Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study.

Background: Male breast cancer (MBC) is an uncommon disease. Few studies have discussed the prognosis of MBC due to its rarity.

Objective: This study aimed to develop a nomogram to predict the overall survival of patients with MBC and externally validate it using cases from China.

Predictive Value of Frailty on Outcomes of Patients With Cirrhosis: Systematic Review and Meta-Analysis.

Background: Frailty is one of the most common symptoms in patients with cirrhosis. Many researchers have identified it as a prognostic factor for patients with cirrhosis. However, no quantitative meta-analysis has evaluated the prognostic value of frailty in patients with cirrhosis.

Factors correlated with demoralization among cancer patients: A systematic review and meta-analysis.

Objectives: Demoralization isa common psychological problem in cancer patients. The purpose of this study is to systematically evaluate the correlated factors of demoralization among cancer patients. We also summarized the available evidence, effect estimates, and the strength of statistical associations between demoralization and its associated factors.

Prevalence and unfavourable outcome of oral frailty in older adult: a systematic review and meta-analysis.

Background And Objective: Oral frailty (OF) refers to a decline in oral function amongst older adult that often occurs alongside declines in cognitive and physical abilities. We conducted a study to determine the prevalence and unfavourable outcomes of OF in the older adult population to provide medical staff with valuable insights into the associated disease burden.

Methods: From inception to March 2024, we systematically searched six key electronic databases: PubMed, Web of Science, Embase, Cochrane Library, Scopus, and CINAHL to identify potential studies that reported the prevalence or unfavourable outcomes of OF amongst older adult.

Quality of life and its predictors among breast cancer patients treated with surgery-a retrospective minimum 3-year follow-up study.

Aim: Quality of life (QoL) has been identified as an important indicator of positive outcomes among breast cancer (BC) survivors. However, the status and predictors of QoL in China remain unclear. This retrospective follow-up study aimed to examine the QoL levels among BC patients following surgery and to assess the influence of sociodemographic, clinical, and psychological factors on QoL.

Prevalence and associated factors of psychological distress among patients with breast cancer: a systematic review and meta-analysis.

Objective: This paper aims to evaluate the literature on the prevalence of psychological distress and its associated factors in patients with breast cancer.

Design: Systematic review and meta-analysis.

Data Sources: PubMed, Web of Science, Embase, the Cochrane Library, China National Knowledge Infrastructure and Wanfang were searched from inception to 11 June 2024.

T lymphocyte and neutrophil/lymphocyte ratio in patients with radiation-induced oral mucositis after intensity-modulated radiation therapy for head and neck cancer: A retrospective single-center study.

To investigate T lymphocyte, neutrophil/lymphocyte ratio (NLR) and their impact on patients with radiation-induced oral mucositis (RIOM) after intensity-modulated radiotherapy for head and neck cancer. The clinical data of 148 patients diagnosed with head and neck cancer from January 2016 to January 2019 were retrospectively analyzed. Patients were divided into RIOM group (n = 42 cases) and non-RIOM group (n = 106 cases), based on whether they developed RIOM after intensity-modulated radiation therapy.

Surface Reconstruction From Point Clouds: A Survey and a Benchmark.

Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete point cloud observation is a long-standing problem in computer vision and graphics research. The problem is technically ill-posed, and becomes more difficult considering that various sensing imperfections would appear in the point clouds obtained by practical depth scanning. In literature, a rich set of methods has been proposed, and reviews of existing methods are also provided.

EPMF: Efficient Perception-Aware Multi-Sensor Fusion for 3D Semantic Segmentation.

We study multi-sensor fusion for 3D semantic segmentation that is important to scene understanding for many applications, such as autonomous driving and robotics. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to effectively exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds.

Exploring the Experiences and Support of Nurses as Second Victims After Patient Safety Events in China: A Mixed-Method Approach.

Aim: To investigate the current status of experience and support of nurses as second victims and explore its related factors in nurses.

Design: A sequential, explanatory, mixed-method study was applied.

Methods: A total of 406 nurses from seven tertiary hospitals in China were chosen as participants between September to October 2023.

Relation between frailty and adverse outcomes in elderly patients with gastric cancer: a scoping review.

