Plasmodium knowlesi, a malaria parasite of Macaca fascicularis and Macaca nemestrina (long-tailed and pig-tailed macaques respectively), occur throughout Southeast Asia, especially Malaysian Borneo. Other naturally-acquired human infections with malaria parasites from macaques in Southeast Asia are P. cynomolgi, P. inui-like, P. coatneyi and P. simiovale. In Sarawak, Malaysian Borneo, M. fascicularis and M. nemestrina from only the Kapit Division have been examined previously for malaria parasites. In order to determine the distribution of P. knowlesi and other zoonotic malaria parasites, 73 macaque blood samples derived from 7 other administrative divisions in Sarawak were studied. Of 45 blood samples from M. fascicularis and 28 from M. nemestrina tested by nested PCR assays, 23 (51.1%) M. fascicularis and 15 (53.6%) M. nemestrina samples were positive for Plasmodium DNA. Thirty-two of these macaques from 7 divisions sampled, harboured either single (n = 12), double (n = 9), triple (n = 7) or quadruple (n = 4) infections of P. knowlesi, P. inui, P. cynomolgi and P. coatneyi, while the infecting species of Plasmodium could not be identified for 6 samples. P. knowlesi was detected in 15.5% (7/45) M. fascicularis and in 7.1% (2/28) M. nemestrina sampled. Despite the small number of samples analysed from each administrative division, the current study indicates that macaques infected with the zoonotic malaria parasites P. knowlesi, P. cynomolgi, P. inui and P. coatneyi are widely distributed throughout Sarawak, Malaysian Borneo. Travelers to forested areas in Sarawak should be made aware of the potential risk of acquiring zoonotic malaria.Human infections with  Brugia malayi2. There are more than 30 species of Plasmodium which are capable of infecting primates out of over 200 species of Plasmodium identified to date4. Human infections with simian malaria parasites were thought to be extremely rare until a large focus of human infections with P. knowlesi was reported in 2004 in the Kapit division of Sarawak, Malaysian Borneo5. Since then, human P. knowlesi infections have been reported throughout Malaysia and in all countries in Southeast Asia except Timor Leste14. Human knowlesi malaria cases are of public health concern in Malaysia, where from 2018 to 2020 they constituted all the 8,500 indigenous cases of malaria, mainly in the states of Sabah and Sarawak, Malaysian Borneo 16. In Sarawak, knowlesi malaria cases have continued to increase from the 120 cases first described in the Kapit Division in 2004 to between 759 and 1,247 annual cases from 2016 to 2020, with 14 deaths . There have also been case reports of tourists acquiring knowlesi malaria following visits to forested areas in Sarawak19 and Southeast Asia20.Non-human primates are reservoir hosts for malaria parasites and many other infective agents including Hepatitis B, Simian retroviruses, Macacine herpesvirus 1 and P. knowlesi are primarily Macaca fascicularis (long-tailed macaque) and M. nemestrina (pig-tailed macaque)4. P. knowlesi infections in these species, the most common non-human primates in Southeast Asia, have been reported from Thailand, Malaysia, Philippines and Laos26. There have also been reports of natural infections of P. knowlesi in a Presbytis melalophos (banded leaf monkey) in Peninsular Malaysia27, and in Trachypithecus obscurus (dusky leaf monkey) and Macaca arctoides (stump-tailed macaque) in Thailand28. Apart from P. knowlesi, there are other malaria parasites of M. fascicularis and M. nemestrina which can infect humans. A case of naturally-acquired P. cynomolgi was reported in Peninsular Malaysia in 201429. This was followed by descriptions of other human P. cynomolgi infections in Cambodia, Malaysia and Thailand33. More recently, human infections with P. inui, P. coatneyi, P. simiovale and P. inui-like parasites have been described in Malaysia35. In South America, zoonotic malaria cases originating from other non-human primates have been reported; P. brasilianum acquired from Alouatta seniculus (red howler monkey)36 and P. simium from Alouatta guariba (brown howler monkey)38.The natural hosts for M. fascicularis and M. nemestrina in Sarawak State were confined to the Kapit Division39. In order to determine the distribution of P. knowlesi and other zoonotic malaria parasites, macaque blood samples collected from 7 other administrative divisions in Sarawak were examined with molecular detection assays.Previous studies on malaria parasites of In Sarawak, permits are required from the Sarawak Forestry Department and the Sarawak Biodiversity Centre for the collection of samples from animals and for the use of these samples for research. The protocols for capture, collection of blood samples and release of wild macaques, together with collection of blood samples from captive macaques were approved by the Sarawak Forestry Department . In addition, a permit to access and collect animal blood samples was obtained from the Sarawak Biodiversity Centre (Permit Number: SBC-RP-0081-BS). All methods were performed in accordance with relevant guidelines and regulations.A veterinarian collected blood samples from the femoral vein of each wild macaque following anaesthesia by intramuscular injection of tiletamine and zolazepam  at a dose of 5 mg/kg. The blood collection was performed using 21G × 1” eedle attached to a 5 ml syringe , where not more than 5 ml blood was drawn from each macaque for the study purposes. Blood samples were collected at the trap sites and the animals were released after they had recovered from anaesthesia. Similar procedures were employed when venous blood samples were obtained from captive macaques at Matang Wildlife Centre and Semenggoh Wildlife Rehabilitation Centre in the Kuching division. For macaques kept as pets, blood spots on filter paper were collected by pricking the end of the tails of the animals by staff of the Sarawak Health Department during their visits to malaria affected communities. This study was approved by the Medical Ethics Committee of Universiti Malaysia Sarawak and was performed in concordance with the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines.. fascicularis and 28 M. nemestrina that were either wild (n = 21) or captive (n = 52). The wild macaques were sampled at the following administrative divisions in Sarawak, with the number collected and dates of sampling in brackets: Sarikei , Kuching , Limbang , Miri  and Sri Aman  , Samarahan , Limbang , Miri . The macaques at the wildlife centres were not born in captivity and were kept in enclosures that were not netted. No information was collected regarding the period of captivity of the macaques at the wildlife centres or at the rural communities. Each of the wild and captive macaques was only sampled once. The macaques were identified by the veterinarians and by the staff of the Sarawak Health Department based on the length of the tails of the macaques; M. fascicularis have long tails (32–75 cm) and M. nemestrina have very short tails (< 25 cm). The veterinarians at the wildlife centres were informed which macaques were malaria-positive and were advised to treat them with antimalarial drugs. None of the wild macaques that were trapped nor those kept as pets were treated with antimalarial drugs.Blood samples were obtained from 45 Mild n = 2 or capti41.DNA was extracted from dried blood spots on filter paper using InstaGene Matrix (BioRad Laboratories) and from frozen blood samples using the QIAamp DNA Micro Kit (QIAGEN) as described previouslyPlasmodium DNA42. All Plasmodium-positive samples were then analysed by nested PCR assays with PCR primers specific for P. knowlesi, P. cynomolgi, P. inui, P. fieldi and P. coatneyi as described previously21.DNA samples were initially analysed using nested-PCR assays for detecting M. fascicularis and 53.6% (15/28) M. nemestrina) were positive for Plasmodium DNA  of wild M. fascicularis and 26.9% (7/26) of captive ones were Plasmodium-positive. For M. nemestrina, both samples from the wild and 50% (13/26) of those from captive animals were Plasmodium-positive.Of the 73 macaque blood samples examined with nested PCR assays, 51.1% (23/45) Plasmodium-positive M. fascicularis samples were collected from all the 7 administrative divisions sampled , double (n = 9), triple (n = 7) or quadruple (n = 4) infections (Table P. knowlesi was detected in 15.5% (7/45) M. fascicularis and in 7.1% (2/28) M. nemestrina sampled. For 6 of these samples (3 each from M. fascicularis and M. nemestrina), the species of Plasmodium could not be identified with PCR primers specific for P. knowlesi, P. cynomolgi, P. inui, P. fieldi and P. coatneyi.In 84% (32/38) ngle n = 2, double46. In the current study, for 6 macaque samples that were Plasmodium-positive in nested PCR assays, the species of Plasmodium could not be identified with nested PCR assays using species-specific primers for P. knowlesi, P. cynomolgi, P. inui, P. fieldi and P. coatneyi. Similar observations have been reported in studies of blood samples from macaques47 and humans34, and during entomological studies to incriminate vectors of malaria47. For these 6 samples the approximately 240 bp fragments that were amplified by PCR with the Plasmodium-specific primers, rPLU3 and rPLU4, were not sequenced. Previously, we sequenced these fragments from a number of samples that were Plasmodium-positive but negative with the species-specific PCR primers and found through the Basic Local Alignment Search Tool (BLAST) searches that they had high similarity to species of Plasmodium, including P. inui and P. knowlesi. However, it was not possible to confirm the identity of the infecting species by phylogenetic analyses of these and referral sequences since the bootstrap values were low and it was not possible to differentiate between the various species of Plasmodium. It is possible that the parasite counts in these 6 macaques were low but were sufficiently amplified by the genus-specific primers which target all the copies of both the A- and S-forms of the small subunit (SSU) rRNA genes42. However, with the species-specific primers that target either the S- or the A-type SSU rRNA genes, sufficient PCR amplification was probably not attained and no amplicons were detected by gel electrophoresis. An alternative explanation is that these macaques may have been infected by P. simiovale, which has been demonstrated in M. fascicularis in Sarawak39, and/or by one or more species of Plasmodium for which PCR primers are currently unavailable.Molecular detection methods such as nested PCR assays have proved to be more sensitive and specific than microscopy for the detection of malaria parasites in humans, non-human primates and mosquitoesPlasmodium was noted in the current study where 84.2% (32/38) of the Plasmodium-positive macaques had either double, triple or quadruple infections with P. knowlesi, P. inui, P. cynomolgi and P. coatneyi. Similar observations were made in our previous study in the Kapit Division of Sarawak where 101 of 108 wild macaques were Plasmodium-positive and 91% (91/101) had mixed species infections21. Multiple