Toward a meningitis-free world

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

Background and Aims

This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis.

Methods

On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements.

Results

After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms.

Conclusion

The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.

A re-assessment of 4CMenB vaccine effectiveness against serogroup B invasive meningococcal disease in England based on an incidence model

BACKGROUND

The four-component serogroup B meningococcal 4CMenB vaccine (Bexsero, GSK) has been routinely given to all infants in the United Kingdom at 2, 4 and 12 months of age since September 2015. After 3 years, Public Health England (PHE) reported a 75% [95% confidence interval 64%; 81%] reduction in the incidence of serogroup B invasive meningococcal disease (IMD) in age groups eligible to be fully vaccinated. In contrast, vaccine effectiveness (VE) evaluated in the same immunization program applying the screening method was not statistically significant. We re-analyzed the data using an incidence model.

METHODS

Aggregate data-stratified by age, year and doses received-were provided by PHE: serogroup B IMD case counts for the entire population of England (years 2011-2018) and 4CMenB vaccine uptake in infants. We combined uptake with national population estimates to obtain counts of vaccinated and unvaccinated person-time by age and time. We re-estimated VE comparing incidence rates in vaccinated and non-vaccinated subjects using a Bayesian Poisson model for case counts with person-time data as an offset. The model was adjusted for age, time and number of doses received.

RESULTS

The incidence model showed that cases decreased until 2013-2014, followed by an increasing trend that continued in the non-vaccinated population during the immunization program. VE in fully vaccinated subjects (three doses) was 80.1% [95% Bayesian credible interval (BCI): 70.3%; 86.7%]. After a single dose, VE was 33.5% [12.4%; 49.7%]95%BCI and after two doses, 78.7% [71.5%; 84.5%]95%BCI. We estimated that vaccination averted 312 cases [252; 368]95%BCI between 2015 and 2018. VE was in line with the previously reported incidence reduction.

CONCLUSIONS

Our estimates of VE had higher precision than previous estimates based on the screening method, which were statistically not significant, and in line with the 75% incidence reduction previously reported by PHE. When disease incidence is low and vaccine uptake is high, the screening method applied to cases exclusively from the population eligible for vaccination may not be precise enough and may produce misleading point-estimates. Precise and accurate VE estimates are fundamental to inform public health decision making. VE assessment can be enhanced using models that leverage data on subjects not eligible for vaccination.

Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study

Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0-14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.

The Impact of Nucleotide Sequence Analysis on Meningococcal Vaccine Development and Assessment

Since it became available as a routine tool in biology, the determination and analysis of nucleotide sequences has been applied to the design of vaccines and the investigation of their effectiveness. As vaccination is primarily concerned with the interaction of biological molecules with the immune system, the utility of sequence data is not immediately obvious and, indeed, nucleotide sequence data are most effective when used to complement more conventional immunological approaches. Here, the impact of sequencing on the field of vaccinology will be illustrated with reference to the development and implementation of vaccines against Neisseria meningitidis (the meningococcus) over the 30-year period from the late-1980s to the late-2010s. Nucleotide sequence-based studies have been important in the fight against this aggressive pathogen largely because of its high genetic and antigenic diversity, properties that were only fully appreciated because of sequence-based studies. Five aspects will be considered, the use of sequence data to: (i) discover vaccine antigens; (ii) assess the diversity and distribution of vaccine antigens; (iii) determine the evolutionary and population biology of the organism and their implications for immunization; and (iv) develop molecular approaches to investigate pre- and post-vaccine pathogen populations to assess vaccine impact. One of the great advantages of nucleotide sequence data has been its scalability, which has meant that increasingly large data sets have been available, which has proved invaluable in the investigation of an organism as diverse and enigmatic as the meningococcus.

NadA3 Structures Reveal Undecad Coiled Coils and LOX1 Binding Regions Competed by Meningococcus B Vaccine-Elicited Human Antibodies

Neisseria meningitidis serogroup B (MenB) is a major cause of sepsis and invasive meningococcal disease. A multicomponent vaccine, 4CMenB, is approved for protection against MenB. Neisserial adhesin A (NadA) is one of the main vaccine antigens, acts in host cell adhesion, and may influence colonization and invasion. Six major genetic variants of NadA exist and can be classified into immunologically distinct groups I and II. Knowledge of the crystal structure of the 4CMenB vaccine component NadA3 (group I) would improve understanding of its immunogenicity, folding, and functional properties and might aid antigen design. Here, X-ray crystallography, biochemical, and cellular studies were used to deeply characterize NadA3. The NadA3 crystal structure is reported; it revealed two unexpected regions of undecad coiled-coil motifs and other conformational differences from NadA5 (group II) not predicted by previous analyses. Structure-guided engineering was performed to increase NadA3 thermostability, and a second crystal structure confirmed the improved packing. Functional NadA3 residues mediating interactions with human receptor LOX-1 were identified. Also, for two protective vaccine-elicited human monoclonal antibodies (5D11, 12H11), we mapped key NadA3 epitopes. These vaccine-elicited human MAbs competed binding of NadA3 to LOX-1, suggesting their potential to inhibit host-pathogen colonizing interactions. The data presented provide a significant advance in the understanding of the structure, immunogenicity and function of NadA, one of the main antigens of the multicomponent meningococcus B vaccine.IMPORTANCE The bacterial microbe Neisseria meningitidis serogroup B (MenB) is a major cause of devastating meningococcal disease. An approved multicomponent vaccine, 4CMenB, protects against MenB. Neisserial adhesin A (NadA) is a key vaccine antigen and acts in host cell-pathogen interactions. We investigated the 4CMenB vaccine component NadA3 in order to improve the understanding of its immunogenicity, structure, and function and to aid antigen design. We report crystal structures of NadA3, revealing unexpected structural motifs, and other conformational differences from the NadA5 orthologue studied previously. We performed structure-based antigen design to engineer increased NadA3 thermostability. Functional NadA3 residues mediating interactions with the human receptor LOX-1 and vaccine-elicited human antibodies were identified. These antibodies competed binding of NadA3 to LOX-1, suggesting their potential to inhibit host-pathogen colonizing interactions. Our data provide a significant advance in the overall understanding of the 4CMenB vaccine antigen NadA.

