Using an interactive web application to identify pre-exposure prophylaxis adherence among men who have sex with men.

Background: Men who have sex with men (MSM) are at a high risk for HIV infection. While pre-exposure prophylaxis (PrEP) is an effective oral preventive strategy, its success is largely dependent on consistent medication adherence. Objective: The aim of this study was to develop the machine learning web application and evaluate the performance in predicting PrEP adherence. Methods: The PrEP prospective cohort study of the MSM population conducted in Western China from 2019 to 2023, and we collected adherence data and personal characteristics data from 747 MSM. Predictor variables were screened and the performance of several machine learning methods in predicting nonadherent behaviors were compared. Results: A total of 11 candidate variables were screened that predicted nonadherent behaviors. We developed and evaluated five machine learning models that performed well in predicting adherence. Attitudes of male sexual partners, self-efficacy, HIV testing, number of male sexual partners, and risk perception were the most important predictors of adherence. The optimal prediction model was displayed in a shiny web application for online calculation of the probability of occurrence of nonadherent behaviors among MSM. Conclusions: Machine learning performed well in predicting nonadherent behaviors among MSM. An interactive and intuitive web application can help identify individuals who may have nonadherent behaviors, resulting in improved medication adherence and increased prevention efficacy.

Development and validation of a web application to collect food supply data associated with their nutritional composition and environmental impacts.

Measuring what people eat is a major methodological challenge. We developed and validated a new web-based method to record actual food supply data that would be less time consuming than the original method (i.e., food supply diary). Through the COD-Appro web application, participants can enter their food supplies by selecting a food item from a list based on items included in French food databases; thus automatically associated with nutrient composition and environmental impacts data. We recruited 102 participants who used COD-Appro during one month, among which 30 also used a classical paper-based food supply diary to be able to compare the two methods. A feedback questionnaire was completed by all the participants. The two methods showed no difference between the price and the quantity of food purchases when compared by food groups (n = 10) which confirms that the new method was robust compared to the original method. In addition, participants reported good usability of the COD-Appro web application and its use saved a considerable amount of time to the experimenters by integrating data entry and pairing with food databases (nutrient composition and environmental impacts) into the data collection step performed by the participants themselves.•Web-based food supply data collection•Association with nutrient composition and environmental impacts•External validity compared to original method (i.e., food supply diary).

PhageGE: an interactive web platform for exploratory analysis and visualization of bacteriophage genomes.

BACKGROUND: Antimicrobial resistance is a serious threat to global health. Due to the stagnant antibiotic discovery pipeline, bacteriophages (phages) have been proposed as an alternative therapy for the treatment of infections caused by multidrug-resistant pathogens. Genomic features play an important role in phage pharmacology. However, our knowledge of phage genomics is sparse, and the use of existing bioinformatic pipelines and tools requires considerable bioinformatic expertise. These challenges have substantially limited the clinical translation of phage therapy. FINDINGS: We have developed PhageGE (Phage Genome Explorer), a user-friendly graphical interface application for the interactive analysis of phage genomes. PhageGE enables users to perform key analyses, including phylogenetic analysis, visualization of phylogenetic trees, prediction of phage life cycle, and comparative analysis of phage genome annotations. The new R Shiny web server, PhageGE, integrates existing R packages and combines them with several newly developed functions to facilitate these analyses. Additionally, the web server provides interactive visualization capabilities and allows users to directly export publication-quality images. CONCLUSIONS: PhageGE is a valuable tool that simplifies the analysis of phage genome data and may expedite the development and clinical translation of phage therapy. PhageGE is publicly available at https://jason-zhao.shinyapps.io/PhageGE_Update/.

Haxe as a Swiss knife for bioinformatic applications: the SeqPHASE case story.

