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Respiratory syncytial virus (RSV) is a major cause of severe acute lower respiratory tract infections (ALRI), with incomplete elucidation of its pathogenesis and intricate host-virus crosstalk constraining the development of effective therapies. The post-transcriptional interplay among microRNAs (miRNAs), RNA-binding proteins (RBPs), and alternative splicing (AS) is a critical, yet underexplored host response layer. We integrated and analyzed three public datasets (GSE231784, GSE231788, and GSE155151) of RSV-infected A549 cells, screened differentially expressed miRNAs, identified conserved differentially expressed RBPs, used SUVA to detect AS events, and constructed a miRNA-RBP-AS network to find core axis. This integrated bioinformatics approach enabled systematic dissection of multi-layered post-transcriptional regulation during RSV infection. Our analysis revealed that RSV infection substantially alters host post-transcriptional regulation, with 86 conserved differentially expressed RBPs and 94 conserved AS events identified. A potential miRNA-RBP-AS axis was uncovered via regulatory network construction. Crucially, this axis suggests that the downregulation of let-7 family miRNAs (e.g., let-7f-2-3p, let-7a-3p) is strongly associated with the upregulation of the RBP genes SAMD4A and ENOX1. The expression of these RBPs, in turn, correlates with the AS of host genes that are implicated in viral replication and immunity, such as TNIP1 and NR5A2. These findings propose that RSV may exploit this post-transcriptional crosstalk to modulate host immunity and facilitate viral replication. Through bioinformatics analysis, this study provides a novel, system-level map of RSV-host interactions and identifies the let-7-SAMD4A/ENOX1-TNIP1/NR5A2 axis as a promising new target for future antiviral strategies, though this requires further experimental validation.

BackgroundAt our hospital's university, the data integration center (DIC) has the primary task of importing data from various systems, including unstructured medical reports and structured laboratory data into the openEHR data repository. As we participate in the German Medical Informatics Initiative (MII), we have an agreement to share common consent data for research purposes. The requirement for this is that the data must be available in the FHIR format.ObjectivesAs a result, we have to transform our openEHR into FHIR data. Both formats are different, and simply programming a module to transform each openEHR template into a FHIR resource can be time-consuming and neither supportable nor updatable.MethodsWe have designed a method and developed an open-source software artifact that transforms an openEHR composition into a single FHIR resource or a bundle of resources, through the dynamic declaration of openEHR fields to API methods. The mappings can be updated at runtime, while the software artifact allows developers and end users to test and validate the mappings.ResultsWe currently use our software, which is able to do robust transformations and has, until now, successfully stored and processed over 30 million FHIR bundles.

Systematic collection of social determinants of health (SDoH) data remains inconsistent across health care settings, despite its critical impact on patient outcomes. Large language model-powered chatbots offer promise for scalable SDoH data collection, but rigorous, feasible evaluation methods for patient-facing applications are lacking. This study aimed to describe an efficient, iterative, multidisciplinary approach for developing and evaluating a patient-facing SDoH chatbot using synthetic data and case simulation, with the goal of optimizing both chatbot performance and the evaluation rubric prior to clinical deployment. A 10-criterion evaluation rubric was adapted from established health care AI frameworks and applied to 27 synthetic clinical scenarios representing diverse SDoH profiles. Scenarios were role-played by a licensed clinical social worker, and chatbot-patient interactions were rated by 3 members of the research team that were multidisciplinary experts: a social worker, a nurse practitioner, and a physician. Quantitative analysis used percent agreement and Fleiss κ to characterize chatbot performance and rater consensus, with percent agreement selected due to the high prevalence of ceiling effects in several domains. Qualitative analysis synthesized rater feedback to guide iterative refinement of both chatbot prompts and rubric domains. Across 27 simulated cases, the chatbot received high proportions of positive ratings for accurate interpretation (agreement=0.98%, 95% CI 0.91-0.99), communication quality, and cultural sensitivity (agreement=0.99%, 95% CI 0.93-1.00), and appropriately adaptive questioning (agreement=0.99%, 95% CI 0.93-1.00). Lower performance was observed in domain focus and completeness (agreement=0.51%, 95% CI 0.40-0.61), completeness of data capture (agreement=0.59%, 95% CI 0.48-0.69; Fleiss κ=0.18), and safety (agreement=0.69%, 95% CI 0.58-0.78; Fleiss κ=-0.04), prompting targeted adaptations. Qualitative feedback highlighted the importance of distinguishing screening from clinical interviewing capabilities and informed the refinement of the rubric, including clarifying the definition of safety to focus on recognition of physical and mental health emergencies. This study describes a formative feasibility approach for iterative refinement of a patient-facing SDoH chatbot and its evaluation rubric using synthetic case simulation. Future work will include independent external raters, patient stakeholders, repeated scenario testing, and prospective clinical evaluation.

