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Digital biomarkers such as wearables are of increasing interest in monitoring rheumatic diseases, but they usually lack disease specificity. In this study, we apply convolutional neural networks (CNN) to real-world hand photographs in order to automatically detect, extract, and analyse dorsal finger fold lines as a correlate of proximal interphalangeal (PIP) joint swelling in patients with rheumatoid arthritis (RA). Hand photographs of RA patients were taken by a smartphone camera in a standardized manner. Overall, 190 PIP joints were categorized as either swollen or not swollen based on clinical judgement and ultrasound. Images were automatically preprocessed by cropping PIP joints and extracting dorsal finger folds. Subsequently, metrical analysis of dorsal finger folds was performed, and a CNN was trained to classify the dorsal finger lines into swollen versus non-swollen joints. Representative horizontal finger folds were also quantified in a subset of patients before and after resolution of PIP swelling and in patients with disease flares. In swollen joints, the number of automatically extracted deep skinfold imprints was significantly reduced compared to non-swollen joints (1.3, SD 0.8 vs. 3.3, SD 0.49, p < 0.01). The joint diameter/deep skinfold length ratio was significantly higher in swollen (4.1, SD 1.4) versus non-swollen joints (2.1, SD 0.6, p < 0.01). The CNN model successfully differentiated swollen from non-swollen joints based on finger fold patterns with a validation accuracy of 0.84, a sensitivity of 88%, and a specificity of 75%. A heatmap of the original images obtained by an extraction algorithm confirmed finger folds as the region of interest for correct classification. After significant response to disease-modifying antirheumatic drug ± corticosteroid therapy, longitudinal metrical analysis of eight representative deep finger folds showed a decrease in the mean diameter/finger fold length (finger fold index, FFI) from 3.03 (SD 0.68) to 2.08 (SD 0.57). Conversely, the FFI increased in patients with disease flares. In conclusion, automated preprocessing and the application of CNN algorithms in combination with longitudinal metrical analysis of dorsal finger fold patterns extracted from real-world hand photos might serve as a digital biomarker in RA.

Background: Assuring adequate antibiotic tissue concentrations at the point of infection, especially in skin and soft tissue infections, is pivotal for an effective treatment and cure. Despite the global issue, a reliable AB monitoring test is missing. Inadequate antibiotic treatment leads to the development of antimicrobial resistances and toxic side effects. β-lactam antibiotics were already detected in sweat of patients treated with the respective antibiotics intravenously before. With the emergence of smartphone-based biosensors to analyse sweat on the spot of need, next-generation molecular digital biomarkers will be increasingly available for a non-invasive pharmacotherapy monitoring. Objective: Here, we investigated if the glycopeptide antibiotic vancomycin is detectable in sweat samples of in-patients treated with intravenous vancomycin. Methods: Eccrine sweat samples were collected using the Macroduct Sweat Collector®. Along every sweat sample, a blood sample was taken. Bio-fluid analysis was performed by Ultra-high Pressure Liquid Chromatograph-Tandem Quadrupole Mass Spectrometry coupled with tandem mass spectrometry. Results: A total of 5 patients were included. Results demonstrate that vancomycin was detected in 5 out of 5 sweat samples. Specifically, vancomycin concentrations ranged from 0.011 to 0.118 mg/L in sweat and from 4.7 to 8.5 mg/L in blood. Conclusion: Our results serve as proof-of-concept that vancomycin is detectable in eccrine sweat and may serve as a surrogate marker for antibiotic tissue penetration. A targeted vancomycin treatment is crucial in patients with repetitive need for antibiotics and a variable antibiotic distribution such as in peripheral artery disease to optimize treatment effectiveness. If combined with on-skin smartphone-based biosensors and smartphone applications, the detection of antibiotic concentrations in sweat might enable a first digital, on-spot, lab-independent and non-invasive therapeutic drug monitoring in skin and soft tissue infections.

Anxiety, characterized by pervasive feelings of nervousness and worry, may differentially impact cognitive problem-solving across various domains, including spatial and social contexts. This study investigates the relationship between distinct types of anxiety and cognitive performance, aiming to elucidate the underlying neural mechanisms involved. Sixty participants (54 females, Mage = 22.46, SD = 4.50; 6 males, Mage = 20.83, SD = 2.31) were divided into three groups: Control ( N  = 20), Social Anxiety ( N  = 20), and Spatial Anxiety ( N  = 20). During a one-hour electroencephalography (EEG) session, participants completed the Colour Stroop task, Flanker Spatial task, and Social Stroop task. Event-related potential (ERP) analysis revealed that the control group exhibited significantly lower amplitudes in N2, N3, early P3, LPP across all three tasks compared to both anxiety groups. No evidence was found for domain-specific effects. These findings suggest a shared neural processing mechanism of anxiety which encompasses different cognitive domains and highlights the complexity of interaction between different types of anxiety and cognitive performance.

