Search Results (2,701)

During pregnancy, hormonal, metabolic, and immunological changes influence the composition and function of maternal microbial communities. Increasing evidence suggests that the maternal microbiota—particularly in the vaginal, gut, and oral environments—plays a significant role in maintaining pregnancy homeostasis and supporting fetal development. In healthy pregnancies, the vaginal microbiota is typically dominated by Lactobacillus species, which help maintain a low vaginal pH and protect against ascending infections. However, disruption of this balance (vaginal dysbiosis) has been associated with obstetrical complications such as intrauterine infection and preterm birth. Similarly, the maternal gut microbiota undergoes trimester-specific changes that contribute to metabolic adaptations required for fetal growth, while alterations in microbial composition have been linked to metabolic disorders including gestational diabetes mellitus and preeclampsia. Changes in oral microbiota and periodontal disease have also been associated with adverse pregnancy outcomes through systemic inflammatory pathways and potential microbial translocation to the placenta. Recent advances in sequencing technologies have improved the understanding of host–microbiome interactions in pregnancy, although the existence of a placental microbiome remains controversial. Overall, maternal microbiota plays an important role in pregnancy physiology, and its dysregulation may contribute to obstetrical complications. Understanding these mechanisms may facilitate the development of microbiome-based diagnostic and therapeutic strategies in maternal–fetal medicine. Full article

Intestinal fungal overgrowth (IFO) is an increasingly recognized yet underexplored component of gut dysbiosis with potential implications for gastrointestinal and systemic disease. While bacterial microbiota have historically garnered research attention, recent advances in sequencing technologies have highlighted the importance of the gut mycobiome in maintaining intestinal homeostasis. Disruption of fungal–bacterial balance, particularly involving Candida albicans, C. tropicalis, and C. glabrata, may contribute to symptom generation through immune activation, epithelial barrier dysfunction, biofilm formation, and the production of toxic metabolites such as acetaldehyde and candidalysin. Emerging clinical evidence suggests that IFO is associated with persistent gastrointestinal symptoms, including bloating, abdominal discomfort, and altered bowel habits, particularly in patients who do not respond to conventional therapies targeting bacterial overgrowth. Furthermore, fungal dysbiosis involving Malassezia restricta and Saccharomyces cerevisiae has been associated with inflammatory bowel disease, metabolic disorders, and systemic immune dysregulation; however, the nature and directionality of these relationships remain incompletely understood. Despite increasing recognition, the diagnosis of IFO remains challenging due to a lack of standardized criteria and validated non-invasive tools. Therapeutic strategies, including antifungal agents such as fluconazole and nystatin, as well as microbiome-targeted interventions, show promise but require further validation. This review provides a comprehensive synthesis of current evidence regarding the epidemiology, pathophysiology, clinical manifestations, diagnostic challenges, and therapeutic implications of IFO, with particular emphasis on species-specific mechanisms. Recognition of the intestinal mycobiome as a potentially important component of gut health may provide new perspectives for understanding gastrointestinal disorders and inform future precision medicine approaches. Full article

Per- and polyfluoroalkyl substances (PFASs) present a critical challenge to global public health and environmental integrity due to the exceptional stability of the carbon–fluorine (C–F) bond. This review synthesizes current knowledge on PFAS physicochemical properties, exposure pathways, and toxicological outcomes, while evaluating global regulatory efficacy. A central problem addressed in this review is the widening discrepancy between rigid, yet deeply fragmented, international regulatory frameworks and the increasingly complex, non-linear epidemiological data regarding PFAS health risks. While historical paradigms focused heavily on direct carcinogenicity, recent high-resolution data reveal significant heterogeneity and methodological inconsistencies in cancer links. Instead, robust evidence points to severe systemic toxicities—including hepatotoxicity, immunotoxicity, and maternal–fetal disruptions—frequently driven by mixture co-exposures and sex-specific metabolic dimorphisms. Furthermore, the industrial transition to short-chain substitutes has inadvertently compounded the crisis due to their high environmental mobility and resistance to conventional water treatment. By critically evaluating these toxicological and regulatory contradictions, this review demonstrates that current substance-by-substance legislative models fail to mitigate real-world pollution trends. Ultimately, we emphasize the urgent need to transition to holistic mixture modeling, implement unified class-based global regulations, and accelerate advanced destructive remediation technologies to mineralize the resilient C–F bond. Full article

