Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI.

This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 to May 2022 were retrospectively enrolled from four medical centers. A cfVB-Net network was used to automatically segment PitNET multiparameter MRI. Radiomics features were extracted from the MRI, and the radiomics score (Radscore) of each patient was calculated. To predict histological subtypes, the Gaussian process (GP) machine learning classifier based on radiomics features was performed. Multi-classification (six-class histological subtype) and binary classification (PRL vs. non-PRL) GP model was constructed. Then, a clinical-radiomics nomogram combining clinical factors and Radscores was constructed using the multivariate logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic (ROC) curves. The PitNET auto-segmentation model eventually achieved the mean Dice similarity coefficient of 0.888 in 1206 patients (mean age 49.3 ± SD years, 52% female). In the multi-classification model, the GP of T2WI got the best area under the ROC curve (AUC), with 0.791, 0.801, and 0.711 in the training, validation, and external testing set, respectively. In the binary classification model, the GP of T2WI combined with CE T1WI demonstrated good performance, with AUC of 0.936, 0.882, and 0.791 in training, validation, and external testing sets, respectively. In the clinical-radiomics nomogram, Radscores and Hardy' grade were identified as predictors for PRL expression. Machine learning and radiomics analysis based on multiparameter MRI exhibited high efficiency and clinical application value in predicting the PitNET histological subtypes.

Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS: In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). RESULTS: The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. CONCLUSION: The DLR model is an effective method for identifying the primary source of liver metastases.

Toll-interacting protein is activated by the transcription factor GATA1 and Sp1 to negatively regulate NF-κB and MAPK pathways in the Japanese eel (Anguilla japonica).

Toll-interacting protein (Tollip) serves as a crucial inhibitory factor in the modulation of Toll-like receptor (TLR)-mediated innate immunological responses. The structure and function of Tollip have been well documented in mammals, yet the information in teleost remained limited. This work employed in vitro overexpression and RNA interference in vivo and in vitro to comprehensively examine the regulatory effects of AjTollip on NF-κB and MAPK signaling pathways. The levels of p65, c-Fos, c-Jun, IL-1, IL-6, and TNF-α were dramatically reduced following overexpression of AjTollip, whereas knocking down AjTollip in vivo and in vitro enhanced those genes' expression. Protein molecular docking simulations showed AjTollip interacts with AjTLR2, AjIRAK4a, and AjIRAK4b. A better understanding of the transcriptional regulation of AjTollip is crucial to elucidating the role of Tollip in fish antibacterial response. Herein, we cloned and characterized a 2.2 kb AjTollip gene promoter sequence. The transcription factors GATA1 and Sp1 were determined to be associated with the activation of AjTollip expression by using promoter truncation and targeted mutagenesis techniques. Collectively, our results indicate that AjTollip suppresses the NF-κB and MAPK signaling pathways, leading to the decreased expression of the downstream inflammatory factors, and GATA1 and Sp1 play a vital role in regulating AjTollip expression.

Gene inversion led to the emergence of brackish archaeal heterotrophs in the aftermath of the Cryogenian Snowball Earth.

Land-ocean interactions greatly impact the evolution of coastal life on earth. However, the ancient geological forces and genetic mechanisms that shaped evolutionary adaptations and allowed microorganisms to inhabit coastal brackish waters remain largely unexplored. In this study, we infer the evolutionary trajectory of the ubiquitous heterotrophic archaea Poseidoniales (Marine Group II archaea) presently occurring across global aquatic habitats. Our results show that their brackish subgroups had a single origination, dated to over 600 million years ago, through the inversion of the magnesium transport gene corA that conferred osmotic-stress tolerance. The subsequent loss and gain of corA were followed by genome-wide adjustment, characterized by a general two-step mode of selection in microbial speciation. The coastal family of Poseidoniales showed a rapid increase in the evolutionary rate during and in the aftermath of the Cryogenian Snowball Earth (∼700 million years ago), possibly in response to the enhanced phosphorus supply and the rise of algae. Our study highlights the close interplay between genetic changes and ecosystem evolution that boosted microbial diversification in the Neoproterozoic continental margins, where the Cambrian explosion of animals soon followed.

