Shigella sonnei is the most common agent of shigellosis in high-income countries, and causes a significant disease burden in low- and middle-income countries. Antimicrobial resistance is increasingly common in all settings. Whole genome sequencing (WGS) is increasingly utilised for S. sonnei outbreak investigation and surveillance, but comparison of data between studies and labs is challenging. Here, we present a genomic framework and genotyping scheme for S. sonnei to efficiently identify genotype and resistance determinants from WGS data. The scheme is implemented in the software package Mykrobe and tested on thousands of genomes. Applying this approach to analyse >4,000 S. sonnei isolates sequenced in public health labs in three countries identified several common genotypes associated with increased rates of ciprofloxacin resistance and azithromycin resistance, confirming intercontinental spread of highly-resistant S. sonnei clones and demonstrating the genomic framework can facilitate monitoring the spread of resistant clones, including those that have recently emerged, at local and global scales. Whole genome sequencing is increasingly being adopted for Shigella sonnei outbreak investigation and surveillance, but there is no global classification standard. Here, the authors develop and validate a genomic framework implemented using open-source software, and demonstrate its application using surveillance data. Shigella spp are Gram-negative bacterial pathogens that cause shigellosis . Shigella are transmitted via the faecal-oral route and estimated to cause ~188 million infections annually, leading to ~160,000 deaths mainly in young children1. In low- and middle-income settings, most of the Shigella disease burden of shigellosis is in children under five years2, however in high-income countries Shigella is frequently detected in returned travellers or men who have sex with men (MSM)4. Shigella sonnei is the most frequently isolated agent of shigellosis in high-income countries and in those that are economically developing6. S. sonnei emerged recently (~350 years ago7), share a single serotype, and display limited genomic diversity ). These properties make it difficult to differentiate and track S. sonnei strains7, motivating adoption of whole-genome sequencing (WGS) for research and public health surveillance of this organism8. Core-genome MLST (cgMLST) is available via the Escherichia coli scheme in EnteroBase9 but has not been widely adopted for S. sonnei surveillance, and most public health labs and research studies rely on the higher-resolution technique of single nucleotide variant (SNV)-based phylogenetics analysis.S. sonnei is divided into five major lineages10. Several WGS studies have investigated regional S. sonnei epidemiology and population structure, including in Asia14, Australia4, the Middle East15, South America10, and the United Kingdom18; and defined additional sub-lineage-level phylogenetic groups of local epidemiological importance, associated with features such as ciprofloxacin-resistance19, transmission within Orthodox Jewish communities15, or transmission amongst MSM17. S. sonnei from Asia, Europe, Australia and North America have for >20 years been dominated by Lineage 3 strains that are resistant to early first-line antimicrobials  due to the presence of antimicrobial resistance (AMR) genes acquired horizontally via the small plasmid spA and a chromosomal Tn7-like transposon21. Resistance to chloramphenicol and/or ampicillin is also observed 22). Reduced susceptibility to fluoroquinolones has emerged on multiple occasions and in multiple locations through acquisition of point mutations within the quinolone resistance determining region (QRDR) of gyrA11. Resistance to ciprofloxacin has emerged at least once via the accumulation of three QRDR mutations (2 in gyrA and one in parC) in a South Asian sublineage that has since been detected on multiple continents23. Resistance to the last few remaining drugs is increasing through the acquisition and maintenance of plasmids carrying mph(A) and ermB (azithromycin resistance) or extended-spectrum beta-lactamase (ESBL) genes (ceftriaxone resistance), often in combination with additional aminoglycoside resistance genes24.The global population of S. sonnei is a notifiable infection and subject to public health surveillance and outbreak investigations, which are increasingly conducted using WGS28. However, the lack of a defined global genomic framework and accompanying genotype nomenclature hampers both local reporting, outbreak detection, and patterns of spread within regions. For example, most S. sonnei WGS studies have reported which of the five major lineages their novel isolates belong to, but have had to download public reference genome data, construct whole genome alignments, and infer phylogenies to achieve this basic identification27. Studies of MSM S. sonnei in different settings have designated different names for the same lineages18, obscuring the fact that the same clones are spreading amongst MSM communities in different countries, and the only way to recognize this currently is through construction of whole-genome phylogenies incorporating data from multiple prior studies29.In many countries, Mycobacterium tuberculosis30 and Salmonella enterica serovar Typhi31, which display similarly low levels of genomic diversity to S. sonnei. These frameworks enable fast and accurate typing of clinical isolates from WGS data without the need for time-consuming comparative genomics or phylogenetics, facilitating straightforward identification of  epidemiologically important lineages from WGS data.WGS-based genotyping frameworks based on single nucleotide variants (SNVs) have been widely adopted for the bacterial pathogens S. sonnei and (i) propose a hierarchical SNV-based genotyping scheme, which we define using 1935 globally distributed genomes; (ii) implement the scheme within the free and open-source Mykrobe32 software alongside detection of genetic determinants that are highly predictive of fluoroquinolone susceptibility phenotypes in S. sonnei28; and (iii) validate this approach to genotyping using an additional 2015 genomes that were sequenced in public health laboratories and deposited in the publicly available GenomeTrakr database. Code is available at https://github.com/katholt/sonneityping. By applying this novel genotyping framework to S. sonnei WGS data generated in public health laboratories on three continents, we demonstrate the utility of the new scheme for tracking and reporting emerging AMR clones both within and between jurisdictions.Here, we describe the global population structure for S. sonnei genomes from eight studies16 as our “discovery” dataset , followed by Lineage 2 (10.7%), Lineage 5 (1.4%), Lineage 1 (0.9%) and Lineage 4 (n = 1). The pairwise core-genome SNV distance distribution revealed peaks and troughs which we used to set thresholds to define clusters at different levels of resolution  and subclades .The recombination-filtered core-genome maximum likelihood phylogeny inferred from these genomes Fig.  was robuion Fig. . A thresM. tuberculosis and S. Typhi schemes, this hierarchical nomenclature facilitates easy recognition of relationships between genotypes; e.g., subclades 3.6.1, 3.6.2, …, 3.6.N are sister groups in the whole-genome phylogeny, nested within clade 3.6, which falls within Lineage 3  were represented by isolates from just one  or two  continents. At the other extreme, clades 3.6 and 3.7 were widely distributed, with representatives on all six continents . Subclades showed even greater geographic specificity, with 74% (n = 71) represented by a single continent only and 72% (n = 69) represented by a single region only. Fifty-nine subclades (61%) were dominated by genomes from a single country.Whilst the discovery set is not a systematic sampling across geographic regions, it can provide some preliminary insights into the global distribution of nts Fig. . Clades S. sonnei genotyping scheme is to facilitate identification and communication about subtypes of public health interest, we reviewed the position of genetic clusters that have been described in the literature as being of epidemiological importance , and MSM communities in Australia (3.6.1.1.2) or the UK (3.6.1.1.3.1) , with isolation dates between 2015 and 2019.We identified marker SNVs unique to each genotype  belonged to clade 3.6. Genotype 3.6.1.1.2 (CipR.MSM5) was the most prevalent, assigned to 26.6% of the genomes, followed by 3.6.1.1  (Supplementary Table n = 16), followed by the USA (n = 13); likely due to a high number of travel-associated cases. All GenomeTrakr genomes deposited from Israel were identified as 3.7.30.4  or 3.7.30.4.1 ; these genotypes were also detected amongst UK and USA genomes.We identified 17 different genotypes, all belonging to clades 3.6 or 3.7  and mapped the genotype assignments to this tree . All groups of isolates sharing a genotype assignment based on marker SNVs constituted monophyletic clades within the core-genome phylogeny, consistent with the expected behaviour of the scheme. This was true for all levels in the hierarchical scheme, including clades, subclades, and higher-resolution epidemiological groups.To verify the genotyping scheme accurately captured the population structure present in the GenomeTrakr isolates, we constructed a core-genome phylogeny including both the validation set and Lineage 3 discovery set ) were found in all lineages but were most widely distributed across clades of Lineage 3  followed by Lineage 2 (81% of clades) , the class II integron integrase (intA2) and AMR genes in the integron cassette  were absent from Lineage 1 but found in Lineages 2 and 3 (40–41% of clades) and the single Lineage 5 clade , 3.7 (99%) and 2.12 (88%), where it was typically accompanied by spA genes ) resulting in resistance to co-trimoxazole , catA1 and blaOXA-1, which are known to mobilise together on the SRL, co-occurred in clades 3.3, 3.4 and 3.5, consistent with prior reports of SRL in clade Latin America IIIa (3.4)10. Acquired genes blaTEM-1 and sul1 were also found at low frequencies across diverse genotypes  Fig. . Tn7 tran = 61/6715 genomes had GyrA-S83L+GyrA-D87G). QRDR triple mutants, associated with ciprofloxacin resistance, were detected only in the CipR sublineage (genotype 3.6.1.1) which harbours GyrA-S83L+GyrA-D87G+ParC-S80I. The emergence and evolutionary dynamics of this CipR sublineage from within the Central Asia IIIa clade (genotype 3.6) were recently described in a detailed phylodynamic study of fluoroquinolone resistant isolates from diverse sources by The et al.14. That study divided the Central Asia III clade into two populations: Pop1, with either GyrA-D87Y or GyrA-S83L arising on two independent occasions in South Asia in the mid-1990 s; and Pop2, which arose from Pop1 genomes carrying GyrA-S83L in South Asia in the early 2000s, and then acquired GyrA-D87G and ParC-S80I to become fluoroquinolone resistant before spreading geographically14. Applying our new genotyping scheme to the genomes from The et al.14  and its subclades 