Some strains of Plasmodium falciparum, a human malaria parasite, evade the complement-like system of Anopheles gambiae mosquitoes.

Plasmodium falciparum lines differ in their ability to infect mosquitoes. The Anopheles gambiae L3-5 refractory (R) line melanizes most Plasmodium species, including the Brazilian P. falciparum 7G8 line, but it is highly susceptible to some African P. falciparum strains such as 3D7, NF54, and GB4. We investigated whether these lines differ in their ability to evade the mosquito immune system. Silencing key components of the mosquito complement-like system [thioester-containing protein 1 (TEP1), leucine-rich repeat protein 1, and Anopheles Plasmodium-responsive leucine-rich repeat protein 1] prevented melanization of 7G8 parasites, reverting the refractory phenotype. In contrast, it had no effect on the intensity of infection with NF54, suggesting that this line is able to evade TEP1-mediated lysis. When R females were coinfected with a line that is melanized (7G8) and a line that survives (3D7), the coinfection resulted in mixed infections with both live and encapsulated parasites on individual midguts. This finding shows that survival of individual parasites is parasite-specific and not systemic in nature, because parasites can evade TEP1-mediated lysis even when other parasites are melanized in the same midgut. When females from an extensive genetic cross between R and susceptible A. gambiae (G3) mosquitoes were infected with P. berghei, encapsulation was strongly correlated with the TEP1-R1 allele. However, P. falciparum 7G8 parasites were no longer encapsulated by females from this cross, indicating that the TEP1-R1 allele is not sufficient to melanize this line. Evasion of the A. gambiae immune system by P. falciparum may be the result of parasite adaptation to sympatric mosquito vectors and may be an important factor driving malaria transmission.

Defibrotide interferes with several steps of the coagulation-inflammation cycle and exhibits therapeutic potential to treat severe malaria.

OBJECTIVE: The coagulation-inflammation cycle has been implicated as a critical component in malaria pathogenesis. Defibrotide (DF), a mixture of DNA aptamers, displays anticoagulant, anti-inflammatory, and endothelial cell (EC)-protective activities and has been successfully used to treat comatose children with veno-occlusive disease. DF was investigated here as a drug to treat cerebral malaria. METHODS AND RESULTS: DF blocks tissue factor expression by ECs incubated with parasitized red blood cells and attenuates prothrombinase activity, platelet aggregation, and complement activation. In contrast, it does not affect nitric oxide bioavailability. We also demonstrated that Plasmodium falciparum glycosylphosphatidylinositol (Pf-GPI) induces tissue factor expression in ECs and cytokine production by dendritic cells. Notably, dendritic cells, known to modulate coagulation and inflammation systemically, were identified as a novel target for DF. Accordingly, DF inhibits Toll-like receptor ligand-dependent dendritic cells activation by a mechanism that is blocked by adenosine receptor antagonist (8-p-sulfophenyltheophylline) but not reproduced by synthetic poly-A, -C, -T, and -G. These results imply that aptameric sequences and adenosine receptor mediate dendritic cells responses to the drug. DF also prevents rosetting formation, red blood cells invasion by P. falciparum and abolishes oocysts development in Anopheles gambiae. In a murine model of cerebral malaria, DF affected parasitemia, decreased IFN-γ levels, and ameliorated clinical score (day 5) with a trend for increased survival. CONCLUSION: Therapeutic use of DF in malaria is proposed.

Using Machine Learning Technology (Early Artificial Intelligence-Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study.

