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Abstract Background Paclitaxel dose-dense regimen has been controversial in clinical trials in recent years. This systematic review and meta-analysis tried to evaluate the efficacy and safety of paclitaxel dose-dense chemotherapy in primary epithelial ovarian cancer. Methods An electronic search following PRISMA guidelines was conducted (Prospero registration number: CRD42020187622), and then a systematic review and meta-analysis of included literature were initiated to determine which regimen was better. Results Four randomized controlled trials were included in the qualitative evaluation, and 3699 ovarian cancer patients were included in the meta-analysis. The meta-analysis revealed that the dose-dense regimen could prolong PFS (HR0.88, 95%CI 0.81–0.96; p = 0.002) and OS (HR0.90, 95%CI 0.81–1.02; p = 0.09), but it also increased the overall toxicity (OR = 1.102, 95%CI 0.864–1.405; p = 0.433), especially toxicity of anemia (OR = 1.924, 95%CI 1.548–2.391; p

Increased ovarian fibrosis and an expanded stromal cell compartment are the main characteristics of aging ovaries. However, the molecular mechanisms and the key factor of stromal cells underlying ovarian aging remain unclear. Here, we explored single-cell transcriptomic data of ovaries from the adult mouse (4,363 cells), young (1,122 cells), and aged (1,479 cells) non-human primates (NHPs) to identify expression patterns of stromal cells between young and old ovaries. An increased number of stromal cells (p = 0.0386) was observed in aged ovaries of NHPs, with enrichment processes related to the collagen-containing extracellular matrix. In addition, differentially expressed genes of stromal cells between young and old ovaries were regulated by ESR1 (p = 7.94E-08) and AR (p = 1.99E-05). Among them, EGFR was identified as the common target and was highly expressed (p = 7.69E-39) in old ovaries. In human ovaries, the correlated genes of EGFR were associated with the process of the cell-substrate junction. Silencing of EGFR in human ovarian stromal cells led to the reduction of cell-substrate junction via regulating phosphorylation modification of the AKT-mTOR signaling pathway and stromal cell marker genes. Overall, we identified high levels of EGFR for stromal cells in ovarian aging, which provides insight into the cell type-specific molecular mechanisms underlying ovarian aging at single-cell resolution.

Abstract Background Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. Methods We retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset. Results Six features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979–1.000) in internal validation cohort and 0.999 (95% CI 0.998–1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance. Conclusions The developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry ( ChiCTR2000032161 ). Graphical abstracthelper lymphocytve vv

Summary: 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

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 .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).

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: National Science Foundation of China.

Fully Ceramic Microencapsulated (FCM) fuel, consisting of tristructural-isotropic (TRISO) and SiC matrix, has engaged significant attention owing to its unmatched accident tolerance and excellent heat transfer efficiency. The effective thermal conductivity (ETC) is of great significance when evaluating the thermal efficiency and safety of nuclear fuel. In this study, the finite element method (FEM) is used to model the ETC of full 3D FCM pellets. The effects of several factors on the ETC of the FCM were mainly investigated, such as the volume fraction, components properties, and the distribution of TRISO particles. The numerical ETC was compared to analytical models in the literature, the most appropriate analytical model was recommended and the accuracy of the developed numerical model was verified. The performed calculation showed that the ETC of the pellet is negatively correlated with the volume fraction of the TRISO particles. In addition, we found that the distribution of particles has a noticeable effect on the ETC of the pellet, and the relationship is fitted by data mining. Based on the results of the calculations, two routes to improve the ETC of the FCM pellet are proposed, one is to increase the thermal conductivity of the buffer layer and the matrix in the TRISO particle and the other is to disperse the TRISO particles by optimizing the preparation process. The present study provides theoretical support for the analysis and improvement of the FCM design and fabrication in the future.