Salmonella spp. can lead to co-infection of these two pathogens, especially in the tropics where malaria is endemic. Moreover, few literatures suggested that malaria infection was associated with Salmonella bacteremia. Therefore, this study quantified pooled prevalence of typhoidal/non-typhoidal Salmonella (NTS) and probability of typhoidal/NTS and malaria co-infection among febrile patients. The systematic review protocol was registered at PROSPERO (CRD42021252322). Studies on co-infection of typhoidal/NTS and malaria were searched in PubMed, Scopus, and Web of Science. The risk of bias of the included studies was assessed using the checklist for analytical cross-sectional studies developed by the Joanna Briggs Institute. Meta-analyses on the following criteria were performed: (1) pooled prevalence of typhoidal/NTS and malaria co-infection among febrile patients, (2) pooled prevalence of typhoidal/NTS among malaria patients, (3) pooled prevalence of malaria infections among patients with Salmonella spp. infection, and (4) probability of typhoidal/NTS and malaria co-infection among febrile patients. Additionally, the case fatality rate and mean difference of malarial parasitemia between typhoidal/NTS and malaria co-infection and Plasmodium monoinfection were also determined. The subgroup analyses of typhoidal/NTS, regions (Africa and Asia), countries, time (publication year), characteristics of participants, and diagnostic tests for identifying Salmonella spp. were also conducted. A sensitivity test was performed to determine the robustness of the study outcomes. Publication bias among the included studies was evaluated using the funnel plot and Egger’s test. All analyses were performed using Stata version 15  with a p-value < 0.05 indicating statistical significance. Eighty-one studies that met the eligibility criteria were included in the analyses. Of the 73,775 study participants, 4523 had typhoidal/NTS and malaria co-infections. The pooled prevalence rates of typhoidal/NTS and malaria co-infection among febrile patients were 14%  and 1%  using the Widal test and culture methods for identifying Salmonella spp., respectively. The pooled prevalence rates of typhoidal/NTS infection among patients with malaria were 31%  and 3%  using the Widal test and culture methods for identifying Salmonella spp., respectively. The pooled prevalence rates of malaria infection among patients with typhoidal/NTS were 17%  and 43% , respectively. Malaria infection was associated with typhoidal/NTS in children aged < 15 years . The case fatality rate in patients with malaria and NTS co-infections was 16% . From the view of the present study, the inappropriate use of the Widal test for Salmonella spp. diagnosis can overestimate the prevalence of typhoidal/NTS and malaria co-infections. Malaria infection associated with typhoidal/NTS in children and the high case fatality rates among few patients with co-infections were highlighted. Future prospective longitudinal studies using the appropriate and confirmatory dsiagnosis for Salmonella spp. infections are highly recommended to ensure the real prevalence of co-infection and highlight the outcome of co-infection for providing adequate treatment in febrile patients who live in areas where malaria is endemic, such as tropical Africa and India.The geographical overlaps of malaria parasites and  Most of these cases  were found in Nigeria (27%), the Democratic Republic of the Congo (12%), Uganda (5%), Mozambique (4%), and Niger (3%), whereas 51% of malaria deaths occurred in Nigeria (23%), the Democratic Republic of the Congo (11%), the United Republic of Tanzania (5%), Mozambique (4%), Niger (4%), and Burkina Faso (4%)1. Although the incidence of malaria declined from 2000 to 2019, its diagnosis among febrile patients in malaria-endemic settings remains challenging; malaria and typhoid and non-typhoid fever are co-endemic and have similar clinical signs and symptoms.Malaria is a major public health problem in tropical and subtropical countries. The World Health Organization (WHO) reported 229 million cases of malaria and 409,000 deaths in 2019Salmonella species are Gram-negative bacilli members of Enterobacteriaceae and associated with human infection3. Salmonella is comprised of two major species, namely, Salmonella enterica and Salmonella bongori. S. enterica are classified into six serotypes, which are differentiated based on their antigenicity3. Several Salmonella enterica serotypes cause typhoid fever. S. typhi and S. paratyphi, collectively referred to as typhoidal Salmonella, are the most common species that cause enteric fever (typhoid fever and paratyphoid fever). While both fevers share clinical symptoms, paratyphoid fever tends to be more benign5. Paratyphoid fever is most commonly acquired by ingesting contaminated food or water5. Humans are key reservoir hosts for typhoidal Salmonella and contribute to disease transmission and dissemination5. Typhoid fever is characterized by gastroenteritis and presents with nonspecific clinical symptoms, such as high fever, fatigue, headache, malaise, abdominal pain, nausea, vomiting, constipation, and diarrhea6. These symptoms are indistinguishable from other causes of fever, such as malaria5. The complications of typhoid fever include septicemia, meningitidis, and immunological symptoms7. Typhoid fever may be mild or severe, and complications may contribute to typhoid-related deaths8. Non-typhoid fever is a febrile illness caused by non-typhoidal Salmonella (NTS), including S. enteritidis and S. typhimurium9. NTS infections most often cause mild gastroenteritis, which is usually self-limiting7. Recently, NTS infections have been associated with septicemia and high mortality rates in immunocompromised patients in sub-Saharan Africa10.Salmonella infection rate is high in low- and middle-income countries with > 100 per 100,000 infected people annually12. Typhoid fever is an important cause of morbidity and mortality worldwide, with an estimated 16–33 million cases and 500,000 to 600,000 deaths annually13. Typhoid is endemic in developing countries, especially Africa, whereas developed countries have a much lower incidence. The majority of patients in developed countries are travelers returning from endemic areas14. In developing countries, especially in Southeast Asia and Africa, NTS is endemic and is a global burden, contrary to typhoidal Salmonella14. Poor water quality, poor handwashing habits, and consumption of untreated drinking water or unsafe food are the main causes of typhoid fever15. Therefore, people with low socioeconomic status and poor or improper hygiene have high risk of fecal–oral enteric infections, including typhoidal and NTS. The most recent meta-analysis revealed that household behaviors, including poor hygiene and consumption of unsafe food and untreated water, increase the risk of typhoid transmission16. Among children in Africa, NTS is a leading cause of bacteremia, whereas typhoid fever has a relatively low burden19. Another study demonstrated that typhoid fever is more common in older children with a period of fever, whereas non-typhoidal bacteremia frequently develops in younger children of poorly educated women or women with low socioeconomic status20.The Salmonella infection, especially from blood, bacteriological culture is the gold standard21. The sensitivity of the culture method depends on the blood volume, antibiotic treatment, affected individual, disease duration, and presence of bacteremia22. Blood cultures have a sensitivity of 40–80%23. Moreover, they are most sensitive in the first week of infection as circulating bacterial concentrations peak at that time23. Stool and rectal swab cultures have lower sensitivity than blood cultures24. However, sensitivity can be enhanced by culturing from three specimens or performing multiple cultures from a single stool specimen24. Culture methods are less frequently employed in developing countries because of the high cost and requirements for good laboratory facilities and highly trained professionals25. Serological diagnoses of infections are conducted using the Widal test. This test measures the antibody titers specific for Salmonella O (somatic) and H (flagella) antigens7. The Widal test is widely used in numerous countries where trained technicians and laboratory facilities are limited25. Other useful methods for the diagnosis of Salmonella infection include enzyme-linked immunosorbent assay (ELISA), which detect IgM and IgG antibodies against Salmonella surface molecules, and molecular methods, such as nested multiplex polymerase chain reaction (PCR) and real-time PCR, which target Salmonella virulence genes. The real-time PCR test is highly specific and sensitive and has faster turnaround times than culture methods26.For the diagnosis of typhoidal 28, recent malaria29, anemia29, severe malarial anemia30, jaundice, and hypoglycemia20. Previous studies also demonstrated that NTS infection is associated with more severe anemia and malaria compared with typhoidal Salmonella or other bacteremia infections31. Another study demonstrated that NTS infections were associated with previous antimalarial treatment and malarial complications . Furthermore, a systematic review demonstrated a higher case fatality rate in children who were co-infected with NTS compared with those infected with malaria alone; however, the study had limitations on high heterogeneity between studies, inclusion of recent malaria infection, use of antigen-based rapid diagnostic tests (RDTs), study design, quality of microbiological data, and publication bias, making the meta-analysis potentially misleading32. To the best of our knowledge, meta-analyses determining the association between malaria and typhoid/non-typhoid fever have not been well