Background: Given the disruption of the global market, technological factors are impacting food supply chains (FSCs). As a result, traceability is emerging as a potential solution for faster and more secure decision-making within FSCs. Methods: This research aims to define a traceability model for a shrimp food supply chain (FSC) in Ecuador using structural equation modelling which insists not only on the main factors that explain its overall performance but also on the effects of the changes in agents’ behaviours. The research was conducted between March and December 2025. A 41-item questionnaire was used for data collection and was administered to 73 stakeholders. The information was reduced and assembled in five main factors. A structural equation model was applied to explore the impact of agents’ coordination, digital transformations, and customer satisfaction on the traceability of the shrimp FSC. Results: The results show that customer satisfaction is broadly affected by the improvements in chain traceability. Furthermore, the results demonstrate the relevance of coordination, digitalization, and traceability as key factors for strengthening the FSC’s performance. Conclusions: The results could contribute to Sustainable Development Goals 12 and 17 and be applicable to other agri-food chains. Full article

AI technologies are changing the world of work in ways that are hard to predict, and this uncertainty is felt particularly strongly by young people who are just beginning to think about their futures. This study explores how high school students in Turkey perceive AI’s potential impact on their career choices, using Social Cognitive Career Theory (SCCT) and Uncertainty Management Theory (UMT) as interpretive lenses rather than formally tested models. SCCT helps frame AI as an environmental force that shapes how students think about their career options, while UMT helps explain how students emotionally and cognitively respond to uncertainty that cannot easily be resolved. Using a cross-sectional survey of 354 12th-grade students, we developed and validated the AI-Related Career Perception Questionnaire (AICP-Q), which yielded four factors: AI Anxiety and Career Precarity, AI Literacy and Technological Awareness, Proactive Career Adaptation, and Socio-Technical Uncertainty. Students showed moderate AI awareness but relatively high levels of socio-technical uncertainty. Academic track emerged as an exploratory statistical correlate of AI Anxiety, a descriptive association suggesting that students’ sense of threat from AI may relate more to the specific skill demands of their chosen field than to the prestige of their school, though no causal inference can be drawn from these cross-sectional data. A key finding is “the planning gap”: students recognized the potential career disruptions associated with AI but did not consistently respond with adaptive behaviors. Drawing on UMT, we advance the tentative hypothesis, to be tested in future research, that this pattern may relate to a lack of the appraisal resources needed to translate awareness into action; because these constructs were not directly measured, this remains an interpretive suggestion rather than an empirical finding. Full article

A newly isolated red-pigmented yeast, Sporobolomyces reniformis EMCC1691, was evaluated for its biotechnological potential in an integrated case study aimed at developing an efficient microbial cell factory for the valorization of delactosed whey. Fermentation trials in 5 L bioreactors demonstrated robust yeast growth on this dairy by-product, with complete consumption of glucose (21.86 g/L) and galactose (20.36 g/L), leading to the accumulation of approximately 6172 mg/L of lipids and 5634 µg/L of total carotenoids. Fatty acid analysis revealed a final concentration of 3924 mg/L, mainly represented by oleic (2037 mg/L), palmitic (779 mg/L), stearic (403 mg/L), and linoleic (362 mg/L) acids. HPLC analysis showed a pigment profile dominated by torularhodin, torulene, γ-carotene, and β-carotene. To complement downstream processing, the fermented culture was spray-dried into a stable powder and subsequently subjected to a simple, cost-effective, and unconventional mechanical pretreatment using a hydraulic press. This post-drying operation ensured extensive cell-wall disruption without the use of chemical agents or specialized equipment, thereby significantly enhancing the recoverability of intracellular lipids and carotenoids through supercritical CO₂ extraction. Under optimized conditions, SFE-CO₂ with ethanol recovered 92.18 ± 1.61 µg/g of total carotenoids, achieving an extraction efficiency of 84% relative to organic solvent extraction (109.17 ± 2.10 µg/g). Importantly, fermentation also reshaped the fatty acid composition of delactosed whey, shifting it toward a profile enriched in monounsaturated and polyunsaturated fatty acids, thereby further highlighting the metabolic impact and bioconversion potential of S. reniformis EMCC1691. Overall, this work highlights the technological relevance of a recently characterized yeast species and its potential to convert dairy by-products into high-value compounds within a proof-of-concept microbial cell factory framework, paving the way for future scale-up investigations. Full article

