In Uganda, frequent shortages of antihypertensive medications hinder continuity of care, undermining blood pressure management. Building on preliminary ethnographic research, this study evaluates a community-led, mobile-wallet-based pooling intervention-MoPuleesa-designed to improve medication access at a rural clinic in Nakaseke District, Uganda. Over a 7-month period, 183 patients enrolled and were linked to a digital savings platform that required monthly contributions of 5000 UGX (∼USD 1.39) into a communal fund to bulk-purchase medications at a discounted cost. Using survey data, transaction logs, and clinic records, we assessed contribution behavior, risk of adverse selection, equity, changes in medication availability, and patient blood pressure levels. On average, 48% participants contributed each month. Contribution rates showed no significant differences across education levels or medication costs, suggesting minimal equity concerns or adverse selection. Government pharmacies fulfilled only 8% of total prescriptions; however, for contributors, MoPuleesa closed 84% of the remaining medication gap. However, despite improvements in medication supply, we did not observe statistically significant improvements in blood pressure. Our findings demonstrate the feasibility and effectiveness of mobile money pooling in addressing chronic medication shortages. MoPuleesa achieved broad participation and equitable outcomes in a resource-constrained setting and significantly improved medication availability. We conclude that mobile-based fund pooling for medication can significantly improve medication supply and, with improvements in eligibility assessments, could serve as a complementary or intermediate solution to structural barriers in under-resourced health systems.
Medical imaging plays a critical role in diagnosing and managing acute conditions such as Acute Respiratory Distress Syndrome (ARDS), particularly in intensive care settings. However, radiological data are often siloed across Picture Archiving and Communication Systems (PACS), with limited interoperability, traceability, and patient control. This paper proposes and validates a blockchain-enabled architecture that integrates radiological imaging data into a patient-controlled digital wallet (BioWallet) ecosystem. The system combines verifiable credentials, decentralized identifiers (DIDs), and a FHIR-compliant backend to ensure secure, auditable, and standards-based access to DICOM images and associated metadata. The ledger stores only consent and audit hashes, while clinical data remain off-chain and correctable within FHIR/EHR systems, ensuring auditability without hindering rectification. A validation scenario replicating an ICU emergency was conducted using synthetic ARDS-COVID19 cases to assess latency, consent enforceability, and user experience. Results showed a 67% reduction in image access time compared to traditional systems, 100% success in blocking unauthorized access, and high clinician satisfaction. The architecture supports FAIR-compliant reuse of annotated imaging datasets, enhances transparency in image-driven research, and aligns with GDPR and future European digital identity frameworks. This work demonstrates the feasibility and value of a decentralized, patient-centric approach to imaging data governance in high-stakes clinical environments.
After the US trauma system contracted in the 1990s with the loss of financially vulnerable trauma centers (TCs), a subsequent re-expansion took place. Many new trauma centers are for-profit (FPTCs) and may provide redundant care for affluent populations. Little is known about their role in the national trauma system. We hypothesized that FPTC catchments would provide redundant coverage to affluent populations. Data for the contiguous United States were obtained from the 2020 Decennial Census or American Community Survey. Population-weighted centroids of each census tract, as well as TC locations, were geocoded in ArcGIS Pro. FPTC status was obtained from the Centers for Medicare and Medicaid Services. Road and traffic data were obtained from Esri StreetMaps Premium. The shortest travel time from each population-weighted centroid was calculated to the nearest TC to delineate coverage areas and populations. This analysis was repeated without FPTCs to assess contributions to the trauma system. Data were exported to Stata for further analysis. In total, 83,713 population-weighted centroids and 2,044 TCs were captured. About 11% of tracts were primarily served by 223 FPTCs, representing the closest TC for 43M people. While overall transport times were similar between FPTCs and NPTCs (14.0 vs. 14.2 min, p=ns), the removal of FPTCs did not change national transport times (median 0 min; IQR, 0, 0; range, 0-98). FPTC catchment populations were associated with more urban [odds ratio (OR), 1.02, 95% CI, 1.01-1.02, p<0.001], Hispanic (OR, 1.14, 95% CI, 1.14-1.15, p<0.001), uninsured (OR, 1.65, 95% CI, 1.57-1.73, p<0.001) or Medicare covered (OR, 1.04, 95% CI, 1.02-1.07, p<0.001) populations. Most FPTCs provide redundant care to populations that are often less insured, of similar poverty levels and of different demographics as compared with nonprofit centers. However, a small subset of FPTCs provides access to care for populations who would face long transport times without them.(J Trauma Acute Care Surg. 2026;00:000-000. Copyright © 2026 Wolters Kluwer Health, Inc. All rights reserved.). Epidemiological; Level III.
As Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) expand, self-custody wallets have become the primary interface for user sovereignty. However, existing solutions suffer from critical limitations, including static authentication frameworks that compromise usability, a lack of real-time risk awareness, and inadequate key recovery mechanisms that often lead to permanent asset loss or reliance on centralized custodians. Furthermore, current wallets frequently expose transaction metadata, undermining user privacy. To address these systemic flaws, we present a modular self-custody wallet that incorporates a context-aware risk engine for real-time transaction scoring, risk-based adaptive authentication, and a dual-path decentralized key-recovery layer combining DAO-governed Shamir secret sharing with a zk-SNARK-verified fallback. The architecture further includes programmable policy enforcement and a zero-knowledge swap layer with stealth addressing to decouple front-end activity from on-chain data. The design integrates smart contracts on EVM chains and Solana through provider adapters and executes on-device ML inference to minimize latency. Experimental results demonstrate that the proposed system reduces privacy leakage probability to 5% (compared to 85% in standard architectures) and accelerates key recovery from over 24 h to approximately 8 seconds using zk-SNARKs, all while achieving 93.6% risk classification accuracy. The proposed CAPPR-Wallet advances self-custody by combining context adaptivity, privacy, and recoverability without centralized trust.
The decentralized and pseudonymous nature of cryptocurrency has facilitated its extensive use in illicit activities, including money laundering, tax evasion, and ransomware. Limiting such activities requires a well-established forensic framework. However, a dedicated methodology for examining cryptocurrency wallets remains underdeveloped. This study presents a systematic forensic analysis of Electrum wallets installed on virtual machines running Windows 10, outlining the wallet taxonomy and meticulously listing all artifacts. This study primarily focuses on memory forensics, with most of the analysis devoted to memory-based artifacts extracted from five distinct memory dump scenarios. Artifacts extraction were performed using Volatility 3 plugins, in conjunction with Python-based analysis scripts, within a Kali Linux environment. Following the memory-based analysis, a limited disk examination was conducted after wallet inactivity or system shutdown to assess whether any residual Electrum artifacts persisted beyond memory. The research examines the artifacts retrievable from wallet files, both before and after backup, and compares these results with those obtained from other methods reported in the literature. The experimental outcomes demonstrate the impact of this research on the successful extraction of private keys, wallet addresses, extended public keys, wallet files, and transaction IDs. The extracted Electrum addresses and private keys provided access to critical wallet details, and unspent Bitcoin were successfully recovered using these keys, confirming the feasibility of forensic cryptocurrency recovery and revealing data of high evidentiary value to the digital forensic community.
With the widespread adoption of cryptocurrencies, the ability to conduct continuous offline payments has increasingly become a critical technological requirement. In network-constrained scenarios, current dual-offline payment technologies are useful for single transactions. However, their limitations in continuous payment scenarios have become increasingly evident, making them unable to meet real-world application needs. This has prompted the industry to demand more urgent innovations in research on continuous offline payment capabilities. To address these challenges, this paper proposes a continuous dual-offline payment system capable of supporting multiple continuous payments. The system integrates elliptic curve cryptography (ECC) and zero-knowledge proof (ZKP) technology to generate secure asset credentials, ensuring both immutability and privacy credentials throughout the offline payment lifecycle. A dynamic credential decomposition mechanism enables the splitting of input credentials into change credentials and receipt credentials, facilitating uninterrupted dual-offline payments between hardware wallets. Additionally, it incorporates a batch verification scheme based on smart contracts, utilizing zero-balance verification and chained hash tracing to ensure payment uniqueness and prevent double-spending attacks, thereby guaranteeing the verifiability and validity of payment settlements. Experimental evaluations demonstrate that the proposed system reduces gas consumption per payment and improves execution efficiency during batch processing, combining high security with strong performance. This research provides a feasible solution for the application of digital currencies in offline scenarios, carrying significant theoretical value and practical significance for driving technological innovation and application expansion in the cryptocurrency field. In addition to cryptocurrency payments, the proposed system is also applicable to IoT and sensor network environments. Many IoT devices operate in disconnected or network-limited areas and require secure micro-transactions. Our dual-offline payment mechanism supports such scenarios, as the main cryptographic operations are lightweight enough for typical IoT hardware. This further extends the practical value of our system beyond traditional cryptocurrency payments.
