Plasmodium vivax is transmitted most across the country of Indonesia. The country has set out a malaria elimination program by 2030. The information on genetic diversity of malarial parasites relates to malaria transmission in an endemic area may provide the information that can help the malaria control program to achieve the target. Therefore, the purpose of this study was to determine the genetic diversity of the Pvmsp-1 gene in Central Kalimantan Province.P. vivax and mixed infection samples were amplified to nested PCR for genotyping of Pvmsp-1 allelic variation in segments F1, F2, and F3.Samples were 140 of archived Giemsa-stained blood smear and rapid detection test. Samples were divided into the indigenous and migrant populations. After confirmation by single-step PCR, only Genotyping of 23 PCR positive samples resulted in 13 genotypes. In segment F1, three allelic variants type A containing subtype A1 , A2 (350 bp), A3 (150 bp), and type B (100 bp). In segment F2, mono genotypes were detected as variant type A  and type B3 (150 bp), multiple genotypes were detected as type B containing subtype B1 (250 bp), B2 (200 bp), and B3 (150bp). In segment F3, three allelic variants generated from four mono genotypes were type A (350 bp), type B (300 bp), and two type C (250 bp).The low allelic variation of Pvmsp-1 gene may reflect the actual situation of the low malaria endemic status of the study sites. Malaria in Indonesia remains an important health burden, and 10.4% was contributed to the malaria cases worldwide, and as second contributor after India . However, there was a declined of malaria cases in Indonesia from 465,700 in 2010 to 235,700 in 2020, followed by the decreased of national annual parasite incidence (API) from 1.96 in 2010 to 0.87 in 2020 . Indonesia has set out a plan to eliminate malaria in the whole country by 2030, with phased target by region depending upon malaria endemicity . To achieve the target of malaria elimination, the movement of individuals from malaria endemic regions to non-endemic areas must be controlled to limit reintroduction of malaria to non-endemic areas but also to limit global genetic diversity and its spread in various areas .Central Kalimantan is one of the developing provinces in Indonesia with the majority of the population working in the agriculture, plantation and mining sectors. This has led to a large number of new land clearing for oil palm plantations and the increase in traditional gold mining practices, which is one of the reasons this province has not succeeded in eliminating malaria incidence . In 2019, API in this province was 0.1. Three out of 14 districts are now still doing efforts to achieve malaria elimination, such as Kapuas, Gunung Mas, and Murung Raya Districts where the endemicity is low . Palangka Raya City, the capital of this province has been certified to be free of malaria . The indigenous people of Kalimantan (Borneo) Island is called Dayak . The illegal gold mining attracted migrants to come to the island for settling. The migrant people came from outside the districts even from outside the island. The migrant people was categorized as non Dayak people who have resided in the study area for more than three months. The migrant people come from different ethnicities and origin of stay possibly have different immune status against malaria and bring in malaria parasite that ultimately cause genetic diversity of parasites . Human migration is a major factor in the reemergence and spread of malaria that transmitted to the local population . Assessing malaria parasites genetic diversity may be useful to elucidate the transmission dynamic and re-introduction of malaria in the area .Plasmodium vivax infection is transmitted across most the Indonesian archipelago . During 2019, the P. vivax cases was estimated to be lower (37.19%) than Plasmodium falciparum (60.88%) . P. vivax is one of the human malaria parasite species causing benign malaria . The vivax cases can lead into debilitating illness , and widely distributed , and more difficult to control and eliminate than P. falciparum in areas where they are sympatric . Hypnozoite is dormant stage of P. vivax can cause relapse in weeks or months after the initial infection, complicate the disease identification either as re-infection or recurrence, and cause possible failure of the treatment .Unlike P. falciparum, the in vitro continuous culture of P. vivax is still difficult to establish . Blood from P. vivax-infected patients is the only source of samples for molecular studies . Currently, during the Covid-19 pandemic, malaria remains a health problem worldwide and affects various aspects of life. The pandemic has caused a problem in malaria field survey especially in collecting the fresh blood samples from patients. Alternatively, the archived Giemsa-stained blood smears (GSBS) and rapid diagnostic tests (RDT) were used to isolate the parasites DNA to achieve this purpose.P. vivax populations is essential that may contribute to malaria control and elimination. Plasmodium vivax merozoite surface protein-1 gene (Pvmsp-1) is the genetic marker that most commonly used to determine the genetic diversity of P. vivax. The Pvmsp-1 gene contains 13 alleles conserve and highly variable blocks. The variable blocks are containing block 2 (F1 region), 6-8 (F2 region), and 10 (F3 region) . Analysis of Pvmsp-1 for F1, F2 and F3 segments allelic variation using polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) in Thailand has been reported to have 5, 2, and 3 allelic variants, respectively , in Pakistan were 23, 41 and 23 allelic variants, respectively , and in India were 6, 2, and 3 variants, respectively .Merozoite surface protein-1 is a protein found on the surface of merozoites which is important for parasite invasion into erythrocytes and is a potential vaccine candidate . Investigating the genetic diversity of P. vivax in Central Kalimantan Province, Indonesia has not been systematically studied. Investigations on the Pvmsp-1 allelic variation in Indonesia are very limited. Therefore, this study aims to determine the PCR-based allelic variation of the Pvmsp-1 from indigenous and migrant populations in Central Kalimantan Province as background information to guide in understanding the transmission of P. vivax infection in these areas.The genetic population structure of Samples used in this study were archived Giemsa-stained thick and thin blood smears (GSBS) and rapid detection test (RDT) cartridges collected during 2017 to 2020 from public health service (Puskesmas) of two districts, Kapuas District and Gunung Mas District, where malaria is still endemic and one municiplity  where malaria is eliminated. The samples were then differentiated into samples from indigenous and migrant people based on the data from the health centers. The blood films have been examined to be positive for malaria parasite by two certified malaria microscopists. The specimens were transferred to our laboratory by packages delivery service in 2021. The study has been approved by the Ethical Review Committee of Faculty of Dental Medicine Universitas Airlangga, Surabaya, Indonesia as stated on the certificate number 008/HRECC.FODM/I/2021.In order to perform PCR amplification, the DNA was extracted from archived GSBS and used RDT cartridges. Each GSBS were scratched off by using a sterile scalpel. Each individual RDT cartridges was opened using scissors and forceps and the nitrocellulose strip was removed from the plastic case, then any plastic covering on the strip was stripped off. The proximal third of the test strip containing DNA was cut and subsequently used for DNA extraction as described elsewhere . The collected materials from blood smears and RDTs were then transferred to a sterile tube. The DNA extraction was done using QIAamp DNA Blood Mini Kit  following manufacturer’s instruction.Plasmodium. Primers used in this PCR were based on the sequences of small-sub unit ribosomal RNA (ssu-rRNA) of P. falciparum and P. vivax. This PCR system used 3 primers in one tube which amplified sequence common to rRNA of both Plasmodium, and specific for each P. vivax and specific for P. falciparum. The three primers produced a 266 bp product specific for P. vivax, and a 346 bp product specific for P. falciparum. The PCR products were then verified through electrophoresis on 2% agarose gel and visualized on the UV light and documented. The positive samples determined by PCR were then used for nested PCR amplification to analyze the allelic variation of Pvmsp-1.The DNA was then amplified for single step PCR using primers and PCR condition based on previously described  for the confirmation of species of The three polymorphic regions of PvMSP-1 gene, namely F1, F2 and F3 were analyzed according to the previously described method with slight modification . Total volume of PCR mixture was 20 µl, containing 12.5 µl of 2x PCR mixture , 5 µl of DNA template and 20 pmol of each primer. Primers and PCR condition were as described  with slight modification. PCR products were directly analyzed using electrophoreses in agarose gels. Five microliters of PCR product were mixed with 1µl of loading buffer and applied to 2% agarose gel. The fragments of DNA were visualized by UV transillumination after ethidium bromide staining. The size of amplified fragments was compared by size to a 100 bp ladder marker .The frequency of each Pvmsp-1 allele was calculated based on the presentation of samples containing that allele from the total sample in each population. Allelic variation was determined based on the number and the size of PCR fragment of each allele in each sample. The Multiplicity of Infection (MOI) was defined as the number of parasitic genotypes per infection and was obtained by dividing the total number of fragments by the number of positive isolates containing allele in the same loci. Isolates with more than one genotype were considered as a polyclonal infection while the presence of a single allele was considered as monoclonal infection.A total of 140 samples consisted of 75 GSBS, and 65 RDT cartridges. Based on the data obtained from health centers, the samples were then differentiated into 85 samples of indigenous people, and 55 samples were from migrant people. The basic characteristic of the samples is shown in The archived GSBS and RDT samples were collected from patients mainly aged 15-65 years old, male patients was the most found, and gold miner was the most occupation among the patients in both population .P. falciparum microscopic samples were identified as same species. From 64 P. vivax microscopic samples only one sample (1.35%) were identified as P. falciparum, 3 (4.05%) were