Search Results (466)

Background/Objectives: Poor bone quality is strongly associated with adverse surgical events. Although dual-energy X-ray absorptiometry (DXA) remains the gold standard for bone mineral density (BMD) assessment, logistical barriers may limit its preoperative application. The Endplate Bone Quality (EBQ) score is an MRI-derived metric quantifying subchondral bone quality at the vertebral endplate with demonstrated predictive value for cage subsidence following lumbar interbody fusion. However, EBQ has been measured exclusively at the operative level in surgical cohorts. This study aimed to assess level-specific EBQ scores across the entire lumbar spine and compare distributions across age, sex and osteoporosis subgroups. Methods: A single-institution retrospective review of T1-weighted lumbar MRI studies from patients evaluated for lower back pain from 2020 to 2025 was performed. EBQ was independently scored by two blinded raters at each disc space from L1–L2 to L5–S1 using 3 mm endplate ROIs normalized to a CSF ROI at L3. Interrater reliability was assessed via ICC, Pearson correlation, and RMSE. Patients were stratified by age (≤60 vs. >60 years), sex, and osteoporosis status, and subgroup comparisons were performed for overall and level-specific EBQ score. Results: A total of 96 patients with an average age of 61.0 ± 9.42 years were included in this study. The majority of patients included were female (87.5%), and 18.8% had been diagnosed with osteoporosis. EBQ scores demonstrated a progressive caudal increase across all subgroups from L2–L3 to L5–S1. Overall interrater reliability was acceptable (ICC = 0.76), with level-specific ICCs ranging from 0.70 to 0.83. No significant differences were observed between age or sex subgroups. Osteoporotic patients demonstrated significantly higher EBQ at L1–L2, L2–L3, and overall (all p < 0.05), with no significant differences at L3–L4 through L5–S1. Conclusions: This study provides normative, level-specific EBQ reference data throughout all levels of the lumbar spine. The increase in EBQ scores seen among caudal levels and reduced osteoporotic discriminatory power support the importance of level-specific context when interpreting EBQ thresholds. These findings may support future studies evaluating threshold development for EBQ. Full article

During warm intervals such as the Early Eocene, megathermal vegetation belts expanded toward higher latitudes, displacing mesothermal and microthermal biota. Here, we examine the diversity and paleoclimate of the Early Eocene Ligorio Márquez Formation (LMF) in the context of other Paleogene Patagonian palynofloras, and we model the potential distribution of Nothofagus using Early Eocene climate simulations. From 35 processed samples, 20 yielded palynomorphs and 85 morphospecies were distinguished. We hypothesize that species richness in the LMF is comparable to other Eocene microfloras, and that climate models will confirm mesothermal conditions for this formation while identifying western Gondwana as the primary region of climatic suitability for Nothofagus. Our results indicate that the LMF hosted a diverse flora under mesothermal, humid-temperate conditions (Köppen–Geiger climate Cfa, within the broader Cf no-dry-season regime). Ecological niche modeling further indicates that western Gondwana (South America, the Antarctic Peninsula, New Zealand, and Australia) provided broadly suitable climatic conditions for Nothofagus. In Experiment 1 (modern-to-Eocene transfer), Maxnet models showed high discriminatory power (AUC_test = 0.86–0.88) with low omission at P10 (OR_P10 = 0.099–0.128). In Experiment 2 (Eocene-to-Eocene calibration), performance was consistently high across GCMs (AUC_test = 0.87–0.98; OR_P10 = 0.091–0.182). However, conditions across Antarctica were likely challenging, limiting its effectiveness as a dispersal corridor during the Eocene. Finally, our results suggest that the ancient South Pacific High influenced the northern distributional limit of Nothofagus in South America. Full article

