Emerging Mechanisms Linking High-Fat Diet and Endometrial Cancer: Insights into the Role of Gut Microbiota and Metabolic Dysregulation

High-fat diet (HFD) consumption has been recognized as a significant risk factor for the development of endometrial carcinoma (EC). Emerging research highlights gut microbiota as crucial mediators of HFD-induced systemic effects, which not only promote metabolic disorders such as obesity, insulin resistance, and systemic inflammation but also lead to profound alterations in gut microbiota composition. These changes subsequently influence estrogen metabolism, inflammatory signaling pathways, and endometrial remodeling, thereby exacerbating cellular proliferation and atypical changes within the endometrium. The underlying mechanisms may involve dysbiotic shifts in intestinal flora that contribute to increased endotoxemia, compromised intestinal barrier function, and chronic low-grade inflammation. This review synthesizes current findings on how HFD-induced gut microbiota dysbiosis and metabolic dysregulation contribute to the pathogenesis of EC while highlighting potential preventive and therapeutic strategies

Interactions between Pro-inflammatory Cytokines and Estrogen Receptors in Endometrial Cancer

Endometrial cancer (EC) is a hormone-driven malignancy in which estrogen receptor (ER) signaling plays a central role. Meanwhile, chronic inflammation, particularly mediated by pro-inflammatory cytokines such as interleukin-6 (IL-6) and interleukin-17 (IL-17), has emerged as a key contributor to endometrial cancer progression. This review examines the interplay between IL-6, IL-17, and estrogen receptors (ERα and ERβ) in endometrial cancer cells, highlighting how these cytokines regulate ER expression and function through multiple signaling pathways, including the Janus kinase/signal transducer and activator of transcription (JAK/STAT), nuclear factor-κB (NF-κB), and mitogen-activated protein kinase (MAPK) pathways. IL-6 and IL-17 have been shown to upregulate ERα and suppress ERβ, thereby enhancing estrogen-mediated tumor proliferation and potentially contributing to hormonal therapy resistance. Moreover, evidence suggests a bidirectional feedback loop in which estrogen signaling further amplifies cytokine production, creating a self-sustaining inflammatory environment that promotes tumor progression. Understanding this cytokine–ER crosstalk provides novel insights into endometrial cancer pathogenesis and reveals potential therapeutic targets. Strategies that combine endocrine therapy with anti-inflammatory agents or cytokine pathway inhibitors may help overcome resistance and improve clinical outcomes in selected patients. Further mechanistic studies and clinical trials are needed to validate the prognostic and therapeutic relevance of IL-6 and IL-17 in hormone-responsive endometrial cancer

Mass Cytometry Defines Virus-Specific CD4 + T Cells in Influenza Vaccination

The antiviral response to influenza virus is complex and multifaceted, involving many immune cell subsets. There is an urgent need to understand the role of CD4+ T cells, which orchestrate an effective antiviral response, to improve vaccine design strategies. In this study, we analyzed PBMCs from human participants immunized with influenza vaccine, using high-dimensional single-cell proteomic immune profiling by mass cytometry. Data were analyzed using a novel clustering algorithm, denoised ragged pruning, to define possible influenza virus-specific clusters of CD4+ T cells. Denoised ragged pruning identified six clusters of cells. Among these, one cluster (Cluster 3) was found to increase in abundance following stimulation with influenza virus peptide ex vivo. A separate cluster (Cluster 4) was found to expand in abundance between days 0 and 7 postvaccination, indicating that it is vaccine responsive. We examined the expression profiles of all six clusters to characterize their lineage, functionality, and possible role in the response to influenza vaccine. Clusters 3 and 4 consisted of effector memory cells, with high CD154 expression. Cluster 3 expressed cytokines like IL-2, IFN-γ, and TNF-α, whereas Cluster 4 expressed IL-17. Interestingly, some participants had low abundance of Clusters 3 and 4, whereas others had higher abundance of one of these clusters compared with the other. Taken together, we present an approach for identifying novel influenza virus-reactive CD4+ T cell subsets, a method that could help advance understanding of the immune response to influenza, predict responsiveness to vaccines, and aid in better vaccine design

