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PubMed2026

Combining neuromelanin-sensitive MRI and quantitative susceptibility mapping for enhanced diagnosis and differentiation of parkinson's disease: A systematic review.

BACKGROUND: Loss of dopaminergic neurones and iron deposition in the substantia nigra pars compacta (SNpc) are two major pathological hallmarks of Parkinson's disease (PD). Such changes can be visualised by advanced techniques including neuromelanin-sensitive MRI (NM-MRI) and quantitative susceptibility mapping (QSM). This systematic review investigates the diagnostic performance and methodological development of the integrated use of NM-MRI and QSM in PD. METHODS: The systematic search was performed in four databases (Scopus, PubMed, ScienceDirect, and Web of Science) according to the PRISMA 2020 guidelines until July 2026. Bias was assessed using QUADAS-2 and certainty of evidence was assessed using GRADE. RESULTS: Seventeen studies with 2228 participants were included. Combined NM-MRI and QSM consistently showed reduced neuromelanin volume/contrast and increased iron deposition in the SNpc of PD patients compared to healthy controls. Multimodal integration yielded a significant improvement in diagnostic accuracy (AUC values 0.86-0.99), and was able to successfully differentiate PD. Recent methodological advances included simultaneous acquisition sequences (e.g. MTC-GRE, STAGE, setMag) and AI-driven automated segmentation, which led to significantly reduced scan times and improved reproducibility. CONCLUSION: The combination of NM-MRI and QSM has a synergistic effect and provides powerful complementary biomarkers for the diagnosis and differential diagnosis of PD.

Humans↗
PubMed2026

Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.

PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.

Humans↗
PubMed2026

Prion disease mimicking rapidly progressive Alzheimer disease: case series and systematic review.

BACKGROUND: Prion disease and Alzheimer disease (AD) are common causes of rapidly progressive dementia (RPD). Although most patients with prion disease are distinguished by MRI and CSF findings, selected cases mimic rapidly progressive AD. We characterized AD-prion disease mimics within a prospective cohort and the extant literature to identify the clinical features and tests that support accurate diagnoses in these patients. METHODS: Patients with prion disease initially diagnosed as rapidly progressive AD were identified from a prospective cohort study at Mayo Clinic (February 2020-June 2026) and through systematic review of MEDLINE and Embase. RESULTS: Of 204 patients with RPD, five (2.5%) were initially diagnosed with clinically probable AD but ultimately determined to have prion disease. Systematic review identified 10 additional cases (n=15, median age-at-onset, 59 years; 67% male). Presentations reproduced amnestic (53%), dysexecutive (27%), primary progressive aphasia (13%), and posterior cortical atrophy (7%) AD phenotypes; median time from AD diagnosis to consideration of prion disease was 2 months. Diffusion-weighted MRI abnormalities were absent in Mayo Clinic cases and absent/equivocal (n=2) or overlooked (n=8) in published cases. CSF biomarkers were consistent with AD in 6/9 tested patients, with elevated total tau levels in 11/13 patients and total-tau/p hosphorylated-tau181 ratios in 5/9 patients. Real-time quaking-induced conversion assays for prions were positive in the CSF of 9/12 patients. Prion disease was confirmed by neuropathology (n=7), genetics (n=2), or real-time quaking-induced conversion (n=6) assays. CONCLUSIONS: Prion disease may rarely mimic rapidly progressive AD. Disproportionate elevations in CSF total-tau levels or total-tau/p hosphorylated-tau181 ratios should prompt consideration of prion disease.

Humans↗
PubMed2026

Exploratory proteomic and metabolomic profiling of pleural effusions identifies histone H4 and alanine as promising complementary markers for pleural tuberculosis.

The diagnosis of pleural tuberculosis (Pl-TB) remains challenging. Histopathological analysis and pathogen detection in pleural biopsies are informative but limited. We investigated differentially expressed proteins and metabolites in pleural effusions from patients with Pl-TB, malignancies, and other pathologies. A proteomic analysis of pooled pleural effusions identified 45 proteins exclusively detected or upregulated in Pl-TB samples, many linked to infectious processes. Conversely, 18 proteins were uniquely found or upregulated in malignant pleural effusions, mainly associated with detoxification and hemostasis. To validate these findings, we employed targeted proteomics in individual samples. Eight proteins were validated: S100-A9, histone H4, insulin-like growth factor-binding protein 2, fibrinogen beta chain, ficolin-3, immunoglobulin heavy constant alpha 1, sulfhydryl oxidase 1, and histidine-rich glycoprotein. Additionally, NMR-based metabolomics identified 13 metabolites with differential abundance between Pl-TB and non-TB samples. Notably, N-acetyl-glycoprotein and the branched-chain amino acids, alanine and lysine differed between groups. Proteomic and metabolomic analyses revealed distinct molecular profiles between Pl-TB and non-TB patients, despite intra-group variability. To address this, we applied classification models. Histone H4 and alanine consistently emerged as discriminative features. Overall, this study provides novel insights into the molecular landscape of Pl-TB. The combined quantification of proteins and metabolites may improve differential diagnosis, although should be further validated in larger, independent cohorts before clinical application.

