Search Results (2,381)

This study presents a structural screening and molecular electrostatic potential (MEP) computational analysis of various organoboronic acids as effective co-formers in pharmaceutical crystal engineering. The strong predictability of dimeric B(OH)₂ homosynthons allows for precise control of molecular self-assembly within the crystal structure. Concurrently, the presence of the boron atom imparts significant biomedical importance to these systems due to its documented efficacy in boron neutron capture therapy (BNCT) and non-enzymatic diagnostic glucose sensors for diabetes management. By developing multi-component co-crystals, boronic acids play a key role in improving critical pharmaceutical parameters, such as aqueous solubility, bioavailability, and overall drug absorption. In this work, we analyze their binding behavior with a diverse array of active pharmaceutical ingredients (APIs) through the lens of various xanthine derivatives (caffeine and theophylline) and nitrofurazone. The successful co-crystallization with these molecules demonstrates the broad compatibility of boronic structures. Conversely, despite highly favorable theoretical predictions for donor–acceptor compatibility, systematic laboratory screening revealed that co-crystallization with other well-known APIs, such as paracetamol and amikacin, fails, leading exclusively to the isolation of the unreacted starting materials. Therefore, by comparing successful structures and collecting data from the resulting negative outcomes, this study provides a systematic approach for the evaluation and selection of future co-formers. Full article

Fitness landscapes have long served as a conceptual tool in evolutionary biology. In recent years, however, they have shifted from being viewed primarily as theoretical constructs to being used to interpret experimental data and, increasingly, to guide evolutionary outcomes in biomedical systems. In this review, we trace that progression across major landscape formalisms, including Fisher’s geometric model, Wright’s adaptive landscape, fitness seascapes, and empirical fitness landscapes. We aim to highlight the strengths of each model and its potential for use in cancer research. Rather than treating these frameworks as isolated models, we show how they collectively reflect the field’s maturation: from describing adaptation in idealized settings to quantifying epistasis and evolutionary accessibility, to interpreting experimental genotype–fitness data, and finally to motivating strategies for steering evolution in contexts such as treatment resistance in cancer. We argue that the central promise of the fitness landscape framework now lies not only in explaining evolutionary dynamics, but also in enabling predictive interventions and overcoming cancer evolution. Full article

Seroprevalence surveys constitute a critical tool for estimating population exposure to infectious agents, particularly during public health emergencies. In the context of the coronavirus disease 2019 (COVID-19) pandemic, such surveys revealed that the circulation of Severe Acute Respiratory Syndrome Coronavirus 2 in Africa had been largely underestimated. The validity of these surveys, however, depends not only on the reliability of diagnostic tests but also on operational conditions, which are frequently challenged by fear, misinformation, and logistical constraints. In the Democratic Republic of the Congo, the National Institute of Biomedical Research conducted two seroprevalence surveys in Kinshasa in 2020 on behalf the project “Appui à la Riposte Africaine à l’Epidémie de COVID-19” (ARIACOV). This study described the challenges encountered and the adaptive strategies employed during these surveys, in order to improve future investigations. The study population comprised 41 eligible field workers who participated in the 2020 ARIACOV seroprevalence surveys in Kinshasa, including 28 interviewers and 13 specimen collectors. Of these, 24 individuals (16 interviewers and 8 specimen collectors) initially confirmed their participation. Ultimately, 15 participants took part in the focus group discussions, comprising 11 interviewers (39.3% of the 28 eligible interviewers) and 4 specimen collectors (30.8% of the 13 eligible specimen collectors). We collected data through two focus groups held in a single session, using a pre-tested semi-structured guide. Discussions were audio-recorded and complemented by observational notes until empirical saturation was achieved. We applied deductive thematic analysis, following Braun and Clarke’s framework, to three predefined domains: transportation, survey procedures, and sample management. The application of systematic manual coding, combined with researcher triangulation, reinforced the rigor and enhanced the credibility of the findings. The findings highlighted community mistrust, driven by rumors, apprehension regarding blood collection, financial suspicions, and inadequate communication. In addition, logistical and transportation challenges significantly constrained survey feasibility. Nonetheless, a range of adaptive strategies were implemented, enabling the successful conduct of the surveys despite the pandemic context and limited resources. The participants’ narratives suggest that strengthening field workers’ training, improving transparency, and working alongside community health workers could contribute to enhance the effectiveness of serological surveys during health crises in resource-constrained environments. Full article

