Search Results (43,836)

Background: High-dose melphalan 140–200 mg/m² (HDM) with autologous stem cell transplant (ASCT) is standard first-line treatment in multiple myeloma (MM), yet standard BSA-based dosing results in wide variation in systemic exposure (AUC). We developed a pharmacokinetic (PK)-guided dosing strategy using a 100 mg/m² first dose, enabling real-time PK assessment and individualized adjustment for the second dose. We report final results of Phase A of our multi-center Phase 1 trial (NCT04483206, MyMel) evaluating feasibility and accuracy of this personalized approach. Methods: Patients received melphalan 100 mg/m² on Day −3, and seven PK samples were collected and shipped overnight for LC-MS/MS analysis. Real-time AUC estimation using noncompartmental analysis (NCA) guided Day −1 dosing to achieve pre-specified AUC targets (13.5 or 14.5 mg × h/L). For comparison, post hoc Bayesian estimation using a nonlinear mixed effects (NLME) model was performed. Sparse sampling designs were evaluated using NONMEM. Results: All 20 patients successfully received PK-guided dosing, with Day −1 doses determined within 48 h. PK-guided dosing reduced AUC variability (CV 4.20–5.62%), with 19 of 20 achieving AUCs within ±10% of the target, compared to what would have been achieved by BSA dosing (CV 9.74–15.34%). NLME improved accuracy, particularly in patients with missing samples, and maintained performance using only four PK time points. Conclusions: This study demonstrates that PK-guided dosing is accurate and feasible with HDM-ASCT. NLME enhances accuracy and enables simplified sampling. Phase B will identify maximum tolerated systemic exposure of seven additional AUC cohorts using the NLME model and a four-sample design. Full article

Cardiovascular disease (CVD) is closely linked to metabolic disorders such as obesity, dyslipidemia, and hepatic steatosis. This study investigated the anti-thrombotic and metabolic effects of KoreaGinseng F Max (KGF), a standardized extract rich in ginsenosides, in high-fat diet (HFD)-induced obese rats, and complementary in silico analyses were used to explore putative molecular targets and pathways. The extract was standardized to contain 36.97 mg/g of ginsenosides Rg1, Rb1, and Rf. Male rats were administered KGF (50, 100, or 200 mg/kg) orally for six weeks. KGF significantly improved lipid profiles by reducing serum triglycerides, total cholesterol, and low-density lipoprotein (LDL) levels. Histological analysis revealed a dose-dependent reduction in hepatic steatosis and adipocyte size. Potential anti-thrombotic activity was evaluated using a FeCl₃-induced carotid artery thrombosis model, with aspirin (30 mg/kg) included as a positive control. KGF200 delayed thrombus formation and produced a carotid blood flow pattern comparable to that observed in the aspirin-treated group, without significant alterations in serum ALT, AST, BUN, or creatinine levels. To further generate mechanistic hypotheses, complementary in silico analyses, including target prediction, GO/KEGG enrichment, network analysis, and molecular docking, were performed using the marker compounds. Eight overlapping genes, including STAT3, PTAFR, VEGFA, FGF2, HPSE, IL2, HSP90AA1, and LGALS3, associated with thrombotic regulation were identified. Pathway analysis suggested that PI3K–Akt signaling, calcium signaling, Th17 cell differentiation, and proteoglycan/ECM-related signaling may represent putative pathway-level mechanisms underlying the observed protective effects. Molecular docking suggested possible interactions between the marker ginsenosides and several predicted hub targets. Collectively, these findings suggest that KGF may have potential for further investigation as a natural product-derived material for improving HFD-associated metabolic and thrombotic dysfunction, while the predicted multi-target and multi-pathway effects require further experimental validation. Full article

