Scientific Advances Research Articles

Two Decades of Progress in Personalized Medicine of Colorectal Cancer in Serbia-Insights from the Institute for Oncology and Radiology of Serbia.

It is projected that, by 2040, the number of new cases of colorectal cancer (CRC) will increase to 3.2 million, and the number of deaths to 1.6 million, highlighting the need for prevention strategies, early detection and adequate follow-up. In this study, we aimed to provide an overview of the progress in personalized medicine of CRC in Serbia, with results and insights from the Institute for Oncology and Radiology of Serbia (IORS), and to propose guidance for tackling observed challenges in the future. Epidemiological data were derived from official global and national cancer registries and IORS electronic medical records. Germline genetic testing for Lynch syndrome was performed by Next Generation Sequencing. RAS and BRAF mutation analyses were performed using qPCR diagnostic kits. Epidemiology and risk factors, prevention and early detection programs, as well as treatment options and scientific advances have been described in detail. Out of 103 patients who underwent germline testing for Lynch syndrome, 19 (18.4%) showed a mutation in MMR genes with pathogenic or likely pathogenic significance and 8 (7.8%) in other CRC-associated genes (APC, CHEK2, MUTYH). Of 6369 tested patients, 50.43% had a mutation in KRAS or NRAS genes, while 9.54% had the V600 mutation in the BRAF gene. Although significant improvements in CRC management have occurred globally in recent years, a strategic approach leading to population-based systemic solutions is required. The high incidence of young-onset CRC and the growing elderly population due to a rise in life expectancy will be especially important factors for countries with limited healthcare resources like Serbia.

Employing artificial intelligence to steer exascale workflows with colmena

Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. Our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing.

Enhancing Large Language Model Comprehension of Material Phase Diagrams through Prompt Engineering and Benchmark Datasets

Large Language Models (LLMs) excel in fields such as natural language understanding, generation, complex reasoning, and biomedicine. With advancements in materials science, traditional manual annotation methods for phase diagrams have become inadequate due to their time-consuming nature and limitations in updating thermodynamic databases. To overcome these challenges, we propose a framework based on instruction tuning, utilizing LLMs for automated end-to-end annotation of phase diagrams. High-quality phase diagram images and expert descriptions are collected from handbooks and then preprocessed to correct errors, remove redundancies, and enhance information. These preprocessed data form a golden dataset, from which a subset are used to train LLMs through hierarchical sampling. The fine-tuned LLM is then tested for automated phase diagram annotation. Results show that the fine-tuned model achieves a cosine similarity of 0.8737, improving phase diagram comprehension accuracy by 7% compared to untuned LLMs. To the best of our knowledge, this is the first paper to propose using LLMs for the automated annotation of phase diagrams, replacing traditional manual annotation methods and significantly enhancing efficiency and accuracy.

Current advances in physiological science. In commemoration of centennial anniversary of the Department of Human and Animal Physiology of Moscow State University

Hereby we briefly review the articles that comprise the special issue of “Lomonosov Biology Journal” (“Vestnik Moskovskogo universiteta. Seria 16. Biologia”), which commemorates the centennial anniversary of the Department of Human and Animal Physiology of Lomonosov Moscow State University, founded by prominent Russian physiologist Alexander F. Samoilov. The main stages of the Department’s history are briefly described. The published studies of the leading Russian researchers, including the scientific research group from Department of Human and Animal Physiology, are discussed within the scope of main current challenges of physiological science.

Medical ethics and internal medicine

Recent scientific and technological advances in medicine have raised a number of ethical and regulatory problems, to which medicine alone can no longer provide adequate answers. Bioethics, as an interdisciplinary reflection, essentially deals with the ethical and regulatory aspects posed by biology in general; medical ethics, on the other hand, deals with the more specific aspects of medicine.