Development of a combined model incorporating clinical characteristics and magnetic resonance imaging features to enhance the predictive value of a prognostic model for locally advanced cervical cancer

ObjectiveThis study aimed to develop non-invasive predictive tools based on clinical characteristics and magnetic resonance imaging (MRI) features to predict survival in patients with locally advanced cervical cancer (LACC), thereby facilitating clinical decision-making.MethodsWe conducted a retrospective analysis of clinical and MRI data from LACC patients who underwent radical radiotherapy at our center between September 2012 and May 2020. Prognostic predictors were identified using single-factor and multifactor Cox analyses. Clinical and MRI models were established based on relevant features, and combined models were created by incorporating MRI factors into the clinical model. The predictive performance of the models was evaluated using the area under the curve (AUC), consistency index (C-index), and decision curve analysis (DCA).ResultsThe study included 175 LACC patients. Multivariate Cox analysis revealed that patients with FIGO IIA-IIB stage, ECOG score 0-1, CYFRA 21-1<7.7 ng/ml, ADC ≥ 0.79 mm^2/s, and Kep ≥ 4.23 minutes had a more favorable survival prognosis. The clinical models, incorporating ECOG, FIGO staging, and CYFRA21-1, outperformed individual prognostic factors in predicting 5-year overall survival (AUC: 0.803) and 5-year progression-free survival (AUC: 0.807). The addition of MRI factors to the clinical model (AUC: 0.803 for 5-year overall survival) increased the AUC of the combined model to 0.858 (P=0.011). Similarly, the combined model demonstrated a superior predictive ability for 5-year progression-free survival, with an AUC of 0.849, compared to the clinical model (AUC: 0.807) and the MRI model (AUC: 0.673). Furthermore, the C-index of the clinical models for overall survival and progression-free survival were 0.763 and 0.800, respectively. Upon incorporating MRI factors, the C-index of the combined model increased to 0.826 for overall survival and 0.843 for progression-free survival. The DCA further supported the superior prognostic performance of the combined model.ConclusionOur findings indicate that ECOG, FIGO staging, and CYFRA21-1 in clinical characteristics, as well as ADC and Kep values in MRI features, are independent prognostic factors for LACC patients undergoing radical radiotherapy. The combined models provide enhanced predictive ability in assessing the risk of patient mortality and disease progression

Layer-by-Layer Epitaxy of Multilayer MoS2 Wafers

Two-dimensional (2D) semiconductor of MoS2 has great potential for advanced electronics technologies beyond silicon1-9. So far, high-quality monolayer MoS2 wafers10-12 are already available and various demonstrations from individual transistors to integrated circuits have also been shown13-15. In addition to the monolayer, multilayers have narrower band gaps but improved carrier mobilities and current capacities over the monolayer5,16-18. However, achieving high-quality multilayer MoS2 wafers remains a challenge. Here we report the growth of high quality multilayer MoS2 4-inch wafers via the layer-by-layer epitaxy process. The epitaxy leads to well-defined stacking orders between adjacent epitaxial layers and offers a delicate control of layer numbers up to 6. Systematic evaluations on the atomic structures and electronic properties were carried out for achieved wafers with different layer numbers. Significant improvements on device performances were found in thicker-layer field effect transistors (FETs), as expected. For example, the average field-effect mobility ({\mu}FE) at room temperature (RT) can increase from ~80 cm2V-1s-1 for monolayer to ~110/145 cm2V-1s-1 for bilayer/trilayer devices. The highest RT {\mu}FE=234.7 cm2V-1s-1 and a record-high on-current densities of 1.704 mA{\mu}m-1 at Vds=2 V were also achieved in trilayer MoS2 FETs with a high on/off ratio exceeding 107. Our work hence moves a step closer to practical applications of 2D MoS2 in electronics.Comment: 13 pages,4 Figure

