Application of CRISPR-Based Human and Bacterial Ribosomal RNA Depletion for SARS-CoV-2 Shotgun Metagenomic Sequencing.

OBJECTIVES: The aim of this study is to evaluate the effectiveness of a CRISPR-based human and bacterial ribosomal RNA (rRNA) depletion kit (JUMPCODE Genomics) on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shotgun metagenomic sequencing in weakly positive respiratory samples. METHODS: Shotgun metagenomics was performed on 40 respiratory specimens collected from solid organ transplant patients and deceased intensive care unit patients at UCLA Medical Center in late 2020 to early 2021. Human and bacterial rRNA depletion was performed on remnant library pools prior to sequencing by Illumina MiSeq. Data quality was analyzed using Geneious Prime, whereas the identification of SARS-CoV-2 variants and lineages was determined by Pangolin. RESULTS: The average genome coverage of the rRNA-depleted respiratory specimens increased from 72.55% to 93.71% in overall samples and from 29.3% to 83.3% in 15 samples that failed to achieve sufficient genome coverage using the standard method. Moreover, rRNA depletion enhanced genome coverage to over 85% in 11 (73.3%) of 15 low viral load samples with cycle threshold values up to 35, resulting in the identification of genotypes. CONCLUSION: The CRISPR-based human and bacterial rRNA depletion enhanced the sensitivity of SARS-CoV-2 shotgun metagenomic sequencing, especially in low viral load samples.

Environmental genomic surveillance of SARS-CoV-2 in wastewater in Rio de Janeiro, Brazil.

Wastewater-based epidemiology can be a complementary approach for monitoring SARS-CoV-2 prevalence, diversity, and geographic distribution. It is a complementary approach regarding its prevalence and diversity, and geographic distribution. The study aimed to evaluate the genetic diversity of SARS-CoV-2 in two wastewater treatment plants (WWTPs) in Rio de Janeiro, Brazil. Samples were collected over a period of January to December 2021 and were concentrated with PEG8000 and the presence of SARS-CoV-2 was detected using E and N1 genes. Partial sequencing of the SARS-CoV-2 genomes resulted in the identification of variants of concern and variants of interest throughout the collection period. It was possible to identify the Mu, Delta, Gamma and Omicron variants in WWTP1; on the contrary, no variants were observed in WWTP2. To the best of our knowledge, we detected the variant Mu (B.1.621) containing characteristic mutations (S:E484K, S:N501Y) from WWTP, for the first time, in Brazil. Another Mu variant detected from clinical surveillance was announced one month after our finding. The detection of SARS-CoV-2 in wastewater can serve as a tool to monitor the prevalence and epidemiology in each community, helping to understand the spread of the virus among the population.

Metagenomics characterization of respiratory viral RNA pathogens in children under five years with severe acute respiratory infection in the Free State, South Africa.

Prompt detection of viral respiratory pathogens is crucial in managing respiratory infection including severe acute respiratory infection (SARI). Metagenomics next-generation sequencing (mNGS) and bioinformatics analyses remain reliable strategies for diagnostic and surveillance purposes. This study evaluated the diagnostic utility of mNGS using multiple analysis tools compared with multiplex real-time PCR for the detection of viral respiratory pathogens in children under 5 years with SARI. Nasopharyngeal swabs collected in viral transport media from 84 children admitted with SARI as per the World Health Organization definition between December 2020 and August 2021 in the Free State Province, South Africa, were used in this study. The obtained specimens were subjected to mNGS using the Illumina MiSeq system, and bioinformatics analysis was performed using three web-based analysis tools; Genome Detective, One Codex and Twist Respiratory Viral Research Panel. With average reads of 211323, mNGS detected viral pathogens in 82 (97.6%) of the 84 patients. Viral aetiologies were established in nine previously undetected/missed cases with an additional bacterial aetiology (Neisseria meningitidis) detected in one patient. Furthermore, mNGS enabled the much needed viral genotypic and subtype differentiation and provided significant information on bacterial co-infection despite enrichment for RNA viruses. Sequences of nonhuman viruses, bacteriophages, and endogenous retrovirus K113 (constituting the respiratory virome) were also uncovered. Notably, mNGS had lower detectability rate for severe acute respiratory syndrome coronavirus 2 (missing 18/32 cases). This study suggests that mNGS, combined with multiple/improved bioinformatics tools, is practically feasible for increased viral and bacterial pathogen detection in SARI, especially in cases where no aetiological agent could be identified by available traditional methods.

