Biosensors and Bioelectronics

Amyotrophic lateral sclerosis (ALS) lacks a validated blood-based diagnostic, and the field is increasingly moving from single-molecule markers toward integrative, multi-component signatures. Here we present a liquid-biopsy strategy that transduces disease dependent serum-nanoparticle interactions into a learnable near-infrared spectral phenotype. A sensor array of twelve DNA-functionalized single-walled carbon nanotube (SWCNT) chiralities, functionalized with (GT)6 ssDNA coupled with a deep learning model was tested on serum from 20 ALS patients and 19 age- and sex-matched controls (n = 39, TargetALS). Our multiplexed sensor design (12 SWCNT chiralities) and data acquisition strategy based on excitation-emission matrices acquired at three timepoints (0, 6, 24 h) was conceived to maximize sensor carried information. Indeed, we show that the array generates partially independent temporal dynamics across chiralities governed primarily by tube diameter. To decode this multiplexed, time-resolved signal, we trained a dual-objective convolutional autoencoder that jointly optimizes reconstruction and classification, achieving 84.6% cross-validated accuracy (AUC = 0.87). Selected latent features were reproducible across an independent same-subject experimental batch and correlated with serum neurofilament light chain, linking the spectral phenotype to a clinically relevant neurodegeneration marker. Mass spectrometry supported a molecular basis for discrimination, revealing an ALS-biased protein corona enriched in adaptive-immune and inflammatory proteins. Together, these results establish proof of principle that time-resolved, multi-chirality SWCNT spectral sensing can compress complex serum composition into a reproducible near-infrared biomarker signature for ALS.

Brain-derived tau (BD-tau) is an emerging blood-based biomarker for neurodegeneration, yet there are currently limited well validated BD-tau assays available for research and clinical use. To enhance access to this vital biomarker for neurological disorders including traumatic brain injury (TBI), we developed a novel blood-based immunoassay for BD-tau on the ultra-sensitive Quanterix HD-X platform using Single Molecule Array technology. Analytical validation assessed dilution linearity, specificity, precision, detection limits, and spike recovery, each recording robust metrics in agreement with international expert recommendations. The assay demonstrated robust validation metrics, achieving between-run stability of 95% when analyzing aliquots from six independent plasma and serum samples across five analytical runs. It also showed strong dilution linearity when diluted four-fold and achieved over 90% recovery when spiked with cerebrospinal fluid. Next, we evaluated the clinical utility of the assay in cohorts of individuals with traumatic brain injury (TBI), where strong performances were recorded whether using the 2-step or 3-step assay formats ({rho}= 0.94; p < 0.0001). Furthermore, plasma BD-tau distinguished samples from TBI patients based on time from injury and severity (AUC=0.93). Plasma BD-tau differentiated between favorable and unfavorable functional outcomes in the acute-severe group. Our findings underscore the significant potential of the BD-tau assay as a biomarker for TBI in the severe phase.

AIMS Cardiometabolic risk factors may impair health by altering the autonomic modulation of the cardiovascular system, a physiological process described by heart rate (HR) circadian oscillations. However, the impact of cardiometabolic health determinants on HR circadian oscillations remains scarcely characterized in real-world, population-based settings. To address this, we applied digital health technologies to investigate how cardiometabolic health determinants shape HR circadian oscillations in a real-world cohort of individuals free of cardiometabolic diseases. METHODS First, a 10-fold cross-validation of a model was performed, aiming at mitigating wearables measurement error caused by motion artifacts. This process was informed by 10,056 epochs of concurrent wearable-derived and polysomnographic HR assessment, yielding an average 1.3 bpm reduction in wearables measurement error. We subsequently applied this model to over 2 million 1-minute epochs of HR data, derived from 7-day continuous actigraphic recordings of 245 individuals free of cardiometabolic disorders. Functional-on-scalar regression modelling and both parametric and nonparametric analyses characterized HR circadian profiles and their relationships with demographics, lifestyle, chronotype, sleep health, and chronic insomnia diagnosis. A 6-dimension sleep health index was calculated. RESULTS Sex, chronotype, and sleep health predominantly shaped HR circadian oscillations. In detail, females consistently showed higher HR across the 24 hours. Moreover, chronotype was associated to a phase shift in HR circadian profiles, with later timings corresponding to eveningness. Notably, sleep health impacted HR circadian oscillations in a dose-dependent fashion: each additional impaired sleep dimension was associated with a 1.2 bpm HR increase during nighttime, alongside reduced circadian robustness and delayed oscillation timings. Finally, the earlier occurrence of morning HR peaks served as a digital biomarker of insomnia (80% specificity, 74% sensitivity). CONCLUSIONS This work provides a digital health framework to characterize HR circadian oscillations in free-living populations and supports its clinical utility in capturing the autonomic disruptions related to cardiometabolic health determinants.