Humans↗
PubMed2026

Respiratory-onset peripartum cardiomyopathy: a systematic review of diagnostic pitfalls and clinical outcomes.

INTRODUCTION: Peripartum cardiomyopathy (PPCM) may initially present with prominent respiratory symptoms that resemble primary pulmonary disease, particularly in late pregnancy and the early postpartum period. In clinical practice, this presentation often triggers alternative diagnostic pathways, introducing delay at a time when rapid cardiac assessment is critical. Although respiratory-dominant presentations are repeatedly described across case-based and observational reports, they have not been systematically examined as a distinct diagnostic pathway within the PPCM literature. CONTENT: This PRISMA-guided systematic review synthesized evidence relating to respiratory-onset presentations of PPCM. Major databases and registers were searched comprehensively. Following screening of 589 records and full-text assessment of 145 reports, 49 studies met inclusion criteria. Twenty studies were qualitatively prioritized for narrative synthesis using ROBIS-informed methodological appraisal. Evidence was examined across diagnostic misclassification patterns, cardiopulmonary mechanisms, differential diagnoses, investigative strategies, and acute and longitudinal management considerations. SUMMARY: Respiratory-led presentations were commonly misattributed to asthma, pneumonia, pulmonary embolism, or perioperative causes, with diagnostic delay frequently reported. Across heterogeneous study designs, cardiogenic pulmonary edema with left-ventricular systolic dysfunction emerged as a recurring unifying mechanism. Early use of echocardiography, natriuretic peptides, and targeted imaging consistently aided differentiation from primary respiratory pathology. Severe clinical deterioration was often described in the context of delayed recognition. OUTLOOK: Respiratory-onset PPCM represents a high-risk diagnostic pathway rather than a discrete disease entity. Prospective registries, standardized diagnostic algorithms, and closer integration of obstetric and cardiopulmonary care are needed to refine early recognition and improve maternal outcomes.

Humans↗
PubMed2026

Diagnostic Value and Limitations of Dermoscopy in Humans and Animals: A Critical Comparative Analysis.

BACKGROUND: Dermoscopy is a noninvasive imaging technique that is well-established in human dermatology, where it enhances the diagnosis of neoplastic, inflammatory, infectious, and alopecic skin disorders. In veterinary dermatology, its use is expanding yet remains heterogeneous and largely descriptive, despite growing evidence of conserved dermoscopic patterns across species. HYPOTHESIS/OBJECTIVES: To review the applications of dermoscopy in veterinary dermatology, and to provide a comparative analysis of dermoscopic features observed in dogs, cats, and horses in relation to corresponding findings in human dermatology. MATERIALS AND METHODS: A systematic review of the literature reporting dermoscopic findings in veterinary dermatology was conducted in accordance with PRISMA guidelines. PubMed, Scopus and Google Scholar were searched for studies published up to 30 July 2025. Eligible studies included original articles describing dermoscopic features in dogs, cats, or horses. Extracted data included species, dermatological condition, dermoscopic findings, device type and histopathological correlation, when available. Levels of evidence were assessed using the Oxford Centre for Evidence-Based Medicine criteria. RESULTS: Thirty studies met the inclusion criteria. Most were descriptive case reports or case series. Dermoscopy was applied to a wide range of conditions, including alopecias, parasitic infestations, dermatophytosis, neoplastic and sebaceous lesions, inflammatory dermatoses, and congenital vascular anomalies. Recurrent dermoscopic features showed strong similarities to those described in human dermatology, although species-specific anatomical differences influenced interpretation. CONCLUSIONS: Dermoscopy represents a valuable adjunct diagnostic tool in veterinary dermatology, with clear translational relevance. Standardisation of terminology and further prospective studies are required to support its broader clinical integration.

Animals↗
PubMed2026

Artificial intelligence-assisted detection and optical differentiation of colorectal lesions in Lynch syndrome surveillance (CADLY2): a multicentre, open-label, randomised controlled superiority trial.

