Current and Emerging Methods of Antibiotic Susceptibility Testing

Platinum nanoparticles-based electrochemical H2O2 sensor for rapid antibiotic susceptibility testing

With the increase of antimicrobial resistance, rapid antibiotic susceptibility testing (AST) to guide precise antibiotic administration has become increasingly important. However, current gold standard AST approaches tend to take up to 24-48 h. In this work, based on the nature of catalase-positive bacteria decomposing H2O2, we developed a rapid, portable, straightforward, and cost-effective phenotypic AST approach by detecting residual H2O2 using a Pt nanoparticles-based electrochemical sensor. The pulse current of the sensor exhibited a linear increase with rising H2O2 concentration, demonstrating a high sensitivity of ∼382.2 μA cm-2 mM-1. This approach showed superb diagnostic performance, with an area under the curve of 1 for 24 clinical samples of Escherichia coli and Staphylococcus aureus, with a total detection time of 60 and 45 min, respectively. Furthermore, the performance of the sensor showed no degradation even after 100 detections, promising a substantial reduction in AST costs. Overall, the proposed approach exhibited immense potential for diagnosing bacterial antibiotic resistance.

Current disease treatments for the ornamental pet fish trade and their associated problems

The trade in live ornamental fishes to be held as companion animals or displayed in public aquaria has an estimated global annual value of US$15–20 billion. Supply chains for ornamental pet fishes often involve many more parties than for fish farmed as food fishes, and at each stage, fishes are exposed to stressors including handling, confinement, crowding, mechanical disturbance, and poor water quality. If chronic, these stressors can compromise their immune system, making fishes more susceptible to pathogens. Mortality and morbidity from infectious disease can result in considerable welfare impacts and massive economic losses for the industry, and the range of infective agents seen in ornamental species is well documented. However, treating these diseases is not straightforward with practices varying greatly across the trade and with several approaches having unintended consequences, such as the emergence of resistant strains of pathogens. While disease treatments for a handful of fish species (e.g., koi, goldfish) have received focused research attention, for the home aquarium owner, there is an increasing reliance on products based on natural compounds which have received far less scientific attention. This review aims to highlight the gaps in our knowledge surrounding the range of disease treatments used across the ornamental pet fish trade, with a particular focus on freshwater tropical species destined for home aquaria. Consideration is given to the potential problems arising from these treatments, including microbial resistance and effects of treatments themselves on fish health and welfare.

Rethinking Biomedical Titanium Alloy Design: A Review of Challenges from Biological and Manufacturing Perspectives

Current biomedical titanium alloys have been repurposed from other industries, which has contributed to several biologically driven implant failure mechanisms. This review highlights the added value that may be gained by building an appreciation of implant biological responses at the onset of alloy design. Specifically, the fundamental mechanisms associated with immune response, angiogenesis, osseointegration and the potential threat of infection are discussed, including how elemental selection can modulate these pivotal systems. With a view to expedite inclusion of these interactions in alloy design criteria, methods to analyze these performance characteristics are also summarized. While machine learning techniques are being increasingly used to unearth complex relationships between alloying elements and material properties, much is still unknown about the correlation between composition and some bio-related properties. To bridge this gap, high-throughput methods are also reviewed to validate biological response along with cutting edge manufacturing approaches that may support rapid discovery. Taken together, this review encourages the alloy development community to rethink their approach to enable a new generation of biomedical implants intrinsically designed for a life in the body, including functionality to tackle biological challenges thereby offering improved patient outcomes.

Advancements of paper-based sensors for antibiotic-resistant bacterial species identification

Evolution of antimicrobial-resistant bacterial species is on a rise. This review aims to explore the diverse range of paper-based platforms designed to identify antimicrobial-resistant bacterial species. It highlights the most important targets used for sensor development and examines the applications of nanosized particles used in paper-based sensors. This review also discusses the advantages, limitations, and applicability of various targets and detection techniques for sensing drug-resistant bacterial species using paper-based platforms.

Rapid Antibiotic Susceptibility Determination by Fluorescence Lifetime Tracking of Bacterial Metabolism

To combat the rise of antibiotic-resistance in bacteria and the resulting effects on healthcare worldwide, new technologies are needed that can perform rapid antibiotic susceptibility testing (AST). Conventional clinical methods for AST rely on growth-based assays, which typically require long incubation times to obtain quantitative results, representing a major bottleneck in the determination of the optimal antibiotic regimen to treat patients. Here, we demonstrate a rapid AST method based on the metabolic activity measured by fluorescence lifetime imaging microscopy (FLIM). Using lab strains and clinical isolates of Escherichia coli with tetracycline-susceptible and resistant phenotypes as models, we demonstrate that changes in metabolic state associated with antibiotic susceptibility can be quantitatively tracked by FLIM. Our results show that the magnitude of metabolic perturbation resulting from antibiotic activity correlates with susceptibility evaluated by conventional metrics. Moreover, susceptible and resistant phenotypes can be differentiated in as short as 10 min after antibiotic exposure. This FLIM-AST (FAST) method can be applied to other antibiotics and provides insights into the nature of metabolic perturbations inside bacterial cells resulting from antibiotic exposure with single cell resolution.

Low-cost, real-time detection of bacterial growth via diffraction-based sensing

The emergence of antibacterial resistance impacts healthcare networks globally, with mortality rates and linked burdens of infection disproportionately affecting the developing world. Rapid alternatives to antibiotic susceptibility testing (AST) allow for swifter, more effective treatment, though they are limited in use in low-resource settings due to significant cost barriers. Herein we demonstrate a simple, cost-effective diffraction sensing-based approach for rapidly detecting bacterial growth (a precursor to AST). Diffraction gratings (1D, lined) directly comprised of our test bacteria (Escherichia coli DH5α) were produced using soft agar-based gel templates designed to direct bacterial attachment and produce a near-zero background signal. The diffraction spot intensities from the live bacterial gratings were monitored in growth and no growth (ampicillin) conditions at room temperature, using a simple fixed laser and photodetector setup. Growth-induced differences in signal were observed within 10-20 minutes, highlighting the sensitivity of this approach and its potential to be adapted as a rapid and accessible AST alternative.

Blackthorn fruit peel polyphenol extracts and photodynamic effect under blue light against Listeria monocytogenes

Photodynamic inactivation is an emerging antimicrobial treatment that can be enhanced by employing exogenous photosensitizers to eradicate foodborne pathogens. This study investigated a novel combinatory strategy to eradicate Listeria monocytogenes using blackthorn fruit peel (BFP) and blue light (BL). Extracts of BFP were characterized in terms of polyphenolic content, individual constituents, and antioxidant and antimicrobial activity. The concentration of phenolic compounds and antioxidant activity were both found to be determinants of antimicrobial activity. It was further speculated that flavonols, predominantly quercetin and rutin, were responsible for the activity of BFP against L. monocytogenes. A combination of BFP and BL resulted in a rapid inactivation of the pathogen by up to 4 log CFU/mL at 58.5 J/cm2, corresponding to 15 min BL illumination. Flow cytometry analysis revealed that the bacterial cells lost activity and suffered extensive membrane damage, exceeding 90% of the population. After photosensitizing L. monocytogenes with the BFP constituents quercetin and rutin, a 1.3-log reduction was observed. When applied together, these compounds could inflict the same damaging effect on cells as they did individually when effects were added. Therefore, the results indicate that BFP represents a natural source of (pro-)photosensitizers, which act additively to create inactivation effects. This study may help identify more effective plant-based photosensitizers to control L. monocytogenes in food-related applications.

Molecular detection of Enterobacter hormaechei in bovine respiratory disease

Bovine respiratory disease (BRD) develops from complex interactions among environmental, host and pathogenic factors. This study aimed to phenotypically identify Enterobacter hormaechei isolated from cattle with BRD and assess antimicrobial susceptibility and determining the molecular phylogeny of local E. hormaechei strains. Between November 2023 and March 2024, nasal swabs were collected from 93 cattle with BRD, before culturing for phenotypic analysis, and performing the polymerase chain reaction (PCR) for molecular characterisation. Of the 93 samples evaluated, 15.79% and 24.56% tested positive for E. hormaechei isolates on culture and PCR, respectively. The local isolates exhibited high resistance to amoxicillin, ampicillin, amikacin, nalidixic acid and ceftazidime; high susceptibility to azithromycin, levofloxacin, gentamicin, ofloxacin, cefepime, ceftriaxone, cefotaxime, nitrofurantoin, ceftazidime and ciprofloxacin; and moderate susceptibility to ciprofloxacin, colistin, imipenem and meropenem. Multiple sequence alignment, phylogenetic tree analysis and homology sequence identification, showed that the five positive isolates were similar to the reference isolate. To the best of our knowledge, this is the first time that E. hormaechei has been isolated in cattle with BRD in Iraq. Because phenotype-based assays show limited accuracy to identify species, we recommend molecular and phylogenetic analysis be included in all similar studies in the future.

