BULLETIN OF THE SCHOOL OF ORIENTAL AND AFRICAN STUDIES-UNIVERSITY OF LONDON最新文献

To determine the prevalence of tonsilloliths in CT PNS (Computed Tomography ParaNasal Sinuses) of patients with and without features of chronic rhinosinusitis. 97 CT PNS of the patients with features of chronic rhinosinusitis were included in the study group, and 124 CT PNS of cases without features of chronic rhinosinusitis were taken as the control group. All 221 CT PNS were then evaluated for the presence of tonsillar calcifications indicative of tonsilloliths and the prevalence of the same in the study and control groups. 97 of the 221 CT PNS evaluated showed features of chronic rhinosinusitis. 60 of these 97 CT PNS showed features of tonsillolith in one or both tonsils. Of these 60 cases, 58 had maxillary sinusitis, and 17 had pansinusitis. Most of the cases had small tonsilloliths (1-3 mm), and only one case had a large tonsillolith (> 6 mm). At the same time, 27 out of the remaining 124 CT PNS without chronic rhinosinusitis showed the presence of tonsilloliths in one or both tonsils. The prevalence of tonsilloliths is significantly higher in patients with chronic rhinosinusitis than in the control group. The presence of tonsilloliths in patients with chronic rhinosinusitis indicates repeated inflammation of the tonsils due to sinusitis. Such chronic inflammation of the mucosa of the pharynx should prompt more aggressive treatment of chronic rhinosinusitis.

In many fields of clinical medicine and blood transfusion, the human leukocyte antigen (HLA) system is crucial. Alloimmunization happens as a result of an immune response to foreign antigens encountered during blood transfusion. This gives rise to alloantibodies against red blood cells (RBCs), HLA, or human platelet antigen (HPA). HLA alloimmunization following allogeneic transfusion was shown to be a result of contaminating white blood cells (WBCs) present in the product. It is a common complication of transfusion therapy that leads to difficulties in clinical intolerance and refractoriness to platelet transfusion during patient management. Single-donor platelets, prophylactic HLA matching, leukoreduction, and irradiation of cellular blood products are some of the mechanisms to prevent HLA alloimmunization during a blood transfusion. Now, the best approach to reduce the occurrence of primary HLA alloimmunization is the removal of WBCs from the blood by filtration.

Background: Aspirin-exacerbated respiratory disease (AERD) is an acquired inflammatory condition characterized by the presence of asthma, chronic rhinosinusitis with nasal polyposis, and respiratory hypersensitivity reactions on ingestion of aspirin or other nonsteroidal anti-inflammatory drugs (NSAIDs). Despite AERD having a classic constellation of symptoms, the diagnosis is often overlooked, with an average of greater than 10 years between the onset of symptoms and diagnosis of AERD. Without a diagnosis, individuals will lack opportunities to receive effective treatments, such as aspirin desensitization or biologic medications.

Objective: Our aim was to develop a combined algorithm that integrates both natural language processing (NLP) and machine learning (ML) techniques to identify patients with AERD from an electronic health record (EHR).

Methods: A rule-based decision tree algorithm incorporating NLP-based features was developed using clinical documents from the EHR at Mayo Clinic. From clinical notes, using NLP techniques, 7 features were extracted that included the following: AERD, asthma, NSAID allergy, nasal polyps, chronic sinusitis, elevated urine leukotriene E4 level, and documented no-NSAID allergy. MedTagger was used to extract these 7 features from the unstructured clinical text given a set of keywords and patterns based on the chart review of 2 allergy and immunology experts for AERD. The status of each extracted feature was quantified by assigning the frequency of its occurrence in clinical documents per subject. We optimized the decision tree classifier's hyperparameters cutoff threshold on the training set to determine the representative feature combination to discriminate AERD. We then evaluated the resulting model on the test set.

Results: The AERD algorithm, which combines NLP and ML techniques, achieved an area under the receiver operating characteristic curve score, sensitivity, and specificity of 0.86 (95% CI 0.78-0.94), 80.00 (95% CI 70.82-87.33), and 88.00 (95% CI 79.98-93.64) for the test set, respectively.

Conclusions: We developed a promising AERD algorithm that needs further refinement to improve AERD diagnosis. Continued development of NLP and ML technologies has the potential to reduce diagnostic delays for AERD and improve the health of our patients.