Database Retrieval Research Articles

Efficient UAV localization using combined autoencoder and SIFT

In GNSS-denied environments, accurate Unmanned Aerial (UAV) localization faces significant challenges. This paper introduces a vision-based localization method combining autoencoder and SIFT algorithms, referred to as AE+SIFT. The method compresses high-resolution map images into low-dimensional vectors, which are stored in a database for efficient retrieval. During the localization process, UAV images are encoded and matched with the database, followed by SIFT and homography projection for precise positioning. The AE+SIFT approach enhances localization accuracy, achieving an average coordinate error of 3.94 meters relative to the ground truth. Notably, when UAV images are misaligned with reference images, our method outperforms the existing AE method in terms of accuracy.

HealthWise: AI-Powered Medical Chatbot with Llama 2

This research paper presents the development of a medical chatbot utilizing cutting- edge technologies, specifically the Langchain framework and the Llama 2 language model, in conjunction with the PineCone database for efficient data storage and retrieval. The primary objective of this project is to create an intelligent conversational agent capable of providing reliable medical information, assisting users in symptom assessment, and guiding them towards appropriate healthcare resources. The integration of Langchain facilitates seamless interaction between the user interface and the underlying LLM, enabling natural language understanding and generation. Leveraging the advanced capabilities of the Llama 2 model ensures that the chatbot can engage in meaningful dialogues while maintaining context and relevance. Additionally, PineCone serves as a robust solution for managing and indexing medical data, enhancing the chatbot's ability to deliver accurate and context-aware responses. This study explores the design, implementation, and potential implications of our medical chatbot, contributing to the emerging field of AI-driven healthcare solutions and underscoring its role in improving patient engagement and access to medical advice. Keywords – Medical Chatbot, Langchain, Llama-2, Pine Cone, Natural Language Processing, Artificial Intelligence, Machine Learning, Generative Artificial Intelligence.

Plaid fabric image retrieval based on hand-crafted features and relevant feedback

Common fabric image retrieval methods ignore the diversity and dynamism of user demands, the results are determined by the query image and cannot be dynamically adjusted. To solve this problem, this study proposes a novel image retrieval method for plaid fabrics based on hand-crafted features and relevant feedback. First, local texture descriptors are extracted by the local binary pattern on the separated images which are processed by Fourier transform. Global texture descriptors are extracted by scale-invariant feature transform (SIFT) and vector of locally aggregated descriptors (VLAD). Second, color moments with image partitioning are extracted to characterize spatial color information of plaid fabric images. Third, the extracted features are fused by the weight allocation for similarity measurement. Finally, the relevant feedback based on meta learning is involved to realize personalized adjustment and optimization of retrieval results. An image retrieval database is built as the benchmark by collecting over 44, 000 plaid fabric samples from the factory. Experiments show that precision and recall at rank eight reach to 70.6% and 62.6%, respectively, and mAP reaches to 0.690. Results prove that the proposed strategy is feasible and effective, which can realize plaid fabric image retrieval fast and efficiently.

Mechanism Exploration of Astaxanthin in the Treatment of Adriamycin-induced Cardiotoxicity Based on Network Pharmacology and Experimental Validation.

Astaxanthin (AXT), a natural antioxidant recognized for its therapeutic potential in cancer and cardiovascular diseases, holds promise in mitigating adriamycin-induced cardiotoxicity (AIC). Nevertheless, the underlying mechanisms of AXT in AIC mitigation remain to be elucidated. Consequently, this study endeavors to elucidate the mechanism of AXT against AIC, employing an integrated approach. Network pharmacology, molecular docking, and molecular dynamics simulations were harnessed to explore the molecular mechanism underlying AXT's action against AIC. Furthermore, the in-vitro AIC model was established with the H9c2 cell to generate transcriptome data for validation. A total of 533 putative AXT targets and 1478 AIC-related genes were initially screened by database retrieval and bioinformatics analysis. A total of 248 potential targets of AXT against AIC and several signaling pathways were identified by network pharmacology and enrichment analysis. Two core genes (CCL2 and NOS3) and the AGE-RAGE signaling pathway in diabetic complications were further highlighted by transcriptome validation based on the AIC in-vitro model. Additionally, molecular docking and dynamics analyses supported the robust binding affinity of AXT with the core targets. The study suggested that AXT might ameliorate AIC through the inhibition of CCL2 and NOS3 as well as AGE-RAGE signaling, which provide a theoretical basis for the development of a strategy against AIC.

