Search Results (312)

Background/Objectives: Rheumatoid neutrophilic dermatosis (RND) is a rare extra-articular manifestation of rheumatoid arthritis (RA) with variable clinical presentations. Although typically non-blistering, a rare bullous or vesiculobullous subtype has been described, mainly in patients with seropositive and active RA, and may mimic autoimmune blistering diseases. The objective of this review was to systematically summarize the clinical, histopathological, immunopathological, and therapeutic features of vesiculobullous rheumatoid neutrophilic dermatosis. Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines utilizing the PubMed, MEDLINE, and Google Scholar databases, which were searched through December 2025. Case reports and case series describing vesiculobullous or bullous RND with extractable patient-level data were included. Non-English articles were translated. Demographic, clinical, histopathological, immunopathological, microbiological, and therapeutic data were extracted and analyzed using Statistica 12.0 software. Results: Results were synthesized descriptively due to clinical heterogeneity and limited sample size. Thirty reported cases were identified, of which 28 non-duplicate cases were included. The mean patient age was 60.8 ± 14.9 years, with a female predominance (male-to-female ratio, 1:2.5). Most patients were of Asian descent (67.9%). Bullous or vesicular lesions most frequently involved the lower legs (64.3%), palms and soles (41.7%), and thighs (35.7%). Rheumatoid factor data were available in 67.9% of patients, all indicating high RA activity. Histopathological examination was reported in 71.4% of cases and most commonly demonstrated a predominantly neutrophilic infiltrate, often dense and extending throughout the dermis, with subepidermal blister formation being the most frequent pattern. Direct immunofluorescence, serological testing for autoimmune bullous diseases, and microbiological investigations were predominantly negative. Dapsone and systemic corticosteroids, alone or combined with RA-specific therapies, were the most commonly used treatments. Conclusions: This review represents the most comprehensive synthesis to date focused exclusively on the bullous/vesiculobullous subtype of RND, highlighting key diagnostic features such as neutrophil-predominant histopathology, negative direct immunofluorescence, and favorable response to dapsone. Full article

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is one of the most common extra-articular manifestations of rheumatoid arthritis (RA) and a leading cause of mortality in RA patients. The diverse and nonspecific clinical presentations of RA-ILD make early diagnosis particularly challenging. In recent years, with a deeper understanding of the pathogenesis of RA-ILD and rapid advancements in medical imaging, artificial intelligence (AI) technologies, and biomarker research, notable progress has been achieved in the diagnostic approaches for RA-ILD. This review summarizes the latest research developments in the diagnosis of RA-ILD, with a focus on the clinical practice guidelines released in 2025. It discusses the application of high-resolution computed tomography (HRCT), the potential of AI in assisting HRCT-based diagnosis, and the discovery and validation of biomarkers. Furthermore, the review addresses current diagnostic challenges and explores future directions, providing clinicians and researchers with a cutting-edge perspective on RA-ILD diagnosis. Full article

Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can support more precise and resilient cocoa management across heterogeneous smallholder landscapes. A PRISMA-guided systematic review of peer-reviewed literature published between 2000 and 2024 was conducted, yielding 49 core studies analyzed alongside supporting evidence. The synthesis evaluates regenerative agronomic outcomes, UAV-derived multispectral, thermal, and structural diagnostics, and AI-based analytical approaches for stress detection, yield estimation, and management zoning. Results indicate that regenerative practices consistently improve soil health and yield stability, while UAS data enhance spatial targeting of rehabilitation, shade management, and stress interventions. AI models further improve predictive capacity and decision relevance when aligned with data availability and institutional context, although performance varies across systems. Reported yield stabilization or improvement typically ranges from 12–30% under integrated approaches, with concurrent reductions in fertilizer and water inputs where spatial targeting is applied. The review concludes that effective scaling of RA–UAS–AI systems depends less on technical sophistication than on governance arrangements, extension integration, and cooperative service models, positioning these tools as enabling components rather than standalone solutions for sustainable cocoa intensification. Full article

