Search Results (1,341)

One of the aggressive and lethal cancers, pancreatic ductal adenocarcinoma (PDAC) is characterized by poor prognosis and resistance to conventional treatments. Moreover, the tumor immune microenvironment (TIME) plays a crucial role in the progression and therapeutic resistance of PDAC. It is associated with T-cell exhaustion, leading to the progressive loss of T-cell functions with an impaired ability to kill tumor cells. Therefore, this study employed single-cell RNA sequencing (scRNA-seq) analysis of a publicly available human PDAC dataset, with cells isolated from the primary tumor and adjacent normal tissues, identifying upregulated genes of T-cells and cancer cells in two groups (“cancer cells_vs_all-PDAC” and “cancer-PDAC_vs_all-normal”). Common and unique markers of cancer cells from both groups were identified. The Reactome pathways of cancer and T-cells were selected, while the genes implicated in those pathways were used to perform PPI analysis, revealing the hub genes of cancer and T-cells. The gene expression validation of cancer and T-cells hub-genes was performed using GEPIA2 and TISCH2, while the overall survival analysis of cancer cells hub-genes was performed using GEPIA2. Conclusively, this study unraveled 16 novel markers of cancer and T-cells, providing the groundwork for future research into the immune landscape of PDAC, particularly T-cell exhaustion. However, further clinical studies are needed to validate these novel markers as potential therapeutic targets in PDAC patients. Full article

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being. Full article

Extremophilic microorganisms offer an untapped potential for producing unique bioactive metabolites with therapeutic applications. In the current study, bacterial isolates were obtained from samples collected from Chamalang cave located in Kohlu District, Balochistan, Pakistan. The cave-derived isolate C1 (Rhodococcus jialingiae) exhibits prominent antibacterial activity against multidrug-resistant pathogens (MDR), including Escherichia coli, Staphylococcus aureus, and Micrococcus luteus. It also demonstrates substantial antioxidant activity, with 71% and 58.39% DPPH radical scavenging. Optimization of physicochemical conditions, such as media, pH, temperature, and nitrogen and carbon sources and concentrations substantially enhanced both biomass and metabolite yields. Optimal conditions comprise specialized media, a pH of 7, a temperature of 30 °C, peptone (1.0 g/L) as the nitrogen source, and glucose (0.5 g/L) as the carbon source. HPLC and QTOF-MS analyses uncovered numerous metabolites, including a phenolic compound, 2-[(E)-3-hydroxy-3-(4-methoxyphenyl) prop-2-enoyl]-4-methoxyphenolate, Streptolactam C, Puromycin, and a putative aromatic polyketide highlighting the C1 isolate chemical. Remarkably, one compound (C₁₄H₃₆N₇) demonstrated a special molecular profile, signifying structural novelty and warranting further characterization by techniques such as ¹H and ¹³C NMR. These findings highlight the biotechnological capacity of the C1 isolate as a source of novel antimicrobials and antioxidants, linking environmental adaptation to metabolic potential and supporting natural product discovery pipelines against antibiotic resistance. Full article

Underwater acoustic (UWA) communication supports many critical applications but still faces several physical-layer signal processing challenges. In response, recent advances in machine learning (ML) and deep learning (DL) offer promising solutions to improve signal detection, modulation adaptability, and classification accuracy. These developments highlight the need for a systematic evaluation to compare various ML/DL models and assess their performance across diverse underwater conditions. However, most existing reviews on ML/DL-based UWA communication focus on isolated approaches rather than integrated system-level perspectives, which limits cross-domain insights and reduces their relevance to practical underwater deployments. Consequently, this systematic literature review (SLR) synthesizes 43 studies (2020–2025) on ML and DL approaches for UWA communication, covering channel estimation, adaptive modulation, and modulation recognition across both single- and multi-carrier systems. The findings reveal that models such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and generative adversarial networks (GANs) enhance channel estimation performance, achieving error reductions and bit error rate (BER) gains ranging from ${10}^{-3}$ to ${10}^{-6}$. Adaptive modulation techniques incorporating support vector machines (SVMs), CNNs, and reinforcement learning (RL) attain classification accuracies exceeding 98% and throughput improvements of up to 25%. For modulation recognition, architectures like sequence CNNs, residual networks, and hybrid convolutional–recurrent models achieve up to 99.38% accuracy with latency below 10 ms. These performance metrics underscore the viability of ML/DL-based solutions in optimizing physical-layer tasks for real-world UWA deployments. Finally, the SLR identifies key challenges in UWA communication, including high complexity, limited data, fragmented performance metrics, deployment realities, energy constraints and poor scalability. It also outlines future directions like lightweight models, physics-informed learning, advanced RL strategies, intelligent resource allocation, and robust feature fusion to build reliable and intelligent underwater systems. Full article

