An innovative user-friendly platform for Covid-19 pandemic databases and resources

A comprehensive analysis of diabetic patient data before and during the COVID-19 pandemic - Lessons from the MÉRY diabetes database (MDD)

AIMS

Our study examined changes in average blood glucose levels (ABG), measurement frequency (MF), and data uploading (DU) before and during the COVID-19 pandemic in 882-day spans, which were divided into further 20-week intervals to highlight the pandemic's impact.

METHODS

T-Tests assessed the statistical significance of blood glucose data from 26,655/20,936 patients and 19.5/16.6 million records during pre-COVID/COVID.

RESULTS

During COVID, patients had significantly lower ABG levels (9.1/8.9 mmol/L, p < 0.001). Weekly DU decreased (155,945/128,445, p < 0.05), while daily MF increased (0.83/0.87, p < 0.001). Comparing the last 20 weeks pre-COVID to the first 20 weeks during COVID, ABG levels were lower (9.0 /8.9, p < 0.01), MF increased (0.83 /0.99, p < 0.001), and DU decreased (153,133/145,381, p < 0.05). In the initial 20 weeks of COVID compared to the second 20 weeks of COVID, ABG increased (8.9/9.1, p < 0.01), MF decreased (0.99/0.95, p < 0.001), and DU decreased (145,381/140,166, p < 0.05). Our most striking observation was the temporary dramatic fall in glucose uploads during the first few weeks of COVID. The changes of ABG and MF values were statistically significant, but were not deemed clinically relevant.

CONCLUSIONS

Despite COVID's prolonged impact, diabetic patients showed improved attitudes. A significant drop in data uploads occurred during the first 20 weeks of COVID; home office and lockdowns apparently disrupted patient routines.

CO-19 PDB 2.0: A Comprehensive COVID-19 Database with Global Auto-Alerts, Statistical Analysis, and Cancer Correlations

Biological databases serve as critical basics for modern research, and amid the dynamic landscape of biology, the COVID-19 database has emerged as an indispensable resource. The global outbreak of Covid-19, commencing in December 2019, necessitates comprehensive databases to unravel the intricate connections between this novel virus and cancer. Despite existing databases, a crucial need persists for a centralized and accessible method to acquire precise information within the research community. The main aim of the work is to develop a database which has all the COVID-19-related data available in just one click with auto global notifications. This gap is addressed by the meticulously designed COVID-19 Pandemic Database (CO-19 PDB 2.0), positioned as a comprehensive resource for researchers navigating the complexities of COVID-19 and cancer. Between December 2019 and June 2024, the CO-19 PDB 2.0 systematically collected and organized 120 datasets into six distinct categories, each catering to specific functionalities. These categories encompass a chemical structure database, a digital image database, a visualization tool database, a genomic database, a social science database, and a literature database. Functionalities range from image analysis and gene sequence information to data visualization and updates on environmental events. CO-19 PDB 2.0 has the option to choose either the search page for the database or the autonotification page, providing a seamless retrieval of information. The dedicated page introduces six predefined charts, providing insights into crucial criteria such as the number of cases and deaths', country-wise distribution, 'new cases and recovery', and rates of death and recovery. The global impact of COVID-19 on cancer patients has led to extensive collaboration among research institutions, producing numerous articles and computational studies published in international journals. A key feature of this initiative is auto daily notifications for standardized information updates. Users can easily navigate based on different categories or use a direct search option. The study offers up-to-date COVID-19 datasets and global statistics on COVID-19 and cancer, highlighting the top 10 cancers diagnosed in the USA in 2022. Breast and prostate cancers are the most common, representing 30% and 26% of new cases, respectively. The initiative also ensures the removal or replacement of dead links, providing a valuable resource for researchers, healthcare professionals, and individuals. The database has been implemented in PHP, HTML, CSS and MySQL and is available freely at https://www.co-19pdb.habdsk.org/. Database URL: https://www.co-19pdb.habdsk.org/.

