A significant proportion of individuals who are heavily exposed to infectious tuberculosis patients do not acquire Mycobacterium tuberculosis (Mtb) infection, as detected by an interferon gamma release assay (IGRA). We examined circulating metabolite profiles and metabolic genotypes in 199 heavily exposed IGRA-negative tuberculosis household contacts in Indonesia. Based on differentially abundant metabolites, activity of several pathways including arachidonic acid, arginine and proline, glutathione, and tryptophan metabolism, correlated with a negative IGRA at three months. SNPs near PRODH (involved in arginine and proline metabolism) were associated with circulating proline concentrations and persistently negative IGRA results, while SNPstudys near AFMID (involved in tryptophan metabolism) were associated with IGRA conversion. For further validation, plasma metabolomic profiles were correlated with mycobacterial growth inhibition by peripheral blood mononuclear cells from individuals in a low-incidence setting. Lower circulating concentrations of six metabolites, including leukotriene B4, proline, glycine, and tryptophan correlated with better growth control in non-exposed healthy individuals, as well as with stronger protection against IGRA-conversion in the Indonesian tuberculosis household contacts. Collectively, these data support the notion that circulating metabolites may impact innate host defense against Mtb infection, and that metabolic interventions may prevent tuberculosis infection and disease.

A significant proportion of individuals heavily exposed to infectious tuberculosis patients do not acquire Mycobacterium tuberculosis (Mtb) infection, as detected by an interferon gamma release assay (IGRA). Trained immunity may contribute to this host resistance to Mtb infection, also termed early clearance. From a prospective tuberculosis household study in Indonesia we selected 80 heavily exposed IGRA-negative household contacts, of whom 40 converted their baseline-negative IGRA to positive after three months (IGRA converters) and 40 remained IGRA-negative (early clearers). From all individuals we measured circulating β-D-glucan and used their serum for induction of trained immunity in vitro, using peripheral blood mononuclear cells from healthy unexposed Dutch donors, that were stimulated with unrelated stimuli six days after exposure to serum from household contacts. β-D-glucan concentrations and positivity did not correlate with early clearance, nor with serum-induced in-vitro trained immunity as measured by heterologous cytokine responses. These findings suggest that early clearance is unlikely to be maintained by circulating β-D-glucan or other serum factors present at exposure to Mtb, and may instead depend on cell-intrinsic or local host processes not captured by serum-based assays.

Background Certain micronutrient levels have been associated with the risk of developing TB disease. We explored the possible association of selected at-risk micronutrient levels with the development of Mycobacterium tuberculosis (M.tb) infection.Methods This cohort study in Bandung, Indonesia, followed Interferon Gamma Release Assay (IGRA) negative TB case contacts with a repeat IGRA test at 3 mo. At baseline, blood was analysed for haemoglobin, 25-hydroxyvitamin D, retinol-binding protein, C-reactive protein, alpha-1-acid glycoprotein, serum transferrin receptor (sTfR), ferritin, zinc and selenium. Total body iron was calculated using ferritin and sTfR status. Associations between case contact micronutrient concentration and IGRA conversion were estimated using Poisson regression.Results Of 430 contacts, 115 (27%) underwent IGRA conversion. Ferritin concentration (adjusted for inflammation) was positively associated with risk of IGRA conversion (incidence rate ratio [IRR] for ferritin=1.17; 95% CI 1.01 to 1.35; p=0.03), but other select micronutrients were not. This association held for ferritin in the final multivariable model (IRR=1.27; 95% CI 1.09 to 1.47; p=0.002).Conclusions The risk of developing M.tb infection, as defined by IGRA conversion, is associated with increasing ferritin. Interventions in TB case contacts to temporarily reduce iron levels, including considering withholding any iron supplementation, may be worthy of evaluation.

AbstractSome individuals, even when heavily exposed to an infectious tuberculosis patient, do not develop a specific T-cell response as measured by interferon-gamma release assay (IGRA). This could be explained by an IFN-γ-independent adaptive immune response, or an effective innate host response clearing Mycobacterium tuberculosis (Mtb) without adaptive immunity. In heavily exposed Indonesian tuberculosis household contacts (n = 1347), a persistently IGRA negative status was associated with presence of a BCG scar, and - especially among those with a BCG scar - with altered innate immune cells dynamics, higher heterologous (Escherichia coli-induced) proinflammatory cytokine production, and higher inflammatory proteins in the IGRA mitogen tube. Neither circulating concentrations of Mtb-specific antibodies nor functional antibody activity associated with IGRA status at baseline or follow-up. In a cohort of adults in a low tuberculosis incidence setting, BCG vaccination induced heterologous innate cytokine production, but only marginally affected Mtb-specific antibody profiles. Our findings suggest that a more efficient host innate immune response, rather than a humoral response, mediates early clearance of Mtb. The protective effect of BCG vaccination against Mtb infection may be linked to innate immune priming, also termed ‘trained immunity’.

