Contemporary clinical medicine relies on the integration of clinical observation with physiological and pathological mechanisms to improve diagnosis, therapeutic decision-making, and patient outcomes. However, most current biomedical research interprets these mechanisms predominantly through the lens of upward emergence, according to which higher-order biological [...] Read more.

Contemporary clinical medicine relies on the integration of clinical observation with physiological and pathological mechanisms to improve diagnosis, therapeutic decision-making, and patient outcomes. However, most current biomedical research interprets these mechanisms predominantly through the lens of upward emergence, according to which higher-order biological functions arise from the interaction of simpler lower-level components. Although indispensable for understanding visceral diseases, this perspective provides only partial access to biological complexity. Accumulating evidence from neuroscience, developmental biology, endocrinology, psychiatry, and regenerative medicine shows that higher-level systemic functions can also reorganize, modulate, or generate lower-level structures, a phenomenon known as downward emergence. Together, upward and downward emergence form a bidirectional framework that more accurately reflects the complex organizational pattern of biological systems. This editorial argues that clinical practice and biomedical research must explicitly acknowledge this bidirectional dynamic, as many diseases (including malignancy) cannot be fully understood through upward emergence alone. Downward emergent processes explain phenomena such as morphogenesis, regeneration, matrix remodeling, immunological reprogramming, endocrine-neurovegetative integration, and forms of pathological transformation that are difficult to interpret through classical reductionism. Viewing cancer as the pathological expression of a disturbed supracellular program provides a coherent explanation of its complex biology and highlights the possibility that malignant progression could be responsive to higher-order regulatory instructions. In this context, theJournal of Mind and Medical Sciences is undertaking a conceptual and editorial realignment, positioning itself as a journal of bidirectional emergence in health and disease. Rather than diminishing its clinical mission, this shift strengthens it by providing a more comprehensive framework for understanding physiological and pathological organization, one that integrates structure–function and function–structure relationships. As medicine moves toward increasingly integrative and mechanistic models of disease, adopting a bidirectional perspective becomes not only scientifically justified but also necessary for advancing diagnostic accuracy, therapeutic innovation, and the development of novel supracellular strategies for human health.Full article

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Background/Objectives: Differentiating benign, premalignant, and early malignant vocal fold lesions is challenging. Perpendicular vascular changes (PVCs) per the European Laryngological Society (ELS) are key malignancy indicators. Enhanced contact endoscopy with narrow-band imaging (NBI-CE) visualizes intrapapillary capillary loops (IPCLs) at high magnification, independent [...] Read more.

Background/Objectives: Differentiating benign, premalignant, and early malignant vocal fold lesions is challenging. Perpendicular vascular changes (PVCs) per the European Laryngological Society (ELS) are key malignancy indicators. Enhanced contact endoscopy with narrow-band imaging (NBI-CE) visualizes intrapapillary capillary loops (IPCLs) at high magnification, independent of gross morphology. However, defining malignancy as any PVC increases sensitivity but lowers specificity—particularly in papillomas—whereas limiting malignancy to narrow-angle PVC improves specificity but risks false negatives and reduced reproducibility.Methods: We intraoperatively evaluated 146 histology-proven vocal fold lesions using NBI-CE. Six raters (three experienced otolaryngologists, three PhD students) classified vascular patterns. Two approaches were tested: (1) malignancy = narrow-angle PVC; (2) malignancy = any PVC. Outcomes were accuracy, sensitivity, specificity, and interrater agreement.Results: Approach (1) had higher specificity but lower sensitivity than (2) (~85% vs. ~70% specificity; ~50% vs. ~80% sensitivity). Accuracy did not differ significantly. Experienced raters showed higher interrater agreement and a more favorable sensitivity–specificity balance. Common errors were false positives in papillomas and false negatives in dysplasia/early carcinoma.Conclusions: PVC assessment with NBI-CE is feasible and informative. Choosing between “any PVC” and “narrow-angle only” entails a sensitivity–specificity trade-off and depends on lesion type and experience. Refined ELS descriptors and automated analysis may improve reproducibility and accuracy.Full article

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Deep learning (DL) methods have revolutionized natural language processing (NLP), enabling industrial documentation systems to process and generate text with high accuracy and fluency. Modern deep learning models, such as transformers and recurrent neural networks (RNNs), learn contextual relationships in text, making them [...] Read more.

Deep learning (DL) methods have revolutionized natural language processing (NLP), enabling industrial documentation systems to process and generate text with high accuracy and fluency. Modern deep learning models, such as transformers and recurrent neural networks (RNNs), learn contextual relationships in text, making them ideal for analyzing and creating complex industrial documentation. Transformer-based architectures, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), are ideally suited for tasks such as text summarization, content generation, and question answering, which are crucial for documentation systems. Pre-trained language models, tuned to specific industrial datasets, support domain-specific vocabulary, ensuring the generated documentation complies with industry standards. Deep learning-based systems can use sequential models, such as those used in machine translation, to generate documentation in multiple languages, promoting accessibility, and global collaboration. Using attention mechanisms, these models identify and highlight critical sections of input data, resulting in the generation of accurate and concise documentation. Integration with optical character recognition (OCR) tools enables DL-based NLP systems to digitize and interpret legacy documents, streamlining the transition to automated workflows. Reinforcement learning and human feedback loops can enhance a system’s ability to generate consistent and contextually relevant text over time. These approaches are particularly effective in creating dynamic documentation that is automatically updated based on data from sensors, registers, or other sources in real time. The scalability of DL techniques enables industrial organizations to efficiently produce massive amounts of documentation, reducing manual effort and improving overall efficiency. NLP has become a fundamental technology for automating the generation, maintenance, and personalization of industrial documentation within the Industry 4.0, 5.0, and emerging Industry 6.0 paradigms. Recent advances in large language models, search-assisted generation, and multimodal architectures have significantly improved the accuracy and contextualization of technical manuals, maintenance reports, and compliance documents. However, persistent challenges such as domain-specific terminology, data scarcity, and the risk of hallucinations highlight the limitations of current approaches in safety-critical manufacturing environments. This review synthesizes state-of-the-art methods, comparing rule-based, neural, and hybrid systems while assessing their effectiveness in addressing industrial requirements for reliability, traceability, and real-time adaptation. Human–AI collaboration and the integration of knowledge graphs are transforming documentation workflows as factories evolve toward cognitive and autonomous systems. The review included 32 articles published between 2018 and 2025. The implications of these bibliometric findings suggest that a high percentage of conference papers (69.6%) may indicate a field still in its conceptual phase, which contextualizes the article’s emphasis on proposed architecture rather than their industrial validation. Most research was conducted in computer science, suggesting early stages of technological maturity. The leading countries were China and India, but these countries did not have large publication counts, nor were leading researchers or affiliations observed, suggesting significant research dispersion. However, the most frequently observed SDGs indicate a clear health context, focusing on “industry innovation and infrastructure” and “good health and well-being”.Full article

