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Background
The role of cytokines in the pathophysiology, diagnosis, and prognosis of small fiber neuropathy (SFN) is incompletely understood. We studied expression profiles of selected pro- and anti-inflammatory cytokines in RNA from white blood cells (WBC) of patients with a medical history and a clinical phenotype suggestive for SFN and compared data with healthy controls.
Methods
We prospectively recruited 52 patients and 21 age- and sex-matched healthy controls. Study participants were characterized in detail and underwent complete neurological examination. Venous blood was drawn for routine and extended laboratory tests, and for WBC isolation. Systemic RNA expression profiles of the pro-inflammatory cytokines interleukin (IL)-1ß, IL-2, IL-8, tumor necrosis factor-alpha (TNF) and the anti-inflammatory cytokines IL-4, IL-10, transforming growth factor beta-1 (TGF) were analyzed. Protein levels of IL-2, IL-8, and TNF were measured in serum of patients and controls. Receiver operating characteristic (ROC)-curve analysis was used to determine the accuracy of IL-2, IL-8, and TNF in differentiating patients and controls. To compare the potential discriminatory efficacy of single versus combined cytokines, equality of different AUCs was tested.
Results
WBC gene expression of IL-2, IL-8, and TNF was higher in patients compared to healthy controls (IL-2: p = 0.02; IL-8: p = 0.009; TNF: p = 0.03) and discriminated between the groups (area under the curve (AUC) ≥ 0.68 for each cytokine) with highest diagnostic accuracy reached by combining the three cytokines (AUC = 0.81, sensitivity = 70%, specificity = 86%). Subgroup analysis revealed the following differences: IL-8 and TNF gene expression levels were higher in female patients compared to female controls (IL-8: p = 0.01; TNF: p = 0.03). The combination of TNF with IL-2 and TNF with IL-2 and IL-8 discriminated best between the study groups. IL-2 was higher expressed in patients with moderate pain compared to those with severe pain (p = 0.02). Patients with acral pain showed higher IL-10 gene expression compared to patients with generalized pain (p = 0.004). We further found a negative correlation between the relative gene expression of IL-2 and current pain intensity (p = 0.02). Serum protein levels of IL-2, IL-8, and TNF did not differ between patients and controls.
Conclusions
We identified higher systemic gene expression of IL-2, IL-8, and TNF in SFN patients than in controls, which may be of potential relevance for diagnostics and patient stratification.
Background
Ischemic stroke immediately evokes a strong neuro-inflammatory response within the vascular compartment, which contributes to primary infarct development under vessel occlusion as well as further infarct growth despite recanalization, referred to as ischemia/reperfusion injury. Later, in the subacute phase of stroke (beyond day 1 after recanalization), further inflammatory processes within the brain parenchyma follow. Whether this second wave of parenchymal inflammation contributes to an additional/secondary increase in infarct volumes and bears the potential to be pharmacologically targeted remains elusive. We addressed the role of the NLR-family pyrin domain-containing protein 3 (NLRP3) inflammasome in the subacute phase of ischemic stroke.
Methods
Focal cerebral ischemia was induced in C57Bl/6 mice by a 30-min transient middle cerebral artery occlusion (tMCAO). Animals were treated with the NLRP3 inhibitor MCC950 therapeutically 24 h after or prophylactically before tMCAO. Stroke outcome, including infarct size and functional deficits as well as the local inflammatory response, was assessed on day 7 after tMCAO.
Results
Infarct sizes on day 7 after tMCAO decreased about 35% after delayed and about 60% after prophylactic NLRP3 inhibition compared to vehicle. Functionally, pharmacological inhibition of NLRP3 mitigated the local inflammatory response in the ischemic brain as indicated by reduction of infiltrating immune cells and reactive astrogliosis.
