Identification Of Pathogens Research Articles

Plant Disease: A Growing Threat to Global Food Security

The escalating global population has led to an increased demand for both quantity and quality in food production. Throughout history, plant diseases have posed significant threats to agricultural output by causing substantial food losses annually while also compromising product quality. Accurate identification of pathogens, clarifying the pathogenic mechanism of pathogens, and understanding the interaction between pathogens and hosts are important for the control of plant diseases. This Special Issue, “Research Progress on Pathogenicity of Fungi in Crops”, belongs to the section “Pest and Disease Management” of Agronomy. It contains research papers on the identification and phylogeny of fungal pathogens, the molecular genetics of plant fungal pathogens, the molecular mechanisms of fungal pathogenicity, and the molecular basis of the interaction between fungi and crops. These studies encapsulate efforts to understand disease systems within current genomics, transcriptomics, proteomics, and metabolomics studies, highlighting research findings that could be future targets for crop disease and pest control. The studies presented in this Special Issue promote the progress of fungal pathogenicity research in crops and provide a scientific basis for future disease control, which is of great significance for sustainable agricultural development and global food security.

Intraoperative Polymerase Chain Reaction from Cardiac Valve Tissue Is Beneficial for Guiding Further Therapy in Patients with Infective Endocarditis.

Background: Infectious endocarditis (IE) remains a critical condition despite all the medical advances in recent decades. Reliable pathogen identification is indispensable for precise therapy. The aim of this study was to evaluate the diagnostic and therapeutic benefit of additional polymerase chain reaction (PCR) in comparison with microbiological culture alone based on intraoperative tissue sampling for patients operated on due to IE. Methods: A total of 224 patients diagnosed with acute or subacute IE were analyzed. Intraoperatively resected infectious tissue was analyzed using both PCR and microbiological culture. Subsequently, the results of the detection of bacteria obtained based on intraoperative measurements from tissue via culture and PCR were compared with preoperative blood culture results. Furthermore, we evaluated the therapeutic impact of the culture and/or PCR results obtained from cardiac tissue. Results: The 224 patients were 63 ± 17 years old, and 64 (29%) were female. In total, 149 (67%) suffered from aortic valve endocarditis, 45 (45%) had mitral valve endocarditis, and 39 (18%) were afflicted with double-valve endocarditis. Prosthetic valve endocarditis was present in 70 (31%) patients. Pathogens were detected in 70% of the cases analyzed via PCR using cardiac valve tissue and in 25% of those analyzed via a culture of cardiac valve tissue; this figure was only 64% for preoperative blood culture. Overall, a pathogen was identified in 197 patients (88%), leading to antibiotic therapy. Targeted antibiotic therapy, based on the PCR results, was carried out in 37 cases and was conducted based on a culture from cardiac valve tissue in three cases. Finally, in 12% of patients, the causative pathogen remained unclear. Conclusions: For patients suffering endocarditis, PCR analysis is indispensable and superior to preoperative blood culture and intraoperative culture in detecting bacteria. Based on PCR testing, antibiotic therapy can be individually adjusted. The high precision of pathogen identification may lead to a significant reduction in IE-associated morbidity and mortality.

Hook-Like DNAzyme-Activated Autocatalytic Biosensor for the Universal Detection of Pathogenic Bacteria.

DNAzyme-based assays have found extensive utility in pathogenic bacteria detection but often suffer from limited sensitivity and specificity. The integration of a signal amplification strategy could address this challenge, while the existing combination methods require extensive modification to accommodate various DNAzymes, limiting the wide-spectrum bacteria detection. We introduced a novel hook-like DNAzyme-activated autocatalytic nucleic acid circuit for universal pathogenic bacteria detection. The hook-like connector DNA was employed to seamlessly integrate the recognition element DNAzyme with the isothermal enzyme-free autocatalytic hybridization chain reaction and catalytic hairpin assembly for robust exponential signal amplification. This innovative autocatalytic circuit substantially amplifies the output signals from the DNAzyme recognition module, effectively overcoming DNAzyme's inherent sensitivity constraints in pathogen identification. The biosensor exhibits a strong linear response within a range of 1.5 × 103 to 3.7 × 107 CFU/mL, achieving a detection limit of 1.3 × 103 CFU/mL. Noted that the sensor's adaptability as a universal detection platform is established by simply modifying the hook-like connector module, enabling the detection of various pathogenic bacteria of considerable public health importance reported by the World Health Organization, including Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, and Salmonella typhimurium. Additionally, the specificity of DNAzyme in bacterial detection is markedly improved due to the signal amplification process of the autocatalytic circuit. This hook-like DNAzyme-activated autocatalytic platform presents a versatile, sensitive, and specific approach for pathogenic bacteria detection, promising to significantly expand the applications of DNAzyme in bacteria detection.

