As technology continues to advance, it is important to understand how website-based tools can support quality improvement. Website-based tools refer to resources such as toolkits that users can access and use autonomously through a dedicated website. This review examined how website-based tools can support healthcare professionals with quality improvement, including the optimal processes used to develop tools and the elements of an effective tool. A systematic search of seven databases was conducted to include articles published between January 2012 and January 2024. Articles were included if they were peer reviewed, written in English, based in health settings, and reported the development or evaluation of a quality improvement website-based tool for professionals. A narrative synthesis was conducted using NVivo. Risk of bias was assessed using the Mixed Methods Appraisal Tool. All papers were independently screened and coded by two authors using a six-phase conceptual framework by Braun and Clarke. Eighteen studies met the inclusion criteria. Themes identified were tool development processes, quality improvement mechanisms and barriers and facilitators to tool usage. Digitalizing existing quality improvement processes (n = 7), identifying gaps in practice (n = 6), and contributing to professional development (n = 3) were common quality improvement aims. Tools were associated with the reported enhancement of accuracy and efficiency in clinical tasks, improvement in adherence to guidelines, facilitation of reflective practice, and provision of tailored feedback for continuous quality improvement. Common features were educational resources (n = 7) and assisting the user to assess current practices against standards/recommendations (n = 6), which supported professionals in achieving better clinical outcomes, increased professional satisfaction and streamlined workflow in various settings. Studies reported facilitators to tool usage including relevance to practice, accessibility, and facilitating multidisciplinary action, making these tools practical and time-efficient for healthcare. However, barriers such as being time consuming, irrelevant to practice, difficult to use, and lack of organizational engagement were reported. Almost all tools were co-developed with stakeholders. The co-design approaches varied, reflecting different levels of stakeholder engagement and adoption of co-design methodologies. It is noted that the quality of included studies was low. These findings offer valuable insights for future development of quality improvement website-based tools in healthcare. Recommendations include ensuring tools are co-developed with healthcare professionals, focusing on practical usability and addressing common barriers to enhance engagement and effectiveness in improving healthcare quality. Randomized controlled trials are warranted to provide objective evidence of tool efficacy.

The tumor immune microenvironment (TIME) is closely associated with tumor formation, particularly linked to the human papillomavirus (HPV), and regulates tumor initiation, proliferation, infiltration, and metastasis. With the rise of immunotherapy, an increasing amount of sample data used for TIME exploration is available in databases. However, no currently available web tool enables a comprehensive exploration of the TIME of HPV-associated cancers by leveraging these data. We have developed a web tool called HPV-associated Tumor Immune MicroEnvironment ExploreR (HPVTIMER), which provides a comprehensive analysis platform that integrates over 10,000 genes and 2290 tumor samples from 65 transcriptome data sets across 8 cancer types sourced from the Gene Expression Omnibus (GEO) database. The tool features four built-in analysis modules, namely, the differential expression analysis module, correlation analysis module, immune infiltration analysis module, and pathway analysis module. These modules enable users to perform systematic and vertical analyses. We used several analytical modules in HPVTIMER to briefly explore the role of CDKN2A in head and neck squamous cell carcinomas. We expect that HPVTIMER will help users explore the immune microenvironment of HPV-associated cancers and uncover potential immune regulatory mechanisms and immunotherapeutic targets. HPVTIMER is available at http://www.hpvtimer.com/.

OBJECTIVE: To develop and evaluate a mobile health application, the Cancer Risk Calculator (CRC), aimed at improving public health literacy by providing personalized information on cancer risks and preventive measures.
METHODS: The CRC was developed through a comprehensive process involving the identification of necessary content, integration of average cancer risks using data from reliable sources, creation of a novel risk model emphasizing modifiable factors, and the application\'s development for easy access. The application covers 38 cancer types, 18 subtypes, and approximately 790 risk factors, utilizing data from the Surveillance, Epidemiology, and End Results Program and scientific literature.
RESULTS: CRC offers users personalized risk assessments across a broad range of cancers, emphasizing modifiable risk factors to encourage preventive behaviors. It distinguishes itself by covering more cancer types and risk factors than existing tools, with preliminary user feedback indicating its utility in promoting health literacy and lifestyle changes.
CONCLUSIONS: The CRC application stands out as an innovative tool in health informatics, significantly enhancing public understanding of cancer risks. Its development underscores the potential of digital health technologies to bolster preventive healthcare strategies through improved health literacy.
CONCLUSIONS: The Cancer Risk Calculator is a pivotal development in mobile health technology, offering comprehensive and personalized insights into cancer risks and prevention. It serves as a valuable resource for public health education, facilitating informed decisions and lifestyle modifications for cancer prevention.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.

Mining gene expression data is valuable for discovering novel biomarkers and therapeutic targets in hepatocellular carcinoma (HCC). Although emerging data mining tools are available for pan-cancer-related gene data analysis, few tools are dedicated to HCC. Moreover, tools specifically designed for HCC have restrictions such as small data scale and limited functionality. Therefore, we developed IHGA, a new interactive web server for discovering genes of interest in HCC on a large-scale and comprehensive basis. Integrative HCC Gene Analysis (IHGA) contains over 100 independent HCC patient-derived datasets (with over 10,000 tissue samples) and more than 90 cell models. IHGA allows users to conduct a series of large-scale and comprehensive analyses and data visualizations based on gene mRNA levels, including expression comparison, correlation analysis, clinical characteristics analysis, survival analysis, immune system interaction analysis, and drug sensitivity analysis. This method notably enhanced the richness of clinical data in IHGA. Additionally, IHGA integrates artificial intelligence (AI)-assisted gene screening based on natural language models. IHGA is free, user-friendly, and can effectively reduce time spent during data collection, organization, and analysis. In conclusion, IHGA is competitive in terms of data scale, data diversity, and functionality. It effectively alleviates the obstacles caused by HCC heterogeneity to data mining work and helps advance research on the molecular mechanisms of HCC.

暂无摘要。

暂无摘要。

Recently, sequencing technologies have become readily available, and scientists are more motivated to conduct metagenomic research to unveil the potential of a myriad of ecosystems and biomes. Metagenomics studies the composition and functions of microbial communities and paves the way to multiple applications in medicine, industry, and ecology. Nonetheless, the immense amount of sequencing data of metagenomics research and the few user-friendly analysis tools and pipelines carry a new challenge to the data analysis.Web-based bioinformatics tools are now being developed to facilitate the analysis of complex metagenomic data without prior knowledge of any programming languages or special installation. Specialized web tools help answer researchers\' main questions on the taxonomic classification, functional capabilities, discrepancies between two ecosystems, and the probable functional correlations between the members of a specific microbial community. With an Internet connection and a few clicks, researchers can conveniently and efficiently analyze the metagenomic datasets, summarize results, and visualize key information on the composition and the functional potential of metagenomic samples under study. This chapter provides a simple guide to a few of the fundamental web-based services used for metagenomic data analyses, such as BV-BRC, RDP, MG-RAST, MicrobiomeAnalyst, METAGENassist, and MGnify.

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.