BACKGROUND: Amid the COVID-19 pandemic, there has been a need for rapid social understanding to inform infodemic management and response. Although social media analysis platforms have traditionally been designed for commercial brands for marketing and sales purposes, they have been underused and adapted for a comprehensive understanding of social dynamics in areas such as public health. Traditional systems have challenges for public health use, and new tools and innovative methods are required. The World Health Organization Early Artificial Intelligence-Supported Response with Social Listening (EARS) platform was developed to overcome some of these challenges. OBJECTIVE: This paper describes the development of the EARS platform, including data sourcing, development, and validation of a machine learning categorization approach, as well as the results from the pilot study. METHODS: Data for EARS are collected daily from web-based conversations in publicly available sources in 9 languages. Public health and social media experts developed a taxonomy to categorize COVID-19 narratives into 5 relevant main categories and 41 subcategories. We developed a semisupervised machine learning algorithm to categorize social media posts into categories and various filters. To validate the results obtained by the machine learning-based approach, we compared it to a search-filter approach, applying Boolean queries with the same amount of information and measured the recall and precision. Hotelling T2 was used to determine the effect of the classification method on the combined variables. RESULTS: The EARS platform was developed, validated, and applied to characterize conversations regarding COVID-19 since December 2020. A total of 215,469,045 social posts were collected for processing from December 2020 to February 2022. The machine learning algorithm outperformed the Boolean search filters method for precision and recall in both English and Spanish languages (P<.001). Demographic and other filters provided useful insights on data, and the gender split of users in the platform was largely consistent with population-level data on social media use. CONCLUSIONS: The EARS platform was developed to address the changing needs of public health analysts during the COVID-19 pandemic. The application of public health taxonomy and artificial intelligence technology to a user-friendly social listening platform, accessible directly by analysts, is a significant step in better enabling understanding of global narratives. The platform was designed for scalability; iterations and new countries and languages have been added. This research has shown that a machine learning approach is more accurate than using only keywords and has the benefit of categorizing and understanding large amounts of digital social data during an infodemic. Further technical developments are needed and planned for continuous improvements, to meet the challenges in the generation of infodemic insights from social media for infodemic managers and public health professionals.

Piloting a Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI Tools.

Artificial intelligence (AI) offers the potential to support healthcare delivery, but poorly trained or validated algorithms bear risks of harm. Ethical guidelines stated transparency about model development and validation as a requirement for trustworthy AI. Abundant guidance exists to provide transparency through reporting, but poorly reported medical AI tools are common. To close this transparency gap, we developed and piloted a framework to quantify the transparency of medical AI tools with three use cases. Our framework comprises a survey to report on the intended use, training and validation data and processes, ethical considerations, and deployment recommendations. The transparency of each response was scored with either 0, 0.5, or 1 to reflect if the requested information was not, partially, or fully provided. Additionally, we assessed on an analogous three-point scale if the provided responses fulfilled the transparency requirement for a set of trustworthiness criteria from ethical guidelines. The degree of transparency and trustworthiness was calculated on a scale from 0% to 100%. Our assessment of three medical AI use cases pin-pointed reporting gaps and resulted in transparency scores of 67% for two use cases and one with 59%. We report anecdotal evidence that business constraints and limited information from external datasets were major obstacles to providing transparency for the three use cases. The observed transparency gaps also lowered the degree of trustworthiness, indicating compliance gaps with ethical guidelines. All three pilot use cases faced challenges to provide transparency about medical AI tools, but more studies are needed to investigate those in the wider medical AI sector. Applying this framework for an external assessment of transparency may be infeasible if business constraints prevent the disclosure of information. New strategies may be necessary to enable audits of medical AI tools while preserving business secrets.

Contact tracing indicators for COVID-19: Rapid scoping review and conceptual framework.

BACKGROUND: Contact tracing is one of the key interventions in response to the COVID-19 pandemic but its implementation varies widely across countries. There is little guidance on how to monitor contact tracing performance, and no systematic overview of indicators to assess contact tracing systems or conceptual framework for such indicators exists to date. METHODS: We conducted a rapid scoping review using a systematic literature search strategy in the peer-reviewed and grey literature as well as open source online documents. We developed a conceptual framework to map indicators by type (input, process, output, outcome, impact) and thematic area (human resources, financial resources, case investigation, contact identification, contact testing, contact follow up, case isolation, contact quarantine, transmission chain interruption, incidence reduction). RESULTS: We identified a total of 153 contact tracing indicators from 1,555 peer-reviewed studies, 894 studies from grey literature sources, and 15 sources from internet searches. Two-thirds of indicators were process indicators (102; 67%), while 48 (31%) indicators were output indicators. Only three (2%) indicators were input indicators. Indicators covered seven out of ten conceptualized thematic areas, with more than half being related to either case investigation (37; 24%) or contact identification (44; 29%). There were no indicators for the input area "financial resources", the outcome area "transmission chain interruption", and the impact area "incidence reduction". CONCLUSIONS: Almost all identified indicators were either process or output indicators focusing on case investigation, contact identification, case isolation or contact quarantine. We identified important gaps in input, outcome and impact indicators, which constrains evidence-based assessment of contact tracing systems. A universally agreed set of indicators is needed to allow for cross-system comparisons and to improve the performance of contact tracing systems.