Patients requiring breast reconstruction following radiation therapy or prior failed autologous breast reconstruction (ABR) or implant-based breast reconstruction (IBBR) represent a challenging cohort and often present with compromised vascularity, scarred anatomy, and subsequent increased rates of complications. In this review, we discuss microsurgical [...] Read more.

Patients requiring breast reconstruction following radiation therapy or prior failed autologous breast reconstruction (ABR) or implant-based breast reconstruction (IBBR) represent a challenging cohort and often present with compromised vascularity, scarred anatomy, and subsequent increased rates of complications. In this review, we discuss microsurgical strategies designed to optimize donor tissue in these challenging clinical scenarios, including the use of stacked or bipedicled flaps, and the utility of intraoperative indocyanine green angiography. We also review approaches to alternate recipient vessel selection in the suboptimal chest, and we address specific strategies for the revision setting, like soft tissue support and hybrid reconstruction with ABR and IBBR. By synthesizing the current literature and expert experience, this narrative review provides a practical framework for microsurgeons managing complex breast reconstruction in higher-risk patients.Full article

KY₃F₁₀:Yb³⁺, Tm³⁺ upconversion microparticles (UCMPs) with varying Mn²⁺ doping concentrations were synthesized via a hydrothermal method. Under 980 nm laser excitation, the sample with 3 mol% Mn²⁺ doping demonstrated markedly enhanced luminescence performance, exhibiting [...] Read more.

KY₃F₁₀:Yb³⁺, Tm³⁺ upconversion microparticles (UCMPs) with varying Mn²⁺ doping concentrations were synthesized via a hydrothermal method. Under 980 nm laser excitation, the sample with 3 mol% Mn²⁺ doping demonstrated markedly enhanced luminescence performance, exhibiting a significant intensity increase compared to undoped samples. The as-synthesized UCMPs were successfully incorporated into an anti-counterfeiting ink. Target information was encrypted using a hash function to generate a QR code, which was then screen-printed onto substrate materials. Under 980 nm laser irradiation, the printed QR code exhibited visible blue fluorescence with high stability, confirming its anti-counterfeiting capability. Furthermore, an image recognition system for anti-counterfeiting, based on a hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture, was developed on the Matlab platform. The system achieved 100% recognition accuracy for the luminescent QR code patterns, providing valuable insights for the development of deep learning-based image anti-counterfeiting technologies.Full article

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Horseradish (Armoracia rusticana L.) root (HRP) and leaf (HLP) pomaces, by-products of juice production by cold-pressing, were analyzed as a novel potential source of natural antioxidants. Chromatography analysis (UHPLC Q-ToF MS) of the bioactive compounds of pomaces was performed along with spectrophotometric [...] Read more.

Horseradish (Armoracia rusticana L.) root (HRP) and leaf (HLP) pomaces, by-products of juice production by cold-pressing, were analyzed as a novel potential source of natural antioxidants. Chromatography analysis (UHPLC Q-ToF MS) of the bioactive compounds of pomaces was performed along with spectrophotometric determination of total phenolic content (TPC), total flavonoid content (TFC), total phenolic acid (hydroxycinnamic) content (TPAC), and antioxidant capacity (via 1,1-diphenyl-2-picrylhydrazyl (DPPH^•) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic-acid) (ABTS^•+) radicals’ scavenging activity and ferric reducing antioxidant power (FRAP)). The concentrations of TPC, TFC, and TPAC differed among the pomaces, significantly favoring HLP. However, both horseradish pomaces (HRP and HLP) contained a considerable amount of various phenolics, with kaempferol and its glucosides dominating. In addition, they exhibit pronounced antioxidant activity, which is confirmed by all three methods used (DPPH, ABTS, and FRAP). These results highlight the potential of valorizing horseradish processing waste as a natural, reliable source of health-promoting bioactive compounds and functional ingredients in food products, thereby fortifying food, preventing oxidation, and prolonging shelf-life. In addition, this study supports endeavors to reduce food waste by providing new insights into the valorization of horseradish pomace, thus contributing to sustainable development and environmental protection.Full article

Urban parks, as vital components of urban green infrastructure, can improve urban ecological environments, showcase urban culture, and offer spaces for human recreation and exercise. However, human activities in these parks also produce severe disruption to soil ecosystems. Studying the effects of recreational [...] Read more.