Background: Molecular testing is recommended to refine risk stratification in indeterminate thyroid nodules (Bethesda III–IV), but data on dual-gene (BRAF and RAS) testing using fresh FNA washout specimens are limited. We aimed to evaluate the performance of BRAF and RAS mutation analysis from fresh thyroid FNA washout material, with a focus on indeterminate cytology. Methods: We retrospectively analyzed 1139 patients who underwent washout-based molecular testing between May 2022 and October 2024 at a tertiary endocrine center. Of these, 307 had available histopathologic results after surgery. Primary outcomes were sample adequacy, mutation spectrum, and diagnostic metrics (sensitivity, specificity, PPV, NPV, and accuracy). Analyses were repeated under two assumptions that classified borderline/low-risk neoplasms as benign vs. malignant, and within the Bethesda III–IV subset. Results: Adequate material for molecular analysis was obtained in 1037/1139 samples (90.9%). In the operated cohort (n = 307), malignant lesions comprised 31.9% and low-risk neoplasms 8.5%. When borderline lesions were considered benign, mutation positivity yielded a sensitivity of 48.0%, a specificity of 89.6%, a PPV of 75.9%, an NPV of 71.9%, and an accuracy of 72.9%. In Bethesda III–IV nodules (n = 153), sensitivity, specificity, and accuracy were 41.0%, 85.2%, and 66.0% (malignant assumption). Isolated BRAF positivity showed high specificity (~96.7%) with modest sensitivity. Conclusions: Our findings extend current diagnostic approaches by showing that dual-gene (BRAF and RAS) testing from fresh FNA washouts is technically feasible (≥90% adequacy) and provides high specificity with modest sensitivity for malignancy in indeterminate nodules. In settings lacking comprehensive commercial panels, this low-complexity approach offers a practical adjunct to cytology and imaging for preoperative decision-making. Full article

Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic–spatial inference framework (LLM-SSIF), a lightweight semantic–spatial pipeline that translates POI texts into interpretable, fine-grained spatial evidence through an end-to-end workflow that couples scalable label expansion with scale-controlled spatial diagnostics at a 500 m resolution. A key advantage of LLM-SSIF is its deployability: LoRA-based parameter-efficient fine-tuning of an open LLM enables lightweight adaptation under limited compute while scaling fine-label coverage. Trained on a nationwide cuisine-labeled dataset (~220,000 records), the model achieves strong multi-class short-text recognition (macro-F1 = 0.843) and, in the Guangzhou–Shenzhen demonstration, expands usable fine-category labels by ~14–15× to support grid-level inference under long-tail sparsity. The spatial module then isolates cuisine-specific over/under-representation beyond overall restaurant intensity, revealing contrasting cultural configurations between Guangzhou and Shenzhen. Overall, LLM-SSIF provides a reproducible and transferable way to translate unstructured POI texts into spatial–statistical evidence for comparative urban analysis. Full article

Inflammatory bowel disease (IBD) and rheumatoid arthritis (RA) are chronic inflammatory diseases that share immune dysregulation and mitochondrial dysfunction. Understanding the molecular mechanisms linking these diseases to mitochondrial dysfunction is crucial for developing novel diagnostic and therapeutic strategies. Datasets related to IBD and RA were obtained from the Gene Expression Omnibus database. Differentially expressed mitochondrial dysfunction-related genes (MDRGs) were identified using differential expression analysis. Weighted gene co-expression network analysis was performed to identify crosstalk genes (CGs). Logistic regression and support vector machine (SVM) models were constructed using least absolute shrinkage and selection operator regression to identify hub genes. Additionally, the differential expression and diagnostic value of the hub genes were verified using quantitative reverse transcriptase–polymerase chain reaction and validation sets. Finally, immune infiltration analysis was conducted to assess the role of immune cells in IBD and RA. A total of 87 CGs associated with mitochondrial dysfunction were identified between IBD and RA, among which PDIA4 and DUSP6 were identified as hub genes. Twenty proteins, including ERO1A, MAPK7, and P4HB, were identified as key proteins that interacted with PDIA4 and DUSP6. The area under the curve (AUC) of the ROC curves for IBD and RA based on the DUSP6 and PDIA4 diagnostic models were 0.664 and 0.856, respectively. The qRT-PCR results indicated that PDIA4 and DUSP6 were overexpressed in IBD and RA. Seven immune cell types, including activated B cells, activated dendritic cells, and eosinophils showed significant differences in the IBD and RA groups. Our findings highlight the close association between IBD, RA, and mitochondrial dysfunction. PDIA4 and DUSP6 may serve as potential biomarkers of mitochondrial dysfunction in patients with IBD and RA. Full article