Revolutionizing infectious disease management in low-resource settings: The impact of rapid diagnostic technologies and portable devices

Revolutionizing infectious disease management in low-resource settings hinges on the integration of rapid diagnostic technologies and portable devices. These innovations offer transformative potential by providing timely, accurate, and cost-effective diagnosis, thereby enhancing patient outcomes and curbing the spread of infectious diseases. Rapid diagnostic technologies, including point-of-care tests and molecular diagnostics, enable swift identification of pathogens, allowing for immediate and appropriate treatment. This is particularly crucial in low-resource settings where laboratory infrastructure is often limited, and delays in diagnosis can lead to increased morbidity and mortality. Portable diagnostic devices, such as handheld PCR machines and mobile phone-based diagnostic tools, further extend the reach of healthcare services to remote and underserved areas. These devices are designed to be user-friendly, requiring minimal training for healthcare workers, and can operate in challenging environments without the need for extensive power supplies or refrigeration. The impact of these technologies is profound. By reducing the time to diagnosis, rapid diagnostic tests can decrease the misuse of antibiotics, helping to combat antibiotic resistance—a major global health threat. Additionally, early and accurate diagnosis facilitates better disease surveillance and outbreak management, enabling quicker public health responses. Cost-effective and scalable, these technologies also offer significant economic benefits, reducing the burden on healthcare systems and improving overall health equity. This review underscores the critical role of rapid diagnostic technologies and portable devices in revolutionizing infectious disease management in low-resource settings. Their implementation not only improves individual patient care but also strengthens public health infrastructure, ultimately contributing to global health security and the fight against infectious diseases. Keywords: Infection Disease, Diagnostic Technologies, Management.

Identification and Antimicrobial Susceptibility Patterns of Neisseria gonorrhoeae, Ureaplasma spp. and Mycoplasma spp. Isolated from Tribal Women

Sexually transmitted infections (STIs) are a major public health problem worldwide with significant social and economic implications. Effective control and prevention strategies necessitate a thorough understanding of the prevalence, isolation, and identification of STI pathogens. The present study aims to provide a comprehensive analysis of the isolation, identification, prevalence, and antimicrobial susceptibility pattern of STI pathogens based on culture method analysis. Endocervical /vaginal swab samples from female patients symptomatic for STI were cultured on different selective and differential media and pathogens were identified by colony morphology and biochemical tests. Antimicrobial Susceptibility Test (AST) of isolated and identified culture pathogen was performed by using Kirby-Bauer disc diffusion method. Among 209 endocervical/vaginal swab samples from symptomatic patients, 126 (60.28%) tested positive and 83 (39.71%) negative. Ureaplasma spp. (n = 100) was the most prevalent isolate, constituting 79.36% of culture-positive samples, followed by N. gonorrhoea (n = 99) at 78.57%, and Mycoplasma spp. (n = 41) at 32.54% individually and in combination. AST analysis revealed erythromycin (74%), ofloxacin (69%), and roxithromycin (64%) as the most resistant antibiotics for Ureaplasma spp. N. gonorrhoea showed the highest resistance to cefixime (78.79%), followed by ofloxacin (75.76%) and erythromycin (69.7%). Azithromycin and erythromycin exhibited 100% resistance against Mycoplasma spp. The study provides information on the prevalent bacterial pathogens involved in STIs among women in Anuppur and Shahdol districts, Madhya Pradesh. Understanding the diversity, distribution patterns and antibiotic sensitivity of these pathogens is crucial for developing targeted interventions and effective prevention strategies in such resource-limited areas.

Enhancing Pathogen Detection in Implant-Related Infections through Chemical Antibiofilm Strategies: A Comprehensive Review.

