Search Results (6,448)

Dengue fever, with a high proportion of asymptomatic infections, poses a major global public health challenge that traditional surveillance systems frequently underestimate. Wastewater-based epidemiology (WBE) has emerged as a promising approach to monitoring infectious diseases beyond enteric viruses. Dengue virus is shed in urine, feces, and saliva, providing a biological basis for wastewater detection alongside clinical surveillance. This systematic review and meta-analysis synthesize current evidence on dengue virus (DENV) detection in wastewater and evaluate methodological factors influencing detection success in WBE. A systematic literature search using selected databases and predetermined keywords, followed by eligibility screening, resulted in ten studies being included, covering community surveillance and experimental trials. DENV ribonucleic acids (RNA) were most consistently detected and enriched in wastewater solids, indicating this matrix as the most reliable for surveillance. Among concentration methods, ultrafiltration achieved the highest viral recovery efficiency, while reverse transcription digital polymerase chain reaction (RT-dPCR) demonstrated superior sensitivity and precision compared to those of reverse transcription quantitative polymerase chain reaction (RT-qPCR), particularly at low viral concentrations. Storage at −80 °C was critical for preserving RNA integrity. The meta-analysis yielded a pooled DENV positivity rate of 24% (95% CI: 20–28%) after exclusion of outliers. Overall, solid-phase analysis combined with RT-dPCR represents the most sensitive methodological approach across the included studies. Harmonized protocols are needed to support future translation of dengue WBE into community surveillance as current evidence mainly demonstrates methodological feasibility and provides a technical foundation for future public health integration. Therefore, further longitudinal and multi-site validation is required to establish its broader applicability for dengue surveillance. Full article

Silver diamine fluoride (SDF) is a key preservation-based intervention in pediatric dentistry. It can arrest many cavitated lesions, reduce treatment burden, and expand access for children who cannot receive conventional restorative care. This viewpoint article offers a reasoned, heuristic framework for calibrating SDF guidance to the strength of the underlying evidence. It does not present a systematic review or formal policy standards. Foundational trials support the clinical usefulness of 38% SDF. The 2017 AAPD guidelines provided conditional recommendations based on low-quality evidence. The current challenge is no longer whether to endorse SDF but how to calibrate guidance on its implementation. Later studies addressing intervals and implementation often have open-label designs, small samples, single centers, or overlapping data sources. Mechanistic and microbiome studies support biological plausibility, but policy should not treat them as definitive evidence. We propose a hypothesis-generating framework that separates claims about the existence of an effect (for which there is stronger directional support) from claims about its optimal conditions (which remain more uncertain), highlights dataset overlap, and matches recommendation strength to study quality. The framework supplements GRADE and provides illustrative upgrade pathways. The goal is to preserve SDF access while making guidelines more transparent, credible, and precise. Full article

Improving crop yields while reducing environmental impacts remains a major challenge for smallholder agriculture, where heterogeneous management practices often limit the performance of technologies. This study developed a Select–Analyze–Design–Evaluate (SADE) framework to enhance the effectiveness of sustainable winter wheat technologies in smallholder farming systems. The framework was implemented in two villages on the North China Plain during a four-year field-based study (2017–2021), combining farmer follow-up surveys with field trials. During the Select stage, baseline data identified widely adopted technologies with substantial performance variability. Accordingly, delayed nitrogen application in Nanxia Village and precision seeding in Wangzhuang Village were selected as priority technologies for targeted diagnosis and improvement. During the Analyze stage, regression models identified key agronomic constraints: nutrient management in Nanxia, and sowing date and nitrogen management in Wangzhuang. Following this diagnosis, village-specific strategies were designed, implemented, and evaluated through multi-stakeholder collaboration. In Nanxia, yield, benefit–cost ratio, and nitrogen recovery efficiency increased by 7.9%, 21.5%, and 23.5%, respectively, while greenhouse gas emissions decreased by 21.5%. In Wangzhuang, the corresponding changes were 11.2%, 48.7%, 45.7%, and −22.9%, respectively. These findings demonstrate that SADE offers a practical pathway for sustainable smallholder agriculture. Full article

