Search Results (8,063)

Rare-earth elements (REEs), which include the entire lanthanide series together with scandium and yttrium, have unique electronic configurations and coordination chemical properties that provide them with special magnetic, optical, and redox abilities. Generally used for diagnostic imaging and theranostic applications, increasing evidence emphasizes their potential as direct anticancer agents. This review aims to present a thorough investigation of the studies published in the last ten years that focus on the intrinsic anticancer properties of REE-based molecular complexes and nanostructures, without discussing their recognized imaging functions. Rare-earth compounds exhibit selective cytotoxicity against malignant cells via mechanisms that mainly include modulations in the generation of reactive oxygen species, mitochondrial dysfunctions, interaction with DNA molecules, apoptosis, and ferroptosis induction, as well as radiosensitization. Molecular complexes that are based on the trivalent coordination chemistry of REEs enable them to target biomolecules like DNA and serum albumin. Nanostructured systems, on the other hand, render tumors more responsive to treatment by improving the cellular uptake, enabling surface functionalization, and controlling ROS generation. Terbium, thulium, yttrium, scandium, ytterbium, cerium, erbium, dysprosium, and europium show different levels of anticancer activity in both in vitro and in vivo cancer models. They often exert more toxicity in tumor cells than in normal tissues, thus exhibiting selective anticancer effects. The findings collectively underscore the therapeutic potential of REE-based compounds as novel metal-based anticancer agents and advocate for additional mechanistic and translational research to enhance their clinical applicability. Full article

Adrenocortical carcinomas (ACCs) are rare, aggressive tumors often discovered incidentally. These malignancies may present with abnormal hormone secretion or, as in some cases, as non-functioning masses causing discomfort. We present a case of brain metastasis in a patient with a giant ACC. A 50-year-old man presented with headache and dizziness. A computed tomography (CT) scan showed an intracranial lesion within the parenchyma measuring 73*60*46 mm with left internal temporal involvement, abundant vasogenic edema and compressing the lateral left ventricle. Further imaging investigations identified a large necrotic tissue mass measuring 15 cm, located on both sides of the right diaphragmatic dome, in the middle posterior region. Hormonal workup was conducted and excluded a functional adrenal tumor. A CT-guided biopsy was performed, confirming ACC. Despite medical management, the patient’s condition deteriorated rapidly, with the cerebral metastasis proving fatal. This case underscores the challenges posed by advanced ACC, particularly when associated with atypical metastatic sites. Giant ACC, though rare, presents significant diagnostic and therapeutic challenges. Surgical excision with appropriate oncologic management can lead to favorable outcomes. This report contributes to the limited literature on cerebral metastases in ACC, aiming to enhance awareness among clinicians managing this rare entity. Full article

Background/Objectives: The objective of this scoping review was to map and critically describe emerging speech-in-noise assessment tools developed over the last decade for the evaluation of hearing loss beyond conventional audiological measures. Methods: This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive literature search was performed in the PubMed/MEDLINE, Scopus, and Embase databases. A comprehensive review of studies describing novel or emerging SIN-based assessment tools was conducted, with a particular emphasis on those including adult participants with normal hearing and hearing loss. Results: Nine studies met the inclusion criteria and were included in the review. The identified tools cover a range of methodological innovations, including advanced digits-in-noise paradigms, antiphasic and binaural presentation modes, optimized adaptive procedures, and digital or automated testing platforms. Several studies also incorporated artificial intelligence-based approaches, such as machine learning, text-to-speech, and automatic speech recognition, to enhance test development, administration, and hearing loss classification. Across all studies, SIN measures demonstrated the ability to reliably differentiate between normal hearing listeners and individuals with hearing loss and to provide complementary information beyond pure-tone audiometry. Conclusions: Emerging speech-in-noise tools show considerable potential to improve the functional assessment of hearing loss and to support more sensitive, accessible, and scalable approaches for hearing evaluation. Further research is required to assess their clinical integration and long-term impact on hearing screening and diagnostic pathways. Full article

