Search Results (953)

Background/Objectives: Emerging evidence links diabetes to increased cancer risk. This study aimed to assess the association between diabetes mellitus (DM)(type 1, type 2, or gestational) and the development of head and neck cancer. Methods: An umbrella review was conducted using systematic searches in Cochrane, EBSCO, Wiley, ScienceDirect, and PubMed (January 2000–January 2024), registered in PROSPERO (CRD42024512151). Included were systematic reviews (SRs) and meta-analyses (MAs) of observational studies. Article selection followed the PRISMA guidelines; the quality and risk of bias of the selected studies were assessed with the Joanna Briggs Institute Checklist. The GROOVE tool was used to identify double counting. Two independent reviewers screened studies, with a third resolving disagreements. Results: Seven SRs were included. While DM has been widely examined in cancer research, few studies specifically targeted head and neck cancers. Of the 20 associations between various cancer sites and diabetes types, 9 (45%) showed a statistically significant positive correlation. The strongest evidence was for overall cancer risk (RR = 1.22, 95% CI: 1.16–1.29, p < 0.001). Oral cancer showed elevated risks (RRR = 1.13, p = 0.009; OR = 1.32, p < 0.001; HR = 1.73, p < 0.05; RR = 1.28, p < 0.05). Increased risks were also observed for oropharyngeal (RR = 1.18; HR = 1.53), head and neck (HR = 1.47), and nasopharyngeal cancer (OR = 1.40), all p < 0.05. Heterogeneity was low in two reviews, unreported in one, and high in four. Five SRs reported associated risk factors. Conclusions: While some associations between DM and cancer appear significant, evidence remains limited and inconsistent, particularly for oral cancer. Further standardized, high-quality research is needed to clarify the link across head and neck cancer subtypes. Full article

The establishment and management of nature reserves play a crucial role in protecting biodiversity and supporting sustainable agriculture. This study focuses on 2538 nature reserves in 22 provinces, 5 autonomous regions and 4 municipalities directly under the central government in mainland China. Integrating GIS spatial statistics, imbalance index, and geodetector models, we reveal critical insights: (1) Pronounced spatial inequity is observed, where a small number of eastern provinces dominate the total reserve count, highlighting significant regional disparities in ecological resource allocation. The sparse kernel density in western regions, indicating sparse reserve coverage. The Standard Deviation Ellipse highlights directional dispersion and human-ecological conflicts in high-density zones. (2) Key sustainability indicators driving reserve distribution include: total water resources, water resources per capita, forest area. (3) The spatial distribution of China’s nature reserves, along with factors such as altitude, river distribution, and transportation infrastructure, plays a crucial role in their development. This research provides theoretical support for the scientific planning and policy-making of nature reserves in China and offers practical guidance for optimizing and adjusting sustainable agricultural development. The study emphasizes the vital functions of nature reserves in maintaining ecosystem balance, enhancing regional climate resilience, and serving as biodiversity reservoirs. This research offers strategic insights for integrating nature reserve spatial planning with sustainable agricultural development policies, providing a scientific basis for optimizing the eco-agricultural interface in China. Full article

To reveal the meso-mechanical essence of deep rock mass failure and capture precursor information, this study focuses on soft rock failure mechanisms. Based on the discontinuous medium discrete element method (DEM), we employed digital image correlation (DIC) technology, acoustic emission (AE) monitoring, and particle flow code (PFC) numerical simulation to investigate the failure evolution characteristics and AE quantitative representation of soft rocks. Key findings include the following: Localized high-strain zones emerge on specimen surfaces before macroscopic crack visualization, with crack tip positions guiding both high-strain zones and crack propagation directions. Strong force chain evolution exhibits high consistency with the macroscopic stress response—as stress increases and damage progresses, force chains concentrate near macroscopic fracture surfaces, aligning with crack propagation directions, while numerous short force chains coalesce into longer chains. The spatial and temporal distribution characteristics of acoustic emissions were explored, and the damage types were quantitatively characterized, with ring-down counts demonstrating four distinct stages: sporadic, gradual increase, stepwise growth, and surge. Shear failures predominantly occurred along macroscopic fracture surfaces. At the same time, there is a phenomenon of acoustic emission silence in front of the stress peak in the surrounding rock of deep soft rock roadway, as a potential precursor indicator for engineering disaster early warning. These findings provide critical theoretical support for deep engineering disaster prediction. Full article