species of Plasmodium in each macaque host, as reported in that21 and other studies39, may not accurately reflect the actual number of species of Plasmodium harboured by each macaque, since blood samples at only a single time point were obtained and analysed. It has been noted that parasite counts fluctuate and can even change daily in non-human primates that have been kept in captivity4. In addition, erythrocytes containing late asexual stages of certain parasites like P. coatneyi sequester4, so the infection would not be detected by PCR analysis of blood samples if the sample was collected when the majority of the parasites were late stages. Sequential samples would provide more accurate information on the prevalence of each species of Plasmodium in a host in nature but it would not be possible to undertake these investigations on wild macaques due to ethical and logistic reasons.The complex nature of infections with M. fascicularis and M. nemestrina infected with zoonotic malaria parasites to inform the public of the risks of acquiring zoonotic malaria. Previous studies in Sarawak were confined to the Kapit Division and in the current study macaques from another 7 divisions were examined39. A total of 32 macaques infected with either single or multiple species of the zoonotic malaria parasites P. knowlesi, P. cynomolgi, P. inui and P. coatneyi were found in all the 7 administrative divisions of Sarawak studied, from the southernmost division of Kuching to the northernmost division of Limbang. A limitation of the current study is the relatively small number of samples examined, involving between one to 14 macaques from each administrative division. There is a need for more extensive studies, involving a larger number of samples from varied locations at each administrative division to determine accurately the prevalence of malaria infection among macaques at these locations. Furthermore, these studies should focus on sampling wild macaques rather than captive ones. In the current study, the prevalence of malaria was much higher in wild than in captive macaques. This difference is primarily due to the mobility of the macaques, greater abundance of the mosquito vectors of P. knowlesi and other simian malaria parasites in the forests rather than close to human habitats, and to the feeding behaviour of the vectors. The vectors of P. knowlesi in Sarawak, Sabah, Peninsular Malaysia and Vietnam are forest-dwelling mosquitoes and a study in Sarawak, found that the vector, Anopheles latens, feeds predominantly in the forest and the forest fringe, rather than in longhouse communities49. Due to the forest habitats and feeding behaviour of the vectors, wild macaques would be at risk of acquiring malaria while foraging for food or while resting at night on trees in the forest. In contrast, some of the captive macaques in the current study were housed in wildlife centres situated away from malaria transmission areas, and those kept as pets by residents of rural communities would have limited exposure to mosquito vectors as they were tethered. Therefore, examination of blood samples from wild as opposed to captive macaques would provide more accurate information on zoonotic malaria parasites that are present at areas surrounding each sampling site. Such information would be essential for informing the local population and visitors to rural areas of Sarawak of the risks of acquiring zoonotic malaria. Combined with data from concurrent entomological and community-based studies, they would provide valuable information on malaria transmission and assist in the implementation of appropriate prevention and control measures for zoonotic malaria.It is important to determine the geographical range of P. knowlesi, P. cynomolgi, P. inui and P. coatneyi, are found in M. fascicularis and M. nemestrina that are widely distributed throughout Sarawak, Malaysian Borneo. P. knowlesi is the main cause of malaria in Sarawak and has the potential to cause fatalities51 so the local rural population and travelers to habitats of these macaques in Sarawak should be made aware of the potential risk of acquiring P. knowlesi and other zoonotic malaria parasites.In conclusion, despite the small number of macaques sampled at each administrative division, the current study indicates that that the zoonotic malaria parasites


Plasmodium falciparum strains circulating in North Lakhimpur area of Assam in the pre-artemisinin era and compare the findings with current diversity patterns.Northeast India shares its international border with Southeast Asia and has a number of malaria endemic zones. Monitoring genetic diversity of malaria parasites is important in this area as drug resistance and increasing genetic diversity form a vicious cycle in which one favours the development of the other. This retrospective study was done to evaluate the genetic diversity patterns in msp-1 (block-2), msp-2 (block-3) and the glurp RII region of P. falciparum using nested PCR.Genomic DNA extraction was done from archived blood spot samples collected in 2006 from malaria-positive cases in Lakhimpur district of Assam, Northeast India. Three antigenic markers of genetic diversity were studied – msp-1, eight genotypes were detected; K1 , MAD20  and RO33  allelic families were noted. Among msp-2 genotypes, 22 distinct alleles were observed out of which FC27 had six alleles and IC/3D7 had 16 alleles. In RII region of glurp, nine genotypes were obtained. Expected heterozygosity (HE) values of the three antigenic markers were 0.72, 0.81 and 0.88, respectively. Multiplicity of infection (MOI) values noted were 1.28, 1.84 and 1.04 for msp-1, msp-2 and glurp, respectively.Allelic diversity was examined in 71 isolates and high polymorphism was observed. In P. falciparum msp (block-2 of msp-1 and block-3 of msp-2) and the glurp RII region in Northeast India in the pre-artemisinin era when chloroqunine was the primary drug used for uncomplicated falciparum malaria. Comparison with current studies have revealed that the genetic diversity in these genes is still high in this region, complicating malaria vaccine research.Results suggest a high level of genetic diversity in  Plasmodium, five species of which are known to cause primary human infection. In tropical and subtropical regions, malaria has become a major health problem. The majority of mortality occurs in young children below 5 years of age residing in tropical regions. In 2020, the estimated global malaria case burden was 241 million with 627 000 deaths [Plasmodium falciparum causes a severe form of malaria with high morbidity and mortality and there are reports of dissemination of drug-resistant strains from northeast India to other parts of the country [Malaria is caused by the genus P. falciparum is MSP [PfMSP-1/2/4/5/10 family consists of glycosyl phosphatidyl inositol (GPI) anchored proteins and PfMSP-3/6/7/9 family members are soluble MSP proteins but they belong to other anchored GPI proteins [Msp-1(block-2), msp-2 (block-3) and glurp (RII-region) genes have highly polymorphic variations throughout the world and are expressed throughout the life cycle of P. falciparum [Msp-1 contains 17 blocks of which block-2 has K1, MAD20 and RO33 allelic families [msp-2 contains two alleles viz. FC27 and IC/3D7 [Msp-based vaccines are difficult to achieve because of the presence of several polymorphic genes and high allelic diversity [A number of vaccines have been tried against the erythrocytic stage of the malaria parasite using different types of surface proteins like merozoite surface protein (MSP), apical membrane antigen-1 (AMA-1) and erythrocytic binding antigen-175 (EBA-175). The most abundant surface antigen in m is MSP . PfMSP-1lciparum . Msp-1 cd IC/3D7 . Msp-basiversity . The mosiversity . Multipliversity . Currentiversity .P. falciparum was first reported from Karbi-Anglong district in Assam in 1973 and in 1979, sulfadoxine/pyrimethamine (SP) resistance in P. falciparum was also reported in the same area [P. falciparum was reported from Changlang, Arunachal Pradesh district (NMEP 1997). With widespread resistance, the anti-malarial drug policy was changed for Northeast India in 2008, heralding the beginning of artemisinin based combination therapies (ACT) [P. falciparum to the rest of India [P. falciparum glurp, msp-1 and msp-2 antigens in isolates from Lakhimpur district, Assam.In India, chloroquine (CQ)-resistant ame area . In latees (ACT) . It is p2 and population of 10,42,137 as per 2011 census data. This district receives high rainfall  with an average temperature of 23.8 °C. The region also contains many tea gardens and dense forests. This region was selected as the study site since it is one of the malaria endemic district of Assam and shares its northern border with Arunachal Pradesh, which is again a malaria endemic state of Northeast India.The samples utilized in the present study were archived samples collected in 2006 from Nowboicha Primary Health Centre area, Lakhimpur, Assam . It is sP. falciparum (HRP2) and P. vivax (LDH) followed by blood smear preparation for microscopy. Finger-prick blood samples from RDT positive, mono-infective, uncomplicated (as per WHO classification) P. falciparum cases were collected in Whatman filter paper (FTA card) after obtaining written consent from the participants. A total of 100 FTA card blood samples were collected, air-dried and preserved at −20 °C. The archived samples from 2006 were maintained in our department and 71 of these isolates were used for the current study. The remaining 29 FTA card blood samples could not be restored; they were spoiled and fragmented due to the storage problem in the freezer. The samples used in the current study were completely anonymised and no identifying features, names or other patient characteristics  have been incorporated in the current study findings.Patients having primary malaria like clinical symptoms such as intermediate fever, chill, vomiting and body ache were screened by rapid diagnostic test (RDT) kit  for Genomic DNA was isolated from each sample using six dried blood spots, 3 mm in diameter by Harris Uni-Core puncher. Between each sample, the Harris Uni-Core puncher was cleaned properly using 70 % ethanol followed by autoclaved double distilled water. DNA was isolated with the help of commercial kits  and stored at −20 °C for further processing.Plasmodium species identification was done using nested PCR targeting the 18S small subunit of ribosomal RNA gene (rRNA) specific for Plasmodium species as described earlier [Plasmodium species-specific PCR, amplification bands of 120 bp indicate P. vivax and 205 bp indicates P. falciparum. Laboratory-adapted 3D7 and DD2 strains were used as positive controls. earlier . In the msp-1 (block 2), msp-2 (block 3) and region 2 (RII) of glurp and their allelic variants. K1, MAD20 and RO33 families of msp-1, FC27 and 3D7/IC families of msp-2 and region 2 (RII) of glurp were amplified using allele-specific primers as previously described [Pfmsp-1, nPCR was undertaken according to the protocol by Soulama et al. [Pfmsp-1, Pfmsp-2 