3.6.1, 3.6.2, 3.6.3, 3.6.4; and Pop2 maps to sublineage 3.6.1.1  including its subgroups 3.6.1.1.1 , 3.6.1.1.2 , 3.6.1.1.3 (n = 16) and 3.6.1.1.3.1 .QRDR SNVs were detected in 50.4% of all genomes, distributed across six clades and 12 subclades Fig. , consistmph(A) and ermB were detected at high frequency in genotypes 3.6.1.1.2  and 3.7.29.1.2.1 ; mph(A) was present alone in the single 1.2.1 genome , and alone or with ermB at lower frequency (<65%) amongst other Lineage 3 genotypes  genomes carried mph(A) in addition to Tn7 and spA genes, rendering them resistant to azithromycin, ciprofloxacin and first-line drugs , leaving extended-spectrum cephalosporins as the last remaining oral drug. ESBL genes were detected only sporadically, at low frequencies in clades 3.6 (13%) and 3.7 (12%), across 25 distinct genotypes  . Concerningly, we detected 40 genomes with resistance determinants for azithromycin, third-generation cephalosporins and fluoroquinolones, all within CipR genotype 3.6.1.1 . These genomes were isolated between 2014 and 2019, and were found in genomes from England (n = 21), Australia (n = 13), the USA (n = 3), Vietnam (n = 3) and the Netherlands (n = 2) (further details below).Determinants of resistance to azithromycin and extended-spectrum cephalosporins were rare and concentrated mainly in clades 3.6 and 3.7. The plasmid-borne azithromycin resistance genes S. sonnei genotyping framework can facilitate the rapid tracking and reporting of emerging AMR trends across jurisdictions, we applied it to genomic surveillance data from Victoria, Australia (n = 644), England (n = 2867) and USA (n = 711) generated over a 4-year period (2016–2019) (data in Supplementary Data S. sonnei notifications in Victoria)4 and all those sent to the Public Health England Gastrointestinal Bacteria Reference Unit28 (provided direct from the reference laboratories for the present study); and ~5% of those notified in the USA (sourced from the public GenomeTrakr database33). The total time taken to generate genotyping reports (including QRDR mutations) for all n = 4222 isolates was ~40 sec – 1 min per isolate using Mykrobe, with raw Illumina sequence files (fastq format) as input.To demonstrate how the USA n = 71 generatS. sonnei samples from each country, beginning at ≤25% in 2016 and reaching 85% in Australia, 39% in England and 44% in USA in 2019  all resistant isolates in all three countries derive from the previously-described CipR sublineage 3.6.1.1 that emerged from South Asia in the early 2000s; (ii) the reported spread of resistant S. sonnei in MSM communities in Australia and England involves the same strain ; (iii) this strain represents a clonal subgroup of the CipR sublineage  that has disseminated intercontinentally over the last few years and become responsible for the majority of fluoroquinolone resistant S. sonnei infections in all three countries. .Figure mph(A)) in each country, and the genotypes responsible. Resistance rates were high (>50%) in Australia across the whole period, reflecting documented outbreaks in the MSM community4. In England and the USA, rates increased between 2016 and 2019, from 13 to 38% in England and 15 to 45% in the USA  + genomes differed markedly between countries in 2016, dominated by 3.7.29  in Australia, 3.7.25  and 3.6.1.1.2  in England, and 3.6.1.1 , 3.7.29  and 3.6.1.1.2  in the USA  + isolates in Australia, 62% in England and 93% in USA  in all three countries in 2019 , and 36 of these were QRDR triple mutants belonging to 3.6.1.1 (CipR). The latter were concentrated in genotypes 3.6.1.1, 3.6.1.1.1 and 3.6.1.1.2 and comprised 13 Australian genomes , 20 English genomes  and 3 USA genomes   reads for eight of these genomes. Correct genotypes were returned in all cases (providing the --ont flag to Mykrobe when using the long reads as input). This overall approach is designed to facilitate easy communication between laboratories and jurisdictions, and straightforward comparison of pathogen populations over time, allowing for rapid identification of clonal dissemination and AMR trends.The genotyping scheme was constructed with a view towards stability, prioritising as markers SNVs that are found in highly conserved core genes that are not under adaptive selection in the population. We also endeavoured to make the scheme backwards compatible by identifying and designating unique genotypes for S. sonnei causing disease in each jurisdiction. They also clearly identify common ciprofloxacin and azithromycin resistant clones that have spread globally and are now present in all three jurisdictions, which was previously not obvious from individual studies, as there was no common nomenclature.To demonstrate the utility of the genotyping scheme for public health applications, we applied it to summarise large genomic surveillance datasets from three jurisdictions in Australia, England and the USA. The results provide a straightforward view of temporal trends in the populations of S. sonnei in reference laboratories, the first step of which usually involves comparison of newly sequenced genomes to those from prior cases in the same jurisdiction  and/or other jurisdictions . Notably, the public health labs in Australia, England and USA from which we sourced genomic surveillance data . The resulting genotyping information can be easily interpreted, compared and stored in (non-sequence) databases for future reference, facilitating epidemiological investigations without need for direct comparisons with any other genome sequences. Detailed phylogenetic analysis can then be applied to subsets of isolates that share the same or similar genotypes, if needed to address specific questions . Notably, such phylogenetic analyses are essential to identify the emergence of novel clusters from within currently defined genotypes. If a high-quality S. sonnei isolated from MSM in Switzerland recently reported identifying the strains as belonging to the same clones spreading through MSM communities in England. To achieve this identification, they had to download English genome data, compare their newly isolated genomes to these using read mapping, and construct and interpret whole genome phylogenies29. A similar study using WGS to investigate a ciprofloxacin-resistant outbreak in California used the same informatics approach to conclude that the local strain belonged to the previously described ciprofloxacin resistant lineage originating in South Asia23. Another recent study from Switzerland used WGS to investigate an increase in ESBL S. sonnei using a combination of cgMLST and phylogenetics36. Using our new genotyping approach, all of these identifications could be made within minutes of obtaining sequence data, with no need for external comparative data, background knowledge of S. sonnei genetics, or complex computational infrastructure and expertise , including loci under positive selection such as gyrA and parC, which are used to define core genome sequence types (cgSTs). EnteroBase also assigns cgSTs to hierarchical clusters (HC) with stable names, using the HierCC algorithm9, at various locus-distance thresholds . HC5 is widely used for detecting outbreak clusters of Salmonella and E. coli in foodborne surveillance41, however clustering of S. sonnei at HC5 (or even HC10) breaks apart epidemiologically recognised outbreak clusters such as the MSM clades 3.6.1.1.3.1 (CipR.MSM1), 3.6.1.1.2 (CipR.MSM5) and 3.7.25 (MSM4)  are much more informative as they communicate relationships between clusters. Perhaps for these reasons, there is to-date only one published report of S. sonnei analysis that utilised cgMLST for public health investigation36, and they too relied on additional SNV-based phylogenetics to place their local isolates in the context of global populations as this was not easily ascertained using cgMLST  was extracted from their respective publications16 (Supplementary Table S. sonnei reference genome 53 G (accession NC_016822) using RedDog . Briefly, RedDog maps reads with Bowtie2 v2.2.944 with the sensitive local parameter, then uses SAMtools v1.145 to retain high quality SNV calls . SNVs detected in repetitive regions where SNV calls are dubious  were removed . SNVs associated with recombination were identified and excluded using Gubbins v2.3.246 (n = 208 SNVs across 10 genomes). The final alignment consisted of 23,673 SNVs across 1935 genomes. Ancestral alleles at these SNV sites were extracted from five E. coli genomes  using the mapping procedure described above, and were included in the alignment for the purpose of outgroup rooting the tree. A maximum likelihood (ML) phylogeny was inferred using IQ-TREE v247 using a GTR substitution model  to the pairwise SNV distance matrix. The resulting dendrogram was then cut at the aforementioned thresholds (using the R function cutree) to cluster isolates into discrete groups representing lineages, clades and subclades. The resulting groups were compared to the ML phylogeny to check that each was monophyletic (using the function is.monophyletic in the ape package48 for R); a small number of groups were non-monophyletic and were broken up into smaller groups  to result in a final set of monophyletic clusters. The SNV alignment was also analysed using the R package fastbaps49 to partition the data into groups using Bayesian clustering   was used to determine the boundaries of the local subtree sharing the defining features of the group  over nonsynonymous (n = 8) or intergenic SNVs (n = 1), and where multiple synonymous SNVs were available we prioritised genes with the lowest ratio of nonsynonymous:synonymous SNVs  as these are less likely to be under selection in the population and thus serve as stable marker SNVs. The list of SNV markers is given in Supplementary Data We identified potential marker SNVs for each of the 147 genotypes by mapping SNVs onto the branches of the ML phylogeny using SNPPar32 to probe for these marker SNVs and assign S. sonnei genotypes. Probes to detect changes at specific S. sonnei QRDR codons  were also included in the S. sonnei panel in Mykrobe32 (v0.9.0) software available at https://github.com/Mykrobe-tools/mykrobe, using the probe panel stored in Figshare (10.6084/m9.figshare.13072646). Mykrobe outputs were then parsed using a custom Python script which tabulates results across a set of samples and summarises the support for each genotype call (https://github.com/katholt/sonneityping). We tested the Mykrobe S. sonnei genotyper using the discovery genome set as input , to confirm that the genotype and QRDR mutations reported by this implementation were correct. Code was also tested using Oxford Nanopore (ONT) long reads (fastq format) using the --ont flag in Mykrobe.We modified the Mykrobe genotyping softwareS. sonnei genomes downloaded from the GenomeTrakr project33 on 7 May 7 2019 (listed in Supplementary Data n = 260 SNVs identified as recombinant across 11 genomes), and a ML phylogeny . All genomes were assigned a genotype using Mykrobe32 v0.9.0, and these were compared to the ML phylogeny to check that each genotype was monophyletic as expected (using the function