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Sarcoidosis-Related Cardiomyopathy: Current Knowledge, Difficulties, and Potential Views State-of-the-Art Assessment.
BACKGROUND Previous studies have shown that macrophage migration inhibitory factor (MIF) is involved in the pathogenesis of asthma. This study aimed to investigate whether serum MIF reflects a therapeutic response in allergic asthma. METHODS We enrolled 30 asthmatic patients with mild-to-moderate exacerbations and 20 healthy controls, analyzing the parameter levels of serum MIF, serum total immunoglobulin E (tIgE), peripheral blood eosinophil percentage (EOS%), and fractional exhaled nitric oxide (FeNO). Lung function indices were used to identify disease severity and therapeutic response. RESULTS Our study showed that all measured parameters in patients were at higher levels than those of controls. After one week of treatment, most parameter levels decreased significantly except for serum tIgE. Furthermore, we found that serum MIF positively correlated with EOS% as well as FeNO, but negatively correlated with lung function indices. Receiver operator characteristic (ROC) curve analysis indicated that among the parameters, serum MIF exhibited a higher capacity to evaluate therapeutic response. The area under the curve (AUC) of MIF was 0.931, with a sensitivity of 0.967 and a specificity of 0.800. CONCLUSIONS Our results suggested that serum MIF may serve as a potential biomarker for evaluating therapeutic response in allergic asthma with mild-to-moderate exacerbations.The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis (ARN). The potential application of artificial intelligence (AI) algorithms in these areas of clinical research has not been reported previously. The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs. A total of 149 wide-angle fundus photographs from 40 eyes of 32 ARN patients were collected, and the U-Net method was used to construct the AI algorithm. Thereby, a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time. This algorithm had an area under the receiver operating curve of 0.92, with 86% sensitivity and 88% specificity in the detection of retinal necrosis. For the purpose of retinal necrosis evaluation, necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples (R2=0.7444, P less then 0.0001) and therapeutic response of ARN (R2=0.999, P less then 0.0001). Therefore, our AI algorithm has a potential application in the clinical aided diagnosis of ARN, evaluation of ARN severity, and treatment response monitoring.Dysregulated crosstalk between different signaling pathways contributes to tumor development, including resistance to cancer therapy. In the present study, we found that the mitogen-activated extracellular signal-regulated kinase (MEK) inhibitor trametinib failed to suppress the proliferation of PANC-1 and MGC803 cells by activating the Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) signaling pathway, while the JAK2 inhibitor fedratinib failed to inhibit the growth of the PANC-1 cells upon stimulation of extracellular signal-regulated kinase (ERK) signaling. In particular, the most prominent enhancement of the anti-proliferative effect resulted from the concurrent blockage of the JAK2/STAT3 and ERK signaling pathways. Furthermore, the combination of the two inhibitors resulted in a reduced tumor burden in mice. Our evidence suggests novel crosstalk between JAK2/STAT3 and ERK signaling in gastric cancer (GC) and pancreatic ductal adenocarcinoma (PDAC) cells and provides a therapeutic strategy to overcome potential resistance in gastrointestinal cancer.Starch is the predominant compound in bulb scales, and previous studies have shown that bulblet development is closely associated with starch enrichment. However, how starch synthesis affects bulbification at the molecular level is unclear. In this study, we demonstrate that Lilium brownii var. giganteum, a wild lily with a giant bulb in nature, and L. brownii, the native species, have different starch levels and characteristics according to cytological and ultra-structural observations. We cloned the complete sequence of three key gene-encoding enzymes (LbgAGPS, LbgGBSS, andLbgSSIII) during starch synthesis by rapid amplification of 5' and 3' complementary DNA (cDNA) ends (RACE) technology. Bioinformatics analysis revealed that the proteins deduced by these genes contain the canonical conserved domains. Constructed phylogenetic trees confirmed the evolutionary relationships with proteins from other species, including monocotyledons and dicotyledons. The transcript levels of various tissues and time course samples obtained during bulblet development uncovered relatively high expression levels in bulblets and gradual increase expression accompanying bulblet growth. Moreover, a set of single nucleotide polymorphisms (SNPs) was discovered in the AGPS genes of four lily genotypes, and a purifying selection fashion was predicted according to the non-synonymous/synonymous (Ka/Ks) values. Taken together, our results suggested that key starch-synthesizing genes might play important roles in bulblet development and lead to distinctive phenotypes in bulblet size.To overcome the computational burden of processing three-dimensional (3D) medical scans and the lack of spatial information in two-dimensional (2D) medical scans, a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2D convolutional neural networks (2D-CNNs). In order to combine the low-level features and high-level features, we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process. Further, in order to resolve the problems of the blurred boundary of the glioma edema area, we superimposed and fused the T2-weighted fluid-attenuated inversion recovery (FLAIR) modal image and the T2-weighted (T2) modal image to enhance the edema section. For the loss function of network training, we improved the cross-entropy loss function to effectively avoid network over-fitting. On the Multimodal Brain Tumor Image Segmentation Challenge (BraTS) datasets, our method achieves dice similarity coefficient values of 0.84, 0.82, and 0.83 on the BraTS2018 training; 0.82, 0.85, and 0.83 on the BraTS2018 validation; and 0.81, 0.78, and 0.83 on the BraTS2013 testing in terms of whole tumors, tumor cores, and enhancing cores, respectively. Experimental results showed that the proposed method achieved promising accuracy and fast processing, demonstrating good potential for clinical medicine.In this study, the fibers of invasive species Agave americana L. and Ricinus communis L. were successfully used for the first time as new sources to produce cytocompatible and highly crystalline cellulose nanofibers. Cellulose nanofibers were obtained by two methods, based on either alkaline or acid hydrolysis. The morphology, chemical composition, and crystallinity of the obtained materials were characterized by scanning electron microscopy (SEM) together with energy-dispersive X-ray spectroscopy (EDX), dynamic light scattering (DLS), X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy. The crystallinity indexes (CIs) of the cellulose nanofibers extracted from A. americana and R. communis were very high (94.1% and 92.7%, respectively). read more Biological studies evaluating the cytotoxic effects of the prepared cellulose nanofibers on human embryonic kidney 293T (HEK293T) cells were also performed. The nanofibers obtained using the two different extraction methods were all shown to be cytocompatible in the concentration range assayed (i.e., 0‒500 µg/mL). Our results showed that the nanocellulose extracted from A. americana and R. communis fibers has high potential as a new renewable green source of highly crystalline cellulose-based cytocompatible nanomaterials for biomedical applications.Jujube (Ziziphus jujuba Mill.), a highly nutritious and functional fruit, is reported to have various health benefits and has been extensively planted worldwide, especially in China. Many studies have shown that bioactive components derived from jujube fruit have significant nutritional and potential biological effects. In this paper, the latest progress in research on major bioactive compounds obtained from jujube is reviewed, and the potential biological functions of jujube fruit resources are discussed. As a dietary supplement, jujube fruit is well recognized as a healthy food which contains a variety of bioactive substances, such as polysaccharides, polyphenols, amino acids, nucleotides, fatty acids, dietary fiber, alkaloids, and other nutrients. These nutrients and non-nutritive phytochemicals obtained from jujube fruit have physiological functions including anticancer, antioxidant, anti-inflammatory, anti-hyperlipidemic, anti-hyperglycemic, immunoregulatory, neuroprotective, sedative, and antiviral functions. Of note is that new constituents, including alkaloids, dietary fiber, and other bioactive substances, as well as the antiviral, hypoglycemic, lipid-lowering, and neuroprotective effects of jujube fruit, are systematically reviewed here for the first time. Meanwhile, problems affecting the exploitation of jujube fruit resources are discussed and further research directions proposed. Therefore, this review provides a useful bibliography for the future development of jujube-based products and the utilization of jujube nutritional components in functional foods.
The programmed death ligand 1 (PD-L1) SP142 assay with a 1% immune cell (IC) cutoff is approved for the selection of advanced triple-negative breast cancer (TNBC) patients for atezolizumab treatment. We aimed to evaluate the interobserver concordance of PD-L1 scoring and inter-assay variability of various PD-L1 assays in TNBC.

