Abstract: Biochar is increasingly being explored beyond its established environmental and agricultural uses as a functional material that interacts with microorganisms, cells, and biological molecules. However, biochar is not a uniform material, and its biological effects cannot be interpreted independently of feedstock composition, production conditions, particle size, surface chemistry, and post-production modification. This review synthesizes current knowledge on the relationships between these physicochemical characteristics and the biological responses associated with biochar and biochar-based materials. Particular attention is given to adsorption, electron transfer, microbial habitat formation, biomolecule binding, and mineral precipitation as mechanisms that may underlie changes in oxidative balance, antimicrobial activity, animal physiology, cellular responses, and tissue regeneration. The review also critically considers emerging nanobiochar and engineered composites investigated as drug carriers, wound-healing materials, regenerative scaffolds, and platforms for anticancer therapy. Importantly, reported effects often depend on highly heterogeneous materials and experimental models, and the biological contribution of the biochar matrix is not always clearly distinguished from that of incorporated metals, polymers, drugs, or other functional components. Current evidence therefore supports biochar as a tunable platform rather than a universally bioactive material. Its successful development for biological and biomedical applications will require standardized production and characterization, and mechanism-oriented experimental designs.

Abstract: Background/Objectives: Diabetes impose a substantial public health burden. Machine learning models trained on symptom data may help identify individuals whose recorded symptom profiles warrant confirmatory clinical assessment. This study compared ten en-semble models for symptom-based classification of a dataset-provided diabetes-status la-bel. Methods: The UCI Early-Stage Diabetes Risk Prediction Dataset was used, comprising 520 observations, 251 unique predictor profiles, 16 predictors, and a binary positive/negative diabetes-status label. All observations were retained, while identical predictor profiles were kept within the same partition during data splitting, nested cross-validation, hy-perparameter optimization, and calibration to reduce information leakage. The weighted F1-score was the optimization objective and primary model-selection metric throughout the analysis. Results: Bagging Extra Tree achieved the highest observed mean weighted F1-score (94.07% ± 7.99%) and MCC (0.876 ± 0.167) and was selected as the final model under the prespecified criterion. An exploratory Friedman analysis indicated variation in model rankings across folds (p = 0.001859), although no Nemenyi-adjusted pairwise comparison reached the nominal significance level. On the profile-disjoint internal holdout set, the calibrated model achieved a weighted F1-score of 0.9224, an MCC of 0.8207, a ROC-AUC of 0.9844, and a Brier score of 0.0528. Random Forest was selected in the unique-profile sensitivity analysis but yielded lower holdout performance, indicating sensitivity to the analytical unit. Conclusions: These findings provide internal evidence that tree-based ensembles can clas-sify the dataset-provided diabetes-status label under profile-disjoint evaluation. They do not establish prospective risk prediction or clinical diagnostic utility; independent exter-nal validation is required before clinical implementation.

Abstract: Background/Objectives: Mobility is a key determinant of autonomy, participation, and quality of life among nursing home residents. Despite its importance, residents of long-term care facilities are often highly inactive and experience progressive mobility decline. Kinaesthetics is a movement-oriented nursing concept that aims to enhance residents’ own movement resources and participation in everyday care activities. However, robust evidence regarding its effectiveness in long-term care settings remains limited. This study investigated whether the implementation of a comprehensive kinaesthetics programme was associated with differences in mobility development among nursing home residents compared with usual care. Methods: A quasi-experimental longitudinal study was conducted in 12 long-term care facilities in southern Germany. Six facilities implemented a comprehensive kinaesthetics programme, while six facilities served as controls. Resident mobility was assessed using the EBoMo instrument, which measures independence in 11 mobility-related activities (range 11–44 points). Two paired samples were created for two observation periods (2019–2021 and 2021–2022). Changes in mobility status were analysed using group comparisons and multivariable linear regression models adjusted for baseline mobility, age, sex, and care level. Results: A total of 142 residents were included in the 2019–2021 analysis and 107 residents in the 2021–2022 analysis. Baseline characteristics were comparable between intervention and control groups. Between 2019 and 2021, mobility declined in both groups but less strongly in the intervention group (−2.02 vs. −4.39 EBoMo points). This difference showed borderline statistical significance in unadjusted analyses (p = 0.051) but was not significant after adjustment (p = 0.097). No significant group differences were observed between 2021 and 2022. In both regression models, baseline mobility was the strongest predictor of follow-up mobility status. Conclusions: The findings provide no clear evidence that the comprehensive kinaesthetics programme significantly improved residents’ mobility compared with usual care. Although a trend towards a smaller decline in mobility was observed during the first observation period, this effect was not confirmed in subsequent analyses. Further research with larger samples and additional outcome measures is needed to clarify the potential benefits of kinaesthetics-based care in long-term care settings.

Abstract: LATERAL ORGAN BOUNDARIES DOMAIN (LBD) transcription factors are plant-specific regulators that govern plant cellular plasticity. On one hand, LBD proteins transduce auxin and wound signals to initiate cell dedifferentiation, callus formation and de novo organogenesis, functioning as key drivers of plant regeneration in both dicots and monocots. On the other hand, they actively respond to a wide range of abiotic stresses including drought, salinity as well as pathogen-induced biotic stress. LBDs maintain ROS homeostasis and osmotic balance, and modulate ABA, JA signaling pathways to enhance plant stress tolerance. This review summarizes the structural characteristics, evolutionary features and dual roles of LBDs, analyzes current research limitations, and puts forward epigenetic and breeding-oriented research directions. It provides valuable theoretical references for studying plant plasticity and improving crop genetic transformation and stress resistance.Unlike previous reviews that separately summarize LBD developmental or stress functions, this work systematically integrates the crosstalk between regeneration and environmental adaptation, and proposes a unified growth-stress trade-off regulatory framework.

Abstract: Interest in the properties of miRNAs has significantly declined in recent years, largely due to inadequate conceptualizations of their interactions with mRNA. Recent publications have shown a growing tendency to consider fully complementary miRNA interactions with mRNA of transcription factor genes. In the present study, we examined the miRNAs identified by Backes et al. which we have designated as BmiRNA to distinguish them from those in the NCBI database and other standard miRNAs. The MirTarget software was utilized to determine the quantitative physicochemical characteristics of the interactions between BmiRNAs and target mRNAs. BmiRNAs differ significantly from other miRNAs across numerous properties. We observed that BmiRNAs bind with full complementarity to the mRNAs of numerous transcription factor genes. Specifically, it is demonstrated that the mRNAs of ZNF genes and other transcription factors bind to BmiRNAs with full complementarity. Nearly all these BmiRNAs exhibited binding within the 3′UTR region. We identified BmiRNAs that interact with multiple target genes, as well as target genes that interact with multiple BmiRNAs, and defined groups of BmiRNAs that bind to the mRNAs of multiple genes. Furthermore, we identified BmiRNAs interacting with the mRNAs of transcription factor genes with high free energy. These results, regarding the interaction of BmiRNAs with the mRNAs of a significant number of genes, support the hypothesis that miRNAs serve as critical regulators of human genome expression.

Abstract:

Background/Objectives: Betanidin glycosides (betacyanins) are plant dietary pigments with reported significant benefits on human health. Betacyanin research has largely focused on betanin common in some plant foods like beets, amaranth, quinoa, cacti fruits , whereas the biological properties of structurally diverse gomphrenin derivatives remain insufficiently characterized. Gomphrenin-type betacyanins dietary source is predominantly Malabar spinach (Basella alba L.) but its health-promoting and disease preventing properties remain understudied. This study investigated whether differences in the composition of gomphrenin-rich preparations influence their effects on inflammatory and oxidative cellular responses. Methods: Betacyanin-rich preparations were obtained from Basella alba f. rubra fruits and Gomphrena globosa L. flowers using chromatographic purification. Their betacyanin profiles were characterized by LC-DAD-ESI-MS and total betacyanin contents were determined spectrophotometrically. The biological effects of crude and purified preparations (1–100 μg/mL) were evaluated in Caco-2 intestinal epithelial cells and human polymorphonuclear neutrophils (PMN). IL-8 secretion in Caco-2 cells, TNF-α, IL-8, and IL-1β secretion in LPS-stimulated PMN, ROS generation in f-MLP-stimulated PMN, and cell viability were assessed. Results: Purification resulted in preparations with markedly different betacyanin profiles. The gomphrenin-enriched BE₂ contained 312 ± 83 mg/g total betacyanins, whereas the acylated-betacyanin-enriched BE₃ contained 188 ± 12 mg/g and was dominated by globosin and other hydroxycinnamoyl derivatives. Despite its lower total betacyanin content, BE₃ displayed stronger inhibition of TNF-α, IL-8, and IL-1β secretion than BE₂; at 100 μg/mL, these cytokine levels were 39.17%, 44.61%, and 58.93% of the LPS control, respectively. All preparations reduced IL-8 secretion in Caco-2 cells by more than 50% at the highest concentration and the Basella preparations effectively suppressed neutrophil ROS generation. No significant cytotoxicity was observed. Conclusions: The biological activity of betalain-rich preparations was not determined by total betacyanin content alone but was associated with their qualitative pigment composition and broader chemical matrix. The stronger anti-inflammatory activity of the acylated-betacyanin-enriched preparation highlights hydroxycinnamoylated gomphrenins as candidates for further investigation, while the complex nature of the preparations warrants further studies to determine the contribution of individual pigments to the observed biological effects.

Abstract: The Revised Multicultural Ideology Scale (MCI-r) has been validated in five Western contexts, each retaining the same four subscales and setting aside Essentialistic Boundaries and Extent of Differences. We report its first administration outside Europe and North America, in 1,200 nationally distributed South Korean adults, and no established structure achieved acceptable fit as specified. The four-factor solution preferred in every prior study was inadmissible in two of three subsamples under robust maximum likelihood and in the full sample under an alternative estimator, because Cultural Maintenance and Social Interaction were not separable (disattenuated r = 1.03; latent r = .896 with method variance modelled). Misfit was organised by item wording direction rather than content. A single orthogonal factor loading the eight negatively worded items raised fit from CFI = .563 to .901 and absorbed 31.6% of their variance; a random intercept representing a general tendency to agree fitted better still on every index, locating the effect in acquiescence rather than in negative wording as such. The only subscales to form an acceptable model were the two containing no negatively worded items, which are also the two set aside in every prior validation — though that advantage is confounded with wording composition and is not interpretable substantively. Mixed-worded subscales cannot be assumed to function equivalently outside the contexts in which they were developed.

Abstract: Early weight-loss responses to oral glucagon-like peptide-1 (GLP-1) receptor agonists vary substantially, yet the longitudinal clinical characteristics associated with super-response remain incompletely understood. Here using de-identified electronic health records, we studied 9,287 GLP-1 receptor agonist (GLP-1 RA) treatment-naive patients with a prescription for oral Wegovy. Of 8,381 patients with an index weight, 3,011 had a follow-up weight recorded between 30 and 180 days (early clinical observation period) and were classified by their best percent weight change as non-responders (no loss), responders (>0–5% loss), strong responders (>5–10%), or super-responders (>10%). Body weight was traced back five years, and HbA1c, blood pressure, comorbidities, dose escalation, and phenotypes extracted from clinical notes using AI-augmented curation were compared across these treatment response groups. At treatment initiation, super-responders weighed less than non-responders (94.1 versus 101.7 kg; P<0.001). Five years before treatment, super-responders weighed 7.6% less than at the index date, compared with 0.8% less among non-responders (P<0.001 across groups). Super-responders subsequently lost 7.2% by day 60 and 11.4% by day 90, returning on average to their weight from five years earlier within two months. Age, sex, type 2 diabetes, HbA1c, and blood pressure did not differ across groups at treatment initiation, and neither HbA1c nor blood pressure differed one, two, or five years earlier. Super-responders more often reached significantly higher doses early in treatment than non-responders: approximately 37% versus 24% were prescribed >1.5 mg at day 30 (P=0.001), 16% versus 9% were prescribed >4 mg at day 60 (P=0.056), and 6% versus 2% were prescribed >9 mg at day 90 (P=0.022; Fisher's exact test). Systolic blood pressure at day 90 fell by 10.2 mmHg in super-responders versus 3.3 mmHg in non-responders (P=0.006 across groups; P=0.003 for trend). Among 1,978 phenotypes AI-curated from unstructured clinical notes, before the first prescription, severe obesity was documented in 19.4% of super-responders versus 36.1% of non-responders (P<0.001; q=0.003). Super-responders had more pretreatment documentation of disordered eating (7.9% versus 3.1%) and less sleep-disordered breathing (35.2% versus 48.1%) and impaired fasting glucose (3.0% versus 7.5%) than non-responders, although differences were not significant after multiple-testing adjustment (all q≥0.26). Among GLP-1 RA-naive patients initiating oral Wegovy, greater early weight-loss response was associated with a more strongly rising weight trajectory over the preceding five years rather than with baseline glycemic or hemodynamic characteristics. In this observational study, pre-treatment weight trajectory and dosage emerged as associates of early oral semaglutide response, but the outcome-defined groups and short follow-up make these findings hypothesis-generating and warrant prospective validation.

Abstract: Deep learning classifiers trained on structural magnetic resonance imaging (MRI) are increasingly proposed for early detection of Alzheimer’s disease (AD) and mild cognitive impairment (MCI), yet the pipelines that produce these models depend on shared datasets, pre-trained weights, and federated updates that an adversary can poison. A backdoored diagnostic model behaves correctly on ordinary scans but collapses to an attacker-chosen label whenever a hidden trigger is present, which turns a prognostic instrument for cognitive aging into a silent source of misdiagnosis. This article proposes MedBackdoorGuard, a defense framework that detects and mitigates backdoor attacks in MRI-based AD and MCI classifiers without prior knowledge of the trigger. The framework couples spectral latent screening of training data with latent trigger isolation in activation space, then immunizes the model through trigger-sensitive neuron pruning and fine-tuning under an anatomical attention consistency constraint that anchors the decision on medial temporal structures. A runtime inference guard adds superimposition entropy and anatomically informed reconstruction to purify suspicious scans at deployment. Across ADNI, OASIS-1, and a public four-class dementia MRI benchmark under patch, blended, warping, and frequency triggers, MedBackdoorGuard reduces the mean attack success rate from 97.4% to 3.0% while holding clean accuracy within 0.6 percentage points of the undefended model, reaches a poisoned-sample detection AUROC of 97.4%, and identifies 59 of 60 backdoored models. Compared with the strongest defense baseline, attack success rate falls by 15.1 percentage points and clean accuracy rises by 1.7 percentage points. The framework restores MCI recall on triggered scans from 3.9% to 88.3%, which is the outcome that matters for early detection in cognitive aging.

Abstract: It is generally accepted that sensory neurons transduce “information” from their environment into biologically relevant signals, allowing for adaptation and body homeostasis. However, it has not been clear what sort of information metric is generated by, or flows through, the neuron. In this work, we stated this problem based on the physical foundations of a conductance-based model of neuronal excitability, starting from the mother equations of an ensemble of N identical ion channels. For any number and nature of channel states, we show that the Fisher information in the output (membrane conductance, potential or current) about the input (the stimulus) arises as the intrinsic information metric of the gating dynamics of the ensemble. We found that the Fisher information is a primary component of the fluctuations of the neuronal membrane conductance, along with a parameter accounting for how deterministic or stochastic the ensemble is (a function of N). We also obtain a relation for the rate of transitions of the neuron, from a given configuration with n open channels to another, which is the primary origin of the intrinsic noise in the absence of artificial stimulation. This result establishes an important analytical correlation between the fluctuations of the neuron output signal, the information it carries (in Fisher’s terms), its physical and kinetic properties, and the corresponding stimuli. This might constitute a theoretical foundation for: i) designing of strategies for stochastic resonance-based computing procedures inspired by the neural intrinsic noise, and ii) modulating neural function by tuning its intrinsic Fisher information or the expected value of conductance fluctuations, with that of a binomially distributed external noise. For channels with inactive states, the model suggests the simultaneous use of two different signals of such noise. Such proposals constitute a new paradigm, which qualitatively differs from the traditional Gaussian white or Lévy noises.

Abstract: Several members of the transient receptor potential (TRP) channel superfamily are notorious for been activated by chemical, thermal and mechanical stimuli. Despite the obvious advances resulting from functional studies and structural characterizations, the mechanisms of TRP channel gating remain obscure. Amongst all, the mechanically-induced gating is probably the most intriguing activation mode of these channels. The main objective of this study was to provide a theoretical framework serving to assess the determinants of TRP channel mechanosensitivity. We hypothesized that, as for thermal stimuli and chemical agonists, the mechanically-induced activation is boosted by the weak voltage dependence of TRP channels. To assess this, we extended a gating model that was previously shown to describe the thermal activation of multiple sensory TRP channels, by considering an additional linear free energy term associated to an external mechanical stimulus. The resulting model predicts that the thermal and mechanical sensitivities are determined by the balance between an energy component associated to the protein volume and another associated to the protein-membrane surface. The model supports the hypothesis linking the mechanical- and voltage-dependent gating by predicting inverse relationships between the mechanical sensitivity and the gating valence. In addition, it serves to explain how TRP channels can act as secondary mechanosensors by being stimulated by second messengers generated upon mechanical activation of signaling pathways. Finally, we delineate experimentally testable-hypotheses that may result in further understanding of TRP channel mechanosensitivity and discuss the relevance of this property for mechanotransduction.

Abstract: Chronic obstructive pulmonary disease (COPD) and inflammatory bowel disease (IBD) are heterogeneous inflammatory disorders. COPD can be classified into emphysema and chronic bronchitis, whereas IBD can be classified into Crohn’s disease and ulcerative colitis. We hypothesize that COPD, like IBD, is also an autoimmune disease. However, the detailed immunopathogenesis of these diseases remains unclear. In this review, we summarize experimental, epidemiological, and clinical evidence linking autoimmunity to these disorders. Emphysema and Crohn’s disease are proposed to be TH1-dominant autoimmune disorders, whereas chronic bronchitis and ulcerative colitis are proposed to be TH22-dominant autoimmune disorders. A more detailed understanding of the immunopathogenesis of these diseases may support more specific and effective therapeutic strategies.

Abstract: Background: To determine the molecular relationship between pyrazinamide (PZA) and bedaquiline (BDQ) resistance mechanisms in multidrug-resistant tuberculosis (MDR-TB) and drug-susceptible Mycobacterium tuberculosis (M. tuberculosis) isolates to improve treatment strategies. Methods: Phenotypic drug susceptibility testing (DST), resistance gene amplification and sequencing (pncA, panD, hadC, and Rv0678), and efflux pump gene expression (mmpS5 and mmpL5) by qRT-PCR were performed on 71 clinical MTBC isolates from seven provinces in Southern Türkiye (2022-2023). Results: PZA resistance was closely associated with MDR-TB (χ² = 38.0, p < 0.0001), exclusively detected in MDR isolates (42% overall; 73% of MDR). pncA mutations were detected in 77% of PZA-resistant isolates with strong genotype-phenotype correlation (p = 0.009, r = 0.45), while panD mutations were present in 40% and showed less association (p = 0.14). No hadC or Rv0678 mutations were identified despite significant overexpression of efflux genes mmpS5 and mmpL5 in MDR strains (p < 0.001). Conclusions: PZA and BDQ resistance arise through distinct, non-overlapping mechanisms. PZA-resistant MDR-TB without Rv0678 mutations confirms bedaquiline's independent therapeutic value and success in treating PZA-resistant cases.

