Automatized detection of uniparental disomies in a large cohort.

Uniparental disomy (UPD) is the inheritance of both homologues of a chromosome from only one parent. The detection of UPDs in sequencing data is not well established and a common gap in genetic diagnostics. We applied our in-house UPD detection pipeline to evaluate a cohort of 9212 samples, including multigene panels as well as exome sequencing data in a single, duo or trio constellation. We used the results to inform the design of our publicly available web app altAFplotter. UPDs categorized as heterodisomy, whole chromosome or segmental isodisomy were identified and validated with microsatellites, multiplex ligation-dependent probe amplification as well as Sanger sequencing. We detected 14 previously undiagnosed UPDs including nine isodisomies, four segmental isodisomies as well as one heterodisomy on chromosome 22. We characterized eight findings as potentially causative through homozygous pathogenic variants or imprinting disorders. Overall, our study demonstrates the utility of our UPD detection pipeline with our web app, altAFplotter, to reliably identify UPDs. This not only increases the diagnostic yield of cases with growth and metabolic disturbances, as well as developmental delay, but also enhances the understanding of UPDs that may be relevant for recurrence risks and genetic counseling.

A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis.

BACKGROUND: Eosinophilic esophagitis (EoE) is a chronic immune-mediated rare disease, characterized by esophageal dysfunctions. It is likely to be primarily activated by food antigens and is classified as a chronic disease for most patients. Therefore, a deeper understanding of the pathogenetic mechanisms underlying EoE is needed to implement and improve therapeutic lines of intervention and ameliorate overall patient wellness. METHODS: RNA-seq data of 18 different studies on EoE, downloaded from NCBI GEO with faster-qdump ( https://github.com/ncbi/sra-tools ), were batch-corrected and analyzed for transcriptomics and metatranscriptomics profiling as well as biological process functional enrichment. The EoE TaMMA web app was designed with plotly and dash. Tabula Sapiens raw data were downloaded from the UCSC Cell Browser. Esophageal single-cell raw data analysis was performed within the Automated Single-cell Analysis Pipeline. Single-cell data-driven bulk RNA-seq data deconvolution was performed with MuSiC and CIBERSORTx. Multi-omics integration was performed with MOFA. RESULTS: The EoE TaMMA framework pointed out disease-specific molecular signatures, confirming its reliability in reanalyzing transcriptomic data, and providing new EoE-specific molecular markers including CXCL14, distinguishing EoE from gastroesophageal reflux disorder. EoE TaMMA also revealed microbiota dysbiosis as a predominant characteristic of EoE pathogenesis. Finally, the multi-omics analysis highlighted the presence of defined classes of microbial entities in subsets of patients that may participate in inducing the antigen-mediated response typical of EoE pathogenesis. CONCLUSIONS: Our study showed that the complex EoE molecular network may be unraveled through advanced bioinformatics, integrating different components of the disease process into an omics-based network approach. This may implement EoE management and treatment in the coming years.

SmartMoms - a web application to raise awareness and provide information on postpartum depression.

BACKGROUND: Postpartum depression is a major public health concern, which is associated with negative consequences for both mothers and children. Unfortunately, many affected women neither understand the warning signs of postpartum depression nor do they know where to seek help. The aim of this study was to evaluate the feasibility of SmartMoms, a German mobile web application (web app) designed to inform women about postpartum depression, support them, and provide an easily accessible self-screening instrument. METHODS: After its development, SmartMoms was distributed through healthcare providers and social media. Feasibility was assessed by examining (1) the experience of postpartum women with the web app, (2) user behaviour, and (3) the experience of healthcare providers with the web app and its distribution. A mixed methods approach was used, including online surveys, usage data, and interviews. RESULTS: Most women used SmartMoms to prevent postpartum depression and rated the web app as good (on average 4.36 out of 5 stars). The majority of women (62.2%) accessing the self-screening instrument showed a risk for postpartum depression (Edinburgh Postnatal Depression scale score ≥ 12). Most providers (n = 12/13) felt supported through SmartMoms in discussing postpartum depression and considered it a useful offer. Suggestions for improvement were provided. CONCLUSIONS: SmartMoms meets the needs and expectations of mothers and healthcare providers interested in postpartum depression but should be further adapted to include more specific support options and additional information for professionals.