Background: Playing an exemplary role, frailty have crucial effect on the preoperative evaluation of elderly patients. Previous studies have shown that frailty is associated with complications and mortality in patients with gastric cancer (GC). However, with the development of the concept of "patient-centered", the range of health-related outcomes is broad.

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training.

Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons.

Prevalence and unfavorable outcome of frailty in older adults with gastric cancer: a systematic review and meta-analysis.

Background And Aim: Previous studies reported inconsistent results on the prevalence and prognostic implications of frailty among older adults with gastric cancer. This systematic review synthesized available literature pertaining on this topic to establish the prevalence and unfavorable outcomes of frailty in older adults with gastric cancer.

Methods: A comprehensive search was conducted across multiple English databases including PubMed, Cochrane Library, CINAHL, Embase, and Web of Science as well as Chinese databases, namely, CNKI, Wan Fang, and CBM, from inception to July 4, 2023, to identify potential studies.

[Retracted] Anti‑Semaphorin‑7A single chain antibody demonstrates beneficial effects on pulmonary inflammation during acute lung injury.

[This retracts the article DOI: 10.3892/etm.2018.

Counterfactual Supervision-Based Information Bottleneck for Out-of-Distribution Generalization.

Learning invariant (causal) features for out-of-distribution (OOD) generalization have attracted extensive attention recently, and among the proposals, invariant risk minimization (IRM) is a notable solution. In spite of its theoretical promise for linear regression, the challenges of using IRM in linear classification problems remain. By introducing the information bottleneck (IB) principle into the learning of IRM, the IB-IRM approach has demonstrated its power to solve these challenges.

Correlates of stigma for patients with breast cancer: a systematic review and meta-analysis.

Objective: This study was conducted to examine the factors associated with stigma in breast cancer women.

Methods: PubMed, Embase, the Cochrane Library, Web of Science, and two Chinese electronic databases were electronically searched to identify eligible studies that reported the correlates of stigma for patients with breast cancer from inception to July 2022. Two researchers independently performed literature screening, data extraction, and risk of bias assessment.

Convolutional Fine-Grained Classification With Self-Supervised Target Relation Regularization.

Fine-grained visual classification can be addressed by deep representation learning under supervision of manually pre-defined targets (e.g., one-hot or the Hadamard codes).

Muscone improves hypoxia/reoxygenation (H/R)-induced neuronal injury by blocking HMGB1/TLR4/NF-κB pathway modulating microRNA-142.

Previous reports have indicated that natural muscone has neuroprotective effects against cerebral hypoxia injury; however, little is known in regards to its pharmacological mechanism. In this study, we tried to evaluate the neuroprotective effects and mechanisms of muscone against cerebral hypoxia injury using an model. The cerebral hypoxia injury cell model was produced by hypoxia/reoxygenation (H/R).

Learning and Meshing from Deep Implicit Surface Networks Using an Efficient Implementation of Analytic Marching.

Reconstruction of object or scene surfaces has tremendous applications in computer vision, computer graphics, and robotics. In this paper, we study a fundamental problem in this context about recovering a surface mesh from an implicit field function whose zero-level set captures the underlying surface. Given that an MLP with activations of Rectified Linear Unit (ReLU) partitions its input space into a number of linear regions, we are motivated to connect this local linearity with a same property owned by the desired result of polygon mesh.

Content-aware convolutional neural networks.

Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution traverses the input images/features using a sliding window scheme to extract features. However, not all the windows contribute equally to the prediction results of CNNs.

Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation Using Structurally Regularized Deep Clustering.

Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution diverges from the target one. Mainstream UDA methods strive to learn domain-aligned features such that classifiers trained on the source features can be readily applied to the target ones. Although impressive results have been achieved, these methods have a potential risk of damaging the intrinsic data structures of target discrimination, raising an issue of generalization particularly for UDA tasks in an inductive setting.

SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces From RGB Images.

This paper focuses on the challenging task of learning 3D object surface reconstructions from RGB images. Existing methods achieve varying degrees of success by using different surface representations. However, they all have their own drawbacks, and cannot properly reconstruct the surface shapes of complex topologies, arguably due to a lack of constraints on the topological structures in their learning frameworks.