Positive Samples Research Articles

Characterization of Weissella Species during a 3-Year Observational Study - An Emerging Threat.

Weissella species are mistaken for Lactobacillus or Leuconostoc because of their Gram-staining property and resistance to vancomycin. In this study, we aimed to evaluate the demographic pattern, presenting symptoms, risk factors, associated pathologies, and clinical outcomes in patients with Weissella infection. We also analyzed the antibiotic susceptibility pattern of the Weissella species isolated. This retrospective observational study was done from January 2021 to August 2023 in a tertiary care referral center in Uttar Pradesh. All Weissella species isolated from blood cultures or cerebrospinal fluid (CSF) samples during this period were included in the study. Twenty-four-hour growth from a positive blood culture or CSF sample was identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Demographic and clinical details of the patients were extracted from the hospital information system. Kirby-Bauer disk diffusion was used for antibiotic susceptibility testing. During the 3-year study, 13 isolates of Weissella species were identified in our laboratory. Among the patients, male predominance was seen. The age range of the patients was 5-53 years. The samples were blood samples (n = 11) and CSF samples (n = 2). Of these isolates, 12 (92.3%) were identified as Weissella confusa and 1 (7.69%) was identified as Weissella viridescens. Common risk factors were anemia, prolonged hospital stay, indwelling catheter, chemotherapy, and chronic kidney disease. All the isolates in this study were sensitive to amikacin, daptomycin, amoxicillin-clavulanate, minocycline, and linezolid. Death was attributed to Weissella infection in three patients. Weissella species are opportunistic organisms that need to be identified quickly and precisely to guarantee the right course of treatment.

Inactivation of Desiccation-Resistant Salmonella on Apple Slices Following Treatment with ε-Polylysine, Sodium Bisulfate, or Peracetic Acid and Subsequent Dehydration

Salmonella is capable of surviving dehydration within various foods, such as dried fruit. Dried fruit, including apple slices, have been the subject of product recalls due to contamination with Salmonella. A study was conducted to determine the fate of Salmonella on apple slices, following immersion in three antimicrobial solutions (viz., ε-polylysine [epsilon-polylysine or EP], sodium bisulfate [SBS], or peracetic acid [PAA]), and subsequent hot air dehydration. Gala apples were aseptically cored and sliced into 0.4 cm thick rings, bisected, and inoculated with a five-strain composite of desiccation-resistant Salmonella, to a population of 8.28 log CFU/slice. Slices were then immersed for 2 min in various concentrations of antimicrobial solutions, including EP (0.005, 0.02, 0.05, and 0.1%), SBS (0.05, 0.1, 0.2, and 0.3%), PAA (18 or 42 ppm), or varying concentrations of PAA + EP, and then dehydrated at 60°C for 5 h. Salmonella populations in positive control samples (inoculated apple slices washed in sterile water) declined by 2.64 log after drying. In the present study, the inactivation of Salmonella, following EP and SBS treatments, increased with increasing concentrations, with maximum reductions of 3.87 and 6.20 log (with 0.1 and 0.3% of the two compounds, respectively). Based on preliminary studies, EP concentrations greater than 0.1% did not result in lower populations of Salmonella. Pretreatment washes with either 18 or 42 ppm of PAA inactivated Salmonella populations by 4.62 and 5.63 log, respectively, following desiccation. Combining PAA with up to 0.1% EP induced no greater population reductions of Salmonella than washing with PAA alone. The addition of EP to PAA solutions appeared to destabilize PAA concentrations, reducing its biocidal efficacy. These results may provide antimicrobial predrying treatment alternatives to promote the reduction of Salmonella during commercial or consumer hot air drying of apple slices.