Developing Culturally Tailored Mobile Web App Education to Promote Breast Cancer Screening: Knowledge, Barriers, and Needs Among American Indian Women.

American Indian (AI) women face disproportionate rates of breast cancer mortality and cancer disparities. This study conducted qualitative research to assess perspectives of AI women towards breast cancer screening, knowledge, barriers, and needs about mobile web app-based education to promote breast cancer screening. This study, in collaboration with the Yankton Sioux Tribe (YST), followed a community-based participatory research approach and conducted two focus groups with a total of 22 YST women aged 40-70 years living on reservation in rural South Dakota. Each group consisted of 11 local professionals working in healthcare and social services and community members. A grounded theory was used for the qualitative analysis. A large portion of participants reported having prior knowledge about breast cancer and screening methods, yet lacked awareness of the detailed procedure and recommended guidelines. Competing priorities and cost of mammograms were noted as major barriers to screening. Participants wanted to learn-in a convenient and easy-to-understand manner-more about breast cancer and prevention from a credible source. Both groups were favorable toward novel educational tools, such as the mobile web app education, and cited potential health benefits, particularly for women aged 40s to 60s. Our findings highlighted the importance of creating effective, culturally tailored educational interventions built into programs specific to AIs to increase understanding about breast cancer screening and promote screening behaviors among AI women. Particular attention to how AIs' culture, beliefs, and barriers are implicated in screening behaviors could help with developing culturally tailored health education tools for this population.

LCCNN: a Lightweight Customized CNN-Based Distance Education App for COVID-19 Recognition.

In the global epidemic, distance learning occupies an increasingly important place in teaching and learning because of its great potential. This paper proposes a web-based app that includes a proposed 8-layered lightweight, customized convolutional neural network (LCCNN) for COVID-19 recognition. Five-channel data augmentation is proposed and used to help the model avoid overfitting. The LCCNN achieves an accuracy of 91.78%, which is higher than the other eight state-of-the-art methods. The results show that this web-based app provides a valuable diagnostic perspective on the patients and is an excellent way to facilitate medical education. Our LCCNN model is explainable for both radiologists and distance education users. Heat maps are generated where the lesions are clearly spotted. The LCCNN can detect from CT images the presence of lesions caused by COVID-19. This web-based app has a clear and simple interface, which is easy to use. With the help of this app, teachers can provide distance education and guide students clearly to understand the damage caused by COVID-19, which can increase interaction with students and stimulate their interest in learning.

Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization.

Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs is laborious even for expert users, and rendering often requires substantial time and computer resources. We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot .

Complex Reporting of the COVID-19 Epidemic in the Czech Republic: Use of an Interactive Web-Based App in Practice.

