Search Results (3,600)

Background: A significant body of research has identified ongoing unmet needs among cancer survivors. However, there is limited information about the survivorship experiences of patients in low- and middle-income countries. This study examined the experiences of colorectal cancer (CRC) survivors in Thailand to understand their post-treatment needs and priorities. Methods: A qualitative descriptive study using semi-structured interviews was conducted with 24 colorectal cancer survivors purposively recruited from a large university hospital in Bangkok, Thailand. The interviews lasted 60–90 min, were audio-recorded, and transcribed verbatim. The data were analyzed using hybrid content analysis, guided by the five domains of the Survivors Unmet Needs Survey (SUNS): information, emotional health, financial concerns, access and continuity of care, and relationships. Results: Participants had a mean age of 57 (SD = 10.9) years. Most were male (58.3%) and diagnosed at a late stage (62.5%). All participants had undergone surgical treatment, and the average time since treatment completion was approximately 3.85 (SD = 2.8) years. Participants most often reported unmet needs in the information and emotional health domains. They also described inadequate physical infrastructure (e.g., overcrowded clinic spaces) and minimal involvement of multidisciplinary providers. Despite these challenges, survivors expressed strong trust in physicians and a preference for physician-led care. Telehealth was viewed as a potential solution to reduce access barriers and improve continuity of care. Conclusions: Guided by the SUNS framework, this study identified significant unmet needs among colorectal cancer survivors in Thailand, particularly in health information, psychological support, and care system infrastructure. Strengthening health information delivery, establishing dedicated survivorship clinics, and exploring scalable care models supported by telehealth could help bridge these gaps and promote more equitable survivorship care in low-resource settings. Full article

Background: Current guidelines endorse the integration of exercise into cancer care. The diagnosis of cancer and its treatment, however, may introduce factors that make exercise engagement difficult, especially for individuals with advanced stages of disease. In this paper, we describe the baseline screening and triage process implemented for the Alberta Cancer Exercise (ACE) hybrid effectiveness-implementation study and share findings that highlight the multifaceted complexity of the process and the direct role of the clinical exercise physiologist (CEP). Methods: ACE was a hybrid effectiveness-implementation study examining the benefit of 12-week cancer-specific community-based exercise program. The ACE screening process was developed by integrating evidence-based guidelines with oncology rehabilitation expertise to ensure safe and standardized participation across cancer populations. The screening process involved four steps: (1) a pre-screen for high-risk cancers, (2) completion of a cancer-specific intake form and the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+), (3) a CEP-led interview to further evaluate cancer status, cancer-related symptoms and other health issues (performed in-person or by phone), and (4) a baseline fitness assessment that included measurement of vital signs. Results: A total of 2596 individuals registered and underwent prescreening for ACE with 2570 (86.6%) consenting to participate. After full screening including the baseline fitness testing, 209 participants (8.1%) were identified as requiring further medical clearance. Of these, 191 (91.4%) had either a high-risk cancer, metastatic disease or were in the palliative end-stage of cancer, and 161 (84.3%) reported cancer-related symptoms potentially affecting their ability to exercise. In total, 806 (31.4%) participants were triaged to CEP-supervised in-person programming, 1754 (68.2%) participants to ACE community programming, and 8 (0.3%) specifically to virtual programming (post-COVID-19 option). Conclusions: The findings highlight the complexity and challenges of the screening and triage process, and the value of a highly trained CEP-led iterative approach that included the application of clinical reasoning. Full article

L-carnitine supplementation enhances fatty acid oxidation and antioxidant defense mechanisms in livestock. Twenty-four wether lambs were randomly assigned to one of three dietary treatments: a control (basal diet), a low-L-carnitine diet (0.01%), or a high-L-carnitine diet (0.05%). After a 15-day adaptation period, all lambs underwent a 45-day experimental phase to assess the impact of L-carnitine supplementation on performance, carcass, muscle, and antioxidant capacity. Although growth performance did not show significant differences (p > 0.05), muscle pH increased in the L-carnitine group (p < 0.05), while drip loss decreased in both the low- and high-dose groups (p < 0.05). The a* value of the Longissimus dorsi muscle significantly increased (p < 0.01), enhancing meat color saturation (p < 0.05), with a reduction in the hue angle observed in the high-dose group (p < 0.01). L-carnitine supplementation led to an elevated myoglobin content and a higher proportion of oxymyoglobin, driven by a significant increase in MRA activity (p < 0.01). The concentration of L-carnitine was positively correlated with the a* value, which in turn was associated with higher total myoglobin content and a greater proportion of oxymyoglobin. In terms of antioxidant capacity, the L-carnitine group exhibited a significant increase in superoxide dismutase content. Moreover, there was upregulation of TFR1 and CAT expression at the mRNA level, while HEPH showed downregulation (p < 0.01). Significant increases were observed in both content and mRNA expression of LDHB and NADHBR5 (p < 0.01). The addition of 0.05% L-carnitine to the diet significantly enhanced muscle color stability in hybrid sheep. This improvement was primarily driven by a significant increase in MRA activity in the high-dose group, which facilitated the conversion of metmyoglobin to oxymyoglobin, thereby significantly boosting meat quality prospects for the sheep industry. Full article