is.monophyletic in the ape package48 for R). In addition, we confirmed that all marker SNVs remained non-homoplasic amongst this larger set of genomes, using output from SNPPar .We used an independent validation dataset  a total of 2015 50 and the CARD database52 for a further 2644 genomes sourced from public databases, and results combined with those from the discovery and validation sets yielding a total of n = 6595 S. sonnei genomes for analysis. Genotypes with at least ten representatives in this data set , and AMR genes detected in at least two genomes, were included in the analysis of AMR frequencies within genotypes .To assess the correspondence between genotypes defined here within the CipR clade (genotype 3.6.1.1 and its subtypes) and the two subpopulations  defined previously by The et al.S. sonnei genotyping framework to analyse 4222 genomes sequenced by public health reference labs between 2016 and 2019 in Australia (n = 6444), England (n = 286717), and the USA . All genomes were genotyped, and AMR determinants were identified as above. Azithromycin resistance was predicted based on presence of mph(A), ESBL/carbapenemase production based on known beta-lactamase alleles , and ciprofloxacin resistance based on the combination of three QRDR mutations . Data were analysed in R and visualised using the ggplot2 package (Figs. We applied the ge Figs. .Further information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Thrombocytopenia is the most common hematological abnormality in patients with acute malaria. This study aimed to determine the role of thrombocytopenia as a diagnostic marker for malaria in patients with acute febrile illness. P ≤ 0.05 was considered statistically significant.  A cross-sectional health facility-based study was conducted on 423 consecutively selected acute febrile patients at Ataye District Hospital from February to May 2019 GC. A complete blood count and malaria microscopy were performed for each acute febrile patient. ROC curve analysis was performed to calculate sensitivity, specificity, positive predictive value, and negative predictive value of platelet count in predicting malaria. A P. vivax and 18 (24.66%) were P. falciparum. The prevalence of thrombocytopenia among malaria patients (79.5%) was significantly higher than those in malaria negative acute febrile patients (13.7%), P < 0.001. About 67% malaria-infected patients had mild to moderate thrombocytopenia and 12.3% had severe thrombocytopenia. The ROC analysis demonstrated platelet counts <150,000/μl as an optimal cutoff value with 0.893 area under the curve, 79.5% sensitivity, 86.3% specificity, 95.3% negative predictive value, and 54.7% positive predictive value to predict malaria.  Out of the 423 acute febrile patients, 73 (17.3%) were microscopically confirmed malaria cases and the rest 350 (82.7%) patients had negative blood film results. Of the microscopically confirmed malaria cases, 55 (75.34%) were  Malaria is still among the major public health problems in the country. Thrombocytopenia is a very good discriminatory test for the presence or absence of malaria with 79.5% sensitivity and 86.3% specificity. Therefore, this may be used in addition to the clinical and microscopic parameters to heighten the suspicion of malaria. Plasmodium species. Globally, there were an estimated 228 million malaria cases and 405, 000 deaths in 2018, and 93% of the malaria cases and 94% of the deaths were in the African Region [Plasmodium falciparum and Plasmodium vivax [Malaria is a life-threatening infectious disease caused by protozoan parasites of the P. falciparum and P. vivax infections [Malaria parasite is a blood parasite that causes different hematological abnormalities such as anemia, thrombocytopenia, lymphopenia, monocytosis, eosinopenia, and rarely disseminated intravascular coagulation –9. Thosefections . The mecfections –16.There are some studies that reported the presence of thrombocytopenia is a predictor of acute malaria in patients with acute febrile illness in endemic areas –20. HoweP. vivax and P. falciparum are main plasmodium species responsible for malaria infection in the district.A cross-sectional health facility-based study was conducted at Ataye District Hospital from February to May 2019 GC. The Hospital is found in Ataye Town, Ataye District, North Shewa Zone of the Amhara Region, Ethiopia. Ataye Town is located at an elevation of 1468 meters above sea level and about 270 km away from the capital city Addis Ababa. Ataye District is one of the hot and malaria endemic areas in Amhara Region, Ethiopia. Malaria is one of the major health problems of the district. Among the plasmodium species, A total of 423 consecutively selected acute febrile patients were enrolled from Ataye District Hospital. Acute febrile patients with pregnancy, HIV, known hematological malignancies, bleeding disorders, and antimalarial therapy were excluded from the study.Plasmodium was detected in a minimum of 200 oil immersion fields. On thick blood smears, detection of any levels of asexual forms of malarial parasites was considered malaria-positive and if no parasites were seen after examining 1000 white blood cells labeled as negative. The blood smear examination and complete blood count were performed by two experienced medical laboratory scientists. These laboratory professionals had trainings on malaria microscopy. Standard operating procedures (SOPs) were strictly followed in each step to maintain the quality of the laboratory results.Semistructured questionnaires were used to collect sociodemographic and clinical data of the study participants by trained clinical nurses. A complete blood count (CBC) and malaria microscopy were performed for each acute febrile patient. Sysmex KX-21N  automated hematological analyzer was used to determine the complete blood count . Giemsa-stained thin and thick blood smears were used to diagnose malaria. Thin smears were considered positive for malaria if one or more malarial parasites were seen and negative if no asexual form of 3/μL. Thrombocytopenia was further classified into mild thrombocytopenia (platelet counts from 100 to 150 × 103/µL), moderate thrombocytopenia (platelet counts from 50 to 100 × 103/µL), and severe thrombocytopenia (platelet counts less than 50 × 103/µL) [Thrombocytopenia was defined as a total platelet count <150 × 10 103/µL) .t-test for continuous variables and the chi-square test for categorical variables to compare means and proportions. ROC curve analysis was performed to calculate sensitivity, specificity, positive predictive value, and negative predictive value of platelet count in predicting malaria. In all cases, a P value less than 0.05 was considered statistically significant.The data were entered and analyzed using a Statistical Package for the Social Science (SPSS) Version 20 statistical software. We used the Ethical approval was obtained from the Research and Ethics Review Committee of College of Medicine and Health Sciences, Wollo University. Permission to conduct the study was also obtained from Ataye District Hospital. Written informed consent from adult participants and assent for children under 18 years of age were obtained before enrolment into the study. To ensure confidentiality, participants' data were linked to a code number. Any abnormal test results of participants were communicated to the concerned body in the hospital.n = 143; 33.8%) were within 15 to 24 years of age. More than half of the study participants were females (54.8%) and rural residents (53%). About 24.8% of participants were illiterate and 25.8% were housewives. Thrombocytopenia was noted in 106 (25.1%) acute febrile patients. Fifty-four (12.8%) patients had mild thrombocytopenia, 43 (10.2%) had moderate thrombocytopenia, and 9 (2.1%) had severe thrombocytopenia , and most of the study participants (ytopenia .P. vivax positive and 18 (24.66%) were P. falciparum. There was no statistically significant difference in malaria infection between male and female acute febrile patients . Malaria infection was more prevalent in rural residents (22.8%) than urban residents (11.1%), (P = 0.002).Out of the 423 acute febrile patients, 73 (17.3%) were microscopically confirmed malaria cases and 350 (82.7%) had negative blood films . Of the P < 0.001). The prevalence of thrombocytopenia among malaria patients (79.5%) was significantly higher than those of malaria negative acute febrile patients (13.7%), P < 0.001. Regarding to the severity of thrombocytopenia, about 67% malaria-infected patients had mild to moderate thrombocytopenia and 12.3% had severe thrombocytopenia  than in P. vivax (3.6%) infected study participants. Most of the malaria negative acute febrile patients (86.3%) had normal platelet count , the optimum cutoff point of platelet count to differentiate malaria infection from acute febrile patients was less than 150 × 103/µl with sensitivity of 79.5% and specificity of 86.3%  of the platelet count was 0.893 , followed by P.falciparum (25%). This was in agreement with other studies [P. falciparum [Malaria is one of the high burden diseases in developing countries. Ethiopia is one of the sub-Saharan countries highly endemic to malaria where an estimated 68% of the population lives in malarious areas , 5. In t studies , 29, 30,lciparum , 28.Plasmodium infection. The association of thrombocytopenia and malaria infection was in agreement with previous studies [P. falciparum (83.3%) and P. vivax (78.2%) infected patients (P = 0.7482) which is similar to a study done by Kassa et al. [P. falciparum than P. vivax [P. vivax infected patients [In this study, mean platelet counts were significantly reduced in malaria-infected patients than those with nonmalaria. Thrombocytopenia occurred in 79.5% of malaria-infected patients. These findings imply that thrombocytopenia may be a marker of  studies , 31–33. a et al. . In contP. vivax  and Shaipatients –16. In cpatients . A studypatients . There apatients . In contpatients .μl as an optimal cutoff value with 79.5% sensitivity, 86.3% specificity, 95.3% negative predictive value, and 54.7% positive predictive value. The area under the curve of the platelet count was 0.893 which means thrombocytopenia is a good discriminatory test for the presence or absence of malaria. These values are very close to studies done in Thailand that found a sensitivity of platelet count <150,000/μl for diagnosing malaria to be 85%, specificity 85%, negative predictive value 97%, and the positive predictive value 48% [In this study, the ROC analysis demonstrated platelet counts <150,000/alue 48%  and in Lalue 48% . Our resalue 48%  and Tangalue 48% .Malaria is still among the major public health problems in the country. The prevalence of thrombocytopenia was significantly higher among malaria patients than malaria negative acute febrile patients in Ataye Hospital. Thrombocytopenia is a good discriminatory test for the presence or absence of malaria with 79.5% sensitivity and 86.3% specificity. Therefore, this may be used in addition to the clinical and microscopic parameters to heighten the suspicion of malaria.