Background/Objectives: Embryo implantation is a highly regulated, multistep process requiring precise synchronization between a developmentally competent blastocyst and a receptive endometrium. Despite advances in reproductive medicine, implantation failure remains a major limiting factor in assisted reproductive technology (ART), particularly in cases of recurrent implantation failure (RIF). This review aims to summarize current knowledge on the molecular, cellular, and immunological mechanisms governing embryo–endometrial interaction and to evaluate contemporary strategies for optimizing implantation outcomes. Methods: This narrative review synthesizes the current literature on embryo implantation, including studies addressing uterine receptivity, etiological factors contributing to implantation failure, and emerging diagnostic and therapeutic approaches. The review integrates findings from molecular biology, clinical ART practices, and bioengineering-based models. Key areas include transcriptomic tools such as endometrial receptivity analysis, time-lapse imaging, artificial-intelligence-based embryo selection, and advanced in vitro models (e.g., microfluidic “womb-on-a-chip” systems and three-dimensional embryo–endometrial platforms). The literature was identified through major biomedical databases, following a structured but non-systematic approach. Results: Implantation success is dependent on a complex interplay of hormonal regulation, gene expression, immune modulation, and embryo quality. Disruption of uterine receptivity during the window of implantation is a critical contributor to infertility and RIF. Multiple factors—including genetic abnormalities, maternal age, lifestyle influences, immunological imbalance, uterine pathology, and chronic endometrial conditions—are implicated in implantation failure. Emerging technologies, such as AI-assisted embryo selection, transcriptomic profiling, and advanced in vitro implantation models, provide enhanced insight into implantation dynamics and offer potential for improved clinical outcomes. Conclusions: Advances in understanding embryo implantation and the development of innovative diagnostic and therapeutic technologies hold significant promise for improving reproductive success. However, further research, validation, and standardization are required before these approaches can be fully integrated into routine clinical practice. A more personalized and mechanism-based approach to implantation may ultimately enhance ART outcomes and reduce the burden of infertility. Full article

Adaptive Sidelobe Cancellation (ASLC) is a core technology for modern radar systems to suppress active sidelobe jamming. From the perspective of disrupting the ASLC system’s ability to stably track the jamming direction, this paper proposes a distributed jamming method based on random phase perturbation. The method employs two spatially separated jamming sources that simultaneously transmit coherent signals. By actively applying controllable random jumps to the relative phase between the two sources, the equivalent wavefront direction of the synthesized signal at the radar receiver changes rapidly, forming a non-stationary jamming that destroys the null-tracking capability of ASLC. An analytical model of the ASLC cancellation ratio (CR) under random phase perturbation is established, with a focus on analyzing the effects of time synchronization accuracy and phase synchronization accuracy on jamming performance. Monte Carlo simulation results show that the proposed method can reduce the average ASLC CR from 26.80 dB to 20.29 dB (a decrease of 6.51 dB). Under identical conditions, this performance is comparable to asynchronous blinking jamming while requiring no precise timing matching, and outperforms multi-source saturation jamming in resource efficiency (two vs. four jammers). This study provides promising simulation-level evidence for the effectiveness of the proposed jamming method. The quantitative results and sensitivity analyses offer a simulation-level theoretical reference for parameter design of distributed cooperative jamming. Further validation in semi-physical simulations or field trials is necessary before claiming engineering readiness. Full article

With the expansion of modern protected agriculture, the amount of post-harvest tomato biomass has increased sharply. Conventional unmanaged disposal practices disrupt carbon flows and cause substantial environmental emissions. Tomato plant residues (TPRs), which are rich in lignocellulose and selected high-value secondary metabolites, have considerable potential as feedstocks for green industrial materials. However, their complex biophysical properties, high physiological moisture content, and recalcitrant cell-wall barriers hinder large-scale processing. This review systematically examines the mechanisms and process architectures for converting TPRs into macromolecular products. First, it analyzes cross-scale anatomical heterogeneity and dynamic rheological properties of TPRs, defining their physicochemical boundaries as industrial precursors. Second, it summarizes the development of physical field-coupled equipment, ranging from anti-tangling harvest-shredding to die-roller densification. Furthermore, it examines the core mechanisms of multi-field-coupled pretreatment technologies, including steam explosion, deep eutectic solvents (DES), and mechanochemistry, in deconstructing vascular skeletons and reducing multiphase mass-transfer resistance. Finally, this review discusses reconstruction pathways for TPR-derived components in advanced polymer materials, including biodegradable nanocellulose films, bio-based composites, aerogels, and lignin-based polyurethane networks. Overall, it links microscopic reaction kinetics with macroscopic equipment engineering, proposes a closed-loop material conversion system from in-field volume reduction to cascaded biorefinery, and provides an engineering framework for future multi-machine intelligent collaboration and continuous production across the industrial chain. Full article