Cancer treatment has shifted toward personalized therapy based on molecular profiling, particularly in advanced disease. Existing circulating tumor DNA panels are often broad, generating many non-actionable variants and incurring costs that limit routine use in molecular tumor boards. We developed and validated a manufacturer-independent, 109-gene liquid biopsy-centered pan-cancer open next generation sequencing panel (LION panel), combined with an in-house bioinformatic pipeline to support clinical decision-making. A total of 87 samples were analyzed, including 17 reference samples, 21 healthy blood donor controls, and 49 patient samples including nine tumor entities. The LION panel achieved 92% sensitivity and 99% specificity in reference samples, with high concordance to digital droplet PCR (r = 0.99). It detected variant allele frequencies as low as 0.05% (tumor-informed) and 0.5% (tumor-uninformed). Clinical concordance reached 82% with blood-based digital droplet PCR and 75% with whole exome tissue sequencing. In representative cases, variant dynamics correlated with disease progression and revealed additional targetable variants. Overall, the LION panel supports clinical decision-making by enabling identification of targetable variants, disease monitoring, and detection of treatment resistance, particularly when tumor tissue is unavailable.

Background: Chronic diseases and symptom syndromes often develop after prolonged biological changes that may precede formal diagnosis. RNA-based metatranscriptomics captures active microbial and human gene expression and may provide a functional layer for disease risk evaluation. To address this translational gap, we developed and validated a Disease Risk Score (DRS) framework that integrates metatranscriptome-derived pathway activity scores from stool, saliva, and blood samples, and evaluated its potential clinical utility as an adjunct risk-evaluation tool. Methods: DRS uses disease-specific sets of pathway activity scores derived from stool and saliva microbial functions, stool and saliva microbial taxa, and blood human gene expression. For each disease, 'not optimal' pathway scores are aggregated into a normalized cumulative odds ratio, or cOR, using score-level odds ratios, statistical significance, and literature-supported biological relevance derived from a Development Cohort of 22,369 individuals. A cOR [&ge;] 5 is defined as high risk. Performance is evaluated in an independent Validation Cohort of 15,908 individuals using self-reported diseases as the reference. Disease support requires both significant cOR separation between self-reported and not-reported (Cohen's d [&ge;] 0.2) and risk ratio enrichment of self-reported disease among individuals classified as high risk (95% CI of Risk Ratio > 1). Results: Of 20 initially evaluated diseases, 15 meet the prespecified validation criteria on the independent validation cohort: ADHD, anxiety, chronic fatigue syndrome, depression, GERD, hypertension, inflammatory bowel disease, IBS-C, IBS-D, insomnia, MASLD, obesity, obstructive sleep apnea, Sjogren's syndrome, and type 2 diabetes. Five selected clinical scenarios illustrate how DRS can support clinician-mediated decision making, including IBS subtype reclassification, improved diagnostic acceptance in IBS-D, personalized lifestyle counseling in MASLD and early type 2 diabetes, and diagnostic uncertainty in atypical GERD. Conclusions: DRS is a metatranscriptomics-based risk-stratification framework that aggregates active microbial and human pathway signals into interpretable disease-specific risk estimates across a wide range of disease conditions. Validation against self-reported disease labels in an independent cohort shows significant risk enrichment for each of 15 diseases. DRS is intended as an adjunct to clinical evaluation: a decision support tool in situations where routine care encounters uncertainty, delay, or low patient engagement. Future prospective studies using clinically adjudicated endpoints are needed to assess calibration and clinical outcomes.