Metagenomics of gut microbiome for migratory seagulls in Kunming city revealed the potential public risk to human health.

BACKGROUND: Seagull as a migratory wild bird has become most popular species in southwest China since 1980s. Previously, we analyzed the gut microbiota and intestinal pathogenic bacteria configuration for this species by using 16S rRNA sequencing and culture methods. To continue in-depth research on the gut microbiome of migratory seagulls, the metagenomics, DNA virome and RNA virome were both investigated for their gut microbial communities of abundance and diversity in this study. RESULTS: The metagenomics results showed 99.72% of total species was bacteria, followed by viruses, fungi, archaea and eukaryota. In particular, Shigella sonnei, Escherichia albertii, Klebsiella pneumonia, Salmonella enterica and Shigella flexneri were the top distributed taxa at species level. PCoA, NMDS, and statistics indicated some drug resistant genes, such as adeL, evgS, tetA, PmrF, and evgA accumulated as time went by from November to January of the next year, and most of these genes were antibiotic efflux. DNA virome composition demonstrated that Caudovirales was the most abundance virus, followed by Cirlivirales, Geplafuvirales, Petitvirales and Piccovirales. Most of these phages corresponded to Enterobacteriaceae and Campylobacteriaceae bacterial hosts respectively. Caliciviridae, Coronaviridae and Picornaviridae were the top distributed RNA virome at family level of this migratory animal. Phylogenetic analysis indicated the sequences of contigs of Gammacoronavirus and Deltacoronavirus had highly similarity with some coronavirus references. CONCLUSIONS: In general, the characteristics of gut microbiome of migratory seagulls were closely related to human activities, and multiomics still revealed the potential public risk to human health.

Respiratory metagenomics: route to routine service.

PURPOSE OF REVIEW: The coronavirus disease 2019 pandemic demonstrated broad utility of pathogen sequencing with rapid methodological progress alongside global distribution of sequencing infrastructure. This review considers implications for now moving clinical metagenomics into routine service, with respiratory metagenomics as the exemplar use-case. RECENT FINDINGS: Respiratory metagenomic workflows have completed proof-of-concept, providing organism identification and many genotypic antimicrobial resistance determinants from clinical samples in <6 h. This enables rapid escalation or de-escalation of empiric therapy for patient benefit and reducing selection of antimicrobial resistance, with genomic-typing available in the same time-frame. Attention is now focussed on demonstrating clinical, health-economic, accreditation, and regulatory requirements. More fundamentally, pathogen sequencing challenges the traditional culture-orientated time frame of microbiology laboratories, which through automation and centralisation risks becoming increasingly separated from the clinical setting. It presents an alternative future where infection experts are brought together around a single genetic output in an acute timeframe, aligning the microbiology target operating model with the wider human genomic and digital strategy. SUMMARY: Pathogen sequencing is a transformational proposition for microbiology laboratories and their infectious diseases, infection control, and public health partners. Healthcare systems that link output from routine clinical metagenomic sequencing, with pandemic and antimicrobial resistance surveillance, will create valuable tools for protecting their population against future infectious diseases threats.

Viral metagenomics unveils MW (Malawi) polyomavirus infection in Brazilian pediatric patients with acute respiratory disease.

Viral metagenomics has been extensively applied for the identification of emerging or poorly characterized viruses. In this study, we applied metagenomics for the identification of viral infections among pediatric patients with acute respiratory disease, but who tested negative for SARS-CoV-2. Twelve pools composed of eight nasopharyngeal specimens were submitted to viral metagenomics. Surprisingly, in two of the pools, we identified reads belonging to the poorly characterized Malawi polyomavirus (MWPyV). Then, the samples composing the positive pools were individually tested using quantitative polymerase chain reaction for identification of the MWPyV index cases. MWPyV-positive samples were also submitted to respiratory virus panel testing due to the metagenomic identification of different clinically important viruses. Of note, MWPyV-positive samples tested also positive for respiratory syncytial virus types A and B. In this study, we retrieved two complete MWPyV genome sequences from the index samples that were submitted to phylogenetic inference to investigate their viral origin. Our study represents the first molecular and genomic characterization of MWPyV obtained from pediatric patients in South America. The detection of MWPyV in acutely infected infants suggests that this virus might participate (coparticipate) in cases of respiratory symptoms. Nevertheless, future studies based on testing of a larger number of clinical samples and MWPyV complete genomes appear to be necessary to elucidate if this emerging polyomavirus might be clinically important.