BACKGROUND: Artificial intelligence (AI)-based computer-aided detection (CADe) systems improve adenoma detection in average-risk colorectal cancer screening. Meanwhile, evidence in Lynch syndrome surveillance is sparse and inconsistent. We assessed the effect of CADe on adenoma detection during Lynch syndrome surveillance. Computer-aided optical diagnosis (CADx) performance for optical differentiation of colorectal lesions was evaluated as a secondary aim. METHODS: CADLY2 was an international, multicentre, open-label, randomised controlled superiority trial at nine specialised hereditary cancer surveillance centres in Belgium, Germany, the Netherlands, and Spain. Adults aged 18 years or older with genetically confirmed Lynch syndrome scheduled for surveillance colonoscopy were randomly assigned (1:1) to high-definition white-light (HD-WL) colonoscopy alone or to HD-WL colonoscopy with computer-aided assistance from CAD EYE (Fujifilm, Tokyo, Japan). CAD EYE was used for CADe during withdrawal and for CADx after lesion detection. Randomisation was done centrally through a secure web-based system using Pocock's minimisation algorithm with a stochastic component and was stratified by centre, sex, previous colorectal cancer, underlying pathogenic variant, and interval since previous colonoscopy. Allocation concealment was ensured through the centralised web-based system. Patients were masked to group allocation until the start of withdrawal in procedures with mild sedation, or until completion of the procedure in procedures with propofol-based sedation. Endoscopists were not masked. The primary outcome was adenoma detection rate, defined as the proportion of patients with at least one histopathologically confirmed adenoma, analysed in the full analysis set (defined as all randomly allocated patients with available data for the primary outcome). The diagnostic performance of the CADx system was evaluated as a secondary outcome. The safety analysis set comprised all randomly allocated patients who underwent a study colonoscopy. This study is registered with the German Clinical Trials Register, DRKS00030695, and is completed. FINDINGS: Between May 9, 2023, and Oct 30, 2025, 757 patients were randomly allocated to HD-WL colonoscopy (377 patients) or to AI-assisted colonoscopy (380 patients); 733 patients were included in the full analysis set (369 HD-WL and 364 AI-assisted). The median age was 49 years (IQR 38-59) in the HD-WL group and 50 years (38-59) in the AI-assisted group; 213 (58%) were female and 156 (42%) male in the HD-WL group, and 207 (57%) were female and 157 (43%) male in the AI-assisted group. The adenoma detection rate was 30·9% (114 of 369 patients) with HD-WL versus 33·8% (123 of 364 patients) with CADe assistance (odds ratio 1·14 [95% CI 0·83-1·57], p=0·41). For CADx differentiation of neoplastic versus non-neoplastic lesions in the paired lesion-level analysis, with histopathology as the reference standard and sessile serrated lesions and traditional serrated adenomas classified as non-neoplastic, CADx sensitivity was 85·9% (95% CI 82·0-89·1) and specificity was 91·4% (89·4-93·0). Three adverse events occurred in the AI-assisted group: two mild post-polypectomy bleedings and one serious pulmonary embolism or deep venous thrombosis unrelated to the procedure. No adverse events occurred in the HD-WL group. INTERPRETATION: CADe-assisted colonoscopy did not show the absolute improvement in adenoma detection rate that was assumed in the prespecified sample-size calculation. CADx did not clearly improve lesion differentiation beyond expert optical diagnosis in expert Lynch syndrome surveillance settings. FUNDING: Third-party research funding of the National Center for Hereditary Tumor Syndromes, University Hospital Bonn.

Humans↗
PubMed2026

Non-parametric differential methylation analysis characterizes histotype-specific promoter regions in epithelial ovarian cancer.

Epithelial ovarian cancer (EOC) is a heterogenous disease with frequent late-stage diagnosis and high mortality rates, for which no reliable screening tests exist. In recent years, epigenetic biomarkers in the form of DNA methylation in CpG-rich regions have gained increased attention in the scientific community due to their robust nature and accessibility, allowing for diagnosis without the need for invasive surgery. In this study, we investigated the aberrant methylation of promoter regions in early stage EOC through non-parametric methods, with the purpose of characterizing candidate epigenetic biomarkers. The approach was used on a cohort of early stage EOC samples, and results were compared to existing programs for differential methylation. Significant regions were then used to construct a CpG panel for stratifying EOC histotypes through predictive classification in external data. Identified promoter regions were highly reproducible across cohorts, and the constructed CpG model stratified histotypes in external cohorts through predictive classification. Comparisons against other DMP and DMR callers showed a degree of homogeneity between results but also revealed promoter regions that were overlooked despite clear signs of aberrant methylation. Finally, EOC histotypes were found to differ in their methylation distribution types, and results indicate that methods sensitive to non-normally distributed data may be poorly suited to compare groups with different distribution types. The non-parametric approach identified aberrantly methylated promoter regions that were highly reproducible across cohorts. Results from predictive classification indicate that these regions may be useful for the purpose of EOC histotype stratification.

Humans↗
PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗
PubMed2026

Blood Bile Acids for Inflammatory Bowel Disease Diagnosis and Disease Activity Assessment: A Metabolomics Meta-Analysis.

Alterations in circulating bile acids (BAs) have been reported in inflammatory bowel disease (IBD), but the consistency of these changes across clinically relevant comparisons remains unclear. Our goal was to investigate systemic BA alterations in IBD using a metabolomics meta-analysis with an exploratory analysis of BA-related gene expression as a supporting context. A systematic review and meta-analysis of 28 metabolomics studies examined blood BA profiles associated with IBD, IBD diagnosis, and disease activity assessment. Univariate analysis and logistic regression modeling of two independent IBD cohorts explored the blood BA-related genes and IBD. Across 28 studies that comprised 5056 IBD patients, 1721 healthy controls, and 314 non-IBD patients, 131 BAs were reported. Eight predefined clinical comparisons were eligible for the meta-analysis. Lower secondary BA levels were consistently observed in IBD patients compared with controls, between UC and CD, and in active versus remission patients. Deoxycholic acid, glycodeoxycholic acid, and taurodeoxycholic acid were frequently decreased, whereas glycocholic acid was increased in certain comparisons. Transcriptomics analyses revealed differential expression of several BA-related genes in blood, including SLC51A, ABCB4, and ACOT8, across the comparisons. Our findings identify consistent circulating BA alterations in IBD and highlight the relevance of blood BA for future biomarker research in the diagnosis and disease activity assessment.

Humans↗
PubMed2026

Urinary Small Extracellular Vesicle DNA as a Biomarker for the Non-Invasive Diagnosis of Bladder Cancer.

Existing diagnostic technologies for bladder cancer (BC) suffer from low sensitivity, low specificity, or a lack of validation. Therefore, validated, non-invasive diagnostic biomarkers with high sensitivity and specificity for early detection of BC are needed to complement and improve upon the limitations of existing diagnostic methods. We used low-pass whole genome sequencing (LP-WGS) technology to detect copy number variations (CNVs) in small extracellular vesicle (sEV) DNA isolated from urine samples of patients. Based on these results, we constructed and validated a diagnostic model to differentiate between benign and malignant bladder lesions. We conducted a receiver operating characteristic analysis and calculated the area under the curve (AUC) to evaluate the performance of the diagnostic model. The urine sEV-DNA LP-WGS data revealed CNV differences between benign and malignant samples. The diagnostic model achieved an AUC of 0.953, a sensitivity of 86.7%, and a specificity of 100% in the training cohort and an AUC of 0.985, a sensitivity of 90%, and a specificity of 100% in the validation cohort. Even at the lowest coverage depth of 0.01X, the performance of the diagnostic model remained relatively robust. Notably, the performance of this diagnostic model surpassed that of the biomarker neuron-specific enolase (sensitivity: 85.7% vs. 64.3%; specificity: 100% vs. 87.5%) and urinary cytology (sensitivity: 100% vs. 66.7%; specificity: 100% vs. 94.1%). Our study demonstrates that urine sEV-DNA exhibits high discriminatory power in distinguishing between benign and malignant bladder lesions, making it a promising tool for auxiliary diagnosis of BC.

Humans↗
PubMed2026

First Report of Fibromyxoma in a Greater Amberjack (Seriola dumerili) From Aquarium of Genoa.

In teleosts, mesenchymal tumours like fibromas are frequently observed, whereas their malignant counterparts, fibrosarcomas, occur only sporadically. In our study, an adult female greater amberjack (Seriola dumerili) reared at the Aquarium of Genoa developed a slow-growing mass protruding from the ventral right side of the head. The tissue was firm and white greyish with localized cranial haemorrhaging on the external surface. Cut-section examination showed a homogeneous, dry, whitish and richly vascularized tissue layout. Tissue samples from the mass were stained with Masson's Trichrome, Alcian Blue-PAS (pH 2.5), and Toluidine blue for differential diagnosis. Microscopically, it was characterized by alternating collagenous spindle-cell septa and hypocellular metachromatic myxoid areas positive for acidic mucopolysaccharides and glycosaminoglycans. The absence of cellular atypia, mitotic activity and necrosis confirms the diagnosis of fibromyxoma. Documenting rare tumours in aquarium fish expands comparative pathology literature and highlights public aquaria as valuable research platforms for long-term health monitoring under controlled husbandry conditions.

fibromyxoma↗
PubMed2026

From host response to genomic targets: electrochemical biosensing of tuberculosis biomarkers.