The public use of early-stage scientific advances in carbon dioxide removal: a science-technology-policy-media perspective

Abstract While Carbon Dioxide Removal (CDR) solutions are considered essential to meet Paris Agreement objectives and curb climate change, their maturity and current ability to operate at scale are highly debated. The rapid development, deployment, and diffusion of such methods will likely require the coordination of science, technology, policy, and societal support. This article proposes a bibliometric approach to quantify the public use of early-stage research in CDR. Specifically, we employ generalized linear models to estimate the likelihood that scientific advances in eight different carbon removal solutions may induce (i) further production of scientific knowledge, (ii) technological innovation, and (iii) policy and media discussion. Our main result is that research in CDR is of significant social value. CDR research generates significant, positive, yet heterogeneous spillovers within science and from science to technology, policy, and media. In particular, advances in Direct Air Capture spur further research and tend to result in patentable technologies, while Blue Carbon and Bio-energy with Carbon Capture and Storage appear to gain relative momentum in the policy and public debate. Moreover, scientific production and collaborations cluster geographically by type of CDR, potentially affecting long-term carbon removal strategies. Overall, our results suggest the existence of coordination gaps between science, technology, policy, and public support.

THE USE OF PREIMPLANTATION DIAGNOSIS IN THE GENERATION OF SAVIOR SIBLINGS: A multidisciplinary study

Preimplantation Genetic Diagnosis (PGD) is a technique used in IVF to analyze embryos and check for the presence of genetic diseases, allowing the selection of healthy embryos. The medicine baby is a child generated for the purpose of treating a sick sibling, being a relevant practice in the medical field because it enables the cure of certain pathologies through the donation of tissues such as blood and bone marrow. The importance of PGD is highlighted, as it allows diagnosing and selecting embryos without genetic diseases, although it is necessary to consider ethical limits to preserve the integrity and dignity of the future child. The objective of this study was to analyze the criteria used in the Preimplantation Genetic Diagnosis for the generation of a drug baby. An exploratory literature review was carried out in databases such as Pubmed, Virtual Health Library, Google Scholar and journals between the years 2012 and 2023, in addition, the film "A proof of love" was analyzed to complement the investigation. While PDG and the drug baby generation advance healthcare in revolutionary ways, it is important to address ethical and social issues comprehensively and sensitively by ensuring that scientific advances are used responsibly and for the benefit of all involved.

SISTEMA PARA DETECÇÃO DE VAZAMENTO DE GÁS

Gas poisoning, such as Liquefied Petroleum Gas (LPG), is responsible for around 19,000 deaths annually, due to its high flammability and toxic effects when inhaled in large quantities(Science Advances, 2024). In response to this, a prototype low-cost LPGgas detection system was developed, based on Arduino. The system uses sensors to identify leaks and trigger alerts via LEDs, buzzer, fan and relay, an LCD board and an android app that receives the data sent viaBluetooth to the owner. Tests have confirmed the system’s efficiency in detecting dangerous levels of gas, with positive results for residential and commercial safety.

Organotin(IV) complexes of tridentate (ONO) hydrazone ligands: synthesis, spectral characterization, antituberculosis, antimicrobial, anti-inflammatory, molecular docking and cytotoxicity studies.

Infectious diseases have a significant impact in the historical trajectory of humanity, exerting profound influence on societies, driving advancements in medical science, and significantly impacting individuals on a worldwide scale. Consequently, this research endeavours to identify potent agents combatting tuberculosis, inflammation, and microbial deformities. The investigation focuses on hydrazones (1,2) endowed eight organotin(IV) complexes, where hydrazones were derived from 2-acetyl-1H-indene-1,3(2H)-dione and 2-phenoxypropanehydrazide/2-(2,4-dichlorophenoxy)propanehydrazide. All compounds underwent thorough characterization utilizing a variety of spectral and analytical techniques including, multinuclear NMR, FT-IR, HRMS, UV-Vis, SEM-EDAX, TGA, XRD, molar conductance measurements, establishing the pentacoordinated environment around tin(IV) ion with tridentate (ONO) mode of chelation of hydrazones. Powder XRD revealed the ligand's crystalline and complexes' semi-crystalline nature, while thermal analysis indicated two-step decomposition leaving tin oxide residue. In vitro evaluations utilize microplate alamar blue assay for assessing anti-tuberculosis activity, serial dilution technique for antimicrobial efficacy, and bovine serum albumin method for evaluating anti-inflammatory properties. The complexes exhibited higher biological activities than their respective ligands and the activity of the complexes follow the order: Ph2SnL1-2 > Bu2SnL1-2 > Et2SnL1-2 > Me2SnL1-2. Among them, phenyl complex (10) [Ph2SnL2] displays superior efficacy against TB dysfunction (MIC: 0.0180 ± 0.009μmol/mL) and also demonstrates exceptional potency in combating inflammation (IC50: 7.27 ± 0.04μM), and microbial (MIC: 0.0045μmol/mL) infections, comparable to standard drugs. Additionally, cytotoxicity testing against vero cell line revealed decreased toxicity at lower concentrations, and attenuated by chelation. Phenyl complex (10) [Ph2SnL2] shows promising cytotoxicity at 3.12µg/mL (19.29 ± 0.09%). Further, The diphenyltin(IV) complex (10), identified as the most effective against TB, shows stronger binding to key 3PTY protein residues (-42.2kJ/mol) compared to ligand (2) (-33.4kJ/mol), correlating with its superior anti-tuberculosis potency in biological assays. This comprehensive approach aims to actively contribute to ongoing initiatives addressing infectious diseases, thereby advancing global health and overall well-being.