Background: Matrix metalloproteinase (MMP) activity has an important role in lung cavitary formation occurred in pulmonary tuberculosis (TB). Low number and viability of CD4 +T-lymphocytes in patients with TB/HIV co infection leads to impaired neutrophils production, causing further impaired MMPs production. Objective: To explore association of neutrophils and lymphocytes count to MMP-8 and MMP-9 among pulmonary TB patients with cavitary lesion and HIV co-infection. Methods: We conducted a cross-sectional study using a purposive sampling technique among patients with noncavitary TB (n = 50), cavitary TB (n = 50) and TB/HIV (n = 27). Complete blood count was examined, including neutrophils and lymphocytes count. MMP-8 and MMP-9 were measured from plasma samples using ELISA method. Statistical analysis was conducted to determine the relation between neutrophils, lymphocytes and MMPs. Result: MMP-8 and MMP-9 were positively correlated with neutrophils, but not to lymphocytes in all groups. Neutrophils, lymphocytes, and MMP-9 were significantly lower in TB/HIV co-infection, whereas MMP-8 was higher compared to new pulmonary TB. Interestingly, in cavitary TB, low lymphocytes were significantly correlated with higher level of MMP-8 and larger extent of lung affected. Conclusion: MMP-8 and MMP-9 are associated with neutrophil count, suggesting that neutrophils contribute significantly to their secretion. MMP-8 is significantly higher in TB/HIV co-infection and extent of lung damage in cavitary TB with lower lymphocyte count. This study suggests that lower lymphocyte level is related to higher neutrophil orchestrated inflammation, leading to tissue destruction.

Models of contact tracing often over-simplify the effects of quarantine and isolation on disease transmission. We develop a model that allows us to investigate the importance of these factors in reducing the effective reproduction number. We show that the reduction in onward transmission during quarantine and isolation has a bigger effect than tracing coverage on the reproduction number. We also show that intuitively reasonable contact tracing performance indicators, such as the proportion of contacts quarantined before symptom onset, are often not well correlated with the reproduction number. We conclude that provision of support systems to enable people to quarantine and isolate effectively is crucial to the success of contact tracing.

In Indonesia, BCG vaccine protection against Mycobacterium tuberculosis infection decreased with increasing exposure to the pathogen. We aimed to validate these findings in Africa. Poisson regression was used to estimate BCG protection, stratified by pathogen exposure using an exposure score, against enzyme-linked immunospot assay conversion at 3 months in 220 Gambian case contacts. Although the interaction between BCG and exposure was not significant (P=.13), BCG protection was strongest in the lowest-exposure tertile (relative risk, 0.35 [95% confidence interval, .15-.82; P=.02] vs 0.50 [.30-.83; P=.008] and 0.71 (.45-1.13; P=.1] for the middle and highest-exposure tertiles, respectively. These results are consistent with those from Indonesia.

Background: OraQuick (R) is a rapid test with high specificity demonstrated in non-dengue endemic settings. However, reports of false positive OraQuick (R) results suggest poor specificity in the context of dengue fever. Objectives: To assess the specificity of OraQuick (R) for HIV-1/2 in patients with dengue fever. Study design: In a study performed across two Singapore hospitals, adult participants meeting WHO 2009 criteria for probable dengue (fever >37.5 degrees C plus two other clinical or haematological criteria) were identified at hospital outpatient clinics from April 2012 to July 2013. Eligible participants were asked for informed consent to complete a questionnaire on HIV risk factors, as well as HIV testing by OraQuick (R), fourth-generation EIA and NAAT. Dengue testing was by Dengue Duo NS1Ag + Ab Combo kits. Confirmed dengue was defined as NS1-positive and probable dengue as IgM-positive. Results: Of 152 eligible patients, 82 consented to inclusion in the study. Fifty-two of these had dengue; 43 confirmed and 9 probable cases. All patients with dengue had a negative OraQuick (R) result, negative EIA and undetectable HIV-1 RNA, corresponding to a specificity of 100 %. Conclusions: OraQuick (R) has high specificity in the context of dengue infection. It can be used to diagnose HIV-associated illness as a cause of fever in dengue endemic settings.