Statistical adaptive modeling for kitchen waste detection in complex scenes

Automatic detection of kitchen waste is of great significance, which provides support for its subsequent full quantitative consumption and harmless treatment. In addition, manual sorting of kitchen waste is inefficient and toxic waste is harmful to human health, making automatic detection of kitchen waste technology crucial. Automatic detection of kitchen waste in complex scenes faces the challenge of diversity of category outlines and uneven distribution. In this paper, we propose a detector based on statistical adaptive modeling(namely SA-Det) for the automatic detection of kitchen waste in complex scenes. Firstly, to solve the issue of diversity of category outlines, we propose a category statistics adaptive (CSA) module. The CSA module constructs dynamic thresholds for each instance to accurately assign positive and negative samples by fusing category statistics and instance shape information, thereby improving detection performance. Moreover, to solve the issue of uneven distribution, we propose a distribution adaptive (DA) module, which dynamically adjusts the loss weights by adaptively sensing the number of labels during training process. Extensive experiments on our constructed kitchen waste dataset (KWD) demonstrate that SA-Det consistently and significantly improves the performance of existing state-of-the-art methods (e.g., Rotated_RetinaNet (Lin et al., 2017) and RoI_Transformer (Ding et al., 2019) by around 2% to 3.5%.

Applying self-supervised learning to network intrusion detection for network flows with graph neural network

Graph Neural Networks (GNNs) have garnered intensive attention for Network Intrusion Detection System (NIDS) due to their suitability for representing the network traffic flows. However, most present GNN-based methods for NIDS are supervised or semi-supervised. Network flows need to be manually annotated as supervisory labels, a process that is time-consuming or even impossible, making NIDS difficult to adapt to potentially complex attacks, especially in large-scale real-world scenarios. The existing GNN-based self-supervised methods focus on the binary classification of network flow as benign or not, and thus fail to reveal the types of attack in practice. This paper studies the application of GNNs to identify the specific types of network flows in an unsupervised manner. We first design an encoder to obtain graph embedding, that introduces the graph attention mechanism and considers the edge information as the only essential factor. Then, a self-supervised method based on graph contrastive learning is proposed. The method samples center nodes, and for each center node, generates subgraph by it and its direct neighbor nodes, and corresponding contrastive subgraph from the interpolated graph, and finally constructs positive and negative samples from subgraphs. Furthermore, a structured contrastive loss function based on edge features and graph local topology is introduced. To the best of our knowledge, it is the first GNN-based self-supervised method for the multiclass classification of network flows in NIDS. Detailed experiments conducted on four real-world datasets (NF-Bot-IoT, NF-Bot-IoT-v2, NF-CSE-CIC-IDS2018, and NF-CSE-CIC-IDS2018-v2) systematically compare our model with the state-of-the-art supervised and self-supervised models, illustrating the considerable potential of our method. Our code is accessible through https://github.com/renj-xu/NEGSC.

An MCDM approach for Reverse vaccinology model to predict bacterial protective antigens

Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets.

Similarity-guided graph contrastive learning for lncRNA-disease association prediction

The increasing research evidence indicates that long non-coding RNAs (lncRNAs) play important roles in regulating biological processes and are closely associated with many human diseases. Computational methods have emerged as indispensable tools for identifying associations between long non-coding RNA (lncRNA) and diseases, primarily due to the time-consuming and costly nature of traditional biological experiments. Given the scarcity of verified lncRNA-disease associations, the intensifying focus on deep learning is playing a crucial role in refining the accuracy of predictive models. Moreover, the contrastive learning method exhibits a clear advantage in situations where data is scarce or annotation costs are high. In this paper, we leverage the advantages of graph neural networks and contrastive learning to innovatively propose a similarity-guided graph contrastive learning (SGGCL) model for predicting lncRNA-disease associations. In the SGGCL model, we employ a novel similarity-guided graph data augmentation method to generate high-quality positive and negative sample pairs, addressing the scarcity of verified data. Additionally, we utilize the RWR algorithm and a graph convolutional neural network for contrastive learning, facilitating the capture of global topology and high-level node embeddings. The experimental results on several datasets demonstrate the superior predictive performance and scalability of our method in lncRNA-disease association prediction compared to state-of-the-art methods.