A case for investment in clinical metagenomics in low-income and middle-income countries.

Clinical metagenomics is the diagnostic approach with the broadest capacity to detect both known and novel pathogens. Clinical metagenomics is costly to run and requires infrastructure, but the use of next-generation sequencing for SARS-CoV-2 molecular epidemiology in low-income and middle-income countries (LMICs) offers an opportunity to direct this infrastructure to the establishment of clinical metagenomics programmes. Local implementation of clinical metagenomics is important to create relevant systems and evaluate cost-effective methodologies for its use, as well as to ensure that reference databases and result interpretation tools are appropriate to local epidemiology. Rational implementation, based on the needs of LMICs and the available resources, could ultimately improve individual patient care in instances in which available diagnostics are inadequate and supplement emerging infectious disease surveillance systems to ensure the next pandemic pathogen is quickly identified.

Genomic Analysis of the Suspicious SARS-CoV-2 Sequences in the Public Sequencing Database.

SARS-CoV-2 has infected more than 600 million people. However, the origin of the virus is still unclear; knowing where the virus came from could help us prevent future zoonotic epidemics. Sequencing data, particularly metagenomic data, can profile the genomes of all species in the sample, including those not recognized at the time, thus allowing for the identification of the progenitor of SARS-CoV-2 in samples collected before the pandemic. We analyzed the data from 5,196 SARS-CoV-2-positive sequencing runs in the NCBI's SRA database with collection dates prior to 2020 or unknown. We found that the mutation patterns obtained from these suspicious SARS-CoV-2 reads did not match the genome characteristics of an unknown progenitor of the virus, suggesting that they may derive from circulating SARS-CoV-2 variants or other coronaviruses. Despite a negative result for tracking the progenitor of SARS-CoV-2, the methods developed in the study could assist in pinpointing the origin of various pathogens in the future. IMPORTANCE Sequences that are homologous to the SARS-CoV-2 genome were found in numerous sequencing runs that were not associated with the SARS-CoV-2 studies in the public database. It is unclear whether they are derived from the possible progenitor of SARS-CoV-2 or contamination of more recent SARS-CoV-2 variants circulated in the population due to the lack of information on the collection, library preparation, and sequencing processes. We have developed a computational framework to infer the evolutionary relationship between sequences based on the comparison of mutations, which enabled us to rule out the possibility that these suspicious sequences originate from unknown progenitors of SARS-CoV-2.

Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome.

Our knowledge of the role of the gut microbiome in acute coronavirus disease 2019 (COVID-19) and post-acute COVID-19 is rapidly increasing, whereas little is known regarding the contribution of multi-kingdom microbiota and host-microbial interactions to COVID-19 severity and consequences. Herein, we perform an integrated analysis using 296 fecal metagenomes, 79 fecal metabolomics, viral load in 1378 respiratory tract samples, and clinical features of 133 COVID-19 patients prospectively followed for up to 6 months. Metagenomic-based clustering identifies two robust ecological clusters (hereafter referred to as Clusters 1 and 2), of which Cluster 1 is significantly associated with severe COVID-19 and the development of post-acute COVID-19 syndrome. Significant differences between clusters could be explained by both multi-kingdom ecological drivers (bacteria, fungi, and viruses) and host factors with a good predictive value and an area under the curve (AUC) of 0.98. A model combining host and microbial factors could predict the duration of respiratory viral shedding with 82.1% accuracy (error ± 3 days). These results highlight the potential utility of host phenotype and multi-kingdom microbiota profiling as a prognostic tool for patients with COVID-19.