conducted, and information is not updated. Therefore, the present study aimed to quantify the pooled prevalence, probability, and outcome of typhoidal/NTS and malaria co-infection among febrile patients who were suspected of having these two diseases.The geographical overlaps of malarial parasites and typhoidal/NTS can lead to co-infection of these two pathogens, especially in the tropics where malaria is endemic. The overlap in the clinical symptoms of malaria and non-malaria febrile illness or co-infection of these two pathogens may lead to the misdiagnosis of one disease. Previous studies conducted in Africa demonstrated that bacteremia caused by NTS was associated with malaria parasitemia33.The protocol of systematic review was registered at PROSPERO (CRD42021252322) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statementPotentially relevant articles in PubMed, Web of Science, and Scopus were searched using the combined search terms presented in Supplementary Table Observational studies in the English language that reported concurrent malaria and typhoidal/NTS infection were included in the study. Studies reporting data that could not be extracted, case–control studies, experimental studies, animal studies, case reports, and case series were excluded.Plasmodium monoinfection, diagnostic test for malaria, and diagnostic test for typhoid (best diagnostic test). Any disagreement on data extraction between the two authors was resolved by a third author (PW) for the final decision.Potentially relevant articles were selected by two authors  using the eligibility criteria. First, the duplicates from the three databases were removed. Second, the remaining studies were screened for titles and abstracts, and any non-related studies were excluded. Third, the full texts of the remaining studies were examined, and any non-related studies were excluded with reasons. Then, the remaining studies were included in the systematic review and meta-analysis. Any disagreement on the study selection between the two authors was resolved by reaching a consensus after the discussion. Data extraction was performed by two authors  using the pilot standardized datasheets. The following information was obtained from each study: first author names, publication year, study sites (country and region), year the study was conducted, study design, characteristics of participants including age and sex, number of co-infections, number of malaria cases, number of typhoid/non-typhoid cases, number of case fatality in co-infection and 34. The checklist is comprised of eight categories (yes/no/unclear/not applicable answers) based on the design, conduct, and analysis. Studies with yes answers in all eight categories were considered to have low risk of bias , whereas those that complied with four to six categories were considered to have a moderate risk of bias . Any study that complied with less than four categories was considered to have a high risk of bias  and thus excluded from the present study. The risk of bias was evaluated by two authors . If the two authors disagreed on the risk of bias assessment, a third author (PW) was responsible for the final decision.The risk of bias of the included studies was evaluated using the checklist for analytical cross-sectional studies developed by the Joanna Briggs InstituteSalmonella spp. infection, (4) comparison of typhoidal/NTS infection among patients with severe and non-severe malaria, (5) association between malaria and typhoidal/NTS infections, (6) case fatality rate among patients with typhoidal/NTS and malaria co-infection, and (7) difference in mean parasitemia level between patients with typhoidal/NTS and malaria co-infection and those with Plasmodium spp. monoinfection.The outcomes of this study were as follows: (1) pooled prevalence of typhoidal/NTS and malaria co-infection among febrile patients, (2) pooled prevalence of typhoidal/NTS infection among patients with malaria, (3) pooled prevalence of malaria infection among patients with Salmonella spp. infection, case fatality rate among patients with typhoidal/NTS and malaria co-infection, and comparison of typhoidal/NTS infection rates among patients with severe and non-severe malaria were estimated using random-effect models, assuming heterogeneity of the included studies. The results of the individual studies are presented in the forest plots as the point estimates  and 95% confidence interval (CI). The association between Plasmodium spp. and Salmonella spp. infections was determined using the random-effects model and expressed as odds ratio with 95% CI. The difference in mean parasitemia level between patients with typhoidal/NTS and malaria co-infection and Plasmodium spp. monoinfection was estimated using the random-effects model and expressed as weighted mean difference (WMD) with 95% CI. The heterogeneity among the included studies was assessed using Cochran’s Q and I2 statistics. Cochran’s Q < 0.05 or I2 > 50% indicated substantial heterogeneity among the included studies. If no substantial heterogeneity existed, the fixed-effects model was employed to estimate the effect size . The subgroup analysis of typhoidal/NTS, regions (Africa and Asia), countries, time (publication year), characteristics of participants, and diagnostic tests for identifying Salmonella spp. were conducted to explore the source(s) of heterogeneity among the overall effect estimate. Sensitivity analyses of the probability of Plasmodium spp. and Salmonella spp. co-infection were performed using the random- and fixed-effects models after excluding outliers.The pooled prevalence rate of typhoidal/NTS and malaria co-infection among febrile patients, typhoidal/NTS infection rate among patients with malaria, malaria infection rate among patients with Publication bias among the included studies was evaluated using a funnel plot between the effect size (ES) and standard error of the ES (seES). A funnel plot with asymmetrical distribution indicated publication bias. Egger’s test was employed if the funnel plot asymmetry was caused by the small study effect. A contour-enhanced funnel plot was also utilized to find the possible causes of funnel plot asymmetry among the included studies. The significance of contour-enhanced funnel plots (p < 0.01) indicated that the cause of funnel plot asymmetry might be more likely other factors such as heterogeneity, selection bias, and quality of the included studies than publication bias.74 were included. Thirty-eight studies108 from additional searches of reference lists and Google Scholar were included. Thus, 81 studies met the eligibility criteria and thus included in the qualitative and quantitative analyses . After removal of 245 duplicated studies, the titles and abstracts of 305 studies were screened. After excluding 232 unrelated studies, 73 were retained for full-text examination. A total of 30 studies were excluded for the following reasons: 11 studies had no malaria and typhoid co-infection cases, 9 studies had no typhoid cases, data could not be extracted from 4 studies, 3 were case–control studies, 1 was an experimental study, 1 was an animal study, and 1 study was a case report. Finally, 43 studies107, Cameroon 93, Ghana 85, Kenya 80, Tanzania 46, Malawi30, Burkina Faso86, Mozambique79, Sierra Leone87, the Democratic Republic of the Congo49, Ethiopia45, Gabon55, and Gambia31, and one study was conducted in Burkina Faso, Ethiopia, Ghana, Guinea-Bissau, Kenya, Madagascar, Senegal, South Africa, Sudan, and Tanzania62. The Asian studies were conducted in India 108, Pakistan 102, Myanmar78, and Vietnam63. One study was conducted in Sweden69.The characteristics of the included studies are presented in Table 108, 8 studies enrolled pregnant women101, 6 studies enrolled patients with severe malaria85, 9 studies enrolled malaria positive patients88, 5 studies enrolled typhoid/non-typhoid-positive patients49, and one study enrolled children with pathogenic bacteremia20. Co-infections with malaria and typhoidal/NTS were reported in 4,523 cases from 73,775 total patients enrolled in the 81 included studies. Co-infections with malaria and typhoidal Salmonella spp., including S. typhi and S. paratyphi, were reported in 3813 cases from 56 studies108. Co-infections with malaria and NTS, such as S. typhimurium, were reported in 707 cases from 18 studies93. Co-infections with malaria and both typhoidal and NTS spp. were reported in 13 studies93.Among the 81 studies included in the analysis, 49 studies (60.5%) enrolled febrile patientsSalmonella spp. infection was identified using blood cultures 108, Widal test 103, stool cultures 107, RDTs 91, and molecular methods 64. One study95 employed both blood and stool cultures. Some studies used combinations of methods to identify Salmonella spp. infection. However, only a definitive method was demonstrated in this qualitative synthesis. For the identification of malaria, Plasmodium spp. infections were identified via microscopy alone 108, microscopy/RDT 104, RDT alone 101, molecular method55, microscopy/RDT/molecular method69, not specified70, and ELISA29.Among the 81 studies included in the present study, 30 (37%) were rated as low risk of bias, whereas 51 studies had a moderate risk of bias . Studies with a high risk of bias were removed during the study selection  using the Widal test, 1%  using blood culture, 1%  using RDTs, 1%  using a molecular method, 7%  using stool cultures, and 6% (95% CI 4–9%) using a combination of blood and stool cultures  and Nigeria , whereas lower prevalence rates were detected in Sierra Leone , Ethiopia , Pakistan , Ghana , India , and Tanzania  . Contrarily, lower prevalence was reported in Nigeria , India , Tanzania , Ghana , and Kenya  . The prevalence of co-infections was 4% in children , 8% in the