Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

Poly[diammonium [(μ4-butane-1,2,3,4-tetracarboxylato)zincate] tetrahydrate]

In the title compound, {(NH4)2[Zn(C8H6O8)]·4H2O}n, the asymmetric unit contains one ammonium cation, half of a butane-1,2,3,4-tetracarboxylate anion, one Zn2+ cation and two water molecules. The butane-1,2,3,4-tetracarboxylate ligand is located about an inversion centre at the mid-point of the central C—C bond. The Zn2+ cation is situated on a twofold rotation axis and is surrounded by four O atoms from four symmetry-related butane-1,2,3,4-tetracarboxylate anions in a distorted tetrahedral environment. In turn, each anion coordinates to four Zn2+ cations. The bridging mode of the anions leads to a three-dimensional framework structure with channels extending along [110] and [010] in which the ammonium cations and the water molecules are located. N—H...O and O—H...O hydrogen bonding between the cations and water molecules and the uncoordinating O atoms of the carboxylate groups consolidates the crystal packing

A research of heat transfer enhancement of rectangular channel with dimples

this thesis is aimed to do numerical simulations on rectangular channel with a new model of dimple in different condition of channel height, dimple depth, dimple distance by using CFD program and RNG k-ε model. It is concluded that, the existence of dimple has changed the velocity and direction of the fluid, as well as the original flow status. Dimple increased the heat exchange area of rectangular channel and broke the boundary layer, enhanced the flow rate of fluid, as a result of enhanced heat transfer performance

Epidermal resident γδ T cell development and function in skin

Epidermal resident γδ T cells, or dendritic epidermal T cells (DETCs) in mice, are a unique and conserved population of γδ T cells enriched in the epidermis, where they serve as the regulators of immune responses and sense skin injury. Despite the great advances in the understanding of the development, homeostasis, and function of DETCs in the past decades, the origin and the underlying molecular mechanisms remain elusive. Here, we reviewed the recent research progress on DETCs, including their origin and homeostasis in the skin, especially at transcriptional and epigenetic levels, and discuss the involvement of DETCs in skin diseases

The fecal microbiota composition of boar Duroc, Yorkshire, Landrace and Hampshire pigs

Objective To investigate the effect of host genetics on gut microbial diversity, we performed a structural survey of the fecal microbiota of four purebred boar pig lines: Duroc, Landrace, Hampshire, and Yorkshire. Methods The V3–V4 regions of the 16S rRNA genes were amplified and sequenced. Results A total of 783 operational taxonomic units were shared by all breeds, whereas others were breed-specific. Firmicutes and Bacteroidetes dominated the majority of the fecal microbiota; Clostridia, Bacilli, and Bacteroidia were the major classes. Nine predominant genera were observed in all breeds and eight of them can produce short-chain fatty acids. Some bacteria can secrete cellulase to aid fiber digestion by the host. Butyric, isobutyric, valeric, and isovaleric acid levels were highest in Landrace pigs, whereas acetic and propionic acid were highest in the Hampshire breed. Heatmap was used to revealed breed-specific bacteria. Principal coordinate analysis of fecal bacteria revealed that the Landrace and Yorkshire breeds had high similarity and were clearly separated from the Duroc and Hampshire breeds. Conclusion Overall, this study is the first time to compare the fecal microbiomes of four breeds of boar pig by high-throughput sequencing and to use Spearman’s rank correlation to analyze competition and cooperation among the core bacteria

Genomic Identification and Expression Analysis of the Cathelicidin Gene Family of the Forest Musk Deer

The forest musk deer (Moschus berezovskii) is a small-sized artiodactyl species famous for the musk secreted by adult males. In the captive population, this species is under the threat of infection diseases, which greatly limits the increase of individual numbers. In the present study, we computationally analyzed the repertoire of the cathelicidin (CATHL) family from the genome of forest musk deer and investigated their expression pattern by real-time PCR. Our results showed that the entire genome of forest musk deer encodes eight cathelicidins, including six functional genes and two pseudogenes. Phylogenetic analyses further revealed that all forest musk deer cathelicidin members have emerged before the split of the forest musk deer and cattle and that forest musk deer CATHL3L2 and CATHL9 are orthologous with two cattle pseudogenes. In addition, the gene expression results showed that the six functional genes are not only abundantly expressed in the spleen and lung, but are also differently expressed in response to abscesses, which suggests that forest musk deer cathelicidins may be involved in infections. Taken together, identification and characterization of the forest musk deer cathelicidins provide fundamental data for further investigating their evolutionary process and biological functions