The gut virome in health and disease: new insights and associations.

PURPOSE OF REVIEW: Recent years have seen great strides made in the field of viral metagenomics. Many studies have reported alterations in the virome in different disease states. The vast majority of the human intestinal virome consists of bacteriophages, viruses that infect bacteria. The dynamic relationship between gut bacterial populations and bacteriophages is influenced by environmental factors that also impact host health and disease. In this review, we focus on studies highlighting the dynamics of the gut virome and fluctuations associated with disease states. RECENT FINDINGS: Novel correlations have been identified between the human gut virome and diseases such as obesity, necrotizing enterocolitis and severe acute respiratory syndrome coronavirus 2 infection. Further associations between the virome and cognition, diet and geography highlight the complexity of factors that can influence the dynamic relationship between gut bacteria, bacteriophages and health. SUMMARY: Here, we highlight some novel associations between the virome and health that will be the foundation for future studies in this field. The future development of microbiome-based interventions, identification of biomarkers, and novel therapeutics will require a thorough understanding of the gut virome and its dynamics.

Pathogenic and metagenomic evaluations reveal the correlations of porcine epidemic diarrhea virus, porcine kobuvirus and porcine astroviruses with neonatal piglet diarrhea.

Porcine epidemic diarrhea virus (PEDV) frequently causes diarrhea outbreaks. However, whether newly discovered enteric viruses such as porcine kobuvirus (PKV) and porcine astroviruses (PAstVs) are also correlated with diarrhea is still unclear. Diarrhea outbreaks were reported in a PEDV-vaccinated pig farm in Xinjiang Uygur Autonomous Region of China from 2019 to 2020. PEDV was a common pathogen detected in fecal samples by routine RT-PCR assays. The PEDV positive fecal sample was used for pathogenic analysis due to the failure isolation of PEDV. The challenged neonatal piglets appeared watery diarrhea within one day post infection (dpi) and all died within 6 dpi. Histopathological and immunohistochemical examinations supported that PEDV is a major pathogen causing intestinal lesions. To further explore enteric viruses associated with neonatal piglet diarrhea, metagenomics sequencing was performed for the diarrheic piglets. Remarkably, PKV was the most abundant virus (58.33%) followed by PEDV (34.45%) and PAstVs (7.22%), which were also confirmed by real-time RT-PCR assays. Significant in vivo replications of PEDV and PKV could only be observed in challenged piglets whilst PAstVs maintained similar virus loads in both challenged and mock infected piglets. Overall, this study provides first pathogenic and metagenomic evidence that significant proliferations of PEDV and PKV are closely associated with severe diarrhea in neonatal piglets, while PAstVs likely play limited roles in neonatal piglet diarrhea.

A metagenomic DNA sequencing assay that is robust against environmental DNA contamination.

Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present Sample-Intrinsic microbial DNA Found by Tagging and sequencing (SIFT-seq) a metagenomic sequencing assay that is robust against environmental DNA contamination introduced during sample preparation. The core idea of SIFT-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied SIFT-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of sepsis and inflammatory bowel disease in blood.

VirusTaxo: Taxonomic classification of viruses from the genome sequence using k-mer enrichment.

Classification of viruses into their taxonomic ranks (e.g., order, family, and genus) provides a framework to organize an abundant population of viruses. Next-generation metagenomic sequencing technologies lead to a rapid increase in generating sequencing data of viruses which require bioinformatics tools to analyze the taxonomy. Many metagenomic taxonomy classifiers have been developed to study microbiomes, but it is particularly challenging to assign the taxonomy of diverse virus sequences and there is a growing need for dedicated methods to be developed that are optimized to classify virus sequences into their taxa. For taxonomic classification of viruses from metagenomic sequences, we developed VirusTaxo using diverse (e.g., 402 DNA and 280 RNA) genera of viruses. VirusTaxo has an average accuracy of 93% at genus level prediction in DNA and RNA viruses. VirusTaxo outperformed existing taxonomic classifiers of viruses where it assigned taxonomy of a larger fraction of metagenomic contigs compared to other methods. Benchmarking of VirusTaxo on a collection of SARS-CoV-2 sequencing libraries and metavirome datasets suggests that VirusTaxo can characterize virus taxonomy from highly diverse contigs and provide a reliable decision on the taxonomy of viruses.