Peripheral facial palsy (PFP) causes pronounced facial asymmetry and functional impairment, highlighting the need for reliable, objective assessment. This study presents a novel, fully automated, reference-free method for quantifying facial symmetry using artificial intelligence (AI)-based facial landmark detection. A total of 405 datasets from 198 PFP patients were analyzed, each including nine standardized facial expressions covering both resting and dynamic movements. AI detected 478 landmarks per image, from which 225 paired landmarks were used to compute local asymmetry angles. Systematic evaluation identified 91 highly informative landmark pairs, primarily around the eyes, nose and mouth, which simplified the analysis and enhanced discriminatory power, while also enabling region-specific assessment of asymmetry. Statistical evaluation included Kruskal–Wallis H-tests across clinical scores and Spearman correlations, showing moderate to strong associations (0.32–0.73, p < 0.001). The fully automated pipeline produced reproducible results and demonstrated robustness to head rotation. Intuitive full-face angle maps allowed direct assessment of asymmetry without a reference image. This AI-driven approach provides a robust, objective, and visually interpretable framework for clinical monitoring, severity classification, and treatment evaluation in PFP, combining quantitative precision with practical applicability. Full article

Background: Chronic liver disease (CLD) in children requires long-term monitoring. Liver biopsy and transient elastography (TE) are resource-intensive methods that require specialized equipment and trained personnel. Simple indirect fibrosis scores based on routine laboratory parameters offer a potentially cost-effective alternative but have not been systematically evaluated in pediatric populations with diverse CLD etiologies. Objectives: This study aimed to assess the performance of several indirect fibrosis and cirrhosis scores in predicting significant (≥F2) and advanced (≥F3) fibrosis and cirrhosis (F4) in children with CLD using TE as a comparator. Methods: We retrospectively reviewed medical records of children with CLD evaluated at a tertiary center between January 2023 and June 2025. TE results and routine laboratory data were used to calculate fibrosis scores, including APRI, FIB-4, FibroIndex, FORNS, GPR, GUCI, King’s score, and Lok’s index. ROC analyses were performed to assess each score’s ability to discriminate significant fibrosis, advanced fibrosis and cirrhosis. Optimal cut-offs were established using the Youden index. Results: GPR showed the strongest concordance with TE-based fibrosis classification across both fibrosis thresholds, achieving an AUROC of 0.835 for significant fibrosis and a superior 0.917 for advanced fibrosis. FibroIndex and APRI also demonstrated good discriminatory power for advanced disease. Utilizing mathematically optimized cut-offs, GPR (0.45) and APRI (0.84) achieved good negative predictive values (100% and 95%) and sensitivities (100% and 85%) for advanced fibrosis, establishing them as potentially valuable screening tools. For cirrhosis detection (F4), Lok’s Index performed best (AUROC 0.854). Conclusions: In this diverse pediatric cohort, simple indirect scores—particularly GPR, APRI, and FibroIndex—demonstrated the highest concordance relative to TE findings, with negative predictive values up to 100% for GPR. This indicates that they can serve as reliable first-line screening tools when TE is unavailable. While their good negative predictive values allow for the confident exclusion of severe disease—potentially sparing many children from invasive testing—their low positive predictive values limit their role in definitive diagnosis. The systematic failure of adult-derived, age-dependent formulas in this cohort underscores the critical need for specialized pediatric biomarkers. Full article

This study frames next-day ETF market behavior as a binary regime classification problem—distinguishing between “oscillating” days, on which intraday price movements remain within a defined threshold, and “trending” days, on which movements exceed that threshold. This framing is economically motivated: active traders employing Martingale-style strategies and ETF options traders require precisely this type of regime prediction to manage risk and time positions. Using 25 years of daily data (2000–2024) for four major ETFs—IWM (Russell 2000), SPY (S&P 500), QQQ (Nasdaq-100), and DIA (Dow Jones)—the study trains and evaluates Random Forest and Neural Network classifiers enriched with macroeconomic announcement indicators and technical features (VIX, RSI, ATR) under a rolling window cross-validation framework. Oscillation is defined as daily intraday price movements within thresholds of 0.5%, 0.75%, and 1%; movements exceeding these levels constitute trending behavior. At the 0.5% threshold—the most practically relevant given typical ETF transaction costs—Neural Networks outperform a naive classifier by 13.4% for IWM, 15.4% for SPY, 4.7% for QQQ, and 3.2% for DIA. AUC values exceed 0.5 in most configurations, with stronger discrimination observed for SPY (AUC up to 0.74) and IWM (AUC up to 0.59) than for QQQ and DIA at some thresholds. Results are stronger for some ETFs and thresholds than others, and cases where AUC approaches 0.5 are explicitly acknowledged as reflecting limited discriminatory power. Full article

