Search Results (11,311)

In response to the plastic pollution crisis, bioplastics emerged as a sustainable alternative. However, low degradation rate and abiotic decomposition generate micro- and nanoplastics. These particles enter the food chain, establishing oral intake as a key route of human exposure. This review gathered studies on the biotransformation of bioplastics in the gastrointestinal tract and on their toxicity in human cells and murine models. Most studies focused on polylactic acid particles due to widespread use in food packaging. Under simulated gastrointestinal conditions in vitro, particles were modulated, resulting in cavity and pore formation, fragmentation, lipase competition, protein corona formation, and alterations in the gut microbiota (including Selenomonadaceae, Bifidobacterium, and Prevotellaceae). Also, particle breakdown increases surface area, enhancing interactions with biomeiolecules and causing higher in vitro and in vivo toxicity. Indeed, pro-inflammatory cytokine secretion, oxidative stress induction, and redox imbalance were found in both models. In mice, alterations in gut microbiota involving Bacillales indirectly mediated hepatotoxicity, leading to uric acid and triglyceride accumulation. Furthermore, microbiota adaptation over time was suggested with an increase in microorganisms and the potential conversion of L-lactic into harmful D-lactic acid. Despite limited studies, this review highlighted that ingested bioplastic-derived micro- and nanoplastics can lead to toxic effects. Full article

This article examines the emerging role of artificial intelligence in mental health contexts, with a particular focus on psychotherapy and the risks associated with deploying large language models (LLMs) in sensitive clinical domains. It aims to provide a broad review of current literature, highlighting key risks of general-purpose artificial intelligence (AI) systems, while also exploring the potential of clinically oriented LLMs for therapist training, supervision, and professional development. It discusses several key concerns, including AI-related psychosis, the development of parasocial attachments, and the growing number of crisis-related interactions users have with general-purpose AI models. These challenges raise important questions about the safety, reliability, and ethical management of AI systems when individuals seek support during periods of psychological crisis. Beyond identifying these risks, the article explores the potential of clinical LLMs specifically designed for mental health applications. In particular, AI can serve as a tool for therapists’ training, supervision, and professional development, offering simulated clinical scenarios, structured feedback, and support for reflective practice. The article concludes by outlining key directions for the responsible development of therapeutic AI. These include the importance of human oversight, the use of specialized and clinically informed training datasets, advances in model fine-tuning and safety alignment, and the establishment of clear professional guidelines and regulatory frameworks. Together, these developments may help ensure that AI technologies are integrated into mental healthcare in ways that prioritize safety, ethical practice, and the continued central role of human clinicians. Full article

Upper Northern Thailand continues to face a protracted structural crisis from fine-particulate matter (PM_2.5), primarily driven by biomass burning and wildfires. Conventional mechanical capture systems, such as cyclones, often suffer a drastic efficiency drop when treating sub-micron particles. This study introduces an innovative Evaporative Condensation Growth Scrubber (ECGS) designed to bridge this technological gap by promoting the growth of fine particles through heterogeneous nucleation. Experimental testing across 10 different nozzle configurations was conducted to optimize the system’s performance. The results revealed that the ECGS system significantly outperformed the dry cyclone (Baseline) across all nine testing configurations. While the Baseline showed inherent limitations in capturing sub-micron particles, the ECGS demonstrated relative efficiency improvements ranging from 39.53% to 83.23% for PM_2.5, and 26.10% to 61.50% for PM₁₀ compared to the baseline. Optimal performance was achieved using a 90-degree injection angle and a 10 cm distance, which created a complete spray curtain and maximized collision probability. Under these conditions, the outlet PM_2.5 concentration stabilized at 11.81 µg/m³ within 180 s of water injection. Crucially, despite sensor interference caused by high relative humidity, the system’s effectiveness was confirmed by a significant difference in performance in PM₁₀ and PM_2.5 removal. The PM₁₀ collection efficiency outperformed that of PM_2.5 by 28.82%, providing empirical evidence that PM_2.5 particles successfully acted as nuclei for condensation and grew into the larger PM₁₀ size range. This particle growth enabled more effective centrifugal separation, demonstrating that the ECGS system offers a viable and efficient solution for fine particle removal in highly polluted environments. Full article