Organ Donation Process for Transplantation: A Scoping Review

Objectives: To identify studies in the literature that address the strategies used in the organ donation process. Method: Scoping review based on the Joanna Briggs Institute ( JBI) methodology, in the databases Medical Literature and Retrieval System Online (MEDLINE), Scopus, Web of Science, Latin American and Caribbean Literature in Health Sciences (LILACS), and Embase via the Virtual Health Library (VHL), following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist, carried out from August to December 2023. Results:"irty-three studies were eligible, which showed that the donation process must happen systematically, following the steps: 1) identi#cation of patients suspected of having brain death (BD); 2) opening the protocol and notifying organizations; 3) con#rmation of the diagnosis of BD; 4) communicating the death to the family; 5) conducting the interview; 6) consent to donation; 7) donor maintenance; 8) procurement and allocation of organs; 9) transplant; and 10) follow-up. Conclusion: Donation requires a multifaceted approach, including awareness, investment, evaluation and research.

Integrating network pharmacology, molecular docking, and experimental validation to reveal the mechanism of Radix Rehmanniae in psoriasis.

Radix Rehmanniae (RR) plays an important role in treating psoriasis. However, the active compounds of RR and potential mechanisms are unclear. The current study was designed to investigate the potential active ingredients, targets, and mechanisms of RR in treating psoriasis through network pharmacology, molecular docking, and vitro experiments. Initially, the TCMSP database and literature retrieval were used to access the active ingredients of RR. The psoriasis target proteins were obtained from Therapeutic Target Database, OMIM, GeneCards, and DrugBank databases. The target proteins were then converted into target genes using Uniprot. Secondly, overlapping genes were obtained through Venn online tool. Then, protein-protein interactions network diagram is finished by STRING database. Next, Cytoscape software was used to acquire the top 10 hub proteins; gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis were then used to predict possible mechanisms. Afterwards, molecular docking validation of the active ingredients with the main targets was performed by AutoDock software. Finally, lipopolysaccharides induced RAW264.7, to assess the effects and molecular mechanisms by MTT, RT-qPCR, and Western blot assays. Overall, there are 20 effective compounds and 33 targets involved in biological processes including apoptosis, intracellular signaling, vasodilation, and mitogen-activated protein kinase (MAPK) signaling cascade. The docking results showed strong binding capacity between the active ingredients and targets. We verified aucubin as the key active ingredient, tumor necrosis factor α, and IL6 as the core targets, and focused on the p38MAPK protein pathway. Cellular experiments showed that aucubin down-regulated the phosphorylated p38MAP protein and reduced the expression of tumor necrosis factor α mRNA, IL6 mRNA, and IL1βmRNA. In summary, RR is featured with multicomponent, multi-target, and multi-pathway in treating psoriasis; the preliminary mechanism may be associated with the down-regulation of p38MAPK phosphorylation and curbing the expression of inflammatory factor by aucubin. This paper provides the scientific basis for Traditional Chinese medicine treating psoriasis.

CPEQA: A Large Language Model Based Knowledge Base Retrieval System for Chinese Confidentiality Knowledge Question Answering

Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, particularly in the construction of the intelligent question-answering system. These systems, especially in specialized fields, usually rely on NLP through the retrieval of corpus and answering databases to efficiently provide accurate and concise answers. This paper focuses on the national confidentiality publicity and education field, aiming to address the dilemma of inaccurate knowledge retrieval in this field. Therefore, we design an intelligent confidentiality question-answering system CPEQA by comprehensively utilizing the LLMs platform and information retrieval technique. CPEQA is capable of providing professional answers to questions about Chinese confidentiality publicity and education raised by users. Additionally, we also integrate the conventional database retrieval technique and LLMs into the database query construction, enabling CPEQA to perform real-time queries and data analysis for both single-table and multi-table querying tasks. Through extensive experiments with generated query sentences, we show both methodological comparisons and empirical evaluations of CPEQA’s performance. Experimental results indicate that CPEQA has achieved competitive results on answering precision, recall rate and other metrics. Finally, we explore the challenges of the CPEQA system associated with these techniques and outline potential avenues for future research in this emerging field.