and glurp allelic variants, primary PCR was performed in a 20 µl reaction volume containing 1 µL of DNA, 0.125 mM (for Pfmsp-1), 0.25 mM (for Pfmsp-2 and glurp) of primer (IDT) and 1X readymade PCR Master Mix (Promega). Primary PCR cycling conditions for all three genes were: initial denaturation for 5 min at 95 °C followed by 25 cycles of denaturation for 1 min at 94 °C, annealing for 2 min at 58 °C and extension for 2 min at 72 °C. This was followed by a final extension at 75 °C for 5 min. Secondary PCR reactions were run on a Biorad C1000 Thermal cycler using 2 µl of primary PCR product as template, 0.25 mM primer (IDT) and 1X readymade PCR Master Mix (Promega). Secondary cycling conditions of PCR were as follows: one step denaturation for 5 min at 95 °C followed by 25 cycles of denaturation for 1 min at 94 °C, annealing at 61 °C for 2 min (for Pfmsp-1 and Pfmsp-2), 58 °C for 2 min (for glurp) and extension for 2 min at 72 °C. A final extension step was carried out at 75 °C for 5 min.Nested PCR was performed to amplify the escribed . For deta et al. . For detPfmsp-1 and Pfmsp-2 the alleles were considered identical if the fragment sizes were within 10 bp; a larger interval of 50 bp was considered for glurp as previously described [In allele-specific nested PCRs, the amplified DNA fragments were grouped according to their sizes: for escribed .msp1, msp2 and glurp genes, PCR fragments were grouped as previously described [HE) was estimated as described previously elsewhere [To identify MOI obtained in escribed . Monocloescribed . Expectelsewhere .Plasmodium species varies among the eight Northeastern states of India. In Assam, predominant species is the P. falciparum. Therefore, the study was restricted to this Plasmodium species only. Apart from P. falciparum and P. vivax, other human malaria parasites reported from Assam are P. ovale and P. malariae. The first and only P. ovale case reported was from Jorhat district of Assam [P. malariae case was reported from Assam and from two other states of Northeast India, viz: Arunachal Pradesh and Tripura, the same species was also reported [Plasmodium based on their genetic diversity pattern. Although P. vivax is co-endemic in northeastern states of India, it still remains neglected.The distribution pattern of of Assam . One P. reported . So far,P. falciparum positive cases were included in this study. All these samples were mono-infection cases and were positive for P. falciparum by genus specific PCR. Out of these samples, Pfmsp1, Pfmsp2 and Pfglurp genes could be successfully amplified individually in 64 (90.14 %), 63 (88.73 %) and 65 (91.54 %) samples, respectively. A total of eight different allelic variants of msp-1, 22 allelic variants of msp-2 and eight allelic variants of glurp were detected. The MOI detected for msp-1, msp-2 and glurp were 1.28, 1.84 and 1.04, respectively.Dried blood spots collected from 71 Pfmsp-1 locus, the dominant allelic family was MAD20  with six different allele fragments in the range of 180–280 bp; the 220 bp fragment size was predominant . Among the nine allelic families of MAD20, the following allelic combinations were observed: 180 and 220 bp (n=1), 190 and 200 bp (n=1), 190 and 220 bp (n=4), 200 and 250 bp (n=2) and 220 and 280 bp (n=2). Both K1  and RO33  allelic families showed only one allelic variant of 200 and 190 bp, respectively. The frequency of monoclonal infection and polyclonal infection was 87.5 % (n=56) and 12.5 % (n=8), respectively. The detected MOI value and HE were 1.28 and 0.81  with 16 different fragments (490–700 bp) and the predominant fragment size was 510 bp . In the IC/3d7 allelic family, among the 16 alleles, multiple bands were observed in nine allele families. Out of these, the 510 and 610 bp combination was predominant , followed by 520 and 620 bp (n=5), 520 and 610 bp (n=2), 710 and 620 bp (n=2), 490 and 510 bp (n=2), 500 and 610 bp (n=1), 610 and 710 bp (n=1), 700 and 810 bp (n=1), 490 and 520 bp (n=1). FC27 allelic family  showed six different allele fragments (250–400 bp) and 310 bp  fragment size was predominant. The frequency of monoclonal infection and polyclonal infection were 71.43 % (n=45) and 28.57 % (n=18), respectively. The detected MOI value for Pfmsp-2 was 1.84 and expected heterozygosity was 0.88  were observed. The most commonly detected allelic variant was the 800 bp fragment, which was denoted as genotype VI  followed by 900–950 bp genotype IX . In both the genotypes IV and V, the frequency of allelic variants was 7.35 % (n=5). Genotype VII was detected in four samples (5.88 %) and genotype I and II were determined in only one sample each (1.47 %). Among the nine different allele fragments, multiple infections were found in three cases (n=62) and 4.61 % (n=3), respectively. The detected MOI value and expected heterozygosity were 1.04 and 0.72, respectively , msp-2 (FC27 and IC/3d7) and RII region of glurp were observed.Various parts of Northeast India are highly malaria endemic where persistent malaria transmission is reported . Numeroumsp-1 is dimorphic and contains three genotypes  and sequence analysis has revealed that it consists of 5300 bases which code for more than 1700 amino acids [n=44) allele of msp-1 is predominant followed by RO33 (n=22) and K1 (n=6) which is in contrast to reports from North-West Colombia [msp-1 K1 (38) allele and Arunachal Pradesh reported that the msp1-RO33 allele was dominant and both these findings are in contrast to our study findings [msp-1 alleles had changed in high transmission seasons and one dominant allele was replaced by another allele type [MSP-1 has a molecular weight of 195 KDa and is present in both merozoites and mature schizonts; no acids . In our Colombia , IndonesColombia , EthiopiColombia . MyanmarColombia . Tripuraele type .Our samples were collected before ACT drug policy was implemented in Northeast India when CQ was the first-line malaria treatment and our results are similar to that observed in North-West Colombia (1997) where CQ was the first line therapy for malaria . The chamsp-2 allelic combinations were observed; the 510 bp with 610 bp combination was dominant. These results are in contrast with a previous Indian study carried out in Orissa, Madhya Pradesh and Rajasthan [P. falciparum isolates, only the FC-27 allele type was reported [msp-1 alleles, this discrepancy could be due to the pattern of disease transmission intensity, change in drug policy, anti-malarial drug pressure and higher level of intragenic recombination in a parasite population [MSP-2 is a small molecular protein 45–52 KDa) located in chromosome-2 and is expressed in merozoites. It is mainly associated with high anti-immunogenicity and increased disease morbidity . The cur KDa locareported . From mareported , Gambia reported , Colombireported , Hondurareported  and Papureported , the FC-pulation .PfGLURP antigen induces protective immunity and thereby has a vital role in malaria vaccine development [Pfglurp RII region. The first study in Assam, Northeast India reported ten different types of glurp alleles based on their base pair sizes [glurp were reported before and after ACT implementation [glurp were reported from Northeast Indian states of Arunachal Pradesh [glurp alleles in Assam, which is similar to previous reports from Northeast India. In low endemic areas like Central South America [glurp allele has been reported to range from two to four; while in endemic regions like India [glurp allele frequency is higher.GLURP (glutamate-rich protein) is exo-antigenic in nature and has a molecular weight of 220 KDa. elopment . In Nortir sizes . Similarentation . Later,  Pradesh  and Trip Pradesh . Our stu America , Hondura America , Colombi America  and Braz America , the freke India , Thailanke India  and Sudake India , the gluHE are indirect measures of parasite genetic diversity in a particular region. The degree of transmission intensity usually regulates the rate of the MOI but the relationship of parasite prevalence and degree of endemicity with MOI is still inconclusive [msp-1, msp-2 and glurp were found to range from 1.04 to 1.84. MOI of the polymorphic genes was similar to those reported earlier from Indian studies [Pfmsp-1 in the current study were marginally lower than those observed in Chhattisgarh [Pfmsp-1 MOI values than Indian studies [Pfmsp-2 MOI values observed in the present study were, however, higher as compared to most Indian studies [Pfglurp MOI values did not show much variation in the pre-artemisinin vs. artemisinin era studies from Southeast Asia including India [HE) values observed in our study for Pfmsp-1 and Pfmsp-2 were slightly higher than observed in Malaysia [Pfglurp, it was slightly lower than those reported earlier from Tripura and Assam, Northeast India [HE values over the years may be due to differences in geographical locations, sampling intervals and subsequent changes in the malaria drug policy in Northeast India.MOI and nclusive . In this studies . Considettisgarh  and Tripttisgarh . Some ar studies . Pfmsp-2 studies ; but low studies . Pfglurpng India . HeterozMalaysia  but compMalaysia  whereas,st India . Slight P. falciparum clinical isolates from 2006 in Assam, Northeast India before the implementation of ACT. While the malaria burden has reduced in the study area and entire Northeast India drastically over the years, a similar trend in reduction of P. falciparum genetic diversity parameters was not observed in comparison with newer studies. Information from wide-scale studies on genetic diversity of the malaria parasite may help in a better understanding of the population dynamics of the parasite and how this contributes towards the development of an efficient vaccine for elimination of malaria over time.The present study evaluated the antigenic diversity of