New Zealand could be the first country in the world to eliminate tuberculosis (TB). We propose a TB elimination strategy based on the eight-point World Health Organization (WHO) action framework for low incidence countries. Priority actions recommended by the WHO include 1) ensure political commitment, funding and stewardship for planning and essential services; 2) address the most vulnerable and hard-to-reach groups; 3) address special needs of migrants and cross-border issues; 4) identify active TB and undertake screening for latent tuberculosis infection (LTBI) in recent TB contacts and selected high-risk groups, and provide appropriate treatment; 5) optimise the prevention and care of drug-resistant TB; 6) ensure continued surveillance, programme monitoring and evaluation and case-based data management; 7) invest in research and new tools; and 8) support global TB prevention, care and control. In New Zealand, central government needs to take greater responsibility for TB policy and programme governance. Urgent action is required to prevent TB in higher risk groups including Māori communities, and to enable immigration screening to detect and treat LTBI. Clinical services need to be supported to implement new guidelines for LTBI that enable better targeting of screening and shorter, safer treatment regimens. Access to WHO recommended treatment regimens needs to be guaranteed for drug-resistant TB. Better use of existing data could better define priority areas for action and assist in the evaluation of current control activities. Access to GeneXpert® MTB-RIF near the point of care and whole genome sequencing nationally would greatly improve clinical and public health management through early identification of drug resistance and outbreaks. New Zealand already has a world-class TB research community that could be better deployed to assist high-incidence countries through research and training.

