Search Results (5,804)

Flow cytometry is an essential diagnostic method in hematology, and one of its main applications is the assessment of the clonality of mature B cells. We present a case report of a patient referred for the investigation of absolute lymphocytosis. The flow cytometry study revealed an increased percentage of B cells, but it could not establish B-cell clonality, based on the study of surface light chains in combination with the pattern of expression of mature B-cell markers. The diagnosis of Persistent Polyclonal B-cell Lymphocytosis (PPBL) was considered in the differential diagnosis as the mature B cells were found to be immunophenotypically memory B cells. However, due to the markedly elevated count of B cells, molecular testing with Polymerase Chain Reaction (PCR) for B-cell clonality based on IGH (Immunoglobulin Heavy Chain) gene rearrangements was performed, and it revealed the presence of two clones of B cells. Approximately one year later, the same work-up was repeated in the patient’s bone marrow aspirate. By flow cytometry, a distinct clonal B-cell population was isolated, while the molecular testing with PCR for B cell clonality based on IGH heavy-chain gene rearrangements revealed the presence of three clones of B cells. In addition, evaluation of the sample with high-dimensional mass cytometry showed the presence of four major immunophenotypically abnormal B-cell subsets. Full article

Introduction: Aquatic chemical pollution is among the most worrying threats to ecosystem health. There is an ever-increasing variety of pollutant substances detected across the source-to-sea continuum, causing loss of biodiversity and ecological disequilibrium. Achieving cleaner and healthier systems relies on carrying out sustained, cost-effective, diagnosis and aquatic effects monitoring, within the adaptive management cycle. The available methods are, however, cumbersome, which creates a clear need for innovative expeditious approaches for low-cost surveillance monitoring. In the last decade, Raman Spectroscopy (RS) has gained wide recognition for application to biological questions, for its ability to uncover the complexity of molecules and their interactions. Various fields, from pharmacology to disease diagnosis and prognosis, have suffered an innovation revolution through the application of RS. In this technique inelastic light scattering of a small part of photons of an incident electromagnetic monochromatic light beam (ranging from near-infrared to visible or ultraviolet) is caused by the molecular vibration of chemical bonds. This results in shifts in energy, which indicate discrete vibrational modes of polarisable molecules, providing qualitative and quantitative assessments of the chemical composition and molecular structure of the sample. The technique shows high sensitivity, no need for sample preparation and the possibility of use in non-invasive and label-free analysis. Objective: The aim of this work is to present and discuss evidence about the application of Raman Spectroscopy (RS) to environmental diagnosis and aquatic effect monitoring of pollution. Methodology: The technique was applied to different biological models, i.e., diatoms, zebrafish embryos and larvae and freshwater snails. Quality assessments with diatoms were tested in environmental monitoring, while assessments with other models were done upon exposure to metals and organic contaminants. Results and conclusions: The Raman spectra obtained from the samples analysed comprised bands detected within the 800 to 2000 cm⁻¹ wavenumber range. These were related to bond vibrations of carbohydrates, DNA phosphate groups, proteins or CH, NH and OH stretching in lipids and proteins. Data analysis using chemometric methods clearly distinguished pollutant exposure from control sites or treatments, pointing out the potential for surveyance monitoring. The next steps include the comparison with other sensitive methods (e.g., locomotion and avoidance behaviours, omics methods) to assess efficiency and bring further mechanistic understanding. Full article

Background/Objectives: Nurses’ engagement in research is essential to strengthen evidence-based practice, knowledge translation, and quality of care. However, individual, organisational, and cultural barriers may limit nurses’ participation in research activities. This study aimed to cross-culturally adapt and psychometrically test the Italian version of the Barriers to Nurses’ Participation in Research Questionnaire within the Italian cultural and healthcare organisational context, and to explore perceived obstacles to research engagement among nurses in Italy. Methods: A cross-sectional methodological study was conducted. The instrument was translated, back-translated, reviewed by the original instrument developer and an expert panel, and evaluated for content validity by 12 clinical research professionals. Data were collected online between September and October 2024 from 196 nurses working across Italian healthcare settings, including hospitals, university hospitals, IRCCS, primary care, and private hospitals. Exploratory Structural Equation Modelling was used to examine the factor structure. Results: A total of 196 nurses were enrolled in the study. A two-factor structure was identified, comprising Research Resources and Personal Relevance of Research, which explained 35.37% and 25.14% of the variance, respectively. Both factors demonstrated good reliability. The most prominent barrier was the lack of incentive or reward for nurses to engage in research, whereas the least relevant barrier was the perception that research was not interesting or valuable. Greater barriers were reported by younger nurses, those with fewer years of experience, and those without specific research training. Lack of time to conduct research emerged as a pervasive obstacle across the sample. Conclusions: The Italian version of the Barriers to Nurses’ Participation in Research Questionnaire provides preliminary evidence of validity and reliability for assessing perceived barriers to research participation among Italian nurses. Owing to the structural modifications introduced during adaptation, the instrument should be interpreted as a culturally adapted and modified Italian version rather than as a direct replication of the original structure. Its use may support organisational diagnosis, research mentorship, training planning, and future research-capacity-building initiatives, although further validation in larger and more heterogeneous samples is warranted. Full article