Adoption of digital tools in the context of the COVID-19 pandemic in the Region of the Americas - the Go.Data experience.

The COVID-19 pandemic has accelerated the growth of digital health tools. Although a number of different tools exist to support field data collection in the context of outbreak response, they have not been sufficient. This prompted the World Health Organization (WHO) to collaborate with the Global Outbreak Alert and Response Network (GOARN) and GOARN partners to develop a comprehensive system, Go.Data. Go.Data, a digital tool for outbreak response has simplified how countries operationalize and monitor case and contact data. Since the start of the pandemic, WHO and GOARN partners have provided support to Go.Data projects in 65 countries and territories, yet the demand by countries to have documented success cases of Go.Data implementations continues to grow. This viewpoint documents the successful Go.Data implementation frameworks in two countries, Argentina and Guatemala and an academic institution, the University of Texas at Austin.

Risk factors for severe outcomes following 2009 influenza A (H1N1) infection: a global pooled analysis.

BACKGROUND: Since the start of the 2009 influenza A pandemic (H1N1pdm), the World Health Organization and its member states have gathered information to characterize the clinical severity of H1N1pdm infection and to assist policy makers to determine risk groups for targeted control measures. METHODS AND FINDINGS: Data were collected on approximately 70,000 laboratory-confirmed hospitalized H1N1pdm patients, 9,700 patients admitted to intensive care units (ICUs), and 2,500 deaths reported between 1 April 2009 and 1 January 2010 from 19 countries or administrative regions--Argentina, Australia, Canada, Chile, China, France, Germany, Hong Kong SAR, Japan, Madagascar, Mexico, The Netherlands, New Zealand, Singapore, South Africa, Spain, Thailand, the United States, and the United Kingdom--to characterize and compare the distribution of risk factors among H1N1pdm patients at three levels of severity: hospitalizations, ICU admissions, and deaths. The median age of patients increased with severity of disease. The highest per capita risk of hospitalization was among patients <5 y and 5-14 y (relative risk [RR] = 3.3 and 3.2, respectively, compared to the general population), whereas the highest risk of death per capita was in the age groups 50-64 y and ≥65 y (RR = 1.5 and 1.6, respectively, compared to the general population). Similarly, the ratio of H1N1pdm deaths to hospitalizations increased with age and was the highest in the ≥65-y-old age group, indicating that while infection rates have been observed to be very low in the oldest age group, risk of death in those over the age of 64 y who became infected was higher than in younger groups. The proportion of H1N1pdm patients with one or more reported chronic conditions increased with severity (median = 31.1%, 52.3%, and 61.8% of hospitalized, ICU-admitted, and fatal H1N1pdm cases, respectively). With the exception of the risk factors asthma, pregnancy, and obesity, the proportion of patients with each risk factor increased with severity level. For all levels of severity, pregnant women in their third trimester consistently accounted for the majority of the total of pregnant women. Our findings suggest that morbid obesity might be a risk factor for ICU admission and fatal outcome (RR = 36.3). CONCLUSIONS: Our results demonstrate that risk factors for severe H1N1pdm infection are similar to those for seasonal influenza, with some notable differences, such as younger age groups and obesity, and reinforce the need to identify and protect groups at highest risk of severe outcomes. Please see later in the article for the Editors' Summary.

Use of a Drosophila model to identify genes regulating Plasmodium growth in the mosquito.

We performed a forward genetic screen, using Drosophila as a surrogate mosquito, to identify host factors required for the growth of the avian malaria parasite, Plasmodium gallinaceum. We identified 18 presumed loss-of-function mutants that reduced the growth of the parasite in flies. Presumptive mutation sites were identified in 14 of the mutants on the basis of the insertion site of a transposable element. None of the identified genes have been previously implicated in innate immune responses or interactions with Plasmodium. The functions of five Anopheles gambiae homologs were tested by using RNAi to knock down gene function followed by measuring the growth of the rodent parasite, Plasmodium berghei. Loss of function of four of these genes in the mosquito affected Plasmodium growth, suggesting that Drosophila can be used effectively as a surrogate mosquito to identify relevant host factors in the mosquito.

Mosquito immune responses and compatibility between Plasmodium parasites and anopheline mosquitoes.

