The relationship between malnutrition and malaria was examined in a cohort of 6–59 month-old children in rural Gambia, in an area of seasonal malaria transmission. The study used data from a clinical trial in which a cohort of children was established and followed for clinical malaria during the 2011 transmission season. A cross-sectional survey to determine the prevalence of malaria and anaemia, and measure the height and weight of these children was carried out at the beginning and end of the transmission season. Standard anthropometric indices  were calculated using At the beginning of the transmission season, 31.7% of children were stunted, 10.8% wasted and 24.8% underweight. Stunting was more common in Fula children than other ethnicities and in children from traditionally constructed houses compared to more modern houses. Stunted children and underweight children were significantly more likely to have mild or moderate anaemia. During the transmission season, 13.7% of children had at least one episode of clinical malaria. There was no association between stunting and malaria incidence . Malaria was not associated with differences in weight or height gain.Chronic malnutrition remains a problem in rural Gambia, particularly among the poor and Fula ethnic group, but it was not associated with an increased risk of malaria.ISRCTN01738840, registered: 27/08/2010 (Retrospectively registered).Trial registration: ISRCTN,  In sub-Saharan Africa (SSA), children under five years are the most vulnerable to malaria and malnutrition , 2. Despvice versa is difficult to elucidate and studies have shown contradictory results. For example, some studies have demonstrated an increased risk of malaria associated with malnutrition  using spectrophotometers , and measure weight and height. The weight of each child was measured by two fieldworkers using hanging scales that were recalibrated frequently. Children under 2 years-old had their recumbent height measured, whilst older children were measured standing using a stadiometer (Leicester Portable Height Measure). Socio-demographic factors such as gender, ethnicity, housing construction , village, bank of the river the village was located on, use of an LLIN the previous night and study arm (house treated with IRS or not) were collected at the first survey.This sub-study obtained additional written consent from the caregivers of children aged 6–59 months to collect anthropometric measures during the second year (2011) of the trial. Malaria episodes during the transmission season were identified by passive case detection. Children were surveyed at the beginning (June 2011) and end (January 2012) of the transmission season to determine the prevalence of z-scores) below the WHO growth standard reference mean. Z-scores of more than 4 or less than -4 were excluded. Mild, moderate and severe anaemia were defined as a haemoglobin level of less than 11, 8 and 5 g/dl, respectively. Houses were said to be traditional if more than 50% of the rooms had mud walls and a thatched roof, and modern if otherwise.Clinical malaria was defined as an axillary temperature of ≥ 37.5 °C and/or a history of fever in the past 48 h, and a positive rapid diagnostic test . After a malaria episode, the child was not considered at risk of malaria for the next 28 days. Anthropometric indices were expressed in relation to the World Health Organization (WHO) child growth standards  and werez-scores calculated against WHO child growth standards are only valid up to this age).The dataset was restricted to children present at both cross-sectional surveys. Children who were 5 years or older were excluded from the analysis of the second cross-sectional survey .The prevalence of A total of 2527 children aged between 6 and 59 months were surveyed at the first cross-sectional survey. The mean age of enrolment was 2.79 years (interquartile range = 2.20) and children were predominantly of Fula (42.5%) or Mandinka (47.7%) ethnicity Table . The triP = 0.004; underweight = 24.8%, P < 0.001) (Table P < 0.001), while the mean haemoglobin level was significantly lower at the end  than at the beginning of the transmission season . Prevalence of mild anaemia was the same between surveys (both 58.4%), though moderate anaemia was significantly higher at the end of the transmission season . No severe anaemia cases were found at the beginning of the transmission season, while 6 children (0.2%) were severely anaemic at the end.Comparing clinical characteristics of the children between the two cross-sectional surveys, children were significantly less likely to be wasted or underweight at the end of season survey  compared to the baseline survey . Similarly, the mean age was 32.7 months (95% CI: 31.6–33.9) for underweight children and 34.4 months (95% CI: 33.7–35.1) for non-underweight ones (P = 0.02). There was no significant difference in the mean age of stunted  and non-stunted children . Ethnicity was strongly associated with malnutrition, with Fula children more likely to be stunted  and underweight  than Mandinka children (Table P < 0.001). Living in a traditional house was not significantly associated with wasting or underweight.During the first survey, children that were wasted or underweight were significantly younger. The mean age was 30.0 months (95% CI: 28.2–31.9) for wasted children and 34.4 months (95% CI: 33.8–35.1) for non-wasted ones (P < 0.001) and were significantly more likely to be mildly or moderately anaemic . Children that were malnourished at the baseline survey were no more likely to be parasitaemic than non-malnourished children, with about 5% of children parasitaemic, irrespective of malnutrition status. At the end of season survey, stunted, wasted and underweight children had a significantly lower mean haemoglobin level than their well-nourished counterparts, and stunted and underweight children were significantly more likely to have mild or moderate anaemia. Parasitaemia prevalence at the end of the transmission season was similar between stunted and non-stunted, wasted and non-wasted and underweight or non-underweight children.Children stunted at the baseline survey had a significantly lower mean haemoglobin level  than non-stunted children . With every year of age gained, children had 1.4 times the odds of clinical malaria  and those living on the north bank of the river had approximately half the odds of malaria compared to those living on the south bank . There was some indication that Mandinka were more likely to have malaria than Fula children, although the result was of borderline significance . Sleeping under an LLIN and in a house that received IRS were not found to be associated with malaria risk.During the transmission season, 13.7% of children 347/2527) in the cohort experienced at least one clinical malaria episode. Most of these episodes occurred in October (39%) and November (34%). In bi-variable analysis, stunting appeared mildly protective against clinical malaria  (Table 27 in theP = 0.8). Similarly, there was no difference in height gain between children that had and those that did not have malaria . There was a reduction in mean haemoglobin concentration of 0.17 g/dl (95% CI: 0.11–0.22) among children who did not have malaria and 0.20 g/dl (95% CI: 0.07–0.33) in children who suffered from at least one malaria episode. After adjusting for confounders and clustering, children with malaria experienced significantly higher loss in haemoglobin concentration compared to children who did not have malaria over the transmission season .The mean weight gain between the two surveys among children who had at least one episode of clinical malaria  was similar to those that did not have malaria  , was common in this cohort of children, with a third of them stunted at the end of the malaria transmission season. This corroborates the results of the 2010 Multiple Indicator Cluster Survey (MICS) which shows a 35.6% stunting prevalence in the URR . SimilarOur analysis identified several risk factors for malnutrition in the URR including ethnicity and poor housing, the latter finding suggesting that, as would be expected, chronic malnutrition is associated with poverty , 38. FeeSimilar to other surveys in The Gambia , 38, 40,Stunting and underweight were strongly associated with anaemia at both the beginning and end of the malaria transmission season. This is an important finding since anaemia is a major cause of mortality in children aged under five years of age . The aetP. falciparum infection at either cross-sectional survey, nor was there an association between malnutrition  at the baseline survey and malaria incidence during the transmission season. This finding is supported by a systematic review of observational studies which concluded that there was no association between anthropometric parameters and malaria incidence [There was no association between malnutrition and ncidence . Althougncidence  were posncidence , includincidence .There was indication of a higher clinical malaria incidence in Mandinka compared to Fula children, although the result was of borderline significance. Previous studies have shown conflicting results with Greenwood et al.  reportinP. falciparum and its associated growth suppressing immune response which could have led to a lack of differences between the two groups of children. Lastly, changes in feeding or other health behaviours directed towards children who were sick with malaria in order to aid their recovery may also have impacted on their weight or height gain between the two surveys. Children who had malaria during the transmission season showed significantly greater reductions in haemoglobin concentration than those who did not suffer from malaria. This was unsurprising since blood stage infection with malaria parasites induces anaemia. The reduction in haemoglobin concentration over the transmission season also seen among children not suffering from malaria suggests possible nutritional deficiency or inflammation/infection, including perhaps asymptomatic malaria infection [Malaria did not have any influence on weight and height gain during the malaria transmission season. Two recent systematic reviews concluded that there was no association between malaria and malnutrition , 25. Hownfection .Observational study designs such as that employed are limited in the extent to which we can determine causal relationships due to possible biases and residual confounding. The study did not measure factors such as diet, micronutrient status and other infections such as diarrhoea which can confound the relationship between malaria and stunting , 54. TheWhile stunting was not a risk factor for malaria in this study, the high levels of stunting and anaemia identified, particularly among the Fula, require urgent attention. Poverty leading to insufficient food or lack of variety of food, poor status of women, absence of a support network, and alternative cultural beliefs about the origin and treatment of malnutrition have been identified as contributing to poor child nutrition practices in The Gambia . Malnutr