Lentinula edodes (shiitake) is a globally significant edible mushroom, with China being its largest producer. Efficient breeding is fundamental to sustaining its industry, yet it is often hindered by the labor-intensive and time-consuming nature of traditional mating type identification methods. To develop a rapid genotyping tool, we elucidated the polymorphism of key mating-type genes and established a practical molecular marker system. In this study, we focused on four major cultivated L. edodes varieties in China (0912, 9608, L808, and W1). Whole-genome sequencing, assembly, and annotation revealed the allelic diversity of HD1 of the A mating type and rcb1 and rcb5 of the B mating type in monokaryotic strains of these varieties. Sequence alignment indicated that HD1 could be classified into five types, rcb1 into four types, and rcb5 into four types. Based on SNPs and InDels, co-dominant primers capable of distinguishing all allelic types of HD1, rcb1, and rcb5 were designed, generating clear molecular fingerprints for the 0912, 9608, L808, and W1 varieties. Notably, this system also demonstrated robust discriminatory power when applied to different Chinese varieties and international L. edodes varieties from Japan, Korea, Thailand, and Canada, confirming its reliability across diverse genetic backgrounds. This highly accurate and efficient marker system offers robust theoretical and technical support for parental selection, germplasm identification, and new variety protection for L. edodes, presenting significant potential to improve horticultural mushroom production efficiency. Full article

Water quality plays a central role in determining the environmental performance of pond-based tropical aquaculture systems. This study aimed to evaluate the relative environmental performance of different tropical pond-based aquaculture systems by identifying multivariate water quality patterns that allow their discrimination and comparison under commercial production conditions. Four pond-based production systems were evaluated: an aquaponic system (APS), a recirculating aquaculture system (RAS), a conventional earthen pond system (CEP), and an integrated rice–chame system (RCS). Fourteen physicochemical water quality variables were monitored throughout the production cycle under real commercial conditions using a comparative observational design. Multivariate discriminant analysis was applied to identify the variables with the highest discriminatory power and evaluate the ability of water quality patterns to correctly classify observations among production systems. The results revealed a clear multivariate separation between technologically intensive systems (APS and RAS) and less intensive and integrated systems (CEP and RCS), reflecting distinct water quality structures and environmental functioning. Variables associated with mineralization and nutrient dynamics, including electrical conductivity, dissolved solids, turbidity, phosphates, chlorides, dissolved oxygen, nitrites, and temperature, contributed most strongly to system discrimination. The discriminant functions achieved a high overall correct classification rate, demonstrating the robustness of the multivariate approach. These findings support the use of water quality variables as consistent environmental signatures for distinguishing tropical pond-based aquaculture systems, providing an operational framework for assessing their relative environmental performance. Discriminant analysis emerges as a valuable tool for system characterization and comparative evaluation, supporting environmentally informed management and optimization of chame aquaculture under tropical conditions. Although water quality represents a robust integrative indicator, it captures only one dimension of environmental performance, and additional factors such as production efficiency, energy use, and effluent characterization should be incorporated in future studies to achieve a comprehensive sustainability assessment. Full article