The Kimchi Premium—the persistent price wedge between Bitcoin on Korean and global exchanges—has resisted standard no-arbitrage explanations for over seven years, raising the question of how macro-financial shocks transmit into this segmented market. Prior work relies on static, linear estimators applied over short lag horizons, leaving the timing, nonlinearity, and regime dependence of the Premium’s adjustment largely untested. We address this gap using daily data from December 2017 to December 2025 ($T=2105$) and rolling windows of 60, 120, and 240 trading days. Linear dynamics are tested with Granger causality (GC) and—because our return series are strongly leptokurtic (Bitcoin excess kurtosis $=7.29$, S&P 500 $=14.77$)—complemented by the Kraskov k-nearest-neighbor transfer entropy (TE) estimator, which captures conditional dependence in higher moments. Inference rests on a stationary bootstrap with pre-specified lag grids to avoid optimistic argmax bias, block-permutation tests for window-level detection rates, and Benjamini–Hochberg and Storey q-value corrections for multiple testing. Robustness is examined through conditional GC and conditional TE controlling for USD/KRW as a common factor, Toda–Yamamoto tests on price levels, a percentage-premium specification, a U.S. trading-day shift to address asynchrony, and winsorization sensitivity. We deliberately adopt a conservative inference stance at the panel level: window-level detection rates and pointwise bootstrap p-values are reported, but claims of “causality” are reserved for the within-window descriptive ranking of channels and horizons, with the panel-level null assessed by block-permutation and Benjamini–Hochberg corrections. Four empirical patterns emerge under this framing. First, Johansen tests identify a single cointegrating vector between Korean and global Bitcoin prices (trace $=91.99$ vs. 5% critical value $15.49$), establishing the Premium as a stationary deviation from long-run parity, while none of the four macro indicators cointegrate with global Bitcoin. Second, GC detection rates for the Premium concentrate at the 240-day horizon: Gold → Premium reaches $23.3\%$ (95% Wilson CI $[19.3\%,27.8\%]$), KOSPI 200 → Premium $16.3\%$, and USD/KRW → Premium $16.8\%$. Third, the Kraskov TE reveals an asymmetry invisible to linear tests: for Gold → Premium at $w=240$, the median optimal lag is five days, against one day for Gold → Bitcoin (chi-square $p=0.017$). Percentage-premium detection rates are substantially higher (e.g., $59.1\%$ for Gold → Premium at $w=240$), indicating that the dollar-wedge specification understates causal strength. Fourth, block-permutation tests do not reject the global null of no window-level excess rejection, and Benjamini–Hochberg rejects no pair at $\alpha =0.05$; we therefore read the detection-rate evidence as descriptive of localized, crisis-dependent transmission episodes rather than as panel-level rejection of pointwise non-causality, and the paper’s contribution is accordingly positioned at the level of channel ranking, lag structure, and regime decomposition rather than at the level of blanket causality claims. Crisis decomposition shows transmission is concentrated in the ETF-Halving regime ($29.8\%$ mean GC detection) and below $4\%$ during the Terra–Luna ($2.9\%$) and Russia–Ukraine ($3.6\%$) episodes. The findings situate the Premium as a stationary error-correction term whose adjustment is dominated by exchange-rate and commodity channels rather than U.S. equities, with implications for arbitrage models, regulatory monitoring, and information-flow analyses of segmented crypto markets. Full article