The aim of present study was to assess the karyotypes of amniotic fluid cells and find the frequency of chromosomal abnormalities and their significance in clinical setting. A total of 15,401 pregnant women were assessed from March 2016 to May 2019, and 14,968 amniotic fluid samples were successfully cultured. These fetuses were grouped according to different indications including advanced maternal age, abnormal nuchal translucency (NT) values, positive first/second trimester screening results, high risk NIPT results, very low PAPP-A and free β-hCG multiples of the normal median (MoM) results, abnormal ultrasound findings or previous history of chromosomal abnormalities. Results indicated the presence of normal karyotype in 90.2%  of fetuses. Totally, 46.4%  of fetuses were 46,XX and 43.8%  had 46,XY chromosome pattern. A total of 1077 abnormal karyotypes were found among 14,968 fetuses, thus the rate of abnormal fetuses was calculated to be 7.2% . Meanwhile, a total of 394 cases (2.8%) had a normal polymorphism in their karyotype. In other words, abnormal karyotypes were detected in one of 13.9 cases of patients underwent amniocentesis. Down syndrome, Edward’s syndrome, abnormal mosaicisms and Patau’s syndrome were detected in 4.4% , 0.57% , 0.49%  and 0.24%  of cases, respectively. Sex chromosomal abnormalities including Klinefelter syndrome, Turner syndrome and 47,XXX karyotype were detected in 64 cases (0.43%). In this article, the rates of chromosomal abnormalities are compared between different groups of patients based on the advanced maternal age, abnormal NT values, very low PAPP-A and free β-hCG MoMs results, and positive FTS results. The current investigation provides insight into the most appropriate indications for amniocentesis in Iran. These abnormalities are not curable at present, and are associated with both financial and psychological burden. These disorders have a combined frequency of 1 in 153 births, necessitating conduction of screening programs for identification of affected fetuses at appropriate gestational age2. Measurement of biochemical markers in maternal circulation, ultrasonographic methods, and non-invasive prenatal diagnosis (NIPT) tests based on cell-free fetal DNA in maternal plasma have been proposed as efficient screening methods2. Moreover, Committee Opinion of ACOG and SMFM have reaffirmed their reviews in 2020 and made recommendations regarding the application of newer genetic technologies  in the prenatal setting3. However, due to the limitation of access to a high resolution CMA, the karyotype method is still used as the best diagnostic method during pregnancy. The appropriate cut-off point for conduction of amniocentesis is a subject of debates nowadays. Karyotype analysis not only helps in the identification of chromosomal abnormalities in fetus, but also offers the scientific basis for either continuation or termination of pregnancy and reduction of the incidence of birth defects4. In the current study, we provide an overview of the detected chromosomal abnormalities in a large cohort of Iranian pregnant women underwent amniocentesis due to different indications including advanced maternal age, abnormal nuchal translucency (NT) values, positive first/second trimester screening (FTS/STS) results, high risk NIPT results, very low PAPP-A and free BhCG multiples of the normal median (MoM) result, abnormal ultrasound findings or previous history of chromosomal abnormalities. Then, we compare the frequencies of chromosomal abnormalities between different groups of patients based on the advanced maternal age, abnormal NT values, very low PAPP-A and free β-hCG Moms results and positive first FTS results.Chromosomal abnormalities are considered as an important cause of congenital defects and intellectual disabilityIn the present retrospective study, we collected the results of 15,401 amniocentesis samples sent for analysis to Genome-Nilou Medical Laboratory, Tehran, Iran. Amniocenteses were performed between March 2016 and May 2019. Detailed demographic and clinical data were collected through filling questionnaires, direct interview with pregnant women and assessment of medical records. The results of serum free β-hCG, PAPP-A, β-hCG, AFP, uE3 and inhibin A MoMs and other clinical and sonographic reports of patients were documented. The study protocol was approved by the ethical committee of Tehran University of Medical Science and the study protocol was performed in accordance with the relevant guidelines. Informed written consent forms were obtained from study participants.2 incubator . Standard Giemsa banding was used for assessment of chromosomes. Chromosomal analyzes were performed simultaneously by an FDA approved software  and manually by an expert Cytogentist using an Invert Microscope .Since Nilou laboratory is a referral center, in two third of cases serum markers, particularly STS and sequential tests were checked in other centers. Women underwent cytogenetic analyses after appropriate genetic counseling with complete explanation of methods, risks, and indications. Samples were collected by expert perinatologists or Maternal–Fetal medicine specialists through ultrasound-guided transabdominal puncture and the amniotic fluid samples were taken in two separate tubes and sent to the laboratory. Amniotic fluid cells of each tube were cultured in long-term cell cultures in Amniomax medium . Each medium was incubated separately at 37 °C in COFor about 58.5% of patients, QF-PCR  test was requested at the same time. The main reason for requesting this test was gestational age ≥ 17 W + 0D, according to the time limit for legal termination of affected pregnancies in Iran which is 18 W + 6D.P values < 0.05 were considered as significant. The Kruskal–Wallis test was used as a non-parametric test equivalent to ANOVA for comparing two or more independent samples with similar or different sample sizes. This test was used for comparison of MoM values of circulatory markers between pregnancies with normal and abnormal karyotypes.Statistical analyses were performed using SPSS Statistics software version 21 . Chi-2 test was used for assessment of association between categorical variables. The relationships between quantitative variables were evaluated using ANOVA test. A total of 15,401 amniocenteses were performed in the center. Karyotype analyses were not performed in 802 cases due to different reasons . Normal polymorphisms were detected in 2.6% of the assessed fetuses with 46,XX or XY, inv(9)  being the most frequent polymorphism. Sex chromosomal abnormalities including Klinefelter syndrome, Turner syndrome, 47,XXX karyotypes were detected in 64 cases (0.43%).Down syndrome, Edward’s syndrome, abnormal mosaicisms and Patau’s syndrome were detected in 4.4% , 0.57% , 0.49%  and 0.24%  of cases, respectively. The rate of male fetuses with Down syndrome to female fetuses with Down syndrome has been calculated to be 54%/45.6%, indicating higher rates of this abnormality among male fetuses . Higher and lower frequencies of chromosomal abnormalities were detected in 35–39 and < 20 years age groups, respectively. Moreover, the highest frequencies of chromosomal abnormalities (excluding Down syndrome) and normal polymorphisms were reported among patients less than 20 years old which is possibly due to the fact that in this age group only patients with abnormal screening results underwent amniocentesis. Finally, Chi-2 tests showed significant increase in chromosomal abnormalities parallel with increase in maternal age from 25 years (P < 0.001). However, the frequency of normal polymorphisms was not associated with maternal age.Then, we assessed the association between chromosomal abnormalities and maternal age Table . ResultsSubsequently, we assessed association between the frequency of chromosomal abnormalities and maternal weight as well as gestational age Table .Table 6AP = 0.001). Therefore, the frequency of normal polymorphisms was higher in obese patients. Yet, there was no association between chromosomal abnormalities and maternal weight.ANOVA test showed that mean maternal weight was higher in those having normal polymorphisms (P < 0.001). Frequency of Down syndrome in the former group was 33.8%, while in the latter group was 24.4%. Other chromosomal abnormalities were detected in 8.7% and 10.6% of consanguineous and non-consanguineous marriages, respectively. Table The frequency of chromosomal abnormalities was higher in non-consanguineous marriages compared with consanguineous ones . In fact, 92% of fetuses with normal karyotypes, 92.7% of fetuses with normal polymorphisms, 69.3% of Down syndrome fetuses and 80% of fetuses with other chromosomal abnormalities had NT values < 3 mm. On the other hand, 3.7% of fetuses with normal karyotypes, 2.6% of fetuses with normal polymorphisms, 20.7% of Down syndrome fetuses and 16.2% of fetuses with other chromosomal abnormalities had NT values more than 3.5 mm. It is worth mentioning that although the frequency of chromosomal abnormalities increases when NT increases, the majority of cases with chromosomal abnormalities are detected in the fetuses with NT < 3 mm, because the number of patients in this group is approximately 9.6 times that of patients with an NT greater than 3 mm. Therefore, one per 6.9 fetuses with NT > 3 mm, one per 3.2 fetuses with NT > 3.5 and one per 16.1 fetuses with NT < 3 mm would be diagnosed as having Down syndrome and other chromosomal abnormalities. If we set the threshold at NT value of 3 mm (instead of 3.5 mm), we could detect 60 (10%) additional cases of Down syndrome and 11 (3.8%) additional cases of other chromosomal abnormalities. However, when the threshold is set at NT > 3.5 mm, referral for amniocentesis decreases from 9.4 to 4.8%. With the advent of NIPT, we can simultaneously lower the amniocentesis rate (by applying cut-off point of 3.5 mm for NT) and detecting additional cases of chromosomal abnormalities, particularly Down syndrome cases.In brief, 9.4% of fetuses had NT values greater than 3 mm. Notably, 4.8% of fetuses had NT values more than 3.5 mm. Chi-2 test showed higher prevalence of chromosomal abnormalities in fetuses with NT values higher than 3 or 3.5 mm . One per 1.3 pregnancies that underwent amniocentesis due to free β-hCG levels > 5 MoM, one was diagnosed as Down syndrome or other chromosomal abnormalities. Therefore, free β-hCG levels > 5 MoM is an important indicator for chromosomal abnormalities  had Down syndrome and 16 patients (9.5%) had other chromosomal abnormalities. In other words, from 52.2 patients referred for amniocentesis just for intermediate risk for Trisomy 21, only one case of chromosomal abnormality was diagnosed Table .Table 10The prevalence of trisomies 13 and 18 with risk scores up to 1:250 cut-off is not high. With cut-off point of 1:250 for these trisomies, it is possible to detect 86.5% of Down Syndrome cases, 100% of Edward’s