Advancing Open Science

Background/Objectives: Triple-negative breast cancer (TNBC) lacks targeted therapies and has a poor prognosis. Wild ginseng (Panax ginseng) is traditionally valued for its medicinal properties, but its scarcity limits therapeutic application. Adventitious root culture technology provides a sustainable source of wild ginseng-derived bioactive compounds. This study investigated the anticancer effects of wild ginseng adventitious root extract (WGAR) on MDA-MB-231 TNBC cells and elucidated the underlying molecular mechanisms. Methods: WGAR was prepared from cultured adventitious roots of 100-year-old wild ginseng, and its chemical composition was analyzed by LC-MS/MS. Anticancer effects were evaluated using MTT assay, acridine orange/propidium iodide (AO/PI) staining, Matrigel invasion assay, Western blot analysis, and proteome profiler array. Molecular docking was performed to predict interactions between WGAR constituents and target proteins poly (ADP-ribose) polymerase (PARP)-1 and β-catenin. Results: LC-MS/MS analysis tentatively identified 17 compounds, including ginsenosides (Rg3, Rh1, Rf) and terpenoids (ursolic acid). WGAR reduced cell viability with an IC50 of 79 μg/mL at 48 h, inducing 51.2% cell death. WGAR activated the intrinsic apoptotic pathway through sequential caspase-9 and caspase-3 activation, followed by PARP cleavage, and was associated with changes in epithelial–mesenchymal transition (EMT)-related markers (reduced N-cadherin, Slug, and β-catenin) alongside decreased inhibitory Ser9 phosphorylation of GSK-3β. Proteome array analysis revealed suppression of ECM remodeling proteins (tenascin C, u-PA) and inflammatory mediators (IL-6, CXCL8). Molecular docking predicted that selected WGAR constituents, particularly terpenoid-type compounds, may potentially interact with PARP-1 and β-catenin; however, these in silico findings are hypothesis-generating and require experimental validation. Conclusions: WGAR exerts multi-target anticancer effects on TNBC cells through apoptosis induction and EMT suppression associated with modulation of GSK-3β/β-catenin signaling, suggesting its potential as a source of therapeutic agents for TNBC.

Background: Late-onset depression (LOD) represents a distinct clinical and biological phenotype emerging in the context of global population ageing. This study aims to synthesize current evidence on the epidemiology, risk factors, mechanistic pathways, and therapeutic approaches of LOD, integrating biological, psychological, and social dimensions. Methods: This narrative review synthesizes recent evidence across epidemiology, clinical symptomatology, neurobiology, and treatment. Where conceptually appropriate or empirically overlapping, we incorporate findings from the broader late-life depression (LLD) literature. Results: LOD emerges (as a distinct clinical and biological entity in later life) as a clinically and biologically meaningful presentation of depression in later life, representing a minority of depressive cases. It is defined by prominent apathy, psychomotor slowing, and cognitive impairment, and is closely linked to frailty, medical comorbidity, and heightened dementia risk. Pathophysiological mechanisms converge on vascular, inflammatory, oxidative, and neuroplasticity pathways, while psychosocial adversity further shapes onset and course. Treatment prioritizes efficacy and tolerability amid multiple morbidity; SSRIs and SNRIs are first-line, with pro-dopaminergic or dual-action agents addressing anhedonia and apathy, and neuromodulation or augmentation strategies reserved for resistance. Integrative approaches combining pharmacotherapy, psychotherapy, and lifestyle interventions are essential to optimize outcomes in aging populations. Conclusions: Late-onset depression (is a distinct, biologically and psychosocially driven disorder) represents a biologically and psychosocially enriched subtype in its own within the spectrum of late-life depression, requiring integrated, personalized care. Addressing neurovascular mechanisms, psychosocial adversity, and prevention through coordinated geriatric and psychiatric strategies may improve outcomes in aging populations.