Urban parks, as vital components of urban green infrastructure, can improve urban ecological environments, showcase urban culture, and offer spaces for human recreation and exercise. However, human activities in these parks also produce severe disruption to soil ecosystems. Studying the effects of recreational disturbances on soil properties and microbial communities is crucial for conserving urban biodiversity and maintaining ecosystem services. This study investigated the effects of human recreational disturbances (HRDs) on soil physicochemical properties and bacterial communities in four forest stands (Phellodendron amurense Rupr (Phe amu),Salix matsudana Koidz. (Sal mat),Pinus tabuliformis var. mukdensis (Pin tab), andPicea asperata Mast. (Pie asp)) in Changchun’s South Lake Park. The results showed that HRD significantly reduced soil water content (SWC) and total phosphorus (TP) while increasing soil bulk density (SBD) and pH. Soil organic carbon (SOC) and total nitrogen (TN) increased inPhe amu andPie asp soils but decreased inSal mat andPin tab soils (p < 0.05). Electrical conductivity (EC) changes were inversely related to SOC and TN trends. Dominant bacterial phyla includedActinobacteriota,Proteobacteria,Acidobacteriota, andChloroflexi. HRD reduced bacterial species richness and diversity by 5.3% and 7.6%, respectively. SWC and SBD were key factors influencing bacterial community dynamics, with SBD affectingBacteroidota,Proteobacteria, andMyxococcota, and SWC impactingProteobacteria,Bacteroidota, andActinobacteriota. These findings provide insights for urban park management, supporting soil microbial diversity and sustainable urban ecosystem development.Full article

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Background: Pathogenesis of aortic stenosis (AS) involves lipid infiltration, inflammation, and oxidative stress, which drive calcification of the aortic valve and progression to heart failure (HF). Fatty acids (FAs) play a crucial role in these processes. A treatment option for severe symptomatic AS [...] Read more.

Background: Pathogenesis of aortic stenosis (AS) involves lipid infiltration, inflammation, and oxidative stress, which drive calcification of the aortic valve and progression to heart failure (HF). Fatty acids (FAs) play a crucial role in these processes. A treatment option for severe symptomatic AS in elderly and high-risk patients is transcatheter aortic valve implantation (TAVI). Objective: To investigate the change in FA profiles in patients undergoing TAVI. Methods: This single-center prospective study included 25 patients with severe AS qualified for TAVI procedure. Blood samples were collected before TAVI and after six months. FA profiles were analyzed by gas chromatography-electron ionization mass spectrometry. Results: Notable changes were identified in FA profiles, including a reduction in docosahexaenoic acid (DHA) levels (117 ± 48.0 µM vs. 141 ± 53.0 µM,p = 0.001) and an increase in alpha-linolenic acid (ALA) concentration (32.8 ± 12.3 µM vs. 19.9 ± 6.40 µM,p = 0.003) six months post-TAVI. Additionally, significant elevations were noted in specific medium-chain FAs (C12) and branched-chain fatty acids (iso C16, iso C17 and anteiso C15, anteiso C17) at six months after TAVI. However, total n-3 polyunsaturated fatty acids (n3 PUFA) levels decreased (p = 0.039), while n-6 polyunsaturated fatty acids (n6 PUFA) levels exhibited no significant overall change at this time point. Decrease in mean pressure gradient (PG) was negatively correlated with eicosapentaenoic acid (EPA), DHA, n-3 docosapentaenoic acid (DPA n3) and n3 PUFA levels in a six-month observation. Conclusions: Our results underscore the complex interplay between cardiac intervention and FA changes, providing novel insights into the metabolic impact of TAVI on FAs serum profile.Full article

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The rise in antimicrobial resistance and tolerance over time represents a significant threat to human and animal health. This has led to a notable increase in interest within the scientific community in the development of herbal-based therapeutic alternatives to antibiotics. The present study [...] Read more.

The rise in antimicrobial resistance and tolerance over time represents a significant threat to human and animal health. This has led to a notable increase in interest within the scientific community in the development of herbal-based therapeutic alternatives to antibiotics. The present study aimed at evaluating the in vitro antimicrobial activity of 18 essential oils (EOs) against a total of 17 strains belonging to both Gram-positive and Gram-negative bacteria, by employing the spot-on-agar method using two different culture media, Brain Heart Infusion (BHI) and Mueller–Hinton (MH). The antimicrobial properties of these essential oils were investigated, as well as their antimicrobial stability over a period of seven days. The overall efficacy of the EOs remained consistent over seven days, regardless of the solid medium used. However, the antimicrobial effects of the EOs were more pronounced in BHI than in MH for Gram-positive bacteria. While variations in antimicrobial activity were recorded among different species and strains, oregano EO proved to be the most effective against both Gram-positive and Gram-negative bacteria, followed by thyme and tea tree. The findings of this study support the notion that EOs could be employed as a promising therapeutic strategy to combat antibiotic-resistant bacteria, thereby enhancing the efforts aimed at addressing antibiotic resistance. Furthermore, the divergent antimicrobial effects exhibited by the two growth media employed here may facilitate the establishment of standardised protocols for the testing of EOs against bacteria.Full article

Remote photoplethysmography (rPPG) is a non-contact technology that estimates physiological signals, such as Heart Rate (HR), by capturing subtle skin color changes caused by periodic blood volume variations using only a standard RGB camera. While cost-effective and convenient, it suffers from a fundamental [...] Read more.