Isolation and molecular characterization of very virulent infectious bursal disease virus from Egypt

In Egypt, the poultry industry is plagued by the significant economic losses caused by the infectious bursal disease virus (IBDV). In the present study, a total of 30 diseased commercial chicken flocks aged 23–41 days old in three Egyptian governorates (Fayoum, Beni-Suef, and Minya) having symptoms consistent with IBDV were examined. The collected bursal samples (n=30, ten pooled bursae for each flock) were screened by RT-PCR to amplify the IBDV VP2 gene. Results revealed that 20 out of 30 samples were positive, as indicated by the amplification of 620 bp of the VP2 gene. The twenty positive samples were inoculated into specific pathogen-free embryonated chicken eggs (SPF-ECEs) via the chorioallantoic-membrane (CAM) route to isolate the suspected virus. Eight samples were successfully isolated and were subjected to a partial sequence of VP2 (targeting the HVR). The nucleotide sequences and phylogenetic analysis revealed that the examined isolates exhibited amino acids A222, I256, I294, and S299, which are extremely conserved among very virulent (vv)IBDV. The amino acid sequence analysis of the eight isolates revealed a high degree of similarity (97-100%) in between and 90-100% similarity compared to other Egyptian vvIBDV strains. In contrast, lower similarity was observed with attenuated IBDV vaccine strains D78, Lukert, and Bursavac (91-93%, 91-93%, and 92-94%, respectively). In conclusion, this study underscores the importance of ongoing surveillance of the IBDV situation in the field, as well as the need for further research to investigate the efficacy of current vaccination strategies to curb IBDV infection.

Molecular Detection and Characterization of Newcastle Disease Virus from Chickens in Mid-Rift Valley and Central Part of Ethiopia.

Newcastle disease (ND) is a highly infectious poultry disease that causes major economic losses worldwide. The disease is caused by Newcastle Disease Virus (NDV) and early detection and identification of the viral strain is essential. Having knowledge of the NDV strain genotype that circulates in some regions would help in designing an effective vaccine to control the disease. In this regard, there is little information on NDV strain in chickens in mid Rift Valley and the central part of Ethiopia. Therefore, the purpose of this study was to detect and characterize NDV strain genotype from chickens in mid-Rift Valley and the central part of Ethiopia and test whether this NDV strain genotype matches the vaccine strain currently used in the study area. A total of 98 samples: 78 (tracheal and cloacal) swabs from chicken pools of five and 20 tissue samples were collected. To detect NDV strain, conserved region of the virus Matrix (M) gene was amplified by qRT-PCR. To characterize NDV strain genotypes, M-gene positive samples were specifically re-amplified by conventional PCR targeting the Fusion (F) gene region and sequenced by Sanger method. 13.26% of tested samples were positive for NDV strain in the study area with statistically significant difference (P<0.05) among the study sites. Further characterization of the F genes from NDV strain isolates by phylogenetic analysis indicated that one field isolate clustered with genotype VII whereas three of the isolates clustered to genotype I, II, and III. The isolate of the current NDV strain vaccine in use in the study area clustered with genotype II. The current study indicates the existence of different NDV strain genotype from that of the vaccine strain currently used. Even though large-scale characterization of several isolates is required at national level, the current study laid baseline information for the existence of variations between field NDV strain genotype and vaccine strain currently used against ND in the country.

PNNCLR: Stochastic pseudo neighborhoods for contrastive learning based unsupervised representation learning problems

Nearest neighbor (NN) sampling provides more semantic variations than predefined transformations for self-supervised learning (SSL) based image recognition problems. However, its performance is restricted by the quality of the support set, which holds positive samples for the contrastive loss. In this work, we show that the quality of the support set plays a crucial role in any nearest neighbor based method for SSL. We then provide a refined baseline (pNNCLR) to the nearest neighbor based SSL approach (NNCLR). To this end, we introduce pseudo nearest neighbors (pNN) to control the quality of the support set, wherein, rather than sampling the nearest neighbors, we sample in the vicinity of hard nearest neighbors by varying the magnitude of the resultant vector and employing a stochastic sampling strategy to improve the performance. Additionally, to stabilize the effects of uncertainty in NN-based learning, we employ a smooth-weight-update approach for training the proposed network. Evaluation of the proposed method on multiple public image recognition and medical image recognition datasets shows that it performs up to 8 percent better than the baseline nearest neighbor method, and is comparable to other previously proposed SSL methods. The code is available at https://github.com/mb16biswas/pnnclr.

