Cerebrospinal fluid (CSF) protects the central nervous system (CNS) and analyzing CSF aids the diagnosis of CNS diseases, but our understanding of CSF leukocytes remains superficial. Here, using single cell transcriptomics, we identify a specific location-associated composition and transcriptome of CSF leukocytes. Multiple sclerosis (MS) – an autoimmune disease of the CNS – increases transcriptional diversity in blood, but increases cell type diversity in CSF including a higher abundance of cytotoxic phenotype T helper cells. An analytical approach, named cell set enrichment analysis (CSEA) identifies a cluster-independent increase of follicular (TFH) cells potentially driving the known expansion of B lineage cells in the CSF in MS. In mice, TFH cells accordingly promote B cell infiltration into the CNS and the severity of MS animal models. Immune mechanisms in MS are thus highly compartmentalized and indicate ongoing local T/B cell interaction.

Alemtuzumab is a humanized monoclonal antibody against CD52 (cluster of differentiation 52) and is approved for the therapy of relapsing-remitting multiple sclerosis. The application of alemtuzumab leads to a rapid, but long-lasting depletion predominantly of CD52-bearing B and T cells with reprogramming effects on immune cell composition resulting in the restoration of tolerogenic networks. Alemtuzumab has proven high efficacy in clinical phase II and III trials, where interferon β-1a was used as active comparator. However, alemtuzumab is associated with frequent and considerable risks. Most importantly secondary autoimmune disease affects 30%–40% of patients, predominantly impairing thyroid function. Extensive monitoring and early intervention allow for an appropriate risk management. However, new and reliable biomarkers for individual risk stratification and treatment response to improve patient selection and therapy guidance are a significant unmet need. Only a deeper understanding of the underlying mechanisms of action (MOA) will reveal such markers, maximizing the best potential risk-benefit ratio for the individual patient. This review provides and analyses the current knowledge on the MOA of alemtuzumab. Most recent data on efficacy and safety of alemtuzumab are presented and future research opportunities are discussed.

Background: The interaction of coagulation factors has been shown to go beyond their traditional roles in hemostasis and to affect the development of inflammatory diseases. Key molecular players, such as fibrinogen, thrombin, or factor XII have been mechanistically and epidemiologically linked to inflammatory disorders like multiple sclerosis (MS), rheumatoid arthritis (RA), and colitis. Objectives: To systematically review the evidence for a role of coagulation factors, especially factor XII, fibrinogen, and thrombin in inflammatory disorders like MS, RA, and bowel disorders. Methods: A systematic literature search was done in the PubMed database to identify studies about coagulation factors in inflammatory diseases. Original articles and reviews investigating the role of the kallikrein–kinin and the coagulation system in mouse and humans were included. Results: We identified 43 animal studies dealing with inflammatory disorders and factors of the kallikrein–kinin or the coagulation system. Different immunological influences are described and novel molecular mechanisms linking coagulation and inflammation are reported. Conclusion: A number of studies have highlighted coagulation factors to tip the balance between hemostasis and thrombosis and between protection from infection and extensive inflammation. To optimize the treatment of chronic inflammatory disorders by these factors, further studies are necessary.

Significance: Neuroinflammation is a hallmark of multiple sclerosis and is linked to neurodegeneration. This study provides pathophysiological insights into the cross-dependency between neuroinflammation and choroid plexus characteristics in both mice and humans. Our work relates an enlargement of choroid plexus volume to ongoing neuroinflammation and emerging clinical disability in two large cohorts of multiple sclerosis patients as well as in two mouse models, the cuprizone diet-related demyelination and the experimental autoimmune encephalomyelitis. Choroid plexus characterization as measured by high-resolution MRI thus represents a reliable and translatable interspecies marker for the quantification of neuroinflammation and disease trajectories that is strongly associated with functional outcomes.

Background and Objectives To evaluate the long-term safety and efficacy of tocilizumab (TCZ), a humanized anti–interleukin-6 receptor antibody in myelin oligodendrocyte glycoprotein–IgG–associated disease (MOGAD) and neuromyelitis optica spectrum disorders (NMOSD). Methods Annualized relapse rate (ARR), Expanded Disability Status Scale score, MRI, autoantibody titers, pain, and adverse events were retrospectively evaluated in 57 patients with MOGAD (n = 14), aquaporin-4 (AQP4)-IgG seropositive (n = 36), and seronegative NMOSD (n = 7; 12%), switched to TCZ from previous immunotherapies, particularly rituximab. Results Patients received TCZ for 23.8 months (median; interquartile range 13.0–51.1 months), with an IV dose of 8.0 mg/kg (median; range 6–12 mg/kg) every 31.6 days (mean; range 26–44 days). For MOGAD, the median ARR decreased from 1.75 (range 0.5–5) to 0 (range 0–0.9; p = 0.0011) under TCZ. A similar effect was seen for AQP4-IgG+ (ARR reduction from 1.5 [range 0–5] to 0 [range 0–4.2]; p < 0.001) and for seronegative NMOSD (from 3.0 [range 1.0–3.0] to 0.2 [range 0–2.0]; p = 0.031). During TCZ, 60% of all patients were relapse free (79% for MOGAD, 56% for AQP4-IgG+, and 43% for seronegative NMOSD). Disability follow-up indicated stabilization. MRI inflammatory activity decreased in MOGAD (p = 0.04; for the brain) and in AQP4-IgG+ NMOSD (p < 0.001; for the spinal cord). Chronic pain was unchanged. Regarding only patients treated with TCZ for at least 12 months (n = 44), ARR reductions were confirmed, including the subgroups of MOGAD (n = 11) and AQP4-IgG+ patients (n = 28). Similarly, in the group of patients treated with TCZ for at least 12 months, 59% of them were relapse free, with 73% for MOGAD, 57% for AQP4-IgG+, and 40% for patients with seronegative NMOSD. No severe or unexpected safety signals were observed. Add-on therapy showed no advantage compared with TCZ monotherapy. Discussion This study provides Class III evidence that long-term TCZ therapy is safe and reduces relapse probability in MOGAD and AQP4-IgG+ NMOSD.

Background: Large language models (LLMs) have demonstrated impressive performances in various medical domains, prompting an exploration of their potential utility within the high-demand setting of emergency department (ED) triage. This study evaluated the triage proficiency of different LLMs and ChatGPT, an LLM-based chatbot, compared to professionally trained ED staff and untrained personnel. We further explored whether LLM responses could guide untrained staff in effective triage. Objective: This study aimed to assess the efficacy of LLMs and the associated product ChatGPT in ED triage compared to personnel of varying training status and to investigate if the models’ responses can enhance the triage proficiency of untrained personnel. Methods: A total of 124 anonymized case vignettes were triaged by untrained doctors; different versions of currently available LLMs; ChatGPT; and professionally trained raters, who subsequently agreed on a consensus set according to the Manchester Triage System (MTS). The prototypical vignettes were adapted from cases at a tertiary ED in Germany. The main outcome was the level of agreement between raters’ MTS level assignments, measured via quadratic-weighted Cohen κ. The extent of over- and undertriage was also determined. Notably, instances of ChatGPT were prompted using zero-shot approaches without extensive background information on the MTS. The tested LLMs included raw GPT-4, Llama 3 70B, Gemini 1.5, and Mixtral 8x7b. Results: GPT-4–based ChatGPT and untrained doctors showed substantial agreement with the consensus triage of professional raters (κ=mean 0.67, SD 0.037 and κ=mean 0.68, SD 0.056, respectively), significantly exceeding the performance of GPT-3.5–based ChatGPT (κ=mean 0.54, SD 0.024; P<.001). When untrained doctors used this LLM for second-opinion triage, there was a slight but statistically insignificant performance increase (κ=mean 0.70, SD 0.047; P=.97). Other tested LLMs performed similar to or worse than GPT-4–based ChatGPT or showed odd triaging behavior with the used parameters. LLMs and ChatGPT models tended toward overtriage, whereas untrained doctors undertriaged. Conclusions: While LLMs and the LLM-based product ChatGPT do not yet match professionally trained raters, their best models’ triage proficiency equals that of untrained ED doctors. In its current form, LLMs or ChatGPT thus did not demonstrate gold-standard performance in ED triage and, in the setting of this study, failed to significantly improve untrained doctors’ triage when used as decision support. Notable performance enhancements in newer LLM versions over older ones hint at future improvements with further technological development and specific training.