Illicit drug use has been rapidly increasing in South Korea, particularly among individuals in their 20s, contributing to a growing burden of substance use disorder (SUD). However, treatment infrastructure remains limited. Nationwide, only a small number of inpatient treatment hospitals are available, and treatment capacity in the Seoul metropolitan area is insufficient to meet growing demand. In addition, community-based addiction services are scarce, creating barriers to continuous care. Digital therapeutics (DTx) have emerged as a promising approach to improve treatment accessibility and continuity of care. In particular, cognitive behavioral therapy (CBT)-based DTx such as RESET-O, developed in the United States and authorized by the US Food and Drug Administration, have demonstrated clinical benefits in supporting addiction recovery. Building on this concept, our team developed D-STOP, a CBT-based DTx intervention designed to support individuals with SUD in the Korean clinical context. This study aims to evaluate the efficacy of D-STOP as an adjunctive DTx intervention for patients with SUD. This study is a randomized controlled clinical trial designed to evaluate the efficacy of D-STOP in individuals diagnosed with SUD. Following an initial screening assessment, 118 participants meeting the diagnostic criteria will be enrolled. Participants will be recruited from psychiatry departments at addiction treatment and clinical care institutions in Chuncheon, Seoul, Daegu, and Changnyeong, South Korea. During the 12-week intervention period, the experimental group will receive D-STOP in addition to treatment as usual, whereas the control group will receive treatment as usual alone. D-STOP delivers structured CBT-based modules and motivational enhancement interventions through a digital platform. During scheduled study visits, participants will also receive therapeutic feedback from psychiatrists or trained study staff. The primary end point is the abstinence success rate during weeks 9 to 12 of treatment. Logistic regression analysis will be used to estimate treatment effects and evaluate superiority compared with the control group. The study protocol was approved by the institutional review board of Hallym University Chuncheon Sacred Heart Hospital on April 14, 2025. Funding began on April 1, 2023. Data collection started on August 4, 2025, and is expected to be completed by November 30, 2026. As of March 6, 2026, a total of 65 participants have been enrolled. An interim analysis of the primary efficacy outcome has been conducted based on the data available at the time of analysis; however, statistical significance has not been established due to the limited sample size. The final analysis is expected to be published in April 2027. DTx have the potential to expand access to evidence-based addiction treatment in resource-constrained settings. This study will provide clinical evidence on the efficacy and feasibility of D-STOP as a digital treatment support tool for patients with SUD.

Sighing has been proposed as a primary respiratory reset mechanism that is often linked to emotional regulation. However, evidence linking sighing to anxiety relies heavily on laboratory studies, which lack ecological validity. It remains unclear whether ambulatory sighing dynamics in free-living settings reflect momentary (state) symptom fluctuations or enduring (trait) pathology. Evidence from daily-life monitoring is needed to evaluate sighing dynamics as a candidate digital biomarker for anxiety disorders (ADs). This study aimed to evaluate daily-life sighing dynamics as a candidate digital biomarker of anxiety by disentangling state and trait anxiety-sigh associations and comparing these dynamics between individuals with ADs and healthy controls (HCs). A secondary objective was to assess the feasibility, signal quality, and joint data coverage of a synchronized ecological momentary assessment (EMA) and respiratory inductance plethysmography (RIP) protocol. We conducted an intensive longitudinal study integrating smartphone-based EMA with continuous RIP using a Hexoskin smart shirt. Thirty-eight adults were enrolled (n=15 with ADs; n=23 HCs) and completed four 36-hour intensive monitoring blocks distributed over 1 to 2 weeks. Participants wore the Hexoskin RIP smart shirt during each 36-hour block and completed 6 randomly timed EMA prompts per day during the daytime hours (9 AM to 9 PM). Sighs were operationally defined as breaths with tidal volume &#x2265;2 &#xd7; each participant's median tidal volume. Each EMA entry was linked to the preceding 5-minute respiratory window, and the primary outcome was sigh proportion (sigh breaths/total breaths) per window. Multilevel generalized linear mixed models were used to analyze anxiety-sigh coupling, decomposing anxiety into within-person (state) and between-person (trait) components. The analysis included 1279 synchronized psychophysiological windows from 33 participants. Five participants were excluded due to insufficient valid synchronized windows. In the primary beta-binomial model of sigh proportion, higher between-person (trait) anxiety was significantly associated with a lower overall sigh proportion (odds ratio [OR] 0.80, 95% CI 0.74-0.87; P<.001), while HCs showed a lower baseline sigh probability than the anxiety disorder group (OR 0.78, 95% CI 0.65-0.93; P=.005). The within-person anxiety-by-group interaction was significant (OR 1.14, 95% CI 1.03-1.26; P=.01), indicating that sighing tended to increase with higher momentary anxiety in HCs but was attenuated in participants with ADs. Feasibility was high (EMA completion: 1626/2046, 79.5%; high-quality respiratory samples: 10.85/12.90 million, 84.1%; EMA-RIP linkage: 1319/1626, 81.1%). Daily-life sighing dynamics showed a state-trait dissociation, with reduced state-dependent coupling in ADs versus HCs, supporting sighing as a candidate digital biomarker of anxiety. The synchronized EMA-RIP protocol was feasible and yielded high-integrity real-world data.

Accurate stratification of Hodgkin lymphoma (HL) by immunologic/histological subtypes and Epstein-Barr virus (EBV) status is essential for epidemiological and translational research, yet large-scale testing is impractical and expensive. Digital pathology models that utilize routinely used hematoxylin and eosin (H&E) whole-slide images (WSIs) could close this gap. We developed and validated a hierarchical Vision Transformer pipeline that aggregates cell-, patch-, and region-level context to predict EBV status and the three most prevalent immunological/histological HL subtypes: nodular sclerosis (NS), mixed cellularity (MC), and nodular lymphocyte-predominant HL (NLPHL)-from H&E-stained WSIs, and additionally evaluated a standard attention-based multiple-instance learning (ABMIL) baseline for direct architectural comparison. The development pool comprised 1643 HL cases (1952 WSIs) from 18 Danish hospitals and was used for hospital-preserving 5-fold cross-validation; external validation was performed on an independent hold-out cohort of 458 cases (532 WSIs) from five hold-out hospitals. For subtype prediction, analyses were restricted to the 1560 cases belonging to NS, MC, or NLPHL. On the external EBV cohort ( N = 458 ) the hierarchical pipeline achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.73 (95% confidence interval (CI) 0.68-0.77), precision-recall (PR)-AUC 0.57 (95% CI 0.49-0.66) with recall (sensitivity) 0.74 (95% CI 0.67-0.80) and macro-F1 score 0.60 (95% CI 0.54, 0.65). For 3-class subtype prediction on 359 external cases, discrimination reached ROC-AUC 0.84 (95% CI 0.80-0.88) and PR-AUC 0.63 (95% CI 0.56-0.71) with a macro-F1 of 0.56 (95% CI 0.48, 0.64) and macro-recall 0.55 (95% CI 0.47, 0.63); residual errors were dominated by NS-MC confusions. An ABMIL baseline using the same patch embeddings achieved ROC-AUC 0.76 (95% CI 0.72-0.81) for EBV and 0.89 (95% CI 0.86-0.92) subtype prediction, outperforming the hierarchical model on both tasks. This multicenter study shows that both hierarchical and attention-based architectures can determine EBV status and major HL subtypes directly from routine H&E slides with externally validated performance across hospitals, whereas the finding that the simpler baseline outperformed the hierarchical model suggests that strong foundation-model embeddings combined with attention-based pooling may reduce the need for explicit multi-scale modelling in cohorts of this size.