Enzyme-free biosensor utilizing chitosan-capped ZnS doped by Mn nanomaterials for tetracycline hydrochloride detection

Tetracycline hydrochloride is a widely used antibiotic for treating bacterial infections, but its misuse poses serious health risks. Therefore, it is crucial to accurately detect tetracycline in complex matrices. In this study, we propose a simple, enzyme-free absorbance biosensor for tetracycline detection based on the optical properties of chitosan-capped ZnS doped with Mn nanomaterials. The biosensor can detect tetracycline in a range from 13.1 pM to 72.2 pM, with the best detection limit being 2.13 pM in deionized water. It can also differentiate tetracycline from ampicillin, penicillin, cephalexin, amoxicillin, and glucose within the aforementioned range. Moreover, this novel sensor has proven reliable over time, and its performance has been demonstrated in tap water and milk. The results have the potential to revolutionize antibiotic monitoring in clinical and environmental settings, thus contributing to the global fight against antibiotic resistance.

Bioluminescent Whole-Cell Bioreporter Bacterial Panel for Sustainable Screening and Discovery of Bioactive Compounds Derived from Mushrooms

This study presents a rapid and comprehensive method for screening mushroom extracts for the putative discovery of bioactive molecules, including those exhibiting antimicrobial activity. This approach utilizes a panel of bioluminescent bacteria, whose light production is a sensitive indicator of various cellular effects triggered by the extracts, including disruption of bacterial communication (quorum sensing), protein and DNA damage, fatty acid metabolism alterations, and oxidative stress induction. The bioassay's strength is its ability to efficiently analyze a large number of extracts simultaneously while also assessing several different mechanisms of toxicity, significantly reducing screening time. All samples analyzed exhibited more than one cellular effect, as indicated by the reporter bacteria. Four samples (C. cornucopioides, F. fomentarius, I. obliquus, and M. giganteus) displayed the highest number (six) of possible mechanisms of antibacterial activity. Additionally, combining extraction and purification protocols with a bioluminescent bacterial panel enables simultaneous improvement of the desired antimicrobial properties of the extracts. The presented approach offers a valuable tool for uncovering the diverse antimicrobial mechanisms of mushroom extracts.

Validating the utility of heavy water (Deuterium Oxide) as a potential Raman spectroscopic probe for identification of antibiotic resistance

The impact of microbial infections is increasing over time, and it is one of the major reasons for death in both developed and developing countries. colistin is considered as the antibiotic of last choice for infections brought by major multidrug-resistant (MDR), gram-negative bacteria such as Enterobacter species, Acinetobacter species, and Pseudomonas aeruginosa. Existing approaches to diagnose these resistant species are relatively slow and take up to 2 to 3 days. In this work, we propose a novel interdisciplinary method based on Raman spectroscopy and heavy water to identify colistin-resistant microbes. Our hypothesis is based on the fact that resistant bacteria will be metabolically active in the culture medium containing antibiotics and heavy water, and these bacteria will take up deuterium instead of hydrogen to newly synthesized lipids and proteins. This effect will generate a 'C - D' bond-specific Raman spectral marker. Successful identification of this band in the spectral profile can confirm the presence of colistin-resistant bacteria. We have validated the efficacy of this approach in identifying colistin-resistant bacteria spiked in artificial urine and have compared sensitivity at different bacterial concentrations. Overall findings suggest that heavy water can potentially serve as a suitable Raman probe for identifying metabolically active colistin-resistant bacteria via urine under clinically implementable time and can be used in clinical settings after validation.

An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks

Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used in clinical diagnostics for rapid species identification. Mining additional data from said spectra in the form of antimicrobial resistance (AMR) profiles is, therefore, highly promising. Such AMR profiles could serve as a drop-in solution for drastically improving treatment efficiency, effectiveness, and costs. This study endeavors to develop the first machine learning models capable of predicting AMR profiles for the whole repertoire of species and drugs encountered in clinical microbiology. The resulting models can be interpreted as drug recommender systems for infectious diseases. We find that our dual-branch method delivers considerably higher performance compared to previous approaches. In addition, experiments show that the models can be efficiently fine-tuned to data from other clinical laboratories. MALDI-TOF-based AMR recommender systems can, hence, greatly extend the value of MALDI-TOF MS for clinical diagnostics. All code supporting this study is distributed on PyPI and is packaged at https://github.com/gdewael/maldi-nn.

CRISPR/Cas9-Mediated Genome Editing of T4 Bacteriophage for High-Throughput Antimicrobial Susceptibility Testing

The accurate and effective determination of antimicrobial resistance is essential to limiting the spread of infectious diseases and ensuring human health. Herein, a simple, accurate, and high-throughput phage-based colorimetric sensing strategy was developed for antimicrobial susceptibility testing (AST). Taking advantage of the CRISPR/Cas9 system, the genome of the T4 phage was modularly engineered to carry lacZ-α (lacZa), a marker gene encoding the α-fragment of β-galactosidase (β-gal). T4lacZa phages were identified by blue-white selection and then used for a biosensing application. In this strategy, the bacterial solution is exposed to the T4lacZa phage, causing target bacteria to overexpress β-gal. Upon the addition of a colorimetric substrate, the β-gal initiates an enzymatic reaction, resulting in a solution color change from yellow to red. This sensing strategy offers a visual way to monitor bacterial growth in the presence of antibiotics, enabling the determination of bacterial antimicrobial susceptibility. As a proof of concept, our developed sensing strategy was successfully applied to identify 9 different multidrug-resistant Escherichia coli (E. coli) in urine samples with 100% specificity. Compared with conventional disk diffusion susceptibility tests, the engineered phage-based sensing strategy can shorten the detection time by at least half without losing detection sensitivity, providing an alternative high-throughput method for AST in clinical diagnosis.

Real-time flow cytometry to assess qualitative and quantitative responses of oral pathobionts during exposure to antiseptics

Antiseptics are widely used in oral healthcare to prevent or treat oral diseases, such as gingivitis and periodontitis. However, the incidence of bacteria being tolerant to standard antiseptics has sharply increased over the last few years. This stresses the urgency for surveillance against tolerant organisms, as well as the discovery of novel antimicrobials. Traditionally, susceptibility to antimicrobials is assessed by broth micro-dilution or disk diffusion assays, both of which are time-consuming, labor-intensive, and provide limited information on the mode of action of the antimicrobials. The abovementioned limitations highlight the need for the development of new methods to monitor and further understand antimicrobial susceptibility. In this study, we used real-time flow cytometry, combined with membrane permeability staining, as a quick and sensitive technology to study the quantitative and qualitative responses of two oral pathobionts to different concentrations of chlorhexidine (CHX), cetylpyridinium chloride (CPC), or triclosan. Apart from the real-time monitoring of cell damage, we further applied a phenotypic fingerprinting method to differentiate between the bacterial subpopulations that arose due to treatment. We quantified the pathobiont damage rate of different antiseptics at different concentrations within 15 minutes of exposure and identified the conditions under which the bacteria were most susceptible. Moreover, we detected species-specific and treatment-specific phenotypic subpopulations. This proves that real-time flow cytometry can provide information on the susceptibility of different microorganisms in a short time frame while differentiating between antiseptics and thus could be a valuable tool in the discovery of novel antimicrobial compound, while at the same time deciphering their mode of action.

IMPORTANCE

With increasing evidence that microorganisms are becoming more tolerant to standard antimicrobials, faster and more accessible antimicrobial susceptibility testing methods are needed. However, traditional susceptibility assays are laborious and time-consuming. To overcome the abovementioned limitations, we introduce a novel approach to define antimicrobial susceptibility in a much shorter time frame with the use of real-time flow cytometry. Furthermore, phenotypic fingerprinting analysis can be applied on the data to study the way antiseptics affect the bacterial cell morphology over time and, thus, gain information on the mode of action of a certain compound.