Inborn errors of immunity (IEIs) encompass a heterogeneous group of more than 550 genetic conditions with variable ages of onset. A significant proportion of IEI arises from genetic variants that may not yet be fully elucidated or recorded in existing genomic databases. Molecular diagnoses are achieved in approximately 15–35% of IEI cases, yet in only 9–20% of individuals with predominant antibody deficiencies, particularly in adult cohorts. We aimed to evaluate whole genome sequencing (WGS) diagnostic yield in adults suspected to have IEI. Clinical assessments of the patients were carried out at tertiary medical institutions in Timisoara and Bucharest, Romania. The study cohort included a consecutive series of 21 adult patients (aged 19–60 years) with IEI phenotype, who underwent genetic analysis, using WGS as the first diagnostic approach. A definitive molecular diagnosis was confirmed in only 9.5% (2/21) of the participants, in LRBA and BTK genes. Variants of uncertain significance (VUS) were detected in three patients (13.6%) in TNFRSF13B, COPA, GATA2 genes. For about half of the cohort the onset of the disease was noted in childhood. WGS as a first-line diagnostic strategy in a cohort of adults with IEI yielded a low diagnostic rate. There were significant delays in genetic diagnosis, as half of the cohort experienced childhood-onset symptoms. Results suggest that adult IEI diagnosis remains challenging, necessitating functional studies and longitudinal re-evaluation of genomic data. Full article

Background: Pleuroparenchymal fibroelastosis (PPFE) is a rare fibroelastotic lung disease characterized histologically by dense pleural and subpleural fibrosis with upper-lobe predominance. In clinical practice, diagnosis often relies on characteristic radiologic findings, as surgical lung biopsy is rarely feasible. Unlike idiopathic pulmonary fibrosis, robust radiologic criteria validated against biopsy-proven cohorts remain limited, and the diagnostic performance of imaging alone is incompletely defined. Although initially described as idiopathic, PPFE is increasingly recognized in secondary settings, including connective tissue disease-associated interstitial lung disease (CTD-ILD), where it frequently overlaps with more common fibrotic patterns. Methods: We conducted a focused narrative review of the literature on PPFE in CTD-ILD, synthesizing evidence on morphology, epidemiology, clinical course, prognostic implications, and proposed pathobiological mechanisms, with emphasis on distinguishing true PPFE from PPFE-like lesions. Results: CTD-associated PPFE is associated with accelerated lung function decline, increased risk of pneumothorax, and poorer outcomes, particularly in systemic sclerosis and rheumatoid arthritis. However, distinguishing true PPFE from radiologic mimics remains challenging, and diagnostic approaches rely heavily on imaging without robust histopathologic validation. Proposed mechanisms include epithelial injury, immune dysregulation, and vascular or lymphatic abnormalities, although causal links remain unproven. Significant gaps persist regarding natural history and therapeutic responsiveness. Conclusions: Earlier identification of PPFE in CTD-ILD is important, as misclassification may delay risk stratification and management. Longitudinal imaging, multidisciplinary evaluation, and standardized diagnostic criteria are needed to improve clinical care and guide future research. Full article

Background: Hyperparathyroidism is a common endocrine disorder, and its diagnosis can be complex. Various indices based on blood biomarkers have been proposed to improve diagnostic accuracy. The objective of this systematic review was to analyze the diagnostic utility of different indices in primary hyperparathyroidism. Methods: A systematic review was performed with searches up to January 2026. Risk of bias was assessed, and a meta-analysis was conducted for indices with two or more studies, calculating sensitivity, specificity, and other accuracy measures. The certainty of the evidence was evaluated using the GRADE system. Results: Twelve studies were included. The calcium–phosphorus ratio demonstrated a sensitivity of 91.6%, specificity of 89.3%, and an area under the curve of 0.957. The parathyroid function index showed a sensitivity of 94.4% and specificity of 94.2%; however, this finding is based on only two studies and requires validation in larger cohorts. The Wisconsin index also showed good performance. Other indices, including the Ca × Cl/P ratio (evaluated in a single study), yielded promising results but with very limited evidence that precludes firm conclusions. All indices performed poorly in cases with normal calcium. Certainty assessment indicated moderate evidence for the main indices and low or very low evidence for the others. Conclusions: The calcium–phosphorus ratio and the parathyroid function index are valid and useful tools for the diagnosis of primary hyperparathyroidism, with excellent performance. The calcium–phosphorus ratio is especially valuable due to its simplicity and accessibility for screening. No index should be used in isolation; integration with clinical evaluation remains essential. Full article

