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Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. Our study aimed to exploit this correlation to improve the performance of both of the IDH mutation status identification and epilepsy diagnosis models in patients with glioma II-IV. 399 patients were retrospectively enrolled and divided into a training (n = 279) and an independent test (n = 120) cohort. Multi-center dataset (n = 228) from The Cancer Imaging Archive (TCIA) was used for external test for identification of IDH mutation status. Region of interests comprising the entire tumor and peritumoral edema were automatically segmented using a pre-trained deep learning model. Radiomic features were extracted from T1-weighted, T2-weighted, post-Gadolinium T1 weighted, and T2 fluid-attenuated inversion recovery images. We proposed an iterative approach derived from LASSO to select features shared by two tasks and features specific to each task, before using them to construct the final models. Receiver operating characteristic (ROC) analysis was employed to evaluate the model. The IDH mutation identification model achieved area under the ROC curve (AUC) values of 0.948, 0.946 and 0.860 on the training, internal test, and external test cohorts, respectively. The epilepsy diagnosis model achieved AUCs of 0.924 and 0.880 on the training and internal test cohorts, respectively. The proposed models can identify IDH status and epilepsy with fewer features, thus having better interpretability and lower risk of overfitting. This not only improves its chance of application in clinical settings, but also provides a new scheme to study multiple correlated clinical tasks. [ABSTRACT FROM AUTHOR]

Genetic alterations play a pivotal role in leukemic clonal transformation, significantly influencing disease pathogenesis and clinical outcomes. Here, we report a novel fusion gene and investigate its pathogenic role in acute lymphoblastic leukemia (ALL). We engineer a transposon transfection system expressing the TOP2B::AFF2 transcript and introduce it into Ba/F3 cells. Functional studies, including proliferation, cell cycle, and apoptosis assays, were conducted to assess the fusion gene's impact. In vitro assays reveal that the TOP2B::AFF2 fusion significantly enhances Ba/F3 cell proliferation and G1/S phase transition while suppressing differentiation and apoptosis. This study identifies TOP2B::AFF2 as a potential oncogenic driver. However, further validation through in vivo studies are warranted to fully elucidate the fusion gene's role in leukemogenesis. [ABSTRACT FROM AUTHOR]

To determine the risk factors for poor prognosis of influenza-associated encephalopathy (IAE), 56 eligible children with IAE who were treated in the pediatric intensive care unit of Wuhan Children's Hospital from January 2022 to December 2023 were selected for retrospective analysis and grouped according to poor prognosis or not, and independent risk factors for poor prognosis were found by regression analysis. Results showed 26 children (26/30, 46.4%) had a poor prognosis. In the univariate analysis, the poor prognosis group compared with the clinically cured group showed a significant increase in the number of days of hospitalization (3.0 vs. 9.5 days, P < 0.001), high-sensitivity C-reactive protein (6.80 vs. 1.88 mg/L, P = 0.003), interleukin-6 (20.26 vs. 8.24 pg/mL, P = 0.001), interleukin-10 (11.75 vs. 4.72 pg/mL, P = 0.003), alanine aminotransferase (104.0 vs. 20.0 U/L, P = 0.011), aspartate azelotransferase (186.5 vs. 37.0 U/L, P = 0.003), serum albumin (37.99 vs. 40.76 g/L, P = 0.042), prothrombin time (13.2 vs. 11.4 s, P = 0.017), D-dimer (4.34 vs. 0.44 mg/L FEU, P < 0.001), peripheral blood CD19 B-cell count (35.11 vs. 32.75 cells/µL, P = 0.018), and cerebrospinal fluid chloride (126.82 vs. 125.50 mmol/L, P = 0.027) were statistically different in the above 11 indicators. After binary logistic regression analysis, it was concluded that D-dimer was an independent risk factor for poor prognosis (odds ratio = 1.440, 95% confidence interval 1.052–1.972, P = 0.023), and the area under the curve (95% confidence interval) was 0.802 (0.680–0.924), P < 0.001. When D-dimer was ≥ 1.18 mg/L FEU, the occurrence of poor prognosis was predicted with sensitivity and specificity of 65.4% and 96.7%, respectively. In conclusion, IAE has a high incidence of poor prognosis, in which D-dimer is a possible risk factor with discriminatory value in assessing the occurrence of poor prognosis. However, due to the limitations of retrospective single-center small sample size data, more confirmation from multicenter large sample size studies is needed in the future. [ABSTRACT FROM AUTHOR]