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

IL-36 antagonism blunts the proliferation and migration of oral squamous cell carcinoma cells.

IL-36 is known to mediate inflammation and fibrosis. Nevertheless, IL-36 signalling axis has also been implicated in cancer, although understanding of exact contribution of IL-36 to cancer progression is very limited, partly due to existence of multiple IL-36 ligands with agonistic and antagonistic function. Here we explored the role of IL-36 in oral squamous cell carcinoma (OSCC). Firstly, we analyzed expression of IL-36 ligands and receptor and found that the expression of IL-36γ was significantly higher in head and neck cancer (HNSCC) than that of normal tissues, and that the high expression of IL-36γ predicted poor clinical outcomes. Secondly, we investigated the direct effect of IL-36γ on OSCC cells and found that IL-36γ stimulated proliferation of OSCC cells with high expression of IL-36R expression. Interestingly, IL-36γ also promoted migration of OSCC cells with low to high IL-36R expression. Critically, both proliferation and migration of OSCC cells induced by IL-36γ were abrogated by anti-IL-36R mAb. Fittingly, RNA sequence analysis revealed that IL-36γ regulated genes involved in cell cycle and cell division. In summary, our results showed that IL-36γ can be a tumor-promoting factor, and targeting of IL-36R signalling may be a beneficial targeted therapy for patients with abnormal IL-36 signalling.

Preoperatively Predicting Ki67 Expression in Pituitary Adenomas Using Deep Segmentation Network and Radiomics Analysis Based on Multiparameter MRI.

RATIONALE AND OBJECTIVES: Ki67 proliferation index is associated with more aggressive tumor behavior and recurrence of pituitary adenomas (PAs). Recently, radiomics and deep learning have been introduced into the study of pituitary tumors. The present study aimed to investigate the feasibility of predicting the Ki67 proliferation index of PAs using the deep segmentation network and radiomics analysis based on multiparameter MRI. MATERIALS AND METHODS: First, the cfVB-Net autosegmentation model was trained; subsequently, its performance was evaluated in terms of the dice similarity coefficient (DSC). In the present study, 1214 patients were classified into the high Ki67 expression group (HG) and the low Ki67 expression group (LG). Analyses of three classification models based on radiomics features were performed to distinguish HG from LG. Clinical factors, imaging features, and Radscores were collectively used to create a nomogram in order to effectively predict Ki67 expression. RESULTS: The cfVB-Net segmentation model demonstrated good performance (DSC: 0.723-0.930). Overall, 18, 15, and 11 optimal features in contrast-enhanced (CE) T1WI, T1WI, and T2WI were obtained for differentiating between HG and LG, respectively. Notably, the best results were presented in the bagging decision tree when CE T1WI and T1WI were combined (area under the receiver operating characteristic curve: training set, 0.927; validation set, 0.831; and independent testing set, 0.825). In the nomogram, age, Hardy' grade, and Radscores were identified as risk predictors of high Ki67 expression. CONCLUSION: The deep segmentation network and radiomics analysis based on multiparameter MRI exhibited good performance and clinical application value in predicting the expression of Ki67 in PAs.

Unlocking the link: how hippocampal glutathione-glutamate coupling predicts cognitive impairment in multiple sclerosis patients.

Cognitive impairment is a common symptom of multiple sclerosis and profoundly impacts quality of life. Glutathione (GSH) and glutamate (Glu) are tightly linked in the brain, participating in cognitive function. However, GSH-Glu couplings in cognitive brain regions and their relationship with cognitive impairment in relapsing-remitting multiple sclerosis (RRMS) remains unclear. Forty-one RRMS patients and 43 healthy controls underwent magnetic resonance spectroscopy to measure GSH and Glu levels in the posterior cingulate cortex, medial prefrontal cortex and left hippocampus. Neuropsychological tests were used to evaluate the cognitive function. The Glu/GSH ratio was used to indicate the coupling between GSH and Glu and was tested as a predictor of cognitive performance. The results show that RRMS patients exhibited reduced hippocampal GSH and Glu levels, which were found to be significant predictors of worse verbal and visuospatial memory, respectively. Moreover, GSH levels were dissociated from Glu levels in the left hippocampus of RRMS patients. Hippocampal Glu/GSH ratio is significantly correlated with processing speed and has a greater predictive effect. Here we show the hippocampal Glu/GSH ratio could serve as a new potential marker for characterizing cognitive impairment in RRMS, providing a new direction for clinical detection of cognitive impairment.

Planktonic microbial signatures of sinking particle export in the open ocean's interior.

A considerable amount of particulate carbon produced by oceanic photosynthesis is exported to the deep-sea by the "gravitational pump" (~6.8 to 7.7 Pg C/year), sequestering it from the atmosphere for centuries. How particulate organic carbon (POC) is transformed during export to the deep sea however is not well understood. Here, we report that dominant suspended prokaryotes also found in sinking particles serve as informative tracers of particle export processes. In a three-year time series from oceanographic campaigns in the Pacific Ocean, upper water column relative abundances of suspended prokaryotes entrained in sinking particles decreased exponentially from depths of 75 to 250 m, conforming to known depth-attenuation patterns of carbon, energy, and mass fluxes in the epipelagic zone. Below ~250 m however, the relative abundance of suspended prokaryotes entrained in sinking particles increased with depth. These results indicate that microbial entrainment, colonization, and sinking particle formation are elevated at mesopelagic and bathypelagic depths. Comparison of suspended and sinking particle-associated microbes provides information about the depth-variability of POC export and biotic processes, that is not evident from biogeochemical data alone.

CRFB5a, a Subtype of Japanese Eel (Anguilla japonica) Type I IFN Receptor, Regulates Host Antiviral and Antimicrobial Functions through Activation of IRF3/IRF7 and LEAP2.

IFNAR1, one of the type I IFN receptors, is crucial to mammalian host defense against viral invasion. However, largely unknown is the immunological role of the fish teleost protein IFNAR1, also known as CRFB5. We have successfully cloned the whole cDNA of the Japanese eel's (Anguilla japonica) CRFB5a homolog, AjCRFB5a. The two fibronectin-3 domains and the transmembrane region (238-260 aa) of AjCRFB5a are normally present, and it shares a three-dimensional structure with zebrafish, Asian arowana, and humans. According to expression analyses, AjCRFB5a is highly expressed in all tissues found, particularly the liver and intestine. In vivo, Aeromonas hydrophila, LPS, and the viral mimic poly I:C all dramatically increased AjCRFB5a expression in the liver. Japanese eel liver cells were reported to express AjCRFB5a more strongly in vitro after being exposed to Aeromonas hydrophila or being stimulated with poly I: C. The membranes of Japanese eel liver cells contained EGFP-AjCRFB5a proteins, some of which were condensed, according to the results of fluorescence microscopy. Luciferase reporter assays showed that AjCRFB5a overexpression strongly increased the expression of immune-related genes in Japanese eel liver cells, such as IFN1, IFN2, IFN3, IFN4, IRF3, IRF5, and IRF7 of the type I IFN signaling pathway, as well as one of the essential antimicrobial peptides LEAP2, in addition to significantly inducing human IFN-promoter activities in HEK293 cells. Additionally, RNA interference (RNAi) data demonstrated that knocking down AjCRFB5a caused all eight of those genes to drastically lower their expression in Japanese eel liver cells, as well as to variable degrees in the kidney, spleen, liver, and intestine. Our findings together showed that AjCRFB5a participates in the host immune response to bacterial infection by inducing antimicrobial peptides mediated by LEAP2 and favorably modulates host antiviral immune responses by activating IRF3 and IRF7-driven type I IFN signaling pathways.

Research progress and applications of colorful Brassica crops.

MAIN CONCLUSION: We review the application and the molecular regulation of anthocyanins in colorful Brassica crops, the creation of new germplasm resources, and the development and utilization of colorful Brassica crops. Brassica crops are widely cultivated: these include oilseed crops, such as rapeseed, mustards, and root, leaf, and stem vegetable types, such as turnips, cabbages, broccoli, and cauliflowers. Colorful variants exist of these crop species, and asides from increased aesthetic appeal, these may also offer advantages in terms of nutritional content and improved stress resistances. This review provides a comprehensive overview of pigmentation in Brassica as a reference for the selection and breeding of new colorful Brassica varieties for multiple end uses. We summarize the function and molecular regulation of anthocyanins in Brassica crops, the creation of new colorful germplasm resources via different breeding methods, and the development and multifunctional utilization of colorful Brassica crop types.

