Search Results (28,198)

Background: Immunosuppressive therapy (methotrexate and biological agents) for juvenile idiopathic arthritis (JIA) is associated with an increased risk of severe infections, higher infection rates, treatment interruptions, failure to achieve disease remission, and recurrent disease flares. Our study aimed to evaluate the safety and efficacy of simultaneous immunization with 13-valent polysaccharide conjugate vaccines (PCV13) against S. pneumoniae (SP) and Hemophilus influanzae type b infections (HibV) in children with JIA without systemic manifestations. Methods: A total of 371 non-systemic JIA patients who received 13PCV and HibV were included in this prospective cohort study. In every patient, we evaluated clinical, laboratory, anti-SP, and anti-Hib IgG antibodies before vaccination, three weeks after, and six months after, and all adverse events (AEs) were collected during the study. The number and duration of acute respiratory infection (ARI) episodes and requirements for antibacterial treatment and AE six months before and after the baseline were collected. Results: The levels of the Ig G anti-SP and anti-Hib antibodies increased in the 3 weeks after vaccination; then, anti-SP antibodies slightly decreased and anti-Hib antibodies remained increased during the whole study, as well as in a part of the patients with a protective titer. During the study, there were no patients with significant flares, and the main JIA outcomes gradually decreased during the trial. The number of patients with uveitis remained equal, as well as the part of the patients with active, low-active, and inactive uveitis. There was no significant rise in the hs-CRP or S100 protein after the vaccination. Previous or ongoing treatment with non-biological (p = 0.072) and biological (p = 0.019) disease-modified anti-rheumatic drugs affected the Hib and did not affect the anti-SP protective titer at the end of the study. Within 6 months following vaccination, the number of ARI episodes (p < 0.001) and the number of courses of antibacterial treatment (p < 0.0001) decreased twice. The median duration of ARI episodes decreased four times (p < 0.0001). Mild AEs (injection site reactions and short-term fever episodes) were found in 58 (15.6%) patients with JIA, and 1 patient (0.2%) developed an SAE. Conclusions: Simultaneous vaccination against pneumococcal and Hib infections reduces the frequency and duration of episodes of ARI, as well as the number of courses of antibacterial drugs, and does not lead to significant JIA flares. The number of reported AEs is consistent with what was expected. Full article

To maintain relevance, youth tobacco control programs must leverage popular social media platforms and address evolving behaviors. Recognizing this, the South Carolina Department of Public Health and Rescue Agency implemented culturally tailored social media campaigns (Down and Dirty, Fresh Empire), a broad vaping social media campaign (Behind the Haze), and an Instagram-based vaping cessation program (Quit the Hit, QTH). This study examines program impact. The social media campaigns were evaluated via online cross-sectional surveys in 2019–2023. Analyses examined awareness and reception overall and among target audiences and compared knowledge, attitudes, and beliefs between campaign-aware and unaware participants. The impact of QTH was assessed via online baseline and follow-up surveys in 2021–2023. Analyses examined program feedback and changes in cessation confidence and tobacco use from baseline to follow-up. Over one-third of participants recalled the social media campaigns, and recall of featured facts was higher among the campaign-aware participants than the unaware participants. QTH participants’ confidence in quitting increased from baseline to follow-up, while any 30-day vaping and average number of days of vaping decreased. This innovative social media program reached high-risk youth with educational content and empowered teens to quit vaping, providing a model for comprehensive youth tobacco control programs. Full article

Improving indoor air quality (IAQ) in daycare centers is essential due to children’s vulnerability to pollutants and prolonged indoor exposure. To address these challenges, energy recovery ventilators (ERVs) with varying filtration efficiencies were evaluated through field measurements and CONTAM simulations. Baseline assessments of CO₂ and PM_2.5 levels revealed significant impacts from outdoor pollutant infiltration. ERVs successfully reduced CO₂ concentrations, maintaining levels below 1000 ppm during most occupancy periods. However, low-efficiency filters (MERV 8 or lower) permitted outdoor particulate matter infiltration, increasing indoor PM_2.5 levels. High-performance filters (MERV 13 or higher) reduced indoor PM_2.5 concentrations by up to 50%, significantly improving air quality. Findings emphasize the necessity of combining high-efficiency filtration with ERVs to mitigate pollutant infiltration and ensure healthy indoor environments. Policymakers and practitioners are urged to implement ventilation systems equipped with MERV 13 or higher filters, particularly in regions with high outdoor pollution. These strategies are critical for safeguarding children’s health and meeting IAQ standards in daycare facilities. Full article

