(1) Background: Head and neck cancer treatment, including advanced techniques like Volumetric Modulated Arc Therapy (VMAT), presents challenges for maintaining patient quality of life (QoL). Thus, thoroughly investigating how radiation therapy (RT) affects patients has been proved essential. Derived by that, this study aims to understand the complex interactions between not only RT and QoL but also symptom severity, and treatment-related toxicities in three distinct time points of patient’s treatment; (2) Methods: To achieve that, EORTC-QLQ-C30 and EORTC QLQ-H&N35 questionnaires were used in combination with EORTC_RTOG scoring criteria and Spearman’s rho statistical analysis for 74 patients with cancer undergoing VMAT radiation therapy; (3) Results: The results revealed a significant improvement in the Overall Health Index post-treatment, indicating a temporary decline during therapy followed by subsequent recovery, often surpassing pre-treatment QoL levels. Concurrently a reduction in symptomatology was observed, notably in pain, swallowing difficulties, and dry mouth, aligning with prior research indicating decreased symptom burden post-treatment. However, Spearman’s correlation coefficient analysis at two distinct time points during therapy uncovered varying degrees of correlation between dosimetric data at Organs at Risk (OARs) and reported symptoms, highlighting potential limitations in using QoL questionnaires as sole indicators of treatment efficacy. Our investigation into the correlation between dosimetric data, toxicity, and symptoms focused on the relationship between radiation doses and oral mucositis levels, a common toxicity in head and neck cancer patients. Significant associations were identified between toxicity levels and dosimetric parameters, particularly with OARs such as the parotid glands, oral cavity, and swallowing muscles, underlining the utility of the EORTC method as a reliable toxicity assessment tool; (4) Conclusions: To summarize, current research attempts to underscore the importance of refining QoL assessments for enhanced patient care. The integration of dosimetric data, symptom severity, and treatment-related toxicities in the QoL outcomes of head and neck cancer patients undergoing VMAT radiation therapy, can lead towards the optimization of treatment strategies and the improvement of patient outcomes in future patient-centered radiation therapy practices. Full article

The cooling performance of the energy-saving water spray window cooling system under summer conditions in Nanning city in southern China, is experimentally examined in this study. By constructing two identical test rooms for comparison, in the experiment we analyzed the effects of water temperature (22 °C, 26 °C, 30 °C), spray frequency (8, 10, 12, 15 min intervals), glass structure (single-layer, double-layer, triple-layer), air conditioning temperature (26 °C, 27 °C, 28 °C, 29 °C), and outer lamination thickness (30, 50, 100, 200 mm) on the cooling effect of windows, aiming to determine the optimal operating conditions. The experimental outcomes demonstrate that the appropriate operating mode for the water spray window system involves using the coldest water source (22 °C), with a spray interval of 8 min, a three-layer air cavity sprayed glass construction, an air cavity thickness set at 100 mm, and the best air conditioning temperature (26 °C). The study found that the water spray system can reduce the outer glass surface temperature of the window by 6 °C and the inner surface temperature by 2 °C. Moreover, the more glass layers and the thicker the lamination, the higher the energy saving rate; with a maximum energy saving rate of 35.19%. The water spray window has good adaptability and significant energy-saving effects in Southeast Asia. By scientifically selecting energy-saving glass types and fine-tuning operating modes, it is expected that efficient building energy conservation in hot climates can be achieved. Full article

Policymakers, governments, and Islamic financial institutions are increasingly focusing on sustainable development, leading to an in-depth examination of current sustainable finance practices, projects, and product portfolios. This study examines the role of Islamic sustainable finance (ISF) in promoting Sustainable Development Goals (SDGs) to avert financial risk in the management of Islamic Finance Products (ISFP). Through qualitative analysis, the study conducts a critical literature review (CLR) that incorporates conceptual, theoretical, and empirical perspectives on ISF and SDGs and addresses two specific research questions. Our study examines over 48 journals from 2010 to 2024 and provides insights into how ISF advances the SDGs across all environmental, social, and economic dimensions. It also highlights that ISF promotes green entrepreneurship by investing in sustainable projects, supporting SMEs, and offering alternative financing. ISF also promotes financial stability, justice, and growth and is consistent with the principles of Maqasid al-Shari’ah. Key ISF mechanisms that promote the SDGs include Islamic Green Sukuk, Socially Responsible Investment Funds, Islamic Microfinance, and Islamic Impact Investing. Integrating Islamic ethical principles into financial activities is crucial for inclusive and sustainable economic development. These qualitative insights are critical for policymakers, Islamic financial institutions, Halal entrepreneurs, environmentalists, and investors to understand the potential of Islamic social finance (ISF) to support sustainable practices, projects, and portfolios. Furthermore, the ISFs alignment with Maqasid al-Shari’ah highlights its importance in promoting sustainable development while mitigating financial risk in ISFPs management. The study offers robust contributions to the existing literature to provide comprehensive insights into how ISF can be effectively used to promote SDGs. Full article

