Search Results (94)

In modern medical education, a robust understanding of statistics is essential for fostering critical thinking, informed clinical decision-making, and effective communication. This paper explores the synergistic value of early and continued statistical education for medical students and residents, particularly in relation to the expanding field of sports supplementation and its impact on athletic performance. Early exposure to statistical principles enhances students’ ability to interpret clinical research, avoid cognitive biases, and engage in evidence-based practice. Continued statistical learning throughout residency further refines these competencies, enabling more sophisticated analysis and application of emerging data. The paper also addresses key challenges in integrating statistics into medical curricula—such as limited curricular space, student disengagement, and resource constraints—and proposes solutions including interactive learning, case-based teaching, and the use of public datasets. A unique emphasis is placed on connecting statistical literacy to the interpretation of research in sports science, particularly regarding the efficacy, safety, and ethical considerations of sports supplements. By linking statistical education to a dynamic and relatable domain like sports performance, educators can not only enrich learning outcomes but also foster lasting interest and competence in quantitative reasoning. This integrated approach holds promise for producing more analytically proficient and clinically capable physicians. Full article

The discovery of effective therapeutics against Alzheimer’s disease (AD) and other neurological disorders remains a significant challenge. Artificial intelligence (AI) tools are of considerable interest in modern drug discovery processes and, by exploiting machine learning (ML) algorithms and deep learning (DL) tools, as well as data analytics, can expedite the identification of new drug targets and potential lead molecules. The current study was aimed at assessing the role of AI-based tools in the discovery of new drug targets against AD and other related neurodegenerative diseases and their efficacy in the discovery of new drugs against these diseases. AD represents a multifactorial neurological disease with limited therapeutics available for management and limited efficacy. The discovery of more effective medications is limited by the complicated pathophysiology of the disease, involving amyloid beta (Aβ), neurofibrillary tangles (NFTs), oxidative stress, and inflammation-induced damage in the brain. The integration of AI tools into the traditional drug discovery process against AD can help to find more effective, safe, highly potent compounds, identify new targets of the disease, and help in the optimization of lead molecules. A detailed literature review was performed to gather evidence regarding the most recent AI tools for drug discovery against AD, Parkinson’s disease (PD), multiple sclerosis (MLS), and epilepsy, focusing on biological markers, early diagnoses, and drug discovery using various databases like PubMed, Web of Science, Google Scholar, Scopus, and ScienceDirect to collect relevant literature. We evaluated the role of AI in analyzing multifaceted biological data and the properties of potential drug candidates and in streamlining the design of clinical trials. By exploring the intersection of AI and neuroscience, this review focused on providing insights into the future of AD treatment and the potential of AI to revolutionize the field of drug discovery. Our findings conclude that AI-based tools are not only cost-effective, but the success rate is extremely high compared to traditional drug discovery methods in identifying new therapeutic targets and in the screening of the majority of molecules for clinical trial purposes. Full article

(1) Background: Oral cancer (OC) is one of the most frequently diagnosed human cancers and remains a challenge for biologists and clinicians. More than 90% of OC cases are squamous cell carcinomas (OSCCs). Despite the use of modern diagnostic and prognostic methods, the 5-year survival rate remains unsatisfactory due to the late diagnosis of the neoplastic process and its resistance to treatment. This comprehensive review aims to present the latest literature data on the use and effectiveness of saliva as a non-invasive biomarker in patients with oral cancer. (2) Methods: The article reviews the current literature on the use of salivary omics biomarkers as an effective method in diagnosing and modifying treatment in patients with OSCC; the research corpus was acquired from the PubMed/Google/Scopus/Cochrane Library/Web of Science databases in accordance with the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA 2020) guidelines. (3) Results: The identification of salivary omics biomarkers involved in carcinogenesis and neoplastic transformation may be a potential alternative to traditional invasive diagnostic methods. Saliva, being both an abundant reservoir of organic and inorganic components derived from epithelial cells as well as a cell-free environment, is becoming an interesting diagnostic material for studies in the field of proteomics, genomics, metagenomics, and metabolomics. (4) Conclusions: Saliva-based analysis is a modern and promising method for the early diagnosis and improvement of treatment outcomes in patients with OSCC and oral potentially malignant disorders (OPMDs), with high diagnostic, therapeutic, and prognostic potential. Full article