Addressing the environmental challenges posed by the massive stockpiling of phosphogypsum (PG) has become a global concern, highlighting the urgency of developing large-scale, low-cost and resource-efficient utilization approaches for PG. This study was conducted in the rocky desertification areas of southwestern China, where land and water resources are scarce. Two land creation techniques—layered reconstruction (GA) and integrated construction (GB)—were adopted with modified PG to systematically investigate their impacts on soil properties and potato growth, yield and quality. The results showed that both techniques significantly improved soil conditions and enhanced potato yield and quality, with each presenting distinct characteristics in soil improvement. Specifically, the GA technique showed relatively better performance in soil nutrient enrichment, while the GB technique was more conducive to enhancing soil enzyme activity. Compared with the local red soil control, both techniques reduced heavy metal accumulation in potato tubers; however, Pb and Cd contents still exceeded national food safety limits, indicating potential food safety risks. In summary, land creation using modified PG can effectively increase arable land area, improve soil quality in rocky desertification regions, and simultaneously promote potato yield and quality. Nevertheless, as the current results are based on a single-season field trial, they cannot reflect the long-term patterns of heavy metal migration and accumulation. Therefore, for large-scale application, it is necessary to strengthen the monitoring of heavy metal levels in imported soil and long-term regional environmental impacts so as to ensure the quality and safety of agricultural products from reclaimed land. Full article

Difficult-to-treat systemic lupus erythematosus (D2T-SLE) remains a major unmet challenge in contemporary lupus care, yet it continues to be defined predominantly by clinical non-response rather than underlying biology. Current biomarkers largely quantify inflammatory burden, immune complex activity, or organ damage and do not reliably capture persistent activation of pathogenic pathways under therapy. Emerging multi-omics, single-cell, and longitudinal studies suggest that, in a subset of patients, apparent treatment failure may reflect incomplete attenuation of dominant immune circuits rather than uniformly elevated inflammation. We propose molecular refractoriness in systemic lupus erythematosus (SLE) as sustained, pathway-level immune activity despite apparently adequate, mechanism-directed therapy. We outline the major immune programs implicated in this process—including interferon-enriched, B-cell/plasmablast-associated, neutrophil extracellular trap (NET)-related, cytotoxic T-cell, and cytokine-associated states—and discuss their relevance for biomarker development and precision trial design. Importantly, we emphasize that interferon gene signatures (IGS) should be interpreted as context-dependent and non-specific markers of interferon responsiveness, reflecting combined activity of type I, II, and III interferons, and functioning primarily as predictive rather than mechanistic biomarkers. We further highlight critical limitations of a purely endotype-based model, including the need to distinguish true molecular refractoriness from damage-dominant and pseudo-refractory states, as well as the emerging role of immune-reset strategies such as cluster of differentiation 19 (CD19)-directed chimeric antigen receptor T-cell (CAR-T) therapy, which may overcome refractoriness independently of specific pathway dominance. These observations suggest that difficult-to-treat SLE encompasses biologically heterogeneous states that may not be fully captured by pathway-resolved stratification alone. Reframing D2T-SLE as a biologically heterogeneous state of incomplete immune attenuation may help bridge the gap between clinical treatment failure and mechanism-informed precision medicine in systemic lupus erythematosus. Full article

During seafloor terrain mapping missions conducted by AUVs, positioning error accumulation occurs inevitably over long distances due to the unavailability of global satellite navigation signals underwater. Moreover, the alternating distribution of flat and undulating regions on the seafloor renders single-constraint-based bathymetric SLAM methods prone to performance degradation in complex environments. To address these challenges, this paper proposes a multi-constraint hybrid strategy SLAM framework for AUV-based seafloor terrain mapping, grounded in an analysis of error accumulation mechanisms and constraint failure characteristics. The framework establishes a hierarchical and progressive constraint architecture to enable collaborative optimization across different spatial scales and topographic conditions. At the foundational pose estimation stage, multi-source trajectory information is fused to ensure continuity and stability in pose computation. In the local consistency constraint stage, an improved point cloud registration method combined with a neighborhood survey-line constraint mechanism is introduced to enhance geometric consistency among survey lines in feature-sparse regions. At the global optimization stage, a loop closure detection strategy is designed based on topographic statistical features, incorporating adaptive thresholds and correlation metrics to achieve robust introduction of global constraints. By flexibly integrating direct registration and feature-matching strategies according to topographic characteristics, the framework fully leverages the advantages of multi-constraint cooperative optimization. The proposed method is validated by the field data. Experimental results on real lake-trial data show that, relative to the baseline configurations evaluated under identical noise-injection conditions, the MCHS-SLAM framework yields more concentrated consistency-error distributions with markedly shorter large-error tails, and exhibits improved error suppression relative to the reference trajectory. This work presents a methodological framework for high-quality seafloor terrain mapping under heterogeneous terrain conditions, providing a basis for future extensions toward onboard real-time deployment. Full article