Analytical validation of a standardized scoring protocol for Ki67: phase 3 of an international multicenter collaboration

Pathological analysis of the nuclear proliferation biomarker Ki67 has multiple potential roles in breast and other cancers. However, clinical utility of the immunohistochemical (IHC) assay for Ki67 immunohistochemistry has been hampered by unacceptable between-laboratory analytical variability. The International Ki67 Working Group has conducted a series of studies aiming to decrease this variability and improve the evaluation of Ki67. This study tries to assess whether acceptable performance can be achieved on prestained core-cut biopsies using a standardized scoring method. Sections from 30 primary ER+ breast cancer core biopsies were centrally stained for Ki67 and circulated among 22 laboratories in 11 countries. Each laboratory scored Ki67 using three methods: (1) global (4 fields of 100 cells each); (2) weighted global (same as global but weighted by estimated percentages of total area); and (3) hot-spot (single field of 500 cells). The intraclass correlation coefficient (ICC), a measure of interlaboratory agreement, for the unweighted global method (0.87; 95% credible interval (CI): 0.81–0.93) met the prespecified success criterion for scoring reproducibility, whereas that for the weighted global (0.87; 95% CI: 0.7999–0.93) and hot-spot methods (0.84; 95% CI: 0.77–0.92) marginally failed to do so. The unweighted global assessment of Ki67 IHC analysis on core biopsies met the prespecified criterion of success for scoring reproducibility. A few cases still showed large scoring discrepancies. Establishment of external quality assessment schemes is likely to improve the agreement between laboratories further. Additional evaluations are needed to assess staining variability and clinical validity in appropriate cohorts of samples

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.published_or_final_versio

The Role of Periplasmic Disulfide Bond Status in the Regulation of the Salmonella SPI1 Type Three Secretion System

97 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.Salmonella enterica serovar Typhimurium injects a set of effector proteins into the host cell cytoplasm via the Salmonella Pathogenicity Island I (SPI1) type III secretion system (T3SS) to induce inflammatory diarrhea and bacterial uptake into intestinal epithelial cells. The master SPI1 regulatory gene hilA is controlled directly by three AraC-like regulators: HilD, HilC and RtsA. A periplasmic disulfide bond oxidoreductase DsbA is required for SPI1 T3SS function. RtsA directly activates dsbA and deletion of dsbA leads to loss of SPI1-dependent secretion. We have studied the dsbA phenotypes by monitoring expression of SPI1 regulatory, structural, and effector genes. In this work, we present evidence that loss of DsbA independently affects SPI1 regulation and SPI1 function. The dsbA-mediated feedback inhibition on SPI1 transcription is not due to defects in the SPI1 T3SS apparatus. Rather, the transcriptional response is dependent on both the flagellar protein FliZ and the RcsCDB system, which also affects fliZ transcription. Thus, the status of disulfide bonds in the periplasm affects expression of the SPI1 system indirectly via regulation of the flagellar apparatus. RcsCDB can also affect SPI1 independently of FliZ. FliZ-mediated induction of hilA expression is through HilD, while RtsA and HilC act as amplifiers of the signal. Preliminary data show that FliZ regulates HilD at the level of HilD protein. Salmonella enterica serovar Typhimurium also encodes a paralogous pair of proteins to DsbA and DsbB, DsbL and DsbI, downstream of a periplasmic arylsulfate sulfotransferase (ASST). Here we show that DsbL and DsbI function as a redox pair for disulfide bond formation and, as such, affect transcription of the SPI1 type three secretion system genes and activation of the RcsCDB system. In contrast to DsbA/DsbB, however, the DsbL/DsbI system cannot catalyze the disulfide bond formation in flagellar assembly. We further demonstrate that DsbL and DsbI are required for ASST activity in Salmonella.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD