Development and validation of a new scoring system for the early diagnosis of tuberculous meningitis in adults

Interactions between CNS and immune cells in tuberculous meningitis

The central nervous system (CNS) harbors its own special immune system composed of microglia in the parenchyma, CNS-associated macrophages (CAMs), dendritic cells, monocytes, and the barrier systems within the brain. Recently, advances in the immune cells in the CNS provided new insights to understand the development of tuberculous meningitis (TBM), which is the predominant form of Mycobacterium tuberculosis (M.tb) infection in the CNS and accompanied with high mortality and disability. The development of the CNS requires the protection of immune cells, including macrophages and microglia, during embryogenesis to ensure the accurate development of the CNS and immune response following pathogenic invasion. In this review, we summarize the current understanding on the CNS immune cells during the initiation and development of the TBM. We also explore the interactions of immune cells with the CNS in TBM. In the future, the combination of modern techniques should be applied to explore the role of immune cells of CNS in TBM.

Development and validation of a new model for the early diagnosis of tuberculous meningitis in adults based on simple clinical and laboratory parameters

BACKGROUND

The differential diagnosis between tuberculous meningitis (TBM) and viral meningitis (VM) or bacterial meningitis (BM) remains challenging in clinical practice, particularly in resource-limited settings. This study aimed to establish a diagnostic model that can accurately and early distinguish TBM from both VM and BM in adults based on simple clinical and laboratory parameters.

METHODS

Patients diagnosed with TBM or non-TBM (VM or BM) between January 2012 and October 2021 were retrospectively enrolled from the General Hospital (derivation cohort) and Branch Hospital (validation cohort) of Ningxia Medical University. Demographic characteristics, clinical symptoms, concomitant diseases, and cerebrospinal fluid (CSF) parameters were collated. Univariable logistic analysis was performed in the derivation cohort to identify significant variables (P < 0.05). A multivariable logistic regression model was constructed using these variables. We verified the performance including discrimination, calibration, and applicability of the model in both derivation and validation cohorts.

RESULTS

A total of 222 patients (70 TBM and 152 non-TBM [75 BM and 77 VM]) and 100 patients (32 TBM and 68 non-TBM [31 BM and 37 VM]) were enrolled as derivation and validation cohorts, respectively. The multivariable logistic regression model showed that disturbance of consciousness for > 5 days, weight loss > 5% of the original weight within 6 months, CSF lymphocyte ratio > 50%, CSF glucose concentration < 2.2 mmol/L, and secondary cerebral infarction were independently correlated with the diagnosis of TBM (P < 0.05). The nomogram model showed excellent discrimination (area under the curve 0.959 vs. 0.962) and great calibration (P-value in the Hosmer-Lemeshow test 0.128 vs. 0.863) in both derivation and validation cohorts. Clinical decision curve analysis showed that the model had good applicability in clinical practice and may benefit the entire population.

CONCLUSIONS

This multivariable diagnostic model may help clinicians in the early discrimination of TBM from VM and BM in adults based on simple clinical and laboratory parameters.

Divergent neuroimmune signatures in the cerebrospinal fluid predict differential gender-specific survival among patients with HIV-associated cryptococcal meningitis

Introduction

Survival among people with HIV-associated cryptococcal meningitis (CM) remains low, particularly among women, despite the currently optimal use of antifungal drugs. Cryptococcus dissemination into the central nervous system [brain, spinal cord, and cerebrospinal fluid (CSF)] elicits the local production of cytokines, chemokines, and other biomarkers. However, no consistent diagnostic or prognostic neuroimmune signature is reported to underpin the risk of death or to identify mechanisms to improve treatment and survival. We hypothesized that distinct neuroimmune signatures in the CSF would distinguish survivors from people who died on antifungal treatment and who may benefit from tailored therapy.

Methods

We considered baseline clinical features, CSF cryptococcal fungal burden, and CSF neuroimmune signatures with survival at 18 weeks among 419 consenting adults by "gender" (168 women and 251 men by biological sex defined at birth).