Remote photoplethysmography (rPPG) is a non-contact technology that estimates physiological signals, such as Heart Rate (HR), by capturing subtle skin color changes caused by periodic blood volume variations using only a standard RGB camera. While cost-effective and convenient, it suffers from a fundamental limitation: performance degrades severely in dynamic environments due to susceptibility to noise, such as abrupt illumination changes or motion blur. This study presents a deep learning framework that combines two structural modifications to ensure robustness in dynamic environments, specifically modeling movement noise and illumination change noise. The proposed framework structurally cancels global disturbances, such as illumination changes or global motion, through a dual-branch pipeline that encodes the face and background in parallel after Video Color Magnification (VCM) and then performs differencing. Subsequently, it utilizes a structure that injects a Temporal Shift Module (TSM) into the Spatio-Temporal Feature Extraction (SSFE) block to preserve long- and short-term temporal correlations and smooth noise, even amidst short and irregular movements. We measured MAE, RMSE, and correlation on the standard dataset UBFC-rPPG under four noise conditions: clean, illumination change noise, Movement Noise, Both Noise and the real-world in-vehicle dataset MR-NIRP (Stationary and Driving). Experimental results showed that the proposed method achieved consistent error reduction and correlation improvement compared to the VS-Net baseline in the illumination change noise-only and combined noise environments (UBFC-rPPG) and in the high-noise driving scenario (MR-NIRP). It maintained competitive performance in motion-only noise. Conversely, a modest performance disadvantage was observed under clean conditions (UBFC) and quasi-clean stationary conditions (MR-NIRP), interpreted as a design trade-off focused on global noise cancellation and temporal smoothing. Ablation studies demonstrated that the dual-branch pipeline is the primary contributor under illumination change noise, while TSM is the key contributor under movement noise, and that the combination of both elements achieves optimal robustness in the most complex scenarios.Full article

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Background: There are important gaps in describing the associations between variants found by GWAS and various phenotypes. Prior reports suggest that SNPs in regulatory regions should be further investigated to uncover these associations. Thus, this study involved a novel approach, along with [...] Read more.

Background: There are important gaps in describing the associations between variants found by GWAS and various phenotypes. Prior reports suggest that SNPs in regulatory regions should be further investigated to uncover these associations. Thus, this study involved a novel approach, along with Pharmacoepigenomics, prompting a new coined term “CpG-PGx SNP”.Methods: The rationale behind our analysis strategy was based on the impact of SNPs playing dual roles both in the CpG site disruption/formation and having PGx associations. Thus, we employed GeneCards (relevance score), PharmGKB (significantp-value), and GWAS catalog data for each gene (p < 5 × 10⁻⁸). Following the obtainment of the 25 best-scored genes of four major epigenetic processes (methylation, demethylation, acetylation, and deacetylation), we generated two lists of candidate genes, including potential CpG-PGx SNPs and possible CpG-PGx SNPs. Results: Among 2900 significant PGx annotations, we found 99 potential CpG-PGx SNPs related to 16 genes.CYP2B6,CYP2C19,CYP2D6, andCOMT genes were the top genes. Additionally, we found 1230 significant GWAS-based SNPs, among them 329 CpG-SNPs related to 48 genes with at least one CpG site disruption/formation. The top gene with the highest CpG-SNPs wasTET2, followed byJMJD1C andHDAC9. Importantly, we detected some synonymous variants in the Epigenetically Modifiable Accessible Region (EMAR), which can provide insights into undiscovered roles of these SNPs. We identified 173 CpG-Disruptive SNPs, 155 CpG-Forming SNPs, and just 1 CpG SNP with both impacts.Conclusions: In conclusion, here we introduce CpG-PGx SNP for the first time and suggest three major genes playing crucial roles in Pharmacoepigenomics (PEpGx),CYP2D6 as the heart of PEpGx, andTET2 with the highest possibility of having CPG-PGx SNPs. We believe that this approach will help the scientific community to utilize “CpG-PGx SNP” to unravel complex disease-driven genetic and epigenetic interactions, yielding therapeutic opportunities.Full article

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Background: Abdominal ultrasound is prone to hepatocellular carcinoma (HCC) surveillance failure, particularly in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) or alcohol-associated liver disease (ALD), prompting growing interest in blood-based biomarkers as an alternative strategy.Methods: We conducted a case–control study evaluating [...] Read more.

Background: Abdominal ultrasound is prone to hepatocellular carcinoma (HCC) surveillance failure, particularly in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) or alcohol-associated liver disease (ALD), prompting growing interest in blood-based biomarkers as an alternative strategy.Methods: We conducted a case–control study evaluating two blood-based biomarker panels, GAAD and GALAD, for detection of early-stage HCC (Barcelona Clinic Liver Cancer (BCLC) stage 0 or A) in patients with MASLD or ALD cirrhosis. Blood specimens were collected within 6 months of HCC diagnosis (cases); controls were patients with cirrhosis but without HCC. GAAD and GALAD scores were measured using the Roche Elecsys platform, applying validated cutoffs of 2.57 and 2.47, respectively. Sensitivity and specificity were compared between the panels and versus ultrasound plus alpha fetoprotein (AFP) using McNemar’s chi square test.Results: Of 152 patients (56.6% men), 46.7% had HCC (54.9% BCLC 0/A) and 53.3% had cirrhosis without HCC. GAAD and GALAD each achieved a sensitivity of 87.2% for early-stage HCC, with specificities of 69.1% and 67.9%, respectively. In paired analyses (n = 90), GAAD had higher sensitivity for any-stage HCC (89.5% vs. 68.4%,p = 0.046) but lower specificity (71.8% vs. 93.0%,p = 0.006) than ultrasound plus AFP. GAAD and GALAD demonstrated consistently higher sensitivity than ultrasound plus AFP across subgroups by age, sex, cirrhosis etiology, and Child Pugh class.Conclusion: In this case–control study of patients with non-viral cirrhosis, GAAD and GALAD demonstrated high sensitivity for early-stage HCC. These findings highlight the potential of blood-based biomarkers to improve HCC surveillance in contemporary populations.Full article

The safe operation of hydrogen transmission pipeline stations is paramount for the widespread adoption of hydrogen energy. This study addresses the significant hazard of hydrogen leakage in high-pressure pipeline stations by employing numerical simulations to investigate the dispersion behavior under various conditions. It [...] Read more.

