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Background/Objectives: Cerebrospinal fluid (CSF) rhinorrhoea carries a significant risk of life-threatening intracranial complications. This review provides a contemporary overview of current management strategies for CSF rhinorrhoea. Methods: We conducted a literature review, examining studies from Medline, Embase, and Google Scholar published within the last 20 years. This narrative synthesis summarises the current and future trends in the management of CSF rhinorrhoea. Results: The management of CSF leaks requires a multidisciplinary approach, encompassing a thorough clinical assessment, targeted diagnostic testing, and a spectrum of surgical and non-surgical interventions. Endoscopic techniques, particularly the use of vascularised flaps such as the nasoseptal flap, has become central to anterior skull base reconstruction. Numerous graft and flap choices provide tailored solutions based on defect size and CSF flow characteristics, with reported success rates exceeding 90%. Conclusions: Endoscopic repair of CSF rhinorrhoea continues to evolve, with modern techniques significantly enhancing success rates and reducing morbidity. Further understanding of underlying aetiologies, advances in technology, and refinement in surgical technique are areas for future innovation in CSF rhinorrhoea management. [ABSTRACT FROM AUTHOR]

Introduction: Volcano seismology has successfully predicted several eruptions and includes many reliable methods that have been adopted extensively by volcanic observatories; however, there are several problems that still lack solutions. Meanwhile, the overwhelming success of data-driven models to solve predictive complex real-world problems positions them as an effective addition to the monitoring systems deployed in volcanological observatories. Methods: By applying signal processing techniques on seismic records, we extracted four different seismic features, which usually change their trend when the system is approaching an eruptive episode. We built a temporal matrix with these parameters then defined a label for each temporal moment according to the real state of the volcanic activity (Unrest, Pre-Eruptive, Eruptive). To solve the remaining problem developing early warning systems that are transferable between volcanoes, we applied our methodology to databases associated with different volcanic systems, including data from both explosive and effusive episodes, recorded at several volcanic scenarios with open and closed conduits: Mt. Etna, Bezymianny, Volcán de Colima, Mount St. Helens and Augustine. Results and Discussion: This work proposes the use of Neural Networks to classify the volcanic state of alert based on the behaviour of these features, providing a probability of having an eruption. This approach offers a Machine Learning tool for probabilistic short-term volcanic eruption forecasting, transferable to different volcanic systems. This innovative method classifies the state of volcanic hazard in near real-time and estimates a probability of the occurrence of an eruption, resulting in a period from at least hours to several days to forecast an eruption. [ABSTRACT FROM AUTHOR]

Objective: Functional endoscopic sinus surgery for chronic rhinosinusitis improves sinus drainage and intranasal medication delivery. This study compares medication delivery with commonly used devices in normal and altered anatomy (post functional endoscopic sinus surgery) using sinus surgery models (Phacon). Methods: Medication delivery was simulated via nasal drops, nasal spray and an irrigation device (Neilmed Sinus Rinse). Coverage was then calculated from endoscopic pictures taken at various anatomical sites in the normal nose and post functional endoscopic sinus surgery. Results: In the normal nose, nasal spray did not penetrate the sphenoid sinus, and drops bypassed the vestibule anteriorly. Neilmed Sinus Rinse provided superior coverage at the sphenoid site following sphenoidectomy and the frontal site following Draf III. After ethmoidectomy, nasal drops overall provided less coverage than the other methods. Conclusion: Neilmed Sinus Rinse generally provided the best distribution, followed by the nasal spray and then nasal drops. The type and extent of surgery also affects medication delivery. [ABSTRACT FROM AUTHOR]

The search for pre‐eruptive observables that can be used for short‐term volcanic forecast remains a scientific challenge. Pre‐eruptive patterns in seismic data are usually identified by analyzing seismic catalogs (e.g., the number and types of recorded seismic events), the evolution of seismic energy, or changes in the tensional state of the volcanic medium as a consequence of changes in the volume of the volcano. However, although successful volcanic predictions have been achieved, there is still no generally valid model suitable for a large range of eruptive scenarios. In this study, we evaluate the potential use of Shannon entropy as short‐term volcanic eruption forecasting extracted from seismic signals at five well studied volcanoes (Etna, Mount St. Helens, Kilauea, Augustine, and Bezymianny). We identified temporal patterns that can be monitored as short‐term eruptive precursors. We quantified the decay of Shannon entropy prior to eruptions, noting that changes appear between 4 days and 12 hr before. When Shannon entropy is combined with the temporal evolution of other features (i.e., energy, kurtosis, and the frequency index), we can elaborate physical models according to the occurring volcanic processes. Our results show that pre‐eruptive variation in Shannon entropy is a confident short‐term volcanic eruption monitoring tool. Plain Language Summary: Volcanic eruptions represent a major natural hazard. Despite decades of research, the forecasting of volcanic eruptions remains a scientific challenge. Subsurface volcanic processes generate seismic waves, which can be measured at the surface using seismometers. To date, the most successful examples of eruption forecasting have been based on seismic data. However, we still lack a early warning model that can be applied across the wide range of eruption styles seen around the world. In this study, we implemented a new approach for the analysis of seismo‐volcanic data aimed at warning eruptions. We used advanced signal processing algorithms to analyze continuous seismic signals from a suite of well‐studied volcanoes (Mount St. Helens, Mt. Etna, Kilauea, Augustine, and Bezymianny) in order to create a new database of features found within the seismic signals. We found that pre‐eruptive variation in the Shannon entropy (a statistical parameter associated to the coherence of the seismic sources) of seismic signals offers a successfully feature for short‐term volcanic eruption alert. The relationship between pre‐eruptive seismic signals and Shannon entropy is based on changes in the probability distributions of the type of seismic waves, independent of the signal source. Key Points: While successful volcanic forecasting has been achieved, there is no generally valid model suitable for large range of eruptive scenariosWe used feature extraction to analyze seismic data from five well studied volcanoes to identify short‐term eruptive precursorsShannon entropy has a uniform temporal pattern of pre‐eruptive change and is a recurrent and transferable feature [ABSTRACT FROM AUTHOR]