Implant-related infections (IRIs) represent a significant challenge to modern surgery. The occurrence of these infections is due to the ability of pathogens to aggregate and form biofilms, which presents a challenge to both the diagnosis and subsequent treatment of the infection. Biofilms provide pathogens with protection from the host immune response and antibiotics, making detection difficult and complicating both single-stage and two-stage revision procedures. This narrative review examines advanced chemical antibiofilm techniques with the aim of improving the detection and identification of pathogens in IRIs. The articles included in this review were selected from databases such as PubMed, Scopus, MDPI and SpringerLink, which focus on recent studies evaluating the efficacy and enhanced accuracy of microbiological sampling and culture following the use of chemical antibiofilm. Although promising results have been achieved with the successful application of some antibiofilm chemical pre-treatment methods, mainly in orthopedics and in cardiovascular surgery, further research is required to optimize and expand their routine use in the clinical setting. This is necessary to ensure their safety, efficacy and integration into diagnostic protocols. Future studies should focus on standardizing these techniques and evaluating their effectiveness in large-scale clinical trials. This review emphasizes the importance of interdisciplinary collaboration in developing reliable diagnostic tools and highlights the need for innovative approaches to improve outcomes for patients undergoing both single-stage and two-stage revision surgery for implant-related infections.

Occurrence, isolation, and identification of emergent pathogens of taxon Vibrio from the Danube river in Bratislava city, Slovakia

The occurrence of Vibrio cholerae, Vibrio metschnikovii and Vibrio fluvialis, important aquatic human pathogens of genus Vibrio, were determined in the surface water of the Danube River in Bratislava. The isolates were identified by biochemical tests. Biochemical characteristics of environmental isolates could be slightly different from reference and clinical strains which is reflected in dissimilarity rate, which was in our case not sufficient. A molecular method called amplified ribosomal DNA restriction analysis was used for confirmation of phenotypic identification. The species identity of 19 out of 22 isolates was confirmed, in the case of 3 misidentified ones, the identity of the genus did not change. The identity of all V. cholerae isolates was confirmed by PCR using species-specific gene called ompW. Also, main virulence factors of these isolates were determined. None of the isolates possessed all three virulence factors necessary for the induction of cholera.

Rapid Detection of Uropathogens Using an Integrated Multiplex Digital Nucleic Acid Detection Assay Powered by a Digital-to-Droplet Microfluidic Device.

The digital nucleic acid detection assay features the capability of absolute quantitation without the need for calibration, thereby facilitating the rapid identification of pathogens. Although several integrated digital nucleic acid detection techniques have been developed, there are still constraints in terms of automation and analysis throughput. To tackle these challenges, this study presents a digital-to-droplet microfluidic device comprising a digital microfluidics (DMF) module at the bottom and a droplet microfluidics module at the top. Following sample introduction, the extraction of nucleic acid and the dispensation of nucleic acid elution for mixing with the multiple amplification reagents are carried out in the DMF module. Subsequently, the reaction droplets are transported to the sample inlet of the droplet microfluidic module via a liquid outlet, and then droplet generation in four parallel units within the droplet microfluidics module is actuated by negative pressure generated by a syringe vacuum. The digital-to-droplet microfluidic device was employed to execute an integrated multiplex digital droplet nucleic acid detection assay (imDDNA) incorporating loop-mediated isothermal amplification (LAMP). This assay was specifically designed to enable simultaneous detection of four uropathogens, namely, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Enterococcus faecalis. The entire process of the imDDNA is completed within 75 min, with a detection range spanning 5 orders of magnitude (9.43 × 10-2.86 × 104 copies μL-1). The imDDNA was employed for the detection of batched clinical specimens, showing a consistency of 91.1% when compared with that of the conventional method. The imDDNA exhibits simplicity in operation and accuracy in quantification, thus offering potential advantages in achieving rapid pathogen detection.

Innovative approaches to microbial identification enhancing accuracy and speed in infectious disease diagnostics