Cluster of the e-Libraries of the Students’ Scientific Society at the University of Library Studies and Information Technologies in Sofia, Bulgaria

The information resources of the Students’ Scientific Society (SSS) at the University of Library Studies and Information Technologies (ULSIT) in Bulgaria are presented in the current article as an example of reference-information mapping. The retrospective channel of this modeling, structured in 29 e-libraries (2005–), designed for universities, libraries and civil society, was defined as an interdisciplinary cluster of the informatization in Bulgaria with wide international resonance. The methodology of development of the e-libraries and the used terminology define them as a secondary-documental resources. The semantic aspects of the е-libraries are shown as a unified system that is rooted in the universal humanitarian studies and virtually organized around the model of theoretical and practical methodological directions for organization of reference information retrieval databases. The perspectives of the development of similar information resources in the future are revealed. The creation of these e-libraries was part of a pedagogical practice through which students of Library Studies and Information Technologies learned information modeling in an open laboratory environment.

The Ice Cloud Imager: retrieval of frozen water column properties

Abstract. The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS-SG) will provide novel measurements of ice hydrometeors. ICI is a passive conically scanning radiometer that will operate within a frequency range of 183 to 664 GHz, helping to cover the present wavelength gap between microwave and infrared observations. Reliable global data will be produced on a daily basis. This paper presents the retrieval database to be used operationally and performs a final pre-launch assessment of ICI retrievals. Simulations are performed within atmospheric states that are consistent with radar reflectivities and represent the three-dimensional (3D) variability of clouds. The radiative transfer calculations use empirically based hydrometeor models. Azimuthal orientation of particles is mimicked, allowing for the consideration of polarisation. The degrees of freedom (DoFs) of the ICI retrieval database are shown to vary according to cloud type. The simulations are considered to be the most detailed performed to this date. Simulated radiances are shown to be statistically consistent with real observations. Machine learning is applied to perform inversions of the simulated ICI observations. The method used allows for the estimation of non-Gaussian uncertainties for each retrieved case. Retrievals of ice water path (IWP), mean mass height (Zm), and mean mass diameter (Dm) are presented. Distributions and zonal means of both database and retrieved IWP show agreement with DARDAR. Retrieval tests indicate that ICI will be sensitive to IWP between 10−2 and 101 kg m−2. Retrieval performance is shown to vary with climatic region and surface type, with the best performance achieved over tropical regions and over ocean. As a consequence of this study, retrievals from real observations will be possible from day one of the ICI operational phase.

Determination of FGFR1 Functions in Cytarabine Treatment of Acute Myeloid Leukemia Through Bioinformatics Analysis

Aim: Among the most prevalent subtypes of acute leukemia is acute myeloid leukemia (LAML). Consequently, it is essential to understand the molecular causes of LAML and find its predictive and diagnostic biomarkers. The aim of this study is to determine the molecular functions of fibroblast growth factor receptor 1(FGFR1) involved in LAML pathogenesis and its potential therapeutic effect for AML treatment. Methods: The molecular docking interaction of the Cytarabine with its target FGFR1 was examined. The Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2), and UALCAN tools database were used to obtain the LAML gene expression datasets. Gene functional annotation was performed to investigate the DEGs' possible role. Using the Interactive Gene database retrieval tool (STRING) and a few chosen hub modules from the GeneMANIA database, the protein- protein interaction (PPI) network were constructed. A survival analysis was performed on the effects of hub genes on the overall survival of LAML patients. Results: As a result of docking, a strong interaction was observed between cytarabine and FGFR1. It has been discovered that cytarabine can reverse FGFR1 expression. The survival study results showed an association between the prognosis of AML patients and one of the central genes, FGFR1. Conclusion: The expression profile and functions of FGFR1 were determined in LAML patients. It has been shown that FGFR1 can be a viable therapeutic target for LAML and a possible biomarker for diagnosis.

