Lower extremity muscle activation patterns during running in individuals with and without anterior cruciate ligament reconstruction.

Bloodstream infections in immunocompromised adult patients.

Some individuals consume food before or after exercise to compensate for energy expended. If this behaviour is prevalent, it could have implications for population-level health. We estimated the prevalence of eating in response to exercise (before and after exercise) and explored associated factors using an online questionnaire. A nationally representative sample of UK adults engaging in weekly exercise (N=1054) reported how their eating behaviours (portion size, frequency, perceived healthiness) change on exercise days compared to non-exercise days. Other measures included demographics (e.g., sex, dieting status), exercise characteristics (frequency, intensity, duration, mode), exercise motivation and enjoyment, the Three-Factor Eating Questionnaire (dietary restraint and disinhibition), and the Diet-Related Beliefs of Exercisers Scale (DBES) (sub-scales: food as reward, nutritional replenishment, healthy eating and refrain from eating). Associations with eating in response to exercise were examined using multiple logistic regression. Eating in response to exercise was reported by 24% (n=251) of the sample, and occurred most often after exercise (17%, n=178). The DBES food as reward sub-scale (OR=2.26, 95% CI 1.91, 2.67) and the DBES nutritional replenishment sub-scale (OR=1.42, 95% 1.13, 1.79) were strongly and positively associated with eating in response to exercise. Internal motivation for exercise (OR=1.53, 95% CI 1.21, 1.93), strength training (OR=1.74, 95% CI 1.28, 2.36), and disinhibition (OR=1.12, 95% CI 1.07, 1.17) were also positively associated with eating in response to exercise. We conclude that nearly a quarter of regular exercisers report consuming more food on exercise days and that reducing this behaviour has the potential to improve population-level health.

HLFFN: A high-low frequency feature fusion network for pre-stack AVO inversion

A time–frequency feature fusion method for contactless heart rate estimation using mmWave FMCW radar

Anastomotic strictures and leaks are severe complications following esophageal atresia (EA) repair. We hypothesized that esophageal injections of botulinum toxin type A (BTX-A) could mitigate these complications through muscle relaxation. This randomized, controlled, blinded animal trial evaluated the feasibility and safety of a long- and short-gap porcine EA model. Furthermore, the effect of BTX-A as a surgical adjunct on anastomotic outcomes were exploratory assessed. Twenty-four animals received BTX-A or saline pre- or intraoperative to a three- or one-centimeter esophageal resection and primary anastomosis. The primary feasibility endpoint was animal completion rate at postoperative day 14, while stricture severity was the primary exploratory outcome. Secondary exploratory outcomes were anastomotic leakage frequency, and biomechanical and histological characteristics. Animal completion rate was 62.5% (15/24). There was no difference in stricture severity between the intervention and control groups (Esophageal Anastomotic Stricture Index 0.41 [IQR 0.26-0.55] versus 0.35 [IQR 0.27-0.58]; p = 0.73). One animal in the control group suffered an anastomotic leak. Biomechanical properties and anastomotic healing did not differ. While the procedure was technically feasible, refinement is necessary to reduce attrition rate and establish a safe model for long-term evaluation. More studies are needed to determine whether BTX-A can provide a reduction in anastomotic complications.

Balance impairment is one of the major problems commonly experienced by older adults as a result of degenerative processes, leading to an increased risk of falls. Falls may cause fear of recurrent falling, reduced physical activity, loss of physical capability, and may eventually result in disability and psychological consequences such as depression. Structured, occupation-based physical activity has been shown to provide various benefits for older adults, including improving physical function, slowing functional decline associated with degenerative processes, reducing fall risk, and enhancing postural control. This study aimed to analyze the effect of the occupation-based Lee Silverman Voice Treatment (LSVT) BIG method on functional balance in older adults. A quantitative approach with a pre-experimental pretest–posttest design was employed. A total of 10 participants were recruited using a total sampling technique and underwent 16 intervention sessions over a 4-week period, with each session lasting 60 minutes. Measurement instruments included a respondent characteristic questionnaire, the Berg Balance Scale as the primary outcome measure, and the Canadian Occupational Performance Measure (COPM) for intervention planning. The results indicated that individual factors (age, Body Mass Index, and comorbidities), as well as physical activity characteristics (frequency, intensity, and duration of exercise), were not significantly associated with functional balance in this sample (p > 0.05). In contrast, the paired t-test analysis demonstrated a significant effect of the occupation-based LSVT BIG intervention on functional balance in older adults (p < 0.001). These findings suggest that the occupation-based LSVT BIG method may be considered a promising intervention approach for improving functional balance in older adults.

