Dermoscopy Differential Diagnosis Explorer (D3X) Ontology to Aggregate and Link Dermoscopic Patterns to Differential Diagnoses: Development and Usability Study.

BACKGROUND: Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand. OBJECTIVE: In this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses. METHODS: Existing ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers. RESULTS: D3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory-driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain. CONCLUSIONS: The D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice.

Successful Treatment of Refractory Orbital Plasmacytoma with Chimeric Antigen Receptor T Cell Therapy: A Case Report and Review of the Literature.

Orbital plasmacytoma is a rare plasma cell tumor that may arise as an aggressive form of extramedullary multiple myeloma. Treatment modalities include surgical excision, radiation, and chemotherapy. Chimeric antigen receptor T cell therapy is currently reserved for refractory disease. The authors present a case of a 69-year-old woman with an extensive orbital plasmacytoma refractory to multimodal therapy who was treated with idecabtagene vicleucel chimeric antigen receptor T cell therapy. Four days after infusion, the patient exhibited grade 1 cytokine release syndrome, which resolved with tocilizumab. The orbital plasmacytoma significantly decreased in size 1 month after treatment and demonstrated complete serological response and sustained tumor burden reduction at 10-month follow-up. This case highlights the efficacy of chimeric antigen receptor T cell therapy for refractory orbital plasmacytoma and calls attention to potential inflammatory toxicities.

A promising response of refractory uterine leiomyosarcoma to pembrolizumab and temozolomide combination therapy.

Uterine leiomyosarcomas (ULMS) are a rare cause of uterine malignancy. Due to their aggressive nature, resistance to standard therapy and high rates of recurrence, they are associated with a very poor prognosis, irrespective of their stage at diagnosis. A 68-year-old female, Chinese patient was diagnosed with Stage IVB uterine leiomyosarcoma with lung metastasis. Disease progression occurred after fourth-line treatment and the patient was commenced on pembrolizumab and temozolomide combination therapy. At the time of writing, our patient has experienced partial response to treatment with minimal adverse effects at cycle 7 of Pembrolizumab in combination with Temozolomide.

Fatigue and Quality of Life in Children with Hearing Loss or Obstructive Sleep Apnea.

OBJECTIVE: To investigate the fatigue levels of children with hearing loss (HL) and obstructive sleep apnea (OSA), hypothesizing that the fatigue experienced by children with HL is under-recognized. STUDY DESIGN: Cross-sectional survey. METHODS: We identified children aged 2-18 with HL, OSA, sleep-disordered breathing (SDB), and controls from a pediatric otolaryngology clinic and sleep center. Children and/or parents completed the Pediatric Quality of Life Inventory Multidimensional Fatigue Scale (PedsQL MFS), Hearing Environments And Reflection on Quality of Life (HEAR-QL), and OSA-18. RESULTS: Responses of 50 children with HL, 79 with OSA, and 18 with SDB were compared with those of 49 recruited controls (RC) and literature controls (LC). Children with HL or OSA had higher fatigue than controls in the PedsQL MFS self-reported (HL 65.4, OSA 54.7, RC 71.8, LC 80.5, p < 0.001) and parent-reported (HL 64.6, OSA 59.3, RC 75.2, LC 89.6, p < 0.001). Children with HL had Cognitive Fatigue similar to that of children with OSA (self 60.4 vs. 49.5, p = 0.170; parent 56.0 vs. 56.7, p = 0.998), though with decreased Sleep/Rest Fatigue (self 67.8 vs. 56.3, p = 0.033; parent 69.8 vs. 57.5, p = 0.001). Children with HL or OSA had lower disease-related quality of life (QOL) than controls in the HEAR-QL and OSA-18, respectively. Stratification with disease severity revealed no differences in fatigue. CONCLUSION: Children with HL or OSA experience higher fatigue and lower QOL than controls. Similar Cognitive Fatigue in both groups suggests under-recognized fatigue in children with HL. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:443-451, 2024.

Evaluation of Machine Learning Approaches for Predicting Warfarin Discharge Dose in Cardiac Surgery Patients: Retrospective Algorithm Development and Validation Study.