As Chinese manufacturing enterprises became more deeply integrated into global value chains, they faced increasingly restrictive U.S. export controls that limited their access to foreign technologies and critical intermediate inputs. Using firm-level data from Chinese listed manufacturing firms over 2006–2015 and the U.S. Entity List, this paper systematically examines the impact of export controls on China’s export technology complexity and explores the underlying mechanisms. The study shows that U.S. export controls significantly reduce manufacturing enterprises’ export technological complexity. The negative effect is more pronounced among enterprises in eastern China, state-owned enterprises, large enterprises, and enterprises operating in high-technology industries. Mechanism analysis shows that export controls suppress the growth of export technological complexity by increasing transaction costs and disrupting supply chains. Although the disruption of innovation chains may stimulate firms’ indigenous innovation, the overall effect of export controls remains negative. Our findings provide theoretical and practical insights for China’s strategies to respond to export controls, enhance the technology complexity of manufacturing exports, and strengthen its position in the global value chain. Full article

The axis linking the gut to the brain to the immune system connects all tissues involved—bacteria, immune cells, metabolism and the CNS—through a multidirectional communication network. Several studies have confirmed that when this axis is disrupted, it can be responsible for Alzheimer’s disease, Parkinson’s disease, obesity, type 2 diabetes, and NAFLD, and the main consequences come from increased systemic inflammation, altered regulation of immune cells, the production of microbial metabolites that alter signals to the immune cells and nervous system, increase in oxidative stress, breakdown of the gut barrier, and more. In recent years, advanced multi-omics technologies, such as metagenomics, transcriptomics, metabolomics, proteomics, and single-cell sequencing, have provided significant advancement in our understanding of all of the interacting nodes involved in the gut–brain–immune axis. These advanced sequencing technologies can characterize the microbial communities, host immune cells, metabolic profiles, and the degree of cell heterogeneity during a specific disease. Combining multi-omics information can reveal a few shared pathways between neurodegenerative and metabolic disorders, such as NF-κB, NLRP3 inflammasome activation, mitochondrial dysfunction, changes in SCFA metabolism, and the alteration of microbial populations in Alzheimer’s and Parkinson’s disease; metabolic dysbiosis and increased risk for Parkinson’s disease; or changes in gut-to-brain-to-immune signaling contributing to diabetes complications and NAFLD. Artificial intelligence (AI) and machine learning are becoming promising tools for detecting biomarkers from these datasets, extracting knowledge, interpreting systems biology, and helping with developing precision medicine. In this review, we summarize current evidence that supports the role of the gut–brain–immune axis in neurodegenerative and metabolic diseases, highlighting results gained with the utilization of multi-omics approaches. We will describe the key microbial, immune, and metabolic pathways involved in pathogenesis and therapeutic approaches including psychobiotics, tailored nutrition, modulation of the microbiome, and metabolite interventions, discussing future perspectives of the translation of the gut–brain–immune axis knowledge into clinical practice. Full article

Social media platforms increasingly function as informal education systems for programming learning, yet the systemic support structures these environments provide remain poorly understood. We analyzed 40,004 comments from programming tutorial videos on a major social media platform (2016–April 2025) to identify patterns of learner support needs at scale. Using BERTopic, we identified twelve discussion themes. We then consolidated these themes into a learner-needs typology based on their dominant support functions: instructional-oriented needs, operational support needs, and knowledge-constructionneeds. We mapped this typology onto the Community of Inquiry (CoI) framework to assess its explanatory coverage. This mapping revealed a critical systemic gap. Operational support needs, covering environment configuration, tool integration, dependency management, and technical troubleshooting, constituted the largest category (44.53% of theme-level discourse), exceeding both knowledge-construction needs (28.42%) and instructional-oriented needs (26.95%). Learners repeatedly described these infrastructure-level challenges as disrupting their attempts to engage with content, execute code for testing ideas, and coordinate with peers, yet these operational readiness needs are not fully specified by CoI’s traditional presences. Social presence did not emerge as a standalone theme at the topic-modeling level; rather, social cues were often embedded within task-oriented troubleshooting. Based on these findings, we propose Technical Presence as a context-sensitive extension to the CoI framework, defined as the extent to which a learning community enables operational readiness through accessible infrastructure support and collaborative troubleshooting. As an infrastructural support condition, Technical Presence supports operational readiness within tool-dependent, practice-based learning: when learners report infrastructure failure, the conditions for enacting instructional design, cognitive inquiry, and peer collaboration are correspondingly weakened. These findings carry implications for content creators, platform developers, and education system designers seeking to strengthen the infrastructural foundations of technology-enhanced learning at scale. Full article