Microscopic diagnosis of Giemsa stained thick and thin blood films by skilled microscopists has remained the gold standard laboratory method for the diagnosis of malaria. However, there is a scarcity of qualified laboratory professionals for correctly diagnosing malaria using microscopy. The aim of this study was to evaluate the performance of laboratory professionals on malaria microscopy at health facilities in Bahir Dar city administration, Northwest Ethiopia.A cross-sectional study was conducted from January to March 2017 in Bahir Dar City. A total of 87 laboratory professionals participated in the selected health facilities, with a response rate of (100%). Standardized pre-validated slide panels and questionnaire were distributed to laboratory professionals by the principal investigator. The panel slides were comprised of 5 positives and 3 negative blood films. The laboratory professionals were requested to report the parasite density using semi-quantitative (+) and per micro-liter methods. Their performances of slide readings were compared with the experts’ readings. Agreement in detecting malaria parasites between laboratory professionals and expert was estimated using the kappa score.The overall sensitivity and specificity of the laboratory professionals in detecting malaria parasites were 83% and 97%, respectively. Similarly, positive predictive values of 98.1% and negative predictive values of 77.7% were reported. The percent agreement between laboratory professionals and expert microscopist in the detection of malaria parasites was 88.5% with a Kappa index of 0.78. Percent agreement in species identification and reporting of Pf/Pv mixed infections were 27.2% and 22.4%, respectively. About 62.2% of the laboratory professionals reported parasite density using semi-quantitative method. While none of them reported per micro-liter method.The current study showed that laboratory professionals had good performance in parasite detection. However, poor performance was seen in both species identification and reporting of Pf/Pv mixed infections. Malaria is a big problem in the world . AccordiHowever, the diagnosis of malaria has still relied on the clinical presentation . Hence, Plasmodium species using microscopy.Thus, data are lacking on laboratory professionals’ performance in detection and identification of malaria parasites in the study area. Therefore, the current study aimed to evaluate the performance of laboratory professionals on detection and identification of A cross-sectional study was conducted from January to March 2017 on performance evaluation of laboratory professionals on malaria microscopy at health facilities in Bahir Dar city administration. A total of 92 laboratory professionals were working in government hospitals, health centers, and private hospitals. We included 87 laboratory professionals in the study. The remaining five laboratory professionals were excluded from the study since they were not regularly involved in malaria microscopy.Bahir Dar city is situated on the Southern shore of Lake Tana, having an elevation of 1840 meters above sea level. It has a total population of 221,991  [P. falciparum (pf), P. vivax (PV) and P. falciparum/P. vivax (pf/PV) mixed infections. Malaria negative panel slides were prepared from non-malaria infected individuals. Each of the collected blood samples was transferred into EDTA containing glass tubes. The samples were processed within one hour of the collection to preserve leukocyte and parasite morphology. Two experienced validators prepared and validated the panel slides at Amhara Public Health Institution (APHI). Five slides from malaria positive patients and three slides from negative individuals were prepared for thick and thin blood films to be dispatched for each laboratory professionals.Four ml of venous blood was collected from each of the patients within the age ranges of 20-25years confirmed with for A total of 736 (460 positive and 276 negatives) validated slides were prepared with Pf, Pv and Pf/Pv accounting for 184, 184 and 92 slides, respectively.For thick and thin blood films preparation, 6 μl and2 μl blood were smeared on a slide. The thick and thin films dried overnight, and the thin films were fixed by dipping in absolute methanol for 10–20 seconds. The slides were stained with 3% Giemsa for 30–45 minutes [Plasmodium positive or negative), and identification and quantification of Plasmodium species. Slides were considered negative if no Plasmodium parasites were found in 100 fields [The blood films were, validated and interpreted using three diagnostic criteria: The presence or absence of malaria parasites; Identification of the species and quantification of parasitic load. Then, laboratory professionals were asked to report the detection  pre-tested and standardized the study questionnaire with laboratory professionals at Merawi District Hospital. The questionnaire was designed to collect information about laboratory professionals’ performance. Information collected include: age, sex, educational level, type of health facility, status of in-service training, work experience, the routine practice of the professionals, and a number of slides done per person/per day. The data were collected by the PI.Strict adherence to the standard operational procedures (SOPs) was assured during slide Preparation. Two validators confirmed the slides. The same microscope mark (CX21) was used to minimize the possible instrumental error. Two validators were participated to confirm the slides. Moreover, the same microscope mark (CX21) was used to minimize the possible instrumental error.The quality of panel slides was properly checked according to WHO standard before dispatching to the laboratory professionals. Finally, the discordant results were rechecked by the principal investigator, along with other certified laboratory professionals who did not participate in the initial preparation and validation processes.Plasmodium parasites were computed using sensitivity, specificity, percent agreement, kappa, positive and negative predictive value.Data were entered and analyzed using Statistical Package for Social Sciences (SPSS version 23). Level of performances on detection, species identification and quantification of Ethical clearance was obtained from Bahir Dar University, College of Medicine and Health Sciences ethical review committee on Ref no: Regional Health Bureau/Research and Technology transfer 1/524/2017 dated on 02/02/ 2017. Then after, permission letters were obtained from Bahir Dar city administration health department. Written informed consent forms were signed by the participating health professionals and blood sample providers. No personal and health facility identifier was included. Individuals were given a unique code number.All 87 laboratory professionals included in the study participated. Females accounted for 48 (55.2%) and 60 (69%) of the participants were between 20–30 years of age. Regarding their educational status, 49 (56.3%) were diploma holders. Forty-five (51.7%) of them were working in government health facilities. Of the total 87 laboratory professionals, 77 (88.5%) of them had experiences on malaria diagnoses greater than or equals to two years. More than half, 49 (56.3%) of the laboratory professionals took training on malaria diagnosis .Of the total 696 dispatched slides, 616 (88.5%) were correctly reported. Of the 435 positive slides, 362 (83.2%) were correctly detected as positive and the rest false positive. Of the 261 malaria negative slides, 254 (97.3%) were correctly detected as negative .Among 362 positively reported slides, the proportion of correct report for Pf, Pv and Pf/Pv mixed infections were 149 (41.2%), 132 (36.5%) and 81 (22.4%), respectively. Moreover, the proportion of correct species identification was 245 (67.7%). Furthermore, the rate of species identification was 120 (80.5%), 103 (78%) and 22 (27.2%) respectively, for Pf, Pv and Pf/Pv mixed infections .P. vivax positive slides was 52 (30.2%) slides 18.6% (32/172) in (1+) and 11.6% (20/172) in (3+) slides. However, none of them reported in the micro-liter method (The proportion of correct report of parasite density was 13.4% (23/172) and 18.6% (32/172) for (Pf 1+) and (Pf 4+) Slides. Similarly, the proportion of correct parasite density report in r method .Plasmodium parasites were 83% and 97%, respectively, and positive and negative predictive values were 98.1% and 77.7%, respectively. Similarly, sensitivity and specificity of professionals in species identification of Plasmodium parasites were 56.3% and 97.3%, respectively. The agreement between the laboratory professionals and validators' on detection and identification of Plasmodium parasites were 88.5% (Kappa = 0.78) and 72% (kappa = 0.47), respectively , 83.5% (kappa = 0.69) and 68% (kappa = 0.41), respectively  and 6 (6.9%) were graded as expert, 32(36.8%) and 24 (27.7%) reference and 18(20.7%) and 57(65.5%) were labeled as in training, respectively based on WHO classification .There is evidence for the scarcity of qualified laboratory professionals to correctly diagnose, and report malaria in Ethiopia . This haThe proportion of correct report 88.5% in this study was consistent with a study done in Hawassa 88%  and ZambThe current study showed that 2.7% of the participants reported false positive results. However, it was lower than the study done in Hawassa 6.8% , in TigrPlasmodium infections, identification of single or mixed Plasmodium species infections, and reporting of the laboratory result as per the WHO guideline.Even if, 56.3% of them took In-service training, yet none of them reported the parasite density as per the WHO guideline. This might be due to poor training quality on malaria microscopy. Thus, the training given on QC for microscopy should target on: Detection of single or mixed Plasmodium parasites was 88.5%. This was similar to a study done in Hawassa 88% [In the present study, the agreement between the validators and laboratory professionals on detection of assa 88% . But it assa 88%  and Hawaassa 88% .This difPlasmodium parasites were 83% and 97%, respectively. Sensitivity and specificity of laboratory professionals were nearly similar to the study conducted in Zambia 88% and 91% [The overall, sensitivity and specificity of laboratory professionals on detection of  and 91% . The sen and 91% . The lowPlasmodium species was 72% (kappa = 0.47) and it was defined as ‘moderate agreement’ based on the Kappa index [P. falciparum and P. vivax species were nearly similarly 80.5% and 78%, respectively. The overall rate of species identification was consistent with a study conducted in North Gondar 69% [Plasmodium species densities at all. These reports were consistent with a study done in Hawassa 28% [In the current study, overall agreement on the identification of pa index . Laboratndar 69% . Howeverndar 69%  and Addindar 69% . Laboratndar 69% , Hawassandar 69%  and in tndar 69% . Of the assa 28% .The overall agreement of on laboratory professionals’ performance in species identification among government hospitals, health centers, and private hospitals were 67.2% (kappa = 0.40), 83.5% (kappa = 0.69) and 68% (kappa = 0.41), respectively. These results at health centers in this study area were higher than those health centers in Hawassa 69% . It coulIt is also noted that low sensitivity and specificity of malaria diagnosis were observed in the government and private hospitals as compared to government health centers. This might be due to the Ethiopian health facility referral system. According to this referral system, patients regardless of their cause first visit health centers, then, those patients who were not treated at health centers are referred to hospitals. It is because of this reason that almost all malaria patients who did not develop severe malaria are treated at the health center level. Thus, laboratory professionals who were working at Ethiopian health centers experience in the diagnosis of malaria as compared to hospitals. As a result, more training on malaria microscopy is given at health centers than hospitals.Plasmodium species. Consistent findings were observed from Addis Ababa as 8.3% were at expert level [In this study, 65.5% of laboratory professionals were classified as 'In-training' and 6.9% were at expert level in the identification of rt level .In this study performance of laboratory professionals’ in smearing preparation, working solution, staining of blood films, washing habit, and drying conditions for malaria diagnosis were not done.The current study showed that laboratory professionals had good performance in parasite detection according to WHO criteria. However, they had poor performance in species and in mixed infections identification. Therefore, EQA should be starting them to fill their gaps.