This paper explores how smart governance can reduce financial risks in the Iraqi banking industry by focusing on the adoption of the use of artificial intelligence (AI) technologies to increase financial stability and operational efficiency. The study uses a combination of quantitative indicators of the dimensions of smart governance (standards, policies, practices, information, technologies, and skills) and financial risk dimensions (market, credit, operational, and investment portfolio risks) based on the secondary data provided by the Central Bank of Iraq covering the years 2023 and 2024. The results reveal a strong progression in all aspects of smart governance during the study time in the form of the rising number of licensed electronic payment providers, the rise of the percentage of current deposits, and the further use of bank accounts and electronic wallets. Also, the human resources in the banking industry have been enhanced with expertise in professional development initiatives. In the financial risk sector, the outcomes are a decline in non-performing loans, an upward trend in the ratio of credit to deposits and an increase in total deposits and total credit facilities, hence, an indication of an improvement in the ability to handle risk. The testing of hypothesis confirms that a strong governing process, including standards, policies, practices, information management, technology adoption and development of human skills, has a positive influence on financial risk management in the AI environment. The research suggests enhancing regulatory systems, increasing digital transformation programs, investing in human-capital growth, and introducing AI-based analytical solutions to guarantee long run sustainability and stability of the financial sector.
Leaf-based leather is a biodegradable, negative carbon emissions, and economically suitable material compared to the conventional leather-making process. In this research, jute leaf (30 gm), cellulose (2 gm), and natural rubber latex (10 ml) composition combined composite exhibited superior tensile strength (9.58 MPa). Fourier transform infrared spectroscopy (FTIR) showed that formed aryl groups in this composite material indicated jute leaves crosslink with natural rubber latex and cellulose. Scanning electron microscopy (SEM) also represents porosity and reduced fiber pull-out. However, when thermoplastic polyurethane (TPU) was heat-compressed with this composite material, it enhanced tensile strength properties (28.9 MPa) and elongation (15.3%). Due to TPU crosslinking, FTIR confirms aryl signatures and urethane linkages formed by hydroxyl-NCO reactions, enhancing chain interactions and mechanical integrity, and SEM shows a porous microstructure supporting cohesion and interfacial adhesion. Contact-angle measurements (~ 85°) indicate the same hydrophobicity, comparable to animal leather (~ 90°). The jute leaf composite degraded within 4 months in the soil, whereas the TPU-compressed variant biodegraded within 6 months. This work presents a sustainable jute leaf bio-composite to replace leather in products such as backpacks, wallets, bags, book and file covers, automotive or home décor, creating bioeconomic opportunities in Bangladesh.
Invasive pulmonary aspergillosis (IPA) is a common cause of fungal infection acquired in intensive care unit (ICU) whose clinical landscape may differ according to SARS-CoV2 infection status. We included all mechanically ventilated patients hospitalized (≥ 48 h) participating in ICU of the REA-REZO network during a 6-year period. Among IPA patients, initial characteristics and outcomes were compared according to COVID-19. Additionally, to account for potential confounders, we performed an inverse probability of treatment weighting (IPTW). Rates of IPA were also compared according to SARS-CoV2 infection. Finally, we investigated risk factors associated with mortality among IPA patients using a Cox model regression. Among 120 993 patients included during the study period, IPA was diagnosed in 254 patients. COVID-19 Associated Pulmonary Aspergillosis (CAPA) patients were significantly older (median age 69 [62-73] years versus 63 [56-70] years; p < 0.001), less immunosuppressed (23.5% versus 32.4%; p = 0.001) and less severe at baseline (median SAPS II score 45 [35-54] versus 55 [39-65]; p < 0.001) compared to non-COVID-IPA patients. CAPA patients exhibited both a longer duration of mechanical ventilation (16 [9-33] days versus 8 [4-26] days; p = 0.002) and a longer ICU length of stay (LOS) (18 [10-38] days versus 14 [6-30] days, p = 0.015) after IPA diagnosis. However, survival did not differ according to COVID-19 status either in the raw population (log-rank test; p = 0.43) or after weighted Cox regression (HR 0.92 [95%CI 0.61-1.38]; p = 0.69). Incidence of IPA was higher in COVID-19 patients (incidence rate ratio: 8.45 [95%CI 6.59-10.87]; p < 0.001). In this large multicenter cohort, IPA had a similar impact on survival depending on SARS-CoV2 infection status. However, despite lower severity, CAPA patients experienced longer duration of mechanical ventilation and LOS.
Acute hypoxaemic respiratory failure (ARF) is the leading cause of intensive care unit (ICU) admission among immunocompromised patients. However, contemporary data regarding the epidemiology, management, and outcomes of ARF in this population remain scarce. We aimed to identify predictors of mortality and intubation in immunocompromised patients admitted to the ICU with ARF. This retrospective observational study was conducted in 103 ICUs in 26 countries. Adults (≥18 years) with ARF and immunodeficiency were eligible for inclusion. Patient data, including information on the nature of underlying immunosuppression, the cause of ARF, and the oxygenation strategy, were obtained from electronic medical records or medical charts. The primary outcome was to report 30-day mortality and identify associated factors in patients with complete data for all variables. Cox proportional hazards models were used to identify variables associated with mortality, and differences between groups were compared with χ2 tests or two-sided Wilcoxon rank-sum tests, with p values of less than 0·05 considered significant. 9854 immunocompromised patients with ARF admitted to participating ICUs between Jan 1, 2017, and Dec 31, 2023, were included in the study. The median age was 64 years (IQR 54-71); 3941 (40·0%) patients were female and 5913 (60·0%) were male. The main causes of immunodeficiency were a haematological malignancy (4759 [48·3%] of 9854 patients) or solid malignancy (3818 [38·7%] patients). Infection was the leading cause of ARF (6610 [62·0%] of 9854 patients); 5288 (53·7%) patients had more than one contributing cause of ARF, and no cause was identified in 1490 (15·1%) patients. The median partial pressure of oxygen in arterial blood (PaO2)/fractional concentration of oxygen in inspired air (FiO2) ratio was 198 [IQR 141-208]. The 30-day mortality rate was 47·3% (4662 patients). Predictors of higher mortality were older age (hazard ratio 1·01 [IQR 1·00-1·02]), higher Charlson Comorbidity Index score (1·04 [1·01-1·07]), higher Frailty Index score (1·22 [1·16-1·28]), longer time from hospital to ICU admission (1·02 [1·01-1·03]), higher respiratory rate (1·02 [1·02-1·03]), coma at ICU admission (2·04 [1·72-2·43]), invasive fungal infection as cause of ARF (1·82 [1·45-2·28]), disease-specific infiltrates (1·73 [1·32-2·26]), unidentified cause of ARF (2·16 [1·74-2·68]), and use of vasoactive drugs (2·45 [2·10-2·86]) or renal replacement therapy (2·07 [1·74-2·48]). Protective factors included receipt of a solid organ transplant (0·62 [0·49-0·79]), systemic vasculitis or connective tissue disease (0·61 (0·47-0·78]), higher PaO2/FiO2 ratio (0·78 [0·72-0·84]), receipt of high-flow nasal oxygen therapy (0·78 [0·64-0·95]), and cardiogenic pulmonary oedema (0·67 [0·51-0·89]). In this large international cohort of immunocompromised patients with ARF, we identified key risk and protective factors for mortality and intubation. These findings could improve outcomes by informing timely clinical decisions, goals-of-care discussions, and management in this vulnerable population. Kirsten and Freddy Johansen Foundation and Groupe de Recherche en Réanimation Onco-Hématologique.