Background/Objectives: The escalating crisis of antibiotic resistance and the inherent limitations of conventional antibiotics necessitate the development of innovative therapeutic strategies. Targeted drug delivery (TDD) offers a powerful approach to enhance efficacy, minimize systemic toxicity, and circumvent bacterial resistance. This systematic review aims to evaluate the potential of unique bacterial transport systems (BTSs), surface specific receptors and intracellular enzymes as platforms for TDD via the “Trojan Horse” strategy (THS). Methods: A comprehensive literature review was conducted, focusing on studies that investigated the specificity and mechanisms of BTSs responsible for the uptake of metabolites that are essential for and unique to bacteria. This includes an analysis of transport systems for siderophores, bacteria-specific sugars, cell wall components, D-amino acids, and vitamins. We assessed preclinical and clinical examples of drug conjugates utilizing these pathways, as well as emerging platforms such as bacteriophage-derived proteins, antibody–antibiotic conjugates, and bacterial extracellular vesicles (EVs). Results: BTSs demonstrate high specificity for their cognate substrates, providing effective molecular gateways for TDD of drugs photosensitizers and diagnostic probes in form of conjugates. The siderophore–cephalosporin conjugate cefiderocol represents a clinically validated example, having received FDA approval. Preclinical studies further reveal that conjugates utilizing sugars (e.g., maltose, trehalose) and vitamins (e.g., B12) can significantly enhance antibiotic uptake and activity against both Gram-positive and Gram-negative pathogens, including drug-resistant strains. Emerging platforms like bacteriophage endolysins and engineered EVs show promise for overcoming biological barriers such as bacterial outer membranes and intracellular host niches. Conclusions: The THS leveraging BTSs represents a clinically viable and promising avenue for next-generation antibacterial therapies. Advantages of BTS include overcoming bacterial resistance, such as reduced membrane permeability and efflux pumps, enabling the “revival” of antibiotics that are poorly permeable or toxic, increasing their local concentration at the target site and reducing side effects on host cells. While significant progress has been made, a striking disconnect persists between the hundreds of conjugates demonstrating potent in vitro activity and the limited agent that has achieved clinical use. This in vitro–in vivo gap reflects, in large part, the early stage of this field rather than a fundamental failure. Further research is critically needed not only to identify novel BTSs and optimize drug-linker chemistry, but also to systematically address the translational barriers—including poor pharmacokinetics, immunogenicity, and unexpected toxicity—that have prevented most promising candidates from advancing beyond preclinical evaluation. Full article

Plastic pollution is a global environmental crisis, and microbial degradation represents a promising remediation strategy. While bacteria have been widely studied, yeasts offer unique advantages for plastic degradation due to their metabolic versatility, stress tolerance, and enzymatic capabilities. However, plastic degradative yeasts have not been reviewed comprehensively. Although several yeasts capable of degrading polyethylene terephthalate (PET) or polyethylene (PE) have been reported (e.g., Moesziomyces antarcticus, Candida tropicalis, Yarrowia lipolytica and Rhodotorula mucilaginosa), degraders of other plastic types are less studied. Although some yeasts can assimilate carbon from plastics, the diversity of yeasts capable of participating in plastic mineralization remains vastly underexplored. In recent years, yeast cell surface display systems for bacterial PETase and fungal cutinase have been developed, demonstrating promising PET degradation efficiency. However, PETase is feedback-inhibited by the intermediate product mono(2-hydroxyethyl)terephthalate (MHET). Systems synergizing PETase with MHETase have shown superior stability during long-term PET degradation and enable large-scale depolymerization of PET waste. For high-crystallinity PET, fungal hydrophobins can be used to modify the surface hydrophobicity of PETase-displaying yeast cells, facilitating their attachment to hydrophobic PET surfaces and ultimately enhancing the degradation efficiency of the whole-cell biocatalyst. Limitations of current research and future directions are also discussed. Full article

Background/Objectives: Many frontline healthcare professionals had not previously faced a crisis of the magnitude of the COVID-19 pandemic, and prolonged exposure to high-stress clinical environments may adversely affect psychological well-being. This study aimed to assess and compare burnout severity among critical care nurses in two clinical settings—one hospital in Croatia and one in Poland—with particular attention to emotional exhaustion, depersonalization, and personal accomplishment in the post-pandemic period. Methods: A cross-sectional comparative design was conducted across two hospitals (Croatia and Poland). Data were collected from 346 critical care nurses between September and December 2023. Burnout was assessed using the Maslach Burnout Inventory Human Services Survey, analyzed primarily as continuous scores across its three dimensions. Results: No statistically significant differences were observed between the two groups in continuous burnout scores (Emotional Exhaustion p = 0.224, Depersonalization p = 0.852, Personal Accomplishment p = 0.636, total MBI score p = 0.394). Secondary cut-off-based analyses yielded some categorical differences, including a higher proportion classified as having high burnout in the Polish sample (43.2%) than in the Croatian sample (31.5%); however, these findings were exploratory and should not be interpreted as overriding the primary continuous-score results. Regression analyses demonstrated low explanatory power, with education level emerging as a significant predictor only in the Croatian sample (OR = 0.320, 95% CI: 0.125–0.824, p = 0.018). Conclusions: Burnout severity did not differ significantly between the two clinical settings when assessed using continuous measures. These findings suggest that burnout among ICU nurses may be driven primarily by shared occupational and organizational stressors rather than setting-specific differences. Categorical findings should be interpreted as complementary and exploratory. Full article