The human small intestine (SI) plays a central role in nutrient processing, host-microbe interactions, and immune regulation, yet remains poorly characterized due to the lack of minimally disruptive sampling methods. Here, we present a protocol for deploying, recovering, and analyzing samples collected using an ingestible device that enables multi-region, lumen-targeted SI sampling during normal digestion. The device incorporates a ~30-cm collapsible tube wound into pH- or time-responsive layers that sequentially unfurl in situ, typically capturing three spatially ordered samples with high yield and reliable retrieval. This protocol outlines study design, participant handling, device recovery, contamination control, and standardized workflows for analyses, including cell quantification, culturomics, sequencing, and metabolomics. We further describe benchmarking approaches for evaluating spatial resolution and strategies for assay prioritization when sample volume is limiting. By reducing participant burden and facilitating integration with stool, saliva, and clinical metadata, this approach enables longitudinal and large-cohort studies linking SI microbial ecology and host physiology to human health.

The Empatica E4 wristband provides continuous multi-modal physiological monitoring including blood volume pulse (BVP), electrodermal activity (EDA) and skin temperature (TEMP) but its validity for sleep-stage-specific autonomic and thermoregulatory monitoring has not been systematically evaluated against concurrent polysomnography (PSG). Using the Wearanize+ dataset which provides synchronised PSG, Empatica E4, and Zmax EEG recordings from 100 home-recorded participants; a systematic validation of Empatica E4 physiological signals against PSG ground truth across five sleep stages was conducted. Of 100 participants, 92 had Empatica data; 69 met Zmax EEG signal quality criteria and formed the analysis sample. Heart rate (HR) from the pre-computed Empatica HR channel showed valid stage-specific patterns (Wake: 70.9 bpm, N3: 61.2 bpm) and moderate inter-device MeanNN correspondence with PSG ECG (Spearman r=0.35-0.42 across stages). Skin temperature showed the expected thermoregulatory pattern (Wake: 33.92C, N3: 35.48C) and is recommended for downstream analyses. Tonic EDA showed an inverted stage pattern attributable to wrist sweat accumulation during deep sleep, representing a known confound for wrist-worn EDA during sleep. Phasic EDA showed plausible patterns and may be used with caution. These findings establish a validated feature set for Empatica E4 sleep research and directly inform multimodal psychiatric biomarker studies using the Wearanize+ dataset.

Background Continuous health monitoring enables early detection of diseases and improves therapeutic outcomes. Non-intrusive biosignal sensors, such as capacitive ECG (cECG), offer a practical solution for daily monitoring in private environments, such as smart homes and vehicles. However, artifacts reduce signal quality and compromise reliability. Methods Following a registered report protocol (Warnecke JM et al. Plos One. 2021; 16(7):e0254780), we record data of 44 subjects and develop an artifact index for cECG. We use three signal quality indices (SQIs): the correlation of QRS complexes (corSQI), the R-peak detection consistency (bSQI) and the absolute amplitude ratio (aSQI). Our index classifies overlapping 10s segments with a step-width of 2s into clean or artifact segments. We label a 2s interval as artifacts if all five overlapping segments indicate artifacts. We record cECGs using an armchair with integrated electrodes in a single-arm study involving 44 subjects performing two activities -- reading and watching television (TV); for 11 minutes each. We record a time-synchronized reference ECG with skin electrodes on the chest. To evaluate the artifact index, we compare it with manually generated ground truth. Moreover, we evaluate the clothing materials cotton, linen, jeans, and polyester in 5 subjects. Results Watching TV results in longer, continuously clean signal durations than reading. On average, 88.3% of the signal has a minimum continuous clean duration of 10s, versus 79.8% during reading. All clothing configurations achieve a clean signal duration exceeding 10s. Among the SQI metrics, bSQI performs best, achieving an accuracy of 90.7% and an F1 score of 79.9%. Combining the three SQI metrics in a voting approach improves accuracy to 92.0% and F1 score to 82.1%. Discussion Our artifact index automatically distinguishes clean from artifact cECG segments, promoting health monitoring in unsupervised real-world settings, earlier disease detection, and preventive health management. A limitation is the investigation of only two scenarios (reading and watching TV).