P. falciparum infections were associated with a 39% increase in age-specific risk of eBL, suggesting that malaria reduction may substantially reduce eBL incidence and mortality. Because the impact of malaria on eBL appears to be cumulative, evaluation of these efforts should account for lags between declines in malaria incidence and eBL incidence.Endemic Burkitt’s lymphoma (eBL) is the most common pediatric cancer in malaria-endemic regions of sub-Saharan Africa and is universally fatal if untreated. Previous research has suggested an etiological link between malaria exposure and eBL. In this study, we used spatially detailed data on malaria parasite infection and eBL incidence in sub-Saharan Africa to assess the strength of this association. We found each additional 100 lifetime  Plasmodium falciparum, among children in these regions, we used high-resolution spatial data from the Malaria Atlas Project to estimate the annual number of P. falciparum infections from 2000 through 2016 for each of 49 districts within the study region. Cumulative P. falciparum exposure, calculated as the sum of annual infections by birth cohort, varied widely, with a median of 47 estimated infections per child by age 10, ranging from 4 to 315 infections. eBL incidence increased 39% for each 100 additional lifetime P. falciparum infections (95% CI: 6.10 to 81.04%) with the risk peaking among children aged 5 to 11 and declining thereafter. Alternative models using estimated annual P. falciparum infections 0 to 10 y before eBL onset were inconclusive, suggesting that eBL risk is a function of cumulative rather than recent cross-sectional exposure. Our findings provide population-level evidence that eBL is a phenotype related to heavy lifetime exposure to P. falciparum malaria and support emphasizing the link between malaria and eBL.Endemic Burkitt lymphoma (eBL) is a pediatric cancer coendemic with malaria in sub-Saharan Africa, suggesting an etiological link between them. However, previous cross-sectional studies of limited geographic areas have not found a convincing association. We used spatially detailed data from the Epidemiology of Burkitt Lymphoma in East African Children and Minors (EMBLEM) study to assess this relationship. EMBLEM is a case–control study of eBL from 2010 through 2016 in six regions of Kenya, Uganda, and Tanzania. To measure the intensity of exposure to the malaria parasite,  Plasmodium (P.) falciparum malaria is holoendemic  is the most common pediatric cancer in equatorial regions of sub-Saharan Africa (SSA) where oendemic . The canMYC gene . eBL has malaria , both of malaria , 5. The  B cells , 7.P. falciparum malaria and eBL incidence in SSA  study, which enrolled eBL cases with associated geographical data in the study regions from 2010 to 2016   data from 2001 to 2017 released by MAP at 5 sq. km. units resolution to estimate P. falciparum parasite prevalence in all birth cohorts by calendar year for each of the 49 districts in our dataset. Using the geographic birth cohort PfPR2-10 data, we calculated the corresponding P. falciparum entomological inoculation rate (PfEIR), which is the number of infectious mosquito bites (used interchangeably with inoculations) per person, per year, in all the geographical areas. PfPR2-10 is directly proportional to PfEIR in areas of high malaria transmission  observed from November 2010 to September 2016 was 59,793,102 person-years, including 29% from Kenya, 48% from Tanzania, and 23% from Uganda. SI Appendix, Fig. S3 shows the distribution of person-time, which included only Ugandan children under observation 2010 to 2011 and children from the three countries 2012 to 2016. The median age of children during the study period (2010 to 2016) was 8 yo in Kenya and Tanzania and 7 yo in Uganda, with a male:female ratio slightly greater than 1 in all countries.The EMBLEM study areas and underlying population at risk are represented in P. falciparum infections per child by calendar year in the study areas 2000 to 2016 is shown in SI Appendix, Fig. S4, Upper panels. The graphs show that P. falciparum infections were higher in Uganda than in Kenya and Tanzania for all periods. P. falciparum infections had an overall decrease over calendar years of observation in all regions, albeit with some fluctuation in the trends. Infections in Uganda decreased from 41 infections per child in 2000 to 5 in 2013 and then increased to 24 in 2016, about half the burden in 2000. Infections in Kenya decreased from 21 per child per year in 2000 to 3 in 2005, where they remained at a plateau (~1.5) until 2013, followed by a slight upsurge to 7 in 2016. Infections in Tanzania declined from 8 to 1 between 2000 and 2016, without an upsurge. The long-term declines in annual P. falciparum infections per child coincide with the implementation of area-wide Government- and donor-supported malaria prevention programs in these countries  during the three selected calendar years 2012, 2014, and 2016 for Uganda, Tanzania, and Kenya to illustrate the age-related increase in the number of infections seen typically in areas of holoendemic malaria , and lower and comparable in Kenya and Tanzania. For example, the cumulative number of infections at ages 1, 5, and 12 y in 2012 was 16, 99, and 260 for Uganda; 3, 18, and 101 for Kenya and 3, 15, and 52 for Tanzania. Consistent with the declining incidence of annual infections described above , the cohort-based analysis shows that the cumulative P. falciparum infections in children older than 10 y were lower in each subsequent year for children of the same age  or demographic data (n = 11). Our analysis is based on a sample of 552 eBL cases with complete data required for inclusion in the spatial analysis , with 267 cases from Uganda, 102 from Tanzania, and 183 from Kenya. The mean age of included cases was 8.3 y (SD: ±3.7), with 63% of cases being male. There were no age and sex differences between the excluded and included cases.Of the 862 suspected eBL cases enrolled, 165 were excluded after pathology and clinical review, 46 were excluded because they were born prior to 2000 and would not have SI Appendix, Fig. S4 (Lower) shows the eBL incidence in the three countries, suggesting a slight increase in eBL incidence during the first 1 to 2 y at the beginning of the EMBLEM study in each country, followed by a downtrend in all countries . Univariate Moran’s local indicators of spatial autocorrelation (LISA) analyses suggest spatial clustering of both total eBL incidence and cumulative estimated P. falciparum incidence over the study period. The significant univariate LISA values for both eBL incidence and cumulative estimated P. falciparum incidence, calculated by calendar year, were then summed over the observation period (2010 to 2016) and demonstrated strong clustering patterns of both metrics; however, the spatial overlap between clusters of cumulative P. falciparum incidence and eBL incidence was imperfect .I LISA values for average cumulative P. falciparum and average eBL incidences showed 4 clusters with significant concordant (high–high or low–low) values and 3 with significant discordant values (high–low) . However, bivariate Moran’s I LISA calculated by year for annual cumulative P. falciparum incidence and eBL incidence only showed discordant pairs, with 7 discordant bivariate Moran’s I LISA values and 0 concordant values.Bivariate Moran’s P. falciparum incidence and annual risk of eBL. The model converged, with the P. falciparum infections, as illustrated in SI Appendix, Fig. S8, we present a sample of age-specific risks to illustrate how these risks vary with age. The probability of successful P. falciparum infection varies with age as a function of exposure to infectious mosquitoes and acquired immunity to P. falciparum, which increases with age. Thus, comparing the IRR to that of a 10 yo with 100 cumulative P. falciparum infections, we see that the IRR for eBL was not significantly above unity before age 5 and after age 11, regardless of cumulative P. falciparum burden but was significantly elevated between age 5 and 11 y. In P. falciparum infection(s) for each year of life . Compared to a child who accumulated 6 infections by age 5 (the lightest infection plausible in our study area), we found that eBL risk rises significantly above unity for ages 5 to 11 y for 10 and 20 annual infections, and that the risk in that fixed age group is higher for 20 infections than 10 infections. Alternative models, using annual estimated number of P. falciparum infections with different lag periods (0 to 10 y) before eBL onset, which are more similar to previous research . These findings may demonstrate that a single year’s measure of P. falciparum is inadequate to represent the lifetime P. falciparum burden, leading to nondifferential misclassification of the exposure and biasing results toward the null. This suggests that an individual’s cumulative or lifetime P. falciparum exposure, rather than a cross-sectional metric of annual infection burden, is the more relevant predictor of eBL incidence. Finally, compared to models with any or all lag periods, the model employing a cumulative metric of P. falciparum exposure explained more of the spatial variation in eBL incidence, as evidenced by smaller magnitude and lower variation of EA-level random intercepts.We used a negative binomial regression model to explore the relationship between estimated lifetime research  were lesP. falciparum infections and risk of developing eBL in children in Uganda, Tanzania, and Kenya. Also, the eBL risk was higher with a higher infection burden in children of a fixed age group  and a protective factor in older children, perhaps by decreasing the risk of successful infection given inoculation  provides a rational explanation to link cumulative exposure of culation , 37. HowP. falciparum exposure and eBL than has been possible to-date. We derived multiple metrics of P. falciparum infection  using computational methods, the established relationships between MAP estimates of PfPR2-10 and PfEIR, and assumptions about transmission efficiency  . These regions were targeted because they have high malaria incidence, which was confirmed through age-weighted prevalence of asymptomatic P. falciparum infection (40 to 55%) , we calculated the amount of person-time at risk as the person time per fraction of each year during which case ascertainment was active in that region.The study population comprised children aged 0 to 15 y residing in six regions covered by the EMBLEM study in six regions of northwestern Kenya, northeastern Tanzania, and northern Uganda  . The two to 55%) , 48–50 ao 55%) 48. The eBL, using district or regional average population growth rates as previously described (The population of children at risk of eBL at each census enumeration area (EA) was estimated using data obtained (by SMM for the EMBLEM study) from each country’s statistical bureau and interpolated at NCIescribed , 48, 49.SI Appendix, Fig. S2 and temporally as shown in SI Appendix, Fig. S3A. Population data were aggregated to the district level , which was used as the unit for spatial analysis to achieve better statistical stability.The average population under observation per year was 8,541,872 , yielding a total population of 59,793,102 person-years that were contributed by children 0 to 15 y old  distributed spatially as shown in P. falciparum using high-resolution age-standardized estimates of PfPR2-10 from MAP . The parameters are specific to children and the formula has been modified to estimate annual PfEIR instead of monthly.ith observation of the jth district as a function of β, a vector of estimated coefficients, and X, a vector of the covariates age, sex, country, year. The random effect, iμ, allows the estimated count of eBL cases to be systematically higher or lower in some districts.PfPR in children because it is correlated with the PfEIR, which represents the number of infectious mosquito bites per person in a defined period , mesoendemic (10 to 50%), and hyperendemic (50 to 75%) categories. Holoendemic malaria, representing the most intense transmission, is defined as PfPR >75% in children aged 0 to 12 mo. This metric PfPR2-10 is a relevant predictor for eBL because it covers the age range when exposure to P. falciparum is thought to modulate eBL risk and it is amenable to mathematical or statistical modeling . Then, assuming a transmission