Announcements

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/.

The following is an interview with Dr. Karuna Nambi Gowri:

1. Can you tell us a bit about your background and what your research focuses on?
My basic major is textile technology with a focus on textile and materials chemistry. During my PhD, I looked at different bioresorbable polymeric materials that could be used as absorbable sutures, and so my research focus is completely on the realm of sustainable biomaterials used for different applications, ranging from traditional textile finishes and biomedical textiles to, currently, non-woven and paper manufacturing.

2. What made you decide to publish a bioengineering article? Why did you choose MDPI’s Bioengineering journal?
I was involved in the realm of biomedical textile research and looking at different polymeric biomaterials that can be engineered and fabricated into innovative wound closure devices, barbed or knotless sutures. Since my research focus was to engineer polymeric biomaterials into these barbed sutures, we anticipated that Bioengineering would be a good place to get our review manuscript published. And this is the main reason we resorted to publishing it in the Bioengineering journal. The other reason why we chose to publish this journal is because of the wide scope of the journal itself.

3. Was it important to you that the journal is open access? How does open access publishing advance the field of bioengineering?
It was important to publish this article in an open access journal since we wanted this to be a baseline for future researchers who like to work on barbed sutures. If it is an open access manuscript, then it would be beneficial to them to actually review it and progress their research from there instead of going through a whole lot of research done over two decades. And open access publishing actually helps advance this field of bioengineering by creating more visibility of the work that was done previously, which would be a win-win situation for both the authors of the article and also the readers who read the article.

4. What do you hope that readers will get from your paper?
We looked through the literature to identify different barbed sutures and their properties that were already published and available for review and evaluation. But there weren’t many review manuscripts for barbed sutures from a material standpoint and there were review articles from clinical standpoints. And so our hope was that this would be a good collection of all the previous works that have been done and published in the area of barbed sutures, and that this manuscript would give a broad overview of how research on barbed sutures was and how it is expected to improve in the future.

5. What critical scientific or engineering problems did your research initially aim to address?
The critical scientific problem that we wanted to address in this research on barbed sutures was to see if we could make the fabrication of these barbed sutures more efficient and commercially scalable with easy manipulation of the barb geometries and parameters. So, in order to address this issue, we looked at switching the currently existing mechanical fabrication to a laser fabrication technique which would enable us to manipulate the barb geometries and parameters in a faster and more precise way, making it suitable for use in a lot more complicated surgical procedure.

6. What are the current bottlenecks in this field, and how did you identify your research’s breakthrough point?
The current bottleneck in the fabrication of barbed sutures is that all the commercially available barbed sutures have the same barb parameters irrespective of the surgery they are to be used in. However, from the literature and previous research studies, it can be seen that different surgical procedures require different barb parameters, since different procedures involve different tissue compositions and structures. So, our research aim was to identify alternative fabrication techniques to better manipulate the barb parameters, considering the final surgical procedure they could be used in. The breakthrough was that we were able to prove that an ultrashort pulsed laser system can be used to fabricate barbed sutures with customized barb parameters that are needed for their use for different surgical procedures.

7. Why do you think this article has been highly cited?
I think the reason for the high citation of this article is because this review manuscript is a collection of all the previous work that focuses on barbed sutures, inclusive of both patents and manuscripts. I guess it is easier for all future researchers who are willing to work in this area of barbed sutures, this article is like a compiled manuscript which has details from research done for over two decades in this area of biomedical textiles.

MDPI Singapore successfully hosted its first in-person Engineering Multidisciplinary Editorial Board Meeting on 19 May 2025.

We are deeply thankful for the enthusiastic participation and strong support of our Editorial Board Members (EBMs). It was a valuable opportunity to connect face-to-face and discuss the future development of the engineering subject area. We would like to invite everyone to jointly review the content of this meeting, and we look forward to welcoming even more EBMs to our future gatherings.

The meeting kicked off with a light icebreaker, where participants introduced themselves and shared their academic backgrounds and experience with MDPI. This was followed by a brief overview of MDPI’s presence in the engineering field—showcasing our journals, performance statistics, and outreach activities.