Background/Objectives: Traditional East Asian medicine (TEAM), comprising traditional Korean medicine (TKM), traditional Chinese medicine (TCM), and Japanese Kampo, is increasingly recorded in electronic health records, and sharing these data depends on interoperability standards. Yet, TEAM concepts rest on conceptual foundations differing from those of biomedical standards. Prior reviews examined traditional medicine’s own systems, not how TEAM concepts fit into mainstream biomedical standards. This review examined how TEAM concepts have been mapped or modeled into international health informatics standards. Methods: Following JBI methodology and PRISMA-ScR guidelines with a Population, Concept, and Context framework, we searched PubMed, Embase, Scopus, and IEEE Xplore for English reports from January 2016 to March 2026, with gray literature and citation searching. Screening was conducted by a single reviewer with senior verification and a 10% inter-rater reliability check. Findings were synthesized descriptively by functional category and clinical object type. Results: Of 16,763 records, 10 studies (2017 to 2026) were included; seven addressed TCM, three TKM, and none Kampo. Terminology work predominated, most often SNOMED CT, UMLS, and MeSH. The OMOP Common Data Model was the only data model, and no exchange standard such as HL7 FHIR appeared. Representability followed a gradient by clinical object type: acupuncture points anchored directly to terminologies, whereas only 1 of 260 ICD-11 Chapter 26 patterns had a SNOMED CT equivalent. Symptoms and disorders fit only partially, and compound herbal medicines required extension or decomposition. Most studies supplemented standards locally, and none of their additions were incorporated into a released standard. Conclusions: The work identified here has centered on establishing shared meaning rather than moving data between systems, and because supplementation stays external, the same gaps recur. Because the search covered only English-language, internationally indexed reports, these findings, including the absence of Kampo, reflect internationally visible work rather than the full literature published in Chinese, Korean, and Japanese national databases. Priorities are upstreaming TEAM content, encoding it in exchange standards, and building dedicated content for patterns. Full article

Identifying the associations between non-coding RNAs and drug resistance (RDRAs) is crucial for uncovering resistance mechanisms and screening effective biomarkers. However, existing graph-based methods typically rely on purely data-driven learning and commonly assume a simplified noise independence hypothesis, overlooking the complex dependencies among feature, structural, and label noise in biomedical networks. This leads to issues such as spurious associations, poor generalization, and lack of interpretability for noisy association prediction tasks. To address these challenges, we propose Prior-RDRGSE, a prior-knowledge-guided dependency-aware graph learning framework. This framework integrates both dependency-aware graph noise modeling and domain knowledge into graph representation learning. Specifically, we first construct a heterogeneous bipartite graph and employ a deep generative inference encoder to jointly infer the underlying clean graph structure and the association signals, thereby explicitly modeling and purifying the intertwined complex noise within the network. Next, we design a resistance-semantics-conditioned interaction module that injects disease-specific and mechanism-related semantic priors into attention queries, explicitly guiding subnetwork interactions in a biologically plausible manner. Furthermore, we introduce a resistance consistency constraint based on KL divergence, which regularizes model training by aligning the learned association distribution with prior distributions derived from clinical and literature data. Comprehensive experiments demonstrate that Prior-RDRGSE achieves state-of-the-art performance in RDRA prediction and significantly outperforms existing methods. Full article

Objectives: Candidate estrogen receptor alpha (ERα) antagonists should combine strong inhibitory activity with favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET)-related properties. This study developed an integrated framework for multi-endpoint prediction, multi-objective optimization in molecular descriptor space, and prioritization of feasible solutions. Methods: A dataset of 1974 compounds with 729 molecular descriptors, pIC₅₀ values, and five binary ADMET-related endpoints was analyzed. Key descriptors were identified through two-stage cross-method and cross-endpoint screening. One regression model for pIC₅₀ and five independent classification models for the ADMET-related endpoints were developed and evaluated separately. The final selected models were incorporated into a two-phase hybrid adaptive multi-objective evolutionary algorithm (2P-HAMOEA). Feasible Pareto solutions were ranked using objective weighting, multi-criteria decision-making, reliability-based fusion, and a performance uncertainty index-based adjustment. Results: The final dataset comprised 1875 compounds and 24 key molecular descriptors. The three-model stacking ensemble for pIC₅₀ achieved an R² of 0.650 in the internal holdout set, and the AUC values of the five ADMET models ranged from 0.878 to 0.979. Under the prespecified criteria, 2P-HAMOEA generated 155 feasible Pareto solutions. Its generational distance was significantly lower than that of four comparator algorithms, whereas its inverted generational distance and hypervolume were not superior to those of most comparators. pIC₅₀ received the highest fused weight (0.328), and pairwise rank correlations among TOPSIS, VIKOR, and WASPAS exceeded 0.90. The top 20% of RWS-ranked solutions (n = 31) were used for descriptor-interval analysis. Conclusions: The framework offers an early-stage, descriptor-level way to examine endpoint-specific predictions before alternative multi-endpoint profiles are compared. The Pareto solutions are numerical descriptor profiles rather than explicit or experimentally validated ERα antagonist structures; independent validation and molecular structure generation remain necessary. Full article

