STUDY OBJECTIVES:Sleep staging is usually performed by manual scoring of polysomnography (PSG), which is expensive, laborious, and poorly scalable. We propose an alternative to PSG for ambulatory sleep staging using wearable photoplethysmography (PPG) recorded by a smartwatch and automated scoring. METHODS:We previously trained a deep learning model on public datasets, with the specific purpose of performance generalizability to unseen datasets. In the present work, the model was assessed on two datasets of reflective PPG collected from wrist-worn devices: (1) 68 overnight recordings and (2) for the first time, 493 long-term recordings each lasting for 24 hours (170 subjects). Findings were compared either to (1) expert scored sleep stages from PSG for the night recordings or (2) actigraphy for the long-term recordings. RESULTS:For the overnight recordings, the PPG-based model achieved 78.7% accuracy and a Cohen's κ of 0.68 on reflective PPG collected using wrist-worn devices compared to PSG using a 4-class setup (wake, N1, and N2 combined, N3 and REM), and a sleep/wake accuracy of 94.1%, with a Cohen's κ of 0.71. For the long-term recordings, a sleep/wake accuracy of 92.5% with a Cohen's κ of 0.80 was achieved when compared to a state-of-the-art actigraphy-based deep learning model. CONCLUSIONS:This state-of-the-art accuracy achieved on wrist-worn devices represents a significant advancement for home sleep monitoring and a valuable alternative to PSG-based sleep staging. Additionally, our model demonstrated promising results on long-term ambulatory recordings, paving the way towards continuous ambulatory monitoring of sleep stages and sleep-wake cycles. Statement of Significance Sleep staging is crucial to diagnose sleep disorders, but traditional methods are laborious and costly. We developed a sleep staging model that demonstrates high performance and exceptional generalization to unseen datasets, including those from wrist-worn devices, thereby possibly enabling accurate sleep staging from wearable technology. Furthermore, we evaluated the model's performance on 24-hour recordings of subjects of various health conditions, offering valuable insights for clinical applications and future research. These advancements significantly enhance the feasibility of continuous sleep monitoring at home, a low-cost, scalable, and comfortable alternative to current methods.

Fabry disease (FD) is a multisystemic disease affecting the heart and the kidneys of affected patients. In addition to FD-specific treatment, patients require concomitant medication for cardio- and nephroprotection. Sodium-dependent glucose transporter 2 inhibitors (SGLT2i) are recommended for patients with heart failure and/or kidney disease, but efficacy data for FD are scarce. In this multicenter study (n = 8), the effects of SGLT2i therapy after 12 months of treatment in 48 patients (12 females) on FD-specific therapy were examined. Patients were retrospectively analyzed at three time points (before SGLT2i: T-1; SGLT2i start: T0; and end of observation: T+1). Patients showed advanced cardiac manifestations with a high frequency of left ventricular hypertrophy (LVH) (females: 81.8

Functional chest imaging using electrical impedance tomography (EIT) has experienced an impressive technological development since its invention in the early eighties of the last century. The number of experimental and clinical studies using this technology is continuously rising, and the increasing availability of devices approved for clinical use accelerates and diversifies its applications in patients. EIT is predominantly used in intensive care units but its utilisation in operating theatres, delivery rooms, pulmonary function laboratories and even remote outpatient settings is growing. Chest EIT is mainly applied to determine the regional distribution of pulmonary ventilation, aeration changes, and respiratory system mechanics both during mechanical ventilation and spontaneous breathing, but an increase in the use of chest EIT for imaging lung perfusion and cardiac action has recently been noted. The ongoing innovation of both EIT hardware and software, the new application fields, and the rising number of users of this technology require consensus on EIT terminology and definitions. This secures a common framework for conducting EIT studies, patient examinations and guarantees unified analysis of EIT data, documentation, reporting and comparability of findings. Our article provides a comprehensive consensus document on EIT terminology and definitions generated by EIT experts of the international TRanslational EIT development stuDy group in cooperation with the producers of EIT technology. It not only updates and extends the first consensus EIT terminology published in 2017, but also offers a new taxonomy of EIT measures, systematically based on the quantification of ventilation-related, heartbeat-related, and contrast-enhanced EIT signals. Thanks to its clear structure with tabulated recommended EIT terms, abbreviations, comprehensible explanations, notes, extensive literature sources and parameter calculations, EIT researchers, clinical users and manufacturers may use this document as a reference source of information relevant for chest EIT.