Background/Objectives: The COVID-19 pandemic intensified concerns regarding the resilience and financing architecture of mental health services, yet it remains unclear whether crisis-induced adjustments fundamentally altered hospital case-mix complexity or merely exposed pre-existing structural configurations. This study examines the relationship between financing regimes and case-mix complexity in psychiatric hospitals in Romania, a Central and Eastern European health system characterized by mixed financing arrangements and pronounced interregional heterogeneity. Methods: Using administrative data comprising 752 hospital section–year observations (2019–2024), we identify structural financing–organization regimes through a two-step clustering procedure (hierarchical Ward method followed by K-means refinement) based on revenue composition, expenditure allocation, workforce structure, and operational pressure indicators. Results: Three distinct regimes emerge, reflecting persistent institutional configurations rather than temporary crisis-induced groupings. Chi-square tests confirm that regime membership is statistically independent of pandemic timing. A multivariate regression model controlling for financing composition and expenditure structure shows that structural variables (particularly the share of contract-based revenues and the allocation of expenditures) exert systematic and economically meaningful effects on the case-mix index (CMI). Pandemic and post-pandemic indicators do not retain robust explanatory power once structural determinants are accounted for. Regional robustness analyses further demonstrate that financing architecture consistently outweighs temporal shock effects in explaining territorial variation in clinical complexity. Conclusions: The findings suggest that psychiatric hospital case-mix dynamics are structurally embedded within differentiated financing regimes whose influence persists beyond crisis periods. By integrating regime identification with outcome modeling in a Central and Eastern European context, this study contributes to the international literature on health system resilience and highlights the primacy of institutional financing architecture over episodic shock effects in shaping hospital complexity. Full article

The United States faces a maternal health crisis marked by stark racial disparities. Although doula support has emerged as an evidence-based intervention to improve perinatal outcomes by addressing social determinants of health, its integration into healthcare systems remains limited. This qualitative study, informed by phenomenological principles, examined multi-level experiences, perceived barriers, and perceived facilitators of integrating doula services into perinatal care systems and their intersection with health equity goals. We conducted 17 semi-structured interviews with 20 participants across Nebraska and Tennessee, including doulas, midwives, physicians, Medicaid administrators, and public health professionals, and analyzed data using reflexive thematic analysis guided by the Socio-Ecological Model. Three themes emerged: the integration paradox, an overarching theme capturing tensions between doula independence and healthcare system demands for standardization, including divergent views on practice models, provider dynamics, and certification; sustainable financing as the prevailing barrier, encompassing grant limitations, private pay inequities, absent Medicaid reimbursement, and the need for cost-effectiveness evidence; and cultural concordance as the prevailing facilitator, including cultural matching, addressing social determinants, and lived experience as motivation. Sustainable doula integration requires reconciling system demands for standardization with the relational, culturally responsive characteristics that define effective care, through Medicaid reimbursement pathways and policy reforms developed in partnership with doula communities. Full article