Background/Objectives: Bones are essential components of the human body, providing structural support, enabling mobility, storing minerals, and protecting internal organs. Bone fractures (BFs) are common injuries that result from excessive physical force and can lead to serious complications, including bleeding, infection, impaired oxygenation, and long-term disability. Early and accurate identification of fractures through radiographic imaging is critical for effective treatment and improved patient outcomes. However, manual evaluation of X-rays is often time-consuming and prone to diagnostic errors due to human limitations. To address this, artificial intelligence (AI), particularly deep learning (DL), has emerged as a powerful tool for enhancing diagnostic precision in medical imaging. Methods: This research introduces a novel convolutional neural network (CNN) model, the Multi-Level Feature Fusion Network (MLFNet), designed to capture and integrate both low-level and high-level image features. The model was evaluated using the Bone Fracture Multi-Region X-ray (BFMRX) dataset. Preprocessing steps included image normalization, resizing, and contrast enhancement to ensure stable convergence, reduce sensitivity to lighting variations in radiographic images, and maintain consistency. Ablation studies were conducted to assess architectural variations, confirming the model’s robustness and generalizability across data distributions. MLFNet’s high accuracy, interpretability, and efficiency make it a promising solution for clinical deployment. Results: MLFNet achieved an impressive accuracy of 99.60% as a standalone model and 98.81% when integrated into hybrid ensemble architectures with five leading pre-trained DL models. Conclusions: The proposed approach supports timely and precise fracture detection, optimizing the diagnostic process and reducing healthcare costs. This approach offers significant potential to aid clinicians in fields such as orthopedics and radiology, contributing to more equitable and effective patient care. Full article

Agarwood, valued for its resin, has long been used in perfumery, incense, and traditional medicine. Its resin is primarily derived from species of Aquilaria and is produced through a still-unknown process in response to biotic or abiotic stress. Concerns regarding agarwood’s sustainability and conservation have emerged because of the substantial loss of natural resources due to overharvesting and illegal trade. To address these concerns, artificial techniques are being used to produce agarwood. The mechanism underlying agarwood production must be elucidated to enhance yield. The authentication of agarwood species is challenging because of morphological similarities between pure and hybrid Aquilaria species. Techniques such as DNA barcoding, molecular marker assessment, and metabolomics can ensure accurate identification, facilitating conservation. Artificial intelligence and machine learning can support this process by enabling rapid, automated identification on the basis of genetic and phytochemical data. Advances in resin induction methods (e.g., fungal inoculation) and chemical induction treatments are improving yield and quality. Endophytic fungi and bacteria promote resin production at minimal harm to the tree. Agarwood’s pharmacological potential—antimicrobial, anti-inflammatory, and anticancer effects—has driven research into bioactive compounds such as sesquiterpenes and flavonoids for the development of novel drugs. This systematic review synthesized current evidence on species authentication, induction techniques, and pharmacological properties. The findings may guide future research aimed at ensuring sustainable use and enhancing the medicinal value of agarwood. Full article

Pancreatic pseudocysts (PPCs) are frequent complications of acute and chronic pancreatitis, characterized by encapsulated collections of pancreatic fluid. Historically managed by open surgical approaches, treatment paradigms have significantly evolved with advancements in imaging and minimally invasive techniques. This review outlines the historical progression and current standards in PPC management, covering conservative, surgical, laparoscopic, and endoscopic interventions. Conservative management remains a valid first-line option for asymptomatic, stable pseudocysts, particularly in the absence of complications. Surgical techniques, once the mainstay, such as marsupialization and internal drainage procedures (cystogastrostomy, cystojejunostomy, and cystoduodenostomy), now serve as alternatives when less invasive methods fail. Laparoscopic approaches offer reduced morbidity and faster recovery, especially for complex or inaccessible PPCs. However, endoscopic drainage, particularly endoscopic ultrasound-guided transmural drainage using plastic or metal stents—especially lumen-apposing metal stents (LAMSs)—has become the preferred modality due to its efficacy, safety profile, and cost effectiveness. Emerging technologies, including robotic-assisted surgery and hybrid techniques, promise further refinement in PPC management. This review synthesizes current evidence and expert guidelines, providing a comprehensive overview of evolving strategies and future directions in the treatment of PPCs. Full article