Electrochemical biosensors on microfluidic chips as promising tools to study microbial biofilms: a review

Microbial biofilms play a pivotal role in microbial infections and antibiotic resistance due to their unique properties, driving the urgent need for advanced methodologies to study their behavior comprehensively across varied environmental contexts. While electrochemical biosensors have demonstrated success in understanding the dynamics of biofilms, scientists are now synergistically merging these biosensors with microfluidic technology. This combined approach offers heightened precision, sensitivity, and real-time monitoring capabilities, promising a more comprehensive understanding of biofilm behavior and its implications. Our review delves into recent advancements in electrochemical biosensors on microfluidic chips, specifically tailored for investigating biofilm dynamics, virulence, and properties. Through a critical examination of these advantages, properties and applications of these devices, the review highlights the transformative potential of this technology in advancing our understanding of microbial biofilms in different settings.

Oral Staphylococcus Species and MRSA Strains in Patients with Orofacial Clefts Undergoing Surgical Rehabilitation Diagnosed by MALDI-TOF MS

This study investigated the occurrence and dynamics of oral Staphylococcus species in patients with orofacial clefts undergoing surgical rehabilitation treatment. Patients (n = 59) were statistically stratified and analyzed (age, gender, types of orofacial clefts, surgical history, and types of previous surgical rehabilitation). Salivary samples were obtained between hospitalization and the return to the specialized medical center. Microbiological diagnosis was performed by classical methods, and MALDI-TOF MS. MRSA strains (SCCmec type II, III, and IV) were characterized by the Decision Tree method. A total of 33 (55.9%) patients showed oral staphylococcal colonization in one, two, or three sampling steps. A high prevalence has been reported for S. aureus (including HA-, MRSA and CA-MRSA), followed by S. saprophyticus, S. epidermidis, S. sciuri, S. haemolyticus, S. lentus, S. arlettae, and S. warneri. The dynamics of oral colonization throughout surgical treatment and medical follow-up may be influenced by (i) imbalances in staphylococcal maintenance, (ii) efficiency of surgical asepsis or break of the aseptic chain, (iii) staphylococcal neocolonization in newly rehabilitated anatomical oral sites, and (iv) total or partial maintenance of staphylococcal species. The highly frequent clinical periodicity in specialized medical and dental centers may contribute to the acquisition of MRSA in these patients.

Cell morphology as biomarker of carbapenem exposure

Characterizing the physiological response of bacterial cells to antibiotics is crucial for designing diagnostic techniques, treatment choices, and drug development. While bacterial cells at sublethal doses of antibiotics are commonly characterized, the impact of exposure to high concentrations of antibiotics on bacteria after long-term serial exposure and their effect on withdrawal need attention for further characterization. This study investigated the effect of increasing imipenem concentrations on carbapenem-susceptible (S) and carbapenem-resistant (R) E. coli on their growth adaptation and cell surface structure. We exposed the bacterial population to increasing imipenem concentrations through 30 exposure cycles. Cell morphology was observed using a 3D laser scanning confocal microscope (LSCM) and transmission electron microscope (TEM). Results showed that the exposure resulted in significant morphological changes in E. coli (S) cells, while minor changes were seen in E. coli (R) cells. The rod-shaped E. coli (S) gradually transformed into round shapes. Further, the exposed E. coli (S) cells' surface area-to-volume ratio (SA/V) was also significantly different from the control, which is non-exposed E. coli (S). Then, the exposed E. coli (S) cells were re-grown in antibiotic-free environment for 100 growth cycles to determine if the changes in cells were reversible. The results showed that their cell morphology remained round, showing that the cell morphology was not reversible. The morphological response of these cells to imipenem can assist in understanding the resistance mechanism in the context of diagnostics and antibacterial therapies.

Machine Learning Assisted MALDI Mass Spectrometry for Rapid Antimicrobial Resistance Prediction in Clinicals

Antimicrobial susceptibility testing (AST) plays a critical role in assessing the resistance of individual microbial isolates and determining appropriate antimicrobial therapeutics in a timely manner. However, conventional AST normally takes up to 72 h for obtaining the results. In healthcare facilities, the global distribution of vancomycin-resistant Enterococcus fecium (VRE) infections underscores the importance of rapidly determining VRE isolates. Here, we developed an integrated antimicrobial resistance (AMR) screening strategy by combining matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) with machine learning to rapidly predict VRE from clinical samples. Over 400 VRE and vancomycin-susceptible E. faecium (VSE) isolates were analyzed using MALDI-MS at different culture times, and a comprehensive dataset comprising 2388 mass spectra was generated. Algorithms including the support vector machine (SVM), SVM with L1-norm, logistic regression, and multilayer perceptron (MLP) were utilized to train the classification model. Validation on a panel of clinical samples (external patients) resulted in a prediction accuracy of 78.07%, 80.26%, 78.95%, and 80.54% for each algorithm, respectively, all with an AUROC above 0.80. Furthermore, a total of 33 mass regions were recognized as influential features and elucidated, contributing to the differences between VRE and VSE through the Shapley value and accuracy, while tandem mass spectrometry was employed to identify the specific peaks among them. Certain ribosomal proteins, such as A0A133N352 and R2Q455, were tentatively identified. Overall, the integration of machine learning with MALDI-MS has enabled the rapid determination of bacterial antibiotic resistance, greatly expediting the usage of appropriate antibiotics.

Precision medicine in practice: unravelling the prevalence and antibiograms of urine cultures for informed decision making in federal tertiary care- a guide to empirical antibiotics therapy

Background and Objectives

Urinary tract infections (UTIs), one of the most prevalent bacterial infections, are facing limited treatment options due to escalating concern of antibiotic resistance. Urine cultures significantly help in identification of etiological agents responsible for these infections. Assessment of antibiotic susceptibility patterns of these bacteria aids in tackling the emerging concern of antibiotic resistance and establishment of empirical therapy guidelines. Our aim was to determine various agents responsible for urinary tract infections and to assess their antibiotic susceptibility patterns.

Materials and Methods

This cross-sectional study was performed over a period of six months from January 2023 to July 2023 in Department of Microbiology of Pakistan Institute of Medical Sciences (PIMS).

Results

Out of 2957 positive samples, Gram negative bacteria were the most prevalent in 1939 (65.6%) samples followed by Gram positive bacteria in 418 (14.1%) and Candida spp. in 269 (9.1%) samples. In gram negative bacteria, Escherichia coli (E. coli) was the most prevalent bacteria isolated from 1070 samples (55.2%) followed by Klebsiella pneumoniae in 397 samples (20.5%). In Gram positive bacteria, Enterococcus spp. was the most common bacteria in 213 samples (51%) followed by Staphylococcus aureus in 120 samples (28.7%). Amikacin was the most sensitive drug (91%) for Gram negative bacteria. Gram positive bacteria were most susceptible to linezolid (97%-100%).

Conclusion

The generation of a hospital tailored antibiogram is essential for the effective management of infections and countering antibiotic resistance. By adopting antimicrobial stewardship strategies by deeper understanding of sensitivity patterns, we can effectively combat antibiotic resistance.

Rapid and Simple Morphological Assay for Determination of Susceptibility/Resistance to Combined Ciprofloxacin and Ampicillin, Independently, in Escherichia coli

Current antibiograms cannot discern the particular effect of a specific antibiotic when the bacteria are incubated with a mixture of antibiotics. To prove that this task is achievable, Escherichia coli strains were treated with ciprofloxacin for 45 min, immobilized on a slide and stained with SYBR Gold. In susceptible strains, the nucleoid relative surface started to decrease near the MIC, being progressively condensed as the dose increased. The shrinkage level correlated with the DNA fragmentation degree. Ciprofloxacin-resistant bacilli showed no change. Additionally, E. coli strains were incubated with ampicillin for 45 min and processed similarly. The ampicillin-susceptible strain revealed intercellular DNA fragments that increased with dose, unlike the resistant strain. Co-incubation with both antibiotics revealed that ampicillin did not modify the nucleoid condensation effect of ciprofloxacin, whereas the quinolone partially decreased the background of DNA fragments induced by ampicillin. Sixty clinical isolates, with different combinations of susceptibility-resistance to each antibiotic, were co-incubated with the EUCAST breakpoints of susceptibility of ciprofloxacin and ampicillin. The morphological assay correctly categorized all the strains for each antibiotic in 60 min, demonstrating the feasible independent evaluation of a mixture of quinolone and beta-lactam. The rapid phenotypic assay may shorten the incubation times and necessary microbial mass currently required for evaluation.

