Search Results (1,425)

Background: Accurately predicting protein druggability is crucial for successful drug development, as it significantly reduces the time and resources required to identify viable drug targets. However, existing methods often face trade-offs between accuracy, efficiency, and interpretability. This study aims to introduce a lightweight framework designed to address these challenges effectively. Methods: We present a lightweight framework that embeds proteins into four biologically informed, non-Euclidean metric spaces, derived from analyses of amino acid sequences, predicted secondary structures, and curated post-translational modification (PTM) annotations. These representations capture key features such as hydrophobicity profiles, PTM densities, spatial patterns, and secondary structure composition, providing interpretable proxies for structure-related determinants of druggability. This approach enhances our understanding of protein functionality while improving druggability predictability in a biologically relevant context. Results: Evaluated on an Aspirin-binding protein dataset using leave-one-out cross-validation (LOOCV), our distance-based ensemble achieves 92.25% accuracy (AUC = 0.9358) in the whole-protein setting. This performance significantly outperforms common sequence-only baselines in the literature while remaining computationally efficient. Conclusions: On a refined single-chain subset, our framework demonstrates performance comparable to established feature engineering pipelines, highlighting its potential effectiveness in practical applications. Together, these results strongly suggest that biologically grounded, non-Euclidean embeddings provide an effective and interpretable alternative to resource-intensive 3D pipelines for target assessment in drug discovery. This approach not only enhances our ability to assess protein druggability but also streamlines the overall process of target identification and validation. Full article

Human immunodeficiency virus (HIV), comprising two distinct types, HIV-1 and HIV-2, remains one of the most significant global health challenges, originating from multiple cross-species transmissions of simian immunodeficiency viruses (SIVs) in the early 20th century. This review traces the evolutionary trajectory of HIV from zoonotic spillover to its establishment as a global pandemic. HIV-1, the principal strain responsible for AIDS, emerged from SIVcpz in Central African chimpanzees, with phylogenetic evidence indicating initial human transmission between the 1920s and 1940s in present day Democratic Republic of Congo. The virus disseminated through colonial trade networks, reaching the Caribbean by the 1960s before establishing endemic transmission in North America and Europe. HIV’s extraordinary genetic diversity—driven by high mutation rates (~10⁻⁵ mutations per base per replication cycle) and frequent recombination events—has generated multiple groups, subtypes, and circulating recombinant forms (CRFs) with distinct epidemiological patterns. HIV-1 Group M, comprising subtypes A through L, accounts for over 95% of global infections, with subtype C predominating in sub-Saharan Africa and Asia, while subtype B dominates in Western Europe and North America. The extensive genetic heterogeneity of HIV significantly impacts diagnostic accuracy, antiretroviral therapy efficacy, and vaccine development, as subtypes exhibit differential biological properties, transmission efficiencies, and drug resistance profiles. Contemporary advances, including next-generation sequencing (NGS) for surveillance, broadly neutralizing antibodies for cross-subtype prevention and therapy, and long-acting antiretroviral formulations to improve adherence, have transformed HIV management and prevention strategies. NGS enables near real-time surveillance of drug resistance mutations and inference of transmission networks where it is available, although access and routine application remain uneven across regions. Broadly neutralizing antibodies demonstrate cross-subtype efficacy, while long-acting formulations have the potential to improve treatment adherence. This review synthesizes recent evidence and offers actionable recommendations to optimize clinical and public health responses—including the routine use of genotypic resistance testing where feasible, targeted use of phylogenetic analysis for outbreak investigation, and the development of region-specific diagnostic and treatment algorithms informed by local subtype prevalence. While the understanding of HIV’s evolutionary dynamics has substantially improved and remains essential, translating this knowledge into universally implemented intervention strategies remains a key challenge for achieving the UNAIDS 95-95-95 targets and the goal of ending AIDS as a public health threat by 2030. Full article