Accurate tumor segmentation is crucial for cancer diagnosis and treatment planning. We developed a hybrid framework combining complementary convolutional neural network (CNN) models and advanced post-processing techniques for robust segmentation. Model 1 uses contrast-limited adaptive histogram equalization preprocessing, CNN predictions, and active contour refinement, but struggles with complex tumor boundaries. Model 2 applies noise-augmented preprocessing and iterative detection to enhance the segmentation of subtle and irregular regions. The outputs of both models are merged and refined with edge correction, size filtering, and a spatial intensity metric (SIM) expansion to improve under-segmented areas, an approach that achieves higher F1 scores and intersection over union scores. The developed framework highlights the potential in combining machine learning and image-processing techniques to develop reliable diagnostic tools. Full article

Influenza A viruses that cause pandemics, as well as other harmful pathogens (e.g., SARS-CoV-2 variants), are known as the ‘silent bioterrorists’ of the 21st century. Due to high mutability, anti-influenza chemotherapeutic treatment is a vital defense strategy to combat both seasonal and pandemic influenza strains, especially when vaccines fail. Consequently, the development of novel therapies to combat this serious threat is of great concern. Hence, in this study, 3-(2-thienyl) acrylic acid (TA) was converted into amides of anti-influenza drugs (aminoadamantanes and oseltamivir) through TBTU-mediated coupling. The crystal structures of the thienyl-based amide hybrids (TA-Am (1), TA-Rim (2), TA-Os-OEt (3), and TA-OsC (4)) were also investigated using single-crystal X-ray diffraction, powder X-ray diffraction (PXRD), and differential scanning calorimetry (DSC). Moreover, the antiviral activities of the hybrids against influenza virus A/Fort Monmouth/1/1947 (H1N1), clinically isolated influenza strain A/Wuhan/359/1995 (H3N2), and oseltamivir-resistant A/Jinnan/15/2009 (H1N1) were evaluated in vitro. Amongst the tested thienyl-based amides, bisamide 8 (Boc-Os-Hda-TA) exhibited the most potent activity against influenza virus A (A/Wuhan/359/1995) with an IC₅₀ value of 18.52 μg/mL and a selectivity index (SI) = 13.0. Full article

Photocatalysis is a promising strategy for the treatment of dangerous chemical pollutants in the ocean. In this work, a stable copper-based photocatalyst, i.e., {[Cu(BPA)₂]·2I₃}_n (1, BPA = 4,4′-bipyridinium-N-pentanoic acid), exhibited excellent degradation performance in dye pollutant in seawater. According to the structural analysis, this photocatalyst consists of 1-D cationic [Cu(BPA)₂]_n²ⁿ⁺ infinite chain and two I³⁻ polyiodide anions. In the [Cu(BPA)₂]_n²ⁿ⁺ chain, the distorted CuO₄N₂ octahedra are bridged by asymmetric viologen ligand (BPA), which result in a 1-D ladder-shaped chain. Strong C–H···O/I hydrogen bonds contribute to the formation of a 2-D layer along bc-plane, in which I³⁻ anions are stacked among the cationic chains. The strong adsorption from ultraviolet to visible regions together with its high charge separation efficiency implies its usage as excellent visible-light-driven catalysis. Interestingly, good photocatalytic performance for the degradation of Rhodamine B (RhB) in seawater can be observed by using this hybrid as photocatalyst. In detail, 90.6% degradation ratio of RhB can be achieved in 150 min under visible light, which was monitored on a UV–Vis spectrum. This work could pave the way for new ocean pollutant treatments for shipping accidents. Full article

