Browse Articles
Discover research articles across all indexed journals
Investigation and enhancement of stress-dependent compliance characteristics in deep in-situ stress measurements based on anelastic strain recovery (ASR) method
Single-cell transcriptomic profiling of C. elegans Q neuroblast lineage during migration and differentiation
Proper migration and differentiation of neuroblasts into neurons are essential for the development of a healthy nervous system. In this context, the asymmetrical migration of Caenorhabditis elegans Q neuroblasts provides a powerful model for studying the genetic aspects of neuronal migration in vivo at single-cell resolution. We isolated Q lineage cells at various stages of development using FACS and employed single-cell RNA sequencing to investigate the molecular mechanisms underlying the migration and differentiation of these neuroblasts. We created a robust transcriptomic differentiation map of the Q neuroblast lineage and used established markers to identify each cell in the lineage. Our results revealed novel genes not previously described in these cells and linked the expression of known genes to specific stages of Q lineage progression. Furthermore, functional enrichment and imaging provided evidence that the parent Q cells are initially specified with an epithelial-like identity and undergo epithelial-mesenchymal transition during the early stages of migration. We also identified novel Wnt-related expression, including left-right asymmetric expression of cwn-1 and cwn-2 , and the involvement of the Wnt/β-catenin asymmetry pathway in the Q lineage. Our work offers a high-resolution view of neuroblast development, showcasing the power of single-cell transcriptomics to reveal stage-specific regulatory programs.
Deep learning–based basilar artery wall and lumen segmentation from 1-mm MR vessel wall imaging
Predicting short-term mortality in severe cirrhosis: An interpretable machine learning model integrating routine clinical indicators
Background The critical need for precise risk stratification in severe liver cirrhosis is underscored by its substantial 30-day mortality rates, demanding reliable tools to guide clinical interventions. Objective To establish a machine learning-driven prognostic model for short-term mortality prediction in decompensated cirrhosis through comprehensive analysis of critical care data. Methods This retrospective cohort study analyzed 1,044 carefully curated cases from the MIMIC-IV database, randomly divided into training (n = 740) and validation (n = 304) sets. We developed a machine learning model incorporating multidimensional clinical parameters, with rigorous evaluation and internal validation. Short-term survival was analyzed via bootstrap-validated Cox proportional hazards regression. Prognostic heterogeneity across international normalized ratio (INR)-based strata was examined. Results The final prediction model incorporated eight significant predictors: age (OR 1.051, 95% CI 1.033–1.070), INR (OR 1.423, 95%CI 1.231–1.644), creatinine (OR 1.171, 95%CI 1.071–1.208), platelets (OR 0.995, 95%CI 0.993–0.997), white blood cell (OR 1.116, 95%CI 1.078–1.155), total bilirubin (OR 1.027, 95%CI 1.002–1.052), peptic ulcer (OR 0.336, 95%CI 0.134–0.845), and Aspartate Aminotransferase/Alanine Aminotransferase (AST/ALT) (OR 1.508, 95%CI 1.294–1.757). The model demonstrated excellent discrimination with an AUC of 0.846 in the training cohort. Cox regression analysis confirmed these findings and identified additional associations with aspartate aminotransferase and red blood cell levels. Furthermore, the indicators within the model provide accurate predictions for the clinical outcomes of patients suffering from severe cirrhosis. Subgroup analysis revealed significant mortality variations across different INR ranges ( P < 0.001). Conclusions Our prediction model identifies high-risk cirrhotic patients and highlights critical prognostic factors, offering clinicians a valuable tool for risk stratification and timely intervention. The strong correlation between laboratory markers, complications, and outcomes underscores the importance of close monitoring in this population. However, our model is an initial step, effective within the ICU but requiring external, multi-center studies to broaden its clinical applicability, which is a clear priority for our future work.
The diagnostic value of CDC20 for malignant pleural effusion of lung adenocarcinoma
Earliest millet cultivation reflects steppe connections, dietary flexibility, and resilience in Bronze Age northern Greece
This paper explores early broomcorn millet (hereafter millet) cultivation in Greece during the Bronze Age. The primary archaeobotanical data for this study derive from the site of Skala Sotiros on the island of Thasos in northern Greece. The site provides unique insights into localized Bronze Age agricultural practices, revealing both divergence from southern Greece agricultural systems and potential influences from exchange networks that linked northern Greece to the southern Balkans and the Pontic steppe–Black Sea region. Systematic sampling of the Bronze Age layers at Skala Sotiros has yielded a diverse assemblage with a notable abundance of millet ( Panicum miliaceum ), a crop almost absent from contemporary southern Greece. Recent radiocarbon dates on millet grains from Skala Sotiros contribute new evidence toward understanding the routes through which millet could have been introduced into the region during the Bronze Age. This study explores the interplay of environmental and cultural factors in the dispersal of millet in Greece, considering environmental stress, cultural dynamics, population movements, and interaction networks. The extensive review of archaeobotanical data across Greece demonstrates how the cultivation of millet may have served as a culinary identity signifier, providing further evidence of differences between northern and southern Greece.
