Search Results (320)

LoRaWAN has become a widely adopted communication technology for Internet of Things applications because of its long-range communication capability and low power consumption. However, resource-constrained LoRaWAN networks remain vulnerable to physical-layer attacks such as jamming and battery-depletion attacks. This paper investigates architectural compression and Knowledge Distillation (KD) for lightweight multi-attribute Physical Layer Authentication (PLA) in LoRaWAN networks. A higher-capacity Teacher model is used to train progressively smaller Student models, and three Student-training strategies—Direct training, Hard-KD, and Soft-KD—are systematically compared to determine whether Teacher supervision provides an advantage beyond architectural compression alone. The framework uses the Received Signal Strength Indicator (RSSI), altitude, and the battery level as input attributes and is evaluated offline using a device-aware semi-synthetic dataset derived from the publicly available Brno LoRaWAN dataset. The resulting dataset contains Normal Communication, Jamming Attack, and Battery Depletion Attack traffic. The experimental results show that Student (8) preserves a classification performance close to that of the Teacher across all three training strategies, achieving Macro F1-scores of 90.619 ± 0.056% for Direct training, 90.591 ± 0.069% for Hard-KD, and 90.626 ± 0.083% for Soft-KD, compared with 91.007% for the Teacher. Student (8) reduces the number of trainable parameters from 2435 to 59 (97.6%) and the serialised model size from 46.65 KB to 2.80 KB (94.0%), while exhibiting a substantially higher measured batch-processing throughput under the evaluated CPU-based implementation. A feature-ablation analysis further indicates that the RSSI and battery level provide the dominant discriminatory information, while the altitude provides a smaller but measurable contextual contribution. The comparable performance of Direct, Hard-KD, and Soft-KD demonstrates that KD does not consistently outperform direct training for the evaluated Student architectures; rather, architectural compression itself accounts for much of the retained performance. These findings demonstrate that compact Student architectures can substantially reduce the model complexity while preserving a strong classification performance within the evaluated multi-attribute PLA framework, supporting the further investigation of lightweight network-side security mechanisms for LoRaWAN deployments. Full article

Hepatocellular carcinoma (HCC) remains difficult to control because recurrence, impaired hepatic reserve, and treatment resistance limit durable benefit. Natural products and traditional Chinese medicine (TCM) provide resources that range from drug-lead discovery to adjunctive multicomponent therapy. This review integrates pharmacological and clinical evidence for purified compounds, semisynthetic derivatives, extracts, formulas, and delivery systems. It focuses on metabolic reprogramming and redox homeostasis, stress responses and regulated cell death, tumor cell plasticity and vascular remodeling, and the immune microenvironment and host response. Recent studies have strengthened selected mechanistic claims through chemical probes, functional perturbation, and resistance models. Clinical research has concentrated on recurrence control after surgery or minimally invasive treatment and on combinations with transarterial chemoembolization, targeted agents, and immunotherapy. Randomized trials and prospective cohorts suggest potential benefit in specific settings, although product standardization, external validation, and long-term follow-up remain limited. Major translational barriers include uncertain active constituents, inadequate batch comparability, missing tumor-exposure data, and sparse herb–drug interaction studies. Future development should match target validation, pharmacokinetics, safety assessment, and clinical endpoints to each product class and clarify whether a candidate is best positioned as a drug lead, adjunctive therapy, or supportive intervention. Full article

In vitro neurovascular unit (NVU) models are essential for reproducing blood–brain barrier (BBB) transport and neurovascular cell interactions. However, the literature remains fragmented: biomaterial chemistry, fabrication parameters and organ-on-a-chip architecture are commonly evaluated in isolation, while inconsistent reporting of matrix properties, processing history, cell source, flow and barrier readouts prevents head-to-head comparison and the extraction of transferable design rules. To address this gap, this review integrates biomaterials, manufacturing technologies and organ-on-a-chip engineering within a unified material–process–structure–function framework. We translate endothelial junctions, basement-membrane components and perivascular cells into experimentally actionable material requirements; compare natural, synthetic, semisynthetic and decellularized extracellular-matrix hydrogels; and examine crosslinking, peptide functionalization, stimuli responsiveness, composite-network formation and preparation methods. Findings from Transwell, microfluidic, tubular, self-assembled and 3D-bioprinted BBB systems are used to relate matrix stiffness, degradability, ligand density, permeability, device-body material and fabrication route to barrier maturation, analytical access and reproducibility. By defining matched controls and minimum reporting requirements for chemistry, mechanics, transport and processing, this review provides a practical basis for next-generation BBB models that can improve permeability and efficacy screening in drug discovery, reproduce disease- and patient-specific barrier dysfunction, and support individualized response testing with iPSC- or patient-derived cells. Full article

