Identification Of Bacterial Pathogens Research Articles

Application of 3D printing in bacterial detection

Diseases caused by bacterial infections, especially pathogen variations and drug-resistant bacteria, are a serious threat to human health globally. Accurate and rapid identification of bacterial pathogens is essential for early diagnosis of infections and timely treatment of disease. At present, the routine diagnostic methods for identifying bacterial pathogens in clinic are bacterial culture, immunological detection, genetic analysis, etc. These methods occupy foundational position for bacterial detection that favors for clinical diagnosis of pathogen infection in daily life. Notably, 3D printing technology, together with 3D bioprinting technology, provides more options for bacterial detection due to its personalized design and manufacturing. Compared with traditional methods, 3D printing provides a more convenient, rapid and accurate bacterial detection platform, thus meeting the urgent needs of clinical and scientific research. Here, we have summarized the research progress and given a comprehensive review of 3D printing technology in bacterial detection, including bacterial detection sensors, microfluidic chips, bioprinting microarray technology, AI and spectroscopy technology, providing a scientific reference and filling in gaps in 3D printing-assistance bacterial detection. At the same time, technical advantages, challenges and future development trends will also be discussed in related healthcare fields.

Identification of Bacterial Pathogens in Organic Food of Animal Origin in Poland

The consumption of organic food has increased in recent years. In organic rearing animals are exposed to outdoor conditions, which may increase their risk of infection from various pathogens. In the present study the occurrence of the most significant foodborne pathogenic bacteria in organic meat and ready-to-eat organic meat products was assessed. Out of 100 raw organic meat samples tested, 72 were contaminated with bacterial pathogens. The highest percentage of contaminated samples was observed in poultry meat (92.5%) followed by pork meat (66.7%). Furthermore, 50.0% of beef origin samples were positive for the bacteria tested. L. monocytogenes was found in 39.0% of samples, S. aureus was identified in 37.0%, Campylobacter in 20.0%, Salmonella in 8.0% and Shigatoxin-producing E. coli in 4.0% of raw meat samples. In 31.0% of samples a co-occurrence of two (83.9%) or three (16.1%) pathogens was observed. Among 100 samples of organic meat products tested, only L. monocytogenes was found in 5.0% of samples. The result of the present study indicated that organic food may be a source of harmful microorganisms that may pose foodborne infections to consumers.

A study on pathogenic aerobic bacteria of surgical site infection and their antimicrobial susceptibility pattern in patients attending a hospital in Bhubaneswar, Odisha

Abstract: Introduction: Identification of bacterial pathogens and their antibiotic susceptibility testing are vital for the effective treatment of surgical site infection (SSI). Objective: The objective of the study was to study the bacterial pathogens in patients with SSI and their antibiotic susceptibility pattern. Methods: Pus samples were collected using sterilized swab stick, and then, the samples were cultured in MacConkey and Blood agar and incubated for 24 h in bacterial incubator. The identification was done by using colony morphology, Gram staining technique, and biochemical tests. After identification, the antibiogram of organisms was done using Mueller–Hinton agar. Results: A total of 76 pus samples were collected from different specialties from SSIs. Out of which 53 samples were culture positive. The number of male patients who had SSI was higher than females. Maximum SSIs were observed in the orthopedics department followed by the surgery department and gynecology department. SSIs in surgery and orthopedics department were commonly seen in the age group 41–60 years. In the obstetrics and gynecology department, SSIs were seen mostly in the age group 21–40 years. In the surgery and orthopedics department, Staphylococcus aureus was the predominant organism and Klebsiella spp was the most common Gram-negative isolate. In the gynecology department, S. aureus was the predominant organism followed by Acinetobacter spp. Maximum sensitivity to Vancomycin and Linezolid was seen as the only Gram-positive isolate in all specialties. Gram-negative isolates from the Enterobacteriaceae family of all specialties were sensitive to tigecycline followed by imipenem and meropenem. Nonfermenters were mostly sensitive to polymyxin b/colistin, imipenem, and meropenem. Conclusion: The study gives an insight into aerobic bacterial pathogens and their antibiotic-sensitivity patterns isolated from SSI and there should be surveillance of SSI which helps to reduce the rate of SSI as well as appropriate use of antibiotics.