not specified (NS) age group (95% CI 1–15%), and 4% in adults  , 1% in the NS group , and 1% in children   and 1%   and 1% in Asia   and 1% in 2009, 2012, 2014, and 2015 and 0% in 2003, 2006, 2013, and 2018 , and time (publication year) was performed using the data from studies using blood culture for typhoidal/NTS identification3%) Fig. . Subgrou108. The studies were divided into groups based on diagnostic tests for Salmonella spp. The pooled prevalence rates of typhoidal/NTS infection among patients with malaria were 31%  using the Widal test, 5%  using RDTs, 3%  using blood culture, 2%  using molecular methods, 12%  using stool cultures, and 27% (95% CI 17–39%) using a combination of blood and stool cultures  and Nigeria , whereas lower prevalence rates were reported in Burkina Faso , Pakistan , Sierra Leone , Ethiopia , Tanzania , Ghana , and India  , whereas lower prevalence rates were reported in Myanmar , India , Tanzania , Ghana , Kenya , and Sweden  , 12% in children , 20% in the NS age group , and 11% in adults  , 1% in the NS age group , 3% in adults , and 2% in children   and 1% in patients aged ≤ 3 years   Fig. . Among t8%) Fig. . Subgrou3%) Fig. .Figure 1108. Results showed that the prevalence rates of typhoidal and NTS infection among patients with malaria were 6%  and 2%  , 6% in Asia , and 1% in Europe (95% CI 0–1%)  in 2016 , 8% in 2012 (95% CI 3–18%) and 7% in 2018 . The low prevalence of typhoidal Salmonella infection infection among patients with malaria was demonstrated in 2016 (13%), 2003 (2%), 2013 (2%), and 2009 (1%) , and time (publication year) was performed using the data from studies using blood culture for typhoidal/NTS identification7%) Fig. . Subgrou1%) Fig. . Subgrou1%) Fig. . Subgrou108 and 6 studies that enrolled patients with severe malaria85. All 30 studies employed the blood culture method to identify Salmonella spp. infections. The pooled prevalence rates of typhoidal/NTS infection were 2% in patients with severe malaria  and 3% in patients with non-severe malaria   Fig. .Figure 2Salmonella spp. infection was estimated from three studies49. The pooled prevalence rate of malaria infection in patients with typhoidal Salmonella spp. was 17% in children    Fig. . The poo1%) Fig. .Figure 2Plasmodium spp. and Salmonella spp. co-infections was estimated from 46 studies108, which reported the following parameters: total number of Plasmodium spp. and Salmonella spp. co-infections, total number of malaria, total number of malaria without typhoid, and total number of febrile patients without malaria/typhoid. Plasmodium spp. and Salmonella spp. co-infections in all age groups occurred by chance , whereas Plasmodium spp. and Salmonella spp. co-infections in children did not . No association between Plasmodium spp. and Salmonella spp. infections was observed in the NS age group  or adults . Overall, Plasmodium spp. and Salmonella spp. co-infection occurred by chance  , while one study63 reported the case fatality rate in patients with malaria and typhoidal Salmonella co-infections at 33% (95% CI 6–79%)   than those with Plasmodium spp. monoinfection . The study by Bassat et al.79 also showed a lower mean hematocrit in patients with co-infections  than those with Plasmodium spp. monoinfection .A limited number of studies reported clinical outcomes of patients with co-infections Table . Five st104, the probability of Plasmodium spp. and Salmonella spp. co-infection was estimated from 43 studies108. Overall, Plasmodium spp. and Salmonella spp. co-infection occurred by chance     and NTS (43%) was also detected. The highest prevalence of co-infections detected using the Widal test was observed in Cameroon40, followed by Nigeria103 and Sierra Leone87, compared with Ghana, India, Ethiopia, Tanzania, and Pakistan. In using blood cultures, the gold standard method for the identification of Salmonella spp., the results indicated that the highest prevalence of co-infection was reported in Nigeria83 compared with India, Tanzania, Ghana, and Kenya. Based on these results, typhoid/non-typhoid and malaria co-infection among febrile patients frequently occurred in Nigeria. In 2020, Nigeria accounted for the most malaria cases (27%) and malaria-related deaths (23%) worldwide1. Moreover, typhoid fever is a major disease in Nigeria due to increased urbanization, insufficient water supply, movement of immigrant workers, poor processing of human waste, and overuse of antibiotics109. Due to the co-endemicity of these two pathogens, the possibility of co-infection might increase in this country.The present meta-analysis revealed a high prevalence of malaria and typhoidal/NTS co-infections among febrile patients detected using the Widal test (14%) and a low prevalence of malaria and typhoidal/NTS co-infections among febrile patients detected using blood cultures (1%). Moreover, the meta-analysis demonstrated that the prevalence of typhoidal/NTS infection among patients with malaria using the Widal test was high (31%), whereas the prevalence of typhoid/non-typhoid using blood culture was low (3%). A high prevalence of malaria infections among patients with typhoidal 83. In the meta-analysis of typhoid among patients with malaria, the highest prevalence of typhoid among patients with malaria was noted in Nigeria57. These results suggested an increasing episode of persistent fever among patients with S. typhi and P. falciparum infections in Nigeria. For NTS infection among patients with malaria, the prevalence was higest in Kenya, and NTS infection was the most common bacteremia in children with malaria28. The high rate of bacteremia in patients with malaria in Nigeria might be due to the high prevalence of NTS infections and malnutrition28.Using the data from studies perfoming blood culture to identify typhoidal/NTS infection, the subgroup analysis of typhoidal/NTS infection demonstrated low prevalence of malaria and typhoid co-infections among febrile patients (1%) and low prevalence of typhoid among patients with malaria (6%). Moreover, the low prevalence of malaria and NTS co-infections among febrile patients (1%) and NTS infection among patients with malaria (2%) was observed. The highest prevalence of malaria and typhoid co-infections among febrile patients was reported in Nigeria, suggesting that malaria and typhoid are indeed halo-endemic in this area108. In Africa, although the pooled prevalence of typhoidal/NTS infection among patients with malaria was lower than those in Asia; the results of individual studies were heterogenous. For example, the high prevalence of typhoidal/NTS infection among patients with malaria were reported by four studies conducted in Nigeria83, while a lower prevalence was reported by other studies included in the meta-analysis.Using the data from studies performing blood culture to identify typhoidal/NTS infection, the subgroup analysis of regions demonstrated that the prevalence of malaria and typhoidal/NTS co-infections were 1% in both Africa and Asia. However, the prevalence of typhoidal/NTS among patients with malaria was higher in Asia (6%) than those of Africa (2%). The difference in the prevalence of typhoidal/NTS co-infections between two regions might be caused by the heterogeneity of the prevalence estimates between two regions or real difference caused by environmental factors. For example, studies in India suggested that malaria and typhoid are endemic because of poor hygiene and environmental factorsSalmonella infections among patients with malaria was highest in 2016, while lower prevalence was reported in before and after 2016. In 2016, three studies conducted in Nigeria and India104 reported the highest prevalence rates of typhoid among patients with malaria. The peak of typhoid among patients with malaria in 2016 was different from those of NTS infections among patients with malaria. The subgroup analysis showed that the peak prevalence rate of NTS infection among patients with malaria was highest in 2011, lower in 2012–2016, and 2001. These results indicated that the prevalence of NTS might decreased with time in 2011–2016, while the prevalence of typhoid among patients with malaria might not depend on time, which are needed to be further investigated.Using the data from studies performing blood culture to identify typhoidal/NTS infection, the subgroup analysis of time (year of publication) showed that the prevalence of malaria and typhoid co-infections among febrile patients, and typhoidal 110, while the lower rate of NTS infection occurred in children aged less than 12 years old, and the proportion of hospitalization was decreased with age111. These age groups were supported by the subgroup analysis of age that the prevalence of typhoidal/NTS was higher in adults than in children. Nevertheless, as the limitation of age information in studies reported typhoidal/NTS co-infections among febrile patients, the subgroups analysis of age might not represent the exact difference in the prevalence of typhoidal/NTS co-infections between adults and children.Using the data from studies performing blood culture to identify typhoidal/NTS infection, the subgroup analysis of age of patients demonstrated that the prevalence rate of typhoidal/NTS infection among patients with malaria was higher in adults (3%) compared to that in children (2%). The previous study showed that peaks of NTS infection occurred in children aged < 2 years and adults aged 25–40 yearsSalmonella species/serotypes by the Widal test and cross-reactivity with other Enterobacteriaceae7. Moreover, false-positive Widal tests have been reported in patients with malaria and other infections5. The malaria Plasmodium may share similar strong immunogenic antigens with the typhoidal Salmonella (S. typhi); thus, Plasmodium infections could induce the generation