Tuberculosis (TB) remains one of the leading causes of death from a single infectious agent worldwide, with timely diagnosis continuing to be a major challenge, particularly in resource-limited settings. Conventional TB diagnostic methods are limited by low sensitivity, long turnaround times, and an inability to reliably differentiate latent from active disease. Biomarker-based diagnostic strategies have therefore gained increasing attention as they offer the potential to improve early detection, disease differentiation, and treatment monitoring. Herein, we examine electrochemical biosensing strategies for TB diagnostics using a biomarker-class-driven framework, covering host-response biomarkers (IFN-γ and TNF-α), pathogen-derived antigens (ESAT6, CFP10, CFP10-ESAT6, MPT64, Ag85, HspX and LpqH), cell-wall signatures and whole-cell markers (LAM and whole cell Mtb), and genomic markers (Mtb DNA and IS6110). Through structured comparison of recognition elements, biointerface designs, signal amplification strategies, electrochemical techniques, matrices, and validation levels, this review identifies the most promising technical approaches for different TB biomarker classes. It further highlights key translational bottlenecks, including limited clinical validation, buffer-based testing, complex multistep amplification, redox-probe dependence, matrix fouling, and insufficient evidence of manufacturability. This review therefore provides practical guidance for developing electrochemical TB biosensors that are analytically sensitive, clinically relevant, and suitable for decentralized diagnostic applications.

Biosensing Techniques↗
PubMed2026

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗
PubMed2026

Assessing the Frequency of VEXAS-Related Canonical UBA1 Mutations in Myelodysplastic Syndrome Patients.

OBJECTIVES: Somatic mutations in the UBA1 gene cause VEXAS syndrome, which presents with inflammatory and hematological symptoms. Case studies show a strong overlap between VEXAS and myelodysplastic syndrome (MDS). Recognizing VEXAS is important for differential diagnosis in patients with both inflammation and MDS, as accurate identification guides treatment. The study focuses on determining how often canonical UBA1 mutations linked to VEXAS occur in MDS patients. METHODS: Patients diagnosed with MDS were enrolled in the study, and genomic DNA was isolated from bone marrow FFPE samples. Molecular analysis was performed using a specifically designed ARMS-PCR approach. Additionally, protein-protein interaction (PPI) studies combined with bioinformatic analyses were carried out to explore potential links between UBA1 and pyroptosis. RESULTS: Among the 149 MDS patients analyzed, none exhibited high-Variant Allele Frequency (VAF) the canonical UBA1 point mutations linked to VEXAS syndrome. PPI analysis revealed a possible association between UBA1 and the NLRP3 inflammasome component. CONCLUSIONS: Expanding the sample size and using targeted NGS or ddPCR would improve mutation detection sensitivity and could reveal UBA1 canonical and non-canonical variants and more accurately estimate the frequency of VEXAS-related mutations in the MDS population.

Humans↗
PubMed2026

Epigenetics and In Silico Transcriptome Analysis of Pediatric Acute Myeloid Leukemia.

Pediatric acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy that accounts for about 15%-20% of childhood leukemias. Despite therapeutic advances, relapses remain common, and survival for high-risk patients is below 60%. Unlike adult AML, pediatric AML displays distinct genetic mutations, including FLT3-ITD, NPM1, KMT2A rearrangements, and core-binding factors (CBF) fusions, as well as extensive epigenetic dysregulation. Aberrant DNA methylation, histone modifications, and altered non-coding RNA expressions disrupt hematopoietic differentiation and activate oncogenic transcriptional networks. Recent advances in silico transcriptomic analysis have transformed the study of pediatric AML by integrating gene expression and epigenetic data to identify molecular drivers and regulatory networks. Computational RNA-seq pipelines and pathway analyses have highlighted key epigenetic regulators, including DNMT3A, TET2, and HDACs, as potential therapeutic targets. Multi-omics approaches combining transcriptomic, methylomic, and chromatin accessibility data are increasingly used to define biomarkers for diagnosis, prognosis, and therapeutic response. This review provides a comprehensive overview of the molecular and epigenetic landscape of pediatric AML, emphasizing the power of in silico transcriptome analysis to uncover disease mechanisms, refine patient stratification, and guide the development of precision-based epigenetic therapies aimed at improving long-term outcomes in children with AML.

Humans↗
PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