Compared with the mitigation and suppression approaches of most Western countries, elimination can minimise direct health effects and offer an early return to social and economic activity On 23 March 2020, New Zealand committed to an elimination strategy in response to the coronavirus disease 2019 (COVID-19) pandemic. Prime Minister Jacinda Ardern announced that on 26 March, NZ would commence an intense lockdown of the country (the highest level of a four-level response framework1). At the time, NZ had just over 100 COVID-19 cases and no deaths, so this "go early, go hard" approach surprised many. However, there were compelling reasons for NZ to pursue elimination.2 In this article we describe why an elimination strategy made sense for NZ, the distinguishing features of this approach, some of the challenges and how they can be overcome, and where we go from here. Until early March 2020, the NZ response to COVID-19 followed the existing pandemic plan, which was based on a mitigation approach for managing pandemic influenza.3 The plan includes steps designed to slow entry of the pandemic, prevent initial spread and then apply physical distancing measures progressively to flatten the curve and avoid overwhelming health services. Because pandemic influenza cannot be contained (except by extreme measures such as total border closure), there was a presumption that case- and contact-based management would fail and the country would inevitably progress to widespread community transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most Western countries across Europe and North America were following the mitigation approach. However, it was performing poorly, with COVID-19 cases overwhelming health services. These countries were then switching to a suppression strategy.4 This strategy involved intense physical distancing and travel restrictions (lockdowns) to suppress virus transmission. A few countries were continuing with a version of mitigation labelled "herd immunity", by which they planned to manage the rate of infection in such a way as to avoid overwhelming the health care system and build up enough recovered and likely immune people in the population to ultimately interrupt virus transmission. This approach proved difficult to manage and was largely abandoned (except perhaps by Sweden). Most low and middle income countries could do very little to manage the pandemic except by applying limited mitigation measures. Vietnam was a notable exception, implementing stringent control measures including quarantine, contact tracing, border controls, school closures and traffic restrictions while case numbers were still low. A number of island states, such as Samoa, Tonga and the Cook Islands, adopted an exclusion approach, primarily by closing their borders to incoming travellers. By early March the evidence base for elimination was growing, with the increasing realisation that COVID-19 was markedly different to pandemic influenza in terms of its transmission dynamics.5 A watershed moment was the report of the World Health Organization joint mission to China, which confirmed that the pandemic there had been contained even after widespread community transmission had commenced.6 There was also strong evidence for early success of the elimination approach in Taiwan,7 Hong Kong8 and South Korea.9 The concept of elimination is well known to infectious disease epidemiologists.10 It refers to the reduction of the incidence of a disease to zero in a defined geographical area. While absence of disease is the ultimate goal, elimination criteria for highly infectious diseases such as measles allow for occasional outbreaks or imported cases, provided they are stamped out within a defined time period.11 By contrast, eradication means that the incidence of a disease has been reduced to zero at the global level, at least outside laboratories. There is no established definition for COVID-19 elimination. Preliminary thinking suggests that such a definition would need to include a defined period of absence of new cases (perhaps 28 days, which is twice the maximum 14-day incubation period).12 This definition would also require a high performing surveillance system and would exclude cases infected outside the country and detected in new arrivals while under isolation or quarantine.12 By late July 2020, NZ had experienced no instances of community-based transmission for more than 80 days and could be considered to have attained elimination. This status can take weeks or even months to achieve, and countries could potentially move in and out of this state depending on their success in containing the pandemic. At the time NZ chose an elimination strategy, the exact nature of this response and its full justification had not been articulated. The health impact of a poorly contained pandemic had been modelled using a range of scenarios,13 demonstrating clear health gains if a widespread pandemic could be prevented in NZ. There was also a concern to avoid repeating the catastrophic impact of previous influenza pandemics on Māori and to protect neighbouring Pacific Islands.14 The net economic consequences of an elimination strategy were uncertain and extremely difficult to estimate. An additional challenge was that both the pandemic and its response were likely to have a disproportionate impact on disadvantaged populations. While an elimination strategy would have huge economic and social costs, the alternatives (suppression and mitigation) would almost certainly have been far more damaging because of the need to continue costly physical distancing measures until a vaccine or other intervention became available. An advantage of a successful elimination strategy was that it would provide a medium term exit path for a return to domestic economic activity without the constraints of circulating SARS-CoV-2. Neither mitigation nor suppression provide a firm exit strategy, particularly given major uncertainties about coronavirus immunity and the potential for ongoing epidemic transmission for months to years under some scenarios.15 As with all COVID-19 strategies, the ultimate exit path will depend on developing effective vaccines and therapeutics. Elimination requires an array of control measures tailored to local needs and to the transmission characteristics of the organism concerned. For COVID-19, the major components are similar to those used for pandemic control more generally. The main difference is the intensity and timing of their application (Box). COVID-19 elimination requires a very strong emphasis on border management to keep the virus out. That intervention would usually be combined with case and contact management to stamp out transmission, along with highly developed surveillance and testing to rapidly identify cases and outbreaks. If started early, these measures may be sufficient for elimination without the need for lockdowns, as was achieved in Taiwan. An elimination strategy requires highly functioning public health infrastructure. Similar to many other countries, NZ has supplemented traditional approaches with newer tools, such as the use of digital technology to speed up contact tracing.16 The NZ COVID Tracer app is now operational,17 although it has yet to be used for contact tracing given the lack of community cases. Additional surveillance approaches can be used to provide increased assurance of elimination (eg, sentinel surveillance, sewage testing). However, even in the presence of a highly sophisticated surveillance system, transmission will continue if isolation and quarantine adherence is suboptimal. The COVID-19 pandemic was halted in China, demonstrating that there are no absolute biological barriers to its elimination.6 Having no important animal or environmental reservoirs is a necessary condition, and this appears to be the case for SARS-CoV-2 (although its actual origin in nature has not been determined, so cases could in theory arise from this source). The combination of high infectiousness and presymptomatic transmission poses challenges for control.18 Fortunately, its relatively long incubation period (about 5 days) makes contact tracing and quarantining effective, unlike for influenza.5 Changing human behaviour to reduce transmission is challenging with a virus as infectious as SARS-CoV-2. This is why mandated extreme physical distancing and movement control (lockdown) may be needed. The intense lockdown carried out in NZ suppressed transmission and gave the country time to expand border controls, improve contact tracing, and undertake large scale testing. Coming out of lockdown (which began progressively on 28 April) must be managed carefully, as the goal is to emerge into a country that is free from community transmission (unlike the lockdowns in countries pursuing mitigation or suppression). Widespread use of face masks was not a feature of the NZ strategy but might in future reduce the need for lockdowns.19 Successful implementation of an elimination strategy requires early risk assessment, effective response planning, infrastructure, resources and political will. The global response to SARS-CoV-2 has been described as the "greatest science policy failure of our generation".20 An elimination strategy could potentially have been widely used to contain COVID-19 and protect populations in countries across the globe. NZ and Australia appear to have joined a small group of countries and jurisdictions pursuing an explicit, or implied, elimination goal, albeit by different strategies. Others including mainland China, Hong Kong, Taiwan, South Korea, Vietnam and a number of small island states and territories. This set of countries is likely to expand in the future. It is not hard to imagine travel between them being relaxed once the risks are well understood and can be managed. It may be time for these countries to actively share knowledge and evidence about the approaches that are supporting them to contain and eliminate COVID-19. There are multiple potential future scenarios. By pursuing and maintaining an elimination strategy, countries can prevent disease and death from COVID-19 and avoid further exacerbation of existing health inequities. They can also move from having to manage ongoing pandemic transmission within their populations to being able to make informed strategic choices about prevention and control options such as vaccines and therapeutics as they become available. We thank our many colleagues who have contributed to development of the NZ elimination strategy, notably Professor Nick Wilson at the University of Otago, Wellington. We also acknowledge funding support from the Health Research Council of NZ (20/1066), which did not have any role in the planning, writing or publication of the work or the decision to publish. No relevant disclosures. Commissioned; externally peer reviewed.

