Browse Articles

RETRACTED ARTICLE: Identification of novel IL17-related genes as prognostic and therapeutic biomarkers of psoriasis using comprehensive bioinformatics analysis and machine learning

Lethal toxicity of metformin on zebrafish during early embryonic development by multi-omics analysis

Cellular landscape of avian intestinal organoids revealed by single cell transcriptomics

Abstract Studies of the avian gastrointestinal tract, where nutrient absorption and key host-pathogen interactions occur, have been strongly enabled by the development of intestinal organoid models. Here we report a single cell transcriptomic atlas of intestinal organoid cells derived from embryos of broiler and layer chickens, capturing mesenchymal, epithelial, endothelial, immune and neuronal cell lineages. Eight inferred mesenchymal subpopulations reflect anatomically distinct intestinal layers, including fibroblasts, telocytes, myofibroblasts, smooth myocytes, pericytes, and interstitial cells of Cajal. Identified heterogeneity within the epithelial lineage included enterocytes, goblet cells, Paneth cells, tuft cells, and diverse enteroendocrine cell subtypes. Additionally, we identified candidate macrophages, monocytes, γδ T cells, NK cells and granulocytes. Layer and broiler organoids showed significant differences in cell-specific transcriptome, most pronounced in epithelial cells, pointing to divergent selection on intestinal physiology. Our analysis finally provides a catalogue of novel cell marker genes to enable future research of chicken intestinal organoids.

Association between the atherogenic index of plasma and the systemic immuno-inflammatory index using NHANES data from 2005 to 2018

Trends in acute glomerulonephritis mortality among older adults from 1992 to 2021

Human type I interferons protect Vero E6 and ARPE-19 cells against West Nile virus and are neutralized by pathogenic autoantibodies

Aerodynamic performance modeling method of high-altitude propellers across the entire flight envelope

Prediction of acute skin toxicity in tomotherapy of breast cancer using skin DVH data

The impact of ingestion of Bifidobacterium longum NCC3001 on perinatal anxiety and depressive symptoms: a randomized controlled trial

Windblown dust in the Tarim basin, Northwest China

Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques

Abstract Ventilator-associated pneumonia significantly increases morbidity, mortality, and healthcare costs among patients with traumatic brain injury. Accurately predicting risk can facilitate earlier interventions and improve patient outcomes. This study leveraged the MIMIC III database, identifying traumatic brain injury cases through standardized clinical criteria. A rigorous data preprocessing workflow included missing value imputation, correlation checks, and expert-driven feature selection, reducing an initial set of features to a subset of critical predictors encompassing demographics, comorbidities, laboratory values, and clinical interventions. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied within a five-fold cross-validation framework, ensuring a balanced training set while maintaining an unbiased validation process. Six machine learning models, including Support Vector Machine, Logistic Regression, Random Forest, XGBoost, Artificial Neural Network, and AdaBoost, were trained using extensive hyperparameter tuning. Comprehensive evaluations were conducted based on multiple metrics, including Area Under the Curve (AUC), accuracy, F1 score, sensitivity, specificity, Positive Predictive Value, and Negative Predictive Value. XGBoost emerged as the top performing algorithm, achieving an AUC of 0.94 and an accuracy of 0.875 on the test set, marking substantial improvements over previously reported best results. An ablation study validated the necessity of each retained feature, indicating that any feature removal led to a decline in model performance. Furthermore, SHAP analysis underscored ICU length of stay, hospital length of stay, serum potassium, and blood urea nitrogen as key contributors to ventilator associated pneumonia risk. Overall, the results demonstrate that advanced ensemble learning, meticulous feature selection, and effective class imbalance handling can significantly enhance early detection in traumatic brain injury cases. These findings have meaningful clinical implications, offering a framework for more timely interventions, optimized resource allocation, and improved patient care in critical settings.