syndrome cases, 92.9% of Patau’s syndrome cases and 70.6% of other chromosomal abnormalities. In other words, from 5.3 patients referred for amniocentesis just for this indication, only one case of chromosomal abnormality was diagnosed.With the cut-off point of 1:250 for trisomy 18, it was possible to detect 91.7% of Down Syndrome cases, 100% of Edward syndrome cases, 75.0% of Patau’s syndrome cases and 77.8% of other chromosomal abnormalities. In other words, from 4.3 patients referred for amniocentesis just for this indication, only one case of chromosomal abnormality was diagnosed Table .Table 115. In the study conducted by Zhang et al., a total of 388 abnormal karyotypes were found among 13,795 fetuses, and the rate of chromosomal abnormalities was 2.813%  or one per 35.6 cases. The main indications of amniocentesis in this study were high-risk serum screening, paternal/maternal abnormality, abnormal signs of ultrasound screening and advanced maternal age. Aneuploidies, autosomal structural abnormalities and mosaicisms were the most common identified karyotypes with frequencies of 59.8% (232/388), 24.7% (96/388) and 12.4% (48/388), respectively. Other uncommon abnormal karyotypes included marker chromosome , sex chromosomal structural abnormality  and triploidy 5. Our study is concordant with Li et al. study6 where among 4191 amniotic fluid specimens from Linyi, China in 2016–2017, a total of 358 abnormal karyotypes were detected, delineating the rate of 8.54% (358/4191 or one per 11.7 cases). Advanced maternal age and abnormal serological screening results were the major prenatal indications for pregnant women with chromosomal abnormalities. Autosomal aneuploidy was the most common pattern accounting for 64.53% of cases, of which 173 (48.32%) cases were trisomy 21, which was the main type of abnormal karyotypes, followed by trisomy 18 (14.25%). There were 38 cases with sex chromosome aneuploidies, including 47,XXY, 47,XXX, 47,XYY and 45,X accounting for 10.61% of the total chromosome abnormalities6. Our study is comparable with Xiao et al. study7. Among 12,365 pregnant women in their study, foetal abnormal karyotype was found in 428 (3.46% or one per 28.9). The detection rates of abnormal karyotype were 57.4% (1 per 1.7) in either a mother or father with chromosomal abnormality, 8.5% in the pregnant women with pathological ultrasound finding (1 per 11.8), 2.79% (1 per 35.8) in the pregnant women with advanced age (35 years and over) and 2.23% (1 per 44.8) in the women with abnormal maternal serum screening (MSS) tests7.In the current study, we provided an overview of the cytogenetic analyses in a large cohort of Iranian pregnant women referred for amniocentesis due to different indications, particularly advanced maternal age and positive FTS results. Based on the defined indications for amniocentesis, the best OAPR was calculated for free βHCG < 0.2 MoM, i.e. one chromosomal abnormality per 1.1 amniocenteses. High risk NIPT, PAPP-A MoM < 0.26, free BhCG MoM > 5.0 were the other amniocentesis indications with appropriate OAPR values. We detected abnormal karyotypes in one per 13.9 cases of patients underwent amniocentesis . Down syndrome, Edward’s syndrome, abnormal mosaicisms and Patau’s syndrome, sex chromosomal abnormalities  and normal polymorphisms were detected in 4.4%, 0.57%, 0.49% , 0.24%, 0.43% and 2.8% of cases, respectively. This finding is not concordant with Zhang et al. study in Shanghai, China4 study in Changsha, China, 2013. They found that positive screening, advanced maternal age, and ultrasonography abnormality were the top 3 indications of amniocentesis and cordocentesis. They found 25 abnormal karyotypes in a total of 669 patients, including 6 cases of trisomy 21, 4 sex chromosomal abnormalities, 7 autosomal balanced translocations, 1 marker chromosome, and 7 mosaics4. This study is also comparable with Kessler et al.8 study in Port Alerge, Brazil, 2008. Among 879 cases, they detected 74 abnormal karyotypes (8.4%), the majority of them being found when the prior indication was fetal malformation8. Moreover, Caron et al.9 have shown that the overall frequency of chromosomal abnormalities for advanced maternal age (≥ 35 years) is 1.79%. In this group, 21% of all abnormalities were structural rearrangements (including markers) and less than half of all abnormalities were trisomy 219.This study is also comparable with Wang et al.9. This finding is concordant with Kagan et al. study10. They have shown that in the risk group between 1:101 and 1:1500, conduction of 3.6% extra procedures would lead to 7.2% improvement in DR10. Therefore, it is rational to suggest additional diagnostic tests or NIPT in women with defined risks between 1:251 and 1:150010. Lindquist et al. have reported the highest frequency of chromosomal abnormalities in pregnancies with an FTS risk of more than 1:10 or MoM of serum markers less than 0.2. In fact, about half of pregnancies with MoM of serum markers less than 0.2 had risk values higher than 1:1011. Vogel et al. have conducted a retrospective study of 575 pregnancies underwent invasive testing due to an FTS risk higher than 1:300. They detected 22 cases of trisomies 21, 18 and 13, 14 cases of other aneuploidies and 15 cases with a pathogenic or probably pathogenic copy number variant (CNV). They have shown that with cut-off point of 1:150 in Contingent NIPT/invasive test, 100% of pathogenic CNVs can be detected. Authors have suggested application of cut-off point of 1:300 and PAPP-A MoM ≤ 0.2 for suggestion of amniocentesis and request for CMA. This study has also reported association between high NT and pathogenic CNVs and the presence of the majority of trisomies in the risk group of 1:50 and greater12.The most important findings of the current study are identification of frequency of chromosomal abnormalities in each risk group based on the results of FTS. In FTS, we have demonstrated that with cut-off point of 1:250 for Trisomy 21, Trisomy 18 and Trisomy 13, the detection rates (DRs) were 79.1% (432/546), 90% (81/90) and 86.6% (123/142) for all abnormal chromosomal patterns. In other words, the poorest OAPR was obtained for intermediate risk for trisomy 21 used for FTS (one per 52.2 cases), so it seems that considering the intermediate risk in the first trimester screening is not remarkably effective and it is better to consider other follow up strategies  for this group to achieve 18.5% (101/546) improvement in DR. Huang et al. have shown in the risk group of 1:200 or greater, they could detect 222 of Down syndrome cases from 253 total cases (DR = 88%). They have suggested conduction of NIPT for the intermediate risk group (1:201–1:1500). This approach has led to detection of 18 additional cases of Down syndrome, 7% improvement in DR and 16% increase in false positive rate of the screening protocolAlthough the above mentioned studies might indicate that FTS can be supplemented by NIPT, sonograms, and possibly sequential screening, the model proposed in the current study can be applied in situations where NIPT/ultrasound is of limited availability.7. They showed in the pregnant women with abnormal MSS, 111 foetuses had abnormal karyotype, but only 36 foetuses had trisomy 13, 18 or 21. They have concluded that ultrasound is an important approach to prevent the birth of foetuses with chromosomal disease. Moreover, they have stated that non-invasive prenatal DNA detection cannot completely replace invasive prenatal diagnosis and MSS. Finally, they have proposed genetic amniocentesis for the pregnant women with certain indications7. Therefore, because in these cases there is a possibility of atypical chromosomal abnormalities , we strongly recommended amniocentesis instead of NIPT in two of above-mentioned indications.The second important finding in the current study is that the frequency of other chromosomal abnormalities rather than Down Syndrome in patients with free βHCG < 0.2 MoM and PAPP-A MoM < 0.26 are 1–3 and 3–2, respectively. Our study is concordant with Xiao et al. study13. In this study, among total 75 children with trisomy 21, there were 42 males and 33 females. The sex ratio was 1.30 which was not statistically significant (P > 0.05). However, a similar sex ratio (1.36) was reported in a larger sample size which was statistically significant (P < 0.01) 13.Two other findings in recent study are remarkably interesting. First, the rate of male fetuses with Down syndrome to female fetuses with Down syndrome has been calculated to be 54%/45.6% (the sex ratio is 1.18), indicating higher rates of this abnormality among male fetuses. This is an important finding that although amniocentesis requests are higher in girls than in boys, the detected chromosomal abnormalities are higher in boys than in girls, which may mean that screening protocols are far more effective in boys than girls. In other words, male fetuses are more frequently affected with Down syndrome compared with female fetuses in spite of higher rate of positive screening results in female fetuses. This study is comparable with Vermra et al. studyThe association between abnormal βHCG and PAPP-A levels and abnormal findings other than trisomy 21 has also been reported in other studies. Thus, the present study has verified this finding in our population. The same is applicable for the higher male/female ratio of trisomy 21.It is worth mentioning that for other chromosomal abnormalities rather than trisomy 21, cut-off points of 1:10 and 1:11–1:250 are not significantly different. Thus, it is not possible to define a cut-off point for high risk of these chromosomal abnormalities.Moreover, the presence of chromosomal abnormalities was higher in non-consanguineous marriages compared with consanguineous ones. This is probably because the most common cause of miscarriage under 12 weeks is genetic disorders, which can be more in this group of patients, and as a result, children with problems have been aborted earlier than 12 weeks. Thus, consanguinity may remain an indication for FTS although not as the indication for amniocentesis.Taken together, in the current study, we have presented the results of amniotic cells karyotypes in a large cohort of Iranian women underwent amniocentesis due to different indications and compared the results within these groups. We have also proposed three additional indications i.e. high risk NIPT, PAPP-A MoM < 0.26 and free BhCG MoM > 5.0 as the most important indications with appropriate OAPR values. Two latter indications have not been previously reported as indicators of invasive tests with high OAPR values. Thus, the present finding is an important novel finding of the current study. We have also shown that intermediate risk in FTS is not an appropriate indication for amniocentesis. Thus, elimination of this indication can improve the efficacy of screening protocols and reduce the number of unnecessary requests for amniocentesis.