identified as P. vivax, and 19 (25.68%) were mix infection of both species. Only samples positive P. vivax and mix infection were used for nested PCR. One sample detected as P. malariae by microscopic examination, but negative by PCR. There was no false positive in this PCR. The results of single step PCR were presented in Thirty-five (25%) out of 140 samples were detected positive by single step PCR, consisted of 25 samples (71.43%) of microscopic origin, and 10 samples (28.58) of RDT origin. False negative by this PCR was 105 samples, consisted of 50 samples (47.62%) from microscopy samples, and 55 (52.38%) from RDT samples. Eleven samples (18.03%) out of 61 samples of P. vivax and 20 of mix infection, 13 samples among them were from indigenous, and 10 samples were from migrant population. Only 6 samples (26.09%) out of 23 were detected containing Pvmsp-1 gene, where each three of which were from indigenous and migrant population, respectively. Genotyping based on the size of PCR fragments in segment F1, F2 and F3 within small number of isolates 13 genotypes were observed in both indigenous and migrant populations. In segment F1, three allelic variants generated from one multiple genotypes, and two mono genotypes. The multiple genotypes were determined as variant type A containing subtype A1 , A2 (350 bp), A3 (150 bp). Mono genotypes were detected as type B (350 bp). In segment F2, three allelic variants were generated from two mono genotype (type A and type C), and one multiple genotype (type B). Mono genotypes were detected as variant type A  and type C (150 bp). Multiple genotypes were detected as type B containing sub type B1 (250 bp), B2 (200 bp), and B3 (150 bp). In segment F3, three allelic variants generated from four mono genotypes were type A (350 bp), type B (300 bp), and two type C (250 bp). The detail results are summarized in Polymorphism of segment F1, F2, and F3 of Pvmsp-1 gene was performed in 23 samples, consisted of three samples of Anopheles mosquito during the journey to and from gold mining . Based on some information collected from various local newspapers and news web pages, illegal gold mining which creates many deserted digging holes has become a major concern for the Central Kalimantan Provincial Government because it causes environmental damage and becomes a breeding site for the Anopheles mosquito.This study is the first report on the allelic diversity of Pvmsp-1 gene among the samples collected from the indigenous and migrant population of Central Kalimantan Province, Indonesia. Most male adult patients related to gold miner as the most occupation in studied areas. All miners were male aged between 15-65 years old. The gold miners are the most popular contributors to the total number of malarial cases in this study, where in indigenous population was 59 (42.14%), and migrant population was 47 (33.57%). Illegal gold mining mostly are located on the slopes of mountain. The difficult access to reach it may contributes to the increased of malaria cases due to the increasing contact with Plasmodium clearly indicate that, DNA could be isolated from GSBS and RDT using the described method and that PCR system using this DNA worked well for the detection and genotyping of Plasmodium species. Mixed infection was more detectable by single step PCR (20 samples or 14.29%) than both microscopy and RDT. Misdiagnosis on microscopy is often associated with the reading proficiency of the microscopist and the blood film preparation although the finding of parasite on Giemsa-stained blood smear is a gold standard of malaria diagnosis. Although RDT is also recommended by WHO for rapid use in the diagnosis of malaria in certain circumstances, the false negative on RDT due to the improper use of RDT, storage and parasite density may occur.During the Covid-19 pandemic, many health services and program were substantially impacted including malaria active and passive cases detection and surveillance. The archived GSBS and RDTs PCR analysis apparently provide a reliable, alternative tool to evaluate the ongoing malaria endemic situation as well as the malaria transmission dynamics in the study sites . The results of molecular identification of P. vivax infection across sites was highly significant in contrast with that of P. falciparum, which was not significantly different .Genotyping parasite population can give the information on parasite diversity, population structure and the undergoing transmission intensity . Based on the Pvmsp-1 genetic marker, this study has found 13 allelic variants in segment F1, F2, and F3 of this gene. Although the number of samples was very low, the results revealed a low allelic variation among both population with mostly mono genotype of F1, F2, or F3 and multiple genotypes of F1 and F2 was only found in one isolate. The findings confirm the low endemic status of the studied districts as evidenced by the low API of Central Kalimantan Province, the ages of patients, which were only adult patients. A positive correlation between the rate of polyclonal infections and endemicity of malaria endemic provinces has been observed in parasite populations from Indonesia, where variation in the polyclonal P. vivax also can be caused by reactivation of dormant-hypnozoite during blood stage infection . Multiple clone activation of hypnozoite can cause relapse in P. vivax infection, which resulted from either homologous or heterologous hypnozoite or both .Multiple genotypes reveal a multiclonal infection can be the result of several bites of infected mosquitoes, or a single mosquito bite which is containing sporozoites that genetically are different . Genetic recombination of sexual stages in a mosquito host is continually occurred in natural population, which enable the rapid evolution of new strains, and resulting in multiple strains of the parasite which transmitted simultaneously, producing multiclonal infection in human host . Multiclonal infection in The low allelic variation might reveal the low transmission of malaria. Population genetic studies have demonstrated that areas with higher malaria transmission tend to have higher genetic diversity . The problem is that there is no easy way to directly translate parasite genetic diversity into a level of parasite transmission . The small number of samples in this study might not represent the entire studied area, but at least it has provided the genetic information of malaria based on the allelic diversity of the Pvmsp-1 gene which has been recorded as basic data of molecular epidemiology in the studied area.Limitation of this study is mainly attributed to the quantity and quality of archived specimens used for DNA analysis. Nevertheless, owing to the impaired health surveillance and service due to pandemics, the current tool may provide further confirmation and/or validation to the results of blood film microscopy reading and RDT.The low allelic variation of Pvmsp-1 gene observed in this study may reflect the actual situation of the low malaria endemic status of the study sites. Further, systematic archived samples analysis in different areas of the districts and Province may better reflect the ongoing malarial situation.The authors declare that there is no conflict of interest regarding this study.Pvmsp-1:Plasmodium vivax merozoite surface protein-1PCR:Polymerization Chain Reactionbp:base pairAPI:Annual Parasite IncidenceGSBS:Giemsa-Stained Blood SmearsRDT:Rapid Diagnostic TestPCR-RFLP:Polymerase Chain Reaction and Restriction Fragment Length PolymorphismMOI:Multiplicity of Infection.


Light microscopy and rapid diagnostic tests are currently being used for diagnosing malaria in routine clinical care settings. Many clinicians have however raised questions about the competencies of laboratory staff who perform these tests and the performance of these diagnostic methods. This study aimed at assessing the performance of microscopy and two rapid diagnostic test kits in the hands of routine laboratory staff compared to expert microscopy as well as assess the performance of clinical diagnosis.this was a cross sectional study involving 799 participants of all ages who visited the out patient department of the University of Cape Coast Hospital with symptoms suggestive of malaria.when the different methods were compared to expert microscopy, the rapid diagnostic test kits had the highest sensitivities, Wondfo 94.83% (95% CI: 85.62-98.20) and CareStart 91.38 (95% CI: 81.02-97.14). Microscopy by laboratory staff had a sensitivity of 68.79 (95% CI: 55.46-80.46) whilst clinical diagnosis had the lowest sensitivity of 17.24 (95% CI: 8.59-29.43). Cohen´s kappa coefficient was used to measure the level of agreement of the methods with expert microscopy. Microscopy by laboratory staff, CareStart and Wondfo showed substantial measures of agreement .these findings suggest that clinical diagnosis is highly unreliable whilst rapid diagnostic tests and microscopy performed by routine laboratory staff could be trusted by clinicians as reliable diagnostic methods. Malaria is hyper-endemic in Ghana and it present to the country, a serious health problem. According to the 2019 Ghana malaria indicator survey . It was Since many clinicians continue to trust their clinical suspicion, and have concerns about the reliability of the various rapid diagnostic test kits and the expertise of health facility workers who perform microscopy, there is a need to determine the accuracy of clinical diagnosis compared with laboratory diagnosis. Performance of RDTs compared to microscopy has been studied under different settings globally with variable findings. However, most of these studies have assessed the performance of these diagnostic methods in the hands of expert microscopists and other specially trained study staff. Only few studies  have proStudy design: this was an analytical cross-sectional hospital-based study conducted from March 2020 through May 2020.Population: the study involved patients who visited the out patient department of the University of Cape Coast (UCC) Hospital and were either diagnosed clinically of malaria or sent to the laboratory on suspicion of malaria. The UCC Hospital is located on the University of Cape Coast campus, northern part of Cape Coast, Ghana.Sampling: participants were recruited based on the following inclusion and exclusion criteria.Inclusion criteria: all patients irrespective of age and sex, suspected of malaria infection and asked to report to the laboratory with a malaria test (microscopy and/or RDT) request form. Also, all patients irrespective of age and sex who present to the pharmacy with a prescription for an anti-malaria drug without prior malaria testing were recruited.Exclusion