A graph with $q(a+t)$ vertices is known as a q-broom-like graph $K_q\sqcap B(a;t)$, which is produced by the hierarchical product of the complete graph $K_q$ by the rooted broom $B(a;t)$, where $q\ge 3,a\ge 1$ and $t\ge 1$. A numerical quantity associated with graph structure is called a topological index. The inverse sum indeg index (shortened to $ISI$ index) is a topological index defined as $ISI\left(G\right)={\displaystyle \sum _{v_i{v}_j\in E\left(G\right)}}{\displaystyle \frac{d_{v_i}{d}_{v_j}}{d_{v_i}+d_{v_j}}}$, where $d_{v_i}$ is the degree of the vertex $v_i$. In this paper, we take into consideration the $ISI$ index for q-broom-like graphs and perform a thorough analysis of it. We find the $ISI$ spectrum of q-broom-like graphs and derive the closed formulas for their $ISI$ index and $ISI$ energy. We also characterize extremal graphs and arrange them according to their $ISI$ index and $ISI$ energy, respectively. Further, a quantitative structure–property relationship is used to predict six physicochemical properties of sixteen alkaloid structures using $ISI$ index and $ISI$ energy. Both graph invariants have significant correlation values, indicating the accuracy and utility of the findings. The conclusions made in this article can help chemists and pharmacists research alkaloids’ structures for applications in industry, pharmacy, agriculture, and daily life. Full article

The cements industry is increasingly under pressure to reduce carbon emissions while maintaining performance standards. Limestone calcined clay cement (LC³) presents a promising low-carbon alternative; however, its performance depends significantly on the type and reactivity of clay used. This study investigates the effect of three common low-grade clay minerals—kaolinite, montmorillonite, and illite—on the behavior of LC³ blends. The clays were thermally activated and characterized using X-ray diffraction (XRD), thermogravimetric analysis (TGA), X-ray fluorescence spectroscopy (XRF), and Blaine air permeability testing to evaluate their mineralogical composition, thermal behavior, chemical content, and fineness. Pozzolanic reactivity was assessed using the modified Chapelle test. Microstructural development was examined through scanning electron microscopy (SEM) of the hydrated specimens at 28 days. The results confirmed a strong correlation between clay reactivity and hydration performance. Kaolinite showed the highest reactivity and fineness, contributing to a dense microstructure with reduced portlandite and enhanced formation of calcium silicate hydrate. Montmorillonite demonstrated comparable strength and favorable hydration characteristics, while illite, though less reactive initially, showed acceptable long-term behavior. Although kaolinite delivered the best overall performance, its limited availability and higher cost suggest that montmorillonite and illite represent viable and cost-effective alternatives, particularly in regions where kaolinite is scarce. This study highlights the suitability of regionally available, low-grade clays for use in LC³ systems, supporting sustainable and economically viable cement production. Full article

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks. Full article

The Lahore–Islamabad Motorway (M-2) is a critical transportation corridor in Pakistan, where contamination in roadside dust by potentially toxic elements (PTEs) presents potential environmental and health concerns. This study evaluates the concentration, spatial distribution, and ecological risks of PTEs (Mn, Ni, Cr, Cu, Pb, Zn, Cd, Ag, Fe) in road dust along the M-2. PTE concentrations were determined using standard protocols and by analysis using an atomic absorption spectrometer. The findings indicate substantial variability in metal concentrations, with Fe (CV% = 9.35%) and Pb (CV% = 7.06%) displaying the highest consistency, whereas Ni exhibited the greatest fluctuation (CV% = 168.80%). Contamination factor analysis revealed low to moderate contamination for Ni and Fe, while Zn contamination was significant in 60% of samples. Cr and Cd exhibited persistently high contamination, and Pb was uniformly elevated across all locations. Ecological risk assessment categorized Ni, Zn, and Cu as low-risk elements, while Pb posed a substantial risk. Cd concentrations indicated high to extreme ecological hazards, emphasizing the necessity for urgent mitigation measures. Factor analysis suggested an interaction of various sources, including industrial, vehicular emissions, and construction materials. Strengthened pollution control strategies and systematic monitoring are essential for mitigating contamination and ensuring environmental sustainability along the motorway. Full article