This study comparatively evaluates the performance of coated cutting tools in machining Ti6Al4V titanium alloy, which is widely used in the aerospace and defense industries as well as in biomedical applications. Surface milling operations were performed under both dry and wet conditions using Al-Ti-N, Al-Ti-N/Ti-Si-N, Al-Cr-N, Al-Ti-Si-N, and uncoated tungsten carbide tools. The data obtained were analyzed based on tool wear, surface roughness, and chip formation. The results demonstrate that coating type and cutting parameters significantly affect tool life and surface quality. This study aims to contribute to tool selection processes for the above-mentioned industrial applications. Full article

Background/Objectives: Large language models and automated analytical tools show potential for biomedical text understanding, knowledge integration, and single-cell data interpretation, but interpretations of specific gene regulatory relationships still require empirical grounding. For TF–target relationships, one relevant constraint is whether curated regulatory edges show detectable expression-level statistical evidence in the single-cell data being interpreted, because such evidence may vary across cellular states, conditions, and regulatory mechanisms. We therefore evaluated the statistical visibility of known transcription factor (TF)–target relationships in single-cell expression space to aid the interpretation of regulatory inference and automated-tool outputs. Methods: We performed two complementary analyses: first, testing whether curated TF–target edges showed stronger pair-level associations than matched background gene pairs; and second, assessing whether high-scoring TF–gene pairs corresponded to existing regulatory knowledge and whether their target genes showed pathway coherence. Results: Across four peripheral blood mononuclear cell (PBMC) datasets, four adult tissues, and seven adult cell types, curated TF–target relationships showed weak but reproducible statistical visibility rather than strong separation. For all five association metrics, the mean area under the precision–recall curve (AUPRC) was only slightly above the random-ranking baseline of 0.5. High-scoring pairs were more often supported by curated resources, and their target genes showed context-specific Hallmark pathway coherence. Conclusions: Single-cell expression associations can provide useful but limited statistical clues for TF–target regulation and should be interpreted as complementary rather than definitive regulatory evidence. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive decline, memory impairment, and functional deterioration. With the rapid growth of the aging population, AD has become a major global health challenge, imposing substantial burdens on patients, families, and healthcare systems. Despite extensive research, early and accurate diagnosis of AD remains challenging due to disease heterogeneity, overlapping clinical manifestations, and the lack of easily accessible, highly sensitive, and specific diagnostic markers. Recent advances in biomedical technologies, including neuroimaging, multi-omics profiling, electronic health records, and digital health tools, have generated large-scale and heterogeneous datasets, providing new opportunities for improving AD diagnosis. However, extracting clinically meaningful information from these complex data sources remains difficult using conventional statistical approaches. Artificial intelligence (AI) has progressively transformed AD diagnosis by evolving from traditional machine learning (ML) approaches based on handcrafted feature engineering to deep learning (DL) models capable of automated representation learning and multimodal information integration. More recently, large language models (LLMs) have further expanded the scope of AI-driven AD diagnosis by enabling contextual understanding of unstructured clinical information, knowledge-guided reasoning, and integration of multimodal biomedical evidence. This transition reflects a shift from feature-based prediction toward more flexible and intelligent diagnostic frameworks. This review synthesizes recent advances in AI-based AD diagnosis, tracing the evolution from traditional ML to DL and LLMs. Particular emphasis is placed on the emerging role of LLMs in extracting disease-related information from speech and clinical narratives, integrating heterogeneous biomedical data sources, and enabling multimodal frameworks for AD assessment. Full article