criteria: excluded were all patients taking anti-malarial drugs at the time of study or have taken anti-malarial drugs within 2 weeks prior to reporting to the hospital.Sample size calculation: sample size was calculated using 95% confidence, an estimated malaria prevalence of 18% [2 P(1-P)/d2 where n = sample size, Z = Z statistic for a level of confidence, P = expected prevalence or proportion and d = precision was used for the sample size calculations. The resulting minimum sample size required was 226. However, in order to increase the statistical power of the study a final total sample size of 799 was used.Sampling technique and recruitment: about 25 patients report every day for a malaria test at the UCC hospital laboratory. Systematic random sampling technique was used to recruit 8 consented patients into the study every day using a sampling interval of 3. In order to reduce the possible coercive influence of clinicians on patients´ acceptance to be part of the study, the recruitment was done by 2 (two) trained research assistants who approached potential participants for their consent whilst they were waiting for their turn at the laboratory. Also, all patients who report to the pharmacy with a malaria prescription without prior malaria test were recruited by the attending pharmacist/pharmacy technician using census sampling. Microscopy and rapid diagnostic tests using two different RDT kits were performed for each participant by hospital laboratory staff as well as experts.Data collection procedures: a questionnaire was used to collect data on demographic characteristics of participants through an interview. A data extraction form was used to document test results and adherence to test results by clinicians.Blood sampling: approximately 3mL of blood samples was taken by trained study staff into an ethylene diamine tetra acetate (EDTA) tubes using the venous sampling technique.Rapid diagnostic tests: about 0.5mL of the blood was used to perform the malaria RDT tests following the manufacturer´s protocols. The RDT kits used were P. falciparum specific CareStart malaria HRP2 Test Kit and pan-specific Wondfo malaria HRP2/pLDH test kit.Microscopy: about 1mL of the same blood was used to prepare the thick and thin films which were stained with Giemsa. After laboratory staffs have performed the tests, two experts using the same samples prepared and read blood film slides independently. All laboratory work was conducted at the laboratory of the University of Cape Coast Hospital. Thick blood films were used to estimate the level of parasitaemia by counting the number of parasites against 200 white blood cells with the assumption that each subject had 8,000 white blood cells/µL of blood. A minimum of 200 fields was examined before declaring slides negative for malaria parasite. Blood smears with discordant expert microscopist results  was re-examined by a third independent microscopist, and parasite densities calculated by averaging the two closest counts.Haemoglobin determination: about 0.5mL of blood was used to determine the haemoglobin level of participants.Ethical consideration: ethical clearance was obtained from the institutional review board of University of Cape Coast (UCCIRB). Permission was also obtained from the management of the University of Cape Coast Hospital before sampling began. The main areas of concern in ethical involvement with participants included the issues of privacy, anonymity, confidentiality and safety of patients. These issues were addressed by training all those involved in the study to ensure confidentiality. Also, no names, or any form of personal identification was used. A coding system was used to identify participants. Samples collected from participants were handled solely by trained laboratory staffs. Subjects diagnosed with malaria were treated according to the Ghana Ministry of Health guidelines.Data analysis: data collected was entered into Microsoft Excel and imported into SPSS version 23.0 for analysis. All patients prescribed anti-malaria drugs without a laboratory test was categorized clinically diagnosed. Using expert microscopy as gold standard, the sensitivities and specificities of clinical diagnosis, microscopy and rapid diagnostic tests (two different kits) performed by hospital laboratory were determined. Chi-square was used to analyze associations between patient characteristics such as age and sex and malaria test results. Cohen´s kappa coefficient was used to measure the level of agreement between the various methods  methods. P-values less than 0.05 was considered significant.A total of 799 participants aged 3 months to 86 years were involved in the study. Most participants (55.9%) were females. The average age of participants was 23.9 ± 17.7 years. In general, males (25.02±16.6 years) were older than females (22.62±19.00 years) with no significant difference in age, t(797)=1.898, p=0.058, between them. Children under-five years involved in the study were 147 (18.4%). Prevalence of malaria: using expert microscopy as a gold standard a prevalence rate of 7.3% (58/799) was found among the study population. Among these, 11(19.0%) were children under 5 years old. There was no significant association between expect microscopy positivity and age group  as well as sex . there was a higher preponderance of malaria parasitaemia among females (55.2%) compared with males (44.2%). This difference was however not statistically significant . Malaria prevalence varied among the population using the different diagnostic tools. The least prevalence was obtained using laboratory staff performed microscopy as shown in P. falciparum only 54(93.1%), P. falciparum and P. malariae mixed infections, 2(3.4%) and P. malariae only 2(3.4%). Thus P. falciparum accounted for majority (96.6%) of all cases in this study. The geometric mean of asexual parasitaemia was 14663.34 parasites/µl (range: 123-102220 parasites/µl). When age was stratified into <5 years and >5 years, there was no significant association  between parasite density and age.Predictive indices of the various diagnostic tests: using expert microscopy as the gold standard, the CareStart and Wondfo RDT kits recorded the highest sensitivities of 91.38% and 94.83% respectively. The highest specificity of 98.92% was recorded by microscopy performed by laboratory staff. Clinical diagnosis recorded the poorest sensitivity of 17.24%. Influence of malaria test results on treatment: out of the total of 689 who were suspected and sent to the laboratory for malaria tests, 107 returned to clinicians with a positive result either by expert microscopy, laboratory staff performed microscopy, or any of the RDTs used. Thus 582 participants submitted negative test results to clinicians. All participants with positive results received appropriate antimalaria drugs, however, 85(14.6%) were given antimalaria drugs even with negative test results. A higher proportion of children under 5 years (20.0%) with negative results received antimalaria drugs compared to participants above 5 years old (13.2%) with no significant difference . The study therefore found an adherence rate of 87.67% to malaria test results among clinicians. Whilst there was a 100% compliance to positive test results, compliance to negative test results was 85.4% as shown in The results of this study suggests a relatively low burden of malaria among the study population compared to the prevalence of 18% found in an earlier study conducted in the study area . The retpfHRP 2 detecting RDTs have the potential to overestimate malaria prevalence. This is because, the HRP 2 antigens can persist for up to 2 weeks after treatment [pfHRP 2 which has since been corrected [The obviously subjective nature of microscopy is an indication that other parasitological tests must be employed to improve diagnosis of malaria in routine clinical settings. This study therefore evaluated the performance of two RDT kits as compared to expert microscopy. The high predictive indices exhibited by both RDT kits in this study is similar to what was found in a Kenyan study . The RDTreatment . Again torrected . There horrected ,21.Clinical diagnosis of malaria in this study had a sensitivity of 17.2% which is very low compared to that of laboratory staff performed microscopy and rapid diagnostic tests. This study findings suggests that over 80% of all managed empirically for malaria did not actually have malaria. Such low sensitivities for clinical diagnosis were also found in some earlier studies ,23. A stLimitations: this study has some few limitations. The study did not make use of polymerase chain reaction which is known to have the highest sensitivity in the diagnosis of malaria to assess the diagnostic accuracy of the malaria RDT kits as well as the competence of the laboratory staff involved in the study. This was however, minimized by the use of highly trained experts in malaria diagnosis. Again, the study did not investigate what could possibly cause the non-malaria fevers among participants who tested negative to the parasitological tests.Findings from this study suggests that when microscopy and rapid diagnostic tests are performed by health workers in routine clinical settings, they compare favourably with expert microscopy. Clinical diagnosis on the other hand was found to be highly unreliable with poor predictive indices. Clinicians continue to prescribe antimalaria drugs for patients with negative malaria test results.Recommendations: to improve the diagnostic accuracy of malaria in clinical settings and therefore increase clinicians trust in malaria test results, both microscopy and rapid diagnostic tests should be performed if possible, for all suspected cases of malaria. Health facilities should develop and implement quality assurance programs that will ensure that optimum quality parasitological tests for malaria are performed by health workers. There should be regular training on malaria tests and case management for clinicians as well as laboratory staff. Clinicians should especially be trained on management of non-malarial febrile conditions and the harmful effects of the over reliance on clinical findings only in the diagnosis of malaria.Some clinicians do not trust malaria test results due to perceived incompetence of routine laboratory staff in the performance of the various malaria tests;Some clinicians diagnose and manage malaria without parasitological evidence.Parasitological tests performed by routine laboratory staff are comparable to those performed by experts and could be reliably used to manage malaria;Clinical diagnosis of malaria cannot be relied upon to accurately diagnose malaria even in malaria endemic regions.