of antibodies against S. typhi antigens, leading to cross-reactivity and false-positive results47. Furthermore, malaria loading strongly correlated with Salmonella antibody titers in numerous studies47. This cross-reaction of typhoidal/NTS antibodies with malarial antigen leads to overdiagnosis of typhoid fever112. The Widal test also generates false-negative results if patients are tested during the early phase of typhoid fever5. The high prevalence of typhoid fever may also be due to poor interpretation of the Widal test when diagnosing typhoid fever113. Nevertheless, in Africa and other territories, the Widal test is the most common diagnostic tool used for typhoid fever; owing to its low cost, ease of performance, and minimal training and equipment requirements. Of note, false-positive results of the Widal tests in febrile patients suspected of having Salmonella spp. infection may lead to incorrect treatment for malaria parasites. Thus, careful interpretation of the Widal test for the diagnoses of typhoid fever in resource-poor countries is required, as the overdiagnosis of typhoid fever can lead to unnecessary treatment of patients with antibiotics, microbial resistance, and poor outcome. The use of Widal test alone for the diagnosis of typhoid fever will cause misdiagnoses.The present meta-analysis demonstrated a wide gap in prevalence of malaria and typhoid/non-typhoid co-infections among febrile patients as measured by the Widal test and blood culture in analysis. The high rate of typhoid/non-typhoid and malaria co-infections detected using the Widal test and low rate of co-infections detected using blood cultures might be due to the lack of differentiation between Salmonella spp. infection may underestimate Salmonella spp. infections, as blood culture has a lower sensitivity compared with the Widal test. Negative blood culture test results may be noted in patients with acute disease before the antibody response5. Based on the results of this study, the Widal test should not be used alone but in combination with blood/stool cultures. Therefore, a combination of the Widal test and blood and stool cultures is an excellent choice for diagnosing Salmonella spp. infection among febrile patients or patients with malaria. Although the high laboratory expenses for combination testing are difficult to overcome, the use of more than one diagnostic method to identify Salmonella spp. infections among patients with malaria is important to prevent incorrect treatment and misdiagnoses of malaria and other acute febrile illnesses. Infections caused by typhoidal Salmonella, including S. typhi and S. paratyphi, and the associated serious complications require treatment with antibiotics, including chloramphenicol, cefixime, amoxicillin, trimethoprim/sulfamethoxazole, azithromycin, aztreonam, and cefotaxime, to prevent severe illness and death114. NTS infections do not usually require treatment with antibiotics. However, complications, such as septicemia and meningitis, require treatment with ciprofloxacin, ceftriaxone, and ampicillin, according to the WHO114. Presently, antibiotic resistance of Salmonella species is an emerging threat, so reliable diagnostic test and appropriate treatments for typhoid/non-typhoid fever are important.Using blood cultures alone to identify Salmonella spp. bacteremia developed in approximately 2% of patients with severe malaria. This occurrence was not much different from the Salmonella spp. bacteremia pooled prevalence of 3% in patients with non-severe malaria. Several mechanisms have been suggested to elucidate why patients with malaria may be predisposed to Salmonella spp. infection and bacteremia. First, immunosuppression occurs during malaria infection and treatment115. Second, malaria can lead to hemolysis, which may predispose patients to infection with Gram-negative bacteria, such as typhoidal Salmonella/NTS spp.69. Third, changes in iron storage metabolism from malaria-induced hemolysis cause neutrophil dysfunction and increased susceptibility118. Increased free iron from hemolysis may also promote the survival of Salmonella spp.19. Fourth, the sequestration of parasitized red blood cells in the intestine causes reduced blood flow in the mucosal gut barrier, which increases intestinal susceptibility to bacterial infection120. The high rate of NTS bacteremia is well described in patients with malaria-related severe anemia121. Severe anemia and hemolysis increase the iron level in the blood and tissues; therefore, pathogens can be actively transported, and iron acquisition is easier121. Based on our results, the increased risk of typhoidal Salmonella bacteremia in patients with severe malaria might reflect the high rate of parasite sequestration and vital organ dysfunction. Moreover, bacteremia cannot be excluded from patients with severe malaria; severe malaria is difficult to distinguish from bacterial sepsis85. Therefore, the WHO guidelines for malaria recommend that children with severe falciparum malaria in high-transmission areas should receive empirical broad-spectrum antibacterial therapy. However, empirical antibiotics should not be administered to adults with severe malaria unless there is clear evidence of bacterial infection122. In the low-transmission areas, WHO suggests that physicians should determine whether patients should receive antibiotics depending on the patient’s condition or parasitemia levels, but patients with severe malaria should not be routinely treated with antibiotics123. In addition to the WHO guidelines, two studies conducted in Myanmar78 stated that “clinicians should have a lower threshold for commencing empirical antibacterial therapy in adults diagnosed with falciparum malaria in these locations than is presently recommended.”The present meta-analysis demonstrated that Salmonella spp. infection in children73. Although the meta-analysis did not provide a significant outcome, individual studies demonstrated significantly higher odds ratios of co-infection in Nigeria101, Cameroon40, India104, and Pakistan102 and significantly lower odds ratio of co-infection in Nigeria51, Cameroon93, Pakistan70, Ghana53, Tanzania44, Kenya60, and India71. Based on these results, the probability of co-infection varies. The fixed-effects model indicated that typhoid/non-typhoid and malaria co-infection did not occur by chance or that there was an association between typhoid/non-typhoid and malaria co-infection in some way. Further studies are required to investigate this association.The present meta-analysis revealed that typhoidal/NTS and malaria co-infection occurred by chance when the random-effects model was employed to combine the effect estimates. However, the subgroup analysis demonstrated a decreased odds ratio of co-infection in children aged < 15 years, indicating that the current malaria infection was negatively associated with typhoidal/non-typhoidal 92. These three studies enrolled patients with severe malaria and co-infected with NTS and indicated that both diseases facilitate the higher fatality rate than those of the malaria or NTS infection alone. Moreover, the meta-analysis of two studies80 showed a higher mean parasitemia level in patients with malaria and co-infected with NTS compared to those with malaria alone , but it is important to note the limitation in the number of included studies in the analysis. Therefore, there is a need to investigate if co-infection of malaria and NTS leads to poor outcome or demonstrated the association of both diseases.The present meta-analysis of case fatality rate of patients with co-infection demonstrated the high rate of mortality (16%) without heterogeneity among the three included studiesSalmonella spp. and malaria co-infection. Therefore, data were not available to determine the differences between co-infected patients and mono-infected patients. Second, the number of studies evaluating the occurrence of Salmonella spp. bacteremia in patients with severe malaria was limited; therefore, the pooled prevalence of Salmonella spp. bacteremia in patients with severe malaria might not represent all patients with severe malaria. Third, the heterogeneity among the included studies used to determine the probability of typhoidal/non-typhoidal Salmonella spp. and malaria co-infection was high; therefore, the association between typhoidal/non-typhoidal Salmonella spp. and malaria co-infection should be carefully interpreted with the results from the sensitivity test. Compared with the previous systematic review32, the present study excluded studies with recent malaria infection; most included studies used microscopy rather than RDTs for malaria detection; and there was no publication bias among the included studies.This study had several limitations. First, most included studies were cross-sectional studies that determined the prevalence of typhoidal/non-typhoidal Salmonella spp. and malaria co-infection occurred by chance or not, healthcare providers must provide support to patients with nonspecific clinical symptoms of malaria or typhoidal/non-typhoidal diseases. In the present study, malaria associated with typhoidal/NTS infection in children and the high case fatality rate among few co-infected patients were highlighted. Future prospective longitudinal studies using the appropriate and confirmatory diagnosis for Salmonella spp. infections are highly recommended to ensure the real prevalence of co-infection and highlight the outcome of co-infection for providing adequate treatment of co-infections in febrile patients who live in areas where malaria is endemic like tropical Africa or India.In conclusion, whether typhoidal/non-typhoidal Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Figure S5.Supplementary Figure S6.Supplementary Table S1.Supplementary Table S2.