This study is the first to validate and compare the STATSports Apex 10 and 18 Hz. Between-analysis (t-test) for total distance and Vpeak reported non-significant differences. Apex units reported a small error of around 1–2% compared to the criterion distances during 400-m, 128.5-m circuit, 20-m trials, and Vpeak. In conclusion, both units could be used with confidence to measure these variables during training and match play.The aims of this study were (i) to investigate the criterion validity (vs. gold standard measurements) of the 10 and 18 Hz STATSports Apex units for measuring distances and peak speed (Vpeak) outcomes and (ii) to investigate the between-unit variability. Twenty university students were enrolled in the study . The criterion validity was tested by comparing the distances recorded by the units with ground truth reference . Vpeak values were compared with those determined by a gold standard criterion device  during a linear 20-m sprint. The distance biases for the Apex 10 Hz in the 400-m trial, 128.5-m circuit, and 20-m trial were 1.05 ± 0.87%, 2.3 ± 1.1%, and 1.11 ± 0.99%, respectively, while for the Apex 18 Hz the biases were 1.17 ± 0.73%, 2.11 ± 1.06%, and 1.15 ± 1.23%, respectively. Vpeak measured by the Apex 10 and 18 Hz were 26.5 ± 2.3 km h In team sport context, the common technologies utilized to quantify external load parameters are: global navigation satellite systems (GNSS), video tracking systems (VTS), and local positioning systems (LPS/radar) . PreviouGNSS devices present large variability in accuracy among different manufacturers’ models . TherefoGreater accuracy in the data collected may help sport scientists through understanding the players’ performances over a given training session, as well as small variations worth of interest in the players’ physical loads and efforts load among sessions. This is particularly relevant at professional and elite levels, where small differences can have a meaningful impact on performance outcomes throughout the season. By increasing the sampling frequency of the GNSS devices have resulted in several improvements in terms of data quality, however, some limitations continue to exist. Previous studies, employing 10 Hz GNSS devices, have shown low validity and reliability when tested in sport-specific circuits, as well as during high intensity short shuttle runs and change-of-direction maneuvers . NeverthThe STATSports Apex unit is an athlete-tracking system released in August 2017, and it is widely used in professional clubs . A previous validation of the STATSports Viper GNSS unit was performed in 2016, and it reported a distance bias of 2.5% during 20-m running activity and a small to moderate bias in speed (3–9%) during 5–20-m short shuttle runs . HoweverTo date, no validation and comparison studies of the Apex 10 and 18 Hz exist; therefore, it would be acknowledgeable to report accuracy data of such units and existing differences between the models, which could offer important practical applications for practitioners working in sport contexts. This need is further required when considering that GNSS-derived data is utilized to manage players’ training loads, recovery strategies, workload implementations, and subsequent training periodization . The valTwenty physical active male and female university students were enrolled  in this descriptive study (data recorded in 2018). The experimental protocol was in accordance with the Declaration of Helsinki for study on human subjects. The Institutional Ethics Board of the University of Suffolk  approved the experimental protocol. A written informed consent was obtained from the participants of this study.Figure 1A), and a 20-m trial (linear running) . ATS II radar uses high frequency radio waves and measures changes of speed of a moving object (Doppler radar). Radar gun and laser devices are considered to be gold standard instruments for evaluating Vpeak  data were collected outdoor on an athletic track, in absence of high and large buildings in the surrounding area to enhance satellites’ reception . Both Aprunning) . The parng Vpeak . The radng Vpeak . The speng Vpeak . Stalkerng Vpeak .Participants completed a 400-m trial at a self-selected speed (jogging pace), a 128.5-m trial, a 20-m trial (jogging pace), and a 20-m sprint at maximum speed. Each participant was verbally instructed before each trial to perform the correct procedure. Participants performed a familiarization trial (week 1) before the beginning of the experimental trial. A 400-m trial, 128.5-m circuit, 20-m trial, and 20-m sprint were performed by the participants of this study . The experimental session was performed during a sunny day without rain or clouds.Figure 2). Apex 10 Hz is a multi-GNSS augmented unit, which is capable of acquiring and tracking multiple satellite systems  concurrently to provide the best possible positional information. In contrast, Apex 18 Hz units have a higher sampling frequency than Apex 10 Hz, but its acquisition system is based only on GPS. GNSS data (speed and distance) recorded by the units were downloaded and further analyzed by the STATSports Apex Software .The Apex units (10 and 18 Hz) were turned on about 10–15 min before the beginning of the test, while the subjects were familiarized with the equipment as well as the protocol procedures. Apex units present the following characteristics: dimension 30 mm (wide) × 80 mm (high), weight 48 g, 100 Hz gyroscope, 100 Hz tri-axial accelerometer, and 10 Hz magnetometer. Prior to the experiments, both Apex unit models  were placed on the back of the participant, midway between the scapulas   of the statistical distribution. Validity was assessed by calculating the bias (%) between the known distance and the unit (absolute error). Bias was interpreted as poor (>10%), moderate (5–10%), or good (<5%) . A paire(CI 90%) . Effect (CI 90%) . Intracl(CI 90%) . The bet-1 and criterion was 26.3 ± 2.4 km h-1. Mean difference (90% CI) was 0.17 , t = 1.012, p = 0.32, ES (90% CI) = 0.08 , trivial. The absolute error of the Apex 10 Hz was 0.62 ± 0.45 km h-1 and the bias was 2.36 ± 1.67% (good). ICC between Apex 10 Hz and radar gun Vpeak was nearly perfect .The Shapiro–Wilk test confirmed the assumption of normality of the statistical distribution. Apex 10 Hz GNSS distance covered in the 400-m trial, 128.5-m circuit, and 20-m trial was 398.7 ± 7.6 m, 131.4 ± 1.4 m, 20.07 ± 0.29 m, respectively, with an absolute error of 4.19 ± 3.48 m, 2.85 ± 1.4 m, and 0.22 ± 0.20 m, respectively. The bias in each trial was 1.05 ± 0.87%, 2.3 ± 1.1%, and 1.11 ± 0.99%, respectively. Vpeak measured by the Apex 10 Hz was 26.5 ± 2.3 km h-1 and criterion was 26.3 ± 2.4 km h-1. Mean difference (90% CI) was 0.24 , t = 1.973, p = 0.064, ES (90% CI) = 0.08 , trivial. The absolute error of the Apex 18 Hz was 0.52 ± 0.30 km h-1 and the bias was 2.02 ± 1.24% (good). ICC between Apex 18 Hz and radar gun Vpeak was nearly perfect .Apex 18 Hz distance covered in the 400-m trial, 128.5-m circuit, and 20-m trial was 396.8 ± 11.6 m, 131.2 ± 1.3 m, and 20.19 ± 0.28 m, respectively, with an absolute error of 4.7 ± 2.9 m, 2.7 ± 1.4 m, and 0.23 ± 0.25 m, respectively. The bias in each trial was 1.17 ± 0.73%, 2.11 ± 1.06%, and 1.15 ± 1.23%, respectively. Vpeak measured by the Apex 18 Hz was 26.5 ± 2.6 km ht = 0.653, p = 0.522; in the 128.5-m circuit of 0.15 , t = 0.319, p = 0.753; in the 20-m trial of -0.12 , t = 1.45, p = 0.163; or in the 20 m sprint Vpeak of -0.04 , t = 0.403, p = 0.691, ICC (CI 90%) = 0.96 .Between-analysis (Apex 10 Hz vs. Apex 18 Hz) did not find any meaningful differences in the 400-m trial  1.78 , Table 1).The aims of this study were to validate and to compare the STATSports units (Apex 10 and 18 Hz) when measuring distance and Vpeak outcomes during sports-specific activities. The main findings of this research were that the STATSports Apex (10 and 18 Hz) reported a small bias (<5%) for distance measures  and Vpeak (20 m sprint), thus supporting the validity of both Apex models. Trivial (ES) differences were found between the Apex 10 and 18 Hz in the parameters analyzed   in team sports . PreviouApex reported lower or equal bias than previous wearable devices analyzed, such as 9.6–32.4% in TD (20 m sprint) MinimaxX team (1 Hz), 1.7–6.7% in TD (30 m sprint) MinimaxX v4.0 (10 Hz), and 2.9–7.7% in TD (30–40 m print) SPI-Pro (5 Hz) . A recenIn the current study, both Apex 10 and 18 Hz units resulted in small bias in distance measures during the 400-m, 128.5-m, and 20-m trials, as well as in Vpeak during the 20-m sprint. These scores are smaller than the bias reported for TD and Vpeak of previous Viper models. A previous study, employing the STATSports Viper (10 Hz) units, found a bias ranging from of 8.7 to 3.4%  in speed outcomes during 5–20-m running activity associated to this model . SeveralAn innovative finding of the current study is the comparison between Apex 10 and 18 Hz on TD and Vpeak. The between-unit analysis (Apex 10 Hz vs. Apex 18 Hz) found trivial to small differences in the 400-m trial (ES = 0.18), 128.5-m circuit (ES = 0.11), 20-m trial (ES = 0.42), and in 20-m sprint Vpeak (ES = 0.03). Following these results, it may be concluded that these two Apex models do not present meaningful differences in the parameters evaluated, therefore sport scientist and coaches could use both models interchangeably in their practice (even if consistency in the model utilization should be recommended). This is the first study to evaluate and compare such units, thus it is not possible to make any comparisons with other research work published. A future study could replicate the current study, analyzing Apex units.This study presents three main limitations: firstly, we evaluated sports-specific movements by linear running activity and circuits involving human participants. It is well known that this approach (largely used in the literature) might present errors, and therefore the results reported in the current study should be considered carefully. For instance, during the 400-m trial and the 128.5-m circuit, the variability due to the human movement inconsistency could have affected the findings. Sports scientists and coaches should consider that research studies involving human subjects might show some inconsistency between the designs. The application of mechanical devices collecting at higher frequency than the human movement could be used to test distance and speed. Secondly, the Vicon motion analysis system has been recently proposed as the new gold standard technology for evaluating sports-specific movements . Such teThe evaluation of GNSS Apex 10 and 18 Hz validity is a critical step for its application in team sports and for research purposes. Apex units are largely utilized in team sports . This study provides innovative findings and offers important implications for sports scientists and researchers involved with such technologies for practical and research purposes. Apex units (10 and 18 Hz) showed good levels of accuracy (bias < 5%) in sport specific metrics. Practitioners can be mindful that the units analyzed in this study can be used to evaluate distance covered during linear running and sports-specific activity. Moreover, Apex units can be used to evaluate Vpeak in sports, since non-significant and trivial differences were found compared to criterion Vpeak (radar gun). External load interpretation and the associated decision making processes can only be made when the validity of GNSS technology is well known. Coaches and sport scientists can use the metrics derived from STATSports Apex units, which have been analyzed in this study, to quantify players’ workload during training sessions and to optimize the overall training periodization.MB was the main investigator. GC was involved in the data analysis and statistics. AS had a critical role for data recording. AI was the supervisor of the project.AI was employed at the time of the study by Maccabi Tel Aviv FC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor declared a past co-authorship with one of the authors AI.