BACKGROUND: The beginning of the coronavirus disease (COVID-19) epidemic dates back to December 31, 2019, when the first cases were reported in the People's Republic of China. In the Czech Republic, the first three cases of infection with the novel coronavirus were confirmed on March 1, 2020. The joint effort of state authorities and researchers gave rise to a unique team, which combines methodical knowledge of real-world processes with the know-how needed for effective processing, analysis, and online visualization of data. OBJECTIVE: Due to an urgent need for a tool that presents important reports based on valid data sources, a team of government experts and researchers focused on the design and development of a web app intended to provide a regularly updated overview of COVID-19 epidemiology in the Czech Republic to the general population. METHODS: The cross-industry standard process for data mining model was chosen for the complex solution of analytical processing and visualization of data that provides validated information on the COVID-19 epidemic across the Czech Republic. Great emphasis was put on the understanding and a correct implementation of all six steps (business understanding, data understanding, data preparation, modelling, evaluation, and deployment) needed in the process, including the infrastructure of a nationwide information system; the methodological setting of communication channels between all involved stakeholders; and data collection, processing, analysis, validation, and visualization. RESULTS: The web-based overview of the current spread of COVID-19 in the Czech Republic has been developed as an online platform providing a set of outputs in the form of tables, graphs, and maps intended for the general public. On March 12, 2020, the first version of the web portal, containing fourteen overviews divided into five topical sections, was released. The web portal's primary objective is to publish a well-arranged visualization and clear explanation of basic information consisting of the overall numbers of performed tests, confirmed cases of COVID-19, COVID-19-related deaths, the daily and cumulative overviews of people with a positive COVID-19 case, performed tests, location and country of infection of people with a positive COVID-19 case, hospitalizations of patients with COVID-19, and distribution of personal protective equipment. CONCLUSIONS: The online interactive overview of the current spread of COVID-19 in the Czech Republic was launched on March 11, 2020, and has immediately become the primary communication channel employed by the health care sector to present the current situation regarding the COVID-19 epidemic. This complex reporting of the COVID-19 epidemic in the Czech Republic also shows an effective way to interconnect knowledge held by various specialists, such as regional and national methodology experts (who report positive cases of the disease on a daily basis), with knowledge held by developers of central registries, analysts, developers of web apps, and leaders in the health care sector.

FastqCleaner: an interactive Bioconductor application for quality-control, filtering and trimming of FASTQ files.

BACKGROUND: Exploration and processing of FASTQ files are the first steps in state-of-the-art data analysis workflows of Next Generation Sequencing (NGS) platforms. The large amount of data generated by these technologies has put a challenge in terms of rapid analysis and visualization of sequencing information. Recent integration of the R data analysis platform with web visual frameworks has stimulated the development of user-friendly, powerful, and dynamic NGS data analysis applications. RESULTS: This paper presents FastqCleaner, a Bioconductor visual application for both quality-control (QC) and pre-processing of FASTQ files. The interface shows diagnostic information for the input and output data and allows to select a series of filtering and trimming operations in an interactive framework. FastqCleaner combines the technology of Bioconductor for NGS data analysis with the data visualization advantages of a web environment. CONCLUSIONS: FastqCleaner is an user-friendly, offline-capable tool that enables access to advanced Bioconductor infrastructure. The novel concept of a Bioconductor interactive application that can be used without the need for programming skills, makes FastqCleaner a valuable resource for NGS data analysis.

GOnet: a tool for interactive Gene Ontology analysis.

BACKGROUND: Biological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns. Unfortunately, there is a gap between machine-readable output of GO software and its human-interpretable form. This gap can be bridged by allowing users to simultaneously visualize and interact with term-term and gene-term relationships. RESULTS: We created the open-source GOnet web-application (available at http://tools.dice-database.org/GOnet/ ), which takes a list of gene or protein entries from human or mouse data and performs GO term annotation analysis (mapping of provided entries to GO subsets) or GO term enrichment analysis (scanning for GO categories overrepresented in the input list). The application is capable of producing parsable data formats and importantly, interactive visualizations of the GO analysis results. The interactive results allow exploration of genes and GO terms as a graph that depicts the natural hierarchy of the terms and retains relationships between terms and genes/proteins. As a result, GOnet provides insight into the functional interconnection of the submitted entries. CONCLUSIONS: The application can be used for GO analysis of any biological data sources resulting in gene/protein lists. It can be helpful for experimentalists as well as computational biologists working on biological interpretation of -omics data resulting in such lists.

From eHealth to iHealth: Transition to Participatory and Personalized Medicine in Mental Health.

Clinical assessment in psychiatry is commonly based on findings from brief, regularly scheduled in-person appointments. Although critically important, this approach reduces assessment to cross-sectional observations that miss essential information about disease course. The mental health provider makes all medical decisions based on this limited information. Thanks to recent technological advances such as mobile phones and other personal devices, electronic health (eHealth) data collection strategies now can provide access to real-time patient self-report data during the interval between visits. Since mobile phones are generally kept on at all times and carried everywhere, they are an ideal platform for the broad implementation of ecological momentary assessment technology. Integration of these tools into medical practice has heralded the eHealth era. Intelligent health (iHealth) further builds on and expands eHealth by adding novel built-in data analysis approaches based on (1) incorporation of new technologies into clinical practice to enhance real-time self-monitoring, (2) extension of assessment to the patient's environment including caregivers, and (3) data processing using data mining to support medical decision making and personalized medicine. This will shift mental health care from a reactive to a proactive and personalized discipline.