Variants in the FBXO7 gene are a recognized cause of juvenile-onset autosomal recessive parkinsonism (PARK15) with a heterogeneous phenotype. There is limited number of reported cases in the literature. To report a 19-year-old Indian man with juvenile-onset parkinsonism harbouring a novel FBXO7 variant and to review the published literature on FBXO7-associated parkinsonism/parkinsonism-pyramidal syndrome, focusing on the clinical, imaging, and genetic spectrum and possible genotype-phenotype correlations. We evaluated an Indian male with FBXO7-associated juvenile parkinsonism and reviewed previously published cases from the literature to assess the clinical, imaging, and genetic spectrum. A total of 32 cases of FBXO7-associated parkinsonism, including our patient, were identified. The median age at onset was 17 years, median age at presentation was 28 years, with slight male predominance and a median disease duration of 5 years. Symmetrical juvenile-onset parkinsonism with tremor and postural instability was the commonest presentation. Dementia, dystonia, pyramidal signs, and gaze abnormalities were less frequent associated features. Most patients showed levodopa responsiveness, although motor complications and psychiatric adverse effects were common. Neuroimaging findings were heterogeneous, ranging from cortical atrophy to pallidal hypointensity and presynaptic dopaminergic deficits on dopamine transporter imaging. Twenty variants were identified, with the homozygous nonsense variant c.1492C > T (p.Arg498*) being the most frequently reported. FBXO7-associated parkinsonism typically presents as symmetrical juvenile-onset levodopa-responsive parkinsonism, although early-onset parkinsonism is known. Dementia, dystonia, and pyramidal signs are less frequent associated features. FBXO7 variants should be considered in patients with symmetrical juvenile/early-onset parkinsonism, especially when accompanied by levodopa-induced psychiatric adverse effects.

Inflammatory bowel diseases (IBD), including Crohn's disease (CD), ulcerative colitis (UC), and IBD-unclassified (IBD-U), are increasingly recognized across Eastern Europe, including Romania, where historical data indicated low incidence. Contemporary real-time epidemiological data for our country are scarce. This study evaluated the short-term frequency and epidemiological characteristics of IBD patients presenting to major Romanian gastroenterology centers. We conducted a prospective, cross-sectional observational study over a 14-day period in November 2024 across 18 university-affiliated tertiary gastroenterology clinical sites in Romania. All consecutive adult patients with confirmed IBD were enrolled using a centralized online platform. Demographics, disease type and phenotype, severity, and treatment were recorded and analyzed descriptively. A total of 1,045 patients were registered: 52.4% CD, 46.9% UC, and 0.7% IBD-U. Geographical distribution revealed a statistically significant variation, with Crohn's disease being more frequent in Southern Romania, while UC predominated in the Eastern and Central-western regions (p=0.0009). Most patients resided in urban areas, and the majority were in clinical remission at presentation. Phenotypic analysis revealed ileocolonic CD (L3) and left-sided/pancolitis UC (E2/E3) as most frequent. Severe disease history was more common in CD, and prior surgery was significantly higher in CD than UC. Smoking and appendectomy were more frequently associated with CD as previously reported. Therapeutic patterns reflected disease type: anti-TNF use predominated in CD, while other biologics and small molecules were more common in UC. Regional differences in therapy were observed, with southern centers showing higher use of novel therapies, likely reflecting a more mature IBD population, with a higher CD prevalence. Notably, the number of IBD diagnoses increased over time, correlating with Romania's GDP growth (R² = 0.89, p < 0.001), suggesting that socioeconomic factors may influence disease recognition and diagnosis. This study offers the most recent snapshot of IBD epidemiology in Romania, highlighting a transition toward medium-incidence patterns and growing clinical complexity. These findings provide evidence for the need to establish nationwide population-based surveillance systems and healthcare planning initiatives aimed at mitigating the rising burden of IBD.

As the power source for high-frequency transmitters in ion cyclotron resonance heating (ICRH) systems, high-power radio-frequency (RF) tetrodes present significant challenges in circuit modeling due to their complex operational principles and inherently interdisciplinary nature. This study addresses this challenge by developing a PSpice circuit model for a high-power RF tetrode through the fitting of its output characteristics, transfer parameters, and distributed parameters. Furthermore, the circuit modeling of a 1.5-MW ICRH transmitter and its associated high-frequency Inductor-Capacitor (LC) resonant circuit was completed. The model’s functionality and frequency characteristics were subsequently verified via simulation against experimental data. Simulation results demonstrate that the numerical simulations of the proposed high-power RF tetrode model, the 1.5-MW ICRH transmitter circuit, and its high-frequency LC resonant circuit align well with the actual electrical characteristics of the 1.5 MW ICRH transmitter system. Consequently, this model can be effectively utilized for parameter optimization and performance analysis of ICRH systems.