Oxygen level alters energy metabolism in bovine preimplantation embryos

Abstract Mammalian preimplantation embryo development is a complex sequence of events. This period of development is sensitive to oxygen (O 2 ) levels that can affect various cellular processes. We compared the influence of O 2 tension by culturing embryos either in normoxic (20% O 2 ) or physiological hypoxic (6% O 2 ) conditions, or sequential low O 2 concentration starting with 6% O 2 until 16-cell stage and then switching to ultrahypoxic conditions (2% O 2 ). Due to ethical concerns, we used bovine as an animal model with a good similarity of embryogenesis to human. We found that the cleavage rate was not affected by O 2 levels but there was a clear difference in blastocyst formation rate. In hypoxia, 36% of embryos reached blastocyst stage while in normoxia only 13%. In ultrahypoxia conditions only 4.6% of embryos developed up to blastocyst stage. Transcriptomic profiles showed that normoxic conditions slowed down oocyte transcript degradation which is a prerequisite for reprogramming of the embryonic cell lineages. There were also clear differences in the expression of key metabolic enzymes between hypoxic and normoxic conditions at the blastocyst stage. Both hypoxic and ultrahypoxic conditions seemed to induce appropriate energy production by upregulating genes involved in glycolysis and lipid metabolism typical to in vivo embryos. In contrast, normoxic conditions failed to upregulate glycolysis genes and only depended on oxidative phosphorylation metabolism. We conclude that constant hypoxia culture of in vitro embryos provided the highest blastocyst formation rate and appropriate energy metabolism. Normoxia altered the energy metabolism and decreased the blastocyst formation rate. Even though ultrahypoxia at blastocyst stage resulted in the lowest blastocyst formation, the transcriptional profile of surviving embryos was normal.

Optimizing the multi-model ensemble of CMIP6 GCMs for climate simulation over Bangladesh

Central retinal artery catheterization for retinal artery occlusion with balanced salt solution

Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children

Plasticulture detection at the country scale by combining multispectral and SAR satellite data

Abstract The use of plastic films has been growing in agriculture, benefiting consumers and producers. However, concerns have been raised about the environmental impact of plastic film use, with mulching films posing a greater threat than greenhouse films. This calls for large-scale monitoring of different plastic film uses. We used cloud computing, freely available optical and radar satellite images, and machine learning to map plastic-mulched farmland (PMF) and plastic cover above vegetation (PCV) (e.g., greenhouse, tunnel) across Germany. The algorithm detected 103 103 ha of PMF and 37 103 ha of PCV in 2020, while a combination of agricultural statistics and surveys estimated a smaller plasticulture cover of around 100 103 ha in 2019. Based on ground observations, the overall accuracy of the classification is 85.3%. Optical and radar features had similar importance scores, and a distinct backscatter of PCV was related to metal frames underneath the plastic films. Overall, the algorithm achieved great results in the distinction between PCV and PMF. This study maps different plastic film uses at a country scale for the first time and sheds light on the high potential of freely available satellite data for continental monitoring.

Orbital angular momentum detection of vortex beams by #-type lines

Comparative study on bivariate statistical characteristics of drought in Shandong using SPI and SPEI

Analysis of combining ability and heterosis based on controlled pollination populations of eucalypt

A fiber optic approach for cement placement and hydration assessment of deep geothermal boreholes

Abstract Achieving well integrity is mandatory for a geothermal well’s safe and sustainable operation. One of the most critical steps is the success of the primary cementing. Conventional monitoring only shows discrete snapshots after completion of the cement job. However, optical fiber sensors enable monitoring of the entire cementing process. Here, we investigate the cement placement and early hydration for a surface casing at a geothermal site in Munich, Germany. We show that distributed dynamic strain rate sensing (DDSS or DAS) allows for tracking rising fluid interfaces, determining the setting time of cement, and assessing the cement job’s success at each depth. We used DDSS and DTS (distributed temperature sensing) with a fiber optic cable permanently deployed behind the casing and combined the results with operational data, a model for the rise of fluids in the borehole, and laboratory experiments to estimate the cement setting phase. Our approach enables monitoring all phases of primary cementing, which can increase the success rate of achieving well integrity. Furthermore, it can reduce costs and improve society’s acceptance of deep geothermal wells in urban areas.