Search Results (14,102)

Background and Objectives: The significance of periodontal disease as a public health issue prompts the exploration of effective treatments, including the potential use of tocopherol (Vitamin E) due to its anti-inflammatory and antioxidant properties. Materials and Methods: The PICO statement (Population, Intervention, Comparator, Outcome) was as follows: In patients with periodontal disease, does tocopherol (Vitamin E) supplementation compared to no supplementation or insufficient Vitamin E intake improve clinical outcomes such as gingival inflammation, pocket depth, and clinical attachment levels? This study searched through PubMed, Scopus, and Web of Science up to June 2024 focused on studies involving human subjects with various forms of periodontal disease, analyzing the impact of tocopherol through dietary or supplementary intake. Primary outcomes evaluated included improvements in gingival inflammation, pocket depth, and clinical attachment levels, with data synthesis conducted according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Quality assessment and risk of bias were meticulously performed for the included observational studies and randomized controlled trials. Results: The meta-analysis incorporated 8 studies that were used for data extraction, totaling 12,832 patients, revealing a heterogeneous response to tocopherol supplementation, with a pooled odds ratio for efficacy in reducing periodontal disease severity at about 0.97 (95% CI: 0.96–0.98). Noteworthy findings indicated a statistically significant increase in clinical attachment loss and pocket depth with odds ratios ranging from 1.15 to 9.33 when Vitamin E was insufficient. However, the considerable heterogeneity (I² = 88.35%) underscores variations in tocopherol’s effectiveness across different populations and study designs. Conclusions: While tocopherol supplementation shows a modest benefit in managing periodontal disease, particularly in reducing clinical attachment levels and pocket depth, the variability in outcomes emphasizes the necessity for more research to establish standardized treatment protocols and dosages. Full article

Background: Colorectal cancer (CRC) is characterized by a pro-inflammatory microenvironment and features high-energy-supply molecules that assure tumor growth. A still less studied macromolecule is inorganic polyphosphate (iPolyP), a high-energy linear polymer that is ubiquitous in all forms of life. Made up of hundreds of repeated orthophosphate units, iPolyP is essential for a wide variety of functions in mammalian cells, including the regulation of proliferative signaling pathways. Some evidence has suggested its involvement in carcinogenesis, although more studies need to be pursued. Moreover, iPolyP regulates several homeostatic processes in animals, spanning from energy metabolism to blood coagulation and tissue regeneration. Results: In this study, we tested the role of iPolyP on CRC proliferation, using in vitro and ex vivo approaches, in order to evaluate its effect on tumor growth. We found that iPolyP is significantly increased in tumor tissues, derived from affected individuals enrolled in this study, compared to the corresponding peritumoral counterparts. In addition, iPolyP signaling occurs through the TRPM8 receptor, a well-characterized Na⁺ and Ca²⁺ ion channel often overexpressed in CRC and linked with poor prognosis, thus promoting CRC cell proliferation. The pharmacological inhibition of TRPM8 or RNA interference experiments performed in established CRC cell lines, such as Caco-2 and SW620, showed that the involvement of TRPM8 is essential, greater than that of the other two known iPolyP receptors, P2Y1 and RAGE. The presence of iPolyP drives cancer cells towards the mitotic phase of the cell cycle by enhancing the expression of ccnb1, which encodes the Cyclin B protein. In vitro 2D and 3D data reflected the ex vivo results, obtained by the generation of CRC-derived organoids, which increased in size. Conclusions: These results indicate that iPolyP may be considered a novel and unexpected early biomarker supporting colorectal cancer cell proliferation. Full article

Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs, such as model non-convergence, lengthy training periods, and insufficient diversity of solutions, this study introduces a novel multi-objective combinatorial optimization algorithm based on DRL. The proposed algorithm employs a uniform weight decomposition method to simplify complex multi-objective scenarios into single-objective problems and uses asynchronous advantage actor–critic (A3C) instead of conventional REINFORCE methods for model training. This approach effectively reduces variance and prevents the entrapment in local optima. Furthermore, the algorithm incorporates an architecture based on graph transformer networks (GTNs), which extends to edge feature representations, thus accurately capturing the topological features of graph structures and the latent inter-node relationships. By integrating a weight vector layer at the encoding stage, the algorithm can flexibly manage issues involving arbitrary weights. Experimental evaluations on the bi-objective traveling salesman problem demonstrate that this algorithm significantly outperforms recent similar efforts in terms of training efficiency and solution diversity. Full article