Background: Colon capsule endoscopy (CCE) or panenteric capsule endoscopy (PCE) offers a promising, non-invasive diagnostic approach for patients with iron deficiency anaemia (IDA). However, high rates of conversion to conventional colonoscopy (CCC) following capsule procedures reduce cost-effectiveness and patient satisfaction. Optimising the faecal immunochemical test (FIT) threshold may improve patient stratification and reduce unnecessary conversions in future applications within the IDA diagnostic pathway. Methods: The CLEAR IDA study was a multicentre, retrospective observational study conducted across four UK hospitals. Data were collected over a six-month study period and included patients referred via the two-week-wait (2WW) cancer pathway for iron deficiency, with or without anaemia, over a 12-month timeframe. Colonoscopy findings were analysed and extrapolated using NHS England’s CCE-to-colonoscopy referral criteria to assess the predictive value of FIT for colorectal cancer (CRC), polyp burden, and CCC using ROC curve analysis. The optimal FIT threshold was identified through three complementary approaches: threshold-based analysis, decision curve analysis, and cost–benefit modelling. Results: A total of 1531 patients were analysed; only 1.6% underwent small bowel capsule endoscopy. The diagnostic accuracy (AUC) of FIT for predicting CRC, polypoidal lesions, and CCC was 0.78, 0.58, and 0.69, respectively. Threshold-based analysis identified FIT = 15 µg/g as the lowest level at which CCC rates significantly increased (p = 0.02; OR = 1.87; 95% CI: 1.07–3.14). Decision curve analysis showed a maximum net benefit at FIT = 17.6 µg/g, while cost–benefit modelling identified 9 µg/g as the most cost-effective. Raising the threshold to 10 µg/g resulted in a net loss of GBP –294.4 per patient. An optimal cost-effective FIT threshold range was identified between 10 and 17.6 µg/g. The threshold selection should be tailored to local service capacity and resource availability. Conclusions: While FIT alone is an imperfect triage tool, optimising thresholds between 10 and 17 µg/g may enhance cost-effectiveness and guide appropriate PCE use in IDA. Full article

Green leadership is often praised for promoting sustainability, while hospitals in reactive or resource-constrained contexts lack the infrastructure to support leadership-led environmental change, indicating that leadership without operational capacity offers little impact. Moreover, the inconsistencies between green human resource practices and environmental performance suggest that green leadership might lead to symbolic gestures rather than real improvements without a robust environmental culture or internal accountability systems. Amid intensifying environmental regulations and sustainability mandates in healthcare, this study investigates how green transformational leadership addresses the contradiction between hospitals’ resource-intensive operations and environmental accountability. Drawing on Dynamic Capabilities Theory (DCT), the research highlights policy-driven imperatives for hospitals to build adaptive leadership models that meet sustainability goals. Using data from 312 junior doctors and nurses in private hospitals, analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM), the study identifies green attitude, green empowerment, and green self-efficacy as key mediators in enhancing environmental performance. Contributions of this study include (1) applying DCT to healthcare sustainability, (2) integrating psychological drivers into leadership–performance models, and (3) emphasizing nurses’ pivotal roles. The results of the study indicate that leaders who prioritize sustainability inspire staff to adopt eco-friendly practices, aligning with SDG 3, i.e., good health and well-being; SDG 12, i.e., responsible consumption and production; and SDG 7, i.e., affordable and clean energy. The findings provide actionable insights for hospital administrators and policymakers striving for environmentally accountable healthcare delivery. Full article

Zinc oxide (ZnO) photocatalysts are recognized for their ease of synthesis, cost-effectiveness, efficiency, scalability, and environmental compatibility, making them highly suitable for addressing wastewater contamination. In this study, various compositions of kaolinite were used for the hydrothermal deposition of ZnO, including 0.5%, 0.75%, 1%, and 1.25%. The main purpose of this study was to evaluate the effect of kaolinite toward the enhanced performance of ZnO through modification of particle size, morphology and surface functional groups. Several analytical techniques were employed to obtain structural and optical results, including scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and UV–visible spectroscopy, revealing significant changes in particle shape, particle size, surface functional groups, and optical band gap when kaolinite was added. The ZnO/kaolinite composite (sample 4) with 1.25% kaolinite content demonstrated outstanding photocatalytic performance for the degradation of methylene blue in natural sunlight. For sample 4, 15 mg of the dye in a 3.4 × 10⁻⁵ M dye solution exhibited a degradation efficiency of 99%. In contrast, when using 15 mg of catalyst dose and 1.5 × 10⁻⁵ M dye solution, the degradation efficiency was observed to be almost 100%, thus indicating that catalyst dose and dye concentration affect degradation efficiency. The reusability test revealed that sample 4 retained degradation efficiency of 98% after five cycles without showing any morphological changes. By decorating ZnO with kaolinite mineral clay, this study provides exciting findings and insights into the development of low-cost photocatalysts, which could be used to produce solar-powered hydrogen and treat wastewater. Full article