Mapping and Identifying Candidate Genes Enabling Cadmium Accumulation in Brassica napus Revealed by Combined BSA-Seq and RNA-Seq Analysis.

Rapeseed has the ability to absorb cadmium in the roots and transfer it to aboveground organs, making it a potential species for remediating soil cadmium (Cd) pollution. However, the genetic and molecular mechanisms underlying this phenomenon in rapeseed are still unclear. In this study, a 'cadmium-enriched' parent, 'P1', with high cadmium transport and accumulation in the shoot (cadmium root: shoot transfer ratio of 153.75%), and a low-cadmium-accumulation parent, 'P2', (with a cadmium transfer ratio of 48.72%) were assessed for Cd concentration using inductively coupled plasma mass spectrometry (ICP-MS). An F2 genetic population was constructed by crossing 'P1' with 'P2' to map QTL intervals and underlying genes associated with cadmium enrichment. Fifty extremely cadmium-enriched F2 individuals and fifty extremely low-accumulation F2 individuals were selected based on cadmium content and cadmium transfer ratio and used for bulk segregant analysis (BSA) in combination with whole genome resequencing. This generated a total of 3,660,999 SNPs and 787,034 InDels between these two segregated phenotypic groups. Based on the delta SNP index (the difference in SNP frequency between the two bulked pools), nine candidate Quantitative trait loci (QTLs) from five chromosomes were identified, and four intervals were validated. RNA sequencing of 'P1' and 'P2' in response to cadmium was also performed and identified 3502 differentially expressed genes (DEGs) between 'P1' and 'P2' under Cd treatment. Finally, 32 candidate DEGs were identified within 9 significant mapping intervals, including genes encoding a glutathione S-transferase (GST), a molecular chaperone (DnaJ), and a phosphoglycerate kinase (PGK), among others. These genes are strong candidates for playing an active role in helping rapeseed cope with cadmium stress. Therefore, this study not only sheds new light on the molecular mechanisms of Cd accumulation in rapeseed but could also be useful for rapeseed breeding programs targeting this trait.

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.

Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images.

BACKGROUND: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear. PURPOSE: To distinguish primary site of BM and identify the best DL models. STUDY TYPE: Retrospective. POPULATION: A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non-small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included. FIELD STRENGTH/SEQUENCE: A 3-T, T1 turbo spin echo (T1-TSE), T2-TSE, T2FLAIR-TSE, DWI echo-planar imaging (DWI-EPI) and contrast-enhanced T1-TSE (CE T1-TSE). ASSESSMENT: Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three-dimensional residual network (3D-ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI, CE-T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non-lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non-lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the model by heatmaps. STATISTICAL TESTS: The area under the receiver operating characteristics curve (AUC) assess each classification performance. RESULTS: 3D ResNet18 with Grad-CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non-lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE-T1WI performed best for lung cancer vs. non-lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE-T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non-lung cancer, SCLC vs. NSCLC. Grad-CAM helped for model visualization by heatmaps that focused on tumor regions. DATA CONCLUSION: DL models may help to distinguish the origins of BM based on MRI data. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Neurochemical and functional reorganization of the cognitive-ear link underlies cognitive impairment in presbycusis.

Recent studies suggest that the interaction between presbycusis and cognitive impairment may be partially explained by the cognitive-ear link. However, the underlying neurophysiological mechanisms remain largely unknown. In this study, we combined magnetic resonance spectroscopy (MRS) and resting-state functional magnetic resonance imaging (fMRI) to investigate auditory gamma-aminobutyric acid (GABA) and glutamate (Glu) levels, intra- and inter-network functional connectivity, and their relationships with auditory and cognitive function in 51 presbycusis patients and 51 well-matched healthy controls. Our results confirmed reorganization of the cognitive-ear link in presbycusis, including decreased auditory GABA and Glu levels and aberrant functional connectivity involving auditory networks (AN) and cognitive-related networks, which were associated with reduced speech perception or cognitive impairment. Moreover, mediation analyses revealed that decreased auditory GABA levels and dysconnectivity between the AN and default mode network (DMN) mediated the association between hearing loss and impaired information processing speed in presbycusis. These findings highlight the importance of AN-DMN dysconnectivity in cognitive-ear link reorganization leading to cognitive impairment, and hearing loss may drive reorganization via decreased auditory GABA levels. Modulation of GABA neurotransmission may lead to new treatment strategies for cognitive impairment in presbycusis patients.

Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study.

Purpose: To develop and validate a clinical-radiomics nomogram based on radiomics features and clinical risk factors for identification of human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer (BC). Methods: Two hundred and thirty-five female patients with BC were enrolled from July 2018 to February 2022 and divided into a training group (from center I, 115 patients), internal validation group (from center I, 49 patients), and external validation group (from centers II and III, 71 patients). The preoperative MRI of all patients was obtained, and radiomics features were extracted by a free open-source software called 3D Slicer. The Least Absolute Shrinkage and Selection Operator regression model was used to identify the most useful features. The radiomics score (Rad-score) was calculated by using the radiomics signature-based formula. A clinical-radiomics nomogram combining clinical factors and Rad-score was developed through multivariate logistic regression analysis. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 2,553 radiomics features were extracted, and 21 radiomics features were selected as the most useful radiomics features. Multivariate logistic regression analysis indicated that Rad-score, progesterone receptor (PR), and Ki-67 were independent parameters to distinguish HER2 status. The clinical-radiomics nomogram, which comprised Rad-score, PR, and Ki-67, showed a favorable classification capability, with AUC of 0.87 [95% confidence internal (CI), 0.80 to 0.93] in the training group, 0.81 (95% CI, 0.69 to 0.94) in the internal validation group, and 0.84 (95% CI, 0.75 to 0.93) in the external validation group. DCA illustrated that the nomogram was useful in clinical practice. Conclusions: The nomogram combined with Rad-score, PR, and Ki-67 can identify the HER2 status of BC.

A Calcineurin Regulator MoRCN1 Is Important for Asexual Development, Stress Response, and Plant Infection of Magnaporthe oryzae.

The calcium/calcineurin signaling pathway plays a key role in the development and virulence of plant pathogenic fungi, but the regulation of this signaling pathway is still not clear. In this study, we identified a calcineurin regulator MoRCN1 in the plant pathogenic fungus Magnaporthe oryzae and found it is important for virulence by regulating the calcineurin pathway. MoRCN1 deletion mutants were severely decreased in colony growth and conidia formation. More importantly, the deletion of MoRCN1 led to a significant reduction in virulence due to defects in appressorium formation and invasive growth. The ΔMorcn1 mutants were more sensitive to different stresses and induced host ROS accumulation, suggesting a role of MoRCN1 in stress adaptation. We found that MoRCN1 directly interacted with the calcineurin catalytic subunit MoCNA and affected its protein stability, which was therefore important for regulating the calcineurin pathway. Transcriptome analysis showed that MoRCN1 significantly activated 491 genes and suppressed 337 genes in response to calcium ion, partially overlapped with the MoCRZ1-bound genes. Gene Ontology and KEGG pathway analyses indicated that MoRCN1-regulated genes were enriched in stress adaptation, lipid metabolism, and secondary metabolite biosynthesis, reflecting a function of MoRCN1 in host cell adaptation. Altogether, these results suggest MoRCN1 functions as a regulator of the calcium/calcineurin signaling pathway for fungal development and infection of host cells.

eIF6 is potential diagnostic and prognostic biomarker that associated with 18F-FDG PET/CT features and immune signatures in esophageal carcinoma.