Chlorodrepanis virens) have undergone strong selection by avian malaria and evolved increased resilience (the ability to recover from infection), while populations at high elevation with few vectors have not undergone selection and remain susceptible. We investigated how experimentally elevated corticosterone affects the ability of high- and low-elevation male Amakihi to cope with avian malaria by measuring innate immunity, hematocrit and malaria parasitemia. Corticosterone implants resulted in a decrease in hematocrit in high- and low-elevation birds but no changes to circulating natural antibodies or leukocytes. Overall, leukocyte count was higher in low- than in high-elevation birds. Malaria infections were detected in a subset of low-elevation birds. Infected individuals with corticosterone implants experienced a significant increase in circulating malaria parasites while untreated infected birds did not. Our results suggest that Amakihi innate immunity measured by natural antibodies and leukocytes is not sensitive to changes in corticosterone, and that high circulating corticosterone may reduce the ability of Amakihi to cope with infection via its effects on hematocrit and malaria parasite load. Understanding how glucocorticoids influence a host's ability to cope with introduced diseases provides new insight into the conservation of animals threatened by novel pathogens.Vertebrates confronted with challenging environments often experience an increase in circulating glucocorticoids, which result in morphological, physiological and behavioral changes that promote survival. However, chronically elevated glucocorticoids can suppress immunity, which may increase susceptibility to disease. Since the introduction of avian malaria to Hawaii a century ago, low-elevation populations of Hawaii Amakihi ( Summary: Amakihi innate immunity, as measured by natural antibodies and leukocytes, is not sensitive to changes in corticosterone, but high circulating corticosterone may reduce the ability of Amakihi to cope with avian malaria infection via its effects on hematocrit and malaria parasite load. When vertebrates are challenged by acute environmental perturbations, the hypothalamic–pituitary–adrenal (HPA) axis is activated and several endocrine changes occur, including increased synthesis of glucocorticoid hormones. The surge in circulating glucocorticoids causes morphological, physiological and behavioral changes that promote survival during and after exposure to an environmental stressor . HoweverSistrurus miliarius, Plethodon shermani, Corticosterone (CORT) is the primary glucocorticoid in birds, reptiles, adult amphibians and many rodents . ElevatePasser domesticus) experience avian malaria relapse in the spring when their CORT levels are high, and malaria parasite loads increased in infected sparrows treated with CORT for 10 days  and its main vector, the southern house mosquito (Culex quinquefasciatus), are cold intolerant  is the only honeycreeper that has maintained stable populations in both high- and low-elevation habitats of Hawaii  and weak selection (high-elevation populations) by avian malaria on Hawaii Island. We hypothesized that CORT affects avian malaria resilience in Amakihi through its modulation of immunity and hematocrit. To test this hypothesis, we experimentally elevated CORT levels in high- and low-elevation adult (age 2+ years) male Amakihi for a period of 4 days using Silastic implants, and we measured the effect of CORT treatment on innate immunity and hematological health. We predicted that the CORT treatment would result in an increase in circulating baseline CORT levels and a decrease in immunity and hematocrit of both high- and low-elevation birds. Additionally, among birds infected with avian malaria, we predicted that CORT treatment would cause an increase in circulating malaria parasite load. To assess immunity, we utilized the hemolysis–hemagglutination assay  and we qChlorodrepanis virens (Gmelin 1788), is a small passerine endemic to the Hawaiian Islands  using 3 m-high mist nets between 07:00 h and 17:00 h in March and April of 2019.The Amakihi,  Islands . Amakihi Islands . Birds wSex and age were determined upon capture by visual inspection of plumage and the presence or absence of a brood patch, which develops only in females . We restN=20) and low-elevation (N=20) adult males were housed in individual cages (90×60×30 cm) located in one of two housing rooms (5.5×3.75×2.5 m) within the aviary. Each cage was equipped with multiple perches, supplemental heat (150 W ceramic heat lamps), Nektar-Plus  and several solid foods  ad libitum. Birds were maintained on the natural photoperiod and ambient temperatures (13–26°C) via a translucent roof and continuous air ventilation with the outdoors. Prior to experimentation, birds were given 10–15 days to acclimate to captivity and allow baseline CORT to return to close to naturally observed levels (High-elevation (d levels .N=10 each per elevation). Sample sizes were chosen based on expected effect sizes and statistical power in similar laboratory studies on immunity (e.g. N=4–6 birds per round), each including equal numbers of control and experimental birds and involving both high- and low-elevation birds, from 18 March to 28 April 2019.Birds were randomly assigned to either a control or experimental CORT implant group . Plasma and packed cells were separated and stored at −30°C for a maximum of 9 months before use in hormone, immunity and malaria diagnostic assays.Blood samples were collected between 08:30 h and 10:00 h on days 0 (immediately before implant placement), 2 and 4  of the experiment by pricking the alar vein with a sterile 26G needle and collecting blood into heparinized microhematocrit tubes. Baseline CORT samples were obtained within 3 min of entering the aviary to obtain baseline or near-baseline levels , and allPlasma CORT was quantified by radioimmunoassay as described and validated for this species . ReconstN=11/110, all reagents scaled accordingly) because of low sample volumes. All plates included a positive control (pooled chicken sample) and negative control (plasma replaced by 0.01 mol l−1 PBS). Samples were randomized across plates and scored blind to sample identity by one observer (G.R.N.). Our results are limited to agglutination score only, because complement activity was not high enough in Amakihi plasma to quantify hemolysis. Agglutination score was calculated as log2(W+1), where W was the last dilution well in which agglutination was detected.We used the protocol described in Plasmodium parasites were quantified by examining blood smears  and of those slides, the mean (±s.e.m.) number of fields scored per slide was 62.2±2.69. To account for possible variation related to fields scored per slide, the parameter was included as a blocking variable in leukocyte and Plasmodium parasite models.Circulating leukocytes and d smears . To prodPacked red blood cell volume in centrifuged capillary tubes was measured blind to sample identity by one observer (G.R.N.) using a Critocaps micro-hematocrit capillary tube reader card.5 red blood cells , we used a modified version of the nested PCR method from od cells .http://www.R-project.org/) using linear mixed models . Baseline CORT and agglutination data were log transformed, leukocyte data were square root transformed, and Plasmodium data were cube root transformed to obtain normality of residuals; hematocrit data were normally distributed. The corrected Akaike's information criterion  and Akaike weights  were used to compare the series of possible models and the null model, and to choose the best-fit models among them  tests (emmeans package).Data were analyzed with R Statistical Analysis Software version 3.5.0 (ong them . All modong them . Coeffic−1), agglutination score, leukocytes per milliliter of blood, hematocrit and Plasmodium parasites per milliliter of blood. We included aviary room  and number of fields scored per slide  as blocking factors and individual bird identity nested within experimental round (1–8) as random effects. Hematocrit can decrease as a result of repeated blood sampling  and separate analyses of lymphocytes and heterophils per milliliter of blood produced the same supported models as analyses of pooled leukocytes, so we chose to present analyses of total leukocytes per milliliter of blood. Models including malaria infection as a predictor variable were run using only low-elevation bird data because of collinearity between malaria infection and elevation. All results are presented as means±s.e.m.We tested for the effect of implant type , experiment day  and bird elevation  or malaria infection status  and their interactions on five response variables: baseline CORT . Baseline CORT was significantly higher in birds with CORT implants than with sham implants on day 2  and day 4  but not on day 0 . In analyses including malaria infection status as a predictor variable of baseline CORT (low-elevation birds only), the same predictor variables were included in the best-fit model , but the interaction among malaria infection status, experiment day and implant type was included in the next best supported model . Post hoc analyses of the latter revealed that baseline CORT was significantly lower in infected than uninfected birds on day 0 .Experiment day, implant type and their interaction were included in the best-fit model of baseline CORT in high- and low-elevation birds (Table S1), and the best-fit model for both analyses was the null model .Neither implant type nor experiment day was a good predictor of agglutination score in high- and low-elevation birds or in only low-elevation birds , and implant type and the interaction between implant type and elevation were also included in the next best supported model . The number of leukocytes per milliliter of blood was significantly higher in low- compared with high-elevation birds  but not in low-elevation birds , and infected birds tended to have a higher leukocytes per milliliter of blood than uninfected birds .Elevation was included in the best-fit model of leukocytes per milliliter of blood in high- and low-elevation birds . Hematocrit decreased over the experimental period for birds with both CORT implants  and sham implants . Again, hematocrit decreased over the experimental period  and was lower in birds with CORT than with sham implants .Hematocrit in high- and low-elevation birds was best