Vitiligo is a chronic acquired depigmenting disorder characterized by progressive melanocyte loss. Its pathogenesis involves a multifactorial interplay among oxidative stress, impaired melanocyte regeneration, immune-mediated cytotoxicity, and neuroendocrine dysregulation. In recent years, traditional Chinese medicine (TCM)-derived active ingredients have attracted increasing research interest as potential interventions for vitiligo, although most isolated compounds remain at the preclinical stage. This review summarizes pharmacological and natural compounds applied in vitiligo treatment and highlights their regulatory mechanisms on core pathogenic modules. A comprehensive analysis of natural and semisynthetic active ingredients is provided, covering their preclinical and clinical evidence, mechanisms of action, and therapeutic relevance. Multiple ingredients, including baicalein, quercetin, curcumin, paeoniflorin, kaempferol, glycyrrhizin, and epigallocatechin-3-gallate (EGCG), have demonstrated antioxidant, anti-inflammatory, immunomodulatory, and neuroprotective effects that are involved in the pathogenesis of vitiligo. Some TCM-derived interventions, such as Ginkgo biloba L. extract and compound glycyrrhizin, have been evaluated clinically, whereas most isolated active compounds, including baicalein and quercetin, remain at the preclinical stage. Overall, TCM-derived active ingredients represent a promising therapeutic strategy for vitiligo. However, continued translational and clinical research is still required to optimize formulations, dosing regimens, and safety profiles, thereby facilitating their integration into routine clinical practice. Full article

This paper develops a unified asymptotic theory for inverse-probability-weighted conditional U-statistics of arbitrary fixed order in the presence of missing-at-random responses and infinite-dimensional functional covariates. The target is a conditional higher-order functional generated by a measurable response kernel and evaluated locally on a separable Banach space. Localization is formulated through delta sequences, providing a common framework for kernel, partition, regressogram, orthogonal series, and related smoothing procedures without recourse to finite-dimensional density arguments. For bounded kernels, we establish uniform almost-complete convergence over pseudo-compact functional domains and obtain a sharp decomposition into deterministic localization bias and stochastic fluctuation. The latter is governed by the localized-kernel variance, the envelope of the delta sequence, the metric complexity of the indexing domain, and the small-ball concentration of the functional covariate. Unbounded kernels are treated under explicit weighted moment, truncation, and summability conditions. The feasible theory quantifies the additional perturbation induced by estimating the propensity score and identifies conditions under which this first-stage uncertainty is asymptotically negligible. Pointwise distributional theory is derived through a denominator linearization combined with the Hoeffding decomposition of the centered localized kernel. The Gaussian limit is driven by the first projection, while the higher-order canonical components are shown to be negligible under explicit local-mass, moment, and noncancellation assumptions. This yields oracle-equivalent feasible inference, a consistent first-projection variance estimator, and asymptotically valid studentized confidence intervals. A finite-grid adaptive comparison principle is also developed for data-driven resolution selection. The scope of the theory is illustrated through conditional rank functionals, discrimination with incomplete labels, metric-learning criteria, and functional prediction. Synthetic and semi-synthetic studies based on functional classification, phoneme log-periodograms, and growth trajectories document the finite-sample interaction between covariate-dependent label observation, local information loss, propensity estimation, and inverse-weighting variance. Full article

Lignans and related neolignans are a diverse class of phenolic natural products with broad biological activities and great potential for pharmaceutical applications. This article summarizes representative subclasses of lignans and compares their antitumor and antioxidant properties from a structural and mechanistic perspective. Aryltetrahydronaphthalene lignans, represented by podophyllotoxin and its semi-synthetic derivatives, provide the clearest baseline for their mechanism of action by disrupting microtubules and inhibiting topoisomerase II. Other subclasses, including dibenzylbutyrolactone, arylnaphthalene, dibenzocyclooctadiene, furofuran lignans, glycosides, and bisphenolic neolignans, exhibit anticancer and redox-regulating effects. Future research should focus on linking structural diversity with validated molecular targets, pharmacokinetic properties, and in vivo efficacy to better define their translational potential. Full article