BACKGROUND: Although eukaryotic initiation factor 6 (eIF6) is a novel therapeutic target, data on its importance in the development of esophageal carcinoma (ESCA) remains limited. This study evaluated the correlation between eIF6 expression and metabolic analysis using fluorine-18 fluorodeoxyglucose (18F-FDG) -Positron emission tomography (PET) and immune gene signatures in ESCA. METHODS: This study employed The Cancer Genome Atlas (TCGA) to analyze the expression and prognostic value of eIF6, as well as its relationship with the immune gene signatures in ESCA patients. The qRT-PCR and Western blot analyses were used to profile the expression of eIF6 in ESCA tissues and different ESCA cell lines. The expression of tumor eIF6 and glucose transporter 1 (GLUT1) was examined using immunohistochemical tools in fifty-two ESCA patients undergoing routine 18F-FDG PET/CT before surgery. In addition, the cellular responses to eIF6 knockdown in human ESCA cells were assessed via the MTS, EdU, flow cytometry and wound healing assays. RESULTS: Our data demonstrated that compared with the normal esophageal tissues, eIF6 expression was upregulated in ESCA tumor tissues and showed a high diagnostic value with an area under curve of 0.825 for predicting ESCA. High eIF6 expression was significantly correlated with shorter overall survival of patients with esophagus adenocarcinoma (p = 0.038), but not in squamous cell carcinoma of the esophagus (p = 0.078). In addition, tumor eIF6 was significantly associated with 18F-FDG PET/CT parameters: maximal and mean standardized uptake values (SUVmax and SUVmean) and total lesion glycolysis (TLG) (rho = 0.458, 0.460, and 0.300, respectively, p < 0.01) as well as GLUT1 expression (rho = 0.453, p < 0.001). A SUVmax cutoff of 18.2 led to prediction of tumor eIF6 expression with an accuracy of 0.755. Functional analysis studies demonstrated that knockdown of eIF6 inhibited ESCA cell growth and migration, and fueled cell apoptosis. Moreover, the Bulk RNA gene analysis revealed a significant inverse association between eIF6 and the tumor-infiltrating immune cells (macrophages, T cells, or Th1 cells) and immunomodulators in the ESCA microenvironment. CONCLUSION: Our study suggested that eIF6 might serve as a potential prognostic biomarker associated with metabolic variability and immune gene signatures in ESCA tumor microenvironment.

A holistic genome dataset of bacteria, archaea and viruses of the Pearl River estuary.

Estuaries are one of the most important coastal ecosystems. While microbiomes and viromes have been separately investigated in some estuaries, few studies holistically deciphered the genomes and connections of viruses and their microbial hosts along an estuarine salinity gradient. Here we applied deep metagenomic sequencing on microbial and viral communities in surface waters of the Pearl River estuary, one of China's largest estuaries with strong anthropogenic impacts. Overall, 1,205 non-redundant prokaryotic genomes with ≥50% completeness and ≤10% contamination, and 78,502 non-redundant viral-like genomes were generated from samples of three size fractions and five salinity levels. Phylogenomic analysis and taxonomy classification show that majority of these estuarine prokaryotic and viral genomes are novel at species level according to public databases. Potential connections between the microbial and viral populations were further investigated by host-virus matching. These combined microbial and viral genomes provide an important complement of global marine genome datasets and should greatly facilitate our understanding of microbe-virus interactions, evolution and their implications in estuarine ecosystems.

Low-flow intussusception and metastable VEGFR2 signaling launch angiogenesis in ischemic muscle.

Efforts to promote sprouting angiogenesis in skeletal muscles of individuals with peripheral artery disease have not been clinically successful. We discovered that, contrary to the prevailing view, angiogenesis following ischemic muscle injury in mice was not driven by endothelial sprouting. Instead, real-time imaging revealed the emergence of wide-caliber, primordial conduits with ultralow flow that rapidly transformed into a hierarchical neocirculation by transluminal bridging and intussusception. This process was accelerated by inhibiting vascular endothelial growth factor receptor-2 (VEGFR2). We probed this response by developing the first live-cell model of transluminal endothelial bridging using microfluidics. Endothelial cells subjected to ultralow shear stress could reposition inside the flowing lumen as pillars. Moreover, the low-flow lumen proved to be a privileged location for endothelial cells with reduced VEGFR2 signaling capacity, as VEGFR2 mechanosignals were boosted. These findings redefine regenerative angiogenesis in muscle as an intussusceptive process and uncover a basis for its launch.