Initial attitude calibration is a critical yet challenging phase in hardware-in-the-loop (HIL) testing for space docking, often hindered by cumbersome procedures, safety concerns, and reliance on external equipment. This paper introduces a human–robot collaborative calibration method based on H∞ robust control. The core objective is to achieve symmetric pose alignment between docking mechanisms by allowing the operator to manually guide the test device, thereby rapidly obtaining initial attitude calibration results. An interactive model incorporating a time delay is established. Using H∞ synthesis, a stabilizing controller is designed to accurately track low-frequency operator commands while strongly suppressing high-frequency disturbances. Notably, the H∞ framework reconstructs an ideal interactive symmetry in human–robot collaboration by compensating for delays and disturbances. The solution to the Riccati equation within a game-theoretic framework effectively achieves symmetric optimization that balances tracking accuracy with safety constraints. Experimental results demonstrate that the method successfully compensates for system delays, enabling symmetric pose alignment while maintaining smooth and continuous motion of the docking mechanism. It also faithfully translates the operator’s low-frequency traction intent into motion. By retaining contact forces/torques within safe thresholds, the method balances interaction safety with operational precision, ultimately providing a reliable solution for initial attitude calibration in space docking HIL tests. Full article

Background: Assessing the efficacy of combination therapies in hepatocellular carcinoma (HCC) requires both accurate tumor delineation and biologically validated prediction of therapeutic response. Conventional MRI-based criteria, which rely primarily on tumor size, often fail to capture treatment efficacy due to tumor heterogeneity and pseudo-progression. This study aimed to develop and biologically validate a multi-task deep learning model that simultaneously segments HCC tumors and predicts treatment outcomes using clinically relevant multi-parametric MRI in a preclinical rat model. Methods: Orthotopic HCC tumors were induced in rats assigned to Control, Sorafenib, NK cell immunotherapy, and combination treatment groups. Multi-parametric MRI (T1w, T2w, and contrast enhanced MRI) scans were performed weekly. We developed a U-Net++ architecture incorporating a pre-trained EfficientNet-B0 encoder, enabling simultaneous segmentation and classification tasks. Model performance was evaluated through Dice coefficients and area under the receiver operator characteristic curve (AUROC) scores, and histological validation (H&E for viability, TUNEL for apoptosis) assessed biological correlations using linear regression analysis. Results: The multi-task model achieved precise tumor segmentation (Dice coefficient = 0.92, intersection over union (IoU) = 0.86) and reliably predicted therapeutic outcomes (AUROC = 0.97, accuracy = 85.0%). MRI-derived deep learning biomarkers correlated strongly with histological markers of tumor viability and apoptosis (root mean squared error (RMSE): viability = 0.1069, apoptosis = 0.013), demonstrating that the model captures biologically relevant imaging features associated with treatment-induced histological changes. Conclusions: This multi-task deep learning framework, validated against histology, demonstrates the feasibility of leveraging widely available clinical MRI sequences for non-invasive monitoring of therapeutic response in HCC. By linking imaging features with underlying tumor biology, the model highlights a translational pathway toward more clinically applicable strategies for evaluating treatment efficacy. Full article

Background/Objective: Accurate quantification of RNA is critical for RNA-based experiments such as polysome profiling and RT-qPCR. These techniques often rely on control RNA to ensure consistency and reliability across experiments. Commonly used spike-in controls, including in vitro-synthesized mRNA or ERCC mixes, are expensive and time-consuming, limiting accessibility for many laboratories. This study aims to evaluate the use of cross-species total RNA as a cost-effective and reliable spike-in control. Methods: We developed a method using total RNA from a non-homologous species—specifically, yeast RNA—as a spike-in control for experiments involving human cells. The approach was tested across multiple RNA-based assays to assess its impact on quantification accuracy, reproducibility, and interference with endogenous RNA measurements. Additionally, we applied this method to evaluate the translation efficiency of Bcl-xL mRNA in mammalian cells under hypertonic stress. Results: Cross-species spike-in RNA demonstrated minimal interference with experimental outcomes and provided consistent normalization across replicates. The use of yeast RNA enabled accurate fold-change calculations and improved detection of experimental variability. In the case study involving Bcl-xL mRNA, the spike-in control facilitated reliable assessment of translation efficiency under stress conditions. Conclusions: Using total RNA from a non-related species as a spike-in control offers a practical, economical alternative to conventional methods. This approach enhances the reliability of RNA quantification without compromising experimental integrity, making it especially valuable for resource-limited settings, particularly in polysome and RT-qPCR workflows. Full article

