Tumor-on-chips (ToCs) are useful platforms for studying the physiology of tumors and evaluating the efficacy and toxicity of anti-cancer drugs. However, the design and fabrication of a ToC system is not a trivial venture. We introduce a user-friendly, flexible, 3D-printed microfluidic device that can be used to culture cancer cells or cancer-derived spheroids embedded in hydrogels under well-controlled environments. The system consists of two lateral flow compartments (left and right sides), each with two inlets and two outlets to deliver cell culture media as continuous liquid streams. The central compartment was designed to host a hydrogel in which cells and microtissues can be confined and cultured. We performed tracer experiments with colored inks and 40 kDa fluorescein isothiocyanate dextran to characterize the transport/mixing performances of the system. We also cultured homotypic (MCF7) and heterotypic (MCF7-BJ) spheroids embedded in gelatin methacryloyl hydrogels to illustrate the use of this microfluidic device in sustaining long-term micro-tissue culture experiments. We further demonstrated the use of this platform in anticancer drug testing by continuous perfusion of doxorubicin, a commonly used anti-cancer drug for breast cancer. In these experiments, we evaluated drug transport, viability, glucose consumption, cell death (apoptosis), and cytotoxicity. In summary, we introduce a robust and friendly ToC system capable of recapitulating relevant aspects of the tumor microenvironment for the study of cancer physiology, anti-cancer drug transport, efficacy, and safety. We anticipate that this flexible 3D-printed microfluidic device may facilitate cancer research and the development and screening of strategies for personalized medicine.

UNASSIGNED: At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application.
UNASSIGNED: We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality.
UNASSIGNED: EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT.
UNASSIGNED: This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.

BACKGROUND: The potential of health apps for health promotion and disease prevention is widely recognized. However, uptake is limited due to barriers individuals face in finding suitable and trustworthy apps, such as the overwhelming amount of available health apps. Therefore, the health app platform \"FitKnip\" was developed, enabling individuals to purchase preselected, trustworthy health apps with a budget of 100 euros (a currency exchange rate of EUR €1=US $1.0831 is applicable). The platform aimed to empower individuals to improve their health and vitality, ultimately supporting a more healthy society.
OBJECTIVE: The primary aim of this study was to evaluate the health app platform in terms of feasibility and acceptability. Potential effects on health empowerment and health outcomes were secondarily explored.
METHODS: This quantitative study was part of a mixed methods study with a prospective pre-post interventional design. We collected web-based user data, and self-reported web-based questionnaires were collected over 5 measurements over an 8-month period. Use statistics were tracked on the platform, including the number of purchased apps and euros spent per user registered within the health app platform. We measured the user-friendliness of the health app platform using the System Usability Scale (SUS) and satisfaction using the Client Satisfaction Questionnaire-8 (CSQ-8) and several 10-point Likert items. We asked participants to indicate, on a scale from 1 (not at all) to 10 (completely), how much the health app platform contributed to various areas related to health empowerment. We assessed health-related quality of life by the 12-item Short-Form Health Survey (SF-12) and one\'s perceived level of stress by the 10-item Perceived Stress Scale (PSS-10).
RESULTS: A total of 1650 participants were included, of whom 42% (685/1650) bought at least 1 app. The majority of those purchased one app (244/685, 35.6%). The health app platform was rated as user-friendly (SUS mean 66.5, SD 20.7; range 66.5-70.0), and the acceptability of the health app platform was moderate (CSQ-8 mean 20.0, SD 1.5; range 19.6-20.0). Results furthermore showed that participants were generally satisfied to highly satisfied with the ease of the payment system to purchase apps on the platform (median 8, IQR 7-10), the look and feel of the platform (median 7, IQR 6-8), as well as the provided budget of 100 euros (median 9, IQR 7-10). Participants were less satisfied with the amount (median 6, IQR 4-7) and diversity (median 6, IQR 4-7) of apps offered on the platform.
CONCLUSIONS: A health app platform is a promising initiative to enhance public health. Feasibility and acceptability are critical for success, as they ensure that such a platform is accessible, user-friendly, and meets end users\' needs and preferences. This can help to increase uptake, engagement, and ultimately the platform\'s adoption and effectiveness.

