Construction and validation of a novel ferroptosis-related signature for evaluating prognosis and immune microenvironment in ovarian cancer

Ovarian cancer (OV) is the most lethal form of gynecological malignancy worldwide, with limited therapeutic options and high recurrence rates. However, research focusing on prognostic patterns of ferroptosis-related genes (FRGs) in ovarian cancer is still lacking. From the 6,406 differentially expressed genes (DEGs) between TCGA-OV (n = 376) and GTEx cohort (n = 180), we identified 63 potential ferroptosis-related genes. Through the LASSO-penalized Cox analysis, 3 prognostic genes, SLC7A11, ZFP36, and TTBK2, were finally distinguished. The time-dependent ROC curves and K-M survival analysis performed powerful prognostic ability of the 3-gene signature. Stepwise, we constructed and validated the nomogram based on the 3-gene signature and clinical features, with promising prognostic value in both TCGA (p-value < .0001) and ICGC cohort (p-value = .0064). Gene Set Enrichment Analysis elucidated several potential pathways between the groups stratified by 3-gene signature, while the m6A gene analysis implied higher m6A level in the high-risk group. We applied the CIBERSORT algorithm to distinct tumor immune microenvironment between two groups, with less activated dendritic cells (DCs) and plasma cells, more M0 macrophages infiltration, and higher expression of key immune checkpoint molecules (CD274, CTLA4, HAVCR2, and PDCD1LG2) in the high-risk group. In addition, the low-risk group exhibited more favorable immunotherapy and chemotherapy responses. Collectively, our findings provided new prospects in the role of ferroptosis-related genes, as a promising prediction tool for prognosis and immune responses, in order to assist personalized treatment decision-making among ovarian cancer patients

A model local interpretation routine for deep learning based radio galaxy classification

Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted to classify source radio morphology from survey image data using deep learning algorithms (i.e., Convolutional Neural Networks), they concentrated on model robustness most time. It is unclear whether a model similarly makes predictions as radio astronomers did. In this work, we used Local Interpretable Model-agnostic Explanation (LIME), an state-of-the-art eXplainable Artificial Intelligence (XAI) technique to explain model prediction behaviour and thus examine the hypothesis in a proof-of-concept manner. In what follows, we describe how \textbf{LIME} generally works and early results about how it helped explain predictions of a radio galaxy classification model using this technique.Comment: 4 pages, 1 figure, accepted summary paper for URSI GASS 2023 J0

Application of remote online learning in oral histopathology teaching in China

The aim of this study was to investigate the application of remote learning and virtual microscopy in oral histopathology teaching, a unique experience in China. The oral histopathology teaching in Nanjing Medical University has been extraordinary. 98 third-year dental students of Grade 2016 took oral histopathology theoretical course face-to-face in 2019 (Traditional group). The 94 participants of Grade 2017 took online oral histopathology course using digital methods(E-Learning platform and Virtual Simulation Experiment Teaching Center for Dentistry) in 2020. During the practical laboratory sessions, the students in both Traditional group and Online group observed the same glass slides for morphological learning. A questionnaire survey explored students' attitudes towards the remote online learning. Results: The mean Theory test scores of the Online group (80.93±12.15) were significantly higher than those of the Traditional group (73.65±8.46) (P 85) of the Online group (54%) was also significantly higher than that of the Traditional group (15%) (P< 0.01). Furthermore, both remote learning and virtual microscopy courses were well accepted by students according to the questionnaire. This study found that remote learning and virtual technology have a positive impact on oral histopathology. The findings reveal that the application of remote online learning has enhanced oral histopathology teaching in China

Discovery of 16 new z ∼ 5.5 quasars: filling in the redshift gap of quasar color selection

We present initial results from the first systematic survey of luminous z ∼ 5.5 quasars. Quasars at z ∼ 5.5, the post-reionization epoch, are crucial tools to explore the evolution of intergalactic medium, quasar evolution, and the early super-massive black hole growth. However, it has been very challenging to select quasars at redshifts 5.3 ≤ z ≤ 5.7 using conventional color selections, due to their similar optical colors to late-type stars, especially M dwarfs, resulting in a glaring redshift gap in quasar redshift distributions. We develop a new selection technique for z ∼ 5.5 quasars based on optical, near-IR, and mid-IR photometric data from Sloan Digital Sky Survey (SDSS), UKIRT InfraRed Deep Sky Surveys—Large Area Survey (ULAS), VISTA Hemisphere Survey (VHS), and Wide Field Infrared Survey Explorer. From our pilot observations in the SDSS-ULAS/VHS area, we have discovered 15 new quasars at 5.3 ≤ z ≤ 5.7 and 6 new lower redshift quasars, with SDSS z band magnitude brighter than 20.5. Including other two z ∼ 5.5 quasars already published in our previous work, we now construct a uniform quasar sample at 5.3 ≤ z ≤ 5.7, with 17 quasars in a ∼4800 square degree survey area. For further application in a larger survey area, we apply our selection pipeline to do a test selection by using the new wide field J-band photometric data from a preliminary version of the UKIRT Hemisphere Survey (UHS). We successfully discover the first UHS selected z ∼ 5.5 quasar

A Novel pyroptosis-related signature for predicting prognosis and evaluating tumor immune microenvironment in ovarian cancer

Abstract Ovarian cancer (OV) is the most fatal gynecological malignant tumor worldwide, with high recurrence rates and great heterogeneity. Pyroptosis is a newly-acknowledged inflammatory form of cell death with an essential role in cancer progression, though studies focusing on prognostic patterns of pyroptosis in OV are still lacking. Our research filtered 106 potential pyroptosis-related genes (PRGs) among the 6406 differentially expressed genes (DEGs) between the 376 TCGA-OV samples and 180 normal controls. Through the LASSO-Cox analysis, the 6-gene prognostic signature, namely CITED2, EXOC6B, MIA2, NRAS, SETBP1, and TRPV46, was finally distinguished. Then, the K-M survival analysis and time-dependent ROC curves demonstrated the promising prognostic value of the 6-gene signature (p-value < 0.0001). Furthermore, based on the signature and corresponding clinical features, we constructed and validated a nomogram model for 1-year, 2-year, and 3-year OV survival, with reliable prognostic values in TCGA-OV (p-value < 0.001) and ICGC-OV cohort (p-value = 0.040). Pathway analysis enriched several critical pathways in cancer, refer to the pyroptosis-related signature, while the m6A analysis indicated greater m6A level in high-risk group. We assessed tumor immune microenvironment through the CIBERSORT algorithm, which demonstrated the upregulation of M1 Macrophages and activated DCs and high expression of key immune checkpoint molecules (CTLA4, PDCD1LG2, and HAVCR2) in high-risk group. Interestingly, the high-risk group exhibited poor sensitivity towards immunotherapy and better sensitivity towards chemotherapies, including Vinblastine, Docetaxel, and Sorafenib. Briefly, the pyroptosis-related signature was a promising tool to predict prognosis and evaluate immune responses, in order to assist decision-making for OV patients in the realm of precision medicine

The effects of diverse microbial community structures, driven by arbuscular mycorrhizal fungi inoculation, on carbon release from a paddy field

Arbuscular mycorrhizal fungi (AMF) play a key role in regulating the carbon cycle in terrestrial ecosystems. However, there is little information on how AMF inoculation affects the carbon fluxes of paddy fields, which are major sources of global carbon emissions. We, therefore, designed an experiment to study the effects of AMF inoculation on methane and carbon dioxide emissions from a paddy field. Results showed that: (1) Among the tested factors, the C/N ratio was the main environmental determinant of microbial community structure in the investigated soil; (2) compared with traditional fertilisation (control), the soil C/N ratio increased by 2.1~15.2% and 1.4~10.5% as a result of AMF application alone (M) or in combination with mineral fertiliser (FM) throughout the growing season, respectively. This change shifted microbial community composition to higher G+/G- bacterial and fungal/bacterial ratios; (3) the microbial community change favoured soil carbon retention. Methane (CH4) emission peaks were reduced by 59.4% and 76.0% versus control in the M treatment and by 52.5% and 29.4% in the FM treatment in the midseason and end-of-season drainage periods, and CO2 emission peaks were reduced by 70.1% and 52.3% in the M plots and by 55.4% and 66.4% in the FM plots

The Learning Curve of Laparoendoscopic Single-Site Surgery in Benign Gynecological Diseases

Objective To analyze and draw the learning curve of laparoendoscopic single-site surgery (LESS) in various benign gynecological diseases, so as to provide a reference for applying this cutting-edge technique. Methods A retrospective analysis of LESS was conducted. Factors influencing the LESS learning process were assessed using Cox’s proportional hazards regression. The cumulative sum (CUSUM) value and the learning curve were calculated and visualized based on operation time (OT), blood loss (BL), conventional laparoscopic surgery (CLS), conversion rate (CV), and complications (CP). The CUSUM value was defined as the sum of CUSUMOT, CUSUMBL, CUSUMCV, and CUSUMCP. Results A total of 445 cases, including adnexectomies (n = 147), ovarian cystectomies (n = 175), and myomectomies (n = 123) were analyzed. Multivariate regression analysis indicated that adhesion grade (HR, 1.462; 95% CI, 1.016–1.994; p = .045), surgical type (HR, 1.283; 95% CI, 1.042–1.429; p = .024), and surgeon CLS experience (HR, 1.372; 95% CI, 1.097–2.246; p = .012) were independent factors predicting surgeons’ mastery of the LESS technique. Among gynecologists with CLS experience, the cutoff points were 17, 20, and 27 cases for adnexectomy, ovarian cystectomy, and myomectomy, respectively. For those without CLS experience, the corresponding cutoff values were 19, 27, and 35 cases. Conclusion The learning curve of LESS for benign gynecological diseases indicates a stepwise process, during which the surgeon’s CLS experience is the key, especially in ovarian cystectomy and myomectomy. For the training of young gynecologists, CLS should be emphasized in the early stage, and LESS should be introduced gradually

Additional file 4 of A Novel pyroptosis-related signature for predicting prognosis and evaluating tumor immune microenvironment in ovarian cancer

Supplementary Material 4 Figure 2 The clinical features of OV patients, stratified by the pyroptosis-associated 6-gene signature

Additional file 2 of A Novel pyroptosis-related signature for predicting prognosis and evaluating tumor immune microenvironment in ovarian cancer

Supplementary Material 2 Table 2 Overview of the six differentially expressed pyroptosis-related