Patients with myasthenia gravis (MG) may produce autoantibodies neutralizing type I interferons (AAN-I-IFN), which have been shown to underlie severe viral diseases, including critical COVID-19 pneumonia, in patients without MG. We studied an international cohort of 85 unvaccinated SARS-CoV-2-infected MG patients with no antiviral treatment. Hypoxemic pneumonia occurred in 48 of these patients, including 22 (45.8%) with AAN-I-IFN, which neutralized both IFN-α2 and IFN-ω in 14 (29.2%) patients. Six (16.2%) of the remaining 37 patients had AAN-I-IFN, which neutralized both IFN-α2 and IFN-ω in three patients. The risk of hypoxemic pneumonia was greater in MG patients with AAN-I-IFN neutralizing 10 ng/mL of both IFN-α2 and IFN-ω (odds ratio and 95% confidence interval (OR [95% CI]): 12.7 [2.1-78.9], p=0. 0010) or IFN-α2 at any dose (4.7 [1.5-15.0], p=0.0054) than in those without such autoantibodies. The risk of AAN-I-IFN production was much higher in MG patients than in the general population (28.9 [10.8-77.7], p=4.9×10-27). Fourteen patients had thymoma, which increased the risk of AAN-I-IFN (64% versus 27%, (OR [95% CI]: 5.6 [1.6-19.4], p=0.0050) and hypoxemic pneumonia (9.2 [1.9-44.2]; p=0.0019). Thymoma is, thus, associated with a higher risk of producing AAN-I-IFN, and these autoantibodies are associated with a higher risk of developing life-threatening COVID-19 pneumonia in patients with MG.
The Poisson-logistic (pogit) model is widely used for count data with latent intensities, with applications including under-reporting correction and share-of-wallet estimation, yet existing estimation methods do not scale well to large datasets. We propose a new expectation-maximization (EM) algorithm for the standard pogit model based on Polya-Gamma data augmentation, which yields a conditionally Gaussian complete-data likelihood with closed-form EM-updates. The resulting EM algorithm has low per-iteration cost and naturally accommodates computational enhancements, including quasi-Newton acceleration and mini-batch implementations. These features enable efficient inference on datasets with millions of observations. Simulation studies and real-data applications demonstrate substantial computational improvements without loss of statistical accuracy, and comparisons with direct maximum-likelihood optimization routines show that the proposed method provides a scalable and competitive alternative for large-scale pogit estimation.
Recent guidelines recommend maintenance and reliever therapy (MART) as the preferred inhaler regimen for patients with moderate to severe asthma. However, updated asthma action plans (AAPs), specifically tailored to MART, have not been developed for the US population. We sought to design a MART-specific AAP that is accessible, pictorial, and low-burden for clinicians to use during time-constrained clinical encounters. Together with health communication design experts, we used a multiphase, mixed-methods approach involving semistructured interviews with 7 adult patients with asthma and 6 clinicians at community health centers while exploring preferences for AAP content and design. Thematic analyses informed our design process, which used the 5E Design Thinking Framework. Our team ultimately developed 2 MART-specific AAPs and assessed their readability using a Flesch-Kincaid test. Patients prioritized simple visuals, concise instructions, and clear guidance for managing worsening symptoms as key to a new MART-specific AAP. Clinicians emphasized the importance of time efficiency when completing AAPs. Using this feedback, we developed a 4" × 6" wallet-sized trifold AAP with a Flesch-Kincaid grade level of 3.7 specifically tailored for MART. Because guidelines endorse MART as the preferred asthma therapy, new and updated MART-specific AAPs are important to facilitate optimal asthma care. Our team codesigned 2 MART-specific AAPs based on patient and clinician feedback.
Regional citrate anticoagulation (RCA) is currently the first-line strategy during continuous renal replacement therapy (CRRT). No international recommendation exists concerning the assessment of ionized calcium (iCa) prior to RCA-CRRT initiation and data on the consequences of pre-existing ionized hypocalcemia are lacking. This study aimed to investigate the frequency of iCa measurements prior to RCA-CRRT and assess the consequences of ionized hypocalcemia in patients undergoing RCA-CRRT. This retrospective, single-center study included all patients treated with RCA-CRRT between June 2021 and April 2023. Calcium target range was considered achieved during RCA-CRRT when the iCa levels were stable within the protocol range (1.12-1.20 mmol/L) over two consecutive measurements performed in a 6-h timeframe. RCA-CRRT related adverse events were screened during the first 72 h of RCA-CRRT. Among the 200 patients screened, 117 (59%) had an iCa measurement prior to RCA-CRRT initiation. Among them, 70 (60%) had normal iCa concentration, 22 (19%) had hypocalcemia which was corrected by calcium chloride, and 25 (21%) had ionized hypocalcemia without any correction prior to CRRT initiation. In all patients presenting hypocalcemia, mean iCa reached normal values 12 h after RCA-CRRT initiation. Calcium target range was achieved in 59 (50%) patients within the first 72 h, whereas only 5 (4.3%) of subjects that remained on CRRT at 72 h failed to achieve homeostasis. Patients who presented with severe hypocalcemia (< 1 mmol/L) during the first 12 h of RCA-CRRT had more frequently pre-existing severe hypocalcemia. Ionized calcium blood measurement is not systematically performed prior to RCA-CRRT initiation. However, within the 12 h following RCA-CRRT initiation, most patients achieved normal calcium concentrations including those with pre-existing hypocalcemia.