Artificial intelligence (AI) is reshaping the field of diagnostic radiology; however, its applications in interventional radiology and pediatric interventional radiology (PIR) remain limited despite clear clinical needs and the rich multimodal data environment characteristic of pediatric procedural care. In this narrative review, I summarize the current state of AI technologies relevant to PIR and outline future perspectives for their clinical integration. Peer-reviewed literature and position statements identified through MEDLINE/PubMed, Embase, Scopus, and major society publications up to the first quarter of 2026 are synthesized, focusing on AI applications across the PIR care pathway, including dose-sparing image acquisition and reconstruction, automated image interpretation and computer-aided diagnosis, data-driven procedural planning and navigation, and post-procedural risk prediction and monitoring. After briefly introducing core machine learning and deep learning concepts, pediatric-specific challenges are discussed, including radiation sensitivity, growth-related anatomical variability, regulatory constraints, and the scarcity of large, annotated datasets, as well as existing and emerging applications along the PIR care pathway: AI-assisted dose reduction and image reconstruction, automated image interpretation, segmentation, and computer-aided diagnosis; data-driven procedural planning, including three-dimensional modelling, augmented reality, AI-enabled/AI-adjacent robotics, and AI-directed procedural navigation; and post-procedural risk prediction and outcome monitoring. Finally, emerging paradigms, including explainable AI, federated learning, and multimodal integration, are highlighted, and research priorities, collaborative frameworks, and governance principles required to ensure safe, equitable, and effective AI deployment in PIR are outlined. In doing so, this review delineates the current evidence gaps and priority directions for clinically meaningful AI adoption in PIR. Although AI has the potential to improve patient care, it has not yet been specifically designed, validated, or deployed in children. Existing work demonstrates feasibility across the PIR workflow, but most tools remain weakly linked to pediatric clinical endpoints. Full article

IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts, rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown and a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at an AUROC of 0.93), detecting 100% of injected faults, with a mean delay under 25 min. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW’s shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW’s shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together, these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems. Full article

Background/Objectives: Shift work is a form of circadian dysregulation, which has been associated with adverse reproductive health outcomes. However, the association between circadian dysregulation and ovarian reserve remains uncertain. The present study examines whether shift work is associated with lower AMH levels in women seeking fertility treatment. Methods: This retrospective cohort study includes female patients aged 20–39 years presenting between February 2023 and June 2024. Patients were excluded if they had only one ovary, a current cancer diagnosis, or past chemotherapy use. Demographic and medical data were obtained from the electronic medical record. AMH levels were compared between daytime workers and shift workers. Results: A total of 1135 patients met inclusion criteria. The median age was 35 years (IQR 32–37). Of these, 89% (n = 1014) reported daytime work, and 11% (n = 121) reported shift work, comprising 102 working rotating shifts, seven working night shifts, and 12 working evening shifts. Daytime-only workers had a median AMH of 17.20 pmol/L (9.1–30.0). Combined shift workers had a median AMH of 17.10 pmol/L (8.1–31.0). There was no statistically significant difference in AMH levels between daytime workers and shift workers (p = 0.935). Although not significant, the odds of having low AMH levels (<7 pmol/L) were 25% higher among shift workers compared to daytime workers (OR 1.246, p = 0.345). Conclusions: In this cohort, AMH levels did not significantly differ between daytime and shift workers, offering reassurance to individuals required to engage in shift work. Future research should include larger cohorts and incorporate more comprehensive measures of circadian disruption. Full article

Dental diseases have long been taught and treated as separate entities: cariology, operative dentistry, endodontics, and periodontology, each working within its own boundaries. However, increasing biological and clinical evidence suggests that this classified view does not fully reflect how disease progresses in the mouth. Instead, dental disease should be understood as a continuum within the interconnected tooth–pulp–periodontium complex. This review provides current evidence showing how dental caries can serve as the starting point of a process that can progress through pulpitis and apical periodontitis and eventually affect surrounding periodontal tissues. Caries is now widely known as a biofilm-driven and host-influenced condition shaped by ecological imbalance rather than specific pathogens alone. As lesions penetrate deeper into dentin, the structure becomes more permeable, permitting diffusion of microbial metabolites and signaling molecules toward the pulp. This initiates a multifaceted inflammatory reaction within the pulp tissue. At this stage, pulpitis becomes a critical turning point, where the outcome depends on microbial load, lesion activity, host response, and quality of clinical intervention. If the disease is not well controlled, it may lead to pulp necrosis, allowing infection to spread beyond the root canal and initiate periapical inflammation. Through anatomical pathways such as apical foramina and lateral canals, these processes can extend further, sometimes resembling or overlapping with periodontal disease. This overlap creates diagnostic challenges, as conventional tests may not always distinguish between conditions. A structured, pathway-based diagnostic approach is therefore essential. From a treatment perspective, this continuum model highlights early intervention, minimally invasive care, preservation of pulp vitality when possible, and maintenance of a strong coronal seal. Ultimately, stronger integration across dental disciplines can improve diagnosis, guide treatment decisions, support long-term tooth preservation, and promote unified dental education. This article presents a narrative review supported by a structured literature search and proposes a clinically actionable framework that extends established endodontic–periodontal concepts upstream to include caries initiation and restorative modulation. Full article

Metallurgical lifting equipment operates under prolonged heavy-load, high-impact, and complex working conditions. The resulting failure reports contain rich field knowledge applicable to fault diagnosis and predictive maintenance. Nevertheless, reliably extracting traceable, structured knowledge from procedural and implicit maintenance records remains a significant challenge. To address this, the paper proposes a generative information extraction method for large language models (LLMs) that integrates ontology schema with inference chain constraints, targeting knowledge extraction and knowledge graph construction from failure reports of metallurgical lifting equipment, named generative constrained information extraction for operations and maintenance (GCIE-OM). A domain ontology schema is first constructed, defining seven entity types and nine relation types to establish explicit knowledge boundaries for structured LLM generation. An inference chain-assisted structured parsing method, termed IC-ASP, is then designed to guide the model through a sequential extraction pipeline comprising scene identification, scope of entity boundary, inference of relation type, evidence traceability with localization, and triple output. This stepwise process strengthens the model’s capacity to comprehend equipment hierarchies, fault evolution chains, and maintenance action logic. Building on this, ChatGLM or LLaMA serves as the backbone model and is adapted to the target domain via LoRA fine-tuning. Entity alignment and character-level source localization mechanisms are further introduced to establish precise mappings between generated outputs and their textual evidence in the source documents. The extracted results are ultimately converted into standardized knowledge triples and stored in a Neo4j graph database. Based on this, a prototype system for generative information extraction is designed and implemented to demonstrate the practical effectiveness and adaptability of the proposed method. Experimental results show that the proposed method outperforms baseline methods across entity recognition, relation extraction, and structured output quality, providing robust knowledge support for fault tracing and predictive maintenance of metallurgical lifting equipment. Full article

Alzheimer’s disease and related dementias (ADRD) are neurodegenerative conditions characterized by progressive cognitive and functional decline. AD pathology is associated with extracellular amyloid-β plaques, intracellular tau neurofibrillary tangles, synaptic dysfunction, and neuronal loss. AD accounts for approximately 60–80% of dementia cases globally. In 2022, AD was the seventh leading cause of death in the United States, and the number of Americans aged 65 and older living with Alzheimer’s dementia is projected to increase substantially by 2060. Despite decades of research, AD/ADRD data resources remain fragmented across clinical, imaging, genetic, genomic, and therapeutic domains. This paper addresses that gap by providing a centralized review of widely used AD/ADRD databases and computational methods. We first summarize computational approaches used to analyze these datasets, including machine learning (ML), natural language processing (NLP), and biomedical imaging. We then review eight databases classified into three categories: Clinical and Population Data, Genetics and Genomics, and Drug Discovery and Therapeutics. Finally, we discuss real-world applications, including early diagnosis, clinical decision support, personalized medicine, and drug-mechanism analysis. This review identifies opportunities for future work in data harmonization, cross-database compatibility, and robust, generalizable AI models for AD/ADRD research. Full article

Aim: To evaluate the status of the nutritional management of diet-treated IEM in Brazil from the perspectives of healthcare professionals, patients, and families. Methods: Data were collected through two nationwide digital questionnaires administered to healthcare professionals involved in dietary management (n = 37) and to patients and caregivers (n = 278), addressing professional training, workload, access to resources, treatment adherence, and socioeconomic factors. Results: Healthcare professionals from 20 out of the 26 Brazilian states participated, most of them female (81%) and dietitians (81%). Although more than half had over 10 years of experience, 59% considered their training insufficient to work with IEM. Only 19% reported exclusive dedication to the field, and 54% were the sole professional responsible for dietary prescriptions at their center. Weekly workload dedicated to IEM varied widely. Among the patients and families, phenylketonuria (60.4%) and glycogen storage disease (25.9%) were the most frequent conditions. Higher educational level and longer time since diagnosis were associated with a better understanding of dietary management (p < 0.05). Among patients on protein-restricted diets, most reported regular use of protein substitutes, although 92% reported poor palatability and 36% reported supply problems. Access to special low-protein foods was limited, and over half of the families reported some level of food insecurity. Conclusions: Significant systemic, logistical, and socioeconomic barriers to optimal dietary management of IEM persist in Brazil, highlighting the need for strengthened public policies, professional training, and equitable access to dietary resources. Full article

The emergence and increasing use of artificial intelligence (AI) in healthcare have paved the way for highly personalized and time-saving approaches in the field of precision medicine. It can be applied to determine a prognosis, diagnosis, and recommended treatment, and may also be used for patient monitoring. As AI applications become more widely available, reliable and easy to use, they are rapidly reshaping the traditional roles of professionals and patients in the therapeutic relationship. On the positive side, professionals may have more time to communicate with patients and provide individualized care, whereas patients may become more empowered and autonomous due to AI-facilitated personalized information and monitoring. On the negative side, AI applications threaten to reduce the role of professionals to a mediating one in clinical decision-making, provide patients with misinformation, and lead to misunderstandings that hinder patients’ autonomy. In this narrative review, we examine the main ethical issues related to the AI-induced shift in roles in the therapeutic relationship, within four inter-related themes: the validity of claims that algorithms outperform humans in certain tasks; the ways in which AI saves time for health professionals but also takes time to properly explain and implement; the issues of trust and accountability, especially if AI suggestions lead to patient harm; and what AI’s alleged cost-effectiveness means for professionals’ employment and remuneration. Across the three roles, we find a common pattern: AI tends to absorb the technical and data-processing parts of clinical work while leaving its relational core to humans. Physicians move toward oversight and interpretation, nurses retain the attentiveness and responsiveness that define care, and patients gain tools for self-management that can widen autonomy or, left unguided, erode it. Whether the overall effect is benign depends less on the technology than on how outperformance is evidenced, how the freed time is used, how trust and accountability are anchored in people, and how cost pressures are managed. The article concludes with some suggestions for prudent use of AI in healthcare, indicating the appropriate measures that can be used to harness the power of AI without damaging the traditional cornerstones of the therapeutic relationship. Full article

Background/Objectives: Despite hematoxylin and eosin (H&E) staining remaining the cornerstone of histopathological diagnosis, substantial intra- and inter-laboratory variability persists. This issue is increasingly relevant in Digital Pathology, where staining inconsistency may affect whole-slide image interpretation and the performance of image analysis algorithms. In the present work, we evaluated the diagnostic adequacy and non-inferiority of a novel tabs-based H&E histochemical staining method compared with conventional liquid reagents. Methods: Fifty formalin-fixed paraffin-embedded tissue samples from routine practice were sectioned in duplicate and stained either conventionally or using H&E Stain Tabs. After slide review, 14 representative tissue samples were selected, scanned at 40× magnification, and used to generate 24 matched image pairs at different magnifications. A blind online survey was completed by 13 expert pathologists using high-quality monitors. Participants assessed overall staining preference and rated stromal, epithelial, cytoplasmic, and nuclear staining quality. Non-inferiority was tested using a predefined margin of −0.10, and paired rating differences were analyzed using the Wilcoxon signed-rank test. Results: Across 312 paired evaluations, the tabs-based method was preferred in 120 cases (38.5%), conventional staining in 118 cases (37.8%), and no preference was expressed in 74 cases (23.7%). The tabs-based method met the criterion for non-inferiority compared with standard staining (z = 2.7). Rating-scale analysis showed significantly better stromal evaluation with the tablet-based method (z = 2.638; p = 0.008), whereas no significant differences were observed for epithelial, cytoplasmic, or nuclear staining. All evaluated images were considered diagnostically adequate. Conclusions: The tabs-based H&E stain was non-inferior to the conventional method and showed particularly favorable performance in the assessment of stromal components. These findings support its potential role in improving staining reproducibility and standardization, particularly in Digital Pathology workflows where pre-analytical and analytical consistency is critical. Full article

