Search Results (2,735)

Background: Tumor necrosis factor-α (TNFα) inhibitors have significantly improved outcomes in children with non-systemic juvenile idiopathic arthritis (JIA), achieving long-term clinical remission for many patients. However, the optimal strategy for TNF-α inhibitor withdrawal remains unknown, whether through abrupt discontinuation, gradual dose reduction, or interval extension. Objective: We aim to identify patient-, disease-, and treatment-related predictors of successful TNF-α inhibitor withdrawal in children with non-systemic JIA. Methods: In this prospective, randomized, open-label, single-center study, 76 children with non-systemic JIA in stable remission for ≥24 months on etanercept or adalimumab were enrolled. At the time of TNF-α inhibitor discontinuation, all patients underwent a comprehensive evaluation, including a clinical examination, laboratory tests (serum calprotectin [S100 proteins] and high-sensitivity C-reactive protein [hsCRP]), and advanced joint imaging (musculoskeletal ultrasound and magnetic resonance imaging [MRI]) to assess subclinical disease activity. Patients were randomized (1:1:1, sealed-envelope allocation) to one of three predefined tapering strategies: (I) abrupt discontinuation; (II) extension of dosing intervals (etanercept 0.8 mg/kg every 2 weeks; adalimumab 24 mg/m² every 4 weeks); or (III) gradual dose reduction (etanercept 0.4 mg/kg weekly; adalimumab 12 mg/m² every 2 weeks). Follow-up visits were scheduled at 3, 6, 9, 12, and 18 months to monitor for disease relapse. Results: Higher baseline Childhood Health Assessment Questionnaire (CHAQ) scores (≥2), elevated serum calprotectin [S100 proteins] and hsCRP levels at withdrawal, imaging evidence of subclinical synovitis, and a history of uveitis were all significantly associated with increased risk of flare. No significant associations were found for other clinical or demographic characteristics. Conclusions: Early significant clinical response, absence of subclinical disease activity, and concomitant low-dose methotrexate therapy were key predictors of sustained drug-free remission. These findings may inform personalized strategies for biologic tapering in pediatric JIA. Full article

With the increasing frequency of maritime activities, large-scale man overboard incidents raise higher demands on maritime search and rescue (SAR) decision-making. Most existing drift models are designed for single-person-overboard situations and have limited ability to model multiple-persons-in-water (MPIW) scenarios. To address this gap, this study proposes a drift trajectory prediction method for MPIW based on full-scale field experiments in the Xisha Sea, South China Sea. In December 2023, six drift experiments were carried out, providing 57 h of tracking data under typical conditions with wind speeds from 0.17 to 7.77 m/s and surface current speeds from 0.06 to 0.96 m/s. Two basic MPIW scenarios were considered, side-by-side connection and random connection, and four MPIW drift models were built for upright 3-person (UP_3), upright 5-person (UP_5), upright–facedown–upright (U-F-U) and facedown 2-person (FD_2). The corresponding wind-induced drift coefficients were estimated. The stochastic variability of the crosswind leeway (CWL), including sign-change frequency and the probability of positive CWL, was systematically analyzed. For unconstrained regressions, the downwind leeway slope coefficients range from −2.96% to −12.61%, while CWL slope coefficients range from 1.01% to 2.78%, depending on group configuration. Monte Carlo simulations were then used to compare different model groups. In typical test cases, the proposed MPIW models reduce the normalized cumulative error for 11 h trajectory prediction from 0.18–0.23 to 0.08–0.17, indicating a clear improvement in the accuracy of search area delineation for group drowning scenarios. The results provide a useful reference for MPIW drift prediction and SAR decision-making in similar offshore and deep-water environments. Full article