With the rising automation and complexity level of industrial systems, the efficiency and accuracy of fault diagnosis have become a critical challenge. The convolutional neural network (CNN) has shown some success in the fault diagnosis field. However, typical convolutional kernels are commonly fixed-sized, which makes it difficult to capture multi-scale features simultaneously. Additionally, the use of numerous fixed-size convolutional filters often results in redundant parameters. During the feature extraction process, the CNN often struggles to take inter-channel dependencies and spatial location information into consideration. There are also limitations in extracting various time-scale features. To address these issues, a fault diagnosis method on the basis of a dual-path mixed convolutional attention-BiGRU network (DMCA-BiGRUN) is proposed for industrial processes. Firstly, a dual-path mixed CNN (DMCNN) is designed to capture features at multiple scales while effectively reducing the parameter count. Secondly, a coordinate attention mechanism (CAM) is designed to help the network to concentrate on main features more effectively during feature extraction by combining the channel relationship and position information. Finally, a bidirectional gated recurrent unit (BiGRU) is introduced to process sequences in both directions, which can effectively learn the long-range temporal dependencies of sequence data. To verify the fault diagnosis performance of the proposed method, simulation experiments are implemented on the Tennessee Eastman (TE) and Continuous Stirred Tank Reactor (CSTR) datasets. Some deep learning methods are compared in the experiments, and the results confirm the feasibility and superiority of DMCA-BiGRUN. Full article

Hydrogen is a promising zero-carbon fuel for internal combustion engines; however, the geometric optimization of injectors for low-pressure direct-injection (LPDI) systems under lean-burn conditions remains underexplored. This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). The CFD model was validated using particle image velocimetry (PIV) data from non-reacting flow experiments conducted in an optically accessible research engine developed by Sandia National Laboratories, ensuring accurate prediction of in-cylinder flow structures. The optimization focused on two critical geometric parameters: injector hole count and injection angle. Partial indicated mean effective pressure (pIMEP) and in-cylinder NO_x emissions were selected as conflicting objectives to balance performance and emissions. Adaptive mesh refinement (AMR) was employed to resolve transient in-cylinder flow and combustion dynamics with high spatial accuracy. Among 22 evaluated configurations including both capped and uncapped designs, the injector featuring three holes at a 15.24° injection angle outperformed the baseline, delivering improved mixture uniformity, reduced knock tendency, and lower NO_x emissions. These results demonstrate the potential of geometry-based optimization for advancing hydrogen-fueled LPDI engines toward cleaner and more efficient combustion strategies. Full article

Background/Objectives: Children with cancer suffer due to the underlying disease and prescribed cancer-directed therapies, and non-pharmacologic modalities may offer improved symptom control without additional medications. We sought to elicit knowledge, attitudes, and beliefs of Pediatric Hematology Oncology (PHO) providers surrounding the incorporation of acupuncture for symptom management for their patients. Methods: A cross-sectional survey instrument was created, formatted, and delivered to physicians and advanced practice providers (APPs) at a single US pediatric cancer center. Survey responses were summarized by descriptive statistics. Results: A total of 78 PHO clinicians participated (response rate 29%). Most participants were interested in learning more about acupuncture (n = 42, 56.0%), yet rarely (n = 17, 22.7%) or never (n = 46, 61.3%) recommend acupuncture to patients. Most (n = 51, 73.9%) noted that they would support institutional development of an acupuncture program. Over half (n = 37, 52.2%) indicated their threshold for minimum hematologic indices for acupuncture includes a platelet count greater than 20,000 and absolute neutrophil count (ANC) greater than 500 (n = 37, 54.4%). Approximately two-thirds (n = 52, 66.7%) of participants noted that acupuncture could improve their patient’s quality of life, and most (n = 46, 67.6%) were not worried about harm. Conclusions: Acupuncture for symptom management is an evidenced-based, guideline-concordant recommendation for adults with cancer, but robust data in the pediatric oncology population are lacking. PHO providers do not routinely recommend acupuncture for patients but note that it may improve quality of life. Given their high symptom burden, rigorous studies of non-pharmacologic strategies for pediatric symptom management are vital. Acupuncture should be examined as a potential beneficial adjunct. Full article

