Announcements

The following is an interview with Prof. Mesin:

1. Can you tell us a bit about your background and what your research focuses on?
I am an electronics engineer with a Ph.D. in applied mathematics. Currently, I serve as an Associate Professor in biomedical engineering. My main research interests lie in the processing and analysis of biomedical data (both signals and images). One focus of my research is on EEG signal acquisition, preprocessing, and functional connectivity analysis.

2. What made you decide to publish a bioengineering article? Why did you choose Bioengineering MDPI?
As an Associate Editor of Bioengineering, I had the opportunity to contribute a review paper in an area closely aligned with the journal’s scope. The choice of MDPI was driven by its strong reputation for rapid and transparent peer review, as well as its commitment to open access dissemination.

3. What was your experience publishing with Bioengineering MDPI?
The experience was very positive. The review process was efficient and timely, and the editorial staff ensured high-quality editing and publication standards.

4. Was it important to you that the journal is open access? How does open access publishing advance the field of bioengineering?
Yes, open access is particularly important for researchers like me. I tend to focus on the scientific substance of my work and less on promotion. Unfortunately, this sometimes means that even my most innovative work goes under-recognized. Open access provides a valuable opportunity for broader dissemination, making research accessible to a larger audience regardless of institutional affiliation or financial resources.

5. What do you hope that readers will get from your paper?
The paper offers a comprehensive tutorial review of data-driven methods for assessing EEG-based functional connectivity. It aims to serve as a reference for researchers by summarizing methods, clarifying technical details, and discussing the impact of acquisition and preprocessing on connectivity estimates.

6. What critical scientific or engineering problems did your research initially aim to address?
We aimed to address the complexity of accurately inferring functional connectivity from EEG data. This includes methodological challenges in preprocessing, signal interpretation, and the selection and validation of appropriate connectivity metrics.

7. What are the current bottlenecks in this field, and how did you identify your research’s breakthrough point?
Key bottlenecks include variability in preprocessing methods, lack of standardization, and the difficulty of distinguishing true from spurious connectivity. Our review highlights the importance of methodological rigor and proposes emerging solutions, such as high-order interactions and graph-based approaches, to advance the field.

8. Which technologies or tools played pivotal roles in designing your methodology?
Our work focuses primarily on data-driven methodologies, including both linear and nonlinear statistical tools. Concepts such as Granger causality, transfer entropy, and multivariate autoregressive modeling are central, alongside tools from information theory and graph theory.

9. Have your experiments or theoretical models undergone significant adjustments? What motivated those changes?
Yes. Throughout the years, our approaches have evolved to incorporate more sophisticated multivariate methods and to address limitations identified in simpler bivariate analyses. These changes were motivated by a deeper understanding of EEG complexity and the risk of misinterpreting indirect connections.

10. Are there any untold “behind-the-scenes” stories worth sharing about this work?
The paper was the result of a genuine collaborative effort between two groups with complementary expertise. Early discussions revealed the need for a unified framework that could bridge theoretical developments and practical EEG applications, which ultimately shaped the paper’s structure.

11. Why do you think this article has been highly cited?
Citation patterns are influenced by complex and often unstable dynamics. A well-known phenomenon is that highly cited papers tend to attract further citations, especially when they receive attention early after publication. In our case, the open access format ensured wide visibility from the outset, and the comprehensive scope of the review likely made it a useful reference for researchers entering or working within the field. Moreover, the collaboration among experienced authors with established scientific reputations may have added credibility and contributed to its early recognition.

12. Are there follow-up studies planned based on this paper’s findings?
Yes, several ongoing studies in our groups are extending the reviewed methodologies to real-world EEG datasets, particularly in clinical and cognitive neuroscience applications.

13. Did your research involve cross-disciplinary collaboration? How did teamwork shape the outcomes?
Definitely. My group was more focused on EEG acquisition and preprocessing, while Prof. Faes’ group contributed theoretical expertise in connectivity metrics. This synergy was essential to producing a balanced and integrative review.