Heterodera glycines Ichinohe) is a highly destructive pathogen for soybean production worldwide. The use of resistant varieties is the most effective way of preventing yield loss. Handou 10 is a commercial soybean variety with desirable agronomic traits and SCN resistance, however genes underlying the SCN resistance in the variety are unknown. An F2:8 recombinant inbred line (RIL) population derived from a cross between Zheng 9525 (susceptible) and Handou 10 was developed and its resistance to SCN HG type 2.5.7 (race 1) and 1.2.5.7 (race 2) was identified. We identified seven quantitative trait loci (QTLs) with additive effects. Among these, three QTLs on Chromosomes 7, 8, and 18 were resistant to both races. These QTLs could explain 1.91–7.73% of the phenotypic variation of SCN’s female index. The QTLs on chromosomes 8 and 18 have already been reported and were most likely overlapped with rhg1 and Rhg4 loci, respectively. However, the QTL on chromosome 7 was novel. Candidate genes for the three QTLs were predicted through genes functional analysis and transcriptome analysis of infected roots of Handou 10 vs. Zheng 9525. Transcriptome analysis performed also indicated that the plant–pathogen interaction played an important role in the SCN resistance for Handou 10. The information will facilitate SCN–resistant gene cloning, and the novel resistant gene will be a source for improving soybeans’ resistance to SCN.Soybean cyst nematode (SCN;  Heterodera glycines Ichinohe) is a devastating pest affecting soybean (Glycine max [L.] Merr.) production worldwide  and Rhg4 (GmSHMT08), were cloned and functionally analyzed  around the rhg1-b locus and the gene GmSHMT (Glyma.08G108900) around the Rhg4 locus were identified as major-effect gene to SCN resistance  from the cross between Zheng 9525 and Handou 10. Zheng 9525 was cultivated by Henan Academy of Agricultural Sciences, whereas Handou 10 was cultivated by Henan Jintun Seed Industry Co., Ltd., and Handan Academy of Agricultural Sciences. The seeds of nine differential cultivars, PI88788, Peking, PI437654, PI209332, PI548316, PI89772, PI90763, Pickett, and Lee, were obtained from Henan Academy of Agricultural Sciences.A recombinant inbred line (RIL) mapping population of 392 FHandou 10, Zheng 9525, and nine differential cultivars were evaluated for the resistance to SCN  in a climate room in Henan Academy of Agricultural Sciences. Plastic cups (Ø 6 cm × h 12 cm) were filled with soil infected by SCN , respectively. Lees were planted in SCN-infested soil, and after 30 days, we collected cysts from the roots using a 710–250 μm sieve tower. Cysts were collected from the 250 μm sieve and rinsed. The eggs were collected by breaking open cysts with a rubber stopper and collecting the eggs on a sieve stack consisting of 250 μm – 75 μm – 25 μm sieves. The mixture from the 25 μm sieve was backwashed into a 50 mL plastic conical tube. A 40% sucrose solution was added to the tubes, stirred, and centrifuged at 2,000 rpm for 5 min. Eggs in the middle layer or supernatant were then collected over a 25 μm sieve. Handou 10, Zheng 9525, and nine differential cultivars were transplanted with five replicates. Each replicate was one plant in a plastic cup (Ø 6 cm × h 12 cm). Five days after transplantation, seedlings were inoculated with about 4,000 eggs per cup. The plants grew at 70 to 80% relative humidity, 28–24 (L/D), and a photoperiod of 16 h: 8 h (L:D) and were watered daily.The SCN resistance of the 392 RILs and the parents (Zheng 9525 and Handou 10) were evaluated for SCN resistance against HG type 1.2.5.7 (race 2) and HG type 2.5.7 (race 1) in a climate room. Five days after sowing, two plants of each line were transplanted into a plastic cup (Ø 6 cm × h 12 cm) with three replicates, each replicate had two plants. Five days after transplantation, seedlings were inoculated with about 4,000 eggs per cup. The growth conditions were the same as the above.Thirty days after inoculation, nematode cysts were collected from the roots of each replicate and counted by an image analysis software . A femal2:8 lines from Zheng 9525 × Handou 10 were sequenced by HiSeq X Ten at Huada Gene Technology Co., Ltd. . The sequences of the parents were aligned with the reference genome (Gmax_275_Wm82.a2) using SOAP2 (Leaf samples of each progeny line and their parents were collected at the seedling stage. DNA was extracted by the plant genomic DNA kit . Zheng 9525 and Handou 10 were sequenced by Illumina HiSeq 4000, whereas 392 RILng SOAP2  and singng SOAP2 . The SNPInstead of using the SNPs as such for linkage mapping, a sliding window-based approach was used to identify bin markers where consecutive SNPs were merged into one bin. We extract the bin area on each chromosome as bin marker, only the bin markers without segregation distortion are selected to construct a map. A genetic map with the bin markers was constructed with JoinMap 4.0 using the maximum likelihood mapping algorithm . Groups Additive QTLs of SCN resistance with FI to HG types 2.5.7 (race 1) and 1.2.5.7 (race 2), were analyzed using the composite interval mapping (CIM) method in WinQTLCart 2.5 software . The walThe seedlings with consistent growth were selected to transplant into a plastic cup with sterile soil after Handou 10 and Zheng 9525 were sown in vermiculite, with one plant per cup. Five days later, well-developed plants of the same size were selected to inoculate 4,000 or 0 SCN HG type (1.2.5.7) (race 2) eggs per cup, with three replicates per genotype of each treatment, and five plants for each replicate. Ten days after inoculation, roots were collected, washed, frozen in liquid nitrogen and stored at -80°C until use. Total RNA was extracted by TRIzol (Invitrogen). Each biological replicate contained pooled roots from five individual plants. The RNA transcriptome sequencing and preliminary data analyzes were carried out by Shenzhen Huada Gene Technology Co., Ltd. (China). For each replicate, a mRNA library was constructed and sequenced using the DNBSEQ platform. Adaptor reads, reads with an unknown base N greater than 5%, and low-quality reads  were filtered out of the raw data to obtain high-quality (clean) reads using SOAPnuke (v1.4.0) . Clean r3 The statistical enrichment of DEGs in KEGG pathways was accomplished with the R (version 3.1.1) function phyper.4 Then FDR was performed on p-value, and q-values of ≤ 0.05 was considered to be significantly enriched.Significant DEGs were annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG).The QTLs that could be detected for the resistance of Handou 10 to SCN HG type 2.5.7 (race 1) and 1.2.5.7 (race 2) were considered major QTLs. The physical positions of the major QTL intervals were identified according to the above genetic map and the reference genome (“Williams 82.a2.v1”) . The DEGrhg1 and Rhg4 were referred to the reports from DNA was extracted by the plant genomic DNA kit . DNA concentration and quality were measured with NanoDrop 2000 . According to the SNPs between Zheng 9525 and Handou 10, the KASP  markers were designed by LGC Science (Shanghai) Ltd. The primer sequences of KASP markers linked to Handou 10, Zheng 9525, and nine differential cultivars (with Lee as a susceptible control) were screened for SCN resistance. Handou 10 was resistant to SCN HG type 2.5.7 (race 1), moderately resistant to SCN HG type 1.2.5.7 (race 2) and moderately susceptible to HG type 1.2.3.5.6.7 (race 4) . Among tZheng 9525 and Handou 10 were re-sequenced using an Illumina HiSeq4000 platform with sequencing average depths of 11.94× and 16.94×, respectively . ApproxiThe average sequencing depth of 392 RILs was 1.94× and the coverage rate was 62.38% . The seqA high-density genetic map was constructed by joining 5,233 bin markers  were derived from the resistant parent Handou 10, and the resistance alleles of the remaining two QTLs (SCN_12_3 and SCN_15_4) were derived from the susceptible parent. Three QTLs  were detected in race 1 and race 2. The percentage of the explained variance of the identified QTLs varied from 1.91 to 7.73%. According to their physical position in the genome, the QTL intervals of SCN_8_2 (6.75–10.35 Mb) and SCN_18_6 (0.05–3.16 Mb) overlapped with the rhg1 and Rhg4 loci (see Footnote 2), respectively.Seven QTLs for SCN resistance with FI were detected on six chromosomes by CIM, which were designated as ectively . The resWe analyzed the transcriptomes of the resistant and susceptible parents infected with race 2. Each sample produced 6.45 G data on average . The aveThe KEGG analysis showed that 2100 DEGs were assigned to 132 pathways, and the plant–pathogen interaction pathway (ko04626) was the top enriched pathway  with 213SCN_7_1, SCN_8_2, and SCN_18_6, respectively  in rhg1 were all expressed in the resistance of Handou 10 according to the transcriptome data  and Rhg4  for Handou 10 were the same as those in Peking, whereas the genotypes of those  located at SCN_7_1 for Handou 10 were consistent with those in PI437654  are responsible for the resistance provided by rhg1-b . In our study, these three candidate genes in rhg1-a might be regulate the SCN resistance of Handou 10. However, the mechanisms of rhg1-a and rhg1-b to SCN resistance are not same . In this study, Handou 10 was identified as being resistant against HG type 2.5.7 (race 1), and moderately resistant against HG type 1.2.5.7 (race 2); additionally, its resistance was identified as being Peking-type based on the analysis of the QTL , KASP mahg1/Rhg4 . SCN_8_2software . SCN_8_2software . Accordisoftware  for the ectively . The resPI089772 . The rhge to SCN . Three gy rhg1-b . In our not same . For rhgsistance . In this by qPCR  and thisSCN_7_1 is an important QTL to SCN resistance for Handou 10. Some QTLs , which were up–regulated between infected and uninfected SCN in Handou 10 and down–regulated in Zheng 9525 according to transcriptome data. Recently, the expression of Glyma.07G139800 was verified by qPCR  might be the important candidate gene to SCN resistance according to KEGG analysis and genes function.Soybean resistance to SCN is regulated by multiple genes. In our study the plant-pathogen interaction pathway was the most enriched KEGG pathway between infected Handou 10 and Zheng 9525, and between infected Zheng 9525 and uninfected Zheng 9525, whereas the MAPK signaling pathway was the most enriched KEGG pathway between infected and uninfected Handou 10. Plants and pathogens should be studied together as an interacting system . SoybeanCAR005982.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NGDC WL, JW, and HW conceived the project and designed the experiments. HW, YL, JL, HL, YW, CL, SW, and HZ performed the experiments. HW, WL, JW, and QS participated in the data analysis and manuscript revised. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


National Early Warning Scores (NEWS2) are used to detect all-cause deterioration. While studies have looked at NEWS2, the use of virtual consultation and remote monitoring of patients with COVID-19 mean there is a need to know which physiological observations are important.To investigate the relationship between outcome and NEWS2, change in NEWS2 and component physiology in COVID-19 inpatients.A multi-centre retrospective study of electronically recorded, routinely collected physiological measurements between March and June 2020. First and maximum NEWS2, component scores and outcomes were recorded. Areas under the curve (AUCs) for 2-day, 7-day and 30-day mortality were calculated.Of 1263 patients, 26% died, 7% were admitted to intensive care units (ICUs) before discharge and 67% were discharged without ICU. Of 1071 patients with initial NEWS2, most values were low: 50% NEWS2=0–2, 27% NEWS2=3–4, 14% NEWS2=5–6 and 9% NEWS2=7+. Maximum scores were: 14% NEWS2=0–2, 22% NEWS2=3–4, 17% NEWS2=5–6 and 47% NEWS2=7+. Higher first and maximum scores were predictive of mortality, ICU admission and longer length of stay. AUCs based on 2-day, 7-day, 30-day and any hospital mortality were 0.77 (95% CI 0.70 to 0.84), 0.70 (0.65 to 0.74), 0.65 (0.61 to 0.68) and 0.65 (0.61 to 0.68), respectively. The AUCs for 2-day mortality were 0.71 (0.65 to 0.77) for supplemental oxygen, 0.65 (0.56 to 0.73) oxygen saturation and 0.64 (0.56 to 0.73) respiratory rate.While respiratory parameters were most predictive, no individual parameter was as good as a full NEWS2, which is an acceptable predictor of short-term mortality in patients with COVID-19. This supports recommendation to use NEWS2 alongside clinical judgement to assess patients with COVID-19. National Early Warning Scores (NEWS2) are used to detect all-cause deterioration and improve outcomes in the general population.During the COVID-19 pandemic, it is not always possible to calculate a full NEWS2 due to remote consultation and management of patients on COVID-19 virtual wards.Patient physiology may not behave as anticipated