Development of novel digital PCR assays for the rapid quantification of Gram-negative bacteria biomarkers using RUCS algorithm

Rapid and accurate identification of bacterial pathogens is crucial for effective treatment and infection control, particularly in hospital settings. Conventional methods like culture techniques and MALDI-TOF mass spectrometry are often time-consuming and less sensitive. This study addresses the need for faster and more precise diagnostic methods by developing novel digital PCR (dPCR) assays for the rapid quantification of biomarkers from three Gram-negative bacteria: Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa.Utilizing publicly available genomes and the rapid identification of PCR primers for unique core sequences or RUCS algorithm, we designed highly specific dPCR assays. These assays were validated using synthetic DNA, bacterial genomic DNA, and DNA extracted from clinical samples. The developed dPCR methods demonstrated wide linearity, a low limit of detection (∼30 copies per reaction), and robust analytical performance with measurement uncertainty below 25 %. The assays showed high repeatability and intermediate precision, with no cross-reactivity observed. Comparison with MALDI-TOF mass spectrometry revealed substantial concordance, highlighting the methods’ suitability for clinical diagnostics.This study underscores the potential of dPCR for rapid and precise quantification of Gram-negative bacterial biomarkers. The developed methods offer significant improvements over existing techniques, providing faster, more accurate, and SI-traceable measurements. These advancements could enhance clinical diagnostics and infection control practices.

Development of novel nanostructured biosensors for rapid detection of pathogens in clinical diagnostics

<p class="MsoNormal" style="text-align: justify; text-justify: inter-ideograph; line-height: 120%; mso-pagination: none; layout-grid-mode: char; mso-layout-grid-align: none; margin: 12.0pt 0cm 6.0pt 0cm;"><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; font-family: 'Times New Roman',serif;">The prompt and precise identification of microorganisms is crucial for successful clinical diagnostics and the prevention of infectious disease outbreaks. Traditional diagnostic methods often suffer from limitations such as extended processing durations, elevated expenses, and the necessity for specialized laboratory equipment. In this research, we propose the development of novel nanostructured biosensors that utilize the distinct characteristics of nanomaterials to improve the accuracy, specificity, and efficiency of identifying pathogens. These biosensors are created with the intention of offering point-of-care testing functionality, thus rendering them appropriate for utilization in a range of clinical settings. The integration of advanced nanotechnology with bioanalytical methods aims to create a reliable system for the real-time identification of bacterial, viral, and fungal pathogens. This review encompasses the design, fabrication, and testing of the biosensors, along with a comprehensive analysis of their performance in comparison to conventional diagnostic techniques. The results demonstrate the potential of nanostructured biosensors to revolutionize pathogen detection, offering significant improvements in efficiency and accuracy, which are essential for timely medical intervention and public health management.</span></p>

Microbiological profile and antibiotic susceptibility pattern of chronic dacryocystitis at a tertiary eye care centre in Western India

: Chronic dacryocystitis is a low-grade infection & inflammation of the lacrimal sac. If neglected the infection may extend to surrounding orbit, brain and paranasal sinuses leading to sight and/or life-threatening complications. Thus appropriate, effective and timely antibiotic treatment/ prophylaxis is desirable. The present study was aimed to document the current microbiological profile and antibiotic susceptibility in adult patients of chronic dacryocystitis seen at our tertiary eye care centre in western India.The study was a cross-sectional, prospective, observational study. The study included 60 adult patients of chronic dacryocystitis. In every case two samples from the lacrimal sac collected via the regurgitation technique or by lacrimal passage syringing were sent for microbiological evaluation. Gram staining was used for identification of bacterial pathogen. KOH mount was used for identification of fungi. The second swab was used for performing culture -sensitivity. The Clinical and Laboratory Standard Institute Guidelines were used to know the antimicrobial sensitivity. Kirby-Bauer disk diffusion method was used for this purpose. The statistical analysis was done using descriptive statistics as mean, standard deviation, median and percentages. Microsoft excel worksheet was used. 60 samples were taken. 53/60 (88.3%) samples showed microbial isolates after 24-48 hours of incubation. 78.3% were gram positive organisms and 10% were gram negative organisms. The most common gram-positive organism was (68.3%) and the most common gram-negative organism was (8.3%). Amongst the antibiotics the most sensitive antibiotic was Ampicillin + Sulbactam (92.5%) and the most resistant antibiotic was Ciprofloxacin (3.8%). The antibiotic most sensitive to gram positive organisms was Ampicillin and Sulbactam, Cephalexin, Linezolid, Cloxacillin and Vancomycin. The antibiotic most sensitive to gram negative organisms was Gentamicin, Amikacin, Gatifloxacin, Ceftazidime, Meropenem and Polymixin -B. The current microbiological profile and antibiotic susceptibility of the microorganisms responsible for chronic dacryocystitis is an invaluable tool in the treatment of chronic dacryocystitis with the most appropriate and effective antibiotic.