Abstract: Autonomous artificial intelligence (AI) agents are increasingly transacting with one another and with digital services without direct human mediation, and dollar-pegged stablecoins have emerged as the dominant settlement medium for this machine-to-machine (M2M) commerce. Industry data indicate that agent-initiated stablecoin settlements grew from a negligible base to tens of millions of dollars across hundreds of millions of micro-transactions within roughly a year, with a single stablecoin issuer accounting for the overwhelming majority of settled value. This concentration, combined with the sub-second, high-frequency, low-value nature of agent payments, raises under-examined questions about transaction failure, liquidity adequacy, and settlement risk that existing payment-systems literature — built largely around human-initiated, lower-frequency transactions — does not directly address. This study develops and empirically tests a structural model in which liquidity adequacy, settlement latency/throughput constraints, issuer credibility and reserve transparency, and protocol interoperability jointly predict transaction failure likelihood, which in turn predicts settlement risk exposure and, ultimately, organizational intention to adopt stablecoin-based agent payment infrastructure; regulatory clarity is modeled as a moderator of the interoperability–failure relationship. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS on survey data from 385 professionals across blockchain engineering, AI agent development, fintech risk/compliance, and treasury functions, the measurement model demonstrates strong reliability and convergent/discriminant validity (all composite reliabilities > 0.90; all AVE > 0.66; HTMT < 0.65), and the structural model explains 40.3% of variance in transaction failure likelihood and 36.2% in settlement risk exposure. All four exogenous predictors significantly influence transaction failure likelihood, transaction failure likelihood significantly predicts settlement risk exposure (β = 0.501, p < .001), and regulatory clarity significantly moderates the interoperability–failure relationship. Bootstrapped mediation confirms that transaction failure likelihood partially mediates the effect of protocol interoperability on settlement risk (indirect effect = −0.172, 95% CI [−0.221, −0.124]). The paper contributes a domain-specific construct set and validated instrument for agentic-payment risk, offers evidence-based guidance for stablecoin issuers, protocol designers, and enterprises deploying autonomous agents, and outlines a concrete pathway — including a fully documented replication dataset — for extending this model with primary field data.

Abstract: Federated learning offers a practical route for collaborative land-cover mapping when satellite imagery cannot be pooled centrally, yet two issues remain: non-IID client data can weaken segmentation and shared model updates can still leak information. This study evaluates a privacy-preserving federated semantic-segmentation framework on DeepGlobe. The 803 labeled images were split at source-image level into 562/120/121 train/validation/internal-test images; each image was padded to 2560 × 2560 and divided into 25 non-overlapping 512 × 512 patches. Five configurations were compared: centralized U-Net, vanilla FedAvg, FedAvg with client-local region-aware contrastive regularization, FedAvg with client-side DP-SGD, and a combined private-contrastive variant using fixed data-independent prototypes. Under one fixed five-client Dirichlet partition (α = 0.5), contrastive FedAvg achieved the highest observed non-private validation performance (mIoU = 0.4062; Macro F1 = 0.5344) versus vanilla FedAvg (0.3712; 0.5011). DP-SGD provided formal patch-level (ε, δ)-Differential Privacy under the stated assumptions, with maximum ε = 4.8201 at δ = 10-5, but mIoU fell to 0.1300 and training became substantially slower. The combined private variant reached mIoU = 0.1362 at ε = 4.9958. These single-run validation results reveal a clear privacy-utility-computation trade-off.