Accurate tail risk forecasting in emerging markets is frequently compromised by the nonlinear dynamics and time-varying long memory of high-frequency volatility. In this study, we employ multifractal detrended fluctuation analysis (MF-DFA) to decode the complex market behavior, revealing pronounced multifractality and strong persistence that defy the static assumptions of classical linear models. The multifractal analysis is only used for research motivation and model design, not as input features for the model. To bridge the gap between fractal diagnostics and predictive modeling, we propose an attention-based dynamically reweighted SA-HAR-J-Net framework. This architecture uniquely integrates HAR-style multi-horizon inputs with a bidirectional LSTM (BiLSTM) encoder and a temporal self-attention mechanism. Crucially, the attention module functions as a dynamic reweighting system, allowing the model to adaptively emphasize historical patterns that receive higher attention weights under changing market conditions, thereby mimicking the time-varying correlations inherent in multifractal processes. Furthermore, we incorporate jump proxies and realized higher moments to enhance the capture of extreme tail dynamics. Utilizing a strict expanding-window out-of-sample protocol, the proposed method achieves significantly lower quantile loss and superior calibration relative to established econometric and machine learning benchmarks for Value-at-Risk (VaR) forecasting. This work provides a robust framework for tail risk monitoring by effectively aligning deep learning architectures with the stylized facts of multifractal markets. Full article

Intestinal infections caused by Salmonella enterica serovar Typhi (S. Typhi) remain a global health concern, making preventive strategies and diagnostic tools essential. This study aimed to identify mimotope peptides of the Vi antigen using phage display and assess their recognition by rabbit and 46 human sera, as well as their potential for diagnosis and immunogen design. Rabbits were immunized with the Vi antigen (AgVi) from S. Typhi ATCC 6539, and sera-derived IgG was used for phage biopanning. DNA sequences from selected phagotopes were synthesized as Salmonella mimotope peptides (SMPs), either linear or KLH-conjugated. Their reactivity was tested with ELISAs against AgVi and SMPs, using both rabbit sera and 46 human serum samples. Ten phagotopes were identified, with a consensus motif (D/G–A/V–x–P–x–x–G–x–x–x–x–x), suggesting α-helix structures. Immunization with KLH-conjugated peptides generated specific antibodies, particularly SMPVi/5 and SMPVi/10, which recognized AgVi and their respective peptides. Competitive inhibition assays confirmed that SMPVi/5 reduced the anti-AgVi binding in a dose-dependent manner. In human sera, AgVi recognition occurred in 52% of samples, while SMPVi/5 and SMPVi/10 were recognized in 45%. Overall, SMPVi/5 demonstrated immunogenicity and functional mimicry, supporting its use as a synthetic reagent for serological assays and as a candidate for immunogen design. Full article

Background/Objectives: Specific Learning Disorders are lifelong neurodevelopmental conditions that persist in adulthood, yet research has traditionally focused on children. In adults, there is significant heterogeneity in cognitive profiles and a lack of consensus on how to operationalize these disorders. This study aims to map the variability in cognitive functioning in university students with Specific Learning Disorders and investigate whether cognitive profiles differ across diagnostic categories and comorbidities. Methods: A retrospective analysis was conducted on the clinical documentation of 166 university students with a diagnosis of Specific Learning Disorders. Participants were categorized into three subgroups: predominant reading disorder, predominant arithmetic disorder, and mixed learning disorder. Cognitive functioning was assessed using Wechsler scales indices. Data were analyzed using linear mixed-effects models and Latent Profile Analysis. Results: Across the sample, reasoning abilities were significantly higher than cognitive efficiency, with working memory consistently emerging as a core weakness. The mixed-disorder group exhibited the lowest cognitive scores and the greatest working memory deficits. Latent Profile Analysis identified two distinct latent subgroups: a “Low Profile” characterized by weaker working memory and a “High Profile” characterized by stronger reasoning and balanced efficiency. Diagnostic labels were only partially aligned with these profiles; while the mixed-disorder group was overrepresented in the “Low Profile,” substantial intra-individual variability existed across all diagnostic categories. Conclusions: The findings suggest that traditional categorical labels for Specific Learning Disorders have limited explanatory power in adulthood, given the high heterogeneity of cognitive functioning. Cognitive weaknesses, particularly in working memory, persist even in high-achieving university students. Clinical and educational support should shift from a label-based approach toward a dimensional, profile-based model to better address the unique strengths and vulnerabilities of adults with Specific Learning Disorders. Full article

Background and Clinical Significance: Dentinogenesis imperfecta is a hereditary dentin disorder that compromises tooth structure, esthetics, and function. Case Presentation: We report the case of a 1.5-year-old female presenting with generalized discoloration of the primary dentition and intermittent sensitivity to thermal stimuli. The diagnosis of dentinogenesis imperfecta was established based on characteristic clinical features, radiographic findings, and a positive family history. The patient was followed longitudinally from 2020 to 2025, with documentation of diagnostic findings, radiographic changes, therapeutic interventions, and outcomes. Management included placement of composite veneers on the maxillary incisors for esthetic rehabilitation and sealants on second primary molars as a preventive measure. Although various management approaches have been described in the literature, evidence regarding optimal strategies and long-term outcomes in the primary dentition remains limited. This case highlights the occurrence of asymptomatic periapical pathology and root resorption despite minimal clinical symptoms, underscoring the challenges of relying on symptom-based assessment alone. Conclusions: Early diagnosis, regular radiographic monitoring, and individualized, risk-based treatment planning are essential in managing dentinogenesis imperfecta. This case emphasizes the importance of recognizing asymptomatic disease progression and integrating psychosocial considerations into comprehensive care. Full article

This Data Descriptor presents an anonymized, shuffled dataset of creatinine-normalized urinary metabolite measurements from 73 Bulgarian children with autism spectrum disorder (ASD), released to support reuse in secondary analyses and cross-cohort comparisons. The public release represents a pathway-oriented 24-marker subset from a broader urinary diagnostic panel, assembled as a self-contained resource for investigators working in these metabolic domains. Spot urine results are provided as individual-level values after creatinine normalization; for trimethylamine, values below the limit of quantification (LOQ) were replaced with LOQ/2. The deposit contains measurements for 24 urinary markers grouped into three functional classes (neurotransmitters and aromatic amino acid precursors; one-carbon/methylation and vitamin-related metabolites; and energy metabolism/organic acids with microbiome-related amines). The underlying cohort comprised children aged 3–13 years, and no contemporaneous neurotypical control group was enrolled. Second-morning, midstream, acid-stabilized spot urine samples were collected within the provider’s workflow; metabolites were measured by LC–MS/MS, and spot urinary creatinine was measured enzymatically for normalization. The release includes the results table in both XLSX and CSV formats, a reference limits and units file for contextual interpretation, a data dictionary, a README, a changelog, and SHA-256 checksums for integrity verification. The public files contain de-identified analytical variables only and omit individual-level demographics, dates, standalone urinary creatinine, and richer clinical metadata to preserve anonymity. Full article

Osteoarthritis (OA) is a complex degenerative joint disease for which early diagnosis and clear molecular characterization remain limited. DNA methylation has been increasingly recognized as an important regulatory factor in OA pathogenesis. In this study, we proposed an integrative computational framework combining statistical analysis, machine learning, deep learning, and functional genomics to identify and validate OA-associated genes and methylation biomarkers for diagnostic and biological interpretation. Candidate CpG sites were obtained using two complementary strategies: differential methylation analysis and selection of loci located near transcription start sites of previously reported OA-related genes. Key features were further refined using support vector machine recursive feature elimination and random forest algorithms. Based on the selected loci, we developed a feature-fusion diagnostic model that combines Transformer and convolutional neural networks with adaptive weighting to capture both global dependency structures and local methylation patterns. A panel of 220 methylation sites demonstrated stable and reproducible diagnostic performance in an independent cohort. Functional annotation and pathway analysis highlighted several established OA-associated genes, including TGFBR2, SMAD3, PPARG, and MAPK3, and suggested INHBB as a potential novel effector gene, with additional support for AMH and INHBE involvement. Overall, this study presents a robust methylation-based framework for identifying key OA-associated genes and provides new insights into the epigenetic mechanisms underlying OA. Full article