Evaluating dynamic models for rigid-foldable origami: unveiling intricate bistable dynamics of stacked-Miura-origami structures as a case study.

Recent advances in origami science and engineering have particularly focused on the challenges of dynamics. While research has primarily focused on statics and kinematics, the need for effective and processable dynamic models has become apparent. This paper evaluates various dynamic modelling techniques for rigid-foldable origami, particularly focusing on their ability to capture nonlinear dynamic behaviours. Two primary methods, the lumped mass-spring-damper approach and the energy-based method, are examined using a bistable stacked Miura-origami (SMO) structure as a case study. Through systematic dynamic experiments, we analyse the effectiveness of these models in predicting bistable dynamic responses, including intra- and interwell oscillations, in different loading conditions. Our findings reveal that the energy-based approach, which considers the structure's inertia and utilizes dynamic experimental data for parameter identification, outperforms other models in terms of validity and accuracy. This model effectively predicts the dynamic response types, the rich and complex nonlinear characteristics and the critical frequency where interwell oscillations occur. Despite its relatively increased complexity in model derivation, it maintains computational efficiency and shows promise for broader applications in origami dynamics. By comparing model predictions with experimental results, this study enhances our understanding of origami dynamics and contributes valuable insights for future research and applications. This article is part of the theme issue 'Origami/Kirigami-inspired structures: from fundamentals to applications'.

The Future of the Translation Profession in the Era of Artificial Intelligence. Survey Results from Polish Translators, Translation Trainers, and Students of Translation

Technological advancements in computer science have led to the progressive change of the translation profession. In particular, this concerns machine translation, which has fundamentally changed the role of the translator. AI-driven tools, such as neural machine translation systems, have significantly improved translation speed and accuracy, with the translator role resembling that of an editor or proofreader. While translators have viewed the technological progress as a threat to their jobs, machine translation comes with some persistent issues, such as bias or inaccuracies stemming from insufficient context understanding and cultural nuances. Accordingly, the question of the future of human translation is more complex than it may initially appear. This paper presents the results of a pilot study on the perception of the future of the translation profession in the era of artificial intelligence.

Strengthening the traditional medicine regulation, education and practice in Nigeria

Background: Despite tremendous advances in medical sciences still a large section of the population in rural areas is heavily dependent on indigenous and Traditional Medicine (TM) for treatment. Moreover, there has been a resurgence of interest in the use of Complementary and Alternative Medicine (CAM), globally. Objective: To review the overall regulator structure for the control of TM and the course curriculum for TM education in Nigeria. Method: Information on CAM and TM was collected via online search. Results: Responding to this unprecedented situation government of many countries have taken proactive steps to streamline and regulate the practitioners of CAM and TM in their countries as mandated by WHO. In many African countries because of the availability of research and training programs many traditional herbal medicines are now being sold over the counter. However, the situation is different in Nigeria, even though TM is very popular across the country, structured institutionalized teaching and research activity on TM are inadequate. Conclusion: Health is a key component in determining the Human Development Index (HDI) of a country. CAM and TM practitioner may play an important role in improving the HDI score of the country. Government-sponsored education & training program for TM practitioners is crucial.

