Search Results (2,055)

Background/Objectives: Malnutrition is highly prevalent in patients with acute leukemia and is frequently underrecognized at diagnosis. Traditional screening tools based on anthropometry often fail to identify early nutritional deterioration. This study aimed to evaluate the prognostic utility of a comprehensive morphofunctional assessment—including bioelectrical impedance vector analysis (BIVA), handgrip strength (HGS), and muscle ultrasound—conducted at diagnosis and after induction therapy, to evaluate the prognostic association with 12-month mortality. Methods: In this prospective cohort study, 52 adult patients with newly diagnosed acute leukemia were enrolled between November 2022 and November 2024 at two tertiary hospitals in Málaga, Spain. Nutritional status was determined using GLIM criteria. Morphofunctional assessment included BIVA-derived phase angle (PhA), HGS via dynamometry, and rectus femoris ultrasound. A second evaluation was performed prior to haematopoietic stem cell transplantation. Mortality at 12 months was the primary outcome. Logistic regression and ROC analysis were used to assess prognostic associations. Results: At baseline, 65.4% of patients were classified as malnourished. After three months, patients showed significant declines in PhA (−0.55°, p < 0.001), body cell mass (−3.15 kg, p < 0.01), skeletal muscle mass (−1.66 kg, p < 0.01), and rectus femoris cross-sectional area (−0.36 cm², p = 0.011). Baseline malnutrition (OR = 6.88; 95% CI: 1.17–40.38; p = 0.033) and PhA decline ≥ 0.90° were both independently associated with higher 12-month mortality. Conclusions: Early morphofunctional assessment using GLIM criteria, BIVA, and muscle ultrasound identifies patients at nutritional and functional risk. PhA decline during treatment was associated with higher 12-month mortality, supporting the need for early, personalized nutritional intervention in leukemia care. Full article

This article introduces a framework for scientific and professional language training tailored for STEM (Science, Technology, Engineering and Mathematics) specialists, emphasising the integration of digital technologies and artificial intelligence (AI) in language education. The framework aims to develop students’ research communication skills and digital competencies, which are essential for effective participation in both national and international scientific discourse. The article discusses contemporary trends in STEM education, emphasising the importance of interdisciplinary approaches, project-based learning, and the utilisation of digital tools to boost language skills and scientific literacy. The article outlines the development and deployment of a digital platform aimed at supporting personalised and adaptive learning experiences, integrating various educational technologies and approaches. Empirical research conducted through a pedagogical experiment demonstrates the effectiveness of the framework, showing significant improvements in students’ academic and linguistic competencies across multiple modules. The findings highlight the importance of combining language training with STEM education to equip future engineers for the challenges of a globalised and digitalised professional world. This work reports on the “Enhancing Scientific and Professional Language Learning for Engineering Students in Kazakhstan through Digital Technologies” project conducted at Saginov Technical University (STU) in Kazakhstan and funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19678460). The research contributes to the ongoing discussion on improving language teaching in STEM fields, offering a framework that aligns with current educational demands and technological progress. Full article

Acute respiratory distress syndrome (ARDS) is a major cause of morbidity and mortality despite decades of progress in ventilatory support. Mechanical ventilation, while essential for oxygenation, may exacerbate lung injury through excessive mechanical power delivery, even when using lung-protective strategies. Extracorporeal carbon dioxide removal (ECCO₂R) was conceived to enable “ultra-protective” ventilation, allowing for further reductions in tidal volume and respiratory rate by selectively removing CO₂ at low extracorporeal blood flows, typically between 0.3 and 1.0 L/min. This physiological decoupling of ventilation and gas exchange aims to mitigate ventilator-induced lung injury (VILI) while maintaining adequate acid–base homeostasis. Although early physiological studies demonstrated feasibility, large, randomized trials have failed to show a survival benefit and have raised concerns about bleeding and technical complications. Recent evidence suggests that these neutral outcomes may stem from the biological and physiological heterogeneity of ARDS rather than from inefficacy of the intervention itself. Patients with high driving pressures, poor compliance, or hyperinflammatory phenotypes may derive greater benefit from ECCO₂R-mediated mechanical unloading. Ongoing technological improvements, including circuit miniaturization, enhanced biocompatibility, and integration with renal replacement therapy, have improved safety and feasibility, yet the procedure remains complex and resource-intensive. Future research should focus on phenotype-enriched trials and the integration of ECCO₂R into precision ventilation frameworks. Ultimately, ECCO₂R should be regarded not as a universal therapy for ARDS but as a targeted physiological tool for selected patients in experienced centers. Full article

