Citations

Abstract Background Deep brain stimulation (DBS) is a widely used therapy for neurologic and psychiatric disorders. Conventional DBS delivers highly regular stimulation patterns that suppress pathological activity but can induce stimulation-related side effects, limiting the therapeutic window. Introducing controlled temporal variability through stochastic pulse timing may represent an alternative programming dimension to improve tolerability while preserving clinical benefit. Methods An adult in their 60’s with bilateral Vim DBS underwent evaluation of tonic, pink-noise, and white-noise stimulation patterns delivered through his chronically implanted Boston Scientific Genus system using the Chronos research platform. We assessed tremor and stimulation-induced side effects using accelerometry, spiral drawing tasks, standardized speech recordings, and patient-reported paresthesias. Results Pink noise stimulation preserved meaningful tremor suppression while improving tolerability compared with conventional tonic 130 Hz stimulation. Under tonic stimulation, dysarthria and paresthesias were prominent at 2.0 mA, narrowing the usable therapeutic window. In contrast, pink noise maintained tremor control across the same amplitude range with reduced side-effect burden. White noise stimulation demonstrated intermediate effects, providing improved tolerability relative to tonic stimulation but less tremor suppression than pink noise. Findings were consistent across accelerometry and functional drawing tasks. Conclusion This study provides first-in-human evidence that temporally structured stochastic pulse timing can preserve therapeutic benefit while expanding the tolerable stimulation range relative to tonic DBS. These findings suggest that temporal structure represents a clinically meaningful programming dimension that may broaden the DBS therapeutic window using software based updates to existing hardware. Further evaluation in larger cohorts is warranted.

Abstract Introduction Acute interstitial nephritis is an important differential diagnosis in patients with deteriorating kidney function. Diagnosis currently requires kidney biopsy, an invasive procedure associated with risks. We hypothesized that urinary T cells may serve as a non-invasive biomarker for acute interstitial nephritis. Methods A total of 320 patients undergoing clinically indicated kidney biopsy were enrolled in a discovery cohort at Charité Berlin (n = 80), an internal validation cohort at Charité (n = 100), and an external validation cohort at The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou (n = 140). Urinary immune cells were assessed by flow cytometry. Renal T cell infiltration was evaluated by immunofluorescence in kidney biopsy specimens from the discovery and internal validation cohorts, including 16 patients with acute interstitial nephritis and 9 patients without acute interstitial nephritis. Additionally, CXCL9 was measured by ELISA in 102 urine samples from these cohorts. Results Across all cohorts, 27 patients (8.4%) were diagnosed with acute interstitial nephritis. In the discovery cohort, multiple urinary T cell subsets were increased in acute interstitial nephritis, with activated CD4 + effector memory T cells expressing CD38 and HLA-DR showing the strongest diagnostic performance. This marker outperformed urinary monocytes, eosinophils, and CXCL9 and was validated in both independent cohorts. Across all cohorts, the area under the receiver operating characteristic curve was 0.84 and increased to 0.91 after exclusion of 8 patients receiving corticosteroids. A cutoff of 211 activated CD4 + effector memory T cells per 100 mL urine yielded a sensitivity of 78% and a specificity of 81%. Urinary activated CD4 + effector memory T cell counts correlated with renal CD4 + and CD4 + CD38 + T cell infiltration in acute interstitial nephritis. Conclusions Urinary activated CD4 + effector memory T cells expressing CD38 and HLA-DR represent a promising non-invasive biomarker for the diagnosis of acute interstitial nephritis. Lay Summary Acute interstitial nephritis is a frequent cause of acute kidney injury and is usually diagnosed by kidney biopsy, an invasive procedure that carries risks. We investigated whether immune cells in urine could help identify AIN in a less invasive manner. In this multicenter study, urine samples from 320 patients undergoing kidney biopsy in Germany and China were analyzed. We found that a specific population of activated T cells was markedly increased in the urine of patients with AIN compared with patients with other kidney diseases. This urinary marker outperformed other immune cell types and the inflammatory cytokine CXCL9 and was successfully validated in independent cohorts from both Germany and China. Importantly, urinary activated T cell counts correlated with immune cell infiltration within the kidney. These findings suggest that urinary activated T cells may serve as a non-invasive biomarker to support the diagnosis of AIN and facilitate earlier clinical decision-making.

Abstract Aims Cardiovascular disease (CVD) is the leading cause of mortality in chronic kidney disease (CKD). While CKD is known to give rise to systemic inflammation, its inciting factors remain poorly defined. Oxalate, long implicated in rare genetic kidney disorders, accumulates with decreased kidney function and has emerged as a driver of inflammation and independent risk factor for CVD. Here, we investigate the immunological mechanisms linking oxalate nephropathy to systemic inflammation, cardiac damage and kidney injury. Methods and results Oxalate nephropathy was induced in C57BL6/N mice through an oxalate-enriched diet. Oxalate induced systemic immune activation, renal fibrosis, and adverse cardiac remodelling, including pulmonary congestion with systolic and diastolic dysfunction. Flow cytometry analysis identified interleukin (IL)-17A as a dominant inflammatory effector, with expansion of Th17 and Th17-like Treg in the kidney, intestine, and spleen. Bulk mRNA sequencing confirmed these findings in kidney and heart. In line, plasma IL-17A was increased in oxalate-fed mice. Confirming the oxalate-IL-17A relationship, plasma IL-17A was elevated in patients with primary hyperoxaluria. Gut microbiome analysis by 16S amplicon sequencing showed only mild oxalate-induced alterations in mice. However, soluble oxalate directly enhanced Th17 polarization and disrupted mitochondrial respiration in vitro. In vivo, antibody-mediated IL-17A blockade improved kidney function, cardiac fibrosis, reduced neutrophil infiltration, and partially restored cardiac function in oxalate-fed mice. Conclusion Our study identifies oxalate as a systemic immunometabolic stressor and IL-17A as a central mediator of oxalate-induced cardiorenal injury. These findings establish the oxalate-IL-17A axis as a mechanistic link between CKD and CVD and suggest IL-17A inhibition as a potential therapeutic strategy to reduce cardiovascular damage in CKD.