Antibiotic abuse-induced residues pose a severe threat to human health, yet traditional detection methods suffer from large sample consumption and complicated pretreatment, which fail to meet practical needs. Terahertz (THz) spectroscopy, with its molecular fingerprinting capability, holds great promise for drug detection. Here, we propose a THz metasurface for accurate qualitative and quantitative analysis of antibiotics. First, quantum chemical calculations reveal the spectral response of antibiotics in the THz band and the formation mechanism of characteristic fingerprint peaks. Then, leveraging the metasurface resonance spectral dip induced by molecular fingerprint peaks, we extract the specific information via spectral inversion. After that, a joint feature space is constructed by combining resonant frequency shift and differential absorption intensity, and the characteristic frequency is further integrated to form a multidimensional detection system. As a proof of concept, the metasurface realizes qualitative and quantitative detection of two antibiotics with a limit of detection as low as 1.11 μg/mm2 and effectively identifies trace norfloxacin in complex mouse blood matrices. Combined with machine learning, the metasurface efficiently identifies multiple antibiotics on our experimental dataset. This work presents a label-free approach enabling direct qualitative and quantitative detection of trace antibiotics via a single THz metasurface, offering new insight for trace identification of drug molecules, with potential application prospects in biomedical detection applications.

Introduction Recreational drugs affect the cardiovascular system through distinct mechanisms; however, data regarding their cardiovascular impact in the emergency department setting is limited. This study aimed to assess the incidence of cardiovascular effects following recreational drug use in presentations to the emergency department, identify the main drug groups involved, and compare cases with and without cardiovascular effects. Methods Data were extracted from the European Drug Emergency Network (Euro-DEN Plus) dataset from October 2013 to December 2021. Recreational drugs were categorised into ten main drug groups: opioids, cocaine, crack cocaine, cannabis, 3,4-methylenedioxymethamfetamine, amfetamine-type stimulants, gamma-hydroxybutyrate and gamma-butyrolactone, hallucinogens, benzodiazepines, and ketamine. Results Among 59,571 presentations, 13,905 (23.3%) involved cardiovascular effects. Cocaine (OR 3.19, 95% CI 2.99–3.39) and 3,4 methylenedioxymethamphetamine (OR 1.18, 95% CI 1.13–1.23) showed the strongest associations with cardiovascular features, including chest pain, palpitations, hypertension, and arrhythmias. Opioids (OR 0.35, 95% CI 0.31–0.38) and benzodiazepines (OR 0.38, 95% CI 0.32–0.44) were associated with less frequent cardiovascular features. Patients with cardiovascular features exhibited higher median values for temperature, heart rate, blood pressure, and respiratory rate (p <0.001). Cardiovascular features were associated with an increased risk of intubation (OR 1.91, 95% CI 1.70–2.15), critical care admission (OR 2.18, 95% CI 2.00–2.38), and mortality (OR 15.8, 95% CI 7.36–33.9). Discussion Cardiovascular effects were common in acute recreational drug toxicity. Cocaine and amfetamine-type stimulants increased the risk of chest pain and arrhythmias, with chest pain being a key indicator of acute coronary syndrome. Cardiovascular effects were more frequently observed with cocaine than with crack cocaine. Cannabis was positively associated with palpitations but not arrhythmias. Gamma-hydroxybutyrate and gamma-butyrolactone, opioids, and benzodiazepines were linked to hypotension. The presence of cardiovascular effects was associated with worse outcomes, underscoring the need for thorough cardiac assessment. Conclusions Cardiovascular effects were present in almost a quarter of emergency department presentations with acute recreational drug toxicity, particularly involving cocaine and 3,4 methylenedioxymethamphetamine.