Mitochondria are vital organelles for human cells with fundamental roles in major metabolic processes such as calcium homeostasis, ATP production, apoptosis and signal transduction. Defective morphology and activity of these organelles have been tightly associated with the pathological onset of severe human disorders, including cardiovascular diseases. Targeting mitochondrial dysfunction has been an area of extensive research encompassing several approaches ranging from pharmacological agents to mitochondrial replacement techniques. Among them, mitochondrial transplantation has been a rapidly evolving approach, especially in the field of cardiovascular dysfunction for the restoration of injured or damaged myocardial cells. Various methods including tunneling nanotubes, nanoblade and “mitopunch” ensure the effective mitochondrial transfer from the donor to the recipient cell, with the internalization of the organelles, via endocytosis, enabling functional restoration. Results of preclinical and clinical trials involving mitochondrial transfer support the application of this technique in improving the function of the myocardium after damage caused by ischemia reperfusion injury. Herein, we discuss the beneficial role of mitochondrial transplantation in cardiovascular diseases and the current technical challenges of mitochondrial isolation, preservation, and targeted delivery, as well as their role in advancing precision medicine, offering a patient tailored therapeutic approach. Full article

Despite major advances in early breast cancer detection and therapeutic strategies, locoregional and distant recurrences, as well as the development of a second primary breast cancer, remain major clinical challenges. Current prognostic tools primarily rely on tumor-specific features, such as the histological grade, hormone receptor status, and proliferative index, and, more recently, on molecular signatures aimed at improving risk stratification and predicting recurrence. However, these approaches remain imperfect, and there is an urgent need to develop complementary strategies. Growing attention has been focused on the tumor microenvironment and the surrounding non-tumoral tissue, which may harbor clinically relevant molecular alterations. Emerging evidence suggests that DNA methylation changes can be detected in the adjacent and contralateral breast tissue and reflect early steps of carcinogenesis or predisposition to tumor development. This phenomenon, often referred to as field cancerization, raises new questions about the dynamics of cancer development. The aim of this work is to provide an integrative overview of DNA methylation alterations in normal breast tissue, including peritumoral and contralateral areas, and to examine their potential as predictive biomarkers of recurrence, based on the available data from tumoral tissue. In theory, these applications seem promising, but their role needs to be confirmed in large prospective trials, in order to overcome barriers to clinical implementation. The currently available evidence does not support a role for DNA methylation in the selection of locoregional and systemic treatment strategies, particularly with a view to reducing the rising number of uni- and bilateral mastectomies performed without any demonstrated survival benefit. Full article

Cancer remains the leading cause of death in domestic dogs. Conventional therapeutic approaches, including surgery, chemotherapy, and radiotherapy, frequently fail to achieve sustained remission or stabilization. Oncolytic virotherapy, a rapidly advancing therapeutic modality in human oncology, is emerging as a novel strategy in veterinary medicine. This systematic review summarizes current knowledge on the application of oncolytic viruses (OVs) in canine cancer treatment, focusing on their mechanisms of action, safety profiles, and clinical efficacy. We evaluate diverse OV platforms, including myxoma virus, reovirus, vesicular stomatitis virus, canine adenoviruses, vaccinia virus, Sendai virus, and Newcastle disease virus, across preclinical and clinical studies in dogs with various malignancies. While several OVs have demonstrated favorable tolerability and modest antitumor activity, key challenges such as pre-existing immunity, optimization of dosing regimens, and rational combination strategies remain to be addressed. This review emphasizes the translational significance of canine studies for both veterinary and human oncology, underscoring the critical need for rigorously designed clinical trials to refine virotherapy protocols and expand therapeutic options for canine cancer patients. Full article