Weekday commuting and weekend recreation are two mobility domains through which urban spatial structure shapes residents’ well-being and urban functioning, yet direct empirical evidence on how they are related remains limited. This study investigates how weekday commuting costs and weekend recreational mobility conditions are related within a jobs–housing–recreation spatial framework, using individual-level location-based services (LBS) data from the central urban area of Chongqing, China. Generalized additive models reveal a nonlinear and range-dependent commuting–recreation relationship. Distance-based and driving-time specifications provide the main evidence for a U-shaped relationship, whereas transit-time specifications do not clearly reproduce this pattern, reflecting short-distance cost overestimation and spatially shared public-transport constraints rather than realised mobility conditions. From a spatial-configuration perspective, this pattern suggests that work-related and recreational mobility conditions are unevenly combined across residential locations, rather than simply aligned or opposed. It also suggests that relatively favourable commuting and recreational mobility conditions can coexist within some residential contexts. Rather than establishing a universal rule, the Chongqing case provides a testable hypothesis that may be relevant to large cities with uneven and partially aligned employment, housing, transport, and recreational opportunities. The study provides an empirical entry point for integrated spatial-performance diagnosis and future evaluation of alternative jobs–housing–recreation configurations. Full article

A stable gut microbial community (SGMC) created by the culture of fecal samples in a chemostat-based gut model is a physiologically relevant, reproducible tool for ecological research on the gut microbiome. It is also a subject of continuous improvement for the model to better mimic the in vivo system. In this research, compositions of fecal homogenates and the derived SGMC were analyzed based on shotgun metagenomic sequencing. It was found that taxa in the fecal inocula could be divided into resilient or sensitive types according to their response to environmental factors; taxa were sorted into three subsets—shared, emerged, and lost—by how they were represented in the established communities. The first two subsets consisted of resilient species, constituting an SGMC, which was suitable for long-term, ecological research. In contrast, species of the lost subset were sensitive to the new environmental conditions and were missing from the SGMC. However, these lost species may carry host-dependent information that is useful or even critical for drug and functional foods development or nutrition research. The differentiation between the three subsets reveal the community shift and metabolic profile along with SGMC formation and could serve as a diagnosis for understanding the extent to which the SGMC mimics, or differs from, the actual ecosystem in vivo or the donor’s fecal samples, which may be useful in work to upgrade in vitro models. Full article

Purpose: Adjuvant radiotherapy (RT) for ductal carcinoma in situ (DCIS) remains a widely discussed topic. While it has been shown to significantly reduce recurrence rates, it does not improve overall survival (OS) and may therefore lead to overtreatment in some cases. This study aimed to assess patient acceptance, quality of life (QoL), and decisional regret (DR) in relation to late adverse effects. Methods: Clinical data from patients who underwent RT for DCIS between 2008 and 2020 were retrospectively collected, while follow-up questionnaires evaluated QoL and DR. High-risk DCIS was defined as tumor size ≥ 2.5 cm, poor differentiation (G3), age ≤ 50, and symptomatic diagnosis. Low-risk disease was defined as tumor size < 2 cm, G1/G2, age > 50, and mammographic diagnosis. Patients who did not meet all criteria for either category were classified as intermediate risk, therefore reflecting everyday work and clinical practice. Questionnaire data were analyzed using both scale-level and item-level approaches and Ipsilateral breast recurrence-free survival (IBRFS) rates were calculated using the Kaplan–Meier method. Results: Among 307 cases offered RT, only 24 declined, indicating high treatment acceptance; an additional 20 cases lacked sufficient documentation, did not receive RT at our center despite acceptance, or discontinued RT prematurely. During the study period, late toxicities occurred at acceptable frequencies: fibrosis in 6.1%, hyperpigmentation in 6.8%, telangiectasia in 8%, and edema in 1.5% of cases with excellent control rates of 90.0%. The response rate to the EORTC QLQ-C30 and Ottawa Decisional Regret Scale (DRS) was 35.4%, of whom 70 cases belonged to the intermediate-risk group. These respondents reported high QoL and low levels of DR. Conclusions: RT for intermediate grade DCIS is accepted and tolerated well, and neither late adverse effects nor QoL outcomes were associated with DR. These findings support recommending RT in this borderline-indication group. Full article