The rapid and accurate identification of microbial pathogens is crucial for effective management of infectious diseases. Traditional methods, though reliable, are often time-consuming. Recent advancements in molecular diagnostics, mass spectrometry, and next-generation sequencing (NGS) have revolutionized the field by enhancing both the speed and accuracy of microbial identification. Polymerase chain reaction (PCR) and its derivatives, such as real-time PCR, enable rapid amplification of genetic material, significantly reducing diagnostic times. Techniques like nucleic acid sequence-based amplification (NASBA) and loop-mediated isothermal amplification (LAMP) further extend these capabilities to resource-limited settings by operating at constant temperatures. Mass spectrometry, particularly matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF), provides rapid and accurate microbial identification by analyzing protein profiles. This method has high throughput, is cost-effective, and offers the ability to detect antimicrobial resistance, significantly impacting clinical decision-making and patient management. NGS technologies offer comprehensive pathogen detection by sequencing entire microbial genomes. This approach is invaluable in diagnosing complex infections, tracking outbreaks, and identifying antibiotic resistance genes, though its high cost and complexity limit widespread use. The integration of these innovative technologies into clinical practice enhances the ability to diagnose and manage infectious diseases effectively. Molecular diagnostics, mass spectrometry, and NGS each bring unique strengths, from rapid and precise pathogen detection to comprehensive genetic analysis and resistance profiling. These advancements contribute to improved patient outcomes and more effective public health strategies. The continued development and implementation of these technologies are essential to address the evolving challenges in microbial diagnostics, paving the way for more precise and personalized approaches in infectious disease management.

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks.

Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.

Exploring Microorganisms Associated to Acute Febrile Illness and Severe Neurological Disorders of Unknown Origin: A Nanopore Metagenomics Approach.

Acute febrile illness (AFI) and severe neurological disorders (SNDs) often present diagnostic challenges due to their potential origins from a wide range of infectious agents. Nanopore metagenomics is emerging as a powerful tool for identifying the microorganisms potentially responsible for these undiagnosed clinical cases. In this study, we aim to shed light on the etiological agents underlying AFI and SND cases that conventional diagnostic methods have not been able to fully elucidate. Our approach involved analyzing samples from fourteen hospitalized patients using a comprehensive nanopore metagenomic approach. This process included RNA extraction and enrichment using the SMART-9N protocol, followed by nanopore sequencing. Subsequent steps involved quality control, host DNA/cDNA removal, de novo genome assembly, and taxonomic classification. Our findings in AFI cases revealed a spectrum of disease-associated microbes, including Escherichia coli, Streptococcus sp., Human Immunodeficiency Virus 1 (Subtype B), and Human Pegivirus. Similarly, SND cases revealed the presence of pathogens such as Escherichia coli, Clostridium sp., and Dengue virus type 2 (Genotype-II lineage). This study employed a metagenomic analysis method, demonstrating its efficiency and adaptability in pathogen identification. Our investigation successfully identified pathogens likely associated with AFI and SNDs, underscoring the feasibility of retrieving near-complete genomes from RNA viruses. These findings offer promising prospects for advancing our understanding and control of infectious diseases, by facilitating detailed genomic analysis which is critical for developing targeted interventions and therapeutic strategies.

Status of Cassava Witches' Broom Disease in the Philippines and Identification of Potential Pathogens by Metagenomic Analysis.

Cassava witches' broom disease (CWBD) is one of the most devastating diseases of cassava (Manihot esculenta Crantz), and it threatens global production of the crop. In 2017, a phytoplasma, Candidatus Phytoplasma luffae (Ca. P. luffae), was reported in the Philippines, and it has been considered as the causal agent, despite unknown etiology and transmission of CWBD. In this study, the nationwide occurrence of CWBD was assessed, and detection of CWBD's pathogen was attempted using polymerase chain reaction (PCR) and next-generation sequencing (NGS) techniques. The results showed that CWBD has spread and become severe, exhibiting symptoms such as small leaf proliferation, shortened internodes, and vascular necrosis. PCR analysis revealed a low phytoplasma detection rate, possibly due to low titer, uneven distribution, or absence in the CWBD-symptomatic cassava. In addition, NGS techniques confirm the PCR results, revealing the absence or extremely low phytoplasma read counts, but a surprisingly high abundance of fastidious and xylem-limited fungus, Ceratobasidium sp. in CWBD-symptomatic plants. These findings cast doubt over the involvement of phytoplasma in CWBD and instead highlight the potential association of Ceratobasidium sp., strongly supporting the recent findings in mainland Southeast Asia. Further investigations are needed to verify the etiology of CWBD and identify infection mechanisms of Ceratobasidium sp. to develop effective diagnostic and control methods for disease management.

Rapid Classification and Differentiation of Sepsis-Related Pathogens Using FT-IR Spectroscopy.

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes.