Biological drugs, including monoclonal antibodies, represent a revolutionary strategy in all fields of medicine, offering promising results even in the treatment of osteoarthritis (OA). However, their safety and efficacy have not been fully validated, highlighting the need for in-depth studies. Therefore, we provided a comprehensive systematic review of the intra-articular use of monoclonal antibodies for the treatment of OA in animal models, reflecting ongoing efforts to advance therapeutic strategies and improve patient outcomes. A systematic literature search was conducted in December 2023 following the PRISMA guidelines, using the Web of Science, Google Scholar, and PUBMED databases. Out of a total of 456, 10 articles were included in the study analyzing intra-articular antibodies and focusing on various targets, including vascular endothelial growth factor (VEGF), nerve growth factor (NGF), interleukin 4-10 (IL4-10), tumor necrosis factor α (TNF-α), a disintegrin and metalloproteinase with thrombospondin motifs 5 (ADAMTS5), and matrix metalloproteinase 13 (MMP-13). Most studies administered the antibodies weekly, ranging from 1 to 10 injections. Animal models varied, with mean follow-up periods of 8.9 ± 4.1 weeks. The methods of assessing outcomes, including pain and morpho-functional changes, varied. Some studies reported only morphological and immunohistochemical data, while others included a quantitative analysis of protein expression. In conclusion, monoclonal antibodies represent a promising avenue in the treatment of OA, offering targeted approaches to modulate disease pathways. Further research and clinical trials are needed to validate their safety and efficacy, with the potential to revolutionize the management of OA and reduce reliance on prosthetic interventions. Full article

Objectives. There is a significant correlation between EEG microstate and the neurophysiological basis of mental illness, brain state, and cognitive function. Given that the unclear relationship between network dynamics and different microstates, this paper utilized microstate, brain network, and control theories to understand the microstate characteristics of short-term memory task, aiming to mechanistically explain the most influential microstates and brain regions driving the abnormal changes in brain state transitions in patients with schizophrenia. Methods. We identified each microstate and analyzed the microstate abnormalities in schizophrenia patients during short-term memory tasks. Subsequently, the network dynamics underlying the primary microstates were studied to reveal the relationships between network dynamics and microstates. Finally, using control theory, we confirmed that the abnormal changes in brain state transitions in schizophrenia patients are driven by specific microstates and brain regions. Results. The frontal-occipital lobes activity of microstate D decreased significantly, but the left frontal lobe of microstate B increased significantly in schizophrenia, when the brain was moving toward the easy-to-reach states. However, the frontal-occipital lobes activity of microstate D decreased significantly in schizophrenia, when the brain was moving toward the hard-to-reach states. Microstate D showed that the right-frontal activity had a higher priority than the left-frontal, but microstate B showed that the left-frontal priority decreased significantly in schizophrenia, when changes occur in the synchronization state of the brain. Conclusions. In conclusion, microstate D may be a biomarker candidate of brain abnormal activity during the states transitions in schizophrenia, and microstate B may represent a compensatory mechanism that maintains brain function and exchanges information with other brain regions. Microstate and brain network provide complementary perspectives on the neurodynamics, offering potential insights into brain function in health and disease. Full article