Unlocking the therapeutic potential of Nigella sativa extract: phytochemical analysis and revealing antimicrobial and antioxidant marvels

The growing global threat of antimicrobial resistance endangers both human and animal life, necessitating the urgent discovery of novel antimicrobial solutions. Medicinal plants hold promise as sources of potential antimicrobial compounds. In this study, we investigated the phytochemical constituents and microbicidal capabilities of the ethanolic extract from Nigella sativa (black seed). Gas chromatography analysis (GC) identified 11 compounds, among them thymoquinone, and thymol, contributing to antimicrobial and antioxidant properties. Antimicrobial assays demonstrated notable inhibition zones against broad spectra of bacteria, including Pseudomonas aeruginosa, Escherichia coli, Salmonella typhi, Staphylococcus aureus, Enterobacter, and Bacillus subtilis, along with potent antifungal activity against Aspergillus niger, Penicillium, and Candida albicans. Notably, when combined with antibiotics, the extract displayed exceptional synergistic antimicrobial efficacy. The black seed extract demonstrated membrane-damaging activity and disrupted virulence factors that protect microbes from antimicrobial agents, including the formation of bacterial biofilm and protease secretion. Thymoquinone, the primary active constituent of the extract, exhibited similar antimicrobial and ant virulence properties. In silico analysis targeting key regulators of quorum sensing and biofilm formation in P. aeruginosa, such as RhlG, LasR, and PqsR, showed a remarkable affinity of thymol and thymoquinone for these targets. Moreover, the N. sativa extract exhibited dose-dependent cytotoxicity against both the promastigote and amastigote forms of Leishmania tropica parasites, hinting at potential antiparasitic activity. In addition to its antimicrobial properties, the extract displayed potential antioxidant activity at a concentration of 400 μg/mL.

Progressive ulcerative keratitis in dogs in the United Kingdom: Microbial isolates, antimicrobial sensitivity, and resistance patterns

OBJECTIVES

The objective of the study was to identify bacterial pathogens and their antimicrobial sensitivity profile associated with cases of canine progressive ulcerative keratitis.

MATERIALS AND METHODS

Analysis of microbial culture and sensitivity results from dogs with progressive ulcerative keratitis presenting to a UK referral practice between December 2018 and August 2020.

RESULTS

Positive bacterial cultures were obtained from 80/148 (54%) of the canine ulcers sampled with 99 bacterial isolates cultured. Streptococcus canis (n = 29), Pseudomonas aeruginosa (n = 19), and Staphylococcus pseudintermedius (n = 16) were the most common isolates. Pseudomonas aeruginosa was more likely to be isolated whether the ulcer was clinically malacic at the time of sampling (OR = 10.1, p < .001). Ulcers treated prior to culture with fusidic acid were 7.6 times more likely to be positive than those treated with any other antimicrobial(s). Bacterial isolates demonstrated resistance against neomycin (85%), fusidic acid (78%), and tetracycline (68%). Conversely, isolates were most likely to be sensitive to gentamicin (88%), ofloxacin (77%), ciprofloxacin (73%), and chloramphenicol (64%). Antimicrobial combinations of chloramphenicol or gentamicin with a fluoroquinolone (ofloxacin or ciprofloxacin) or chloramphenicol combined with gentamicin were the most effective on in vitro analysis (over 90% susceptibility of all isolates).

CONCLUSION

The most common bacterial species associated with canine progressive ulcerative keratitis in a UK referral population were S. canis, P. aeruginosa, and S. pseudintermedius. Combination antimicrobial therapy is recommended pending culture and sensitivity results given the varied antimicrobial susceptibility profiles and significant bacterial in vitro resistance to antimicrobial monotherapy.

TGC-ARG: Anticipating Antibiotic Resistance via Transformer-Based Modeling and Contrastive Learning

In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein sequences as input, utilizes SCRATCH-1D for protein secondary structure prediction, and employs feature extraction techniques to derive distinctive features from both sequence and structural data. Subsequently, a Siamese network is employed to foster a contrastive learning environment, enhancing the model's ability to effectively represent the data. Finally, a multi-layer perceptron (MLP) integrates and processes sequence embeddings alongside predicted secondary structure embeddings to forecast ARG presence. To evaluate our approach, we curated a pioneering open dataset termed ARSS (Antibiotic Resistance Sequence Statistics). Comprehensive comparative experiments demonstrate that our method surpasses current state-of-the-art methodologies. Additionally, through detailed case studies, we illustrate the efficacy of our approach in predicting potential ARGs.

Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of Salmonella Typhimurium

Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Current susceptibility testing approaches limit our ability to rapidly distinguish between antimicrobial-susceptible and -resistant organisms. Salmonella Typhimurium (S. Typhimurium) is an enteric pathogen responsible for severe gastrointestinal illness and invasive disease. Despite widespread resistance, ciprofloxacin remains a common treatment for Salmonella infections, particularly in lower-resource settings, where the drug is given empirically. Here, we exploit high-content imaging to generate deep phenotyping of S. Typhimurium isolates longitudinally exposed to increasing concentrations of ciprofloxacin. We apply machine learning algorithms to the imaging data and demonstrate that individual isolates display distinct growth and morphological characteristics that cluster by time point and susceptibility to ciprofloxacin, which occur independently of ciprofloxacin exposure. Using a further set of S. Typhimurium clinical isolates, we find that machine learning classifiers can accurately predict ciprofloxacin susceptibility without exposure to it or any prior knowledge of resistance phenotype. These results demonstrate the principle of using high-content imaging with machine learning algorithms to predict drug susceptibility of clinical bacterial isolates. This technique may be an important tool in understanding the morphological impact of antimicrobials on the bacterial cell to identify drugs with new modes of action.

A comparison of antimicrobial regimen outcomes and antibiogram development in microbial keratitis: a prospective cohort study in Alexandria, Egypt

INTRODUCTION

Antimicrobial resistance in microbial keratitis has not been previously explored in Alexandria. We aim to recommend effective therapies through identification of etiological agents, determination of antimicrobial susceptibilities, and comparing outcomes of empiric topical antimicrobials.

METHODS

In this 2022 prospective cohort conducted in Alexandria Main University Hospital cornea clinic, antimicrobial susceptibilities of isolated microorganisms from corneal scrapings were detected and antibiograms were developed. Bacterial (BK), fungal (FK), or mixed fungal/bacterial keratitis (MFBK) patients on empiric regimens were compared for ulcer healing, time-to-epithelialization, best-corrected visual acuity, interventions, and complications.

RESULTS

The prevalent microorganisms in 93 positive-cultures were coagulase-negative staphylococci (CoNS, 30.1%), Pseudomonas aeruginosa (14%), and Aspergillus spp. (12.9%). CoNS were susceptible to vancomycin (VAN, 100%) and moxifloxacin (MOX, 90.9%). Gram-negative bacteria showed more susceptibility to gatifloxacin (90.9%) than MOX (57.1%), and to gentamicin (GEN, 44.4%) than ceftazidime (CAZ, 11.8%). Methicillin-resistance reached 23.9% among Gram-positive bacteria. Fungi exhibited 10% resistance to voriconazole (VRC). Percentages of healed ulcers in 49 BK patients using GEN + VAN, CAZ + VAN and MOX were 85.7%, 44.4%, and 64.5%, respectively (p = 0.259). Their median time-to-epithelialization reached 21, 30, and 30 days, respectively (log-rank p = 0.020). In 51 FK patients, more ulcers (88.9%) healed with natamycin (NT) + VRC combination compared to VRC (39.1%) or NT (52.6%) (p = 0.036). Their median time-to-epithelialization was 65, 60, and 22 days, respectively (log-rank p < 0.001). The VRC group required more interventions (60.9%) than NT + VRC-treated group (11.1%) (p = 0.018). In 23 MFBK patients, none healed using NT + CAZ + VAN, while 50% healed using VRC + CAZ + VAN (p = 0.052). Regimens had comparable visual outcomes and complications.

CONCLUSION

Based on the higher detected susceptibility, we recommend empiric MOX in suspected Gram-positive BK, gatifloxacin in Gram-negative BK, and GEN + VAN in severe BK. Due to better outcomes, we recommend NT + VRC in severe FK.

TRIAL REGISTRATION

ClinicalTrials.gov identifier, NCT05655689. Registered December 19, 2022- Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT05655689?cond=NCT05655689.&draw=2&rank=1.