BACKGROUND: Functional screens based on dsRNA-mediated gene silencing identified several Anopheles gambiae genes that limit Plasmodium berghei infection. However, some of the genes identified in these screens have no effect on the human malaria parasite Plasmodium falciparum; raising the question of whether different mosquito effector genes mediate anti-parasitic responses to different Plasmodium species. RESULTS: Four new An. gambiae (G3) genes were identified that, when silenced, have a different effect on P. berghei (Anka 2.34) and P. falciparum (3D7) infections. Orthologs of these genes, as well as LRIM1 and CTL4, were also silenced in An. stephensi (Nijmegen Sda500) females infected with P. yoelii (17XNL). For five of the six genes tested, silencing had the same effect on infection in the P. falciparum-An. gambiae and P. yoelii-An. stephensi parasite-vector combinations. Although silencing LRIM1 or CTL4 has no effect in An. stephensi females infected with P. yoelii, when An. gambiae is infected with the same parasite, silencing these genes has a dramatic effect. In An. gambiae (G3), TEP1, LRIM1 or LRIM2 silencing reverts lysis and melanization of P. yoelii, while CTL4 silencing enhances melanization. CONCLUSION: There is a broad spectrum of compatibility, the extent to which the mosquito immune system limits infection, between different Plasmodium strains and particular mosquito strains that is mediated by TEP1/LRIM1 activation. The interactions between highly compatible animal models of malaria, such as P. yoelii (17XNL)-An. stephensi (Nijmegen Sda500), is more similar to that of P. falciparum (3D7)-An. gambiae (G3).

September through October 2010 multi-centre study in the Netherlands examining laboratory ability to detect enterovirus 68, an emerging respiratory pathogen.

During September and October 2010, the Dutch Public Health Institute detected an enterovirus (EV) 68 (EV68) epidemic in the Netherlands through general practitioner-based surveillance of acute respiratory infections. EV68 shares phenotypic and genotypic properties with human rhinovirus (HRV). Despite increased EV and HRV detections, Dutch clinical laboratories did not identify EV68. To assess the capability of Dutch clinical laboratories to detect EV68, ten laboratories with more than eight detected EV and HRV cases in September and October 2010 provided information about their detection algorithms and testing results for a 2010 Dutch EV68 strain. For EV detection mostly stool specimens (median 49%), respiratory specimens (median 27%) and cerebrospinal fluid (median 22%) were used. For HRV detection only respiratory specimens were used. Except for the Seeplex® RV15ACE EV-specific assay, all EV and 73% of HRV assays, including those of the Public Health Institute, were able to detect EV68. Two-step EV RT-PCR protocols were the most sensitive. Thus, laboratories might have misidentified EV68 as HRV. In addition, EV68 cases might have also been missed because patients with respiratory diseases are usually not tested for EV infection. Therefore, clinical laboratories should include EV detection in the differential diagnosis of patients presenting with respiratory symptoms.

Emergence and epidemic occurrence of enterovirus 68 respiratory infections in The Netherlands in 2010.

Following an increase in detection of enterovirus 68 (EV68) in community surveillance of respiratory infections in The Netherlands in 2010, epidemiological and virological analyses were performed to investigate the possible public health impact of EV68 infections. We retrospectively tested specimens collected from acute respiratory infections surveillance and through three children cohort studies conducted in The Netherlands from 1994 through 2010. A total of 71 of 13,310 (0.5%) specimens were positive for EV68, of which 67 (94%) were from symptomatic persons. Twenty-four (34%) of the EV68 positive specimens were collected during 2010. EV68-positive patients with respiratory symptoms showed significantly more dyspnea, cough and bronchitis than EV68-negative patients with respiratory symptoms. Phylogenetic analysis showed an increased VP1 gene diversity in 2010, suggesting that the increased number of EV68 detections in 2010 reflects a real epidemic. Clinical laboratories should consider enterovirus diagnostics in the differential diagnosis of patients presenting with respiratory symptoms.

Seasonality and prevalence of Leishmania major infection in Phlebotomus duboscqi Neveu-Lemaire from two neighboring villages in central Mali.