Background: Circulating cytokines and their soluble receptors in body fluids have been implicated in the pathogenesis of multiple sclerosis (MS). Alterations in serum levels of pro- and anti-inflammatory cytokines and/or their soluble receptors can dysregulate central nervous system (CNS) signaling pathways and, therefore, may serve as potential biomarkers for the diagnosis of MS. Therefore, the primary end-point of this study is to investigate the utility of various cytokines and their soluble receptors as diagnostic biomarkers in MS. The secondary outcome is also to assess whether these cytokines are useful in differentiating the severity of MS. Methods: In this case–control study, we compared a panel of pro-inflammatory interleukins (ILs), including IL18 and tumor necrosis factor-alpha (TNFα), soluble IL receptors (sIL7Rα and sIL2Rα), and insulin-like growth factor-1 (IGF-1) in 45 MS patients and in 45 healthy control individuals matched for sex and age. Associations of these biomarkers with age, disease severity (Expanded Disability Status Scale [EDSS]), disease duration, and age at first MS symptom onset were also assessed. Results: Serum levels of cytokines and soluble IL receptors were elevated in MS patients compared to healthy controls. IGF-1 was lower (p < 0.001) in the MS patients than in the healthy individuals. The serum level of IGF-1 was higher (p < 0.01) in the remitting-relapsing phase compared to the primary progression and secondary progression stages. Similarly, only IGF-1 was more elevated (p < 0.01) in the mild stage compared to the moderate stage based on the EDSS score. Receiver operating characteristic (ROC) curve analysis demonstrated that IL18 had excellent discriminatory power for the diagnosis of MS (p < 0.001), with an area under the curve (AUC) of 0.96 ± 0.017, followed by IGF-1 (p < 0.001), which showed strong diagnostic performance (AUC = 0.873 ± 0.037). Soluble (s) IL2Rα exhibited fair diagnostic accuracy (p < 0.001; AUC = 0.717 ± 0.054). In contrast, sIL7Rα and TNFα showed poor discriminatory power despite statistical significance (p < 0.01), with AUC values of 0.675 ± 0.057 and 0.687 ± 0.056, respectively. Results of regression analysis revealed that EDSS, duration of disease, and use of any treatment had no impact on the cytokines. Similarly, no significant correlations were noted between these confounders and cytokines, except a moderate negative correlation (−0.418) between IGF-1 and EDSS. Conclusions: IL18 and IGF-1 have the potential to be used as biomarkers in distinguishing MS from healthy individuals. However, both biomarkers failed to demonstrate the discrimination between various phenotypic patterns of disease, limiting their utility for disease stratification. Future studies with larger, longitudinal cohorts and multi-marker panels are warranted to validate these results and to explore whether combining cytokines with imaging or genetic markers can improve prognostic precision. Full article

Osteosarcoma is the most frequent cause of primary malignant bone tumors in childhood and adolescence. It is aggressive and may be associated with early metastasis, making patient management difficult. In this research, modern AI models for the diagnosis and prognosis of osteosarcoma were screened and analyzed. Our review searched for articles that used AI for the diagnosis and prognosis of osteosarcoma over the past 10 years, including AI in predicting the staging of tumors, predicting chemotherapy response, identifying prognostic biomarkers and assessing risk of metastasis. The models performed well based on AUC and C-index, with considerable discriminatory power, and were superior to the classical clinical methods analyzed. Through the identification of already existing deficiencies in the literature, this review pointed out a need for future research trends to explore with respect to prospective validation, multimodal data fusion and translation of AI tools into clinical routine. Full article

Introduction: Various scoring systems are applied to burn patients to assess the perioperative and mortality risks as well as comorbidities. Objective: The purpose of this study was to compare the predictive accuracies for mortality of different scoring systems: the Abbreviated Burn Severity Index (ABSI), Bogenhausen ABSI (BABSI), American Society of Anesthesiologists (ASA) classification, Charlson Comorbidity Index (CCI) and modified Frailty Index-5 (mFI-5). Materials and Methods: We retrospectively analyzed 644 burn patients treated at one burn center between September 2018 and May 2022. Results: Median scores were 5 (range: 1–16), 5 (range: 2–17.5), 2 (range: 1–5), 0 (range: 0–14) and 0 (range: 0–5) for the ABSI, BABSI, ASA, CCI and mFI-5, respectively. Significantly different median score results were observed between survivors and non-survivors: ABSI: 5 vs. 10; BABSI: 5 vs. 10.5; ASA: 2 vs. 4; CCI: 0 vs. 5; and mFI-5: 0 vs. 2 (p < 0.001 for all scores). Predictive accuracies were excellent for the BABSI (AUC = 0.963), ABSI (AUC = 0.952), and ASA (AUC = 0.916), whereas fair predictive accuracies were found for the CCI (AUC = 0.851) and mFI-5 (AUC = 0.760). Good calibration was observed for the BABSI, ABSI, CCI, and mFI-5, whereas calibration was poor for the ASA. Conclusion: All five scores significantly differentiate between survivors and non-survivors. However, the strongest discriminatory power and best calibration for mortality prediction were observed for the BABSI and ABSI scores. Therefore, the application of both scores is recommended in daily routine. Full article