Digital wellness policies in universities often rely on uniform daily screen time caps, yet platform-specific risks may shape student outcomes as strongly as total time online. This paper presents a methodological proof of concept: it demonstrates an analytical pipeline that converts multi-platform outcome data into a graduated, affordance-informed policy typology, rather than offering validated platform-risk classifications or ready-to-implement policy. Using a publicly available student dataset covering twelve social networks, we aggregate four harm-aligned outcomes—addictive use, poorer mental health, shorter sleep duration, and interpersonal conflict—to the platform level and apply hierarchical clustering to derive a four-tier risk typology. We stress that the clustering and the subsequent principal component analysis operate on only twelve platform-level observations, not on the 705 individual students. We then project the tiers onto a two-dimensional risk map and simulate three illustrative policy regimes: a uniform daily cap, a tier-calibrated cap, and an affordance-targeted short-video cap. In this illustrative dataset, the tier-calibrated cap reproduces roughly seventy percent of the harm reduction in a blanket cap while directly affecting about three-quarters of students, and the three regimes deliver near-equivalent harm reduction per percent of students affected. Because the source data show characteristics consistent with synthetic or semi-synthetic simulation and lack established provenance, all quantitative results are presented as demonstrations of the pipeline. We describe the sensitivity of the typology to clustering choices, the ethical risks of tiered classification, and the replication required before any tier could inform practice. Full article

Background/Objectives: Opiates comprise naturally occurring opium alkaloids and their semisynthetic derivatives. Routine urine drug screening relies on enzyme immunoassays (EIAs) to rapidly detect opiate exposure; however, EIAs provide limited insight into opiate-associated metabolic patterns. Methods: We retrospectively analyzed liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-qToF-MS) datasets from comprehensive urine drug screening of 363 patients at the University of Pittsburgh Medical Center Clinical Toxicology Laboratory. Multiple statistical analyses were applied to identify the features associated with opiate (OPIA)-EIA-positive, oxycodone (OXY)-EIA-positive, and 6-monoacetylmorphine (6MAM)-EIA-positive specimens (42, 34, and seven specimens, respectively) designated as EIA-associated discovery feature sets. The feature sets selected by ≥2 statistical analyses were defined as EIA-associated consensus feature set and further evaluated using MS-FINDER for feature annotation. Results: Among 14,883 features, 138, 121, and 104 features were assigned to the OPIA-, OXY-, and 6MAM-EIA discovery feature sets, respectively. Consensus feature sets included oxycodone/opiate metabolites, acetaminophen metabolites, and norfentanyl for OPIA-EIA; oxycodone metabolites, α-phenylalanylaspartic acid, and 4-pyridoxic acid for OXY-EIA; and norfentanyl, 6-monoacetylmorphine, and 3-hydroxycotinine artifact for 6MAM-EIA. Conclusions: These metabolomic patterns indicate a dominant exposure gradient model, in which OXY-EIA-positive specimens primarily reflect prescribed oxycodone exposure, 6-MAM-EIA-positive specimens reflect illicit heroin/fentanyl exposure with polysubstance/recreational use signature, and OPIA-EIA-positive specimens occupy an intermediate, mixed profile shaped by immunoassay cross-reactivity and real-world co-exposures. Associations involving α-phenylalanylaspartic acid and 4-pyridoxic acid are hypothesis-generating and require further validation. These findings illustrate the value of archived clinical toxicology datasets for metabolomic discovery and as a foundation for sentinel laboratory-based surveillance of evolving drug and chemical exposures. Full article

Background: Polygodial (9), a drimane sesquiterpene dialdehyde from Drimys winteri, has not previously been examined against carbohydrate-hydrolyzing enzymes. Methods: Regioselective Wittig olefination at C12 gave the enoate 10; reduction of the remaining C11 aldehyde with NaBH₄ was followed by spontaneous intramolecular conjugate addition, affording the annellated tetrahydrofuran 12a and the bridged ether 12b. Results: All three derivatives inhibited α-glucosidase and α-amylase more strongly than the parent compound. Compound 12a was the most active α-glucosidase inhibitor (IC₅₀ = 53.98 ± 3.0 µM, against 90.36 ± 4.0 µM for acarbose) and, in docking, the only derivative to occupy the acarbose-binding site of the enzyme (−8.1 kcal/mol). However, this 12a–α-glucosidase pose was not maintained during the 200 ns simulations. The enoate 10 was the most active α-amylase inhibitor (IC₅₀ = 42.32 ± 2.1 µM, against 78.24 ± 3.9 µM for acarbose; −8.5 kcal/mol). Over 200 ns of molecular dynamics, the 10–α-amylase complex remained associated, with binding attributed by MM-GBSA mainly to van der Waals and lipophilic terms. Conclusions: Converting the dialdehyde into an enoate or a cyclic ether increases carbohydrase inhibition and determines which of the two enzymes is preferentially inhibited. Full article