Haxe is a general purpose, object-oriented programming language supporting syntactic macros. The Haxe compiler is well known for its ability to translate the source code of Haxe programs into the source code of a variety of other programming languages including Java, C++, JavaScript, and Python. Although Haxe is more and more used for a variety of purposes, including games, it has not yet attracted much attention from bioinformaticians. This is surprising, as Haxe allows generating different versions of the same program (e.g. a graphical user interface version in JavaScript running in a web browser for beginners and a command-line version in C++ or Python for increased performance) while maintaining a single code, a feature that should be of interest for many bioinformatic applications. To demonstrate the usefulness of Haxe in bioinformatics, we present here the case story of the program SeqPHASE, written originally in Perl (with a CGI version running on a server) and published in 2010. As Perl+CGI is not desirable anymore for security purposes, we decided to rewrite the SeqPHASE program in Haxe and to host it at Github Pages (https://eeg-ebe.github.io/SeqPHASE), thereby alleviating the need to configure and maintain a dedicated server. Using SeqPHASE as an example, we discuss the advantages and disadvantages of Haxe's source code conversion functionality when it comes to implementing bioinformatic software.

Bioinformatics Study on Site-Specific Variations of Eotaxin-3, a Key Chemokine in Eosinophilic Esophagitis (EoE).

Eotaxin-3 is a key chemokine with a relevant role in eosinophilic esophagitis, a rare chronic immune/antigen-mediated inflammatory disorder. Eotaxin-3 is a potent activator of eosinophil emergence and migration, which may lead to allergic airway inflammation. We investigated, using bioinformatics tools, the protein structure and the possible effects of the known variations reported in public databases. Following a procedure already established, we created a 3D model of the whole protein and modeled the structure of 105 protein variants due to known point mutations. The effects of the amino acid substitution at the level of impact on protein structure, stability, and possibly function were detected by the bioinformatics procedure and described in detail. A web application was implemented to browse the results of the analysis and visualize the 3D models, with the opportunity of downloading the models and analyzing them using their own software. Among 105 amino acid substitutions investigated, the study evidenced in 44 cases at least one change in any of the investigated structural parameters. Other six variations are also relevant, although a structural effect was not detected by our analysis, because they affected amino acids highly conserved, which suggests a possible function role. All these variations should be the object of particular attention, as they may induce a loss of functionality in the protein.

Integration of dynaMic water extents towards imProved lake wAter suRface Temperature (IMPART).

Lake water surface temperature (LWST) is a critical component in understanding the response of freshwater ecosystems to climate change. Traditional estimation of LWST estimation considers water surface bodies to be static. Our work proposes a novel open-source web application, IMPART, designed for estimating dynamic LWST using Landsat reflectance and MODIS temperature datasets from 2004 to 2022. Results presented globally for over 342 lakes reveal a root mean square deviation of 0.86 °C between static and dynamic LWST. Additionally, our results demonstrate that 57% of the lakes exhibit a statistically significant difference between the static and dynamic LWST values. Improved LWST will ultimately enhance our ability to comprehensively monitor and respond to the impacts of climate change on freshwater ecosystems worldwide. Furthermore, based on the Koppen-Geiger climate classification, our zonal analysis demonstrates the deviation between static and dynamic LWST. It identifies specific zones where considering waterbodies as dynamic entities is essential.

Developing an Open-Source, User-Friendly, OWASP-Compliant Architecture for Healthcare Web Application Testing.

Nowadays, web applications are fundamental in the healthcare sector. However, with the widespread use of this technology, risks related to cybersecurity attacks also increase. To mitigate this phenomenon, every 3-4 years, the nonprofit foundation Open Worldwide Application Security Project (OWASP) compiles a top 10 ranking of the most critical web application security risks. Along with the top 10 Web Application Security Risks, OWASP also provides the Web Security Testing Guide, which offers comprehensive guidelines for conducting security tests. This guide includes suggestions for specific tools to use when performing different tests, among other valuable insights. However, the use of these recommended tools can be costly and can require advanced technical skills and a deep understanding of security best practices and web technologies. In addition, since the OWASP work on web security is generic, it would be useful to restrict and adapt it to the healthcare area. This would help in reducing the overhead when dealing with the needed tools. The goal of this study is to make web application security assessment in healthcare more accessible by developing tools that simplify the process and makes it user- friendly. Before developing such tools, an in-depth feasibility study must be conducted to verify the existence of open-source libraries to carry out the necessary testing procedures. It will be also necessary to identify how tools could be simplified and enhanced when focusing on healthcare.