Results

Survival at 18 weeks was significantly lower among women than among men {47% vs. 59%, respectively; hazard ratio (HR) = 1.4 [95% confidence interval (CI), 1.0 to 1.9; p = 0.023]}. Unsupervised principal component analysis (PCA) demonstrated divergent neuroimmune signatures by gender, survival, and intragender-specific survival. Overall, women had lower levels of programmed death ligand 1, Interleukin (IL) (IL-11RA/IL-1F30, and IL-15 (IL-15) than men (all p < 0.028). Female survivors compared with those who died expressed significant elevations in levels of CCL11 and CXCL10 chemokines (both p = 0.001), as well as increased T helper 1, regulatory, and T helper 17 cytokines (all p < 0.041). In contrast, male survivors expressed lower levels of IL-15 and IL-8 compared with men who died (p < 0.044).

Conclusions

Survivors of both genders demonstrated a significant increase in the levels of immune regulatory IL-10. In conclusion, the lower survival among women with CM was accompanied by distinct differential gender-specific neuroimmune signatures. These female and male intragender-specific survival-associated neuroimmune signatures provide potential targets for interventions to advance therapy to improve the low survival among people with HIV-associated CM.

MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations

Tuberculous meningitis (TBM) is not only one of the most fatal forms of tuberculosis, but also a major public health concern worldwide, presenting grave clinical challenges due to its nonspecific symptoms and the urgent need for timely intervention. The severity and the rapid progression of TBM underscore the necessity of early and accurate diagnosis to prevent irreversible neurological deficits and reduce mortality rates. Traditional diagnostic methods, reliant primarily on clinical findings and cerebrospinal fluid analysis, often falter in delivering timely and conclusive results. Moreover, such methods struggle to distinguish TBM from other forms of neuroinfections, making it critical to seek advanced diagnostic solutions. Against this backdrop, magnetic resonance imaging (MRI) has emerged as an indispensable modality in diagnostics, owing to its unique advantages. This review provides an overview of the advancements in MRI technology, specifically emphasizing its crucial applications in the early detection and identification of complex pathological changes in TBM. The integration of artificial intelligence (AI) has further enhanced the transformative impact of MRI on TBM diagnostic imaging. When these cutting-edge technologies synergize with deep learning algorithms, they substantially improve diagnostic precision and efficiency. Currently, the field of TBM imaging diagnosis is undergoing a phase of technological amalgamation. The melding of MRI and AI technologies unquestionably signals new opportunities in this specialized area.

Current Insights into Diagnosing and Treating Neurotuberculosis in Adults

Neurotuberculosis has the highest morbidity and mortality risk of all forms of extrapulmonary tuberculosis (TB). Early treatment is paramount, but establishing diagnosis are challenging in all three forms of neurotuberculosis: tuberculous meningitis (TBM), spinal TB and tuberculomas. Despite advancements in diagnostic tools and ongoing research aimed at improving TB treatment regimens, the mortality rate for neurotuberculosis remains high. While antituberculosis drugs were discovered in the 1940s, TB treatment regimens were designed for and studied in pulmonary TB and remained largely unchanged for decades. However, new antibiotic regimens and host-directed therapies are now being studied to combat drug resistance and contribute to ending the TB epidemic. Clinical trials are necessary to assess the effectiveness and safety of these treatments, addressing paradoxical responses in neurotuberculosis cases and ultimately improving patient outcomes. Pharmacokinetic-pharmacodynamic analyses can inform evidence-based dose selection and exposure optimization. This review provides an update on the diagnosis and treatment of neurotuberculosis, encompassing both sensitive and resistant antituberculosis drug approaches, drawing on evidence from the literature published over the past decade.

