detection of maternal GBS in vaginal swabs during pregnancy. Our study has demonstrated that the sensitivity and specificity of the VOC analysis by GC-IMS for the detection of GBS from vaginal swabs was 0.81 (95% confidence interval [CI], 0.71-0.89) and 0.97 (95% CI, 0.91-1) respectively. We conclude that the use of VOCs as biomarkers for the detection of maternal GBS in the vagina is a novel tool. As this test produces results within minutes and is of low unit test cost, it has the potential to be used in clinical settings, where fast diagnosis is important, for example, a patient in early labour.

Emerging ultrafast nucleic acid amplification technologies for next-generation molecular diagnostics

Over the last decade, nucleic acid amplification tests (NAATs) including polymerase chain reaction (PCR) were an indispensable methodology for diagnosing cancers, viral and bacterial infections owing to their high sensitivity and specificity. Because the NAATs can recognize and discriminate even a few copies of nucleic acid (NA) and species-specific NA sequences, NAATs have become the gold standard in a wide range of applications. However, limitations of NAAT approaches have recently become more apparent by reason of their lengthy run time, large reaction volume, and complex protocol. To meet the current demands of clinicians and biomedical researchers, new NAATs have developed to achieve ultrafast sample-to-answer protocols for the point-of-care testing (POCT). In this review, ultrafast NA-POCT platforms are discussed, outlining their NA amplification principles as well as delineating recent advances in ultrafast NAAT applications. The main focus is to provide an overview of NA-POCT platforms in regard to sample preparation of NA, NA amplification, NA detection process, interpretation of the analysis, and evaluation of the platform design. Increasing importance will be given to innovative, ultrafast amplification methods and tools which incorporate artificial intelligence (AI)-associated data analysis processes and mobile-healthcare networks. The future prospects of NA POCT platforms are promising as they allow absolute quantitation of NA in individuals which is essential to precision medicine.

Volatiles of pathogenic and non-pathogenic soil-borne fungi affect plant development and resistance to insects

Plants are ubiquitously exposed to a wide diversity of (micro)organisms, including mutualists and antagonists. Prior to direct contact, plants can perceive microbial organic and inorganic volatile compounds (hereafter: volatiles) from a distance that, in turn, may affect plant development and resistance. To date, however, the specificity of plant responses to volatiles emitted by pathogenic and non-pathogenic fungi and the ecological consequences of such responses remain largely elusive. We investigated whether Arabidopsis thaliana plants can differentiate between volatiles of pathogenic and non-pathogenic soil-borne fungi. We profiled volatile organic compounds (VOCs) and measured CO₂ emission of 11 fungi. We assessed the main effects of fungal volatiles on plant development and insect resistance. Despite distinct differences in VOC profiles between the pathogenic and non-pathogenic fungi, plants did not discriminate, based on plant phenotypic responses, between pathogenic and non-pathogenic fungi. Overall, plant growth was promoted and flowering was accelerated upon exposure to fungal volatiles, irrespectively of fungal CO₂ emission levels. In addition, plants became significantly more susceptible to a generalist insect leaf-chewing herbivore upon exposure to the volatiles of some of the fungi, demonstrating that a prior fungal volatile exposure can negatively affect plant resistance. These data indicate that plant development and resistance can be modulated in response to exposure to fungal volatiles.

Root renovation: how an improved understanding of basic root biology could inform the development of elite crops that foster sustainable soil health

A major goal in agricultural research is to develop 'elite' crops with stronger, resilient root systems. Within this context, breeding practices have focussed on developing plant varieties that are, primarily, able to withstand pathogen attack and, secondarily, able to maximise plant productivity. Although great strides towards breeding disease-tolerant or -resistant root stocks have been made, this has come at a cost. Emerging studies in certain crop species suggest that domestication of crops, together with soil management practices aimed at improving plant yield, may hinder beneficial soil microbial association or reduce microbial diversity in soil. To achieve more sustainable management of agricultural lands, we must not only shift our soil management practices but also our breeding strategy to include contributions from beneficial microbes. For this latter point, we need to advance our understanding of how plants communicate with, and are able to differentiate between, microbes of different lifestyles. Here, we present a review of the key findings on belowground plant-microbial interactions that have been made over the past decade, with a specific focus on how plants and microbes communicate. We also discuss the currently unresolved questions in this area, and propose plausible ways to use currently available research and integrate fast-emerging '-omics' technologies to tackle these questions. Combining past and developing research will enable the development of new crop varieties that will have new, value-added phenotypes belowground.

Effect of film thickness in gelatin hybrid gels for artificial olfaction

Artificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 μm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98%), in particular when using optical signals from a single film composition with 30 μm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.

Protein- and Peptide-Based Biosensors in Artificial Olfaction

Animals' olfactory systems rely on proteins, olfactory receptors (ORs) and odorant-binding proteins (OBPs), as their native sensing units to detect odours. Recent advances demonstrate that these proteins can also be employed as molecular recognition units in gas-phase biosensors. In addition, the interactions between odorant molecules and ORs or OBPs are a source of inspiration for designing peptides with tunable odorant selectivity. We review recent progress in gas biosensors employing biological units (ORs, OBPs, and peptides) in light of future developments in artificial olfaction, emphasizing examples where biological components have been employed to detect gas-phase analytes.