Microbial peptides activate tumour-infiltrating lymphocytes in glioblastoma.

Microbial organisms have key roles in numerous physiological processes in the human body and have recently been shown to modify the response to immune checkpoint inhibitors1,2. Here we aim to address the role of microbial organisms and their potential role in immune reactivity against glioblastoma. We demonstrate that HLA molecules of both glioblastoma tissues and tumour cell lines present bacteria-specific peptides. This finding prompted us to examine whether tumour-infiltrating lymphocytes (TILs) recognize tumour-derived bacterial peptides. Bacterial peptides eluted from HLA class II molecules are recognized by TILs, albeit very weakly. Using an unbiased antigen discovery approach to probe the specificity of a TIL CD4+ T cell clone, we show that it recognizes a broad spectrum of peptides from pathogenic bacteria, commensal gut microbiota and also glioblastoma-related tumour antigens. These peptides were also strongly stimulatory for bulk TILs and peripheral blood memory cells, which then respond to tumour-derived target peptides. Our data hint at how bacterial pathogens and bacterial gut microbiota can be involved in specific immune recognition of tumour antigens. The unbiased identification of microbial target antigens for TILs holds promise for future personalized tumour vaccination approaches.

Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls.

Fatigue is the most common symptom among multiple sclerosis (MS) patients and severely affects the quality of life. We investigate how perceived fatigue can be predicted using biomarkers collected from an arm-worn wearable sensor for MS patients (n = 51) and a healthy control group (n = 23) at an unprecedented time resolution of more than five times per day. On average, during our two-week study, participants reported their level of fatigue 51 times totaling more than 3,700 data points. Using interpretable generalized additive models, we find that increased physical activity, heart rate, sympathetic activity, and parasympathetic activity while awake and asleep relate to perceived fatigue throughout the day-partly affected by dysfunction of the ANS. We believe our analysis opens up new research opportunities for fine-grained modeling of perceived fatigue based on passively collected physiological signals using wearables-for MS patients and healthy controls alike.

Clinical and Immunologic Effects of Paraprobiotics in Long-COVID Patients: A Pilot Study.

BACKGROUND AND OBJECTIVES: After the enormous health burden during the acute stages of the COVID-19 pandemic, we are now facing another important challenge, that is, long-COVID, a clinical condition with often disabling signs and symptoms of the neuropsychiatric, gastrointestinal, respiratory, cardiovascular, and immune systems. While the pathogenesis of this syndrome is still poorly understood, alterations of immune function and the gut microbiota seem to play important roles. Because affected individuals are frequently unable to work for prolonged periods and suffer numerous health compromises, effective treatments represent a major unmet medical need. Multiple potential therapies have been tried, but none is approved yet. Approaches that are able to influence the immune system and gut microbiota such as probiotics and paraprobiotics, i.e., nonviable probiotics, seem promising candidates. We, therefore, evaluated the clinical and immunologic effects of paraprobiotics in a small pilot study. METHODS: A total of 6 patients with long-COVID were followed systematically for more than 12 months after disease onset using standardized validated questionnaires, a smartphone app, and wearable sensors to assess neurocognitive function, fatigue, depressiveness, autonomic nervous system alterations, and quality of life. We then offered patients defined paraprobiotics for 4 weeks and evaluated them at the end of the treatment period using the same questionnaires, smartphone app, and wearable sensors. In addition, a comprehensive immunophenotyping and gut microbiota analysis was performed before and after treatment. RESULTS: Improvements in several of the neurologic symptoms such as dysautonomia, fatigue, and depression were documented using both patient-reported outcomes and data from the smartphone app and wearable sensors. Of interest, the expression of activation markers on some immune cell populations such as B cells and nonclassical monocytes and the expression of toll-like receptor 2 (TLR2) on T cells were reduced after paraprobiotics treatment. DISCUSSION: Our results suggest that paraprobiotics might exert positive effects in patients with long-COVID most likely by modulating immune cell activation and expression of TLR2 on T cells. Further studies with paraprobiotics should confirm the promising observations of this small pilot study and hopefully not only improve the outcome of long-COVID but also unravel the pathomechanisms of this condition. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that paraprobiotics increase the probability of favorable changes of clinical and immunologic markers in patients with long-COVID.

Modeling multiple sclerosis using mobile and wearable sensor data.

