Search Results (124)

Background: Paramagnetic manganese (Mn(II)) has emerged as a promising alternative to gadolinium-based contrast agents (GBCAs) due to its favorable magnetic properties. Despite extensive research, no Mn-based agent has yet achieved clinical translation. Because free Mn(II) is toxic, macromolecular complexes incorporating stable macrocyclic DOTA chelators conjugated to polysaccharides may enhance coordination stability and improve the safety profile of Mn(II)-based contrast agents. Methods: Two chemical routes, maleimide- and ester-mediated, were evaluated for covalent coupling of DOTA-based macrocyclic ligands to the backbone of selected poly- and oligosaccharides. Subsequently, DOTA-modified carboxymethyldextran, aminodextran, and chitosan oligosaccharide were labeled with paramagnetic Mn(II) under mild conditions. ATR-FTIR confirmed the successful conjugation of DOTA chelators to the sugar backbone. The conjugates were further characterized by DLS, ICP-MS, and FPLC. In vitro relaxivity was measured at high field strength to evaluate MRI performance. In vivo contrast efficacy was first assessed using in ovo MRI in chicken embryos and subsequently evaluated by biodistribution studies in nude mice. Results: In vitro relaxivity studies demonstrated higher signal enhancement of the poly-/oligosaccharide-DOTA-Mn(II) conjugates compared with MnCl₂ and the clinical agent gadoteridol (ProHance^®). In ovo MRI showed persistent vascular enhancement up to 120 min, while in nude mice, contrast enhancement was observed in the liver, kidneys, and gallbladder 40 min post-injection. Conclusions: Mn(II)-tagged sugar-based imaging probes may offer a promising non-gadolinium alternative to GBCAs, with tunable biodistribution profiles depending on carrier molecular weight. Full article

Tobacco smoke exposure not only increases the risks of lung cancer and cardiovascular disease, but can be a stressor contributing to mental illness. It is important to clarify the relationship between chronic tobacco smoke exposure and mental stress from the perspective of disease prevention. We developed a simple and highly sensitive method for simultaneously analyzing nine biomarkers: nicotine and cotinine (tobacco smoke exposure markers); cortisol, testosterone, and dehydroepiandrosterone (stress-related markers); and serotonin, melatonin, dopamine, and oxytocin (relaxation-related markers). Biomarkers were extracted and concentrated by in-tube solid-phase microextraction with a Supel-Q PLOT capillary, followed by separation and detection within 7 min using liquid chromatography–tandem mass spectrometry on a Discovery HS F5 column. Calibration curves using stable isotope-labeled internal standards showed good linearity (0.005–100 ng mL⁻¹) with detection limits of 0.09–13.5 pg mL⁻¹. Intra-day and inter-day precision had relative standard deviations below 7.2% and 15.5% (n = 6), respectively, with recovery rates of 84.0–108.8%. The automated method requires only ultrafiltration of hair methanol extract, enabling non-invasive pg-level analysis using just a few milligrams of hair. Hair analysis reflects an association between chronic tobacco smoke exposure and stress. This method is effective for analyzing the relationship between long-term tobacco smoke exposure and chronic stress. Full article

Existing contrastive deep graph clustering methods typically employ fixed-threshold strategies when constructing positive and negative sample pairs, and fail to integrate both graph structure information and clustering structure information effectively. However, this fixed-threshold and binary partitioning approach is overly rigid, limiting the model’s utilization of potentially learnable samples. To address this problem, this paper proposes a contrastive deep graph clustering model with a progressive relaxation weighting strategy (DCPRES). By introducing the progressive relaxation weighting strategy (PRES), DCPRES dynamically allocates sample weights, constructing a progressive training strategy from easy to difficult samples. This effectively mitigates the impact of pseudo-label noise and enhances the quality of positive and negative sample pair construction. Building upon this, DCPRES designs two contrastive learning losses: an instance-level loss and a cluster-level loss. These respectively focus on local node information and global cluster distribution characteristics, promoting more robust representation learning and clustering performance. Extensive experiments demonstrated that DCPRES significantly outperforms existing methods on multiple public graph datasets, exhibiting a superior robustness and stability. For instance, on the CORA dataset, our model achieved a significant improvement over the static approach of CCGC, with the NMI increasing by $4.73\%$, the ACC by $4.77\%$, the ARI value by $7.03\%$, and the F1-score by $5.89\%$. It provides an efficient and stable solution for unsupervised graph clustering tasks. Full article

Parkinson’s disease (PD) is characterized by motor symptoms, with key diagnostic features, such as rigidity, traditionally assessed through subjective clinical scales. This study proposes a novel hybrid framework integrating model-driven biomechanical features (damping ratio, decay rate) and data-driven statistical features (maximum detail coefficient) from wearable sensor data during a modified pendulum test to quantify shoulder girdle rigidity objectively. Using weak supervision, these features were unified to generate robust labels from limited data, achieving a 10% improvement in PD/healthy control classification accuracy (0.71 vs. 0.64) over data-driven methods and matching model-driven performance (0.70). The damping ratio and decay rate, aligning with Wartenberg pendulum test metrics like relaxation index, revealed velocity-dependent aspects of rigidity, challenging its clinical characterization as velocity-independent. Outputs correlated strongly with UPDRS rigidity scores (r = 0.78, p < 0.001), validating their clinical utility as novel biomechanical biomarkers. This framework enhances interpretability and scalability, enabling remote, objective rigidity assessment for early diagnosis and telemedicine, advancing PD management through innovative sensor-based neurotechnology. Full article

Background: This study used inertial measurement unit (IMU)-based posture angle estimates to define proxy risk labels and investigated whether these labels can be predicted from demographic, anthropometric, and lifestyle variables through machine learning analysis. Methods: Thirty healthy individuals aged 18–25 years were included. Demographic and anthropometric data and information on daily living activities were collected. The IMU sensors were placed at vertebral levels C1, C7, T5, T12, and L5. Participants were instructed to stand in an upright posture, followed by a relaxed daily posture. Anatomic postural changes between these positions were analyzed. Cervical lordosis, thoracic kyphosis, lumbar lordosis, and scoliosis risks were predicted using machine learning algorithms, including Random Forest (RF) and Artificial Neural Networks (ANN). Results: Incorrect postures during desk work and phone use were associated with an increased likelihood of posture-related deviations, such as cervical lordosis, thoracic kyphosis, and lumbar lordosis. Conversely, daily physical activity reduced these deviations. Using LOSO and stratified cross-validation with imbalance handling, balanced accuracies ranged between 0.55 and 0.82 across targets, with majority-class baselines between 0.53 and 0.87. For cervical lordosis risk, RF achieved a 0.82 balanced accuracy (95% CI: 0.74–0.97), while other categories showed a moderate but consistent performance. AUPRC values exceeded baseline levels across all models. Conclusions: IMU-based posture angle estimates can be used to identify posture-related risk categories. In this study, ML models have demonstrated predictive relationships with demographic, anthropometric, and lifestyle variables. These findings provide exploratory evidence based on IMU-derived proxy labels in a small cohort of healthy young adults. They represent exploratory indicators of postural deviation rather than clinical outcomes and may motivate future studies on preventive strategies. Importantly, the results remain underpowered relative to the a priori power targets and should be interpreted qualitatively. Full article

Both neuropathic and nociplastic pain (non-nociceptive pain) are characterized by a similar pattern of clinical symptoms, including numbness, dysesthesia, tingling, and pricking. Whereas nociplastic pain results from altered nociception without indication of tissue damage or a somatosensory system lesion, neuropathic pain is caused by a disease or lesion affecting the somatosensory system. The available therapeutic options consist of antiepileptic drugs, antidepressants, and muscle relaxants. Unfortunately, symptoms are often refractory, and increasing drug dosage may lead to adverse events. In this narrative review, we searched PubMed, MEDLINE, Cochrane, and EMBASE databases from their inception up to 26 July 2025, using the key words “duloxetine,” “pregabalin,” and then ‘‘combination,’’ “nociplastic pain,” “neuropathic pain,” “efficacy,” “safety,” “pharmacology,” “pharmacokinetic,” and “pharmacodynamic.” We evaluated the role of combination therapy with duloxetine, a serotonin–norepinephrine reuptake inhibitor, and pregabalin, an antiseizure medication that acts on voltage-gated calcium channels α2δ subunit, in patients with neuropathic or nociplastic pain. The literature data indicate that combination therapy has synergistic effects, leading to fewer adverse events in specific categories of patients. Available evidence showed that combination therapy is generally not inferior to monotherapy, with slight differences in safety outcomes depending on supplementation, drug labels, and titration. These results indicate that even if not superior, combination therapy may be an alternative to monotherapy in selected patients: those who experience side effects from higher dosages of duloxetine or pregabalin and for whom symptom relief from dose reduction alone is not possible; those who use medications that interact with duloxetine; those who suffer from anxiety–depression, where pain is closely linked to mental symptoms; and those who have central neuropathic pain (often refractory). Full article

The field of human–robot collaboration (HRC) still lacks research studies regarding the evaluation of mental workload (MWL) through objective measurement to assess the mental state of operators in assembly tasks. This research study presents a comparative neuroergonomic analysis to evaluate the mental workload and productivity in three laboratory experimental conditions: in the first, the participant assembles a component without the intervention of the robot (standard scenario); in the second scenario, the participant performs the same activity in collaboration with the robot (collaborative scenario); in the third scenario, the participant is fully guided in the task in collaboration with the robot (collaborative guided scenario) through a system of guiding labels according to Poka-Yoke principles. The assessment of participants’ mental workload is shown through combinative analysis of subjective (NASA TLX) and objective (electroencephalogram—EEG). Objective MWL was assessed as the power waves ratio β/α (Beta—stress indicator, Alpha—relaxation indicator). Furthermore, the research used observational measurements to calculate the productivity index in terms of accurately assembled components across the three scenarios. Through ANOVA RM, mental workload significantly decreased in the activities involving the cobot. Also, an increase in productivity was observed shifting from the manual scenario to the cobot-assisted one (18.4%), and to the collaborative guided scenarios supported by Poka-Yoke principles (33.87%). Full article

The accelerating pace of digital life has intensified psychological strain, increasing the demand for accessible and systematized emotional support tools. Relaxing video games—defined as low-pressure, non-competitive games designed to promote calm and emotional relief—offer immersive environments that facilitate affective engagement and sustained user involvement. This study proposes a computational framework that integrates sentiment analysis and topic modeling to investigate the affective mechanisms and behavioral dynamics associated with relaxing gameplay. We analyzed nearly 60,000 user reviews from the Steam platform in both English and Chinese, employing a hybrid methodology that combines sentiment classification, dual-stage Latent Dirichlet Allocation (LDA), and multi-label mechanism tagging. Emotional relief emerged as the dominant sentiment (62.8%), whereas anxiety was less prevalent (10.4%). Topic modeling revealed key affective dimensions such as pastoral immersion and cozy routine. Regression analysis demonstrated that mechanisms like emotional relief (β = 0.0461, p = 0.001) and escapism (β = 0.1820, p < 0.001) were significant predictors of longer playtime, while Anxiety Expression lost statistical significance (p = 0.124) when contextual controls were added. The findings highlight the potential of relaxing video games as scalable emotional regulation tools and demonstrate how sentiment- and topic-driven modeling can support system-level understanding of affective user behavior. This research contributes to affective computing, digital mental health, and the design of emotionally aware interactive systems. Full article

Migraine is a common disabling primary headache disorder with significant personal and socio-economic impacts. A combination of medication and non-pharmacological therapies is essential for migraine management. Outpatient multidisciplinary headache therapy has not yet been evaluated in Switzerland. This study evaluates the effectiveness of the headache management program at Inselspital, Bern University Hospital, in improving headache-related disability in migraine patients. This open-label pilot study used prospectively assessed routine data from our headache registry. Participants aged 18 years or older with a diagnosis of migraine, confirmed by a headache specialist, were included. The program consisted of seven weekly sessions, each with a 50 min educational lecture and a 30 min progressive muscle relaxation (PMR) exercise. Primary outcomes were headache-related impact and disability, measured by the Headache Impact Test 6 (HIT-6) and Migraine Disability Assessment (MIDAS). Secondary outcomes included symptoms of anxiety, measured by the Generalized Anxiety Disorder 7-item scale (GAD-7), and symptoms of depression, assessed using the eight-item Patient Health Questionnaire depression scale (PHQ-8). Data were analysed using paired t-test and Wilcoxon signed rank tests. Significant improvements were observed in HIT-6 scores (pre-program: 65.2; post-program: 61.9; p = 0.012) and MIDAS scores (pre-program: 38; post-program: 27; p = 0.011), while PHQ-8 also showed a statistically significant reduction. Although the GAD-7 scores improved numerically, this change was not statistically significant. These findings suggest that the headache management program may reduce headache burden and disability; however, further research with larger samples is needed to confirm these preliminary results. Full article

Electron paramagnetic resonance (EPR) spectroscopy is gaining increasing recognition in research on various nanostructures. In the case of iron oxide nanoparticles, EPR measurements offer the possibility of determining the magnetic phase and the exact type (Fe₃O₄, γ-Fe₂O₃, α-Fe₂O₃, or a combination) of the core material. Furthermore, the EPR technique enables the study of relaxation processes, estimation of the effective and surface anisotropy constants, and assessment of the influence of sample aging on the magnetic properties of nanoparticles. The scope of the information obtained can be further expanded by utilizing spin labeling of polymer-coated nanoparticles. By analyzing the signals from the attached nitroxide, one can determine certain properties of the coating and its interactions with the environment (e.g., body fluids, cells, tissues) and also perform imaging of nanoparticles in various media. In some cases, EPR can help monitor the encapsulation of active substances and their release processes. Unfortunately, despite the enormous potential, not all of the possibilities offered by EPR are routinely used in nanoscience. Therefore, the present article aims not only to present the current applications and existing trends but also to indicate directions for future EPR research, constituting a road map. Full article

Emotion recognition using an odor-induced electroencephalogram (EEG) has broad applications in human-computer interaction. However, existing studies often rely on subjective self-reporting to label emotion, lacking objective verification. While the β/α ratio has been identified as a potential objective indicator of arousal in EEG spectral analysis, its value in emotion recognition remains underexplored. This study ensured the authenticity of emotions through self-reporting and EEG spectral analysis of 50 adults after inhaling sandalwood essential oil (SEO) or bergamot essential oil (BEO). Classification models were built using discriminant analysis (DA), support vector machine (SVM), and random forest (RF) algorithms to identify low or high arousal emotions. Notably, this study introduced the β/α ratio as a novel frequency domain feature to enhance model performance for the first time. Both self-reporting and EEG spectral analysis indicated that SEO promotes relaxation, whereas BEO enhances attentiveness. In model testing, incorporating the β/α ratio enhanced the performance of all models, with the accuracy of DA, SVM, and RF increasing from 70%, 75%, and 85% to 75%, 80%, and 95%, respectively. This study validated the authenticity of emotions by employing a combination of subjective and objective methods and highlighted the importance of β/α in emotion recognition along the arousal dimension. Full article

Zinc oxide (ZnO) and iron oxide (IO) nanoparticles have been identified as promising candidates for biomedical applications, based on their unique physicochemical properties. The association of these nanoparticles in a single system creates a bimodal entity, allowing the excellent luminescent properties of ZnO quantum dots to be combined with the contrast agent of IO for magnetic resonance imaging (MRI). The present study focuses on the luminescent and MRI properties of a new poly(lactic-co-glycolic acid) (PLGA) nanocarrier system formulation containing ZnO NPs and IO NPs in different nominal ratios. Microscopic analysis (TEM and SEM) reveals a circular morphology with IO and ZnO NPs. The average diameter of the particles was determined to be 220 nm, as measured by DLS. The luminescence results indicate that the PLGA system shows strong emission in the visible range, and the MRI analysis shows a high r2 relaxivity of 171 mM⁻¹ s⁻¹ at 7T. The optimized formulation, exhibiting a molar ratio of Fe:Zn ranging from 1:10 to 1:13 (mol:mol), demonstrates superior fluorescence and MRI performance, underscoring the significance of nanoparticle composition in bimodal imaging applications. The systems evaluated demonstrate no toxicity in the THP-1 cells for doses of up to 128 µg mL⁻¹, with efficient labeling after 4 h of incubation, yielding images of strong luminescence and T2 contrast. The PLGA:ZnO:IO system demonstrates considerable potential as a bimodal platform for diagnostic imaging. Full article

Image-guided drug delivery is a method for tracking drug carriers for activation in specific lesions in the body. Image guidance uses the labelling of the drug or carrier and a clinically approved imaging modality. MRI (magnetic resonance image)-guided drug delivery has been considered for focused ultrasound tumour-targeted drug release. Liposomes are labelled for MRI tracking and the confirmation of drug delivery. In this study, we prepared two lipids conjugated to Gd-DOTA that confer MR imaging properties. Two lipid conjugates to DOTA, a C₁₈ (LCA-1) and a C₁₆ (LCA-2), were synthesised. The lipids were combined at different ratios within the lipid mix, and we investigated their effects on the liposome’s T_m using DSC (differential scanning calorimetry) and on relaxivity using NMR. The results show that when different combinations of LCA-1 and LCA-2 were introduced into the liposomes, their ratio affected both thermal drug release and relaxivity. As these lipids are part of the liposomal membrane, they confer tracking ability, and their effect on relaxivity due to thermal release could enable the confirmation of liposomal drug release using MRI at clinically relevant magnetic field strengths. Full article

This study addresses a specialized variant of the full-truckload delivery problem inspired by a Turkish logistics firm that operates in the liquid transportation sector. An exact algorithm is proposed for the relevant problem, to which no exact approach has been applied before. Multiple customer and trailer types, as well as washing operations, are introduced simultaneously during the exact solution process, bringing new aspects to the exact algorithm approach among full-truckload systems in the literature. The objective is to minimize transportation costs while addressing constraints related to multiple time windows, trailer types, customer types, product types, a heterogeneous fleet with limited capacity, multiple departure points, and various actions such as loading, unloading, and washing. Additionally, the elimination or reduction of waiting times is provided along transportation routes. In order to achieve optimal solutions, an exact algorithm based on the column generation method is proposed. A route-based insertion algorithm is also employed for initial routes/columns. Regarding the acquisition of integral solutions in the exact algorithm, both dynamic and static sets of valid inequalities are incorporated. A label-setting algorithm is used to generate columns within the exact algorithm by being accelerated through bi-directional search, ng-route relaxation, subproblem selection, and heuristic column generation. Due to the problem-dependent structure of the column generation method and acceleration techniques, a tailored version of them is included in the solution process. Performance analysis, which was conducted using artificial input sets based on the real-life operations of the logistics firm, demonstrates that optimality gaps of less than 1% can be attained within reasonable times even for large-scale instances relevant to the industry, such as 120 customers, 8 product and 8 trailer types, 4 daily time windows, and 40 departure points. Full article

Peptides are an important class of biomolecules that perform many physiological functions and which occupy a significant and increasing share of the pharmaceutical market. Methods to determine the solid-state structures of peptides in different environments are important to help understand their biological functions and to aid the development of drug formulations. Here, a new magic-angle spinning (MAS) solid-state nuclear magnetic resonance (SSNMR) approach is described for the structural analysis of uniformly ¹³C-labeled solid peptides. Double-quantum (DQ) coherence between selective pairs of ¹³C nuclei in peptide backbone and side-chain CH₃ groups is excited to provide restraints on (i) ¹³C–¹³C internuclear distances and (ii) the relative orientations of C–H bonds. DQ coherence is selected by adjusting the MAS frequency to the difference in the resonance frequencies of selected nuclear pairs (the rotational resonance condition), which reintroduces the dipolar coupling between the nuclei. Interatomic distances are then measured using a constant time SSNMR experiment to eliminate uncertainties arising from relaxation effects. Further, the relative orientations of C–H bond vectors are determined using a DQ heteronuclear local field SSNMR experiment, employing ¹³C–¹H coupling amplification to increase sensitivity. These methods are applied to determine the molecular conformation of a uniformly ¹³C-labeled peptide, N-formyl-l-methionyl-l-leucyl-l-phenylalanine (fMLF). From just six distance and six angular restraints, two possible molecular conformations are determined, one of which is in excellent agreement with the crystal structure of a closely related peptide. The method is envisaged to a useful addition to the SSNMR repertoire for the solid-state structure determination of peptides in a variety of forms, including amyloid fibrils and pharmaceutical formulations. Full article