As a globally significant oilseed and food crop, peanuts exhibit significant quality changes influenced by storage conditions. This study monitored six key quality indicators—including fatty acid content, carbonyl content, peroxide value, acid value, phenylacetaldehyde and moisture content—in peanut samples stored for 30 weeks under varying temperature and humidity conditions. A Deep Clustering Network (DCN) was employed for quality grading, yielding superior results compared to Deep Empirical Correlation (DEC) and K-Means++ clustering methods, thereby establishing effective quality grading standards. Building upon this, a D-SCSformer time series prediction model was constructed to forecast quality indicators. Through dimensionality-segmented embedding and statistical feature fusion, it achieved strong predictive performance (MSE = 0.2012, MAE = 0.2884, RMSE = 0.4387, and R² = 0.9998), reducing MSE by 57.9%, MAE by 35.4%, and RMSE by 34.1%, while improving R² from 0.9996 to 0.9998 compared to the mainstream Crossformer model. This study provides technical support and a decision-making basis for temperature and humidity regulation and shelf-life management during peanut storage.

Background/Objectives: The emergence of immune-evasive SARS-CoV-2 variants highlights the need for adaptable vaccine strategies. Trimeric receptor-binding domain (tRBD) antigens offer structural and immunological advantages over monomeric RBDs, but DNA vaccine efficacy has been limited by inefficient antigen expression, particularly in non-dividing antigen-presenting cells. Although cytoplasmic transcription–based DNA platforms have been developed to overcome nuclear entry barriers, their utility for antigen structure–function optimization remains underexplored. This study evaluated whether integrating a rationally designed trimeric RBD with a T7-driven cytoplasmic transcription system could enhance immunogenic performance. Methods: A DNA vaccine encoding a tandem trimeric SARS-CoV-2 RBD was delivered using a T7 RNA polymerase-driven cytoplasmic transcription system. In vitro antigen expression was assessed following Lipofectamine 3000-mediated transfection. In vivo, mice were immunized with the SM-102-based Rpol/tRBD/LNP formulation, and immunogenicity was assessed by antigen-specific antibody titers, serum neutralizing activity, and T-cell response profiling, together with basic safety/tolerability evaluations. Results: The T7-driven cytoplasmic transcription system markedly increased antigen mRNA and protein expression compared with conventional plasmid delivery. Rpol/tRBD vaccination induced higher anti-RBD IgG titers, enhanced neutralizing antibody activity, and robust CD8⁺ T cell responses relative to monomeric RBD and plasmid-based trimeric RBD vaccines. Immune responses were Th1-skewed and accompanied by germinal center activation without excessive inflammatory cytokine induction, body-weight loss, or hepatic and renal toxicity. Conclusions: This study demonstrates that integrating rational trimeric antigen engineering with direct cytoplasmic transcription enables balanced and well-tolerated immune activation in a DNA vaccine context. The T7 autogene-based platform provides a flexible framework for antigen structure–function optimization and supports the development of next-generation DNA vaccines targeting rapidly evolving viral pathogens.

Objectives: This study aims to evaluate whether contemporary artificial intelligence (AI), including convolutional neural networks (CNNs) for medical imaging and large language models (LLMs) for language processing, could replace physicians in the near future and to identify the principal clinical, technical, and regulatory barriers. Methods: A narrative review is conducted on the scientific literature addressing AI performance and reproducibility in medical imaging, LLM competence in medical knowledge assessment and patient communication, limitations in out-of-distribution generalization, absence of physical examination and sensory inputs, and current regulatory and legal frameworks, particularly within the European Union. Results: AI systems demonstrate high accuracy and reproducibility in narrowly defined tasks, such as image interpretation, lesion measurement, triage, documentation support, and written communication. These capabilities reduce interobserver variability and support workflow efficiency. However, major obstacles to physician replacement persist, including limited generalization beyond training distributions, inability to perform physical examination or procedural tasks, susceptibility of LLMs to hallucinations and overconfidence, unresolved issues of legal liability at higher levels of autonomy, and the continued requirement for clinician oversight. Conclusions: In the foreseeable future, AI will augment rather than replace physicians. The most realistic trajectory involves automation of well-defined tasks under human supervision, while clinical integration, physical examination, procedural performance, ethical judgment, and accountability remain physician-dependent. Future adoption should prioritize robust clinical validation, uncertainty management, escalation pathways to clinicians, and clear regulatory and legal frameworks.