BACKGROUND: Warfarin dosing in cardiac surgery patients is complicated by a heightened sensitivity to the drug, predisposing patients to adverse events. Predictive algorithms are therefore needed to guide warfarin dosing in cardiac surgery patients. OBJECTIVE: This study aimed to develop and validate an algorithm for predicting the warfarin dose needed to attain a therapeutic international normalized ratio (INR) at the time of discharge in cardiac surgery patients. METHODS: We abstracted variables influencing warfarin dosage from the records of 1031 encounters initiating warfarin between April 1, 2011, and November 29, 2019, at St Michael's Hospital in Toronto, Ontario, Canada. We compared the performance of penalized linear regression, k-nearest neighbors, random forest regression, gradient boosting, multivariate adaptive regression splines, and an ensemble model combining the predictions of the 5 regression models. We developed and validated separate models for predicting the warfarin dose required for achieving a discharge INR of 2.0-3.0 in patients undergoing all forms of cardiac surgery except mechanical mitral valve replacement and a discharge INR of 2.5-3.5 in patients receiving a mechanical mitral valve replacement. For the former, we selected 80% of encounters (n=780) who had initiated warfarin during their hospital admission and had achieved a target INR of 2.0-3.0 at the time of discharge as the training cohort. Following 10-fold cross-validation, model accuracy was evaluated in a test cohort comprised solely of cardiac surgery patients. For patients requiring a target INR of 2.5-3.5 (n=165), we used leave-p-out cross-validation (p=3 observations) to estimate model performance. For each approach, we determined the mean absolute error (MAE) and the proportion of predictions within 20% of the true warfarin dose. We retrospectively evaluated the best-performing algorithm in clinical practice by comparing the proportion of cardiovascular surgery patients discharged with a therapeutic INR before (April 2011 and July 2019) and following (September 2021 and May 2, 2022) its implementation in routine care. RESULTS: Random forest regression was the best-performing model for patients with a target INR of 2.0-3.0, an MAE of 1.13 mg, and 39.5% of predictions of falling within 20% of the actual therapeutic discharge dose. For patients with a target INR of 2.5-3.5, the ensemble model performed best, with an MAE of 1.11 mg and 43.6% of predictions being within 20% of the actual therapeutic discharge dose. The proportion of cardiovascular surgery patients discharged with a therapeutic INR before and following implementation of these algorithms in clinical practice was 47.5% (305/641) and 61.1% (11/18), respectively. CONCLUSIONS: Machine learning algorithms based on routinely available clinical data can help guide initial warfarin dosing in cardiac surgery patients and optimize the postsurgical anticoagulation of these patients.

DEVO: an ontology to assist with dermoscopic feature standardization.

BACKGROUND: The utilization of dermoscopic analysis is becoming increasingly critical for diagnosing skin diseases by physicians and even artificial intelligence. With the expansion of dermoscopy, its vocabulary has proliferated, but the rapid evolution of the vocabulary of dermoscopy without standardized control is counterproductive. We aimed to develop a domain-specific ontology to formally represent knowledge for certain dermoscopic features. METHODS: The first phase involved creating a fundamental-level ontology that covers the fundamental aspects and elements in describing visualizations, such as shapes and colors. The second phase involved creating a domain ontology that harnesses the fundamental-level ontology to formalize the definitions of dermoscopic metaphorical terms. RESULTS: The Dermoscopy Elements of Visuals Ontology (DEVO) contains 1047 classes, 47 object properties, and 16 data properties. It has a better semiotic score compared to similar ontologies of the same domain. Three human annotators also examined the consistency, complexity, and future application of the ontology. CONCLUSIONS: The proposed ontology was able to harness the definitions of metaphoric terms by decomposing them into their visual elements. Future applications include providing education for trainees and diagnostic support for dermatologists, with the goal of generating responses to queries about dermoscopic features and integrating these features to diagnose skin diseases.

Primary bulbar conjunctival basal cell carcinoma: A clinical-pathologic report and literature review.