To describe the incidence and clinical profile of graft rejection in high-risk penetrating keratoplasty (PK) recipients treated with a compounded topical formulation of 0.02% tacrolimus combined with 1% prednisolone. This retrospective observational study included 41 eyes from 41 high-risk PK patients operated between 2019 and 2023 at the Oftalmosalud Eye Institute (Lima, Peru), with a minimum follow-up of 12months. All patients received adjunctive immunoprophylaxis with topical 0.02% tacrolimus and 1% prednisolone, initiated on the first postoperative day and maintained throughout follow-up. High-risk PK refers to corneal transplantation in eyes with predisposing factors for immune-mediated graft rejection. Graft rejection episodes were classified as reversible or irreversible based on clinical course and treatment response. Graft rejection occurred in 15 of 41 eyes (36.6%), with 60% of these episodes being reversible and 40% irreversible. The remaining 26 eyes (63.4%) maintained clear grafts without signs of rejection. The most frequent high-risk clinical features were corneal neovascularization in 21 eyes (51.2%), ocular surface disease in 9 eyes (21.9%), and persistent inflammation in 7 eyes (17.0%). No serious adverse events related to topical tacrolimus were observed. Topical 0.02% tacrolimus in combination with prednisolone may represent a safe adjunctive strategy in high-risk PK. These findings support the incorporation of topical tacrolimus into immunosuppressive regimens for high-risk corneal transplantation. Prospective controlled trials are warranted to confirm its long-term efficacy and safety.

To examine screening mammograms assigned high-risk scores by two artificial intelligence (AI) models, 2 and 4 years prior to screen-detected cancers. This retrospective study was based on data from 130,031 screening examinations performed in BreastScreen Norway (2008-2018), processed by two AI models: a commercial model (A) and an in-house model (B). The study sample included women with screen-detected cancer, and two prior consecutive screening examinations assigned the highest 10% of AI risk scores. Two radiologists conducted an informed review of three consecutive screening mammograms per woman, 4 and 2 years prior to, and at diagnosis. Descriptive statistics assessed the location of AI markings, mammographic features, histopathological tumor characteristics, and visibility of malignancy preceding diagnosis. Model A and B assigned high-risk scores at both prior mammograms for 43 and 47 cases, respectively, with 29 cases selected by both, yielding 61 sets of three consecutive examinations. AI markings corresponded to the cancer location in at least one view in 61% and 57% of cases 4 years prior to diagnosis for models A and B, respectively, while radiologists classified 89% as true negative or minimal sign non-specific. At diagnosis, spiculated mass (28%) and density with calcifications (20%) were the most frequent mammographic features but were absent 2 and 4 years prior. The AI models identified breast cancer in a substantial part of screening mammograms 2 and 4 years preceding diagnosis, but only a few demonstrated suspicious findings according to radiologists. Question Understanding the correspondence between AI markings, cancer location, and mammographic features is important to evaluate AI models' potential to enhance early breast cancer detection. Findings AI markings corresponded to the cancer location 4 and 2 years prior to diagnosis, with evolving mammographic features, yet radiologists classified most examinations as negative. Clinical relevance The discrepancy between AI-identified breast cancers years before diagnosis and radiologists classifying the same cases as negative highlights both AI's potential and the challenges of how best to implement it to enhance early detection.

Surface defect detection in aluminum profiles remains challenging due to complex textures, illumination variations, reflective noise, and subtle small-scale defects. Conventional YOLO-based detectors rely primarily on spatial-domain features and fail to exploit complementary frequency and gradient information, which is essential for detecting weak, texture-oriented, and elongated defects. This paper proposes Adaptive Triple-Domain YOLOv8, a real-time framework that integrates spatial, frequency (Discrete Wavelet Transform), and gradient (Gabor filter) features within a unified architecture. An adaptive attention-based fusion module dynamically integrates multi-domain features, enabling defect-specific discrimination while preserving high inference speed. Experiments on the Tianchi benchmark demonstrate that the proposed method consistently outperforms spatial-domain, dual-domain, and recent detectors, achieving a mAP@0.5 of 96.7% and a mAP@0.5:0.95 of 73.2% at over 100 FPS. The method significantly improves the detection on low-contrast, texture-dominated, and elongated defects, providing a favorable balance between detection accuracy and real-time efficiency for industrial aluminum surface inspection.

Bioacoustic processing and analyses of mouse vocalizations: Current methods and future directions.

