Folia Med (Plovdiv). 2025 Mar 21;67(2). doi: 10.3897/folmed.67.e137670.
ABSTRACT
Maxillofacial space infection refers to infections in the potential spaces and fascial planes of the maxillofacial region. The primary objective was identifying predictive variables associated with increased hospital stay in patients with odontogenic maxillofacial space infections.
PMID:40270143 | DOI:10.3897/folmed.67.e137670
Folia Med (Plovdiv). 2025 Mar 21;67(2). doi: 10.3897/folmed.67.e145825.
ABSTRACT
Malignant pleural effusions are a common and debilitating complication of advanced malignancies, affecting approximately one million patients annually. This condition leads to significant morbidity and a decline in quality of life. Accurate diagnosis and effective management are critical yet challenging due to the overlap in biochemical markers between malignant and benign pleural effusions.
PMID:40270142 | DOI:10.3897/folmed.67.e145825
Am J Rhinol Allergy. 2025 Apr 24:19458924251335075. doi: 10.1177/19458924251335075. Online ahead of print.
ABSTRACT
BackgroundPatients with chronic rhinosinusitis (CRS) refractory to medical management often require endoscopic sinus surgery (ESS). Oral corticosteroids (OCSs) are frequently prescribed postoperatively, but the evidence of their efficacy is limited.ObjectiveThe purpose of this study is to evaluate the efficacy of OCS use in patients with CRS following ESS.MethodsA systematic search was performed to identify studies examining the use of OCSs in patients undergoing ESS for CRS. The primary outcomes were sinonasal outcome test (SNOT) and Lund-Kennedy (LK) endoscopic scores. Secondary outcomes were visual analog scale (VAS) scores. Meta-analysis was conducted using a fixed effects model with a heterogeneity test via the I2 statistic.ResultsThe search yielded 1899 articles, and 22 were included in the qualitative analysis, 14 of which were randomized controlled trials with 793 total patients. OCS use differed based on type, dosage, and duration. Studies included in meta-analysis did not show a significant difference in SNOT (Standardized Mean Difference [SMD] -0.03, Confidence Interval [CI] -0.47-0.40, I2 0%), LK (SMD -0.20 CI -0.57-0.17 I2 58%), or VAS (SMD 0.19 CI -0.25-0.63 I2 54%) scores between steroid and non-steroid groups. Two studies that assessed OCSs in the allergic fungal rhinosinusitis (AFRS) subtype of CRS showed significant improvement in outcomes. Two additional studies examined OCS versus itraconazole in AFRS, with both groups showing improvement but neither one with greater significance.ConclusionThis study showed no significant difference in SNOT, LK, or VAS scores in patients with CRS who received OCSs following ESS. Given the limited number of studies in the analysis, further investigations are warranted before making recommendations.
PMID:40270102 | DOI:10.1177/19458924251335075
Clin Appl Thromb Hemost. 2025 Jan-Dec;31:10760296251322779. doi: 10.1177/10760296251322779. Epub 2025 Apr 24.
ABSTRACT
BackgroundAtrial fibrillation (AF) after acute pulmonary embolism (PE) may lead to a poor prognosis. We conducted this study to explore influencing factors of new-onset AF in patients after acute PE.MethodsPatients with objectively confirmed acute PE at the China-Japan Friendship Hospital from first January 2015 to 31st May 2022 were retrospectively included in the study, and patients with new-onset AF confirmed by electrocardiography were defined as the case group. The control group was obtained from the above PE patients without new-onset AF in age matching. Patients with a history of AF, pulmonary hypertension, acute myocardial infarction, valvular heart disease and hyperthyroidism were excluded. Logistic regression was conducted to identify potential influencing factors for the development of new-onset AF in patients with acute PE. To assess the prediction value of potential variables, receiver operating characteristic curves were plotted.ResultsAmong 853 patients diagnosed with acute PE, 732 patients met the including criteria, and 29 patients with new-onset AF were identified. The median age of all patients was 74 years, with 77.6% being male. No statistically significant differences were observed between the case and control groups regarding demographic characteristics. Patients with new-onset AF had significantly enlarged right atrium, right ventricle and left atrium in echocardiography compared with control group, but no significant differences were observed in the diameter of the left ventricle and the proportion of left ventricular ejection fraction (LVEF) ≤ 40%. Right atrial enlargement (OR, 4.19; 95%CI, 1.24-15.09; P = 0.023), left atrial enlargement (OR, 14.76; 95%CI, 4.79-57.28; P < 0.001) and the simplified pulmonary embolism severity index (sPESI) (OR, 2.04; 95%CI, 1.19-3.67; P = 0.012) were associated with increased risk of new-onset AF.ConclusionsBoth severity of acute PE and enlargement of left and right atrium were associated with an increased risk of new-onset AF in patients with acute PE. In patients with high-risk acute PE, greater vigilance is needed for the occurrence of new-onset AF.