The safe operation of hydrogen transmission pipeline stations is paramount for the widespread adoption of hydrogen energy. This study addresses the significant hazard of hydrogen leakage in high-pressure pipeline stations by employing numerical simulations to investigate the dispersion behavior under various conditions. It specifically focuses on the complex interplay between meteorological factors, operational parameters, and station layout. A key finding is that the structural configuration of obstacles—namely their height and distance from the leakage source—serves as the dominant mechanism controlling the evolution of the hazard radius, overshadowing the influence of traditional parameters like wind speed and leak diameter in obstructed environments. Based on this insight, a novel and robust predictive model for the dynamic hazard radius was developed using multiple regression analysis. The model accurately quantifies the impact of leakage duration, obstacle spacing, and obstacle height, achieving an excellent fit (R² = 0.9848) with a prediction error of less than 5% compared to simulation data. This study provides valuable insights for defining risk zones and supports the development of effective safety measures and emergency response strategies for hydrogen infrastructure, thereby contributing to the secure and sustainable deployment of hydrogen energy.Full article

Background/Objectives: Melphalan-based percutaneous hepatic perfusion (M-PHP) became approved in 2023 for treatment of liver-dominant metastatic uveal melanoma (mUM). Patients with liver-dominant mUM have a poor overall survival (OS) ≤ 12 months; however, the reported OS benefit from M-PHP varies in clinical trials from [...] Read more.

Background/Objectives: Melphalan-based percutaneous hepatic perfusion (M-PHP) became approved in 2023 for treatment of liver-dominant metastatic uveal melanoma (mUM). Patients with liver-dominant mUM have a poor overall survival (OS) ≤ 12 months; however, the reported OS benefit from M-PHP varies in clinical trials from 9.6 to 27.4 months and remains uncertain. Here, we report the OS outcome after 10 years’ experience with M-PHP treatment of patients with liver-dominant mUM. Methods: A total of 38 consecutive patients (19 women, median age 57.7 years) with liver-dominant mUM underwent 99 M-PHP procedures (median: 2.6 M-PHP/patient) between April 2014 and March 2024 at our institution. OS outcomes were retrospectively analyzed using Kaplan–Meier methods and Cox proportional hazard models. Results: Median OS was 29.1 months after first M-PHP treatment (median follow-up: 25.8 months). The estimated percentage of patients surviving at 1, 2, and 3 years was 79.5%, 53.2%, and 28.5%, respectively. Each additional M-PHP cycle was associated with about 40% reduction in the risk of death (hazard ratio = 0.414). Median OS was numerically improved by 8.4 months with ≥3 versus ≤2 cycles of M-PHP administered (29.8 versus 21.4 months,p = 0.058). No treatment-related deaths occurred. Conclusions: This study found a clinically meaningful OS benefit in M-PHP-treated patients with liver-dominant mUM, reaching nearly 2.5-year median OS with ≥3 M-PHP cycles administered. This finding supports the need to account for the institutional volume and experience with the M-PHP procedure in both clinical practice and research, and may provide an OS reference for estimating OS gains in the evolving therapeutic landscape for mUM patients.Full article

In recent years, cloud computing and edge computing have flourished, establishing data centers as pivotal hubs for information exchange. However, the security for the interconnection of these data centers faces increasingly severe challenges. Traditional cryptographic techniques face potential risks of being compromised under [...] Read more.

In recent years, cloud computing and edge computing have flourished, establishing data centers as pivotal hubs for information exchange. However, the security for the interconnection of these data centers faces increasingly severe challenges. Traditional cryptographic techniques face potential risks of being compromised under the threat of quantum computing, whereas quantum key distribution (QKD), which possesses information-theoretic security, provides an effective foundation for secure data center interconnection. This paper focuses on the QKD-enabled cross-domain data center interconnection network, delving into the multi-dimensional resource (i.e., computing, wavelength, and key resources) allocation problem. By constructing a QKD-enabled cross-domain data center interconnection network model, it integrates key resources with traditional computing and wavelength resources, forming a multi-dimensional resource allocation framework. Furthermore, we design two heuristic algorithms, i.e., the local balancing factor-based multi-dimensional resource allocation (LBF-MDRA) and the global balancing factor-based multi-dimensional resource allocation (GBF-MDRA) algorithms, which rationally perform virtual network function (VNF) node selection and efficiently allocate multi-dimensional resources. Simulation results indicate that the LBF-MDRA and GBF-MDRA algorithms can increase the success probability of cross-domain service requests by 24.53% and 30.91% compared to the benchmark algorithm, respectively.Full article

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Adding plasmonic nanostructures to perovskite solar cells (PSCs) can boost light absorption, but often at the cost of new electronic losses. Based on 3D FDTD simulations, this study demonstrates how Au@Al₂O₃ core-shell nanostructures can overcome this fundamental trade-off through a [...] Read more.