14. How did early career researchers or students contribute to this work?
They played a fundamental role. Early career researchers carried out most of the literature review and technical analysis, under the supervision of senior authors who guided the overall structure and interpretation.

15. What was the greatest technical or theoretical challenge during this research, and how did you overcome it?
One major challenge was organizing a vast and fragmented literature into a coherent and accessible framework. We addressed this by systematically categorizing connectivity metrics across time, frequency, and information-theoretic domains, and by explicitly linking them through their mathematical foundations.

16. Were there difficulties in data acquisition or experimental reproducibility? How were they resolved?
While the paper is primarily a review, we are well aware that reproducibility is a major issue in EEG research. We emphasized best practices in preprocessing and model validation to improve reproducibility and highlighted the importance of transparent methodological reporting.

17. Did ethical concerns (e.g., gene editing, biosafety, etc.) arise? How were they addressed?
No specific ethical concerns arose in this work, as it is a review of methodological approaches and did not involve new experimental protocols with human subjects.

18. Which technological directions in bioengineering deserve the most attention over the next 5 years?
In the coming years, I believe particular attention should be given to the integration of multiple neuroimaging modalities, which can offer complementary spatial and temporal resolution for studying brain function. The development of non-invasive brain–computer interfaces will continue to open new possibilities for clinical and assistive technologies. Equally promising is the evolution of adaptive neurostimulation systems, enabling a deeper and more personalized interaction between the user and the device. Lastly, the incorporation of explainable artificial intelligence into biomedical signal processing holds great potential for improving both diagnostic transparency and clinical trust in AI-based systems.

19. How is AI reshaping bioengineering research in disruptive ways?
AI enables the analysis of complex, high-dimensional data and fostering real-time decision-making in clinical contexts. In EEG analysis, deep learning is being explored for source localization, artifact rejection, and connectivity estimation, though interpretability remains a key challenge.

20. What learning resources would you recommend for newcomers entering this field?
I recommend starting with foundational texts on signal processing and time series analysis, followed by specialized literature on EEG analysis and functional connectivity. Our review paper itself offers a structured entry point. Practical experience with tools like MATLAB, Python, and EEGLAB is also invaluable.

We are pleased to share that Bioengineering (ISSN: 2306-5354) has received an increased CiteScore of 5.3 in June 2025. The CiteScore ranks the journal 90 out of 163 titles (Q3) in the “Bioengineering” category, an impressive achievement for a journal running in Volume 12.

You can find more statistics on our website: https://www.mdpi.com/journal/bioengineering/stats.

The current CiteScores measure the average number of citations within a journal over a four-year window (2021–2024). The Scopus database provides a comprehensive suite of metrics that support informed publishing strategies, research evaluation and enable benchmarking of journal performance.

This achievement reflects the collective efforts of our authors, reviewers, and editors. Together we will continue to track the progress of Bioengineering and its growing impact in biomedical engineering.

We are pleased to announce that the Young Women in Engineering Award is open for nominations. This award was established to acknowledge the achievements of young women investigators in the field of engineering and to inspire young women to consider research and careers in engineering. All the nominations will be assessed by an Award Evaluation Committee, and winners will be announced online on 8 March 2026.

Prizes:

• CHF 1000;
• An electronic certificate;
• A voucher to waive the article processing charges (APCs) for one submission to a journal within the field of engineering (subject to peer review)—valid for one year.

Number of winners: 2.

Eligibility and requirements:

• This role is designated for female candidates;
• Must have received their Ph.D. no more than 10 years prior to 31 December 2025;
• Must have produced groundbreaking research and made a significant contribution to the advancement of engineering;
• Must be nominated by senior scientists.

List of documents for nomination:

• Detailed curriculum vitae, including an updated publication list and a list of the researcher’s own research grants;
• Scanned copy of doctorate certificate;
• Signed nomination letters from two established senior scientists.

Schedule:

Open for nominations: 23 June 2025;
Nomination deadline: 30 November 2025;
Winner announcement: 8 March 2026.