The cumulative impact of multidomain adverse exposures on abdominal aortic aneurysm (AAA) risk and the underlying metabolic pathways remain insufficiently understood. We analyzed 304,482 UK Biobank participants free of aortic aneurysm at baseline. Twenty-six exposures were grouped into five domains to derive domain specific and overall exposure scores. Associations with incident AAA were assessed using Cox models. To explore potential metabolic pathways, we applied a two stage NMR metabolomics framework combining multivariable linear regression, metabolite specific Cox models, and a 10-fold cross validated elastic net Cox model to generate a metabolite score. Mediation analyses and XGBoost models were also performed. During a mean follow up of 14.9 years, 1,671 participants developed AAA. A higher overall exposure score was associated with an increased risk of incident AAA (per SD increase: HR, 1.08; 95% CI, 1.07-1.11). Among domain specific scores, the socioeconomic, social psychology, and lifestyle scores were independently associated with AAA, whereas the environmental pollution and living environment scores were not. Current smoking showed the strongest association among individual exposures (HR, 7.95; 95% CI, 6.81-9.27). Overall exposure burden was associated with broad metabolomic perturbations, and a 13-metabolite score was significantly associated with AAA (per SD increase: HR, 1.38; 95% CI, 1.35-1.41), mediating 5.78% of this association. Adding exposure and metabolite scores improved prediction beyond clinical factors alone. Greater multidomain adverse exposure burden was associated with higher incident AAA risk, partly through metabolic signatures related to inflammation and lipoprotein metabolism, and may provide incremental value for AAA risk stratification.

Our study aimed to evaluate procalcitonin (PCT) as a prognostic marker in advanced odontogenic infections. In addition, we sought to identify further prognostic markers that could be used to predict the length of hospital stay (LOS). We retrospectively analysed 61 patients who were hospitalised between January and August 2020 for surgical abscess drainage in the Department of Oral and Maxillofacial Surgery at the University Hospital of Bonn. Univariable logistic discrete hazard models were used to assess the association between baseline levels as well as temporal changes in laboratory parameters (PCT, C-reactive protein (CRP), and leukocytes) and LOS. In addition, a tree-based approach for modelling discrete time-to-event data was used to identify risk groups of patients with a prolonged LOS. Median LOS were 5, 7, and 9 days for different PCT concentrations&#x2009;<&#x2009;0.1&#xa0;&#xb5;g/l, 0.1-0.5&#xa0;&#xb5;g/l, and >&#x2009;0.5&#xa0;&#xb5;g/l, respectively (p&#x2009;<&#x2009;0.001). The data-driven tree identified three subgroups with median LOS of 4, 6, and 10 days based on preoperative CRP and PCT levels. Patients with CRP&#x2009;>&#x2009;153&#xa0;mg/l had a median LOS of 10 days, while those with CRP&#x2009;&#x2264;&#x2009;153&#xa0;mg/l had median LOS of 6 or 4 days depending on whether PCT was >&#x2009;0.03&#xa0;&#xb5;g/l or &#x2264;&#x2009;0.03&#xa0;&#xb5;g/l. PCT may serve as a useful prognostic marker for estimating LOS, even in severe odontogenic infections. Exploratory decision tree analysis demonstrated that the combined assessment of PCT and CRP levels could help stratify patients into distinct LOS subgroups, which may facilitate clinical decision-making and more accurate estimation of treatment outcomes.

