Search Results (601)

The 9th National Congress of the Italian Society for Virology (SIV-ISV), entitled “One Virology—One Health”, took place in Turin at the Centro Congressi Lingotto from 22 to 24 June 2025. The meeting highlighted recent multidisciplinary and translational developments in virology, with a strong focus on the integration of the One Health perspective. Major themes included viral emergence and surveillance, genomic sequencing and bioinformatics, virus–host interactions, viral immunology and vaccines, structural and physical virology, environmental and food virology, zoonoses and animal infections, diagnostics and antiviral therapy, virus-based biotechnology and plant virology. The Congress aimed to: (i) bring together clinicians, basic researchers, veterinarians, environmental microbiologists, bioinformaticians, public-health professionals and industry to share methodologies and best practices; (ii) provide an interactive scientific environment promoting discussion and collaboration between senior investigators and trainees through plenaries, joint society sessions, invited talks, oral communications selected from abstracts, poster sessions, and mentoring panels; and (iii) identify priorities and inspire new research directions at the interface of human, animal and environmental health. More than 400 participants from national and international institutions attended the meeting, featuring distinguished plenary speakers, joint sessions with global networks, and numerous presentations of original unpublished data. This report summarizes the meeting’s scientific highlights, cross-disciplinary discussions, and proposed actions to strengthen One Health surveillance, computational infrastructures, and translational applications of viral biology. Full article

Background: Respiratory diseases (RDs), including asthma, COVID-19, chronic obstructive pulmonary disease (COPD), and pneumonia, remain a major global health challenge, contributing substantially to global morbidity and mortality. Conventional diagnosis relies heavily on clinicians’ expertise to interpret respiratory sounds and radiographic images, a process that can be subjective, time-consuming, and prone to inter-observer variability. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled automated diagnostic approaches that can improve the efficiency, consistency, and scalability of respiratory disease detection. However, existing research remains fragmented across different data modalities. Methods: This review systematically analyzes recent studies on AI-based respiratory disease detection using both visual modalities (e.g., chest X-rays, computed tomography (CT) scans, and ultrasound) and audio modalities (e.g., cough and breath sounds). To provide a comprehensive perspective, the reviewed literature is organized using a unified taxonomy that categorizes existing approaches into three main groups: audio-based, visual-based, and audio–visual-based methods. In addition, two conceptual frameworks are proposed to illustrate representative pipelines for audio-based and visual-based respiratory disease classification. Results: The analysis reveals that most existing studies focus on single-modality approaches, while multimodal integration remains relatively underexplored. Only a limited number of studies combine audio and visual data within unified frameworks, primarily due to the scarcity of synchronized multimodal datasets collected from the same patients. The proposed taxonomy and conceptual frameworks provide a structured basis for comparing existing methods, identifying methodological trends, and highlighting key research gaps in multimodal respiratory disease detection. Conclusions: Future research should prioritize the development of multimodal datasets, robust evaluation protocols, and interpretable and lightweight AI models suitable for real-world clinical deployment. Advancing multimodal integration has the potential to significantly enhance the accuracy, reliability, and clinical applicability of AI-driven respiratory disease diagnosis systems. Full article

Background/Objectives: Despite the growing burden of type 2 diabetes in Palestine and the central role of patient-centred care (PCC) in high-quality primary healthcare, evidence on PCC from the perspective of people with diabetes remains limited. This study aimed to assess patient-centredness in governmental primary healthcare centres in the West Bank from the perspective of adults with diabetes. Methods: A cross-sectional study was implemented in three primary healthcare directorates covering north, south, and central West Bank (WB). The perspectives of patients with type 2 diabetes mellitus (DM) on patient-centredness were investigated using an Arabic-translated version of the PPPC-R questionnaire. A total of 450 eligible patients were approached using non-probability convenience and quota sampling across the three directorates between August and September 2025. We used R (version 4.5.1) for the analysis. Results: A total of 417 patients completed the questionnaire (response rate 91%). Participants were (50.4%) women and (49.6%) men, with a mean age of 54.6 years (SD = 12.9). Participants reported moderate overall PCC perceptions (M = 2.82, SD = 0.50), with the highest mean scores for Enhancing the Clinician–Patient Relationship (M = 2.90, SD = 0.52), followed by Understanding the Whole Person (M = 2.77, SD = 0.56) and Finding Common Ground (M = 2.71, SD = 0.71). After adjustment for sociodemographic variables in multivariable analysis HC3-robust regression models, no predictor remained independently significant, and the models explained only a modest share of variance (R² ≈ 0.03–0.06). Conclusion: Perceived patient-centredness of diabetes care in governmental PHC clinics in the West Bank was moderate and varied by geographic and contextual factors. Findings suggest a need for targeted quality improvement initiatives to strengthen PCC in diabetes services and to expand the research to other governorates to obtain a clearer picture of the regional disparities within the Palestinian PHC system. Full article

Background/Objectives: Childhood obesity is a complex, multifactorial condition that requires comprehensive and sustained interventions. Despite the central role of dietary modification in obesity management, current approaches to dietary counselling remain heterogeneous and often fail to account for the broader environmental and social determinants of eating behavior. In pediatric populations, dietary habits are strongly influenced by family dynamics, home food environments, school settings, and peer interactions, highlighting the need for system-oriented intervention models. Methods: This structured narrative review with conceptual framework development presents an integrative framework for dietary counselling in pediatric obesity, combining evidence-based nutritional strategies with behavioral and environmental approaches. The paper synthesizes current literature on early-life habit formation, family-based behavioral treatment, feeding practices, and environmental determinants of dietary behavior. Results: The proposed framework emphasizes the role of the family as the primary therapeutic unit and highlights the importance of modifying the home food environment and implementing gradual, achievable changes through the “small steps” approach. A structured, visit-based model of dietary counselling is introduced, integrating dietary assessment, patient education, and behavioral strategies. Additionally, the influence of external environments—including schools, peer groups, and public health systems—is considered to provide a comprehensive understanding of factors shaping dietary behaviors in children. Conclusions: The proposed system-oriented framework offers practical guidance for clinicians and public health practitioners and supports the development of more effective and sustainable interventions. Integrating individual, family, and environmental perspectives may improve adherence to dietary recommendations and enhance long-term outcomes in pediatric obesity management. Full article

Religious psychopathology as a field lies at the intersection of psychiatry, theology, and culture. It addresses scientific discoveries and questions relating to the manifestation of mental health disorders that are expressed through religious content, ideation, and/or behavior. Religious psychopathology, being a multifaceted phenomenon, challenges clinicians, researchers, and religious leaders because it is non-trivial to distinguish between culturally normative religious experiences and pathological symptoms. The present integrative narrative review examines historical perspectives, diagnostic challenges, clinical manifestations, cultural considerations, therapeutic interventions, neurobiological models, ethical issues, and future directions in the field of religious psychopathology. It focuses primarily on literature from 2013 to 2025, while also incorporating selected foundational historical, theoretical, and clinical sources necessary for conceptual clarification. A special emphasis is placed on culturally informed and interdisciplinary approaches. Particular focus is given to approaches that respect spiritual frameworks while concurrently promoting evidence-based mental health care. Full article

Background/Objectives: Autistic traits are distributed dimensionally across psychiatric populations, yet their systematic assessment in mood and psychotic spectrum disorders remains limited. While elevated autistic traits have been documented in schizophrenia spectrum disorders, evidence in bipolar disorder (BD) and major depressive disorder (MDD) is scarce, and no studies have applied the clinician-rated PANSS Autism Severity Score (PAUSS) to mood disorder populations. This study aims to investigate the presence and severity of autistic traits across psychotic spectrum disorder (PSD), BD, and MDD in an outpatient sample using the PAUSS. Methods: In this cross-sectional naturalistic outpatient study, clinically stable adult patients with MDD, BD, or PSD, without autism spectrum disorder, were assessed with the Brief Psychiatric Rating Scale (BPRS) and PAUSS. Group comparisons, adjusted models, correlation analyses, principal component analysis, and multinomial logistic regression were performed. Results: A total of 165 patients were included (MDD, n = 84, BD, n = 45, PSD, n = 36). Compared with the mood disorder groups, PSD patients were younger and showed higher BPRS scores. PSD was also characterized by significantly higher PAUSS total, social, and communication scores, whereas PAUSS RRB did not differ in univariate analyses. In the overall sample, BPRS severity correlated positively with all PAUSS dimensions, while age showed only weak or non-significant associations. Diagnosis-stratified analyses revealed that the association between psychopathology and autistic traits was present in MDD and BD, but not in PSD. PCA showed that autistic trait dimensions converged on a broad common profile and differed across diagnostic groups, with PSD showing the most distinct pattern. In multinomial logistic regression, higher BPRS, higher PAUSS social and communication scores, and younger age independently distinguished PSD from MDD and BD; PAUSS RRB showed an inverse association only in the multivariable model. Conclusions: This study supports a transdiagnostic perspective on autistic traits in adult psychiatric populations, highlighting disorder-specific differences across diagnostic categories. Social and communication impairments emerged as key dimensions distinguishing PSD from mood disorders. Assessing autistic traits in psychiatric settings may improve diagnostic precision and inform personalized, stratified treatment approaches. Full article