Monitoring activities of daily living (ADLs) in the home is a promising approach for tracking dementia progression in older adults. While ambient sensor-based ADL systems are well-studied, most existing ADL recognition systems rely on globally trained models that ignore the spatial organization of in-home activities. In real deployments, where training data are sparse and highly home-specific, global transformer models may fail to capture room-dependent behavioral structure. We propose a deterministic Mixture of Experts (MoE) architecture for in-home ADL recognition, in which each expert is a compact transformer specialized to one room of the home (bedroom, kitchen, bathroom, living area). Input segments are routed using a deterministic gating strategy based on room-level motion activity and time-of-day priors for sleep-related behaviors. Unlike learned routing networks, the proposed gate encodes domain knowledge about where ADLs are likely to occur, reducing model complexity under limited per-home training data. By decomposing ADL recognition into room-specific activity spaces, the proposed architecture reduces competition between dominant and low-frequency activities under highly imbalanced residential data. We evaluated the system on data collected via low-cost ambient sensors (motion, light, temperature, humidity) and Raspberry Pi edge devices across five homes, with ground-truth ADL labels provided by participants and caregivers. Across the five homes, the proposed MoE consistently outperformed global transformer, 1D CNN, and Random Forest baselines, achieving macro-F1 scores ranging from 0.60 to 0.88, highlighting the importance of home-specific modeling in real-world deployments. These findings suggest that room-aware expert specialization may provide a practical and interpretable strategy for low-data ADL recognition in real-world residential environments.

Background Thalassemia is one of the most common monogenic disorders worldwide, current screening strategies combining hematological testing with molecular assays still carry a risk of missed diagnoses and undesirable efficiency, particularly for complex structural variants and rare mutations. Methods In this prospective double-blind, multicenter cohort study of 3,842 participants (3,362 pregnant women and 480 male partners), we conducted a head-to-head comparison to systematically evaluate the incremental clinical value and detection performance of single-molecule nanopore sequencing in thalassemia (SMITH) against conventional hematological testing and next-generation sequencing (NGS). Findings The overall concordance rate between NGS and SMITH was 98.6% (3789/3842). The discrepant cases (n=53) were directly attributed to the superior detection capabilities of SMITH, which successfully identified complex structural rearrangements-including 45 -globin gene triplications and four HK alleles-that were missed by NGS. Furthermore, SMITH accurately detected four rare variants (c.134_135insT/, c.-22(C>T)/, {beta}N/{beta}c.316-290delinsAGGGCAATAATTT and {beta}3.5 kb deletion/{beta}N ) and resolved ten trans and three cis configurations within the globin gene allele. Clinically, these technical advantages translated to a 9.3% (5/54) increase in the detection rate of high-risk prenatal couples, effectively preventing one birth affected by moderate-to-severe thalassemia. Additionally, SMITH corrected a diagnostic discrepancy in one case (HK vs. -3.7), sparing the couple from an unnecessary invasive procedure. Interpretation Our findings demonstrate that SMITH provides a powerful platform for resolving globin gene rearrangements, detecting rare variants, and enabling direct haplotype phasing. By effectively eliminating diagnostic blind spots, SMITH is expected to become an optimal method for thalassemia prevention programs. Funding This study was supported by Chinese National Natural Science Foundation Projects 81760037 and 82271894.