Cleft lip is a craniofacial anomaly, a developmental defect originating in the embryonic period. Cleft lip repair is most often performed between 3 and 6 months after birth. The scar that develops after repair is aesthetically and functionally improved through revision using various grafting techniques. The V-shaped mini-flap method is frequently used for the closure of mini-microform cleft lips. The aim of this study was to present the cosmetic and functional results. This was a retrospective observational study conducted from June 1, 2018 to June 30, 2024 including all patients who underwent secondary cleft lip revision. All patients had previously undergone primary cleft lip reconstruction, and all underwent revision for secondary cleft lip repair. A V-shaped mini flap was used to correct vermilion deviation, and a dermofat graft was used to prevent collapse. Some patients underwent simultaneous alveolar bone grafting. Numerical data were calculated by frequency analysis. The sample consisted of 30 patients who underwent secondary cleft lip revision. After primary cleft lip repair, whistling deformity occurred in 23.3% of the patients, vermilion irregularity in 26.7%, depression in 23.3%, pigmentation in 73.3%, and abnormal lip movements in 56.7%. Cleft lip was bilateral in 20% of patients and 40% had a history of concomitant cleft palate. Intraoperatively, 60% of the patients underwent V-shaped mini flap and 40% underwent V-shaped mini flap&#x2005;+&#x2005;alveolar bone graft. After secondary revision, hematoma developed in 6.7%, graft loss in 20%, and donor site morbidity in 13.4%, wound dehiscence, seroma in 10%, cyst formation in 6.7%, 10% of the patients were reoperated on. Secondary revision with a V-shaped mini flap or V-shaped mini flap&#x2005;+&#x2005;alveolar bone graft appears to be somewhat advantageous in correcting cosmetic and functional defects that arise after primary cleft lip repair, as it is associated with few manageable complications and may require a third cleft lip repair.

Electrocardiograms (ECGs) are used in clinical medicine because their waveforms provide rich information reflecting heart states. It is believed that a specialist with extensive training can identify representative abnormalities and their predictive signs through template matching with typical cases. Furthermore, it is increasingly required to identify the cause of the abnormality, which is difficult even for specialists. To provide clues to internal heart states, we propose a deep neural network-based model composed of one encoder network and multiple decoder networks for converting a 12-lead ECG into multiple microscopic physical and chemical cardiac parameters that represent internal heart states and are potentially essential factors of ECG signals, such as multiple ionic parameters related to cardiac electrophysiology. Data pairs consisting of cardiac parameters and ECGs are needed for training the model, but these are unavailable in principle. Therefore, we propose to generate such data by using a white-box model that can simulate the physical behavior of the heart. The proposed method was experimentally evaluated in terms of the mean absolute error of cardiac parameters taking continuous values and in terms of the accuracy of those taking discrete values. The results showed that the method could accurately estimate these parameters. Moreover, we investigated the estimation errors in detail by visualizing the distributions of the estimated cardiac parameters around the ground-truth values. Our method can help doctors monitor heart conditions by automatically estimating cardiac parameters from ECGs. In addition, it can be used to discover relationships among cardiac parameters, ECG waveforms, and heart diseases. However, before our method can be used in actual clinical practice, it remains to be verified that it works correctly with real ECGs.

Reinforcement learning provides a framework for studying how individuals adjust their behavior through repeated interaction and feedback in social dilemmas. In Q-learning, exploration controls how often agents choose actions other than those favored by their current learned Q-values. Yet, the existing models usually treat the exploration rate as a constant parameter. In systems with social evaluation, however, trial-and-error behavior carries different costs and opportunities for agents with different reputations, making exploration dependent on social standing rather than uniform across agents. Herein, we develop a spatial prisoner's dilemma model in which Q-learning agents adapt their exploration rates according to local reputation differences, while reputation is updated through an asymmetric, state-dependent rule. The results show that adaptive exploration and asymmetric reputation updating each promote cooperation, but their combination produces a stronger reinforcing effect than either mechanism alone. Low-reputation agents explore more and can recover reputation through cooperation, while high-reputation agents explore less and avoid reputation losses caused by defection. This mechanism also reorganizes cooperation in space, producing a stable checkerboard-like coexistence at intermediate reputation concern. In addition, cooperation is most vulnerable at intermediate baseline exploration rates, whereas stronger asymmetric reputation updating mitigates this exploration-induced disruption. These results suggest that reputation can act not only as a record of past behavior but also as a dynamic signal that regulates exploratory behavior during learning and thereby stabilizes cooperation.