AIMSWe aimed to determine the effectiveness of surveillance using testing for SARS-CoV-2 to identify an outbreak arising from a single case of border control failure in a country that has eliminated community transmission of COVID-19: New Zealand.METHODSA stochastic version of the SEIR model CovidSIM v1.1 designed specifically for COVID-19 was utilised. It was seeded with New Zealand population data and relevant parameters sourced from the New Zealand and international literature.RESULTSFor what we regard as the most plausible scenario with an effective reproduction number of 2.0, the results suggest that 95% of outbreaks from a single imported case would be detected in the period up to day 36 after introduction. At the time point of detection, there would be a median number of five infected cases in the community (95% range: 1-29). To achieve this level of detection, an ongoing programme of 5,580 tests per day (1,120 tests per million people per day) for the New Zealand population would be required. The vast majority of this testing (96%) would be of symptomatic cases in primary care settings and the rest in hospitals.CONCLUSIONSThis model-based analysis suggests that a surveillance system with a very high level of routine testing is probably required to detect an emerging or re-emerging SARS-CoV-2 outbreak within five weeks of a border control failure in a nation that had previously eliminated COVID-19. Nevertheless, there are plausible strategies to enhance testing yield and cost-effectiveness and potential supplementary surveillance systems such as the testing of town/city sewerage systems for the pandemic virus.

A SEIR simulation model for the COVID-19 pandemic was developed (<http://covidsim.eu>) and applied to a hypothetical European country of 10 million population. Our results show which interventions potentially push the epidemic peak into the subsequent year (when vaccinations may be available) or which fail. Different levels of control (via contact reduction) resulted in 22% to 63% of the population sick, 0.2% to 0.6% hospitalised, and 0.07% to 0.28% dead (n=6,450 to 28,228). ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Nil ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All the necessary data can be generated by the users themselves since the model is freely available online with a data file export capacity. * #### Parameters N : Population size X : Number of initial infections t max : Day after introducti on of the infection when the transmissi on potiential is highest Q max : Maximum isolation capacity ![Graphic][1]</img> : Time at which isolation measures start ![Graphic][2]</img> : Time at which isolation measures end c Home : Fraction of contacts which are prevented for cases who are in home isolation c Cont : Fraction of contacts which are prevented ![Graphic][3]</img> : Time at which contact reduction starts ![Graphic][4]</img> : Time at which contact reduction ends c ( t ) : Fraction of contacts which are reduced at time t y : Force of infection which originates fromoutside of the population (e.g. via travellers) R : Average value of the basic reproduction number a : Amplitude of the seasonal fluctuatio n of R D E : Average duration of the latent period n E : Number of stages for the latent period ε : Stage transition rate in the latent period ( ε = n E / D E ) D P : Average duration of the prodoromal period n P : Number of stages for the prodromal period φ : Stage transition rate in the prodromal period ( φ = n P / D P ) i P : Relative infectiousness during prodromal period D I : Average duration of the symptomatic period n I : Number of stages for the symptomatic period γ : Stage transition rate in the symptomatic period ( γ = n I / D I ) β I ( t ) : Effective contact rate of individual s in the symptomatic period at time t β I ( t ) = R /( i P D P + D I )× (1+ a cos( t / 365)) β P ( t ) : Effective contact rate of individual s in the prodromal period at time t ( β P ( t ) = β I ( t ) i P ) p Sick : Fraction of infected individual s who become sick p Consult : Fraction of sick individual s who seek medical help p Hosp : Fraction of sick individual s who are hospitalized p ICU : Fraction of hospitalized individual s who are admitted to the ICU p Death : Fraction of sick individual s who die from the disease [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif [3]: /embed/inline-graphic-3.gif [4]: /embed/inline-graphic-4.gif