Objectives: With the growing importance of mobile technology and artificial intelligence (AI) in healthcare, the development of automated cardiac diagnostic systems has gained strategic significance. This review aims to summarize the current state of knowledge on the use of AI in the analysis of electrocardiographic (ECG) signals obtained from wearable devices, particularly smartwatches, and to outline perspectives for future clinical applications. Methods: A narrative literature review was conducted using PubMed, Web of Science, and Scopus databases. The search focused on combinations of keywords related to AI, ECG, and wearable technologies. After screening and applying inclusion criteria, 152 publications were selected for final analysis. Conclusions: Modern AI algorithms—especially deep neural networks—show promise in detecting arrhythmias, heart failure, prolonged QT syndrome, and other cardiovascular conditions. Smartwatches without ECG sensors, using photoplethysmography (PPG) and machine learning, show potential as supportive tools for preliminary atrial fibrillation (AF) screening at the population level, although further validation in diverse real-world settings is needed. This article explores innovation trends such as genetic data integration, digital twins, federated learning, and local signal processing. Regulatory, technical, and ethical challenges are also discussed, along with the issue of limited clinical evidence. Artificial intelligence enables a significant enhancement of personalized, mobile, and preventive cardiology. Its integration into smartwatch ECG analysis opens a path toward early detection of cardiac disorders and the implementation of population-scale screening approaches. Full article

The sudden and unusual nature of natural disasters in China underscores the complexity and difficulty inherent in China’s natural disaster emergency management (NDEM). National-level NDEM plays a critical role in bridging China’s emergency management capacity with modernization efforts, serving as a foundation for integrating development with security. In this study, we analyzed 250 NDEM-related policies issued by the Chinese central government between 1954 and 2022 as research objects. Using a five-factor analysis framework, we tracked the evolution of China’s NDEM by analyzing the time of issue, policy authority, departmental cooperation, policy themes, and policy tools. Our findings indicated that the cooperation networks exhibited growing complexity and expanding collaboration in the early stages but shifted to a more centralized and streamlined structure after the year 2018. The themes of NDEM policies reflect the modernization needs of emergency management, with science and technology proving essential to advancing NDEM capabilities. However, the structure of NDEM policy tools requires further optimization. Accordingly, we propose policy recommendations aimed at enhancing interdepartmental cooperation, accelerating the integration of science and technology in NDEM, and optimizing and adjusting the configuration of policy tools. Full article

This study aims to recover, interpret and analyse the early meteorological observations made in Venice by Bernardino Zendrini from 1738 to 1743. Zendrini used a cistern barometer, an Amontons-type air thermometer and an additional mercury thermometer, i.e., a de l’Isle one. By comparing and interpreting the existing details, the instruments have been re-imagined, interpreted and discussed; finally their unknown scale and calibration points have been calculated. The barometer readings needed standard corrections, which were not known at that time. The scale of the air thermometers was in inches of mercury. Zendrini used a reversed scale, with boiling water set to 0, but neglected the second calibration point and the length of the tube. In addition, he gave the thermoscopic readings without the corresponding pressures. The methodology for the calibration, validation and transformation of the readings into modern units, i.e., hPa and °C, is carefully discussed. This paper provides and analyses new data, and improves our knowledge about the history of science, meteorological measurements, instruments and observations in the first half of the 18th century. Full article

This paper explores the evolution and contextual background of an 18th-century dyer’s manuscript originating in Antwerp, covering the period between 1778 and 1802. This manuscript offers a unique glimpse into the operational practices of a small enterprise specializing in red hues for a middle-class clientele. The manuscript includes dye recipes, accounting records, and business correspondence, along with dyed textile samples that provide a tangible connection between written instructions and their visual outcomes. Our study aims to go beyond content analysis to examine the manuscript as a dynamic document in which the dyer’s craft knowledge and experiential learning are visibly embedded. Unlike most available technical treatises, this manuscript appears to be an evolving draft marked by corrections and additions. This fluidity in structure sheds light on the process of knowledge formation and codification in the craft, aligning with devices of precise knowledge transmission and especially with the concept of “codification of error” (Codification of error refers to how early modern artisans and scholars began systematically recording mistakes in their work rather than hiding them. This shift recognized failure as a valuable part of the learning process, helping to refine techniques and support more empirical, experimental approaches to knowledge in the crafts and sciences)—an approach developed within the artisan community to refine practices over time and theorized by Professor Sven Duprè. Through a selection of annotated pages, we highlight the manuscript’s traces of iterative thought and method development. We propose that these elements illustrate the dialectic between transmitted knowledge and individual experimentation, where mistakes, followed by correction, reflection, and refinement, play a central role. Additionally, we discuss the manuscript as evidence of the thin boundaries between practical trade knowledge and the field of scientific inquiry. Through the abovementioned and the comparison with contemporary manuals, this research positions the manuscript as a valuable case study in understanding craft knowledge evolution and its transmission within the historical context of 18th-century European textile dyeing. Full article