Aquaculture is a crucial global food production sector that faces challenges in water quality management, food safety, and stress-related health concerns in aquatic species. Cortisol, a key stress biomarker in fish, and Escherichia coli (E. coli) contamination in bivalve mollusks are critical indicators that require sensitive and real-time detection methods. Liquid crystal (LC)-based immunosensors have emerged as a promising solution for detecting biological analytes due to their high sensitivity, rapid response, and label-free optical detection capabilities. Therefore, this study explores the development and application of LC-based immunosensors for the detection of cortisol in artificial and real recirculating aquaculture system (RAS) samples, as well as E. coli in real contaminated water and clam samples during the depuration processes of bivalve mollusks. The biosensors exhibited the capacity to detect cortisol with a response time in seconds and a limit of detection (LOD) of 0.1 ng/mL. Furthermore, they demonstrated specificity to cortisol when tested against different interfering substances, including testosterone, glucose, and cholesterol. Furthermore, it was possible to correlate cortisol concentrations in different filtration stages and track E. coli contamination during depuration. The results confirm the feasibility of LC-based immunosensors as a user-friendly, portable, and efficient diagnostic tool for aquaculture applications. Full article

Background: Distinguishing between osteoarthritis (OA), rheumatoid arthritis (RA), and psoriatic arthritis (PsA) remains challenging despite different underlying mechanisms. Synovial fluid reflects metabolic changes within affected joints, yet comprehensive metabolomic comparisons across these conditions are limited. We aimed to identify disease-specific metabolic signatures in synovial fluid that could improve differential diagnosis and reveal therapeutic targets. Methods: We collected synovial fluid from 39 patients (20 OA, 5 RA, and 14 PsA) during routine knee arthrocentesis between January 2023 and February 2024. Following metabolite extraction, we performed untargeted metabolomic profiling using quadrupole time-of-flight liquid chromatography–mass spectrometry (Q-TOF LC/MS). Data underwent multivariate statistical analysis, including principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA), to identify discriminatory metabolites. Results: While unsupervised analysis showed overlap between groups, supervised PLS-DA achieved clear metabolic separation. RA samples showed elevated itaconic acid, indicating inflammatory macrophage activation, and increased O-acetylserine, suggesting altered one-carbon metabolism. Hypoxanthine was decreased, which reflected severe metabolic stress. PsA exhibited the unique elevation of 4,4-dimethylcholestane and 2-oxoarginine. These metabolites have previously been unreported in this disease. OA demonstrated increased hippuric acid and indoleacetic acid, which are both gut microbiota products, supporting the gut–joint axis hypothesis. Conclusions: Each arthritis type displayed distinct metabolic fingerprints in synovial fluid. Candidate discriminatory metabolites, including gut-derived metabolites in OA and specific lipid alterations in PsA, open new diagnostic and therapeutic avenues. Given the limited RA sample size (n = 5), RA-related results should be viewed as exploratory and requiring validation in larger independent cohorts. These metabolites may, after rigorous validation in larger and independent cohorts, contribute to multi-metabolite biomarker panels for earlier diagnosis and to the rational design of targeted therapeutics addressing disease-specific metabolic disruptions. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide. Despite advances in screening and therapeutic strategies, early detection and individualized treatment remain major challenges. In recent years, an expanding repertoire of biomarkers has emerged, spanning genomic, transcriptomic, proteomic, and metabolomic signatures. Epigenetic features, such as DNA methylation panels, as well as non-coding RNAs and the gut microbiome, hold potential not only for improving early diagnosis but also for refining prognosis and predicting therapeutic responses within the framework of precision oncology. This narrative review provides an updated, integrative overview of CRC diagnostic, prognostic, and predictive biomarkers. We distinguish established markers already in clinical practice, such as RAS and BRAF mutations, HER2 amplification, microsatellite instability/mismatch repair deficiency (MSI/dMMR), and widely investigated molecular alterations including TP53 mutations and immune-checkpoint-related markers, from novel biomarkers with growing translational potential. We also discuss the implementation challenges of these biomarkers in clinical practice, including issues related to validation, standardization, and cost-effectiveness, as well as the multi-modal approach for the development of composite diagnostic panels. Full article