predicted by a model that included implant type, experiment day and their interaction  and 0 high-elevation birds tested positive for avian malaria via nested PCR at all three sampling points, and infection rates differed significantly by elevation . Plasmodium parasites were detected via microscopy in a subset of blood smears from PCR-diagnosed infected birds (N=7/19) and in no blood smears from PCR-diagnosed uninfected birds (N=0/76). Implant type was included as a predictor of Plasmodium parasites per milliliter of blood in a statistically supported model that had nearly the same AICc as the best-fit model, the null model (ΔAICc=0.3). The number of Plasmodium parasites per milliliter of blood was significantly higher in birds with CORT than with sham implants  and anemia, which weakens birds and can increase mortality , where birds that received several days of CORT treatment via injections had greater Plasmodium relictum counts than control birds . Many individuals experienced an increase in baseline CORT that was within the biological range observed in free-living Amakihi males on Hawaii Island  by elevated CORT does not seem to be the mechanism by which CORT affects avian malaria resilience, at least on the time scale tested here (4 days of treatment), but additional research involving other aspects of innate immunity is needed. These findings highlight the importance of considering physiology in the context of emerging infectious diseases and raise new questions regarding relationships between glucocorticoids and disease susceptibility.Chronically elevated glucocorticoids can be immunosuppressive and may reduce a host's ability to resist or cope with infectious diseases . The goa


Background: Malaria among pregnant women is one of the major causes of maternal and infant mortality and morbidity, especially in high-risk areas. Therefore, our study identified the burden of malaria for pregnant women, non-pregnant women, and children under 5 years of age, and malaria service health facilities in Bannu district, Khyber Pakhtunkhwa, Pakistan.Methods: A cross-sectional study was conducted. In this survey, 15,650 individuals were surveyed, and 1,283 were malaria-positive detected. The data were collected from 80 different healthcare centers. SPSS version 23 was used for data analysis. ArcGIS version 10.8 was used for study area mapping.Results: Malaria was detected in 23.3% of children under five, 4.4% of pregnant women, and 72.3% of non-pregnant women, respectively. Moreover, P. falciparum, P. vivax, and mixed infection had a prevalence of 2.1, 96.8, and 1.1%. The most often used and effective medications to treat malaria were chloroquine (29.7%) and primaquine (69.4%).Conclusion: This study's findings depict that malaria's prevalence in the non-pregnant women's group was high. Additionally, P. vivax infection was found to be more prevalent than other types of malaria infection. Due to the scarcity of healthcare facilities in this endemic region, special attention should be directed to strengthening the malaria surveillance and eradication programs. Plasmodium falciparum  is the most important infection. Similarly, Plasmodium vivax (P. vivax) is a major threat to pregnant women: about 90 million are susceptible every year in Asia. With 300 to 600 million cases occurring every year and almost 2.2 billion people at high risk, malaria is a global concern  in the neighboring areas from North Waziristan to Khyber Pakhtunkhwa Province . A totalEighty healthcare centers were equipped with microscopes, rapid diagnostic testing (RDT), and both were surveyed in the Bannu district. In this study, 15,650 people were interviewed, and 1,283 people were found to have malaria, according to the results The cluster sampling technique was carried out where the whole population was divided into clusters, and a random sample of these clusters was selected. All of the observations in the selected cluster are included in the final selection process. It can be understood as simple random sampling as described, except that the primary sampling unit is a cluster or group of people instead of the individual. Cluster sampling is typically used when the researcher cannot get a complete list of the population members. It was also used when a random sample would produce a list of subjects so widely scattered that surveying them would prove far too expensive.This study's main data collection technique was a questionnaire that includes two types: a malaria infection registration form for high-risk groups  and a malaria facility service form for healthcare centers. Demographic data, including gender, age, residence, healthcare center name, and personal code, have been merged into the database. The malaria facility service form mainly includes malaria diagnosis information and healthcare center treatment. Pregnant women, non-pregnant women, and children under 5 years of age positive cases were included, while >5 years of age male positive infections and negative cases were excluded from this study.Different quality control activities were applied to ensure the data collected in this study, such as re-examining the slides, training data collectors, supervision by qualified experts, and monitoring integrity and completeness of data.2) test was used to explore the relationship between categorical variables. The level of significance was considered at the probability level (P ≤ 0.05).Data analysis was performed with SPSS version 23 . Descriptive statistics were given as means (± SD) for continuous variables and as frequency and %age for categorical variables. Chi-square  were malaria-positive, of which children under 5 years of age, pregnant women, and non-pregnant women were 23.3% (21.0–25.6 CI 95%), 4.4% (3.4–5.6 CI 95%), and 72.3% (69.9–74.7 CI 95%). P. vivax, 2.1% (1.3–2.9 CI 95 %) for P. falciparum, and 14 (1.1%) with (0.5–1.6 CI 95 %) for mixed infection, as shown in P. vivax and P. falciparum was 0.02. The prevalence of malaria in children under 5 years of age, pregnant women, and non-pregnant women were 23.3, 4.4, and 72.3%, respectively. Compared with other age groups, the prevalence was surprisingly high in non-pregnant women, as shown in P. falciparum were fever with chills and sweating. Nevertheless, the prominent symptoms of P. vivax infection were headache and fever with chills and myalgia, as shown in P. falciparum, while thrombocytopenia was reported in P. vivax patients. Other laboratory measures, including mean corpuscular volume, lymphocytes, neutrophils, urea, creatinine, SGPT, and bilirubin level, are more likely to be consistent in both species, as shown in A total of 1,283 individuals were positive for malaria tested using different diagnostic techniques like microscopy, RDT, and both. The overall average malaria prevalence in the three groups was 8.2%. Among the enrolled patients, 96.8% (95.9–97.7% CI 95%) of the 1,283 patients tested positive for The primary treatment used for malaria infection reported by the health centers was primaquine, followed by chloroquine . The totThe incidence and prevalence of malaria can be effectively reduced through active and passive diagnosis . AccuratP. falciparum, 1,242 (96.8%) with (95.9–97.7 CI 95%) positive for P. vivax, and 14 (1.1%) with (0.5–1.6 CI 95%) showed mixed infection . The average ratio of P. vivax and P. falciparum was 0.02. The prevalence of Malaria in Children under 5 years of age, pregnant women, and non-pregnant women were 23.3, 4.4, and 72.3%, respectively. High prevalence existed in non-pregnant women as compared to the other groups. The dominance of P. vivax is consistent with other parts carried out by Pakistan studies  with (1.3–2.9 CI 95%) positive for  studies , 25–27,  studies . Anotherprovince . In othein Buner  to 25% iin Buner , 31. In in Buner .Our study also shows a high prevalence of malaria in non-pregnant women, consistent with other studies in Pakistan , but difP. falciparum endemic countries have gradually updated their treatment policies, from monotherapy using chloroquine, amodiaquine, and sulfadoxine-pyrimethamine to the currently recommended combination therapy based on artemisinin. Combination therapies are highly effective and well-tolerated, which reduced the mortality and morbidity of malaria worldwide. Unfortunately, P. falciparum in Southeast Asia has recently developed artemisinin resistance, threatening these gains  and early treatment. Since 2010, all se gains .According to our results, the primary treatment used for malaria infection reported by the health centers was primaquine, followed by chloroquine . The totfalciparum malaria  of Khyber Pakhtunkhwa Province. Therefore, the results of the research work lack generalization to be considered for the whole country. In addition, due to the cross-sectional nature of the study, causality cannot be assumed in the association between risk factors and malaria prevalence. A more extended study should be conducted to determine the seasonal changes and other risk factors of the malaria epidemic in this region.P. vivax infection was found high than other malaria infections. The primary treatment used for malaria infection reported by the health centers was primaquine, followed by chloroquine. In the Bannu district, the health care facilities for malaria services appeared weak. There is a strong need for attention to those living in this district and reinforcing malaria control or elimination strategies.The findings of this study reveal that the prevalence of malaria in the non-pregnant women's group was more affected by malaria. The original contributions presented in the study are included in the article/supplementary files, further inquiries can be directed to the corresponding authors.The studies involving human participants were reviewed and approved the University of Science and Technology Bannu, Pakistan. Permission to conduct the research and relevant information was obtained by the relevant authorities at the Directorate of the Malaria Control Programme, Pakistan. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.HQ and MK were responsible for data collection and wrote the paper. AA, AK, and UA collected data. AK and YS review the paper. All authors contributed to the article and approved the submitted version.This work was supported by the Department of Imaging, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, PR China.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.