Recently, extracellular vesicles (EVs) have emerged as pivotal mediators of intercellular communication that reflect physiological homeostasis and pathological alterations. By encapsulating diverse biomolecules, including proteins, nucleic acids, and lipids, EVs mirror the molecular signatures of their parent cells, thereby positioning EV-based biosensing as a transformative platform for noninvasive diagnostics, prognostic prediction, and therapeutic monitoring. This review provides a comprehensive overview of the current state and clinical translation of EV biosensing technologies. Herein, we have discussed ongoing efforts toward standardization and analytical validation (e.g., MISEV2023 and EV-TRACK) and evaluated advances in sensing modalities such as surface plasmon resonance (SPR), electrochemical, fluorescence, and magnetic detection systems, which have significantly improved analytical performance in terms of sensitivity and specificity. Furthermore, we highlight recent developments in multiplexed and multiomics integration at the single-EV level and the application of machine learning to enhance diagnostic accuracy and interpret biological heterogeneity. The clinical relevance of EV biosensing has been explored across multiple disease domains, including oncology, neurology, and cardiometabolic and infectious diseases, with an emphasis on translational progress toward standardized, regulatory-compliant, and scalable platforms. Finally, this review identifies key challenges in manufacturing scale-up, quality control, and point-of-care deployment and proposes a unified framework to accelerate the adoption of EV biosensing as a cornerstone of next-generation precision diagnostics and personalized medicine. Full article

Background: Coronary artery bypass grafting (CABG) is a well-established revascularization strategy for patients with multivessel coronary artery disease. The effectiveness of CABG is significantly influenced by antiplatelet therapy aimed at maintaining graft patency and reducing thrombotic complications. However, substantial inter-individual variability exists in platelet function responses to standard therapies such as aspirin and clopidogrel, leading to antiplatelet resistance. This variability has been linked to increased risks of myocardial infarction, stroke, and early graft failure. Platelet function testing (PFT) offers a potential strategy to identify resistance and guide more personalized antiplatelet therapy. This study aims to evaluate the association between perioperative platelet function test results and clinical outcomes following CABG. By assessing platelet responsiveness at multiple timepoints and correlating findings with postoperative events, the study seeks to determine whether PFT can stratify risk and improve patient management. Methods: This is a prospective, single-centre, observational cohort study conducted at a tertiary NHS cardiac surgery centre. Patients having elective or urgent isolated CABG will be enrolled and undergo perioperative PFT using the TEG6s system. Clinical outcomes will be monitored for 12 months postoperatively, with primary endpoints assessing the correlation between platelet function results and major adverse cardiovascular and cerebrovascular events (MACCE). Secondary endpoints will include the prevalence of antiplatelet resistance, demographic predictors, and the feasibility of integrating PFT into clinical workflows. Results: This study will report the prevalence of aspirin and clopidogrel resistance in CABG patients based on TEG6s PFT, as well as the correlation between platelet function results and MACCE, postoperative bleeding, and the need for surgical re-exploration. Additionally, it will examine the associations between demographic and clinical factors—such as diabetes status, renal function, BMI, and surgical technique—and variability in platelet responsiveness. The feasibility of incorporating PFT into perioperative workflows will also be evaluated, assessing whether results could support personalized antiplatelet management in future clinical trials. Conclusions: Findings from this study will provide real-world evidence regarding platelet function variability in CABG patients and suggest that PFT may identify those at increased risk of thrombotic complications. This exploratory analysis supports the need for larger interventional trials aimed at optimizing individualized postoperative antiplatelet therapy to improve surgical outcomes. Full article

Background: Artificial intelligence (AI) is transforming medicine by supporting data-driven diagnosis, prognosis, and personalized care. In rheumatology, AI applications are rapidly expanding in imaging, disease monitoring, and therapeutic decision support. This review aimed to summarize current evidence on AI in osteoporosis and chronic inflammatory rheumatic diseases, with a focus on methodological robustness and clinical applicability. Methods: A narrative review was conducted following SANRA criteria. PubMed and the Cochrane Library were systematically searched for studies published between January 2015 and July 2025 using MeSH terms and free-text keywords related to AI, osteoporosis, and inflammatory rheumatic diseases. A total of 323 articles were included. Results: Machine learning and deep learning models show strong performance in osteoporosis for predicting bone mineral density (BMD), bone loss, and fractures. In chronic inflammatory rheumatic diseases, AI improves imaging interpretation, particularly for sacroiliitis. AI tools also demonstrate potential for predicting disease risk and activity, diagnostic support and treatment response. Hybrid models combining imaging, clinical, and biological data appear particularly promising. However, most studies rely on retrospective single-center datasets, with limited external validation, suboptimal explainability, and scarce evidence of real-world implementation. Conclusions: AI holds significant promise for advancing diagnosis and personalized management in osteoporosis and rheumatic diseases. However, major challenges persist, including heterogeneous data quality, inconsistent methodological reporting, limited clinical validation, and barriers to integration into routine practice. Bridging the gap between algorithmic performance and clinical impact will require prospective studies, robust validation frameworks, and strategies to build trust among clinicians and patients. Full article

Introduction: Personality disorders are enduring, maladaptive patterns that impair social and vocational functioning. The DSM-5 Alternative Model for Personality Disorders (AMPD) distinguishes Criterion A (personality functioning: identity, self-direction, empathy, intimacy) from Criterion B (maladaptive trait domains: negative affectivity, detachment, antagonism, disinhibition, psychoticism). We frame this study within Criterion B, supporting the use of a dimensional approach that complements (rather than replaces) normative models like the Five-Factor Model (FFM) and addresses cross-cultural gaps amid Saudi Arabia’s rapid sociocultural change such as the reforms associated with Vision 2030. Given Saudi Arabia’s collectivist orientation and evolving sociocultural norms under Vision 2030, the dimensional approach of the AMPD Criterion B offers a culturally sensitive lens for capturing personality pathology beyond Western-centric diagnostic models. Aim: We aimed to examine how PID-5-BF maladaptive trait domains vary across key sociodemographic factors in Saudi adults. Subjects and Methods: This was a quantitative, cross-sectional analytical study conducted among Saudi adults using the PID-5-BF Convenience sampling was performed via the dissemination of an online survey; we aimed for 377 participants and obtained 343 completed responses (~91% of the target sample). For trait assessment, we used the PID-5-BF; analyses compared domains across sociodemographic groups. Results: Females showed a higher negative affect; participants ≤ 30 years exhibited higher psychoticism than those >40; and single individuals reported lower detachment and psychoticism than their married peers. Conclusions: Gender, age, and marital status are associated with differences in maladaptive trait expression, supporting the need for culturally tailored screening and interventions in Saudi mental health services. These findings should be interpreted with caution given the fact that WhatsApp-based convenience sampling was used, which may bias the results as the respondents were more likely to live in urban areas, be educated, and be technologically proficient. Full article

Background: Artificial intelligence (AI) is rapidly transforming clinical medicine by enabling earlier disease detection, personalized risk stratification, precision diagnostics, and optimized therapeutic decision-making across multiple specialties. Methods: This narrative review synthesizes the most recent evidence from prospective randomized controlled trials, large cohort studies, and real-world implementations of AI in cardiology, pulmonology, neurology, hepatology, pancreatic diseases, and other key areas of internal medicine. Studies were selected based on clinical impact, external validation, and regulatory approval status where applicable. Results: AI systems now outperform traditional clinical tools in numerous high-stakes applications: >88% freedom from atrial fibrillation at 1 year with AI-guided ablation, noninferior stent optimization versus OCT guidance, >95% sensitivity for atrial fibrillation and low ejection fraction detection on single-lead ECG, substantial increases in adenoma detection rate and melanoma triage accuracy, automated pancreatic cancer detection on routine CT with 89–90% sensitivity, and significant improvements in palliative care consultation rates and post-PCI outcomes using AI-supported telemedicine. Over 850 FDA-cleared AI devices exist as of November 2025, with cardiology and radiology dominating clinical adoption. Conclusions: AI has transitioned from experimental to clinically indispensable in multiple specialties, delivering measurable reductions in mortality, morbidity, hospitalizations, and healthcare resource utilization. Remaining challenges include external validation gaps, bias mitigation, and the need for large-scale prospective trials before universal implementation. Full article

Mass spectrometry-based metabolomics is a valuable tool for advancing pediatric health research. Along with nuclear magnetic resonance, it enables detailed biochemical analysis from minimal sample volumes, a critical feature for pediatric diagnosis. Metabolomics supports early detection of inherited metabolic disorders, monitors metabolic changes during growth, and identifies disease markers for a range of conditions, including metabolic, neurodevelopmental, oncological, and infectious diseases. Integrating metabolomic data with genomic, proteomic (i.e., multi-omics approaches), and clinical information enables more precise and preventive care by enhancing risk assessment and informing targeted treatments. However, routine clinical use faces several challenges, including establishing age- and sex-specific reference ranges, standardizing sample collection and processing, ensuring consistency across platforms and laboratories, expanding reference databases, and improving data comparability. Ethical and regulatory issues, including informed consent, data privacy, and equitable access, also require careful consideration. Advances in high-resolution and single-cell metabolomics, artificial intelligence for data analysis, and cost-effective testing are expected to address these barriers and support broader clinical adoption. As standards and data-sharing initiatives grow, metabolomics will play an increasingly important role in pediatric diagnostics and personalized care, enabling earlier disease detection, improved treatment monitoring, and better long-term outcomes for children. Full article

Wearable multi-modal body fluid monitoring enables continuous, non-invasive, and context-aware assessment of human physiology. By integrating biochemical and physical information across multiple modalities, wearable systems overcome the limitations of single-marker sensing and provide a more holistic view of dynamic health states. This review offers a system-level overview of recent advances in multi-modal body fluid monitoring, structured into three hierarchical dimensions. We first examine sensing-combination strategies such as multi-marker analysis within single fluids, coupling biochemical signals with bioelectrical, mechanical, or thermal parameters, and emerging multi-fluid acquisition to improve analytical accuracy and physiological relevance. Next, we discuss platform-integration mechanisms based on biochemical, physical, and hybrid sensing principles, along with monolithic and modular architectures enabled by flexible electronics, microfluidics, microneedles, and smart textiles. Finally, the data-processing patterns are analyzed, involving cross-modal calibration, machine learning inference, and multi-level data fusion to enhance data reliability and support personalized and predictive healthcare. Beyond summarizing technical advances, this review establishes a comprehensive framework that moves beyond isolated signal acquisition or simple metric aggregation toward holistic physiological interpretation. It guides the development of next-generation wearable multi-modal body fluid monitoring systems that overcome the challenges of high integration, miniaturization, and personalized medical applications. Full article

Background and Objectives: Contrast-enhanced mammography (CEM) provides both morphological and functional information and may reflect breast cancer biology similarly to Magnetic Resonance Imaging (MRI). Materials and Methods: This single-center retrospective study included 399 women with Breast Imaging Reporting and Data System (BI-RADS) category 0 screening mammograms who subsequently underwent CEM. A total of 76 malignant lesions (68 invasive cancers, 8 ductal carcinoma in situ (DCIS)) with complete imaging and pathology data were analyzed. Invasive cancers were classified into luminal A, luminal B, luminal B/Human Epidermal Growth Factor Receptor 2 (HER2)-positive, HER2-enriched, and triple-negative, and grouped as luminal (Group 1) versus HER2-positive/triple-negative (Group 2). Results: Luminal subtypes predominated (47 of 68, 69%), while 21 of 68 (31%) were HER2-positive or triple-negative. Most cancers appeared as masses with spiculated margins and heterogeneous enhancement. Significant differences were observed in mass shape (p = 0.03) and internal enhancement (p = 0.01). Luminal tumors were more often irregular and spiculated with heterogeneous enhancement, whereas the HER2-positive/triple-negative tumors more frequently appeared round with rim or homogeneous enhancement. Deep learning-derived malignancy scores (iCAD ProFound AI^®) demonstrated good diagnostic performance (area under the curve (AUC) = 0.744, 95% confidence interval (CI) 0.654–0.821, p < 0.001). The median AI score was significantly higher in malignant compared with benign lesions (70% [interquartile range (IQR) 47–93] vs. 38% [IQR 25–61]; Mann–Whitney U test, p < 0.001). Among malignant lesions, iCAD scores varied across molecular subtypes, with higher median values observed in Group 1 versus Group 2 (87% vs. 55%), although the difference was not statistically significant (Mann–Whitney U test, p = 0.35). Conclusions: CEM features mirrored subtype-specific phenotypes previously described with MRI, supporting its role as a practical tool for enhanced tumor characterization. Although certain imaging and AI-derived parameters differed descriptively across subtypes, no statistically significant differences were observed. As deep-learning models continue to evolve, the integration of AI-enhanced CEM into clinical workflows holds strong potential to improve lesion characterization and risk stratification in personalized breast cancer diagnostics. Full article

This study takes the source region of the Yellow River from 2000 to 2024 as the research area, and integrates multi-source remote sensing, long-term meteorological observation, and land use data from 2000 to 2024. Using GIS spatial analysis, the standard ellipse model, and a geographic detector, this study systematically depicts the spatio-temporal heterogeneity and multi-scale evolution trend of soil and water conservation services, and then quantifies the spatial differentiation of the contribution rate of climate fluctuation, land use transformation, and human activity intensity to service change. The results showed the following: (1) The land use pattern in the source region of the Yellow River showed a one-way transformation of “grassland dominated, forest land increased alone, and the rest decreased”. The net increase in forest land 204.3 km² was all from the transformation of grassland. The vegetation coverage increased by 9.9%, and the low-value area of soil and water conservation services in the northwest continued to expand. (2) The overall moving distance of the center of gravity of soil and water conservation service capacity is not significant compared with the spatial scale of the source area of the Yellow River. The standard deviation ellipse of each year also did not show systematic and large changes in area, shape, or direction. (3) Annual mean temperature (Q = 0.590) and vegetation coverage (Q = 0.527) are the most influential single factors, while the interaction between annual mean temperature and precipitation (bidirectional enhancement) is the most stable synergistic driving combination. The single-factor Q values of topography and human activities were <0.10. (4) Climate and economic factors are the key factors driving the spatial differentiation of soil and water conservation service capacity, and the role of each driving factor has an optimal range to reduce the risk of soil erosion. The optimal range of population density is 7~9 person/km², the optimal range of average GDP is 11,900~14,100 yuan/km², the optimal range of annual average temperature is 1.71~3.47 °C, the optimal range of annual precipitation is 682~730 mm, the optimal range of vegetation coverage is 81.7~100%, and the optimal range of altitude is 3390~3740 m. The optimal range of slope is 18.3~24.3°. The optimal range of soil moisture is 26.7~29.4%. The optimal range of grazing intensity is 0.352~0.652. The study proposes countermeasures such as strict control of development in high-value areas of soil and water conservation services and key ecological restoration in low-value areas, the establishment of breeding bases and catchment areas in low-precipitation areas to cope with climate change, the optimization of grazing strategies, so as to provide scientific support for the stability of alpine grassland ecosystem services, and the high-quality development of the Yellow River Basin. Full article

Stress is a pervasive global health concern that adversely contributes to morbidity and reduced productivity, yet it often remains unquantified due to its subjective and variant presentation. Although artificial intelligence offers an encouraging path toward automated monitoring of mental states, current state-of-the-art approaches are challenged by the reliance on single-source data, sparsity of labeled samples, and significant class imbalance. This paper proposes NeuroStrainSense, a novel deep multimodal stress detection model that integrates three complementary datasets—WESAD, SWELL-KW, and TILES—through a Transformer-based feature fusion architecture combined with a Variational Autoencoder for generative data augmentation. The Transformer architecture employs four encoder layers with eight multi-head attention heads and a hidden dimension of 512 to capture complex inter-modal dependencies across physiological, audio, and behavioral modalities. Our experiments demonstrate that NeuroStrainSense achieves a state-of-the-art performance with accuracies of 87.1%, 88.5%, and 89.8% on the respective datasets, with F1-scores exceeding 0.85 and AUCs greater than 0.89, representing improvements of 2.6–6.6 percentage points over existing baselines. We propose a robust evaluation framework that quantifies discrimination among stress types through clustering validity metrics, achieving a Silhouette Score of 0.75 and Intraclass Correlation Coefficient of 0.76. Comprehensive ablation experiments confirm the utility of each modality and the VAE augmentation module, with physiological features contributing most significantly (average performance decrease of 5.8% when removed), followed by audio (2.8%) and behavioral features (2.1%). Statistical validation confirms all findings at the p < 0.01 significance level. Beyond binary classification, the model identifies five clinically relevant stress profiles—Cognitive Overload, Burnout, Acute Stress, Psychosomatic, and Low-Grade Chronic—with an expert concordance of Cohen’s κ = 0.71 (p < 0.001), demonstrating the strong ecological validity for personalized well-being and occupational health applications. External validation on the MIT Reality Mining dataset confirms the generalizability with minimal performance degradation (accuracy: 0.785, F1-score: 0.752, AUC: 0.849). This work underlines the potential of integrated multimodal learning and demographically aware generative AI for continuous, precise, and fair stress monitoring across diverse populations and environmental contexts. Full article