Background/Objectives: The use of large language models (LLMs) by patients seeking information about their diagnosis and treatment is rapidly increasing. While their application in healthcare is still under scientific investigation, the demand for these models is expected to grow significantly in the coming years. This study evaluates the accuracy of three publicly available AI tools—GPT-4, Gemini, and Bing—in interpreting MRI reports and suggesting treatments for patients with vestibular schwannomas (VS). To evaluate and compare the diagnostic accuracy and treatment recommendations provided by GPT-4, Gemini, and Bing for patients with VS based on MRI reports, while addressing the growing use of these tools by patients seeking medical information. Methods: This retrospective study included 35 consecutive patients with VS treated at a university-based neurosurgery department. Anonymized MRI reports in German were translated to English, and AI tools were prompted with five standardized verbal prompts for diagnoses and treatment recommendations. Diagnostic accuracy, differential diagnoses, and treatment recommendations were assessed and compared. Results: Thirty-five patients (mean age, 57 years ± 13; 18 men) were included. GPT-4 achieved the highest diagnostic accuracy for VS at 97.14% (34/35), followed by Gemini at 88.57% (31/35), and Bing at 85.71% (30/35). GPT-4 provided the most accurate treatment recommendations (57.1%, 20/35), compared to Gemini (45.7%, 16/35) and Bing (31.4%, 11/35). GPT-4 correctly recommended surgery in 60% of cases (21/35), compared to 51.4% for Bing (18/35) and 45.7% for Gemini (16/35). The difference between GPT-4 and Bing was statistically significant (p-value: 0.02). Conclusions: GPT-4 outperformed Gemini and Bing in interpreting MRI reports and providing treatment recommendations for VS. Although the AI tools demonstrated good diagnostic accuracy, their treatment recommendations were less precise than those made by an interdisciplinary tumor board. This study highlights the growing role of AI tools in patient-driven healthcare inquiries. Full article

Accurate grade prediction in porphyry copper deposits requires not only capturing spatial continuity but also accounting for geological controls. This study evaluates the added value of incorporating alteration and mineralization domains into machine learning (ML) models for copper grade estimation at the Iju porphyry Cu deposit, Iran. We compare four scenarios: spatial coordinates only, coordinates + alteration, coordinates + mineralization, and coordinates + both domains. A three-stage workflow was developed, in which Random Forest classifiers—optimized with Particle Swarm Optimization (PSO-RF)—classify alteration and mineralization zones, which are later integrated into regression models for ore grade prediction. Model performance was assessed using nested spatial cross-validation and benchmarked against Support Vector Machines (SVM). In comparative analysis, the PSO-RF framework consistently outperformed SVM, achieving more balanced accuracy between training and testing data and demonstrating greater robustness to class imbalance in domain classification. Moreover, results show that combining alteration and mineralization domains improves predictive performance (R² = 0.78; RMSE was reduced by 5.6% relative to coordinates-only). Although numerically moderate, this reduction in error translates into more reliable tonnage and grade estimations near cut-off grades, thereby enhancing the economic confidence of resource evaluations. These findings demonstrate that integrating multiple geological domains can improve both the accuracy and interpretability of ML-based grade models, providing a practical and reproducible workflow for porphyry copper resource evaluation. Full article

The fast advancement of artificial intelligence presents ethical challenges that exceed the scope of traditional moral theories. This paper proposes a value-centered framework for AI ethics grounded in axiology, which distinguishes intrinsic values like dignity and fairness from instrumental ones such as accuracy and efficiency. This distinction supports ethical pluralism and contextual sensitivity. Using Multi-Criteria Decision Analysis (MCDA), the framework translates values into structured evaluations, enabling transparent trade-offs. A healthcare case study illustrates how ethical outcomes vary across physician, patient, and public health perspectives. The results highlight the limitations of single-theory approaches and emphasize the need for adaptable models that reflect diverse stakeholder values. By linking philosophical inquiry with governance initiatives like Responsible Artificial Intelligence (AI) and Digital Humanism, the framework offers actionable design criteria for inclusive and context-aware AI development. Full article