Despite their success in speech processing, neural networks often operate as black boxes, prompting the following questions: What informs their decisions, and how can we interpret them? This work examines this issue in the context of lexical stress. A dataset of English disyllabic words was automatically constructed from read and spontaneous speech. Several convolutional neural network (CNN) architectures were trained to predict stress position from a spectrographic representation of disyllabic words lacking minimal stress pairs (e.g., initial stress WAllet, final stress exTEND), achieving up to 92% accuracy on held-out test data. Layerwise relevance propagation, a technique for neural network interpretability analysis, revealed that predictions for held-out minimal pairs (PROtest vs proTEST) were most strongly influenced by information in stressed versus unstressed syllables, particularly the spectral properties of stressed vowels. However, the classifiers also attended to information throughout the word. A feature-specific relevance analysis is proposed, and its results suggest that the best-performing classifier is strongly influenced by the stressed vowel's first and second formants, with some evidence that its pitch and third formant also contribute. These results reveal deep learning's ability to acquire distributed cues to stress from naturally occurring data, extending traditional phonetic work based around highly controlled stimuli.
Cognitive impairments following acquired brain injury (ABI) often hinder individuals' ability to manage finances independently, a key component of autonomy and quality of life. While mobile applications (apps) offer promising support for budgeting, the identification of accessible and clinically validated tools remains limited by the unregulated and heterogeneous nature of commercial app markets. This study aimed to compare three methods for identifying mobile budgeting apps for use by individuals with ABI. We applied three app identification methods: (1) a weighted keyword algorithm (AppGuide), (2) a Boolean keyword query on Apple and Google Play stores and (3) an AI-assisted query using ChatGPT. All methods used identical inclusion criteria, emphasising bilingual availability (English, French), cost accessible at an affordable cost, and platform compatibility. Apps passing automated filters were manually screened to evaluate performance and retrieval efficiency. Out of 84,827 apps identified via Method 1, 13,862 met automated criteria, and 62 were selected for manual review. Method 2 yielded 121 apps, 62 of which were screened. Method 3 generated five apps. After manual screening and testing, one app from Method 1 (Spendee©) and three apps from Method 3 (Spendee©, Wallet App©, Money Manager©) met all inclusion criteria. No apps from Method 2 were retained. This study presents a novel, clinically relevant methodological approach to guide the identification of mobile apps potentially suitable for prescription to users with cognitive impairments following ABI. It demonstrates how integrating complementary methods can enhance the precision and clinical relevance of app selection. This study presents a novel, clinically relevant methodological approach to guide the identification of mobile apps potentially suitable for individuals with cognitive impairments following ABIIntegrating complementary search methods can enhance the precision and clinical relevance of app selectionUnstandardised metadata and opaque algorithms in commercial app stores limit the accuracy and reliability of app identification for clinical decision-makingGenerative AI shows clinical potential but raises challenges related to reproducibility and transparencyBilingual app selection highlights the need for inclusive and transferable identification strategies across linguistic contexts.
Nocardiosis is a serious infection in immunosuppressed patients, especially transplant recipients. The slow-growing phenotype of the bacterium and the variety of symptoms complicate diagnosis and delay antimicrobial therapy, resulting in high mortality rates despite effective treatments. A further complication is that some nocardiosis patients test positive in fungal diagnostics that detect (1,3)-β-D-glucan (the Fungitell assay), but the basis for this cross-reactivity remains unknown. We demonstrate that nocardial cell wall arabinogalactan is a cryptic antigen responsible for cross-reactivity in the Fungitell assay and that this antigen is revealed in vivo following bacterial cell lysis. We further show that the reactivity results from a β-glucose substitution of the galactan domain, a modification specific to nocardia, and identify the optimal antigen as a tetramer of the trisaccharide repeating unit. By providing structural evidence for Fungitell cross-reactivity during nocardiosis, this work paves the way for developing specific diagnostic tools that are currently lacking.