probability  of 10%  and its association with eBL.We selected the d period . PfPR2-1modeling . The MAPmodeling . We noteommunity . It excl) of 10% , 28, we P. falciparum was correlated with eBL in time and space, we created a variable to capture the cumulative number of P. falciparum infections a child in a particular area acquires by summing the estimated infections for each annual birth cohort within each district. Because high-resolution PfPR2-10 data have only been available from 2000 onward, this variable could not be estimated for children born before 1999 in Uganda or before 2001 in Kenya and Tanzania, so these birth cohorts are excluded from our analysis. Thus, eBL outcomes were considered only for those birth cohorts whose cumulative infections over their entire lifespan could be estimated. For a child who developed eBL, the cumulative number of estimated P. falciparum infections at the age of diagnosis was considered their exposure, whereas for children who did not develop eBL, their number of estimated infections continued to increase up to a maximum determined by their age. The 552 children diagnosed with eBL are not removed from the at-risk population because their person-time was considered negligible relative to the population denominators. We note that although the number of cumulative P. falciparum infections increases with age, this increase does not result in increased malaria-related morbidity or mortality because children generally develop premunition (anti-disease and anti-parasite immunity), which reduces the risk for malaria and ultimately causes the PfPR2-10 to plateau and decrease . The EMBLEM study implemented regular public awareness campaigns to sensitize the community about eBL and encourage referral of suspected cases to collaborating hospitals with the capacity to diagnose and treat eBL in the six regions. The study facilitated the diagnosis and treatment of patients referred to the hospitals. Information on which cases were included in the analysis can be found in SI Appendix, Fig. S5. The EMBLEM study collected detailed information on the cases in this analysis, including geographic origin (Study Population). The time trends in eBL incidence over the study period were examined to determine its correlation with P. falciparum incidence and the impact of awareness campaigns .We used eBL case data prospectively ascertained by the EMBLEM study from the defined study areas from 2010 through 2016 , Uganda National Council for Science and Technology (H816), Tanzania National Institute for Medical Research (NIMR/HQ/R.8c/Vol. IX/1023), Moi University/Moi Teaching and Referral Hospital (000536), and National Cancer Institute (10-C-N133) and did not completely overlap spatially.P. falciparum Incidence. District-level eBL incidence was calculated as the age-specific number of cases identified in that country divided by the total age-specific person-time at risk in that country (see Study Population). Overall eBL incidence in each district was calculated as the number of cases per million children in each age and sex category, per year, and per district and was expressed as cases per million children. Within each 5-y age group, we made the simplifying assumption that every year of age represented an equal share of the population. We investigated trends in eBL and cumulative estimated P. falciparum incidence by first producing maps of the spatiotemporal distribution of both conditions, averaged over the study period, and calculated the Spearman correlation coefficient between average eBL incidence and average cumulative P. falciparum infections across all 49 districts. Next, we identified patterns of spatial clustering of cumulative estimated P. falciparum incidence and of eBL incidence using Moran’s Local Indicator of Spatial Autocorrelation (LISA) statistics  to account for possible overdispersion. A random intercept was included for each district because there are repeated measurements of each spatial unit over multiple years. Fixed effects were also included for the relationship between age, sex, country, and calendar year. To ensure that our cumulative-exposure metric was preferable to a cross-sectional measurement of malaria exposure, we compared the performance of this model to those using cross-sectional annual exposures by age and location. Results from this analysis are described in the Appendix. All models were fitted using the “brms” package for Bayesian hierarchical regression modeling for R 4.0.0, using default priors for both regression and variance parameters Click here for additional data file.


Thomson-Luque et al. Nature Communications 10.1038/s41467-021-25062-z (2021)Plasmodium falciparum causing severe malaria are younger than those causing uncomplicated malaria1. Thomson-Luque et al.2 subsequently claimed that in ours, and other datasets, circulating parasitemia inversely correlated with estimated parasite age and that this was because the P. falciparum that cause severe malaria express PfEMP1s that are more cytoadherent, leading to earlier parasite sequestration in the microvasculature and reduced splenic clearance. Here, we show that in our dataset circulating parasitemia and the proportion of total parasites that are circulating do not correlate with circulating parasite age, hence our data do not support their hypothesis.We previously showed in Papua that circulating P. falciparum parasites in severe malaria are younger than those causing uncomplicated malaria1 and they suggested that this difference confounded comparisons between transcriptomes of parasites causing severe and uncomplicated malaria. This is precisely the reason why we developed and applied mathematical approaches to control for parasite stage variation prior to identifying genes upregulated in severe malaria by differential gene expression (DGE) analysis. It appears that Thomson-Luque et al. missed these details which were outlined in our methods and results. Thus, they may have misinterpreted our differentially expressed geneset in their reanalysis. Thomson-Luque et al. themselves showed that the genes that we identified as upregulated in severe malaria were not expressed earlier than the genes upregulated in uncomplicated malaria . Thus, our analysis successfully controlled for parasite stage variation which did not confound our differentially expressed geneset.Thomson-Luque et al. confirmed our discovery that circulating var gene de novo assemblies and compared these parasite densities, but only 35 of these were used for DGE due to drug treatment prior to admission or insufficient sequence coverage1. Parasitemias were significantly higher in the 16 severe malaria cases used for DGE  than in the 19 uncomplicated malaria cases used for DGE (median 0.59% IQR 0.092–1.18) p = 0.0136 two-sided Mann–Whitney test U = 78. Thus, because we had not provided the individual parasite densities Thomson-Luque et al. understandably but incorrectly assumed the samples used for DGE also did not differ in parasitemia.Thomson-Luque showed that the genes upregulated in severe malaria in our differentially expressed geneset were not expressed earlier than the genes upregulated in uncomplicated malaria. They claimed that this similarity in expression timing was due to a lack of difference in parasitemia between our severe malaria and uncomplicated malaria patients. In our paper we analysed 44 patient samples for our r = 0.2033 95% CI −0.1494 to 0.5101 p = 0.2415) or asexual, non-ring stage parasites estimated by our mixture model (Spearman r = −0.2088 95% CI −0.5143 to 0.1438 p = 0.2288) . However, our data do not support these correlations. We did not provide the parasitemias for the individual patients in our publication but we have reanalysed our data and compared the proportions of parasite stages in our samples to the parasitemias. There is no correlation in our dataset between the parasitemia and the proportion of ring stage parasites . There is a correlation between levels of normalised reads of PF3D7_1420400 and proportions of ring stages  or proportion of asexual non-ring stages (Spearman r = −0.4608 95% CI −0.6782 to −0.1693 p = 0.0024). That the normalised glycine tRNA ligase reads have a positive correlation with the proportion of ring stages and a negative correlation with the proportion of non-ring asexual stages suggests that the levels of glycine tRNA ligase transcripts better reflects youth of parasite than parasitemia, consistent with the majority of RNAseq datasets prepared using the same approach as our own and available on plasmodb7. Thus, the inferred relationship between raw reads of glycine tRNA ligase and circulating parasitemia in the samples of Tonkin-Hill et al. was incorrect and the observed trends in gene expression of the Tonkin-Hill et al. samples in Fig. 1c of Thomson-Luque et al. are not associated with increasing parasitemia.In their Fig. 1c, Thomson-Luque et al. ranked 41 of our patient samples by the RNAseq readcounts of a single glycine tRNA ligase gene PF3D7_1420400 in lieu of the individual parasitemias. This gene is described as a housekeeping gene and its level of expression is inferred to represent parasitemia. However, in the 41 samples with adequate sequence coverage for expression analysis from Tonkin-Hill et al.Ptot) calculated from HRP2 levels. Ptot estimates both sequestered and circulating parasites8 and thus more directly measures the removal of parasites from circulation by cytoadhesion than inferring cytoadhesion from differences in circulating parasitemia. Ptot can also be affected by variation in the parasite multiplication rate, duration of infection, volume of distribution, and inter-individual variation in PfHRP2 half-life but is nonetheless a better univariate correlate with clinical outcome and prognostic indicators of severity than is peripheral blood parasitemia8. Ptot could be calculated for 21 uncomplicated and 16 severe malaria samples1. √Ptot was higher in severe malaria  than in uncomplicated malaria  in these samples . However, the ratio of circulating parasitemia/Ptot, i.e., a relative measure of the proportion that circulating parasites constitute of the body’s total parasite load, did not correlate with the proportion of circulating parasites that were rings  or asexual non-ring stages  (Fig. Thomson-Luque et al. used staging of transcriptomes of circulating parasites to infer relationships between parasite stage and circulating parasite density and proposed that earlier sequestration occurs in high parasitemia/severe malaria. However, we present data suggesting that in our samples this was not the case using estimated total parasite biomass (var gene expression was reduced in severe cases when in fact we reported that “There was no difference between severe malaria and uncomplicated malaria in total var gene expression”1. Thomson-Luque et al. appear to have misinterpreted our statement that “var gene expression was modulated”, which referred to a histone methyl transferase involved in var gene silencing and switching that was downregulated in severe malaria. Thus, we were referring to potentially altered var gene switching or silencing, which would affect which var genes were expressed not the total level of var gene expression.Our data do support part of the hypothesis of Thomson-Luque et al., notably the established role of PfEMP1-mediated sequestration in severe disease. While that was a principal focus of our study, Thomson-Luque et al. interpreted our manuscript as reporting 1. Our interpretation of these data differs significantly from that of Thomson-Luque et al. because in our data neither parasitemia (Fig. var gene assemblies. The identification of surface expressed PfEMP1s will require development of PfEMP1 variant-specific detection reagents.In summary, we previously reported that parasites circulating in severe malaria were younger than those in uncomplicated malariamia Fig.  correlatFurther information on research design is available in the Reporting Summary