This study aimed to clarify aerosol exposure risks throughout the workflow of a Biosafety Level 2 (BSL-2) polymerase chain reaction (PCR) laboratory, validate the suitability of the {Phi}X174 bacteriophage as an indicator virus, and provide evidence for biosafety control measures. The {Phi}X174 bacteriophage was used to simulate viral samples, and a concentration-bacteriophage plaque standard curve was constructed (R2=0.998). Five operational steps in a simulated PCR laboratory were quantitatively monitored for aerosol concentration using double-layer agar plates, with blank controls used to eliminate interference. Statistical analysis was employed to identify risk differences. Sample homogenization ((5.67 {+/-} 1.23) x 104 plaque-forming units (PFU)/m3) and nucleic acid extraction ((3.45 {+/-} 0.89) x 104 PFU/m3) were identified as high-/very high-risk steps. The viral load in the samples was strongly positively correlated with the aerosol concentration (r = 0.926, P <0.001), with aerosol levels linearly decreasing with increasing distance in high-risk steps. The {Phi}X174 bacteriophage demonstrated high detection sensitivity (101 PFU/ml) and demonstrated safety compatibility with BSL-2 laboratories. Aerosol risks in PCR laboratories exhibit step-specific differentiation, and {Phi}X174 serves as an ideal indicator virus. Proposed strategies such as equipment upgrades and personal protective equipment (PPE) grading can reduce exposure risks.

NAT2 is an important pharmacogene which encodes the N-acetyltransferase 2 enzyme that is involved in the metabolism of multiple medications, and variants in this gene can affect patient response to these medications. CPIC has published a clinical guideline for prescribing hydralazine using NAT2 genotypes. Just prior to the guideline, updated NAT2 star allele numbering and definitions were released, differing somewhat from the historical nomenclature. Clinical pharmacogenomic testing panels often test for the most common star alleles, so knowledge of the most common updated NAT2 star alleles is critical for the implementation of the CPIC NAT2/hydralazine guideline. We first determine NAT2 diplotype frequencies from UK Biobank (UKBB) 200k phased genomes, then analyzed allele, diplotype, and phenotype population frequencies from the All of Us Research program, PennMedicine BioBank (PMBB) and UKBB 500k datasets. We found that analyzing NAT2 diplotypes from phased data provides critical information for algorithms designed to predict diplotypes from unphased data. We observed that NAT2*5, *6, and *4 were the most common star alleles in that order, and the top 11 most frequent NAT2 star alleles were the same across all biobanks. However, differences in star allele frequencies across biogeographical populations were observed. The largest difference led to a higher frequency of NAT2 poor metabolizer phenotypes as compared to rapid and intermediate metabolizer phenotypes in all global populations except in the EAS population, where NAT2 poor metabolizers were in the minority.

Background: Circulating tumour DNA (ctDNA) liquid biopsy is now established across oncology for early cancer detection, minimal residual disease surveillance, and treatment monitoring. Detection thresholds for all current ctDNA assays are derived empirically through receiver operating characteristic analysis on training cohorts - a statistically valid but theoretically uninformed approach that does not specify the minimum detectable tumour fraction given assay technical characteristics, nor identify when increasing sequencing depth ceases to provide additional clinical information. Methods: We model ctDNA detection as a binary hypothesis testing problem with Binomial-distributed mutant allele counts against a sequencing error noise floor. The Neyman-Pearson lemma is applied to derive the uniformly most powerful detector and the minimum detectable tumour fraction in closed form. The sequencing assay is modelled as a binary symmetric channel and Shannon channel capacity is calculated. Empirical validation uses n=61 data points extracted from five published peer-reviewed analytical validation studies across five independent institutions in the US and EU (2018 - 2025): Yu et al. 2022, Stetson et al. 2018, Frydendahl et al. 2023, Northcott et al. 2024, and Cheng et al. 2025. Results: The minimum detectable tumour fraction is derived in closed form as f_min approximately equal to (z_alpha + z_beta) multiplied by the square root of (epsilon divided by N), where N is sequencing depth, epsilon is the platform error rate, and z_alpha, z_beta are standard normal quantiles at the specified false positive and false negative rates. Shannon channel capacity is C = 1 minus H(epsilon) bits per read, where H(epsilon) is binary entropy. Empirical validation yields 84.3% agreement for single-locus assays. Discordance for multi-locus tumour-informed assays (NeXT Personal, duplex WGS) is consistent with the single-locus model scope and identifies the principal theoretical extension required. Conclusions: This framework provides the first formal Neyman-Pearson optimality proof for ctDNA detection, a closed-form detection limit, and a platform-independent efficiency metric for NHS and regulatory standardisation. Keywords: circulating tumour DNA; liquid biopsy; Neyman-Pearson detection; Shannon channel capacity; sequencing depth; limit of detection; minimal residual disease; signal detection theory