Nominations and contact:

All nomination materials and award-related communications must be submitted via our official email: ywe-award@mdpi.com.

Please do not hesitate to contact us if you have any questions. We look forward to receiving your nominations.

Young Women in Engineering Award Team

Dr. Pedro Miguel Rodrigues is one of the authors of the highly cited article entitled “Cardiovascular Diseases Diagnosis Using an ECG Multi-Band Non-Linear Machine Learning Framework Analysis” published in Bioengineering (ISSN: 2306-5354).

The following is an interview with Dr. Pedro Miguel Rodrigues:

1. Can you tell us a bit about your background and what your research focuses on?
I hold a B.Sc., M.Sc., and Ph.D. in biomedical engineering with specialized expertise in signal processing and artificial intelligence. I am a Professor and Vice-Dean for Lifelong Learning and Partnerships at the Faculty of Biotechnology at Universidade Católica Portuguesa, and a researcher at the Centre for Biotechnology and Fine Chemistry in Porto, Portugal. My primary focus involves applying advanced biosignal processing techniques and machine learning methods to clinical datasets. My research interests revolve around developing algorithms designed to detect and monitor diseases (such as Alzheimer’s, Parkinson’s, cardiovascular conditions, and voice disorders) before their earliest symptoms appear. By working with various biosignals—like EEG, ECG and voice signals—and diagnostic imaging inputs such as MRI, I aim to streamline early detection efforts, minimize subjective interpretation errors, and ultimately offer medical doctors more reliable tools for patient diagnostic evaluation.

2. What made you decide to publish a bioengineering article? Why did you choose MDPI’s Bioengineering?
My team and I were motivated to publish in a venue that places equal emphasis on biology and engineering, given our interdisciplinary approach that intertwines computational techniques with clinical applications. MDPI’s Bioengineering appealed to us through its strong open-access principles, robust international readership, and efficient editorial process. Moreover, it provides a platform where researchers from multiple fields can discover how AI-based diagnostic frameworks apply to a variety of medical contexts, making it a compelling choice for dissemination.

3. Was it important to you that the journal is open access? How does open access publishing advance the field of bioengineering?
It is important to publish open access for several reasons. First, open access ensures the work can be readily examined and implemented by researchers and clinicians operating in diverse settings, including resource-limited regions. Second, it expedites the feedback loop between research and practical implementation, as anyone has access to methods to build upon them directly. This widespread accessibility enables faster progress in addressing pressing biomedical challenges, ultimately benefiting the entire field by nurturing collaboration and transparency.

4. What are the current bottlenecks in this field, and how did you identify your research’s breakthrough point?
One of the most persistent challenges is handling the sheer variation in biosignal quality and data formats across different clinical environments. For instance, older EEG or ECG machines often produce outputs that differ in resolution or file structure from newer devices. Our breakthrough came when we applied advanced feature extraction and domain adaptation techniques, enabling the algorithm to effectively learn from multiple datasets, each with its own unique characteristics. Once we observed that our models maintained strong performance across disparate clinical subpopulations, we realized that our approach was both generalizable and scalable.

5. Why do you think this article has been highly cited?
Conditions such as Alzheimer’s disease exert a profound global impact, affecting nearly every healthcare system. Because of this broad relevance, our work was widely cited by researchers confronting similar challenges across a variety of academic institutions, laboratories, and hospitals worldwide. Moreover, publishing in an open-access format greatly accelerated the distribution of our findings, helping us reach a broader audience much more quickly.

6. Are there follow-up studies planned based on this paper’s findings?
Yes, several lines of research are currently in progress. We are broadening our predictive frameworks by adopting a multimodal approach that integrates EEG, MRI, and genetic (including microbiome) data aiming to harness insights that could further enhance early detection. Additionally, we are working with clinicians who track patients over multiple years, allowing us to evaluate our models’ accuracy and adaptability as various diseases progress.