Malformations of cortical development (MCD) are a group of neurodevelopmental disorders caused by abnormalities in cerebral cortex development, leading to conditions such as intellectual disability and refractory epilepsy. The prenatal phenotypes of MCD are complex and non-specific, complicating accurate diagnosis and prognosis assessment. Genetic testing, particularly chromosomal microarray analysis (CMA) and whole-exome sequencing (WES), has become an important tool for prenatal diagnosis. This review synthesizes current research on prenatal MCD, focusing on the integration of imaging and genetic diagnostic strategies based on the biological foundation of cortical development and the classification system of MCD. Prenatal MCD phenotypes show significant developmental stage clustering, with proliferation-phase abnormalities (62.9%) being the most common and microcephaly as the core phenotype. Genetic studies have revealed a high degree of genetic heterogeneity in MCD, with etiologies encompassing chromosomal abnormalities and a wide range of single-gene mutations. These mutations are clustered by phenotype: microcephaly is associated with neuronal proliferation/DNA repair genes; macrocephaly is driven by genes in the PI3K-AKT-mTOR and RAS-MAPK signaling pathways; and gyral and sulcal abnormalities are closely linked to microtubule-associated genes and migration pathways. De novo mutations account for the majority of pathogenic genetic alterations identified in MCD (50.6%); up to 75.1% of pathogenic mutations cannot be detected by routine prenatal screening. Based on this, the review emphasizes that for fetuses with suspected MCD, NGS, with WES at its core, plays an increasingly important role in achieving early and accurate prenatal diagnosis. Future research should prioritize the advancement of integrated diagnostic methods and large-scale cohort studies to further elucidate genotype–phenotype associations. Full article

Background/Objectives: Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder driven by mutations in the NF1 gene, whose pathogenesis centers on the loss of neurofibromin function and subsequent hyperactivation of the RAS/MAPK pathway. Notably, to the best of our knowledge and following a systematic literature search conducted by our research team, no cases of NF1 complicated by severe cardiac structural abnormalities that ultimately lead to cerebral infarction have been reported to date. Thus, it is of paramount importance to avoid missed diagnosis by performing comprehensive cardiac-related examinations in patients with NF1. Case Presentation: A 20-year-old male patient diagnosed with NF1 presented with right-sided limb weakness and was initially identified with cerebral infarction. To clarify the underlying etiology, a comprehensive clinical evaluation was performed, including cardiac imaging assessments (to characterize cardiac structural changes) and whole-exome sequencing (to identify the presence of procoagulant gene mutations). Comprehensive evaluation revealed a spectrum of cardiac structural abnormalities in the patient: aortic valve prolapse with severe regurgitation, non-infective vegetations on the aortic valve leaflets, mild-to-moderate mitral regurgitation, left ventricular hypertrophy and dilation, and left atrial dilation. Whole-exome sequencing detected exclusively a pathogenic variant in the NF1 gene, with no other pathogenic/likely pathogenic variants or thrombophilia-associated polymorphisms being found. Laboratory investigations ruled out infectious etiologies, supporting the notion that NF1-mediated cardiac structural and developmental anomalies are the primary driver of cardiac vegetation formation, given the absence of other identified contributing factors; embolization of one such vegetation ultimately led to both splenic and cerebral infarction. Conclusions: This case emphasizes the necessity of implementing early and proactive cardiac evaluations in patients with NF1. Additionally, for NF1 individuals—particularly those presenting with suggestive vascular or cardiac symptoms—a comprehensive multifactorial assessment of thrombotic risk is critical. Collectively, maintaining clinical vigilance for cardiac abnormalities in NF1 patients and avoiding diagnostic oversight is essential to reduce life-threatening risks. Full article

