Impact of age on genomic alterations and the tumor immune microenvironment in papillary thyroid cancer.

Differentiated thyroid cancer in older adults has been linked to alterations in the mutational landscape and tumor immune cell infiltration that create a tumor-permissive microenvironment. We sought to determine the impact of age on genomic alterations and immune cell composition in papillary thyroid cancer (PTC). Genomic alterations, immune cell composition and clinical data were obtained using The Cancer Genome Atlas (TCGA) and computational immunogenomic analyses. Disease severity was recoded into 3 groups: Group A (T1-2N0M0), Group B (T1-3N1a-1bM0), and Group C (T4NxMx or TxNxM1). Histopathologic subtypes included conventional, follicular-variant, and tall cell variant PTC. Spearman's rank correlation, ANOVA, t-test, and multivariable linear regression were performed. 470 PTC samples were retrieved from the TCGA portal with genomic alteration and immune cell composition data. TERT promoter alterations were more common in patients ≥ 65 years (26% vs 4%, p<0.0001). Tumor mutational burden increased with increasing age (r=0.463, p<0.0001). Increasing age was associated with decreased CD8+ T cells (r=-0.15, p=0.01) using CIBERSORT and decreased B cells (r=-0.13), CD8+ T cells (r=-0.19) and neutrophils (r=-0.14, p<0.05) using TIMER. Multivariate regression found that increasing age was independently associated with increased resting NK cells and resting dendritic cells, and decreased naïve B cells and CD8+ T cells (p<0.05). PTC tumors of older adults are characterized by increased TERT promoter alterations, increased tumor mutational burden, and a decreased cytotoxic CD8+ T and increased resting dendritic cell immune infiltrate. Further studies are needed to determine if these changes in immune cell infiltrate are associated with compromised outcomes.

[Effects of laser combined with periodontal basic treatment on periodontal indices, subgingival microflora, adiponectin, MMP-13 and IL-1ß in gingival crevicular fluid in patients with periodontitis].

PURPOSE: To investigate the effects of laser combined with periodontal basic treatment on periodontal indices, subgingival flora, adiponectin, matrix metalloproteinase-13 (MMP-13) and interleukin-1ß (IL-1ß) in patients with periodontitis. METHODS: A retrospective analysis was performed on 100 patients with periodontitis diagnosed and treated in Hengshui People's Hospital from December 2022 to July 2023. According to treatment methods, the patients were divided into control group (n=51) and experimental group (n=49). The control group received periodontal basic treatment, and the experimental group received laser treatment on the basis of the control group. The periodontal indexes, subgingival microflora, adiponectin, MMP-13, IL-1ß and bone metabolic factors of gingival crevicular fluid before and after treatment were compared between the two groups, as well as the clinical therapeutic effect. Statistical analysis was performed with SPSS 22.0 software package. RESULTS: After treatment, probing depth(PD), bleeding on probing(BOP), gingival index(GI) and plaque index (PLI) in the experimental group were lower than before treatment (P＜0.05), PD, BOP and PLI in the control group were lower than before treatment (P＜0.05), and PD, BOP, GI and PLI in the experimental group were significantly lower than those in control group (P＜0.05). After treatment, Lactobacillus, Clostridium and Bacteroides in both groups were significantly lower than before treatment (P＜0.05), and the experimental group was significantly lower than the control group(P＜0.05). After treatment, adiponectin in gingival crevicular fluid increased in both groups compared with before treatment(P＜0.05), and MMP-13 and IL-1ß in gingival crevicular fluid decreased in both groups compared with before treatment (P＜0.05), and adiponectin in gingival crevicular fluid in the experimental group was significantly higher than that in the control group (P＜0.05), MMP-13 and IL-1ß in the experimental group were significantly higher than that in the control group (P＜0.05). After treatment, procollagenⅠtype N-terminal peptide (PINP), cross linked C-telopeptide of type Ⅰ collagen(CXT) and bone glaprotein (BGP) were significantly higher than those before treatment (P＜0.05), and the experimental group was significantly higher than the control group (P＜0.05). The total effective rate of the experimental group was significantly higher than that of the control group (P＜0.05). CONCLUSIONS: Laser combined with periodontal basic treatment can effectively improve periodontal indexes, reduce subgingival flora, increase the levels of adiponectin and bone metabolic factor in gingival crevicular fluid, reduce the levels of MMP-13 and IL-1ß in gingival crevicular fluid, and improve the clinical therapeutic effect in patients with periodontitis.

