Scientific Reports

Determining physiological stress at high resolution is crucial across diverse settings to enable informed decision-making in the context of health and disease. Saliva-based targeted multi-omics testing provides a powerful, non-invasive method to quantify physiological stress and circadian dynamics at high-frequency. In a laboratory crossover trial with 24-hour sampling comprising 413 saliva samples, we demonstrate high analytical reliability, distinct molecular individuality, and robust acute an...

Rapid risk stratification is essential in the clinic, yet vital signs, laboratory tests, and triage scores may not fully capture risk at presentation. We investigated whether facial photographs taken after emergency admission provide an additional mortality signal. Using 27,660 smartphone facial photographs, we trained deep neural networks to identify mortality risk with a Cox proportional hazards framework. Face-derived risk scores strongly stratified short- and long-term mortality, outperformi...

Respiratory monitoring in daily-life settings is important for health assessment, yet extracting physiologically interpretable information from breathing signals under natural conditions remains challenging, as breathing is inherently dynamic and strongly modulated by behavior. Here, a portable breathing monitoring device based on a flexible lead zirconate titanate sensor is developed to address this challenge. By exploiting polarity-opposed piezoelectric and pyroelectric responses through senso...

Overcrowding of emergency departments (ED) is now a problem of global health care concern due to the increase in patients. Triage systems have been established for a considerable period. However, their reliability in choosing the appropriate patient and the level of service has undergone much scrutiny. In this paper, we describe a comprehensive machine learning framework aimed at predicting critical emergency department outcomes and enabling dynamic routing decisions. Through the MIMIC-IV-ED dat...

Nocturnal glucose regulation is modulated by autonomic and circadian mechanisms, yet their dynamic interplay in apparently healthy, free-living populations remains poorly studied. Here, we assessed 227,860 nights of concurrent sleep data from Ultrahuman AIR ring and M1 continuous glucose monitoring (CGM) system across 5849 adults globally to examine nocturnal cardio-metabolic coupling. We found that higher sleep consistency was inversely associated with glucose variability, and vice versa. Unsup...

1Accurate assessment of patient-ventilator interaction is critical for optimizing respiratory support and detecting harmful dyssynchronies linked to adverse outcomes, including ventilator-induced lung injury and prolonged ICU stays. This requires precise, breath-by-breath segmentation and phase delineation of ventilator waveforms, specifically pressure, flow, and volume. Current reliance on manual annotation limits scalability and consistency, particularly given the variability of waveforms acro...

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative approaches beyond traditional epidemiological models. This study introduces an advanced method for automated model discovery using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, leveraging a dataset from the COVID-19 outbreak in Thuringia, Germany, encompassing over 400,000 patient records and vaccination data. By analysing this dat...

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSTrauma patients present with heterogeneous injury patterns that are challenging to represent in statistical models. Traditional approaches either use high-dimensional one-hot encoding, resulting in sparse features, or aggregate injuries into summary scores that lose patient-specific detail. This study developed data-driven ICD-10 embeddings for trauma injuries and evaluated their ability to preserve injury information. MethodsUsing the National ...

Myocardial infarction (MI) is a major global health concern influenced by diverse risk factors. Despite growing evidence of oral- systemic connections, current MI models largely exclude oral health indicators, reflecting the longstanding separation between dental and medical paradigms. This study introduces a multidomain, interpretable machine learning framework that integrates detailed periodontal and oral hygiene variables, marking one of the first efforts to quantitatively incorporate these f...

Sudden cardiac death risk is 2-3-fold higher in athletes than in non-athletes. We classify sports-related cardiac arrhythmias using a novel explainability framework comprising data analysis, model interpretability, post-hoc visualisation, and systematic assessment. Two neural networks--one with interpretable sinc convolution and one with standard convolution--were trained on general-population ECGs (PhysioNet, n=88,253, 30 arrhythmias, three continents) and tested on professional footballers (PF...