Evaluation of Automated Disk Diffusion Antimicrobial Susceptibility Testing Using Radian® In-Line Carousel

Antimicrobial susceptibility testing (AST) by disk diffusion provides an accurate image of bacterial growth, enabling the detection of culture purity, heterogeneous growth, and antibiotic interactions. However, this manual method is time-consuming and visual interpretation is prone to errors. To overcome these disadvantages, the Radian® In-Line Carousel (Copan, Brescia, Italy) was launched, which is a WASPLab® module dedicated to full automation of (pre)-analytical steps as well as interpretation of disk diffusion AST. However, until now, no evaluation of Radian® against manual disk diffusion has been performed. We assessed the categorical agreement (CA) between standardized disk diffusion (reference method) and Radian® using EUCAST 2021 breakpoints. We tested 135 non-duplicate strains, selected from the National EUCAST challenge panel, clinical strains, and external quality controls. The strains included Enterobacterales (n = 63), Enterococcus faecalis (n = 3), Enterococcus faecium (n = 10), Pseudomonas aeruginosa (n = 16), Staphylococcus aureus (n = 19), coagulase-negative staphylococci (n = 4), and Streptococcus spp. (n = 20). Furthermore, we explored antibiotic disk thermolability in the WASP Radian® carousel by testing 10 ATCC® strains up to 7 days. The observed CA was 95.3%, 96.3%, 93.8%, 97.3% and 98.0% for Enterobacterales, Enterococcus spp., P. aeruginosa, Staphylococcus spp. and Streptococcus spp., respectively, resulting in an acceptable overall CA for all groups. (Very) major error rates were ≤ 5% for all antibiotics. Antibiotic disk thermostability was confirmed up to 4 days in the WASP Radian® In-Line Carousel. The Radian® In-Line Carousel provides a fully automated solution for accurate disk diffusion AST, reducing workload and improving standardization and traceability. In addition, our study demonstrated the thermostability of antibiotic disks up to 4 days in the WASP Radian® In-Line Carousel.

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.

Bullet-related bacterial wound infections among injured personnel at emergency site hospitals in Bahir Dar: prevalence, antimicrobial susceptibility and associated factors

BACKGROUND

Bullet-related bacterial wound infection can be caused by high-velocity bullets and shrapnel injuries. In Ethiopia, significant injuries were reported that may cause severe wound infections, persistent systemic infections and may lead to amputation and mortality. The magnitude, antimicrobial susceptibility profiles, and factors associated with bacterial wound infections among patients with bullet-related injuries are not yet studied particularly at health facilities in Bahir Dar, Northwest Ethiopia. Therefore, this study was aimed to determine the prevalence, bacterial profiles, antimicrobial susceptibility profiles, and factors associated with bacterial infections among patients with bullet-related injuries at referral health facilities in Bahir Dar, Northwest Ethiopia.

METHODS

A Hospital-based cross-sectional study was conducted among patients with bullet-related injuries at three referral health facilities in Bahir Dar from May 25 to July 27, 2022. A total of 384 patients with bullet-related injuries were included in the study. Sociodemographic and clinical data were collected using a structured questionnaire. Wound swabs were collected aseptically and cultured on Blood and MacConkey agar following bacteriological standards. Biochemical tests were performed to differentiate bacteria for positive cultivation and antimicrobial susceptibility profiles of the isolates were done on Muller Hinton agar using the Kirby-Bauer disk diffusion technique according to the 2021 Clinical Laboratory Standard Institute (CLSI) guideline. The data were entered using Epi-Info version 7.3 and analyzed using SPSS version 25. Descriptive data were presented using frequency, percentages, figures, and charts. Logistic regression was carried out to identify factors associated with bacterial wound infections. P-value < 0.05 was considered statistically significant.

RESULTS

The prevalence of bullet-related bacterial wound infection among three referral hospitals in Bahir Dar city was 54.7%. The most commonly isolated Gram-negative organism was Klebsiella spps 49 (23.3%) while among Gram-positive organism, Staphylococcus aureus 58 (27.6%) and coagulase-negative staphylococci (CONS) 18 (8.6%). Contamination, hospitalization and smoking habit were significantly associated with the presence of bullet-related bacterial wound infections. Over 97% multidrug resistant (MDR) bacterial isolates were identified and of theses, E. coli, Proteus species, Citrobactor, and Staphylococcus aureus were highly drug resistant.

CONCLUSION

Increased prevalence of bullet-related bacterial wound infection was noticed in this study. S. aureus followed by Klebsiella species were most commonly isolated bacteria. High frequency of resistance to Ampicillin, Oxacillin, Cefepime, Ceftriaxone, Ceftazidime, Vancomycin, and Norfloxacin was observed. Therefore, proper handling of bullet injuries, prompt investigation of bacterial infections, monitoring of drug sensitivity patterns and antibiotic usage are critical.

Antibody functionalized capacitance sensor for label-free and real-time detection of bacteria and antibiotic susceptibility

The effective management of infectious diseases and the growing concern of antibiotic resistance necessitates accurate and targeted therapies, highlighting the importance of antibiotic susceptibility testing. This study aimed to develop a real-time impedimetric biosensor for identifying and monitoring bacterial growth and antibiotic susceptibility. The biosensor employed a gold 8-channel disk-shaped microelectrode array with specific antibodies as bio-recognition elements. This setup was allowed for the analysis of bacterial samples, including Staphylococcus aureus, Bacillus cereus, and Micrococcus luteus. These microorganisms were successfully cultured and detected within 1 h of incubation even with a minimal bacterial concentration of 10 CFU/ml. Overall, the developed biosensor array exhibits promising capabilities for monitoring S. aureus, B. cereus and M. luteus, showcasing an excellent linear response ranging from 10 to 104 CFU/ml with a detection limit of 0.95, 1.22 and 1.04 CFU/mL respectively. Moreover, real-time monitoring of antibiotic susceptibility was facilitated by changes in capacitance, which dropped when bacteria were exposed to antibiotic doses higher than their minimum inhibitory concentration (MIC), indicating suppressed bacterial growth. The capacitance measurements enabled determination of half-maximal cytotoxic concentrations (CC50) values for each bacteria-antibiotic pair. As a proof-of-concept application, the developed sensor array was employed as a sensing platform for the real time detection of bacteria in milk samples, which ensured the reliability of the sensor for in-field detection of foodborne pathogens and rapid antimicrobial susceptibility tests (ASTs).

Methods for screening and evaluation of antimicrobial activity: A review of protocols, advantages, and limitations

Infectious diseases pose a formidable global challenge, compounded by the emergence of antimicrobial resistance. Consequently, researchers are actively exploring novel antimicrobial compounds as potential solutions. This endeavor underscores the pivotal role of methods employed for screening and evaluating antimicrobial activity-a critical step in discovery and characterization of antimicrobial agents. While traditional techniques such as well-diffusion, disk-diffusion, and broth-dilution are commonly utilized in antimicrobial assays, they may encounter limitations concerning reproducibility and speed. Additionally, a diverse array of antimicrobial assays including cross-streaking, poisoned-food, co-culture, time-kill kinetics, resazurin assay, bioautography, etc., are routinely employed in antimicrobial evaluations. Advanced techniques such as flow-cytometry, impedance analysis, and bioluminescent technique may offer rapid and sensitive results, providing deeper insights into the impact of antimicrobials on cellular integrity. However, their higher cost and limited accessibility in certain laboratory settings may present challenges. This article provides a comprehensive overview of assays designed to characterize antimicrobial activity, elucidating their underlying principles, protocols, advantages, and limitations. The primary objective is to enhance understanding of the methodologies designed for evaluating antimicrobial agents in our relentless battle against infectious diseases. By selecting the appropriate antimicrobial testing method, researchers can discern suitable conditions and streamline the identification of effective antimicrobial agents.

Determination of minimum inhibitory concentrations using machine-learning-assisted agar dilution

Effective policy to address the global threat of antimicrobial resistance requires robust antimicrobial susceptibility data. Traditional methods for measuring minimum inhibitory concentration (MIC) are resource intensive, subject to human error, and require considerable infrastructure. AIgarMIC streamlines and standardizes MIC measurement and is especially valuable for large-scale surveillance activities. MICs were measured using agar dilution for n = 10 antibiotics against clinical Enterobacterales isolates (n = 1,086) obtained from a large tertiary hospital microbiology laboratory. Escherichia coli (n = 827, 76%) was the most common organism. Photographs of agar plates were divided into smaller images covering one inoculation site. A labeled data set of colony images was created and used to train a convolutional neural network to classify images based on whether a bacterial colony was present (first-step model). If growth was present, a second-step model determined whether colony morphology suggested antimicrobial growth inhibition. The ability of the AI to determine MIC was then compared with standard visual determination. The first-step model classified bacterial growth as present/absent with 94.3% accuracy. The second-step model classified colonies as "inhibited" or "good growth" with 88.6% accuracy. For the determination of MIC, the rate of essential agreement was 98.9% (644/651), with a bias of -7.8%, compared with manual annotation. AIgarMIC uses artificial intelligence to automate endpoint assessments for agar dilution and potentially increases throughput without bespoke equipment. AIgarMIC reduces laboratory barriers to generating high-quality MIC data that can be used for large-scale surveillance programs.

