Construction and Validation of a Nomogram to Predict the Postoperative Venous Thromboembolism Risk in Patients with HGSOC.

Venous thromboembolism (VTE) is a common complication in patients with high-grade serous ovarian cancer (HGSOC) after surgery. This study aims to establish a comprehensive risk assessment model to better identify the potential risk of postoperative VTE in HGSOC. Clinical data from 587 HGSOC patients who underwent surgical treatment were retrospectively collected. Univariate and multivariate logistic regression analyses were performed to identify independent factors influencing the occurrence of postoperative VTE in HGSOC. A nomogram model was constructed in the training set and further validated in the verification set. Logistic regression identified age (odds ratio [OR] = 1.063, P = .002), tumor size (OR = 3.815, P < .001), postoperative transfusion (OR = 5.646, P = .001), and postoperative D-dimer (OR = 1.246, P = .003) as independent risk factors for postoperative VTE in HGSOC patients. A nomogram was constructed using these factors. The receiver operating characteristic curve showed an area under the curve (AUC) of 0.840 (95% confidence interval [CI]: 0.782, 0.898) in the training set and 0.793 (95% CI: 0.704, 0.882) in the validation set. The calibration curve demonstrated a good consistency between model predictions and actual results. The decision curve analysis indicated the model benefits at a threshold probability of less than 70%. A nomogram predicting postoperative VTE in HGSOC was established and validated. This model will assist clinicians in the early identification of high-risk patients, enabling the implementation of appropriate preventive measures.

A large-scale fMRI dataset for the visual processing of naturalistic scenes.

One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put a lot of effort into collecting large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.

A large-scale fMRI dataset for human action recognition.

Human action recognition is a critical capability for our survival, allowing us to interact easily with the environment and others in everyday life. Although the neural basis of action recognition has been widely studied using a few action categories from simple contexts as stimuli, how the human brain recognizes diverse human actions in real-world environments still needs to be explored. Here, we present the Human Action Dataset (HAD), a large-scale functional magnetic resonance imaging (fMRI) dataset for human action recognition. HAD contains fMRI responses to 21,600 video clips from 30 participants. The video clips encompass 180 human action categories and offer a comprehensive coverage of complex activities in daily life. We demonstrate that the data are reliable within and across participants and, notably, capture rich representation information of the observed human actions. This extensive dataset, with its vast number of action categories and exemplars, has the potential to deepen our understanding of human action recognition in natural environments.

Comparative Survival Outcomes of Hyperthermic Intraperitoneal Chemotherapy, Intraperitoneal Chemotherapy and Intravenous Chemotherapy for Primary Advanced Ovarian Cancer: A Network Meta-Analysis.

OBJECTIVE: We aimed to compare the survival outcomes and adverse events of hyperthermic intraperitoneal chemotherapy (HIPEC), intraperitoneal chemotherapy (IP)and intravenous chemotherapy (IP)for primary advanced ovarian cancer. METHODS: PubMed, CENTRAL (Cochrane Central Registry of Controlled Trials), Embase, Web of Science and Scopus were searched using multiple terms for primary advanced ovarian cancer, including randomized controlled trials and comparative studies in both Chinese and English (up to date 15 August 2022). Outcomes include overall survival, progression-free survival and adverse events. The data were pooled and reported as hazard ratio (HRs) with 95% confidence intervals. The Newcastle-Ottawa Scales were used to assess the risk of bias in the included comparative study. The Cochrane Collaboration's Risk of Bias Tool was used for randomized controlled trials. RESULTS: In total, 32 studies, including 6347 patients and 8 different platinum-based chemotherapy regimens, were included in this network meta-analysis. Our analysis results showed that HIPEC2 (carboplatin with area under the curve 10) exhibited a statistically significant OS benefit compared to IV, weekly dose-dense chemotherapy and HIPEC1 (cisplatin with 75/100 mg/m2). Intraperitoneal plus intravenous chemotherapy was associated with a statistically significantly better likelihood of overall survival compared to IV. For progression-free survival, our statistical results only suggest a better progression-free survival in ovarian cancer patients treated with HIPEC1 compared with weekly dose-dense chemotherapy. No evidence of difference was observed between the other comparison groups. Compared with the non-HIPEC group, HIPEC may had a higher incidence of electrolyte disturbances (≥grade 3). CONCLUSION: Our statistical analysis suggests that the groups receiving HIPEC2 had a better OS than the groups receiving IV, weekly dose-dense chemotherapy and HIPEC1. For PFS, our analysis only showed HIPEC1 is better than IV. Moreover, HIPEC may lead to a higher incidence of electrolyte disturbances (≥grade 3). HIPEC therapy for advanced ovarian cancer is currently controversial.

DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains.

Deep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks via an end-to-end deep learning strategy. Deep learning allows data representations that have multiple levels of abstraction; however, it does not explicitly provide any insights into the internal operations of DNNs. Deep learning's success is appealing to neuroscientists not only as a method for applying DNNs to model biological neural systems but also as a means of adopting concepts and methods from cognitive neuroscience to understand the internal representations of DNNs. Although general deep learning frameworks, such as PyTorch and TensorFlow, could be used to allow such cross-disciplinary investigations, the use of these frameworks typically requires high-level programming expertise and comprehensive mathematical knowledge. A toolbox specifically designed as a mechanism for cognitive neuroscientists to map both DNNs and brains is urgently needed. Here, we present DNNBrain, a Python-based toolbox designed for exploring the internal representations of DNNs as well as brains. Through the integration of DNN software packages and well-established brain imaging tools, DNNBrain provides application programming and command line interfaces for a variety of research scenarios. These include extracting DNN activation, probing and visualizing DNN representations, and mapping DNN representations onto the brain. We expect that our toolbox will accelerate scientific research by both applying DNNs to model biological neural systems and utilizing paradigms of cognitive neuroscience to unveil the black box of DNNs.