Malaria is a major international public health problem that affects millions of patients worldwide especially in sub-Saharan Africa. Although many tests have been developed to diagnose malaria infections, we still lack reliable diagnostic biomarkers for the identification of disease severity, especially in endemic areas where the diagnosis of cerebral malaria is very difficult and requires the exclusion of all other possible causes. Previous host and pathogen transcriptomic studies have not yielded homogenous results that can be harnessed into a reliable diagnostic tool. Here we utilized a multi-cohort analysis approach using machine-learning algorithms to identify blood gene signatures that can distinguish severe and cerebral malaria from moderate and non-cerebral cases. Using a Regularized Random Forest model, we identified 28-gene and 32-gene signatures that can reliably distinguish severe and cerebral malaria, respectively. We tested the specificity of both signatures against other common infectious diseases to ensure the signatures reliability and suitability as diagnostic markers. The severe and cerebral malaria gene-signatures were further integrated through k-top scoring pairs classifiers into ten and nine gene pairs that could distinguish severe and cerebral malaria, respectively. These signatures have various implications that can be utilized as blood diagnostic tools for malaria severity in endemic countries. Malaria is an important vector-transmitted infectious disease that affect millions of patients worldwide especially in sub-Saharan Africa, with an estimated new 228 million cases and 405,000 deaths in 2018 alone . DespitePlasmodium, where P. falciparum is by far the most relevant  (accessed September 2020). For the GEO query, we used the following search terms: “Plasmodium”, “malaria”, and the filters (organism (Homo sapiens)), study type (expression profiling by array), entry type (Dataset/Series)). The Array Express query was executed using the following search terms: Plasmodium”, “malaria”, and the filters (organism (Homo sapiens)), experiment type (array assay). Initially 89 entries from GEO and 34 entries from Array Express were retrieved. Duplicates and irrelevant studies were excluded, and 19 studies remained and were further refined using the inclusion criteria (below) to identify the final nine studies included in our analysis. We included only studies that had analyzed gene expression in whole blood, PBMC or blood cell components but excluded studies using other tissues, ex vivo experiments, and cell line infection models. The database-search followed the Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) statement and is documented in the PRISMA Flow Diagram  and samples were labeled as severe or non-severe and cerebral or non-cerebral using the phenotype information provided in each dataset. In terms of malaria severity, samples without available annotation were labeled as severe if they have one or more of the following: a) cerebral malaria; b) severe anemia; c) hyperparasitemia). These criteria are based on the World Health Organization (WHO) criteria for the diagnosis of severe malaria infection . SubsequtuneRF” function. This whole process was performed for both phenotypes to identify two small subsets of genes that can distinguish severe from non-severe and cerebral from non-cerebral malaria.To identify parsimonious gene signatures of both severe and cerebral malaria, we performed a feature selecting process using regularized random forest (RRF) models  on the tWe evaluated both signatures on the unseen testing data using different performance metrics including the area under the ROC curve (AUC) and the area under the precision recall curve (AUPRC). To compute the ROC and PRC curves together with the AUC values, we used the predicted class probabilities (ranging from 0 to 1) returned by the RF model together with the true class labels . These pTo examine whether the severe malaria signature can capture some of the molecular changes induced by malaria in non-blood tissues, we applied the signature to a dataset of 20 placental samples (GSE7586), ten of which have placental malaria (PM) and the other ten are from controls. Eight samples have signs of placental inflammation, seven with and one without PM. The signature was used to distinguish PM-positive from PM-negative samples and to distinguish samples with inflammation from inflammation-free samples.Since many infectious diseases may induce similar, non-specific molecular changes in the blood, we proceeded to test the specificity of the two malaria signatures. For this purpose, we used the signatures to classify dengue fever (DF) versus healthy controls and DF versus severe dengue (dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS)) in blood samples from six different datasets . We used DF to test the specificity of our signatures since malaria and DF have a similar geographical distribution, both are mosquito-transmitted, and both share several immunopathogenic features . SimilarSince interpretability of the gene signatures is essential for their potential clinical uses, we proceeded to test if we can simplify the decision rules of the two malaria signatures. For this purpose, we divided the genes comprising the signatures into two sets of up- and down-regulated genes. These were subsequently used to build gene pairs with each pair consisting of an up-regulated and another down-regulated gene. We used the resulting gene pairs to build K-Top Scoring Pairs (K-TSPs) models with the target of identifying a subset of gene pairs that can separate severe from non-severe and cerebral from non-cerebral malaria. The K-TSPs is a rank-based classification method that selects gene pairs (K) whose expression levels consistently switch their ranking between the two classes of interest . Each paWe used R programming language (version 4.0.2) for initial processing and analysis of dataset. The datasets were accessed from the NCBI GEO database using the GEOquery R package. The feature selection processes were performed using the RRF package  and the From the initial datasets acquired by searching public databases, nine matched our predetermined inclusion criteria (see methods). The datasets included samples from 99 healthy controls and 318 malaria patients, from which 137 were asymptomatic or had mild malaria, 51 severe non-cerebral and 130 cerebral malaria. The data summary of the included datasets is shown in : IDH1, ZNF148, SF3B1, TBCD, HDAC5, STK17B, TRA2A, LIFR, ORC2, CHAF1A, DNALI1, CREM, PLXNA2, SLC25A40, MAP2K7, TBC1D2B, XDH, MBTD1, CBX5, PAPPA2, ATP5G3, CNOT7, SCML1, ADAP2, SLC38A2, ZCCHC2, AGPAT3, and USP48  by the RRF model. The 28 genes includend USP48 Figure 1PCA and heatmap plots of the 318 samples for the 59 gene expression data are shown in When evaluated on the unseen testing dataset, both the severe and cerebral malaria signatures showed a good performance. The severe malaria signature was able to distinguish severe from non-severe malaria in the testing dataset with an AUC of 0.85, sensitivity of 0.91, specificity of 0.62, and MCC of 0.54 . New approaches to rapidly diagnose severely affected patients are essential to combat its high mortality rate. The available diagnostic tools lack a reliable and accessible measure to distinguish severe and cerebral malaria from mild cases, especially in high endemicity areas, where the identification of other infections can be confused with malaria asymptomatic parasitemia. Previous postmortem autopsies of fetal cerebral malaria cases indicated that the misdiagnosis of cerebral malaria could reach as high as 23% . In our To select the most relevant genes able to classify disease status in our cohort, we implemented a multi-step analysis, where we combined a data-preprocessing pipeline to ensure reliable integration of samples from different datasets and used a two-step genomics classification model to select the most important features. For the first selection, we used regularized random forests (RRF) techniques, which offer a modification to standard random forest models by introducing a limitation to features used for splitting the trees, meaning that new features are added only when they offer a predictive value superior to those used in previous splits, which ensures choosing the most relevant features to the model accuracy .We identified 28-gene and 32-gene signatures that can reliably distinguish severe and cerebral malaria with an AUC of 0.85 and 0.98, and sensitivity of 0.91 and 0.89, respectively. The high performance of these signatures in the malaria datasets without cross-reacting with other infectious diseases makes them suitable candidates for new diagnostic platforms for malaria severity.These signatures provide a substantial improvement to previously detected host-gene signatures that were mainly focused on distinguishing acute malaria from healthy patients , or harbATP5G3, which was downregulated in the two malaria signatures, plays a part in energy metabolism and energy production. Its downregulation in both types of disease can indicate an infection-induced mitochondrial injury, which can lead to reduced energy production, reducing the capacity of immune cells to stop the infection   , althougTNF) . In our with TNF . Furtherntiation , and itsntiation . Moreoventiation .IDH1) is one of the genes we identified as down-regulated in severe malaria has also been found to be associated with HIV infection. Specifically, Chinn et al. reported that SNPs in IDH1 were significantly associated with HIV infection, three of which were found in transcription factors binding sites ) 1  and MORC Family CW-Type Zinc Finger 2 (MORC2) are associated with a number of neurological disorders including cerebellar ataxia (INF-β delaying the infection resolution (EPHA4) and other Eph receptors are known to be up-regulated after neuronal injury  was found to promote influenza A virus replication and cytokine production  and ArrayExpress under the corresponding accession number. The code for this analysis is available on GitHub and can be accessed using the following link: Conceptualization, MB and MO. Methodology, MB and MO. Software, MB and MO. Validation, MB and MO. Formal analysis, MB and MO. Investigation, MB and MO. Resources, MB and MO. Data curation, MB and MO. Writing—original draft preparation, MB and MO. Writing—review and editing, MB, MO, LM, and GH. Visualization, MB and MO. Supervision, LM and GH. Project administration, MB, MO, LM, and GH. All authors have read and agreed to the published version of the manuscript.MB is supported by the IMM-PACT-Program for Clinician Scientists of the Deutsche Forschungsgemeinschaft  [413517907].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.