IMPORTANCE

This research uses modern artificial intelligence and machine-learning approaches to standardize and automate the interpretation of agar dilution minimum inhibitory concentration testing. Artificial intelligence is currently of significant topical interest to researchers and clinicians. In our manuscript, we demonstrate a use-case in the microbiology laboratory and present validation data for the model's performance against manual interpretation.

Dual-channel fluorescent sensors based on chitosan-coated Mn-doped ZnS micromaterials to detect ampicillin

The global threat of antibiotic resistance has increased the importance of the detection of antibiotics. Conventional methods to detect antibiotics are time-consuming and require expensive specialized equipment. Here, we present a simple and rapid biosensor for detecting ampicillin, a commonly used antibiotic. Our method is based on the fluorescent properties of chitosan-coated Mn-doped ZnS micromaterials combined with the β-lactamase enzyme. The biosensors exhibited the highest sensitivity in a linear working range of 13.1-72.2 pM with a limit of detection of 8.24 pM in deionized water. In addition, due to the biological specificity of β-lactamase, the proposed sensors have demonstrated high selectivity over penicillin, tetracycline, and glucose through the enhancing and quenching effects at wavelengths of 510 nm and 614 nm, respectively. These proposed sensors also showed promising results when tested in various matrices, including tap water, bottled water, and milk. Our work reports for the first time the cost-effective (Mn:ZnS)Chitosan micromaterial was used for ampicillin detection. The results will facilitate the monitoring of antibiotics in clinical and environmental contexts.

Review of methods for detection and characterization of non-tuberculous mycobacteria in aquatic organisms

Mycobacteriosis is an emerging and often lethal disease of aquatic organisms caused by several non-tuberculous mycobacteria (NTM) species. Early diagnosis of mycobacteriosis in aquaculture and aquatic settings is critical; however, clinical diagnoses and laboratory detection are challenging, and the available literature is scarce. In an attempt to fill the gap, here we review the most relevant approaches to detect and characterize mycobacteria in clinical specimens of aquatic organisms. Emphasis is given to recent advances in molecular methods used to differentiate NTM species spanning from targeted gene sequencing to next-generation sequencing. Further, given that there are major gaps in our understanding of the prevalence of the different NTM species, partially because of their distinct requirements for in vitro growth, we also reviewed the most relevant NTM species reported to cause disease in aquatic organisms and their specific in vitro growth conditions. We also highlight that traditional bacterial culture continues to be relevant for NTM identification, particularly in non-automated laboratories. However, for NTM species discrimination, a high level of accuracy can be achieved with MALDI-TOF MS and molecular approaches, especially targeted gene sequencing applied from clinical specimens or from pure NTM isolates.

Discovery of antibiotics that selectively kill metabolically dormant bacteria

There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.

Journey of technological advancements in the detection of antimicrobial resistance

Increased uses rather an extensive misuse of antibiotics due to easy availability and easy access have resulted in antibiotic resistance as a global crisis. The speed of discovery of new antibiotics has slowed down recently. Therefore, there is a need to reduce the rate of increase in resistance against the presently available antibiotics, or else many infections may be left untreatable or difficult to be treated due to the high prevalence of resistance. The judicious use of broad-spectrum antibiotics can control the increase in resistance profile. Various techniques are presently being used for the detection of antibiotic resistance. Conventional phenotypic methods are preferred that are highly reliable but are much more time-consuming. The patients cannot spare more time as the infection keeps increasing. The results with genotypic methods are obtained within 24 h as compared to phenotypic methods. Hence, recent molecular methods like qPCR can be used for detection. In this review, we present an overview of various methods useful for the detection of antibiotic resistance, with emphasis on their advantages and limitations. The review also emphasizes qPCR to be the most preferred method out of all because of various advantageous factors.

A Comprehensive Review on Biofilms in Otorhinolaryngology: Understanding the Pathogenesis, Diagnosis, and Treatment Strategies

Biofilms, structured communities of microorganisms encased in a self-produced matrix, pose significant challenges in otorhinolaryngology due to their role in chronic and recurrent infections affecting the ear, nose, and throat (ENT) region. This review provides an overview of biofilms, emphasizing their formation, pathogenesis, diagnosis, and treatment strategies in otorhinolaryngological disorders. Biofilms are pivotal in chronic rhinosinusitis (CRS), otitis media, laryngopharyngeal reflux (LPR), and tonsillitis, contributing to treatment resistance and disease recurrence. Current diagnostic techniques, including imaging modalities, microbiological cultures, and molecular techniques, are discussed, alongside emerging technologies. Treatment strategies, ranging from conventional antibiotics to alternative therapies, such as biofilm disruptors, phage therapy, and immunomodulation, are evaluated in terms of their efficacy and potential clinical applications. The review underscores the significance of understanding biofilms in otorhinolaryngology and highlights the need for tailored approaches to diagnosis and management to improve patient outcomes.

Characterization of Genomic, Physiological, and Probiotic Features of Lactiplantibacillus plantarum JS21 Strain Isolated from Traditional Fermented Jiangshui

This study aimed to understand the genetic and metabolic traits of a Lactiplantibacillus plantarum JS21 strain and its probiotic abilities through laboratory tests and computer analysis. L. plantarum JS21 was isolated from a traditional fermented food known as "Jiangshui" in Hanzhong city. In this research, the complete genetic makeup of JS21 was determined using Illumina and PacBio technologies. The JS21 genome consisted of a 3.423 Mb circular chromosome and five plasmids. It was found to contain 3023 protein-coding genes, 16 tRNA genes, 64 rRNA operons, 40 non-coding RNA genes, 264 pseudogenes, and six CRISPR array regions. The GC content of the genome was 44.53%. Additionally, the genome harbored three complete prophages. The evolutionary relationship and the genome collinearity of JS21 were compared with other L. plantarum strains. The resistance genes identified in JS21 were inherent. Enzyme genes involved in the Embden-Meyerhof-Parnas (EMP) and phosphoketolase (PK) pathways were detected, indicating potential for facultative heterofermentative pathways. JS21 possessed bacteriocins plnE/plnF genes and genes for polyketide and terpenoid assembly, possibly contributing to its antibacterial properties against Escherichia coli (ATCC 25922), Escherichia coli (K88), Staphylococcus aureus (CMCC 26003), and Listeria monocytogenes (CICC 21635). Furthermore, JS21 carried genes for Na+/H+ antiporters, F0F1 ATPase, and other stress resistance genes, which may account for its ability to withstand simulated conditions of the human gastrointestinal tract in vitro. The high hydrophobicity of its cell surface suggested the potential for intestinal colonization. Overall, L. plantarum JS21 exhibited probiotic traits as evidenced by laboratory experiments and computational analysis, suggesting its suitability as a dietary supplement.

Assessment of Genomic and Metabolic Characteristics of Cholesterol-Reducing and GABA Producer Limosilactobacillus fermentum AGA52 Isolated from Lactic Acid Fermented Shalgam Based on "In Silico" and "In Vitro" Approaches

This study aimed to characterize the genomic and metabolic properties of a novel Lb. fermentum strain AGA52 which was isolated from a lactic acid fermented beverage called "shalgam." The genome size of AGA52 was 2,001,184 bp, which is predicted to carry 2024 genes, including 50 tRNAs, 3 rRNAs, 3 ncRNAs, 15 CRISPR repeats, 14 CRISPR spacers, and 1 CRISPR array. The genome has a GC content of 51.82% including 95 predicted pseudogenes, 56 complete or partial transposases, and 2 intact prophages. The similarity of the clusters of orthologous groups (COG) was analyzed by comparison with the other Lb. fermentum strains. The detected resistome on the genome of AGA52 was found to be intrinsic originated. Besides, it has been determined that AGA52 has an obligate heterofermentative carbohydrate metabolism due to the absence of the 1-phosphofructokinase (pfK) enzyme. Furthermore, the strain is found to have a better antioxidant capacity and to be tolerant to gastrointestinal simulated conditions. It was also observed that the AGA52 has antimicrobial activity against Yersinia enterocolitica ATCC9610, Bacillus cereus ATCC33019, Salmonella enterica sv. Typhimurium, Escherichia coli O157:h7 ATCC43897, Listeria monocytogenes ATCC7644, Klebsiella pneumoniae ATCC13883, and Proteus vulgaris ATCC8427. Additionally, AGA52 exhibited 42.74 ± 4.82% adherence to HT29 cells. Cholesterol assimilation (33.9 ± 0.005%) and GABA production capacities were also confirmed by "in silico" and "in vitro." Overall, the investigation of genomic and metabolic features of the AGA52 revealed that is a potential psychobiotic and probiotic dietary supplement candidate and can bring functional benefits to the host.