Tribology, the science of friction, wear, and lubrication, governs the reliability of nearly every mechanical system. Tribological contacts account for approximately 23% of global energy consumption, including 20% used to overcome friction and 3% associated with remanufacturing worn components. Nevertheless, the field has remained constrained by low experimental throughput, operator variability, and a scarcity of standardized, reusable datasets. This review surveys two converging trends positioned to address these limitations: the development of robotic and automated platforms for tribological experimentation, and the growing application of artificial intelligence to tribological analysis. High-throughput tribometer architectures, robotic specimen preparation, and multi-modal in situ sensing are examined as components of an emerging automated tribometry infrastructure. Supervised learning, physics-informed neural networks, and Bayesian optimization are reviewed as AI methods organized by the data regime in which they operate. The convergence of these trends in closed-loop autonomous tribological experimentation is assessed, including system architecture, optimization target specification, current partial implementations, and tribology-specific integration barriers that distinguish this domain from adjacent self-driving laboratory applications. Application domains spanning industrial machinery, biomedical implants, and aerospace and automotive drivetrains are discussed. Key challenges including dataset standardization, model transferability, and hardware-software integration complexity are identified. Prospects for fully autonomous tribological discovery pipelines are outlined, with emphasis on open-access data infrastructure and physics-constrained learning as the enabling conditions for the field. Full article

Silica aerogels are among the most extraordinary porous materials produced through sol–gel chemistry, distinguished by ultralow density, exceptionally high porosity, large specific surface area, and extremely low thermal conductivity. Despite these characteristics, widespread application of conventional silica aerogels has been constrained by inherent brittleness, poor mechanical strength, and moisture sensitivity. Significant research has therefore focused on silica aerogel composites, in which reinforcing or functional phases—fibers, polymers, carbon nanomaterials, metal oxides, and biopolymers—are integrated into the silica network to enhance mechanical robustness, flexibility, hydrothermal stability, electrical conductivity, catalytic activity, and multifunctionality while largely preserving the parent aerogel’s desirable properties. We review the synthesis, characterization, properties, and applications of silica aerogel composites. Sol–gel processing and drying technologies are discussed, followed by composite-formation strategies and the advanced techniques used to evaluate structural, mechanical, thermal, surface, and functional properties. The effects of reinforcing phases on mechanical performance, thermal conductivity, and hydrothermal stability are analyzed, and current and emerging applications in thermal insulation, environmental remediation, catalysis, acoustic damping, aerospace systems, biomedical engineering, and energy storage are highlighted. Finally, key challenges and future directions involving multifunctional materials, green synthesis, and data-driven materials design are discussed. Full article

Background/Objectives: Early recognition of psychological distress is an important component of integrated healthcare, particularly among patients experiencing social and linguistic vulnerability. This hypothesis-generating study examined how observable expressions of distress were conveyed and addressed in multilingual hospital encounters. Methods: An exploratory qualitative analysis was conducted using fifteen interpreter-mediated consultations purposively selected from the Intercomsalud corpus because verified transcripts were available. The sample included consultations in Arabic (n = 6), Chinese (n = 2), English (n = 5), Russian (n = 1) and Ukrainian (n = 1). Coding distinguished (1) explicit verbal distress, (2) observable emotional cues, and (3) researcher-inferred distress; it also described full, partial or non-relay and the healthcare professional’s observable response within the recording. Results: In the analysed consultations, biomedical and procedural information was generally conveyed in recognisable form. Some explicit distress expressions and observable cues were reduced, reformulated or not explored, whereas other encounters showed sustained acknowledgement. The psychology consultation provided a suggestive, uncontrolled contrast and cannot establish a department effect. Conclusions: The cases illustrate that clinical-content relay and affective recognition can be examined as related dimensions, but the present sample primarily documents accurate clinical-content relay with variable affective recognition and does not empirically validate their full independence. Larger, independently coded studies with follow-up data are needed. Full article