Evaluation of Metagenomic and Targeted Next-Generation Sequencing Workflows for Detection of Respiratory Pathogens from Bronchoalveolar Lavage Fluid Specimens.

Next-generation sequencing (NGS) workflows applied to bronchoalveolar lavage (BAL) fluid specimens could enhance the detection of respiratory pathogens, although optimal approaches are not defined. This study evaluated the performance of the Respiratory Pathogen ID/AMR (RPIP) kit (Illumina, Inc.) with automated Explify bioinformatic analysis (IDbyDNA, Inc.), a targeted NGS workflow enriching specific pathogen sequences and antimicrobial resistance (AMR) markers, and a complementary untargeted metagenomic workflow with in-house bioinformatic analysis. Compared to a composite clinical standard consisting of provider-ordered microbiology testing, chart review, and orthogonal testing, both workflows demonstrated similar performances. The overall agreement for the RPIP targeted workflow was 65.6% (95% confidence interval, 59.2 to 71.5%), with a positive percent agreement (PPA) of 45.9% (36.8 to 55.2%) and a negative percent agreement (NPA) of 85.7% (78.1 to 91.5%). The overall accuracy for the metagenomic workflow was 67.1% (60.9 to 72.9%), with a PPA of 56.6% (47.3 to 65.5%) and an NPA of 77.2% (68.9 to 84.1%). The approaches revealed pathogens undetected by provider-ordered testing (Ureaplasma parvum, Tropheryma whipplei, severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2], rhinovirus, and cytomegalovirus [CMV]), although not all pathogens detected by provider-ordered testing were identified by the NGS workflows. The RPIP targeted workflow required more time and reagents for library preparation but streamlined bioinformatic analysis, whereas the metagenomic assay was less demanding technically but required complex bioinformatic analysis. The results from both workflows were interpreted utilizing standardized criteria, which is necessary to avoid reporting nonpathogenic organisms. The RPIP targeted workflow identified AMR markers associated with phenotypic resistance in some bacteria but incorrectly identified blaOXA genes in Pseudomonas aeruginosa as being associated with carbapenem resistance. These workflows could serve as adjunctive testing with, but not as a replacement for, standard microbiology techniques.

WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs.

The classification of biological sequences is an open issue for a variety of data sets, such as viral and metagenomics sequences. Therefore, many studies utilize neural network tools, as the well-known methods in this field, and focus on designing customized network structures. However, a few works focus on more effective factors, such as input encoding method or implementation technology, to address accuracy and efficiency issues in this area. Therefore, in this work, we propose an image-based encoding method, called as WalkIm, whose adoption, even in a simple neural network, provides competitive accuracy and superior efficiency, compared to the existing classification methods (e.g. VGDC, CASTOR, and DLM-CNN) for a variety of biological sequences. Using WalkIm for classifying various data sets (i.e. viruses whole-genome data, metagenomics read data, and metabarcoding data), it achieves the same performance as the existing methods, with no enforcement of parameter initialization or network architecture adjustment for each data set. It is worth noting that even in the case of classifying high-mutant data sets, such as Coronaviruses, it achieves almost 100% accuracy for classifying its various types. In addition, WalkIm achieves high-speed convergence during network training, as well as reduction of network complexity. Therefore WalkIm method enables us to execute the classifying neural networks on a normal desktop system in a short time interval. Moreover, we addressed the compatibility of WalkIm encoding method with free-space optical processing technology. Taking advantages of optical implementation of convolutional layers, we illustrated that the training time can be reduced by up to 500 time. In addition to all aforementioned advantages, this encoding method preserves the structure of generated images in various modes of sequence transformation, such as reverse complement, complement, and reverse modes.

Diagnosis and analysis of unexplained cases of childhood encephalitis in Australia using metatranscriptomic sequencing.

