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Invasive fungal infections (IFIs) represent a growing global health threat, particularly for immunocompromised populations, with mortality exceeding 1.5 million deaths annually. Despite their clinical and economic burden-costing billions in healthcare expenditures-fungal infections remain underprioritized in public health agendas. This review examines the current landscape of antifungal therapy, focusing on advances, challenges, and future directions. Key drug classes (polyenes, azoles, echinocandins, and novel agents) are analyzed for their mechanisms of action, pharmacokinetics, and clinical applications, alongside emerging resistance patterns in pathogens like and azole-resistant . The rise of resistance, driven by agricultural fungicide use and nosocomial transmission, underscores the need for innovative antifungals, rapid diagnostics, and stewardship programs. Promising developments include next-generation echinocandins (e.g., rezafungin), triterpenoids (ibrexafungerp), and orotomides (olorofim), which target resistant strains and offer improved safety profiles. The review also highlights the critical role of "One Health" strategies to mitigate environmental and clinical resistance. Future success hinges on multidisciplinary collaboration, enhanced surveillance, and accelerated drug development to address unmet needs in antifungal therapy.
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http://dx.doi.org/10.3390/idr17040091 | DOI Listing |
Adv Sci (Weinh)
September 2025
Department of Orthodontics, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing Key Laboratory of Digital Stomatology, NHC Key Laboratory of Digital Stomatology, NMPA Key
Clear aligners offer aesthetic and comfort advantages in orthodontics, yet their ability to deliver effective forces relies heavily on empirical judgment or large-scale optical scanning, lacking real-time quantitative evaluation. Integrating pressure sensors into aligners is a promising solution, but challenges in miniaturization, multi-dimensional sensing, measurement accuracy, and biocompatibility hinder clinical application. Here, an all-in-one Orthodontic Force Acquisition System (OFAS) is presented that enables real-time, 3D force monitoring using a cross-shaped iontronic sensing array and an origami-inspired, wireless battery-free readout circuit miniaturized for single-tooth placement.
View Article and Find Full Text PDFJ Eval Clin Pract
September 2025
Department of Orthopedics and Traumatology, Medical Faculty, University of Health Sciences, Antalya, Turkey.
Aims And Objective: The field of medical statistics has experienced significant advancements driven by integrating innovative statistical methodologies. This study aims to conduct a comprehensive analysis to explore current trends, influential research areas, and future directions in medical statistics.
Methods: This paper maps the evolution of statistical methods used in medical research based on 4,919 relevant publications retrieved from the Web of Science.
Nanoscale
September 2025
School of Biochemical Engineering, Indian Institute of Technology (BHU) Varanasi, Uttar Pradesh 221005, India.
Early-stage cancer diagnosis is considered a grand challenge, and even though advanced analytical assays have been established through molecular biology techniques, there are still clinical limitations. For example, low concentration of target biomarkers at early stages of cancer, background values from the healthy cells, individual variation, and factors like DNA mutations, remain the limiting factor in early cancer detection. Volatile organic compound (VOC) biomarkers in exhaled breath are produced during cancer cell metabolism, and therefore may present a promising way to diagnose cancer at the early stage since they can be detected both rapidly and non-invasively.
View Article and Find Full Text PDFDermatitis
September 2025
From the Department of Dermatology, Venereology and Leprology, All India Institute of Medical Sciences (AIIMS), Bhopal, India.
Contact dermatitis (CD), which includes both allergic CD and irritant CD, is a common inflammatory condition that can pose significant diagnostic challenges. Although patch testing is the gold standard for identifying causative allergens for allergic contact dermatitis (ACD), it is time-consuming, subjective, and requires expert interpretation. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning, have shown promise in improving the accuracy, efficiency, and accessibility of CD diagnosis and management.
View Article and Find Full Text PDFJ Ocul Pharmacol Ther
September 2025
Pharmaceutical Sciences, University of Colorado Denver, Aurora, Colorado, USA.