Background: Gastric cancer (GC) remains a leading cause of cancer mortality. E3 ubiquitin ligases, as central regulators of protein stability and signaling within the ubiquitin–proteasome system, have been implicated in tumor progression, but their functional roles in GC are not well established. Methods: We integrated bioinformatics analysis of TCGA and GEO datasets, in vitro experiments (including cell proliferation, migration, and apoptosis assays), and computational modeling to identify key prognostic factors in GC. Results: We established two molecular subtypes (E3GC1/E3GC2) with distinct clinical outcomes and developed a 10-gene prognostic signature. The model showed moderate predictive accuracy (AUC: 0.61–0.71) and was validated externally. MMRN1 was upregulated in GC cells and its knockdown significantly inhibited malignant phenotypes. Critically, drug sensitivity analysis revealed high-risk patients were more sensitive to proteasome inhibitors (bortezomib), while low-risk patients responded better to taxane-based chemotherapy (docetaxel). Molecular docking predicted a high-confidence interaction between MMRN1 and NEDD4L, suggesting potential ubiquitination regulation. Conclusions: MMRN1 drives GC cell proliferation and migration in vitro and may be regulated by NEDD4L-mediated ubiquitination. Our study provides a foundation for E3 ligase-based patient stratification and personalized therapy selection in GC. While this study provides comprehensive multi-omics evidence supporting the role of MMRN1 in GC progression, its clinical translation is limited by the lack of in vivo validation and direct experimental evidence of NEDD4L-MMRN1 physical interaction. Further studies using animal models and clinical specimens are warranted to confirm these findings. Full article

Geological models form the basis for scientific investigations of both the surface and subsurface of urban environments. Urban cover, however, usually prohibits the collection of new subsurface data. Therefore, models depend on existing subsurface datasets that are often of poor quality and have an uneven spatial and temporal distribution, introducing significant uncertainty. This research proposes a novel method to mitigate uncertainty caused by clusters of uncertain data points in kriging-based geological modelling. This method estimates orientations from clusters of uncertain data and randomly selects points for geological interpolation. Unlike other approaches, it relies on the spatial distribution of the data and translating geological information from points to geological orientations. This research also compares the proposed approach to locally changing the accuracy of the interpolator through data-informed local smoothing. Using the Ouseburn catchment, Newcastle upon Tyne, UK, as a case study, results indicate good correlation between both approaches and known conditions, as well as improved performance of the proposed methodology in model validation. Findings highlight a trade-off between model uncertainty and model precision when using highly uncertain datasets. As urban planning, water resources, and energy analyses rely on a robust geological interpretation, the modelling objective ultimately guides the best modelling approach. Full article

Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) technology offers a promising solution for wheelchair control by translating neural signals into navigation commands. A major challenge—signal noise caused by eye blinks—is addressed in this feasibility study through real-time blink detection and correction. The proposed design utilizes sensors to capture both SSVEP and blink signals, enabling the isolation and compensation of interference, which improves control accuracy by 14.68%. Real-time correction during blinks significantly enhances system reliability and responsiveness. Furthermore, user data and global positioning system (GPS) trajectories are uploaded to the cloud via Wi-Fi 6E for continuous safety monitoring. This approach not only restores mobility for users with physical disabilities but also promotes independence and spatial autonomy. Full article

Background: Stackable metallic or magnetic multi-template systems translate a prosthetically (facially) driven plan into each surgical phase of full-arch rehabilitation. Our objective was to map and critically describe the clinical applications, accuracy, and short-term outcomes of stackable/sequential guides and to illustrate the operational steps with a standardized magnet-retained case. Methods: Following a prospectively registered protocol (OSF, June 2025), we performed a scoping review in accordance with and PRISMA guidance. PubMed, Scopus and Embase were searched to 26 June 2025 for primary human studies using stackable or sequential static guides to place ≥4 implants per arch with immediate (≤72 h) loading. Duplicate-independent screening and data-charting captured guide design, planning platform, surgical accuracy, implant survival, prosthetic outcomes and patient-reported measures. A single non-analytic clinical vignette was included solely to illustrate the facially driven stackable workflow. Results: Eight studies (five countries, 2021–2025) encompassing 351 implants and one additional clinical case met the inclusion criteria. Mechanical indexing predominated (7/9 protocols); only two papers, including our case, used magnetic retention. Mean coronal and angular deviations, reported in two cohorts, were 0.95 mm/2.8° and 0.87 mm/2.67°, respectively—well within accepted thresholds for full-arch guided surgery. Immediate loading was achieved in 100% of arches; cumulative implant survival was 97.1% after 3–12 months. Patient-reported satisfaction exceeded 90 mm on VAS scales when measured. Our case demonstrated 0.90 mm/2.95° accuracy, 100% implant stability ≥ 35 N cm and uneventful provisionalisation at 12 weeks. Conclusions: Early clinical reports show clinically acceptable accuracy and high short-term survival with streamlined workflow. However, evidence remains heterogeneous and short-term; prospective multi-centre studies with standardized accuracy metrics, ≥3-year follow-up, validated PROMs, and cost-effectiveness analyses are still needed. Full article

Backscatter technologies promise to enable large-scale, battery-free sensor networks by modulating and reflecting ambient radio frequency (RF) carriers rather than generating new signals. Translating this potential into practical deployments—such as distributed photovoltaic (PV) power systems—necessitates realistic modeling that accounts for deployment variabilities commonly neglected in idealized analyses, including uncertain hardware insertion loss, non-ideal antenna gain, spatially varying path loss exponents, and fluctuating noise floors. In this work, we develop a practical model for reliable backscatter communications that explicitly incorporates these impairing factors, and we complement the theoretical development with empirical characterization of each contributing term. To validate the model, we implement a frequency-shift keying (FSK)-based backscatter system employing a non-coherent demodulation scheme with adaptive bit-rate matching, and we conduct comprehensive experiments to evaluate communication range and sensitivity to system parameters. Experimental results demonstrate strong agreement with theoretical predictions: the prototype tag consumes 825 µW in measured operation, and an integrated circuit (IC) implementation reduces consumption to 97.8 µW, while measured communication performance corroborates the model’s accuracy under realistic deployment conditions. Full article

An early and precise diagnosis is essential for successful intervention in Alzheimer’s disease (AD), a progressive neurological illness. In this study, we present a deep learning-based framework for multiclass classification of AD severity levels using MRI neuroimaging data. The framework integrates multiple convolutional and transformer-based architectures with a novel hybrid hyperparameter optimization strategy; Snake+EVO surpasses conventional optimizers like Genetic Algorithms and Particle Swarm Optimization by skillfully striking a balance between exploration and exploitation. A private clinical dataset yielded a classification accuracy of 99.81%for the optimized CNN model, while maintaining competitive performance on benchmark datasets such as OASIS and the Alzheimer’s Disease Multiclass Dataset. Ensemble learning further enhanced robustness by leveraging complementary model strengths, and Grad-CAM visualizations provided interpretable heatmaps highlighting clinically relevant brain regions. These findings confirm that hybrid optimization combined with ensemble learning substantially improves diagnostic accuracy, efficiency, and interpretability, establishing the proposed framework as a promising AI-assisted tool for AD staging. Future work will extend this approach to multimodal neuroimaging and longitudinal modeling to better capture disease progression and support clinical translation. Full article

Hyperspectral imaging (HSI) is increasingly used in studies for medical applications as it provides both structural and functional information of biological tissue, enhancing diagnostic accuracy and clinical decision-making. Recently, HSI cameras (HSICs) have been integrated with medical endoscopes (HSIEs), capturing hypercube data beyond conventional white light imaging endoscopes. However, there are currently no cleared or approved HSIEs by the U.S. Food and Drug Administration (FDA). HSI accuracy depends on technologies and experimental parameters, which must be assessed for reliability. Importantly, the reflectance spectrum of a target can vary across different cameras and under different environmental or operational conditions. Thus, before reliable clinical translation can be achieved, a fundamental question must be addressed: can the same target yield consistent spectral measurements across different HSI systems and under varying acquisition conditions? This study investigates the impact of eight parameters—ambient light, exposure time, camera warm-up time, spatial and temporal averaging, camera focus, working distance, illumination angle, and target angle—on spectral measurements using two HSI techniques: interferometer-based spectral scanning and snapshot. Controlled experiments were conducted to evaluate how each parameter affects spectral accuracy and whether normalization can mitigate these effects. Our findings reveal that several parameters significantly influence spectral measurements, with some having a more pronounced impact. While normalization reduced variations for most parameters, it was less effective at mitigating errors caused by ambient light and camera warm-up time. Additionally, normalization did not eliminate spectral noise resulting from low exposure time, small region of interest, or a spectrally non-uniform light source. From these results, we propose practical considerations for optimizing HSI system performance. Implementing these measures can minimize variations in reflectance spectra of identical targets captured by different cameras and under diverse conditions, thereby supporting the reliable translation of HSI techniques to clinical applications. Full article