COVIDHealth: A novel labeled dataset and machine learning-based web application for classifying COVID-19 discourses on Twitter.

The COVID-19 pandemic has sparked widespread health-related discussions on social media platforms like Twitter (now named 'X'). However, the lack of labeled Twitter data poses significant challenges for theme-based classification and tweet aggregation. To address this gap, we developed a machine learning-based web application that automatically classifies COVID-19 discourses into five categories: health risks, prevention, symptoms, transmission, and treatment. We collected and labeled 6,667 COVID-19-related tweets using the Twitter API, and applied various feature extraction methods to extract relevant features. We then compared the performance of seven classical machine learning algorithms (Decision Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest Neighbor, Logistic Regression, and Linear SVC) and four deep learning techniques (LSTM, CNN, RNN, and BERT) for classification. Our results show that the CNN achieved the highest precision (90.41%), recall (90.4%), F1 score (90.4%), and accuracy (90.4%). The Linear SVC algorithm exhibited the highest precision (85.71%), recall (86.94%), and F1 score (86.13%) among classical machine learning approaches. Our study advances the field of health-related data analysis and classification, and offers a publicly accessible web-based tool for public health researchers and practitioners. This tool has the potential to support addressing public health challenges and enhancing awareness during pandemics. The dataset and application are accessible at https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website.

Outbreak detector: a web application to boost disease surveillance systems and timely detection of infectious disease epidemics.

OBJECTIVE: Digital technologies have improved the performance of surveillance systems through early detection of outbreaks and epidemic control. The aim of this study is to introduce an outbreak detection web application called OBDETECTOR (Outbreak Detector), which as a professional web application has the ability to process weekly or daily reported data from disease surveillance systems and facilitates the early detection of disease outbreaks. RESULTS: OBDETECTOR generates a histogram that exhibits the trend of infection within a time range selected by the user. The output comprises red triangles and plus signs, where the former denotes outbreak days determined by the algorithm applied to the data, and the latter represents days identified as outbreaks by the researcher. The graph also displays threshold values and its symbols enable researchers to compute evaluation criteria for outbreak detection algorithms, including sensitivity and specificity. OBDETECTOR allows users to modify algorithm parameters based on their research objectives immediately after loading data. The implementation of automatic web applications results in immediate reporting, precise analysis, and prompt alert notification. Moreover, Public Health authorities and other stakeholders of surveillance can benefit from the widespread accessibility and user-friendliness of these tools, enhancing their knowledge and skills for better engagement in surveillance programs.

Deciphering the needs of patients with hereditary breast and ovarian Cancer in the Process of Genetic Counseling to Inform the Development of a Mobile Support App: a qualitative study in Germany.

Patients with hereditary breast and ovarian cancer (HBOC) are not only concerned about their own health but also about that of their children, grandchildren, and other relatives. Therefore, they have specific needs for information and support. During genetic counseling guidance is provided to HBOC patients and other individuals who may be at risk for familial cancer. The purpose of the study was to identify the needs of HBOC patients during the genetic counseling process that could be addressed by digital solutions. Nine semi-structured qualitative interviews were conducted. Overall, the patients appreciated the personal contact with human geneticists as an especially positive factor in the genetic counseling process. However, patients noted the following needs (1) support in the time following genetic counseling, (2) support before genetic counseling by collecting own and familial medical information, (3) Need for contact options to support services, (4) Need for patient-friendly medical information, (5) Wish for administration-related components in a support app. The results will inform the development of a patient-centered mobile support app.