Background: Interventions to block malaria transmission from humans to mosquitoes are currently in development. To be successfully implemented, key populations need to be identified where the use of these transmission-blocking and/or reducing strategies will have greatest impact.Methods: We used data from a longitudinally monitored cohort of children from Kilifi county located along the Kenyan coast collected between 1998-2016 to describe the distribution and prevalence of gametocytaemia in relation to transmission intensity, time and age. Data from 2,223 children accounting for 9,134 person-years of follow-up assessed during cross-sectional surveys for asexual parasites and gametocytes were used in logistic regression models to identify factors predictive of gametocyte carriage in this cohort.Results: Our analysis showed that children 1-5 years of age were more likely to carry microscopically detectable gametocytes than their older counterparts. Carrying asexual parasites and recent episodes of clinical malaria were also strong predictors of gametocyte carriage. The prevalence of asexual parasites and of gametocyte carriage declined over time, and after 2006, when artemisinin combination therapy (ACT) was introduced, recent episodes of clinical malaria ceased to be a predictor of gametocyte carriage. Conclusions: Gametocyte carriage in children in Kilifi has fallen over time.  Previous episodes of clinical malaria may contribute to the development of carriage, but this appears to be mitigated by the use of ACTs highlighting the impact that gametocidal antimalarials can have in reducing the overall prevalence of gametocytaemia when targeted on acute febrile illness. Causality is complex, but reductions have been attributed to the increased use of insecticide treated nets and the adoption of highly effective artemisinin combination therapies (ACTs) as the first line treatment for malaria4.Considerable progress has been made towards eliminating malaria over the years, with an unprecedented reduction in disease burden between 2000 to 2010, albeit with progress stalling between 2010 to 2015Anopheles mosquitoes. Gametocytes are produced when a proportion of the asexual parasites, an average of 1 gametocyte per 156 asexual parasites5, commit to sexual development during a malaria infection. It is not yet clear what factors drive this commitment but it has been proposed that factors such as drug pressure, an unfavourable environment within the host including host immunity7 and parasite factors such as the contents of extracellular vesicles released from infected erythrocytes9 may play a role. Genetic influences, in particular, variants of the β-globin locus have also been shown to influence gametocyte production in asymptomatic infections where variants that protect against severe malaria10 are associated with an increased rate of gametocyte production12.Malaria is transmitted via gametocytes taken up during a blood meal by female14. Primaquine is active against mature gametocytes, and the World Health Organisation (WHO) recommends a single low dose of 0.25 mg/kg primaquine for use in low transmission areas to reduce malaria transmission15. Although not active against the mature gametocytes, ACTs act against early stage gametocytes and consequently reduce gametocyte carriage6.Older antimalarials such as chloroquine (CQ) and sulfadoxine-pyrimethamine (SP) were active primarily against asexual parasites and had limited activity against gametocytes, particularly when resistance emerged18 or limited to short-term follow up20. We carried out an analysis of data collected over 19 years of follow-up in a longitudinal cohort established at the Kenyan coast, during a period of changing malaria transmission and changing drug use. Here, we sought to describe the distribution and prevalence ofPlasmodium falciparum gametocyte carriage within this cohort and changing prevalence over time, and thus identify potential risk factors for gametocyte carriage.Identifying prognostic indicators of gametocyte carriage is key to the successful implementation of interventions aimed at reducing malaria transmission. Several studies have examined the epidemiology of gametocyte carriage but these have largely been single surveys23. Three cohorts located in areas of varying transmission intensity were included, that is, Ngerenya , Junju (moderate transmission) and Chonyi (high transmission). Malaria transmission intensity is higher during the rainy seasons with the long rainy season occurring between May-July and short rainy season between October-December24. We analysed data from cross-sectional surveys conducted within the three cohorts. Data included in the analyses were from cross-sectional surveys conducted from 1998 to 2016 for Ngerenya ; from 1999 to 2001 for Chonyi; and from 2007 to 2016 for Junju.Cohorts of children recruited into the Kilifi Malaria Longitudinal Cohort study were located in Kilifi County at the Kenyan coast 21–23. 22. This sample size was considered appropriate for a study on the definitions of clinical malaria. Participants willing to continue follow-up were included as a pragmatic study size for future studies. For Junju, participants were recruited from 405 children who previously participated in a malaria vaccine trial23. The sample size was determined based on an expected febrile malaria incidence of 50%. Enrolling 400 children would then allow detection of 35% vaccine efficacy with 80% power. Children born into the households were then subsequently recruited into the three cohorts over time. All study participants had access to healthcare facilities with the same study protocol applied to all cohorts per year. Children were actively monitored for malaria by weekly visits to identify febrile episodes and by cross-sectional surveys for asymptomatic parasitaemia, and exited follow up when they were 15 years of age25.In Ngerenya and Chonyi, households were selected randomly with 72 households selected in Ngerenya (819 participants) and 52 households in Chonyi (783) participants26.To reflect the marked decline in transmission intensity observed in Ngerenya over the follow-up period, for analytical purposes, Ngerenya was divided into Ngerenya early, which included data collected during a period of moderate transmission (1998–2001), and Ngerenya late, which included data collected during the period of moderate to low transmission (2002–2016)Approval for participation in these cohort studies was given by Kenya Medical Research Institute Ethics Review Committee (reference numbers KEMRI/SERU/CGMRC//3149 and SSC1131), and research was conducted according to the principles of the Declaration of Helsinki, which included the administration of informed consenting in the participant’s local language prior to any study procedure. Written informed consent for participation in this study was provided by the parents of the children included in this study.27. Briefly, households in the three cohorts were visited by a field worker every week where axillary temperature was recorded for each study participant. If the participant had a fever or history of fever, they had a blood smear performed by the field worker to diagnose malaria infection. From 2007 onwards, rapid diagnostic tests (RDTs) were available in the field to guide treatment decisions, but even before RDTs were available all febrile malaria episodes were treated. Field workers were resident in the villages where study participants lived, and were available to assess febrile episodes arising before a scheduled visit was planned. Treatment was freely provided by the field workers and the drug administered in a particular year was based on the Government of Kenya national guidelines for treatment of malaria.Active malaria surveillance was performed during weekly follow-up visits, carried out as described previouslyP. falciparum infections were used. There were approximately 364  participants in each survey, and a detailed summary of the cross-sectional surveys included in the analysis with the number of participants attending each survey is presented in Supplementary Table 128.To analyse gametocyte and asexual parasite prevalence, data from cross-sectional surveys mainly taken before the beginning of the long rainy season to assess asymptomatic9 per litre or an RBC count of 5 × 1012 per litre. In total, 100 high-powered fields of a thick film were read before ascertaining that no parasites were present.Thick and thin blood films were taken from all children at each cross-sectional survey and for children presenting with fever during the weekly follow-up visits. The thin blood films were fixed with 100% methanol and stained with 3% Giemsa stain for 45 minutes before being examined for parasites. Thick films were air-dried before staining. If there were more than 25 parasites per high powered field on the thick film then the thin film was used for counting, otherwise the thick film was used. Asexual parasite densities were determined per microliter of blood and were calculated as the number of parasites per 200 white blood cells (WBCs) for thick films or per 500 red blood cells (RBCs) for thin films. The final parasitaemia was then calculated in reference to the actual full blood count (if available) or estimated assuming a WBC count of 8 × 10Gametocytes were counted when observed during application of the protocol for asexual parasites, and hence the numbers of fields examined during which gametocytes may be observed varied depending on the asexual parasitaemia. Malaria parasite and gametocyte counts were determined by two independent readers and discordant readings resolved by a third reader. Quality assurance over the study period included comprehensive microscopy training during induction and at regular intervals using internal and external quality control. For internal quality control, a subset of slides selected quarterly are re-read by the microscopy team and concordance between the results checked. For external quality control, at the beginning of the cohort study, this involved reading reference blood films from a partner lab in the United Kingdom. Currently, external quality control involves participating in three annual surveys carried out by the National Institute of Communicable Diseases (NICD) based in South Africa where they send 20 slides per survey to our lab for proficiency testing.30.A subset of samples were typed for sickle cell genotype and α-thalassaemia, as previously described21. For the weekly follow-up visits, temperature was recorded for the participants, and for those with a fever, a blood film was taken and analysed as above. For children <1 year of age, clinical malaria was defined as a fever (axillary temperature ≥37.5°C) with any parasitaemia while for children between 1–15 years of age malaria was defined as fever accompanied by parasitaemia of ≥2,500 parasites/µl of blood. For estimates of the number of malaria episodes per participant per survey, malaria episodes were considered as unique only if the time difference between two consecutive malaria episodes was ≥28 days. Malaria episodes occurring in the interval between two cross-sectional surveys, the respective survey (survey x) and the prior survey (survey x-1), were identified, summed up and defined as the number of malaria episodes occurring in the period leading up to each respective survey.To determine malaria episodes, data from the weekly follow-up visits were used and malaria episodes defined using previously described cut-offs31: asexual parasite positivity, age, year, number of malaria episodes and whether the participant had a malaria episode, asexual parasite positive blood film or gametocyte positive blood film in the prior cross-sectional survey. The variable asexual parasite positive included all infections – asymptomatic and symptomatic. Age was included as a categorical variable with the age-group ‘5–9 years’ was chosen as the reference group as the numbers in this group were large and allowed clearer presentation of the risks in other groups. Poisson and logistic regression models