Seed dimensions (length and width), surface color, frictional properties (static and kinetic coefficients of friction), thousand-seed weight, and angle of repose are five important categories of seed physical properties. Conventional methods generally measure these properties separately and rely heavily on manual operation, resulting in limited applicability and difficulty in balancing measurement efficiency and accuracy. To address these limitations, this study developed an automated method and an integrated platform incorporating automatic feeding, individual-seed positioning, state recognition, parameter acquisition, and cyclic control. After a single sample loading, the platform sequentially processed individual seeds, measured their dimensions, surface color, and static and kinetic coefficients of friction, and accumulated the measured seeds for subsequent thousand-seed weight and angle-of-repose determination, thereby enabling continuous automated measurement of the five categories of physical properties. Experiments were conducted using maize kernels, red kidney beans, and sunflower seeds to evaluate the measurement performance, repeatability, and cross-material adaptability of the platform. The standard deviations of repeated seed-dimension measurements were below 0.042 mm for all three seed types. The first moments of H and S in the HSV color space and a* and b* in the CIELAB color space were selected for color analysis and exhibited relatively low sensitivity to illumination variation under the tested imaging conditions. Within the stable-sliding intervals, the coefficients of determination for the relationship between actual seed displacement and squared time were all greater than 0.99, supporting the approximation of uniformly accelerated translational motion and the calculation of sliding acceleration for kinetic-friction determination. When the sample size used for thousand-seed weight estimation exceeded 50 seeds, further reductions in the coefficient of variation and average relative error were no greater than 0.2 percentage points. The angle-of-repose measurements distinguished differences in the pile formation characteristics of the three seed types. All three seed types completed the entire measurement procedure, corresponding to a platform adaptation success rate of 100%. In the cross-material validation, the coefficients of variation of all evaluated repeated-measurement indicators were no greater than 2.5%. These results indicate that, under the tested material and experimental conditions, the platform exhibited good operational stability, measurement repeatability, and cross-material adaptability, providing an effective approach for the automated and integrated measurement of multiple seed physical properties. Full article

Online flatness detection of aircraft rudder surfaces serves as a pivotal core procedure for ensuring the manufacturing precision, aerodynamic performance and operational safety of aeronautical components. Traditional plane fitting-based detection approaches are constrained by low detection efficiency, susceptibility to local optimal solutions, weak anti-noise robustness and limited automation capability, which fail to satisfy the micron-level high-precision online detection requirements for curved composite rudder surfaces in batch manufacturing scenarios. To address the aforementioned technical bottlenecks, this study proposes a bidirectionally coupled PSO-SA hybrid optimization algorithm for non-convex minimum zone flatness evaluation of curved rudder surfaces, which overcomes the unidirectional open-loop iteration limitation inherent in conventional serial PSO-SA composite frameworks. Two targeted algorithmic improvements are elaborated in this work: a residual-adaptive nonlinear inertia weight strategy, which dynamically balances global exploration and local exploitation capabilities based on the fluctuation characteristics of free-form surface measurement residuals; and a measurement noise-modified Metropolis acceptance criterion, which substantially enhances the algorithm’s anti-interference performance against on-machine trigger sampling noise. Integrating with the trigger-type on-machine detection hardware of computer numerical control (CNC) machine tools, an integrated online detection system is established to realize the full-process functions of point cloud data acquisition, error compensation, intelligent plane fitting and flatness error evaluation. Meanwhile, the complete technical workflow involving measurement path planning, probe calibration and algorithm iterative solution is systematically illustrated. Comparative simulation experiments implemented on the MATLAB platform demonstrate that the proposed algorithm exhibits superior performance in convergence speed, fitting accuracy and optimization stability over five mainstream algorithms, including standard particle swarm optimization (PSO), standard simulated annealing (SA), comprehensive learning PSO (CLPSO), adaptive cooling SA and conventional serial PSO-SA. On-machine physical measurement experiments are conducted on 24 aircraft rudder workpieces covering aluminum alloy skins and assembled riveted components. After multi-dimensional systematic calibration, the overall detection error of the developed system is controlled within 1 μm. The experimental results indicate that the average flatness error calculated by the proposed bidirectionally coupled PSO-SA algorithm is 29.7 μm, which is 30.1% and 38.5% lower than that of standard PSO and standard SA, respectively, fully complying with the aviation flatness tolerance specification of 0.1–0.3 mm. Moreover, the full detection cycle for a single workpiece is only 2.1 min, achieving a 34.4% reduction in detection time compared with standard PSO and effectively improving the efficiency of online in-process inspection. One-way analysis of variance (ANOVA) combined with Tukey’s posthoc test further verifies that the accuracy superiority of the proposed algorithm is statistically significant. This research provides a targeted theoretical basis and complete engineering implementation scheme for intelligent flatness detection of aerospace curved thin-walled parts, and offers a valuable technical reference for form and position error evaluation of irregular industrial components under noisy measurement conditions. Full article