Plant cell wall enzymatic deconstruction: Bridging the gap between micro and nano scales

Understanding lignocellulosic biomass resistance to enzymatic deconstruction is crucial for its sustainable conversion into bioproducts. Despite scientific advances, quantitative morphological analysis of plant deconstruction at cell and tissue scales remains under-explored. In this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales. By applying this pipeline to poplar wood, dynamics of cellular parameters was computed and cellulose conversion during enzymatic deconstruction was measured. Results showed that enzymatic deconstruction predominantly impacts cell wall volume rather than surface area. Additionally, a negative correlation was observed between pre-hydrolysis compactness measures and volumetric cell wall deconstruction rate, whose strength was modulated by enzymatic activity. Results also revealed a strong positive correlation between average volumetric cell wall deconstruction rate and cellulose conversion rate. These findings link key deconstruction parameters across nano and micro scales.

A Content Analysis of the Vision Statements of Top 10 Leading Universities in Indonesia

This study analyzes the vision statements of Indonesia's top 10 universities, as ranked by Times Higher Education in 2024. Vision statements are crucial for defining a university’s strategic direction, identity, and stakeholder engagement. The content analysis identifies recurring themes, including scientific advancement, innovation, community empowerment, and global competitiveness. While these statements articulate ambitious goals, potential challenges such as symbolic rhetoric and institutional isomorphism may hinder their effectiveness. The findings emphasize the need for vision statements that are actionable and aligned with the institution's mission. The study concludes with recommendations for further research on the practical implementation of these visions and the role of leadership, as well as strategies for embedding visions into institutional culture.

The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors

AbstractGastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.

Transforming Cancer Research through Informatics.

For more than three decades, concurrent advances in laboratory technologies and computer science have driven the rise of cancer informatics. Today, software tools for cancer research are indispensable to the entire cancer research enterprise.

Digital Methods: An STS Challenge to Methodological Digitization in Social Science Research

ABSTRACT The integration of digital technologies into social science has catalyzed the development of fields such as digital sociology, digital humanities, and digital social sciences more broadly. This technological shift has significant methodological implications for social science research, which are being increasingly discussed. This discussion raises critical questions about the potential and limitations of merging traditional social research methodologies with digital innovations. Digitized and digital methods have brought about promises of a new era in social science research. However, some scholars have highlighted the challenges and limitations associated with this methodological transition, including data-relevant considerations, skill and expertise issues, and ethical concerns. From the vantage point of advancements in science and technology studies (STS) – a discipline that has engaged with the methods and practices of knowledge production for some decades – I will explore five additional methodological challenges posed by the digitization of social science research that has been largely overlooked or neglected in the literature of digital methods. Our analysis concludes that while digital methods can serve as valuable complementary tools, they are not sufficient to fully engage with the core practices and phenomena of social science research. Thus, digital and traditional methods can coexist, albeit not in a symmetrical manner.

Potential for advances in data linkage and data science to support injury prevention research

The recent COVID-19 pandemic stimulated unprecedented linkage of datasets worldwide, and while injury is endemic rather than pandemic, there is much to be learned by the injury prevention community from...

WITHDRAWN: A Policy Configured Resource Management Scheme for Ahns Using LR-KMA and WD-BMO

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Socio-Eco-Efficiency in Agroforestry Production Systems: A Systematic Review

Eco-efficiency has emerged to reduce environmental impacts and improve competitiveness in various industries, including agriculture. However, the application of eco-efficiency in agroforestry, incorporating social variables, is a relatively recent development. This study conducted a review of the scientific literature on eco-efficiency to address key questions. The analysis utilized the Web of Science Core Collection database, focusing on categories such as sustainable eco-efficiency, agricultural eco-efficiency, forestry eco-efficiency, and socio-eco-efficiency. The objective of this work is to offer a bibliometric analysis on socio-eco-efficiency in agroforestry production systems. The results revealed three stages in eco-efficiency research: an initial stage with low publication numbers, a second stage marked by significant growth and a focus on sustainable development, and a third stage characterized by rapid growth and increasing interest. In the field of agricultural eco-efficiency, there has been a recent increase in publications, reflecting the growing importance of agriculture in eco-efficiency analyses. China emerged as the leading country in terms of scientific advancements in eco-efficiency. The study also identified key research areas and institutions contributing to the literature on agricultural eco-efficiency. Overall, the findings highlight the expanding interest and future potential of socio-eco-efficiency in agroforestry research.