Background/Objectives: Klebsiella pneumoniae is one of the major causes of severe infections in cancer patients. The rapid and accurate typing of isolates is essential for tracking transmission routes and implementing infection control measures. The IR Biotyper, an automated system based on Fourier transform infrared (FT-IR) spectroscopy, enables fast cluster analysis with reduced cost and turnaround time. The aim of this paper was to compare typing results obtained by the IR Biotyper and pulsed-field gel electrophoresis (PFGE) for the epidemiological surveillance of K. pneumoniae in an oncology hospital. Methods: A total of 137 isolates collected between 2020 and 2023 from both colonization and infection were retrospectively analyzed using PFGE and the IR Biotyper. The discriminatory power of both methods and their concordance were assessed. Results: Both methods demonstrated high discriminatory power. PFGE classified the strains into 59 distinct types (96 including subtypes), while the IR Biotyper differentiated 70 FT-IR types. Concordance between the methods was moderate (adjusted Wallace coefficient: 0.515). PFGE type B was the most prevalent, comprising 43 isolates and subdivided into 16 subtypes. The most frequent FT-IR types were 16 (17 isolates), 10 (8 isolates), and 14 (5 isolates), all corresponding to PFGE type B with different subtypes. The IR Biotyper successfully distinguished isolates within the long-standing PFGE type B clone. Conclusions: The IR Biotyper demonstrated good discriminatory capacity and was able to differentiate isolates within a dominant PFGE clone, supporting its potential as a rapid tool for monitoring clonal spread in oncology settings. However, the moderate concordance with PFGE highlights that further studies are needed to optimize performance and confirm its role as a complementary method for routine hospital epidemiological surveillance. Full article

This study investigates whether multi-component T₂ mapping, using bi-exponential (BE) and stretched-exponential (SE) models, enhances the early detection of knee osteoarthritis (OA) compared with the conventional mono-exponential (ME) approach. T₂ relaxation maps were derived from 26 patients with early-stage OA and 26 healthy controls. To minimize the influence of age-related cartilage changes, all model-derived parameters were adjusted for age prior to analysis. Quantitative T₂ parameters were extracted from six anatomically defined cartilage sub-regions to capture spatially heterogeneous tissue alterations characteristic of early OA. These parameters were then integrated using linear discriminant analysis to assess combined diagnostic performance. Global whole-cartilage analyses demonstrated limited discriminatory power across all models, with area under the receiver operating characteristic curve (AUC) values not exceeding 0.65, indicating that diffuse averaging obscures subtle, localized degeneration. In contrast, sub-regional analysis improved classification accuracy, highlighting the importance of regional assessment in early disease. Among the evaluated models, the BE-T₂ model showed the highest performance, achieving an AUC of 0.68, and marginally outperforming both the SE model (AUC = 0.60) and the ME model (AUC = 0.51). These findings suggest that multi-component T₂ mapping, particularly when applied at a sub-regional level, may offer improved sensitivity to early cartilage compositional changes. Overall, this approach shows strong potential as a noninvasive imaging biomarker for the early detection of knee OA. Full article