Chili products are vulnerable to the addition of unauthorized substances, including Sudan dyes, Rhodamine B, Basic Orange 2, poppy-derived materials and improperly used processing chemicals. Their analysis is complicated by the high contents of lipids, carotenoids, capsaicinoids and other co-extracted matrix components in chili powder, chili oil, chili sauce and composite seasonings. This review critically evaluates conventional and emerging sample-preparation strategies, including solid-phase extraction; the quick, easy, cheap, effective, rugged and safe (QuEChERS) procedure; deep eutectic solvent (DES)-assisted extraction; enhanced matrix removal for lipids (EMR-Lipid); and molecularly imprinted sorbents. Laboratory methods based on high-performance liquid chromatography (HPLC), liquid chromatography–tandem mass spectrometry (LC–MS/MS) and gas chromatography–mass spectrometry (GC–MS) are compared with enzyme-linked immunosorbent assay (ELISA), surface-enhanced Raman spectroscopy (SERS), electrochemical sensors, miniature mass spectrometry and artificial intelligence-assisted hyperspectral imaging (AI–HSI). The comparison considers representative limits of detection and quantification, recovery, precision, sample-preparation burden, cost, portability, validation status and regulatory role. LC–MS/MS remains the preferred confirmatory platform for targeted multi-residue analysis, whereas rapid and portable methods are more appropriate for screening and sample triage. A three-tier workflow linking rapid screening, laboratory confirmation, and emerging-risk identification and traceability is proposed. Future priorities include standardized chili reference materials, open AI training and validation datasets, greener DES-based extraction and interlaboratory validation of field-deployable methods. Full article

Background and Objectives: Obstructive sleep apnea (OSA) is a prevalent sleep-related breathing disorder associated with increased cardiovascular morbidity and mortality. Continuous positive airway pressure (CPAP) is the standard treatment for OSA; however, its effects on cardiovascular outcomes remain incompletely established. This umbrella review synthesized and critically appraised the evidence regarding the effects of CPAP on surrogate, intermediate, and functional cardiovascular outcomes in adults with OSA. Materials and Methods: An umbrella review of systematic reviews and meta-analyses evaluating CPAP therapy in adults with OSA was conducted. A comprehensive literature search was performed in PubMed, Embase, and the Cochrane Library from database inception to 28 February 2026. Eligible reviews assessed cardiovascular outcomes, including blood pressure, cardiac function, autonomic function, cardiovascular biomarkers, and functional cardiovascular measures. Methodological quality was evaluated using AMSTAR 2, and the certainty of evidence for the main outcomes was assessed using the GRADE framework. Results: Thirty-eight systematic reviews and meta-analyses were included. CPAP therapy was consistently associated with reductions in systolic blood pressure (mean difference [MD] −4.8 mmHg) and diastolic blood pressure (MD −3.0 mmHg), with additional improvements in 24 h ambulatory blood pressure. Favorable effects were also observed for left ventricular ejection fraction (MD 3.27), left ventricular diastolic function (weighted mean difference [WMD] 0.22), and sympathetic nervous system activity, reflected by lower circulating noradrenaline levels (standardized mean difference [SMD] −1.10) in randomized controlled trials. Improvements in BNP/NT-proBNP concentrations and New York Heart Association functional class were reported among patients with heart failure, whereas no significant effect was observed on body mass index. The certainty of evidence ranged from low to moderate, and methodological confidence of the included reviews was predominantly moderate, low, or critically low. Conclusions: CPAP therapy was associated with improvements in several surrogate, intermediate, and functional cardiovascular outcomes in adults with OSA, particularly blood pressure, left ventricular function, and sympathetic activity. However, the certainty of evidence ranged from low to moderate, and substantial heterogeneity and methodological limitations warrant cautious interpretation. Current evidence remains insufficient to demonstrate a consistent reduction in major adverse cardiovascular events or cardiovascular mortality. Further high-quality randomized controlled trials with longer follow-up are needed to clarify the long-term cardiovascular impact of CPAP therapy. Full article