Urban infrastructure systems increasingly depend on textual reports generated by citizens, inspection teams, maintenance units, emergency platforms, and smart city services. Accurate identification of critical risks in these reports is essential for enhancing urban resilience and enabling timely decision-making. Nevertheless, urban infrastructure risk classification is challenging due to the brevity, noise, domain specificity, and context dependence of these reports. This study introduces RAFE-XAI, a retrieval-augmented feature engineering and explainable natural language processing framework for urban infrastructure risk classification. The term retrieval-augmented is used here in a classification-oriented sense: retrieved reports are used to construct additional features and evidence, not to generate output text as in Retrieval-Augmented Generation systems. The proposed framework incorporates semantic sentence embeddings, retrieval-based evidence, neighborhood-derived label distributions, domain-specific risk indicators, infrastructure asset cues, location indicators, and evidence-based explainability. The framework does not construct an explicit graph, adjacency matrix, graph neural network, or message-passing mechanism. Instead, retrieval is used to derive neighbor label-distribution features, which are combined with semantic embeddings and interpretable keyword, asset, and location indicators. To assess the effectiveness of this approach, UIR-Text, a semi-synthetic urban infrastructure risk dataset with scenario-level group splitting to mitigate data leakage, was constructed. Experimental results on UIR-Text show that fine-tuned DistilBERT achieves the strongest predictive performance, with Macro-F1 scores of 0.8278 for category classification, 0.9120 for binary critical-risk detection, and 0.3379 for four-level severity classification. Among the explainable feature-engineering models, RAFE-XAI with Random Forest achieves the strongest category classification performance, with Accuracy 0.8400, Macro-F1 0.8043, Weighted-F1 0.8444, and MCC 0.8062. These results suggest that fine-tuned transformers provide the highest predictive performance on this benchmark, while RAFE-XAI offers a transparent retrieval-augmented alternative that exposes retrieved evidence, neighbor label distributions, and domain cues. Four-level severity classification remains challenging, even with fine-tuned DistilBERT, indicating the need for richer impact-aware variables. Full article

Hydrocolloid-based and hydrocolloid-dominant hybrid matrices have developed from conventional rheology modifiers into functional platforms for pharmaceutical and cosmetic delivery. This review focuses on systems in which hydrocolloid chemistry, hydration, ionisation, bioadhesion, and network architecture determine swelling, mechanical behaviour, biocompatibility, and controlled release. The discussion covers alginate, chitosan, hyaluronic acid, pectin, carrageenan, dextran, gellan gum, collagen, cellulose derivatives, and selected hybrid architectures in which synthetic or semi-synthetic components provide a defined responsive function. Rather than treating all smart polymers as a single class, the review compares how pH, enzymatic, redox/ROS, thermo-responsive, magnetic, optical, ultrasound-mediated, and multi-trigger mechanisms operate within hydrocolloid-rich matrices. Pharmaceutical examples are considered across oral, transdermal, injectable depot, wound-healing, and regenerative applications, while cosmetic and cosmeceutical systems are discussed in relation to active stabilisation, dermal residence, barrier support, and personalised skincare. By linking material class, trigger mechanism, route of administration, and translational constraints, the review identifies the main advantages of hydrocolloids as delivery matrices as well as their current limitations, including burst release, modest mechanical strength, hydrophobic-drug loading challenges, sterilisation sensitivity, source variability, and regulatory complexity. Full article

The fruits of Lycopus europaeus L. represent an unusual source of highly oxygenated aromatic abietane diterpenoids. Following our previous identification of euroabienol (1) from this plant material, a phytochemical reinvestigation of the dichloromethane fruit extract was undertaken to characterize related minor constituents. Three new aromatic abietane diterpenoids, 4-epileonubiastrin (2), 3α-acetoxyeuroabienol (3), and 11-deoxyeuroabienol (4), were isolated together with euroabienol (1). Their structures and relative configurations were established by MS, HRMS, IR, and extensive 1D and 2D NMR analyses. Manual iterative full spin analysis of selected ¹H NMR spin systems enabled refined determination of chemical shifts and coupling constants and provided additional support for conformational and configurational assignments, particularly in structurally congested parts of the molecules. To obtain a preliminary indication of biological relevance, compounds 1–4 and the semisynthetic O-methylated euroabienol derivative 5 were evaluated for acetylcholinesterase and urease inhibition. The observed effects were modest: compound 2 showed the highest AChE inhibition, reaching 31% at 50 μM, whereas compound 4 was the most active against jack bean urease, producing 40% inhibition at 100 μM. The study expands current knowledge of L. europaeus fruit diterpenoids and illustrates the value of ¹H NMR simulation as a complementary tool in the elucidation of closely related abietane natural products. Full article