Multimodal artificial intelligence (AI) is driving a paradigm shift in modern biomedicine by seamlessly integrating heterogeneous data sources such as medical imaging, genomic information, and electronic health records. This review explores the transformative impact of multimodal AI across three pivotal areas: biomaterials science, medical diagnostics, and personalized medicine. In the realm of biomaterials, AI facilitates the design of patient-specific solutions tailored for tissue engineering, drug delivery, and regenerative therapies. Advanced tools like AlphaFold have significantly improved protein structure prediction, enabling the creation of biomaterials with enhanced biological compatibility. In diagnostics, AI systems synthesize multimodal inputs combining imaging, molecular markers, and clinical data—to improve diagnostic precision and support early disease detection. For precision medicine, AI integrates data from wearable technologies, continuous monitoring systems, and individualized health profiles to inform targeted therapeutic strategies. Despite its promise, the integration of AI into clinical practice presents challenges such as ensuring data security, meeting regulatory standards, and promoting algorithmic transparency. Addressing ethical issues including bias and equitable access remains critical. Nonetheless, the convergence of AI and biotechnology continues to shape a future where healthcare is more predictive, personalized, and responsive. Full article

After the arrival of Jesuits in China during the late Ming dynasty, they adopted a strategy of aligning Catholicism with Confucianism, referring to themselves as “Western Confucians” to gain sympathy and support from the Chinese literati. However, during the early Qing dynasty, particularly during the late Kangxi era, because of the resurgence of traditional Confucian ideology and the consolidation of Manchu rule, literati attitudes toward Catholicism shifted significantly. This paper uses the early Qing scholar Dong Han (董含, 1626–?) from the Songjiang region as a case study, with his Notes in Three Hills 三冈识略 as the primary textual source, to analyze his critiques of Western learning (including Catholicism and Western science). Additionally, it examines Catholic responses to these critiques, based on the manuscript Refute the False Accusation 辩诬 collected in the BnF (Bibliothèque nationale de France). Through an analysis of Dong Han’s attitude toward Western learning and the responses it provoked, the paper aims to shed light on the changing attitudes of early Qing literati in the Jiangnan area toward Western learning, as well as the challenges faced by Catholicism in establishing their legitimacy in early modern China. It points out that the fundamental differences between Western learning and Confucianism prevented it from making an effective argument for legitimacy in China. The lack of support from the literati and the emperor led to the eventual fate of Catholicism—being prohibited. Full article

This review synthesizes historical and contemporary research on wildlife–vehicle collisions and roadkill, outlining its evolution from early documentation to modern road ecology. It discusses how early efforts in North America and Europe that quantified animal casualties and developed standardized methodologies formed current studies that use advanced geospatial tools, citizen science, and artificial intelligence to analyze spatiotemporal patterns. We examine key ecological, methodological, and economic impacts of roadkill on wildlife populations and human safety, highlighting the role of road density, vehicle speed, and seasonal factors. The framework presented also underscores a commitment to sustainability by integrating environmental conservation with infrastructural development and socio-economic resilience. The review details various mitigation strategies, from fencing and wildlife crossings to dynamic signage, and evaluates their effectiveness in reducing mortality rates, thereby supporting sustainable development in transportation infrastructure and wildlife management. It also identifies research gaps and outlines future directions, advocating for integrated, multidisciplinary approaches to improve wildlife conservation, infrastructure planning, and public awareness in the context of rapidly expanding road networks. Full article

The rapid advancement of materials science has revolutionized total hip arthroplasty (THA), a critical orthopedic procedure aimed at restoring mobility and improving patient quality of life. This review investigates the evolution of biomaterials used in THA, analyzing their mechanical, biological, and chemical properties. The study outlines the transition from early natural materials to modern metals, polymers, and ceramics, highlighting their benefits and limitations in clinical applications. Particular emphasis is placed on the development of advanced materials such as highly cross-linked polyethylene (HXLPE), zirconia-toughened alumina (ZTA), and tantalum alloys (Ta), which demonstrate enhanced biocompatibility, wear resistance, and longevity. By examining emerging trends, including bioactive coatings and nanotechnology, this paper aims to provide a comprehensive understanding of current challenges and future directions in material selection for hip prostheses, ultimately aiming to minimize annual revision rates and improve long-term outcomes. Full article