Nueva aplicación móvil para categorización del vértigo en el nuevo milenio / New mobile application for categorization of vertigo in the new millennium

Introducción: El vértigo se define como la distorsión en la sensación de movimiento propio cuando se realiza un movimiento normal de la cabeza, para su estudio y categorización por el médico general, se recomienda el enfoque TiTrATE, que determina el momento de inicio, duración, evolución y los desencadenantes del vértigo. Objetivo: creación de una aplicación web progresiva (PWA) llamada TiTrATEapp y aplicarla en los pacientes que acudan a la emergencia en el mes de febrero 2023. Método: estudio tipo prospectivo, experimental, observacional, de análisis comparativo. Resultado: se creó una aplicación web progresiva (PWA) llamada TiTrATEapp, basada en el algoritmo TiTrATE, comparándose el tiempo de cálculo de la aplicación del algoritmo de forma manual y digital, obteniendo como resultado que la aplicación digital es más rápida y eficaz. Conclusión: Las aplicaciones móviles son una herramienta objetiva que ayuda los profesionales de la salud a diagnosticar y a tomar decisiones clínicas de forma práctica basadas en la evidencia científica, la digitalización del TiTrATE permite a los médicos realizar un diagnóstico acertado de forma rápida y orientan a la referencia del paciente a la especialidad correcta y la eficiencia de recursos. (AU)

Introduction: Vertigo is defined as the distortion in the sensation of self-movement when a normal movement of the head is made, for its study the TiTrATEapproach is recommended for the general practitioner, which determines the moment of onset, duration, evolution and triggers of vertigo for its study and categorization accurately. Objective: to create a progressive web application (PWA) called TiTrATEapp, Method: Prospective, observational, longitudinal study. Result: creation of a progressive web application (PWA) called TiTrATEapp, based on the TiTrATE algorithm, comparing the calculation time of the application of the algorithm manually and digitally, resulting in the digital application being faster and more efficient. Discussion: Vertigo is a difficult symptom to decipher in the emergency room, due to the subjective description of patients and the wide variety of probable causes, the digitization ofTiTrATE allows to rule out potentially dangerous causes, quickly, accurately. At present there are no studies that measure the effectiveness of mobile applications for the study of vertigo. Conclusion: Mobile applications are an objective tool that helps health professionals diagnose and make clinical decisions in a practical way based on scientific evidence, the digitalization of TiTrATE allows resident doctors to make an accurate diagnosis quickly and guide the patient's referral to the correct specialty and resource efficiency. (AU)

PyChelator: a Python-based Colab and web application for metal chelator calculations.

BACKGROUND: Metal ions play vital roles in regulating various biological systems, making it essential to control the concentration of free metal ions in solutions during experimental procedures. Several software applications exist for estimating the concentration of free metals in the presence of chelators, with MaxChelator being the easily accessible choice in this domain. This work aimed at developing a Python version of the software with arbitrary precision calculations, extensive new features, and a user-friendly interface to calculate the free metal ions. RESULTS: We introduce the open-source PyChelator web application and the Python-based Google Colaboratory notebook, PyChelator Colab. Key features aim to improve the user experience of metal chelator calculations including input in smaller units, selection among stability constants, input of user-defined constants, and convenient download of all results in Excel format. These features were implemented in Python language by employing Google Colab, facilitating the incorporation of the calculator into other Python-based pipelines and inviting the contributions from the community of Python-using scientists for further enhancements. Arbitrary-precision arithmetic was employed by using the built-in Decimal module to obtain the most accurate results and to avoid rounding errors. No notable differences were observed compared to the results obtained from the PyChelator web application. However, comparison of different sources of stability constants showed substantial differences among them. CONCLUSIONS: PyChelator is a user-friendly metal and chelator calculator that provides a platform for further development. It is provided as an interactive web application, freely available for use at https://amrutelab.github.io/PyChelator , and as a Python-based Google Colaboratory notebook at https://colab. RESEARCH: google.com/github/AmruteLab/PyChelator/blob/main/PyChelator_Colab.ipynb .

Ligify: Automated Genome Mining for Ligand-Inducible Transcription Factors.