Convolutional neural networks (CNNs) are commonly employed for demanding applications, such as speech recognition, natural language processing, and computer vision. As CNN architectures become more complex, their computational demands grow, leading to substantial energy consumption and complicating their use on devices with limited resources (e.g., edge devices). Furthermore, a new line of research seeking more sustainable approaches to Artificial Intelligence development and research is increasingly drawing attention: Green AI. Motivated by an interest in optimizing Machine Learning models, in this paper, we propose Optimizing Convolutional Neural Network Architectures (OCNNA). It is a novel CNN optimization and construction method based on pruning designed to establish the importance of convolutional layers. The proposal was evaluated through a thorough empirical study including the best known datasets (CIFAR-10, CIFAR-100, and Imagenet) and CNN architectures (VGG-16, ResNet-50, DenseNet-40, and MobileNet), setting accuracy drop and the remaining parameters ratio as objective metrics to compare the performance of OCNNA with the other state-of-the-art approaches. Our method was compared with more than 20 convolutional neural network simplification algorithms, obtaining outstanding results. As a result, OCNNA is a competitive CNN construction method which could ease the deployment of neural networks on the IoT or resource-limited devices. Full article

Background/Objectives: The complementary feeding period is crucial for addressing micronutrient imbalances, particularly in very low birth weight (VLBW) infants. However, the impact of the timing of solid food introduction on micronutrient intake in a representative VLBW population remains unclear. Methods: This prospective, observational study investigated micronutrient intake during complementary feeding in VLBW infants categorized based on whether solids were introduced early (<17 weeks corrected age (CA)) or late (≥17 weeks CA). Nutritional intake was assessed using a 24 h recall at 6 weeks CA and with 3-day dietary records at 12 weeks and at 6, 9, and 12 months CA. Results: Among 218 infants, 115 were assigned to the early group and 82 to the late group. In total, 114–170 dietary records were valid for the final analysis at each timepoint. The timepoint of solid introduction did not affect micronutrient intake, except for a higher iron and phosphorus intake at 6 months CA in the early group (early vs. late: iron 0.71 vs. 0.58 mg/kg/d, adjusted p-value (p-adj.) = 0.04; phosphorus 341 vs. 286 mg/d, p-adj. = 0.04). Total vitamin D, calcium, zinc, and phosphorus greatly met intake recommendations; however, dietary iron intake was insufficient to equalize the iron quantity from supplements during the second half year CA. While nutrient intakes were similar between infants with and without comorbidities, breastfed infants had lower micronutrient intakes compared with formula-fed infants. Conclusions: This study suggests that micronutrient intakes were sufficient during complementary feeding in VLBW infants. However, prolonged iron supplementation may be necessary beyond the introduction of iron-rich solids. Further research is essential to determine micronutrient requirements for infants with comorbidities. Full article

Ambitious approaches to science teaching feature collaborative learning environments and engage students in rich discourse to make sense of their own and their peers’ ideas. Classroom assessment must cohere with and mutually reinforce these kinds of learning experiences. This paper explores how teachers’ enactment of formative assessment tasks can support such an ambitious vision of learning. We draw on video data collected through a year-long investigation to explore the ways that co-designing formative assessments linked to a learning progression for modeling energy in systems could help teachers coordinate classroom practices across high school physics, chemistry, and biology. Our analyses show that while there was some alignment of routines within content areas, students had differential opportunities to share and work on their ideas. Though the tasks were constructed for surfacing students’ ideas, they were not always facilitated to create space for teachers to take up and work with those ideas. This paper suggests the importance of designing and enacting formative assessment tasks to support ambitious reform efforts, as well as ongoing professional learning to support teachers in using those tasks in ways that will center discourse around students’ developing ideas. Full article