Sickle cell disease (SCD) is a prevalent genetic disorder caused by a mutation in the beta-globin gene. Hyperhemolysis (HS) is a severe complication involving the rapid destruction of both transfused and endogenous red blood cells, commonly found in SCD. This literature review explores the clinical presentation, diagnosis, pathogenesis, and management of HS in SCD. HS can manifest acutely or in a delayed manner, complicating diagnosis due to overlapping symptoms and varying reticulocyte responses. Immunohematological assessments often reveal delayed positivity in direct antiglobulin tests and antibody screens. HS typically presents severe anemia, jaundice, hemoglobinuria, and hemodynamic instability. Diagnostic markers include elevated bilirubin and lactate dehydrogenase levels alongside a reduced reticulocyte count. The management of HS is primarily empirical, with no clinical trials to support standardized treatment protocols. First-line treatments involve steroids and intravenous immunoglobulins (IVIG), which modulate immune responses and mitigate hemolysis. Refractory cases may require additional agents such as rituximab, eculizumab, tocilizumab, and, in some instances, plasma exchange or erythropoietin-stimulating agents. Novel therapeutic approaches, including bortezomib and Hemopure, have shown promise but require further investigation. Current management strategies are empirical, underscoring the need for robust clinical trials to establish effective treatment protocols that ultimately improve outcomes for SCD patients experiencing HS. Full article

This study investigates arthropod assemblage in cowpea, hemp, and watermelon grown both as monocrops and intercrops using three sampling techniques: direct visual counts, sticky cards, and pan traps. A total of 31,774 arthropods were collected, spanning two classes [Arachnida (0.07%) and Insecta (99.93%)], 11 orders, and 82 families representing diverse functional groups. Arachnids were represented by a single family (Araneae). Among insects, the composition included Diptera (36.81%), Thysanoptera (24.64%), Hemiptera (19.43%), Hymenoptera (11.58%), Coleoptera (6.84%), Lepidoptera (0.076%) and Blattodea, Odonata, Orthoptera, Psocodea (≤0.005%). Roughly 10% of the total arthropods were pollinators, while the remainder were primarily herbivores and predators. Apidae were abundant in all treatments except for watermelon monocrops. Intercropping supported more pollinators, particularly Apidae, Halictidae, and Sarcophagidae. However, herbivores dominated (>50%) in each system, largely due to high presence of thrips and cicadellids. Predators accounted for approximately 30%, with dolichopodids (Diptera) being the most dominant. Watermelon yield increased by 30–60% in the intercrop systems. While intercropping increases overall arthropod abundance, it also creates a more balanced community where beneficial organisms are not heavily outnumbered by pests and contributes to enhanced ecological resilience and crop performance. Full article

Due to the limitations of the sowing machine performance and rice seed germination rates, missing seedlings inevitably occur after rice is sown in large fields. This phenomenon has a direct impact on the rice yield. In the field environment, the existing methods for detecting missing seedlings based on unmanned aerial vehicle (UAV) remote sensing images often have unsatisfactory effects. Therefore, to enable the fast and accurate detection of missing rice seedlings and facilitate subsequent reseeding, this study proposes a UAV remote-sensing-based method for detecting missing rice seedlings in large fields. The proposed method uses an improved PCERT-DETR model to detect rice seedlings and missing seedlings in UAV remote sensing images of large fields. The experimental results show that PCERT-DETR achieves an optimal performance on the self-constructed dataset, with an mean average precision (mAP) of 81.2%, precision (P) of 82.8%, recall (R) of 78.3%, and F₁-score (F₁) of 80.5%. The model’s parameter count is only 21.4 M and its FLOPs reach 66.6 G, meeting real-time detection requirements. Compared to the baseline network models, PCERT-DETR improves the P, R, F₁, and mAP by 15.0, 1.2, 8.5, and 6.8 percentage points, respectively. Furthermore, the performance evaluation experiments were carried out through ablation experiments, comparative detection model experiments and heat map visualization analysis, indicating that the model has a strong detection performance on the test set. The results confirm that the proposed model can accurately detect the number of missing rice seedlings. This study provides accurate information on the number of missing seedlings for subsequent reseeding operations, thus contributing to the improvement of precision farming practices. Full article