Background/Objectives: Acute kidney injury (AKI) is a common complication of coronavirus disease-19 (COVID-19), but the impact of baseline kidney function and care processes on outcomes is not well understood. We hypothesized that baseline kidney health status may influence courses and outcomes of AKI. Methods: This is a multinational, multicenter, retrospective cohort study. We included hospitalized adult COVID-19 patients with kidney disease (AKI, end-stage kidney disease (ESKD), chronic kidney disease (CKD), or kidney transplant (KT) recipients) from 1 January 2020 to 31 March 2022, across 52 centers in 23 countries. Patients with no prior kidney function information were classified as acute kidney disease (AKD) if estimated glomerular filtration rate (eGFR) at admission was <60 mL/min/1.73 m² and as no known kidney disease (NKD) if eGFR was ≥60 mL/min/1.73 m². We defined combined outcome as death or non-kidney recovery at hospital discharge. Multivariable binary regression models were applied. Results: Among 4158 patients, 882 had ESKD, and 3038 developed AKI. AKI patients were categorized as NKD (31.8%), AKD (38.6%), CKD (23.3%), and KT recipients (3.3%). NKD patients had higher AKI severity and more intensive care unit care needs. In the multivariable analyses, the risk of the combined outcome was higher in AKD (OR 1.459 [1.061, 2.005]) or CKD (OR 1.705 [1.206, 2.410]) patients, although the risk of in-hospital mortality was similar to NKD. Among the survivors at hospital discharge, the risk of partial or non-recovery was higher in CKD (OR 5.445 [3.864, 7.672]) or KT recipients (OR 4.208 [2.383, 7.429]) compared to NKD. These findings were consistent across income categories. Conclusions: Among AKI patients with COVID-19, nearly two-thirds had underlying kidney dysfunction, with 55% identified as having baseline AKD, which had higher risk of death or non-kidney recovery at discharge compared to NKD. Full article

To enhance maintenance endeavors, it is imperative to gain a deep understanding of system degradation. In systems with degradation-aware control, observing degradation becomes particularly challenging. Even with sensors, such controllers continuously mitigate deviations to ensure the system operates within optimal limits. Here, we propose a framework explicitly tailored for degradation-aware control systems, built upon two main components: (1) degradation modeling to estimate and track hidden degradation over time and (2) a Long Short-Term Memory Autoencoder-Degradation Stage Detector (A-LSTMA-DSD) to define alarm and failure thresholds for enabling condition-based maintenance. In degradation modeling, the framework utilizes actuator measurements to model hidden degradation. Next, an A-LSTMA-DSD model is developed to flag anomalies, based on which alarm and failure thresholds are assigned. These dynamic thresholds are defined to ensure sufficient time for addressing maintenance requirements. Working with real data from a boiler unit in an oil refinery and focusing on steam leakages, our proposed framework successfully identified all failures and on average triggered alarm and failure thresholds 15 and 8 days in advance of failures, respectively. In addition to triggering these thresholds, our system outperforms baseline models, such as CNN, LSTM, ANN, ARIMA, and Facebook Profit, in identifying failures by 60% and 95%, respectively. Full article