Background/Objectives: Type 2 diabetes (T2D) is one of the fastest growing chronic pediatric conditions worldwide. Indigenous children tend to be diagnosed younger, have severe progression of disease, and poorer health outcomes. While biomedical advancements have yielded more effective treatment outcomes, few resources have been implemented to prevent T2D in Indigenous youth. Therefore, an exploratory pilot study was conducted in one Canadian province with an overall purpose of co-creating, pilot-testing, and evaluating an Indigenous community-led T2D education resource called the wîcihitowin: Helping Each Other with Type 2 Diabetes Education Program. Methods: Data for this analysis were derived from a pre- and post-test evaluation of the digital education resource with a purposeful sample of Indigenous youth (n = 87) from two Cree communities living in a western Canadian province. Descriptive statistics, paired t-tests, and correlations were used to measure the differences from pre- to post-participation in the education program. Results: The youth had moderate knowledge baseline scores of diabetes, nutrition, and physical activity. Participants reported improved knowledge on all the education topics after participating in the wîcihitowin: Helping Each Other with Type 2 Diabetes Program. Significant positive correlations were found between all knowledge areas. Conclusions: The digital education resource effectively enhanced youths’ knowledge of diabetes, nutrition, and physical activity. Additionally, these findings highlighted the importance of culturally safe teachings for Indigenous peoples, emphasizing the need for T2D education to be community and culturally driven. These results are particularly important given the lack of existing evidence evaluating the effectiveness of T2D education among Indigenous youth. Full article

Statistical literacy is essential for biomedical researchers, yet doctoral training in applied biostatistics and reproducible data analysis remains limited despite the growing accessibility of open-source tools such as R. This retrospective observational programme evaluation examined three consecutive editions (2021–2023) of an intensive three-day doctoral course in applied biostatistics with R, delivered at the Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC) and the Universidad de Córdoba (UCO). Each edition enrolled 15 doctoral students with a 5:1 student-to-instructor ratio and used a problem-based curriculum of nine modules supported by a purpose-written open-access textbook. End-of-course examination performance was described using a ten-item multiple-choice examination; because the examination was not psychometrically equated across editions, score distributions are reported descriptively by cohort rather than used to infer cross-edition differences in learning or competence. Satisfaction was evaluated across four ACSA-aligned domains and was descriptively high in all editions; in the combined within-participant analysis, the Teaching Team domain was rated higher than the other domains. Self-reported theoretical and practical knowledge ratings increased descriptively from pre- to post-course within each edition. The 2021 ratings were reconstructed retrospectively from qualitative responses and were therefore not pooled or directly compared with the directly scaled 2022–2023 ratings. Qualitative feedback highlighted the theory–practice balance and low student-to-instructor ratio, with longer duration as the principal suggestion. The findings support the feasibility and acceptability of this intensive R-based training model, but the uncontrolled design, non-equated examination, small samples, and self-reported outcomes preclude claims about learning improvement, causal effectiveness, or durable competence. Full article

Hydrogel-and-nanomaterial-integrated wearable biosensor networks are promising platforms for real-time biomedical monitoring because they combine soft biointerfaces, sensitive signal transduction, and wireless data connectivity. Hydrogels provide tissue-like softness, hydration, adhesion, permeability, and biocompatibility, whereas nanomaterials such as graphene, carbon nanotubes, MXenes, metallic nanoparticles, and conductive polymers enhance conductivity, electrochemical activity, optical responsiveness, mechanical durability, and signal amplification. This review summarizes recent advances in hydrogel-and-nanomaterial-integrated wearable biosensors, ranging from soft material interfaces and stand-alone sensing devices to wireless wearable nodes, IoT-connected platforms, and emerging closed-loop sensor–actuator systems. Because these platforms differ substantially in their level of integration and validation, this review distinguishes enabling material and device concepts from fully connected or closed-loop systems. The distinctive contribution of this review is a materials-to-systems, evidence-graded framework that links hydrogel and nanomaterial interface design with sensing mechanisms, wearable sensor-node integration, wireless and IoT connectivity, and closed-loop actuation while distinguishing device-level proof of concept from clinically validated performance. We discuss functional hydrogel design, nanomaterial-based conductive networks, hybrid hydrogel–nanomaterial structures, and key requirements for skin compatibility, adhesion, stretchability, and long-term stability. Major sensing mechanisms and biomedical targets are reviewed, including electrochemical and optical biosensing, mechanical and physiological signal sensing, and sweat biomarker monitoring. We further highlight system-level integration strategies involving wearable sensor nodes, wireless communication, smartphone and cloud connectivity, data processing, power management, security, and reliability. Representative biomedical applications are summarized, including sweat-based metabolic monitoring, smart wound monitoring, hydrogel-based wound dressings, cardiovascular and respiratory monitoring, and motion sensing. Finally, current technical and translational challenges are discussed with emphasis on the distinction between analytical sensing performance, physiological correlation, and clinical validation. Disease-management and closed-loop healthcare applications are discussed as emerging directions that require appropriate human studies, reference-method comparison, agreement analysis, long-term monitoring, and safety validation before clinical implementation. Full article