Plasmodium including Plasmodium malariae, Plasmodium falciparum, Plasmodium cynomolgi, Plasmodium knowlesi, Plasmodium ovale, and Plasmodium vivax are known to infect humans. However, greatest disease burden and fatalities are caused by Plasmodium falciparum. Globally, about 3 billion individuals are at risk of contracting malaria disease every year, with over 400,000 fatalities reported in the Sub-Saharan Africa. World Health Organization (WHO) 2018 malaria report indicated that approximately 405,000 mortalities and 228 million cases were reported worldwide, with Africa carrying the highest disease burden. Over the last decade, there has been a significant decline in malaria deaths and infections, which may be related to the availability of effective diagnostic techniques. However, in certain areas, the rate of decline has slowed or even reversed the gains made so far. Accurate diagnosis, adequate treatment, and management of the disease are critical WHO-set goals of eliminating malaria by 2030. Microscopy, rapid diagnostic tests (RDTs), nucleic acid amplification tests (NAATs), and biosensors are all currently accessible diagnostic methods. These technologies have substantial flaws and triumphs that could stymie or accelerate malaria eradication efforts. The cost, ease, accessibility, and availability of skilled persons all influence the use of these technologies. These variables have a direct and indirect ramification on the entire management portfolio of patients. Despite the overall decline in the malaria disease burden driven partly by new diagnostic technologies, a sobering pattern marked by limited number of studies and spatial as well as temporal heterogeneity remains a concern. This review summarizes the principle, performance, gaps, accomplishments, and applicability of numerous malaria diagnostic techniques and their potential role in reducing the malaria disease burden in Sub-Saharan Africa.Worldwide, transmission of emerging and reemerging malaria infections poses a significant threat to human health in the Sub-Saharan Africa, one that can quickly overwhelm public health resources. While the disease burden of malaria in the Sub-Saharan Africa appears to be on a gradual decline, it is characterized by spatial and temporal variability occasioning a sorry state for the Global South Countries. New evidence on long-term complications of malaria heightens our awareness of its public health impact. Given the likelihood of misdiagnosis, and the unknown levels of malaria transmission across different landscapes, many missed opportunities for prevention occur. Africa's population growth, unplanned urbanization, habitat destruction, and trans-border travel are contributing to a rise in the calamitous epidemiology of malaria. Despite empirical statistics demonstrating a downward trend in the malaria disease burden attributable to the scale-up of multiple control strategies, including new diagnostic technologies, malaria remains a global threat to human health in Sub-Sahara Africa. Malaria is a severe public health threat globally, despite several advancements and innovations in its control. Six species of the genus  Plasmodium malariae, Plasmodium vivax, Plasmodium ovale, Plasmodium falciparum, Plasmodium cynomolgi, and Plasmodium knowlesi are six species of the genus Plasmodium that are known to infect people [Plasmodium falciparum, on the other hand, is responsible for the majority of disease burden and mortality [Despite breakthroughs in malaria control, the illness has long been a major public health burden globally . It, alot people , 5–7. Plortality –10.Every year, around 3 billion people worldwide are at risk of getting malaria, with over 400,000 deaths occurring in Sub-Saharan Africa , 11–16. A significant reduction in malaria burden has been observed internationally over the last decade, which can be linked to successful management efforts , 18–20. Plasmodium falciparum malaria is decreasing in many parts of Africa, it is still characterized by spatial and temporal variability, posing new and evolving challenges for malaria control programs. Large-scale, reliable monitoring systems that track rather than estimating the actual malaria burden over time in large areas of the world are useful [Although e useful .Accurate diagnosis and appropriate treatment of Malaria are required for the WHO-set target of eliminating the disease by 2030 to be achieved , 22, 27.Malaria's global impact on health-care systems and economies necessitated the development of more effective techniques to address gaps in accurate and timely detection –4. This Microscopy, malaria RDTs, nucleic acid amplification tests, and biosensors are among the various malaria diagnostic methods currently in use , 30, 31.Plasmodium parasites in a short amount of time for case management [Light, digital, and fluorescent-assisted microscopy are the three types of microscopy . Microscnagement . The usenagement . Microscnagement , 31–35. nagement . Howevernagement , 30, 34.nagement .Blood film quality, reading and interpretation of data, stochastic variance, and available workload are all factors that influence the quality of outcomes. Furthermore, the readers' competency levels, as well as how they interpret and handle ambiguous results, have a major impact on patient management and disease burden , 21, 32.μL of blood [Plasmodium aldolase in RDTs now on the market is used to detect parasitemia [This is a more rapid method of identifying malaria infection in people, with the ultimate goal of establishing a solid foundation for disease management , 10, 29.of blood . This teof blood . These kof blood . Meanwhiof blood , 34. Theasitemia , 33, 36.Plasmodium falciparum (HRP-2) or HRP2 with a species-specific protein from Plasmodium vivax (Pv-pLDH) [falciparum malaria, and non-falciparum malaria [A significant number of RDTs can simultaneously check and detect two distinct proteins. For example, combining pLDH with a particular protein from Pv-pLDH) , 33, 34. malaria , 30.Plasmodium falciparum infections have been reported all over the world [Performance of RDTs is influenced by manufacturing and environmental factors, especially when it comes to the ability of these kits to identify and quantify malaria parasites , 30. Falhe world , 21, 36.he world , 23, 37.he world . Furtherhe world .In a malaria investigation conducted in febrile children in Calabar, Nigeria, the Paracheck-Pf RDT achieved a sensitivity of 51.4% and a specificity of 73.2%. The percentage of false positives was 26.8%, while the rate of false negatives was 48.6%. The positive predictive value (PPV) was 58.1%, while the negative predictive value (NPV) was 67.6%. A positive likelihood ratio (LR) of 1.92 and a negative LR of 0.67 were also found in the RDT. The RDT test had a 64.1% accuracy rate. According to the findings, variations in the performance of RDT kits can be caused by a variety of reasons, and as a result, they should not be utilized as a stand-alone test kit unless a prior batch/lot validation test has been performed .μL of blood, specificity and sensitivity range from 88 to 94% and 98 to 100%, respectively [This technology has a high level of diagnostic precision , 23. In ectively . It is, ectively , 29, 30.ectively .μL of blood [This is a more advanced method of nucleic acid amplification. Several modifications were suggested to improve visibility of the amplified products, including the use of colorimetric or fluorescent dyes . This meof blood . When coof blood . Howeverof blood , 39.The technique employs a sophisticated primer design that includes 4–6 primers that are specifically intended to target 6–8 areas of a gene of interest . ForwardμL of blood [RNase H, T7 RNA polymerase, and reverse transcriptase are used in this method to amplify RNA targets , 41. Revof blood . This teof blood .  Figureof blood .Biosensors and immunosensors have exploded in popularity in recent years, and they appear to be the most promising sensing technologies, offering a variety of analytical advantages and cost savings , 44. TheElectrochemical biosensors have sparked a lot of interest in clinical diagnostics because of its key design advantages, assay simplicity, and higher analytical performance over traditional laboratory methods . In the When compared to traditional laboratory methods, this technology provides improved analytical results . PfHRP-2To fulfill the malaria Vision 2030 targets, timely accurate diagnosis and tailored treatment are critical in reducing the disease burden in Sub-Saharan Africa. Although most currently available diagnostic tools can detect symptomatic infections with high accuracy, a considerable portion of silent infections still go undiscovered. Furthermore, the sensitivity and specificity of these diagnostic tools vary greatly, which may help or hinder the extinction, eradication, and elimination of malaria globally. The rise in the number of asymptomatic malaria infections necessitates the development of technologies that can detect the parasites' enzymes and end products.Biosensors are extremely accurate, with the highest specificity and sensitivity, despite their low use. Despite the use of malaria RDTs and microscopy, it may be beneficial to prioritize biosensors in malaria diagnosis to reduce the malaria disease burden in Sub-Saharan Africa. Although the malaria disease burden is on the downward trend in Sub-Saharan Africa, lingering uncertainty persists and pockets of infection remain. It is crucial to avoid allowing complacency to take root. It is also instructive to take caution and make the case that using proper diagnostic technologies to improve the quality of malaria diagnosis is critical for Africa. Malaria researchers are faced with a difficult task in adopting new and improved diagnostic technologies to achieve a sustained decline, because history shows that malaria can and is sure to reemerge.


The OFDM chirp signal is suitable for MIMO radar applications due to its large time-bandwidth product, constant time-domain, and almost constant frequency-domain modulus. Particularly, by introducing the time-frequency structure of the non-linear frequency modulation (NLFM) signal into the design of an OFDM chirp waveform, a new OFDM-NLFM waveform with low peak auto-correlation sidelobe ratio (PASR) and peak cross-correlation ratio (PCCR) is obtained. IN-OFDM is the OFDM-NLFM waveform set currently with the lowest PASR and PCCR. Here we construct the optimization model of the OFDM-NLFM waveform set with the objective function being the maximum of the PASR and PCCR. Further, this paper proposes an OFDM-NLFM waveform set design algorithm inspired by alternating optimization. We implement the proposed algorithm by the alternate execution of two sub-algorithms. First, we keep both the sub-chirp sequence code matrix and sub-chirp rate plus and minus (PM) code matrix unchanged and use the particle swarm optimization (PSO) algorithm to obtain the optimal parameters of the NLFM signal’s time-frequency structure (NLFM parameters). Next, we keep current optimal NLFM parameters unchanged, and optimize the sub-chirp sequence code matrix and sub-chirp rate PM code matrix using the block coordinate descent (BCD) algorithm. The above two sub-algorithms are alternately executed until the objective function converges to the optimal solution. The results show that the PASR and PCCR of the obtained OFDM-NLFM waveform set are about 5 dB lower than that of the IN-OFDM. M transmitting/receiving antennas is shown in M signals transmitted by the M antennas are different. The echo of all transmitted signals are collected by each receiving antenna. MIMO diversity gain is achieved by an ideal separation of the echo. The filter mh, m = 1, 2, …, M is matched to the m-th transmit signal. The echo from the other M-1 transmitted signals causes correlation interference at the output of mh. A MIMO waveform set is orthogonal when the cross-correlation function between any pair of M transmit signals is zero. Although orthogonality is not achievable within a limited time-bandwidth product, a low cross-correlation is achievable. Furthermore, the sidelobe