G protein-coupled receptors (GPRs), including pro-inflammatory GPR4 and ovarian cancer GPR1 (OGR1/GPR68), are involved in the pH sensing of the extracellular space and have been implicated in inflammatory bowel disease (IBD). Previous data show that a loss of GPR4 or OGR1 independently is associated with reduced intestinal inflammation in mouse models of experimental colitis. In the present manuscript, we investigated the impact of the simultaneous loss of GPR4 and OGR1 in animal models of IBD. To study the effects of combined loss of Gpr4 Ogr1 in IBD we used the well-established acute dextran sodium sulfate (DSS) and spontaneous Il10^−/− murine colitis models. Disease severity was assessed using multiple clinical scores (e.g., body weight loss, disease activity score, murine endoscopic index of colitis severity (MEICS) and histological analyses). Real-time quantitative polymerase chain reaction (qPCR), Western blot, and flow cytometry were used to investigate changes in pro-inflammatory cytokines expression and immune cells infiltration. We found that a combined loss of GPR4 and OGR1 significantly reduces colon inflammation in IBD relative to single deficiencies as evidenced by reduced body weight loss, disease score, CD4/CD8 ratio, and Il1β, Il6, and Tnf in the colon. Similarly, in the II10 deficiency model, the inflammation was significantly ameliorated upon the simultaneous deletion of GPR4 and OGR1, evidenced by a reduction in the MEICS score, colon length, Tnf and Il1β measurements, and a decrease in the number of macrophages in the colon, as compared to single deletions. Importantly, hydroxyproline levels were decreased close to baseline in Il10^−/− × Gpr4^−/− × Ogr1^−/− mice. Our findings demonstrate that the simultaneous loss of GRP4 and OGR1 functions exerts an additive effect on multiple parameters associated with colonic inflammation. These results further reinforce the hypothesis that chronic inflammatory acidosis is a driver of fibrosis and is dependent on GPR4 and OGR1 signaling. The inhibition of both GPR4 and OGR1 by pH-sensing receptor modulators may constitute as a potential therapeutic option for IBD, as both pH-sensing receptors appear to sustain inflammation by acting on complementary pro-inflammatory pathways. Full article

With the rapid expansion of urban populations and the accelerated pace of urbanization, the concept of urban air mobility (UAM) has emerged. During flights, UAM aircraft need to transmit real-time sensing information to base stations for further processing and analysis. Large-scale real-time data require leveraging the computing capabilities of edge servers at the network edge to reduce transmission delay and energy consumption of UAM aircraft. In cases where edge servers are unable to process information, an unmanned aerial vehicle (UAV) equipped with computing capabilities and operating in low-altitude airspace can serve as a relay to assist in communication and computation. Due to the limited payloads and flight times of UAVs and UAM aircraft, delay and energy consumption within the system pose significant challenges. To tackle these challenges, two fundamental objectives have been proposed: minimizing delay and minimizing energy consumption. Furthermore, an optimization problem has been proposed to minimize the weighted sum of delay and energy consumption. Then, a UAM federated twin delayed deep deterministic policy gradient (UF-TD3) algorithm has been proposed to solve the original problems characterized by complex, non-convex, and inseparable variables. Simulation results show that the proposed UF-TD3 algorithm converges quickly and significantly outperforms four other baseline algorithms in optimizing delay and energy consumption performance. Moreover, compared to the conventional delay minimization strategy and energy minimization strategy, the proposed strategy of minimizing the weighted sum of delay and energy consumption can reduce the delay by 63.8% and reduce energy by 73.96%. Full article

Rice is a staple food for nearly half the global population and, with rising living standards, the demand for high-quality grain is increasing. Chalkiness, a key determinant of appearance quality, requires accurate detection for effective quality evaluation. While traditional 2D imaging has been used for chalkiness detection, its inherent inability to capture complete 3D morphology limits its suitability for precision agriculture and breeding. Although micro-CT has shown promise in 3D chalk phenotype analysis, high-throughput automated 3D detection for multiple grains remains a challenge, hindering practical applications. To address this, we propose a high-throughput 3D chalkiness detection method using micro-CT and VSE-UNet. Our method begins with non-destructive 3D imaging of grains using micro-CT. For the accurate segmentation of kernels and chalky regions, we propose VSE-UNet, an improved VGG-UNet with an SE attention mechanism for enhanced feature learning. Through comprehensive training optimization strategies, including the Dice focal loss function and dropout technique, the model achieves robust and accurate segmentation of both kernels and chalky regions in continuous CT slices. To enable high-throughput 3D analysis, we developed a unified 3D detection framework integrating isosurface extraction, point cloud conversion, DBSCAN clustering, and Poisson reconstruction. This framework overcomes the limitations of single-grain analysis, enabling simultaneous multi-grain detection. Finally, 3D morphological indicators of chalkiness are calculated using triangular mesh techniques. Experimental results demonstrate significant improvements in both 2D segmentation (7.31% improvement in chalkiness IoU, 2.54% in mIoU, 2.80% in mPA) and 3D phenotypic measurements, with VSE-UNet achieving more accurate volume and dimensional measurements compared with the baseline. These improvements provide a reliable foundation for studying chalkiness formation and enable high-throughput phenotyping. Full article