Artificial intelligence (AI) is increasingly integrated into emergency general surgery (EGS), where rapid diagnosis, accurate decision-making, and timely intervention are essential for improving patient outcomes. Recent advances in machine learning, deep learning, computer vision, and predictive analytics have enabled AI-assisted systems to support clinicians throughout the perioperative workflow. Current applications include radiologic image interpretation, diagnosis of acute abdominal conditions, surgical workflow recognition, intraoperative anatomical guidance, postoperative complication prediction, and intensive care monitoring. AI technologies may improve diagnostic accuracy, optimize operative planning, enhance surgical safety, and facilitate personalized perioperative management. In minimally invasive surgery, computer vision and real-time data analysis have shown promising results for intraoperative decision support and surgical education. However, important limitations remain, including concerns regarding data quality, algorithm transparency, ethical governance, regulatory approval, and implementation disparities between healthcare systems. In addition, much of the current evidence is derived from retrospective or highly specialized datasets, limiting broad clinical applicability. This narrative review summarizes the current clinical applications of AI in emergency general surgery and discusses emerging technologies, existing challenges, and future perspectives regarding the integration of AI into acute surgical care. Full article

Pulse oximetry is an essential standard monitor in modern anesthetic practice, enabling continuous noninvasive assessment of arterial oxygen saturation and pulse rate throughout the perioperative period. Since its introduction into clinical medicine, pulse oximetry has significantly improved patient safety by facilitating early detection of hypoxemia and physiologic deterioration. Despite its widespread use, clinicians may underrecognize the technical principles, physiologic assumptions, and limitations that influence measurement accuracy. This review provides a perioperative perspective on pulse oximetry, including the physics of photoplethysmography, sensor technologies, and practical considerations for optimal probe placement and signal acquisition. Sources of inaccuracy such as motion artifact, low perfusion states, dyshemoglobinemias, ambient light interference, skin pigmentation, and venous pulsation are discussed in detail. The review further examines perioperative applications across preoperative evaluation, intraoperative monitoring, and postoperative recovery, while also exploring advanced parameters including perfusion index (PI) and pleth variability index (PVI). Emerging innovations such as multi-wavelength systems and artificial intelligence (AI)-enhanced signal analysis are also highlighted. A comprehensive understanding of pulse oximetry allows anesthesiologists to appropriately interpret monitor data, recognize device limitations, and optimize perioperative patient care. Full article

Sleep disturbances represent one of the most frequent and clinically significant comorbidities in children with autism spectrum disorder (ASD), affecting approximately 50–80% of individuals. Clinically, these disturbances encompass a broad spectrum of disorders, including insomnia, parasomnias, sleep-related movement disorders, and sleep-related breathing disorders, commonly presenting with prolonged sleep latency, frequent nocturnal awakenings, reduced total sleep time, and alterations in sleep architecture. Circadian rhythm dysregulation, abnormalities in neurotransmitter systems such as GABA and serotonin, and altered melatonin signaling have been consistently implicated. These processes may reflect underlying genetic and metabolic influences affecting circadian clock regulation and synaptic function. The management of sleep disturbances in ASD requires a comprehensive approach combining behavioral strategies, caregiver education, and sleep hygiene interventions, while pharmacological options, particularly melatonin, may be considered when non-pharmacological measures are insufficient. Understanding the multifactorial mechanisms underlying sleep disturbances in ASD is essential for improving early recognition and developing individualized therapeutic strategies. This review synthesizes current evidence on the prevalence, biological mechanisms, clinical manifestations, and management of sleep disturbances in ASD, providing an integrated perspective for both clinicians and researchers. Full article

Background/Objectives: Psilocybin-assisted therapy (PAT) is a promising intervention to alleviate existential distress among patients with advanced cancer receiving palliative care. However, evidence on how to integrate PAT into routine oncology and palliative care services remains scarce. This study aimed to examine real-world PAT implementation, identify factors influencing adoption, and estimate integration costs within oncology and palliative care services. Methods: We conducted a single-case implementation study in a large university-affiliated tertiary care center in Canada during the first year following its introduction. Semi-structured interviews with clinicians, managers, and other stakeholders explored barriers, facilitating conditions, and actions needed to support PAT implementation. A budget impact analysis estimated incremental costs associated with delivering PAT. Results: After one year, no patients had received PAT. Ten professionals representing diverse clinical and managerial roles participated in the interviews. While participants viewed PAT favorably, they emphasized the need to align the intervention with existing care pathways and clarify referral processes. Administrative and regulatory procedures, together with logistical constraints related to treatment delivery, were identified as key barriers, whereas perceived clinical relevance and institutional leadership were seen as important facilitators. From the health care system perspective, the estimated cost of delivering a complete PAT intervention ranged from 2648 to 5827 Canadian dollars (CAD) per patient, depending on the scenario examined, excluding the cost of the psilocybin itself. Conclusions: Despite perceived clinical relevance and relatively modest estimated costs, the absence of treated patients after one year highlights the gap between regulatory authorization and effective service uptake. These findings underscore the importance of structured implementation strategies, sustained institutional support, and alignment between regulatory frameworks and clinical workflows to ensure meaningful integration of PAT into routine oncology and palliative care services. Full article

Background and Objectives: Patient safety at hospital interfaces is shaped by organisational fragility, ethical obligations, and anticipated legal exposure. Reporting, disclosure, and speaking up have been studied separately, yet the way these pressures converge in ordinary hospital work remains insufficiently described. Materials and Methods: We conducted a qualitative study in a public hospital in Romania using semi-structured episodic interviews and the critical incident technique. Twelve clinicians participated: six nurses and six physicians working in intensive care, emergency medicine, general surgery, paediatrics, oncology day care, anaesthesia, obstetrics, and internal medicine/cardiology. Interviews were audio-recorded, transcribed verbatim in Romanian, anonymised, and analysed with the framework method from a critical realist perspective. A secondary cross-case coding of all 12 episodes was used for descriptive analytic displays. Results: Four mechanisms organised the material. First, local stop rules and cross-checks created temporary stability at fragile interfaces such as high-alert medication, patient identification, specimen labelling, and transfer documentation. Second, consent and confidentiality were repeatedly compressed by urgency, compromised capacity, public space, and family pressure; legitimacy depended on explicit reasoning rather than documentary completion alone. Third, speaking-up and near-miss reporting were governed by protocol-backed legitimacy, leader response, and the informal cost of interruption. Formal incident reporting was present in one episode, partial in one, and absent in 10. Fourth, documentation and disclosure redistributed accountability. Notes that recorded reasoning supported continuity of care, whereas protective opacity concealed near misses, infrastructural weakness, and interactional pressure. Documentation or disclosure pressure appeared in all 12 episodes. Conclusions: Safety in everyday hospital work was assembled through local barriers, moral triage, and selective visibility. Interface redesign, protected near-miss reporting, psychologically safe escalation, and structured support for urgent consent and post-incident communication would make transparent safety work more sustainable. Trustworthiness was strengthened through reflexive memoing by the physician-interviewer, an audit trail of coding decisions, comparison across professional groups, active attention to negative cases, and iterative assessment of meaning saturation at the level of explanatory mechanisms. Full article

Background/Objectives: Laryngeal disorders are among the most prevalent conditions in otolaryngology, yet they remain challenging to diagnose without specialized expertise. Artificial intelligence (AI) systems leveraging machine learning (ML) and deep learning (DL) have demonstrated promising performance for the automatic detection and classification of voice disorders and laryngeal lesions. Methods: This review synthesizes findings from 88 studies published between 2015 and 2025 on AI-based laryngeal disorder detection, considering physioacoustic mechanisms, databases and acquisition protocols, AI architectures and validation strategies, and diagnostic performance. Results: The current literature supports high internal accuracies for binary healthy versus pathological detection (88–99%); meanwhile, performance decreases for higher-level tasks such as pathophysiological category classification and identification, particularly under external validation. From a clinical perspective, clinicians do not infer specific diagnoses from isolated acoustic parameters such as percent jitter or shimmer. Instead, they rely on how these perturbation patterns dynamically evolve during connected speech, where alterations guide perceptual differentiation between underlying disorders. Recurrent sources of bias include dependence on a limited number of historical vowel-based databases, class and demographic imbalance, and limited ecological validity of recording protocols. Additional concerns involve the predominant use of internal cross-validation and insufficient reproducibility or code sharing. Conclusions: Drawing on the literature, an integrative three-level clinical recognition framework is proposed, delineating realistic use cases for AI as a decision-support tool rather than an autonomous diagnostic system. Key priorities for future personalized medicine and research are also identified, including diversified multi-center datasets, standardized methodological reporting, rigorous external validation, and compliance with regulatory and ethical requirements for medical AI deployment. Full article