Study Design. Post hoc analysis of prospectively-collected data. Objective. To identify factors predicting the most (and least) satisfaction following surgery for cervical spondylotic myelopathy (CSM). Summary of Background Data. Patient satisfaction affects long-term outcomes after surgery but the preoperative factors that influence postoperative satisfaction have not been well studied in patients with CSM. Methods. We conducted a post hoc analysis of prospectively-collected data from the Quality Outcomes Database (QOD) CSM registry cohort. Baseline and surgical characteristics were compared for patients with CSM reporting the most [North American Spine Society Satisfaction Score (NASS) 1] and least (NASS 4) satisfaction 24 months postoperatively. A multivariable logistic regression model was constructed using backward stepwise elimination to determine significant predictors of most versus least satisfaction. Twenty-four-month patient-reported outcomes (PROs) were compared. Results. Overall, 1141 patients were prospectively enrolled with 948 (83.1%) reaching 24-month follow-up. Of these, 699 met prespecified inclusion criteria: 610 (87.3%) were most satisfied and 89 (12.7%) were least satisfied. Though there were mean improvements in all 24-month PROs for the most satisfied cohort ( P <0.001), there were no significant improvements in 24-month PROs for the least satisfied cohort ( P >0.05). Using multivariable logistic regression modeling, a preoperative symptom of upper extremity weakness (OR=2.5, P =0.003), subjective myelopathy (OR=2.0, P =0.04), absence of numbness complaints (OR=2.2, P =0.003), symptom duration under 12 months (OR=1.6, P =0.047), Caucasian race (OR=2.3, P =0.001), participation in activities outside of the home (OR=2.0, P =0.04), anterior approach (OR=2.3, P <0.001), and higher baseline quality of life (OR=4.0, P =0.01) were predictive of the most satisfaction. Conclusion. Compared with the least satisfied, those most satisfied following surgery for CSM more often had a preoperative symptom of arm weakness, subjective myelopathy, the absence of numbness complaints, symptom duration under 12 months, higher baseline quality of life, and participation in activities outside of the home. Compared with a posterior approach, an anterior approach was associated with the most satisfaction. Taken together, early surgical treatment--before myelopathic symptoms significantly impact one's quality of life--may be considered to optimize patient satisfaction following CSM surgery.

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

Abstract Drug-resistant epilepsy affects tens of millions of people worldwide and is associated with considerable morbidity and mortality. Thalamic deep brain stimulation and cortical responsive neurostimulation are proven treatments for focal epilepsy. Both have been used to target a range of thalamic nuclei; yet, the roles of these thalamic nuclei in focal seizure generation remain incompletely understood. Thirteen patients with drug-resistant focal epilepsy undergoing intracranial EEG were consented to undergo investigation of thalamocortical networks. Sampled regions included cortical, mesial temporal, and thalamic brain regions. Visual and spectral analyses were performed to identify seizure onset patterns and correlate thalamic and cortical seizure activity. Thalamic ictal discharges were observed in 89% of seizures. Of these, 56% demonstrated synchronous thalamocortical activity with distinct patterns. These onset patterns included hypersynchronous spiking, low-voltage fast activity, ictal baseline shifts, and broadband suppression. Multiple thalamic nuclei were involved in ictal organization and propagation, with the specific nuclei depending on the cortical seizure network. The thalamus plays a crucial role in focal onset seizure generation and propagation, with distinct seizure onset patterns and nuclei involved. These findings support exploring a broader range of thalamic nuclei in epilepsy neurostimulation and have implications for seizure detection settings in intracranial sensing devices.

Background: Major depressive disorder remains a leading cause of disability. While subgenual cingulate cortex (sgCC) deep brain stimulation (DBS) shows promise for medically refractory depression, clinical outcomes have been heterogeneous, suggesting that individual differences in neural circuitry engagement may critically influence therapeutic efficacy. We aimed to define the electrophysiological signatures of sgCC efferent connectivity using single-pulse electrical stimulation (SPES) with intracranial stereo-EEG (sEEG) to inform rational targeting and physiological biomarkers for sgCC-DBS. Methods: In four patients undergoing clinically indicated sEEG for seizure mapping, SPES was delivered through sgCC pairs, while distributed brain stimulation-evoked potentials (BSEPs) were recorded across cortical and subcortical sites. Responses were characterized using Canonical Response Parameterization to extract reproducible waveforms and per-trial reliability. Results: sgCC stimulation elicited reproducible, spatially organized BSEPs across frontal, limbic, and paralimbic networks, aligning with known anatomical pathways. Frontal recruitment featured robust, lateralized orbitofrontal activation favoring the ipsilateral central, medial OFC and bilateral ventromedial prefrontal responses. Limbic effects demonstrated bilateral cingulate activation with stronger ipsilateral recruitment and lateralized amygdala and hippocampal responses. Paralimbic engagement included insular responses with subject-specific anterior predominance and bi-hemispheric temporal-polar slow-wave deflections. Conclusion: These findings provide direct electrophysiological evidence of distributed, lateralized sgCC divergent network connectivity in the human brain, offering physiologic confirmation of its role in affective circuitry. The observed topography and laterality have direct applications for sgCC-DBS targeting and implicate BSEP signatures as candidate biomarkers to guide patient-specific therapy.