Search Results (442)

Background and Objectives: Reliable pretreatment biomarkers to guide treatment selection in HER2-positive metastatic breast cancer (mBC) remain an unmet need. Systemic inflammatory indices derived from routine blood tests have emerged as accessible prognostic markers. This study evaluated the prognostic value of inflammation-based indices in patients with HER2-positive mBC treated with trastuzumab emtansine (T-DM1). Materials and Methods: In this retrospective single-center cohort study, 50 patients with HER2-positive mBC treated with T-DM1 in the second-line setting were analyzed. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan–Meier method. ROC analysis assessed the prognostic performance of the CRP/albumin ratio (CAO), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII). Variables associated with PFS were further evaluated using multivariable Cox regression. Results: The median follow-up was 46 months. Median OS from initial diagnosis and median PFS from T-DM1 initiation were 96 and 7 months, respectively. Metastatic pattern (p = 0.010), CNS involvement at T-DM1 initiation (p = 0.025), liver metastasis (p = 0.041), and best radiologic response (p < 0.001) were associated with PFS. ROC analysis showed modest discrimination (CAO AUC 0.694, NLR 0.658, PLR 0.646, and SII 0.653). In multivariable analysis, best radiologic response to T-DM1 was strongly associated with progression risk and appeared to reflect treatment sensitivity rather than acting as a pretreatment predictor. Conclusions: T-DM1 provided meaningful disease control in this real-world cohort. Treatment response was the main determinant of progression, while baseline inflammatory markers offered modest complementary prognostic value. These findings may aid patient selection for T-DM1, particularly in settings with limited access to trastuzumab deruxtecan. Full article

Background: The growing complexity and cost of oncohematological treatments has created an urgent need for standardized methodologies capable of enabling inter-institutional comparisons of healthcare expenditure within homogeneous patient groups. Cancer-related pharmaceutical costs vary substantially depending on tumour type, disease stage, and therapeutic approach, making cross-institutional benchmarking challenging due to heterogeneity in patient populations and clinical practice patterns. Therefore, integrating cost analysis with clinically meaningful patient stratification is essential to improve resource allocation and outcome evaluation. Methods: A multicentre working group comprising four tertiary hospitals in Madrid (Spain) was established to develop and preliminarily evaluate a novel classification system for adult oncohematological patients. A standardized methodology was designed to stratify patients into homogeneous groups (PATONCO categories) based on tumor location, therapeutic objective, and clinically relevant biomarkers. A cost indicator was defined as the average cost per patient per month for each PATONCO category. Data were extracted from pharmacy dispensing systems and analyzed using descriptive and inferential statistics, including Kruskal–Wallis and post hoc Dunn tests. Results: A total of 3659 patients were included (3168 oncology; 491 hematology), distributed across 62 programmes (54 oncology; 8 hematology). The PATONCOS tool enabled the identification and validation of a cost indicator (average cost/patient/month per category), allowing inter-hospital comparison. Significant differences in costs were observed across most high-prevalence categories, reflecting variability in drug selection within homogeneous patient groups, as documented by the differential use of specific therapeutic agents across centers. The model demonstrated its capacity to detect intra-group homogeneity and inter-group variability, improving the identification of high-cost patient subgroups and supporting benchmarking across centers. Conclusions: The PATONCOS tool provides a novel, clinically oriented stratification methodology that integrates pharmacotherapy, biomarkers, and disease stage with economic evaluation. This approach enables more accurate comparisons of oncology treatment costs between institutions and may support data-driven decision-making in resource allocation. Its implementation may contribute to more sustainable healthcare systems by aligning clinical practice with economic outcomes. Full article