Biomarker-driven approaches have markedly improved the stratification and management of airway inflammatory diseases. However, in everyday clinical practice, these strategies still rely mainly on systemic indicators, which often provide only an indirect view of the inflammatory processes occurring within the airway mucosa. This limitation becomes particularly evident in chronic conditions such as chronic rhinosinusitis with nasal polyps (CRSwNP), where local inflammatory patterns may not relate to circulating biomarkers. Nasal cytology represents a simple, non-invasive, and reproducible technique that allows direct evaluation of the cellular components of the nasal mucosa. By identifying distinct inflammatory patterns, it offers a real-time snapshot of the local inflammatory microenvironment, bringing the clinician closer to the site of disease. In this hypothesis, we propose that airway inflammation is primarily driven by local cytological patterns. In particular, we suggest that the interaction between eosinophils and mast cells constitutes a key pathogenic axis underlying disease activity, severity, and progression. From a pathophysiological perspective, eosinophils may reflect a more chronic component of inflammation, whereas mast cells are more closely associated with active and dynamic phases of the disease. Their coexistence may therefore identify a state of amplified inflammatory activity, often associated with more severe clinical phenotypes. We further propose that integrating cytological findings into clinical–cytological grading (CCG) systems could improve patient stratification and support more personalized therapeutic strategies. This model is readily testable in current clinical and research settings and may contribute to a progressive shift toward the use of local biomarkers in precision medicine. Full article

The diagnostic field of prostate cancer (PCa) has undergone a significant evolution with the widespread integration of multiparametric magnetic resonance imaging (mpMRI) and mpMRI-targeted biopsies (TBx). This approach has been shown to improve the detection of clinically significant prostate cancer (csPCa) while reducing the overdiagnosis of low-risk disease. However, a conceptual and clinical challenge, which can be referred to as the “Precision Paradox,” has emerged. By directing biopsy cores almost exclusively into the most suspicious MRI lesions, clinicians may inadvertently overrepresent the biological significance of a limited high-grade component. This can lead to grade migration and pathological downgrading at the time of radical prostatectomy (RP). Although downgrading does not automatically equate to clinical overtreatment, it introduces prognostic uncertainty that complicates risk stratification for active surveillance (AS) and focal therapy. This conceptual commentary provides a critical perspective on this diagnostic issue. We synthesize recent meta-analyses to evaluate the true rates of grade mismatch associated with TBx and combined biopsy approaches. Furthermore, we discuss the spatial limitations of biopsy sampling, the pathological mechanisms driving grade discordance, and the clinical relevance of minor high-grade components such as cribriform architecture. Finally, we highlight the role of multi-omics and validated genomic biomarkers in risk models, ultimately fostering improved shared decision-making in the modern mpMRI era. Full article

Morphological disciplines, namely Human Anatomy, Histology, and Embryology, have traditionally provided the foundational knowledge for medical education, offering spatial, cellular, and temporal coordinates of the human body. However, reducing these disciplines to static and purely descriptive learning undermines their deeper purpose: interpreting morphology as the dynamic outcome of biological processes. This review emphasizes three interrelated pillars of morphological sciences—cell differentiation, tissue homeostasis, and organ remodeling—as essential frameworks for understanding both normal physiology and disease pathogenesis. Cell differentiation establishes functional identity, tissue homeostasis ensures structural stability, and organ remodeling enables adaptation to both physiological and pathological stimuli. Dysregulation of these programs underlies a wide range of conditions, from degenerative diseases and chronic inflammation to neoplasms. Integrating classical morphological knowledge with modern approaches—including stem cell biology, organoids, tissue engineering, and computational modeling—enables predictive and regenerative strategies in personalized medicine. Furthermore, recent advances in artificial intelligence applied to histopathology have enhanced our capacity to detect early deviations from homeostasis and guide targeted interventions. By combining spatial, cellular, and molecular perspectives, the morphological sciences can provide clinicians with tools to interpret disease as the result of altered biological programs, anticipate pathology, and design precise therapeutic strategies. This integrated approach highlights the renewed centrality of morphology in contemporary medicine, bridging foundational knowledge with predictive, regenerative, and personalized healthcare. Full article