A shock flash breaking out of a dusty red supergiant.

Shock-breakout emission is light that arises when a shockwave, generated by the core-collapse explosion of a massive star, passes through its outer envelope. Hitherto, the earliest detection of such a signal was at several hours after the explosion1, although a few others had been reported2-7. The temporal evolution of early light curves should provide insights into the shock propagation, including explosion asymmetry and environment in the vicinity, but this has been hampered by the lack of multiwavelength observations. Here we report the instant multiband observations of a type II supernova (SN 2023ixf) in the galaxy M101 (at a distance of 6.85 ± 0.15 Mpc; ref. 8), beginning at about 1.4 h after the explosion. The exploding star was a red supergiant with a radius of about 440 solar radii. The light curves evolved rapidly, on timescales of 1-2 h, and appeared unusually fainter and redder than predicted by the models9-11 within the first few hours, which we attribute to an optically thick dust shell before it was disrupted by the shockwave. We infer that the breakout and perhaps the distribution of the surrounding dust were not spherically symmetric.

Disulfidptosis-related signature elucidates the prognostic, immunologic, and therapeutic characteristics in ovarian cancer.

Introduction: Ovarian cancer (OC) is the deadliest malignancy in gynecology, but the mechanism of its initiation and progression is poorly elucidated. Disulfidptosis is a novel discovered type of regulatory cell death. This study aimed to develop a novel disulfidptosis-related prognostic signature (DRPS) for OC and explore the effects and potential treatment by disulfidptosis-related risk stratification. Methods: The disulfidptosis-related genes were first analyzed in bulk RNA-Seq and a prognostic nomogram was developed and validated by LASSO algorithm and multivariate cox regression. Then we systematically assessed the clinicopathological and mutational characteristics, pathway enrichment analysis, immune cell infiltration, single-cell-level expression, and drug sensitivity according to DRPS. Results: The DRPS was established with 6 genes (MYL6, PDLIM1, ACTN4, FLNB, SLC7A11, and CD2AP) and the corresponding prognostic nomogram was constructed based on the DRPS, FIGO stage, grade, and residual disease. Stratified by the risk score derived from DRPS, patients in high-risk group tended to have worse prognosis, lower level of disulfidptosis, activated oncogenic pathways, inhibitory tumor immune microenvironment, and higher sensitivity to specific drugs including epirubicin, stauroporine, navitoclax, and tamoxifen. Single-cell transcriptomic analysis revealed the expression level of genes in the DRPS significantly varied in different cell types between tumor and normal tissues. The protein-level expression of genes in the DRPS was validated by the immunohistochemical staining analysis. Conclusion: In this study, the DRPS and corresponding prognostic nomogram for OC were developed, which was important for OC prognostic assessment, tumor microenvironment modification, drug sensitivity prediction, and exploration of potential mechanisms in tumor development.

Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study.

BACKGROUND: Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer. METHODS: In this multicentre, retrospective cohort study, we collected 98 laboratory tests and clinical features of women with or without ovarian cancer admitted to three hospitals in China during Jan 1, 2012 and April 4, 2021. A multi-criteria decision making-based classification fusion (MCF) risk prediction framework was used to make a model that combined estimations from 20 AI classification models to reach an integrated prediction tool developed for ovarian cancer diagnosis. It was evaluated on an internal validation set (3007 individuals) and two external validation sets (5641 and 2344 individuals). The primary outcome was the prediction accuracy of the model in identifying ovarian cancer. FINDINGS: Based on 52 features (51 laboratory tests and age), the MCF achieved an area under the receiver-operating characteristic curve (AUC) of 0·949 (95% CI 0·948-0·950) in the internal validation set, and AUCs of 0·882 (0·880-0·885) and 0·884 (0·882-0·887) in the two external validation sets. The model showed higher AUC and sensitivity compared with CA125 and HE4 in identifying ovarian cancer, especially in patients with early-stage ovarian cancer. The MCF also yielded acceptable prediction accuracy with the exclusion of highly ranked laboratory tests that indicate ovarian cancer, such as CA125 and other tumour markers, and outperformed state-of-the-art models in ovarian cancer prediction. The MCF was wrapped as an ovarian cancer prediction tool, and made publicly available to provide estimated probability of ovarian cancer with input laboratory test values. INTERPRETATION: The MCF model consistently achieved satisfactory performance in ovarian cancer prediction when using laboratory tests from the three validation sets. This model offers a low-cost, easily accessible, and accurate diagnostic tool for ovarian cancer. The included laboratory tests, not only CA125 which was the highest ranked laboratory test in importance of diagnostic assistance, contributed to the characterisation of patients with ovarian cancer. FUNDING: Ministry of Science and Technology of China; National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province, China; and Science and Technology Project of Guangzhou, China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study.

Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.

Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study.

BACKGROUND: Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed to develop a deep convolutional neural network (DCNN) model that automates evaluation of ultrasound images and to facilitate a more accurate diagnosis of ovarian cancer than existing methods. METHODS: In this retrospective, multicentre, diagnostic study, we collected pelvic ultrasound images from ten hospitals across China between September 2003, and May 2019. We included consecutive adult patients (aged ≥18 years) with adnexal lesions in ultrasonography and healthy controls and excluded duplicated cases and patients without adnexa or pathological diagnosis. For DCNN model development, patients were assigned to the training dataset (34 488 images of 3755 patients with ovarian cancer, 541 442 images of 101 777 controls). For model validation, patients were assigned to the internal validation dataset (3031 images of 266 patients with ovarian cancer, 5385 images of 602 with benign adnexal lesions), external validation datasets 1 (486 images of 67 with ovarian cancer, 933 images of 268 with benign adnexal lesions), and 2 (1253 images of 166 with ovarian cancer, 5257 images of 723 benign adnexal lesions). Using these datasets, we assessed the diagnostic value of DCNN, compared DCNN with 35 radiologists, and explored whether DCNN could augment the diagnostic accuracy of six radiologists. Pathological diagnosis was the reference standard. FINDINGS: For DCNN to detect ovarian cancer, AUC was 0·911 (95% CI 0·886-0·936) in the internal dataset, 0·870 (95% CI 0·822-0·918) in external validation dataset 1, and 0·831 (95% CI 0·793-0·869) in external validation dataset 2. The DCNN model was more accurate than radiologists at detecting ovarian cancer in the internal dataset (88·8% vs 85·7%) and external validation dataset 1 (86·9% vs 81·1%). Accuracy and sensitivity of diagnosis increased more after DCNN-assisted diagnosis than assessment by radiologists alone (87·6% [85·0-90·2] vs 78·3% [72·1-84·5], p<0·0001; 82·7% [78·5-86·9] vs 70·4% [59·1-81·7], p<0·0001). The average accuracy of DCNN-assisted evaluations for six radiologists reached 0·876 and were significantly augmented when they were DCNN-assisted (p<0·05). INTERPRETATION: The performance of DCNN-enabled ultrasound exceeded the average diagnostic level of radiologists matched the level of expert ultrasound image readers, and augmented radiologists' accuracy. However, these observations warrant further investigations in prospective studies or randomised clinical trials. FUNDING: National Key Basic Research Program of China, National Sci-Tech Support Projects, and National Natural Science Foundation of China.

Preoperative Evaluation of Perineural Invasion in Cervical Cancer: Development and Independent Validation of a Novel Predictive Nomogram.

BACKGROUND: Perineural invasion (PNI) is associated with a poor prognosis for cervical cancer and influences surgical strategies. However, a preoperative evaluation that can determine PNI in cervical cancer patients is lacking. METHODS: After 1:1 propensity score matching, 162 cervical cancer patients with PNI and 162 cervical cancer patients without PNI were included in the training set. Forty-nine eligible patients were enrolled in the validation set. The PNI-positive and PNI-negative groups were compared. Multivariate logistic regression was performed to build the PNI prediction nomogram. RESULTS: Age [odds ratio (OR), 1.028; 95% confidence interval (CI), 0.999-1.058], adenocarcinoma (OR, 1.169; 95% CI, 0.675-2.028), tumor size (OR, 1.216; 95% CI, 0.927-1.607), neoadjuvant chemotherapy (OR, 0.544; 95% CI, 0.269-1.083), lymph node enlargement (OR, 1.953; 95% CI, 1.086-3.550), deep stromal invasion (OR, 1.639; 95% CI, 0.977-2.742), and full-layer invasion (OR, 5.119; 95% CI, 2.788-9.799) were integrated in the PNI prediction nomogram based on multivariate logistic regression. The PNI prediction nomogram exhibited satisfactory performance, with areas under the curve of 0.763 (95% CI, 0.712-0.815) for the training set and 0.860 (95% CI, 0.758-0.961) for the validation set. Moreover, after reviewing the pathological slides of patients in the validation set, four patients initially diagnosed as PNI-negative were recognized as PNI-positive. All these four patients with false-negative PNI were correctly predicted to be PNI-positive (predicted p > 0.5) by the nomogram, which improved the PNI detection rate. CONCLUSION: The nomogram has potential to assist clinicians when evaluating the PNI status, reduce misdiagnosis, and optimize surgical strategies for patients with cervical cancer.

Immunological alternation in COVID-19 patients with cancer and its implications on mortality.

Patients with malignancy were reportedly more susceptible and vulnerable to Coronavirus Disease 2019 (COVID-19), and witnessed a greater mortality risk in COVID-19 infection than noncancerous patients. But the role of immune dysregulation of malignant patients on poor prognosis of COVID-19 has remained insufficiently investigated. Here we conducted a retrospective cohort study that included 2,052 patients hospitalized with COVID-19 (Cancer, n = 93; Non-cancer, n = 1,959), and compared the immunological characteristics of both cohorts. We used stratification analysis, multivariate regressions, and propensity-score matching to evaluate the effect of immunological indices. In result, COVID-19 patients with cancer had ongoing and significantly elevated inflammatory factors and cytokines (high-sensitivity C-reactive protein, procalcitonin, interleukin (IL)-2 receptor, IL-6, IL-8), as well as decreased immune cells (CD8 + T cells, CD4 + T cells, B cells, NK cells, Th and Ts cells) than those without cancer. The mortality rate was significantly higher in cancer cohort (24.7%) than non-cancer cohort (10.8%). By stratification analysis, COVID-19 patients with immune dysregulation had poorer prognosis than those with the relatively normal immune system both in cancer and non-cancer cohort. By logistic regression, Cox regression, and propensity-score matching, we found that prior to adjustment for immunological indices, cancer history was associated with an increased mortality risk of COVID-19 (p < .05); after adjustment for immunological indices, cancer history was no longer an independent risk factor for poor prognosis of COVID-19 (p > .30). In conclusion, COVID-19 patients with cancer had more severely dysregulated immune responses than noncancerous patients, which might account for their poorer prognosis. Clinical Trial: This study has been registered on the Chinese Clinical Trial Registry (No. ChiCTR2000032161).

Heterogeneity of immune microenvironment in ovarian cancer and its clinical significance: a retrospective study.

Treatment of ovarian cancer (OC) remains the biggest challenge among gynecological malignancies. Immune checkpoint blockade therapy is promising in many cancers but shows low response rates in OC because of its heterogeneity. Although the biological and molecular heterogeneity of OC has been extensively investigated, heterogeneity of immune microenvironment remains elusive. We have collected the expression profiles of 3071 OC patients from 22 publicly available datasets. CIBERSORT was applied to infer the infiltration fraction of 22 immune cells among 2086 patients with CIBERSORT P < .05. We then explored the heterogeneity landscape of immune microenvironment in OC at three levels (immune infiltration, prognostic relevance of immune infiltration, immune checkpoint expression patterns). Multivariable Cox regression model was used to investigate the associations between survival risk and immune infiltration. Constructed immune risk score stratified patients with significantly different survival risk (HR: 1.47, 95% CI: 1.31-1.66, P < .0001). The immune infiltration landscape, prognostic relevance of immune cells, and expression patterns of 79 immune checkpoints exhibited remarkable clinicopathological heterogeneity. For instance, M1 macrophages were significantly associated with better outcomes among patients with high-grade, late-stage, type-II OC (HR: 0.77-0.83), and worse outcomes among patients with type-I OC (HR: 1.78); M2 macrophages were significantly associated with worse outcomes among patients with high-grade, type-II OC (HR: 1.14-1.17); Neutrophils were significantly associated with worse outcomes among patients with high-grade, late-stage, type-I OC (HR: 1.14-1.73). The heterogeneous landscape of immune microenvironment presented in this study provided new insights into prognostic prediction and tailored immunotherapy of OC.

Risk factors for developing into critical COVID-19 patients in Wuhan, China: A multicenter, retrospective, cohort study.

BACKGROUND: The ferocious global assault of COVID-19 continues. Critically ill patients witnessed significantly higher mortality than severe and moderate ones. Herein, we aim to comprehensively delineate clinical features of COVID-19 and explore risk factors of developing critical disease. METHODS: This is a Mini-national multicenter, retrospective, cohort study involving 2,387 consecutive COVID-19 inpatients that underwent discharge or death between January 27 and March 21, 2020. After quality control, 2,044 COVID-19 inpatients were enrolled. Electronic medical records were collected to identify the risk factors of developing critical COVID-19. FINDINGS: The severity of COVID-19 climbed up straightly with age. Critical group was characterized by higher proportion of dyspnea, systemic organ damage, and long-lasting inflammatory storm. All-cause mortality of critical group was 85•45%, by contrast with 0•58% for severe group and 0•18% for moderate group. Logistic regression revealed that sex was an effect modifier for hypertension and coronary heart disease (CHD), where hypertension and CHD were risk factors solely in males. Multivariable regression showed increasing odds of critical illness associated with hypertension, CHD, tumor, and age ≥ 60 years for male, and chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), tumor, and age ≥ 60 years for female. INTERPRETATION: We provide comprehensive front-line information about different severity of COVID-19 and insights into different risk factors associated with critical COVID-19 between sexes. These results highlight the significance of dividing risk factors between sexes in clinical and epidemiologic works of COVID-19, and perhaps other coronavirus appearing in future. FUNDING: 10.13039/100000001 National Science Foundation of China.

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Corrigendum: Intense Exercise Promotes Adult Hippocampal Neurogenesis But Not Spatial Discrimination.

[This corrects the article DOI: 10.3389/fncel.2017.00013.].

Intense Exercise Promotes Adult Hippocampal Neurogenesis But Not Spatial Discrimination.

Hippocampal neurogenesis persists throughout adult life and plays an important role in learning and memory. Although the influence of physical exercise on neurogenesis has been intensively studied, there is controversy in regard to how the impact of exercise may vary with its regime. Less is known about how distinct exercise paradigms may differentially affect the learning behavior. Here we found that, chronic moderate treadmill running led to an increase of cell proliferation, survival, neuronal differentiation, and migration. In contrast, intense running only promoted neuronal differentiation and migration, which was accompanied with lower expressions of vascular endothelial growth factor, brain-derived neurotrophic factor, insulin-like growth factor 1, and erythropoietin. In addition, the intensely but not mildly exercised animals exhibited a lower mitochondrial activity in the dentate gyrus. Correspondingly, neurogenesis induced by moderate but not intense exercise was sufficient to improve the animal's ability in spatial pattern separation. Our data indicate that the effect of exercise on spatial learning is intensity-dependent and may involve mechanisms other than a simple increase in the number of new neurons.