Encephalitis is most often caused by a variety of infectious agents identified through diagnostic tests utilizing cerebrospinal fluid. We investigated the clinical characteristics and potential aetiological agents of unexplained encephalitis through metagenomic sequencing of residual clinical samples from multiple tissue types and independent clinical review. Forty-three specimens were collected from 18 encephalitis cases with no cause identified by the Australian Childhood Encephalitis study. Samples were subjected to total RNA sequencing ('metatranscriptomics') to determine the presence and abundance of potential pathogens, and to describe the possible aetiologies of unexplained encephalitis. Using this protocol, we identified five RNA and two DNA viruses associated with human infection from both non-sterile and sterile sites, which were confirmed by PCR. These comprised two human rhinoviruses, two human seasonal coronaviruses, two polyomaviruses and one picobirnavirus. Human rhinovirus and seasonal coronaviruses may be responsible for five of the encephalitis cases. Immune-mediated encephalitis was considered likely in six cases and metatranscriptomics did not identify a possible pathogen in these cases. The aetiology remained unknown in nine cases. Our study emphasizes the importance of respiratory viruses in the aetiology of unexplained child encephalitis and suggests that non-central-nervous-system sampling in encephalitis clinical guidelines and protocols could improve the diagnostic yield.

Metagenomics-enabled microbial surveillance.

Lessons learnt from the COVID-19 pandemic include increased awareness of the potential for zoonoses and emerging infectious diseases that can adversely affect human health. Although emergent viruses are currently in the spotlight, we must not forget the ongoing toll of morbidity and mortality owing to antimicrobial resistance in bacterial pathogens and to vector-borne, foodborne and waterborne diseases. Population growth, planetary change, international travel and medical tourism all contribute to the increasing frequency of infectious disease outbreaks. Surveillance is therefore of crucial importance, but the diversity of microbial pathogens, coupled with resource-intensive methods, compromises our ability to scale-up such efforts. Innovative technologies that are both easy to use and able to simultaneously identify diverse microorganisms (viral, bacterial or fungal) with precision are necessary to enable informed public health decisions. Metagenomics-enabled surveillance methods offer the opportunity to improve detection of both known and yet-to-emerge pathogens.

Genomic characterization of the dominating Beta, V2 variant carrying vaccinated (Oxford-AstraZeneca) and nonvaccinated COVID-19 patient samples in Bangladesh: A metagenomics and whole-genome approach.

Bangladesh is experiencing a second wave of COVID-19 since March 2021, despite the nationwide vaccination drive with ChAdOx1 (Oxford-AstraZeneca) vaccine from early February 2021. Here, we characterized 19 nasopharyngeal swab (NPS) samples from COVID-19 suspect patients using genomic and metagenomic approaches. Screening for SARS-CoV-2 by reverse transcriptase polymerase chain reaction and metagenomic sequencing revealed 17 samples of COVID-19 positive (vaccinated = 10, nonvaccinated = 7) and 2 samples of COVID-19 negative. We did not find any significant correlation between associated factors including vaccination status, age or sex of the patients, diversity or abundance of the coinfected organisms/pathogens, and the abundance of SARS-CoV-2. Though the first wave of the pandemic was dominated by clade 20B, Beta, V2 (South African variant) dominated the second wave (January 2021 to May 2021), while the third wave (May 2021 to September 2021) was responsible for Delta variants of the epidemic in Bangladesh including both vaccinated and unvaccinated infections. Noteworthily, the receptor binding domain (RBD) region of S protein of all the isolates harbored similar substitutions including K417N, E484K, and N501Y that signify the Beta, while D614G, D215G, D80A, A67V, L18F, and A701V substitutions were commonly found in the non-RBD region of Spike proteins. ORF7b and ORF3a genes underwent a positive selection (dN/dS ratio 1.77 and 1.24, respectively), while the overall S protein of the Bangladeshi SARS-CoV-2 isolates underwent negative selection pressure (dN/dS = 0.621). Furthermore, we found different bacterial coinfections like Streptococcus agalactiae, Neisseria meningitidis, Elizabethkingia anophelis, Stenotrophomonas maltophilia, Klebsiella pneumoniae, and Pseudomonas plecoglossicida, expressing a number of antibiotic resistance genes such as tetA and tetM. Overall, this approach provides valuable insights on the SARS-CoV-2 genomes and microbiome composition from both vaccinated and nonvaccinated patients in Bangladesh.