The relationship between malaria infection and malnutrition is complex. Using data from a randomized controlled trial of 450 children 0–5 years of age in Burkina Faso, we examined the effect of malaria infection on short-term changes in anthropometric measures, the effect of malnutrition on malaria infection, and whether age modified the effect of baseline anthropometric measures on malaria infection. Malaria infection, assessed by blood smear microscopy and weight, height, mid-upper arm circumference, height-for-age z-score, weight-for-age z-score, and weight-for-height z-score were measured at three time points: baseline, 2 weeks, and 6 months. We used generalized estimating equations adjusted for sex, age, breastfeeding, maternal education, and study treatment (azithromycin versus placebo) for all analyses. Interaction terms were used to assess effect modification by age. Among the 366 children with no malaria infection at baseline, 43 (11.6%) had malaria infection within 6 months. There were no important differences in anthropometric measures at 2 weeks and 6 months between those with and without malaria infection at baseline. There were no significant differences in prevalence of malaria infection by baseline anthropometric measures. Age (0–30 months versus 30–60 months) modified the effect of baseline weight and height on malaria infection. Among those aged 0–30 months, for each kilogram increase in weight, malaria infection increased by 27% (95% CI: 6–53%), and for each centimeter increase in height, it increased by 9% (95% CI: 1–17%), but there were no differences for those aged 30–60 months. Malnutrition and malaria continue to be major causes of morbidity and mortality in low- and middle-income countries. Globally, 8.9% of the population in 2019 was undernourished,,,––The association between malaria infection and malnutrition is complex, and the directionality of these associations is poorly understood.To better understand the relationships between malaria infection and malnutrition, we used longitudinal 6-month follow-up data from 450 children 0–5 years of age enrolled in a randomized controlled trial in Burkina Faso. The objectives of this study were to examine the effect of malaria infection on short-term changes in anthropometric measures, the effect of malnutrition on malaria infection prevalence, and whether age modified any of these relationships.N = 450) 8 days to 60 months old were recruited. The sample size was determined based on the primary outcome of the trial, which was Shannon’s diversity index of the gut microbiome. A sample size of 450 children (225 per arm) provided at least 80% power for a detectable effect size of 1.02 SDs of Shannon’s diversity with no loss to follow-up.For this secondary analysis, we used data from a randomized controlled trial (GAMIN) conducted between 2019 and 2020 to evaluate the potential effect of azithromycin on intestinal microbiome changes and child growth.Data were collected at three time points: baseline, 2 weeks, and 6 months after enrollment. Anthropometric measures, including weight, height, and mid-upper arm circumference (MUAC) were taken at each study visit. Weight was measured using the SECA 847 scale  to weigh infants and children to the nearest 0.1 kg. The scales were calibrated each morning by using a 5-kg weight test. Length/height was measured using a ShorrBoard®  to the nearest centimeter. MUAC was measured using a standard MUAC tape. Height-for-age z-score (HAZ), weight-for-age z-score (WAZ), and weight-for-height z-score (WHZ) were calculated according to 2006 WHO standards,Potential confounders of the association between malnutrition and malaria infection were selected a priori using a directed acyclic graph and based on previous literature.N = 6) were considered implausible and were thus excluded from analysis.lincom command in Stata) was used to obtain the mean difference at 2 weeks and 6 months. Multivariable models were adjusted for sex, age (in months), breastfeeding, maternal education, and study treatment (azithromycin versus placebo).For all analyses, we used generalized estimating equations (GEEs) with robust standard errors to account for repeated observations in children. Six observations with extreme HAZ, WAZ, or WHZ scores (z-score > 6 or < −6) (N = 366). For this subcohort, we estimated the prevalence ratio of malaria infection at 2 weeks and 6 months for each unit increase in baseline anthropometric measures. GEE models with a binomial family and log link were used to obtain prevalence ratios. The same covariates as above were used in adjusted models for this analysis.To examine the effect of baseline anthropometric measures on point prevalence of malaria infection, we restricted our study sample to those who had no malaria infection at baseline  and baseline anthropometric measures was included in the GEE models to assess effect modification on the multiplicative scale. The effect of each baseline measure on point prevalence of malaria infection within the strata of age was calculated. We plotted the predicted prevalence of malaria infection with baseline anthropometric measures for each age group to visualize any differences in the malnutrition–malaria infection association in younger and older children.As a sensitivity analysis, we assessed the study characteristics of those who were lost to follow-up (LTFU) using logistic regression of predictors of missing visits. Predictors included in the model included baseline malaria infection, baseline anthropometric measures, sex, age group, breastfeeding, maternal education, and study treatment (azithromycin versus placebo).SAS 9.4  was used for data cleaning and descriptive analyses. Stata version 14.2  was used for all other analyses.N = 444), 78 participants (17.3%) had malaria infection at baseline. Half (50.9%) of the children were female, and 58.4% were aged 0–30 months, whereas 41.6% were between 30 and 60 months old. Approximately half of the children (51.1%) were randomized to the treatment arm (azithromycin), and 38.1% were breastfeeding. The mean HAZ, WAZ, and WHZ scores at baseline were −0.9, −0.9, and −0.5, respectively , 43 children (11.6%) had malaria infection during follow-up (at 2 week or 6 month visit). In the unadjusted analysis, we found that increases in baseline weight and height were associated with increased prevalence of malaria infection , we found that results were very similar with regard to point estimates and statistical significance. The only change was for children aged 0–30 months, where the effect of baseline height on the prevalence of malaria infection changed from borderline statistically significant to borderline insignificant . In the sensitivity analysis of LTFU, none of the covariates were significantly associated with missing the third or both follow-up visits.Our findings show that in this study population, baseline malaria infection did not affect growth at measurements 2 weeks and 6 months later. The effect of baseline anthropometric measures on the prevalence of malaria infection varied by child age. An increase in anthropometric measures at baseline increased the prevalence of malaria infection in children aged 0–30 months but not in those 30–60 months. Older children (aged 30–60 months) with lower height, HAZ, and MUAC and younger children (aged 0–30 months) who had higher weight-related measures  had a higher prevalence of malaria infection.,,,Previous studies found mixed results regarding the effect of malaria on growth and malnutrition. Some studies found that malaria could impair growth.,For the effect of baseline anthropometric measures on the prevalence of malaria infection, in the adjusted analysis, there was no substantial increase or decrease in the prevalence of malaria infection for increasing anthropometric measures. Although the findings from previous studies are mixed, this result is in line with some of the studies that found that anthropometric indices were not important predictors of malaria.––In this study, we found that the effect of baseline anthropometric measures on malaria infection prevalence varied by age. In those aged 30–60 months, there was a slight decrease in the prevalence of malaria infection as baseline measures increased, although the CIs did not reach statistical significance. Contrary to some of the studies mentioned above that found no effect, some studies found that malnutrition increasedIn children aged 0–30 months, lower anthropometric measures were protective of malaria infection . In part,,Although the assessment of effect modification was exploratory, differences in the effect of malnutrition on malaria infection by age group may be suggestive of distinct pathophysiologic pathways. Specific micronutrient deficiencies such as vitamin A, zinc, and protein-energy malnutrition have been associated with increased risk of malaria,,,,This study had some limitations. First, we had a relatively small sample size, which could limit our statistical power to detect more subtle differences. However, several other studies, particularly longitudinal studies on this topic, also had similar sample sizes ranging between 202 and 847 participants.This study also had several strengths, including its longitudinal data, which allowed us to establish temporality and comprehensively evaluate bi-directional effects in the same population. This may also be one of the few (if any) studies to assess the role of age in modifying the effect of malnutrition on the prevalence of malaria infection.The effect of malnutrition on the prevalence of malaria infection may vary by age. The prevalence of malaria infection increased with increasing anthropometric measures among children aged 0–30 months, whereas it slightly decreased for children aged 30–60 months. Children aged 30–60 months with lower height, HAZ, and MUAC and younger children (aged 0–30 months) who had higher weight and WAZ and WHZ z-scores had a higher prevalence of malaria infection. We recommend that future studies examine age-related and other factors that may exacerbate or mitigate the effect of malnutrition on malaria infection. It may also be useful to examine these associations at both individual and community levels.