Incorporating STEM (Science, Technology, Engineering, and Mathematics) into early childhood education has been associated with children’s holistic development. STEM education not only enhances critical thinking, creativity, problem-solving, and other 21st-century skills but also contributes significantly to cognitive growth, emotional regulation, and social abilities. Within the early childhood context, the use of play and toys emerges as a natural and powerful medium for introducing STEM concepts in developmentally appropriate and engaging ways. Play and toys have a prominent role, and previous studies have provided strong evidence on their educational benefits. Toys enhanced with technological characteristics (Technology-Enhanced Toys—TETs), such as coding and interactive toys, are increasingly being viewed as cultural tools that mediate learning and nurture cognitive and collaborative skills among young learners. However, the impact TETs have on young children’s STEM learning remains largely unexplored. This qualitative observational study, grounded in a socio-cultural perspective, explored how 37 children aged 3 to 4 years in four early childhood settings in Greece exhibited STEM-related behaviours during free play with technology-enhanced toys. Data were collected through systematic video recordings and written observations over a three-month period that involved interacting with various TETs, such as Bee-Bot, Coko Robot, a remote-controlled dog, and others. Results indicate that playing with TETs enhanced problem-solving, computational thinking, and collaboration, thus affirming the positive influence of digital technology and the potential of TETs to enrich early STEM education. Implications for equity, the importance of teachers’ professional development in effectively integrating TETs into early childhood curricula and the need for further research will also be discussed. Full article

In the aftermath of the COVID-19 pandemic, instructors in higher education have reported a decline in foundational reading habits, particularly in STEM courses where dense, technical texts are common. This study examines a low-barrier instructional intervention that used generative AI (GenAI) to support pre-class preparation in two upper-division biology courses. Weekly AI-generated audio overviews—“podcasts”—were paired with timed, textbook-based online quizzes. These tools were not intended to replace reading, but to scaffold engagement, reduce preparation anxiety, and promote early familiarity with course content. We analyzed student engagement, perceptions, and performance using pre/post surveys, quiz scores, and exam outcomes. Students reported that the podcasts helped manage time constraints, improved their readiness for lecture, and increased their motivation to read. Those who consistently completed the quizzes performed significantly better on closed-book, in-class exams and earned higher final course grades. Our findings suggest that GenAI tools, when integrated intentionally, can reintroduce structured learning behaviors in post-pandemic classrooms. By meeting students where they are—without compromising cognitive rigor—audio-based scaffolds may offer inclusive, scalable strategies for improving academic performance and reengaging students with scientific content in an increasingly attention-fragmented educational landscape. Full article

Torque Teno Virus (TTV), a common and genetically diverse component of the human virome, is not linked to any known disease but reflects immune status. Its plasma viral load has shown clinical relevance in solid organ transplant recipients, correlating it with immunosuppression when present at high levels. However, the clinical significance of TTV viral load in hematopoietic stem cell transplantation (HSCT) recipients is less understood. This systematic review aims to evaluate whether plasma TTV DNA load directly correlates with the degree of T-cell immune reconstitution after HSCT, supporting its potential role as a biomarker for immune competence. The study protocol was registered in the PROSPERO International Prospective Register of Systematic Reviews (CRD420251116208) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Twenty-one studies were included. The results showed concordant data about TTV kinetics with peak levels reaching approximately between +90 to +120 days after transplantation. Contradictory results have instead been found for the association of TTV load with immune parameters (lymphocyte counts, viral opportunistic infection, and development of acute graft versus host diseases). Even if a low-risk bias assessment was classified in most studies (67%), it was possible to identify important clinical and methodological differences between them, which might account for the different findings observed. Therefore, future larger studies with standardized protocols are needed to assess whether TTV viral load can serve as a reliable tool for guiding clinical decisions in the context of HSCT. Full article