Background/Objectives: Duchenne muscular dystrophy (DMD) is a fatal, progressive neuromuscular disorder caused by mutations in the dystrophin gene, leading to the absence of functional dystrophin protein. As the largest gene in the human genome, the DMD locus is highly susceptible to mutations, contributing to a prevalence of approximately 1 in 3800–6300 live male births worldwide. This review aims to provide a comprehensive and critical synthesis of current and emerging therapeutic strategies for DMD. Methods: We conducted a narrative review of the literature, integrating findings from clinical trials, regulatory approvals, and preclinical studies. We categorized therapeutic approaches into mutation-agnostic and mutation-specific strategies, with emphasis on the mechanism of action, clinical progress, and translational limitations. Results: Current standards of care, including corticosteroids and supportive interventions, remain foundational in disease management. Mutation-specific approaches such as exon skipping and adeno-associated virus (AAV)-mediated gene replacement can restore dystrophin expression, although clinical benefit remains variable and is influenced by factors such as mutation type, delivery efficiency, and durability. Emerging genome editing strategies offer the potential for permanent correction but face significant challenges related to delivery, safety, and scalability. Emerging mutation-agnostic therapies targeting inflammation, fibrosis, and membrane instability provide broader applicability but do not directly address the underlying genetic defect. Across modalities, key limitations include modest functional outcomes, safety concerns, and variability in clinical trial endpoints. Conclusions: The DMD therapeutic landscape is rapidly evolving, and future progress will likely depend on optimizing delivery platforms, improving durability, and integrating combination strategies to address the multifaceted nature of disease progression. Full article

Precision alignment and target contact are critical tasks for mobile manipulators in industrial inspection and flexible manufacturing. However, achieving high accuracy after navigation remains challenging due to accumulated errors from mobile base localization, perception noise, and calibration uncertainty. This paper proposes a vision-guided precision alignment framework for mobile manipulators using a single front-facing RGB-D camera. The method integrates YOLO-based target detection, AR marker-assisted plane depth estimation, and depth-based 3D localization within a coarse-to-fine alignment strategy. After navigation, the manipulator first moves to a predefined pre-alignment pose, followed by visual localization and iterative refinement to compensate for residual errors before executing precise target contact. The proposed system is implemented and evaluated in a Gazebo-based simulation environment using a mobile manipulator platform model. In a static touch panel experiment with 50 trials, the system achieves a success rate of 98%, with positioning errors maintained within a millimeter-level range. Simulation results demonstrate that the proposed method provides stable alignment performance in the simulation environment without relying on external sensing devices such as force sensors or multi-camera systems. The proposed approach shows promising potential for precision contact tasks in mobile manipulation. Full article

Deep eutectic solvents (DESs) have emerged as powerful media for enhancing the solubility of poorly water-soluble active pharmaceutical ingredients (APIs). However, their rational design remains challenging due to the complex interplay of intermolecular interactions and non-ideal thermodynamic behavior. This study develops a comprehensive, data-driven taxonomy of solute–solvent systems by integrating COSMO-RS-derived descriptors with principal component analysis (PCA) and unsupervised clustering. This approach establishes a constrained, evidence-based decision framework, which is more appropriate for complex physicochemical systems like DESs than traditional empirical rules. The analysis successfully reduces the multidimensional descriptor space to five physically interpretable axes: solvation driving force, API thermodynamic stability, solvent interaction profile, hydrogen-bond network strength, and hydration effects. Two primary solubilization mechanisms are identified: interaction-driven solvation, characterized by high API–DES affinity, and destabilization-driven solvation. Furthermore, comparison of dry and water-containing systems reveals that water acts as a thermodynamic structuring agent, fundamentally reducing system dimensionality and promoting the emergence of more distinct solvation regimes. Validated through the projection of benzocaine and lidocaine, this framework enables a transition from trial-and-error screening to mechanism-guided formulation design, providing a robust roadmap for navigating the complex solubility landscape of pharmaceutical DESs. Full article