LC-SRM Combined With Machine Learning Enables Fast Identification and Quantification of Bacterial Pathogens in Urinary Tract Infections

Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate antibiotherapy to patients and to avoid the emergence of drug-resistant pathogens. While the gold standard requires 24 h to 48 h of bacteria culture prior to MALDI-TOF species identification, we propose a culture-free workflow, enabling bacterial identification and quantification in less than 4 h using 1 ml of urine. After rapid and automatable sample preparation, a signature of 82 bacterial peptides, defined by machine learning, was monitored in LC-MS, to distinguish the 15 species causing 84% of the UTIs. The combination of the sensitivity of the SRM mode on a triple quadrupole TSQ Altis instrument and the robustness of capillary flow enabled us to analyze up to 75 samples per day, with 99.2% accuracy on bacterial inoculations of healthy urines. We have also shown our method can be used to quantify the spread of the infection, from 8 × 104 to 3 × 107 CFU/ml. Finally, the workflow was validated on 45 inoculated urines and on 84 UTI-positive urine from patients, with respectively 93.3% and 87.1% of agreement with the culture-MALDI procedure at a level above 1 × 105 CFU/ml corresponding to an infection requiring antibiotherapy.

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.

Emerging Methods in the Identification of Bacterial Respiratory Tract Pathogens.

Here, we will review different bacterial causes of respiratory tract infections and discuss the available diagnostic methods. Moreover, we will provide some recently published patents and newer techniques, such as respiratory panels and omics approaches, and express the challenges in this path. Respiratory tract infections (RTIs) include those infections that can lead to the involvement of different respiratory parts, including the sinuses, throat, airways, and lungs. Acute respiratory tract infection is the leading cause of death from infectious illnesses worldwide. According to the World Health Organization, 1.6 to 2.2 million deaths have occurred due to acute respiratory infections in children under five years of age. About 4 million people die annually from respiratory infections, 98% of which are caused by lower respiratory infections. Depending on the type of pathogen, the severity of the infection can vary from mild to severe and even cause death. The most important pathogens involved in respiratory tract infections include Streptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis. The symptoms are often similar, but the treatment can vary greatly. Therefore, correct diagnosis is so important. There are several methods for diagnosing respiratory infections. Traditional tests include the culture of respiratory samples, considered the primary tool for diagnosing respiratory infections in laboratories, and less common standard tests include rapid and antigenic tests. It is essential to think that the culture method is reliable. In the original method of diagnosing respiratory infections, some bacteria were challenging to grow successfully, and many clinical laboratories needed to be equipped for viral cultures. Another issue is the time to get the results, which may take up to 7 days. Rapid and antigenic tests are faster but need to be more accurate. The clinical laboratories are trying to be equipped with molecular methods for detecting respiratory pathogens and identifying the genetic material of the infectious agent in these new methods as the primary method in their agenda.