were evaluated and the best model for the data determined by comparing the Akaike information criterion (AIC). To correct for repeated measures per individual, robust standard errors were calculated with allowance for clustering to account for non-independence of observations. Observations with missing data were excluded from the analyses. Variance inflation factors were also calculated to assess multicollinearity among the covariates included in the model (Supplementary Table 228). Probability values (p) of less than 0.05 were considered statistically significant. All statistical analyses were carried out in R statistical software via RStudio version 1.1.46332.To assess the relationship between variables, Spearman’s rank correlation coefficients were calculated. Models to predict gametocyte positivity were fitted using the following known covariates shown to be associated with gametocyte carriageFor the study, a total of 19,580 observations from 2,703 children  derived P. falciparum asexual parasites or gametocytes over the period of follow-up was analysed for each of the cohorts (p<0.0001) indicating a paralleled decline in sexual and asexual parasitaemia. This was only true, however, in Ngerenya (both early and late transmission periods) and Chonyi. For Junju the temporal variation was more random,  demonstrating the absence of a strong relationship between asexual and sexual parasite prevalence over time in this cohort. On the other hand, gametocyte and asexual parasite densities did not differ significantly over time in the cohorts  and the analysis then restricted to individuals who had had the same number of blood films taken per cohort to avoid bias. The distribution of gametocyte positive events was approximately binomial  with the highest frequency was determined separately . We found a better fit for the model with asexual parasitaemia as a binary variable.We tested associations between the following covariates: asexual parasite positivity, age, cohort, number of malaria episodes and whether an individual was gametocyte positive or asexual parasite positive during the prior survey or had malaria episodes in the prior survey. Additionally, we compared using asexual parasitaemia as a binary variable (positive versus negative) and as a log-transformed continuous variable  , Ngerenya early  and Ngerenya late . However, in Junju the number of malaria episodes was associated with reduced odds of gametocyte positivity . We tested the interaction between the number of malaria episodes and cohort in a logistic regression and confirmed that the variation in effect of malaria episodes was statistically significant (p = 0.0297) (p = 0.0007) and Ngerenya late (p = 0.0008).Associations were consistent when the models were fitted separately for each cohort –Table 70.0297) . Furtherp = 0.0006) while malaria episodes occurring within 28 days of a cross-sectional survey were associated with an approximately threefold increased risk of gametocyte positivity . On the other hand, after 2006 the number of malaria episodes a participant had and malaria episodes occurring within 28 days of a cross-sectional survey ceased to be predictors of gametocyte positivity .Owing to differential associations observed in the cohorts between malaria episodes and gametocyte positivity and the difference in follow-up period, particularly for Junju and Chonyi, we divided the dataset into two time-periods, before 2006 and after 2006. This marked the periods before and after the introduction of ACTs. There was a marked decline in gametocyte prevalence in the period after the introduction of ACTs , droppin28). In this analysis, we found that being under 5 years of age and being gametocyte-positive in the prior survey were associated with an increased odds of gametocyte positivity, while residing in Junju was associated with a decreased odds of gametocyte positivity, consistent with the analysis inAdditionally, we looked at how gametocyte positivity in individuals who tested positive for asexual parasites varied with age, cohort, malaria episodes and parasitaemia (Supplementary Tables 4 and 528). Heterozygosity  and homozygosity  for α-thalassaemia did not appear associated with gametocyte positivity. This was also true for sickle cell trait   .11. The three cohorts were in sublocations within Kilifi County  that represent a low, a mid to low, and a high transmission setting. For the purposes of this analysis, however, Ngerenya was subdivided into Ngerenya early (a period of moderate to high transmission) and Ngerenya late (a period of low transmission).The analysis aimed to describe gametocyte prevalence and distribution over time and varying transmission intensities, and to identify factors associated with gametocyte carriage in three cohorts of children in Kilifi maintained for 3, 12 and 19 years, respectively, in which individual follow-up ran to a maximum of 15 years of age. Identification of these factors could then possibly serve as predictive features that would allow for targeted application of transmission-reducing interventions34. Heterogeneity of transmission with hotspots of malaria has been described35, and transmission has persisted in Junju, whilst in Ngerenya transmission has remained either non-existent or low in some parts. Previous literature has demonstrated reductions in the prevalence of gametocytaemia with increasing age in higher-transmission areas37, consistent with the pattern we here describe in the higher transmission cohorts. Similarly, results obtained in previous studies are confirmed by this analysis, that the likelihood of gametocytaemia increases in the presence of asymptomatic asexual parasitaemia31. Furthermore, it is expected that the prevalence of gametocytaemia would fall in the community as the prevalence of asymptomatic parasitaemia falls38. Moreover, prior episodes of clinical malaria are a well-documented source of gametocytaemia11. The findings in Junju are therefore unexpected: gametocyte prevalence was disproportionately lower compared with the prevalence of asexual parasitaemia  than at higher transmission intensities (Ngerenya early and Chonyi). When gametocytes are seen in the absence of asexual parasitaemia by microscopy, it may be that asexual parasitaemia is present but below the threshold detectable microscopicallyet al. (2015) identified hotspots of gametocyte carriage and these were associated with active malaria transmission11. However, gametocytaemia appeared to follow a binomial distribution in contrast to the negative binomial distribution seen for asexual parasitaemia, suggestive that gametocyte carriage is more evenly distributed in the population compared with asexual parasite carriage, where certain individuals are at considerably greater susceptibility to [re-]infection due to host-related factors25 .Datasets.zip .imm_csbleed_data.tab .imm_weekly_fu_overall.tab .sickle.thal data.tab .https://kemri-wellcome.org/about-us/#ChildVerticalTab_15). Requests for the data can be made to the Data Governance Committee (dgc@kemri-wellcome.org) through the corresponding author.The above raw data that support the findings of this manuscript are under restricted access and available through the KEMRI-Wellcome Trust Research Programme Data Governance Committee if the use of the data is complaint with the consent provided by the participants. Details of the criteria can be found in the KEMRI-Wellcome data sharing guidelines Summary Statistics - csbleed.R Summary Statistics - malaria episodes.RSummary Statistics - wfu.R (wfu - weekly follow-up)Creative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of the I am happy that the authors have addressed all points raised.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors have addressed appropriately all concerns.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. et al have used data collected between 1998 and 2016 to describe the distribution and prevalence ofP. falciparum gametocytaemia in children from 3 regions of Kalifi which were chosen based on malaria transmission intensity.Muthui Thick and thin blood films were collected from children at cross sectional surveys and weekly follow ups over a 19 year period and asexual parasite and gametocyte densities were determined by microscopy. The aim of the study is to identify potential risk factors for gametocyte carriage with the view of identifying target populations for transmission blocking/reducing interventions. The analysis is largely descriptive and the conclusions reached are logical based on the data and findings.Tables 1 and 2. Describe the demographic characteristics of the study population, but it is not clear which data is included in which table. The footnote to Table 1 “malaria episodes calculated from weekly follow-up data for all study participants who had complete data on gametocyte density” is also confusing.Under case detection. “Treatment was freely provided…based on the Government of Kenya national guidelines for treatment of malaria” the drugs actually used are only described in the discussion (paragraph 3). It would be helpful to see this displayed visually on a figure . At the very least I suggest visually indicating the time when ACT was introduced.Figure 10. Prevalence of gametocytaemia and asexual parasitaemia before and after ACT introduction in all cohorts. But, Ngerenya is the only site for which data covering all 19 years is available. Chonyi (high transmission) was only sampled prior to 2006 when ACT was introduced and Junju (moderate transmission) was only sampled after 2006. Given this intermittent sampling of sites with different transmission intensities, can justification for inclusion of all data be included in the manuscript? The manuscript is generally well written and the data clearly presented, however, a couple of points would benefit from additional clarificationI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Tables 1 and 2. Describe the demographic characteristics of the study population, but it is not clear which data is included in which table. The footnote to Table 1 “malaria episodes calculated from weekly follow-up data for all study participants who had complete data on gametocyte density” is also confusing. The data in Table 1 describes the data from the cross-sectional surveys while the data in Table 2 describes the data from the weekly follow-up visits. We agree that the table legends may not be as clear and have therefore renamed the title of Table 1 to read:We would like to sincerely thank you for taking the time to read our manuscript and provide constructive feedback. Please find our responses to your comments below:‘Table 1. Demographic characteristics of study participants participating in the cross-sectional surveys’.Under case detection. “Treatment was freely provided…based on the Government of Kenya national guidelines for treatment of malaria” the drugs actually used are only described in the discussion (paragraph 3). It would be helpful to see this displayed visually on a figure . At the very least I suggest visually indicating the time when ACT was introduced. We agree that it would be useful to show the timelines when the different drug regimens were in use on a graph and have therefore added this to Figure 3 that also shows the number of cross-sectional surveys carried out in a year per cohort.Figure 10. Prevalence of gametocytaemia and asexual parasitaemia before and after ACT introduction in all cohorts. But, Ngerenya is the only site for which data covering all 19 years is available. Chonyi (high transmission) was only sampled prior to 2006 when ACT was introduced and Junju (moderate transmission) was only sampled after 2006. Given this intermittent sampling of sites with different transmission intensities, can justification for inclusion of all data be included in the manuscript? We agree that the three cohorts have been followed up for different lengths of time and over different years. However, our primary objective was to describe parasite prevalence over time and varied transmission intensity and describe factors predictive of gametocyte carriage. Including the three cohorts observed under differing drug regimens and over different years reflects changes in malaria transmission would enable us to tease out the effects of changing drug regimen on the prevalence of gametocytaemia overall.The number of malaria episodes was calculated from the weekly follow-up data for the period leading up to each survey and then this figure assigned to the respective cross-sectional survey. This is what we present as the number of malaria episodes in Table 1.We do acknowledge, however, that the monitoring data for Junju is only available post-ACT introduction and therefore we cannot be certain that the changing epidemiology of parasite prevalence is due to the use of ACTs. Also, as hardly any parasites were detected in Ngerenya after 2006, we do not have sufficient power to detect a difference in parasite prevalence pre- and post-ACT introduction. In mitigation against this Junju and Chonyi are located close to each other with similar populations, and the malaria parasite populations are likely to be mixed over this geographical space. Therefore, it seems more likely that ACTs are the explanation for the changing epidemiological patterns rather than an ecological difference in the settings. We have added a paragraph in the discussion on page 17 after the second paragraph to explain this as below:22and malaria parasites are likely to be mixed over this geographical space, it seems more likely that ACT use explains the changing epidemiological patterns rather than an ecological difference between the settings.’