Seismic events remain a significant threat, causing loss of life and extensive damage in vulnerable regions. Soil liquefaction, a complex phenomenon where soil particles lose confinement, poses a substantial risk. The existing conventional simplified procedures, and some current machine learning techniques, for liquefaction assessment reveal limitations and disadvantages. Utilizing the publicly available liquefaction case history database, this study aimed to produce a rule-based liquefaction triggering classification model using rough set-based machine learning, which is an interpretable machine learning tool. Following a series of procedures, a set of 32 rules in the form of IF-THEN statements were chosen as the best rule set. While some rules showed the expected outputs, there are several rules that presented attribute threshold values for triggering liquefaction. Rules that govern fine-grained soils emerged and challenged some of the common understandings of soil liquefaction. Additionally, this study also offered a clear flowchart for utilizing the rule-based model, demonstrated through practical examples using a borehole log. Results from the state-of-practice simplified procedures for liquefaction triggering align well with the proposed rule-based model. Recommendations for further evaluations of some rules and the expansion of the liquefaction database are warranted. Full article

Rice straw is not easy to decompose, it takes a long time to compost, and the anaerobic bacteria involved in the decomposition process produce a large amount of carbon dioxide (CO₂), indicating that applications for rice straw need to be developed. Recycling rice straw in agricultural crops is an opportunity to increase the sustainability of grain production. Several studies have shown that the probiotic population gradually decreases in the soil, leading to an increased risk of plant diseases and decreased biomass yield. Because the microorganisms in the soil are related to the growth of plants, when the soil microbial community is imbalanced it seriously affects plant growth. We investigated the feasibility of using composted rice stalks to artificially cultivate microorganisms obtained from the Oryza sativa-planted environment for analyzing the mycobiota and evaluating applications for sustainable agriculture. Microbes obtained from the water-submerged part (group-A) and soil part (group-B) of O. sativa were cultured in an artificial medium, and the microbial diversity was analyzed with internal transcribed spacer sequencing. Paddy field soil was mixed with fermented paddy straw compost, and the microbes obtained from the soil used for O. sativa planting were designated as group-C. The paddy fields transplanted with artificially cultured microbes from group-A were designated as group-D and those from group-B were designated as group-E. We found that fungi and yeasts can be cultured in groups-A and -B. These microbes altered the soil mycobiota in the paddy fields after transplantation in groups-D and -E compared to groups-A and -B. Development in O. sativa post treatment with microbial transplantation was observed in the groups-D and -E compared to group-C. These results showed that artificially cultured microorganisms could be efficiently transplanted into the soil and improve the mycobiota. Phytohormones were involved in improving O. sativa growth and rice yield via the submerged part-derived microbial medium (group-D) or the soil part-derived microbial medium (group-E) treatments. Collectively, these fungi and yeasts may be applied in microbial transplantation via rice straw fermentation to repair soil mycobiota imbalances, facilitating plant growth and sustainable agriculture. These fungi and yeasts may be applied in microbial transplantation to repair soil mycobiota imbalances and sustainable agriculture. Full article

The Huizhou region is an important area of traditional Chinese culture, and currently, the state of the village’s surroundings in this area is still not perfect. In this study, seven districts (counties) in the Huizhou region were selected for research. The Rural Habitat Environment (RHES) Indicator Program is based on the concept of Socio-Economic-Natural Complex Ecosystems (SENCE) and constructs 18 metrics in three dimensions. Trends and influencing factors were analyzed using entropy weight TOPSIS and a Grey Relational Analysis (GRA) for the years 2013–2022, and spatial and temporal evolution was measured using Geographic Information Systems (GISs). The findings show that the composite index for the Huizhou region grew from 2013 (0.3197) to 2022 (0.6806). Second, the Tunxi District belongs to the high index–high economy category. The Shexian, Xiuning, and Qimen counties belong to the high index–low economy category. Huizhou District and Huangshan District belong to the low index–high economy category. Yixian County belongs to the low index–low economy category. Third, all districts (counties) show an upward trend, and Huangshan District has the best RHES condition. Shexian County ranks relatively low in the comprehensive index. Full article

