Bioprinting technology has attracted significant attention in the field of tissue engineering, enabling the precise placement of cells, biomaterials, and biomolecules to construct 3D tissue and organ structures. This review explores the feasibility of bioprinting functional organs by assessing current advancements in the field. A poll conducted among people from diverse backgrounds reveals common optimism regarding the future of organ bioprinting and its role in medicine and other fields. The article is conceptualized from a student-to-student perspective to provide a brief overview of key aspects of bioprinting, including bioinks, crosslinking techniques, bioprinting methods, and the maturation process required to develop functional tissues. Furthermore, it highlights recent progress in printing specific tissues as models for studying healthy and diseased tissues as well as implantable grafts. While there are still significant challenges that require the integration of technologies from engineering, biomaterials science, cell biology, physics, and medicine, ongoing research continues to address these complexities. The possibilities of bioprinting tissues and organs go beyond minimizing dependence on animal testing and advancing drug discovery; indeed, this approach also opens the door to accessible personalized medicine and presents a viable solution to the worldwide organ donor shortage.

This paper documents the experience of a methodological approach that integrates offline, personalized, and individual assessment with new digital data processing technologies, based on specific medical needs. In the multicenter non-pharmacological experimental study on aging titled "Fra-set: Identification and Quantification of Frailty" by P. Abete (funded by the National Recovery and Resilience Plan (PNRR) - AGE-it - Ageing Well in an Ageing Society, Spoke 3, Task 1.2 (Definition of a shared minimum dataset and data collection framework: Multidimensional assessment of age-related diseases, multimorbidity, and frailty and related outcomes in health settings), conducted within the study "Metabolic Aspects of Vascular Diseases: Importance in the Development of Atherosclerosis and Identification of New Therapeutic Approaches and Biomarkers" (PRIN 2020), the endpoint is the validation of the diagnostic tool named “fr-AGILE,” which allows for the identification and quantification of frailty in hospitalized elderly patients in facilities with varying levels of care intensity. Although the investigative tools are questionnaires, the research is quantitative. Data collection occurs in non-digital settings, specifically in low, medium, and high-intensity care facilities affiliated with the study in Campania. It utilizes information acquired directly from the patient or caregiver in a detailed and individualized manner, through the administration of scales such as the Edmonton Frail Scale and fr-AGILE, tests administered at clinical stability (pre-discharge). The importance of adherence to informed consent from patients—whose absence constitutes an exclusion criterion from the study; the awareness of a pronounced digital divide within the elderly population; the urgent need for extreme personalization of care; the necessity to identify a medical tool for the identification and quantification of frailty in elderly patients that is simple, quick, and multidimensional (Faller JW et al., 2019); the need to abandon Fried's phenotypic model (the narrow biological paradigm) in favor of a complex bio-psycho-social paradigm that includes, in a multidimensional approach, the estimation of physical status, the psycho-cognitive sphere, functional aspects, and social aspects (P. Abete et al., 2017)—are motivations that dictate the need for a research methodology that integrates offline, individualized, and personalized data collection tools with digital contexts for data analysis, processing, and sharing, as well as internal communication and interconnection between the UOCs and the various professional figures involved in the study. Keywords: Aging, Customization, Fragility, Integrated methodology

Insights into cellulose nanocrystals-based bioinks for 3D bioprinting in regenerative medicine and tissue engineering: A review.

Recent trends in the transformative impact of biomass-derived carbon dots in biomedical science

Microfluidic informatics - A research paradigm for the future of microfluidics.

Undergraduate research programs (URPs) play an important role in preparing the next cohort of professionals in the health research workforce. URPs also provide continuity and structure during times of stress and uncertainty, like the COVID-19 pandemic and racial reckoning of 2020. This mixed-methods study describes the relationships between student stressors and educational experiences while examining program factors that might have mitigated negative consequences. Participants of an NIH-funded URP, BUILD (Building Infrastructure Leading to Diversity), aimed to increase the number of students from underrepresented backgrounds in biomedical and behavioral sciences Ph.D. programs and research careers (N = 45), were surveyed in September 2020 and again in May 2021 to understand their personal, programmatic, and educational-related concerns during the twin pandemic of COVID-19 and racial injustice. Concurrent and longitudinal correlational relationships as well as qualitative data were examined to describe trainee experiences and inform best practices in supporting academic pursuits and well-being. In fall 2020, students reported high levels of mental health and academic concerns. Additionally, there was a wide spectrum of personal needs concerns, and of emotional impacts of anti-Black racism on students. High levels of these concerns and impacts of racial injustice were related to poorer personal resource management and programmatic working relationships, as well as educational and graduation impacts after students completed a virtual academic year. Students continued to feel emotionally and academically impacted by both anti-Asian and anti-Black racism, and a majority also indicated heightened awareness and engagement with racial injustice topics. Finally, results showed that negative early experiences were related to poorer end-of-the-year educational experiences, and in some cases, these relationships were significant only for students with a weaker sense of belonging, resource management skills, or working relationships. Results supported the URPs’ importance of developing belongingness, strong working relationships, and personal management skills, which improved students’ research and academic success, particularly for those with personal, mental health, and/or academic needs or concerns. Building a network of support and these skill sets as undergraduates may have long-reaching effects to help trainees endure and flourish when faced with future challenges.