Discussions then focused on ways to grow and enhance MDPI’s engineering portfolio. Topics included the following:

• Strategies to attract high-quality submissions.
• Improving journals’ visibility.
• Enhancing author support and increasing submission success rates.
• Strengthening the role and engagement of EBMs.
• Identifying emerging research topics.

We wrapped up with an open sharing session, where EBMs reflected on their experiences working with MDPI and offered thoughtful suggestions for improvement. Their ideas were insightful and sparked some great conversations.

We are truly grateful to all attendees for their time, ideas, and commitment. Collaborating with such passionate scholars is an honor, and we look forward to working closely together to elevate research excellence in the engineering field.

Journals our EBMs are affiliated with:

• Applied Sciences;
• Aerospace;
• Bioengineering;
• JMMP.

Six new journals covering a range of subjects launched their inaugural issues in June 2025. We are excited to be able to share with you the newest research rooted in the value of open access. We are pleased to present the latest research and to make it accessible to all.

We extend our sincere thanks to all the Editorial Board Members for their commitment and expertise. Each journal is dedicated to upholding strong editorial standards through a thorough peer review process, ensuring impactful open access scholarship.

Please feel free to browse and discover more about the new journals below.

Journal	Founding Editor-in-Chief	Journal Topics (Selected)
	Prof. Dr. Chang-Pu Sun, China Academy of Engineering Physics, Beijing, China; Computational Science Research Center, China Editorial | View inaugural issue	quantum information and phenomena; condensed matter physics and statistical physics; atomic, molecular, and optical physics; computational physics and mathematical methods View journal scope | Submit an article
	Prof. Dr. Hualiang Lin, Sun Yat-sen University, China; Prof. Dr. Jose L. Domingo, Universitat Rovira i Virgili, Spain Editorial | View inaugural issue	green and organic food; green infrastructures; green exercise; environmental impact of the healthcare sector; effects of climate change on human health | View journal scope | Submit an article
	Prof. Dr. Francesco Veglio, University of L’Aquila, Italy Editorial | View inaugural issue	ultra-pure substances; water purification; air purification; gas purification; inorganic chemical purification; purification technologies View journal scope | Submit an article
	Prof. Dr. Junxing Zheng, Huazhong University of Science and Technology, China Editorial | View inaugural issue	computer-aided design and engineering; artificial intelligence and machine learning; building information modeling (BIM) and digital twins; robotics and automation in construction; smart sensors and Internet of Things (IoT); intelligent control systems and facilities management View journal scope | Submit an article
	Prof. Dr. Assunta Di Vaio, University of Naples Parthenope, Italy Editorial | View inaugural issue	sustainability, managerial, and biodiversity accounting; carbon management accounting; corporate social responsibility; artificial intelligence and disclosure View journal scope | Submit an article
	Prof. Dr. Changjun Liu, Sichuan University, China Editorial | View inaugural issue	bioeffects of electromagnetic waves; electromagnetic science and engineering; microwave, millimeter-wave, and terahertz technologies; metamaterials and metasurfaces; communication, sensing, and networks; energy, power, and sustainable applications; quantum and emerging technologies; artificial intelligence and advanced fabrication View journal scope | Submit an article

Conference: The CRS 2025 Annual Meeting and Exposition
Date: 14–18 July 2025
Location: Philadelphia, USA

This conference will be the 52nd annual conference of the Controlled Release Society. The annual CRS conference is the most important conference in the pharmaceutics field. The main topics of the conference include nanomedicine and nanoscale delivery, immuno delivery, oral delivery, skin and mucosal delivery, ocular delivery, bioengineering, gene delivery and gene editing, alternative methods to animal testing, long-acting drug delivery formulations, delivery to the nervous system, artificial intelligence, and predictive models in pharmaceutical technologies.

The following open access journals will be represented at the event:

• Pharmaceutics;
• Gels;
• Pharmaceuticals;
• Biomedicines;
• Bioengineering;
• Biomolecules;
• Sci. Pharm;
• CIMB;
• Future Pharmacology.

If you plan to attend this conference, please contact us online. Our delegates look forward to meeting you in person at booth #202 and answering any questions you may have. For more information about the conference, please visit the following website: https://crs2025annualmeeting.eventscribe.net.

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.