Abstract: Detecting anatomical tooth landmarks on intraoral 3D scans (IOS) is a key step in digital orthodontics, but supervised methods are limited by the few scans carrying landmark annotations. On the public Teeth3DS+ benchmark every scan has per-vertex tooth segmentation and FDI labels, whereas only a small subset also carries dental landmarks, so landmark detection becomes a semi-supervised problem in which abundant segmentation labels compensate for the missing landmark labels. We pretrain a shared point-cloud encoder on segmentation using all non-test scans, then train a landmark head with an equivariance consistency loss and confidence-filtered pseudo-labels on the unlabelled scans, plus a geometric prior that constrains each predicted landmark to the segmented region of its tooth. All model selection uses a held-out validation split; the test set serves only for final reporting. On the 3DTeethLand test split the method raises the mean Average Precision from 0.702 to 0.753 and lowers the mean radial error from 0.63 to 0.54 mm over a supervised baseline with the same labelled budget, ranking third of seven systems. Existing segmentation labels are thus an effective, low-cost signal for label-efficient 3D landmark detection.

Abstract: Introduction: Coordinating clinical education and training between nursing education institutions and clinical placement sites remains a persistent challenge, contributing to miscommunication, unfulfilled clinical hours, and inconsistent supervision. Mobile technology, and mobile applications specifically, have been proposed as a mechanism to strengthen this coordination. Objective: This manuscript describes the conceptual framework and the design-and-development process of a mobile application (WILApp) intended to coordinate the clinical education and training of undergraduate nursing students at a university in Gauteng, South Africa, and reports the practice-oriented theoretical structure and the nine-step technical development process that produced it. Methods: A design and developmental research (DDR) approach, situated within Phase 2 (the design/development phase) of a four-phase, convergent mixed-methods study, was used. Phase 1 qualitative and quantitative data, analysed respectively through thematic analysis and descriptive statistics and merged using a side-by-side comparison method, together with the literature review and theoretical framework, informed the requirements gathering, technology-stack selection, and user-interface design undertaken in partnership with an appointed information technology (IT) company under a signed non-disclosure and service-level agreement. Pseudonymised registration and role-based access control safeguarded participant data throughout the design and development process. Results: A conceptual framework based on Dickoff, James and Wiedenbach's survey list for practice-oriented theory was developed, specifying the agents, recipients, context, procedure, dynamics, and outcome of the mobile application. The resulting application, WILApp, was built using React Native with a Firebase backend and comprised screens for registration, login, clinical hours tracking, assessment, progress, and data report generation. Development followed nine sequential steps: requirements gathering; user interface and user experience design; technology stack selection; backend development; mobile application development; testing and quality assurance; documentation and release readiness; deployment and support; and maintenance and future enhancements. Conclusion: A theory-driven, iterative design-and-development process, grounded in integrated empirical findings and a practice-oriented conceptual framework, produced a functional, purpose-built mobile application capable of supporting the coordination of clinical education and training. This process offers a replicable model for developing similar digital tools in resource-constrained nursing education contexts.

Abstract: Public debate describes AI agents as lying, cheating, and coordinating. Those descriptions track real hazards, but they import a human moral psychology into systems whose behaviour is better explained by optimisation, scaffolding, and institutional context. This paper develops the alternative without minimising the danger. It begins from three premises: control of AI is not solely an engineering problem; no durable control strategy may assume that overseers remain cognitively superior to the overseen; and systems grown by optimisation are epistemically closer to husbandry than to automotive engineering, so aviation-style certification does not transfer. From these premises the paper derives a Moral Agency Transition: reversible levels of authorised agency in which promotion requires four warrants, including a detection warrant — evidence that independent evaluators can detect the relevant failure classes, not merely that the system can pass them. First, concurrent multiplicity: a deployed model is a fleet of simultaneous, causally disconnected instances, so persistence, provenance, and successor fidelity are defined over a fleet, with explicit merge semantics and divergence tripwires. Second, maintenance attribution: today’s systems do not maintain their own commitments; external pipelines do. Endogenous repair is therefore made an explicit requirement for high-impact levels. Evaluators and institutions are themselves treated as fallible hypotheses with regression triggers, not as final solutions. The result is a falsifiable research programme replacing alignment-as-obedience with accountable, revocable, institutionally embedded delegation.