C-State: an interactive web app for simultaneous multi-gene visualization and comparative epigenetic pattern search.

BACKGROUND: Comparative epigenomic analysis across multiple genes presents a bottleneck for bench biologists working with NGS data. Despite the development of standardized peak analysis algorithms, the identification of novel epigenetic patterns and their visualization across gene subsets remains a challenge. RESULTS: We developed a fast and interactive web app, C-State (Chromatin-State), to query and plot chromatin landscapes across multiple loci and cell types. C-State has an interactive, JavaScript-based graphical user interface and runs locally in modern web browsers that are pre-installed on all computers, thus eliminating the need for cumbersome data transfer, pre-processing and prior programming knowledge. CONCLUSIONS: C-State is unique in its ability to extract and analyze multi-gene epigenetic information. It allows for powerful GUI-based pattern searching and visualization. We include a case study to demonstrate its potential for identifying user-defined epigenetic trends in context of gene expression profiles.

CBioProfiler: A Web and Standalone Pipeline for Cancer Biomarker and Subtype Characterization.

Cancer is a leading cause of death worldwide, and the identification of biomarkers and subtypes that can predict the long-term survival of cancer patients is essential for their risk stratification, treatment, and prognosis. However, there are currently no standardized tools for exploring cancer biomarkers or subtypes. In this study, we introduced Cancer Biomarker and subtype Profiler (CBioProfiler), a web server and standalone application that includes two pipelines for analyzing cancer biomarkers and subtypes. The cancer biomarker pipeline consists of five modules for identifying and annotating cancer survival-related biomarkers using multiple survival-related machine learning algorithms. The cancer subtype pipeline includes three modules for data preprocessing, subtype identification using multiple unsupervised machine learning methods, and subtype evaluation and validation. CBioProfiler also includes CuratedCancerPrognosisData, a novel R package that integrates reviewed and curated gene expression and clinical data from 268 studies. These studies cover 43 common blood and solid tumors and draw upon 47,686 clinical samples. The web server is available at https://www.cbioprofiler.com/ and https://cbioprofiler.znhospital.cn/CBioProfiler/, and the standalone app and source code can be found at https://github.com/liuxiaoping2020/CBioProfiler.

Improving mental health literacy using web- or app-based interventions: A scoping review.

Background: Given the rapid advancement in technology, the Internet has grown to play a significant role in the field of healthcare. Individuals can now access a profusion of easily available materials for self-management of their health. The purpose of this review is to describe Web/App-based interventions that are primarily or secondarily designed to improve mental health literacy (MHL) and to investigate the effectiveness of online interventions for improving mental health. Materials and Method: A scoping review was conducted by searching five databases: PsycINFO, EMBASE, PubMed, CINAHL, and Web of Science. The search was limited to peer-reviewed journals published in English between 2000 and 2022. Studies focusing on enhancements of MHL or its constituent components were included. Results: Twenty-four studies met the inclusion criteria. The interventions primarily targeted patients or individuals exhibiting symptoms of mental disorders, with a higher representation of female participants. All the interventions yielded positive outcomes. The included studies were categorized according to three themes: knowledge, attitude, and self-care skills. Although numerous studies have focused on knowledge improvement, research on interventions targeting self-care skills is scarce. Furthermore, existing literature on knowledge enhancement is limited in terms of the coverage of risk factors. Conclusion: This review indicates gaps in web/app interventions including limited evidence on risk factors, inconsistent help-seeking awareness, and inadequate self-care skills training. Further research is critically needed to address these deficiencies and promote comprehensive MHL.

A scenario-based web app to facilitate patient education in lung tumor patients undergoing video-assisted thoracoscopic surgery: Development and usability testing.