Conclusions
Our results demonstrate that the NLRP3 inflammasome continues to drive neuroinflammation within the subacute stroke phase. NLRP3 inflammasome inhibition leads to a better long-term outcome—even when administered with a delay of 1 day after stroke induction, indicating ongoing inflammation-driven infarct progression. These findings may pave the way for eagerly awaited delayed treatment options in ischemic stroke.
Spinal muscular atrophy (SMA) is a disabling disease that affects not only the patient’s health-related quality of life (HRQoL), but also causes a high caregiver burden (CGB). The aim of this study was to evaluate HRQoL, CGB, and their predictors in SMA. In two prospective, cross-sectional, and multi-center studies, SMA patients (n = 39) and SMA patient/caregiver couples (n = 49) filled in the EuroQoL Five Dimension Five Level Scale (EQ-5D-5L) and the Short Form Health Survey 36 (SF-36). Caregivers (CGs) additionally answered the Zarit Burden Interview (ZBI) and the Hospital Anxiety and Depression Scale (HADS). Patients were clustered into two groups with either low or high HRQoL (EQ-5D-5L index value <0.259 or >0.679). The latter group was mostly composed of ambulatory type III patients with higher motor/functional scores. More severely affected patients reported low physical functioning but good mental health and vitality. The CGB (mean ZBI = 22/88) correlated negatively with patients’ motor/functional scores and age. Higher CGB was associated with a lower HRQoL, higher depression and anxiety, and more health impairments of the CGs. We conclude that patient and CG well-being levels interact closely, which highlights the need to consider the health of both parties while evaluating novel treatments.
Now that mechanical thrombectomy has substantially improved outcomes after large-vessel occlusion stroke in up to every second patient, futile reperfusion wherein successful recanalization is not followed by a favorable outcome is moving into focus. Unfortunately, blood-based biomarkers, which identify critical stages of hemodynamically compromised yet reperfused tissue, are lacking. We recently reported that hypoxia induces the expression of endoglin, a TGF-β co-receptor, in human brain endothelium in vitro. Subsequent reoxygenation resulted in shedding. Our cell model suggests that soluble endoglin compromises the brain endothelial barrier function. To evaluate soluble endoglin as a potential biomarker of reperfusion (-injury) we analyzed its concentration in 148 blood samples of patients with acute stroke due to large-vessel occlusion. In line with our in vitro data, systemic soluble endoglin concentrations were significantly higher in patients with successful recanalization, whereas hypoxia alone did not induce local endoglin shedding, as analyzed by intra-arterial samples from hypoxic vasculature. In patients with reperfusion, higher concentrations of soluble endoglin additionally indicated larger infarct volumes at admission. In summary, we give translational evidence that the sequence of hypoxia and subsequent reoxygenation triggers the release of vasoactive soluble endoglin in large-vessel occlusion stroke and can serve as a biomarker for severe ischemia with ensuing recanalization/reperfusion.
Gait disturbances are common manifestations of Parkinson’s disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine learning approach to approximate IMU angular velocity profiles and subsequently gait events using electromyographic (EMG) channels during overground walking in patients with PD. We recorded six parkinsonian patients while they walked for at least three minutes. Patient-agnostic regression models were trained on temporally embedded EMG time series of different combinations of up to five leg muscles bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement of <50 ms), low numbers of missed events (<2%), and next to no false-positive event detections (<0.1%). Swing and stance phases could thus be determined with high fidelity (median F1-score of ~0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction, which allows the simultaneous acquisition of an electromyographic signal to be performed. This gait analysis approach has the potential to make additional measurement devices such as IMUs and force plates less essential, thereby reducing financial and preparation overheads and discomfort factors in gait studies.