Stress caused by suboptimal temperatures (ST) represents a stress that limits growth in all grasses without inhibiting their activity and induces alterations in photosynthetic performance. We evaluated the responses of photosynthetic parameters and leaf elongation between two groups of grass genotypes with different levels of tolerance to ST, belonging to phylogenetically distant species. Responses to ST depended on the type of parameter and on the genotypic group. Leaf elongation traits showed discriminatory power, especially the area under the leaf elongation curve, which integrated the early and transient effects of stress over time. The photosynthetic parameter PI_ABS showed lower discriminatory power compared with the area under the leaf elongation curve. However, a deeper analysis of other photosynthetic parameters revealed an increase in energetic connectivity between Photosystem II centers in tolerant, but not in sensitive, genotypes. A subsequent analysis of leaf and cellular parameters of early leaf elongation dynamics indicated that ST reduced meristematic activity in all genotypes, but the tolerant genotype group maintained a greater accumulation of mature cells compared with the sensitive genotype group. Overall, the results suggested a response to ST in tolerant genotypes, but not in sensitive genotypes, related to the early dynamics of leaf and cellular growth parameters to partially compensate for the restrictive effect of ST on leaf elongation not recorded. In parallel, they also indicated a response of the tolerant genotypes to ST in terms of photosynthetic parameters, probably as a pathway to maintain cellular homeostasis, to prevent photooxidative damage in PSII under stress. However, the relationship between both responses does not appear to be strictly linear, but rather would be mediated by coordinated adjustments in the temporal dynamics of growth, suggesting a functional integration between photosynthetic performance and the cellular mechanisms that regulate leaf expansion under ST stress. Full article

Background/Aim: This study aimed to evaluate the changes in hematological indices following methotrexate (MTX) initiation and assess their correlation with and predictive value for treatment responses in rheumatoid arthritis (RA) patients. Methods: A retrospective study was conducted on 299 DMARD-naïve RA patients who received MTX monotherapy for 12 weeks. Univariate and multivariate logistic regression identified predictors of remission and low disease activity. Correlation analyses assessed relationships between hematological and disease activity changes. Receiver operating characteristic (ROC) curve analysis evaluated the discriminatory ability of hematological parameters. Results: After 12 weeks of MTX, significant decreases were observed in white blood cell (p = 0.025), neutrophil (p = 0.026), hemoglobin (p = 0.001), and platelet counts (p < 0.001), alongside an increase in red cell distribution width (RDW) (p < 0.001). Multivariate analysis identified only baseline DAS28-CRP (OR: 9826.7, p < 0.001) and CRP (OR: 0.45, p = 0.005) as independent predictors for remission, and baseline swollen joint count, DAS28-CRP, and CRP for LDA. Hematological parameters were not independent predictors. ROC analysis revealed neither baseline values nor changes in hematological indices had satisfactory discriminatory power for remission or LDA. Conclusions: Hematological parameter changes do not serve as robust independent predictors for early treatment response. Clinical disease activity indices remain superior for prognostication in DMARD-naïve patients starting MTX. Full article

This paper addresses the problem of multi-class classification of bacterial microscopic images using a rigorous experimental protocol designed to prevent information leakage and improve performance. The dataset consists of 2034 images representing 33 taxa, organized by class. Data integrity checks confirmed the absence of corrupted or unreadable files. To formalize image characteristics and ensure quality control, indirect geometric and textural features were calculated, including minimum frame size, brightness statistics (mean and standard deviation), Shannon entropy, Laplace variance, and Sobel gradient energy. Quality checks revealed a small proportion of images with extreme brightness (2.5074%), while no samples with critically low sharpness according to the selected criteria were detected. Statistical analysis of interclass differences using the Kruskal–Wallis test with multiple comparison correction demonstrated the high discriminatory power of texture features, specifically gradient energy (ε² = 0.819987) and Laplace variance (ε² = 0.709904). Feature correlations were consistent with their physical interpretation, revealing a strong positive relationship between sharpness and gradient energy. Principal component analysis confirmed a strong structural pattern, with the first two components explaining 75.5766% of the total variance. For a unified comparison, classical machine learning, transfer learning, and modern deep architectures were evaluated within a single protocol. Full article