Portable loop-mediated isothermal amplification device with spectrometric detection for rapid pathogen identification

With the rise in extreme weather due to global warming, coupled with globalization facilitating the spread of infectious diseases, there's a pressing need for portable testing platforms offering simplicity, low cost, and remote transmission, particularly beneficial in resource-limited and non-urban areas. We have developed a portable device using loop-mediated isothermal amplification (LAMP) with spectrometric detection to identify Salmonella Typhimurium DNA. The device utilizes the LinkIt 7697 microcontroller and a microspectrometer to capture and transmit spectral signals in real-time, allowing for improved monitoring and analysis of the reaction progress. We built a hand-held box containing a microspectrometer, thermoelectric cooler, ultraviolet LED, disposable reaction tube, and homemade thermal module, all powered by rechargeable batteries. Additionally, we conducted thorough experiments to ensure temperature accuracy within 1 °C under thermal control, developed a heating module with a LinkIt 7697 IoT development board to heat the DNA mixture to the reaction temperature within 3 min, and integrated foam insulation and a 3D-printed frame to enhance the device's thermal stability. We successfully demonstrated the amplification of Salmonella Typhimurium DNA with an impressive sensitivity of 2.83 × 10−4 ng/μL. A remote webpage interface allows for monitoring the temperature and fluorescence during the LAMP process, improving usability. This portable LAMP device with real-time detection offers a cost-effective solution for detecting Salmonella Typhimurium in food products. Its unique design and capabilities make it a promising tool for ensuring food safety.

Development and evaluation of high-resolution melting assays for direct and simultaneous pathogen identification in bloodstream infections in pediatric oncology patients

Blood culture, the gold-standard method for identifying pathogens in bloodstream infections, is time-consuming and demonstrates low sensitivity. These drawbacks are related to high mortality, especially among pediatric oncology patients presenting febrile neutropenia episodes. Here we describe two novel High-Resolution Melting assays designed for pathogen detection in bloodstream infections. The assays were initially evaluated using five sepsis-associated pathogens. Both assays demonstrated 100 % specificity, detected as low as 100 fg of bacterial DNA, and exhibited reproducibility. Clinical isolates from blood cultures were 100 % identified by both assays. Moreover, blind and direct identification of blood samples from pediatric cancer patients demonstrated sensitivities of 61.5 % and 69.2 % for “Primer Set 1” and “Primer Set 2”, respectively. Our study highlights the potential of HRM-based assays as a rapid and efficient diagnostic approach for sepsis-related microorganisms. Further advancements could enhance their clinical utility for better management of febrile neutropenia episodes, especially in pediatric oncology patients.

Epidemiological characteristics and drug resistance of diarrheagenic Escherichia coli infection in diarrhea patients in Shanghai, 2016-2022

Objective: To understand the infection status, epidemiological characteristics and drug resistance of Diarrheagenic Escherichia coli (DEC) in Shanghai and provide evidence for the disease surveillance. Methods: The epidemiological data of diarrhea cases in Shanghai from 2016 to 2022 were collected from Shanghai Diarrhea Comprehensive Surveillance System, and stool samples were collected from the cases for DEC detection. The drug resistance data was obtained from Chinese Pathogen Identification Network. Statistical analysis was conducted by using χ2 and fisher test. Results: In 24 883 diarrhea cases detected during 2016-2022, the DEC positive rate was 9.13% (2 271/24 883), the single DEC positive rate was 8.83% (2 197/24 883) and the mixed DEC positive rate was 0.30% (74/24 883). The main type of DEC was Enterotoxigenic Escherichia coli (ETEC) [4.33% (1 077/24 883)]. The DEC positive rate was highest in people aged ≤5 years 18.48% (22/119). The annual peak of DEC positive rate was observed during July - September [5.91% (1 470/24 883)]. The DEC positive rate were 9.47% (554/5 847) and 9.02% (1 717/19 036) in urban area and in suburbs, respectively, Enteroaggregative Escherichia coli (EAEC) [3.98% (233/5 847)] and ETEC [4.56% (868/19 036)] were mainly detected. From 2016 to 2019, the DEC positive rate was 9.42% (1 821/19 330), while it was 8.10% (450/5 553) from 2020 to 2022, the main DEC types were ETEC (4.87%, 941/19 330) and EAEC (4.70%, 261/5 553). The multi-drug resistance rate was 40.21% (618/1 537). The top three antibiotics with high drug resistance rates were ampicillin [64.74% (995/1 537)], nalidixic acid [58.49% (899/1 537)] and tetracycline [45.09% (693/1 537)]. Conclusions: Compared with 2016- 2019, a decrease in DEC detection rate was observed during 2020-2022, and the main type of DEC detected shifted from ETEC to EAEC. The prevalence of multi-drug resistance was severe. Therefore, it is necessary to further strengthen the surveillance for DEC drug resistance and standardize the use of clinical antibiotics.