The HABD: Home of All Biological Databases Empowering Biological Research With Cutting-Edge Database Systems

The emergence of computer technologies and computing power has led to the development of several database systems that provide standardized access to vast quantities of data, making it possible to collect, search, index, evaluate, and extract useful knowledge across various fields. The Home of All Biological Databases (HABD) has been established as a continually expanding platform that aims to store, organize, and distribute biological data in a searchable manner, removing all dead and non-accessible data. The platform meticulously categorizes data into various categories, such as COVID-19 Pandemic Database (CO-19PDB), Database relevant to Human Research (DBHR), Cancer Research Database (CRDB), Latest Database of Protein Research (LDBPR), Fungi Databases Collection (FDBC), and many other databases that are categorized based on biological phenomena. It currently provides a total of 22 databases, including 6 published, 5 submitted, and the remaining in various stages of development. These databases encompass a range of areas, including phytochemical-specific and plastic biodegradation databases. HABD is equipped with search engine optimization (SEO) analyzer and Neil Patel tools, which ensure excellent SEO and high-speed value. With timely updates, HABD aims to facilitate the processing and visualization of data for scientists, providing a one-stop-shop for all biological databases. Computer platforms, such as PhP, html, CSS, Java script and Biopython, are used to build all the databases. © 2024 Wiley Periodicals LLC.

COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19

COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.

DBHR: a collection of databases relevant to human research

Background:

The achievement of the human genome project provides a basis for the systematic study of the human genome from evolutionary history to disease-specific medicine. With the explosive growth of biological data, a growing number of biological databases are being established to support human-related research.

Objective:

The main objective of our study is to store, organize and share data in a structured and searchable manner. In short, we have planned the future development of new features in the database research area.

Materials & methods:

In total, we collected and integrated 680 human databases from scientific published work. Multiple options are presented for accessing the data, while original links and short descriptions are also presented for each database.

Results & discussion:

We have provided the latest collection of human research databases on a single platform with six categories: DNA database, RNA database, protein database, expression database, pathway database and disease database.

Conclusion:

Taken together, our database will be useful for further human research study and will be modified over time. The database has been implemented in PHP, HTML, CSS and MySQL and is available freely at https://habdsk.org/database.php.

We have compiled the most recent collection of human research datasets into six categories – DNA database, RNA database, protein database, expression database, pathway database and disease database – on a single platform. In all, 680 human datasets were acquired and incorporated from scientifically published studies. There are several ways to retrieve the data, as well as original links and short descriptions for each database. The primary goal of our research is to store, organize and exchange data in an organized and searchable format. In brief, we have planned the future development of additional features in the database. Our database will be beneficial for future human research studies and will be updated throughout time. We firmly believe that every researcher should have access to essential biological databases, so we have gathered a collection of human-related databases that are regularly used and currently available but have not previously been presented in such a simple and welcoming manner.

iCAV: an integrative database of cancer-associated viruses

To date, various studies have found that the occurrence of cancer may be related to viral infections. Therefore, it is important to explore the relationship between viruses and diseases. The International Agency for Research on Cancer has defined six types of viruses as Class 1 human carcinogens, including Epstein–Barr virus, hepatitis C virus, hepatitis B virus, human T-cell lymphotropic virus, human herpesvirus 8 and human papillomavirus, while Merkel cell polyomavirus is classified as ‘probably carcinogenic to humans’ (Group 2A). Therefore, in-depth research on these viruses will help clarify their relationship with diseases, and substantial efforts have been made to sequence their genomes. However, there is no complete database documenting these cancer-associated viruses, and researchers are not able to easily access and retrieve the published genomes. In this study, we developed iCAV, a database that integrates the genomes of cancer-related viruses and the corresponding phenotypes. We collected a total of 18 649 genome sequences from seven human disease-related viruses, and each virus was further classified by the associated disease, sample and country. iCAV is a comprehensive resource of cancer-associated viruses that provides browse and download functions for viral genomes.

Database URL: http://icav.omicsbio.info/