Microglia maintain neuronal homeostasis through dynamic surveillance strategies that depend on their functional state. In the healthy brain, highly ramified microglia monitor neuronal integrity via motile processes and transient soma contacts. Aging is associated with reduced process motility and diminished expression of homeostatic markers, raising the question of how microglial surveillance adapts to these changes. Here, we used time-lapse imaging of acute cortical slices from young and aged mice to characterize age-dependent alterations in microglial behavior. We found that, unlike microglia in young animals, a subset of aged microglia exhibited pronounced somatic mobility and loss of territorial confinement, resembling migratory behaviors previously described in neurodegenerative conditions. Unsupervised clustering analyses revealed increased heterogeneity in aged microglia, including the emergence of distinct high-mobility subpopulations absent in young brains. Despite these dynamic changes, aged microglia largely retained ramified morphology and did not show increased neuronal envelopment. In parallel, microglia from aged mice displayed prolonged intracellular Ca bursts across all subpopulations, indicating a global shift in functional state. These sustained calcium signals resembled those observed in disease-associated microglia, suggesting that aging induces a primed, partially activated phenotype. Consistent with this interpretation, we observed a trend toward reduced P2Y12 expression in aged microglia. Together, our findings demonstrate that microglia adapt to aging by shifting from process-based surveillance toward increased soma-mediated mobility, accompanied by sustained Ca signaling. This adaptive response may compensate for declining process dynamics while reflecting a transition toward a pro-inflammatory, disease-associated state.

We conducted a survey about recent surgical procedures on a large connected population and requested each individual’s permission to access data from commercial wearable devices they may have been wearing around the time of the procedure. For subcohorts of 66–118 patients who reported having a weight loss procedure and who had dense Fitbit data around their procedure date, we examined several daily measures of behavior and physiology in the 12 weeks leading up to and the 12 weeks following their procedures. We found that the weeks following weight loss operations were associated with fewer daily total steps, smaller proportions of the day spent walking, lower resting and 95th percentile heart rates, more total sleep time, and greater sleep efficiency. We demonstrate that consumer-grade activity trackers can capture behavioral and physiological changes resulting from weight loss surgery and these devices have the potential to be used to develop measures of patients’ postoperative recovery that are convenient, sensitive, scalable, individualized, and continuous.

Peak alpha frequency (PAF) is negatively associated with experimental pain sensitivity. Transcranial alternating current stimulation (tACS) enables shifting neural frequency to modulate sensory perception, yet it has not been tested in pain perception. We hypothesized that tACS application at 1 Hz above the individual PAF (iPAF) would increase PAF and reduce experimental pain ratings. This is a double-blind, sham-controlled, crossover, exploratory study with 15 healthy participants who completed 2 sessions; 20 min of tACS was applied to the somatosensory cortex at 1 Hz above the iPAF or sham stimulation (Intervention), administered in random order. Pain reports (4 time points) in response to a tonic heat pain (THP) stimulus were recorded before and after tACS. Five-minute electroencephalography was recorded in 4 conditions: baseline (iPAF identification), after THP stimulus, after tACS to test its effect on iPAF, and after another THP stimulus to test pain effects on iPAF following tACS. This paradigm examines associations between iPAF and pain, and the effects of pain and tACS on iPAF. There was no effect of real vs. sham tACS on iPAF, with a non-significant Condition × Intervention interaction and a small effect size (partial 0.01 and 0.03). Pain intensity increased across rating time points (  < 0.001), but tACS had no effects. In this small-sample preliminary study, a single tACS session did not affect iPAF or pain ratings in response to THP stimulus. The current study may help shape methodologies for future pain studies investigating the effects of tACS on PAF and pain.

Musical activity requires the coordinated integration of auditory, motor, and cognitive systems, making it an effective framework for studying brain plasticity. Previous studies have explored music training-related alterations in brain structure and function. However, the relationship between structure and function remains insufficiently understood. In this study, we applied a novel morphometric measure, morphometric inverse divergence, to assess the structural decoupling index (SDI) and evaluate differences in structure-function coupling between musicians and non-musicians. A total of 39 participants (22 musicians, 17 non-musicians) were enrolled. Group differences in SDI were examined to assess regional structure-function dependency. Within the musician group, correlation analyses were conducted between SDI and musical training experience and age of onset of training. Compared to non-musicians, musicians exhibited significantly lower SDI in the left superior frontal gyrus, right inferior frontal gyrus, right superior temporal gyrus, right parahippocampal gyrus and left posterior superior temporal sulcus (pSTS). Notably, the SDI in left pSTS showed a significant negative correlation with cumulative musical training experience and a positive correlation with age of onset of training. These findings demonstrate that long-term music training is associated with enhanced structure-function coupling in fronto-temporal networks, particularly in regions supporting auditory-motor integration and memory. Furthermore, the left pSTS appears specifically sensitive to training intensity and timing.

Behaviors arise from coordinated neural activity across diverse spatial and temporal scales. Prior work has linked better task performance and cognitive functioning to patterns of global network connectivity requiring minimal reconfiguration when switching between task demands. This metric indexing similarity in functional connectivity across task and rest has been termed “neural efficiency.” Here we assess stability of neural efficiency over approximately 3 years in adolescence, specificity across two task-rest combinations and associations with anxiety. At approximately age 16 and/or 19, 95 participants completed a resting state scan alongside a cognitive control and/or threat task. Neural efficiency was quantified as partial correlations between intrinsic and task-related functional connectivity patterns across the whole brain. We tested temporal stability across the three-year interval, as well as associations with task performance and anxiety across the two task-rest combinations at the two time points. Neural efficiency values remained relatively stable from mid to late adolescence (ICC[3,1] = 0.51–0.58). The cognitive control task showed higher values than the threat task. Across tasks, neural efficiency was associated with better performance (i.e., reduced interference), although not consistently ( r  = −0.19, p  = 0.26 – r  = −0.37, p = 0.021). These effects did not survive correction for multiple testing. No associations were found between neural efficiency and self/parent-reported anxiety. In sum, the metric shows moderate developmental stability and associations with task performance. Task features impact neural efficiency. Given small sample sizes, findings need to be interpreted cautiously.