I just read the paper about #openalexR, an #rstats package to work with data from #OpenAlex.

Aria, M., Le Trang, Cuccurullo, C., Belfiore, A., & Choe, J. (2024). openalexR: An r-tool for collecting bibliometric data from OpenAlex. The R Journal, 15, 167–180. https://doi.org/10.32614/RJ-2023-089

This package is useful to work with scholarly literature as this demands efficient tools for bibliometric analysis, science mapping, and research assessment. OpenAlex, a large open database of academic metadata, provides free access to comprehensive bibliometric data, but accessing and analyzing this data can be technically challenging. For example, we used an OpenAlex csv data dump in a review about the #Fediverse and open source software [1].

openalexR, an R package, which is offering a user-friendly interface to the OpenAlex API. This package enables researchers to query, download, and analyze metadata on works, authors, institutions, sources, concepts, and more, with minimal coding expertise. Key features include oa_fetch for data retrieval, oa_snowball for automated citation-based literature searches, and oa_ngrams for text analysis. By converting API responses into structured data frames. Thus, it should be a nice tool for bibliometric studies, literature reviews, and science mapping. This might also come handy to identify trends, map collaborations, and assess scholarly impact. Be warned, the OpenAlex data is far from complete or perfect.

[1] Rödiger, S., Kögler, M., Birkholz, M., 2024. Open-Source Software, Fediverse and Custom ROMs as Tools for a Sustainable Internet, in: 2024 Electronics Goes Green 2024+ (EGG). Presented at the 2024 Electronics Goes Green 2024+ (EGG), pp. 1–13. https://doi.org/10.23919/EGG62010.2024.10631179

I just read an interesting paper about #Sweave and tooling for reproducible research by Roger D. Peng, which was very important for #rstats before #RMarkdown and #Quarto. I can recommand to read it.

Peng, R. (2025). Tooling for Reproducible Research: Considerations for the Past and Future of Data Analysis. Austrian Journal of Statistics, 54(3), 1–8. [https://doi.org/10.17713/ajs.v54i3.2052](https://doi.org/10.17713/ajs.v54i3.2052)

It is alos in memoriam of Fritz Leisch (31 July 1968 - 24 April 2024), who made major contributions to this area.

This is the link to the whole issue in the Austrian Journal of Statistics.

[https://ajs.or.at/index.php/ajs/issue/view/120](https://ajs.or.at/index.php/ajs/issue/view/120)

We published another review about Neurodegenerative diseases.

Alzheimer’s, Parkinson’s, Huntington’s, and Amyotrophic Lateral Sclerosis (ALS) affect millions worldwide, posing significant challenges for early detection and effective treatment. But what if the key to unlocking new diagnostics and therapies lies in tiny molecules called microRNAs (miRNAs)? These small, non-coding RNAs regulate gene expression and have emerged as promising biomarkers and therapeutic targets for neurodegenerative diseases. This is something we addressed in our first review:

Azam, H.M.H., Rößling, R.I., Geithe, C., Khan, M.M., Dinter, F., Hanack, K., Prüß, H., Husse, B., Roggenbuck, D., Schierack, P., Rödiger, S., 2024. MicroRNA Biomarkers as Next-Generation Diagnostic Tools for Neurodegenerative Diseases: A Review. Front. Mol. Neurosci. 17. doi.org/10.3389/fnmol.2024.1...

Our second (final) review further explores the of miRNAs in understanding, diagnosing, and treating these debilitating conditions.

Azam, H.M.H., Mumtaz, M., Rödiger, S., Schierack, P., Hussain, N., Aisha, A., 2025. MicroRNAs in neurodegenerative diseases: from molecular mechanisms to clinical biomarkers, detection methods and therapeutic strategies—advances and challenges. Neurol Sci. doi.org/10.1007/s10072-025-0...

Part of this are information about the role of microRNAs in neurodegenerative diseases. This time we have a more bioanalytical/biosensoric perspective. One of the most exciting aspects of miRNA is their potential as less-invasive biomarkers. MiRNAs are released into biofluids like blood and cerebrospinal fluid (CSF) via extracellular vesicles, making them more accessible for diagnostic testing. Advanced detection methods, such as RT-qPCR, next-generation sequencing (NGS), and microfluidic chips, are being developed to measure miRNA levels with high sensitivity and specificity. Emerging technologies, like lateral flow assays and electrochemical biosensors, are also being explored for point-of-care diagnostics, which could revolutionize how we detect and monitor neurodegenerative diseases in clinical settings.

Beyond diagnostics, miRNAs hold promise as therapeutic agents. Researchers are exploring two main strategies, which are miRNA Inhibition and miRNA Replacement. However, challenges remain, particularly in delivering miRNAs to the brain due to the blood-brain barrier. Innovative delivery systems, such as viral vectors, liposomes, and exosomes, are being developed to overcome these barriers and improve the efficacy of miRNA-based therapies.

While the potential of miRNAs is immense, several challenges must be addressed to translate research into clinical practice. These are standardization (there is a need for standardized protocols in miRNA extraction, detection, and data analysis to ensure reproducibility across studies), specificity and Sensitivity (miRNAs often share high sequence similarities, making it difficult to design probes that can distinguish between them. Advanced technologies like locked nucleic acid (LNA) probes and CRISPR-based detection are being explored to improve specificity) and presumably the deciding aspect regulatory approval (miRNA-based diagnostics and therapeutics must meet stringent regulatory requirements, which can delay their clinical adoption).

#NeurodegenerativeDiseases #MicroRNAs #Biomarkers #Therapeutics #Neuroscience #PrecisionMedicine #miRNA