Neo-tetraploid rice is a highly fertile variety created from autotetraploid rice. It demonstrates stronger heterosis and produces stable hybrid progeny. However, there is insufficient data regarding abiotic stress in neo-tetraploid hybrid progeny, especially in relation to salt stress. Two hybrid progenies, high salt-resistance tetraploid rice hybrid progeny (HSRTH) and low salt-resistance tetraploid rice hybrid progeny (LSRTH), were generated by crossing the neo-tetraploid rice cultivars ‘Huaduo 3’ and ‘Huaduo 8’ with the autotetraploid rice Huanghuazhan-4x. Here, we assessed the germination characteristics and seedling growth of two neo-tetraploid hybrids at six NaCl concentrations: 0, 50, 100, 150, 200, and 250 mmol/L. HSRTH demonstrated a higher tolerance to salt stress, achieving a grain germination rate of 48.00 ± 2.63% compared to LSRTH, which reached only 5.00 ± 1.41% under a 250 mmol/L NaCl treatment. Cytological observations showed that the root tip differentiation zone and coleoptiles of HSRTH were less affected by NaCl stress treatment, resulting in fewer cortical cell abnormalities, decreased stele issues, and fewer rhizodermis cell problems, such as shrinkage. Gene expression analysis revealed nine genes that showed differential expression in HSRTH compared to LSRTH. Our study demonstrated that HSRTH showed strong salt stress tolerance, providing a basis for selecting salt-resistant rice germplasm and offering insights for developing salt-tolerant rice varieties using neo-tetraploid resources. Full article

Oxidative stress is the key contributor to the onset of numerous diseases. Herein, we develop tea polyphenol–zinc (Tp-Zn) using a metal–polyphenol coordination strategy through a simple hybrid approach. The product is characterized by methods such as dynamic light scattering (DLS), ultraviolet–visible spectroscopy (UV–vis) and transmission electron microscopy (TEM) to evaluate the particle size and potential of Tp-Zn. Oxidative stress was induced in mice by administering diquat (25 mg/kg body weight) followed by pre-treatment with 210 mg/kg body weight tea polyphenols (TPs), 280 mg/kg body weight Tp-Zn, and 70 mg/kg body weight ZnSO₄ for 7 days. Results showed that Tp-Zn treatment significantly improved intestinal barrier function by preventing the diquat-induced down-regulation of tight junction proteins Zonula Occludens protein 1 (ZO-1) and occludin. It also mitigated liver inflammation and damage, as evidenced by reduced serum levels of Aspartate aminotransferase (AST), Alanine aminotransferase (ALT), and Alkaline phosphatase (AKP). Furthermore, Tp-Zn enhanced the antioxidant response in both the intestine and liver by up-regulating the mRNA expression of antioxidant enzymes and reducing the levels of Malondialdehyde (MDA) and reactive oxygen species (ROS) compared to the diquat group (DIQ group). Also, the detection of ROS in the small intestine confirmed Tp-Zn markedly increased intestinal Nuclear factor erythroid 2-related factor 2 (Nrf2) expression compared to the control group. This study aims to clarify that metal–polyphenol coordination with multifaceted regulation of the inflammatory microenvironment could be a novel approach for preventing or treating oxidative stress-related diseases. Full article

Accurate normalization is crucial for reliable gene expression quantification and depends on stably expressed housekeeping genes (HKGs) as internal controls. However, HKGs expression varies with developmental stage, tissue type, and treatments, potentially introducing bias and compromising data accuracy. Thus, validating candidate reference genes under defined conditions is essential. Reynoutria, also known as giant Asian knotweeds, is a Polygonaceae family genus of several medicinal plants producing a diverse array of specialized metabolites of pharmacological interest. Outside their native range, these plants are also noxious invasive weeds, causing significant environmental and economic threats. Research on stable reference genes in these species is limited, with a primary focus on R. japonica. To enable accurate gene expression analysis related to specialized metabolism and natural product biosynthesis, we aimed to identify the most stable reference genes across the most common species: R. japonica Houtt., R. sachalinensis (F. Schmidt) Nakai, and their hybrid—R. × bohemica Chrtek & Chrtková. In this study, we evaluated twelve candidate HKGs (ACT, TUA, TUB, GAPDH, EF-1γ, UBQ, UBC, 60SrRNA, eIF6A, SKD1, YLS8, and NDUFA13) across three tissue types (rhizomes, leaves, and flowers) from three Reynoutria species sampled at peak flowering. Primer specificity and amplification efficiency were confirmed through standard-curve analysis. We assessed expression stability using ΔCt, geNorm, NormFinder, and BestKeeper, and generated comprehensive rankings with RefFinder. Our integrated analysis revealed organ- and species-dependent stability differences, yet identified up to three reference genes suitable for interspecific normalization in Reynoutria. This represents the first systematic, comparative validation of HKGs across closely related knotweed species, providing a robust foundation for future transcriptomic and functional studies of their specialized metabolism and other biological processes. Full article