Abstract: Background: Antimicrobial resistance (AMR) requires action at two levels that are often separated: safe antibiotic decisions for individual patients and timely recognition of changing resistance ecology at population level. ResistTrack-ID is designed as an AI-assisted clinical decision-support, adherence, stewardship and multimodal surveillance architecture for Indonesia. As artificial intelligence becomes more capable of shaping empirical antibiotic selection, surveillance is no longer only a retrospective reporting function. It becomes part of the model-safety infrastructure, because an AI system can repeatedly apply a resistance pattern that is locally outdated with far greater speed and consistency than an individual clinician. Objective: We propose ResistTrack-ID as an integrated framework that combines patient-level clinical decision support with longitudinal, catchment-based triangulation of clinical microbiology, environmental AMR signals, syndromic patterns and antibiotic-use data. Framework: The clinical core harmonizes electronic health record, diagnostic, microbiology, local antibiogram and treatment-guideline data and produces clinician-facing ranked antibiotic options with explicit uncertainty while retaining clinician authority. A patient-facing component supports treatment completion. The surveillance layer combines continuously collected clinical microbiology with monthly trajectory analysis, hospital and community wastewater from predefined sentinel sites, weekly syndromic signals and antibiotic-use metrics. Rather than flatten these streams into one score, a spatiotemporal engine preserves their different evidentiary meanings and temporal resolutions, evaluates baseline deviation, change points, spatial clusters and lagged relationships, and asks whether signals converge, diverge or precede one another. Alerts trigger intensified sampling, stewardship review, model reassessment or targeted genomic investigation rather than being interpreted as proof of transmission. Evaluation: ResistTrack-ID requires separate validation of clinical prediction, prescribing safety, stewardship outcomes, surveillance lead time, false-alert burden, geographic concordance, data completeness, model drift, usability and cost. Conclusion: ResistTrack-ID reframes AMR control as a connected learning loop between bedside decisions and community-level ecological surveillance. The purpose of surveillance in an AI-assisted system is not merely to draw a richer map of resistance, but to keep the information environment underlying antibiotic recommendations current, geographically grounded, uncertainty-aware and open to correction.

Abstract: Background/Objectives: Quilombola communities preserve knowledge about medicinal plants that may help identify compounds relevant to cardiovascular drug discovery. This study examined agreement between cardiovascular uses reported by elders in the Mumbuca and Prata communities in Tocantins, Brazil, and published pharmacological evidence. Methods: In October 2025, the authors interviewed 10 elders recognized by their communities as holders of medicinal-plant knowledge. The authors collected and botanically identified cited plants and searched Google Scholar, PubMed, and SciELO for pharmacological and phytochemical studies of species associated with cardiovascular conditions. Results: The field study cataloged 81 medicinal-plant taxa. Elders associated 11 (13.6%) taxa with cardiovascular treatment or protection. The literature review identified at least 21 potentially cardioactive substances among these species. Evidence was most consistent for antihypertensive, antioxidant, anti-inflammatory, anticoagulant, and cardioprotective effects. Conclusions: Traditional recommendations and published evidence converged for several species, supporting careful, community-engaged investigation of quilombola knowledge as a source of hypotheses for cardiovascular drug discovery. Evidence gaps identify priorities for pharmacological, toxicological, and clinical research conducted with ethical safeguards and equitable recognition of knowledge holders.