of the auto-correlation of the waveform set should be lowered to improve pulse compression performance. The research of the MIMO radar waveform design focuses on reducing the peak of auto-correlation function sidelobe and cross-correlation function within a limited time-bandwidth product [Multiple-input multiple-output (MIMO) radar uses waveform diversity techniques to improve power efficiency, clutter suppression ability, and other performances. A monostatic MIMO radar with  product ,4,5,6,7.There has been much research on the OFDM waveform for single-input single-output (SISO) radar systems. Initially, OFDM waveforms were used in multi-carrier FMCW radar systems . SubsequIn recent years, with the development of MIMO radar, there has been an increasing number of studies on designing OFDM waveform sets for MIMO radar systems. According to the different sub-carriers, the MIMO radar OFDM waveform sets are divided into two categories: LFM-OFDM ,18,19,20This paper develops the signal model and the optimization model of the OFDM-NLFM waveform set. The time-frequency structure of the OFDM-NLFM waveform set is constructed by sub-chirp signals with different time-width, center frequency, and chirp rates. After determining the number of transmitted signals of the OFDM-NLFM waveform set, the number of sub-bands, and other system parameters, an OFDM-NLFM waveform set is generated by the following steps. The first step is to initialize the NLFM signal’s time-frequency structure (NLFM parameters). Then, the time-frequency structure of the NLFM signal is divided into several sub-bands, or sub-pulses. Next, we replace the nonlinear frequency modulation curve in each sub-band with a linear frequency modulation curve and generate the piecewise chirp signal to approximate the NLFM signal. Finally, the sequence order of sub-chirp signals is permutated according to the sub-chirp sequence code matrix, and the sign of chirp rates is changed according to the sub-chirp rate plus and minus (PM) code matrix. Especially, each row of the sub-chirp sequence code matrix is a permutation that determines the order of the sub-chirp sequence for each transmit signal of the OFDM-NLFM waveform set, and each element of the sub-chirp rate PM code matrix is a 1-bit binary number that determines whether the sub-chirp signal is an up-chirp signal or a down-chirp signal. This paper defines the objective function as the sum of the cross-correlation functions considering that the correlation interferences from the other transmit signals are additive.To design the OFDM-NLFM waveform set with the lowest possible PASR and PCCR, this paper proposes an OFDM-NLFM waveform set design algorithm based on alternating optimization. There are both discrete code matrices and continuous NLFM parameters of the OFDM-NLFM waveform set. The expression between the objective function and the waveform parameters consists of time-frequency structure transformation and correlation function calculation. Hence, the OFDM-NLFM waveform set design problem is a high-dimensional optimization problem with a complex objective function and complicated optimization variables. Dimensionality reduction and decomposition are efficient solutions to a high-dimensional optimization problem with fast execution ,43,44. TThis paper develops the signal model and parameter optimization model of the OFDM-NLFM waveform set. A novel OFDM-NLFM waveform set design algorithm based on alternating optimization is proposed. With the same system parameters as the current optimal IN-OFDM waveform, the PASR and PCCR of the obtained waveform set are about 5 dB lower than that of the IN-OFDM. The rest of this paper is organized as follows. M antennas for transmitting and receiving. An OFDM-NLFM waveform set can be generated as follow. (1) construct the time-frequency structure of the piecewise chirp signal that approximates the origin NLFM signal. Note that the sub-chirp signals of the piecewise chirp signal have different sub-chirp rates and bandwidths. (2) minimize the cross-correlation function by adjusting the sub-chirps order in the time-frequency structure of the piecewise chirp signal. Therefore, the parameters of the OFDM-NLFM waveform set consist of two parts; for each transmit signal, one part is NLFM parameters that control the time-frequency structure, and another part is the sub-chirp sequence code matrix and the sub-chirp rate PM code matrix that controls the order of sub-chirp sequence and the sub-chirp rates, respectively.Consider a monostatic MIMO radar with M transmit signals of the OFDM-NLFM waveform set. Each signal of the OFDM-NLFM waveform set is generated by a time-imitating and sub-chirp rate transforming of a piecewise chirp signal. In this figure, the number of sub-pulses and sub-bands is set to N = 8. T represents pulse duration. Lf and Hf represent the highest and lowest signal frequency, respectively. The red curve on the left represents the time-frequency structure of the original NLFM signal, and the green broken line that approximates the red curve represents the time-frequency structure of the corresponding piecewise chirp signal. The values of f(t) of the NLFM signal by adding several sine function items whose amplitude is controlled by parameters [a and b are adjustable parameters. t and f represent the time and instantaneous frequency, respectively, T. One way to determine the segmentation points of the time-frequency curve is to set time domain segmentation points, which can be expressed asThe first step to constructing the signal shown in rameters . The timf(t) in Equation (1), expressed asf−1 represents the reverse function of f(t). Numerical methods can be adopted when the reverse function is difficult to solve. Equations (2) and (3) show that any two of the three, which are the NLFM parameters, time-domain segmentation points, and frequency-domain segmentation points, determine the time-frequency structure of the piecewise chirp signal. According to the obtained time-frequency structure, the time width, sub-chirp rate, and the carrier frequency of each sub-chirp signal can be determined. The time width isAnother way is to set frequency segmentation points at first. The segmentation points of the time-frequency curve are determined according to the reverse function of The sub-chirp rate isThe center frequency of each sub-band isThe sign of the sub-chirp rate is a vector defined asAccording to Equations (4)–(7), the complex expression of each sub-chirp signal isM transmit signals of the OFDM-NLFM waveform set. The sub-chirp sequence code that determines the order of the time-imitation is defined asn-th sub-chirp signal after the time-imitation equals the M transmit signals of the OFDM-NLFM waveform set isrect(x) is the rectangular window functionThe sub-chirp signals M transmit signals, we have the following waveform parameters:(1)M groups time-frequency structure curve parameters of the NLFM signal (NLFM parameters) can be defined asA total of (2)m-th transmit signal, the time domain segmentation points are M groups segmentation parameters can be defined asSegmentation points parameters in the time domain or frequency domain. The segmentation points and the NLFM parameters in Equation (12) determine the sub-chirp rate, center frequency, time width, and bandwidth of the sub-chirp signal in the sub-band. For the (3)M row vectors. The meaning of every element of the matrix can be seen in Equations (7) and (9). The two matrices can be defined in Equation (14), where The sub-chirp sequence code matrix and the sub-chirp rate PM code matrix. Both matrices are composed of For the OFDM-NLFM waveform set with m-th transmit signal of the MIMO radar OFDM-NLFM waveform set isAccording to the above three groups’ parameters, the expression of the M transmit signals, the aperiodic auto- and cross-correlation function for each pair of signals arei-th transmit signal of the waveform set. Ideal orthogonality means t and i in Equation (20), which is not achievable. In order to optimize the orthogonality and pulse compression performance of the OFDM-NLFM waveform set, the following indicators are optimized by the proposed OFDM-NLFM waveform set design algorithm.For a MIMO radar waveform set with w is the width of the auto-correlation mainlobe. PASR is the peak auto-correlation sidelobe ratio, and PCCR is the peak cross-correlation ratio. The waveform set performs better when its values are lower.In Equations (21) and (22), Based on the signal model of the OFDM-NLFM waveform set and MIMO radar correlation function performance indicators, this section establishes the optimization model for the OFDM-NLFM waveform set. The solution space of this optimization problem consists of the OFDM-NLFM waveform set parameters, including the NLFM parameters, the sub-chirp sequence code matrix, and the sub-chirp rate PM code matrix. Here we do not optimize the segmentation parameters. One reason is that the dimension of the optimization variable will be much higher if the segmentation parameters are included. Another reason is that it is tricky to optimize it using the existing continuous parameter optimization method, as the mapping between time-frequency segmentation parameters and the objective function is very complicated. Therefore, the optimization model established in this paper does not consider the time-frequency segmentation parameters.The solution space and optimization variables of the optimization problem are modeled asX is divided into three parts corresponding to the OFDM-NLFM waveform set parameters shown in Equations (12) and (14).The solution space Ω is composed of three independent sub-spaces, and its optimization variable F2 represents the mapping from the waveform set s(t) to the objective function. The two performance indicators, PASR and PCCR, are actually functions of s(t). F1 represents the mapping from the solution space Ω to the signal space S. According to the definition of the solution space and objective function, the OFDM-NLFM waveform set optimization problem of MIMO radar is modeled asX is an optimization variable satisfying the constraint conditions in Equation (23). X2 controls the sign of the sub-chirp rate in each sub-bands. X3 controls the sequence order of each sub-chirp signal. In short, the optimized objective function value X in the solution space Ω to the waveform set space S, and then mapping the obtained OFDM-NLFM waveform set to the objective function domain.According to the definition of the solution space and the OFDM-NLFM waveform set, the mapping from the solution space to the objective function domain is described asIn the above-mentioned MIMO radar OFDM-NLFM waveform set optimization problem, the solution space is of high dimension, and its objective function is complex. This paper proposes an OFDM-NLFM waveform set design algorithm based on alternating optimization. Especially, we divide the solution space into sub-spaces according to its characteristics, and the optimization problem in sub-spaces is viable to solve.M transmit signals, each of which has N sub-chirp signals, the optimization variable X includes three parts. X1 is NLFM parameters consisting of 2M continuous variables. X2 is a matrix consisting of MN binary bits, with 2MN different values. X3 consists of M permutations, with a total of (N!)M different values. Thus the dimension of the solution space Ω is very high, and the structure of the optimization variable is complex containing continuous, discrete variables and permutations. To map from the solution space to the objective function domain, we first map the solution space to the waveform, and then calculate the performance indicators of the MIMO waveform set. A difficulty is that the simple auto- and cross-correlation functions are not elementary functions, but instead the functionals of the waveform set. To conclude, the objective function does not possess good properties such as continuity and derivability. The OFDM-NLFM waveform set optimization problem defined in Equation (25) is an NP-hard constrained optimization problem with mixed discrete and continuous optimization variables. Its objective function