The role of BioFire Joint Infection Panel in diagnosing periprosthetic hip and knee joint infections in patients with unclear conventional microbiological results.

This study aimed to evaluate the BioFire Joint Infection (JI) Panel in cases of hip and knee periprosthetic joint infection (PJI) where conventional microbiology is unclear, and to assess its role as a complementary intraoperative diagnostic tool. Five groups representing common microbiological scenarios in hip and knee revision arthroplasty were selected from our arthroplasty registry, prospectively maintained PJI databases, and biobank: 1) unexpected-negative cultures (UNCs), 2) unexpected-positive cultures (UPCs), 3) single-positive intraoperative cultures (SPCs), and 4) clearly septic and 5) aseptic cases. In total, 268 archived synovial fluid samples from 195 patients who underwent acute/chronic revision total hip or knee arthroplasty were included. Cases were classified according to the International Consensus Meeting 2018 criteria. JI panel evaluation of synovial fluid was performed, and the results were compared with cultures. The JI panel detected microorganisms in 7/48 (14.5%) and 15/67 (22.4%) cases related to UNCs and SPCs, respectively, but not in cases of UPCs. The correlation between JI panel detection and infection classification criteria for early/late acute and chronic PJI was 46.6%, 73%, and 40%, respectively. Overall, the JI panel identified 12.6% additional microorganisms and three new species. The JI panel pathogen identification showed a sensitivity and specificity of 41.4% (95% confidence interval (CI) 33.7 to 49.5) and 91.1% (95% CI 84.7 to 94.9), respectively. In total, 19/195 (9.7%) could have been managed differently and more accurately upon JI panel evaluation. Despite its microbial limitation, JI panel demonstrated clinical usefulness by complementing the traditional methods based on multiple cultures, particularly in PJI with unclear microbiological results.

Clinical characteristics and risk factors for mortality in pneumonia-associated acute respiratory distress syndrome patients: a single center retrospective cohort study.

Pathogenic diversity may have contributed to the high mortality of pneumonia-associated acute respiratory distress syndrome (p-ARDS). Metagenomics next-generation sequencing (mNGS) serves as a valuable diagnostic tool for early pathogen identification. However, its clinical utility in p-ARDS remains understudied. There are still limited researches on the etiology, clinical characteristics and risk factors for 28-day mortality in p-ARDS patients. A single center retrospective cohort study of 75 p-ARDS patients was conducted. Patients were categorized into survival and deceased groups based on their 28-day outcomes. A comprehensive clinical evaluation was conducted, including baseline characteristics, laboratory indicators, outcomes and pathogen identification by mNGS and traditional microbiological testing. We then evaluated the diagnostic value of mNGS and identified clinical characteristics and risk factors for 28-day mortality in p-ARDS. The overall ICU mortality was 26.67%, and the 28-day mortality was 57.33%, with 32 cases (42.67%) in the survival group, and 43 cases (57.33%) in the deceased group. Patients in the deceased group were older than those in the survival group (68(59,73) years vs. 59(44,67) years, P=0.04). The average lengths of ICU and hospital stay were 9(5,13) days and 14(7,21) days, respectively. The survival group had longer lengths of ICU and hospital stay (ICU: 11(7,17) days and hospital: 17(9,27) days) compared to the deceased group (ICU: 8(4,11) days and hospital: 12(6,19) days) (P<0.05). Survival patients exhibited lower Acute Physiology and Chronic Health Evaluation (APACHE) II score on the 3rd and 7th days, higher lymphocyte counts, higher CD3+ and CD8+ T cell counts compared to deceased patients (P<0.05). Multivariate logistic regression analysis identified age, APACHE II scores on 3rd and 7th days, CD8+ T cell count and length of ICU as independent risk factors for 28-day mortality in p-ARDS patients. mNGS demonstrated a significantly higher overall pathogen detection rate (70/75, 93.33%) compared to the traditional method (50/75, 66.67%, P=0.022). The average turnaround time (TAT) for mNGS was significantly shorter at 1(1,1) day compared to 4(3,5) days for the traditional method (P<0.001). Metagenome next-generation sequencing can be used as a valuable tool for identifying pathogens in p-ARDS, reducing diagnostic time and improving accuracy. Early application of mNGS alongside traditional methods is recommended for p-ARDS. Furthermore, older age, higher APACHE II scores, lower lymphocyte counts and lymphocyte subset counts were associated with increased mortality in p-ARDS patients, highlighting the importance of timely assessment of immune status and disease severity, especially in elderly.