‘We acknowledge that Junju monitoring data is only available post ACT introduction and therefore we cannot analyse parasite prevalence pre- and post-ACT introduction in this cohort. Additionally, while monitoring data for Ngerenya spans pre- and post-ACT introduction, parasite prevalence is infrequent in Ngerenya after 2006 and thus we do not have enough power to detect the changing association between drug regimen and gametocyte prevalence. However, as Junju and Chonyi are located close to each other  and have similar populations, The results presented in the manuscript are scientifically valid. The authors analyze 19 years of data on asexual/gametocyte carriage and predictor factors for gametocyte positivity based on light microscopy data. Although conclusions are clear and supported by the data presented, it was difficult to follow the definitions of the variables used and which data defined them. This could be improved by defining all variables in the 'Case definitions' section and using the same terms consistently through the text and tables. More detail is given below.Methods/Design: 3 cohorts with weekly follow up of clinical cases (febrile cases) + 1 annual survey for asymptomatic infections.Is the 1 annual survey for asymptomatic infections mentioned in the methods section considered a survey ? It was not clear how this data was used.Through the text and in the tables the authors use different terms such as number of malaria episodes, current and previous survey, prior survey. Some of these terms are confusing. For example:"Number of malaria episodes occurring between the current survey and the prior survey were then summed up and analyzed as the number of episodes occurring in the current survey." Is this variable defined by only weekly follow up data or also cross sectional surveys?"Previous survey period interval between the preceding survey and the survey that came before." Is this using both cross-sectional and weekly follow up data? (Definition is found on page 9 of results). Asexual parasite positive. Is it all infections or asymptomatic infections only? Cross-sectionals surveys – numbers per community and year described in a figure. Malaria episode within 28 days of survey, is this variable the same than recent malaria episodes used in the text Study population: There are two tables presenting characteristics of the study population. Does Table 1 include all participants  or does it include only cross-sectional? Or are cross-sectional study participants a sub-group of the cohort participants? While in the text the authors state “The demographic characteristics of study participants participating in the cross-sectional surveys are presented in Table 1, the characteristics of the study participants participating in the weekly follow-up visits are provided in Table 2”, Table 1 footer defines malaria episodes as calculated from weekly follow-up data and does not include cross-sectional in the title. This is confusing. Laboratory investigations: Describes an external and internal quality control of slides. Since the study covers 19 years, is this assurance system covering the full period? What was the result?Results:p = 0.0234), Ngerenya early  and Ngerenya late ". Here is confusing that prior clinical malaria refers to number of malaria episodes, instead of malaria episodes in the previous survey period? (Page 9) "Prior clinical malaria episodes were associated with increased odds of gametocyte positivity in Chonyi  while recent malaria episodes were associated with an approximately threefold increased risk of gametocyte positivity ". The second part of the sentence would be more clear: 'while malaria episodes within 28 days of survey were associated….. '. (Page 13) "We found that before 2006, the number of malaria episodes were associated with an increased risk of gametocyte positivity  – 15 years of age (instead of 15 years).38. Moreover, prior episodes of clinical malaria are a well documented source of gametocytaemia6,11. The findings in Junju are therefore unexpected: gametocyte prevalence was disproportionately lower compared with the prevalence of asymptomatic parasitaemia ". Figure 4 shows asexual parasitemia; do the authors mean asymptomatic infections here? This should be clarified, it is now not clear (in the methods and result section) if authors refer to asymptomatic infections or all asexual infections. (Page 16) "Furthermore, it is expected that the prevalence of gametocytaemia would fall in the community as the prevalence of asymptomatic parasitaemia falls "prior episodes of clinical malaria were modestly protective rather than a risk factor for gametocytaemia. We speculate that a change in anti-malarial drug policy might have caused this variation in effect". Here prior episodes mean episodes within the 28 days of the survey?16,18 showed that gametocyte prevalence was highest in 5–15-year-olds in comparison to their younger and older counterparts". The present study does not compare to older than 15 years old. (Page 19) "Interestingly, this and other studiesI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We would like to sincerely thank you for taking the time to read our manuscript and provide constructive feedback. Please find our responses below:Methods/Design:3 cohorts with weekly follow up of clinical cases (febrile cases) + 1 annual survey for asymptomatic infections.Cross-sectionals surveys – numbers per community and year described in a figure.Is the 1 annual survey for asymptomatic infections mentioned in the methods section considered a survey ? It was not clear how this data was used. Yes, the cross-sectional surveys for asymptomatic infections were conducted within the three cohorts, data from these cross-sectional surveys were then what was used in the analysis to determine parasite prevalence. We have amended this on page 3 in the first paragraph under the sub-heading ‘Study design and data collection’ as below:‘We analysed data from cross-sectional surveys conducted within the three cohorts. Data included in the analyses were from cross-sectional surveys conducted from 1998 to 2016 for Ngerenya ; from 1999 to 2001 for Chonyi; and from 2007 to 2016 for Junju.’And the second paragraph as below:Through the text and in the tables the authors use different terms such as number of malaria episodes, current and previous survey, prior survey. Some of these terms are confusing. For example:Number of malaria episodes occurring between the current survey and the prior survey were then summed up and analyzed as the number of episodes occurring in the current survey."Is this variable defined by only weekly follow up data or also cross-sectional surveys? The variable ‘number of malaria episodes’ was defined only by the data derived from the weekly surveillance and not data from the cross-sectional parasitological surveys. We have clarified this in the first paragraph under the sub-heading ‘Case definitions’ on page 5 as follows:‘Children were actively monitored for malaria by weekly visits to identify febrile episodes and by cross-sectional surveys for asymptomatic parasitaemia…’"Previous survey period interval between the preceding survey and the survey that came before."Is this using both cross-sectional and weekly follow up data? (Definition is found on page 9 of results). The malaria episodes were all calculated from the weekly surveillance and not from the cross-sectional surveys. We agree on reflection that the terminology may not be clear as used and have therefore updated the manuscript as follows:We have amended the first paragraph under the sub-heading ‘Factors predicting gametocyte positivity’ on page 9 to read:‘We tested associations between the following covariates: asexual parasite positivity, age, cohort, number of malaria episodes occurring in the period leading up to a cross-sectional survey and whether an individual was gametocyte positive or asexual parasite positive during the prior survey or had malaria episodes in the period leading up to the prior survey (prior to each respective survey).’i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey’.We have updated the tables and text as well and replaced ‘Malaria episodes in the previous survey period’ with ‘Number of malaria episodes in the prior survey’. We have also changed ‘Gametocyte positive the previous survey’ with ‘Gametocyte positive in the prior survey’ and ‘Asexual parasite positive the previous survey’ with ‘Asexual parasite positive in the prior survey’. We have also amended the footnote beneath the tables to read: ‘Asexual parasite positive. Is it all infections or asymptomatic infections only? The variable asexual parasite positive refers to all infections that were positive for asexual parasites as detected microscopically during the cross-sectional surveys. We have since amended this on page 5 under the paragraph ‘Statistical analysis’ to read:‘Malaria episodes occurring in the interval between two cross-sectional surveys, the respective survey (survey x) and the prior survey (survey x-1), were identified, summed up and defined as the number of malaria episodes occurring in the period leading up to each respective survey.’Malaria episode within 28 days of survey, is this variable the same than recent malaria episodes used in the text Yes, the malaria episodes occurring within 28 days of a cross-sectional survey are the same as recent malaria episodes. We realise that the connection between the two terms was missing in the text and have amended the text in the final paragraph of page 13 to read:‘The variable asexual parasite positive included all infections – asymptomatic and symptomatic.’Study population: There are two tables presenting characteristics of the study population. Does Table 1 include all participants  or does it include only cross-sectional? Or are cross-sectional study participants a sub-group of the cohort participants? The data in Table 1 represents the characteristics of the study participants who participated in the cross-sectional surveys. Participants were recruited to the three cohorts from which each year over the course of follow-up a cross-sectional survey was conducted and weekly surveillance to monitor for malaria episodes. Therefore, the cross-sectional data is derived from the cohort. We have amended this on page 3 in the first paragraph under the sub-heading ‘Study design and data collection’ as below:‘…while malaria episodes occurring within 28 days of a cross-sectional survey were associated with an approximately threefold increased risk of gametocyte positivity…’While in the text the authors state “The demographic characteristics of study participants participating in the cross-sectional surveys are presented in Table 1, the characteristics of the study participants participating in the weekly follow-up visits are provided in Table 2”, Table 1 footer defines malaria episodes as calculated from weekly follow-up data and does not include cross-sectional in the title. This is confusing We agree that this may be unclear and have therefore renamed the title of Table 1 to read:‘We analysed data from cross-sectional surveys conducted within the three cohorts. Data included in the analyses were from cross-sectional surveys conducted from 1998 to 2016 for Ngerenya ; from 1999 to 2001 for Chonyi; and from 2007 to 2016 for Junju.’