Analysis of pancreatic cancer treatment and survival disparities in Florida throughout the Covid-19 pandemic.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) is currently the third-leading cause of cancer-related death in the United States. African Americans (AAs) with PDAC have worse survival in comparison to other racial groups. The COVID-19 pandemic caused significant stress to the healthcare system. We aim to evaluate the pandemic's impact on already known disparities in newly diagnosed patients with PDAC in Florida. METHODS: This is a retrospective analysis of newly diagnosed patients with PDAC in the OneFlorida+ Data Trust based upon date of diagnosis: Pre-pandemic (01/01/2017- 09/30/2019), Transition (10/01/2019-02/28/2020), and Pandemic (03/1/2020-10/31/2020). Primary endpoints are time to treatment initiation and rate of surgery and secondary endpoint is survival time. Disparities due to age, sex, race, and income were also evaluated. Chi-squared or Fisher's exact test when necessary, Kruskal-Wallis test, and Kaplan-Meier analysis with log-rank test were performed to compare the differences between the comparative groups for categorical, quantitative, and survival outcomes, respectively. Multivariable regression analyses were conducted to estimate the effects of cofactors. RESULTS: 934 patients with a median age of 67 years were included. There were 47.8% females and 52.2% males; 19.4% AA, 70.2% Caucasian, 10.4% Other race; median income was $53,551. While we observed a significant reduction in the diagnosis rate of new PDAC cases during the pandemic, there were no significant differences in demographic distributions among the three cohorts. Time to treatment did not significantly change from the pre-pandemic to the pandemic, and no difference was observed across all demographics. Rate of surgery increased significantly from the pre-pandemic (35.8%) to the pandemic (55.6%). AAs in the pre-pandemic cohort had a significantly lower rate of surgery of 25.0% compared to 41.7% in Caucasians. AAs, patients ≥ 67 years, and income < $53,000 had significantly higher hazards to death and shorter median survival time (mST). CONCLUSIONS: While no differences in time to initial treatment are observed among the newly diagnosed PDAC patients, there remain significant disparities in the rate of surgery and overall survival. Observing a significant reduction in diagnosis rate and analyzing disparities can provide insight into the effect of a resource-restricting pandemic for patients with newly diagnosed PDAC.

Characterization of the Difference between Day and Night Temperatures on the Growth, Photosynthesis, and Metabolite Accumulation of Tea Seedlings.

Currently, the effects of the differences between day and night temperatures (DIFs) on tea plant are poorly understood. In order to investigate the influence of DIFs on the growth, photosynthesis, and metabolite accumulation of tea plants, the plants were cultivated under 5 °C (25/20 °C, light/dark), 10 °C (25/15 °C, light/dark), and 15 °C (25/10 °C, light/dark). The results showed that the growth rate of the new shoots decreased with an increase in the DIFs. There was a downward trend in the photosynthesis among the treatments, as evidenced by the lowest net photosynthetic rate and total chlorophyll at a DIF of 15 °C. In addition, the DIFs significantly affected the primary and secondary metabolites. In particular, the 10 °C DIF treatment contained the lowest levels of soluble sugars, tea polyphenols, and catechins but was abundant in caffeine and amino acids, along with high expression levels of theanine synthetase (TS3) and glutamate synthase (GOGAT). Furthermore, the transcriptome data revealed that the differentially expressed genes were enriched in valine, leucine, and isoleucine degradation, flavone/flavonol biosyntheses, flavonoid biosynthesis, etc. Therefore, we concluded that a DIF of 10 °C was suitable for the protected cultivation of tea plants in terms of the growth and the quality of a favorable flavor of tea, which provided a scientific basis for the protected cultivation of tea seedlings.

Efficacy, safety and predictors of combined fruquintinib with programmed death-1 inhibitors for advanced microsatellite-stable colorectal cancer: A retrospective study.