PMID:40270083 | DOI:10.1177/10760296251322779
Sci Rep. 2025 Apr 23;15(1):14063. doi: 10.1038/s41598-025-98134-5.
ABSTRACT
Previous studies have shown that athletes recovering from anterior cruciate ligament (ACL) reconstruction experience a decline in motor cortex excitability and injury-related cortical reorganization, potentially leading to ongoing complications and a higher risk of subsequent injuries. Therefore, incorporating an intervention that consistently delivers sensory inputs to the central nervous system and enhances excitability at both spinal and supra-spinal levels, in addition to exercise therapy, may offer greater benefits. The purpose of this study was to explore whether combining whole body vibration (WBV) with exercise therapy enhances motor cortical excitability in athletes undergoing ACL reconstruction more effectively than exercise therapy alone. Additionally, it aimed to assess whether this combination improves quadriceps strength and reduces functional limitations in daily activities. This study is a randomized, single-blinded, controlled trial. Twenty-six participants were assigned to either the WBV plus exercise therapy group (intervention group) or the exercise therapy-only group (control group). Outcome measures, assessed before and after treatment, included motor cortex excitability [active motor threshold (AMT) and motor-evoked potential amplitude of the quadriceps], isometric peak torque of the quadriceps, and daily functional disabilities using the knee outcome survey activities of daily living scale (KOS-ADL scale). The treatment period consisted of 12 sessions (4 weeks, with 3 sessions per week). A two-way mixed ANOVA was conducted to examine the main effects of group, time and their interactions. The results showed that in the intervention group (WBV plus exercise therapy), AMT significantly decreased (F(1, 12) = 11.35, P = 0.006, η2 = 0.486), while the control group (exercise therapy only) showed no significant change (F(1, 12) = 0.252, P = 0.625, η2 = 0.021). In the intervention group, AMT decreased by 19.47% post-treatment. Both groups showed significant improvements in isometric peak torque and KOS-ADL scores (P < 0.001), with large effect sizes for these parameters. The study concluded that adding WBV to exercise therapy is more effective in increasing motor cortex excitability compared to exercise therapy alone. However, since both groups showed significant improvements in quadriceps peak torque and KOS-ADL scores, it suggests that the addition of WBV did not provide substantial added benefits in enhancing quadriceps strength and improving daily functional abilities. The observed improvements may primarily be attributed to exercise therapy. Nonetheless, it is important to consider the small sample size and low statistical power when interpreting these results.RCT registration: On the Iranian Registry of Clinical Trials (IRCT20220220054078N1).
PMID:40269014 | DOI:10.1038/s41598-025-98134-5
Sci Rep. 2025 Apr 23;15(1):14126. doi: 10.1038/s41598-025-97866-8.
ABSTRACT
In the context of telecommunications, AI enhances network efficiency by predicting and managing traffic. In many decision-making scenarios, decision-makers choose the more flexible structure that can handle all kinds of information. Bipolarity is the only case in which we can discuss the positive and negative aspects of certain scenarios. On one side, AI enhances network efficiency, proactive maintenance, and personalized customer experience but on the other hand, it has also some negative aspects (1) implementing AI infrastructure can be costly (2) Uses of AI in telecommunication may raise data security concerns and user privacy (3) AI can lead to potential issues if system fail or misused. To cover these issues, the idea of an interval-valued bipolar fuzzy soft set (IVBFSS) has been developed that can deal with both positive and negative aspects of AI. Some basic operational laws for IVBPFS numbers are developed. Several fundamental aggregation operators have been introduced like arithmetic average and geometric average aggregation operators, indicating our main contribution. An algorithm is developed to discuss the application perspective of the initiated approaches. We have utilized these developed notions to classify AI-driven techniques in the telecommunications sector to discuss the applicability of the initiated notions. A comparative analysis of the developed approaches shows the advantages and superiority of the introduced work.
PMID:40269011 | DOI:10.1038/s41598-025-97866-8
Sci Rep. 2025 Apr 23;15(1):14137. doi: 10.1038/s41598-025-98112-x.