Ureaplasma species (spp.) are considered commensals of the adult genitourinary tract, but have been associated with chorioamnionitis, preterm birth, and invasive infections in neonates, including meningitis. Data on mechanisms involved in Ureaplasma-driven neuroinflammation are scarce. The present study addressed brain inflammatory responses in preterm lambs exposed to Ureaplasma parvum (UP) in utero. 7 days after intra-amniotic injection of UP (n = 10) or saline (n = 11), lambs were surgically delivered at gestational day 128–129. Expression of inflammatory markers was assessed in different brain regions using qRT-PCR and in cerebrospinal fluid (CSF) by multiplex immunoassay. CSF was analyzed for UP presence using ureB-based real-time PCR, and MRI scans documented cerebral white matter area and cortical folding. Cerebral tissue levels of atypical chemokine receptor (ACKR) 3, caspases 1-like, 2, 7, and C–X–C chemokine receptor (CXCR) 4 mRNA, as well as CSF interleukin-8 protein concentrations were significantly increased in UP-exposed lambs. UP presence in CSF was confirmed in one animal. Cortical folding and white matter area did not differ among groups. The present study confirms a role of caspases and the transmembrane receptors ACKR3 and CXCR4 in Ureaplasma-driven neuroinflammation. Enhanced caspase 1-like, 2, and 7 expression may reflect cell death. Increased ACKR3 and CXCR4 expression has been associated with inflammatory central nervous system (CNS) diseases and impaired blood–brain barrier function. According to these data and previous in vitro findings from our group, we speculate that Ureaplasma-induced caspase and receptor responses affect CNS barrier properties and thus facilitate neuroinflammation.
Background
Eye movement abnormalities are commonplace in neurological disorders. However, unaided eye movement assessments lack granularity. Although videooculography (VOG) improves diagnostic accuracy, resource intensiveness precludes its broad use. To bridge this care gap, we here validate a framework for smartphone video-based nystagmography capitalizing on recent computer vision advances.
Methods
A convolutional neural network was fine-tuned for pupil tracking using > 550 annotated frames: ConVNG. In a cross-sectional approach, slow-phase velocity of optokinetic nystagmus was calculated in 10 subjects using ConVNG and VOG. Equivalence of accuracy and precision was assessed using the “two one-sample t-test” (TOST) and Bayesian interval-null approaches. ConVNG was systematically compared to OpenFace and MediaPipe as computer vision (CV) benchmarks for gaze estimation.
Results
ConVNG tracking accuracy reached 9–15% of an average pupil diameter. In a fully independent clinical video dataset, ConVNG robustly detected pupil keypoints (median prediction confidence 0.85). SPV measurement accuracy was equivalent to VOG (TOST p < 0.017; Bayes factors (BF) > 24). ConVNG, but not MediaPipe, achieved equivalence to VOG in all SPV calculations. Median precision was 0.30°/s for ConVNG, 0.7°/s for MediaPipe and 0.12°/s for VOG. ConVNG precision was significantly higher than MediaPipe in vertical planes, but both algorithms’ precision was inferior to VOG.
Conclusions
ConVNG enables offline smartphone video nystagmography with an accuracy comparable to VOG and significantly higher precision than MediaPipe, a benchmark computer vision application for gaze estimation. This serves as a blueprint for highly accessible tools with potential to accelerate progress toward precise and personalized Medicine.
Inflammation in the brain and gut is a critical component of several neurological diseases, such as Parkinson’s disease (PD). One trigger of the immune system in PD is aggregation of the pre-synaptic protein, α-synuclein (αSyn). Understanding the mechanism of propagation of αSyn aggregates is essential to developing disease-modifying therapeutics. Using a brain-first mouse model of PD, we demonstrate αSyn trafficking from the brain to the ileum of male mice. Immunohistochemistry revealed that the ileal αSyn aggregations are contained within CD11c+ cells. Using single-cell RNA sequencing, we demonstrate that ileal CD11c\(^+\) cells are microglia-like and the same subtype of cells is activated in the brain and ileum of PD mice. Moreover, by utilizing mice expressing the photo-convertible protein, Dendra2, we show that CD11c\(^+\) cells traffic from the brain to the ileum. Together these data provide a mechanism of αSyn trafficking between the brain and gut.
Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool’s training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability.