Search Results (19,294)

Background: Sepsis has heterogeneous etiologies. Although bacteria are the most common causative agents, viral and yeast forms of sepsis also occur. Procalcitonin (PCT) is widely used to monitor severe bacterial infections and may support the differential diagnosis of infection etiology. Methods: We evaluated the diagnostic value of PCT in viral sepsis using PCT levels at ICU admission and PCT kinetics during ICU treatment. Results: During the COVID-19 pandemic, 191 adult ICU patients with sepsis and a positive SARS-CoV-2 PCR test at hospital admission were included and classified into two groups according to the presence or absence of bacterial or yeast co-infection. PCT showed a distinct diagnostic pattern influenced by the presence of co-infection. PCT remained low in isolated viral sepsis, whereas elevated concentrations were associated with superimposed bacterial or yeast co-infection and worse clinical outcomes. Overall mortality was significantly lower in patients with isolated viral sepsis compared to those with co-infection (50 vs. 69%, p = 0.009). Conclusions: In viral sepsis, persistently low PCT concentrations argue against bacterial co-infection, whereas elevated or rising values should prompt increased diagnostic evaluation. Although PCT provides clinically relevant diagnostic information, it must be interpreted cautiously and in conjunction with clinical assessment and microbiological data. PCT should serve as an adjunctive, not a standalone, marker of infection etiology in sepsis. Full article

This study scientifically assessed the safety of the Ming Dynasty official-style timber structure of Taiyuan Chongshan Temple’s Great Mercy Hall, a nationally protected cultural relic. An integrated framework was adopted, including form restoration via 3D laser scanning and manual surveying, damage detection using impedance meters, stress wave tomography and one-dimensional stress wave testing, mechanical analysis with a differentiated material finite element model, and short-term on-site monitoring at risk points. Results showed that the 303.3 mm construction ruler length was restored, with the column grid tilting northwestward; the main structure was hardwood pine, and critical columns had severe localized damage (24% internal damage rate, 13% cross-sectional damage ratio) with 42% residual strength in some members; and the structure remained elastically safe, with material degradation causing 6.3–13.3% linear displacement amplification. Two weak links (eave purlin deflection: 33–37 mm; double-eave golden column axial force concentration: 86.9–88.5 kN) and dougong’s outward inclination due to eccentric compression were identified. Short-term monitoring indicated temperature-driven elastic responses and an 8 mm cumulative residual displacement in the northern single-step beam, and a three-level early warning threshold system was proposed. This study clarified the hall’s state as “overall stable with localized weaknesses”, providing a methodological reference for the preventive protection of similar ancient timber structures. Full article

Background: Physical activity (PA), physical fitness (PF), and motor skills (MS) play crucial roles in overall health and well-being, particularly in early childhood, when habits that affect future health are formed. Methods: This study, involving 299 children (150 boys, 149 girls, mean age 6.9 ± 0.96 years), explored the variance explained by external factors such as socioeconomic status (SES), body composition (BC), sex, and geographical location on motor-related physical fitness (MRPF) and health-related physical fitness (HRPF) in children. Using a variety of assessments, including demographics, anthropometric data, BIA, ActiGraphs, the 20 m shuttle run, 10 and 20 m speed tests, and test items from the Körperkoordinations test für Kinder (KTK) and the TGMD-2, a multiple stepwise regression analysis using SPSS (v 28.0) identified the associated factors. Results: The variables tested show modest explained variance for HRPF, MRPF, and MS, with the largest cumulative explained variance of 26.4%. The explained variances for MRPF and MS were lower (medium to small) than the significant, medium-to-large, explained variances for HRPF. Body fat percentage (BF%), moderate-to-vigorous physical activity (MVPA), parental education and income, and BMI emerged as substantial contributors to HRPF, explaining 12.1% to 26.4% of the variance. Sex, BF%, and quintile status were the most influential associated factors for MRPF, and for MS, BMI and sex emerged as the strongest contributors. Conclusions: These findings underscore the importance of holistic approaches that consider individual factors, such as MVPA, body composition (BC), PA levels, sex, and broader social and economic contexts, to promote children’s well-being. The study emphasises the need for comprehensive strategies to address the multifaceted associations with children’s physical development. Full article

Domestic violence against women is a severe problem in Brazil and worldwide. The Brazilian system presents innovative policies aimed at women’s access to justice, which have not yet been systematized or empirically examined. This article aims to identify, analyze, and categorize the main policies for access to justice for victims of domestic violence in Brazil. Methods used are documentary analysis and data collection were conducted. Data were requested from public institutions and collected from 27 state governments and the federal government. As results, the research found that, in addition to access to justice policies commonly adopted worldwide—such as emergency protective measures, risk assessment forms, call centers for women, and panic buttons, Brazil has implemented several innovations, particularly: (i) multidisciplinary care centers, (ii) specialized police stations, (iii) shelters and rental assistance, (iv) employment quotas, (v) special pensions for orphans, and (vi) aesthetic repair. In conclusion: (i) the ten public policies are effective and have benefited millions of women in Brazil; (ii) there is a need to coordinate these policies, assess their effectiveness, and consolidate data; (iii) new technologies offer opportunities to develop tools that facilitate access to justice and prevent and redress domestic violence; and (iv) these policies may be adopted by other countries, with appropriate contextual adaptations. Full article

Vanilla planifolia, a high-value tropical orchid, is significantly impacted by viral pathogens that threaten its cultivation and productivity. This study employs metagenomic techniques to detect and characterize the viral communities associated with V. planifolia in South Florida. Using high-throughput RNA sequencing, the Cymbidium mosaic virus (CymMV) and Vanilla latent virus (VLV) were prevalent in the plant system, with CymMV being the dominant viral species. Phylogenetic analysis of the CymMV coat protein gene revealed notable genetic divergence in the Homestead isolate, forming a distinct clade from global reference strains, suggesting local adaptation or host-specific evolution. Viral distribution across the plant system revealed higher viral loads in stem tissue, consistent with their role in systemic transport, whereas leaves exhibited greater viral richness, likely due to increased environmental exposure. The low abundance of other viral species, including Garlic viruses and Senna severe yellow mosaic virus, suggests that V. planifolia may harbor a broader range of viral taxa than previously characterized, though these findings represent a preliminary survey of symptomatic plants rather than a comprehensive regional assessment. This study underscores the value of metagenomic approaches for uncovering both well-characterized and novel viruses in plant systems and highlights the need for expanded sampling and continuous viral surveillance to guide disease management strategies in economically important crops such as vanilla. Full article

Credit risk assessment under severe class imbalance requires both structured imbalance correction and principled decision rules, yet most studies treat these as independent steps. This study develops a general integrated three-layer framework for credit risk assessment under class imbalance. The first layer introduces Sequential Hybrid Data Oversampling (SHDO), which sequentially applies five complementary oversampling techniques to enrich minority-class representation in mixed-type credit data. The second layer formulates credit approval as a decision-theoretic optimisation problem: a closed-form optimal threshold is derived under asymmetric costs, extended to constrained portfolios via a Lagrangian formulation with Karush–Kuhn–Tucker conditions, and further extended to minimax-robust decision making under estimation uncertainty. The third layer compares eleven classifiers under a unified evaluation protocol with an ablation isolating the effect of SHDO. The framework is empirically validated on the Home Credit Default Risk dataset, which is used as an illustrative case study rather than defining the scope of the contribution. On the held-out test set, XGBoost trained with SHDO achieves the highest minority-class F1 (0.254), while gradient-boosted models collectively attain ROC-AUC values of 0.713–0.750, outperforming classical baselines (0.540–0.620). The ablation confirms that without SHDO, all models exhibit near-zero minority-class recall despite adequate ranking ability. SHAP analysis on XGBoost confirms that the learned risk structure aligns with established creditworthiness determinants. The decision framework converts these probability estimates into analytically justified approval thresholds responsive to economic parameters, institutional constraints, and estimation uncertainty. Full article

Background: This study aimed to estimate the prevalence and associated demographic factors of autism spectrum disorder (ASD) in children aged 3 to 6 years in Bahrain, as well as to identify co-occurring developmental disorders. Methodology: The study sample comprised 500 children who attended eight health centers across four governorates (Group A) in Bahrain. A second group (Group B) consisted of all children who completed their diagnosis at the Child and Adolescent Psychiatric Unit for ASD from June 2023 to May 2024 to identify associated developmental disorders (n = 232). Group A mothers were interviewed using the M-CHAT-R. For Group B, we used children’s files, the General Intelligence Scale (Stanford-Binet), the M-CHAT-R, the CARS, Conners’ Form, and the Zarit Burden Interview to assess family burden. Additionally, a file review was conducted to determine the presence of intellectual disability (ID) in Group B cases. The Conner-3 Short Forms Test was administered to all cases aged 6 years or older (n = 64), and family burden was assessed using the Zarit Burden Interview Scale for reachable cases (n = 176). Results: Group A findings indicated an overall screen-based/at risk prevalence of ASD of 2.6%, with a higher prevalence in males (1.6%) compared to females (1%). In Group B, there was a78% prevalence of ID and a 17.2% prevalence of attention-deficit/hyperactivity disorder (ADHD). The study also found that the impact of having a child with ASD on the family varied based on the nature and severity of the disorder or disability, with moderate to severe burden reported at approximately 38%. Conclusions: The prevalence of ASD among young children was notably high, particularly among males. The most common comorbidities were ID followed by ADHD. The family burden associated with ASD was significant, with more than one third reporting moderate to severe burden. These data are essential for informing health education and social service planning. Full article

Large imaging and spectroscopic surveys now produce heterogeneous data at a scale that challenges supervised approaches which depend on scarce labels and task-specific retraining. In this paper, we conduct a systematic evaluation and analysis of AstroCLIP, a cross-modal self-supervised model that aligns galaxy images and optical spectra within a shared embedding space. Our overarching aim is to extend the released benchmark with a more fine-grained assessment of robustness and embedding behaviour. Using the released DESI and DESI Legacy Imaging Survey evaluation suite, we first reproduce the main downstream galaxy-property regression results and then extend the evaluation in two novel ways: (i) by stratifying predictive performance across a neighbour-count proxy for local environment density, and (ii) by comparing the suite’s observational categories labelled Low-z (Bright) and High-z (Faint). We further inspect the embedding space using UMAP and unsupervised clustering, and quantify cluster–property agreement using the adjusted mutual information (AMI). Across tasks, spectral embeddings consistently outperform image embeddings; for example, zero-shot prediction reaches $R^2=0.87$ for $log\left(M_{*}\right)$ and $R^2=0.63$ for $log\left(\mathrm{sSFR}\right)$. Under our environment proxy, moderate-density bins often yield the strongest predictive performance, while very sparse or crowded bins tend to underperform. Image-based predictions benefit substantially from the Low-z (Bright) subset, whereas spectral embeddings are more stable across the observational split. At the same time, UMAP and clustering reveal only weak discrete separation by individual physical properties, so the results are most consistent with useful information being encoded in a largely continuous rather than sharply clustered form. Full article

This paper surveys symbolic encoding procedures that can be successfully utilized in various entropy-based applications. The existing studies indicate several important advantages of the symbolic time series analysis (STSA) in finance and economics, specifically in assessing informational content of financial time series. Data symbolization comprises the conversion of a data series of many different possible values into a symbol series of only a few fixed values. The STSA procedures allow for capturing dynamic time-varying patterns of successive values in financial time series. Discretization techniques can reduce the noise and effectively filter the data. Particularly, they are robust to outliers. Moreover, symbolic encoding of information forms the basis for the Shannon’s mathematical theory of communication and the seminal concept of information entropy. Full article

Background/Objectives: The quality of intraoral scanner-derived digital models depends not only on deviation-based accuracy, but also on how scanned surfaces are reconstructed into a polygonal mesh. The aim of this prospective within-subject observational study was to evaluate whether tooth morphology influences local mesh density distribution in intraoral scanner-derived STL models of selected maxillary teeth. Methods: Twenty participants underwent maxillary intraoral scanning using a Medit i900 wired intraoral scanner under standardized clinical conditions. For each participant, the buccal surfaces of the maxillary right central incisor (11), canine (13), first premolar (15), and first molar (16) were selected as regions of interest. Surface area (A), number of vertices (V), and number of faces (F) were recorded, and the surface-normalized mesh density parameters vertices per unit area (V/A) and faces per unit area (F/A) were calculated. Comparisons among tooth types were performed using repeated-measures analysis of variance (ANOVA) with Bonferroni post hoc correction. Results: Significant differences were found among tooth types for both V/A and F/A (p < 0.001). Mean V/A values were 18.2 ± 1.9 for tooth 11, 19.8 ± 1.4 for tooth 13, 23.8 ± 1.7 for tooth 15, and 22.9 ± 2.0 vertices/mm² for tooth 16. Mean F/A values were 34.3 ± 3.6, 37.5 ± 2.7, 44.4 ± 3.3, and 42.9 ± 3.8 faces/mm², respectively. Post hoc comparisons showed significant differences between teeth 11 and 13, 11 and 15, 11 and 16, 13 and 15, and 13 and 16, whereas no significant difference was observed between teeth 15 and 16. Conclusions: Tooth morphology significantly influenced local mesh density distribution in intraoral scanner-derived STL models of selected maxillary teeth. These findings suggest that local anatomical form affects STL mesh reconstruction under standardized in vivo scanning conditions and support local mesh density analysis as a useful complementary approach to conventional deviation-based digital assessment. Full article

Self-contained local forecasting of wind and solar series can improve operational planning of wind farms and photovoltaic (PV) plant day-cycles in addition to numerical models, which are mostly behind time due to high simulation costs. Unstable electricity production requires balancing the availability of renewable energy (RE) with unpredictable user consumption to achieve effective usage. Artificial intelligence (AI) predictive modelling can minimise the intermittent uncertainty in wind and solar resources by trying to eliminate specific problems in RE-detached system reliability and optimal utilisation. The proposed 24 h day-training and prediction scheme comprises the starting detection and the following similarity re-assessment of sampling day-series intervals. Two-point professional weather stations record standard meteorological variables, of which the most relevant are selected as optimal model inputs. Automatic two-layer altitude observation captures key relationships between hill- and lowland-level data, which comply with pattern progress. New biologically inspired differential learning (DfL) is designed and developed to integrate adaptive neurocomputing (evolving node tree components) with customised numerical procedures of operator calculus (OC) based on derivative transforms. DfL enables the representation of uncertain dynamics related to local weather patterns. Angular and frequency data (wind azimuth, temperature, irradiation) are processed together with the amplitudes to solve simple 2-variable partial differential equations (PDEs) in binomial nodes. Differentiated data provide the fruitful information necessary to model upcoming changes in mid-term day horizons. Additional PDE components in periodic form improve the modelling of hidden complex patterns in cycle data. The DfL efficiency was proved in statistical experiments, compared to a variety of elaborated AI techniques, enhanced by selective difference input preprocessing. Successful LSTM-deep and stochastic tree learning shows little inferior model performances, notably in day-ahead estimation of chaotic 24 h wind series, and slightly better approximation of alterative 8 h solar cycles. Free parametric C++ software with the applied archive data is available for additional comparative and reproducible experiments. Full article

In punch-bending of thin stainless steel components, the final product angle is significantly influenced by springback after process completion. In the present study, an attempt has been made to investigate the effect of wire straightening strategies and bending tool parameters on the springback behavior of cold-rolled EN 1.4310 flat wire (3.9 mm × 0.4 mm). The wire, supplied in coiled form with inherent residual stresses, was subjected to single-pass, two-pass, and four-pass three-roller straightening prior to bending. Tensile characterization was performed to examine changes in elastic-plastic flow stress behavior, and the experimentally obtained true stress–true plastic strain data were directly implemented in finite element simulations. A full factorial parametric study was conducted by varying the bending angle (66–90°), bending radius (1.33 mm–2.93 mm), and die-punch clearance conditions. Experimental investigations on a subset process parameters were performed to assess the simulation outcomes. It was observed that the straightening history modifies the springback magnitude. Among the parameters considered, the bending radius was found to be the dominant factor governing springback sensitivity. An increase in the clearance gap between the die and punch produces systematic and monotonic shifts in the final product angle due to an increase in the effective bending radius. Full article

Background/Objectives: Dynamic movement orthoses (DMOs) are elasto-compressive bodysuits used in the rehabilitation of children with motor disabilities, mainly in children with cerebral palsy. Among the DMOs, the FLEXA^® represents one of the most frequently used orthoses in clinical practice due to its adaptability and flexibility. The purpose of this case study is to describe the application of FLEXA^® in an 18-month-old female child with choreic cerebral palsy. Methods: To evaluate the effect of the dynamic movement orthosis (FLEXA^®), the Movement Disorder-Childhood Rating Scale (MD-CRS) 0–3 years was administered. The child was evaluated before the use of the FLEXA^® bodysuit, and with the bodysuit donned approximately 30 min after its application. A follow-up using the same assessments was carried out at 6 months. Results: The results showed an important change in the severity of the movements according to the MD-CRS; mainly, the child’s movement disorder severity changed from a grade five severity (profoundly affected) without the bodysuit to grade three (moderately affected) with the use of the bodysuit. The benefit was maintained during follow-up even without the bodysuit, and a further improvement was observed in assessments with the bodysuit. Conclusions: This case report highlights the potential benefits of dynamic movement orthosis, like the FLEXA^®, in managing movement disorders in children with choreic form of cerebral palsy. Extending the study to a larger sample would help to strengthen the validity of these findings and confirm the beneficial effects of use of DMOs for children with movement disorders. Full article

Ankylosing spondylitis (AS) is a chronic immune-mediated inflammatory disease affecting the axial skeleton, characterized by progressive structural damage and functional impairment. Although biologic therapies targeting tumor necrosis factor and interleukin-17 have improved clinical outcomes, a substantial proportion of patients fail to achieve sustained disease control. Emerging evidence suggests that metabolic alterations may contribute to AS pathogenesis; however, systematic characterization of metabolism-related biomarkers and their regulatory networks remains limited, and the interplay between metabolic dysfunction and immune dysregulation in AS is poorly understood. Two whole-blood GEO datasets (GSE25101, GSE73754; n = 104) were integrated as the primary analytical cohort. A third dataset (GSE11886, n = 18; monocyte-derived macrophages) was included for exploratory cross-tissue analysis. Differential expression analysis identified 847 DEGs, which were refined to 16 metabolism-related genes through weighted gene co-expression network analysis (WGCNA) and GeneCards database filtering. Eleven machine learning algorithms with 5-fold cross-validation were applied to construct diagnostic models and identify hub genes. Validation analyses included immune cell infiltration estimation using CIBERSORT, metabolic pathway activity assessment via ssGSEA, single-cell transcriptomics from GSE268839, functional enrichment through GSEA/GSVA, and chromosomal localization analysis. A competing endogenous RNA (ceRNA) regulatory network was constructed to map post-transcriptional regulation. Natural compounds from 66 AS-treating traditional Chinese medicines were screened against hub genes using deep learning-based binding prediction. Multiple machine learning algorithms achieved comparable cross-validated performance (CV AUC range 0.741–0.836; top five models: 0.805–0.836) using the six hub genes (MFN2, SLC27A3, RHOB, SMG7, AKR1B1, LCOR) identified through SHAP-based feature importance analysis of the PLS model. Leave-one-dataset-out validation between the two whole-blood cohorts showed that all algorithms exceeded an AUC of 0.77 in Round 1 (validate: GSE73754, n = 72; best AUC 0.861), while Round 2 (validate: GSE25101, n = 32) yielded more modest performance (best AUC, 0.715) reflecting the smaller validation sample. Exploratory application to GSE11886 (macrophage-derived samples) showed near-chance performance, consistent with the tissue-source discrepancy. AS patients exhibited significant downregulation of oxidative phosphorylation, TCA cycle, and glycolysis pathways (p < 0.01), accompanied by elevated glutathione metabolism (p < 0.001). Immune cell deconvolution revealed reduced CD8+ T cell proportions correlating with MFN2 downregulation, and increased neutrophil frequencies correlating with SLC27A3 upregulation. Exploratory single-cell analysis indicated that RHOB expression was relatively enriched in border-associated macrophages and fibroblasts, while AKR1B1 was more prominently expressed in vascular endothelial cells and plasmacytoid dendritic cells. The ceRNA network identified 21 miRNAs and 65 lncRNAs forming 86 regulatory interactions, with four key regulatory axes (SATB1-AS1/miR-539-5p/LCOR, FAM95B1/miR-223-3p/RHOB, LINC01106/miR-106a-5p/MFN2, AATBC/miR-185-5p/SMG7) predicted to regulate hub gene expression. Compound screening identified betaine, pyruvic acid, citric acid, etc., as top-ranking candidates, with MFN2 showing the highest binding capacity among hub genes. This study provides an integrative framework linking metabolic reprogramming with immune dysfunction in AS. The six-gene diagnostic signature showed preliminary discriminatory ability in the available datasets, while the ceRNA regulatory network and natural compound screening results prioritize candidate regulatory pathways and compounds for future validation. These findings advance our understanding of AS pathogenesis and may guide future biomarker development and targeted intervention strategies. Full article

Due to their availability and environmental friendliness, alginate polymers are widely used in pharmaceuticals and cosmetics. The most common type of alginate is derived from seaweed and is used to develop topical dosage forms, among other things. However, variability in the seaweed material can lead to instability in the physicochemical parameters. Biotechnologically produced alginate minimizes this drawback through controlled synthesis. However, unlike algal alginates, the safety profile of such polymers has not been well studied. When developing dosage forms intended for wound surfaces, safety is of primary importance. In this study, we developed enzybiotic compositions based on bacterial alginate as an excipient and a novel recombinant modified endolysin, LysSi3-LK, as an antibacterial agent, and assessed their antibacterial properties and safety profile. The study included an in vitro evaluation of the activity spectrum, as well as the cytotoxicity and biocompatibility, of gel and hydrogel compositions. It was demonstrated that bacterial alginate is acceptable for the encapsulation of endolysin. It exhibited medium cytotoxic effects on the HaCaT cells, which were significantly reduced by the LysSi3-LK addition. The migration of cells was diminished following exposure to the gel and hydrogel formulations. However, an improvement in biocompatibility was observed in the cell proliferation assay. Full article