Background: Lipedema is a frequently misdiagnosed condition in women, often mistaken for obesity, which significantly deteriorates both quality of life and physical health. Recognizing the necessity for holistic treatment strategies, research has increasingly supported the integration of specific dietary approaches, particularly ketogenic diets focusing on low-carbohydrate and high-fat intake. Objectives: to evaluate the impact of ketogenic diets on women with lipedema through a systematic review and meta-analysis. Methods: A systematic review and meta-analysis were conducted by reviewing published, peer-reviewed studies addressing the implications of a low-carbohydrate, high-fat (LCHF) ketogenic diet in managing lipedema following comprehensive scrutiny of digital medical databases, such as PubMed, PubMed Central, Science Direct, and the Web of Science. This research was governed by specified parameters, including an established search string composed of search terms and an eligibility criterion (PICO) as denoted by the principal authors. Statistical analysis was carried out using RevMan 5.4.1 software with the Newcastle–Ottawa Scale utilized for quality appraisal of the included studies. Results: Seven studies reporting statistical outcomes were included in the systematic review and meta-analysis following a rigorous quality appraisal and data identification process. Three hundred and twenty-nine female participants were diagnosed with lipedema and treated using a low-carbohydrate, high-fat diet. Data analysis identified the high-fat diet with a mean study duration of 15.85 weeks. Mean Differences (MDs) on changes pre- and post-intervention showed significant reductions in BMI and total body weight [4.23 (95% CI 2.49, 5.97) p < 0.00001 and 7.94 (95% CI 5.45, 10.43) p < 0.00001 for BMI and body weight, respectively]. Other anthropometric measurements, such as changes in waist/hip circumferences and waist/hip ratios, showed a significant reduction in these parameters, with an MD of 8.05 (95% CI 4.66, 11.44) p < 0.00001 and an MD of 6.67 (95% CI 3.35, 9.99) p < 0.0001 for changes in waist and hip circumferences from baseline, respectively. Lastly, changes in pain sensitivity were statistically significant post-intervention [MD 1.12 (95% CI, 0.44, 1.79) p = 0.001]. All studies scored fair on the Newcastle–Ottawa Scale. Conclusions: despite the limited studies and low number of study participants, the review observed a significant reduction in anthropometric and body composition metrics, indicating a potentially beneficial association between LCHF ketogenic diets and lipedema management. Full article

Background/Objectives: The current meta-analysis looks at the effect of ethnicity on the connection between 5-HTTLPR SNPs and PTSD patients in all published genetic association studies. Techniques: In accordance with PRISMA principles, the literature was searched in PubMed, Scopus, and ScienceDirect. A consistent method was followed by two reviewers who independently chose publications for inclusion and extracted data. Using a random-effects model, a meta-analysis of the biallelic and triallelic studies was conducted in order to determine the pooled OR and the associated 95% CI. The impact estimates were corrected for minor study effects, including publication bias, using the trim-and-fill approach. Findings: After 17 studies were deemed eligible for inclusion, the overall sample size was 8838 controls and 2586 PTSD patients, as opposed to 627 and 3524 in the triallelic meta-analysis. The results of our meta-analysis and comprehensive review do not point to a direct main effect of the 5-HTTLPR polymorphisms on PTSD. Nonetheless, preliminary data suggest that ethnicity influences the association between 5-HTTLPR and PTSD. Conclusions: According to our findings, ethnicity—especially African ethnicity—has a major influence on the relationship between 5-HTTLPR and PTSD and needs to be taken into account as a crucial moderating factor in further studies. Full article

Symbolic regression plays a crucial role in machine learning and data science by allowing the extraction of meaningful mathematical models directly from data without imposing a specific structure. This level of adaptability is especially beneficial in scientific and engineering fields, where comprehending and articulating the underlying data relationships is just as important as making accurate predictions. Genetic Programming (GP) has been extensively utilized for symbolic regression and has demonstrated remarkable success in diverse domains. However, GP’s heavy reliance on evolutionary mechanisms makes it computationally intensive and challenging to handle. On the other hand, Particle Swarm Optimization (PSO) has demonstrated remarkable performance in numerical optimization with parallelism, simplicity, and rapid convergence. These attributes position PSO as a compelling option for Automatic Programming (AP), which focuses on the automatic generation of programs or mathematical models. Particle Swarm Programming (PSP) has emerged as an alternative to Genetic Programming (GP), with a specific emphasis on harnessing the efficiency of PSO for symbolic regression. However, PSP remains unsolved due to the high-dimensional search spaces and local optimal regions in AP, where traditional PSO can encounter issues such as premature convergence and stagnation. To tackle these challenges, we introduce Dynamical Sphere Regrouping PSO Programming (DSRegPSOP), an innovative PSP implementation that integrates DSRegPSO’s dynamical sphere regrouping and momentum conservation mechanisms. DSRegPSOP is specifically developed to deal with large-scale, high-dimensional search spaces featuring numerous local optima, thus proving effective behavior for symbolic regression tasks. We assess DSRegPSOP by generating 10 mathematical expressions for mapping points from functions with varying complexity, including noise in position and cost evaluation. Moreover, we also evaluate its performance using real-world datasets. Our results show that DSRegPSOP effectively addresses the shortcomings of PSO in PSP by producing mathematical models entirely generated by AP that achieve accuracy similar to other machine learning algorithms optimized for regression tasks involving numerical structures. Additionally, DSRegPSOP combines the benefits of symbolic regression with the efficiency of PSO. Full article