Gastrointestinal (GI) malignancies—which comprise esophageal, gastric, colorectal, hepatobiliary, and pancreatic cancers—remain a leading global cause of oncologic morbidity and mortality. The prognosis for many patients (especially those diagnosed with advanced-stage disease) remains poor despite conventional therapies—namely, surgery, chemotherapy, and radiation. Immunotherapy, however, has emerged as a new strategy in oncology, and, in particular, the advent of cancer vaccines now provides an investigational approach to improving clinical outcomes in patients with GI malignancies. This review aims to provide a comprehensive overview of multiple vaccine-based strategies developed to better target GI cancers, spanning from early preclinical studies to the most recently completed clinical trials. We first introduce the main vaccine therapy classes and the immunologic rationale underlying each. We then summarize key findings from past and ongoing trials using a cancer-type-based approach, primarily focusing on vaccine safety and immunogenicity, and commenting on limitations in overall efficacy. Finally, we identify the challenges of applying mostly early-phase trials to clinical practice as well as future directions for integrating these vaccine-based approaches into personalized treatments for GI cancer patients. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with prognosis critically dependent on the stage at diagnosis. Traditional tissue biopsy presents well-known limitations, including tumor heterogeneity and invasiveness. Liquid biopsy, encompassing the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and other cell-free biomarkers, has emerged as a transformative approach for non-invasive tumor profiling. This comprehensive narrative review outlines the recent evidence published on the current state and future perspectives of liquid biopsy in CRC, with a focused emphasis on the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in data analysis and clinical translation. Methods: A narrative review of the literature was conducted by searching PubMed/MEDLINE, EMBASE, and ClinicalTrials.gov for articles published between January 2020 and January 2026, using a predefined Boolean search string combining terms related to liquid biopsy biomarkers, colorectal cancer, and artificial intelligence methodologies. Filters were applied to include only English-language human studies. Additional relevant sources were consulted to ensure comprehensive coverage of the available literature. Liquid biopsy platforms, particularly ctDNA sequencing and methylation profiling, demonstrate increasing clinical utility across the CRC care continuum from population screening to post-surgical minimal residual disease (MRD) detection and real-time therapy monitoring. AI-driven analytical frameworks, including Random Forest, Convolutional Neural Networks, LSTM models, and more recently Large Language Models (LLMs), substantially augment the sensitivity and specificity of liquid biopsy interpretation, enabling multimodal data integration. The convergence of liquid biopsy technology and AI-driven analytics represents a paradigm shift toward precision oncology in CRC. Remaining challenges include analytical standardization, model explainability, regulatory harmonization, and equitable access. Future integration of federated learning frameworks and LLM-based clinical decision support tools will be essential for responsible clinical translation. Full article

Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) offer a promising pathway for restoring communication. Decoding tonal languages like Mandarin from EEG remains challenging due to homophones and complex temporal dynamics. This study investigates the decoding of six high-frequency Mandarin action verbs—Chi (eat), He (drink), Chuan (wear), Na (take), Kan (look), and Dai (put on)—from EEG signals. We designed a visual-cue-based overt speech production experiment and collected EEG data from 30 participants during visually guided verb reading aloud. A recurrent neural network framework incorporating dual Long Short-Term Memory (LSTM) layers was implemented to model the long-range temporal dependencies in EEG patterns. The proposed model was compared against a traditional Common Spatial Pattern combined with Support Vector Machine (CSP-SVM) baseline. Our LSTM-based model achieved an average classification accuracy of 69.93% ± 3.07% for the six-class task, significantly outperforming the CSP-SVM baseline (36.53% ± 3.17%). Accuracy exceeded 75% under specific training conditions, including more than 15 training repetitions and a training-data proportion of 38%. Furthermore, the model attained this performance level utilizing approximately 38% of the available trial data for training, demonstrating data efficiency. The results indicate that the LSTM architecture can effectively capture the neural signatures associated with Mandarin verb processing, providing a foundation for developing practical EEG-based assistive communication technologies. The inference latency of the trained model, quantified as the post-training per-trial testing time, was under 2 s, supporting near-real-time applications. Full article