First detection of Rickettsia felis and Ehrlichia canis in the common bed bug Cimex lectularius

Bed bugs, common blood-feeding pests, have received limited attention regarding their potential involvement in emerging pathogen transmission. This study aimed to investigate the main vector-borne bacteria within bed bugs collected from Tunisian governorates and to genetically characterize the identified species. Molecular screening was conducted on field-collected bed bug samples, targeting zoonotic vector-borne bacteria from the Anaplasmataceae family, as well as the genera Rickettsia, Ehrlichia, Bartonella, and Borrelia. A total of 119 Cimex lectularius specimens were collected and grouped into 14 pools based on sampling Tunisian sites. Using genus-specific PCR assays, DNA of Rickettsia and Ehrlichia spp. was detected in a single pool. Sequencing and BLAST analysis of the obtained partial ompB and dsb sequences from positive samples revealed 100% similarity with those of Ehrlichia canis and Rickettsia felis available in GenBank. Obtained partial sequences showed phylogenetic similarity to R. felis and E. canis isolates found in dogs and ticks from American and European countries. To the best of our knowledge, this study is the first to investigate bed bugs in Tunisia and to report the worldwide identification of R. felis and E. canis DNA in the common bed bug, C. lectularius. These findings highlight the need for further research to explore the potential role of bed bugs in the epidemiology of these vector-borne bacteria.

Improved object detection method for unmanned driving based on Transformers

The object detection method serves as the core technology within the unmanned driving perception module, extensively employed for detecting vehicles, pedestrians, traffic signs, and various objects. However, existing object detection methods still encounter three challenges in intricate unmanned driving scenarios: unsatisfactory performance in multi-scale object detection, inadequate accuracy in detecting small objects, and occurrences of false positives and missed detections in densely occluded environments. Therefore, this study proposes an improved object detection method for unmanned driving, leveraging Transformer architecture to address these challenges. First, a multi-scale Transformer feature extraction method integrated with channel attention is used to enhance the network's capability in extracting features across different scales. Second, a training method incorporating Query Denoising with Gaussian decay was employed to enhance the network's proficiency in learning representations of small objects. Third, a hybrid matching method combining Optimal Transport and Hungarian algorithms was used to facilitate the matching process between predicted and actual values, thereby enriching the network with more informative positive sample features. Experimental evaluations conducted on datasets including KITTI demonstrate that the proposed method achieves 3% higher mean Average Precision (mAP) than that of the existing methodologies.

Comparison of different lateral flow assays on bronchoalveolar lavage fluid for invasive aspergillosis screening in non-hematological patients

Several lateral flow assays (LFA) capable of detecting Aspergillus fumigatus in serum and broncho-alveolar lavage fluid (BALF) within the hour, thereby potentially accelerating the screening process, are now commercially available. We prospectively compared three LFA targeting A. fumigatus on BALF collected from non-surgical intensive care patients between June 2022 and February 2023. The three LFA tested were Sõna Aspergillus galactomannan LFA (Immy), Fungadia Aspergillus antigen (Gadia), and AspLFD (OLM Diagnostics). We compared the results of these LFA with those of the galactomannan (GM) PlateliaAspergillus enzyme immunoassay (Bio-Rad), culture on Sabouraud medium and Aspergillus qPCR. We tested 97 BALF samples from 92 patients. In total 84 BALF samples tested negative with all three LFA, and four BALF samples tested positive with the AspLFD assay only (OLM). Only one BALF sample tested positive with the three LFA. In addition, three BALF samples tested positive only with the GM Platelia immunoassay. Four diagnosis of probable invasive aspergillosis were retained for the 92 patients tested. This prospective series included very few positive samples. From a practical point of view, the LFA from OLM presented the simplest protocol for use.