This study investigated the fracture behavior of concrete beams with recycled coarse aggregate (RCA) and recycled fine aggregate (RFA) using the box-counting method to measure crack fractal dimensions under load. Beams with RCA showed higher fractal dimensions due to RCA’s lower elastic moduli and compressive strengths, resulting in reduced deformation resistance, ductility, and more late-stage crack propagation. A direct proportional relationship existed between RCA/RFA replacement ratios and crack fractal dimensions. Second-order and third-order polynomial trend surface-fitting techniques were applied to examine the complex relationships among RFA/RCA dosage, applied load, and crack fractal dimension. The results indicated that the RFA dosage had a negative quadratic influence, while load had a positive linear effect, with dosage impact increasing with load. A second-order functional relationship was found between mid-span deflection and crack fractal dimension, reflecting nonlinear behavior consistent with concrete mechanics. This study enhances the understanding of recycled aggregate concrete beam fracture behavior, with the crack fractal dimension serving as a valuable quantitative indicator for damage state and crack complexity assessment. These findings are crucial for engineering design and application, enabling better evaluation of structural performance under various conditions. Full article

This paper examines the impact of carbon capture and storage (CCS) deployment on national carbon intensity (CI) across 43 countries from 2010 to 2020. Using a dynamic common correlated effects (DCCE) log–log panel, we estimate the elasticity of CI with respect to sectoral CCS facility counts within four income-group panels and the full sample. In the high-income panel, CCS in direct air capture, cement, iron and steel, power and heat, and natural gas processing sectors produces statistically significant CI declines of 0.15%, 0.13%, 0.095%, 0.092%, and 0.087% per 1% increase in facilities, respectively (all p < 0.05). Upper-middle-income countries exhibit strong CI reductions in direct air capture (–0.22%) and cement (–0.21%) but mixed results in other sectors. Lower-middle- and low-income panels show attenuated or positive elasticities—reflecting early-stage CCS adoption and infrastructure barriers. Robustness checks confirm these patterns both before and after the 2015 Paris Agreement and between emerging and developed economy panels. Spatial analysis reveals that the United States and United Kingdom achieved 30–40% CI reductions over the decade, whereas China, India, and Indonesia realized only 10–20% declines (relative to a 2010 baseline), highlighting regional deployment gaps. Drawing on these detailed income-group insights, we propose tailored policy pathways: in high-income settings, expand tax credits and public–private infrastructure partnerships; in upper-middle-income regions, utilize blended finance and technology-transfer programs; and in lower-income contexts, establish pilot CCS hubs with international support and shared storage networks. We further recommend measures to manage CCS’s energy and water penalties, implement rigorous monitoring to mitigate leakage risks, and design risk-sharing contracts to address economic uncertainties. Full article

Respirable particulate matter (RPM) is a major indoor environment concern posing direct health risks. Localized data on RPM exposure remains scarce across different microenvironments in occupational and educational settings. Students in educational settings are increasingly vulnerable to RPM, specifically in the winter season when more activities are carried out indoors and meteorological conditions elevate the PM levels. This study was conducted to assess the personal exposure of university students to RPM within their frequently visited microenvironments (MEs). Forty volunteers were selected, and their exposure to RPM was measured by specifically monitoring their particle mass count (PMC) and particle number count (PNC) in commonly identified MEs. Calibrated air pumps with nylon cyclones and a Dylos DC 1100 Pro were used for this purpose. We found that the mean RPM concentration for personal exposure was 251 µg/m³, significantly exceeding the prescribed National Environmental Quality Standards (NEQS) limit of 35 µg/m³. We also observed a significant correlation between the PNC and PMC in the microenvironments. The assessment of personal exposure to RMP in this study highlights the urgent need for mitigation strategies in educational settings to reduce the personal exposure of students to RMP to reduce their health-related risks. Full article