Improved Methodology for Evaluating Nanomedicine Antibacterial Properties in Biological Fluids

Accurate assessment of nanomedicines' antibacterial properties is pivotal for their effective use in both in vitro and in vivo settings. Conventional antibacterial activity assessment methods, involving bacterial coculture with compounds on agar plates, may not fully suit nanomedicines due to their susceptibility to alterations in physicochemical properties induced by biological fluids. Furthermore, these biological fluids might even enhance the bacterial growth. This study introduces a novel, rigorous, and reproducible methodology for evaluating nanomedicine antibacterial properties using cell culture media (i.e., DMEM-FBS10%). To assess the antibacterial activity of the nanoparticles in cell culture media, superparamagnetic iron oxide nanoparticles (SPIONs) were chosen as the model nanomedicine due to their clinical significance. A comparative analysis between the traditional and our proposed methods yielded contrasting outcomes, shedding light on the significant impact of biological fluids on nanoparticle antibacterial activities. While the conventional approach suggested the antibacterial effectiveness of SPIONs against Staphylococcus aureus, our innovative method unveiled a substantial increase in bacterial growth in the presence of biological fluids. More specifically, we found a significant increase in bacterial growth when exposed to bare SPIONs at various concentrations, while the formation of a protein corona on SPION surfaces could markedly reduce the observed bacterial growth compared to the control group. These findings underscore the necessity for more refined evaluation techniques that can better replicate the in vivo environment when studying the nanomedicine's antibacterial capabilities.

Multipad agarose plate: a rapid and high-throughput approach for antibiotic susceptibility testing

We describe a phenotypic antibiotic susceptibility testing (AST) method that can provide an eightfold speed-up in turnaround time compared with the current clinical standard by leveraging advances in microscopy and single-cell imaging. A newly developed growth plate containing 96 agarose pads, termed the multipad agarose plate (MAP), can be assembled at low cost. Pads can be prepared with dilution series of antibiotics. Bacteria are seeded on the pads and automatically imaged using brightfield microscopy, with a fully automated segmentation pipeline quantifying microcolony formation and growth rate. Using a test set of nine antibiotics with very different targets, we demonstrate that accurate minimum inhibitory concentration (MIC) measurements can be performed based on the growth rate of microcolonies within 3 h of incubation with the antibiotic when started from exponential phase. Faster, reliable and high-throughput methods for AST, such as MAP, could improve patient care by expediting treatment initiation and alleviating the burden of antimicrobial resistance.

A Highly Integrated Lab-on-a-CMOS Platform for Real-Time Monitoring of E. Coli Growth Kinetics

Existing miniaturized and cost-effective solutions for bacterial growth monitoring usually require offline incubators with constant temperature to culture the bio-samples prior to measurement. Such a separated sample preparation and detection scheme requires extensive human intervention, risks contamination, and suffers from poor temporal resolution. To achieve integrated sample preparation and real-time bacterial growth monitoring, this article presents a lab-on-a-CMOS platform incorporates an optical sensor array, temperature sensor array, micro-heaters, and readout circuits. Escherichia coli's (E. coli) optimum growth temperature of 37 °C is achieved through a heat regulation system consisting of two micro-heaters and an on-chip temperature sensor array. A photodiode array with an in-pixel capacitive trans-impedance amplifier to reduce inter-pixel cross-coupling is designed to extract the optical information during bacterial growth. To balance the footprint, power consumption, and quantization speed, a 10 b column successive-approximation register (SAR)/single-slope (SS) dual-mode analog-to-digital converter (ADC) is designed to digitize the temperature and optical signals. Fabricated in a standard 0.18 um CMOS process, the proposed platform can regulate the sample temperature to 37 +/- 0.2/0.3 °C within 32 mins. Enabled by an on-chip heat regulation system and photodetectors, the prototype demonstrates a real-time monitoring of bacterial growth kinetics and antibiotic responses. Minute-level temporal resolution is achieved as this proposed platform is free of extensive and time-consuming human intervention. The proposed platform can be viably used in contamination sensitive applications such as antibiotic tests, stem cell cultures, and organ-on-chips.

Agreement of methods to assess antimicrobial susceptibility using Escherichia coli isolates as target models

Antimicrobial susceptibility tests (AST) conducted in vitro offer a range of methods to assess the antimicrobial resistance (AMR) of microorganisms. Escherichia coli, a widely distributed bacterium, is closely linked to the issue of AMR. In this way, the present study aimed to assess the agreement among different in vitro AST methods, including disk diffusion in agar, broth dilution, and agar dilution method. A total of 100 E. coli isolates were analyzed for their resistance levels against six antibiotics: amoxicillin, ceftiofur, ciprofloxacin, chloramphenicol, tetracycline, and sulfamethoxazole + trimethoprim, using the aforementioned AST methods. Standard breakpoint values were employed to classify isolates as resistant, intermediate, or susceptible, and comparisons among the AST methods were conducted by McNemar's test (P < .05). The obtained data demonstrated equivalence among the AST methods, highlighting the reliability of these standardized classical methodologies. This standardization aids in preventing the inappropriate use of antimicrobials and the dissemination of antimicrobial-resistant microorganisms.

Wastewater surveillance for bacterial targets: current challenges and future goals

Wastewater-based epidemiology (WBE) expanded rapidly in response to the COVID-19 pandemic. As the public health emergency has ended, researchers and practitioners are looking to shift the focus of existing wastewater surveillance programs to other targets, including bacteria. Bacterial targets may pose some unique challenges for WBE applications. To explore the current state of the field, the National Science Foundation-funded Research Coordination Network (RCN) on Wastewater Based Epidemiology for SARS-CoV-2 and Emerging Public Health Threats held a workshop in April 2023 to discuss the challenges and needs for wastewater bacterial surveillance. The targets and methods used in existing programs were diverse, with twelve different targets and nine different methods listed. Discussions during the workshop highlighted the challenges in adapting existing programs and identified research gaps in four key areas: choosing new targets, relating bacterial wastewater data to human disease incidence and prevalence, developing methods, and normalizing results. To help with these challenges and research gaps, the authors identified steps the larger community can take to improve bacteria wastewater surveillance. This includes developing data reporting standards and method optimization and validation for bacterial programs. Additionally, more work is needed to understand shedding patterns for potential bacterial targets to better relate wastewater data to human infections. Wastewater surveillance for bacteria can help provide insight into the underlying prevalence in communities, but much work is needed to establish these methods.IMPORTANCEWastewater surveillance was a useful tool to elucidate the burden and spread of SARS-CoV-2 during the pandemic. Public health officials and researchers are interested in expanding these surveillance programs to include bacterial targets, but many questions remain. The NSF-funded Research Coordination Network for Wastewater Surveillance of SARS-CoV-2 and Emerging Public Health Threats held a workshop to identify barriers and research gaps to implementing bacterial wastewater surveillance programs.