The need for IT specialists in radiation oncology - A position paper by the International Society for radiation oncology Informatics, endorsed by DGMP, SASRO, ÖGRO, ÖGMP, SRO and DEGRO.

A generative vision-language model for holistic pathological assessment using preoperative imaging in hepatocellular carcinoma.

Growth hormone modulates epithelial intercellular junctions structure and intestinal permeability in zebrafish.

Despite major advances in Alzheimer's disease and related diseases (ADRD) research, the translation of discoveries into impactful clinical interventions remains slow. Overwhelming data complexity, fragmented knowledge, and prolonged research cycles hinder progress in understanding and treating neurodegenerative diseases. Artificial intelligence (AI) offers a promising path forward, particularly when developed as a scientist-in-the-loop system that collaborates with researchers throughout the scientific discovery process. This paper introduces the concept of an AI Biomedical Scientist, an intelligent platform designed to support literature synthesis, hypothesis generation, experimental design, and data interpretation. This platform aims to function as a holistic scientific partner, integrating diverse biomedical data and expert reasoning to accelerate discovery. We review commercial and academic efforts and introduce targeted Minimum Viable Products (MVPs) needed for general biomedical research lab utilization of AI, such as robust and accurate tools for literature and data analysis, negative data models, and virtual peer review, with a longer-term vision of foundation models trained directly on biomedical datasets. In AD and neurodegeneration research, such tools are anticipated to deliver efficiency gains ranging from modest improvements in specific research tasks to potential multi-fold accelerations in discovery workflows as systems mature and scale. This review examines the technical foundations, challenges, and anticipated impacts of AI and aims to inform and engage researchers in utilizing these systems to transform biomedical discovery, starting with AD and extending to other complex conditions.

Review of recent advances in pesticide encapsulation using naturally-occurring carbohydrate-based delivery systems: Environmental and economic benefits.

Plant-based natural polymers are gaining attention as ecofriendly alternatives to synthetic materials with applications in food, biomedical, pharmaceutical, and environmental science. Tragacanth gum (TG), a natural exudate obtained from Astragalus species, represents a unique polysaccharide with a complex molecular structure and distinctive rheological properties. It has been traditionally used for centuries as a stabilizer and emulsifier. Recent advances highlight its potential as a multifunctional biopolymer with industrial and biomedical potential. This review explores the structural characteristics, physicochemical properties, and modification strategies of TG, comparing it with other plant derived gums. Special emphasis is given to its applications in drug delivery, tissue engineering, wound healing, biodegradable packaging, and functional food formulation. Strengths such as biocompatibility and gel-forming ability but challenges remain including variability in quality, limited standardization, and issues with large scale production. Emerging trends, such as nanoformulations, hybrid polymer composites, and smart hydrogels, are also discussed. By positioning TG within the broader context of sustainable biomaterials, this review identifies key research gaps and proposes future directions to advance its role in the green polymer economy.