Prokaryotic transcription factors can be repurposed into biosensors for the ligand-inducible control of gene expression, but the landscape of chemical ligands for which biosensors exist is extremely limited. To expand this landscape, we developed Ligify, a web application that leverages information in enzyme reaction databases to predict transcription factors that may be responsive to user-defined chemicals. Candidate transcription factors are then incorporated into automatically generated plasmid sequences that are designed to express GFP in response to the target chemical. Our benchmarking analyses demonstrated that Ligify correctly predicted 31/100 previously validated biosensors and highlighted strategies for further improvement. We then used Ligify to build a panel of genetic circuits that could induce a 47-fold, 5-fold, 9-fold, and 27-fold change in fluorescence in response to D-ribose, L-sorbose, isoeugenol, and 4-vinylphenol, respectively. Ligify should enhance the ability of researchers to quickly develop biosensors for an expanded range of chemicals and is publicly available at https://ligify.groov.bio.

A Novel Web-Based Application for Influenza and COVID-19 Outbreak Detection and Response in Residential Aged Care Facilities.

Unlabelled: The use of innovative digital health technologies in public health is expanding quickly, including the use of these tools in outbreak response. The translation of a digital health innovation into effective public health practice is a complex process requiring diverse enablers across the people, process, and technology domains. This paper describes a novel web-based application that was designed and implemented by a district-level public health authority to assist residential aged care facilities in influenza and COVID-19 outbreak detection and response. It discusses some of the challenges, enablers, and key lessons learned in designing and implementing such a novel application from the perspectives of the public health practitioners (the authors) that undertook this project.

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.

COVID-19 Vaccine Effectiveness and Digital Pandemic Surveillance in Germany (eCOV Study): Web Application-Based Prospective Observational Cohort Study.

BACKGROUND: The COVID-19 pandemic posed significant challenges to global health systems. Efficient public health responses required a rapid and secure collection of health data to improve the understanding of SARS-CoV-2 and examine the vaccine effectiveness (VE) and drug safety of the novel COVID-19 vaccines. OBJECTIVE: This study (COVID-19 study on vaccinated and unvaccinated subjects over 16 years; eCOV study) aims to (1) evaluate the real-world effectiveness of COVID-19 vaccines through a digital participatory surveillance tool and (2) assess the potential of self-reported data for monitoring key parameters of the COVID-19 pandemic in Germany. METHODS: Using a digital study web application, we collected self-reported data between May 1, 2021, and August 1, 2022, to assess VE, test positivity rates, COVID-19 incidence rates, and adverse events after COVID-19 vaccination. Our primary outcome measure was the VE of SARS-CoV-2 vaccines against laboratory-confirmed SARS-CoV-2 infection. The secondary outcome measures included VE against hospitalization and across different SARS-CoV-2 variants, adverse events after vaccination, and symptoms during infection. Logistic regression models adjusted for confounders were used to estimate VE 4 to 48 weeks after the primary vaccination series and after third-dose vaccination. Unvaccinated participants were compared with age- and gender-matched participants who had received 2 doses of BNT162b2 (Pfizer-BioNTech) and those who had received 3 doses of BNT162b2 and were not infected before the last vaccination. To assess the potential of self-reported digital data, the data were compared with official data from public health authorities. RESULTS: We enrolled 10,077 participants (aged ≥16 y) who contributed 44,786 tests and 5530 symptoms. In this young, primarily female, and digital-literate cohort, VE against infections of any severity waned from 91.2% (95% CI 70.4%-97.4%) at week 4 to 37.2% (95% CI 23.5%-48.5%) at week 48 after the second dose of BNT162b2. A third dose of BNT162b2 increased VE to 67.6% (95% CI 50.3%-78.8%) after 4 weeks. The low number of reported hospitalizations limited our ability to calculate VE against hospitalization. Adverse events after vaccination were consistent with previously published research. Seven-day incidences and test positivity rates reflected the course of the pandemic in Germany when compared with official numbers from the national infectious disease surveillance system. CONCLUSIONS: Our data indicate that COVID-19 vaccinations are safe and effective, and third-dose vaccinations partially restore protection against SARS-CoV-2 infection. The study showcased the successful use of a digital study web application for COVID-19 surveillance and continuous monitoring of VE in Germany, highlighting its potential to accelerate public health decision-making. Addressing biases in digital data collection is vital to ensure the accuracy and reliability of digital solutions as public health tools.