Resistance Rates of Mycobacterium tuberculosis Complex Isolates to Primary Anti-tuberculosis Drugs: A 5-Year Retrospective Study

Background/Aim: Tuberculosis remains a major global health problem with a high morbidity and mortality rate, approximately a quarter of the population is infected with tuberculosis. Drug susceptibility testing is an essential tool for identifying and managing drug-resistant tuberculosis. This study was conducted to evaluate the drug susceptibility pattern of Mycobacterium tuberculosis complex strains isolated from a university hospital. Methods: A total of 10900 samples sent to the microbiology laboratory with the suspicion of tuberculosis clinically between January 2018 and January 2022 were analyzed retrospectively. The automated BACTEC MGIT 960 (Becton Dickinson, USA) was used for sample culture and susceptibility testing. The obtained data were statistically analyzed with the Statistical Package for Social Sciences (SPSS version 20). Results: Out of the 154 isolated positive samples, males and females constituted equal parts of the study population (50%). The majority of tuberculosis cases were in the age group 56–75 years (42.2%), Pulmonary TB was detected in (90.3%) of the patients, while extrapulmonary TB cases were observed in (9.7%). As a result of susceptibility studies on positive samples, isoniazid resistance was 5.2%; streptomycin resistance 1.3%; ethambutol resistance was detected at a rate of 0.6%, while no rifampicin resistant sample was found. Both streptomycin and isoniazid resistance were seen together in 1.3% of the samples. Conclusion: A similar resistance pattern of the first-line antituberculosis drugs was observed in other studies conducted in different provinces of Turkey. The absence of multi-drug resistant and extensively drug-resistant tuberculosis in our study indicates that the tuberculosis surveillance program implemented in our region was successful.

Timing and prediction of secondary bacteremia in patients with COVID‐19: A retrospective cohort study

AbstractBackgroundWe aimed to aid the appropriate use of antimicrobial agents by determining the timing of secondary bacteremia and validating and updating clinical prediction models for bacteremia in patients with COVID‐19.MethodsWe performed a retrospective cohort study on all hospitalized patients diagnosed with COVID‐19 who underwent blood culture tests from January 1, 2020, and September 30, 2021, at an urban teaching hospital in Japan. The primary outcome measure was secondary bacteremia in patients with COVID‐19.ResultsOf the 507 patients hospitalized with COVID‐19, 169 underwent blood culture tests. Eleven of them had secondary bacteremia. The majority of secondary bacteremia occurred on or later than the 9th day after symptom onset. Positive blood culture samples collected on day 9 or later after disease onset had an odds ratio of 22.4 (95% CI 2.76–181.2, p &lt; 0.001) compared with those collected less than 9 days after onset. The area under the receiver operating characteristic curve of the modified Shapiro rule combined with blood culture collection on or after the 9th day from onset was 0.919 (95% CI, 0.843–0.995), and the net benefit was high according to the decision curve analysis.ConclusionsThe timings of symptom onset and hospital admission may be valuable indicators for making a clinical decision to perform blood cultures in patients hospitalized with COVID‐19.

Evaluation of Antibiotics by Disk Diffusion and Minimum Inhibitory Concentration Breakpoints in Urinary Tract Infections

Antibiotic resistance (ABR) has made it more challenging to treat uropathogenic organisms. It is impossible to compromise antimicrobial susceptibility testing (AST), which is essential and has a significant impact on infection treatment strategies. Although labor-intensive and technically challenging for everyday laboratory use, the agar dilution technique is appropriate for monitoring and assessing novel antimicrobials. Objective: To determine the minimum inhibitory concentration (MIC), Agar dilution technique and disk diffusion as susceptibility test methodologies. Methods: This study was carried out at Khyber Girls Medical College (KGMC) Peshawar. Keeping in view the Clinical and Laboratory Standards Institute (CLSI) guidelines AST was executed. BIOMÉRIEUX® API® kits and gram staining were utilized for identification of bacteria. The disk diffusion was performed using Thermo ScientificTM OxoidTM antibiotic discs of Co-trimoxazole, Levofloxacin, Nitrofurantoin and Fosfomycin. The MIC and zone of inhibitions for disk diffusion were noted according to the CLSI protocol. Results: 158 culture positive samples were isolated out of 680 total received. Esherechia Coli (E. coli) (74.1%) being the most isolated organism. In comparison of disk diffusion and agar dilution, categorical agreement for Levofloxacin, Cotrimoxazole, Nitrofurantoin and Fosfomycin were (82.28%, 72.15 %, 87.97% and 82.28%) respectively. Kappa coefficients of (0.64, 0.43, 0.57 and 0.37) (p &lt; 0.0001) were calculated for Levofloxacin, Co-trimoxazole, Nitrofurantoin, and Fosfomycin respectively, revealing considerable level of agreement for these antibiotics. Conclusions: It was concluded that Agar dilution is more precise than disk diffusion but being more labor intensive and technical. Disk diffusion can still produce significantly accurate results with less resource consumption.