Background: Patient education (PE) is essential for improving patients' knowledge, anxiety, and satisfaction, and supporting their postoperative recovery. However, the advantages of video-assisted thoracoscopic surgery (VATS)-smaller incisions and faster recovery-can result in shorter hospital stays, making PE more challenging to implement effectively. Multimedia PE can potentially enhance PE, but its effectiveness for patients undergoing VATS is unclear. Objective: This study developed a scenario-based PE web app for lung tumor patients undergoing VATS (SPE-VATS) to facilitate the PE process and evaluated its usability through a clinical trial. Methods: The SPE-VATS provided the experimental group (EG: 32 participants) with interactive scenario, query guidance, diagnostic analysis, experience sharing, and active reminder, while the control group (CG: 32 participants) used pamphlets and videos. The usability of SPE-VATS in terms of postoperative anxiety reduction and patient satisfaction with the app was evaluated using self-reported questionnaires based on the state-trait anxiety inventory, technology acceptance model, system usability scale, and task load index. Results: There was no statistically significant difference in postoperative anxiety reduction between the EG and CG, possibly because 90% of the participants underwent a low-risk surgical type, and VATS is known to be advantageous in alleviating surgical anxiety. However, females and higher educated EG participants showed a non-significant but favorable reduction than their CG counterparts. Moreover, the EG was highly satisfied with the app (rated 4.2 to 4.4 out of 5.0), with no significant gender and education level difference. They particularly valued the interactive scenario, experience sharing, and diagnostic analysis features of SPE-VATS. Conclusions: The SPE-VATS demonstrated its usability and high patient satisfaction, particularly for female and higher educated patients. Low-risk patient predominance and VATS's advantages may explain non-significant postoperative anxiety reduction, warranting further studies on high-risk patients to evaluate the impact of SPE-VATS on clinical practice.

Bibliometric analysis of global research in palliative care for cervical cancer.

Objective: The present study aims to conduct a comprehensive bibliometric analysis of global research in palliative care for cervical cancer, providing insights into publication trends, authorship patterns, influential journals, and thematic concentrations. Methods: A bibliometric analysis approach was employed using metadata extracted from Scopus spanning 2000-2023. The search utilized main terms related to cervical cancer and palliative care. Data analysis and visualization were performed using the Bibliometrix R Package's web app Biblioshiny and VOSviewer software. Results: The study identified 2,492 publications on palliative care for cervical cancer, with a notable peak in 2021. The analysis revealed a diverse publication landscape, encompassing primarily articles. Citation analysis showed a staggering 63,994 citations. The most relevant journals were The Lancet Oncology, Gynecologic Oncology, and International Journal of Gynecological Cancer. The study also highlighted influential authors, institutions, and countries, with Harvard University, the University of Toronto, and the University of Texas MD Anderson Cancer Center leading in publications. Discussion: The findings reflected a growing interest in palliative care for cervical cancer, marked by increasing publications over the years. However, the analysis indicated limited international collaborations, with research efforts concentrated in high-income countries. Thematic areas include surgery, palliative care, chemotherapy, radiotherapy, and quality of life. Thus, further collaborations and research in developing countries are needed. Conclusion: This bibliometric analysis showcased a comprehensive overview of the global research landscape on palliative care for cervical cancer. The study identified trends, key contributors, and thematic concentrations, offering valuable insights for future research directions and enhancing palliative care services. Addressing the identified gaps, fostering international collaborations, and directing research efforts toward developing countries can contribute to the advancement of palliative care for cervical cancer globally.

Electronic data capture in resource-limited settings using the lightweight clinical data acquisition and recording system.

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software's capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future.

Application to combine questioning, stimulus presentation and external measurements in a real-life music eye tracking experiment.