This research investigates the optimization potential of Renewable Energy Communities (RECs) through advanced demand-side management strategies. The study simulates a real distribution network and analyzes load profile optimization in a residential REC configuration, comparing two distinct approaches: Demand-Side Engagement (DSE) and Optimized Demand-Side Management (Opt-DSM). The methodology encompasses load-shifting strategies at the appliance level, progressing from spontaneous behavior patterns to algorithmic optimization. Starting from a baseline scenario of conventional consumption patterns, the research evaluates the effectiveness of both user-driven load shifting (DSE) and automated redistribution through genetic algorithms (Opt-DSM). The analysis framework addresses three key dimensions: economic efficiency through incentive optimization, social cohesion via collaborative engagement, and environmental sustainability through the optimal utilization of locally generated energy. Results demonstrate that enhanced generation-consumption synchronization through Opt-DSM yields superior outcomes for both distribution network performance and participant economics compared to DSE. However, successful implementation requires substantial technological infrastructure investment at individual and community levels, alongside significant modifications to established consumption patterns. This research contributes to the understanding of RECs as innovative socio-technical systems and provides figures to support the analysis related to the balance between technological optimization and user engagement in maximizing shared energy potential. Full article

Introduction: Hepatitis D virus (HDV) infection remains a significant global health challenge due to its severity and high risk of progression to cirrhosis and hepatocellular carcinoma (HCC). Bulevirtide, a novel HDV entry inhibitor, has shown promise in managing chronic hepatitis D by blocking viral entry into hepatocytes. This study evaluated the efficacy and safety of bulevirtide in reducing HDV RNA levels and improving liver function in a real-life cohort of Italian patients with HDV infection. Methods: This multicenter prospective trial enrolled 108 consecutive patients with chronic HDV infection, from June 2023 to June 2024, who received 2 mg/day of bulevirtide in combination with a nucleoside/nucleotide analogue for hepatitis B virus (HBV) infection. Patients with any stage of liver fibrosis or compensated cirrhosis were included. Data collected included demographic and clinical characteristics, liver function tests, HDV RNA levels, and adverse events at baseline and 6 months. Results: The virological response was achieved in 54.6% of patients (n = 59), with 36 demonstrating undetectable HDV RNA levels. Among responders, ALT levels decreased significantly from 67.0 U/mL [IQR 44.0–116.3] to 31.5 U/mL [IQR 24.0–36.5, p = 0.001], and AST levels from 66.0 U/mL [IQR 46.5–91.0] to 32.5 U/mL [IQR 28.0–38.0, p = 0.021]. Median HDV RNA dropped from 29,800 IU/mL [IQR 3100–375,000] to 0 IU/mL [IQR 0–291, p < 0.001]. No significant predictors of response emerged. Mild adverse events, including pruritus (5.6%) and injection-site reactions (1.9%) and flu-like syndrome (0.9) were reported, with no treatment discontinuation. Conclusions: Bulevirtide effectively reduces HDV RNA levels and improves liver function with a favorable safety profile, offering a promising therapeutic option for chronic hepatitis D. Further large-scale studies are needed to confirm these findings and explore long-term outcomes. Full article