Background: In the UK, over 36 million contacts are made annually by people living with dementia (PLWD) to either primary or secondary community mental health services. As dementia progresses, PLWD may experience increased distress and resort to 999 calls for an ambulance, which may in turn result in conveyance to Accident & Emergency (A&E). Nearly 1 million A&E attendances are made by PLWD. This trend is set to rise sharply as the prevalence rates of dementia increase over time and as the condition progresses, with associated healthcare costs impacting overall care delivery. This may lead to reduced resource allocation for dementia emergency services, negatively affecting the experiences of both providers and service users. Aim(s): To explore ways to improve access and quality of care to emergency crisis care for PLWD from the perspective of healthcare staff providing this type of support. Methods: This qualitative study explored (1) the experiences, resources, and needs of healthcare professionals in emergency and community settings to support access for PLWD, and (2) the mechanisms influencing dementia crisis response. The COREQ Checklist was used to improve transparency, credibility, and reproducibility. Inter-rater reliability was calculated. PPIE contributors co-developed recommendations for healthcare professionals, and study findings informed a comic-based dissemination resource shared with third-sector organisations to support community awareness and engagement. Results: Fifteen interviews were held with emergency services staff. Inter-rater reliability was substantial between two raters (k = 0.62). Four overarching themes, with associated subthemes, were identified relating to crisis care delivery, barriers to effective response, and strategies employed to address these challenges. Additional themes captured decision-making processes at key points in the care pathway, including initial crisis response, during intervention, and at discharge from emergency and community services. Decision-making was characterised by the need to balance patient safety with autonomy in determining care in the best interests of PLWD and their informal carers. Discussion: This exploratory study reveals frontline staff perspectives on challenges and actionable strategies for dementia crisis care. Findings support targeted service improvements, cross-sector collaboration, and co-produced resources to enhance outcomes for PLWD and their informal carers.

Introduction Education is a core social determinant of health for children and adolescents. Unfortunately, academic achievement, health, and wellbeing of adolescents have decreased in many developed countries in the past decade. The purpose of the Wellbeing and Education linkages in school-aged children (WELL-ED) study is to examine associations of school absences and academic achievement with use of school-based and community-based health and social welfare services. In addition, we will assess user experiences and multi-sector services pathways of school-aged children for a better understanding of how the service system could respond to the needs of children. Methods and analysis WELL-ED is a large population-based study that combines register data on school absences and educational support from municipalities with register data on healthcare and social service use collected from wellbeing services counties in Finland. The study cohort includes all children who attended mandatory education in public schools in Southern Finland in school year 2023-2024. A smaller cohort of adolescents in school year 8 was invited to complete a user experience survey. The primary outcomes of this study are related to equity of service use. Ethics and dissemination The Regional Committee on Medical Research Ethics of the Helsinki and Uusimaa Hospital District (2803/2024) has approved the WELL-ED study protocol. For the survey, adolescents in year 8 and parents of adolescents younger than 15 provided informed consent. Results will be published in peer-reviewed journals, summaries will be sent to participating municipalities and wellbeing services counties and press releases will be written on key findings.

Abstract Objective: The onset of hypertension occurs at a younger age in China, and the relationship between health literacy and quality of life among middle-aged and older hypertensive patients remains unclear. This study explored whether perceived social support and self-efficacy mediate the association between health literacy and quality of life in middle-aged and older hypertensive patients. Methods: A questionnaire was administered to 1,015 middle-aged and older hypertensive adults from communities in six central provinces of China. The EQ-5D scale, Perceived Social Support (PSS) scale, Self-Efficacy Scale (SES), and Health Literacy Scale (HLS) were used to assess quality of life, social support, self-efficacy, and health literacy, respectively. Mplus 8.3 software was used to construct a structural equation model for path analysis. Results: The mean PSS, SES, HLS, EQ-5D, and EQ-VAS scores were 15.57{+/-}3.45, 10.61{+/-}2.41, 9.49{+/-}2.86, 0.88{+/-}0.18, and 71.06{+/-}17.49, respectively. Health literacy and quality of life scores significantly differed among middle-aged and older hypertensive patients, and both showed positive correlations with perceived social support and self-efficacy (both P<0.001). Perceived social support and self-efficacy exhibited a chain mediated effect on the relationship between health literacy and quality of life (EQ-5D utility index and EQ-VAS), accounting for 28.57% of the total effect of the EQ-5D utility index and 27.26% of that of the EQ-VAS. This study is the first to elucidate the mechanism by which health literacy influences quality of life in middle-aged and older hypertensive patients through the chain-mediated effect of perceived social support and self-efficacy. Conclusion : Health literacy is significantly correlated with quality of life in middle-aged and older hypertensive patients. This correlation can directly or indirectly explain the impact on quality of life through mediating pathways involving perceived social support and self-efficacy. Keywords: hypertensive patients, perceived social support, self-efficacy, health literacy, quality of life, mediating effect