Isolation, Identification And Antibiotic Susceptibility Testing Of Bacterial Pathogens From Blood Cultures Of Neonates With Clinical Signs Of Sepsis In A Tertiary Care Centre

Isolation, Identification And Antibiotic Susceptibility Testing Of Bacterial Pathogens From Blood Cultures Of Neonates With Clinical Signs Of Sepsis In A Tertiary Care Centre

Aeromonas hydrophila Septicaemia in a 6-Month-Old Infant: A Case Report

Background: Aeromonas hydrophila, a Gram-negative, motile, facultative anaerobe, is emerging as a serious pathogen. It is found in water, soil, and food, causing various infections, from mild diarrhoea to life-threatening sepsis. Young children, older people, and immunocompromised people are most susceptible. While treatable with antibiotics, A. hydrophila’s growing resistance to penicillin and other commonly available antibiotics complicates treatment. This case report highlights a rare instance of A. hydrophila sepsis in a young child in rural Gambia. Case summary: A 6-month-old African female child with high fever, cough, fast breathing, and vomiting was diagnosed with septicemia caused by Aeromonas hydrophila, a waterborne bacteria. The infant likely contracted the infection from contaminated fish or water. We started the child on the initial first-line empirical antibiotics but later switched to ceftriaxone due to antibiotic resistance, and the child recovered fully. This case highlights the importance of appropriate diagnosis, antibiotic susceptibility and surveillance of emerging antibiotic-resistant pathogens in young children. Conclusion: This report describes the infection of emerging antibiotic-resistant A. hydrophila sepsis in an African female child. A. hydrophila is an emerging pathogen causing opportunistic infections in both immunocompetent and immunocompromised patients. This study suggests that prompt identification of bacterial pathogens, coupled with targeted antimicrobial therapy guided by local antibiogram data and blood culture for antibiotic susceptibility, is likely a crucial factor for optimal patient outcomes. Furthermore, we advocate for more comprehensive surveillance systems to effectively track and monitor these emerging antibiotic-resistant pathogens.

Bacterial enrichment prior to third-generation metagenomic sequencing improves detection of BRD pathogens and genetic determinants of antimicrobial resistance in feedlot cattle.

Bovine respiratory disease (BRD) is one of the most important animal health problems in the beef industry. While bacterial culture and antimicrobial susceptibility testing have been used for diagnostic testing, the common practice of examining one isolate per species does not fully reflect the bacterial population in the sample. In contrast, a recent study with metagenomic sequencing of nasal swabs from feedlot cattle is promising in terms of bacterial pathogen identification and detection of antimicrobial resistance genes (ARGs). However, the sensitivity of metagenomic sequencing was impeded by the high proportion of host biomass in the nasal swab samples. This pilot study employed a non-selective bacterial enrichment step before nucleic acid extraction to increase the relative proportion of bacterial DNA for sequencing. Non-selective bacterial enrichment increased the proportion of bacteria relative to host sequence data, allowing increased detection of BRD pathogens compared with unenriched samples. This process also allowed for enhanced detection of ARGs with species-level resolution, including detection of ARGs for bacterial species of interest that were not targeted for culture and susceptibility testing. The long-read sequencing approach enabled ARG detection on individual bacterial reads without the need for assembly. Metagenomics following non-selective bacterial enrichment resulted in substantial agreement for four of six comparisons with culture for respiratory bacteria and substantial or better correlation with qPCR. Comparison between isolate susceptibility results and detection of ARGs was best for macrolide ARGs in Mannheimia haemolytica reads but was also substantial for sulfonamide ARGs within M. haemolytica and Pasteurella multocida reads and tetracycline ARGs in Histophilus somni reads. By increasing the proportion of bacterial DNA relative to host DNA through non-selective enrichment, we demonstrated a corresponding increase in the proportion of sequencing data identifying BRD-associated pathogens and ARGs in deep nasopharyngeal swabs from feedlot cattle using long-read metagenomic sequencing. This method shows promise as a detection strategy for BRD pathogens and ARGs and strikes a balance between processing time, input costs, and generation of on-target data. This approach could serve as a valuable tool to inform antimicrobial management for BRD and support antimicrobial stewardship.

Advancing metagenome-assembled genome-based pathogen identification: unraveling the power of long-read assembly algorithms in Oxford Nanopore sequencing.