Phlebotomus duboscqi is the principle vector of Leishmania major, the causative agent of cutaneous leishmaniasis (CL), in West Africa and is the suspected vector in Mali. Although found throughout the country the seasonality and infection prevalence of P. duboscqi has not been established in Mali. We conducted a three year study in two neighboring villages, Kemena and Sougoula, in Central Mali, an area with a leishmanin skin test positivity of up to 45%. During the first year, we evaluated the overall diversity of sand flies. Of 18,595 flies collected, 12,952 (69%) belonged to 12 species of Sergentomyia and 5,643 (31%) to two species of the genus Phlebotomus, P. duboscqi and P. rodhaini. Of those, P. duboscqi was the most abundant, representing 99% of the collected Phlebotomus species. P. duboscqi was the primary sand fly collected inside dwellings, mostly by resting site collection. The seasonality and infection prevalence of P. duboscqi was monitored over two consecutive years. P. dubsocqi were collected throughout the year. Using a quasi-Poisson model we observed a significant annual (year 1 to year 2), seasonal (monthly) and village effect (Kemena versus Sougoula) on the number of collected P. duboscqi. The significant seasonal effect of the quasi-Poisson model reflects two seasonal collection peaks in May-July and October-November. The infection status of pooled P. duboscqi females was determined by PCR. The infection prevalence of pooled females, estimated using the maximum likelihood estimate of prevalence, was 2.7% in Kemena and Sougoula. Based on the PCR product size, L. major was identified as the only species found in flies from the two villages. This was confirmed by sequence alignment of a subset of PCR products from infected flies to known Leishmania species, incriminating P. duboscqi as the vector of CL in Mali.

The Anopheles gambiae oxidation resistance 1 (OXR1) gene regulates expression of enzymes that detoxify reactive oxygen species.

BACKGROUND: OXR1 is an ancient gene, present in all eukaryotes examined so far that confers protection from oxidative stress by an unknown mechanism. The most highly conserved region of the gene is the carboxyl-terminal TLDc domain, which has been shown to be sufficient to prevent oxidative damage. METHODOLOGY/PRINCIPAL FINDINGS: OXR1 has a complex genomic structure in the mosquito A. gambiae, and we confirm that multiple splice forms are expressed in adult females. Our studies revealed that OXR1 regulates the basal levels of catalase (CAT) and glutathione peroxidase (Gpx) expression, two enzymes involved in detoxification of hydrogen peroxide, giving new insight into the mechanism of action of OXR1. Gene silencing experiments indicate that the Jun Kinase (JNK) gene acts upstream of OXR1 and also regulates expression of CAT and GPx. Both OXR1 and JNK genes are required for adult female mosquitoes to survive chronic oxidative stress. OXR1 silencing decreases P. berghei oocyst formation. Unexpectedly, JNK silencing has the opposite effect and enhances Plasmodium infection in the mosquito, suggesting that JNK may also mediate some, yet to be defined, antiparasitic response. CONCLUSION: The JNK pathway regulates OXR1 expression and OXR1, in turn, regulates expression of enzymes that detoxify reactive oxygen species (ROS) in Anopheles gambiae. OXR1 silencing decreases Plasmodium infection in the mosquito, while JNK silencing has the opposite effect and enhances infection.

Reactive oxygen species modulate Anopheles gambiae immunity against bacteria and Plasmodium.

The involvement of reactive oxygen species (ROS) in mosquito immunity against bacteria and Plasmodium was investigated in the malaria vector Anopheles gambiae. Strains of An. gambiae with higher systemic levels of ROS survive a bacterial challenge better, whereas reduction of ROS by dietary administration of antioxidants significantly decreases survival, indicating that ROS are required to mount effective antibacterial responses. Expression of several ROS detoxification enzymes increases in the midgut and fat body after a blood meal. Furthermore, expression of several of these enzymes increases to even higher levels when mosquitoes are fed a Plasmodium berghei-infected meal, indicating that the oxidative stress after a blood meal is exacerbated by Plasmodium infection. Paradoxically, a complete lack of induction of catalase mRNA and lower catalase activity were observed in P. berghei-infected midguts. This suppression of midgut catalase expression is a specific response to ookinete midgut invasion and is expected to lead to higher local levels of hydrogen peroxide. Further reduction of catalase expression by double-stranded RNA-mediated gene silencing promoted parasite clearance by a lytic mechanism and reduced infection significantly. High mosquito mortality is often observed after P. berghei infection. Death appears to result in part from excess production of ROS, as mortality can be decreased by oral administration of uric acid, a strong antioxidant. We conclude that ROS modulate An. gambiae immunity and that the mosquito response to P. berghei involves a local reduction of detoxification of hydrogen peroxide in the midgut that contributes to limit Plasmodium infection through a lytic mechanism.