Background: Research findings have revealed that combining anti-angiogenesis inhibitors with programmed death-1(PD-1) inhibitors can reverse the immunosuppressive tumor microenvironment and enhance the antitumor immune response. To explore the therapeutic options for breaking immune tolerance in microsatellite stability (MSS) or mismatch repair-proficiency (pMMR) advanced colorectal cancer (CRC), we assessed the efficacy, safety and predictors of the fruquintinib and PD-1 inhibitors combination in patients with MSS/pMMR advanced CRC in a real-world environment. Methods: We conducted a single-center retrospective study by collecting relevant data on patients with MSS/pMMR advanced CRC who received fruquintinib coupled with PD-1 inhibitors in the First Affiliated Hospital of Zhengzhou University between August 2019 and November 2021, focusing on progression-free survival. Results: We enrolled 110 eligible patients in this study between August 2019 and November 2021. At the deadline (January 20, 2022), 13 patients had objective responses. The objective response rate was 11.8% (13/110, 95% confidence interval [CI]: 6.4-18.2), the disease control rate was 70.0% (82/110, 95% CI: 60.9-78.2), and the progression-free survival was 5.4 months (95% CI: 4.0-6.8). Liver metastases (hazard ratio [HR]: 0.594, 95% CI: 0.363-0.973, P<0.05), alkaline phosphatase elevation (ALP>160U/L) (HR: 0.478, 95%CI: 0.241-0.948, P<0.05), fibrinogen elevation (FIB>4g/L) (HR: 0.517, 95% CI: 0.313-0.855, P<0.05), and an increase in the ALP level from the baseline after treatment (HR: 1.673, 95% CI: 1.040-2.690, P<0.05) were negative predictors of the progression-free survival. A total of 101 of 110 patients experienced treatment-related adverse events, including 14 who experienced grade 3 or above treatment-related adverse events, and no treatment-related deaths occurred. Hypertension was the most frequently encountered grade 3 treatment-related adverse event. Conclusion: Fruquintinib combined with PD-1 inhibitors has antitumor activity and manageable safety in treating patients with MSS/pMMR advanced CRC. Liver metastases, ALP level and FIB level might be a prediction of the patient response to this therapy.

Hyaluronic Acid-Stabilized Fe3O4 Nanoparticles for Promoting In Vivo Magnetic Resonance Imaging of Tumors.

The use of iron oxide (Fe3O4) nanoparticles as novel contrast agents for magnetic resonance imaging (MRI) has attracted great interest due to their high r 2 relaxivity. However, both poor colloidal stability and lack of effective targeting ability have impeded their further expansion in the clinics. Here, we reported the creation of hyaluronic acid (HA)-stabilized Fe3O4 nanoparticles prepared by a hydrothermal co-precipitation method and followed by electrostatic adsorption of HA onto the nanoparticle surface. The water-soluble HA functions not only as a stabilizer but also as a targeting ligand with high affinity for the CD44 receptor overexpressed in many tumors. The resulting HA-stabilized Fe3O4 nanoparticles have an estimated size of sub-20 nm as observed by transmission electron microscopy (TEM) imaging and exhibited long-term colloidal stability in aqueous solution. We found that the nanoparticles are hemocompatible and cytocompatible under certain concentrations. As verified by quantifying the cellular uptake, the Fe3O4@HA nanoparticles were able to target a model cell line (HeLa cells) overexpressing the CD44 receptor through an active pathway. In addition, we showed that the nanoparticles can be used as effective contrast agents for MRI both in vitro in HeLa cells and in vivo in a xenografted HeLa tumor model in rodents. We believe that our findings shed important light on the use of active targeting ligands to improve the contrast of lesion for tumor-specific MRI in the nano-based diagnosis systems.

Direct photo-oxidation of methane to methanol over a mono-iron hydroxyl site.

Natural gas, consisting mainly of methane (CH4), has a relatively low energy density at ambient conditions (~36 kJ l-1). Partial oxidation of CH4 to methanol (CH3OH) lifts the energy density to ~17 MJ l-1 and drives the production of numerous chemicals. In nature, this is achieved by methane monooxygenase with di-iron sites, which is extremely challenging to mimic in artificial systems due to the high dissociation energy of the C-H bond in CH4 (439 kJ mol-1) and facile over-oxidation of CH3OH to CO and CO2. Here we report the direct photo-oxidation of CH4 over mono-iron hydroxyl sites immobilized within a metal-organic framework, PMOF-RuFe(OH). Under ambient and flow conditions in the presence of H2O and O2, CH4 is converted to CH3OH with 100% selectivity and a time yield of 8.81 ± 0.34 mmol gcat-1 h-1 (versus 5.05 mmol gcat-1 h-1 for methane monooxygenase). By using operando spectroscopic and modelling techniques, we find that confined mono-iron hydroxyl sites bind CH4 by forming an [Fe-OH···CH4] intermediate, thus lowering the barrier for C-H bond activation. The confinement of mono-iron hydroxyl sites in a porous matrix demonstrates a strategy for C-H bond activation in CH4 to drive the direct photosynthesis of CH3OH.

Cancer survival prognosis with Deep Bayesian Perturbation Cox Network.

BACKGROUND: The Cox proportional hazards model with neural networks is widely used to accurately predict survival outcome for choosing cancer treatment strategies. Although this method has shown outstanding performance in many tasks, it has encountered challenges when dealing with high-dimensional datasets. In this study, we point out that the Cox network has estimation bias in processing such datasets with a large number of censored samples. The estimation bias is composed of censored estimation bias and variance estimation bias, which limit the prediction performance of the model. In order to correct this bias, this paper proposes the Deep Bayesian Perturbation Cox Network (DBP), which introduces Bayesian prior knowledge about censored samples to optimize the training process of the neural network. Specifically, the model uses a sampling module called Bayesian Perturbation to approximate the prior knowledge, which can be used as a component for other Cox-based neural networks. RESULTS: The comparison between DBP and the previous model in different kinds of genomic datasets demonstrates that our model has made significant improvements over previous state-of-the-art methods. In addition, the simulation experiments are performed to illustrate how the DBP method addresses the bias caused by Cox Network. In the case study, based on the predicted risks in BRCA data from TCGA, we identify 400 differential expressed genes and 20 KEGG pathways that are associated with breast cancer prognosis, among which 65% of the top 20 genes have been proved by literature review. CONCLUSION: Overall, these results demonstrate that our proposed method is advanced and robust in datasets with a large proportion of censored samples. Besides, it can guide to discover disease-related genes.

An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction.

BACKGROUND: Predicting hepatocellular carcinoma (HCC) prognosis is important for treatment selection, and it is increasingly interesting to predict prognosis through gene expression data. Currently, the prognosis remains of low accuracy due to the high dimension but small sample size of liver cancer omics data. In previous studies, a transfer learning strategy has been developed by pre-training models on similar cancer types and then fine-tuning the pre-trained models on the target dataset. However, transfer learning has limited performance since other cancer types are similar at different levels, and it is not trivial to balance the relations with different cancer types. METHODS: Here, we propose an adaptive transfer-learning-based deep Cox neural network (ATRCN), where cancers are represented by 12 phenotype and 10 genotype features, and suitable cancers were adaptively selected for model pre-training. In this way, the pre-trained model can learn valuable prior knowledge from other cancer types while reducing the biases. RESULTS: ATRCN chose pancreatic and stomach adenocarcinomas as the pre-training cancers, and the experiments indicated that our method improved the C-index of 3.8% by comparing with traditional transfer learning methods. The independent tests on three additional HCC datasets proved the robustness of our model. Based on the divided risk subgroups, we identified 10 HCC prognostic markers, including one new prognostic marker, TTC36. Further wet experiments indicated that TTC36 is associated with the progression of liver cancer cells. CONCLUSION: These results proved that our proposed deep-learning-based method for HCC prognosis prediction is robust, accurate, and biologically meaningful.

Integrating multi-omics data through deep learning for accurate cancer prognosis prediction.

BACKGROUND: Genomic information is nowadays widely used for precise cancer treatments. Since the individual type of omics data only represents a single view that suffers from data noise and bias, multiple types of omics data are required for accurate cancer prognosis prediction. However, it is challenging to effectively integrate multi-omics data due to the large number of redundant variables but relatively small sample size. With the recent progress in deep learning techniques, Autoencoder was used to integrate multi-omics data for extracting representative features. Nevertheless, the generated model is fragile from data noises. Additionally, previous studies usually focused on individual cancer types without making comprehensive tests on pan-cancer. Here, we employed the denoising Autoencoder to get a robust representation of the multi-omics data, and then used the learned representative features to estimate patients' risks. RESULTS: By applying to 15 cancers from The Cancer Genome Atlas (TCGA), our method was shown to improve the C-index values over previous methods by 6.5% on average. Considering the difficulty to obtain multi-omics data in practice, we further used only mRNA data to fit the estimated risks by training XGboost models, and found the models could achieve an average C-index value of 0.627. As a case study, the breast cancer prognosis prediction model was independently tested on three datasets from the Gene Expression Omnibus (GEO), and shown able to significantly separate high-risk patients from low-risk ones (C-index>0.6, p-values<0.05). Based on the risk subgroups divided by our method, we identified nine prognostic markers highly associated with breast cancer, among which seven genes have been proved by literature review. CONCLUSION: Our comprehensive tests indicated that we have constructed an accurate and robust framework to integrate multi-omics data for cancer prognosis prediction. Moreover, it is an effective way to discover cancer prognosis-related genes.

A cadmium TCNQ-based semiconductor with versatile binding modes and non-integer redox states.

The 3-D coordination polymer with the basic formula Cd2(TCNQ)3.5(H2O)2 is the first instance of the coexistence of the bridging modes µ2, µ3 and µ4 for TCNQ in one network. Single crystal measurements revealed the compound to be a good semiconductor with a room temperature conductivity of 5.8 × 10(-3) S cm(-1).