Validation and development of population pharmacokinetic model of vancomycin using a real-world database from a nationwide free web application.

INTRODUCTION: Vancomycin requires a population pharmacokinetic (popPK) model to estimate the area under the concentration-time curve (AUC), and an AUC-guided dosing strategy is necessary. This study aimed to develop a popPK model for vancomycin using a real-world database pooled from a nationwide web application (PAT). METHODS: In this retrospective study, the PAT database between December 14, 2022 and April 6, 2023 was used to develop a popPK model. The model was validated and compared with six existing models based on the predictive performance of datasets from another PAT database and the Kumamoto University Hospital. The developed model determined the dosing strategy for achieving the target AUC. RESULTS: The modeling populations consisted of 7146 (13,372 concentrations from the PAT database), 3805 (7540 concentrations from the PAT database), and 783 (1775 concentrations from Kumamoto University Hospital) individuals. A two-compartment popPK model was developed that incorporated creatinine clearance as a covariate for clearance and body weight for central and peripheral volumes of distribution. The validation demonstrated that the popPK model exhibited the smallest mean absolute prediction error of 5.07, outperforming others (ranging from 5.10 to 5.83). The dosing strategies suggested a first dose of 30 mg/kg and maintenance doses adjusted for kidney function and age. CONCLUSIONS: This study demonstrated the updating of PAT through the validation and development of a popPK model using a vast amount of data collected from anonymous PAT users.

ResearchConnect.info: An Interactive Web-Based Platform for Building Academic Collaborations.

RATIONALE AND OBJECTIVES: Radiology is a rapidly evolving field that benefits from continuous innovation and research participation among trainees. Traditional methods for involving residents in research are often inefficient and limited, usually due to the absence of a standardized approach to identifying available research projects. A centralized online platform can enhance networking and offer equal opportunities for all residents. MATERIALS AND METHODS: Research Connect is an online platform built with PHP, SQL, and JavaScript. Features include project and collaboration listing as well as advertisement of project openings to medical/undergraduate students, residents, and fellows. The automated system maintains project data and sends notifications for new research opportunities when they meet user preference criteria. Both pre- and post-launch surveys were used to assess the platform's efficacy. RESULTS: Before the introduction of Research Connect, 69% of respondents used informal conversations as their primary method of discovering research opportunities. One year after its launch, Research Connect had 141 active users, comprising 63 residents and 41 faculty members, along with 85 projects encompassing various radiology subspecialties. The platform received a median satisfaction rating of 4 on a 1-5 scale, with 54% of users successfully locating projects of interest through the platform. CONCLUSION: Research Connect addresses the need for a standardized method and centralized platform with active research projects and is designed for scalability. Feedback suggests it has increased the visibility and accessibility of radiology research, promoting greater trainee involvement and academic collaboration.

Development of a Web Application for Simulating Plasma Drug Concentrations in Patients with Zolpidem Intoxication.

Zolpidem is a widely prescribed hypnotic Z-drug used to treat short-term insomnia. However, a growing number of individuals intentionally overdose on these drugs. This study aimed to develop a predictive tool for physicians to assess patients with zolpidem overdose. A population pharmacokinetic (PK) model was established using digitized data obtained from twenty-three healthy volunteers after a single oral administration of zolpidem. Based on the final PK model, a web application was developed using open-source R packages such as rxode2, nonmem2rx, and shiny. The final model was a one-compartment model with first-order absorption and elimination with PK parameters, including clearance (CL, 16.9 L/h), absorption rate constant (Ka, 5.41 h-1), volume of distribution (Vd, 61.7 L), and lag time (ALAG, 0.394 h). Using the established population PK model in the current study, we developed a web application that enables users to simulate plasma zolpidem concentrations and visualize their profiles. This user-friendly web application may provide essential clinical information to physicians, ultimately helping in the management of patients with zolpidem intoxication.