Purpose: To enhance the characterization of primary bulbar conjunctival basal cell carcinoma (BCC) clinically and histologically, via report of a case and review of the relevant medical literature. Observations: We report the case of a 73-year-old man with no history of skin cancer who presented with a bulbar conjunctival nodule without connection to the eyelid or caruncle, originally thought to represent a pyogenic granuloma. After one month without improvement on topical prednisolone, excisional biopsy was performed, with routine histopathology and immunohistochemistry. The tumor was found histologically to be primary conjunctival BCC. Immunostaining exhibited negative Ber-EP4 and S100, mostly negative CK7 and EMA, and positive p63. Margins were negative, and the patient had no recurrence six months after excision. Conclusions and Importance: Primary conjunctival BCC must be considered in the differential diagnosis of a conjunctival lesion which may initially appear benign but does not behave as expected clinically. The location of the tumor in the case presented here refutes a prior postulate that primary conjunctival BCC arises from basal adnexal epithelium in the caruncle. Of the immunohistochemical findings, only the Ber-EP4 result differed from the typical immunostaining profile of cutaneous BCC. Further study is needed to determine the frequency of Ber-EP4 positivity in primary conjunctival BCC.

Understanding Information Needs and Barriers to Accessing Health Information Across All Stages of Pregnancy: Systematic Review.

BACKGROUND: Understanding consumers' health information needs across all stages of the pregnancy trajectory is crucial to the development of mechanisms that allow them to retrieve high-quality, customized, and layperson-friendly health information. OBJECTIVE: The objective of this study was to identify research gaps in pregnancy-related consumer information needs and available information from different sources. METHODS: We conducted a systematic review of CINAHL, Cochrane, PubMed, and Web of Science for relevant articles that were published from 2009 to 2019. The quality of the included articles was assessed using the Critical Appraisal Skills Program. A descriptive data analysis was performed on these articles. Based on the review result, we developed the Pregnancy Information Needs Ontology (PINO) and made it publicly available in GitHub and BioPortal. RESULTS: A total of 33 articles from 9 countries met the inclusion criteria for this review, of which the majority were published no earlier than 2016. Most studies were either descriptive (9/33, 27%), interviews (7/33, 21%), or surveys/questionnaires (7/33, 21%); 20 articles mentioned consumers' pregnancy-related information needs. Half (9/18, 50%) of the human-subject studies were conducted in the United States. More than a third (13/33, 39%) of all studies focused on during-pregnancy stage; only one study (1/33, 3%) was about all stages of pregnancy. The most frequent consumer information needs were related to labor delivery (9/20, 45%), medication in pregnancy (6/20, 30%), newborn care (5/20, 25%), and lab tests (6/20, 30%). The most frequently available source of information was the internet (15/24, 63%). PINO consists of 267 classes, 555 axioms, and 271 subclass relationships. CONCLUSIONS: Only a few articles assessed the barriers to access to pregnancy-related information and the quality of each source of information; further work is needed. Future work is also needed to address the gaps between the information needed and the information available.

Abnormal singing can identify patients with right hemisphere cortical strokes at risk for impaired prosody.

ABSTRACT: Despite lacking aphasia seen with left hemisphere (LH) infarcts involving the middle cerebral artery territory, right hemisphere (RH) strokes can result in significant difficulties in affective prosody. These impairments may be more difficult to identify but lead to significant communication problems.We determine if evaluation of singing can accurately identify stroke patients with cortical RH infarcts at risk for prosodic impairment who may benefit from rehabilitation.A prospective cohort of 36 patients evaluated with acute ischemic stroke was recruited. Participants underwent an experimental battery evaluating their singing, prosody comprehension, and prosody production. Singing samples were rated by 2 independent reviewers as subjectively "normal" or "abnormal," and analyzed for properties of the fundamental frequency. Relationships between infarct location, singing, and prosody performance were evaluated using t tests and chi-squared analysis.Eighty percent of participants with LH cortical strokes were unable to successfully complete any of the tasks due to severe aphasia. For the remainder, singing ratings corresponded to stroke location for 68% of patients. RH cortical strokes demonstrated a lower mean fundamental frequency while singing than those with subcortical infarcts (176.8 vs 130.4, P = 0.02). They also made more errors on tasks of prosody comprehension (28.6 vs 16.0, P < 0.001) and production (40.4 vs 18.4, P < 0.001).Patients with RH cortical infarcts are more likely to exhibit impaired prosody comprehension and production and demonstrate the poor variation of tone when singing compared to patients with subcortical infarcts. A simple singing screen is able to successfully identify patients with cortical lesions and potential prosodic deficits.

Comprehensive profiling of the gut microbiota in patients with chronic obstructive pulmonary disease of varying severity.

Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that reduces lung and respiratory function, with a high mortality rate. Severe and acute deterioration of COPD can easily lead to respiratory failure, resulting in personal, social, and medical burden. Recent studies have shown a high correlation between the gut microbiota and lung inflammation. In this study, we investigated the relationship between gut microbiota and COPD severity. A total of 60 COPD patients with varying severity according to GOLD guidelines were enrolled in this study. DNA was extracted from patients' stool and 16S rRNA data analysis conducted using high-throughput sequencing followed by bioinformatics analysis. The richness of the gut microbiota was not associated with COPD severity. The gut microbiome is more similar in stage 1 and 2 COPD than stage 3+4 COPD. Fusobacterium and Aerococcus were more abundant in stage 3+4 COPD. Ruminococcaceae NK4A214 group and Lachnoclostridium were less abundant in stage 2-4, and Tyzzerella 4 and Dialister were less abundant in stage 1. However, the abundance of a Bacteroides was associated with blood eosinophils and lung function. This study suggests that no distinctive gut microbiota pattern is associated with the severity of COPD. The gut microbiome could affect COPD by gut inflammation shaping the host immune system.

Conversational ontology operator: patient-centric vaccine dialogue management engine for spoken conversational agents.

BACKGROUND: Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we take the next step and introduce the Conversational Ontology Operator (COO), the software engine harnessing PHIDO. We also developed a question-answering subsystem called Frankenstein Ontology Question-Answering for User-centric Systems (FOQUS) to support the dialogue interaction. METHODS: We tested both the dialogue engine and the question-answering system using application-based competency questions and questions furnished from our previous Wizard of OZ simulation trials. RESULTS: Our results revealed that the dialogue engine is able to perform the core tasks of communicating health information and conversational flow. Inter-rater agreement and accuracy scores among four reviewers indicated perceived, acceptable responses to the questions asked by participants from the simulation studies, yet the composition of the responses was deemed mediocre by our evaluators. CONCLUSIONS: Overall, we present some preliminary evidence of a functioning ontology-based system to manage dialogue and consumer questions. Future plans for this work will involve deploying this system in a speech-enabled agent to assess its usage with potential health consumer users.

Examining Potential Usability and Health Beliefs Among Young Adults Using a Conversational Agent for HPV Vaccine Counseling.

The human papillomavirus (HPV) vaccine is the most effective way to prevent HPV-related cancers. Integrating provider vaccine counseling is crucial to improving HPV vaccine completion rates. Automating the counseling experience through a conversational agent could help improve HPV vaccine coverage and reduce the burden of vaccine counseling for providers. In a previous study, we tested a simulated conversational agent that provided HPV vaccine counseling for parents using the Wizard of OZ protocol. In the current study, we assessed the conversational agent among young college adults (n=24), a population that may have missed the HPV vaccine during their adolescence when vaccination is recommended. We also administered surveys for system and voice usability, and for health beliefs concerning the HPV vaccine. Participants perceived the agent to have high usability that is slightly better or equivalent to other voice interactive interfaces, and there is some evidence that the agent impacted their beliefs concerning the harms, uncertainty, and risk denials for the HPV vaccine. Overall, this study demonstrates the potential for conversational agents to be an impactful tool for health promotion endeavors.

Recognizing software names in biomedical literature using machine learning.