Retrieval-style In-context Learning for Few-shot Hierarchical Text Classification

Abstract Hierarchical text classification (HTC) is an important task with broad applications, and few-shot HTC has gained increasing interest recently. While in-context learning (ICL) with large language models (LLMs) has achieved significant success in few-shot learning, it is not as effective for HTC because of the expansive hierarchical label sets and extremely ambiguous labels. In this work, we introduce the first ICL-based framework with LLM for few-shot HTC. We exploit a retrieval database to identify relevant demonstrations, and an iterative policy to manage multi-layer hierarchical labels. Particularly, we equip the retrieval database with HTC label-aware representations for the input texts, which is achieved by continual training on a pretrained language model with masked language modeling (MLM), layer-wise classification (CLS, specifically for HTC), and a novel divergent contrastive learning (DCL, mainly for adjacent semantically similar labels) objective. Experimental results on three benchmark datasets demonstrate superior performance of our method, and we can achieve state-of-the-art results in few-shot HTC.

Mapping the landscape of green banking strategies: a bibliometric approach

IntroductionGreen banking (GB) strategies are a set of financial practices and activities implemented by banks to encourage sustainability, environmental responsibility, and low-carbon impact. This bibliometric study examines the present state of research in this field by utilizing the Scopus database for data retrieval and VosViewer for network analysis.MethodA total of 149 researches were retrieved from the Scopus and analyzed using the VosViewer software following the PRISMA guidelines. The study examines year-by-year publication patterns, top-cited articles, contributing countries, journals, co-citation analysis of authors, and co-occurrence analysis of keywords.ResultStrategies such as online banking, solar ATMs, construction of green buildings, and making provisions for green loans are identified as the key GB strategies that can be adopted by the banks. Furthermore, the fundamental challenges banking organizations encounter while implementing GB strategies are underlined.DiscussionThe study suggests that “green brand image,” “green audit,” “environmental economics,” and “carbon footprint” can be considered as an area of interest in future. The study provides researchers, policymakers, regulators, and financial institutions with valuable insights by presenting the current state of green banking research and identifying emerging areas for further exploration. These findings will help others to discover the areas of interest in GB and advancing sustainable finance practices that can foster environmental responsibility in the financial sector.

Energy-saving path planning navigation for solar-powered vehicles considering shadows

Globally, the adverse climate effects caused by greenhouse gas emissions are becoming increasingly apparent, and solutions to increase the use of eco-friendly transportation methods are urgently needed. Introducing solar-powered vehicles (SPVs), which are cars integrated with solar panels capable of generating power, presents a promising solution to reduce urban carbon footprints. However, the low adoption rate of SPVs implies that the benefits—such as environmental friendliness and ability to charge while driving—need to be more palpably experienced by consumers. To address this aspect, in this study, we aimed to develop a navigation system algorithm that guides users along routes that optimize energy consumption and solar energy production from the starting point to the destination. This was done with the objective of providing more tangible benefits from using SPVs. The study focused on the high-traffic urban center of Seoul, where determining solar power availability for a moving SPV is challenging, given the presence of shadows cast by roadside features such as buildings and trees. To achieve this, panoramic images from Google Street View were collected at 10 m intervals from all roads within the research area. From these images, sky and non-sky elements were separated. Subsequently, a hemispherical map was constructed and superimposed with the sun's path. The presence of shadows was determined by assessing whether the sun's path was obstructed by non-sky elements; if the path was unimpeded in the sky, no shadow was recorded. The shadow data obtained at each spot were efficiently stored in a database for quick retrieval and application based on specific locations and departure times. Using this shadow information, the navigation algorithm calculates power generation along a given route and considers the energy consumption of the SPV. Analysis led to the identification of an energy-saving route, which enabled the achievement of energy conservation and CO2 reduction benefits. Furthermore, a comprehensive sensitivity analysis was conducted to examine the impact of four critical parameters—module efficiency, solar panel area, vehicle speed, and departure time—on route selection and net energy consumption. The energy-saving path planning algorithm enhances the economic feasibility of solar charging for SPVs during travel; thus, this study can contribute significantly to the widespread adoption of SPVs, which play a definitive role in reducing transportation's carbon footprint.

The Emergence of N. sativa L. as a Green Antifungal Agent.

Nigella sativa L. has been widely used in the Unani, Ayurveda, Chinese, and Arabic medicine systems and has a long history of medicinal and folk uses. Several phytoconstituents of the plant are reported to have excellent therapeutic properties. In-vitro and in-vivo studies have revealed that seed oil and thymoquinone have excellent inhibitory efficacy on a wide range of both pathogenic and non-pathogenic fungi. The present review aims to undertake a comprehensive and systematic evaluation of the antifungal effects of different phytochemical constituents of black cumin. An exhaustive database retrieval was conducted on PubMed, Scopus, ISI Web of Science, SciFinder, Google Scholar, and CABI to collect scientific information about the antifungal activity of N. sativa L. with 1990 to 2023 as a reference range using 'Nigella sativa,' 'Nigella oil,' 'antifungal uses,' 'dermatophytic fungi,' 'candidiasis,' 'anti-aflatoxin,' 'anti-biofilm' and 'biological activity' as the keywords. Black cumin seeds, as well as the extract of aerial parts, were found to exhibit strong antifungal activity against a wide range of fungi. Among the active compounds, thymoquinone exhibited the most potent antifungal effect. Several recent studies proved that black cumin inhibits biofilm formation and growth. The review provides an in-depth analysis of the antifungal activity of black cumin. This work emphasizes the need to expand studies on this plant to exploit its antifungal properties for biomedical applications.

Comparison of the efficacy and safety of vertebroplasty with different pedicle approaches for osteoporotic vertebral.

To compare the efficacy and safety of vertebroplasty through different pedicle approaches in the treatment of osteoporotic vertebral compression fracture osteoporotic vertebral compression fractures (OVCF) by network meta-analysis. Pubmed, Embase, Cochrane Library, Web of Science. Database for literature retrieval, retrieval time from the establishment of the database to April 2023, the randomized controlled trials of unilateral vertebroplasty (UVP), bilateral vertebroplasty (BVP), unilateral kyphoplasty (UKP), bilateral kyphoplasty (BKP), curved vertebroplasty (CVP) and curved kyphoplasty (CKP) were screened, evaluated and the data were extracted and included in the analysis. STATA 15.0 and ReMan 5.3 were used for data analysis. This study was registered in the National Institute for Health Research (NIHR) with the registration number CRD42023405181. This study included 16 articles with a total of 1712 patients. The order of visual analogue scale (VAS) improvement from good to bad is CVP > BVP > UVP > CKP > BKP > UKP. The order of kyphotic angles improvement from good to bad is CKP > UKP > UKP > UVP > BVP > CVP. The order of bone cement injection from less to more is UVP > CVP > UKP > CKP > BVP > BKP. The order of bone cement leakage rate from less to more is CKP > CVP > UKP > BKP > UVP > BVP. The order of X-ray exposure time from less to more is CKP > CVP > UVP > BVP > UKP > BKP. The order of operation time from less to more is CVP > UVP > UKP > CKP > BVP > BKP. For patients with kyphotic angles, kyphoplasty has unique advantages in improving kyphotic angles. But generally speaking, curved approach can optimize the distribution of bone cement through unilateral approach to achieve the orthopedic effect of bilateral approach, which is a minimally invasive technique with better curative effect and higher safety in the treatment of OVCF.

Enhancing MAUDE Database Utility by GPT-4 and Cause-Effect Visualization.

The MAUDE database is a valuable public resource for understanding malfunctions and adverse events related to medical devices and health IT. However, its extensive data and complex structure pose challenges. To overcome this, we have developed an automated analytical pipeline using GPT-4, a cutting-edge large language model. This pipeline is intended to efficiently extract, categorize, and visualize safety events with minimal human annotation. In our analysis of 4,459 colonoscopy reports from MAUDE (2011-2021), the events were categorized into operational, human factor, and device-related. Ishikawa diagrams visualized a subset stored in a vector database for easy retrieval and comparison through a similarity search. This innovative approach streamlines access to vital safety insights, reducing the workload on human annotators, and holds promise to enhance the utility of the MAUDE database.

Health technologies for tackling client absenteeism in primary and secondary care services.

Tackling client absenteeism in primary and secondary care settings is crucial to ensure the continuity of care for individuals, familiesand communities, as well as preventing waste of resources within healthcare systems. This article is an integrative review to identify advancements in health technologies that addressclient absenteeism in primary and secondary care. The databases Medical Literature and Retrieval System Online (MEDLINE/PubMed®), Scientific Electronic Library Onlineand Virtual Health Librarywere consulted. The inclusion criteria were as follows: full papers, published between 2013 and 2023, in English, Portugueseor Spanish. The descriptors used were the following: patients, mobile applications, health services management, absenteeismand primary care, and secondary care. Eleven articles published from 2014 to 2021 were included. Most articles were identified in the MEDLINE/PUBMED database, employed a randomized controlled trial methodology (36.36%), and were published between 2019 and 2021 (90.0%) in English (63.7%). The applications had managerial, assistiveand/or educational purposes. In addition to absenteeism control, these applications strived to promote client engagement with health services, increase health literacyand tackle structural barriers to care, such as language barriers. Efforts are needed to ensure that providers receive training to educate clients on the applications. Moreover, community-based participatory studies to ensure the feasibility of applications are warranted.