Tobacco mosaic virus (TMV) poses a serious threat to global agricultural production. It is an exceptionally stable virus with a broad host range and is widespread across diverse agroecosystems. Concerning TMV management, plant-derived metabolites have emerged as promising and eco-friendly antiviral agents. To date, numerous plant-derived metabolites with potent anti-TMV activity and their underlying mechanisms of action have been identified. However, a comprehensive understanding of their mechanisms of action is still lacking. This review summarizes the diversity of anti-TMV mechanisms triggered by natural and plant-sourced semisynthetic compounds. These metabolites mainly include alkaloids, flavonoids, terpenoids, phenylpropanoids, and glycosides, which act either directly targeting virus particles or indirectly by eliciting host immunity. Together, these mechanisms form an integrated defence network that restricts viral replication and movement within the host. This mechanistic understanding will be essential for the rational development of sustainable and effective plant-derived antiviral agents. Full article

Artemisia annua contains artemisinin, a sesquiterpene lactone endoperoxide; artemether is a semisynthetic derivative of artemisinin that may offer potential advantages due to its redox-modulating and antimicrobial activities. These compounds have been associated with oxidative-stress modulation and microbial inhibition, making them promising candidates for experimental evaluation in nutritional and post-harvest quality studies. This study evaluated the effect of dietary supplementation with A. annua powder, artemisinin, and artemether on the refrigerated quality of Nile tilapia (Oreochromis niloticus) fillets. A total of 160 Nile tilapia were randomly assigned to four treatments: control (no additive), 1% A. annua powder, artemisinin (9.6 mg/kg feed), or artemether (9.6 mg/kg feed). After 30 days of feeding, 320 fillets were collected and stored under refrigeration at 4 °C. Samples were analyzed immediately after slaughter (day 0) and on days 7, 15, and 30. For each treatment group and sampling time, 20 fillets were used: 10 for microbiological evaluations, including counts of mesophilic and psychrotrophic bacteria, molds and yeasts, sulfite-reducing Clostridium, Enterobacteriaceae, coagulase-positive staphylococci, and coliforms; and 10 for physicochemical analyses, including pH, colorimetry, lipid oxidation through TBARS, and sensory evaluation. All supplemented treatments demonstrated improved microbial stability and lower TBARS values when compared with the control. Spoilage indicators such as discoloration, texture loss, and odor deterioration were also delayed. Artemether showed the most pronounced benefits, with lower microbial loads and oxidation indices for several evaluated parameters. These findings suggest that dietary supplementation with A. annua and its derivatives may help delay post-harvest quality deterioration of tilapia fillets during refrigerated storage. Full article

Semi-synthetic cannabinoids (SSCs) are chemically modified derivatives of naturally occurring phytocannabinoids that have rapidly emerged in commercial cannabis and hemp-derived products, including vape cartridges, edibles, infused oils, and concentrated extracts. Increasing availability of compounds such as hexahydrocannabinol (HHC), HHC analogues, and Δ⁸-tetrahydrocannabinol (Δ⁸-THC) has created significant challenges for forensic toxicology, analytical detection, public health surveillance, and regulatory control. This structured narrative review evaluated current evidence on the forensic, toxicological, pharmacological, and analytical implications of SSCs. The literature published between January 2019 and May 2026 was identified through searches of PubMed, Scopus, and Web of Science using predefined search terms related to SSCs, forensic toxicology, analytical detection, intoxication, metabolism, and public health. Recent evidence demonstrates that HHC-related compounds currently dominate the SSC market and scientific literature. Available studies indicate that SSCs undergo extensive Phase I and Phase II metabolism, producing hydroxylated, oxidized, and glucuronidated metabolites that frequently predominate over parent compounds in biological matrices. This metabolic complexity complicates forensic interpretation, particularly in postmortem investigations and impairment assessments where toxicological reference ranges remain poorly established. Emerging intoxication reports describe prolonged sedation, neuropsychiatric manifestations, cognitive impairment, and severe poisoning associated with HHC analogues, although much of the current evidence remains limited to case reports and small observational studies. From an analytical perspective, conventional toxicology screening methods may fail to detect SSC exposure, necessitating advanced analytical approaches such as liquid chromatography–tandem mass spectrometry (LC–MS/MS), high-resolution mass spectrometry (HRMS), and chiral chromatographic techniques for metabolite identification and epimer differentiation. However, limited reference standards, evolving structural diversity, and regulatory variability across jurisdictions continue to hinder standardized detection and interpretation. Overall, SSCs represent a rapidly evolving class of psychoactive compounds requiring coordinated advancements in forensic toxicology, analytical surveillance, pharmacological characterization, and public health monitoring to improve detection reliability, risk assessment, and regulatory response. Full article