is nonlinear, nonconvex, and non-differentiable. In this case, the common linear programming, continuous optimization, and convex optimization methods fail, and the other optimization design algorithms for the MIMO radar waveform set cannot apply to the OFDM-NLFM waveform set as they are limited to its specific waveform set.For an OFDM-NLFM waveform set with As the objective function is difficult to be simplified, the most direct approach is to separate discrete variables from continuous variables. Alternating optimization is a computational framework to solve high-dimensional optimization problems . AlternaThe optimization of the discrete variable of the OFDM-NLFM waveform set is an unconstrained combinatorial optimization problem. Since the dimension of the discrete variable is very large, it is not feasible to find the global optimal solution accurately. Thus, the CD algorithm can be used to solve the unconstrained optimization problem by conducting approximate minimization along the coordinate direction or in the coordinate hyperplane . This paThe intelligent optimization algorithm is a simple and direct solution for the continuous parameters of the OFDM-NLFM waveform set, considering that the objective function is very complex and difficult to be simplified and transformed. The PSO algorithm is a random search algorithm originally inspired by the foraging behavior of birds . It is fThe overall block diagram of the proposed OFDM-NLFM waveform set design algorithm is shown in Step 1: Select the initial NLFM parameters and construct M time-frequency curves according to Equation (1). The sub-chirp rate PM code matrix with MN elements is generated by random binary numbers. The sub-chirp sequence code matrix is generated by M random permutations. M is the number of transmit signals, and N is the number of the sub-chirp signals of each transmit signal.Step 2: After the parameter initialization step, the time domain segmentation parameters in Equation (13) should be set properly.Step 3: After the initial waveform has been obtained, keep the sub-chirp rate PM code matrix and sub-chirp sequence code matrix unchanged. Optimize NLFM parameters using the PSO algorithm and obtain the current optimal NLFM parameters. Before executing, the PSO algorithm, population size, the maximum number of iterations, and other parameters should be set properly.Step 4: Keep the current optimal NLFM parameters obtained in step 3 unchanged, sub-chirp rate PM code matrix and sub-chirp sequence code matrix are optimized based on the BCD algorithm. Firstly, the matrix to be optimized is divided into several small blocks. Secondly, each block is optimized in turns with other blocks unchanged. Finally, the current optimal sub-chirp rate PM code matrix and sub-chirp sequence code matrix are obtained.Step 5: If the difference between the objective function values before and after step 3 and step 4 is lower than the threshold, the optimization algorithm converges. Output the current optimal NLFM parameters, sub-chirp rate PM code matrix, and sub-chirp sequence code matrix. Otherwise, jump to step 3 and continue.The specific implementation of the above steps is described in the following. M transmit signals is tested under several groups of random initialed sub-chirp rate PM code matrices and sub-chirp sequence code matrices. For each group of the matrices, the test goes through all the values of NLFM parameters with The principle of the parameter initialization for the OFDM-NLFM waveform set is to make the peak of the auto-correlation function sidelobe low. The NLFM parameters in Equation (12) affect the peak of the auto-correlation function sidelobe, measured by the PASR in Equation (21). In order to help select the appropriate initial values of the NLFM parameters, this section traverses the parameter value in a certain range to test its impact on the PASR. The PASR is also affected by the sub-chirp rate PM code matrix and sub-chirp sequence code matrix. Therefore, in this section, the PASR of M = 1 in Equations (12) and (21) because a bigger M is not necessary when PCCR is not analyzed. The PASR under two different groups of sub-chirp rate PM code matrices and sub-chirp sequence code matrices are shown in Set M. Thus the particle dimension is 2M. The interval of the NLFM parameters is k-th iteration of the alternating optimization. Note that they are the initial solution described in Equation (26) when k = 0. Since the PSO algorithm is executed iteratively, the initial population of the k+1-th iteration should include the optimal NLFM parameters in the k-th iteration. Thus, the population of the k+1-th iteration is initialized asq is the number of particles. k-th execution of the PSO. The rest of the particles are initialized by random numbers. Initializing the population in this way ensures the current best solution to be passed to the next generation. After the k+1-th PSO algorithm has been executed, the optimal solution can be expressed asG-th generation population after the k+1-th execution of PSO. G is the maximum generation of the PSO algorithm. Every time after initializing the OFDM-NLFM waveform set parameters or executing of one iteration of alternating optimization, the PSO algorithm is used to optimize the NLFM parameters. According to Equation (23), the number of NLFM parameters is 2k-th execution of the sub-algorithm. These two current optimal values are also the initial solutions of the k+1-th optimization. The optimization algorithm based on BCD proposed in this section is mainly built on the coordinate descent algorithm. The two code matrices are divided into smaller blocks. Each time we update one element or one block of the matrix, the values of the other elements are fixed. Considering that each row of the matrix corresponds to each transmit signal of the OFDM-NLFM waveform set, the above two matrices can be divided into M blocks according to different rows.After obtaining current optimal NLFM parameters by the PSO, the next step of the OFDM-NLFM waveform set design algorithm is to optimize the sub-chirp rate PM code matrix and sub-chirp sequence code matrix. This task is an unconstrained combinatorial optimization problem that can be expressed asThe blocks of the above-mentioned matrix are divided as shown in Equations (32) and (33). In the BCD algorithm, one row of the matrix is updated with the rows corresponding to the other transmit signals unchanged. Each row of the matrices is updated alternately. Therefore, the optimization algorithm proposed in this paper is composed of the two layers of loops:Outer loop: Update the m-th row vector M-th row of the matrices are updated alternately until the objective function converges.Inner loop: Update the n-th column of the row vector N-th columns are updated alternately.i-th outer loop isN. NG is the set of all possible permutations of 1, 2, …, N. The number of the element of the set is N! and the optimization solution is expressed asi+1-th iteration. In the next iteration, we change the value of m and optimize another row of the sub-chirp rate PM code matrix and sub-chirp sequence code matrix. The process of the outer loop can be summarized in Algorithm 1.Algorithm 1: Block coordinate descent algorithm for X2 and X3Input: Initial solution Output: Optimal solution Step 1: m = 1, i = 0, Step 2: Update the m-th row vector Algorithm 2 whose output is the optimal row vector Step 3:Step 4: i = i + 1. If m = 1, otherwise m = m + 1.Step 5: Calculate the objective function using Therefore, the original high-dimensional optimization problem shown in Equation (30) is equal to solving several low-dimensional optimization problems. The optimization variable after the j be iteration counter, and the row vector after the j+1-th iteration can be expressed asThe inner loop calculates the optimal value of j continues to increase until the value of the objective function converges, and the obtained optimal solution is M permutations, the exchange operation of the row vector or the permutation is defined as followsa-th and b-th column of the row vector. According to the definition of the exchange operator, the update algorithm of the row vector can be summarized as in Algorithm 2.Algorithm 2: The update algorithm for the m-th row of X2 and X3Input: Current optimal row vectors Algorithm 1.Output: Optimal row vectors Step 1: n = 1, j = 0, Step 2: Exchange every columns with the n-th column of N new row vectors is obtained as Step 3: Calculate N objective function N new row vectors. Select the minimum objective function l-th value is lowest) and its corresponding row vector is optimal solution expressed as Step 4: The value of the n-th column of Step 5:j = j + 1. If n = 1, otherwise n = n + 1.Step 6: Calculate the objective function using When m = 1, 2, 3, 4.This section designs the OFDM-NLFM waveform set using the proposed algorithm based on alternating optimization in The correlation function of the IN-OFDM waveform is shown in Under the same setting, the sub-chirp rate PM code matrix and sub-chirp sequence code matrix obtained by the proposed OFDM-NLFM waveform set design algorithm are shown in This section analyzes the influence of different random initial values on the final design result. This section randomly generates 10 groups of sub-chirp rate PM code matrix and sub-chirp sequence code matrix as the initial solution for the proposed optimization algorithm. We calculate the MIMO waveform set correlation function indicator under the same radar system parameters in It can be seen from Here we keep the radar system parameters unchanged, as shown in M and the number of sub-bands N, respectively. The bandwidth, pulse width, and sampling frequency are still the same as in M to {2,3,4}, and the number of sub-bands N to {2,3,4,5,6,7,8}. The time-frequency segmentation parameters are set to a uniform segmentation. The indicator values of the waveform set obtained by the proposed algorithm are shown in M and N, and M and N. From the results in the previous section, it is known that the proposed algorithm is less affected by the initialization. The following results are obtained by a single test. It can be seen that the larger the M, the larger the PASR and PCCR, which match the common facts. However, when M = 2, N = 2, PCCR is the lowest. No matter how large the N is, it seems that there cannot be a lower cross-correlation function peak. The reason is that the cross-correlation function between an up-chirp signal and a down-chirp signal is the lowest. When M > 2, the larger the N is, the lower the peak value of the cross-correlation function. Furthermore, the PASR and PCCR will increase rapidly as M increase. The design freedoms of the OFDM-NLFM waveform set are low when M is larger than 4.In order to analyze the result of the proposed OFDM-NLFM waveform set design algorithm based on alternating optimization under different MIMO system parameters, this section changes the number of transmit signals This paper constructs a signal model of the MIMO radar OFDM-NLFM waveform set, and the task is modeled as a complex NP-hard nonlinear and non-convex optimization problem. To solve this optimization problem, we propose an OFDM-NLFM waveform set design algorithm based on alternating optimization. The proposed algorithm uses the PSO algorithm to optimize the continuous NLFM parameters and uses the BCD algorithm to optimize the discrete sub-chirp rate PM code matrix and sub-chirp sequence code matrix. The two sub-steps are executed alternately until the objective function converges. The numerical simulation results show that the OFDM-NLFM waveform set designed by the proposed optimization algorithm is better than that of currently the best IN-OFDM waveform set. The value of PASR is 4.03 dB lower than that of the IN-OFDM waveform set. The PCCR is 5.2845 dB lower than that of the IN-OFDM waveform set. In addition, we also compare the obtained waveform set under different time-domain segmentation parameters and MIMO radar system parameters. Results show that the time-domain segmentation parameters have an influence on the results, and therefore a proper selection is required.