Differential operators often admit multiple algebraically equivalent symbolic formulations, yet those formulations can differ in the organization of their internal structure prior to solution analysis. A reproducible symbolic framework is introduced to compare such formulations at the level of operator expressions. Within a declared symbolic specification consisting of a fixed grammar, an admissible weight class, canonical compression rules, and an admissible family of reformulations, we define four encoding-relative structural descriptors: structural strain τ, structural curvature κ, compressibility σ, and the balance ratio Γ = κ/τ. Structural strain compares an encoding to a designated reference representation, while compressibility measures reduction under canonical symbolic compression. These quantities are deterministic descriptors within the declared encoding class rather than coordinate-free invariants of the underlying operator. The structural length functional underlying these descriptors is developed, canonical compression is formalized, and finite symbolic comparison is distinguished from pathwise symbolic deformation. A robustness theorem shows that, away from the threshold surface Γ = σ, sufficiently small admissible perturbations preserve the induced diagnostic label. A supporting weight-robustness result further shows that qualitative labels persist across a local admissible family of weight choices under corresponding nondegeneracy conditions. The framework serves as a reproducible diagnostic for operator representations alongside Lyapunov, spectral, pseudospectral, and energy-based stability theories. Examples of representative ordinary and partial differential operators illustrate how the descriptors are computed and how they behave under admissible re-expression, while the appendices provide the technical backbone of the paper: formal definitions, reproducibility protocol, extended perturbation arguments, and explicit failure-mode analysis. Additional sensitivity checks regarding encoding, weights, and threshold variation clarify the method’s scope, and explicit failure modes delineate the boundary cases in which the descriptors cease to apply. The main contribution of this study is a formally delimited and reproducible symbolic framework for comparing differential operators under a fixed, declared specification, together with robustness results and worked examples that clarify the method’s scope. Full article

Against the backdrop of intensifying global population aging, ensuring the sustainable provision of age-friendly spaces has become an important domain of urban policy intervention. A close examination of the supply–demand matching of age-friendly spaces is therefore essential for policymakers seeking to achieve social and environmental sustainability in an aging society. However, existing approaches to assessing this alignment primarily rely on quantitative analyses of geographical spatial distribution, lacking methods to evaluate the structural alignment of spatial functional types. To address this gap, this study proposes and validates a type-based quantitative approach to examining the alignment between policy supply and everyday demands for age-friendly spaces. By integrating policy text analysis, questionnaire surveys, activity logs, and behavior snapshots, the study identifies the types of age-friendly spaces mentioned by policies and those demanded in daily life, and quantitatively evaluates their alignment using a matching model. The results show that the older adults’ spatial demands shift progressively from life-oriented spaces to survival-oriented spaces as age increases and health declines. More importantly, a significant structural imbalance is evident: survival-oriented spaces are oversupplied, while life-oriented spaces remain in short supply. This study provides a diagnostic method for assessing the provision of age-friendly spaces and provides practical implications for local governments in formulating more balanced, responsive, and sustainable supply strategies. Full article

Primary Sjögren’s disease (SjD) is characterized by lymphocyte infiltration into exocrine glands. Mitochondrial dysfunction is a critical pathological mechanism underlying SjD, and mitophagy plays a vital role in clearing damaged mitochondria. This study used bioinformatic analysis to explore the potential roles of mitophagy-related genes in SjD pathogenesis and immune infiltration. Bioinformatic analysis was performed on the SjD microarray datasets to identify differentially expressed genes (DEGs). Mitophagy-related DEGs were selected and analyzed using functional enrichment, protein–protein interaction (PPI) networks, and machine learning (Least Absolute Shrinkage and Selection Operator [LASSO] and Random Forest) to identify hub genes. Their diagnostic value was assessed by receiver operating characteristic (ROC) curves. Immune infiltration and its correlation with hub genes were also evaluated. Hub gene expression in the salivary glands of patients was validated using qRT-PCR. Regulatory networks were also predicted. Three hub genes (GABARAPL1, PINK1, and SQSTM1) were identified. They showed high diagnostic specificity and were downregulated in SjD salivary glands. Immune infiltration analysis revealed increased levels of activated natural killer (NK) cells, memory B cells, plasma cells, CD8+ T cells, Tfh cells, and M1 macrophages, but decreased levels of Tregs and M2 macrophages. Hub gene expression was correlated with specific immune cell subsets. Regulatory network predictions highlighted potential upstream regulators and therapeutic compounds. This study identified three mitophagy-related hub genes linked to immune dysregulation in SjD, providing novel insights into disease mechanisms and potential therapeutic targets. Full article