Global commodity shocks are associated with volatility in frontier financial markets, affecting exchange rates and equity indices. This study examined volatility transmission from global commodity shocks to Kenya’s USD/KES exchange rate and the NSE 20 Share Index using daily data from November 1997 to December 2024. While GARCH specifications capture clustering, they are sensitive to structural breaks and regime changes, which distort persistence and weaken risk measures. Machine learning approaches provide alternatives capable of capturing nonlinear dependencies, abrupt volatility bursts, and regime-independent dynamics. Empirical evidence demonstrates that the 2008 Global Financial Crisis and COVID-19 induced permanent volatility regime changes. This study examined volatility transmission from global commodity shocks to a frontier financial market, focusing on the USD/KES and the NSE 20 Share Index. Structural break-detection was integrated through the Iterative Cumulative Sum of Squares algorithm, alongside APARCH, FIGARCH models and ML architectures (XGBoost, LSTM). In Kenya volatility is characterized by strong persistence and long-memory dynamics, with limited evidence of leverage effects. Break-adjusted models improve inference by correcting spurious persistence, while machine learning approaches demonstrate superior tracking of volatility during stress regimes. Volatility transmission is nonlinear, break-sensitive, and state-dependent; hybrid ML–econometric methods enhance crisis forecasting and regime-sensitive financial stability analysis. Full article

Background: The steady rise of prostate cancer in Saudi Arabia signals a critical public health shift that requires immediate investment in early detection and prevention to mitigate a future clinical crisis. Accurate diagnosis using multiparametric MRI and PI-RADS scoring remains challenging, as interpretations are highly experience-dependent and subspecialized radiologists are limited. Methods: To address this gap, this study introduces a novel Dual-Stream Attention Network designed to automate the classification of low-risk (PIRADS 2-3) versus high-risk (PIRADS 4-5) lesions from T2-weighted MRI. Leveraging a ResNet50 backbone, the architecture employs parallel streams for Local and Global Feature Processing, each enhanced by a Channel-Spatial Attention module to highlight diagnostically relevant regions. These features are integrated through a Cross-Stream Fusion mechanism and a gate-controlled Adaptive Feature Fusion module to optimize multi-scale information. The model was developed and validated on a regional dataset of 3850 images from Jazan Specialist Hospital and Prince Mohammed bin Naser Hospital. This research provides a standardized, high-precision diagnostic path tailored to the Saudi Arabian population, conducted under institutional review board approval (No. 25138). Results: The proposed dual-stream attention network achieved an accuracy of 97.8% on the validation set and 96.4% on the test set, demonstrating high performance and generalization capabilities in classifying prostate lesions from Saudi patient populations. Conclusions: The proposed dual-stream architecture with novel attention and fusion mechanisms demonstrates high effectiveness for prostate cancer classification from T2-weighted MRI in Saudi clinical settings. This represents the first deep learning model specifically trained and validated on Saudi Arabian prostate MRI data, with the potential to address the shortage of specialized expertise and improve diagnostic efficiency in the Kingdom. Full article

This study aims to examine the impact of the COVID-19 pandemic on the hospitality sector in Greece during the COVID-19 period. To this end, questionnaires were distributed in 320 enterprises operating throughout Greece exclusively in the hospitality industry. Structural equation modeling (SEM) was employed for analyzing data. The results reveal a structured transmission pathway: Business Survival Anxiety and Psychological Distress intensify Financial Strain; financial pressure constrains Strategic Capability; and diminished strategic flexibility shapes firms’ evaluation of the crisis’s overall impact. Financial Strain emerges as the central mediating mechanism, bridging managerial perceptions and organisational outcomes. These findings confirm that crisis impact is embedded in firm-level dynamics, where psychological pressures, resource constraints, and strategic contraction interact systematically. Ultimately, the study shows that the severity of the pandemic was not assessed solely in terms of immediate revenue loss, but in relation to the erosion of strategic capacity—innovation, investment potential, and long-term competitiveness. Resilience in tourism therefore depends on the alignment between psychological stability, financial robustness, and strategic adaptability. Full article