Background Totally endoscopic aortic root (AR) surgery via right anterior minithoracotomy (RAMT) may reduce surgical trauma and accelerate recovery compared with full sternotomy (FS). However, the approach is technically demanding due to limited access and anatomical complexity. This study compares early clinical outcomes and quality of life (QoL) after RAMT versus FS to evaluate the feasibility and safety of the totally endoscopic approach. Methods This single-center, retrospective study included 149 patients underwent AR surgery via RAMT (n=74) or FS (n=75) between January 2021 and March 2026. Patients with aortic dissection, infective endocarditis, redo surgery, concomitant procedures, or arch replacement were excluded. Operative outcomes, postoperative recovery, 30-day and 1-year mortality were analyzed. QoL was assessed using the Short Form-8 (SF-8) questionnaire. Results The median age was 60.0 years, and 79.9% of patients were male. Bentall procedure was performed in 84.6% of patients, 15.4% underwent a David procedure. Compared with FS-AR, RAMT-AR was associated with shorter median operative time (147.0 vs. 178.0 min; p<0.001), lower median chest drainage volume (650.0 vs. 850.0 mL; p<0.001), and shorter median ICU stay (24.0 vs. 25.0 h; p=0.008) and hospital stay (6.0 vs. 8.0 days; p=0.028). Overall, 30-day and 1-year mortality was 0.7%. SF-8 analysis demonstrated significantly higher physical and mental component scores in RAMT-AR patients. Conclusion In specialized centers, totally endoscopic AR surgery via RAMT is a safe and feasible minimally invasive approach associated with favorable early outcomes and a potential benefit in postoperative physical and mental QoL by reducing surgical trauma.

Background. The emergency department in the United States of America functions as a residual access point for healthcare and social services for populations including rural communities, the uninsured, mental health and addiction patients, and the unhoused. The workforce variable that determines unit function (experience density, the concentration of accumulated clinical judgment within a unit workforce) is not measured in hospital accounting systems. Objective. To document workforce composition changes in U.S. emergency nursing across the 2018 and 2022 cycles of the National Sample Survey of Registered Nurses (NSSRN), and to specify falsifiable predictions for the 2026 cycle. Methods. We analyzed NSSRN public-use files using a four-way ED definition extending Castner et al. (2024) and a hospital-bedside-restricted comparator. Variance estimation used jackknife replicate weights for 2018 and Successive Differences Replication for 2022. Burnout was operationalized using the Norful et al. (2023) leaving-reasons proxy across cycles, with sensitivity analysis using the 2022 direct burnout item. Results. A 15-year trajectory (2008-2022) documents progressive experience-density compression: the ED's 15+ year veteran cohort fell from 41.9% to 28.0% over the decade preceding the pandemic, a loss of nearly a third of the senior cohort and a 19.6% decline in mean experience density, before recovering modestly to 33.3% as veteran nurses remained through the pandemic acute phase, leaving the ED as the youngest hospital setting throughout. Hospital non-ED bedside nurses lost senior tenure between cycles (mean 15.65[-&gt;]14.06 years since first licensure; 15+ year share 43.5%[-&gt;]38.7%), while ED nurses retained their senior tail (mean 11.60[-&gt;]12.58). Burnout endorsement rose sharply in both populations (non-ED 27.3%[-&gt;]46.0%; ED 34.2%[-&gt;]61.2%), with the ED-vs-non-ED gap more than doubling. Controlling for tenure, ED status was not independently associated with burnout in 2018 (OR 1.15, 95% CI 0.83-1.59) but was strongly associated in 2022 (OR 1.92, 95% CI 1.44-2.55; p<.001). The direct burnout item showed a parallel pattern (OR 2.92, 95% CI 1.62-5.28). Conclusions. A pandemic-era setting-specific burnout effect emerged in emergency nursing that workforce-composition controls cannot explain. The 2022 cycle establishes a pre-exit baseline against which the 2026 NSSRN will serve as the falsifiable test of post-Omicron veteran exit. Nursing pipeline replacement lag exceeds the interval before 2026 data arrives; the consequences of inaction fall on populations dependent on ED-based residual access.