China’s Yunnan Province, known for its tea plantations, faces significant challenges in smart pest management due to its ecologically intricate environment. To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using WIoU-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. The research results demonstrate that compared to the original YOLOv8n model, the improved YOLOv8n-WSE-pest model shows increases in the precision, recall, mAP50, and F1 score by 3.12%, 5.65%, 2.18%, and 4.43%, respectively. In external validation, the mAP of the model outperforms other deep learning networks such as Faster-RCNN, SSD, and the original YOLOv8n, with improvements of 14.34%, 8.85%, and 2.18%, respectively. In summary, the intelligent tea garden pest identification model proposed in this study excels at precise the detection of key pests in tea plantations, enhancing the efficiency and accuracy of pest management through the application of advanced techniques in applied science. Full article

Asthma, a complex disease characterized by persistent airway inflammation, remains an urgent global health concern. We explored the critical role of allergic biomarkers and dysregulated immune system in asthma through an extensive literature review in databases such as Web of Science, PubMed, EMBASE, Scopus, and Google Scholar. This review summarizes the growing data on the pivotal role of allergic biomarkers and dysregulated immune system in the development and evolution of asthma. Recent studies have uncovered several biomarkers that elucidate intrinsic allergic mechanisms in individuals with asthma. This article highlights these biomarkers’ potential in predicting asthma onset, assessing its intensity, guiding therapeutic interventions, and tracking disease progression. We also explore the innovative therapeutic prospects arising from the convergence of allergy and dysregulated immune system in asthma and emphasize the potential for precision medicine approaches. Understanding allergic biomarkers intertwined with a dysregulated immune system heralds a new era in asthma treatment and points to improved and individualized treatment modalities. Full article

The increasing prevalence of diabetes worldwide, including in Mexico, presents significant challenges to healthcare systems. This has a notable impact on hospital admissions, as diabetes is considered an ambulatory care-sensitive condition, meaning that hospitalizations could be avoided. This is just one example of many challenges faced in the medical and public health fields. Traditional healthcare methods have been effective in managing diabetes and preventing complications. However, they often encounter limitations when it comes to analyzing large amounts of health data to effectively identify and address diseases. This paper aims to bridge this gap by outlining a comprehensive methodology for non-physicians, particularly data scientists, working in healthcare. As a case study, this paper utilizes hospital diabetes discharge records from 2010 to 2023, totaling 36,665,793 records from medical units under the Ministry of Health of Mexico. We aim to highlight the importance for data scientists to understand the problem and its implications. By doing so, insights can be generated to inform policy decisions and reduce the burden of avoidable hospitalizations. The approach primarily relies on stratification and standardization to uncover rates based on sex and age groups. This study provides a foundation for data scientists to approach health data in a new way. Full article

Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by elevated blood glucose levels and insulin resistance, leading to multiple organ damage with implications for quality of life and lifespan. In recent years, the rising prevalence of T2DM globally has coincided with the digital transformation of medicine and healthcare, including extensive electronic health records (EHRs) for patients and healthy individuals. Numerous research articles as well as systematic reviews have been conducted to produce innovative findings and summarize current developments and applications of data science in the life sciences, medicine and healthcare. The present review is conducted in the context of T2DM and Machine Learning, examining relatively recent publications using tabular data and demonstrating the relevant use cases, the workflows during model building and the candidate predictors. Our work indicates that Gradient Boosting and tree-based models are the most successful ones, the SHAPley and Wrapper algorithms being quite popular feature interpretation and evaluation methods, highlighting urinary markers and dietary intake as emerging diabetes predictors besides the typical invasive ones. These results could offer insight toward better management of diabetes and open new avenues for research. Full article