‘Table 1. Demographic characteristics of study participants participating in the cross-sectional surveys’.Laboratory investigations: Describes an external and internal quality control of slides. Since the study covers 19 years, is this assurance system covering the full period? What was the result? Since the start of the cohort follow-up, there have been internal and external quality control systems in place for the microscopy. The nature of quality assurance, however, has changed over time. In the beginning, the internal quality control involved the selection of 10 slides every three months that were then rotated amongst the microscopists. Concordance among the results was checked and for results that deviated from the average range, the responsible microscopist was required to repeat the readings as a training exercise. External quality assurance involved the same microscopists reading reference slides obtained from labs in the United Kingdom (UK) where their results would then be compared to the standard reported for those slides. Over time, however, the internal quality control has changed whereby 10% of the slides read in a quarter are selected at random by an independent member of the lab, the readings repeated by the microscopists and compared for concordance between the new and the old results. For external quality control, this has changed where we now participate in three surveys per year carried out by the National Institute of Communicable Diseases (NICD) based in South Africa where they send 20 slides per survey to our lab for reading. The results are then compared to the reference results provided by NICD.The number of malaria episodes were calculated from the weekly follow-up data for the period leading up to each survey and then summed up and the figure assigned to the respective cross-sectional survey. This is what we present in Table 1.We have amended the second paragraph on page 5 under the sub-heading ‘Laboratory investigation’ as follows:‘Quality assurance over the study period included comprehensive microscopy training during induction and at regular intervals using internal and external quality control. For internal quality control, a subset of slides selected quarterly is re-read by the microscopy team and concordance between the results checked. For external quality control, at the beginning of the cohort study, this involved reading reference blood films from a partner lab in the United Kingdom. Currently, external quality control involves participating in three annual surveys carried out by the National Institute of Communicable Diseases (NICD) based in South Africa where they send 20 slides per survey to our lab for proficiency testing.’Results:Page 9) "Prior clinical malaria episodes were associated with increased odds of gametocyte positivity in Chonyi , Ngerenya early  and Ngerenya late ". Here is confusing that prior clinical malaria refers to number of malaria episodes, instead of malaria episodes in the previous survey period? We accept that the statement could be clarified and have therefore have amended the statement to read on page 9 in the third paragraph to read: (Page 13) "We found that before 2006, the number of malaria episodes were associated with an increased risk of gametocyte positivity  while recent malaria episodes were associated with an approximately threefold increased risk of gametocyte positivity ". The second part of the sentence would be more clear: 'while malaria episodes within 28 days of survey were associated….. '. We agree and have amended the sentence in the last paragraph on page 13 as follows:‘The number of malaria episodes occurring in the period leading up to a cross-sectional survey were associated with increased odds of gametocyte positivity in Chonyi …’.Table 9 should read 'Number of malaria episodes' instead of 'No of malaria episodes'. We acknowledge this oversight and have amended Table 9 to read ‘Number..’ instead of ‘No..’.‘…while malaria episodes occurring within 28 days of a cross-sectional survey were associated with an approximately threefold increased risk of…’Discussion – 15 years of age (instead of 15 years). We acknowledge this oversight and have edited the text on page 16 line 3 to now read ‘…15 years of age…’.(Page 16) "Furthermore, it is expected that the prevalence of gametocytaemia would fall in the community as the prevalence of asymptomatic parasitaemia falls38. Moreover, prior episodes of clinical malaria are a well-documented source of gametocytaemia6,11. The findings in Junju are therefore unexpected: gametocyte prevalence was disproportionately lower compared with the prevalence of asymptomatic parasitaemia ". Figure 4 shows asexual parasitemia; do the authors mean asymptomatic infections here? This should be clarified, it is now not clear (in the methods and result section) if authors refer to asymptomatic infections or all asexual infections. The main purpose of the cross-sectional parasitological surveys is to assess asymptomatic asexual parasite prevalence in the three cohorts as they were carried out at the end of the dry season when parasite transmission is low. For this reason, in the methods section, we refer to asymptomatic infections.nd paragraph of the discussion on page 16 to read:We, however, acknowledge that the terminology may not be accurately described in the discussion and have amended the 238. Moreover, prior episodes of clinical malaria are a well-documented source of gametocytaemia6, 11. The findings in Junju are therefore unexpected: gametocyte prevalence was disproportionately lower compared with the prevalence of asexual parasitaemia , and malaria episodes occurring in the period before a cross-sectional survey were modestly protective rather than a risk factor for gametocytaemia (Table 6). We speculate that a change in anti-malarial drug policy might have caused this variation in effect.’"prior episodes of clinical malaria were modestly protective rather than a risk factor for gametocytaemia. We speculate that a change in anti-malarial drug policy might have caused this variation in effect". Here prior episodes mean episodes within the 28 days of the survey? Here we refer to the number of malaria episodes occurring in the period leading up to a cross-sectional survey, to improve the clarity of the sentence we have amended it to read:‘Furthermore, it is expected that the prevalence of gametocytaemia would fall in the community as the prevalence of asymptomatic asexual parasitaemia falls (Page 19) "Interestingly, this and other studies16,18 showed that gametocyte prevalence was highest in 5–15-year-olds in comparison to their younger and older counterparts". The present study does not compare to older than 15 years old. In this paragraph, the study we were referring to is the cited study in the previous sentence by Goncalveset al. and not the present study. However, we acknowledge that this may not be clear as written and have therefore amended the sentence in the first paragraph of page 19 to read:‘…and malaria episodes occurring in the period before a cross-sectional survey were modestly protective rather than a risk factor for gametocytaemia (Table 6)’.et al.48 and other studies…’ ‘Interestingly, this previous study by Goncalves


Plasmodium vivax and Plasmodium falciparum malaria to Khatlon region. The National Programme of Malaria Control was successful and malaria transmission was interrupted in 2009. Background. The aim of this study was to investigate the mechanisms of immunological response in Tajik children with tropical Plasmodium falciparum malaria. Materials and Methods. We examined 124 patients with P. falciparum malaria at the age of 6 months up to 14 years that were hospitalized in Clinical Infectious Diseases Hospital in Dushanbe city and in Regional hospital of Khatlon region in the period 2000-2007. In most cases, they were school-age children (56%). The peak incidence was recorded in July-October. Verification of the diagnosis was based on clinical, epidemiological data, and the results of blood microscopy. In all patients, along with the standard, clinical, and laboratory tests, a number of indicators of the immune status were performed that include the T-immunity, the content of serum immunoglobulins of three main classes, the level of circulating immune complexes (CIC), C3 complement, and the concentration of key serum cytokines that have been studied in the dynamics of infectious process. Finding. The study of cellular and humoral immunity in patients with Plasmodium falciparum malaria is an obvious additional criterion in assessing the severity of infection. The imbalance of cytokine profile is an important pathogenic factor in the development of severe and recurrent forms of the disease, since the formation of a defective immune response to parasitic antigens contributes to adverse outcomes. Conclusions. Plasmodium falciparum malaria was characterized by depression of cellular and humoral immunity, the degree of which depended on the severity of the pathological process.The epidemiological situation in Tajikistan Republic deteriorated in the 1990s, when an influx of refugees from Afghanistan resulted in mass importation of Plasmodium falciparum malaria was registered in Tajikistan Republic, the only in the European Region of World Health Organization. The epidemiological situation in the newly independent Tajikistan deteriorated further in 1993, when an influx of refugees from malaria endemic regions of Afghanistan resulted in mass importation of Plasmodium vivax and Plasmodium falciparum malaria to malaria genic areas of Khatlon region, bordering Afghanistan. In 1997, at the peak of the epidemic, 29794 malaria cases were officially reported in the country. Considerable financial, scientific, and practical support from the government and various international organizations played a crucial role in controlling the malaria epidemic [ Gambusia affinis in breading sites, insecticide-treated nets (ITNs), and personnel training resulting in a rapid decrease of malaria morbidity. The P. falciparum malaria patients received radical treatment by artesunate + sulphadoxine/pyrimethamine. In 2000-2008, more than one million of people, including 150,000 school children were subjected to health education activities. In total, 305 laboratory technicians, 1550 physicians, epidemiologists, and entomologists benefitted from malaria training. A number of Afghani health workers were trained in Tajikistan in antimalarial measures in the border areas. The National Control Programme was successful and transmission of P. falciparum was interrupted in 2009 . The analysis of morbidity revealed seasonality: the peak incidence was recorded in July-October. According to “Malaria Case Management Protocol of Tajikistan Republic,” 39 patients were diagnosed with light form of the disease, 60—moderate form, and 25—severe form. After patients' examination, the light and moderate forms of the disease were dominating , mainly due to their early admission. Clinical manifestations of malaria were dependent on the age of children: in very young children, early symptoms of intoxication prevailed, dyspepsia, diarrhea, and fever, it had an intermittent character, and there was no typical malarial paroxysm. Malaria symptoms in children of older age groups did not differ much from the adults.We examined 124 patients withThe verification of the diagnosis was based on clinical, epidemiological data, and the results of blood microscopy. In all patients, along with the standard, clinical, and laboratory tests, a number of indicators of the immune status were performed that include the T-immunity, the content of serum immunoglobulins of three main classes, the level of circulating immune complexes (CIC), C3 complement, and the concentration of key serum cytokines that have been studied in the dynamics of infectious process  and depending on the severity of the disease. P. falciparum malaria, practitioners are guided by WHO recommendations  and ICD-10 , which distinguish uncomplicated, severe, and complicated malaria. In the NIS countries, including the Republic of Tajikistan, in the classification of P. falciparum malaria, the only difference from the WHO classification is that in uncomplicated form there are two degrees of severity: light (low-symptomatic) and medium ; the rest complies with the recommendations of WHO and ICD-10.The criteria for the severity of the disease are height and duration of fever, severity of symptoms of intoxication, lesions of other organs and systems, hepatosplenomegaly, parasitemia, and frequency and duration of malarial paroxysm. In assessing the severity ofFor quantification of CD4+, CD8+, and B lymphocytes (CD20+), the commercial kits of monoclonal antibodies of the company ORTON (USA) were used and the determination was performed on the cytometry counter (FACSСAN). Serum levels of immunoglobulin classes A, M, G, and the third component of complement were determined by radial immunodiffusion in gel; circulating concentrations of immune complexes (CIC) in the blood serum were performed in the reaction with polyethylene glycol.The study of cytokines in pg/ml was determined by the competitive and enzyme-linked immunosorbent assay (ELISA), using commercial “ProCon” company's test systems, Saint Petersburg; measurement of the optical density was performed on Multiscan MCC-340 “Labsystems” (Finland); IFN-g was also tested by ELISA using Biosource test system (USA).Statistical analysis was performed by the method of variation statistics on the PC using the application package “Statistica 6.0”  for the absolute values of calculated average values and the error of the mean (m±m). Pair comparisons of absolute values were carried out according to the Mann-Whitney U-test, which is used to compare independent samples, and Wilcoxon T-test for dependent samples. The differences were statistically considered significant at p<0.05. P. falciparum malaria revealed disturbances of immunoregulatory mechanisms in the different periods of the disease and their severity depends on the severity of the disease. The study of cellular and humoral protective factors revealed that in patients with mild form of P. falciparum malaria, indicators of cellular immunity at all stages of the disease did not differ from those of the control group  , but the immunoregulatory index (IRI) in the peak of disease was reduced (2.0). Low production of early antibodies  in the early stages of the disease contributed to the reduction of the index of antibody activity (IAA). High levels of IgG  and CIC  decreased gradually toward recovery, but also at late stage of recovery in the absence of parasites in the peripheral blood are still significantly higher than the control level . In the peak of the light form of tropical malaria in the composition of CIC, immunoglobulin G was dominated, which, apparently, was associated with a more active IgG binding of the antigenic determinants of Plasmodium falciparum. This was also reflected in the significant increase in the level of the absolute content of B cells in the period of the disease , which is obviously connected with the expressed neoantigenic stimulation of B cell part of the immune system. The values of C3 blood serum in all periods of the disease remained normal. The dynamics of cellular and humoral immunity in patients with mild form of P. falciparum malaria showed a weak immune restructuring of the organism due to brief irritation of the immune system by antigens of the parasite. Obviously, it is linked to more frequent occurrence of relapses and repeated cases of the disease after suffering from mild form of P. falciparum malaria (77.3%).The study of the immune response dynamics in P. falciparum malaria were significantly different from those of the light form. The most pronounced changes were observed at the peak of decease and early recovery: the absolute number of T-lymphocytes was significantly reduced  and CD4 + . In these stages of the disease, the activation of humoral immunity was observed, which was expressed in a significant increase in IgM concentrations of 1,8 ± 0,3 and 1,6 ± 0,58 g/l, against 1,02 ± 0,07 g/l in the control, p <0.01 and the IAA index of 2.4. In the peak of moderate form in the composition of the CIC, large-sized complexes containing IgM were dominated. Active immune restructuring determines a favorable outcome of the disease that is evidenced by the absence of cases of relapses and the formation of asymptomatic carriage with moderate course of illness.Changes of the immune status in the moderate form of P. falciparum malaria was characterized by almost the same quantitative and qualitative changes of cellular immunity, as well as in cases of moderate flow, but there was a significant reduction of IRI during the crisis period . Indicators of humoral immunity did not differ from those of healthy children, indicating the low production of antibodies M and G class with this form of disease severity. In the peak of the disease, a significant increase of CIC was observed  and, as in the moderate form, immune complexes of large dimensions prevailed in their composition containing IgM. The obvious depression of cell humoral protective factors and of high CIC values requires immunotherapy. As a result, studies have found a significant increase of proinflammatory cytokines in almost all periods of the disease with a tendency of reduced rates in the period of late recovery, but not reaching the control values   .γ: in the peak of disease, it was found at low level , with an increase in value in the period of recovery, which is a major factor, activating macrophages and promoting more effective destruction of intracellular pathogens. The findings suggest a substantial suppression of specific antiparasitic immunity in P. falciparum malaria. The level of the studied cytokines correlated with the severity of the disease: the highest rates were found in severe tropical malaria, perhaps due to excessive activity of monocytes/macrophages, responsible for the production of proinflammatory components of regulation, as well as the release of reactive radicals. The involvement of the monocyte-macrophage cells, followed by the active elaboration of the whole complex of biologically active substances, contributing to cellular and circulatory disorders can be traced with moderate and severe P. falciparum malaria; it is probably one of the pathological links of severe anemia, brain damage, and nonspecific inflammation. In considering the role of cytokines in the development of these or other disturbances, one has to take into account the diversity of their spectrum of biological activity [γ, naturally called immune interferon, also has the ability to suppress the proliferation of erythrocyte germ cells [ P. falciparum malaria (p <0.01), suggesting an imbalance of immunoregulatory mechanisms of Th2 type. The predominance of Th2-pathway immune response determines the suppression of cell-mediated immunity in the early stages of the disease. In addition, the imbalance of cell-cell interactions and the reduction of immunomodulatory properties indicate failure of stimulation of own adequate immune response as a reaction to malarial infection caused by P. falciparum. Although replicative P. falciparum malaria pathogen activity must be an inductor of interferon production, it does not happen due to lack of effective immunological response to various stimuli, including proinflammatory cytokines, pathogen itself, and its products of metabolism. This may lead to severe course with the development of serious complications, and the concentration of these cytokines may be a predictor of it. It should be noted that imbalance of immune mechanisms is short and in the recovery period the dominance of Th1-type immune response is observed that is induced by IFN-γ production, as well as lower levels of proinflammatory cytokines that exacerbate cardiovascular and autoimmune processes. Increasing concentrations of IL-4 have compensatory rather than active counterregulatory character to proinflammatory cytokines that implies more stabilizing function in the inflammatory response. Together, these identified features demonstrate the complexity and diversity of processes of immune cytokines in P. falciparum malaria.An exception is the content of IFN-activity , 7. It irm cells . Of part Plasmodium falciparum malaria is characterized by depression of T cell immunity, the extent of which depended on the severity of malaria infection. Please note the low levels of T-lymphocytes in moderate and severe forms of the disease, a significant reduction of T helpers, relatively intact level of T suppressors, and suppression of humoral immunity. These changes are most pronounced in the moderate form of the disease; moreover, they are accompanied by a substantial increase in the functional activity of cells having killer cytotoxic activity. The study of cellular and humoral immunity in patients with tropical malaria is an obvious additional criterion in assessing the severity of infection. The imbalance of cytokine profile in P. falciparum malaria is an important pathogenetic factor in the development of severe and recurrent forms of the disease, since the formation of a defective immune response to parasitic antigens contributes to adverse outcomes.The P. vivax . It was found that the nature of immunological disorders in the tertian malaria, in contrast to the P. falciparum malaria, is due to the high level of CIC in their composition of IgM, in combination with a decrease in the absolute number of peripheral blood lymphocytes. Immunological reactions are not prolonged in time and reflect the adequacy of the immune response. The tertian malaria is characterized by the predominance of Th1-pathway immune response, which contributes to the earliest elimination of infected red blood cells and pathogens from the body and the adequacy of the mechanisms of immunological response. In P. falciparum malaria, as noted above, Th2-pathway of the immune response prevails in the early stages of the disease with a switch in the period of convalescence to Th1-character of lymphocytic reactions (Figures 2 P. falciparum malaria is a predictor of the severe, complicated course of the disease, since the formation of an inadequate immune response to parasitic antigens contributes to the development of adverse outcomes.In parallel and at the same time, the above-mentioned indicators of the immune status and cytokine profile were carried out in children withFigures 2. The per P. falciparum malaria is an objective additional criterion in assessing the severity of the infectious process.The study of cellular and humoral immunity in patients withPlasmodium falciparum malaria was characterized by depression of cellular and humoral immunity, the degree of which depended on the severity of the pathological process. Please pay attention to the low rates of T-lymphocytes in moderate and severe forms of the disease and the significant decrease in T-lymphocytes-helpers with a relatively stable level of T-lymphocytes-suppressors. The most pronounced changes in the moderate form of the disease are accompanied by a significant increase in the functional activity of cells with killer cytotoxic activity and activation of humoral protection factors.