Cementless TKA use as an alternative to cemented TKA in high BMI patients: A systematic review and meta-analysis.

Obesity is prevalent, with nearly one-third of the world's population being classified as obese. In patients with high body mass index (BMI)/body mass undergoing total knee arthroplasty (TKA), there is an increase in strain placed on the implant fixation interfaces. As such, component fixation is a potential concern when performing TKA in the obese patient. To address the growing concerns around the longevity of implant fixation, some have advocated cementless over cemented fixation. However, there is no clear consensus on whether a cementless fixation has more favourable outcomes. The aim of this paper was to present a systematic review and meta-analysis of the existing evidence to establish if cementless TKA has a lower rate of aseptic loosening in high BMI patients when compared to cemented TKA procedures. A systematic review was performed, and the following databases Medical Literature Analysis and Retrieval System Online (1946 to date), PubMed (1966 to date) and Excerpta Medica Database (1974 to date) were searched. All studies comparing cementless to cemented TKA in patients with BMI > 30 were considered. Meta-analysis compared aseptic loosening and all-cause revision between cemented and uncemented implant use in BMI > 30 patients. The search returned 91 articles in total; after duplicates were removed, the yield was 44 studies. Of the remaining studies that were assessed, three studies met the inclusion criteria for meta-analysis. The pooled odds ratio for all-cause revisions was 0.17 (95%, 0.08-0.36) in favour of uncemented implants (p < 0.01). The pooled odds ratio for aseptic loosening was 0.15 (95% confidence interval, 0.02-0.90) in favour of uncemented implants (p = 0.04). Meta-analysis demonstrated a significant decrease in all-cause revisions and revisions for aseptic loosening when using uncemented fixation in high BMI patients when compared to the use of cemented implants. The level of evidence is 1 for our systematic review.

Xstar atomic database: the PyXstar package

We present a progress report on the development of PyXstar, a Python package to manage the data (input, output, intermediate, atomic database, and model-grids) associated with the xstar code for treating photoionized and collisionally ionized plasmas. The PyXstar modular structure and database retrieval scheme are described, and its functionality is illustrated with Python functions and classes for performing database searches. We briefly compare PyXstar with two other Python spectrum modeling tools: PyNeb and PyAtomDB.Graphic abstract

Flexible Fiber-Optic Scope Use in Retrieval Medicine: A Case Series

ObjectiveThe use of flexible fiber-optic scopes is increasing across critical care specialities, but there is limited literature on their use in retrieval medicine. This study aims to describe a case series in which flexible fiber-optic scopes were used by New South Wales Ambulance Aeromedical Operations critical care teams. MethodsA retrospective case series was performed in our service from January 1, 2019, to December 31, 2021. We searched our electronic medical retrieval database for the terms “aScope,” “Awake Fibreoptic Intubation (AFOI),” “Fibreoptic Intubation (FOI),” and “Nasal Endotracheal Tube (ETT)” to identify when a flexible fiber-optic scope was used. ResultsThere were 16 uses of flexible fiber-optic scopes during the study period. The most common procedure was awake nasal fiber-optic intubation, which was performed during 9 cases, 7 of which were successful. Other procedures performed included 4 cases of intubation through a supraglottic airway, 1 case of an asleep fiber-optic intubation, 1 case of flexible nasoendoscopy for upper airway assessment, and 1 case of fiber-optic bronchoscopy via tracheostomy. These procedures were performed by anesthetic, emergency and intensive care doctors. This case series describes 4 of these cases in more detail. ConclusionThis case series demonstrates a variety of procedural and diagnostic uses for flexible fiber-optic scopes in retrieval medicine. These procedures can have an important bearing on decision making and patient management. The training and clinical governance provided by our service in this skill appears to be safe and robust with high success rates of awake fiber-optic intubations.