This paper underlines the significance of free trade agreements in attracting foreign direct investment and their impact on the operational capacities of local firms in host countries. It argues that free trade agreements do not only eliminate barriers to trade, but they also increase the size of the regional market and improve the business environment, making it more attractive to foreign direct investment, along with all the attributes and spillover effects associated with it. While determining the type of spillover effects of foreign direct investment associated with Oman’s trade agreements, this paper uses the Kruskal–Wallis H-test and 438 samples from companies surveyed between 1 August and 31 October 2023 to assess the impact of spillovers on the performance of the surveyed companies. The results reveal that technology transfer, knowledge transfer, labour productivity, product efficiency, capital investments, and job creation have positive effects on the firms’ operational capacities, with technology transfer having the highest impact (27%), followed by labour productivity and job creation (18%). The spillover effects are almost the same for company size and percentage of ownership. They also identified manufacturing and tourism as priority sectors and the availability of a skilled workforce as a major challenge. These findings make original contribution to the field as this is probably the first study to produce a firm-level analysis of spillover effects of foreign direct investment and trade agreements in the context of Oman and the wider Gulf region. The paper concludes with practical implications for policy makers when negotiating trade agreements and designing investment policies to optimize spillover effects on the performance of their domestic firms. Full article

This study investigates the potential of biochar to mitigate drought stress in crops. Biochar amendments are increasingly recognized for their ability to enhance soil properties that promote water retention and plant resilience during dry periods. In a study conducted in Egypt, impacts of the application of sugarcane biochar on cucumber production, water productivity, and quality characteristics under deficient irrigation conditions were evaluated. Increasing the biochar application rate under arid region conditions resulted in significant improvements in cucumber yield, water productivity, vitamin C, soluble sugar content, and total soluble solids. The activated sugarcane biochar exhibited desirable properties, including a large surface area (250 m²g⁻¹) and pore size (0.28 cm³g⁻¹), with a microporous nature. The positive effects observed can be attributed to reduced water volume leaving the root zone, enhanced nutrient absorption, increased water holding capacity, and augmented nutrient availability. Based on the results, it is recommended to implement under-irrigation at 75% of full irrigation and apply a biochar rate of 10 tons per hectare to mitigate the environmental impacts of water scarcity and support the development of sustainable agricultural systems. In conclusion, biochar emerges as a valuable soil amendment for bolstering cucumber production and water use efficiency (WUE) under limited irrigation scenarios. Its potential lies in promoting improved soil moisture retention, enhancing nutrient availability for cucumber plants, and potentially mitigating plant stress. Full article

The increasing utilization of short fiber-reinforced thermoplastics, due to their advantageous mechanical properties and manufacturing convenience, has led to their application in areas traditionally dominated by metals. This shift underscores the importance of understanding the fatigue behavior of these materials. This study evaluates the fatigue behavior of short fiber-reinforced thermoplastics through three characterization methods: continuous fatigue, interrupted fatigue, and the Locati method, with the latter serving as a novel approach for estimating critical fatigue conditions from a single specimen. Continuous fatigue testing provides the baseline for comparison. The effect of load interruption is explored through the interrupted fatigue method. The Locati method, characterized by incrementally increasing load steps until failure, offers a significant benefit by enabling the estimation of critical fatigue conditions efficiently. This research aims to provide a comprehensive understanding of the fatigue behavior of short fiber-reinforced thermoplastics, contributing to the optimization of their use in engineering applications. Full article