Aims: We aimed to determine the length of time from the last detected case of SARS-CoV-2 infection before elimination can be assumed at a country level in an island nation. Methods: A stochastic version of the SEIR model CovidSIM v1.1 designed specifically for COVID-19 was utilised. It was populated with data for the case study island nation of New Zealand (NZ) along with relevant parameters sourced from the NZ and international literature. This included a testing level for symptomatic cases of 7,800 tests per million people per week. Results: It was estimated to take between 27 and 33 days of no new detected cases for there to be a 95% probability of epidemic extinction. This was for effective reproduction numbers (Re) in the range of 0.50 to 1.0, which encompass such controls as case isolation (the shorter durations relate to low Re values). For a 99% probability of epidemic extinction, the equivalent time period was 37 to 44 days. In scenarios with lower levels of symptomatic cases seeking medical attention and lower levels of testing, the time period was up to 53 to 91 days (95% level). Conclusions: In the context of a high level of testing, a period of around one month of no new notified cases of COVID-19 would give 95% certainty that elimination of SARS-CoV-2 transmission had been achieved.

In the battle for control of coronavirus disease-19 (COVID-19), we have few weapons. Yet contact tracing is among the most powerful. Contact tracing is the process by which public-health officials identify people, or contacts, who have been exposed to a person infected with a pathogen or another hazard. For all its power, though, contact tracing yields a variable level of success. One reason is that contact tracing's ability to break the chain of transmission is only as effective as the proportion of contacts who are actually traced. In part, this proportion turns on the quality of the information that infected people provide, which makes human memory a crucial part of the efficacy of contact tracing. Yet the fallibilities of memory, and the challenges associated with gathering reliable information from memory, have been grossly underestimated by those charged with gathering it. We review the research on witnesses and investigative interviewing, identifying interrelated challenges that parallel those in contact tracing, as well as approaches for addressing those challenges.

Immunopathology contributes to high mortality in tuberculous meningitis (TBM) but little is known about the blood and cerebrospinal fluid (CSF) immune response. We prospectively characterised the immune response of 160 TBM suspects in an Indonesian cohort, including 67 HIV-negative probable or definite TBM cases. TBM patients presented with severe disease and 38% died in 6 months. Blood from TBM patients analysed by flow cytometry showed lower αβT and γδT cells, NK cells and MAIT cells compared to 26 pulmonary tuberculosis patients (2.4-4-fold, all p < 0.05) and 27 healthy controls (2.7-7.6-fold, p < 0.001), but higher neutrophils and classical monocytes (2.3-3.0-fold, p < 0.001). CSF leukocyte activation was higher than in blood (1.8-9-fold). CSF of TBM patients showed a predominance of αβT and NK cells, associated with better survival. Cytokine production after ex-vivo stimulation of whole blood showed a much broader range in TBM compared to both control groups (p < 0.001). Among TBM patients, high ex-vivo production of TNF-α, IL-6 and IL-10 correlated with fever, lymphocyte count and monocyte HLA-DR expression (all p < 0.05). TBM patients show a strong myeloid blood response, with a broad variation in immune function. This may influence the response to adjuvant treatment and should be considered in future trials of host-directed therapy.

Tuberculosis (TB) is a serious infectious disease caused by infection with Mycobacterium tuberculosis , and kills more people annually than any other single infectious agent. Although a vaccine is available, it is only moderately effective and an improved vaccine is urgently needed. The ability to develop a more effective vaccine has been thwarted by a lack of understanding of the mechanism of vaccine-induced immune protection. Over recent decades, many novel TB vaccines have been developed and almost all have aimed to generate memory CD4 T cells. In this review, we critically evaluate evidence in the literature that supports the contention that memory CD4 T cells are the prime mediators of vaccine-induced protection against TB. Because of the lack of robust evidence supporting memory CD4 T cells in this role, the potential for B-cell antibody and “trained” innate cells as alternative mediators of protective immunity is explored.