One of the generally accepted constructs of dengue pathogenesis is that clinical disease severity is at least partially dependent upon plasma viremia, yet data on plasma viremia in primary versus secondary infections and in relation to clinically relevant endpoints remain limited and contradictory.Using a large database comprising detailed clinical and laboratory characterization of Vietnamese participants enrolled in a series of research studies executed over a 15-year period, we explored relationships between plasma viremia measured by reverse transcription–polymerase chain reaction and 3 clinically relevant endpoints—severe dengue, plasma leakage, and hospitalization—in the dengue-confirmed cases. All 4 dengue serotypes and both primary and secondary infections were well represented. In our logistic regression models we allowed for a nonlinear effect of viremia and for associations between viremia and outcome to differ by age, serotype, host immune status, and illness day at study enrollment.Among 5642 dengue-confirmed cases we identified 259 (4.6%) severe dengue cases, 701 (12.4%) patients with plasma leakage, and 1441 of 4008 (40.0%) patients recruited in outpatient settings who were subsequently hospitalized. From the early febrile phase onwards, higher viremia increased the risk of developing all 3 endpoints, but effect sizes were modest (ORs ranging from 1.12–1.27 per 1-log increase) compared with the effects of a secondary immune response . The associations were consistent across age, serotype, and immune status groups, and in the various sensitivity and subgroup analyses we undertook.Higher plasma viremia is associated with increased dengue severity, regardless of serotype or immune status. From the early febrile phase onwards, higher plasma viremia increases the risk of subsequent progression to adverse dengue outcomes, with little evidence for effect modification by potential confounders. However, the effect sizes are modest compared with a secondary immune response. In 2019 dengue, the most common mosquito-borne viral pathogen of humans, was identified by the World Health Organization (WHO) as 1 of the top 10 threats to global health [Most symptomatic infections result in a self-limiting nonspecific viral syndrome, but a small minority of patients experience more severe manifestations. Of particular concern is a vasculopathy that causes plasma leakage and may progress to potentially fatal dengue shock syndrome (DSS) , 4. All Most complications manifest during a critical period approximately 4–6 days from symptom onset, as both fever and viremia are resolving , supportThus, although the generally accepted constructs of dengue pathogenesis indicate that disease severity is related to/dependent upon higher plasma viremia, the available evidence is limited and inconsistent. To further explore associations between plasma viremia measured during the febrile phase and final clinical severity, we used a large database comprising detailed clinical and laboratory characterization of several thousand DENV infections among Vietnamese participants enrolled in a series of research studies over a period of 15 years.The study population was derived from 4 prospective observational studies performed in Ho Chi Minh City, Vietnam . BrieflyLaboratory-confirmed dengue was defined by detection of either DENV-RNA, using reverse transcription–polymerase chain reaction (RT-PCR), or dengue NS1 antigen . The RT-We selected 3 clinically relevant endpoints of interest—severe dengue, plasma leakage, and hospitalization . The defP values. We also present the effects of the various factors on the probability of occurrence of each endpoint using the parameter estimates from the logistic regression models.All viremia values were transformed to the base-10 logarithm (log-10). We used logistic regression for all analyses (Supplementary Appendix 4). We used interaction terms to allow relationships between viremia and the study endpoints to differ with the following potential confounders: serotype, immune status, age, and illness day. Preliminary exploration of the data indicated no influence of sex on viremia. We allowed for nonlinear effects of log-10 viremia and age by using restricted cubic splines with knots at the 10th, 50th, and 90th percentiles . ResultsSince the 4 studies differed in design, the pattern of missing data varied. For all 3 endpoints, the primary analysis is a complete-case analysis , excludiFinally, we performed sensitivity analyses to investigate whether the viremia effects differed by (1) the RT-PCR method used and (2) outpatient versus inpatient status at study enrollment. For these analyses we included each variable in the models as a main effect and with an interaction with viremia.All analyses were performed using the statistical software R version 3.4.4  and the A total of 5686 laboratory-confirmed dengue cases were identified from the 4 studies, with 44 subsequently excluded . BaselinThe frequency of the 3 endpoints differed by study , again rThe 3 endpoints were observed much less frequently in the 10 064 individuals for whom dengue was not laboratory confirmed: 0.9% severe dengue, 2.6% plasma leakage, and 21.3% hospitalized . AlthougPlasma viremia levels by illness day at enrollment, serotype, and immune status are shown in From the early febrile phase onwards, higher viremia increased the risk of developing all 3 endpoints—severe dengue, plasma leakage, and hospitalization—in each case with a modest but clearly apparent effect . These eIn the complete-case analysis, the risk for severe dengue, plasma leakage, and hospitalization differed by serotype. The risk for all endpoints was highest for DENV-2 and lowest for DENV-3 infections. For DENV-4, the risks for severe dengue and hospitalization appeared to be borderline higher than for DENV-1 but similar to DENV-3 for plasma leakage . ProbablThe imputed-data analysis showed largely similar results to the complete-case analysis . Higher The models including RT-PCR method or inpatient/outpatient status at enrollment showed similar effects to the main models, with higher viremia increasing the risk for all outcomes in analyses both with and without imputation of missing data . StudiesIn this pooled analysis of 5642 Vietnamese dengue cases we have clearly demonstrated that higher viremia levels during the febrile phase increase the risk for development of vascular leakage and severe dengue as well as the risk of hospitalization. Relative differences were apparent between the serotypes and in the different immune status groups, but the probability of occurrence for all 3 endpoints increased in line with log-10 plasma viremia, with generally consistent findings between the complete-case and imputed-data analyses and in the sensitivity analyses. However, the effect sizes were modest compared with the effects of a secondary immune response, and we did not identify a particular threshold associated with disease progression.Previous efforts to explore relationships between plasma viremia and dengue severity have provided somewhat contradictory findings , 18–25. As expected, we identified secondary immune status as a strong independent predictor of all adverse outcomes. Yet, it is notable that day-specific viremia levels were consistently lower in probable secondary than in probable primary cases, as has been reported previously , 19, 21.Consistent with previous research, the study also confirms that DENV-2 carries the greatest risk of adverse outcomes despite manifesting the lowest daily viremia levels and that viremia is typically highest for DENV-1 , 32, 33.One limitation that could affect the overall generalizability of the findings relates to the age range of the study participants. The focus of the 4 contributing studies was on pediatric dengue, and only study D enrolled adults, who formed less than 15% of the whole study population. We were careful to apply uniform definitions for the 3 endpoints across all 4 studies, but the majority of those classified as severe dengue had DSS  and few cases with severe bleeding or severe organ impairment were identified. Age-related physiological factors increase the risk for DSS in children compared with adults, while intrinsically lower normal platelet ranges and a greater likelihood of pre-existing organ dysfunction mean that bleeding and organ impairment are more likely to occur in older age groups . Thus, wAnother potential limitation involves the use of 2 different RT-PCR techniques. However, our sensitivity analysis showed consistent associations between viremia and adverse outcomes across both methods, indicating that the associations are robust. In line with this, NS1-positive but RT-PCR–negative individuals had a low risk of all outcomes, comparable to individuals with documented low-level viremia. Undoubtedly, some other study participants did have dengue but were both RT-PCR and NS1 negative and thus were not included in this analysis; however, the proportion of missed cases in this very large study is likely to be small.We also acknowledge that retrospective pooling of data from 4 studies led to some heterogeneity and to issues with missing data for certain important variables. However, we overcame these issues through multiple imputation using the MICE method, a well-recognized system for dealing with missing data, and the similarities between the results of the complete-case and imputed-data analyses provide strong reassurance that the overall findings are robust. In real-world practice, to undertake a prospective study of this size with uniform data collection and laboratory evaluations would be extremely difficult, and in fact, the heterogeneities between the studies, taken together with our sensitivity analyses, serve to reinforce the broad generalizability of the results. Although this is the most comprehensive analysis of this nature ever undertaken, in an ideal world additional data from other ethnic groups and/or lower transmission settings where the elderly (a high-risk group missing from our dataset) remain susceptible to dengue would be used to establish conclusively that the findings are generalizable to other populations.In conclusion, following conflicting results from less-comprehensive studies, this work provides clear evidence that, regardless of the infecting serotype or the host’s immune status, higher plasma viremia increases the risk for clinically relevant adverse outcomes associated with dengue infection. In addition to the relevance for pathogenesis studies, this knowledge could be utilized to develop risk-prediction algorithms aimed at enrolling individuals at increased risk of complications in research studies. Additionally, confirmation of a definite relationship between viremia and disease severity reinforces the rationale for targeting viremia as an endpoint in therapeutic intervention trials aimed at preventing and/or treating severe dengue.Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at ciaa1840_suppl_Supplementary_MaterialsClick here for additional data file.