The computational complexity of biological phenomena, exemplified by protein folding and molecular docking, presents significant challenges due to their inherently non-deterministic polynomial-time (NP)-complete characteristics. Recent advancements in probabilistic computing, particularly probabilistic bits (p-bits), offer promising avenues for addressing these complex biological computations through efficient implementation of Ising models. This presentation discusses two pioneering studies demonstrating the efficacy of p-bit-based probabilistic computing systems in solving the 3-dimensional (3-D) protein folding problem and molecular docking, both crucial in biomedical research and pharmaceutical discovery.In addressing the protein folding challenge, a novel p-bit-based probabilistic computational approach has been developed. This approach leverages the well-established hydrophobic-polar (HP) lattice model, extended into 3-D space, to systematically encode amino acid sequences and their spatial constraints into an Ising framework. A sophisticated encoding scheme involving many-body interactions significantly streamlines the energy landscape, reducing complexity and enhancing computational efficiency. Simulation results indicate marked improvements in identifying correct folding configurations, notably demonstrating a substantial reduction—approximately half—in the total number of energy levels for shorter peptide sequences. Furthermore, this approach successfully predicts optimal configurations for peptide sequences containing up to 36 amino acids, reinforcing the robustness and scalability of p-bit probabilistic circuits (p-circuits) in solving biologically pertinent NP-complete problems.Complementing this work, we introduce the first application of p-bit-based probabilistic computing to molecular docking, a critical process in the elucidation of ligand-target interactions essential for drug discovery. Traditional docking methodologies frequently encounter significant obstacles due to the complex combinatorial nature of ligand-receptor interactions. Here, the molecular docking problem is recast as a Maximum Weighted Clique (MWC) optimization within graph theory, permitting its translation into an Ising model that p-circuits can efficiently resolve. Application of this innovative methodology to practical cases, including docking interactions involving the LolA-LolCDE lipoprotein complex and the AF9 YEATS domain with cyclopeptide inhibitors, demonstrates superior accuracy and computational efficiency compared to established quantum-based computational approaches, such as Gaussian Boson Sampling (GBS) and Quantum Approximate Optimization Algorithms (QAOA). Specifically, this p-bit-based method achieved an impressive success rate of approximately 84% in accurately predicting optimal docking conformations.Collectively, these studies underscore the transformative potential of probabilistic computing utilizing p-bits. The successful application of p-circuits to complex biological problems not only highlights their suitability and adaptability to large-scale biological computations but also establishes a foundation for future methodological innovations. By integrating probabilistic computing with biological research, this emerging computational paradigm holds substantial promise for significantly enhancing computational accuracy, efficiency, and capacity in biomedical science and pharmacological discovery.

Background: The integration of 3D bioprinting, biomaterials science, and cellular biology presents a viable strategy for maxillofacial bone regeneration, overcoming the constraints of traditional graft techniques. This review offers a thorough examination of the present condition, obstacles, uses, and future outlook of 3D bioprinting technology in maxillofacial bone regeneration. An essential understanding has been attained by analyzing the technological constraints, specifically in vascularization and neuro-integration, and by delineating the vital translational pathway from benchtop models to clinical application. We have examined several bioprinting techniques—namely extrusion, inkjet, and laser-assisted methods—and the requisite bioinks, emphasizing their physicochemical and biological features vital for osteogenesis. Significant clinical applications, including the treatment of trauma-induced abnormalities and the reconstruction of oncology-related resections, have been emphasized. This review highlights the urgent necessity for established regulatory frameworks and refined printing settings to guarantee effective, functional, and durable bone substitutes, providing a distinct pathway for future research and clinical implementation in this specialized surgical domain. Aim: The purpose of this review was to present a general overview of the current clinical and diagnostic applications of bioprinting in bone tissue engineering for the reconstruction of bone defects. Methods: A search of major scientific databases, including PubMed, Science Direct, Embase, and Cochrane, was conducted. Articles published within the last 10 years that analyze the possible applications of bioprinting in bone tissue fabrication were included. Results: Several bioinks, based on hydrogels and stem cells, can enable the fabrication of such tissues using this technology. This review reports on the processes adopted for the bioprinting of bone tissue, the bioinks used, and cell cultivation methods. Conclusions: Bioprinting represents a promising solution for bone regeneration with potential applications that could revolutionize current surgical practices, despite the many challenges that future research will face.

Science depends on trust, integrity and accountability. Yet compared to related disciplines, political science has been slow to institutionalize responsible conduct of research (RCR) education. This paper addresses that gap by presenting an empirical audit of RCR training across the top 50 political science graduate programmes in the United States. The findings reveal that while methods and quantitative training are near-universal, formal ethics instruction is rare or entirely absent in most programmes. Drawing on lessons from economics, psychology and biomedical sciences, I show how the lack of structured RCR education threatens the credibility, reproducibility and ethical integrity of political science research. I then propose a modular, credit-bearing RCR course tailored to the methodological and normative challenges faced by political scientists, including fieldwork, experimental design, authorship norms and data transparency. By embedding RCR into graduate curricula, political science can better prepare future scholars to navigate ethical dilemmas, prevent misconduct, and align with evolving expectations from funders and journals. This article extends the recent work of Chekenya and Misra by focusing specifically on graduate education in political science, and offers practical guidance for departments, professional associations and educators committed to cultivating a culture of ethical research. JEL Classification: A11, B41, Q10