Non-digital traditional games such as board and card formats are increasingly recognised as valuable tools for active learning in higher education. These analogue approaches promote engagement, collaboration, and conceptual understanding through embodied and social interaction. This scoping review mapped research on the use of traditional, non-digital games as active learning strategies in tertiary education and examined whether the rise in generative artificial intelligence (GenAI) since 2022 has influenced their pedagogical role. Following the PRISMA-ScR framework, a systematic search of Scopus (October 2025) identified 2480 records; after screening, 26 studies met all inclusion criteria (explicitly using card and/or board games). Whilst this was a scoping, not a systematic review, some bias due to using only one database and evidence could have missed some studies. Results analysed the use and impacts of the games and whether AI was a specific driver in its use. Studies spanned STEM, business, health, and social sciences, with board and card games most frequently employed to support engagement, understanding, and collaboration. Most reported positive learning outcomes. Post-2023 publications suggest renewed interest in analogue pedagogies as authentic, human-centred responses to AI-mediated education. While none directly investigated GenAI, its emergence appears to have acted as an indirect catalyst, highlighting the continuing importance of tactile, cooperative learning experiences. Analogue games therefore remain a resilient, adaptable form of active learning that complements technological innovation and sustains the human dimensions of higher-education practice. Full article

The post-implantation phase of mammalian development is crucial yet challenging to study due to ethical and technical constraints, particularly in humans. Recent revolutionary advances in extended in vitro culture systems for mammalian embryos now offer unprecedented windows into this developmental “black box”. This review synthesizes how these platforms, alongside stem cell-derived embryo models, are transforming our ability to model early human development in a dish. We detail the technological evolution from two-dimensional (2D) to three-dimensional (3D) cultures that support mouse, non-human primate, and human embryos through key stages of implantation and gastrulation, recapitulating events like lineage specification and axial patterning. Furthermore, we explore how these models serve as powerful tools for investigating the etiology of early pregnancy failure, screening for developmental toxicity of pharmaceuticals, and deciphering the molecular pathogenesis of birth defects. By bridging fundamental embryology with clinical and pharmacological applications, these innovative models herald a new era in biomedical research, holding significant promise for advancing reproductive medicine and regenerative strategies. Full article

Sorghum (Sorghum bicolor L. Moench) is a major cereal crop cultivated in semi-arid regions, but its yield is often constrained by soilborne fungal pathogens that affect plant growth and grain quality. This study explored how Trichoderma-based bioinoculants restructure the structure and functional composition of fungal communities in distinct sorghum compartments (soil, root, seed, and stem) using ITS amplicon sequencing. Two cultivars, Kalatur and Foehn, were evaluated under control and inoculated conditions. Alpha diversity indices revealed that inoculation reduced overall fungal richness and evenness, particularly in seed and stem tissues, while selectively enhancing beneficial taxa. Beta diversity analyses (PERMANOVA, p < 0.01) confirmed significant treatment-driven shifts in community composition. LEfSe analysis identified Trichoderma and Mortierella as biomarkers of inoculated samples, whereas Fusarium, Alternaria, and Penicillium predominated in controls. The enrichment of saprotrophic and symbiotrophic taxa in treated samples, coupled with the decline of pathogenic genera, indicates a transition toward functionally beneficial microbial assemblages. These results demonstrate that Trichoderma bioinoculants not only suppress fungal pathogens but also promote the establishment of beneficial ecological groups contributing to plant and soil health. The present work provides insight into the mechanisms through which microbial inoculants modulate host-associated fungal communities, supporting their use as sustainable tools for crop protection and microbiome management in sorghum-based agroecosystems. Full article