In the entire group of 70 children serum periostin concentration correlated negatively with peripheral, central, and ambulatory blood pressure, as well as with aortic pulse wave velocity (aPWV). In multivariate analysis, periostin level significantly correlated with age , −0.831–−0.398]), uric acid , body mass index (BMI) Z-score , high-density lipoprotein (HDL)-cholesterol , and triglycerides . Neither the presence of hypertension nor blood pressure and aPWV influenced periostin level. To conclude, the role of serum periostin as a biomarker of elevated blood pressure and arterial damage in pediatric patients with primary hypertension is yet to be unmasked. Age, body mass index, uric acid, and lipid concentrations are key factors influencing periostin level in pediatric patients.Experimental studies suggest that periostin is involved in tissue repair and remodeling. The study aimed to evaluate serum periostin concentration as potential biomarker in pediatric patients with primary hypertension (PH). We measured serum periostin, blood pressure, arterial damage, biochemical, and clinical data in 50 children with PH and 20 age-matched healthy controls. In univariate analysis, children with PH had significantly lower serum periostin compared to healthy peers (35.42 ± 10.43 vs. 42.16 ± 12.82 [ng/mL],  Arterial hypertension (AH), one of the most common diseases worldwide, is a recognized risk factor for renal and cardiovascular diseases . Its preEvaluation of arterial stiffness and common carotid artery intima-media thickness (cIMT) are included in the assessment of hypertension-mediated organ damage in children ,6. IncrePeriostin is a matricellular protein, a member of the fasciclin family . It is pWe hypothesize that serum periostin could serve as a useful biomarker in hypertensive pediatric patients. Hence, the aims of our study were: 1. to compare serum periostin level in pediatric patients with primary hypertension and their healthy peers; 2. to reveal the relation between blood pressure , arterial damage  and serum periostin 3. to reveal other significant determinants of serum periostin 4. to test serum periostin as a potential biomarker of subclinical arterial damage in children and adolescents with PH. p = 0.05, and effect size 0.55 should be ~50 [Sample size was estimated based on available literature on periostin with statistical power 0.8, d be ~50 ,33,34,35Approval of the Bioethical Committee of the Medical University of Warsaw was obtained before the initiation of the research . All procedures involving human participants were in accordance with the highest ethical standards of the institutional research committee and were performed in accordance with the Declaration of Helsinki on the treatment of human subjects and its later amendments. Informed consent was obtained from all participants (≥16 years) and their representatives included in the study.2). These measurements were compared with Polish normative data and expressed as Z-score [In all patients upon admission, we assessed basic anthropometric parameters including height (cm), weight (kg), and body mass index (BMI)  , Biovendor, Brno, Czech Republic) and the results were read using Biochrom Asys UVM 340 Scanning Microplate Reader .2) [Other parameters measured from peripheral blood in all children included: number of neutrophils , lymphocytes , platelets , mean platelet volume , and neutrophil-to-lymphocyte as well as platelet-to-lymphocyte ratios , serum creatinine (mg/dL), uric acid (mg/dL), total, low density lipoprotein (LDL), high-density lipoprotein (HDL) cholesterol (mg/dL), and triglycerides (mg/dL), calcium (mg/dL), phosphate (mg/dL), parathormone , alkaline phosphatase (IU/L), and vitamin D concentration (25(OH)D) (ng/mL). Glomerular filtration rate was estimated according to the revised Schwartz formula (eGFR) (mL/min/1.73 m2) .2/kPa), AIx (augmentation index) (%), PWVbeta (pulse wave velocity beta) (m/s), D_max (mm), D_min (mm) , DATmax  (ms). aPWV and cIMT were presented as numeric values and Z-scores [In all patients with PH, the duration of hypertension was estimated based on medical records and in those receiving pharmacological treatment antihypertensive medications were analyzed. The BP measurement methodology and arterial damage assessment were described in detail in our previous manuscripts ,40,41,42Z-scores ,46.t-test, U Mann–Whitney test, Spearman rank correlation, chi-square test, and Fisher’s exact test. Multivariate analysis was performed using a general regression model. Parameters that correlated with each other with r >0.600 were excluded from the regression model to avoid collinearity. A p-value below 0.05 was considered as statistically significant.Statistical data were analyzed using Dell Statistica 13.0 PL software . All data were reported as absolute numbers, mean ± standard deviation (SD), and interquartile range (IQR). Normality of data distribution was analyzed with the Shapiro–Wilk test. The following tests were used (depending upon variables’ distribution): Student p = 0.038). Children with PH were characterized also by significantly higher weight, BMI, concentrations of uric acid, and triglycerides. HDL-cholesterol levels were significantly lower in the study group compared to healthy children. In the PH group 21 (42%) of all patients were overweight and 7 (14%) were obese. The duration of hypertension in the study group was 18.14 ± 20.86 (4–24) (months). At the moment of evaluation, 27 (54.0%) children received pharmacological antihypertensive treatment—23 were on monotherapy, 3 were treated with 2 medications, and one child received 3 medications. Most commonly used agents were as follows: calcium channel blockers (17 children), angiotensin-converting enzyme inhibitors (9 children), and beta-adrenolytics (3 children). In addition, 3 children were treated with doxazosin, valsartan, and hydrochlorothiazide. Parameters of complete blood count and calcium-phosphate metabolism were presented in Both groups’ basic clinical parameters and results of key laboratory tests, including periostin levels, were depicted in The comparison of peripheral and central blood pressure, arterial stiffness, and intima media thickness in the study and control group are shown in p = 0.004), thicker common carotid artery intima media layer (0.45 ± 0.07 vs. 0.39 ± 0.03 (mm), p < 0.001), and larger common carotid artery  diameters. No differences were found in aortic augmentation index, subendocardial viability ratio, and local stiffness parameters.Children and adolescents with PH had higher office peripheral and central blood pressures, as well as blood pressure measured with ABPM. There was no difference in 24 h ABPM heart rate and pulse pressure (PP), and aortic pulse pressure (AoPP) and both systolic and diastolic nocturnal BP dipping between both groups. Patients with PH were characterized by faster aortic pulse wave velocity (5.17 ± 0.93 vs. 4.49 ± 0.72 (m/s), p = 0.954) and between 10 PH patients treated and 40 patients not treated with renin-angiotensin-aldosterone system inhibitors (34.96 ± 6.47 (30.77–37.41) vs. 35.54 ± 11.27 (27.42–40.20) (ng/mL), p = 0.602). Additionally, no difference between hypertensive boys and girls was found (34.89 ± 9.86 (28.80–37.89) vs. 36.00 ± 11.36 (27.22–41.10) (ng/mL), p = 0.954).Periostin level did not differ between PH patients either on or off antihypertensive drugs (34.89 ± 9.86 (28.80–37.89) vs. 36.00 ± 11.36 (27.22–41.10) (ng/mL), The correlations of periostin with clinical and laboratory parameters in the whole group of 70 children are shown in To verify independent associations between periostin, blood pressure, and arterial stiffness, multivariate analysis was performed . As showOur cross-sectional study analyzed periostin as a possible biomarker of blood pressure and subclinical arterial damage in pediatric patients with primary hypertension. Univariate analysis showed that periostin level was lower in hypertensive individuals as compared to the control group. Moreover, it revealed numerous negative correlations between periostin level, blood pressure, and arterial damage parameters. Nevertheless, these relations disappeared in multivariate analysis leaving only the following significant predictors of periostin: age, BMI Z-score, uric acid, HDL-cholesterol, and triglycerides. In multivariate analysis, neither the presence of hypertension nor blood pressure significantly influenced periostin level. Periostin was found to be involved in tissue repair after vascular injury, e.g., in acute rheumatic fever , hyperteIt is noteworthy that elevated expression of periostin in the arterial wall was found in a hypoxia-induced model of pulmonary hypertension . MoreoveInterleukins (ILs) 3, 4, 6, 13, tumor necrosis factor alpha (TNFα), TGFβ, and vascular endothelial growth factor (VEGF) are the best known inducers of periostin expression and release . In partActivation of the renin–angiotensin–aldosterone system could be a link between periostin and cardiovascular system regulation. One study showed increased periostin levels in rats in response to chronic infusion of angiotensin II . Other pAge was the strongest predictor of periostin level in our cohort. Limited studies showed decrease in periostin levels with age ,35. SimiEpidemiological studies show an association between cardiovascular diseases, hypertension, metabolic syndrome, and high levels of uric acid. In addition, some data suggest that high uric acid levels can predict the development of hypertension . Animal 2 according to Schwartz’s formula, which is known to underestimate glomerular filtration rate in adolescents [In univariate analysis, serum periostin inversely correlated with serum creatinine, which could seemingly indicate the dependence of this marker on renal function. However, it should be noted that patients in both groups had normal renal function . Serlescents . On the lescents  and chrolescents . Also, ilescents .The results of our multivariate analysis demonstrated that periostin level is positively correlated with HDL-cholesterol and negatively with triglyceride concentration as well as with BMI Z-score. HDLs are characterized by a well-established protective effect on the arterial wall and their negative correlations with cardiovascular incidents are well documented . There iThe cross-sectional nature of the study that precludes drawing final casual relationships between the measured parameters is a major limitation of our study. A low number of patients in the control group is another disadvantage. The large heterogeneity in age and BMI of the patients resulted in the demonstration of numerous correlations in univariate analysis that proved to be statistically insignificant in multivariate analysis . Of note, our analysis of immune system activation was limited only to low-precision parameters derived from peripheral complete blood count. In addition, we have not evaluated concentrations of other key players in pathogenesis of tissue damage and repair e.g., TGFβ. Finally, neither urine nor tissue  periostin levels were analyzed in the studied children. Of note, deep analysis of blood pressure and general and local (carotid artery) vascular damage are particular strengths of the study.The role of serum periostin as a biomarker of elevated blood pressure and arterial damage in pediatric patients with primary hypertension is yet to be unmasked. More clinical studies are needed to reveal the changes of periostin in hypertensive patients’ serum and urine, and to clarify its role in this population. Age seems to be the strongest predictor of serum periostin level in pediatric population. We have not found any significant dependences of serum periostin with blood pressure and arterial damage analyzed according to different aspects. On the other hand, there was a significant relation of serum periostin with well-established cardiovascular risk factors. Considering young age of our patients and relatively short duration of PH as compared to adults, it is possible that these correlations have not markedly influenced blood pressure and target organs in children with PH yet.