Background. Long COVID has been associated with increased cardiovascular risk and chronic low-grade inflammation, raising concerns about its long-term metabolic consequences. Given that the Mediterranean diet (MD) has shown beneficial effects on cardiovascular risk factors and inflammation in various populations, it is important to explore its potential impact on individuals with Long COVID. Therefore, the aim is to determine the association of the MD with cardiovascular risk factors (CVRF) and metabolic syndrome (MetS) in Caucasian subjects diagnosed with Long COVID. Methods. Cross-sectional study, 305 subjects diagnosed with Long COVID were included following the WHO criteria. Adherence to MD was evaluated with the MEDAS (Mediterranean Diet Adherence Screener) with 14 items used in Prevention with Mediterranean Diet study (PREDIMED study). The criteria considered to diagnose MetS were blood pressure, glycemia, triglycerides, HDL cholesterol, and waist circumference. Other CVRFs considered were tobacco consumption, total cholesterol, LDL cholesterol, body mass index, and baseline uric acid levels. The association between MD with CVRF and the number and components of MetS was analyzed using multiple regression models and multinomial regression. Results. The mean age was 52.75 ± 11.94 years (men 55.74 ± 12.22 and women 51.33 ± 11.57; p = 0.002), (68% women). The mean of the MEDAS questionnaire was 7.76 ± 2.37. The presented MetS were 23.6% (39.8% men and 15.9% women p < 0.001). In the multiple regression analysis, after adjusting for age and average time from acute COVID-19 infection to the date of inclusion in this study, the mean MD score showed a negative association with uric acid (β = −0.295; 95% CI: −0.496 to −0.093), BMI (β = −0.049; 95% CI: −0.096 to −0.002), the number of MetS components (β = −0.210; 95% CI: −0.410 to −0.010), and waist circumference (WC) (β = −0.021; 95% CI: −0.037 to −0.003) and a positive association with HDL cholesterol (β = −0.018; 95% CI: 0.001 to −0.037). Conclusions. The findings of this study suggest that higher Mediterranean diet scores are associated with lower levels of uric acid, fewer MetS components, smaller waist circumference, and higher HDL cholesterol levels in individuals with Long COVID. Full article

Objective: To analyse variations in axial length (AL), corneal radius (CR) and the AL/CR ratio over one year in eight-year-old schoolchildren, considering sex, ethnicity and refractive error. Methods: Vision screenings were conducted in 16 schools in Terrassa (Barcelona, Spain) with eight-year-old children as part of the CISViT project. Measurements included ocular biometrics (AL and CR) and non-cycloplegic autorefraction for refractive error. Parental questionnaires provided demographic data (birth date, ethnicity). The same procedures were repeated after one year. Results: Ocular biometric parameters differed by sex and ethnicity. Boys and children of Maghreb descent had longer AL and flatter CR than girls and Caucasian children (p < 0.001 for both visits). The AL/CR ratio was higher in boys than girls (p = 0.002 in the initial visit and p = 0.011 in the follow-up visit) but consistent across ethnicities (p = 0.291 and p = 0.390). AL and AL/CR ratio differed significantly by refractive error status (p < 0.001 in both visits), increasing in more myopic children, while CR showed no significant difference. In myopic children, the AL/CR ratio exceeded 3.0, and typical sex-based biometric differences diminished. Growth rates for AL and AL/CR ratio were similar across sex and ethnicity, indicating minimal demographic influence. Conclusions: AL and CR differ significantly by sex and ethnicity, with demographic differences evident in baseline measurements but not in growth rates over one year. The consistency of the AL/CR ratio across ethnicities, despite sex-based differences, supports its utility as a reliable metric for assessing refractive development in diverse populations. Full article

Background/Objectives: Early pediatric cochlear implantation positively impacts early language outcomes. The Teenager and Young Adults Cochlear Implant (TAYACI) study investigates the long-term outcomes of early implantation and factors influencing variability among cochlear implant (CI) users. This article outlines participants’ background, early language outcomes, and multidisciplinary study protocol. Methods and Materials: Individuals aged 12–22 received CIs before 30 months of age, followed-up at the same hearing implant center, and adhered to a standard school curriculum were invited to participate. Out of 109 eligible CI users, 50 participated; 46 agreed to undergo clinical assessments, while four completed questionnaires only. Results: The mean age at the first CI was 15.63 months (SD = 6.0). All but one communicated with spoken language(s). Participants attended mainstream schools and had highly educated parents. Over half (56%) had received Auditory Verbal Therapy in early childhood. Earlier implantation correlated with better language understanding one year post-CI. Conclusions: Earlier implantation was associated with better early language outcomes, with parental education level and early family-centered intervention likely contributing. Future sub-studies will investigate multidisciplinary long-term effects of pediatric cochlear implantation in adolescents. Full article