Identification of Salmonella sp Bacteria in Processed Balado Shrimp Sold in Food Stalls Around Mulyosari Area

Background &amp; Objective: Food quality is an issue of great concern in developing countries. Because many nutrients are obtained through food. Balado shrimp is a typical Padang dish with a spicy, sweet and savoury taste that is widely admired by all ages from children to adults. The presence of pathogenic bacteria, one of which is Salmonella sp, which contaminates balado shrimp food can cause health problems in the human body until death. The purpose of this study was to determine the presence or absence of Salmonella sp bacteria in processed shrimp balado sold in food stalls in the Mulyosari area. Method: In this study use as many as 30 samples of shrimp balado sold in food stalls Mulyosari area. This sample examination uses a streaking plate procedure and IMViC test. Data analysis using descriptive qualitative. Result: The results of this study showed that there were 3 samples identified Salmonella sp. Of the 30 samples, there was a range of 10% contaminated with Salmonella sp bacteria and 90% were not contaminated with Salmonella sp bacteria. Conclusion: This indicates that positive samples of balado shrimp must be further examined to determine the feasibility of consumption.

Phylogenetic Analysis and Mutation of Sars-Cov-2 in Bats in Karst Malang City, Indonesia

Introduction: A group of people in China were hospitalized with an initial diagnosis of pneumonia of unknown cause. The patients were linked to a wholesale wet seafood and animal market in Wuhan, Hubei Province, China. The disease has spread to other provinces in China, Thailand, Japan, and South Korea in less than a month. SARS-CoV-2 was found to originate from bats. Therefore, this research aims to analyze SARS-CoV-2 mutation in bats in Malang Karst, Indonesia. Methods: Other bat body parts used as research samples include the brain, liver, kidneys, intestines, pancreas, fetus, blood, lungs, and ectoparasites. The samples were taken separately and placed in a container containing 10% PBF. For further analysis, we used RNA Extraction, Real-Time PCR, Sequencing, and CoV Gisaid mutation analysis software to analyze the sequencing data. Then, EMBL software will be used to analyze the phylogenetically. Results and Discussion: There was 1 sample that showed a positive result for Covid-19, namely the intestine of the Cynoptera brachyotis species. There were differences between SARS-CoV-2 in bats in Malang Karst in Indonesia compared to SARS-CoV from 2000 to 2019. The spike protein's receptor binding domain (RBD) is the most variable part of the coronavirus genome. Conclusion: From the research results, one positive sample was obtained using Real-Time PCR, and based on mutation analysis, mutations were found in SARS-CoV-2 against the SARS-CoV virus from 2000-2019. Further research is needed, especially regarding SARS-CoV-2 as a vaccine.

Molecular and Insilico Characterization of Antimicrobial Resistant Escherichia fergusonii and Morganella morganii causing Urinary Tract Infections