Hair loss disorders, particularly androgenetic alopecia (AGA), are common conditions that carry significant psychosocial impact. Current standard therapies, including minoxidil, finasteride, and hair transplantation, primarily slow progression or re-distribute existing follicles and do not regenerate lost follicular structures. In recent years, regenerative medicine has been associated with a gradual shift toward approaches that aim to restore follicular function and architecture. Stem cell-derived conditioned media and exosomes have shown the ability to activate Wnt/β-catenin signaling, enhance angiogenesis, modulate inflammation, and promote dermal papilla cell survival, resulting in improved hair density and shaft thickness with favorable safety profiles. Autologous cell-based therapies, including adipose-derived stem cells and dermal sheath cup cells, have demonstrated the potential to rescue miniaturized follicles, although durability and standardization remain challenges. Adjunctive interventions such as microneedling and platelet-rich plasma (PRP) further augment follicular regeneration by inducing controlled micro-injury and releasing growth and neurotrophic factors. In parallel, machine learning-based diagnostic tools and deep hair phenotyping offer improved severity scoring, treatment monitoring, and personalized therapeutic planning, while robotic Follicular Unit Excision (FUE) platforms enhance surgical precision and graft preservation. Advances in tissue engineering and 3D follicle organoid culture suggest progress toward producing transplantable follicle units, though large-scale clinical translation is still in early development. Collectively, these emerging biological and technological strategies indicate movement beyond symptomatic management toward more targeted, multimodal approaches. Future progress will depend on standardized protocols, regulatory clarity, and long-term clinical trials to define which regenerative approaches can reliably achieve sustainable follicle renewal in routine cosmetic dermatology practice. Full article

STEM education increasingly relies on technology-enhanced environments that utilize data-driven strategies, digital tools, and adaptable learning models. To support the evaluation of contemporary STEM teaching methods, this study proposes a multi-criteria analytical framework based on expert assessment. Semi-structured interviews were conducted with 41 experienced teachers from Bulgarian schools (N = 41), who evaluated six key indicators (m = 6) of STEM integration: Effectiveness, Engagement, Applicability, Flexibility, Validity, and Accessibility. The qualitative data were transformed into numerical values and analyzed using the Target Parameter Ranking method. The degree of expert agreement was assessed through the Morris–Kendall coefficient, yielding a statistically significant moderate agreement (w_k = 0.137; χ² = 28.085, df = 5, p = 3.50 × 10⁻⁵ (p < 0.001)). The results indicate that Engagement (W_j = 0.206), Flexibility (W_j = 0.188), and Effectiveness (W_j = 0.186) are the most highly weighted criteria, reflecting teachers’ prioritization of active participation, learning outcomes, and adaptability in technology-rich STEM environments. In comparison, Applicability and Accessibility show higher variability, highlighting their dependence on contextual factors such as infrastructure and resource availability. The proposed framework provides a structured, data-driven basis for evaluating and refining STEM teaching practices and can be integrated into educational decision-support systems. Full article

Prostate cancer (PCa) is a common malignancy among men worldwide. After radical prostatectomy (RP) and radical radiotherapy (RT), patients may experience biochemical recurrence (BCR) of prostate cancer, indicating disease progression. Therefore, it is meaningful to predict and accurately assess the risk of BCR, and a machine-learning-based-model for BCR prediction in PCa based on fatty-acid metabolism and cancer-cell stemness was developed. A stemness prediction model and ssGSEA (single-sample gene set enrichment analysis) empirical cumulative distribution function algorithm were used to score the stemness scoring (mRNAsi) and fatty-acid metabolism of prostate-cancer samples, respectively, and further analysis showed that the two scores of the samples were positively correlated. Based on WGCNA (weighted correlation network analysis), we discovered modules significantly associated with both stemness and fatty-acid metabolism and obtained the genes within them. Then, based on this gene set, 101 algorithm combinations of 10 machine-learning methods were used for training and prediction BCR of PCa, and the model with the best prediction effect was named fat_stemness_BCR. Compared with 23 published PCa BCR models, the fat_stemness_BCR model performs better in TCGA and CPGEA data. To facilitate the use of the model, the trained model was encapsulated into an R package and an online service tool (PCaMLmodel, Version 1.0) was built. The newly developed fat_stemness_SCR model enriches the prognostic research of biochemical recurrence in PCa and provides a new reference for the study of other diseases. Full article