Gas-assisted steam explosion (GASE) disrupts raw material structures and promotes active release, but its effects on the nutritional quality and flavor of edible fungi remain unclear. Therefore, this study assessed the influence of GASE on the nutritional quality and flavor characteristics of Pleurotus eryngii. Using the sample as the raw material, we selected the GASE process parameters through single-factor experiments combined with response surface methodology and confirmation experiments. Subsequently, changes in nutrient contents and volatile/non-volatile flavor profiles were quantitatively characterized under these processing conditions. The results indicated that the selected parameters effectively disrupted the cell wall structure of the sample, resulting in a loose and porous microstructure. Consequently, the levels of protein, polysaccharides, amino acids and vitamins were significantly altered. In terms of flavor, this process modified the relative odor activity values of key aroma compounds, including volatile aldehydes and pyrazines, while also affecting the distribution of non-volatile metabolites. This led to the enrichment of flavor compounds such as nucleotides and their derivatives, and organic acids. This study confirmed that GASE technology can effectively enhance the nutritional quality and flavor characteristics of the mushroom by regulating its microstructure and metabolite composition. Full article

Background: Symbiotic fungi play essential roles throughout the entire cycle of orchid plants, including seed germination, seedling development, and maturation. Dendrobium officinale Kimura & Migo (Orchidaceae) (D. officinale) is a rare and highly valued traditional Chinese medicinal herb. Currently, artificial breeding using tissue culture technology is widely adopted and essential in the Dendrobium industry; however, this approach may impair or disrupt the plant’s ability to establish and maintain symbiotic relationships with mycorrhizal fungi. Methods: In this study, the fungal endophyte community (FEC) in the roots of D. officinale cultivated under four different modes was analyzed using high-throughput sequencing. Correlation analyses were also carried out to examine the relationships between bioactive compounds and the FEC. Results: (1) The FEC in D. officinale roots was dominated by Ascomycota and Basidiomycota, with significant differences in abundance, diversity, and community structure among cultivation modes; (2) the FEC under greenhouse cultivation differed significantly from those under tree epiphytic cultivation in terms of fungal nutritional types and dominant taxa; (3) six major mycorrhizal fungal taxa were identified in Dendrobium roots, although non-mycorrhizal fungi accounted for approximately 97% of the community; and (4) polysaccharide content in Dendrobium stems was positively correlated with certain root fugal endophytes (Exophiala, alaromyces, Pseudodactylaria, and Fellomyces). Conclusions: This study provides a foundation for understating the growth of D. officinale under different cultivation modes and highlights the relationship between bioactive compound accumulation and fungal endophyte communities. Full article

The increasing adoption of Plant Factories with Artificial Lighting (PFAL) has intensified the reliance on Internet of Things (IoT) technologies for real-time monitoring and control of environmental and operational variables. While IoT-based architectures enable precise resource management and productivity optimization, PFAL systems remain highly vulnerable to component failures, sensor malfunctions, communication faults, and energy disruptions, which may compromise crop integrity and system reliability. These risks are particularly critical in low-cost and small-scale PFAL implementations, where maintenance capacity and redundancy are often limited. Existing IoT-based PFAL monitoring systems typically address either hardware or software redundancy in isolation and rarely incorporate a dedicated maintenance-oriented fault detection layer validated under realistic multi-failure scenarios. This study addresses these challenges by proposing a low-cost redundant system architecture for PFAL applications that simultaneously integrates (1) hardware redundancy through multi-sensor configurations; (2) analytical redundancy based on residual generation and threshold-based fault isolation; and (3) a maintenance-oriented fault detection layer capable of identifying abnormal internal device conditions. Experimental validation was conducted using four hardware configurations—Arduino Nano with Ethernet, ESP32, STM32 with Wi-Fi, and STM32 with Ethernet—evaluated across five fault scenarios: dust accumulation, water exposure, high temperature, fire detection, and physical impact. The STM32 with Ethernet configuration consistently achieved the fastest fault detection response times across all tested scenarios. Future work will focus on the integration of machine learning-based predictive maintenance algorithms, multi-node PFAL network deployments, and long-term field validation. Full article