The domain shift problem induced by variable working conditions severely constrains the cross-domain generalization capability of data-driven fault diagnosis models. Existing methods lack collaborative modeling of the multi-scale time-frequency characteristics of vibration signals at the feature extraction level. They also neglect the constraint guidance of fault physical mechanisms on the feature alignment process at the domain adaptation level. To address these deficiencies, this paper proposes a physics-guided cross-domain adaptation framework-a Hierarchical Hybrid Transformer network with Contrastive Learning (PgHHT-CL). The framework comprises three key designs. At the feature encoding level, a hierarchical hybrid Transformer encoder is constructed. It achieves collaborative extraction of transient impulse components and periodic modulation components in vibration signals through gated interactive fusion of local convolutional branches and global self-attention branches across multiple abstraction levels. At the domain adaptation level, a physics-guided cross-domain contrastive learning strategy leverages the order-invariance relationship between fault characteristic frequencies and rotational frequency from bearing dynamics prior knowledge to constrain the construction of cross-domain positive and negative sample pairs. The feature alignment process is thereby required to satisfy physical consistency beyond statistical distribution matching. At the training optimization level, a joint optimization objective integrates classification loss, cross-domain contrastive loss, and physical consistency loss, with a progressive weight adjustment strategy to ensure stable convergence of multi-task learning. Extensive cross-condition transfer experiments on two public bearing datasets from Case Western Reserve University and Paderborn University show that PgHHT-CL achieves average diagnostic accuracies of 94.94 ± 0.32% and 90.26 ± 0.50%, respectively, attaining the highest mean accuracy across all 18 transfer tasks among the representative state-of-the-art baselines selected for comparison. The framework also exhibits notable robustness under large domain shift and strong noise conditions. Ablation experiments and feature visualization analyses further validate the effectiveness and physical interpretability of the physics-guided strategy and hierarchical hybrid architecture.

Purpose The current research seeks to determine the influential attributes of influencers and examine the influence of food influencers (food bloggers) on consumers' purchase intention. Design/methodology/approach Firstly, the content analysis method was applied to identify the characteristics of the influencers, and after extracting factors and characteristics from previous studies, the factors were categorized based on similarities and experts' opinions. Then, an integrated model was designed based on the research hypotheses. In the second step, an online survey was performed on 291 Instagram users. Findings The results of structural equation analysis demonstrated that the influence of three factors: persuasive power, the scope of influence and influencer honesty, is stronger on purchase intention compared to other characteristics. The findings of this paper presented comprehensive recommendations on the paths to success for brands, marketing experts and influencers and provided valuable insights for influencers, food business owners, restaurant managers and audiences to build persuasive influencer marketing strategies and these lead to the consumers' encouragement to buy more. Originality/value The theoretical contribution of this study extends the food influencer literature and by identifying the most frequent and effective features of food industry influencers (key elements of food bloggers persuasion) on the purchase intention of followers on Instagram, it provides insights into how food industry professionals can use social media influencers to develop effective marketing strategies and enhance strong relationships.

In recent years, with the development of medical imaging and deep learning technologies, medical image segmentation has played a crucial role in assisting physicians with diagnosis and treatment. Networks based on encoder-decoder architectures have been widely used in the design of medical image segmentation models. However, a large amount of information, including critical segmentation detail information, is lost in the process of encoder down-sampling. In order to rationally utilize this loss information, this paper proposes a novel network: Loss Information Aggregation Network (LIAUNet), which utilizes loss information and rationally aggregates it. Specifically, a MultiScale Hybrid Pooling Loss Extraction Module (MHPLE) is firstly designed to extract the loss information of four scales in the down-sampling process and to supplement and adaptively adjust it. In addition, we design the Information-Preserving multiscale Attention Aggregator (IMAA) to aggregate the extracted loss information through a multiscale approach, which enhances the features and reduces further loss or sparsity of the loss information. Finally, we add a Spectral-Gated Convolution Block (SGCB) after encoder down-sampling to enhance the network's ability to jointly model frequency and spatial domain features. Experiments on multiple medical image datasets show that, compared with other methods, our method has higher segmentation accuracy. This method provides effective support for the advancement of medical imaging and clinical diagnosis.

Mutational processes, such as the molecular effects of carcinogenic agents or defective DNA repair mechanisms, produce different mutation types with characteristic frequency profiles, known as mutational signatures. Non-negative matrix factorization (NMF) has been successfully used to discover many mutational signatures, yielding novel insights into cancer etiology and informing targeted therapies. However, the NMF model is only a rough approximation to reality, and even small departures from this assumed model can have large negative effects on the accuracy and reliability of the results. We propose BayesPowerNMF, a Bayesian NMF method that provides nonparametric robustness to model misspecification, principled automated selection of the number of latent processes, and uncertainty quantification of model parameters. In extensive simulation studies, we find that our proposed approach recovers more true signatures with greater accuracy than current leading methods. On whole-genome sequencing data for six cancer types from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, we find that our method is able to accurately recover more signatures than the current state-of-the-art.