Nickel (Ni) resources are critical for the development of modern industry. This study investigates the knowledge structure and frontier evolution of Ni deposit research by constructing a domain-specific knowledge graph using bibliometric analysis and semantic extraction from 7090 publications (1966–2024) sourced from the Web of Science Core Collection. The results show that Ni research has three distinct phases: slow growth (1966–1990), early growth (1991–2010), and rapid expansion (2011–present). The collaborative network of institutions in which articles are published forms three regional clusters centered on China, Russia, and Australia. Keyword burst analysis identifies emerging frontiers, including sulfur isotopes, pyrite geochemistry, and LA-ICP-MS applications. Temporal keyword analysis identifies “platinum-group minerals”, “ore-forming fluids”, “isotopic analysis”, and “Eastern Tianshan” interactions as five pivotal research frontiers in nickel deposit studies. The knowledge graph framework demonstrates significant potential in mitigating data fragmentation, enhancing interdisciplinary data accessibility, and guiding future exploration strategies. This study shows the important role of knowledge maps in optimizing the study of nickel deposits. Full article

Historical archives hold untapped potential for understanding long-term biodiversity change. This study introduces computational approaches to historical ecology, combining archival research, text analysis, and spatial mapping to reconstruct past biodiversity patterns. Using the 1845 Bavarian Animal Observation Dataset (AOD1845), a comprehensive survey of vertebrate species across 119 districts, we transform 5400 prose records into structured ecological data. Our analyses reveal how species distributions, habitat associations, and human–wildlife interactions were shaped by land use and environmental pressures in pre-industrial Bavaria. Beyond documenting ecological baselines, the study captures early perceptions of habitat loss and species decline. We emphasise the critical role of historical expertise in interpreting archival sources and avoiding anachronisms when integrating historical data with modern biodiversity frameworks. By bridging the humanities and environmental sciences, this work shows how digitised archives and computational methods can open new frontiers for conservation science, restoration ecology, and Anthropocene studies. The findings advocate for the systematic mobilisation of historical datasets to better understand biodiversity change over time. Full article

In the early 20th century, Chinese Christianity was greatly impacted by the “Anti-Christian Movement”. T. C. Chao, an influential modern Chinese Christian theologian, actively contemplated and responded to accusations such as being “the accomplice of imperialism” and “the enemy of Mr. Science”. T. C. Chao, taking into account the social context and the state of religious development at the time, and drawing inspiration from the Buddhist reforms of his contemporary, Master Taixu, proposed the ideas of the indigenization and contextualization of Christianity. This laid the theoretical foundation for the construction of “Chinese theology” and provided new perspectives and practical directions for the “de-othering” of Chinese Christianity. Full article

Background/Objectives: Artificial intelligence (AI) is rapidly transforming the landscape of modern medicine, offering advanced tools for diagnosing complex conditions. In the realm of venous pathologies such as chronic venous disease (CVD), venous reflux, and deep venous thrombosis (DVT), AI has shown tremendous potential to improve diagnostic accuracy, streamline workflows, and enhance clinical decision-making. This study aims to evaluate the efficacy and feasibility of AI algorithms in diagnosing venous diseases and explore their potential impact on clinical practice. Methods: This paper provides a comprehensive review of key studies documenting the use of AI in venous pathology diagnostics, with different electronic databases being searched, including MEDLINE/Pub Med, Web of Science, Scopus, Embase, ResearchGate, and Google Scholar. Results: Out of 52 reports assessed for eligibility, 43 were excluded according to the preset criteria; therefore, findings from nine major studies involving more than 1000 patients were analyzed. The evaluation shows that AI utilization in the diagnosis of venous pathologies has demonstrated significant improvements. Notably, AI algorithms have achieved an accuracy exceeding 90%, significantly reducing inter-observer variability and ensuring consistent interpretation of ultrasonographic images across different clinicians and settings. Additionally, AI has accelerated diagnostic workflows, decreasing the time required for image analysis by more than 50%. Furthermore, AI has proven capable of detecting subtle abnormalities, such as minor venous reflux or early-stage thrombi, which may be overlooked during manual evaluations. Conclusions: Artificial intelligence represents a transformative innovation in the diagnosis and management of venous diseases. By enhancing diagnostic accuracy, streamlining workflows, and enabling personalized care, AI has the potential to address current challenges in venous diagnostics and improve patient outcomes. The future of AI in venous diagnostics is promising, and several areas of development were noted, including AI algorithms embedding directly into ultrasound devices to provide instantaneous diagnostic insights during patient evaluations; combining AI-processed Doppler data with other imaging modalities, such as computed tomography or MRI, for comprehensive assessments; AI usage in order to predict disease progression and tailor treatment strategies based on individual patient profiles; and constructing large-scale, multicenter datasets to improve the robustness and generalizability of AI algorithms. Full article