Microscopic diagnosis of Giemsa-stained thick and thin blood films has remained the standard laboratory method for diagnosing malaria. High quality performance of microscopists that examine blood slides in health facilities remains critically important.Plasmodium species.A cross-sectional study was conducted to assess the performance of 107 malaria microscopists working at 23 malaria rechecking laboratories in Ethiopia. A set of 12 blood film slides was distributed to each microscopist. Data was collected and exported to SPSS version 20 for analysis. Chi-square, sensitivity, specificity, percent agreement, and kappa scores were calculated to assess performance in detecting and identification of P. malariae  followed by P. ovale . Participants working at hospital laboratories had the highest percentage  of accurate species identification. Study participants that had participated in malaria microscopy and quality assurance trainings were significantly better at quantifying parasite densities (P<0.001).The mean age of the participants was 30 ± 5 yrs and most of them  were working at regional reference laboratories. Overall, the sensitivity of participants in detecting and identifying malaria parasite species was 96.8% and 56.7%, respectively. The overall agreement on detection and identification of malaria species was 96.8% (Kappa = 0.9) and 64.8% (Kappa = 0.33), respectively. The least accurately identified malaria parasite species was The accuracy of parasite identification and quantification differed strongly between participants and expert microscopists. Therefore, regular competency assessment and training for malaria microscopists should be mandatory to assure proper diagnosis and management of malaria in Ethiopia. Plasmodium [P. falciparum, P. malariae, P. ovale, P. vivax or P. knowlesi [Malaria is a mosquito-borne infectious disease of humans and other animals caused by parasitic protozoans of the genus asmodium ,2. In huknowlesi . Globallknowlesi . Every mknowlesi . In Ethiknowlesi -8.Correct diagnosis of malaria is vital to determine disease prevalence and incidence in order to evaluate the impact of malaria control interventions . TraininThe study was conducted from February to May, 2015. Ethiopia is administratively structured with nine regional states and two city administration . A totalWhole blood (3-5 ml) was collected in EDTA tubes from volunteers above 18 years of age. A total of 200 blood film slides and Dried Blood Spot (DBS) were prepared from each donor. Six and two microliters were used to prepare a thick and thin blood film, respectively. Both thick and thin blood films were prepared on a single slide. The thin blood film was fixed with absolute methanol. After drying, both thin and thick films were stained for 30-45 minutes with 3% Giemsa working solution. Blood film for microscopic diagnosis of malaria was made semi-permanent by cover slip using Poly mount  and a cover glass. Six malaria microscopy experts  were involved in the validation process and each of them read 2 blood film slides from each donor. Discordant results among validators on species identification were verified by Polymerase Chain Reaction (PCR).P. falciparum slides with different densities, 2 BF slides with P. vivax, 1 Mixed species slide of P. falciparum and P. vivax, 1 BF slide with P. malariae, and 1 BF slide with P. ovale). These BF slides were used for assessment of parasites detection, stage and species identification. The second set contained 2 positive slides; 1 P. falciparum BF slide with a parasite density of 1891 parasites/μL (with a range of 1419-2363 parasites/μL) and 1 P. falciparum BF slide with a parasite density of 50,659 parasites/μL (with a range of 37990-63323 parasites/μL) were used for the assessment of quantification performance. Based on WHO recommendation, quantification results that deviated ±25% from the mean calculated by expert readers was considered as correct quantification result. A total of 120 minutes (10 minutes per BF slide) were allocated for the exercise [After validation, two sets of blood film (BF) slides with different values were given to study participants. The first set comprised a total of 10 BF slides; 2 negative slides, 8 positive slides , sensitivity, specificity, percent agreement, and kappa scores were calculated to assess laboratory professionals’ performance in detecting and identification l to 90% . Kappa vl to 90% . A percel to 90% ,15.This study was done after obtaining approval from the ethical clearance committee of Addis Ababa University College of Health Sciences, Department of Medical Laboratory Sciences, departmental research and ethics review committee. Information about the study was provided to all malaria microscopists involved in the study and to all concerned bodies at the study sites. All participants were assured about the confidentiality, protection and anonymity of the data. Written informed consent was obtained from voluntary study participants before conducting the study.From a total of 129 malaria microscopists that were engaged in rechecking blood film slides, 107 (83%) participated in the study. The mean age (± SD) of the participants was 30 ± 5 yrs and 90 (84.1%) were men .Based on WHO classification, 78 (72.9%) of the participants were ‘in training’ with respect to species identification. Only 11(10.3%) participants quantified both BF slides correctly while 34 (31.8%) used a non-recommended quantification system . From a The overall agreement on detection of malaria parasites was 96.8% (Kappa = 0.9), which is ‘perfect agreement’ while on identification of malaria species it was 64.8% (Kappa = 0.33) which is a ‘fair agreement’ . False pP. falciparum but only 2.8% of BF slides with P. malariae were identified correctly  BF slides with P. ovale were identified wrongly as P. vivax, and 37 (34.6%) of cases with mixed  BF slides were reported only partially correctly as P. falciparum.The performance of participants in species identification showed that 80.1% of BF slides with orrectly . A totalOut of 107 participants, 97 (90.7%) scored <80% overall, which indicates failure based on WHO and national guidelines ,15. The Participants working at hospital laboratories had the highest percentage agreement with expert readings  while regional reference laboratories had the lowest . Study participants that participated in malaria microscopy and QA training had a better performance on quantification than those who had not received training (P<0.001) .Malaria remains a leading cause of morbidity in Ethiopia . MicroscBoth the sensitivity (96.8%) and specificity (96.7%) of microscopists in this study were higher than in the study conducted in Hawassa town which was 82% and 96.5% , in USA P. falciparum (19.9%) in the current finding was slightly higher than a similar study conducted in Hawassa, where this was about 18% [P.ovale which were identified correctly (32.7%) was higher than the study conducted in North Gondar, northwest Ethiopia in which no microscopist correctly identified P. ovale [P. falciparum malaria in Ethiopia is an artemisinin-based combination therapy (ACT) called artemether-lumefantrine (AL) while the first line treatment for P. vivax is oral chloroquine [Overall, strength of agreement in identification of different species of malaria in the current study (Kappa = 0.33) was lower than the study conducted in North Gondar (Kappa = 0.47)  and Hawabout 18%  but lowebout 18% . Blood fP. ovale . Low peroroquine . CorrectIn our study, the false positivity rate (negative BF slides reported as positive) was 0.8% which was lower than the study conducted in Canada (2.0%) , 6.9% reQuantification performance of malaria microscopists working at sub-regional laboratories was 73.3%, which was higher than malaria microscopist performance from other malaria rechecking laboratories (P=0.003).This might be because most participants that were working at sub-regional laboratories were trained on malaria microscopy and QA training. Study participants that were trained on malaria microscopy and QA performed significantly better on quantification (P<0.001). Correctly-quantified blood film slides in our study were 31.3%, which was lower than 81% of correct quantification reported from the USA . ParticiP. malariae and P.ovale correctly. The ability to identify true positives was lower amongst participants that worked at regional reference laboratories. Participants that were not trained on malaria microscopy displayed very poor performance in parasite quantification. Quantification performance of microscopists working at health centre or hospital laboratories was very low. Therefore, to fill the identified gaps, all stakeholders at all levels have to work on the implementation of regular competency assessments and implement training policy. Demonstration BF slides used for malaria microscopy training have to comprise P. malariae and P.ovale. Regions which are using or which are going to use health centre and hospital laboratories as rechecking sites have to evaluate the performance of laboratories using all external quality assessment methods and have to conduct competency assessment for malaria microscopists that are working at those rechecking sites.Even though the performance of participants was good in detection of malaria using microscopy their agreement with expert microscopists identification of different malaria species and the quantification of parasite densities were very low. Most participants did not identify