Understanding the dynamic characteristics of droplets in the orientated flow channels of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for their effective heat and water management and bipolar plate design. Therefore, the transient transport dynamics of liquid water within orientated gas flow channels (OGFCs) of PEMFCs are investigated, and a two-phase model based on the volume of fluid (VOF) method is established in the current study. Moreover, the impacts of the size of droplets and the geometrical parameters of baffles on the removal dynamics of liquid water are examined. The results show that baffles effectively promote droplet breakup and accelerate their detachment from the Gas Diffusion Layer (GDL) surface by increasing flow instability and local shear forces. The morphology of water is altered by the high velocity of gaseous flow, which can break up into several smaller droplets and distribute them on the surface of GDL by the gas flow. The shape of the liquid water film changes from a regular cuboid to a big droplet due to the surface tension of the liquid water droplets and the hydrophobicity of the GDL surfaces. Increasing the baffle height can reduce the time needed for the removal of droplets. With the increase in L1* from 0.25 to 0.75, the drainage time decreases slightly; however, for L1* increasing from 0.75 to 1.25, the drainage time remains almost the same. The impacts of different leeward lengths, L2*, on the water coverage ratio and pressure drop are minor.

Vascular diseases remain a major global health burden despite remarkable technological advances in vascular surgery and endovascular therapies. Conditions such as peripheral arterial disease, abdominal aortic aneurysm, carotid stenosis, chronic venous disease, diabetic vasculopathies, and vascular chronic ulcers are not only biological entities but are deeply shaped by social structures, cultural norms, and economic inequalities. This article introduces Vascular Sociology as an interdisciplinary field that integrates vascular surgery with medical sociology to provide a more comprehensive understanding of vascular health and disease. Drawing on classical and contemporary sociological theory, including concepts such as social determinants of health, embodiment, illness narratives, and the disease–illness–sickness triad, the article argues that vascular pathology reflects cumulative social exposures across the life course. Socially patterned behaviors, work conditions, food environments, healthcare access, gender norms, and geographic inequalities profoundly influence disease onset, progression, treatment decisions, and outcomes. The paper highlights how surgical success is contingent not only on technical excellence but also on patients’ social contexts, including health literacy, trust in institutions, caregiving resources, and the capacity to adhere to long-term follow-up and rehabilitation. By outlining conceptual foundations, epidemiological evidence, and mixed-methods research strategies, the article positions Vascular Sociology as a framework capable of bridging biomedical knowledge with lived experience. This approach expands the definition of vascular outcomes to include social reintegration, identity transformation, and equity of care, ultimately aiming to improve patient-centered practice, reduce disparities, and inform more socially responsive vascular health policies.

Accurate estimation of crop evapotranspiration (ET) is essential for achieving efficient agricultural water use in the North China Plain. Although machine learning techniques have demonstrated considerable potential for ET simulation, a systematic evaluation of model-architecture suitability and hyperparameter optimization strategies specifically for summer soybean ET estimation in this region is still lacking. To address this gap, we systematically compared several machine learning architectures and their hyperparameter optimization schemes to develop a high-accuracy daily ET model for summer soybean in the North China Plain. Synchronous observations from a large-scale weighing lysimeter and an automatic weather station were first used to characterize the day-to-day dynamics of soybean ET and to identify the key driving variables. Four algorithms—support vector regression (SVR), Random Forest (RF), extreme gradient boosting (XGBoost), and a stacking ensemble—were then trained for ET simulation, while Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Randomized Grid Search (RGS) were employed for hyperparameter tuning. Results show that solar radiation (R_S), maximum air temperature (T_max), and leaf area index (LAI) are the dominant drivers of ET. The Stacking-PSO-F3 combination, forced with Rs, T_max, LAI, maximum relative humidity (RH_max), and minimum relative humidity (RH_min), achieved the highest accuracy, yielding R² values of 0.948 on the test set and 0.900 in interannual validation, thereby demonstrating excellent precision, stability, and generalizability. The proposed model provides a robust technical tool for precision irrigation and regional water resource optimization.