Often thought of as a mesic paradise, forest ecosystems are a mosaic of microhabitats with temporal oscillations that cause significant environmental stresses, providing habitats for extremophilic and extremotolerant fungi. Adapted to survive and thrive under conditions lethal to most mesophiles (e.g., extreme temperatures, pH, water potential, radiation, salinity, nutrient scarcity, and pollutants), these species are increasingly recognized as vital yet underappreciated elements of forest biodiversity and function. This review examines the current understanding of the roles of extremophilic fungi in forests, scrutinizing their presence in these ecosystems with a critical eye. Particularly under severe environmental conditions, extremophilic fungi play a crucial role in forest ecosystems, as they significantly enhance decomposition and nutrient cycling, and foster mutualistic interactions with plants that increase stress resilience. This helps to maintain ecosystem stability. We examine the definition of “extreme” within forest settings, survey the known diversity and distribution of these fungi across various forest stress niches (cold climates, fire-affected areas, acidic soils, canopy surfaces, polluted sites), and delve into their possible ecological functions, including decomposition of recalcitrant matter, nutrient cycling under stress, interactions with plants (pathogenesis, endophytism, perhaps mycorrhizae), bioremediation, and contributions to soil formation. However, the review stresses significant methodological difficulties, information gaps, and field-based natural biases. We recommend overcoming cultural constraints, enhancing the functional annotation of “omics” data, and planning investigations that clarify the specific activities and interactions of these cryptic creatures within the forest matrix to further advance the field. Here, we demonstrate that moving beyond simple identification to a deeper understanding of function will enable us to more fully appreciate the value of extremophilic fungi in forest ecosystems, particularly in relation to environmental disturbances and climate change. Full article

Background: Critical thinking is fundamental for registered nurses (RNs) when making clinical decisions, which impact patient outcomes. This review aimed to identify studies on critical thinking and clinical decision making among nurses in clinical practice and synthesize their findings based on the regional area, observed findings, and predictive factors, and to assess the measurement tools used. Methods: A comprehensive search of the PubMed, Web of Science, CINAHL, and SCOPUS databases up to December 2024 was conducted in accordance with the PRISMA guidelines. The Newcastle–Ottawa Scale was used to assess the quality of included studies. Studies with similarly themed components were grouped for narrative synthesis. A meta-analysis of random-effects model calculations was performed. Results: This review included forty studies (twenty-four on CT, twelve on CDM, four on both) from various WHO regions, revealing diverse findings on observed skills. Ten CT and four CDM measurement tools were identified. Many studies also explored individual and group-level predictive factors for these skills. Meta-analyses of four common tools (CCTDI, NCT4P, CDMNS, and NDMI) showed significant heterogeneity, with statistically significant pooled mean scores. Conclusions: The synthesis highlights the global research on nurses’ critical thinking and clinical decision making, including the exploration of various predictive factors. However, the significant heterogeneity in the findings from meta-analyses of commonly used measurement tools underscores a need for more standardized measurement and analytical approaches, such as multilevel modeling, to better account for the hierarchical nature of potential predictive factors (individual and group levels), which would allow for more reliable comparisons and stronger conclusions in this field. Full article

Catechins, renowned for their health benefits, have unexamined environmental impacts. This study assessed the toxicity of crude catechin and catechin hydrate on invertebrate larvae, plant, and microalgae. The survival rates of Daphnia magna Straus and Artemia salina L. were monitored every 24 h over a three-day period. The germination rate and radicle length of Lactuca sativa L. was measured every 24 h for four days. Inhibitory effects were evaluated in both freshwater and seawater cultures of Chlorella vulgaris Beijerinck, with cell density recorded every 24 h and yield inhibition calculated after 96 h. Results indicated that increasing catechin concentration and exposure duration decreased the survival rate of D. magna and A. salina. Daphnia magna was more sensitive to catechins than A. salina, with 24 h lethal concentration 50 (LC-50) values of 1174 µg/mL compared to 1895 µg/mL for crude catechin, and 54 µg/mL compared to 153 µg/mL for catechin hydrate. The germination rate and radicle length of L. sativa, along with the cell density of C. vulgaris, decreased with increasing catechin concentration, but remained higher even after prolonged exposure. At low catechin concentrations, C. vulgaris cell density exceeded control levels. This study demonstrates that catechins in aquatic environments can significantly impact ecosystems. At certain concentrations, catechins are toxic and potentially lethal to aquatic organisms. Conversely, at lower concentrations, catechins may promote microalgal growth, suggesting a fertilizing effect. Understanding these dynamics is crucial for maintaining the stability of aquatic ecosystems. Full article