Sepsis remains a leading cause of morbidity and mortality worldwide, with survivors often following divergent trajectories: rapid recovery (RAP) or progression to chronic critical illness (CCI). CCI is characterized by persistent organ dysfunction, recurrent infections, and immune dysregulation. Myeloid-derived suppressor cells (MDSCs), which expand in number after sepsis, are implicated in this maladaptive state, yet their epigenetic regulation remains poorly understood. Here, we applied an Omni-ATAC protocol optimized to profile chromatin accessibility in CD66b + MDSCs from healthy participants (HPs) and sepsis patients across time points (day 4, day 14-21, and 6 months) and clinical outcomes (RAP, CCI, and Deceased). Dimensionality reduction analyses of genome-wide chromatin accessibility showed clear separation of sepsis and HP samples. Furthermore, these analyses revealed distinct trajectories post-sepsis diagnosis: RAP samples progressively regained HP-like chromatin states, whereas CCI samples remained epigenetically "locked" in aberrant states. Differential accessibility analysis identified thousands of promoter regions with altered accessibility, including immune checkpoint and inflammatory genes (e.g., ARG1, CD274, S100A8 / 9 ). Pathway analyses predicted global suppression of immune, metabolic, and chromatin remodeling programs in CCI, contrasting with restoration in RAP. These findings from patient-derived CD66b + MDSCs suggest that epigenetic chromatin remodeling underlies divergent recovery trajectories and highlight chromatin-modifying pathways as potential therapeutic targets to restore immune competence in sepsis patients with CCI.
Fiberoptic bronchoscopy in mechanically ventilated ICU patients can markedly increase airway resistance and peak inspiratory pressure (PIP), limit effective tidal volume delivery, and provoke transient hypoxemia, hypercapnia, dynamic hyperinflation, and hemodynamic instability. However, per-procedural ventilator management remains heterogeneous. This single-center trial with before-and-after design assessed the feasibility and safety of implementing a standardized low-flow ventilation protocol, and characterized its physiological effects (particularly peak inspiratory pressure), during bronchoscopy in adults intubated and ventilated in volume-assist-control mode. During the observational phase (n = 36), ventilator settings reflected usual practice (increased pressure alarm and reduced PEEP; no routine flow reduction). Following a 1-month training period, the intervention phase (n = 35) implemented an inspiratory flow rate of 20 L/min, unchanged PEEP, and a reduced respiratory rate to achieve an I:E ratio of 1:2. Physiologic and ventilator data were automatically recorded at 1-min intervals. The primary endpoint was peak inspiratory pressure (PIP), a physiological endpoint reflecting the mechanical effect of the intervention. Secondary endpoints included minute ventilation (MV), end-tidal CO2 (EtCO2), oxygenation metrics, ventilator alarms, and hypotension, feasibility and procedural safety. PIP decreased substantially (45 [35, 65] cmH2O vs. 82 [59, 93] cmH2O; p < 0.001), resulting in a reduction in the time spent with pressure alarms (3.4% vs. 24%; p < 0.0001). However, MV (5.2 [4.6, 5.9] L/min vs. 6.4 [4.1, 7.3] L/min; p = 0.2), and EtCO2 (36 [30, 40] mmHg vs. 31 [27, 39] mmHg; p = 0.4) did not differ significantly. PEEP was maintained in the intervention group (8 [6, 10] cmH2O vs. 2 [0, 5] cmH2O; p = 0.001). The protocol was demonstrated short-term procedural safety signals, as average SpO2 (98.9 [93.7, 100.0]% vs. 99.9 [96.8, 100.0], p = 0.082), and hypotension incidence (8.6% vs. 14%; p = 0.7) did not differ significantly. This protocol implementation study demonstrates that standardized low-flow ventilation with PEEP maintenance is feasible and safe during bronchoscopy in mechanically ventilated patients, with a significant reduction in peak inspiratory pressure as a robust mechanical signal. Alveolar ventilation and hemodynamic tolerance were preserved. These findings support the conduct of further research to evaluate clinical outcomes.
Autoantibodies neutralizing type I interferon (AAN-I-IFNs) underlie life-threatening COVID-19. In hospitalized COVID-19 patients of the DisCoVeRy trial (NCT-04315948), AAN-I-IFNs were associated with higher nasopharyngeal viral load at inclusion (P = 1.4 × 10-3) and delayed viral clearance (P = .013). Blood AAN-I-IFNs, high viral load and delayed clearance were also independent risk factors for critical COVID-19.