Pharmaceutical contaminants such as ibuprofen are increasingly detected in water sources due to widespread use and insufficient removal by conventional treatment processes. Given its persistence and adverse effects on human health and aquatic ecosystems, efficient removal technologies are needed. This study reports the synthesis of a Mg/Al-layered double hydroxide (LDH) hybridized with carbon quantum dots (CQDs) via in situ co-precipitation to enhance adsorptive performance. The hybrid (LDH-CQD) was characterized by FTIR, XRD, DSC, TGA-DTG, SEM-EDS, BET, and pH in the point of zero charge (pH_PZC) analysis. Results indicated a marked increase in surface area (2.89 to 66.9 m²/g), a shift in surface charge behavior (pHpzc from 8.57 to 6.21), and improved porosity. Adsorption experiments using ibuprofen as a model contaminant revealed superior performance of the hybrid compared to pristine Mg/Al-LDH, with a maximum capacity of 22.13 mg·g⁻¹ (% Removal = 88.53%) at 25 ppm, and in lower concentrations (5 and 10 ppm), the hybrid showed 100% removal. Kinetic modeling followed a pseudo-second-order mechanism, and the isotherm was the SIPS model (maximum adsorption capacity = 24.150 mg.g⁻¹). These findings highlight the potential of LDH-CQD hybrid as efficient and tunable adsorbents for removing emerging pharmaceutical pollutants from aqueous media. Full article

Cardiovascular disease remains the world’s leading cause of mortality, yet everyday care still relies on episodic, symptom-driven interventions that detect ischemia, arrhythmias, and remodeling only after tissue damage has begun, limiting the effectiveness of therapy. A narrative review synthesized 183 studies published between 2016 and 2025 that were located through PubMed, MDPI, Scopus, IEEE Xplore, and Web of Science. This review examines CVD diagnostics using innovative technologies such as digital cardiovascular twins, which involve the collection of data from wearable IoT devices (electrocardiography (ECG), photoplethysmography (PPG), and mechanocardiography), clinical records, laboratory biomarkers, and genetic markers, as well as their integration with artificial intelligence (AI), including machine learning and deep learning, graph and transformer networks for interpreting multi-dimensional data streams and creating prognostic models, as well as generative AI, medical large language models (LLMs), and autonomous agents for decision support, personalized alerts, and treatment scenario modeling, and with cloud and edge computing for data processing. This multi-layered architecture enables the detection of silent pathologies long before clinical manifestations, transforming continuous observations into actionable recommendations and shifting cardiology from reactive treatment to predictive and preventive care. Evidence converges on four layers: sensors streaming multimodal clinical and environmental data; hybrid analytics that integrate hemodynamic models with deep-, graph- and transformer learning while Bayesian and Kalman filters manage uncertainty; decision support delivered by domain-tuned medical LLMs and autonomous agents; and prospective simulations that trial pacing or pharmacotherapy before bedside use, closing the prediction-intervention loop. This stack flags silent pathology weeks in advance and steers proactive personalized prevention. It also lays the groundwork for software-as-a-medical-device ecosystems and new regulatory guidance for trustworthy AI-enabled cardiovascular care. Full article

This study investigates the development of a sustainable flame-retardant treatment for cotton fabrics using a hybrid coating composed of chitosan, phytic acid, APTES, and eggshell powder at concentrations of 2% and 4%, applied in one and two cycles. FTIR confirmed the deposition of the organic–inorganic layer through the appearance of characteristic bands. Thermogravimetric analysis (TGA/dTGA) revealed enhanced thermal stability for all treated samples, with increased char yield and a shift in the main cellulose degradation peak. Vertical flammability tests demonstrated that all coated fabrics achieved self-extinguishing behavior within 12 s, meeting NFPA 701 criteria. The 2% eggshell formulation with two applications (S2%-II) exhibited the best balance between flame retardancy and mechanical performance. Tensile tests indicated improved fiber cohesion for treated samples, while SEM micrographs confirmed uniform coating deposition and particle integration. Colorimetric analysis showed that the treatment did not cause a significant change in the natural color of the cotton. Although washing resistance remains a limitation due to the natural origin of the components, the samples remained stable over time without microbial growth or staining, suggesting potential for upholstery and covering fabrics not subjected to domestic washing. The results highlight the feasibility of using agro-industrial waste to create eco-friendly flame-retardant finishes for cotton textiles. Full article