Degenerative diseases progress through gradual cell-intrinsic damage that is difficult to resolve with bulk transcriptomics or discrete cell-state analysis. We introduce a generalizable single-cell and spatial transcriptomics framework that quantifies continuous damage trajectories in vivo using cell-type-specific scores. Applied to chronic kidney disease and metabolic dysfunction-associated steatotic liver disease, the podocyte damage score (PDS) and hepatocyte damage score (HDS) place individual podocytes and hepatocytes on a health-to-damage axis. Across mouse and human single-cell RNA sequencing (scRNA-seq), single-nucleus RNA-seq (snRNA-seq), spatial transcriptomics, proteomics, histology, and clinical datasets, PDS and HDS robustly detect disease-associated damage, align disease models as well as unperturbed cells, and reveal conserved pathways. The PDS links podocyte injury and FSGS progression to circadian gene-expression disruption. The HDS identifies a damage threshold in metabolic dysfunction-associated steatotic liver disease (MASLD)/NASH beyond which hepatocytes adopt senescent, metabolically dysfunctional states. These damage scores enable scalable mapping of degenerative disease progression and stage-specific therapeutic targets.

Kidney transplantation (KTx) is the preferred treatment for kidney failure. However, post-transplant management is challenging due to the limited lifespan of transplanted organs. Current methods for monitoring post-transplant complications are invasive and have limitations. Therefore, there is an urgent need for novel non-invasive biomarkers. This study investigates the proteomic composition of urine to understand renal biology during the process of transplantation and to identify potential markers for outcome prediction. Urine samples were collected from donors before transplantation and from recipients 4 weeks and 1 year after transplantation. Proteomic analysis was performed using mass spectrometry and label-free quantification. Statistical analyses included principal component analysis (PCA) and enrichment analysis. The resulting key findings were confirmed in an independent validation cohort. In addition, correlative regression models to evaluate the relationship between protein abundance and clinical outcomes in the further course after transplantation were performed. 106 urine samples in the setting of 70 kidney transplantations were analyzed. PCA revealed distinct clustering of donor and recipient samples, indicating significant proteomic changes after transplantation. Hierarchical clustering and gene ontology analysis identified molecular changes as a response to transplantation and showed an over-representation of relevant pathways related to inflammation, cell immune response and coagulation in both the original and validation cohorts. Multivariate regression analysis, including linear and logistic regression, identified 11 potential protein biomarkers, including ORM2, IL1RAP, APP, and FABP4 as predictors of eGFR 12 months after transplantation and 1 HP as a predictor of infections within the first year after transplantation, respectively. This study underscores the potential of noninvasive urine proteomics for identifying biological processes involved in kidney transplantation and for enhancing post-transplant monitoring and outcome prediction. We identified 12 potential biomarkers with added value to standard clinical parameters linked to transplant outcomes, which will be promising candidates for future outcome monitoring after KTx.

Background: Tumor necrosis factor-alpha (TNF-alpha) is a cytokine involved in systemic inflammation and has a profound impact on metabolic processes within cells. Studying its effects on kidney organoids can reveal insights into renal inflammatory responses and metabolic dysregulation associated with kidney diseases. This study aimed to characterize metabolic shifts in human kidney organoids upon TNF-alpha stimulation, with a focus on energy metabolism, amino acid metabolism, and uremic toxin production, and to test whether supplementation with TCA cycle metabolites could modulate these effects. Methods: Kidney organoids were exposed to TNF-alpha, followed by proteomics and metabolomics mass spectrometric analyses. We quantified changes in key catalytic enzymes and metabolites involved in major metabolic pathways including the TCA cycle, amino acid metabolism, and the production of uremic toxins. In a follow-up experiment we combined TNF-alpha treatment with a TCA cycle metabolite mix (500µM each of cis-aconitic acid, citric acid, sodium succinate, and L-malic acid) to assess whether TCA cycle metabolites can attenuate the TNF-alpha–induced inflammation. Results: TNF-alpha stimulation resulted in significant metabolic rewiring of the organoid tissue. We observed an increased energy demand evidenced by upregulated production of TCA cycle metabolites. Further, we detected changes in fatty acid, amino acid and nucleotide metabolism. Importantly, co-treatment of organoids with TNF-alpha and the TCA cycle metabolite mix partially ameliorated the inflammatory response: concentrations of C3, VCAM1, and CXCL10 were reduced at both 24h and 48h compared to TNF-alpha alone, suggesting a protective effect of the metabolites. Conclusion: TNF-alpha induces a coordinated inflammatory response and metabolic reprogramming in human kidney organoids. Supplementation with TCA cycle intermediates can mitigate key inflammatory markers, pointing to a potential therapeutic strategy for metabolic modulation in kidney inflammation. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Low nephron endowment constitutes a risk factor for hypertension and renal disease. Epigenetic regulation is crucial for nephron progenitor cell differentiation, affecting nephron number and renal function. The role of many epigenetic modulators, such as Lysine-specific histone demethylase 1a (LSD1 or KDM1A), remains unclear. We used Kdm1a-KO mice to demonstrate that Kdm1a depletion in nephron progenitor cells results in reduced kidney size in neonates and led to glomerulosclerosis, proteinuria, and renal cysts in adults. Notably, Kdm1a deletion in podocytes or tubular cells did not replicate these effects. CRISPR/Cas9-mediated KDM1A deletion in human kidney organoids caused cyst formation and altered gene expression, with snRNA-seq revealing downregulation of podocyte genes and upregulation of metabolic genes. The presence of noncoding RNAs indicated roles in cell proliferation. Our study reveals the critical role of Kdm1a function in nephron development and highlights its affect on transcriptional programming for long-term renal function and susceptibility to cyst formation.

Sleep apnea is a common chronic sleep-related disorder which is known to be a comorbidity for cerebro- and cardio-vascular disease. Diagnosis of sleep apnea usually requires an overnight polysomnography at the sleep laboratory. In this paper, we used a wearable device which measures reflectance photoplethysmography (PPG) at the wrist and upper arm to estimate continuous SpO2 levels during sleep and subsequently derive an oxygen desaturation index (ODI) for each patient. On a cohort of 170 patients undergoing sleep apnea screening, we evaluated whether this ODI value could represent a surrogate marker for the apnea-hypopnea index (AHI) for the diagnosis and severity assessment of sleep apnea. As the ODI was simultaneously obtained at the fingertip, upper arm and wrist, we compared ODI diagnostic performance depending on the measurement location. We then further evaluated the accuracy of ODI as a direct predictor for moderate and severe sleep apnea as defined by established AHI thresholds. We found that ODI values obtained at the upper arm were good predictors for moderate or severe sleep apnea, with 86% accuracy, 96% sensitivity and 70% specificity, whereas ODI values obtained at the wrist were less reliable as a diagnostic tool.

Abstract Dysregulated proteolysis is central to autoimmune pathogenesis. The complement cascade, a major protease network, generates fragments that modulate immunity and tissue injury. We developed a scalable blood plasma N-terminomics workflow that markedly expands detection of proteolytic events in vitro and in vivo. Applied to 143 systemic lupus erythematosus (SLE) patients, Multi-Omics Factor Analysis (MOFA) linked N-terminal signatures to immunological and clinical heterogeneity. This revealed a previously unrecognized complement fragment, C3-LHF1, encompassing the C345C domain and rivaling, based on intensity detected by mass spectrometry, the abundance of canonical fragments like C3a and C3b. C3-LHF1 associated with renal function and remission in lupus nephritis, and exhibited dual functions: inhibiting classical and lectin complement pathways and acting as a partial IL6ST (gp130) agonist, independent of IL6Rα. In human kidney organoids, C3-LHF1 induced JAK/STAT3 signaling, amplified TNFα-driven CXCL10 secretion, and reduced podocyte marker expression, suggesting a role in tissue remodeling. These findings reveal unanticipated complexity in complement-mediated signaling and provide a comprehensive atlas of protein N-termini in human plasma, which enables discovery of novel immunoregulatory mechanisms and therapeutic targets in inflammatory disease.

Introduction Obstructive sleep apnea syndrome (OSAS) is a prevalent sleep disorder associated with significant morbidity and mortality, particularly due to its links with cardiovascular diseases like hypertension (HT). Continuous positive airway pressure (CPAP) remains the standard treatment for OSAS, yet individualized therapy and monitoring are crucial for optimizing patient outcomes. This study explores the feasibility of utilizing connected devices to remotely monitor OSAS patients undergoing CPAP treatment. Methods Ten patients diagnosed with OSAS were enrolled in a prospective observational feasibility study. Participants wore two wearables continuously: CenterPoint Insight Watch ™ for sleep and physical activity monitoring, and Aktiia™ bracelet for blood pressure measurement. CPAP usage data were collected using the DreamStation™ device. Data synchronization and processing were conducted using a dedicated Python script. Primary outcomes included acceptability, compliance, autonomy in device usage, and data quality. Secondary outcomes focused on the feasibility of integrating a centralized platform for analysis. Results Acceptability among patients was reasonable, with 58% consenting to participate. However, two patients discontinued the study due to skin allergies and device interference with professional activities. Most participants demonstrated autonomy in using the devices, although two required assistance with synchronization. Data quality varied, particularly with nocturnal blood pressure measurements, affected by technical issues and individual factors. Integration of data from all devices onto a centralized platform was feasible, enabling comprehensive analysis. Discussion The study highlighted successes in continuous remote monitoring of OSAS patients undergoing CPAP treatment. Challenges included device-related issues and manual data processing. A centralized platform for data integration and analysis proved promising for longitudinal monitoring and personalized healthcare delivery. Conclusion This feasibility study demonstrates the potential of remote monitoring in CPAP-treated OSAS patients. Future efforts should focus on addressing technical challenges and optimizing data integration on a common platform to realize the full benefits of continuous monitoring in personalized healthcare management.