Thirty patients with primary TNBC were selected, and SP142, SP263, 22C3, and E1L3N assays were performed. PD-L1 staining in ICs and tumor cells (TCs) was scored by 10 pathologists who were blinded to the assay. The interobserver concordance among pathologists and the inter-assay variability of the four PD-L1 assays were analyzed. For SP142, the intraobserver concordance among the six pathologists was analyzed after training.

The adjusted means of PD-L1 IC scoring ranged from 6.2% to 12.9% for the four assays; the intraclass correlations showed moderate (0.584-0.649) reader concordance. The PD-L1 IC scoring with a 1% cutoff resulted in identical scoring in 40.0%-66.7% of cases and a poor to moderate agreement (Fleiss κ statistic [FKS] = 0.345-0.534) for the four assays. The SP142 assay had the widest range of positive rate (56.5%-100.0%), lowest number of cases with identical scoring, and lowest FKS at 1% cutoff. Pairwise comparison of adjusted means showed significantly decreased PD-L1 staining in SP142 compared with the other assays in both ICs and TCs. As for the intraobserver concordance in the SP142 assay, the overall percent agreement was 87.8% with a 1% IC cutoff. After training, the proportion of cases with identical scoring at a 1% IC cutoff increased to 70.0%; the FKS also increased to 0.610.

The concordance of PD-L1 IC scoring among pathologists was low, at the 1% cutoff for the SP142 assay without training. SP142 showed the lowest PD-L1 expression in both IC and TC.
The concordance of PD-L1 IC scoring among pathologists was low, at the 1% cutoff for the SP142 assay without training. SP142 showed the lowest PD-L1 expression in both IC and TC.
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