Salmonella is a globally important pathogen and one of the World Health Organization and One Health priority organisms. Reptiles represent environmental reservoirs of Salmonella serovars that can cause reptile-associated salmonellosis (RAS) in humans. Due to distinct biochemical features and uncommon O and H antigen variants, reptile-associated isolates may be difficult to identify using standard microbiological diagnostics. This study analyzed 62 Salmonella isolates obtained from wild and kept snakes in Poland. Samples originated from Natrix natrix, N. tessellata, Coronella austriaca, Zamenis longissimus, Elaphe dione and Nerodia fasciata species. Serovar prediction using SeqSero1.2 was compared with classical slide agglutination. Seventeen serovars were confirmed, with S. enterica subsp. diarizonae (IIIb) 38:r:z being the most frequent. For seven isolates, molecular and serological results were inconsistent. Among three isolates from Coronella austriaca predicted as IIIb 38:z₁₀:z₅₀, three distinct second-phase flagellar phenotypes were detected. Slide agglutination confirmed the presence of serovar 38:z₁₀:z₆, which has not been previously listed in the White–Kauffmann–Le Minor scheme or described in the scientific literature. The findings highlight the utility of genetic serovar prediction while emphasizing the need for continuous validation, particularly for the identification of rare or atypical Salmonella serovars associated with reptiles. Full article

Over the past several decades, rapid advances in molecular genomics have transformed our understanding of thyroid malignancies and are increasingly integrated into international clinical guidelines. Mutational profiles and epigenetic events are now recognized not only as diagnostic and prognostic tools but also as predictors of therapeutic response. Papillary, follicular, oncocytic, medullary, and anaplastic thyroid carcinomas harbor distinct early driver mutations, such as BRAFV600E, RAS, and fusion events (RET, NTRK, and ALK), that cooperate with secondary alterations (TERT promoter, TP53, PIK3CA, and CDKN2A/B loss) to drive dedifferentiation, metastasis, and therapeutic resistance. Insights from The Cancer Genome Atlas (TCGA) and transcriptomic scoring systems (e.g., BRAF–RAS score) now link genotype to tumor morphology, metastatic tropism, and radioactive iodine refractoriness. These molecular insights have been incorporated into updated risk stratification frameworks, preoperative surgical planning, and treatment algorithms, informing the selection of kinase inhibitors, redifferentiation strategies, and enrollment in genotype-directed clinical trials for radioiodine-refractory disease. This review synthesizes recent evidence connecting genomic alterations to clinical behavior and highlights their translation into evolving approaches for thyroid cancer management. Full article

Background/Objective: In recent years, artificial intelligence (AI) has gained increasing prominence in the fields of diagnostic decision-making in medicine. The aim of this study was to compare multidisciplinary team (MDT: rheumatology, pulmonology, thoracic radiology) decisions with single-session plans generated by ChatGPT-4o. Methods: In this cross-sectional concordance study, adults (≥18 years) with confirmed systemic autoimmune rheumatic disease (SARD) and having MDT decisions within the last 6 months were included. The study documented diagnostic, treatment, and monitoring decisions in cases of SARDs by recording answers to six essential questions: (1) What is the most likely clinical diagnosis? (2) What is the most likely radiological diagnosis? (3) Is there a need for anti-inflammatory treatment? (4) Is there a need for antifibrotic treatment? (5) Is drug-free follow-up appropriate? and (6) Are additional investigations required? Consequently, all evaluations were performed with ChatGPT-4o in a single-session format using a standardized single-prompt template, with the system blinded to MDT decisions. All data analyses in this study were conducted using the R programming language (version 4.3.2). An agreement between AI-generated and MDT decisions was assessed using Cohen’s Kappa (κ) statistic where κ (kappa) values represent the level of agreement: <0.20 = slight, 0.21–0.40 = fair, 0.41–0.60 = moderate, 0.61–0.80 = substantial, >0.80 = almost perfect agreement. These analyses were performed using the irr and psych packages in R. Statistical significance of the models was evaluated through p-values, while overall model fit was assessed using the Likelihood Ratio Test. Results: A total of 47 patients were involved in this study, with a predominance of female patients (61.70%, n = 29). The mean age was 61.74 ± 10.40 years. The most frequently observed diagnosis was rheumatoid arthritis (RA), accounting for 31.91% of cases (n = 15). This was followed by cases of anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, interstitial pneumonia with autoimmune features (IPAF), and sarcoidosis. The analyses indicate a statistically significant level of agreement across all decision types. For clinical diagnosis decisions, agreement was moderate (κ = 0.52), suggesting that the AI system can reach partially consistent conclusions in diagnostic processes. The need for an immunosuppressive treatment and follow-up without medication decisions demonstrated a higher level of concordance, reaching the moderate-to-high range (κ = 0.64 and κ = 0.67, respectively). For antifibrotic treatment decisions, agreement was moderate (κ = 0.49), while radiological diagnosis decisions also fell within the moderate range (κ = 0.55). The lowest agreement—though still moderate—was observed in further investigation required decisions (κ = 0.45). Conclusions: In patients with SARDs with pulmonary involvement, particularly in complex cases, concordance was observed between MDT decisions and AI-generated recommendations regarding prioritization of clinical and radiologic diagnoses, treatment selection, suitability for drug-free follow-up, and the need for further diagnostic investigations. Full article

Background: Seronegative rheumatoid arthritis (SNRA), defined by the absence of rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPA), represents 20–30% of rheumatoid arthritis cases. Once considered a milder phenotype, SNRA is now recognised as a heterogeneous entity in which a substantial subset of patients develops structural progression comparable to seropositive RA. The binary RF/ACPA-based definition is increasingly viewed as insufficient, as the broader anti-modified protein antibody (AMPA) family—including antibodies against carbamylated, acetylated and malondialdehyde–acetaldehyde–modified proteins—indicates that many “seronegative” patients may harbour unconventional humoral autoimmunity undetected by standard assays. Objectives: To synthesise contemporary insights into the epidemiology, immunopathology, diagnostic challenges and therapeutic management of SNRA, with emphasis on erosive versus non-erosive phenotypes and the implications of the AMPA paradigm. Methods: A comprehensive literature search of PubMed, Cochrane Library and Google Scholar identified randomised trials, observational cohorts and systematic reviews, with focus on studies published within the past decade. Results: SNRA displays partially distinct immune features, including lower formation of tertiary lymphoid structures and variable activation of innate inflammatory circuits. However, the traditional adaptive–versus–innate dichotomy is overly reductionist. Growing evidence suggests that unconventional humoral responses directed against non-classical post-translational modifications may be present in a proportion of RF/ACPA-negative patients. Additional qualitative dimensions—such as IgA isotypes and fine-specificity profiles—represent further heterogeneity with potential prognostic significance. Although ACPA remains the strongest predictor of erosive progression, up to one-third of seronegative patients develop erosions within five years. The 2010 ACR/EULAR criteria may delay diagnosis in SNRA. Cytokine inhibitors and JAK inhibitors show largely serostatus-independent efficacy, whereas B-cell and T-cell–targeted therapies demonstrate attenuated responses in SNRA. Conclusions: SNRA is clinically and immunologically diverse. Integrating the AMPA framework is essential for refining classification and prognostication. Distinguishing erosive from non-erosive forms may guide treatment, while future work should prioritise biomarkers predicting progression and therapeutic response. Full article