Adding plasmonic nanostructures to perovskite solar cells (PSCs) can boost light absorption, but often at the cost of new electronic losses. Based on 3D FDTD simulations, this study demonstrates how Au@Al₂O₃ core-shell nanostructures can overcome this fundamental trade-off through a dual function of the Al₂O₃ shell, namely its moderate refractive index and excellent passivating properties. In addition, the geometry of Au@Al₂O₃ core–shell nanostructure is optimized to produce a maximum short-circuit current density ($J_{sc}$) of 25 mA cm⁻². The simulations provide mechanism-level design rules that link dielectric choice and geometry to near-field localization and far-field coupling in perovskite absorbers. An experimentally testable parameter window is reported rather than device-level performance claims, with explicit discussion of energy partitioning and stability caveats associated with plasmonic loss in Au and interfacial chemistry.Full article

BTEX compounds (benzene, toluene, ethylbenzene, and xylene isomers) are aromatic hydrocarbons widely used in various industries. Due to their volatility, they become persistent pollutants in workplace air, posing serious risks to worker health. The aim of this study is to systematically map academic [...] Read more.

BTEX compounds (benzene, toluene, ethylbenzene, and xylene isomers) are aromatic hydrocarbons widely used in various industries. Due to their volatility, they become persistent pollutants in workplace air, posing serious risks to worker health. The aim of this study is to systematically map academic publications on BTEX exposure and health effects and to evaluate the impact of exposure levels in industrial settings on worker health. Publications obtained from the Web of Science database between 2010 and 2025 were bibliometrically analyzed in terms of productivity, collaboration networks, thematic trends, and analysis methods. In addition, the sources of BTEX compound dispersion, analysis methods, and industrial hazard classifications were evaluated through content analysis. According to the findings, Iran and China stood out as the most active countries, with publication intensity peaking in 2023. BTEX exposure was observed to be particularly high in the petrochemical sector. However, there is a lack of studies that systematically address the direct effects on worker health. This study aims to contribute to the more effective management of BTEX-related exposure risks by providing decision-makers with scientifically based and interpretable analyses.Full article

Ferroelectricity in hafnium oxide (HfO₂)-based thin films has emerged as a scalable pathway toward CMOS-compatible non-volatile memories and logic devices. This study examines how dopant chemistry and film thickness influence the stabilization of the ferroelectric phase in ALD-grown HfO₂ thin [...] Read more.

Ferroelectricity in hafnium oxide (HfO₂)-based thin films has emerged as a scalable pathway toward CMOS-compatible non-volatile memories and logic devices. This study examines how dopant chemistry and film thickness influence the stabilization of the ferroelectric phase in ALD-grown HfO₂ thin films doped with Zr, Al, and Y. Structural, morphological, and electrical characterizations were carried out using AFM, GIXRD, P–E, in-plane I/W–E, and C–V measurements on films with thicknesses of 7 nm and 100 nm. AFM revealed atomically smooth and dense surfaces (R_q < 0.5 nm), while GIXRD confirmed the stabilization of the orthorhombic Pca2₁ phase in doped 7 nm films and its relaxation toward the monoclinic phase at 100 nm. The 7 nm HfZrO and HfYO films exhibited robust ferroelectric hysteresis with remanent polarization values up to 60 μC·cm⁻², whereas HfAlO showed a narrower but still distinct switching response. In-plane I/W–E characteristics indicated a combination of Poole–Frenkel and injection-limited conduction, consistent with defect-assisted polarization reversal and asymmetric contact barriers. At 100 nm, all films showed reduced polarization and partially dielectric behavior, as corroborated by the C–V data. These results demonstrate that nanoscale confinement, dopant-induced strain, and oxygen vacancy related defect chemistry collectively stabilize the orthorhombic ferroelectric phase, with Zr doping providing the most favorable balance between polarization strength and leakage control.Full article

This study identifies the socio-ecological predictors at each level of the ecosystem that serve as risk or protective factors for the frequency of bullying, cyberbullying, and sexual harassment in U.S. schools, and examines how these predictors vary across the three behaviors, highlighting both [...] Read more.

This study identifies the socio-ecological predictors at each level of the ecosystem that serve as risk or protective factors for the frequency of bullying, cyberbullying, and sexual harassment in U.S. schools, and examines how these predictors vary across the three behaviors, highlighting both common and distinct predictors. Using data from two waves (2018 and 2020) of the School Survey on Crime and Safety (SSOCS), a nationally representative cross-sectional survey of 5132 U.S. public elementary and secondary schools, we conduct a Generalized Ordinal Logistic Regression in hierarchical blocks of mesosystem-, exosystem-, and macrosystem-level factors to examine the relative contribution of each ecological level. We find that both mesosystem (teacher training, school size, school grade) and exosystem (neighborhood crime, parental involvement, and involvement of mental health & social services)-level factors play significant roles in predicting the frequency of bullying, sexual harassment, and cyberbullying. Urbanicity at the macrosystem-level is not a significant predictor of bullying and sexual harassment; however, schools in urban areas are less likely to have frequent and monthly cyberbullying and more likely to have occasional and no cyberbullying compared to schools in non-urban areas. We argue for the importance of multi-level interventions to address peer harassment in schools through evidence-based policies.Full article

Lipid signaling plays a crucial role in how plants perceive and respond to environmental challenges. Among the various lipid mediators, phosphatidic acid (PA) serves as a key metabolic intermediate and second messenger that links membrane dynamics with stress signaling. It is produced rapidly [...] Read more.