Plasmodium spp. and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) co‐infection in pregnant women remains limited, especially in Latin America, where Plasmodium vivax infection is highly prevalent.Malaria‐endemic areas are not spared from the impact of coronavirus disease 2019 (COVID-19), leading to co-infection scenarios where overlapping symptoms impose serious diagnostic challenges. Current knowledge on P. vivax and SARS-CoV-2 co-infection hospitalized in two main malaria referral centers of the Capital District and Bolivar state, Venezuela between March 13, 2020 and December 31, 2021.This is a case series of five pregnant women with Plasmodium spp. was 4.8 ± 2.5 days and 2.8 ± 1.6 days, respectively. The mean hospital stay was 5.4 ± 7 days. Three women recovered and were discharged from the hospital. Two women died, one from cerebral malaria and one from respiratory failure. Three adverse fetal outcomes were registered, two miscarriages and one stillbirth.Clinical and laboratory data from five pregnant women with a mean age of 22 years were analyzed; three of them were in the third trimester of pregnancy. Comorbidities included obesity in two cases, hypertension in one, and asthma in one. Three out of five patients had severe to critical COVID-19 disease. Dry cough, fever, chills, and headache were the most frequent symptoms reported. Laboratory analyses showed elevated aspartate/alanine aminotransferase and creatinine levels, thrombocytopenia, and severe anemia as the most relevant abnormalities. The mean period between symptom onset and a positive molecular test for SARS-CoV-2 infection or positive microscopy for This study documented a predominance of severe/critical COVID-19 disease and a high proportion of adverse maternal–fetal outcomes among pregnant women with malaria and COVID-19 co-infection. More comprehensive prospective cohort studies are warranted to explore the risk factors, management challenges, and clinical outcomes of pregnant women with this co-infection. The impact of coronavirus disease 2019 (COVID-19) in the world has been unprecedented, particularly affecting low- and middle-income countries where healthcare systems are already weakened and overburden by other diseases such as malaria and arboviral diseases .Plasmodium falciparum and SARS-CoV-2 co-infection were typically oligosymptomatic at presentation with mild to moderate parasitemia, thrombocytopenia, lymphopenia, and elevated bilirubin levels [Plasmodium vivax infection is highly prevalent.Most malaria and COVID-19 co-infections had been reported in Africa and Asia, where patients with n levels , 4. Howen levels . Currentn levels , 7, espeP. vivax accounted for 82% of reported cases, followed by P. falciparum (13%), and mixed malaria  [P. vivax and SARS-CoV-2 co-infection in Venezuela.Venezuela remains a malaria breeding ground in the region, representing 34% of cases and 47% of disease-related deaths in America in 2021. Of note, Centro para Estudios sobre Malaria, Capital District and “Ruiz y Páez” University Hospital Complex, Bolivar state, Venezuela  was conducted. Clinical and laboratory data were obtained from the patients’ medical records. Laboratory tests were carried on the hospital as part of the routine management. Malaria diagnosis by microscopy was performed using Giemsa-stained blood smears, but data on parasite density were not available. SARS-CoV-2 infection was confirmed by reverse transcription polymerase chain reaction (RT-PCR) [A retrospective medical record review of all malaria and COVID-19 co-infections in pregnant women registered at the The severity of COVID-19 was characterized as mild , moderate , severe , or critical , according to the National Institutes of Health (United States) guidelines .Centro para Estudios sobre Malaria and “Ruiz y Páez” University Hospital Complex, respectively, were reviewed. The demographic, clinical, and paraclinical profiles of five pregnant women with confirmed co-infection by P. vivax and SARS-CoV-2 were recorded . Three of them were in the third trimester of pregnancy, two suffered from obesity, one was hypertense, and one was asthmatic. Dry cough (4/5), fever (3/5), chills (3/5), and headache (2/5) were the most frequent symptoms reported on admission. Less common symptoms included dyspnea (1/5), arthralgia (1/5), and vomiting (1/5). Mild elevations of hepatic enzymes, creatinine, and urea serum levels were observed. Likewise, patients exhibited mild alterations in the number of platelets, leucocytes, and neutrophils. Only one had severe anemia and none of them had lymphopenia. Three pregnant women recovered and were discharged from the hospital. Two women died, one from cerebral malaria and one from respiratory failure. Three adverse fetal outcomes were registered, two miscarriages and one stillbirth. The mean hospital stay was 5.4 ± 7 days.P. vivax and SARS-CoV-2 co-infection had a mainly mild clinical course [This analysis describes a case series of five pregnant women with malaria and COVID-19 co-infection in Venezuela, with a predominance of severe/critical COVID-19, including two maternal–fetal deaths and one stillbirth. Although some reports showed that l course –14, evenl course , 15, a hl course .Malaria and COVID-19 symptoms may overlap and delay the diagnosis of co-infection. Symptoms on admission in this case series were similar to those previously reported , 15, beiSince malaria and COVID-19 have different incubation periods, one infection could potentiate the subsequent one. To the authors’ knowledge, this has not been addressed or reviewed at full length in other studies. Indeed, only few studies have examined this potential relationship . A unifyPlasmodium spp. [P. vivax [Plasmodium ovale [P. falciparum co-infection were found. Pregnancy-associated malaria increases the risk of anemia, stillbirth, low birth weight, and maternal–fetal death due to systemic and placental inflammatory responses and microvascular dysfunction [A higher risk of adverse outcomes in pregnant women with either ium spp.  or SARS-ium spp. , 25 haveP. vivax , 15 and um ovale . No simifunction , 26. SARfunction , 28. Sevfunction . Thus, dP. vivax [Recently, it was documented a high prevalence of maternal–fetal complications in Venezuela caused mainly by P. vivax , traditiP. vivax , the cliP. vivax . HoweverP. vivax , 33. TheP. falciparum was detected in the Giemsa-stained peripheral blood smears, such possibility cannot be ruled out, since blood samples not taken during the febrile peak may not reveal low level circulating parasites, as most of them remain adhered to vascular endothelia. Because a high level of false-positive results with the SARS-CoV-2 serological assay has been identified in highly malaria-endemic areas [Due to the retrospective nature of this work, several limitations were unavoidable. Clinical and paraclinical information was incomplete for some patients and we did not collect specific outcomes of COVID-19-associated post-acute complications, such as thrombotic events. Moreover, there was an inherent selection bias, as all analyzed cases came from the primary malaria-endemic region of the country and included only hospitalized cases, so these severe maternal–fetal results cannot be extrapolated to populations with different epidemiological and clinical characteristics. As a strength, all malaria and COVID-19 co-infections in the current case series were confirmed by microscopy and RT-PCR, respectively. In the case of the dead pregnant woman with cerebral malaria, although no co-infection by ic areas , 35, rouic areas .This study documented a predominance of severe/critical COVID-19 diseases, as well as a high proportion of adverse maternal–fetal outcomes among malaria co-infected pregnant women. COVID-19 has become an unprecedented challenge to healthcare systems worldwide and has affected malaria control programs by delaying the distribution of insecticide-treated nets, and disrupting early diagnosis and drug supplies, among others , 38. Mal