Introduction: Desmoid fibromatosis (DF) is a rare, locally invasive, and typically non-metastatic neoplasm. Its pathophysiology is poorly understood, and the subtle clinical presentation makes diagnosis challenging. A multidisciplinary approach is necessary to achieve a definitive diagnosis and identify the most effective therapeutic strategy. Because of its heterogeneous presentation and unpredictable behavior, DF cannot be managed through a standardized protocol, and diagnostic and therapeutic decisions must instead be tailored to the individual patient, consistent with a personalized-medicine approach. We present an unusual case in which we aim to evaluate the diagnostic procedures and treatments used and determine whether they align with the recent literature. Materials and Methods: A case report. A 27-year-old man presented with fever and worsening abdominal pain, without clinical signs of intestinal obstruction. He had undergone a sleeve gastrectomy five years earlier. A computed tomography (CT) scan revealed an intra-abdominal mass in the mesohypogastric region, likely originating from the mesentery of the small intestine. The patient underwent a right hemicolectomy extended to the terminal ileum. Histopathological examination and immunohistochemistry confirmed the diagnosis of DF. The patient was discharged on the 5th postoperative day (POD) in good general clinical condition. To date, there are no signs of recurrence. Conclusions: The multidisciplinary approach is essential for managing DF and R0 surgical resection remains the gold standard. The diagnostic and therapeutic pathway followed in our patient appears consistent with the recent literature. This case illustrates how individualized clinical reasoning can be applied even to a rare, heterogeneous neoplasm for which no standardized protocol exists. Furthermore, an established post-surgical management protocol for DF has not yet been developed. Additionally, we observed a possible, hypothesis-generating association between bariatric surgery and the subsequent onset of an intra-abdominal desmoid tumor; given the rarity of desmoid fibromatosis relative to the high volume of bariatric procedures performed worldwide, this observation should not be interpreted as a confirmed correlation, and we conducted a literature review to explore it as a hypothesis. Numerous studies are needed on this matter, but we hope that this case can provide a small starting point for subsequent studies. Full article

Immune checkpoint inhibitors (ICIs) have translated the scope of cancer therapy, but their immune-restorative mechanism can lead to immune-related adverse events (irAEs), which can affect several organs with varying severity. Early identification of patients at risk of irAEs is prudent to inform clinical decisions in precision oncology and remains a challenge as current clinical and biomarker methods still lack predictive accuracy. Artificial Intelligence (AI) presents a promising strategy for improving early detection, risk stratification as well as monitoring of irAEs. This narrative review summarizes the current AI modalities for detecting and predicting irAEs risk occurring with ICI therapy, including clinical Machine Learning models, radiomics-based approaches, natural language processing (NLP) systems and the integration of modalities with multimodal AI frameworks. Clinical Machine Learning models demonstrate moderate predictive performance whereas radiomics-derived modeling appears promising for pneumonitis. NLP and language models have achieved higher accuracy for retrospective irAEs detection. Multimodal AI applications offer theoretical potential through diverse data integration that captures the complex biology of irAEs; however, current evidence is limited. All these modalities face limitations of inadequate sample sizes, retrospective design, heterogeneous outcome definitions, class imbalance, and insufficient external validation. AI-based models have significant potential for personalized immunotherapy monitoring but require prospective multicenter validation, standardized datasets and clinically interpretable frameworks prior to implementation. Future advances in multimodal modeling can also enable precise prediction and early detection of irAEs, ultimately improving the safety and effectiveness of cancer immunotherapy. Full article