Purpose: This single-athlete, longitudinal observational study describes training intensity distribution (TID) across two Olympic preparation cycles (Rio 2016 vs. Tokyo 2021) and explores whether differences in high-intensity exposure coincided with performance outcomes. Methods: An elite male 50 m freestyle specialist (personal best 21.27 s; height: 187 cm, weight: 80 kg, body mass index: 22.9 kg·m⁻², fat-free mass: 75.2 kg, and fat mass: 4.8 kg) was monitored across four mesocycle periods. TID is expressed as % of total swim volume in three zones: Z1 (low intensity), Z2 (threshold), Z3 [high intensity/race-pace, including High Intensity Interval Training (HIIT) and Sprint Interval Training (SIT)]. Both the coach and swimmer signed a written informed consent for the use of their data. Results: For Rio 2016, TID (Z1/Z2/Z3) was as follows: General 80/0/20, Specific 60/0/40, Pre-competition 40/30/30, and Taper 50/20/30, indicating a polarized approach. For Tokyo 2021, TID shifted to: General 85/0/15, Specific 60/0/40, Pre-competition 30/30/40, and Taper 40/20/40. Discussion: In this single athlete, a greater proportion of work in Z3 during the Tokyo cycle, particularly in the Pre-competition and Taper phases, probably coincided with improved performance (21.57 vs. 21.79 s). Conclusions: Although clear causal inference is not possible, these observations depict the probability that sprint-swim preparation for 50 m events needs a training volume oriented to Z3 and relatively less in Z1. However, the study’s design, the methods by which the TID was recorded, etc., limit any generalization about the interpretation of the findings. Therefore, future studies should address these limitations, providing more insights into improving the training on that kind of events. Full article

The convergence of biometric and environmental sensing represents a transformative advancement in wearable technology, moving beyond single-parameter tracking towards a holistic, context-aware paradigm for health monitoring. This review comprehensively examines the landscape of multi-modal wearable devices that simultaneously capture physiological data, such as electrodermal activity (EDA), electrocardiogram (ECG), heart rate variability (HRV), and body temperature, alongside environmental exposures, including air quality, ambient temperature, and atmospheric pressure. We analyze the fundamental sensing technologies, data fusion methodologies, and the critical importance of contextualizing physiological signals within an individual’s environment to disambiguate health states. A detailed survey of existing commercial and research-grade devices highlights a growing, yet still limited, integration of these domains. As a central case study, we present an integrated prototype, which exemplifies this approach by fusing data from inertial, environmental, and physiological sensors to generate intuitive, composite indices for stress, fitness, and comfort, visualized via a polar graph. Finally, we discuss the significant challenges and future directions for this field, including clinical validation, data security, and power management, underscoring the potential of convergent sensing to revolutionize personalized, predictive healthcare. Full article

Purpose: Candida bloodstream infections remain a major global health challenge, with mortality rates approaching 40%. Beyond classical immunocompromised status, recent evidence highlights additional risk factors, including iatrogenic immunosuppression, advanced age, prolonged hospitalization, exposure to broad-spectrum antibiotics, and total parenteral nutrition. While Candida albicans (C. albicans) remains the most common species in Europe and the USA, non-albicans species, particularly Nakaseomyces glabratus (N. glabratus), Candida tropicalis (C. tropicalis), and Candida parapsilosis (C. parapsilosis), are emerging worldwide. Methods: This retrospective observational cohort study was conducted at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” in Salerno, Italy, from January 2015 to December 2024. It included all patients with at least one positive blood culture for Candida species. Demographic data, hospital ward of admission, and antifungal susceptibility profiles were collected and analyzed using SPSS software (IBM SPSS Statistics for Mac, version 30 (IBM Corp., Armonk, NY, USA)). Results: The incidence rate is 48.7 new isolates per one thousand patient-days, with a trend of increasing episodes over time among a total of 364 patients. Most cases occurred in medical wards (59.5%), where patients were older (median age 76 (17). C. albicans accounted for 57.9% of isolates, and a significant association was found between species distribution and hospital unit (p < 0.05). Resistance to fluconazole, voriconazole, and amphotericin B increased among C. albicans, with similar trends in N. glabratus and C. parapsilosis. Conclusions: This large single-center cohort highlights both the persistent dominance of C. albicans and the worrisome rise in resistance among C. parapsilosis. Given the aging patient population and increasing antifungal resistance, local epidemiological data are crucial to guide empirical therapy. Our findings underscore the need for multidisciplinary antifungal stewardship programs to optimize personalized treatment strategies and contain the emergence of resistant strains. Full article