Background: Early identification of metabolic health risk is important for preventive intervention, but routine laboratory testing is not always available in everyday health-management environments. Artificial intelligence models can estimate risk from accessible variables, but purely data-driven models may provide limited clinical interpretability. Objective: This study presents the Advanced Hybrid Inference Model (AIM), a clinically interpretable screening support framework that combines biomarker estimation, Random Forest-based risk prediction, and rule-based clinical interpretation. Methods: AIM was intentionally implemented as a three-stage, Random Forest-centered pipeline: (1) Selected anthropometric and demographic variables were used to estimate clinically relevant metabolic biomarkers when direct measurements were unavailable. (2) A Random Forest model generated metabolic risk estimates from measured or estimated biomarkers and clinical variables. (3) Rule-based interpretation mapped the model outputs and biomarker thresholds to clinically meaningful risk-support messages. Results: Experimental validation was conducted using clinically collected datasets under class-imbalanced conditions. The results indicate that the proposed framework showed exploratory potential for identifying high-risk patterns. These findings suggest that the AIM framework may be useful as a screening-oriented approach. Conclusions: AIM should be interpreted as an exploratory clinical screening support framework that prioritizes interpretability, structured rule-based reasoning, and risk prioritization rather than a diagnostic classifier or universally superior prediction model. Full article

Background/Objectives: Peripheral biomarkers for autism spectrum disorder (ASD) have shown mixed results in previous studies. In this study, complete blood count-derived immune-inflammatory markers, iron and micronutrient levels, and thyroid function were compared between drug-naïve preschoolers newly diagnosed with ASD and healthy controls. Additionally, the relationships between these markers, symptom severity, and developmental skills were examined. Methods: This retrospective case–control study included 62 children with ASD (aged 24–72 months) and 61 age-matched healthy controls. Symptom severity, behavioral traits, and developmental status were assessed using the Childhood Autism Rating Scale (CARS), Autism Behavior Checklist (ABC), and Denver II Developmental Screening Test (DDST), respectively. Composite inflammatory indices were calculated from hemogram data. Statistical analyses incorporated Holm–Bonferroni corrections for multiple comparisons and sex-stratified exploratory analyses of conditional associations using 95% bootstrap confidence intervals based on 5000 resamples. Results: Children with ASD demonstrated significantly lower mean corpuscular volume (MCV; d = 0.66, adj. p = 0.019), lower mean platelet volume (MPV; d = 0.58, adj. p = 0.034), and higher absolute lymphocyte counts (LYMPH; d = 1.10, adj. p = 0.019). Initial group differences in ferritin, serum iron, and transferrin saturation did not survive adjustment (adj. p > 0.05). Composite inflammatory indices were not significantly associated with clinical or developmental scores. Higher CARS and ABC scores correlated with lower personal–social and language scores on the DDST (p < 0.01). Furthermore, exploratory sex-stratified, conditional association analyses suggested preliminary basophil- and lymphocyte-related patterns in girls; however, these findings are strictly hypothesis-generating due to the small female sample size (n = 12). Conclusions: Newly diagnosed, drug-naïve preschoolers with ASD showed a distinct baseline blood profile, including lower MCV and MPV and higher lymphocyte counts. Clinical challenges were most evident in personal–social and language domains. While the primary diagnostic value of routine hemograms in this context appears limited, the exploratory sex-stratified basophil- and lymphocyte-related patterns require validation in adequately powered future cohorts. Full article

Chronic joint disease is a major cause of lameness and reduced performance in sport horses and is characterized by persistent synovial inflammation and protease-mediated matrix degradation. This exploratory prospective pilot study investigated clinical outcomes and synovial biomarker changes following intra-articular administration of an α-2-macroglobulin plasma-derived preparation. Twenty client-owned sport horses were observed in the treatment group (n = 10) or a comparison group (n = 10) and monitored for up to 180 days under field conditions. Clinical outcomes were assessed longitudinally, while synovial fluid was analyzed at baseline and 30 days post-treatment only in treated horses. Mixed-effects analysis showed significant group × time interactions for American Association of Equine Practitioners (AAEP) lameness score, flexion test response, and joint effusion. Treated horses showed early and sustained improvement in clinical scores, whereas minimal changes were observed in the comparison group. At 30 days, treated horses exhibited consistent within-subject reductions in synovial total protein, total nucleated cell count, polynuclear cell percentage, pro-inflammatory cytokines (PGE₂, TNF-α, IL-6, IL-1β), matrix metalloproteinases (MMP-9, MMP-13), sulphated glycosaminoglycans, and neurogenic mediators (NGF, Substance P). These findings indicate a coherent pattern of clinical improvement associated with parallel changes in synovial biomarkers in treated horses. However, as longitudinal biomarker data were not collected in the comparison group, these observations should be interpreted as exploratory and do not establish causality. The observed findings support the rationale for further investigation of protease-targeted approaches in equine joint disease. Full article

Alzheimer’s disease (AD) lacks effective disease-modifying therapies, and extracellular vesicles (EVs) derived from adipose-derived mesenchymal stromal cells (ADMSCs) have emerged as promising therapeutic candidates. In this study, we investigated the brain biodistribution and dose-dependent effects of intranasally administered ADMSC-EVs in female APP/PS1 mice, with age-matched wild-type mice and vehicle-treated transgenic mice serving as controls. EV biodistribution was assessed using PKH26 labeling, cognitive performance was evaluated using the Morris water maze, Y-maze, and novel object recognition tests, and hippocampal amyloid pathology and plasma AD-related biomarkers were analyzed. Intranasally delivered ADMSC-EVs rapidly reached multiple brain regions, including the hippocampus, improved learning and memory performance, and reduced hippocampal amyloid-β 1-42 (Aβ42) deposition and plaque burden. These effects followed a nonlinear dose–response pattern, with reduced efficacy at low doses and no additional benefits at high doses. Notably, partial behavioral and pathological benefits persisted after treatment cessation. Together, these findings show that intranasal ADMSC-EVs exert therapeutic effects in APP/PS1 mice and support the importance of dose optimization and post-treatment durability in the development of EV-based interventions for AD. Full article

Background: Mild Cognitive Impairment (MCI) is an early stage of cognitive decline and a major risk factor for dementia, typically diagnosed using neuropsychological assessments such as memory and executive function tests. While EEG-based detection has been widely explored, most approaches rely on raw signal analysis and computationally intensive deep learning models. In contrast, wearable devices use indirect behavioral proxies (e.g., activity patterns or sleep–wake patterns), limiting diagnostic specificity. Although substantial clinical evidence indicates altered sleep architecture in MCI, the use of sleep stage dynamics for MCI classification remains largely unexplored. Methods: We propose a lightweight and physiologically interpretable framework using novel features based on hypnogram-derived sleep dynamics. The method was evaluated on the MASS SS1 dataset (36 healthy, 17 MCI subjects) using five classifiers—Logistic Regression, Random Forest, XGBoost, Linear SVM, and RBF SVM—with leave-one-subject-out validation and threshold optimization. Results: RBF SVM achieved the best performance (accuracy: 77.4%, balanced accuracy: 78.7%, sensitivity: 82.4%, specificity: 75.0%, ROC AUC: 0.778), followed by Random Forest (accuracy: 77.4%, balanced accuracy: 77.1%) and XGBoost (accuracy: 71.7%, balanced accuracy: 73.0%). Conclusions: This proof-of-concept study demonstrates that features extracted from sleep stage dynamics are effective, non-invasive, and interpretable biomarkers for early MCI detection, with strong potential for integration into wearable sleep monitoring systems. Full article

Given higher compatible rates between oral lichen planus (OLP) and oral lichenoid lesions (OLLs) in histopathological and clinical features, this study aims to delineate a boundary between equivocal OLP and OLLs by biomarkers. The updated OLP diagnostic criteria in 2016 was our guideline in defining study cases of typical OLP and typical OLL with triggers, which include topical offending agents (OLL-agent), dental restorations (OLL-dental), and systemic offending drugs (OLL-drug). The expression intensity and distribution patterns of CD4, CD8, and CGRP in four groups were detected by immunohistochemistry assay (IHC). A total of 79 cases including OLP (24), OLL-agent (15), OLL-dental (21), and OLL-drug (19) were collected from an oral biopsy laboratory. Band-like distribution patterns of CD4 (100%, score 3), CD8 (54.17%, score 2), and CGRP (87.5%, score 3) in the subepithelial regions of the OLP group significantly differ from the OLL groups (each comparison pair, p = 0.0001). The sensitivity of CD4 (100%), specificity of CD4 (83.64%), negative predictive value of CD4 (100%), and accuracy of CD4 (83.80%) in the OLP group provide results for the diagnostic test evaluation. The band-like distribution pattern of CD4 in the subepithelial region may determine OLP when the biopsy specimen does not show typical microscopic features. Full article

Objective and reproducible diagnosis of attention-deficit/hyperactivity disorder (ADHD) remains challenging because of the limited availability of reliable electroencephalography (EEG) biomarkers and the high variability of neural signals. This study proposes a computational framework for ADHD detection based on dynamic functional connectivity and network-index modeling. Multichannel EEG recordings were transformed into temporal connectivity graphs using sliding-window correlations of band-limited amplitude envelopes. Several network-index models were evaluated, including linear, graph-based, recurrent, and hybrid spatio-temporal approaches. The proposed Hybrid Spatio-Temporal Graph Transformer demonstrated moderate, yet reproducible, subject-level classification performance. On the independent test set, the model achieved an accuracy of 63.16%, a balanced accuracy of 62.22%, a sensitivity of 80.00%, a specificity of 44.44%, an F1-score of 69.57%, and an AUC-ROC of 0.7444. Additional analysis of the derived network index demonstrated moderate intergroup separability, with a mean index shift of 1.16, Cohen’s d = 0.73, Pearson’s r = 0.36, and distribution overlap = 0.72. These findings suggest that the proposed framework captures informative spatio-temporal EEG connectivity patterns associated with ADHD; however, the model’s diagnostic applicability should be considered preliminary and requires validation in larger independent cohorts. Full article

Progesterone (P4) exerts important vascular and immunomodulatory effects that influence platelet (PLT) activation and serotonin (5-HT) handling across mammalian species; nevertheless, its role in modulating PLT physiology during diestrus in mares remains poorly defined. This study hypothesized that physiological variations in luteal activity during diestrus are associated with changes in PLT activation and 5-HT-related parameters. The first objective was to determine whether changes in circulating P4 during diestrus are associated with alterations in PLT aggregation, circulating 5-HT, and PLT morphological indices in healthy mares; the second objective was to identify a diestrus day providing consistent physiological conditions for assessing PLT-related biomarkers. Twenty clinically healthy Spanish Purebred mares aged 4–9 years old were monitored. Blood samples were collected on days 5, 14, and 16 post-ovulation, with luteal status confirmed by ultrasonography. P4 concentrations were determined using a solid-phase I-125 radioimmunoassay (RIA), 5-HT concentrations were quantified using a competitive enzyme immunoassay, and PLT indices were measured using an ADVIA 2120i hematology analyzer. Data were compared using appropriate parametric or non-parametric tests after assessing distribution, and correlations were analyzed using rank-based correlation analysis, using Pearson or Spearman coefficients according to variable distribution. P4 concentrations were higher on days 14 and 16 compared with day 5 (p < 0.05), with no significant differences between days 14 and 16. Platelet aggregates (AGREG) showed the greatest variation, with significantly higher values on day 14 compared with day 5 (p < 0.05). In contrast, circulating 5-HT and all PLT morphological indices (PLT count, PCT, MPV, PLCR, PDW, PCDW, MPM, and PMDW) remained unchanged across diestrus. PLT aggregation showed a strong positive association with circulating P4 concentrations (r = 0.88, p < 0.05), whereas no meaningful correlations were observed between 5-HT and AGREG or between 5-HT and PLT morphological parameters. Internal correlations among PLT indices followed expected biological patterns, confirming the stability of structural PLT traits over short physiological intervals. These findings demonstrate that during diestrus, PLT activation—but not PLT morphology or circulating 5-HT—varies in parallel with P4 in mares. Day 14, corresponding to mid-diestrus, characterized by high luteal activity, represents an informative time point for assessing PLT activation and related biomarkers, providing a framework for standardizing sampling protocols for PLT-derived products in equine reproductive medicine. Full article

This study examined alterations in serum redox biomarkers before and one month after administration of the coronavirus disease 2019 (COVID-19) booster (third) doses across four vaccine regimens. A longitudinal cohort of 410 adults was analyzed following homologous Pfizer-BioNTech, Sinopharm [Vero Cell]-Inactivated, Sputnik V, or heterologous Sinopharm/Pfizer vaccination. Serum total proteins, albumin, total thiols, nitrites, ferric-reducing antioxidant power (FRAP), and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging activity were measured, with DPPH interpreted as an ex vivo surrogate of serum radical-scavenging capacity. Additional analyses included stratification by prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, multivariable regression, correlation analysis, effect-size estimation, and sensitivity testing. Booster vaccination was associated with modest but consistent decreases in DPPH activity, albumin, and total proteins, whereas FRAP, nitrite, and total thiol levels remained stable. This pattern supports a transient shift in antioxidant buffering capacity but, by itself, does not exclude oxidative stress, as direct oxidative damage markers were not assessed. The most pronounced changes were observed in Sinopharm-based regimens, particularly in the heterologous Sinopharm/Pfizer group. Prior SARS-CoV-2 infection did not materially alter the qualitative response pattern, whereas older age and comorbidities were associated with greater declines in DPPH activity and albumin. Overall, the findings indicate a modest, transient redox-associated response following booster-induced immune activation and suggest that host-related factors, such as age and comorbidity burden, may accentuate short-term changes in antioxidant buffering capacity. Full article

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long Coronavirus Disease 2019 (long COVID) are complex chronic conditions that often follow infectious triggers with overlapping clinical features but poorly defined pathophysiological relationships. This study aimed to identify disease-specific immune signatures through multiparameter immunophenotyping of monocytes, dendritic cells, and T cell subsets. A total of 207 participants were included (ME/CFS: n = 103; long COVID: n = 63; healthy controls: n = 41). Peripheral blood mononuclear cells were analyzed using multiparameter flow cytometry. Statistical analyses included non-parametric testing, age-adjusted Analysis of covariance (ANCOVA), correlation network analysis, and principal component analysis (PCA). Long COVID was characterized by increased M2-like monocyte polarization, elevated CD80 expression across monocyte subsets, expansion of dendritic cells, and reduced expression of activation markers, indicating persistent immune activation with features of immune exhaustion. In contrast, ME/CFS exhibited reduced costimulatory molecule expression, impaired C-C chemokine receptor type 7 (CCR7)-mediated immune cell trafficking, and less coordinated activation patterns, consistent with a state of immune suppression. Correlation network analysis revealed more extensive and integrated immune interactions in long COVID, while PCA identified distinct immunophenotypic components and enabled moderate discrimination between the two conditions. These findings demonstrate that ME/CFS and long COVID are characterized by distinct immune profiles, supporting the concept of divergent immunopathological mechanisms. The identified signatures may contribute to biomarker development and guide targeted therapeutic approaches. Full article

Automated optical coherence tomography (OCT) diagnosis is clinically important for retinal disease assessment, but image-level classification can be limited by class imbalance and localized pathological patterns. Standard image-level classifiers may compress small pathological regions together with large areas of normal retinal tissue, reducing the contribution of subtle structural biomarkers during global pooling. To address this limitation, we propose Saliency Mask-Guided Local-context Consistency (SMGLC), a training framework that uses saliency maps from a frozen teacher proxy to extract lesion-focused local crops and align their feature representations with the corresponding whole-scan representations. This consistency objective encourages a lightweight student backbone to preserve local pathological cues while retaining a standalone inference pathway. We evaluate SMGLC on OCTDL and OCT2017 against four representative baseline architectures and same-student ablations under a fixed 8:1:1 evaluation protocol. On the OCTDL test split, SMGLC achieves an accuracy of 95.88%, an F1 score of 88.92%, and an AUC of 99.09%. On the OCT2017 test split, it reaches an accuracy of 95.58%, an F1 score of 92.93%, and an AUC of 99.37%. These results show that train-time local-context supervision remains competitive under the revised protocol without requiring an additional saliency branch at inference time. Full article

Background/Objectives: Autism Spectrum Disorder (ASD) is associated with structural brain alterations, particularly involving white matter and connectivity. Artificial intelligence (AI) enhances the detection of subtle neuroanatomical changes. This study aimed to characterize structural abnormalities and volumetric patterns in children with ASD using AI-assisted MRI. Methods: This retrospective study included 90 children diagnosed with ASD. Brain MRI scans were analyzed using the CE-certified AI platform mdbrain. Structural findings were classified into corpus callosum anomalies, white matter signal abnormalities (WMSA), ventriculomegaly, other abnormalities, or no detectable changes. Group differences were assessed using ANOVA and Kruskal–Wallis tests with Tukey post hoc analysis. Logistic regression, principal component analysis (PCA), and linear discriminant analysis (LDA) were applied. Results: WMSA were identified in 23.3% of patients, followed by other anomalies (27.8%), corpus callosum anomalies (8.9%), ventriculomegaly (8.9%), and no abnormalities (31.1%). Total white matter volume was significantly reduced in pathological groups and was the only independent predictor. PCA identified three principal components reflecting shared temporo-parietal covariance, hemispheric asymmetry, and a white matter-related axis. Exploratory LDA demonstrated partial separation among anomaly categories. Conclusions: Children with ASD in this cohort showed heterogeneous but partially structured MRI alterations involving both focal and global volumetric changes. Reduced total white matter volume was the most consistent multivariable association with structural abnormalities. AI-assisted morphometric analysis may support structural phenotyping in ASD. These findings are exploratory and require confirmation in larger, prospectively validated cohorts before biomarker applications can be considered. Full article

Background and Objectives: Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease that progressively leads to brain atrophy and the accumulation of disability over time. In this study, we used FreeSurfer to compare subcortical volumes and cortical surface areas between patients with MS and healthy controls and to investigate how regional atrophy relates to both disease lasting and clinical disability. Materials and Methods: We included 80 participants in this study, 40 patients with clinically definite MS and 40 age- and sex-matched healthy controls, all imaged on a Philips Ingenia 3.0T MRI scanner. High-resolution 3D T1-weighted MPRAGE sequences of the brain were processed using FreeSurfer 7.3.3. MS patients were stratified by disease lasting into two subgroups: ≤5 years (n = 17) and >5 years (n = 23). Subcortical volumes were normalised to estimated total intracranial volume (eTIV). Between-group differences were assessed using Welch’s t-test with Benjamini–Hochberg false discovery rate (FDR) correction. Multiple linear regression models controlled for age, sex, and the Expanded Disability Status Scale (EDSS). Results: We found statistically significant volume reductions in 48 of the 52 normalised regions examined. Thalamic volume showed the most severe reduction (mean—21.6% bilaterally) in MS patients. The corpus callosum, hippocampus, and amygdala were also prominently affected. Receiver operating characteristic (ROC) analysis of mean bilateral thalamic volume yielded an area under the curve (AUC) of 0.822 (95% CI: 0.731–0.913). Cortical surface area did not survive FDR correction in the primary comparison, though nominal reductions emerged in longer-lasting MS patients. EDSS correlated with both thalamic and hippocampal volumes in regression models. Conclusions: FreeSurfer-based volumetric analysis detected widespread grey and white matter volume differences in MS patients relative to matched controls, with changes already present in patients within the first five years of diagnosis. The high proportion of significant regions is consistent with a combined pattern of generalised and regionally accentuated atrophy. Among the regions examined, thalamic volume showed the strongest cross-sectional discrimination (AUC = 0.822; sensitivity 65%, specificity 90%) and the most consistent associations with EDSS; these findings support further evaluation of thalamic volume as a candidate imaging biomarker of neurodegeneration, although its diagnostic performance is moderate and requires external longitudinal validation before clinical deployment. Full article