in patients with COVID-19.In this retrospective multicentre study of over 1200 hospitalised patients with COVID-19, those with higher initial or maximum NEWS2 were more likely to die, require intensive care unit admission and have longer length of stay than patients with lower scores at either time.NEWS2 values had the best predictive value for short-term (2-day) mortality in patients with COVID-19.Individually, the respiratory components  make the greatest contribution to the NEWS2 value.The COVID-19 pandemic resulted in large numbers of patients presenting to hospital with acute respiratory failure. Early in the pandemic, it was recognised that despite hypoxaemia due to severe disease, some patients did not appear to manifest the usual symptoms of respiratory distress and the use of oximetry was promoted to assess oxygen saturation at presentation. In England, a pathway was introduced using oximetry measurement at home to monitor and detect deterioration in patients who did not require hospital admission.The National Early Warning Score (NEWS) was developed in 2012 by the Royal College of Physicians (RCP) to detect all-cause deterioration and improve outcomes in hospital patients.The NEWS2 calculation was included in the pathway for risk stratification of patients with COVID-19 assessed for management at home. There is evidence that NEWS2 is of benefit in out-of-hospital settings as it provides a common language of deterioration,Studies have examined the relationship of NEWS2 to outcome in patients with COVID-19. Almost all are single-centre studies involving small numbers, and many have focused on comparison of NEWS2 with other predictive scores. Unlike previous studies, our aim was to examine initial and maximum NEWS2 values and component scores in hospitalised patients with COVID-19 in the West-of-England. The aim was to determine if NEWS2 could be used to identify deterioration in patients with COVID-19 and then to clarify which, if any, of the individual parameters were most predictive of outcomes due to absolute value or change, to support clinical decision making.This was a pragmatic, multicentre observational cohort study of COVID-19-positive patients admitted to North Bristol NHS Trust, University Hospitals Bristol and Weston NHS Foundation Trust, Great Western Hospitals NHS Foundation Trust and Royal United Hospitals Bath NHS Foundation Trust, in the West-of-England.Patients were eligible if they were aged ≥16 years at admission and had a positive swab taken during their hospital stay or up to 2 weeks before. Patients admitted to hospital more than once following a positive test are described, but only first admissions were included in analyses.Routinely collected electronic hospital data were requested from the four trusts. In each case, observations were measured manually but entered onto an electronic system, which recorded and displayed results. As data were routine and anonymised prior to transfer to the research team, patient consent was not required.Data were requested on eligible patients from the first case in each trust (early March 2020) up until data extraction (approximately end of June 2020). As each trust extracted data at different times, data were amended to reflect the situation on 27 June 2020 (the date of first data extraction). All electronically recorded NEWS2 component physiological measurements were requested, along with date and time of each set of observations. Electronic observations were not recorded in the emergency departments (EDs) except for Great Western or the respiratory admission unit in Bath. NEWS2 is not used in intensive care units (ICUs).Patient age, sex, hospital admission date, COVID-19 swab and result dates, ICU admission, hospital discharge status  and hospital discharge/death date were also requested.Two NEWS2 values were considered for analysis. The initial score and its individual components were based on the first full set of electronically recorded observations taken within 2 days of the patient’s positive swab. Maximum NEWS2 values were the highest calculated NEWS2 values between initial NEWS2 and discharge/death for each patient. For some patients, the maximum score was the first score.Maximum component scores were the highest component scores in this same period but were not always part of the maximum NEWS2.Each component score  and corresponding NEWS2 value were calculated from the physiological measurements listed above following the rules on the NEWS2 scoring card .10.1136/emermed-2020-210624.supp1Supplementary dataNEWS2 change scores were calculated as maximum score minus first score; if a patient only had one score recorded, they were excluded from the change score analysis.NEWS2 values were grouped into four categories for analysis: 0–2, 3–4, 5–6 and 7+, in line with hospital escalation trigger scores of 3, 5 and 7.The primary outcomes were death in hospital with admission to ICU, death without ICU admission, ICU admission prior to discharge or no ICU admission prior to discharge. Outcomes for patients who were still inpatients when data were extracted are described but excluded from all analyses. Time to death for patients who died and post-COVID-19 length of hospital stay (LOS) for patients who survived to discharge were secondary outcomes. Time to death was calculated as date of death minus date of first score. LOS was calculated as discharge date minus COVID-19 swab date.Continuous data were summarised using medians and IQRs. Categorical data were summarised using counts and percentages.The distribution of NEWS2 values and how this differed by mortality/ICU status were explored graphically. This analysis was split into three parts: first NEWS2 values, maximum NEWS2 values and the change from first to maximum. Similarly, distributions of each component score were explored. These analyses were purely descriptive.The sensitivity and specificity of first NEWS2 at different cut-offs were determined for 2-day, 7-day, 30-day and any hospital mortality. Receiver operating characteristic curves (sensitivity against 1-specificity) were constructed and area under the curve (AUC) calculated along with 95% CIs. As prespecified, AUC values of 0.7–0.79 were considered acceptable, 0.8–0.89 excellent and ≥0.9 outstanding.Stata V.15.1 was used to conduct all data checking, cleaning and analyses.Between 11 March and 27 June 2020, 1288 hospitalised patients met the inclusion criteria  of whichThree hundred and thirty-one (26%) died in hospital during their first admission, 40 of whom received ICU care before death. Eighty-three (7%) were admitted to ICU and subsequently discharged, and 849 (67%) were discharged without ICU requirement. Mortality was 4% at 2 days, 15% at 7 days and 26% at 30 days. Men were more likely to die than women (29% vs 23%), and older patients were more likely to die than younger patients .Of those discharged from the hospital, 196/932 (21%) were readmitted ; 147 were readmitted once, 31 twice and 18 three to six times. Twenty-six patients who were discharged following their first admission died during a subsequent admission. One thousand one hundred and forty-four (91%) patients had at least one set of complete electronic observations and 1071 (85%) had ‘first’ scores. Of the 618/1071 people who had their swab on the same day as they were admitted, 55% had their first NEWS2 recorded within 4 hours, 16% between 4 and 8 hours, 19% between 8 and 24 hours, and 10% over 24 hours later.Most patients had low first NEWS2 values: 535 (50%) NEWS2=0–2, 287 (27%) NEWS2=3–4, 150 (14%) NEWS2=5–6 and 99 (9%) NEWS2=7+. However, a greater proportion had a high score at some point during their stay: 156 (14%) had a maximum score of NEWS2=0–2, 251 (22%) NEWS2=3–4, 195 (17%) NEWS2=5–6 and 542 (47%) NEWS2=7+ .Patients with higher first scores were more likely to die than patients with lower first scores , oxygen saturation (39% of patients) and pulse rate (29% of patients) . For theWe found 57/1071 first scores were 3 for low oxygen saturation, and of those 28/57 (49%) had a normal RR. This was more common for patients without supplemental oxygen  than with oxygen . Therefore, silent hypoxia was demonstrated in these patients, although numbers were small. This may be due to high percentage of patients receiving oxygen prior to first NEWS2 (45%).The AUCs for 2-day, 7-day, 30-day and any hospital mortality were 0.77 (95% CI 0.70 to 0.84), 0.70 (0.65 to 0.74),0.65 (0.61 to 0.68) and 0.65 (0.61 to 0.68), respectively . Two-dayThe AUCs for 2-day mortality for individual component scores were 0.71 (95% CI 0.65 to 0.77) for supplemental oxygen, 0.65 (0.56 to 0.73) oxygen saturation, 0.64 (0.56 to 0.73) RR, 0.56 (0.48 to 0.63) SBP, 0.53 (0.46 to 0.61) pulse, 0.53 (0.46 to 0.59) temperature and 0.53 (0.49 to 0.57) consciousness.This study included 1288 COVID-19-positive hospitalised patients in the West-of-England. Patients were more likely to be men and older, and these groups were also more likely to die, in line with other COVID-19 studies.The AUC for 2-day mortality was 0.77, reducing to 0.70 and 0.65 for 7-day and 30-day mortality; these AUCs were considered acceptable at 2 and 7 days according to our predefined criteriaThe main strength of this paper is the inclusion of all patients with a positive COVID-19 diagnosis over a period of more than 3 months admitted to four hospitals. This enables findings to be generalisable across the UK. A limitation was the absence of electronic observations in ICU, three EDs and respiratory admission unit in Bath. This meant that 17% of patients were not included in the analysis of first scores, 11% were not included in maximum score analysis , and maxWe would have liked to look at pre-hospital NEWS2, however without data linkage this would have meant studying suspected COVID-19 status rather than confirmed, and remote GP consultations mean that NEWS2 values in primary care data are often incomplete. We therefore focused on hospital data for confirmed patients with COVID-19. A limitation of this approach was that 45% of patients scored for supplemental oxygen on first score, which does not reflect the situation in primary care. However, it is likely that the score for supplemental oxygen would be replaced by a score for hypoxia in the community. We did not have access to admission symptoms, so it is possible that a proportion of the 402/1071 patients who had swabs taken at least 1 day post-admission were admitted with another diagnosis and acquired COVID-19 in hospital. However, this study was undertaken early in the pandemic when patients were not swabbed unless they had COVID-19 symptoms.This is one of the largest UK multicentre studies of inpatients with confirmed COVID-19. In a systematic review of 18 studies with 6922 participants, only 6 had more than 400 patients.Hypoxia (low oxygen saturation) has been shown to predict COVID-19 mortality in other studies.An evidence review of NEWS2 and COVID-19Is NEWS2 valid as a measure of severity in COVID-19, and does it predict who is likely to deteriorate? We have demonstrated that NEWS2 predicts mortality, particularly short-term mortality.Is a single NEWS2 value sufficiently sensitive and specific? We have shown that a single score can predict short-term mortality, and based on the AUC, NEWS2=4 is the best value to balance sensitivity and specificity. A low score suggests that mortality is unlikely in the subsequent 2 days but over time, scores deteriorate in many patients, so the use of serial scores is likely to be superior to a single score. In either case, NEWS2 should always be used alongside clinical judgement and not as a rule in/out test.Is calculating NEWS2 practical? Some components are measurable at home, but blood pressure and oxygen saturation require equipment. This study shows that blood pressure alone was a poor predictor of short-term mortality, but oxygen saturations and RR are most predictive. This supports the approach of using pulse oximeters for remote monitoringFurther research measuring NEWS2 in patients with COVID-19 in primary/community care is required, but we believe the evidence presented in this study informs the management of patients in these settings, despite being collected in hospitalised patients. NEWS2 in combination with clinical judgement is a systematic way for clinicians to assess and manage patients according to the likelihood of deterioration and provides a standardised language to communicate illness severity.This study has demonstrated that increased NEWS2 is associated with mortality in patients with COVID-19 and is a reasonably good predictor of 2-day mortality. The respiratory components  are the most valuable predictors in the short-term supporting the use of pulse oximeters by COVID-19 oximetry@Home. These findings support the RCP’s recommendations to use NEWS2, alongside clinical judgement, in the assessment of patients with COVID-19.