Biofilm Producing Methicillin-Resistant Staphylococcus aureus (MRSA) Infections in Humans: Clinical Implications and Management

Since its initial description in the 1960s, methicillin-resistant Staphylococcus aureus (MRSA) has developed multiple mechanisms for antimicrobial resistance and evading the immune system, including biofilm production. MRSA is now a widespread pathogen, causing a spectrum of infections ranging from superficial skin issues to severe conditions like osteoarticular infections and endocarditis, leading to high morbidity and mortality. Biofilm production is a key aspect of MRSA's ability to invade, spread, and resist antimicrobial treatments. Environmental factors, such as suboptimal antibiotics, pH, temperature, and tissue oxygen levels, enhance biofilm formation. Biofilms are intricate bacterial structures with dense organisms embedded in polysaccharides, promoting their resilience. The process involves stages of attachment, expansion, maturation, and eventually disassembly or dispersion. MRSA's biofilm formation has a complex molecular foundation, involving genes like icaADBC, fnbA, fnbB, clfA, clfB, atl, agr, sarA, sarZ, sigB, sarX, psm, icaR, and srtA. Recognizing pivotal genes for biofilm formation has led to potential therapeutic strategies targeting elemental and enzymatic properties to combat MRSA biofilms. This review provides a practical approach for healthcare practitioners, addressing biofilm pathogenesis, disease spectrum, and management guidelines, including advances in treatment. Effective management involves appropriate antimicrobial therapy, surgical interventions, foreign body removal, and robust infection control practices to curtail spread within healthcare environments.

Distinguishing Gram-positive and Gram-negative bloodstream infections through leukocytes, C-reactive protein, procalcitonin, and D-Dimer: an empirical antibiotic guidance

This retrospective study aimed to compare the difference of the levels of white blood cells (WBC), C-reactive protein (CRP), procalcitonin, and D-Dimer in the bloodstream infection (BSI) patients, and their values in distinguishing bacterial categories. A total of 847 BSI patients were analysed and divided into Gram-positive BSI (GP-BSI) and Gram-negative BSI (GN-BSI) groups. Most frequently isolated pathogens in GP-BSI were Staphylococcus epidermidis (35.75%), followed by Staphylococcus hominis (18.33%), and Streptococcus haemolyticus (10.16%), while in GN-BSI, Escherichia coli (30.07%), Klebsiella pneumoniae (23.98%), and Acinetobacter baumannii (13.18%) were the most common. The predictive value was evaluated based on 3 years of patient data, which showed an area under the curve (AUC) of 0.828. It was further validated using 2 years of data, which yielded an AUC of 0.925. Significant differences existed in the procalcitonin, D-Dimer, and CRP levels between GN-BSI and GP-BSI. The current results provide a more effective strategy for early differential diagnosis in bacterial categorization of BSI when combining WBC, CRP, procalcitonin, and D-Dimer measurements.

Molecular antimicrobial susceptibility testing in sepsis

Rapidly detecting and identifying pathogens is crucial for appropriate antimicrobial therapy in patients with sepsis. Conventional diagnostic methods have been a great asset to medicine, though they are time consuming and labor intensive. This work will enable healthcare professionals to understand the bacterial community better and enhance their diagnostic capacity by using novel molecular methods that make obtaining quicker, more precise results possible. The authors discuss and critically assess the merits and drawbacks of molecular testing and the added value of these tests, including the shift turnaround time, the implication for clinicians' decisions, gaps in knowledge, future research directions and novel insights or innovations. The field of antimicrobial molecular testing has seen several novel insights and innovations to improve the diagnosis and management of infectious diseases.

Bacterial heavy metal resistance related to environmental conditions

Contaminated water bodies such as rivers provide reservoirs for bacterial resistance. This field study tested the water quality and the bacterial resistance to heavy metals of Qishan River water pollution. Wastewater discharged to environmental surface waters is a major pathway of heavy metals and heavy metal-resistant bacteria. Contaminated water bodies such as rivers provide reservoirs for bacterial resistance. This field study tested the water quality and bacterial resistance to heavy metals of Qishan River water pollution. Guided by our research hypothesis that an overall increase in downstream heavy metal resistance levels was following an increase in human settlements were eight sites sampled along the Qishan River. These were situated upstream and downstream to the confluence of the Qishan River with the Kaoping River. In the laboratory bacterial heavy metal resistance was bio-assayed by disk diffusion and micro-dilution with six widely used heavy metals. The comparison of bacterial resistance was among Qishan River upstream sites (sites 1-6) and downstream sites (sites 7-9). Multi-drug-resistant bacteria and co-resistance against heavy metals and antibacterials appeared at site 8. This research discusses the correlation between environmental factors, and antibacterial and heavy metal resistance. The results provide stakeholders and authorities responsible for environmental pollution with a reference for risk assessment and management of bacterial resistance.

Cost-Efficient Micro-Well Array-Based Colorimetric Antibiotic Susceptibility Testing (MacAST) for Bacteria from Culture or Community

Rapid and cost-efficient antibiotic susceptibility testing (AST) is key to timely prescription-oriented diagnosis and precision treatment. However, current AST methods have limitations in throughput or cost effectiveness, and are impractical for microbial communities. Here, we developed a high-throughput micro-well array-based colorimetric AST (macAST) system equipped with a self-developed smartphone application that could efficiently test sixteen combinations of bacteria strains and antibiotics, achieving comparable AST results based on resazurin metabolism assay. For community samples, we integrated immunomagnetic separation into the macAST (imacAST) system to specifically enrich the target cells before testing, which shortened bacterial isolation time from days to only 45 min and achieved AST of the target bacteria with a low concentration (~103 CFU/mL). This proof-of-concept study developed a high-throughput AST system with an at least ten-fold reduction in cost compared with a system equipped with a microscope or Raman spectrum. Based on colorimetric readout, the antimicrobial susceptibility of the bacteria from microbial communities can be delivered within 6 h, compared to days being required based on standard procedures, bypassing the need for precise instrumentation in therapy to combat bacterial antibiotic resistance in resource-limited settings.

Potential Causes of Spread of Antimicrobial Resistance and Preventive Measures in One Health Perspective-A Review

Antimicrobial resistance, referring to microorganisms' capability to subsist and proliferate even when there are antimicrobials is a foremost threat to public health globally. The appearance of antimicrobial resistance can be ascribed to anthropological, animal, and environmental factors. Human-related causes include antimicrobial overuse and misuse in medicine, antibiotic-containing cosmetics and biocides utilization, and inadequate sanitation and hygiene in public settings. Prophylactic and therapeutic antimicrobial misuse and overuse, using antimicrobials as feed additives, microbes resistant to antibiotics and resistance genes in animal excreta, and antimicrobial residue found in animal-origin food and excreta are animals related contributive factors for the antibiotic resistance emergence and spread. Environmental factors including naturally existing resistance genes, improper disposal of unused antimicrobials, contamination from waste in public settings, animal farms, and pharmaceutical industries, and the use of agricultural and sanitation chemicals facilitatet its emergence and spread. Wildlife has a plausible role in the antimicrobial resistance spread. Adopting a one-health approach involving using antimicrobials properly in animals and humans, improving sanitation in public spaces and farms, and implementing coordinated governmental regulations is crucial for combating antimicrobial resistance. Collaborative and cooperative involvement of stakeholders in public, veterinary and ecological health sectors is foremost to circumvent the problem effectively.

Evaluating the Sensitivity of Different Molecular Techniques for Detecting Mycobacterium tuberculosis Complex in Patients with Pulmonary Infection

This study aimed to evaluate the accuracy of detecting drug-resistant Mycobacterium tuberculosis complex (MTBC)-specific DNA in sputum specimens from 48 patients diagnosed with pulmonary tuberculosis. The presence of MTBC DNA in the specimens was validated using the GeneXpert MTB/RIF system and compared with a specific PCR assay targeting the IS6110 and the mtp40 gene sequence fragments. Additionally, the results obtained by multiplex PCR assays to detect the most frequently encountered rifampin, isoniazid, and ethambutol resistance-conferring mutations were matched with those obtained by GeneXpert and phenotypic culture-based drug susceptibility tests. Of the 48 sputum samples, 25 were positive for MTBC using the GeneXpert MTB/RIF test. Nevertheless, the IS6110 and mtp40 single-step PCR revealed the IS6110 in 27 of the 48 sputum samples, while the mtp40 gene fragment was found in only 17 of them. Furthermore, multiplex PCR assays detected drug-resistant conferring mutations in 21 (77.8%) of the 27 samples with confirmed MTBC DNA, 10 of which contained single drug-resistant conferring mutations towards ethambutol and two towards rifampin, and the remaining nine contained double-resistant mutations for ethambutol and rifampin. In contrast, only five sputum specimens (18.5%) contained drug-resistant MTBC isolates, and two contained mono-drug-resistant MTBC species toward ethambutol and rifampin, respectively, and the remaining three were designated as multi-drug resistant toward both drugs using GeneXpert and phenotypic culture-based drug susceptibility tests. Such discrepancies in the results emphasize the need to develop novel molecular tests that associate with phenotypic non-DNA-based assays to improve the detection of drug-resistant isolates in clinical specimens in future studies.