Background: In atrial fibrillation (AF), cerebral microbleed (CMB) burden guides anticoagulation decisions, yet AF is itself inconsistently associated with CMBs, a paradox unexplained by frameworks that treat CMBs as a unitary marker of small vessel disease. We hypothesized that the white matter hyperintensity (WMH) context in which CMBs arise modifies their vascular meaning, and that this context-dependence underlies the inconsistent AF-CMB association. Methods: From a multicenter Korean stroke registry, we analyzed 5,735 first-ever ischemic stroke patients imaged at nine centers using susceptibility-weighted MRI. WMH volume and CMB count were extracted by validated deep learning pipelines. Patients were cross-classified by age-adjusted WMH residual (median split) and CMB count (2) into four groups. The AF-CMB association was estimated by multivariable logistic regression within each WMH stratum with formal interaction testing. Spatial CMB distribution was analyzed against the Automated Anatomical Labeling atlas. Results: In the full cohort (mean age 69.5 years; 57.7% male), AF was not associated with CMBs (OR 1.04; 95% CI 0.87-1.25). Stratification yielded divergent estimates: the adjusted AF OR was 1.46 (1.11-1.93; P = 0.007) in the WMH-low stratum and 0.95 (0.73-1.22; P = 0.665) in the WMH-high stratum, with significant interaction (OR 0.56; P < 0.001). The discordant phenotype (low WMH, high CMB; 8.9%) was enriched for AF (28.0%) and showed fronto-temporal cortical predominance with deep structure sparing. AF independently reduced the proportion of deep CMBs (IRR 0.80; P = 0.040). The interaction was preserved across prespecified sensitivity analyses. Conclusions: The AF-CMB association is confined to patients with low WMH burden relative to age and is accompanied by a topographically distinct CMB distribution. Clinical assessment of small vessel disease based on WMH alone may overlook a CMB phenotype linked to AF.

Mosquito-borne diseases continue to pose significant public health challenges worldwide, particularly in densely populated regions of South Asia and parts of North America experiencing increasing vector prevalence due to climate and environmental changes. Commercial mosquito repellents are widely used as a primary preventive measure; however, their efficacy, safety, and public health impacts vary depending on formulation, active ingredients, environmental conditions, and user practices. This study presents a comparative evaluation of commonly used mosquito repellent products in South Asia and North America, including coils, vaporizers, sprays, creams, and Natural repellents. The research aims to assess repellent efficacy against major mosquito vectors, evaluate potential health and respiratory effects associated with prolonged exposure, and analyze consumer awareness and usage patterns across different regions. Laboratory-based efficacy testing and field observations were conducted to compare protection duration, repellency rate, and environmental performance under varying climatic conditions. Safety assessments included analysis of chemical composition, indoor air quality impact, and reported adverse health symptoms among users. The findings indicate significant differences in effectiveness and safety profiles among product categories and geographical regions. Synthetic repellents generally demonstrated higher repellency duration, while herbal formulations showed improved safety and environmental compatibility. The study highlights the importance of standardized evaluation protocols, regulatory oversight, and public awareness in promoting safe and effective mosquito control strategies. These findings may support policymakers, healthcare professionals, and manufacturers in improving mosquito repellent technologies and reducing the burden of mosquito-borne diseases globally.