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GD2TIF cells as a platform for single-dose and long-term delivery of biologics
Dynamic caustics by ultrasonically modulated liquid surface
Anti-aging effect of low molecular weight recombinant humanized collagen on photo-aging by activating adherence junction signaling pathways
Skin aging is characterized by a loss of collagen. Collagen stimulates the secretion of extracellular matrix (ECM) components by skin fibroblasts, contributing to anti-wrinkle and skin-firming effects in cosmetic applications. However, the skin barrier poses a significant challenge to collagen absorption, hindering its dermal functionality. Rapid advancements in synthetic biology have enabled the development of recombinant human collagen (RHC) with controllable sequence and molecular weight, enhancing its potential cosmetic applications. Nonetheless, research on the ability of RHC to penetrate the skin and exert anti-aging effects remains limited, and its underlying mechanisms are largely unexplored. To address this gap, we selected low molecular weight recombinant human collagen peptide (LRHC) and evaluated its skin permeability and anti-aging mechanisms. Findings indicated that LRHC significantly promoted fibroblast proliferation and enhanced the transcription of collagen types I and type III. Furthermore, in photoaged nude mouse models, LRHC upregulated the expression of key basement membrane components, including collagen type IV (COL4), collagen type VII (COL7), collagen type XVII (COL17), integrin β4 (ITGB4), and laminin-332 (LN332, formerly LN5), resulting in increased collagen fiber density. Notably, LRHC demonstrated a dermal permeability rate of 74.7 ± 14.2% after 8 hours. Transcriptome sequencing revealed that LRHC may maintain cytoskeletal structure through activation of adherens junction signaling pathways and promote extracellular matrix (ECM) production through activation of transforming growth factor-beta (TGF-β) signaling pathways, thereby achieving anti-aging efficacy.. These findings confirm that LRHC can penetrate the dermis and exert anti-aging effects on skin, potentially through mechanisms mediated by adherens junction signaling pathways. As a functional active ingredient with anti-aging properties, LRHC holds significant potential for cosmetic applications.
Modeling resource consumption in the US air transportation system via minimum-cost percolation
Clinical analysis of dry eye after refractive surgery in army recruits in 2024
β-aminopropionitrile-induced thoracic aortopathy is refractory to cilostazol and sildenafil in mice
Thoracic aortopathies are life-threatening diseases including aneurysm, dissection, and rupture. Cilostazol, a phosphodiesterase (PDE) 3 inhibitor, and sildenafil, a PDE5 inhibitor, have been used clinically for peripheral arterial disease and erectile dysfunction or pulmonary hypertension, respectively. Recent studies report their effects on abdominal aortic aneurysm formation. However, their impacts on thoracic aortopathy remain unknown. In this study, we investigated whether cilostazol and sildenafil affect thoracic aortopathy induced by β-aminopropionitrile (BAPN) administration in mice. Bulk RNA sequencing analysis revealed that BAPN administration upregulated Pde3a transcription in the ascending aorta and Pde5a in both ascending and descending regions before thoracic aortopathy formation. Next, we tested the effects of cilostazol or sildenafil on BAPN-induced thoracic aortopathy. BAPN-administered mice were fed a diet supplemented with either cilostazol or sildenafil. Mass spectrometry measurements determined the presence of cilostazol or sildenafil in the plasma of mice fed drug-supplemented diets. However, neither drug altered BAPN-induced aortic rupture nor aneurysm formation and progression. These results provide evidence that cilostazol and sildenafil did not influence BAPN-induced thoracic aortopathy in mice.
Unidirectional perfect absorption induced by chiral coupling in spin-momentum locked waveguide magnonics
Climate-adaptive energy forecasting in green buildings via attention-enhanced Seq2Seq transfer learning
Abstract Energy consumption forecasting in green buildings remains challenging due to complex climate-building interactions and temporal dependencies in energy usage patterns. Existing prediction models often fail to capture long-term dependencies and adapt to diverse climatic conditions, limiting their practical applicability. This study presents an integrated forecasting framework that combines sequence-to-sequence (Seq2Seq) architecture with reinforcement learning and transfer learning techniques. The framework employs long short-term memory (LSTM) networks enhanced with attention mechanisms to model temporal dependencies and climate variability in energy consumption data. The attention mechanism enables the model to focus on relevant temporal features while transfer learning facilitates adaptation across different climate zones. Experimental validation on two publicly available green building datasets demonstrates superior performance, achieving 96.2% accuracy, mean square error of 0.2635, and coefficient of determination ( $$R^2$$ R 2 ) of 0.98. The proposed framework exhibits strong generalization capabilities across diverse climate conditions and building types. However, the framework requires substantial training data (6-12 months of high-quality sensor data) and shows reduced performance during extreme weather events, with RMSE increases of 15-20% under such conditions. These results suggest significant potential for improving energy management strategies in green buildings, contributing to enhanced energy efficiency and reduced carbon emissions in the construction sector. The framework is applicable to green buildings with reliable sensor infrastructure and adequate historical data, with performance optimized for standard operational conditions.