Uterine Corpus Endometrial Carcinoma (UCEC) represents a common malignant neoplasm in women, with its prognosis being intricately associated with available therapeutic interventions. In the past few decades, there has been a burgeoning interest in the role of mitochondria within the context of UCEC. Nevertheless, the development and application of prognostic models predicated on mitochondrial-related genes (MRGs) in UCEC remains in the exploratory stages. This study utilized RNA sequencing data and clinical information from the TCGA database to identify differentially expressed MRGs (DEMRGs) between UCEC and normal groups that are associated with overall survival (OS). Patients were randomly assigned to training and testing cohorts in a 1:1 ratio. In the training cohort, a risk model based on DEMRGs was developed using Lasso Cox regression analysis. Subsequently, patients in both cohorts were stratified into high-risk and low-risk groups based on their median risk scores. The prognostic performance of the model was validated through Kaplan-Meier survival analysis, ROC curves, and nomograms. Additionally, further analyses including functional enrichment, immune landscape assessment, prediction of response to ICB therapy, mutation profiling, and drug sensitivity analysis elucidated biological distinctions between the identified risk groups. We established a risk model incorporating eight MRGs. Patients classified within he high-risk group exhibited significantly poorer prognoses relative to those in the low-risk group. Functional enrichment analysis identified substantial differences in biological processes and signaling pathways between the high-risk and low-risk cohorts. Immune landscape analysis showed that patients with elevated risk scores exhibited significant immunosuppressive and immune evasion mechanisms. Conversely, low-risk patients exhibited higher expression of human leukocyte antigen (HLA) family members and immune checkpoint genes (ICGs) compared to their high-risk counterparts.Consequently, low-risk patients showed greater responsiveness to immunotherapy and potential small molecule drugs, whereas high-risk patients were more susceptible to chemotherapy. The mitochondrial-related risk model formulated in this study demonstrates efficacy in predicting both prognosis and response to immunotherapy in patients with UCEC, thereby providing a scientific basis for personalized treatment strategies. Future research endeavors should focus on further validating the clinical utility of this model and investigate the specific mechanisms of the identified MRGs in UCEC. [ABSTRACT FROM AUTHOR]

As one of the most destructive and invasive cancers, pancreatic cancer exhibits complex tumor heterogeneity, which has been a major challenge for clinicians in terms of patient treatment and prognosis. The toll-like receptor (TLR) pathway is closely related to the immune microenvironment within various cancer tissues. To explore the development pattern of pancreatic cancer and find an ideal biomarker, our research has explored the mechanism of the TLR pathway in pancreatic cancer. We collected single-cell expression data from 57,024 cells and transcriptomic data from 945 pancreatic cancer patients, and conducted a series of analyses at both the single-cell and transcriptomic levels. By calculating the TLR pathway score, we clustered pancreatic cancer patients and conducted a series of analyses including metabolic pathways, immune microenvironment, drug sensitivity and so on. In the process of building prognostic models, we screened 33 core genes related to the prognosis of pancreatic cancer, and combined a series of machine learning algorithms to build the prognosis model of pancreatic cancer. We used single cell sequencing to clarify the complex intrinsic relationship between TLR pathway and pancreatic cancer. The strongest TLR signals were observed in macrophages and endothelial cells. With the occurrence of pancreatic cancer, the TLR signal of various cell types gradually increased, but with the increase of the malignant degree of ductal epithelial cells, the TLR signal gradually weakened. Cluster analysis showed that patients with the most active TLR pathway had severe dysregulation of immune microenvironment and the worst prognosis. Finally, we combined a series of machine learning algorithms to build a pancreatic cancer prognosis model that includes four genes (NT5E, TGFBI, ANLN, and FAM83A). The model showed strong performance in predicting the survival state of pancreatic cancer samples. We explored the important role of TLR pathway in pancreatic cancer and established and validated a new prognosis model for pancreatic cancer based on TLR-related genes. [ABSTRACT FROM AUTHOR]

Examining how historical and contemporary geographic and environmental factors contribute to genetic divergence at different evolutionary scales is a central yet largely unexplored question in ecology and evolution. Here, we examine this key question by investigating how environmental and geographic factors across different epochs have driven genetic divergence at deeper (phylogeographic) and shallower (landscape genetic) evolutionary scales in the Chinese Tertiary relict tree Emmenopterys henryi. We found that geography played a predominant role at all levels - phylogeographic clades are broadly geographically structured, the deepest levels of divergence are associated with major geological or pre-Quaternary climatic events, and isolation by distance (IBD) primarily explained population genetic structure. However, environmental factors are clearly also important - climatic fluctuations since the Last Interglacial (LIG) have likely contributed to phylogeographic structure, and the population genetic structure (in our AFLP dataset) was partly explained by isolation by environment (IBE), which may have resulted from natural selection in environments with divergent climates. Thus, historical and contemporary geography and historical and contemporary environments have all shaped patterns of genetic structure in E. henryi, and, in fact, changes in the landscape through time have also been critical factors.

We systematically measure the dielectric function of atomically thin MoS2 films with different layer numbers and demonstrate that excitonic effects play a dominant role in the dielectric function when the films are less than 5-7 layers thick. The dielectric function shows an anomalous dependence on the layer number. It decreases with the layer number increasing when the films are less than 5-7 layers thick but turns to increase with the layer number for thicker films. We show that this is because the excitonic effect is very strong in the thin MoS2 films and its contribution to the dielectric function may dominate over the contribution of the band structure. We also extract the value of layer-dependent exciton binding energy and Bohr radius in the films by fitting the experimental results with an intuitive model. The dominance of excitonic effects is in stark contrast with what reported at conventional materials whose dielectric functions are usually dictated by band structures. The knowledge of the dielectric function may enable capabilities to engineer the light-matter interactions of atomically thin MoS2 films for the development of novel photonic devices, such as metamaterials, waveguides, light absorbers, and light emitters.