Multiple sclerosis (MS) is a neurological disease of the central nervous system that is the leading cause of non-traumatic disability in young adults. Clinical laboratory tests and neuroimaging studies are the standard methods to diagnose and monitor MS. However, due to infrequent clinic visits, it is fundamental to identify remote and frequent approaches for monitoring MS, which enable timely diagnosis, early access to treatment, and slowing down disease progression. In this work, we investigate the most reliable, clinically useful, and available features derived from mobile and wearable devices as well as their ability to distinguish people with MS (PwMS) from healthy controls, recognize MS disability and fatigue levels. To this end, we formalize clinical knowledge and derive behavioral markers to characterize MS. We evaluate our approach on a dataset we collected from 55 PwMS and 24 healthy controls for a total of 489 days conducted in free-living conditions. The dataset contains wearable sensor data - e.g., heart rate - collected using an arm-worn device, smartphone data - e.g., phone locks - collected through a mobile application, patient health records - e.g., MS type - obtained from the hospital, and self-reports - e.g., fatigue level - collected using validated questionnaires administered via the mobile application. Our results demonstrate the feasibility of using features derived from mobile and wearable sensors to monitor MS. Our findings open up opportunities for continuous monitoring of MS in free-living conditions and can be used to evaluate and guide the effectiveness of treatments, manage the disease, and identify participants for clinical trials.

Cognitive fatigability assessment test (cFAST): Development of a new instrument to assess cognitive fatigability and pilot study on its association to perceived fatigue in multiple sclerosis.

Background: Fatigue is a common symptom of many diseases, including multiple sclerosis. It manifests as a cognitive or physical condition. Fatigue is poorly understood, and effective therapies are missing. Furthermore, there is a lack of methods to measure fatigue objectively. Fatigability, the measurable decline in performance during a task, has been suggested as a complementary method to quantify fatigue. Objective: To develop a new and objective measurement of cognitive fatigability and investigate its association with perceived fatigue. Methods: We introduced the cognitive fatigability assessment test (cFAST), a novel smartphone-based test to quantify cognitive fatigability. Forty-two people with multiple sclerosis (23 fatigued and 19 non-fatigued, defined by the Fatigue Scale for Motor and Cognitive Functions) took part in our validation study. Patients completed cFAST twice. We used t-tests, Monte Carlo sampling, and area under the receiver operating characteristic curves to evaluate our approach using two sets of proposed metrics. Results: When classifying fatigue, our fatigability metric Δresponse time has a mean area under the receiver operating characteristic curve of 0.74 (95% CI 0.64-0.84), making it the best performing metric for this task. Furthermore, Δresponse time shows a statistically significant difference between the fatigued and non-fatigued groups (t = 2.27, P = .03). Particularly, cognitively-fatigued patients decline in performance, while non-fatigued patients do not. Conclusions: We introduce cFAST, a new instrument to quantify cognitive fatigability. Our pilot study provides evidence that cognitive fatigability assessment test produces a quantifiable drop in cognitive performance in a short period. Furthermore, our results indicate that cFAST may have the potential to serve as a surrogate for subjective cognitive fatigue. cFAST is significantly shorter than the existing fatigability assessments and does not require specialized equipment. Thus, it could enable frequent and remote monitoring, which could substantially aid clinicians in better understanding and treating fatigue.

Continuous monitoring with wearables in multiple sclerosis reveals an association of cardiac autonomic dysfunction with disease severity.

Background: Dysfunction of the autonomic nervous system is common in multiple sclerosis patients, and probably present years before diagnosis, but its role in the disease is poorly understood. Objectives: To study the autonomic nervous system in patients with multiple sclerosis using cardiac autonomic regulation measured with a wearable. Methods: In a two-week study, we present a method to standardize the measurement of heart rate variability using a wearable sensor that allows the investigation of circadian trends. Using this method, we investigate the relationship of cardiac autonomic dysfunction with clinical hallmarks and subjective burden of fatigue and autonomic symptoms. Results: In 55 patients with multiple sclerosis and 24 healthy age- and gender-matched controls, we assessed the cumulative circadian heart-rate variability trend of two weeks. The trend analysis revealed an effect of inflammation (P = 0.0490, SMD = -0.5466) and progressive neurodegeneration (P = 0.0016, SMD = 1.1491) on cardiac autonomic function. No association with subjective symptoms could be found. Conclusions: Trend-based heart rate variability measured with a wearable provides the opportunity for unobtrusive long-term assessment of autonomic functions in patients with multiple sclerosis. It revealed a general dysregulation in patients with multiple sclerosis.