Studying people in real-life situations, such as making music in a vocal group, requires flexible and integrative measurement technology. Therefore, a digital browser-based survey instrument was developed for this study. It was designed to seamlessly introduce questions on participants' mobile devices through external control immediately after relevant events, aiming to achieve high accuracy in self-administered situational questions. In addition, chronological synchronization with supplementary measurements (here eye-tracking and audio recording) was incorporated. Digital features of this web app offer convenient integration into everyday situations, synchronous interviewing of multiple people, and gathering time-based data. Due to the numerous possibilities of the browser-based development interface, various other application areas can be opened up. The contribution of this article is:-App - explanation and offer for use-Feasibility report on the implementation of the app in an eye-tracking study with vocal groups.

An Initial Validation of Community-Based Air-Conduction Audiometry in Adults With Simulated Hearing Impairment Using a New Web App, DigiBel: Validation Study.

BACKGROUND: Approximately 80% of primary school children in the United States and Europe experience glue ear, which may impair hearing at a critical time for speech acquisition and social development. A web-based app, DigiBel, has been developed primarily to identify individuals with conductive hearing impairment who may benefit from the temporary use of bone-conduction assistive technology in the community. OBJECTIVE: This preliminary study aims to determine the screening accuracy and usability of DigiBel self-assessed air-conduction (AC) pure tone audiometry in adult volunteers with simulated hearing impairment prior to formal clinical validation. METHODS: Healthy adults, each with 1 ear plugged, underwent automated AC pure tone audiometry (reference test) and DigiBel audiometry in quiet community settings. Threshold measurements were compared across 6 tone frequencies and DigiBel test-retest reliability was calculated. The accuracy of DigiBel for detecting more than 20 dB of hearing impairment was assessed. A total of 30 adults (30 unplugged ears and 30 plugged ears) completed both audiometry tests. RESULTS: DigiBel had 100% sensitivity (95% CI 87.23-100) and 72.73% (95% CI 54.48-86.70) specificity in detecting hearing impairment. Threshold mean bias was insignificant except at 4000 and 8000 Hz where a small but significant overestimation of threshold measurement was identified. All 24 participants completing feedback rated the DigiBel test as good or excellent and 21 (88%) participants agreed or strongly agreed that they would be able to do the test at home without help. CONCLUSIONS: This study supports the potential use of DigiBel as a screening tool for hearing impairment. The findings will be used to improve the software further prior to undertaking a formal clinical trial of AC and bone-conduction audiometry in individuals with suspected conductive hearing impairment.

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.

A Web-Based, Respondent-Driven Sampling Survey Among Men Who Have Sex With Men (Kai Noi): Description of Methods and Characteristics.

BACKGROUND: Thailand's HIV epidemic is heavily concentrated among men who have sex with men (MSM), and surveillance efforts are mostly based on case surveillance and local biobehavioral surveys. OBJECTIVE: We piloted Kai Noi, a web-based respondent-driven sampling (RDS) survey among MSM. METHODS: We developed an application coded in PHP that facilitated all procedures and events typically used in an RDS office for use on the web, including e-coupon validation, eligibility screening, consent, interview, peer recruitment, e-coupon issuance, and compensation. All procedures were automated and e-coupon ID numbers were randomly generated. Participants' phone numbers were the principal means to detect and prevent duplicate enrollment. Sampling took place across Thailand; residents of Bangkok were also invited to attend 1 of 10 clinics for an HIV-related blood draw with additional compensation. RESULTS: Sampling took place from February to June 2022; seeds (21 at the start, 14 added later) were identified through banner ads, micromessaging, and in online chat rooms. Sampling reached all 6 regions and almost all provinces. Fraudulent (duplicate) enrollment using "borrowed" phone numbers was identified and led to the detection and invalidation of 318 survey records. A further 106 participants did not pass an attention filter question (asking recruits to select a specific categorical response) and were excluded from data analysis, leading to a final data set of 1643 valid participants. Only one record showed signs of straightlining (identical adjacent responses). None of the Bangkok respondents presented for a blood draw. CONCLUSIONS: We successfully developed an application to implement web-based RDS among MSM across Thailand. Measures to minimize, detect, and eliminate fraudulent survey enrollment are imperative in web-based surveys offering compensation. Efforts to improve biomarker uptake are needed to fully tap the potential of web-based sampling and data collection.