The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms

Tuberculous meningitis (TBM) poses a diagnostic challenge, particularly impacting vulnerable populations such as infants and those with untreated HIV. Given the diagnostic intricacies of TBM, there's a pressing need for rapid and reliable diagnostic tools. This review scrutinizes the efficacy of up-and-coming technologies like machine learning in transforming TBM diagnostics and management. Advanced diagnostic technologies like targeted gene sequencing, real-time polymerase chain reaction (RT-PCR), miRNA assays, and metagenomic next-generation sequencing (mNGS) offer promising avenues for early TBM detection. The capabilities of these technologies are further augmented when paired with mass spectrometry, metabolomics, and proteomics, enriching the pool of disease-specific biomarkers. Machine learning algorithms, adept at sifting through voluminous datasets like medical imaging, genomic profiles, and patient histories, are increasingly revealing nuanced disease pathways, thereby elevating diagnostic accuracy and guiding treatment strategies. While these burgeoning technologies offer hope for more precise TBM diagnosis, hurdles remain in terms of their clinical implementation. Future endeavors should zero in on the validation of these tools through prospective studies, critically evaluating their limitations, and outlining protocols for seamless incorporation into established healthcare frameworks. Through this review, we aim to present an exhaustive snapshot of emerging diagnostic modalities in TBM, the current standing of machine learning in meningitis diagnostics, and the challenges and future prospects of converging these domains.

Divergent Neuroimmune Signatures in the Cerebrospinal Fluid Predict Differential Gender-Specific Survival Among Patients With HIV-Associated Cryptococcal Meningitis

Survival among people with HIV-associated cryptococcal meningitis (CM) remains low, exceptionally among women with the increased threat of death on current optimal use of antifungal drugs. Cryptococcus dissemination into the central nervous system (CNS) prompts a neuroimmune reaction to activate pathogen concomitant factors. However, no consistent diagnostic or prognostic immune-mediated signature is reported to underpin the risk of death or mechanism to improve treatment or survival. We theorized that the distinct neuroimmune cytokine or chemokine signatures in the cerebrospinal fluid (CSF), distinguish survivors from people who died on antifungal treatment, who may benefit from tailored therapy. We considered the baseline clinical disease features, cryptococcal microbiologic factors, and CSF neuroimmune modulated signatures among 419 consenting adults by gender (biological sex assigned at birth) (168 females and 251 males) by 18 weeks of survival on antifungal management. Survival at 18 weeks was inferior among females than males (47% vs. 59%; hazard ratio HR=1.4, 95% CI: 1.0 to 1.9, and p=0.023). Unsupervised principal component analysis (PCA) demonstrated the divergent neuroimmune signatures by gender, survival, and intragender-specific survival. Overall, females displayed lower levels of PD-L1, IL-1RA, and IL-15 than males (all p≤0.028). Female survivors compared with those who died, expressed significant fold elevations in levels of CSF (CCL11 - myeloid and CXCL10 - lymphoid chemokine (in both p=0.001), and CSF Th1, Th2, and Th17 cytokines. In contrast, male survivors expressed distinctly lower levels of CSF IL-15 and IL-8 compared with those who died. Survivors of either gender demonstrated a significant increase in the levels of immune regulatory element, IL-10. In the finale, we classified divergent neuroimmune key signatures in CSF by gender, survival, and intragender-specific survival among people with HIV-associated cryptococcal meningitis. These intragender-specific survival associated-neuroimmune signatures, suggests the discrete role of gender immune regulating mechanisms as the possible targets for interventions to advance therapy to improve survival among people with HIV-associated cryptococcal meningitis.

Clinical Management of Pathogen-Negative Tuberculous Meningitis in Adults: A Series Case Study

Tuberculosis remains a serious world public health problem. Tuberculous meningitis (TBM) is the one of most severe forms of extrapulmonary tuberculosis. However, the insensitivity and time-consuming requirement of culturing the pathogen Mycobacterium tuberculosis, the traditional "gold standard" diagnostic test for TBM, often delays timely diagnosis and treatment, resulting in high disability and mortality rates. In our series case study, we present five pathogen-negative TBM cases who received empirical anti-tuberculosis therapy with a good clinical outcome. We describe in detail the clinical symptoms, laboratory test results, and imaging findings of the five patients from symptom onset to dynamic follow-up. We then summarize the similarities of the clinical characteristics of the presented patients, as well as shared features in laboratory and imaging tests, and proceed to analyze the challenges in the timely diagnosis of TBM. Finally, we argue that monitoring of cerebrospinal fluid markers and imaging are critical for the diagnosis and treatment of TBM, and emphasize the importance of differential diagnosis in cases when tuberculous meningitis is highly suspected despite negative findings for that etiology.