Traditionally used for measuring heart rate and oxygen saturation, photoplethysmography (PPG) has emerged as a promising non-invasive alternative for diagnosing sleep related disorders. Unlike the gold-standard polysomnography (PSG) performed in-lab at the hospital, PPG offers a more scalable and cost-effective solution. Recent advancements in deep learning have significantly enhanced the precision of these methods for sleep stage inference. This study extends the evaluation of a PPG-based deep learning model to a clinical cohort of 134 patients with suspected sleep apnea (SA). These participants, enrolled in an ongoing clinical trial, underwent overnight PSG alongside simultaneous recording of PPG and accelerometer signals using CSEM’s wearable devices, positioned at both the wrist and upper arm. When compared to PSG, the PPG-based deep learning model achieved a median accuracy of 80.8% with a Cohen's Kappa of 0.7 in identifying wakefulness, light sleep (S1 + S2), deep sleep (S3), and rapid eye movement (REM) sleep stages using wrist-worn sensors. A reduction in performance was observed when the device was worn at the upper arm, with accuracy decreasing by approximately 6.2% and Cohen’s Kappa by 10%. Additionally, a lightweight alternative of the model leveraging inter-beat-intervals (IBIs) yielded comparable results at the wrist, with no performance degradation at the upper arm, highlighting its potential for deployment in resource-constrained settings. Overall, these findings demonstrate the feasibility of the approach as an accessible complement to PSG for home-based sleep monitoring.

DNA repair is essential for preserving genome integrity. Podocytes, postmitotic epithelial cells of the kidney filtration unit, bear limited regenerative capacity, yet their survival is indispensable for kidney health. Podocyte loss is a hallmark of the aging process and of many diseases, but the underlying factors remain unclear. We investigated the consequences of DNA damage in a podocyte-specific knockout mouse model for DNA excision repair protein Ercc1 and in cultured podocytes under genomic stress. Furthermore, we characterized DNA damage-related alterations in mouse and human renal tissue of different ages and patients with minimal change disease and focal segmental glomerulosclerosis. Ercc1 knockout resulted in accumulation of DNA damage and ensuing albuminuria and kidney disease. Podocytes reacted to genomic stress by activating mTOR complex 1 (mTORC1) signaling in vitro and in vivo. This was abrogated by inhibiting DNA damage signaling through DNA-dependent protein kinase (DNA-PK) and ataxia teleangiectasia mutated (ATM) kinases, and inhibition of mTORC1 modulated the development of glomerulosclerosis. Perturbed DNA repair gene expression and genomic stress in podocytes were also detected in focal segmental glomerulosclerosis. Beyond that, DNA damage signaling occurred in podocytes of healthy aging mice and humans. We provide evidence that genome maintenance in podocytes is linked to the mTORC1 pathway and is involved in the aging process as well as the development of glomerulosclerosis.

Degenerative diseases are marked by the progressive accumulation of cellular damage, leading to impaired cellular function and tissue degeneration. Despite advances in single-cell technologies, capturing the gradual decline of individual cells in vivo remains challenging. Here, we present a novel, universal, cross-model framework for quantifying cellular damage at single-cell resolution, to uncover conserved molecular trajectories of cellular degeneration. This method uses single-cell RNA sequencing data and enables the detection of progressive damage within distinct cell populations under physiological and pathological conditions. We developed the Podocyte Damage Score (PDS) and Hepatocyte Damage Score (HDS) to monitor cellular deterioration in murine models of kidney glomerulosclerosis and liver steatosis, respectively. The application of these scores to both murine and human datasets accurately quantified cellular damage across diverse disease models and distinguished varying degrees of damage even in unperturbed samples. Notably, the PDS revealed circadian gene expression dysregulation as a hallmark of podocyte injury, while the HDS identified a critical threshold of hepatocyte damage leading to cellular senescence and metabolic dysfunction. The approach provides a scalable tool for decoding disease progression and identifying therapeutic targets across degenerative disorders. ### Competing Interest Statement The authors have declared no competing interest.