Ischemia and no obstructive coronary artery disease (INOCA) patients who presented coronary microvascular dysfunction (CMD) demonstrate a poor prognosis, yet the risk factors for CMD remain unclear. Subtle changes in thyroid hormone levels within the normal range, especially the free thyroxine (FT4)/free triiodothyronine (FT3) ratio, have been shown to regulate the cardiovascular system. This prospective study investigated the correlation between FT4/FT3 ratio and CMD in euthyroid patients with INOCA.www.chictr.org.cn/, ChiCTR2000037112) recruited patients with myocardial ischemia symptoms who underwent both coronary angiography (CAG) and myocardial perfusion imaging (MPI) with dynamic single-photon emission computed tomography (D-SPECT). INOCA was defined as coronary stenosis< 50% and CMD was defined as coronary flow reserve (CFR)<2.5. All patients were excluded from abnormal thyroid function and thyroid disease history.This prospective study (Among 71 INOCA patients (15 [21.1%] CMD), FT4 and FT4/FT3 ratio in CMD group were significantly higher and both showed significantly moderate correlation with CFR . The ROC curve revealed that FT4/FT3 ratio had the highest efficacy for predicting CMD with an optimized cutoff value>3.39 . Multivariate logistic regression showed that FT4/FT3 ratio was an independent predictor of CMD .In euthyroid INOCA patients, increased FT4/FT3 ratio levels are associated with the occurrence of CMD, presenting a novel biomarker for improving the risk stratification. More than 50% of patients with myocardial ischemia symptoms who underwent coronary angiography (CAG) did not find an obstructive lesion (coronary stenosis >50%), and these patients are now diagnosed with ischemia and no obstructive coronary artery disease (INOCA) , 2. It wThe effects of abnormal thyroid hormone levels on the cardiovascular system have been well demonstrated , 10. SinThe current gold standard for noninvasive assessment of CFR is positron emission tomography (PET) myocardial absolute blood flow measurement , 15. DueIn this prospective study, we recruited 150 patients with myocardial ischemia symptoms who underwent both CAG and D-SPECT CFR within two days in Shanghai Tenth people’s hospital from 2020 to 2021. After excluding the previous history of myocardial infarction or coronary revascularization, 87 patients who present non-obstructive coronary stenosis (<50%) were diagnosed as INOCA. Further biochemical examinations excluded 16 patients: Hyperthyroidism (n=4); Hypothyroidism (n=6); Subclinical hypothyroidism (n=2); Hashimoto’s thyroiditis (n=4). A total of 71 euthyroid INOCA patients were included in the final study cohort. We divided them into two groups (Fasting blood was obtained to test thyroid hormone levels .Thyroid function profile was measured by direct chemiluminescence method . The reference intervals were as follows: FT3:2.8-6.3pmol/L, FT4:10.5-24.4pmol/L, TT3:1.0-3.0nmol/L, TT4:55.5-161.3nmol/L, TSH:0.38-4.34pmol/L, TGAb:<110IU/ml, TPOAb:<40IU/ml, TRAb:0-1.75IU/L.All patients underwent a one-day rest-stress dynamic and conventional acquisition on D-SPECT . Stop taking β-blockers, theophylline, dipyridamole, and ACEI drugs for at least 12 h before examination. All scans were performed after overnight fasting.For rest imaging, an initial dose of approximately 37 MBq (1 mCi) is required to position the patient’s heart within the field of view and to establish the scanning ROI. The remaining dose of approximately 370 MBq (10 mCi) is then injected into the patient, and the rest dynamic images were acquired by list mode for over 6 min. After a 60-90 min delay, rest perfusion scanning was performed.For stress imaging, no positioning dose is required as the heart location can be identified from the resting activity. Pharmacological stress agent ATP was instilled at a rate of 140 ug/(kg·min) through the venous access and 925 MBq (25 mCi) MIBI was injected 3 min after the start of ATP injection. Then stress dynamic images were acquired in list mode over 6 min. After a 60-90 min delay, stress perfusion scanning was performed. The dynamic imaging protocol is illustrated in Dynamic imaging data is re-binned into 32 frames consisting of 3 sec x 21 frames, 9 sec x 1 frame, 15 sec x 1 frame, 21 sec x 1 frame, 27 sec x 1 frame, and 30 sec x 7 frames. An OSEM technique is used for the reconstruction of dynamic imaging acquisitions, with 4 iterations and 32 subsets. All dynamic data and corresponding perfusion information are analyzed using semi‐quantitative methods in Corridor 4DM software . Left ventricular (LV) endocardial and epicardial surfaces are algorithmically estimated from summed myocardial images beyond the two-minute mark. A midwall surface, determined equally distant between the endocardial and epicardial surfaces, is divided into 460 polar map sectors, where LV myocardial tissue time-activity curves (TAC) are the nearest neighbor sampled at the center of each sector across all time frames. The CFR analysis makes use of ROI blood sampling by averaging a box‐shaped region in the LV blood pool, specifically in the center of the LV on the short axis and centered at the basal valve plane along the long-axis, across all time frames. The size of the ROI is two pixels wide on the short axis and 30mm long on the long-axis to sample both the LV and left atrial cavities. The Myocardial blood flow (MBF) was estimated through the previously established net retention model  and CFR Continuous data were presented as mean ± standard deviation, and categorical data were presented as frequency and percentage. The 2 test was used for categorical data. Independent samples t-test was used to compare the means of continuous variables between the CMD group and non-CMD group. The receiver operating characteristic curve (ROC) was performed to calculate the best cut-off value of thyroid hormone for CMD prediction. Pearson’s correlation was used to clarify the relationship between thyroid hormone and CFR. The least absolute shrinkage and selection operator (LASSO) regression was used to select the variables that entered into multivariate logistic regression. Multivariable logistic regression analysis was used to explore the association between FT4/FT3 ratio and CMD while controlling for potential confounders. Results were presented as odds ratio (OR) and 95% confidence interval (95% CI). A two-sided P value of < 0.05 was considered significant. All statistical analysis were performed using SPSS 16.0 for Windows  and MedCalc 18.0.A total of 71 INOCA patients  were included in this prospective study. Baseline characteristics, cardiovascular risk factors, and followed medications were compared in Thyroid hormone levels, resting/stressing myocardial blood flow, and global CFR was illustrated in As depicted in the for trend=0.006) and age  in Q3 were both associated with an increased risk for CMD.As shown in Our study has the following three main findings. Firstly, FT4 and FT4/FT3 ratio levels were higher in CMD patients; secondly, both FT4 and FT4/FT3 ratios were correlated with CFR; finally, it was demonstrated that the increased FT4/FT3 ratio predicted the risk of CMD in euthyroid INOCA patients, which provide a novel biological marker to improving risk stratification.CMD plays a pivotal role in INOCA patients, which is considered to be the third mechanism causing myocardial ischemia in addition to coronary atherosclerosis and vasospasm , 21. SinPrevious studies have established a strong association between abnormal thyroid hormones and increased cardiovascular risk . To our Our results also showed that FT4 was elevated in CMD patients and increased FT4 levels were correlated with decreased CFR. Although T4 requires conversion to T3 to exert its powerful biological effects, increased FT4 levels can still act directly on thyroid hormone receptors in a variety of tissues to promote excessive NO release from endothelial cells, exaggerated coronary vascular tone, and subsequently leading to endothelial dysfunction . SimilarAnother important finding indicated that age was higher in the CMD group and advancing age was significantly associated with the increased CMD risk. These results may be explained by the fact that aging impairs endothelium-mediated coronary vasodilation and angiogenesis, which in turn promotes structural remodeling of the microvasculature and subsequently leads to reduced perfusion of the microcirculation , 33. SimThis study has several limitations. First, since this study is a single-center prospective study, further sample size expansion and multicenter validation are needed. Second, we require additional follow-up data for this group of patients to clarify the prediction of major adverse cardiovascular events by FT4/FT3 ratio. And some new biological cardiac functional markers associated with thyroid function, such as Brain natriuretic peptide (BNP) , could bTaken over, increased FT4/FT3 ratio is strongly consistent with an increased risk of CMD, the high predictive efficacy for CMD is expected to provide a novel biomarker for early prevention and risk stratification in euthyroid INOCA patients.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Shanghai Tenth people’s hospital ethics committee. The patients/participants provided their written informed consent to participate in this study.FY, QH: conception and design. HZ, WC and YH: data collection and analysis. KS: Language editing and polishing. CX, XF, MF, SQ and XH: data curation. JZ, FH and XZ: Provision of study materials. All authors contributed to the article and approved the submitted version.The study was supported by research grants from the National Key Research and Development Program of China (2020YFA0909000), The National Natural Science Foundation of China (82127807), Shanghai Key Laboratory of Molecular Imaging (18DZ2260400), Clinical Research Plan of SHDC (2020CR4065) and The National Natural Science Foundation of China (No.82071956).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.