ABSTRACT
The Gazelle Optimization Algorithm (GOA) is a recently proposed and widely recognized metaheuristic algorithm. However, it suffers from slow convergence, low precision, and a tendency to fall into local optima when addressing practical problems. To address these limitations, we propose a Multi-Strategy Improved Gazelle Optimization Algorithm (MIGOA). Key enhancements include population initialization based on an optimal point set, a tangent flight search strategy, an adaptive step size factor, and novel exploration strategies. These improvements collectively enhance GOA’s exploration capability, convergence speed, and precision, effectively preventing it from becoming trapped in local optima. We evaluated MIGOA using the CEC2017 and CEC2020 benchmark test sets, comparing it with GOA and eight other algorithms. The results, validated by the Wilcoxon rank-sum test and the Friedman mean rank test, demonstrate that MIGOA achieves average rankings of 1.80, 2.03, 2.03, and 2.70 on CEC2017 (Dim = 30/50/100) and CEC2020 (Dim = 20), respectively, outperforming the standard GOA and other high-performance optimizers. Furthermore, the application of MIGOA to three-dimensional unmanned aerial vehicle (UAV) path planning problems and 2 engineering optimization design problems further validates its potential in solving constrained optimization problems. Experimental results consistently indicate that MIGOA exhibits strong scalability and practical applicability.
PMID:40268989 | DOI:10.1038/s41598-025-98112-x
Sci Rep. 2025 Apr 23;15(1):14045. doi: 10.1038/s41598-025-96223-z.
ABSTRACT
The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains, on the other hand, are adept at learning stable sensory representations given noisy observations, a capacity mediated by a cascade of signal conditioning steps informed by domain knowledge. The olfactory system, in particular, solves a source separation and denoising problem compounded by concentration variability, environmental interference, and unpredictably correlated sensor affinities using a plastic network that requires statistically well-behaved input. We present a data-blind neuromorphic signal conditioning strategy, based on the biological system architecture, that normalizes and quantizes analog data into spike-phase representations, thereby transforming uncontrolled sensory input into a regular form with minimal information loss. Normalized input is delivered to a column of spiking principal neurons via heterogeneous synaptic weights; this gain diversification strategy regularizes neuronal utilization, yoking total activity to the network’s operating range and rendering internal representations robust to uncontrolled open-set stimulus variance. To dynamically optimize resource utilization while balancing activity regularization and resolution, we supplement this mechanism with a data-aware calibration strategy in which the range and density of the quantization weights adapt to accumulated input statistics.
PMID:40268966 | DOI:10.1038/s41598-025-96223-z
Sci Data. 2025 Apr 23;12(1):680. doi: 10.1038/s41597-025-05010-y.
ABSTRACT
The utilization of datasets from beetle hindwings is prevalent in research of morphology and evolution of beetles, serving as a valuable tool for comprehending the evolutionary processes and functional adaptations under specific environmental conditions. However, the collection of hindwing images of beetles poses several challenges, including limited sample availability, complex sample preparation procedures, and restricted public accessibility. Recently, a machine learning technique called Stable Diffusion has been developed to statistically generate diverse images using a pretrained model with prompts. In this study, we introduce an approach utilizing Stable diffusion and ControlNet for the generation of beetle hindwing images, along with the corresponding results obtained from its application to a diverse set of 200 leaf beetle hindwings. To demonstrate the fidelity of the synthetic hindwing images, we conducted a comprarative analysis of three key metrics: Structural Similarity Index (SSIM), Inception Score (IS), and Fréchet Inception Distance (FID), which are crucial for evaluating image fidelity. The results demonstrated a strong alignment between the actual data and the synthetic images, confirming their high fidelity. This novel library of leaf beetle hindwings not only offers morphological image for utilization in machine learning, but also showcases the extensive applicability of the proposed methodology.
PMID:40268959 | DOI:10.1038/s41597-025-05010-y
Nat Commun. 2025 Apr 24;16(1):3841. doi: 10.1038/s41467-025-59243-x.
ABSTRACT
Genetic risk prediction for non-European populations is hindered by limited Genome-Wide Association Study (GWAS) sample sizes and small tuning datasets. We propose JointPRS, a data-adaptive framework that leverages genetic correlations across multiple populations using GWAS summary statistics. It achieves accurate predictions without individual-level tuning data and remains effective in the presence of a small tuning set thanks to its data-adaptive approach. Through extensive simulations and real data applications to 22 quantitative and four binary traits in five continental populations evaluated using the UK Biobank (UKBB) and All of Us (AoU), JointPRS consistently outperforms six state-of-the-art methods across three data scenarios: no tuning data, same-cohort tuning and testing, and cross-cohort tuning and testing. Notably, in the Admixed American population, JointPRS improves lipid trait prediction in AoU by 6.46%-172.00% compared to the other existing methods.
PMID:40268942 | DOI:10.1038/s41467-025-59243-x
