Search Results (2,845)

Background/Objectives: It is of the utmost importance to study environmental bacteria, as these microorganisms remain poorly characterized regarding their diversity, antimicrobial resistance, and impact on the global ecosystem. This knowledge gap is particularly pronounced for marine bacteria. In this study, we aimed to isolate bacteria from different marine samples and to gain insights into the environmental bacterial resistome, an aspect that remains largely neglected. Methods: Bacteria were isolated from several marine sources using two different culture media, and their identification was based on 16S rRNA gene analysis. Whole-genome sequencing was performed for selected isolates belonging to novel taxa. Antimicrobial susceptibility to seven antibiotics was evaluated using the disk diffusion method. Results: A total of 171 bacterial isolates belonging to the phyla Pseudomonadota, Bacteroidota, Planctomycetota, Actinomycetota, and Bacillota were obtained from diverse marine samples. The most abundant group belonged to the class Alphaproteobacteria. Thirty isolates represented novel taxa, comprising 16 new species and one new genus. Despite the challenges associated with determining antibiotic resistance profiles in environmental bacteria, only one isolate (1.8%) was pan-susceptible, whereas 54 (98.2%) showed resistance to at least one of the tested antibiotics. Moreover, 33 isolates exhibited a multidrug-resistant phenotype. Genome analysis of four novel taxa revealed the presence of an incomplete AdeFGH efflux pump. Conclusions: This study highlights the high bacterial diversity in marine environments, the striking prevalence of antibiotic resistance, and the major methodological challenges in studying environmental bacteria. Importantly, it emphasizes the relevance of culturomics-based approaches for uncovering hidden microbial diversity and characterizing environmental resistomes. Full article

Objective: This paper describes the development of a speech intelligibility test in Swiss German dialects, designed for children aged four to nine who are not yet familiar with standard German. Method: Suitable monosyllabic words and trochees in different Swiss German dialects were compiled, illustrated, and evaluated. Picture-pointing test procedures appropriate for children were developed. The selected test words and the pictures representing them were evaluated in a preliminary trial with forty-six normal-hearing children between two and nine years of age. Results: A set of 60 monosyllabic words and 40 trochees was recorded in four different Swiss German dialects as well as in standard German, resulting in a total of 500 recordings. Drawings were created to illustrate each word and found to be appropriate for children aged four years old or older. A non-adaptive and an adaptive test procedure using a weighted up–down method to measure speech reception thresholds in quiet and in noise were developed. Conclusions: A novel test to determine speech intelligibility in children in four different Swiss dialects was developed and evaluated in a pilot study. A validation study with more participants was designed to evaluate the test material and procedures. Full article

Background/Objectives: Sepsis detection remains challenging due to clinical heterogeneity and limitations of traditional scoring systems. This study developed and validated a hospital-wide machine learning model for sepsis detection using retrospectively developed data from prospectively expert-validated cases, aiming to improve diagnostic accuracy beyond conventional approaches. Methods: This retrospective cohort study analysed 218,715 hospital episodes (2014–2018) at a tertiary care centre. Sepsis cases (n = 11,864, 5.42%) were prospectively validated in real-time by a Multidisciplinary Sepsis Unit using modified Sepsis-2 criteria with organ dysfunction. The model integrated structured data (26.95%) and unstructured clinical notes (73.04%) extracted via natural language processing from 2829 variables, selecting 230 relevant predictors. Thirty models including random forests, support vector machines, neural networks, and gradient boosting were developed and evaluated. The dataset was randomly split (5/7 training, 2/7 testing) with preserved patient-level independence. Results: The BiAlert Sepsis model (random forest + Sepsis-2 ensemble) achieved an AUC-ROC of 0.95, sensitivity of 0.93, and specificity of 0.84, significantly outperforming traditional approaches. Compared to the best rule-based method (Sepsis-2 + qSOFA, AUC-ROC 0.90), BiAlert reduced false positives by 39.6% (13.10% vs. 21.70%, p < 0.01). Novel predictors included eosinopenia and hypoalbuminemia, while traditional variables (MAP, GCS, platelets) showed minimal univariate association. The model received European Medicines Agency approval as a medical device in June 2024. Conclusions: This hospital-wide machine learning model, trained on prospectively expert-validated cases and integrating extensive NLP-derived features, demonstrates superior sepsis detection performance compared to conventional scoring systems. External validation and prospective clinical impact studies are needed before widespread implementation. Full article

Background: Trypanosoma cruzi, the causative agent of Chagas disease, remains a major public health concern, and there is a continued need for new antitrypanosomal agents. Thiosemicarbazone (TSC) derivatives have emerged as a promising class of compounds with potential antiparasitic activity. Objectives: This study aimed to report the synthesis, characterization, and biological profiling of a novel series of thiosemicarbazone derivatives as antitrypanosomal agents against Trypanosoma cruzi. Methods: Fourteen new compounds and six previously described analogues were prepared and characterized by ¹H/¹³C nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). As a preliminary in vitro screen, activity was assessed by direct parasite counting in epimastigote and bloodstream trypomastigote forms, as tractable models of replicative and infective stages sharing core metabolic targets with intracellular amastigotes. Epimastigote potency was quantified as half-maximal effective concentrations (EC₅₀) derived from dose–response curves, whereas trypomastigote response was evaluated as percent viability after treatment at a fixed concentration of 20 µM. Mechanistic profiling included inhibition assays against the cysteine protease cruzipain (CZP) and selected redox defense enzymes, complemented by in silico similarity clustering and binding-pose affinity scoring. Results: A nitro-methoxy-substituted TSC showed potent CZP inhibition but limited trypomastigote efficacy, whereas brominated analogues displayed dual-stage activity independent of CZP inhibition. Tanimoto similarity analysis identified distinct structure–activity clusters, linking nitro-methoxy substitution to epimastigote selectivity and brominated scaffolds to broader antiparasitic profiles, with hydrophobicity and steric complementarity as key determinants. Enzymatic assays revealed no significant inhibition of cytosolic tryparedoxin peroxidase (cTXNPx) or glutathione peroxidase type I (TcGPx-I), suggesting redox disruption is not a primary mode of action. In vitro and in silico analyses showed low or no non-specific cytotoxicity under the tested conditions, supporting further optimization of these derivatives as antitrypanosomal preliminary hits. Key hits included derivative 3e (epimastigote EC₅₀ = 0.36 ± 0.02 µM) and brominated analogues 2c and 2e (epimastigote EC₅₀ = 3.92 ± 0.13 and 4.36 ± 0.10 µM, respectively), while docking supported favorable binding-pose affinity (e.g., ΔG_S-pose = −20.78 ± 2.47 kcal/mol for 3e). Conclusions: These results support further optimization of the identified thiosemicarbazone derivatives as preliminary antitrypanosomal hits and provide insight into structure–activity relationships and potential mechanisms of action. Full article

Electroencephalography (EEG) provides valuable information about a subject’s mental state; however, developing reliable classification models remains challenging. One major difficulty lies in defining an effective feature representation, as the wide range of features proposed in the literature often leads to high-dimensional inputs, increasing the risk of overfitting, reducing generalization, and raising computational cost. A further critical challenge is the strong inter-subject variability inherent to EEG data, where distributional shifts frequently cause models trained on one individual to perform poorly on unseen subjects. This work proposes a novel family of correlation-based feature selection methods that explicitly models inter-feature relationships through correlation structures. The objective is to identify features that are simultaneously discriminative across mental states (relaxed and concentrated) and invariant across subjects, thereby improving cross-subject generalization. The proposed methods are evaluated against established feature selection and dimensionality reduction techniques using a leave-one-subject-out experimental protocol, in which models are trained on multiple participants and tested on unseen individuals. Experimental results demonstrate that the proposed approach consistently achieves superior or competitive performance compared to existing methods, particularly under strong inter-subject distribution shifts. In addition, the analysis reveals how preprocessing parameters—such as window length, overlap, and frequency band decomposition—affect classification performance and generalization. Unlike previous EEG feature selection approaches that primarily focus on feature relevance or redundancy, the proposed framework explicitly promotes domain invariance while preserving feature interpretability, without relying on subject-specific calibration. Full article

Background/Objectives: Pacing-induced cardiomyopathy (PiCM) is a recognized complication of chronic right ventricular pacing (RVP), characterized by left ventricular (LV) dysfunction, adverse remodeling, and progression to heart failure. MicroRNAs (miRs) regulate gene expression and play an important role in ventricular remodeling. This study aimed to observe whether dynamic changes in miRs according to a novel peripheral blood mononuclear cell (PBMC)-based approach could serve as early predictive biomarkers of PiCM. Methods: A prospective, single-center cohort study was conducted in adult patients undergoing pacemaker implantation. Clinical characteristics, echocardiographic parameters and expression levels of miR-208b-3p and miR-9 were assessed immediately and 3 months post-pacemaker implantation. PiCM was defined as a ≥10% reduction in LVEF at one year, with no alternative cause. Statistical analyses included correlation testing, ROC curve analysis, and multivariate regression to identify factors associated with PiCM. Results: Among 126 patients, 11.1% developed PiCM. Compared with the non-PiCM group, those who developed PiCM exhibited more pronounced 3-month changes in miR-208b-3p (median Δ₃log miR: +1.3 vs. −0.4, p = 0.013) and miR-9 (median Δ₃log miR: −1.7 vs. +0.21, p = 0.011). In multivariate analyses, Δ₃LV-GLS, Δ₃logmiR-208b-3p, and Δ₃logmiR-9 were associated with a higher likelihood of PiCM. Among PiCM patients, Δ₃logmiR-208b-3p correlated inversely with Δ₃LV-GLS (r = −0.73, p = 0.016), while Δ₃logmiR-9 correlated positively (r = 0.88, p < 0.001). ROC analyses demonstrated good predictive ability for Δ₃LV-GLS (AUC = 0.924), Δ₃log miR-208b-3p (AUC = 0.783), and Δ₃log miR-9 (AUC = 0.835), with no significant differences between curves. Conclusions: Early LV-GLS deterioration and dynamic changes in expression of miR-208b-3p and miR-9 in PBMCs precede overt LV systolic dysfunction. These miRs may serve as early predictive biomarkers for PiCM. Full article

The development of upper-limb prostheses is often hindered by limited dexterity, a restricted workspace, and bulky designs, primarily due to performance limitations in proximal joints like the shoulder and elbow, which contribute to high user abandonment rates. To overcome these challenges, this paper presents a novel, bioinspired, and integrated prosthetic system as an advancement in bionic technology. The design incorporates a shoulder joint based on an asymmetric 3-RRR spherical parallel mechanism (SPM) with actuators embedded within the moving platform, and an elbow joint actuated by low-voltage Shape Memory Alloy (SMA) springs. The inverse kinematics of the shoulder mechanism was established, revealing the existence of up to eight configurations. We employed Multi-Objective Particle Swarm Optimization (MOPSO) to simultaneously maximize workspace coverage, enhance dexterity, and minimize joint torque. The optimized design achieves remarkable performance: (1) 85% coverage of the natural shoulder’s workspace; (2) a maximum von Mises stress of merely 3.4 MPa under a 40 N load, ensuring structural integrity; and (3) a sub-0.2 s response time for the SMA-driven elbow under low-voltage conditions (6 V) at a motion velocity of 6°/s. Both motion simulation and prototype testing validated smooth and anthropomorphic motion trajectories. This work provides a comprehensive framework for developing lightweight, high-performance prosthetic limbs, establishing a solid foundation for next-generation wearable robotics and bionic devices. Future research will focus on the integration of neural interfaces for intuitive control. Full article

Background/Objectives: The rates and predictors of clinical remission, a novel and practical therapeutic goal in severe asthma, have been inconsistently reported across studies. Data on clinical remission in Japanese patients remain limited. The aim of this study was to assess the rate of four-component clinical remission and its predictors in Japanese adult patients with severe asthma. Methods: This retrospective study enrolled adult patients with severe asthma who had initiated biologic therapy at least 12 months prior to inclusion at Nagoya City University Hospital. The primary endpoint was the achievement rate of four-component clinical remission, defined as (1) no maintenance oral corticosteroids (OCS); (2) no exacerbations for 12 months; (3) Asthma Control Test (ACT) score ≥ 20; and (4) forced expiratory volume in one second (FEV₁) ≥ 80% of predicted. The secondary endpoint was to identify factors, including airway structural indices measured using chest computed tomography (CT), associated with clinical remission at 12 months. Results: Among 87 patients with severe asthma, 26 (30%) achieved four-component clinical remission after 12 months of biologic therapy. In univariate analysis, clinical remission was more frequently achieved in patients with chronic rhinosinusitis, higher FEV₁ (% predicted), higher blood eosinophil counts, higher ACT scores, fewer exacerbations in the previous year, higher Lund–Mackay scores, and smaller airway wall thickness and luminal areas on CT (all p < 0.05). Multivariate analysis revealed that higher blood eosinophil counts and fewer exacerbations in the previous year were independently associated with clinical remission (both p < 0.05). Conclusions: After 12 months of biologic therapy, 30% of patients with severe asthma achieved four-component clinical remission. Higher blood eosinophil counts and fewer prior exacerbations were associated with higher remission rates. Full article

Wheel load is a critical information source reflecting the status of vehicle load distribution and motion. Yet, existing in-wheel motor products are primarily designed as propulsion units and inherently lack the load-sensing capabilities required by intelligent vehicles. To address this research gap, this paper presents a novel intelligent electric drive wheel (i-EDW) with an integrated transmission system and a load-sensing unit (LSU). The i-EDW adopts an Axial Flux Permanent Magnet Synchronous Motor (AFPMSM), while the integrated LSU ensures high-precision measurement of six-dimensional wheel forces and moments. According to this multi-axis force information, a real-time estimation and stability control method based on the tire–road friction circle concept is proposed. Instead of the complex decoupling and multi-objective optimization with the multi-actuator systems, this paper focuses on minimizing the tire load rate of i-EDWs, which significantly advances the state of the art in terms of calculation efficiency and respond speed. To validate this theoretical framework, a full-vehicle model equipped with four i-EDWs is developed. In the MATLAB R2022A/Simulink co-simulation environment, a virtual prototype is tested under typical driving scenarios, including the straight-line acceleration and double-moving-lane (DML) steering. The simulation results prove a reliable safety margin from the friction circle boundaries, laying a solid foundation for precise motion control and improved system robustness in future intelligent vehicles. Full article

Background: Population aging increases susceptibility to cognitive decline, anxiety, and metabolic dysregulation, yet safe and effective interventions remain limited. Saffron (Crocus sativus L.) has been traditionally used to enhance mood and cognition, and its main metabolites, crocins and safranal, exert neuroprotective, anxiolytic, and metabolic effects. However, variability in extract composition and frequent adulteration hinder reproducibility. Objectives: To clarify the efficacy of genuine saffron preparations in aging, we investigated a saffron extract standardized for safranal and crocin content (SSE). Methods: Safranal bioavailability was first characterized in rats, followed by an evaluation of behavioral, neuroendocrine, and metabolic outcomes after 35 days of oral SSE administration (25 or 200 mg/kg/day) in 25-month-old male C57BL/6 mice. Behavioral performance was assessed using open field and novel object recognition tests, while molecular analyses targeted neuropeptides in the hypothalamus and amygdala, hippocampal plasticity markers, cortical inflammatory proteins, and hepatic lipid metabolism genes. Results: SSE administration induced a rapid but transient increase in the plasma’s safranal, confirming its bioavailability. In aged mice, the low dose prevented age-related weight loss and modulated hepatic lipid metabolism, whereas the high dose reduced anxiety-like behavior and improved recognition memory. The anxiolytic effects are consistent with elevated hypothalamic Npy, an anxiolytic peptide, reduced amygdalar Crh, a key mediator of stress and anxiety, and decreased hypothalamic Hcrt, an arousal modulator. The improvement in memory is associated with modulation of the cortical and hippocampal inflammatory and endocannabinoid proteins involved in neural plasticity. Conclusions: These findings highlight content-standardized saffron extracts as a promising multi-target nutraceuticals for healthy aging. Full article

Background/Objectives: Falls represent a major health concern for stroke survivors, necessitating effective risk assessment tools. This study proposes the Instrumented Fall Risk Assessment (IFRA) scale, a novel screening tool derived from Instrumented Timed Up and Go (ITUG) test data, designed to capture mobility measures often missed by traditional scales. Methods: We employed a two-step machine learning approach to develop the IFRA scale: first, identifying predictive mobility features from ITUG data and, second, creating a stratification strategy to classify patients into low-, medium-, or high-fall-risk categories. This study included 142 participants, who were divided into training (including synthetic cases), validation, and testing sets (comprising 22 non-fallers and 10 fallers). IFRA’s performance was compared against traditional clinical scales (e.g., standard TUG and Mini-BESTest) using Fisher’s Exact test. Results: Machine learning analysis identified specific features as key predictors, namely vertical and medio-lateral acceleration, and angular velocity during walking and sit-to-walk transitions. IFRA demonstrated a statistically significant association with fall status (Fisher’s Exact test p = 0.004) and was the only scale to assign more than half of the actual fallers to the high-risk category, outperforming the comparative clinical scales in this dataset. Conclusions: This proof-of-concept study demonstrates IFRA’s potential as an automated, complementary approach for fall risk stratification in post-stroke patients. While IFRA shows promising discriminative capability, particularly for identifying high-risk individuals, these preliminary findings require validation in larger cohorts before clinical implementation. Full article

Background: Salmonella is a major zoonotic foodborne pathogen. Conventional poultry vaccines may present limitations in terms of efficacy, safety, and practicality. Objectives: This study focuses on enhancing the immunogenicity and improving the safety of a novel oral vaccination employing inducible toxin–antitoxin (TA) systems, which lead to self-destruction of virulent Salmonella Enteritidis. Methods: A Hok/Sok (HS) TA system was designed to induce cell death upon absence of arabinose. Point mutations were introduced to the Hok toxin promoter to moderate toxin production. A combination of HS and CeaB/CeiB (CC) TA systems was designed to induce cell death both in low di-cation levels or anaerobic conditions. Survival of Salmonella-carrying TA systems was tested in culture and in the Raw264.7 macrophage cell line. One-day old chicks were inoculated with Salmonella carrying the TA system to evaluate bacterial persistence and induction of a protective immune response. Results: Attenuation of the Hok toxin promoter prolonged bacterial survival in vitro. Salmonella carrying the combined TA systems was eliminated completely both in vitro and in inoculated chickens, eliciting high levels of antibodies and conferring protection against challenge with wild-type Salmonella. Conclusions: These findings highlight the potential of the adaptable TA-based vaccination platform to generate safe and efficacious Salmonella vaccines for poultry, contributing to reduced transmission in the food chain. Full article

Background/Objectives: Osteoporosis is a major global health concern, and early risk assessment plays a crucial role in fracture prevention. Although demographic, clinical, and lifestyle factors are commonly incorporated into screening tools, their relative importance within data-driven prediction frameworks can vary substantially across datasets. Rather than aiming to identify novel predictors, this study evaluates the stability and behavior of established osteoporosis risk factors using statistical inference and machine learning-based feature selection methods across heterogeneous data sources. We further examine whether simplified and near-minimal models can achieve predictive performances comparable to that of full-feature configurations. Methods: An open-access Kaggle dataset (n = 1958) and a retrospective clinical dataset from the University of Phayao Hospital (n = 176) were analyzed. Feature relevance was assessed using logistic regression, likelihood ratio testing, MRMR, ReliefF, and unified importance scoring. Multiple predictor configurations, ranging from full-feature to minimal and near-minimal models, were evaluated using decision tree, support vector machine, k-nearest neighbor, naïve Bayes, and efficient linear classifiers. External validation was performed using hospital-based records. Results: Across all analyses, age consistently emerged as the dominant predictor, followed by corticosteroid use, while other variables showed limited incremental predictive contributions. Simplified models based on age alone or age combined with medication-related variables achieved performances comparable to full-feature models (accuracy ≈91% and AUC ≈ 0.95). In addition, near-minimal models incorporating gender alongside age and medications demonstrated a favorable balance between discrimination and computational efficiency under external validation. Although overall performance declined under distributional shift, naïve Bayes and efficient linear classifiers showed the most stable external behavior (AUC = 0.728–0.787). Conclusions: These findings indicate that stability-driven feature selection primarily reproduces well-established epidemiological risk patterns rather than identifying novel predictors. Minimal and near-minimal models—including those incorporating gender—retain acceptable performances under external validation and are methodologically efficient. Given the limited size and single-center nature of the external cohort, the results should be interpreted as preliminary methodological evidence rather than definitive support for clinical screening deployment. Further multi-center studies are required to assess generalizability and clinical relevance. Full article

Current saw- and pin-type lint-cleaning systems used by the ginning industry have challenges retaining lint quality. The objective of the research was to test a novel pneumatic fractionator for the lint cleaning of an Upland cotton variety that was both saw- and roller-ginned. The process variables tested were initial lint moisture content in the range of 5.5–15% w.b., line pressure in the range of 276–552 kPa, and residence time in the range of 15–45 s. Experiments were conducted based on a central composite design. Models based on response surface methodology (RSM) were developed for final lint moisture, total trash extracted during lint cleaning, and High-Volume Instrument (HVI) fiber quality. The RSM models adequately described the pneumatic fractionation process, as indicated by the coefficient of determination, predicted vs. observed plots, and residual values. The results indicated that the interactions among initial lint moisture content, residence time, and line pressure significantly affected lint quality. At the optimized pneumatic fractionator process conditions, the predicted lint quality attributes were better for both roller- and saw-ginned lint compared to lint cleaned with saw- and pin-type lint cleaners. The upper half mean length increased by 1 mm, the uniformity index was higher by 0.5–1 percentage points, the strength was 1–2 g/tex higher, and the short fiber content was reduced by more than one percentage point. Color grades were better for pneumatic fractionated lint compared to saw- and pin-type lint cleaning methods. Lint value was approximately 4 cents/kg higher for both saw- and roller-ginned pneumatic fractionated lint, compared to lint cleaned using saw- and pin-type lint cleaners. The novel pneumatic fractionator, when compared to industry-standard saw- and pin-type lint cleaners, effectively cleaned lint while retaining fiber quality and removing most of the motes and trash. Full article

The vibration monitoring of scaled models in wind tunnels is critical for aerodynamic testing and structural safety. The abrupt onset of flutter or other aeroelastic instabilities poses a significant risk, necessitating the development of real-time, model-agnostic monitoring systems. This paper proposes a novel, generalized health indicator (HI) based on an improved Fisher Discriminant Analysis (FDA) framework for vibration state classification. The core innovation lies in reformulating the FDA objective function to distinguish between stable and dangerous vibration states, rather than tracking degradation trends. To ensure cross-model applicability, a frequency-wise standardization technique is introduced, normalizing spectral amplitudes based on the statistics of a model’s stable state. Furthermore, a dual-mode entropic regularization term is incorporated into the optimization process. This term balances the dispersion of weights across frequency bands (promoting generalizability and avoiding overfitting to specific frequencies) with the concentration of weights on the most informative resonance frequencies (enhancing the sensitivity to dangerous states). The optimal frequency weights are obtained by solving a regularized generalized eigenvalue problem, and the resulting HI is the weighted sum of the standardized frequency amplitudes. The method is validated using simulated spectral data and flight data from a wind tunnel test, demonstrating a superior performance in the early detection of dangerous vibrations and the clear interpretability of critical frequency bands. Comparisons with traditional sparse measures and machine-learning methods highlight the proposed method’s advantages in trendability, robustness, and unique capability for cross-model adaptation. Full article