Artificial intelligence (AI) may influence economic performance through productivity, innovation, and the diffusion of advanced technologies, but direct country-level measures of AI adoption remain limited for small economies. This study examines the association between AI-related technological capability and economic growth in Cyprus. The descriptive analysis covers 1993–2024, while the econometric analysis uses the common annual sample for 2008–2024 after introducing one-period lags. High-technology exports as a percentage of manufactured exports are treated as an indirect indicator of technological sophistication and absorptive capacity, rather than as a direct measure of AI adoption. A sequential distributed lag ordinary least squares framework introduces current and lagged high-technology exports, merchandise trade, and inflation, with heteroskedasticity-consistent HC3 standard errors. A COVID-19 indicator for 2020–2021 and an observation-exclusion sensitivity analysis are used to assess the influence of exceptional pandemic-era movements. The preferred specification explains a substantial share of annual GDP growth variation, but the small sample requires cautious interpretation. The estimates indicate a positive contemporaneous and a negative lagged association for high-technology exports, a stronger lagged than contemporaneous trade association, and opposite-signed current and lagged inflation coefficients. The COVID-19 indicator is statistically insignificant and does not materially alter the principal coefficient pattern. Complementary DESI, Eurostat enterprise AI use, and ICT employment indicators show that Cyprus has broadly adequate digital inputs while enterprise AI adoption has not yet reached the European Union average, indicating considerable scope for further diffusion. The findings are exploratory statistical associations and do not identify causal effects of AI. The study contributes a country-specific macroeconomic assessment of technological capability, digital readiness, and growth in a small, highly open economy. Full article

Fault signals in rotating machinery typically manifest as long time-series data embedded with local high-frequency impulses. Traditional deep learning methods often struggle to simultaneously capture these transient local impacts and model long-term global degradation features. To address this challenge, this paper proposes a novel GCU-SAM enhanced Transformer for intelligent fault diagnosis. The proposed network integrates a Gated Convolutional Unit (GCU) with a Self-Attention Mechanism (SAM). By introducing the GCU as a local inductive bias prior to the global attention module, the model dynamically captures and purifies local impulse responses via reset and update gates. Subsequently, a cascaded multi-head self-attention mechanism models long-sequence global evolution trends, forming an integrated framework for local fine-grained perception and global correlation modeling. Validated on the CWRU bearing and SEU gearbox datasets, the proposed architecture achieves superior diagnostic performance with an average F1-score of 99.01%. Compared to representative baselines, including 1D-CNN, BiLSTM, and the standard Transformer, the GCU-SAM significantly boosts diagnostic accuracy and effectively overcomes the early-stage optimization oscillations inherent in pure attention mechanisms. By achieving a deep multi-scale fusion of local abrupt changes and global degradation trends, the model exhibits exceptional feature-clustering discriminative capability and convergence stability, providing a robust and highly accurate solution for the intelligent fault diagnosis of complex rotating machinery. Full article

Oil contamination of soils in arid regions of Kazakhstan is a critical environmental problem, as extreme temperatures, low humidity and salinity limit traditional bioremediation. A pilot-scale 45-day field experiment at the Karazhanbas oil field (Mangistau Region, Kazakhstan) provided a first field validation of an adsorption-immobilisation bioremediation technology. A consortium of Rhodococcus erythropolis AT7 and Dietzia maris 22K was immobilised on buckwheat and rice husk carriers. Four treatments were tested on 1 × 1 m plots (initial petroleum products 3725 mg/kg): the consortium immobilised on buckwheat husks, on rice husks, free cells, and an untreated control. Petroleum products were measured by FTIR spectroscopy on days 0, 15, 30 and 45. The buckwheat husk variant showed the highest observed degradation efficiency among the tested treatments—94.0 ± 0.5% (3725 to 223 ± 18 mg/kg)—1.6 times higher than rice husk (57.9%) and 1.7 times higher than free cells at the standard dose (54.6%). Degradation followed first-order kinetics (k = 0.0355 day⁻¹; t₁/₂ = 19.5 days), and hydrocarbon-oxidising microorganisms increased to 3.4 × 10⁸ CFU/g in the immobilised treatments. The higher performance of buckwheat husks may be associated with lower lignin content and improved carrier properties, with phenolic compounds such as rutin as a plausible additional factor. These pilot results provide a basis for larger-scale validation in Western Kazakhstan. Full article