Hydrogels are of great importance for functionalizing sensors and microfluidics, and poly(ethylene glycol) dimethacrylate (PEG-DMA) is often used as a viscosifier for printable hydrogel precursor inks. In this study, 1–10 kDa PEG-DMA based hydrogels were characterized by gravimetric and electrochemical methods to investigate the diffusivity of small molecules and proteins. Swelling ratios (SRs) of 14.43–9.24, as well as mesh sizes ξ of 3.58–6.91 nm were calculated, and it was found that the SR correlates with the molar concentration of PEG-DMA in the ink (MCI) (SR = 0.1127 × MCI + 8.3256, R² = 0.9692) and ξ correlates with the molecular weight (M_w) (ξ = 0.3382 × M_w + 3.638, R² = 0.9451). To investigate the sensing properties, methylene blue (MB) and MB-conjugated proteins were measured on electrochemical sensors with and without hydrogel coating. It was found that on sensors with 10 kDa PEG-DMA hydrogel modification, the DPV peak currents were reduced to 92 % for MB, 73 % for MB-BSA, and 23 % for MB-IgG. To investigate the diffusion properties of MB(-conjugates) in hydrogels with 1–10 kDa PEG-DMA, diffusivity was calculated from the current equation. It was found that diffusivity increases with increasing ξ. Finally, the release of MB-BSA was detected after drying the MB-BSA-containing hydrogel, which is a promising result for the development of hydrogel-based reagent reservoirs for biosensing. Full article

Landscape sustainability (LS) has received widespread attention from both scientists and practitioners in recent decades. However, a systematic review of the recent trends in this field remains scarce. Here, we present a bibliometric analysis of 715 English papers published in SCI journals (i.e., indexed by Web of Science) during 2006–2023, whose titles, abstracts, or keywords contain both landscape-related terms and sustainability-related terms. Specifically, we quantitatively analyzed the LS literature using a variety of bibliometric methods and qualitatively analyzed the full text of highly cited seminal literature to cross-validate the bibliometric findings. We found that: (1) LS has entered the exponential growth phase (2013–present) after experiencing the incubation phase (pre–2006) and emerging phase (2006–2013). (2) Early LS themes focused on landscape change and the relationship between landscape ecology and sustainability science, while recent LS themes have shifted towards more sectoral topics, such as the relationship between ecosystem services and human well-being, climate change, and urbanization. (3) The closely connected network of cross-citations in seminal literature indicates strong communication among scientists in the field of LS. However, the participatory and solution-oriented research streams of LS have not been well integrated into the core research network of LS. Our results suggest that LS has evolved into a rapidly growing discipline with a stable scientific community and core research themes. To become a well-established discipline, LS needs to further enhance its unique scientific core rather than only drawing knowledge from other related fields. Constructing social–ecological-centered, solution-oriented landscape pattern analysis theory and methods for LS should be a top priority. Full article

Limited research has been conducted on the regulation of light quality on heterotrophy in in vitro-grown potato plantlets. Here, we investigated the effect of light quality on photosynthetic and heterotrophic abilities as well as microtuber formation and growth of potato plantlets (Solanum tuberosum L. cv. Shepody). Potato plantlets pre-cultivated under white light for 30 days were then transferred to grow under blue (B), green (G), red (R), yellow (Y), and white (W) lights, and parameters including dry weight, photosynthetic pigment, medium solute consumption, δ¹³C value, root activity, and sucrose transport (SUT) gene expression of these plantlets were measured. The results showed that B, G, and W were conducive to the rapid induction of microtubers, while R, and especially Y, delayed microtuber formation. Higher photosynthetic ability was observed in the W treatment, whereas the opposite effect was seen in the monochromatic light treatments. Microtuber growth was primarily dependent on heterotrophy, and B was conducive to microtuber growth. The delay in microtuber formation was related to the high expression of StSUT4 in the root, and better microtuber growth was associated with higher root activity, more medium solute consumption, and a higher expression level of StSUT1 in the roots. Full article

With the degradation of the environment due to increasing ecological destruction and pollution, sustainable development has become the paramount objective of social progress. As a result, the concept of green development has garnered considerable attention, which is an important starting point for China to achieve stable economic development and sustainable ecological development. To achieve high-quality economic progress while advancing environmentally friendly practices, it is imperative to formulate and uphold a sound green credit system. However, the phenomenon of greenwashing by enterprises still exists, which compromises the efficacy of green credit and hinders the long-term sustainable and well-organized progress of green finance. Building on the background of green credit, considering the existence of blockchain and government subsidies and adopting the method of tripartite evolutionary game, this paper examines the strategic decisions made by the government, financial institutions, and small and medium-sized enterprises in the context of greenwashing. An emphasis is placed on the impact of blockchain technology on the three parties involved in the green credit market. The findings demonstrate that blockchain technology can diminish the likelihood of greenwashing by businesses and enhance the impact of government subsidies. However, it cannot replace the regulatory authority of the government in sustainable development. Moreover, excessive subsidies can stimulate more greenwashing practices, but eliminating subsidies does not eradicate the root of greenwashing. To encourage sustainable economic development and minimize corporate defaults, the government ought to reinforce supervision and establish a robust social surveillance and publicity mechanism. This paper broadens the research perspective on the effectiveness of green credit and provides some empirical and theoretical references for further promoting the green transformation of SMEs and the sustainable development of the ecological environment. Full article