Understanding how cells differentiate to their final specialized fates is a fundamental problem in biomedical science. Single-cell multi-omic profiling provides an opportunity to identify dynamic molecular changes, but new computational approaches are needed to realize this potential. In particular, previous methods for RNA velocity inference lack support for multi-lineage, multi-sample, and multi-omic single-cell data and cannot be used to identify differential dynamics. To overcome these challenges, we introduce MultiVeloVAE, a probabilistic framework for multi-sample RNA velocity inference that integrates single-cell RNA and multi-omic data. MultiVeloVAE models gene expression and chromatin accessibility on a shared time scale, performs multi-sample inference from datasets with partially overlapping modalities, accounts for lineage bifurcations, and enables statistical testing of velocity parameters among cell types and over time. Using newly generated 10X Multiome datasets from human embryoid bodies and differentiating macrophage cells, we demonstrate that MultiVeloVAE provides novel insights into chromatin accessibility and gene expression dynamics during development.

BackgroundVideo-sharing sites such as YouTube (Google) and TikTok (ByteDance) have become indispensable resources for learners and educators. The recent growth in generative artificial intelligence (AI) tools, however, has resulted in low-quality, AI-generated material (commonly called “slop”) cluttering these platforms and competing with authoritative educational materials. The extent to which slop has polluted science education video content is unknown, as are the specific hazards to learning from purportedly educational videos made by AI without the use of human discretion.ObjectiveThis study aimed to advance a formal definition of slop (based on the recent theoretical construct of “careless speech”), to identify its qualitative characteristics that may be problematic for learners, and to gauge its prevalence among preclinical biomedical science (medical biochemistry and cell biology) videos on YouTube and TikTok. We also examined whether any quantitative features of video metadata correlate with the presence of slop.MethodsAn automated search of publicly available YouTube and TikTok videos related to 10 search terms was conducted in February and March 2025. After exclusion of duplicates, off-topic, and non-English results, videos were screened, and those suggestive of AI were flagged. The flagged videos were subject to a 2-stage qualitative content analysis to identify and code problematic features before an assignment of “slop” was made. Quantitative viewership data on all videos in the study were scraped using automated tools and compared between slop videos and the overall population.ResultsWe define “slop” according to the degree of human care in production. Of 1082 videos screened (814 YouTube, 268 TikTok), 57 (5.3%) were deemed probably AI-generated and low-quality. From qualitative analysis of these and 6 additional AI-generated videos, we identified 16 codes for problematic aspects of the videos as related to their format or contents. These codes were then mapped to the 7 characteristics of careless speech identified earlier. Analysis of view, like, and comment rates revealed no significant difference between slop videos and the overall population.ConclusionsWe find slop to be not especially prevalent on YouTube and TikTok at this time. These videos have comparable viewership statistics to the overall population, although the small dataset suggests this finding should be interpreted with caution. From the slop videos that were identified, several features inconsistent with best practices in multimedia instruction were defined. Our findings should inform learners seeking to avoid low-quality material on video-sharing sites and suggest pitfalls for instructors to avoid when making high-quality educational materials with generative AI.

Objectives: Physical fitness is vital to sustaining the health of each individual and represents the level of readiness that allows them to perform everyday activities with sufficient energy. The aim of this research was to assess the physical fitness index and to determine its relationship with body composition. Methods: This research included 121 students of the State University of Novi Pazar, Serbia. The modified Harvard step test was used to assess physical fitness, and the body mass index was used to assess body composition. Results: Statistical analysis indicated that the physical fitness of students was not significantly satisfactory, given the large percentage of students with low-average and poor levels of physical fitness. A strong negative correlation between physical fitness index and body mass index was shown by Pearson (−0.720) and Spearman (−0.659) correlation coefficients with a p-value < 0.001. The results of the chi-square test (χ2(3) = 88.94, p < 0.001) also confirm this correlation. Conclusions: This study indicates widespread poor physical fitness among students and highlights the importance of regular exercise as a key factor for improving physical abilities. Given the relatively high prevalence of suboptimal prevalence of suboptimal physical fitness among university students, our findings represent a critical wake-up call for public health authorities, underscoring the urgent need for targeted interventions to reverse this trend and safeguard the health potential of the next generation.