Changing Priorities in Vaccinology: Antibiotic Resistance Moving to the Top

Antimicrobial resistance (AMR) is currently the most alarming issue for human health. AMR already causes 700,000 deaths/year. It is estimated that 10 million deaths due to AMR will occur every year after 2050. This equals the number of people dying of cancer every year in present times. International institutions such as G20, World Bank, World Health Organization (WHO), UN General Assembly, European Union, and the UK and USA governments are calling for new antibiotics. To underline this emergency, a list of antibiotic-resistant "priority pathogens" has been published by WHO. It contains 12 families of bacteria that represent the greatest danger for human health. Resistance to multiple antibiotics is particularly relevant for the Gram-negative bacteria present in the list. The ability of these bacteria to develop mechanisms to resist treatment could be transmitted with genetic material, allowing other bacteria to become drug resistant. Although the search for new antimicrobial drugs remains a top priority, the pipeline for new antibiotics is not promising, and alternative solutions are needed. A possible answer to AMR is vaccination. In fact, while antibiotic resistance emerges rapidly, vaccines can lead to a much longer lasting control of infections. New technologies, such as the high-throughput cloning of human B cells from convalescent or vaccinated people, allow for finding new protective antigens (Ags) that could not be identified with conventional technologies. Antibodies produced by convalescent B cell clones can be screened for their ability to bind, block, and kill bacteria, using novel high-throughput microscopy platforms that rapidly capture digital images, or by conventional technologies such as bactericidal, opsono-phagocytosis and FACS assays. Selected antibodies expressed by recombinant DNA techniques can be used for passive immunization in animal models and tested for protection. Antibodies providing the best protection can be employed to identify new Ags and then used for generating highly specific recombinant Fab fragments. Co-crystallization of Ags bound to Fab fragments will allow us to determine the structure and characteristics of new Ags. This structure-based Ag design will bring to a new generation of vaccines able to target previously elusive infections, thereby offering an effective solution to the problem of AMR.

Community-acquired bacterial meningitis

Meningitis is an inflammation of the meninges and subarachnoid space that can also involve the brain cortex and parenchyma. It can be acquired spontaneously in the community - community-acquired bacterial meningitis - or in the hospital as a complication of invasive procedures or head trauma (nosocomial bacterial meningitis). Despite advances in treatment and vaccinations, community-acquired bacterial meningitis remains one of the most important infectious diseases worldwide. Streptococcus pneumoniae and Neisseria meningitidis are the most common causative bacteria and are associated with high mortality and morbidity; vaccines targeting these organisms, which have designs similar to the successful vaccine that targets Haemophilus influenzae type b meningitis, are now being used in many routine vaccination programmes. Experimental and genetic association studies have increased our knowledge about the pathogenesis of bacterial meningitis. Early antibiotic treatment improves the outcome, but the growing emergence of drug resistance as well as shifts in the distribution of serotypes and groups are fuelling further development of new vaccines and treatment strategies. Corticosteroids were found to be beneficial in high-income countries depending on the bacterial species. Further improvements in the outcome are likely to come from dampening the host inflammatory response and implementing preventive measures, especially the development of new vaccines.

Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines

BACKGROUND

Estimating the effectiveness of meningococcal vaccines with high accuracy and precision can be challenging due to the low incidence of the invasive disease, which ranges between 0.5 and 1 cases per 100,000 in Europe and North America. Vaccine effectiveness (VE) is usually estimated with a screening method that combines in one formula the proportion of meningococcal disease cases that have been vaccinated and the proportion of vaccinated in the overall population. Due to the small number of cases, initial point estimates are affected by large uncertainties and several years may be required to estimate VE with a small confidence interval.

METHODS

We used a Monte Carlo maximum likelihood (MCML) approach to estimate the effectiveness of meningococcal vaccines, based on stochastic simulations of a dynamic model for meningococcal transmission and vaccination. We calibrated the model to describe two immunization campaigns: the campaign against MenC in England and the Bexsero campaign that started in the UK in September 2015. First, the MCML method provided estimates for both the direct and indirect effects of the MenC vaccine that were validated against results published in the literature. Then, we assessed the performance of the MCML method in terms of time gain with respect to the screening method under different assumptions of VE for Bexsero.