7. Which technological directions in bioengineering deserve the most attention over the next 5 years?
Over the next five years, bioengineering stands to benefit significantly from AI-driven multi-modal analytics, which will integrate data from advanced sensors, wearables, imaging technologies, and a rapidly expanding array of IoT devices. By combining these continuous data streams with refined machine-learning frameworks, researchers, and clinicians can achieve real-time diagnostics and interventions that transcend traditional healthcare models. Meanwhile, edge cloud computing will play an indispensable role in reducing latency and safeguarding sensitive patient information by processing data on-site—even in resource-constrained or remote areas—which broadens global access to quality healthcare. These converging technologies promise to redefine clinical decision-making, expand the reach of medical services, and fuel ongoing innovation in patient care throughout the coming decade.

8. How is AI reshaping bioengineering research in disruptive ways?
AI is bridging the gap between massive raw datasets and actionable clinical insights by recognizing patterns previously invisible to human observers. These data-driven approaches reduce costs by streamlining diagnostics, minimizing invasive testing, and guiding targeted research on the most promising therapeutics. As AI-driven platforms increasingly integrate diverse data sources, imaging, molecular, signals and wearable sensor data, they bolster clinicians’ decision-making, foster early detection of health risks and pathological status.

MDPI will attend the 30th Congress of the European Society of Biomechanics (ESB), which will take place from 6 to 9 July 2025 at the ETH Zürich, Zürich, Switzerland.

This year’s theme, “AI in Biomechanics: Opportunities and Challenges”, highlights the growing impact of artificial intelligence on biomechanics. Throughout the congress, we will explore how AI is transforming research, clinical applications, and technological innovation—while also addressing the complexities and ethical considerations that accompany these advancements. The congress will feature a dynamic program of scientific presentations, interactive workshops, award sessions, and debates led by leading experts. Beyond the academic program, a series of social events will provide the perfect setting to connect with colleagues, strengthen collaborations, and experience the vibrant culture of Zürich.

MDPI is excited to participate in this prestigious event. We welcome you to visit MDPI’s booth #14, where you will be able to have face-to-face exchanges with our representatives, learn more about our open access publishing services, and acquire gifts.

The following MDPI journals will be represented:

• Biomechanics;
• Geriatrics;
• Prosthesis;
• Applied Mechanics;
• Biomimetics;
• Biophysica;
• JFMK;
• Muscles;
• Osteology;
• Applied Biosciences;
• Bioengineering;
• BioMed;
• Organoids;
• Sports.

If you plan to attend this event, we encourage you to visit our booth and speak to our representatives. We are eager to meet you in person and assist you with any queries you may have. For more information about the conference, please visit the official website: https://esbiomech2025.org/.

Conference: 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Date: 14–18 July 2025
Location: Copenhagen, Denmark

MDPI will be attending the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society as an exhibitor, welcoming researchers from diverse backgrounds to visit and share their latest ideas.

The 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society will be jointly hosted by the IEEE (Institute of Electrical and Electronics Engineers) and the IEEE Engineering in Medicine and Biology Society (EMBS). The overarching conference theme, “Engineering Medicine, Innovating Healthcare”, underscores the pivotal role of interdisciplinary collaboration and new ways of driving transformative advancements in healthcare delivery and patient outcomes.

We cordially invite you to join us as we embark on a journey to revolutionize healthcare through innovation, AI, and technology integration. We look forward to seeing you in Copenhagen in July 2025!

The following MDPI journals will be represented:

• Diagnostics;
• AI;
• BioMedInformatics;
• JCM;
• Biomimetics;
• Bioengineering;
• IJERPH;
• Diseases;
• Journal of Imaging;
• Sensors;
• Eng;
• Biosensors;
• MTI;
• Reports;
• Anatomia;
• Biomed;
• Surgical Techniques Development;
• Biotech;
• Signals;
• Tomography.

If you are planning to attend this conference, please do not hesitate to start an online conversation with us. Our delegates look forward to meeting you in person and answering any questions that you may have. For more information, please visit https://embc.embs.org/2025/.