The effective closure of the Strait of Hormuz on 28 February 2026 disrupted approximately 20 million barrels (bbl) per day of crude oil transit, constituting the largest supply shock in global oil market history. This study quantifies the resulting economic losses under three blockade-duration scenarios and evaluates the Northern Sea Route (NSR) as a partial mitigation mechanism through a novel framework integrating sustainable supply chain resilience (SSCR), the Triple Bottom Line (TBL), and the United Nations Sustainable Development Goals (SDGs). A 3 × 3 scenario matrix crossing three blockade durations with three NSR utilization levels estimates global and country-level impacts using data from the International Energy Agency (IEA), the International Monetary Fund (IMF), and the Centre for High North Logistics (CHNL). Even under maximum feasible NSR utilization, net environmentally adjustment mitigation offsets only 1.1–3.6% of total global losses, demonstrating that the Northern Sea Route functions as marginal insurance rather than a viable substitute for Hormuz-dependent supply chains. Global Gross Domestic Product (GDP) losses range from USD 330 billion to USD 2.2 trillion, with South Korea (68–70% Middle East crude dependency) and Japan (approximately 95%) disproportionately affected. After TBL environmentally adjustment monetizing CO₂, black-carbon, and icebreaker costs, the NSR mitigates 1.1–3.6% of total losses, functioning as insurance rather than substitution. The SDG assessment reveals a fundamental trade-off: the NSR offsets energy-security losses (SDGs 7, 9) but worsens climate and marine outcomes (SDGs 13, 14). Theoretically, this study proposes “alternative maritime route availability” as a conceptual extension of supply chain resilience (SCRes) capabilities and outlines a sustainability-adjusted resilience score (SARS) framework that, pending further validation, could serve as a replicable assessment tool. These findings underscore that accelerating the energy transition remains the most effective long-term response to chokepoint vulnerability. Full article

Climate change has emerged as a defining global crisis, with the frequency and intensity of climate-induced disasters rising sharply and imposing disproportionate costs on developing economies and small island states. This article examines the role of climate-resilient infrastructure as a central pillar of climate-smart growth, integrating mitigation, adaptation, and long-term development objectives. It situates climate-resilient infrastructure within a planetary health setting, emphasizing the interdependence between human well-being, ecological systems, and infrastructure resilience. Climate-resilient infrastructure, not merely seen as an engineering solution but as a public good that generates significant positive externalities, reduces systemic macroeconomic risk and delivers welfare gains that exceed private financial returns. It discusses the cross-country heterogeneities in resilience outcomes, driven by differences in geographic exposure, economic capacity, institutional quality, and political economy constraints. Building on this, the study advances a welfare-based approach to infrastructure prioritization that incorporates service disruptions, distributional impacts, and fiscal risk, rather than asset values alone. It further outlines policy and financing strategies to bridge the gap between social and private returns, including public investment, concessional finance, blended instruments, and nature-based solutions. By embedding infrastructure within a planetary health lens, the paper argues that resilient systems are critical not only for safeguarding lives and livelihoods, but also for sustaining ecological stability, reducing health risks, and enabling inclusive, sustainable, and climate-smart economic growth. Full article

The Spanish tourism sector is experiencing an unprecedented boom. However, this macroeconomic success coexists with a growing crisis of burnout and job insecurity. While the macroeconomic effects of minimum wage policies are widely debated, the micro-level psychosocial reality of employees operating within these cost-pressured environments remains largely unexplored. This research uses the Job Demands–Resources (JD-R) framework to descriptively explore the current state of employee well-being in the Spanish hotel sector, operating within the macroeconomic context of recent minimum wage increases. Specifically, the study evaluates how environments characterized by high cost-containment pressures are associated with exacerbated labour demands and depleted resources, a pattern consistent with burnout, thus analysing the implications for social sustainability. Our data come from a survey of 384 hotel employees in Spain and were analysed using the Labour Demands–Resources (JD-R) framework and bootstrap methods. The results reveal that employees report very low agreement that their workloads are reasonable and manageable (mean = 1.8/5) and perceive limited development opportunities (mean = 1.9/5), despite acknowledging the importance of well-being for sustainability (mean = 4.8/5). Work intensification is particularly acute in regions with high seasonality and among cleaning staff. Furthermore, sustainability awareness moderates the negative impact of workload on employee engagement. The study concludes that within high-pressure hospitality environments, macroeconomic wage improvements can be offset by a decline in job quality, threatening the long-term social sustainability of the sector. We advocate for more nuanced policies and a shift in human resource management strategy toward genuine investment in human capital. Full article