RESULTS

MCML estimates of VE for the MenC immunization campaign are in good agreement with results based on the screening method and carriage studies, yet characterized by smaller confidence intervals and obtained using only incidence data collected within 2 years of scheduled vaccination. Also, we show that the MCML method could provide a fast and accurate estimate of the effectiveness of Bexsero, with a time gain, with respect to the screening method, that could range from 2 to 15 years, depending on the value of VE measured from field data.

CONCLUSIONS

Results indicate that inference methods based on dynamic computational models can be successfully used to quantify in near real time the effectiveness of immunization campaigns against Neisseria meningitidis. Such an approach could represent an important tool to complement and support traditional observational studies, in the initial phase of a campaign.

Bexsero® chronicle

Neisseria meningitidis causes globally 1·2 million invasive disease cases and 135,000 deaths per year, mostly in infants and adolescents. A century of traditional vaccinology had failed the fight against the serogroup B meningococcus (MenB), mostly prevalent in developed countries. Eighteen years after the publication of the first complete genome sequence from a living organism, thanks to an innovative genome-based approach named 'reverse vaccinology', the first broadly effective MenB vaccine was licensed for use by the European Medical Agency and other authorities, and is being implemented worldwide. Here we review this long and passionate journey, from the disease epidemiology to novel antigen discovery, from vaccine clinical development to public health impact: two decades of scientific and technological innovation to defeat one of the most sudden and devastating invasive diseases.

Structure of the meningococcal vaccine antigen NadA and epitope mapping of a bactericidal antibody

Serogroup B Neisseria meningitidis (MenB) is a major cause of severe sepsis and invasive meningococcal disease, which is associated with 5-15% mortality and devastating long-term sequelae. Neisserial adhesin A (NadA), a trimeric autotransporter adhesin (TAA) that acts in adhesion to and invasion of host epithelial cells, is one of the three antigens discovered by genome mining that are part of the MenB vaccine that recently was approved by the European Medicines Agency. Here we present the crystal structure of NadA variant 5 at 2 Å resolution and transmission electron microscopy data for NadA variant 3 that is present in the vaccine. The two variants show similar overall topology with a novel TAA fold predominantly composed of trimeric coiled-coils with three protruding wing-like structures that create an unusual N-terminal head domain. Detailed mapping of the binding site of a bactericidal antibody by hydrogen/deuterium exchange MS shows that a protective conformational epitope is located in the head of NadA. These results provide information that is important for elucidating the biological function and vaccine efficacy of NadA.

Design and evaluation of meningococcal vaccines through structure-based modification of host and pathogen molecules

Neisseria meningitis remains a leading cause of sepsis and meningitis, and vaccines are required to prevent infections by this important human pathogen. Factor H binding protein (fHbp) is a key antigen that elicits protective immunity against the meningococcus and recruits the host complement regulator, fH. As the high affinity interaction between fHbp and fH could impair immune responses, we sought to identify non-functional fHbps that could act as effective immunogens. This was achieved by alanine substitution of fHbps from all three variant groups (V1, V2 and V3 fHbp) of the protein; while some residues affected fH binding in each variant group, the distribution of key amino underlying the interaction with fH differed between the V1, V2 and V3 proteins. The atomic structure of V3 fHbp in complex with fH and of the C-terminal barrel of V2 fHbp provide explanations to the differences in the precise nature of their interactions with fH, and the instability of the V2 protein. To develop transgenic models to assess the efficacy of non-functional fHbps, we determined the structural basis of the low level of interaction between fHbp and murine fH; in addition to changes in amino acids in the fHbp binding site, murine fH has a distinct conformation compared with the human protein that would sterically inhibit binding to fHbp. Non-functional V1 fHbps were further characterised by binding and structural studies, and shown in non-transgenic and transgenic mice (expressing chimeric fH that binds fHbp and precisely regulates complement system) to retain their immunogenicity. Our findings provide a catalogue of non-functional fHbps from all variant groups that can be included in new generation meningococcal vaccines, and establish proof-in-principle for clinical studies to compare their efficacy with wild-type fHbps.

Vaccines are currently available against several serogroups of Neisseria meningitidis. However broadly effective serogroup B vaccines are still required as capsule-based approaches cannot be implemented with this serogroup because of the risks of auto-immunity. As a result, vaccines based on proteins in the bacterial outer membrane are being developed. Factor H binding protein (fHbp) is an important meningococcal immunogen which is able to bind the human complement regulator factor H (fH) at high affinity; this interaction could impair the efficacy of fHbp-based vaccines. Here we perform structure:function analyses to define non-functional fHbps and to explain the basis for the host specificity of the fHbp:fH interaction. The vaccine candidacy of non-functional fHbps was compared with wild-type proteins in a relevant transgenic model. These findings should allow the design and evaluation of future fHbp vaccines against this important human pathogen.