Long-COVID: assessment of circulating markers suggests no cerebral neuronal damage, neuroinflammation or systemic inflammation–a controlled study
Abstract Long-COVID remains incompletely understood, particularly regarding the roles of peripheral systemic inflammation and neuroinflammation. The persistence and extent of these processes remain debated. We conducted a single-center, age- and sex-matched case–control study at Stavanger University Hospital, Norway, recruiting participants from the general population. Forty-eight long-COVID patients and 48 recovered controls were included at a median of 69 weeks post-SARS-CoV-2 infection. Exclusion criteria included autoimmune or chronic inflammatory diseases, cancer, and other conditions affecting fatigue. Plasma levels of neurofilament light (NfL), glial fibrillary acidic protein (GFAP), triggering receptor expressed on myeloid cells 2 (TREM2), C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) were measured using ultrasensitive NULISA™ technology. CRP, TNF-α, and IL-6 were additionally assessed by a standard hospital laboratory method (CRP) and MSD S-Plex chemiluminescence immunoassay (TNF-α and IL-6 MSD). No significant differences in NfL or GFAP were observed between groups, suggesting no ongoing neuronal injury or neuroinflammation. Routine immunoassays showed no differences for inflammatory markers. In unadjusted analyses using ultrasensitive assays, long-COVID patients showed nominally elevated levels of CRP ( p = 0.04), TNF-α ( p = 0.01), IL-6 ( p = 0.02), and TREM2 ( p = 0.02). However, these differences did not survive correction for multiple comparisons (all false discovery rate-adjusted p > 0.05). The absence of neuroinflammation markers is consistent with the hypothesis that persistent long-COVID symptoms are unlikely due to ongoing neuronal injury or central nervous system inflammation. Alternatively, persisting long-COVID symptoms may reflect a chronic, extremely low-level immune activation, that contributes to fatigue, pain, and other sickness phenomena through mechanisms such as pro-inflammatory signaling in the brain, or epigenetic mechanisms underlying the sickness behavior response. These findings should be considered preliminary and warrant validation in larger, longitudinal cohorts.
A filtering-enhanced MIMO antenna architecture for next-generation multi-user satellite communication
Slope stability prediction via TrAdaBoost transfer learning: integrating physics and data into a double-driven framework
My relationship with my PhD supervisor has become toxic — what do I do?
Eye movement dynamics are a key factor for intra-saccadic motion perception
Evaluation of photobioreactor designs for potential application as microalgal façade systems
Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis
Data-driven machine learning modelling in wire EDM of TiNiCo shape memory alloy
Nano-enabled plant fortification: green-synthesized SiO2 and emamectin benzoate nanoparticles synergistically boost maize defense and agronomic performance against Spodoptera frugiperda infestation
Abstract Zea mays L., a globally vital C₄ cereal, is increasingly threatened by fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith), a destructive and insecticide-resistant pest. This study developed a nano-enabled strategy integrating green-synthesized SiO 2 nanoparticles (GS-SiNPs) for plant fortification with nano-formulated emamectin benzoate (EMB-NPs) for enhanced insecticidal activity. Laboratory bioassays on 4 th -instar FAW larvae evaluated acute toxicity (LC 50 and LC 90 ) and detoxification enzyme activity. A field experiment in Egypt, autumn 2024 used a randomized complete block design to test two foliar sprays on maize in ten treatments with four replicates. Larval counts, leaf damage, anatomy, photosynthesis, leaf area (LA) plant −1 , Si content, and yield were assessed. Laboratorially, LC 90 (ppm) values were 93.6 (EMB-NPs), and 122.7 (EMB bulk), with GS-SiNPs exhibiting the steepest (5.18). GS-SiNPs with EMB bulk or EMB-NPs exhibited LC 50 values of 102.0 and 71.8 ppm, respectively, indicating a synergistic effect of both mixtures. EMB bulk + GS-SiNPs and EMB-NPs + GS-SiNPs suppressed larval detoxification enzymes. Field results revealed 100% initial larval mortality. The ½EMB-NPs + GS-SiNPs reduced leaf damage by 64.2% after the 1 st spray, while ¾EMB-NPs + GS-SiNPs achieved 86.4% after the 2 nd spray. This treatment also induced significant anatomical modification, increasing blade, midvein, and vascular bundle thickness. It enhanced photosynthesis, leaf Si, and LA plant −1 , and boosted yield by 54.5% vis-à-vis control. Combining GS-SiNPs with EMB-NPs, particularly ¾EMB-NPs + GS-SiNPs, enhanced EMB bioefficacy and suppressed FAW detoxification while improving maize’s physio-anatomical resilience. This nano-enabled sustainable strategy offers a dose-efficient and eco-friendly approach for FAW management and maize productivity.