Lipid signaling plays a crucial role in how plants perceive and respond to environmental challenges. Among the various lipid mediators, phosphatidic acid (PA) serves as a key metabolic intermediate and second messenger that links membrane dynamics with stress signaling. It is produced rapidly through the coordinated actions of phospholipase C, phospholipase D and diacylglycerol kinase, and its transient accumulation enables plants to adjust defense and acclimation responses with remarkable precision. Recent studies have shown that PA participates in immune signaling, osmotic regulation, and redox control, functioning at the intersection of membrane remodeling and intracellular signal transduction. Through interactions with hormone signaling, calcium fluxes, and reactive oxygen species production, PA integrates multiple stress-responsive pathways, thereby helping to maintain physiological homeostasis under adverse conditions. This review summarizes current understanding of the biosynthetic regulation and signaling roles of PA, and discusses emerging perspectives that highlight its central role in plant immunity and stress adaptation.Full article

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Background: Cystic fibrosis (CF) is a genetic disorder that disrupts gut microbiota composition, promoting dysbiosis associated with chronic inflammation, impaired nutrient absorption, and poor clinical outcomes. While modulation of the intestinal microbiota through prebiotics, probiotics, and synbiotics has been proposed as a therapeutic [...] Read more.

Background: Cystic fibrosis (CF) is a genetic disorder that disrupts gut microbiota composition, promoting dysbiosis associated with chronic inflammation, impaired nutrient absorption, and poor clinical outcomes. While modulation of the intestinal microbiota through prebiotics, probiotics, and synbiotics has been proposed as a therapeutic strategy, clinical evidence remains limited, especially in children. Objective: This study aimed to evaluate the impact of three supplementation strategies (a prebiotic (β-glucan), a probiotic (Lacticaseibacillus rhamnosus GG), and their synbiotic combination) on the gut microbiota and metabolic activity of a CF child faecal donor using a dynamicin vitro colonic fermentation model (SHIME^®). Methods: Microbial composition (16S rRNA gene sequencing), and metabolic activity (quantification of short-chain fatty acids (SCFAs), ammonia, and lactate) were analysed. Results: Results showed that the prebiotic increased alpha diversity; while both the prebiotic and probiotic treatments significantly reduced Bacillota and increased Bacteroidota, modulating the Bacillota/Bacteroidota ratio. The synbiotic treatment showed the most beneficial overall profile, including enhanced production of SCFAs, particularly butyrate and propionate, and increased abundance ofFaecalibacterium andAgathobacter, which are two bacterial genera generally associated with gut health. Notably, the synbiotic also reduced the relative abundance of potentially pathogenic genera such asVeillonella,Megasphaera, andStenotrophomonas, but paralleled with an increase inClostridium ss 1. Although the probiotic alone showed some positive effects, it was less effective overall compared to the prebiotic and synbiotic approaches. Conclusions: These findings support the potential of synbiotic supplementation as a promising strategy to modulate gut dysbiosis in CF, thoughin vivo studies are needed to confirm the translational relevance of these results.Full article

This study investigated the effects of three cooking methods—fragmenting process (FP), boiling treatment (BT), and high-pressure steam (HPS) treatment—on the structure and volatile compounds (VOCs) of freshLyophyllum decastes. The surface morphology and functional groups ofL. decastes were analyzed by scanning [...] Read more.

This study investigated the effects of three cooking methods—fragmenting process (FP), boiling treatment (BT), and high-pressure steam (HPS) treatment—on the structure and volatile compounds (VOCs) of freshLyophyllum decastes. The surface morphology and functional groups ofL. decastes were analyzed by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR), respectively. The VOCs inL. decastes were analyzed by comprehensive two-dimensional gas chromatography–mass spectrometry (GC×GC-MS) and gas chromatography–ion mobility spectrometry (GC-IMS). SEM results showed that HPS resulted in the most pronounced structural disruption, forming a honeycomb-like porous surface, whereas FP yielded smaller fragments with smoother surfaces. FTIR spectra indicated that none of the treatments significantly altered the characteristic functional groups. A total of 73 VOCs were identified by GC×GC-MS, including 23 hydrocarbons, 14 alcohols, 10 ketones, seven aldehydes, six ethers, three esters, two terpenes, and eight other compounds. Additionally, 22 VOCs were identified by GC-IMS, including seven alcohols, six aldehydes, five esters, three ketones, and one other compound. The four compounds benzaldehyde, benzeneacetaldehyde, (E)-2-hexen-1-ol, and 1-hexanal were detected by both methods. Among the three methods, FP induced the least structural damage and better preserved the VOCs. These results offer theoretical insights and technical support for the flavor-oriented deep processing ofL. decastes.Full article

Biased estimates and fluctuating measures of central tendency are significant impediments to statistical inference and computational data analysis and are often caused by partial and distorted observations. The imputed least-squares-based estimators are very sensitive to non-normality, outliers, and missing data; thus, they cannot [...] Read more.

Biased estimates and fluctuating measures of central tendency are significant impediments to statistical inference and computational data analysis and are often caused by partial and distorted observations. The imputed least-squares-based estimators are very sensitive to non-normality, outliers, and missing data; thus, they cannot guarantee reliability in the presence of anomalous data. In an attempt to overcome these inadequacies, this paper utilizes a geometric fusion scheme, the Minimum Regularized Covariance Determinant (MRCD), to construct high-quality central measures. The suggested mechanism incorporates the concept of geometric dispersion and resistance-based principles of covariance to form stable dispersion structures, irrespective of data contamination and incompleteness. In this computational scheme, three estimators are developed, all of which use adaptive logarithmic transformations to boost efficiency and robustness. The theoretical argument can be characterized by analytical derivations of bias and Mean Squared Error (MSE) in large-sample situations, and empirical gains were verified by large-scale Monte Carlo experiments using synthetic populations and real-world datasets. The proposed MRCDdriven estimators are known to have a lower MSE as well as higher percentage relative efficiency (PRE) as compared to classical estimators. Overall, the findings indicate that the geometric fusion mechanism (MRCD) is a powerful, self-scaling, and statistically sound way of computing central measures in a situation in which information is incomplete and distorted.Full article

This paper introduces a novel approach to handwritten digit recognition based on directional flood simulation and topological feature extraction. While traditional pixel-based methods often struggle with noise, partial occlusion, and limited data, our method leverages the structural integrity of digits by simulating water [...] Read more.