Introduction: The precise control of mechanochemical activation within deep tissues using non-invasive ultrasound holds profound implications for advancing our understanding of fundamental biomedical sciences and revolutionizing disease treatments. However, a theory-guided mechanoresponsive materials system with well-defined ultrasound activation has yet to be explored. Here we present the concept of using porous hydrogen-bonded organic frameworks (HOFs) as toolkits for focused ultrasound (FUS) programmably triggered drug activation to control specific cellular events in the deep brain.Method: We have developed HOFs that could be precisely tailored and selectively activated through non-invasive ultrasound to facilitate remote medication manipulation, offering precise disease treatment in deep tissues. A theoretical model is developed to visualize the mechanochemical scission and ultrasound mechanics, providing valuable guidelines for the rational design of mechanoresponsive materials to achieve programmable control. To demonstrate the practicality of this approach, we encapsulate the designer drug clozapine N-oxide (CNO) into the optimal HOF nanocrystals for FUS-gated release to activate engineered G-protein-coupled receptors in the ventral tegmental area (VTA) of mice and rats and hence achieve targeted neural circuit modulation even at depth 9mm with a latency of seconds.Result: This work presents an ultrasound-activated HOF system with finely tuned interactions at the molecular level through modifying the chemical structure of interaction units. Through the manipulation of hydrogen bond density and the number of aromatic fused rings in the backbone structures of the organic ligands, a theoretical model is developed to explain the structure and functionality relationships in the HOFs, providing valuable guidelines for the precise and rational design of HOF building units at the molecular level to achieve on-demand and programmable drug activation under a desirable ultrasound pressure. By tuning HOF nanocrystals sensitivity to respond to focused ultrasoudn (FUS), we successfully achieve spatiotemporal control of deep brain neural circuits in both mice and rats with a latency of only seconds. The results demonstrate that HOF-enabled sono-chemogenetics can achieve a high temporal resolution and long-period neuromodulation while retaining the benefits of minimal invasiveness.Discussion: Our sono-chemogenetics technology is advancing toward applications in non-human primates and human disease treatment. While the current delivery of viruses and nanocrystals relies on local injection, FUS-mediated blood-brain barrier opening could reduce invasiveness, paving the way for future clinical applications of sono-chemogenetics.Conclusions: Our findings have demonstrated that our Ultrasound-activated HOF technology has the combination of high drug-loading content, high biostability, low immunogenicity and unique ultrasound programmability for non-invasive, precise medication therapy. Our technology is capable of releasing different types of molecule with designable medication activation sensitivity and resolution..

In the work, attention is paid to the role of artificial intelligence (AI) in scientific medical research: from the planning stage to generalization of results and the construction of visualizations. AI can significantly expand the researcher’s capabilities in the process of conducting a study, creating a dissertation, an analytical article, or a scientific report when it is necessary to perform a large amount of technical and analytical work. The purpose: to systematize the possibility of using artificial intelligence at diffe­rent stages of preparation of medical scientific research — from the analysis of sources to summarizing the results. The authors seek to show how AI can facilitate the analytical work of the researcher, leaving the main thing for humans — scientific thinking, logic, interpretation and responsibility for the conclusions obtained. Results. The tools based on AI to plan scientific research such as E-PICOS, MedLine and Cochrane Semantic Scholar, Scite are considered, as well as systems for conducting systematic reviews and meta-analyses: Elicit and ResearchRabbit. The work pays attention to the peculiarities of cooperation with general purpose programs like ChatGPT, Grok, Claude and others when writing programs on R, Python, assistance with Excel formulas for the preparation of graphic materials and statistical calculations. The preparation of scientific publication by means of AI is considered, starting with the verification of grammar and style of texts to the formation of reference lists according to the appropriate standard. This block of the article deals with tools such as Grammarly, QuillBot, SciSpace and others. Particular attention is paid to the opportunities and restriction of AI in scientific work, namely because the effective use of AI requires the researcher to have analytical thinking, a critical approach and a responsible attitude to the results. AI is not an alternative to the researcher, but an intellectual partner, which greatly facilitates the path but does not determine its direction. AI does not replace the researcher’s analytical thinking, but greatly expands their tools, allowing to focus on the essence of the study, and not on routine technical details. Conclusions. Artificial intelligence opens new opportunities in medical research, enables to automate information search, improve analytical processes and visualization of results. Its use helps researchers save time, increase the accuracy of analysis and present data in a convenient and understandable form. However, the key factors in the successful use of AI remain the competence and analytical thinking of the researcher. AI does not replace the scientist — it significantly enhances their capabilities. In the future, a combination of classical scientific approaches and artificial intelligence technologies will be the norm of quality medical research.