We are pleased to announce that the 2nd International Online Conference on Biomimetics (IOCB2025) is scheduled to take place from 16 to 18 September 2025 online. The conference, organized by MDPI’s Biomimetics (ISSN: 2313-7673, Impact Factor: 3.4), aims to provide a forum and a survey for researchers and professionals in the fields of materials science, mechanical engineering, nanotechnology, and biomedicine. Attendance is free.

Conference Chair: Prof. Dr. Andrew Adamatzky
Affiliation: Unconventional Computing Laboratory, University of the West of England (UWE), Bristol, UK

Conference Sessions:
S1. Bioinspired Electronics
Session Chair: Prof. Georgios Sirakoulis, Department of Electrical and Computer Engineering, Democritus University of Thrace, Komotini, Greece;

S2. Bioinspired Computing—Algorithms and Prototypes
Session Chair: Prof. Andrew Adamatzky, Unconventional Computing Laboratory, University of the West of England (UWE), Bristol, UK;

S3. Bioinspired Materials—Structures, Surfaces and Interfaces
Session Chair: Prof. Victor Erokhin, Institute of Materials for Electronics and Magnetism, Italian National Council of Researches (IMEM-CNR), Parma, Italy;

S4. Bioinspired Architecture
Session Chair: Prof. Phil Ayres, Royal Danish Academy, Copenhagen, Denmark;

S5. Bioinspired Robotics
Session Chair: Dr. Alessandro Chiolerio, Italian Institute of Technology, University of the West of England (UWE), Bristol, UK;

S6. Bioinspired Arts
Session Chair: Prof. Eduardo Miranda, School of Art, Design and Architecture, Plymouth University, Plymouth, UK.

Conference Awards:

• Best Oral Presentation Award;
• Best Poster Award.

Prize: The winners will receive a certificate and CHF 200 each

Number of winners: 6.

Guide for Authors:
Please submit your abstract by 16 June 2025: https://sciforum.net/user/submission/create/1296.

Please register freely for the event by 13 September 2025: https://sciforum.net/event/IOCB2025?section=#registration.

For more information, you may refer to the following: https://sciforum.net/event/IOCB2025?section=#instructions.

Notification of Acceptance: 13 August 2025.

We look forward to receiving your contribution to the 2nd International Online Conference on Biomimetics (IOCB2025).

Conference Chairs
IOCB2025 Organizing Team

MDPI is excited to collaborate with Toronto Metropolitan University to host an engaging Scientific Publishing Workshop aimed at empowering scholars with the knowledge and tools needed to succeed in academic publishing. This workshop will delve into the key aspects of MDPI’s open access publishing framework, offering an in-depth exploration of journal selection, the editorial workflow, and the essentials of publication ethics. Participants will also gain practical advice on improving their scientific writing and effectively addressing reviewer comments, with guidance from an experienced professional. This session promises to equip attendees with valuable skills to elevate their publishing journey.

Workshop Highlights:

• Learn How to Better Write and Structure a Research Article;
• How to Choose Where to Publish Academic Work;
• How to Respond to Reviewer Comments;
• Reasons for Rejection During Pre-Check;
• How to Navigate Through Ethics and AI Use.

Date: 15 May 2025
Time: 12:00–1:30 p.m.
Venue: Toronto Metropolitan University

Program:

The First International Organoid Conference (ISFO 2025) is the first congress held by the Internation Society for Organoid (ISFO), which is dedicated to unique advantages in various interdisciplinary fields around organoids, such as disease modeling, clinical immunology, pharmacotoxicology, regenerative medicine and is poised to be one of the most promising research tools for clinic studies and drug development.

The following MDPI journals will be represented at the conference:

• Cells;
• Organoids;
• JDB;
• Bioengineering;
• Biomedicines.

If you are planning to attend the conference mentioned above, please feel free to start an online conversation with us. Our delegates look forward to meeting you in person at booth #29 and answering any questions you may have.

For more information about the conference, please visit the following website: https://www.isorganoid.org/programs/details/26/6.html.