This paper introduces a novel approach to handwritten digit recognition based on directional flood simulation and topological feature extraction. While traditional pixel-based methods often struggle with noise, partial occlusion, and limited data, our method leverages the structural integrity of digits by simulating water flow from image boundaries using a modified breadth-first search (BFS) algorithm. The resulting flooded regions capture stroke directionality, spatial segmentation, and closed-area characteristics, forming a compact and interpretable feature vector. Additional parameters such as inner cavities, perimeter estimation, and normalized stroke density enhance classification robustness. For efficient prediction, we employ the Annoy approximate nearest neighbors algorithm using ensemble-based tree partitioning. The proposed method achieves high accuracy on the MNIST (95.9%) and USPS (93.0%) datasets, demonstrating resilience to rotation, noise, and limited training data. This topology-driven strategy enables accurate digit classification with reduced dimensionality and improved generalization.Full article

Injection molding is a high-volume manufacturing process widely used for producing polymer components; however, its process parameters strongly influence residual stress, warpage, and the resulting mechanical performance. This work presents a comprehensive factorial design and ANOVA to evaluate the simultaneous effects of the [...] Read more.

Injection molding is a high-volume manufacturing process widely used for producing polymer components; however, its process parameters strongly influence residual stress, warpage, and the resulting mechanical performance. This work presents a comprehensive factorial design and ANOVA to evaluate the simultaneous effects of the injection temperature, packing pressure, packing time, and specimen orientation on the warpage, hardness, tensile, and flexural properties of polypropylene plates. The results demonstrate that the injection temperature and packing pressure are the dominant factors affecting the hardness and ultimate tensile strength, whereas warpage is mainly governed by the injection temperature and orientation. Under the tested conditions, certain combinations of injection temperature and packing pressure led to an improved mechanical performance; however, these adjustments also produced reductions in other properties, indicating that the balance among parameters depends on the targeted application rather than a single optimal set. Conversely, the parameter combination that produced the lowest warpage still yielded a significant increase in$E_{sec}$, indicating that reducing the warpage does not necessarily compromise the tensile stiffness. Interestingly, variations in the stress distribution between the tensile and bending tests suggest that the solidification-induced structure of the material influences its mechanical response, with specimens that showed a lower tensile strength exhibiting a comparatively higher resistance under bending. These findings provide new insights into the trade-offs between dimensional accuracy and mechanical performance and offer practical guidelines for optimizing polypropylene injection molding processes.Full article

This study evaluated the effect of the culture system (temporary immersion bioreactor (TIB) vs. solid culture) on the micropropagation efficiency of twoLachenalia cultivars, ‘Rainbow Bells’ and ‘Riana’. Morphological and biochemical responses were analyzed under different immersion frequencies (15 min every 24 h, [...] Read more.

This study evaluated the effect of the culture system (temporary immersion bioreactor (TIB) vs. solid culture) on the micropropagation efficiency of twoLachenalia cultivars, ‘Rainbow Bells’ and ‘Riana’. Morphological and biochemical responses were analyzed under different immersion frequencies (15 min every 24 h, 15 min every 8 h, and 5 min every 8 h) and on solid medium. TIB, regardless of the immersion frequency, was more efficient in terms of biomass growth (3.27–3.95-fold increase) and the number of obtained bulblets (17.80–19.08 bulbs). The response to culture conditions was genotype-dependent. ‘Rainbow Bells’ exhibited higher biomass growth and bulblet number, whereas ‘Riana’ produced fewer but heavier bulblets (4.39 and 2.09 of biomass growth, 23.19 and 9.97 bulbs per 1 g, 0.23 and 0.31 g per bulb, respectively, for ‘Rainbow Bells’ and Riana’). The most effective bulblet multiplication was obtained under the 1 × 15 min regime for ‘Rainbow Bells’; the same frequency promoted bulblet enlargement in ‘Riana’. Principal component analysis (PCA) explained 77% of total variance, revealing strong genotype separation: ‘Rainbow Bells’ clustered with traits linked to growth intensity and phenolic accumulation, while ‘Riana’ correlated with bulb storage parameters. The results support the use of TIBs to improveLachenalia micropropagation, bulb quality, and future automation, and indicate that further research should focus on optimizing culture parameters for each genotype.Full article

Reliable model evaluation is critical in waste management research, where machine learning is increasingly used to inform policy, circular economy strategies and progress towards the United Nations Sustainable Development Goals. However, common evaluation practices often fail to account for key methodological challenges, risking [...] Read more.

Reliable model evaluation is critical in waste management research, where machine learning is increasingly used to inform policy, circular economy strategies and progress towards the United Nations Sustainable Development Goals. However, common evaluation practices often fail to account for key methodological challenges, risking misleading conclusions. This study presents a theoretical analysis supplemented with a practical example of municipal solid waste generation in Ireland to demonstrate how standard evaluation metrics can produce distorted results. In particular, the widespread use of the$R^2$ in waste management/sustainability machine learning is examined, showing its susceptibility to inflation when data exhibit strong correlations, temporal dependence or non-linear model structures. The findings show that reliance on the$R^2$ misrepresents model performance under conditions typical of waste datasets. In the Irish example, the$R^2$ often suggested a degradation of predictive ability even when error-based metrics, such as root mean squared error (RMSE) and mean absolute error (MAE), indicated improvement or stability. These results demonstrate the need for evaluation frameworks that move beyond single, correlation-based metrics. Future work should focus on developing and standardising robust practices to ensure that machine learning can support transparent, reliable and effective decision-making in waste management and circular economy contexts.Full article