Genome-wide identification and functional characterization of magnesium transporter (MGT) gene family in soybean (Glycine max L.) and their expression profiles in response to aphid infestation, dehydration, and salt stresses
The divalent cation, Magnesium (Mg2+), is an essential mineral element for plant growth and development. Magnesium transporter (MGT) plays a vital role in maintaining Mg2 + homeostasis within plant cells. Although extensive research has been conducted in several crop species, no comprehensive study has yet been carried out on the MGT gene family in soybean (Glycine max L.), an economically valuable oil crop species. In this study, a total of 29 MGT genes encoding proteins (GmMGT) were identified in the soybean genome through comprehensive bioinformatics analysis. The GmMGT proteins were subsequently categorized into MRS2, CorA, and NIPA groups, with the majority predicted to be localized to the plasma membrane. Analyses of gene structures, conserved domains, and motifs indicated strong structural and functional similarities across the subgroups. Gene duplication, selection pressure, and synteny analyses demonstrated that GmMGT genes had undergone purifying selection, with only 12 segmentally duplicated gene pairs being identified. Gene ontology (GO) analysis revealed the involvement of all GmMGT genes in organism localization and ion transportation. Cis-acting regulatory element (CAREs) analysis identified 53 CAREs involved in light responsiveness, tissue-specific, phytohormone, and stress responses. Notably, nine major CAREs were abundantly found in the promoter regions of GLYMA.06G159100 and GLYMA.10G180200. Through the promoter analysis, we identified 81 miRNAs and 29 transcription factor families (TFFs), overrepresented under different stress conditions. RNA-seq data from 14 different soybean tissues demonstrated higher expression in flower tissue and lower levels in nodules with GLYMA.05G168200, GLYMA.10G180200, GLYMA.12G030100, GLYMA.12G168000, GLYMA.16G003900, GLYMA.20G210300 exhibiting elevated expression patterns. Transcriptomic analysis further revealed that, 10 GmMGTs were upregulated under biotic stress at 4h, 8h, 24h, and 48h after aphid infestation, with GLYMA.02G285600 and GLYMA.13G368400 being the most upregulated genes. Under abiotic stresses, GLYMA.03G159400, GLYMA.05G196600, and GLYMA.15G125900 were upregulated in response to dehydration, while GLYMA.04G005200, GLYMA.08G126600, GLYMA.10G180200 were induced at 1h, 6h, and 12h under salinity stress. These findings shed light on the versatile roles of GmMGT genes in plant growth, development and stress response, and they may serve as a valuable resource for further functional characterization of GmMGT genes within the soybean genome.
Acoustic generation of orbital currents
Podoplanin expression is associated with local inflammation and survival in glioma
Lifestyle and psychosocial factors in inflammatory bowel disease: Prevalence, impact, motivation, and support needs
Background and aim Lifestyle and psychosocial factors impact mucosal inflammation and well-being of Inflammatory Bowel Disease (IBD) patients. However, lifestyle assessment and interventions are not standard care. The aim of this study was to estimate the occurrence of and gather patients’ perspectives on unfavorable lifestyle and psychosocial factors in individuals with IBD. Methods A multicenter study was conducted, enrolling IBD patients using a telemedicine platform that reports on disease activity, lifestyle, and psychosocial factors. Patients' perspectives were gathered through a nationwide online survey distributed by the Dutch IBD patient organization. Results In the telemedicine cohort (n = 460), 16.3% followed a specific diet, and 50.7% believed diet impacted their disease or quality of life. Additionally, 67.4% did not meet exercise norms, 9.3% smoked, and 8.0% had excessive alcohol consumption (>7 units/week). About one-third experienced high stress, poor sleep regularly, and emotional distress occasionally. In the nationwide survey (n = 1126), most patients (58–91%) believed that stress, unhealthy diet, poor sleep, physical inactivity, and anxiety or depression could cause intestinal symptoms. Around 70% were motivated to change diet, stress management, and physical activity. Less than one-fifth of patients received hospital support, with the majority being satisfied. Approximately 20% of patients desired but lacked support concerning stress, physical activity, diet, and sleep. Conclusions Patients with IBD commonly report unfavorable lifestyle and psychosocial factors, recognize their impact on intestinal symptoms, and are motivated to change, but often lack hospital support. This underscores the importance for systematic incorporation of lifestyle and psychosocial factors into patient-centered IBD care and the potential for targeted interventions.
Author Correction: Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale
IQUP identifies quantitatively unreliable spectra with machine learning for isobaric labeling-based proteomics
Correction: Cohort profile: A prospective cohort study on newlywed couples in rural and poor urban Bangladesh
Characterization of PROTAC specificity and endogenous protein interactomes using ProtacID
Prevalence and determinants of caesarean sections among women in selected health facilities in Njombe region, Tanzania
Arab2Vec: An Arabic word embedding model for use in Twitter NLP applications
The analysis of Arabic Twitter data sets is a highly active research topic, particularly since the outbreak of COVID-19 and subsequent attempts to understand public sentiment related to the pandemic. This activity is partially driven by the high number of Arabic Twitter users, around 164 million. Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, Arab2Vec, that can be used in Twitter-based natural language processing (NLP) applications. Arab2Vec was constructed using a vast data set of approximately 186,000,000 tweets from 2008 to 2021 from all Arabic Twitter sources. This makes Arab2Vec the most up-to-date word embedding model researchers can use for Twitter-based applications. The model is compared with existing models from the literature. The reported results demonstrate superior performance regarding the number of recognised words and F1 score for classification tasks with known data sets and the ability to work with emojis. We also incorporate skip-grams with negative sampling, an approach that other Arabic models haven’t previously used. Nine versions of Arab2Vec are produced; these models differ regarding available features, the number of words trained on, speed, etc. This paper provides Arab2Vec as an open-source project for users to employ in research. It describes the data collection methods, the data pre-processing and cleaning step, the effort to build these nine models, and experiments to validate them qualitatively and quantitatively.