Artificial intelligence (AI) is becoming more and more integrated into manufacturing processes, revolutionizing conventional production, like CNC (Computer Numerical Control) machining. This study analyzes how large language models (LLMs), exemplified by ChatGPT, behave when tasked with G-code optimization for improving surface quality and productivity of automotive metal parts, with emphasis on systematically documenting failure modes and limitations that emerge when general-purpose AI encounters specialized manufacturing domains. Even if software programming remains essential for highly regulated sectors, free AI tools will be increasingly used due to advantages like cost-effectiveness, adaptability, and continuous innovation. The condition is that there is sufficient technical expertise available in-house. The experiment carried out involved milling three identical parts using a Haas VF-3 SS CNC machine. The G-code was generated by SolidCam and was optimized using ChatGPT considering user-specified criteria. The aim was to improve the quality of the part’s surface, as well as increase productivity. The measurements were performed using an ISR C-300 Portable Surface Roughness Tester and Axiom Too 3D measuring equipment. The experiment revealed that while AI-generated code achieved a 37% reduction in cycle time (from 2.39 to 1.45 min) and significantly improved surface roughness (Ra—arithmetic mean deviation of the evaluated profile—decreased from 0.68 µm to 0.11 µm—an 84% improvement), it critically eliminated the pocket-milling operation, resulting in a non-conforming part. The AI optimization also removed essential safety features including tool length compensation (G43/H codes) and return-to-safe-position commands (G28), which required manual intervention to prevent tool breakage and part damage. Critical analysis revealed that ChatGPT failures stemmed from three factors: (1) token-minimization bias in LLM training leading to removal of the longest code block (31% of total code), (2) lack of semantic understanding of machining geometry, and (3) absence of manufacturing safety constraints in the AI model. This study demonstrates that current free AI tools like ChatGPT can identify optimization opportunities but lack the contextual understanding and manufacturing safety protocols necessary for autonomous CNC programming in production environments, highlighting both the potential, but also the limitation, of free AI software for CNC programming. Full article

Mangrove ecosystems are extremely sensitive to compounded stress, as evidenced by the widespread degradation and mortality of the pioneer mangrove species Avicennia marina along the Guangxi coast in recent years. However, research on how mangrove ecosystems respond to compound biotic stressors remains limited. Therefore, the present study aimed to systematically examine the ecological response mechanisms of A. marina under dual threats from the burrowing isopod Sphaeroma terebrans and the defoliating moth Hyblaea puera. Two contrasting sites were selected: Guchengling (subject to chronic stem-boring and sudden defoliator outbreaks) and Tieshangang (free from compounded stress). Photosynthetic capacity, metabolic function, and root structural integrity were all compromised considerably by chronic boring stress. During insect outbreaks, 15.33 ha of mangroves were destroyed due to impairments that breached the ecological threshold. In contrast, the healthier Tieshangang community exhibited strong ecological resilience, with rapid green canopy regeneration following defoliation and notable recovery in the normalized difference vegetation index. To enable early identification and precise intervention in mangrove decline, a comprehensive health index model was developed that includes root–canopy coordination, root length, and boring density. Field validation results, showing 100% agreement with expert evaluations across 19 validation sites (Cohen’s κ = 1.0), confirmed the high accuracy of the model. This study highlights the importance of identifying sensitive zones and undertaking timely ecological restoration, thereby providing a scientific basis and a practical tool that could facilitate early warning and timely management of mangrove degradation events. Full article

Forests play a strategic role in global sustainability, and restoration is essential to meet ESG targets. Seedling quality strongly influences reforestation success, but standardized evaluation protocols are often lacking. This study aimed to identify and prioritize critical technical parameters of forest seedlings and determine the highest-priority factor affecting field performance. A total of 100 seedlings of Handroanthus impetiginosus and Sparattosperma leucanthum were evaluated using Quality Function Deployment (QFD), considering reforestation as the client to translate field performance requirements into nursery-level technical parameters. Seedling characteristics were compared to standards based on the literature and nursery best practices. QFD analysis revealed that stem thickness and integrity, absence of borers, well-developed and firm roots, and complete and healthy leaves were the most critical attributes. Hardiness, combining structural robustness, disease resistance, and vigor, emerged as the central factor. Observed non-conformities included disease (15%), stem bifurcations (10%), and substrate deficiencies (12%). These results demonstrate that QFD is an effective tool for systematically identifying and prioritizing seedling attributes. The study provides a structured approach for nursery evaluation and quality control, supporting informed decision-making to enhance the success of forest restoration projects. Full article