With the rise of Large Language Models (LLMs), distinguishing between genuine and AI-generated content, particularly in finance, has become challenging. Previous studies have focused on binary identification of ChatGPT-generated content, overlooking other AI tools used for text regeneration. This study addresses this gap by examining various AI-regenerated content types in the finance domain. Objective: The study aims to differentiate between human-generated financial content and AI-regenerated content, specifically focusing on ChatGPT, QuillBot, and SpinBot. It constructs a dataset comprising real text and AI-regenerated text for this purpose. Contribution: This research contributes to the field by providing a dataset that includes various types of AI-regenerated financial content. It also evaluates the performance of different models, particularly highlighting the effectiveness of the Bidirectional Encoder Representations from the Transformers Base Cased model in distinguishing between these content types. Methods: The dataset is meticulously preprocessed to ensure quality and reliability. Various models, including Bidirectional Encoder Representations Base Cased, are fine-tuned and compared with traditional machine learning models using TFIDF and Word2Vec approaches. Results: The Bidirectional Encoder Representations Base Cased model outperforms other models, achieving an accuracy, precision, recall, and F1 score of 0.73, 0.73, 0.73, and 0.72 respectively, in distinguishing between real and AI-regenerated financial content. Conclusion: This study demonstrates the effectiveness of the Bidirectional Encoder Representations base model in differentiating between human-generated financial content and AI-regenerated content. It highlights the importance of considering various AI tools in identifying synthetic content, particularly in the finance domain in Pakistan. Full article

In this study, the challenges posed by limited annotated medical data in the field of eye movement AI analysis are addressed through the introduction of a novel physiologically based gaze data augmentation library. Unlike traditional augmentation methods, which may introduce artifacts and alter pathological features in medical datasets, the proposed library emulates natural head movements during gaze data collection. This approach enhances sample diversity without compromising authenticity. The library evaluation was conducted on both CNN and hybrid architectures using distinct datasets, demonstrating its effectiveness in regularizing the training process and improving generalization. What is particularly noteworthy is the achievement of a macro F1 score of up to 79% when trained using the proposed augmentation (EMULATE) with the three HTCE variants. This pioneering approach leverages domain-specific knowledge to contribute to the robustness and authenticity of deep learning models in the medical domain. Full article

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson’s disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F₁ score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson’s Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups. Full article

This study aims to describe the prevalence of malocclusion and identify associated factors in preschool children. Completed in 2022–2023, this cross-sectional study included 523 children aged 26 to 80 months in municipal schools in Salvador. An oral examination was carried out on the children, and a questionnaire was self-administered by the parents. Descriptive analyses and multivariate logistic regression (the backward method, p-value ≤ 0.05, 95% CI) were conducted. The majority of children were female (51.82%), over 54 months old (52.2%), Black or mixed race (90.63%), and not affected by COVID-19 (92.35%). The prevalence of malocclusion was 43.21%, with open bite as the most common condition. There was a significant association between malocclusion and screen time (OR: 1.34; p: 0.116; CI: 1.0–1.94), physical/psychological aggression (OR: 2.55; p: 0.031; CI: 1.0–5.98), consumption of ultra-processed foods (OR: 1.77; p: 0.003; CI: 1.22–2.57), digital suction (OR: 3.1; p: 0.001; CI: 1.56–6.16), and the habit of biting objects (OR: 1.56; p: 0.121; CI: 1.0–2.73). The promotion of comprehensive health in early childhood and psychosocial interventions are recommended, aiming to reduce screen time, aggression, consumption of ultra-processed foods, thumb sucking, and the habit of biting objects to prevent malocclusion. Full article