Software tools now are essential to research and applications in the biomedical domain. However, existing software repositories are mainly built using manual curation, which is time-consuming and unscalable. This study took the initiative to manually annotate software names in 1,120 MEDLINE abstracts and titles and used this corpus to develop and evaluate machine learning-based named entity recognition systems for biomedical software. Specifically, two strategies were proposed for feature engineering: (1) domain knowledge features and (2) unsupervised word representation features of clustered and binarized word embeddings. Our best system achieved an F-measure of 91.79% for recognizing software from titles and an F-measure of 86.35% for recognizing software from both titles and abstracts using inexact matching criteria. We then created a biomedical software catalog with 19,557 entries using the developed system. This study demonstrates the feasibility of using natural language processing methods to automatically build a high-quality software index from biomedical literature.

Early Usability Assessment of a Conversational Agent for HPV Vaccination.

With the emerging use of speech technology in consumer goods, we experimented with the application of conversational agents for the communication of health information relating to HPV vaccine. Research have stated that one-to-one contact between providers and patients have a variety of positive influences on patients' perception towards vaccines, even leading to uptake, compared to paper-based methods. We implemented a Wizard of Oz experiment that counsels adults with children (n=18) on the HPV vaccine, using an iPad tablet and dialogue script developed by public health collaborators, and for early testing of a prospective conversational agent in this area. Our early results show that non-vaccine hesitant parents believed that agent was easy to use and had capabilities needed, despite the desire for additional features. Our future work will involve developing a dialogue engine to provide automated dialogue interaction and future improvements and experimentation for the speech interface.

An Ontology-Powered Dialogue Engine For Patient Communication of Vaccines.

In this study, we introduce an ontology-driven software engine to provide dialogue interaction functionality for a conversational agent for HPV vaccine counseling. Currently, the HPV vaccination rates are low that risks unprotected individuals at being infected with HPV, a virus that leads to life-threatening cancers. In addition, we developed a question answering subsystem to support the dialogue engine. In this paper, we discuss our design and development of an ontology-driven dialogue engine that uses the Patient Health Information Dialogue Ontology, an ontology that we previously developed, and a question answering subsystem based on various previous methods to supplement the dialogue engine's interaction with the user. Our next step is to test the functional ability of the ontology-driven software components and deploy the engine in a live environment to be integrated with a speech interface.

Visualized Emotion Ontology: a model for representing visual cues of emotions.

BACKGROUND: Healthcare services, particularly in patient-provider interaction, often involve highly emotional situations, and it is important for physicians to understand and respond to their patients' emotions to best ensure their well-being. METHODS: In order to model the emotion domain, we have created the Visualized Emotion Ontology (VEO) to provide a semantic definition of 25 emotions based on established models, as well as visual representations of emotions utilizing shapes, lines, and colors. RESULTS: As determined by ontology evaluation metrics, VEO exhibited better machine-readability (z=1.12), linguistic quality (z=0.61), and domain coverage (z=0.39) compared to a sample of cognitive ontologies. Additionally, a survey of 1082 participants through Amazon Mechanical Turk revealed that a significantly higher proportion of people agree than disagree with 17 out of our 25 emotion images, validating the majority of our visualizations. CONCLUSION: From the development, evaluation, and serialization of the VEO, we have defined a set of 25 emotions using OWL that linked surveyed visualizations to each emotion. In the future, we plan to use the VEO in patient-facing software tools, such as embodied conversational agents, to enhance interactions between patients and providers in a clinical environment.

VEO-Engine: Interfacing and Reasoning with an Emotion Ontology for Device Visual Expression.

In order for machines to understand or express emotion to users, the specific emotions must be formally defined and the software coded to how those emotions are to be expressed. This is particularly important if devices or computer-based tools are utilized in clinical settings, which may be stressful for patients and where emotions can dominate their decision making. We have reported our development and feasibility results of an ontology, Visualized Emotion Ontology (VEO), that links abstract visualizations that express specific emotions. Here, we used VEO with the VEO-Engine, a software API package that interfaces with the VEO. The VEO-Engine was developed in Java using Apache Jena and OWL-API. The software package was tested on a Raspberry Pi machine with a small touchscreen display that linked each visualization to an emotion. The VEO-Engine stores input parameters of emotional situations and valences to reason and interpret users' emotions using the ontology-based reasoner. With this software, devices can interfaced wirelessly, so smart devices with visual displays can interact with the ontology. By means of the VEO-Engine, we show the portability and usability of the VEO in human-computer interaction.

Designing an Ontology for Emotion-driven Visual Representations.

Emotions influence our perceptions and decisions and are often felt more strongly in situations related to healthcare. Therefore, it is important to understand how both providers and patients express their emotions in face-to-face scenarios. An ontology is a way to represent domain concepts and the relationships between them in a polyarchical manner. We have created an ontological model called the Visualized Emotion Ontology (VEO) that expresses the semantic definitions and visualizations of 25 emotions based on published research. With VEO, we can augment patient-facing software tools, like embodied conversational agents, to improve patient-provider interaction in clinical environments.

Plexiform fibromyxoma with cotyledon-like serosal growth: A case report of a rare gastric tumor and review of the literature.

Plexiform fibromyxoma is a rare, benign mesenchymal neoplasm that predilects the gastric antrum and has potential for misdiagnosis as a gastrointestinal stromal tumor (GIST). The histology of the tumor is characterized by interwoven fascicular growth of cytologically bland spindled cells within a variably myxoid stroma. The current study reports the clinicopathological and immunohistochemical findings of a plexiform fibromyxoma resected from a 28-year-old Vietnamese female. The patient presented with acute, severe abdominal pain and worsening anemia. The initial fine-needle aspiration and needle core biopsy of the gastric antral mass led to an initial diagnosis of GIST. The subsequent distal partial gastrectomy revealed a 5.5-cm transmural antral mass that ulcerated the overlying mucosa and grew as variably elongated, myxoedematous, polypoid (cotyledon-like) excrescences from the serosal surface. Microscopically, the tumor demonstrated plexiform and multinodular growth of cytologically bland spindled cells proliferating in an abundant myxocollagenous stroma with a prominent capillary network. Tumor cells immunohistochemically expressed smooth muscle actin and CD10, but did not express CD117, Discovered on GIST-1 or nuclear ß-catenin. Follow-up evaluation 23 months post surgery revealed no evidence of residual tumor. A review the cases of this rare entity reported in the English language literature is also provided.

Urinary bladder retroversion and neourethrocystostomy for treatment of inadvertent prostatectomy and urethrectomy in a dog.

CASE DESCRIPTION A 15-month-old male Newfoundland was examined because of an inability to urinate, lethargy, inappetence, and intermittent vomiting that first became evident after bilateral cryptorchidectomy 2 days previously. The patient was referred for further evaluation and treatment. CLINICAL FINDINGS Results of physical examination, serum biochemical analysis, and abdominocentesis led to a diagnosis of uroperitoneum. Retrograde cystography indicated urinary tract obstruction. In view of the history of recent elective cryptorchidectomy, a diagnosis of uroperitoneum with urethral obstruction secondary to iatrogenic prostatectomy and urethrectomy was made. TREATMENT AND OUTCOME During a ventral midline celiotomy, the inadvertent prostatectomy and urethrectomy were found to have resulted in insufficient urethral length for primary repair. Surgical repair of the urethral defect was achieved by means of a novel technique of bladder retroversion and neourethrocystostomy at the apex of the bladder. A urethral stricture evident 1.5 months after surgery was initially treated with balloon dilatation, followed by temporary and then permanent placement of a self-expanding metallic stent. At the last follow-up 6.6 years after stent placement, the dog remained continent while receiving phenylpropanolamine and the owner was highly satisfied with the outcome. CLINICAL RELEVANCE Caudal intraabdominal bladder retroversion with apex neourethrocystostomy may be a viable alternative to more complex urethral lengthening procedures in dogs and can potentially preserve lower urinary tract function. This treatment might be considered for patients with urethral trauma or malignant neoplasia necessitating extensive urethral resection. Urethral strictures may be effectively managed with stenting.