Background/Objectives: Transcranial direct current stimulation (tDCS) and peripheral electrical stimulation (PES) have each demonstrated potential benefits for upper limb motor recovery in stroke patients. Their combined use has been hypothesized to produce synergistic/additive effects by engaging both central and peripheral neuroplasticity. This review aims to provide updated evidence on the effects of combined tDCS with PES on upper limb motor function and activity performance in stroke patients. Methods: Following PRISMA guidelines, six databases were searched. A systematic review and random-effects meta-analysis were completed. Methodological quality was assessed using the PEDro scale, risk of bias was assessed using the version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB 2), and certainty of evidence was assessed using the GRADE approach. Results: Twelve RCTs involving 449 participants were included in this review. Adding motor-level PES to tDCS demonstrated a significant benefit on FMA-WH (MD = 1.53; 95% CI = 0.38 to 2.68; p = 0.009; low certainty; single study), while no significant effects on FMA-UE, MAS, ADL, and activity capacity were observed. Adding tDCS to motor-level PES yielded no significant benefits on FMA-UE and FMA-WH, while significant effects on handgrip strength and activity capacity were seen (with low certainty based on narrative reporting from individual studies). Adding tDCS to sensory-level PES showed no significant effects on FMA-UE, FMA-WH, MAS, muscle strength, and activity capacity. Adding tDCS and motor-level PES to standard therapy showed non-significant effect on FMA-UE, while significant benefits on FMA-WH (MD = 5.17; 95% CI = 4.09 to 6.25; p < 0.001), ADL (MD = 17.07; 95% CI = 15.54 to 18.60; p < 0.001), and MAL-AOU (with low certainty based on a single study) were observed. Adding tDCS and sensory-level PES to standard therapy demonstrated no significant overall effects on FMA-UE&LE (MD = 10.57; 95% CI = −32.29 to 53.43; p = 0.197, I² = 43%), MAS (MD = −2.29; 95% CI = −13.09 to 8.51; p = 0.227; I² = 57%), and ADL (MD = 12.16; 95% CI = −44.69 to 69.02; p = 0.224; I² = 69%) (with low certainty based on multi-arms from a single study). Exploratory meta-analysis suggested a possible significant ADL improvement by combining tDCS and either motor/sensory PES with standard therapy (MD = 14.37; 95% CI = 1.83 to 26.91; p = 0.039; I² = 71%)(with very low certainty). Conclusions: This review revealed that the current evidence, although of very low certainty, does not demonstrate a clear synergistic/additive effect of combined tDCS and PES on upper limb motor function recovery after stroke (which may be due to the substantial heterogeneity and various state-dependent responses across cohorts). Current evidence suggests a potential ADL improvement after stroke by pairing tDCS and PES with conventional rehabilitation; however, this was of very low certainty which requires future studies to further validate. Future studies should use factorial designs (comparing tDCS with PES, tDCS alone, PES alone, and sham or standard therapy control) to further verify the effect of pairing tDCS and PES on upper limb function after stroke. Full article

The sorting of Persian lime Citrus × latifolia (Yu.Tanaka) Tanaka intended for export is still performed manually in many production units, introducing variability and low repeatability. Deep learning-based vision systems offer high accuracy but at a high cost, and with decisions that are difficult to trace against a quality standard. A system was developed that integrates classical computer vision (grayscale conversion, Gaussian filtering, thresholding and edge detection) with three supervised symbolic classifiers (PRISM, ID3 and Naive Bayes) under a hierarchical decision scheme, validated against the criteria of the Mexican Standard NMX-FF-077-1996-SCFI. The models were evaluated using a set of 7017 images, with a 265-image test subset, and the physical prototype was validated with 200 fruits. The system reached an accuracy of 95.1% on the test set and 95.5% during physical operation, with an F1 score of 0.97 for the export-grade class; only 2 of 265 and 1 of 200 non-conforming fruits were wrongly admitted. The cost of the deployed prototype remained at 407 USD. Integrating classical vision with interpretable symbolic rules constitutes an accessible and auditable solution for Persian lime quality control in accordance with the standard, with reproducible performance between algorithmic evaluation and physical operation. Full article