The ever decreasing cost of Next-Generation Sequencing coupled with the emergence of efficient and reproducible analysis pipelines has rendered genomic methods more accessible. However, downstream analyses are basic or missing in most workflows, creating a significant barrier for non-bioinformaticians. To help close this gap, we developed Cactus, an end-to-end pipeline for analyzing ATAC-Seq and mRNA-Seq data, either separately or jointly. Its Nextflow-, container-, and virtual environment-based architecture ensures efficient and reproducible analyses. Cactus preprocesses raw reads, conducts differential analyses between conditions, and performs enrichment analyses in various databases, including DNA-binding motifs, ChIP-Seq binding sites, chromatin states, and ontologies. We demonstrate the utility of Cactus in a multi-modal and multi-species case study as well as by showcasing its unique capabilities as compared to other ATAC-Seq pipelines. In conclusion, Cactus can assist researchers in gaining comprehensive insights from chromatin accessibility and gene expression data in a quick, user-friendly, and reproducible manner.

BACKGROUND: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management.
OBJECTIVE: This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania.
METHODS: This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values.
RESULTS: This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns.
CONCLUSIONS: Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally.
UNASSIGNED: PRR1-10.2196/54388.

Wolfberry, also known as goji berry or Lycium barbarum, is a highly valued fruit with significant health benefits and nutritional value. For more efficient and comprehensive usage of published L. barbarum genomic data, we established the Wolfberry database. The utility of the Wolfberry Genome Database (WGDB) is highlighted through the Genome browser, which enables the user to explore the L. barbarum genome, browse specific chromosomes, and access gene sequences. Gene annotation features provide comprehensive information about gene functions, locations, expression profiles, pathway involvement, protein domains, and regulatory transcription factors. The transcriptome feature allows the user to explore gene expression patterns using transcripts per kilobase million (TPM) and fragments per kilobase per million mapped reads (FPKM) metrics. The Metabolism pathway page provides insights into metabolic pathways and the involvement of the selected genes. In addition to the database content, we also introduce six analysis tools developed for the WGDB. These tools offer functionalities for gene function prediction, nucleotide and amino acid BLAST analysis, protein domain analysis, GO annotation, and gene expression pattern analysis. The WGDB is freely accessible at https://cosbi7.ee.ncku.edu.tw/Wolfberry/. Overall, WGDB serves as a valuable resource for researchers interested in the genomics and transcriptomics of L. barbarum. Its user-friendly web interface and comprehensive data facilitate the exploration of gene functions, regulatory mechanisms, and metabolic pathways, ultimately contributing to a deeper understanding of wolfberry and its potential applications in agronomy and nutrition.

BACKGROUND: The rapid growth of omics technologies has led to the use of bioinformatics as a powerful tool for unravelling scientific puzzles. However, the obstacles of bioinformatics are compounded by the complexity of data processing and the distinct nature of omics data types, particularly in terms of visualization and statistics.
OBJECTIVE: We developed a comprehensive and free platform, CFViSA, to facilitate effortless visualization and statistical analysis of omics data by the scientific community.
METHODS: CFViSA was constructed using the Scala programming language and utilizes the AKKA toolkit for the web server and MySQL for the database server. The visualization and statistical analysis were performed with the R program.
RESULTS: CFViSA integrates two omics data analysis pipelines (microbiome and transcriptome analysis) and an extensive array of 79 analysis tools spanning simple sequence processing, visualization, and statistics available for various omics data, including microbiome and transcriptome data. CFViSA starts from an analysis interface, paralleling a demonstration full course to help users understand operating principles and scientifically set the analysis parameters. Once analysis is conducted, users can enter the task history interface for figure adjustments, and then a complete series of results, including statistics, feature tables and figures. All the graphic layouts were printed with necessary statistics and a traceback function recording the options for analysis and visualization; these statistics were excluded from the five competing methods.
CONCLUSIONS: CFViSA is a user-friendly bioinformatics cloud platform with detailed guidelines for integrating functions in multi-omics analysis with real-time visualization adjustment and complete series of results provision. CFViSA is available at http://www.cloud.biomicroclass.com/en/CFViSA/.