The embodied brain is highly dynamic, changing with every thought, sensory input and motor activity. It keeps us coherent and healthy via its connections to every organ within the body, particularly the heart, which in turn supplies nutrients and oxygen to all bodily organs and the brain. Listening to music can instantly alter brain-body dynamics. Yet, the acoustic, neural, and physiologic parameters and processes that facilitate these effects are not well understood. Here, we tested the hypothe...

Osteoporosis is a silent yet debilitating disease that often remains undetected until fractures occur. While early prediction is crucial, most studies combine male and female datasets to train a single model, introducing bias since osteoporosis risk and progression differ by gender. This study aims to develop gender-specific machine learning models that leverage longitudinal data to predict osteoporosis risk, providing tailored insights for men and women. Data were obtained from two large longit...

Current standard of care imaging practices cannot reliably differentiate among certain renal tumors such as benign oncocytoma and clear cell renal cell carcinoma (RCC), and between low and high grade RCCs. Previous work has explored using deep learning, radiomics, and texture analysis to predict renal tumor subtypes and differentiate between low and high grade RCCs with mixed success. To further this work, large diverse datasets are needed to improve model performance and provide strong evaluati...

ObjectiveIn vitro cytokine release assays are essential tools in immunological research, yet their reliability can be influenced by various pre-analytical factors. This study was initiated to investigate the potential impact of lithium heparin whole blood collection tubes from different manufacturers on assay performance. The objective of this work is to urge the scientific community to consider whole blood collection tubes as a significant and often underestimated source of variability in cell-...

BackgroundFoundation models have emerged as a promising paradigm for medical imaging AI [7], with claims of improved generalization and reduced bias. However, their robustness to technical acquisition parameters remains unexplored. We evaluated whether foundation models exhibit greater robustness to chest radiograph view type (anteroposterior [AP] versus posteroanterior [PA]) compared to traditional convolutional neural networks. MethodsWe compared four model architectures on the RSNA Pneumonia...

BackgroundMeteorological factors such as barometric pressure, humidity, and temperature have been linked to weather-related symptoms in the general population, yet little is known about their influence on athletes daily well-being and performance. Individual variability in weather sensitivity has been reported in biometeorology research, suggesting that only certain individuals exhibit pronounced physiological responses to environmental fluctuations. However, no studies have examined within-pers...

The increasing availability of electronic health records (EHRs) provides opportunities to apply machine learning (ML) methods in support of clinical decision-making. The temporal nature of laboratory values in EHR data records makes them particularly suitable for temporal deep learning (DL) architectures that model patient trajectories over time. However, despite this potential, the application of temporal DL models to longitudinal laboratory data has largely been limited to intensive care unit ...

Handheld ultrasound devices have revolutionized point-of-care diagnostics, but their effectiveness remains limited by operator dependency and the need for specialized training. This paper presents an intelligent guidance and diagnostic assistance system for the handheld wireless ultrasound device, enabling automated carotid artery and thyroid examinations through handheld operation. Drawing inspiration from the Actor-Critic framework, we implement a simulation-based reinforcement learning approa...

Blood-based biomarkers are central to diagnostics, yet current approaches depend on invasive sampling and centralized laboratory infrastructure. At the same time, womens reproductive health remains severely under-monitored: most clinically relevant biomarkers are rarely measured outside fertility clinics, leaving millions without accessible, continuous insight into their reproductive lifespan. Anti-Mullerian hormone (AMH), a key indicator of ovarian reserve and overall reproductive function, sti...

Artificial intelligence systems for chest radiograph interpretation are increasingly deployed in clinical practice, yet current fairness frameworks emphasize demographic subgroup analysis while the relative contribution of technical acquisition parameters to performance disparities remains poorly characterized. We conducted a multi-dataset external validation study analyzing 138,804 chest radiographs from the RSNA Pneumonia Detection Challenge (n=26,684; 22.5% pneumonia prevalence) and NIH Chest...