Wolfiporia extensa (Peck) Ginns (WE) is a fungus widely used in Traditional Chinese Medicine. Its four medicinal parts, Poria (WP), Rubra Poria (RP), Poriae Cutis (PC), and Poria Cum Pini Radix (PPR), are prescribed for distinct therapeutic purposes; however, their quality standards and pharmacological differences remain unclear. This study established reliable quality markers and compared the neuroprotective activities of four medicinal parts of WE. Seven triterpenoids were quantified using validated high-performance liquid chromatography (HPLC) assays, while monosaccharide profiles were analyzed using 1-phenyl-3-methyl-5-pyrazolone-HPLC. The International Commission on Illumination L*a*b* colorimetry and chemometric models were used for authentication. The neuroprotective effects of aqueous extracts were assessed in SH-SY5Y, BV-2, and HOG cell models. Network pharmacology was used to predict compound–target pathway relationships. The content of poricoic acid A (PAA) was the highest in PC; the content of pachymic acid (PA) was the highest in RP; whereas dehydrotumulosic acid (DTUA) was enriched in WP. WP demonstrated the highest mannose and galactose content. The water extract of WP reduced H₂O₂-induced cytotoxicity in SH-SY5Y cells, whereas the PC extract alleviated L-α-lysophosphatidylcholine-induced injury in human oligodendroglioma cells. Network analysis identified PA, PAA, and poricoic acid B (PAB) as candidate bioactive compounds involved in neuroprotective effects. The PA/PAB ratios of ≥10, 1–9, and ≤1 were used to identify WP, RP, and PC, respectively. L* values of >80, 55–79, and <55 effectively discriminated WP, RP, and PC, respectively. This integrative study identified chemical and functional quality markers and demonstrated distinct neuroprotective profiles in different parts of WE, supporting their standardized and rational application in traditional medicine and nutraceuticals. Full article

Meat and meat products are highly susceptible to microbial spoilage, lipid oxidation, moisture loss, discoloration, and sensory deterioration, creating a need for effective, safe, and sustainable packaging solutions. Although previous studies have investigated biodegradable polymers, nanomaterials, and active packaging systems separately, an integrated assessment connecting material design, preservation mechanisms, safety, sustainability, and commercial feasibility remains limited. This review addresses this gap by critically evaluating recent advances in biodegradable nanocomposite films and coatings for meat preservation. Current evidence demonstrates that the incorporation of nanoscale reinforcements and bioactive agents into biopolymer matrices can enhance their mechanical performance, gas and moisture barrier properties, antimicrobial activity, antioxidant capacity, and controlled release behavior. However, these advantages are strongly influenced by the nanofiller characteristics, concentration, dispersion, polymer-nanofiller interactions, food matrix composition, and storage conditions. Excessive nanomaterial incorporation may promote aggregation, induce structural defects, reduce flexibility, and increase migration concerns. Despite promising preservation outcomes, most available studies remain limited to laboratory-scale investigations, variable testing protocols, and insufficient validation under real commercial conditions. Key challenges hindering industrial adoption include nanoparticle migration, long-term safety assessment, regulatory uncertainty, production costs, consumer acceptance, and limited life-cycle evaluation. Future research should focus on safe-by-design formulations, standardized real-food testing, scalable manufacturing approaches, controlled-release technologies, and integrated assessments of preservation efficiency, safety, economic feasibility, and environmental sustainability. Overall, biodegradable nanocomposite packaging represents a promising approach for extending meat shelf life; however, successful commercialization requires balancing enhanced preservation performance with safety assurance and industrial practicality. Full article