Depression is among the leading causes of disability globally. Therefore, exploring the various non-medical treatment options for this condition is particularly important. The aim of the review was to assess the effect of art therapy on depressive symptoms. The foundation of this review is a pre-planned, explorative, secondary analysis of a previously published umbrella review, encompassing the databases Cochrane Library, Embase, MEDLINE, CINAHL, ERIC, American Psychological Association PsycArticles, American Psychological Association PsycInfo, PSYNDEX, the German Clinical Trials Register, and ClinicalTrials.gov. Included were all randomized trials with any patient population receiving active visual art therapy. The outcome was depressive symptoms measured by depression assessment instruments. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and conducted a bias assessment using a modified Cochrane risk of bias tool. Data was pooled using a random-effects model and visualized in forest plots. A pooled standardized mean difference (SMD) with hedges g was calculated to measure the reduction of depressive symptoms. Of 3,100 identified reports we included 26 studies. Of these, 19 studies with 997 patients were eligible for inclusion in the meta-analysis. Overall, we found a standardized mean difference of 0.53 (95% CI: 0.30 to 0.76) for depressive symptoms, favoring the intervention group. Main sources of variation were different types of control groups, methodological quality, and patient populations. Our results suggest that art therapy is associated with improved depressive symptoms. Therefore, art therapy should be accessible as complementary treatment for patients suffering from depressive symptoms.

Lumpy skin disease (LSD) is a vector-borne viral disease of cattle that causes significant production losses. Caused by Lumpy skin disease virus (LSDV), it has recently emerged as a Transboundary Animal Disease (TAD), creating a need for rapid and sensitive diagnostic assays to facilitate disease surveillance and control programs. In this study, a reverse passive latex agglutination test (RPLAT) was developed and validated for the rapid detection of lumpy skin disease virus (LSDV) antigen. Polystyrene Latex beads were coated with hyperimmune serum raised against LSDV in Guinea pig, and assay conditions were optimized for detecting antigen in vitro. We used 173 field samples, including nasal swabs, scabs/skin lesions, and arthropod vectors that were tested alongside PCR as the reference standard. Using receiver operating characteristic curve (ROC) analysis, we determined the assay sensitivity to be 93.4% and specificity to be 94.4% at the optimal cutoff value. The assay area under the curve (AUC) was calculated to be 0.936 (95% CI: 0.889-0.968). Real-time PCR analysis of representative RPLAT-positive samples and all RPLAT-negative samples demonstrated high concordance between the two assays, with 65 of 67 representative RPLAT-positive samples confirmed by qPCR. Optimal detection was observed for samples taken at a late stage of infection, which would correlate with higher antigen amounts during infection. We did not detect any cross-reactivity to other bovine pathogens tested, indicating high analytical specificity of the assay. Results were interpretable within 5&#xa0;min, require limited equipment, and are visually interpreted. Due to the preliminary nature of this study and constraints in sample availability and resources, extensive inter- and intra-laboratory validation was not performed and will be addressed in future studies. Overall, the study demonstrates the preliminary feasibility of a rapid antigen detection assay for field-level screening of LSDV.

Accurate three-dimensional representations of lumbar vertebral anatomy are essential for spinal research and clinical decision-making, particularly biomechanical analyses and the development of patient-specific interventions. However, in many practical settings, only partial information is available, making it difficult to obtain complete vertebral geometries. Statistical Shape Models (SSMs) provide a powerful way to characterize population-level anatomical variability, while Gaussian Process Regression (GPR) enables the reconstruction of full three-dimensional shapes from limited surface information. To reconstruct complete vertebral geometries from partial anatomical information using SSM and GPR and determine the minimum partial information needed for clinically acceptable reconstruction. Thirteen high-resolution CT datasets of healthy adult lumbar spines were segmented. A two-step registration framework was implemented: rigid registration followed by 3D-3D embedded deformation non-rigid registration. Principal Component Analysis (PCA) was used to generate SSMs of the lumbar spine. GPR was then employed for shape reconstruction from partial input data. Reconstruction performance was assessed with a leave-one-out cross-validation method. In the full lumbar spine SSM, the first eight principal modes captured 92.7% of total shape variance. GPR enabled accurate reconstruction of full lumbar spines from sparse partial inputs. Shape reconstruction errors remained within the clinically acceptable range, with Average Distance (AD) within 1.23-3.64 mm, depending on input sparsity. Minimum information analysis revealed that as little as 30.67% surface data per vertebra was sufficient for clinically acceptable reconstructions. Combining SSMs with GPR enables accurate, anatomically realistic reconstruction of the lumbar spine from partial data. To the best of our knowledge, this study represents the first integration of SSMs and GPR for vertebral reconstruction. The proposed approach is suitable for future integration with sparse, ultrasound-derived anatomical information and establishes a foundation for radiation-free 3D guidance systems for lumbar facet joint injections and other spine interventions.

Microglia contribute to central nervous system homeostasis and neuroprotection partly through the release of small extracellular vesicles (sEVs) carrying regulatory cargoes such as microRNAs. Interleukin-4 (IL-4) alters microglial state and secretory output; however, whether sEVs released from IL-4-treated microglia protect neurons against toxic injury, and which cargoes mediate these effects, remains unclear. Here, we investigated the protective effects of sEVs derived from the IL-4-treated HMC3 human microglial cell line in a rotenone-induced injury model in the SH-SY5Y cell line and examined the contribution of microRNA-191-5p to neuroprotection. Small RNA sequencing revealed a distinct miRNA profile in IL-4-sEVs, with microRNA-191-5p emerging as the most statistically significant upregulated candidate. Its enrichment was confirmed by RT-qPCR. PKH67-labeled sEV-associated fluorescence was detected in SH-SY5Y cells, indicating uptake of microglia-derived sEVs by recipient cells. Functionally, pretreatment with IL-4-sEVs significantly reduced rotenone-induced cell death and preserved cell morphology compared with untreated and control sEV-treated cells. To assess the contribution of microRNA-191-5p, IL-4-sEVs were loaded with a microRNA-191-5p antagomir, which reduced microRNA-191-5p levels and partially attenuated the protective effect of IL-4-sEVs. Together, these findings suggest that sEVs derived from the IL-4-treated HMC3 microglial cell line mitigate rotenone-induced injury in the SH-SY5Y cell line in&#xa0;vitro and that microRNA-191-5p contributes, at least in part, to this effect.

Expanded coverage for telehealth during the COVID-19 pandemic allowed providers to bill for telemedicine services that were previously not reimbursable, including telemedicine critical care (TCC) services for critically ill patients. We aimed to characterize TCC billing practices among Medicare beneficiaries before, during, and after the COVID-19 pandemic. This was a serial cross-sectional study of adult Medicare Fee-For-Service beneficiaries with at least one bill for critical care at acute care hospitals from January 2018 to September 2024. TCC billing was identified using provider billing codes; multivariate regression models were used to determine characteristics associated with receipt of TCC. Key outcomes were patient-, provider-, and hospital-level characteristics associated with TCC billing. None. Billing for TCC increased from 0.002% of critical care bills pre-pandemic to 0.01% of critical care bills during and after the pandemic. Patients billed for TCC were disproportionately likely to have COVID-19 but were otherwise relatively similar to critically ill patients not billed for TCC. Internal medicine/critical care providers accounted for the highest proportion of pandemic TCC bills (46.0%). TCC billing occurred more often at minor teaching hospitals (adjusted odds ratio [aOR], 1.21; 95% CI, 1.03-1.43) and at safety-net hospitals (aOR, 1.33; 95% CI, 1.04-1.70). TCC billing was less likely at small-sized hospitals (aOR, 0.39; 95% CI 0.26-0.58) and medium-sized hospitals (aOR, 0.67; 95% CI, 0.47-0.95), government-owned hospitals (aOR, 0.70; 95% CI, 0.57-0.86), for-profit hospitals (aOR, 0.58; 95% CI, 0.48-0.71), rural hospitals (aOR, 0.70; 95% CI, 0.55-0.89), and critical access hospitals (aOR, 0.59; 95% CI, 0.47-0.73). Billing for TCC among hospitalized critically ill Medicare beneficiaries increased during the pandemic but remained low as a proportion of all critical care bills. There was variability in utilization across subspecialties and lesser utilization at rural and critical access hospitals. Further studies are needed to characterize the clinical and economic consequences of this shift.