Microplastics (MPs) are becoming one of the most serious environmental threats worldwide. They have been shown to induce male reproductive toxicity in animal studies. However, evidence of their adverse effects on male reproductive health in human is still lacking. In this study, we evaluated the presence of MPs in human semen and explored their associations with semen quality. A total of 45 semen samples from men attending a fertility center were collected. MPs in the semen samples were analyzed by laser direct infrared (LD-IR) spectroscopy. MPs were found in 34 out of 45 semen samples, with an average abundance of 17.0 (42.0) particles/g. The size of MPs ranged from 20.3 μm to 189.7 μm and the majority (57.8%) were smaller than 50 μm. A total of 15 distinct MPs polymers were identified, and polyethylene (PET) accounted for 35.9% of the total amount of MPs, followed by butadiene rubber (BR, 26.4%) and chlorinated polyethylene (CPE, 12.2%). Analysis of the association of MP exposure with semen quality showed that participants exposed to PET MPs experienced a reduction in sperm progressive motility (20.6% ± 12.8% vs. 34.9% ± 15.9%, p = 0.056). However, no significant association was found between MP exposure and sperm concentration or total sperm count. Our findings confirmed the presence of MPs in human semen and suggested that MP exposure might have adverse impacts on male reproductive health. However, further large-scale studies are needed to confirm these findings. Full article

Background: Chronic lymphocytic leukemia (CLL) is a prevalent hematologic malignancy that predominantly affects elderly individuals, posing significant clinical challenges due to patient comorbidities and inherent resistance to conventional chemotherapy. The emergence of targeted therapies combining venetoclax, a selective inhibitor of the anti-apoptotic protein BCL-2, with anti-CD20 monoclonal antibodies has dramatically transformed the treatment landscape. Methods: This retrospective observational study analyzed the differential impacts of first-line venetoclax-obinutuzumab (VenO) and second-line venetoclax-rituximab (VenR) on immunoglobulin G (IgG) levels and absolute neutrophil count (ANC) in CLL patients. Results: Our findings indicate that during first-line VenO therapy, a significant improvement in ANC levels from baseline was observed, whereas patients undergoing second-line VenR therapy demonstrated limited impact on ANC and the decreasing tendency in IgG levels. Patients treated with VenR had a longer disease history and previous exposure to other treatment regimens, primarily chemoimmunotherapy, which could negatively influence immune recovery, making direct comparisons between these two treatment lines challenging. Although this observational study did not directly compare infection rates, the observed enhancement of ANC levels in patients receiving VenO suggests a potential for lower infection risk compared to pretreated VenR patients. Conclusions: These results underscore the clinical significance of considering both the treatment line and the patient’s prior therapeutic history when selecting venetoclax-based regimens for CLL. The potential association of first-line VenO with improved immunological parameters and the complex impact of prior therapies on immunological recovery with second-line VenR warrant further prospective investigation into the correlation between treatment regimen, patient history, immune function, and infectious complications. Full article

This study introduces a multi-task prediction model, MT-MBLAE, designed to use DNA methylation data from blood to predict the advancement of Alzheimer’s disease. By integrating various modules, including bi-directional long short-term memory (BiLSTM), long short-term memory (LSTM), and RepeatVector, among others, the model encodes DNA methylation profile data, capturing temporal and spatial information from instantaneous DNA methylation spectra data. Leveraging the network properties of BiLSTM and LSTM enables the consideration of both preceding and subsequent information in sequences, facilitating the extraction of richer features and enhancing the model’s comprehension of sequential data. Moreover, the model employs LSTM, time distributed to reconstruct time series DNA methylation profiles. The time-distributed layer applies identical layers at each time step of the sequence, sharing weights and biases uniformly across all time steps. This approach achieves parameter sharing, reduces the model’s parameter count, and ensures consistency in handling time series data. Experimental findings show the excellent performance of the MT-MBLAE model in predicting cognitively normal (CN) to mild cognitive impairment (MCI), and mild cognitive impairment (MCI) to Alzheimer’s disease (AD). Full article