Oxford Nanopore sequencing is one of the high-throughput sequencing technologies that facilitates the reconstruction of metagenome-assembled genomes (MAGs). This study aimed to assess the potential of long-read assembly algorithms in Oxford Nanopore sequencing to enhance the MAG-based identification of bacterial pathogens using both simulated and mock communities. Simulated communities were generated to mimic those on fresh spinach and in surface water. Long reads were produced using R9.4.1+SQK-LSK109 and R10.4 + SQK-LSK112, with 0.5, 1, and 2 million reads. The simulated bacterial communities included multidrug-resistant Salmonella enterica serotypes Heidelberg, Montevideo, and Typhimurium in the fresh spinach community individually or in combination, as well as multidrug-resistant Pseudomonas aeruginosa in the surface water community. Real data sets of the ZymoBIOMICS HMW DNA Standard were also studied. A bioinformatic pipeline (MAGenie, freely available at https://github.com/jackchen129/MAGenie) that combines metagenome assembly, taxonomic classification, and sequence extraction was developed to reconstruct draft MAGs from metagenome assemblies. Five assemblers were evaluated based on a series of genomic analyses. Overall, Flye outperformed the other assemblers, followed by Shasta, Raven, and Unicycler, while Canu performed least effectively. In some instances, the extracted sequences resulted in draft MAGs and provided the locations and structures of antimicrobial resistance genes and mobile genetic elements. Our study showcases the viability of utilizing the extracted sequences for precise phylogenetic inference, as demonstrated by the consistent alignment of phylogenetic topology between the reference genome and the extracted sequences. R9.4.1+SQK-LSK109 was more effective in most cases than R10.4+SQK-LSK112, and greater sequencing depths generally led to more accurate results.IMPORTANCEBy examining diverse bacterial communities, particularly those housing multiple Salmonella enterica serotypes, this study holds significance in uncovering the potential of long-read assembly algorithms to improve metagenome-assembled genome (MAG)-based pathogen identification through Oxford Nanopore sequencing. Our research demonstrates that long-read assembly stands out as a promising avenue for boosting precision in MAG-based pathogen identification, thus advancing the development of more robust surveillance measures. The findings also support ongoing endeavors to fine-tune a bioinformatic pipeline for accurate pathogen identification within complex metagenomic samples.

Real-time 3D tracking of swimming microbes using digital holographic microscopy and deep learning.

The three-dimensional swimming tracks of motile microorganisms can be used to identify their species, which holds promise for the rapid identification of bacterial pathogens. The tracks also provide detailed information on the cells' responses to external stimuli such as chemical gradients and physical objects. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data. We demonstrate that a common neural network (NN) accelerates the analysis of holographic data by an order of magnitude, enabling its use on single-board computers and in real time. We establish a heuristic relationship between the distance of a cell from the focal plane and the size of the bounding box assigned to it by the NN, allowing us to rapidly localise cells in three dimensions as they swim. This technique opens the possibility of providing real-time feedback in experiments, for example by monitoring and adapting the supply of nutrients to a microbial bioreactor in response to changes in the swimming phenotype of microbes, or for rapid identification of bacterial pathogens in drinking water or clinical samples.

IDENTIFICATION OF THE MAIN DISEASES OF WALNUT IN THE SOUTHERN FRUIT-GROWING ZONE OF KAZAKHSTAN

Industrial cultivation of nut crops in the Republic of Kazakhstan is promoted by favourable soil and climate conditions, especially in the southern region, which makes it possible to obtain high yields of this valuable plant in the future. Significant damage to the yield of nut crops is caused by diseases, protection of plantations from which is one of the main measures aimed at increasing their productivity. The most important issue in cultivation of nut crops is its protection from bacterial and fungal pathogens. In Kazakhstan purposeful researches on studying species diversity of diseases and pests of nut crops have not been carried out. The novelty of the conducted research is in the fact that for the first time in Kazakhstan fungal and bacterial diseases of walnut in the conditions of the southern zone of fruit growing on the basis of microbiological and molecular-genetic methods were identified.
 The results of research showed that the majority of isolated pathogens are fungi, as well as several species of bacteria causing diseases of walnut. In total, 4 genera of fungi were isolated: Alternaria, Fusarium as causative agents of fungal diseases; Aspergillus, Penicillium as secondary infestation; also 2 genera of bacteria: Pantoea, Pseudomonas as causative agents of bacterial diseases. Molecular identification of fungal and bacterial pathogens by PCR based on genomic DNA showed that the studied walnut samples were identified as phytopathogenic fungi: Alternaria alternata, Fusarium solani, as well as phytopathogenic bacteria: Pantoea agglomerans, Pseudomonas syringae, Pseudomonas oryzahabitans. The conducted research results will allow to make up a system of protection of walnut from harmful diseases in order to increase the productivity of plantations.

Rapid Detection of Bacterial Pathogens Causing Lower Respiratory Tract Infections via Microfluidic-Chip-Based Loop-Mediated Isothermal Amplification.

Respiratory tract infections (RTIs) are among the most common problems in clinical settings. Rapid and accurate identification of bacterial pathogens will provide practical guidelines for managing and treating RTIs. This study describes a method for rapidly detecting bacterial pathogens that cause respiratory tract infections via multi-channel loop-mediated isothermal amplification (LAMP). LAMP is a sensitive and specific diagnostic tool that rapidly detects bacterial nucleic acids with high accuracy and reliability. The proposed method offers a significant advantage over traditional bacterial culturing methods, which are time-consuming and often require greater sensitivity for detecting low levels of bacterial nucleic acids. This article presents representative results of K. pneumoniae infection and its multiple co-infections using LAMP to detect samples (sputum, bronchial lavage fluid, and alveolar lavage fluid) from the lower respiratory tract. In summary, the multi-channel LAMP method provides a rapid and efficient means of identifying single and multiple bacterial pathogens in clinical samples, which can help prevent the spread of bacterial pathogens and aid in the appropriate treatment of RTIs.

Isolation and identification of bacterial pathogens causing ice-ice disease in Eucheuma cottonii seaweed at Seira Island Waters, Tanimbar Islands District, Maluku, Indonesia

Abstract. Tuhumury NC, Sahetapy JMF, Matakupan J. 2024. Isolation and identification of bacterial pathogens causing ice-ice disease in Eucheuma cottonii seaweed at Seira Island Waters, Tanimbar Islands District, Maluku, Indonesia. Biodiversitas 25: 964-970. Seaweed cultivation has proven to be beneficial and enhances the well-being of communities. Production of seaweed in the waters of Maluku has increased despite facing the problem of ice-ice disease. Therefore, this research aimed to isolate and identify bacteria on the Eucheuma cottonii seaweed species affected by ice-ice disease in the waters of Seira Island, Tanimbar Islands District, Maluku. It was conducted in August 2023 at three observation stations, and water quality samples were collected in situ at all three stations, including temperature, salinity, pH, dissolved oxygen (DO), and turbidity. Furthermore, bacterial isolation and identification were carried out at the Laboratory of Marine Aquaculture, Ambon. Bacteria isolation and purification were performed on TCBS media using the streak plate technique, while identification used the API 20E test. The results showed that water quality parameters supported seaweed growth. Four bacteria that caused ice-ice disease in the waters of Seira were identified as Vibrio alginolyticus, Vibrio fluvalis, Vibrio cholerae, and Aeromonas caviae. Moreover, all four bacteria found were gram-negative and pathogenic. Bacteria belonging to the genera Vibrio and Aeromonas have been proven to cause infections in marine organisms, including seaweed. In conclusion, four types of bacteria were identified in seaweed affected by ice-ice disease.

Pattern of antibiotic resistance in surgical site infections in a tertiary care hospital of Nepal