Background: As cannabis becomes legal in several U.S. states, the risk of THC-induced tachycardia increases. This study aimed to develop and verify a physiologically based pharmacokinetic–pharmacodynamic (PBPK-PD) model to assess the impact of THC and its active metabolite, 11-hydroxy-THC (11-OH-THC), on the heart rate of healthy adults. Methods: A PBPK-PD model for intravenous (IV) 11-OH-THC administration was first developed. Secondly, a PBPK-PD model for IV THC, combined with the metabolized 11-OH-THC, was established, verified, and validated. Direct PD models driven by the plasma, brain, and heart concentrations of THC and 11-OH-THC predicted using our previously verified PBPK model were tested for model development. Finally, the risks of tachycardia at a rest condition from various doses of oral and inhaled THC were simulated for 500 individuals aged 18–65 years, with a sex ratio of 1:1 and a baseline heart rate of 70 beats per minute. Results: The PD model was best described by a direct nonlinear E_max model driven by the sum of the total THC and 11-OH-THC concentrations in their effect compartments linked to their heart compartments. In 42 simulated dosing regimens with THC doses ranging from 2 to 69.4 mg, 97% of the observed heart rates or heart rate changes following THC administration fell within the 5th to 95th percentiles of the model-predicted values. Similarly, for two simulated 11-OH-THC IV doses, 93% of the observations fell within this range. Simulations indicated that half of the simulated population would experience tachycardia at doses of 60 mg and 15 mg of THC for oral and inhaled administration, respectively. The simulated risks of tachycardia based on specific conditions should be interpreted with caution. Conclusions: Our verified PBPK-PD model successfully describes the heart rate changes in healthy adults after IV, oral, and inhaled THC administration. This model provides a tool to predict the effects of THC and its primary metabolite on heart rates, offering valuable insights for assessing the risk of tachycardia in both clinical and recreational cannabis use. Full article

Seismocardiography (SCG) and Gyrocardiography (GCG) use lightweight, miniaturized accelerometers and gyroscopes to record, respectively, cardiac-induced linear accelerations and angular velocities of the chest wall. These inertial sensors are also sensitive to thoracic movements with respiration, which cause baseline wanderings in SCG and GCG signals. Nowadays, accelerometers and gyroscopes are widely integrated into smartphones, thus increasing the potential of SCG and GCG as cardiorespiratory monitoring tools. This study investigates the accuracy of smartphone inertial sensors in simultaneously measuring instantaneous heart rates and breathing rates. Smartphone-derived SCG and GCG signals were acquired from 10 healthy subjects at rest. The performances of heartbeats and respiratory acts detection, as well as of inter-beat intervals (IBIs) and inter-breath intervals (IBrIs) estimation, were evaluated for both SCG and GCG via the comparison with simultaneous electrocardiography and respiration belt signals. Heartbeats were detected with a sensitivity and positive predictive value (PPV) of 89.3% and 93.3% in SCG signals and of 97.3% and 97.9% in GCG signals. Moreover, IBIs measurements reported strong linear relationships (R² > 0.999), non-significant biases, and Bland–Altman limits of agreement (LoA) of ±7.33 ms for SCG and ±5.22 ms for GCG. On the other hand, respiratory acts detection scored a sensitivity and PPV of 95.6% and 94.7% for SCG and of 95.7% and 92.0% for GCG. Furthermore, high R² values (0.976 and 0.968, respectively), non-significant biases, and an LoA of ±0.558 s for SCG and ±0.749 s for GCG were achieved for IBrIs estimates. The results of this study confirm that smartphone inertial sensors can provide accurate measurements of both instantaneous heart rate and breathing rate without the need for additional devices. Full article