This study presents an inexpensive approach for the macro- and microscopic observation of fungal mycelial growth. The \'fungal drops\' method allows to investigate the development of a mycelial network in filamentous microorganisms at the colony and hyphal scales. A heterogeneous environment is created by depositing 15-20 µl drops on a hydrophobic surface at a fixed distance. This system is akin to a two-dimensional (2D) soil-like structure in which aqueous-pockets are intermixed with air-filled pores. The fungus (spores or mycelia) is inoculated into one of the drops, from which hyphal growth and exploration take place. Hyphal structures are assessed at different scales using stereoscopic and microscopic imaging. The former allows to evaluate the local response of regions within the colony (modular behaviour), while the latter can be used for fractal dimension analyses to describe the hyphal network architecture. The method was tested with several species to underpin the transferability to multiple species. In addition, two sets of experiments were carried out to demonstrate its use in fungal biology. First, mycelial reorganization of Fusarium oxysporum was assessed as a response to patches containing different nutrient concentrations. Second, the effect of interactions with the soil bacterium Pseudomonas putida on habitat colonization by the same fungus was assessed. This method appeared as fast and accessible, allowed for a high level of replication, and complements more complex experimental platforms. Coupled with image analysis, the fungal drops method provides new insights into the study of fungal modularity both macroscopically and at a single-hypha level.

BACKGROUND: Epidemiological trends in the United States have shown an increase in HIV cases among young sexual minoritized men. Using mobile health (mHealth), which refers to health services and information delivered or enhanced through the internet and related technologies, is a crucial strategy to address HIV disparities. However, despite its potential, the practical implementation of mHealth remains limited. Additionally, it is important to consider that young individuals may become accustomed to, distracted from, or lose interest in these apps, highlighting the need for regular updates and monitoring of relevant content.
OBJECTIVE: In this study, we sought to highlight the voices of young sexual minoritized men aged 17-24 years and explored preferred mHealth intervention characteristics and willingness to adopt these technologies among a diverse, nationwide sample of young sexual minoritized men.
METHODS: From April to September 2020, we recruited participants through web-based platforms such as social media and geosocial networking apps for men. These individuals were invited to participate in synchronous web-based focus group discussions centered around topics pertaining to HIV testing and prevention and their preferences for mHealth technologies.
RESULTS: A total of 41 young sexual minoritized men, aged between 17 and 24 years, participated in 9 focus group discussions spanning April to September 2020, with 3-7 participants in each group. The findings shed light on three key insights regarding young sexual minoritized men\'s preferences: (1) the need for personalized and representative content, (2) a preference for mobile and web-based simulation of prevention scenarios, and (3) a preference for digital software with individually tailored content. As expected, preference for mHealth apps was high, which supports the potential and need to develop or adapt interventions that use smartphones as a platform for engaging young sexual minoritized men in HIV prevention. This study expands on previous research in multiple meaningful ways, delving into the use and perceptions of mHealth information amid the COVID-19 pandemic. This study also highlighted the importance of streamlined access to health care providers, especially in light of the barriers faced by young people during the COVID-19 pandemic. In terms of presentation and navigation, participants favored a user-friendly design that was easy to use and appropriate for their age, which was effectively addressed through the implementation of web-based simulations.
CONCLUSIONS: Ultimately, this study provides valuable insight into the preferences of young sexual minoritized men when it comes to mHealth interventions and highlights the need for further research in order to develop effective and tailored HIV prevention tools. A future direction for researchers is to evaluate how best to address participants\' desire for personalized content within mHealth apps. Additionally, as technology rapidly evolves, there is a need to re-assess the effectiveness of web-based simulations, particularly those that are used in HIV prevention.

Water-in-oil droplets allow performing massive experimental parallelization and high-throughput studies, such as single-cell experiments. However, analyzing such vast arrays of droplets usually requires advanced expertise and sophisticated workflow tools, which limits accessibility for a wider user base in the fields of chemistry and biology. Thus, there is a need for more user-friendly tools for droplet analysis. In this article, we deliver a set of analytical pipelines for user-friendly analysis of typical scenarios in droplet experiments. We built pipelines that combine various open-source image-analysis software with a custom-developed data processing tool called \"EasyFlow\". Our pipelines are applicable to the typical experimental scenarios that users encounter when working with droplets: i) mono- and polydisperse droplets, ii) brightfield and fluorescent images, iii) droplet and object detection, iv) signal profile of droplets and objects (e.g., fluorescence).