Background: Surgical site infection (SSI) is the second most common hospital-acquired infection. The identification of bacterial pathogens and their antibiotic susceptibility pattern is required for the successful treatment of SSI and curb antimicrobial resistance, which is a major threat globally. This study aimed to assess the microbial profile of the organisms causing SSI and their antibiogram in a tertiary care hospital. Methods: This cross-sectional study was conducted at Microbiology Laboratory of tertiary care hospital, from March 2023 to August 2023. Swab or aspirate specimens were collected aseptically from the surgical site and processed for bacterial isolation following standard bacteriological techniques. Gram’s staining and biochemical tests were performed to identify the organisms at the species level. Antimicrobial susceptibility tests were done following Kirby–Bauer’s disc diffusion method. Statistical analysis was performed using SPSS software version16.0. Results: Out of 405 samples, 92 (22.7%) yielded bacterial growth. Eighty-three culture positive cases (90.2%) were monomicrobial while nine (15.7%) had mixed growth of at least two different bacteria. Out of 108 isolates, 43 (39.8%) were gram-positive and 65 (60.2%) were gram-negative bacteria. Escherichia coli (39.8%) was the most commonly isolated organism. All gram-negative isolates were resistant to ceftriaxone followed by amoxicillin-clavulanic acid (94.0%), amoxicillin (94.0%), cefixime (90.7%), and cefepime (89.8%). Similarly, none of the gram-positive isolates were susceptible to ampicillin, amoxicillin, and ampicillin/sulbactam. The most effective drugs against E. coli were imipenem (100%), followed by tigecycline (96%), meropenem (95.1%), chloramphenicol (84%), amikacin (81.5%), and gentamicin (81.5%). Overall prevalence of MDR was 59.3%. Conclusion: Our study demonstrates increased resistance of both gram-positive and gram-negative organisms to commonly used drugs like cephalosporins and even quinolones. Continued surveillance of antimicrobial susceptibility results at local level, dissemination of data, and prescribing the antibiotics accordingly based on culture and sensitivity results are necessary to ameliorate antimicrobial resistance.

Risk assessment and pathogen profile of surgical site infections in traumatic brain injury patients undergoing emergency craniotomy: A retrospective study.

Emergency craniotomy in patients with traumatic brain injury poses a significant risk for surgical site infections (SSIs). Understanding the risk factors and pathogenic characteristics of SSIs in this context is crucial for improving outcomes. This comprehensive retrospective analysis spanned from February 2020 to February 2023 at our institution. We included 25 patients with SSIs post-emergency craniotomy and a control group of 50 patients without SSIs. Data on various potential risk factors were collected, including demographic information, preoperative conditions, and intraoperative details. The BACT/ALERT3D Automated Bacterial Culture and Detection System was utilized for rapid bacterial pathogen identification. Statistical analyses included univariate and multivariate logistic regression to identify significant risk factors for SSIs. The study identified Klebsiella pneumoniae, Escherichia coli, and Staphylococcus aureus as the most prevalent pathogens in SSIs. Significant risk factors for SSIs included the lack of preoperative antibiotic use, postoperative drainage tube placement, diabetes mellitus, and the incorporation of invasive procedures, all of which showed a significant association with SSIs in the univariate analysis. The multivariate analysis further highlighted the protective effect of preoperative antibiotics and the increased risks associated with anaemia, diabetes mellitus, postoperative drainage tube placement, and the incorporation of invasive procedures. Our research underscores the critical role of factors like insufficient preoperative antibiotics, postoperative drainage, invasive techniques, anaemia, and diabetes mellitus in elevating the risk of surgical site infections in traumatic brain injury patients undergoing emergency craniotomy. Enhanced focus on these areas is essential for improving surgical outcomes.

Microscopic identification of foodborne bacterial pathogens based on deep learning method

Accurate and rapid detection of foodborne bacterial pathogens is critical for food quality control. Nowadays, tracking morphological bacterial properties using microscope is still a priority at the grass-roots food supervision department due to its simplicity and low cost. However, the method requires highly professional personnel and there are certain misjudgments in the process of analysis. Automatically recognizing foodborne pathogen using deep learning algorithm to replace manual microscopy will not only reduce expert cost, artificial misjudgment, and operation time in detection, but also provide more objective and accurate identification. Here, we firstly constructed a high-quality and large-scale dataset of foodborne pathogenic bacteria, allowing the deep learning-based model to be efficiently trained and achieve accurate identification. The deep convolutional neural network-based model is capable of identifying six common foodborne pathogens, including Escherichia coli (O157:H7), Vibrio parahaemolyticus, Staphylococcus aureus, Bacillus cereus, Salmonella typhi, and Streptococcus hemolyticus, with accuracy rates of 90%–100%. This method can assist or replace the manual microscopic inspection step in traditional detection methods, and is promising to break through the traditional approach that heavily relies on manual judgment, greatly reduce the cost of experts and human errors, and provide rapid, accurate, and powerful discriminatory results in large quantities for detection.