Non-hormone-treated beef steers (n = 1625; 371 ± 8.4 kg) were used to determine the impact of a direct-fed microbial (DFM) on growth performance, carcass characteristics, health parameters, and economic outcomes in finishing beef cattle. Steers were blocked based on initial BW, sorted into three optimal marketing groups for each day of enrollment, randomly assigned to one of two treatments (12 replicates per treatment), and fed for an average of 133 days before harvest. Treatments included the following: (1) control (CON) and (2) 50 mg/head per day of a DFM (BDP) containing Lactobacillus animalis 506, Propionibacterium freudenreichii 507, Bacillus licheniformis 809, and Bacillus subtilis 597 (BOVAMINE DEFEND^® Plus). Steers were weighed in drafts by pen on a certified scale at closeout, and both pens within a block were harvested on the same day. Carcass characteristics and liver scores were collected upon slaughter. Data were analyzed as part of a completely randomized block design with the pen as the experimental unit. There was no difference in dry matter intake and final body weight between treatments. The BDP steers exhibited a lower number of total outs (deads and removed) throughout the experiment (p < 0.01). Average daily gain (ADG) tended to be greater (p < 0.06), and feed efficiency (p < 0.01) was greater, for steers receiving BDP. Although there were no statistical differences in hot carcass weight and quality grade between treatments, there was a lower incidence of liver abscess scores (p < 0.01) in the BDP steers. These data suggest that BOVAMINE DEFEND^® Plus supplementation improves growth performance during the finishing phase, reduces overall mortality, and improves feed to gain efficiency. Full article

Recombinant protein expression is a fundamental aspect of both synthetic biology and biotechnology as well as a field unto itself. Microalgae, with their eukaryotic cellular machinery, high lipid content, cost-effective cultivation conditions, safety profile for human consumption, and environmentally friendly attributes, are a promising system for protein expression or metabolic engineering for sustainable chemical production. Amongst the incredible diversity of microalgae species, Chlorella spp. are heavily studied due to their high growth efficiency, potential for low-cost cultivation, and well-characterized scale-up process for large-scale cultivation. This review aims to comprehensively examine the ongoing advancements in the bioengineering of Chlorella spp. for recombinant protein production and its biotechnological applications. This includes genetic elements such as promoters, terminators, reporters and markers, enhancers, and tags successfully used in Chlorella spp. Full article

Glioblastoma is the most common and aggressive type of malignant brain tumor with a poor prognosis due to the lack of effective treatment options. Therefore, new treatment options are required. Sphingolipids are essential components of the cell membrane, while complement components are integral to innate immunity, and both play a critical role in regulating glioblastoma survival signaling. This review focuses on recent studies investigating the functional roles of sphingolipid metabolism and complement activation signaling in glioblastoma. It also discusses how targeting these two systems together may emerge as a novel therapeutic approach. Full article

In the wake of the COVID-19 pandemic, the surveillance and safety measures of indoor Cultural Heritage sites have become a paramount concern due to the unique challenges posed by their enclosed environments and high visitor volumes. This communication explores the integration of Artificial Intelligence (AI) in enhancing epidemiological surveillance and health safety protocols in these culturally significant spaces. AI technologies, including machine learning algorithms and Internet of Things (IoT) sensors, have shown promising potential in monitoring air quality, detecting pathogens, and managing crowd dynamics to mitigate the spread of infectious diseases. We review various applications of AI that have been employed to address both direct health risks and indirect impacts such as visitor experience and preservation practices. Additionally, this paper discusses the challenges and limitations of AI deployment, such as ethical considerations, privacy issues, and financial constraints. By harnessing AI, Cultural Heritage sites can not only improve their resilience against future pandemics but also ensure the safety and well-being of visitors and staff, thus preserving these treasured sites for future generations. This exploration into AI’s role in post-COVID surveillance at Cultural Heritage sites opens new frontiers in combining technology with traditional conservation and public health efforts, providing a blueprint for enhanced safety and operational efficiency in response to global health challenges. Full article

Evidence indicates that chronic social stress plays a significant role in the development of cancer and depression. Although their association is recognized, the precise physiological mechanism remains unknown. In our previous work, we observed that OF1 males subjected to chronic social defiance exhibited anhedonia, and those who developed tumors in the lung showed anxiety-associated behaviors. In this study, we observed that tumor-bearing OF1 mice presented higher levels of 3-HK, and this increase may be due to IDO. No differences in hippocampal catecholamine levels were observed. Our results suggest that a systemic tumor can induce molecular changes in the hippocampal kynurenine pathway that may impact behavior. Full article