Leveraging Machine Learning-Guided Molecular Simulations Coupled with Experimental Data to Decipher Membrane Binding Mechanisms of Aminosterols.

Understanding the molecular mechanisms of the interactions between specific compounds and cellular membranes is essential for numerous biotechnological applications, including targeted drug delivery, elucidation of the drug mechanism of action, pathogen identification, and novel antibiotic development. However, estimation of the free energy landscape associated with solute binding to realistic biological systems is still a challenging task. In this work, we leverage the Time-lagged Independent Component Analysis (TICA) in combination with neural networks (NN) through the Deep-TICA approach for determining the free energy associated with the membrane insertion processes of two natural aminosterol compounds, trodusquemine (TRO), and squalamine (SQ). These compounds are particularly noteworthy because they interact with the outer layer of neuron membranes, protecting them from the toxic action of misfolded proteins involved in neurodegenerative disorders, in both their monomeric and oligomeric forms. We demonstrate how this strategy could be used to generate an effective collective variable for describing solute absorption in the membrane and for estimating free energy landscape of translocation via on-the-fly probability enhanced sampling (OPES) method. In this context, the computational protocol allowed an exhaustive characterization of the aminosterol entry pathway into a neuron-like lipid bilayer. Furthermore, it provided accurate prediction of membrane binding affinities, in close agreement with the experimental binding data obtained by using fluorescently labeled aminosterols and large unilamellar vesicles (LUVs). The findings contribute significantly to our understanding of aminosterol entry pathways and aminosterol-lipid membrane interactions. Finally, the computational methods deployed in this study further demonstrate considerable potential for investigating membrane binding processes.

Bionanosensor utilizing single-layer graphene for the detection of iridovirus.

Iridoviruses, a group of double-stranded DNA viruses, pose a significant threat to various aquatic animals, causing substantial economic losses in aquaculture and impacting ecosystem health. Early and accurate detection of these viruses is crucial for effective disease management and control. Conventional diagnostic methods, including polymerase chain reaction (PCR) and virus isolation, often require specialized laboratories, skilled personnel, and considerable time. This highlights the need for rapid, sensitive, and cost-effective diagnostic tools for iridovirus detection. Single-layer graphene, a two-dimensional material with unique properties like high surface area, excellent electrical conductivity, and chemical stability, has emerged as a versatile platform for biosensing applications. This paper explores the potential of employing single-layer graphene in the development of a bionanosensor for the sensitive and rapid detection of iridoviruses. The aim of the present investigation is to develop a sensor by analyzing the vibrational responses of single-layer graphene sheets (SLGS) with attached microorganisms. Graphene-based virus sensors typically rely on the interaction between the virus and the graphene surface, which lead to changes in the frequency response of graphene. This change is measured and used to detect the presence of the virus. Its high surface-to-volume ratio and sensitivity to changes in its frequency make it a highly sensitive platform for virus detection. We employ finite element method (FEM) analysis to model the sensor's performance and optimize its design parameters. The simulation results highlight the sensor's potential for achieving high sensitivity and rapid detection of iridovirus. Bridged and simply supported with roller support boundary conditions applied at the ends of SLG structure. Simulations have been performed to see how SLG behaves when used as sensors. A single-layer graphene armchair SLG (5,5) with 50-nm length exhibits its highest frequency vibration at 8.66 × 106 Hz, with a mass of 1.2786 Zg. In contrast, a zigzag-SLG with a (18,0) configuration has its lowest frequency vibration at 2.82 × 105 Hz. This aids in comprehending the thresholds of detection and the influence of factors such as size, and boundary conditions on sensor effectiveness. These biosensors can be especially helpful in biological sciences and the medical field since they can considerably improve the treatment of patients, cancer early diagnosis, and pathogen identification when used in clinical environments.