Kingella kingae is recognized as a frequent source of childhood bacteremia and the commonest agent of skeletal system infections in children 6 months - 4 years old. Several factors, including difficulty in detecting this fastidious organism in routine laboratory assays, result in underdiagnosis of the infections. Species-specific nucleic acid amplification assays, however, significantly improve the detection of K. kingae in blood samples. The aim of this study was to detect K. kingae infection in young children in Accra, Ghana.a cross-sectional based study was carried out in three hospitals in Accra. Children with febrile illness and directed by a clinician for blood culture were recruited. Blood samples collected were analysed by culture and polymerase chain reaction (PCR), using universal prokaryotic and K. kingae rtxA primers.blood samples from 232 children (mean age 20.10 ± 12.57 months) were analysed. Bacteremia (72.4%) was the highest clinical diagnosis particularly in the 12-24 months age group. Only 7 (3.1%) samples showed bacterial growth and were negative for Kingella. PCR with universal prokaryotic primers succeeded in 223 (96.1%) out of 232 samples. PCR with K. kingae rtxA toxin primers was positive for 12 (5.4%) samples, all diagnosed as bacteremia, out of the 223 samples. Eleven (91.7%) out of the 12 K. kingae PCR positives were culture-negative.Kingella kingae was detected only by PCR specific for the K. kingae rtxA toxin. Kingella kingae may be a potential cause of bacteremia and hence febrile illness in young children living in Accra, Ghana. Kingella kingae is a fastidious Gram-negative coccobacillus and a normal flora of the oropharynx in young children, with a prevalence rate of 10-12% among 2 - 24-month-old children  was used. The extraction was carried out by using TNES buffer (10 mM Tris-HCl (pH 7.5), 400 mM NaCl, 100 Mm EDTA, 0.60% SDS) protocol. For lysis, 200 µl TNES digestion buffer was added to the filter papers, followed by adding 6 µl of proteinase K (10 mg/ml) to each tube and incubation for 4 hours in a heat block  at 56°C. Samples were retrieved, 100 μl 5M NaCl was added to each tube and mixed briefly. The contents were placed in a freezer for 5 minutes, 20 μl protein precipitator was added to each tube, then the tubes were placed in a freezer for 10 minutes and spun in a centrifuge at 15000 rpm for 10 minutes. The supernatants were transferred into new 1.5 ml Eppendorf tubes, and 400 µl 100% ethanol added to each tube, then rocked gently back and forth. Deoxyribonucleic acid (DNA) precipitation was visible at this moment. Samples were stored in a -21oC for 1 minute, 56°C for 1 minute and 68°C for 2 minutes, with a final extension at 68oC for 10 minutes. Samples positive for 16S bacterial sequences were selected and used for another round of PCR using rtxA toxin gene primers of K. kingae as described previously  and p13B [5´-CGGGATCCCAGGCCCGGGAACGTATTCAC-3´]  to amplieviously . AmplifiK. kingae among the children. Continuous variables such as age were presented in ranges.Data collected was entered and analysed using SPSS version 24.0  to obtain descriptive data. Relevant tables and figures were created from the data to allow for easy analysis and interpretation. Frequency and percentage were used to determine the prevalence of Demographic data: a total of 232 children, 114 (49.1%) males and 118 (50.9%) females, were recruited for the study. Their ages ranged from 6 months to 48 months with a mean age of 20.10 ± 12.57 months.Clinical diagnosis by clinicians: the clinical diagnosis of the children by the clinicians is shown in Culture tests: only seven (3.0%) out of the 232 culture tests were positive, showing bacterial growth. The rest showed no bacterial growth and 2 (0.9%) no bacterial growth but had contamination.Culture results in relation to clinical diagnosis:Detection of bacteria using universal prokaryotic primers: bacterial 16S rDNA fragments of the predicted sizes (ed sizes  were sucK. kingae using rtxA toxin gene primers:Detection of  twelve (5.4%) samples yielded DNA fragments of the predicted size (approximately 1198 bp)   after sc6 months  age grouBacteria rDNA PCR in relation to culture results: out of the 223 positive 16S rDNA samples, 214 (96.0%) were culture-negative, 7 (3.1%) were positive for culture and 2 (0.9%) showed contamination.K. kingae PCR results:Culture results in relation to  comparison of culture results to K. kingae PCR results showed that out of the 12 positive K. kingae detected by PCR, 1 (8.3%) sample was culture positive and 11 (91.7%) samples were negative by the culture methods.K. kingae PCR:Clinical diagnosis in relation K. kingae PCR results. The 12 K. kingae PCR positive samples were all bacteremia cases.K. kingae detection by culture, broad range PCR and the toxin specific rtxA genes were investigated. Kingella kingae is a common etiology of paediatric bacteremia [Kingella kingae may also appear Gram-positive on staining . In addition, blood culture may allow the growth of a small quantity of bacteria potentially considered contaminants. However, many of these issues can be overcome by using nucleic acid amplification assays (NAAAs) like PCR which also reduces the time needed for bacterial identification from 3-4 days to a few hours. Using NAAAS also enables exact identification without suffering the bacteriostatic effect of antimicrobial therapy . Some studies have reported detecting K. kingae from culture-negative specimens by using broad range PCR amplification . For example, a comparative study by Rosey et al. (2007) detected 15 additional cases using a combination of conventional and real-time PCR with broad-spectrum primers as against inoculated blood culture vials and seeded samples of synovial fluid onto solid medium which resulted in the isolation of K. kingae in 6 of 94 (6.4%) children. Using primers targeting the conserved 16S rDNA of bacteria serves as a control to monitor the extraction and absence of PCR inhibitors. PCR-based assays that amplify the 16S rRNA gene results in a 200% improvement in the diagnosis of the organism compared to culture [In this study, cteremia , 10. In cteremia . Culturicteremia , 27. Kin culture .K. kingae, believed to begin with asymptomatic colonization of the respiratory tract by disrupting the oropharyngeal epithelium which facilitates its entry into the bloodstream and damage to deeper tissues, was related to the production of a potent cytotoxin (RTX) which has a disease-promoting effect [et al. . The 12 Kingella kingae positive samples were all bacteremia cases by clinical diagnosis. This was not surprising as K. kingae occult bacteremia is the second most common presentation of K. kingae disease in children [K. kingae bacteremia.Disease caused by g effect . The reset al. 20 reportedre vials . Reporte. kingae . These rchildren , 31. Dubchildren , may notK. kingae is transmitted by respiratory secretions, saliva, and potentially oral contact with contaminated objects. Asymptomatic colonization of the upper respiratory tract by K. kingae is found in children who acquire the infectious agent after six months of life [K. kingae were detected at age 1-3 years. In several studies, almost 90% of the reported cases have occurred in children aged < 5 years, and 60% in those aged < 2 years [As an oropharyngeal colonizer,  of life ; subsequ 2 years , 34. DatKingella kingae was not detected by microbiological means but was detected only by PCR assay specific for the K. kingae RTX toxin. Most PCR positives were within the 12-36 months age group and all bacteremia cases by clinical diagnosis. Hence, K. kingae may be a potential cause of bacteremia and hence febrile illness in young children living in Accra, Ghana.Kingella kingae is a common etiology of paediatric bacteremia;Data on K. kingae infections from sub-Saharan Africa are scarce.First report of K. kingae infection in Accra, Ghana;Kingella kingae may be a potential cause of bacteremia in young children in Accra, Ghana.