Urinary tract infections (UTIs) are a major public health concern causing mortality and morbidity worldwide. There are approximately 13,000 deaths that are linked to UTIs every year. Bacterial antibiotic resistance is increasing globally, making it difficult to treat urinary tract infections. Biochemical tests have resulted in inaccurate identification, due to which wrong empirical treatment of antibiotics is prescribed. The present research work aims to identify rare bacterial isolates along with the assessment of antibiotic resistance patterns. A total of 339 samples were examined for urine cultures. Based on biochemical testing, three positive samples were selected. Culture sensitivity results revealed two multidrug-resistant and one extensively drug-resistant bacteria. MDR strains showed sensitivity to meropenem, gentamicin, cefoperazone-sulbactam, nitrofurantoin, tigecycline and imipenem, and co-amoxiclav while XDR bacteria showed sensitivity to nitrofurantoin and tigecycline. The 16S rRNA sequencing results revealed two Escherichia fergusonii strains and one Morganella morganii strain. Our findings revealed the identification of rare and opportunistic bacteria less reported from Pakistan. In addition, it is the first report of E. fergusonii from Pakistan. In conclusion, using molecular identification of rare and neglected bacteria via PCR is more sensitive and highly specific than other identification methods. In the future, this approach will help in accurate diagnosis and will help in minimizing antibiotic resistance.

Significance of Serological Markers in Diagnosis of Acute Dengue Infection: 2021 Outbreak

Objective: To highlight the significance of Dengue anti-IgM and Dengue NS1 antigen in diagnosing Acute Dengue infection. Study Design: Cross-sectional study. Place and Duration of Study: Virology Department, Armed Forces Institute of Pathology, Rawalpindi Pakistan, from Sep to Nov 2021. Methodology: One hundred and fifty Dengue NS1-positive patients of either gender with age≥5 years were included. ELISA tested patients for detection of Dengue anti-IgM antibody. A questionnaire was used to collect demographic and personal data of each patient. Results: Out of 150 dengue NS1 positive samples, 26(17%) were positive for dengue anti-IgM. In collected samples, Dengue serological markers (NS1 and anti-IgM) positivity was observed based on ‘the day of onset of symptoms. On days 1 and 2, 54 and 44 NS1 positive samples were collected, respectively, but no one was found positive for anti-IgM. Anti-IgM was detected on day-3 and onwards. 27% Dengue anti-IgM positivity was found on day-3, which increased gradually as the time of infection increased. On day four, it was 53%; on day 5, it was 55%; on day-6, it was 60%; on day 7, it was 75%; and on day 8, it was 100%. Conclusions: The findings of present study suggested that anti-Dengue IgM antibodies are less significant than the Dengue NS1 antigen in diagnosing acute dengue infection.

Occurrence of Coxiellosis in ruminants and its associated risk factors

Coxiellosis in animals is caused by the zoonotic pathogen, Coxiella burnetii. Although the disease is of public health importance it remains underdiagnosed and underreported. The cross- sectional study was aimed to estimate the occurrence of the disease in livestock of study area and also to identify the risk factors associated with the disease in animals. Blood, serum, and vaginal swabs samples were collected from 200 ruminants (cattle, sheep, and goats), across various farms in Karnataka, India. These samples were then screened using ELISA and PCR (com1 and IS1111). A questionnaire was administered to the farm owners to collect the risk factor-related information. About 5.26 % cattle, 12.3 % sheep, and 12.5 % goats were positive by ELISA. By PCR, 9.47 % cattle, 9.3 % sheep, and 10 % goats were positive. Overall, the occurrence of 14.73 %, 18.46 % and 17.5 % was estimated in cattle, sheep and goat, respectively. PCR targeting the IS1111 gene detected higher number of samples as positive as compared to the com1 gene PCR. Higher number of vaginal swab samples were detected as positive as compared to blood. History of reproductive disorders (OR: 4.30; 95 %CI:1.95- 9.46), abortion (OR: 30.94; 95 %CI:6.30- 151.84) and repeat breeding (OR:11.36; 95 %CI:4.16- 30.99) were significantly associated with coxiellosis (p < 0.005). Multivariable analysis by logistic regression model analysis suggested retained abortion, repeat breeding and rearing of animal in semi-intensive system as factors significantly associated with the infection. Cultural identification of the PCR positive samples were cultured using embryonated egg propagation and cell culture techniques and positivity was confirmed in six samples. Phylogenetic analysis of the com1 and IS1111 gene revealed clustering based on similar geographic locations. The study estimated the occurrence of the disease in the study area and identified the potential risk factors.