CLEC14A was up-regulated in hepatocellular carcinoma and may function as a potential diagnostic biomarker

Objective: The current work aimed to investigate the expression and potential clinical significance of C-type Lectin domain family 14 (CLEC14A) in hepatocellular carcinoma. Methods: The relative expressions of CLEC14A in the Hepatocellular Carcinoma (HCC) tissue and adjacent normal tissue of 105 HCC patients were examined using RT-qPCR methods. Furthermore, Receiver Operating Characteristic (ROC) curve was drawn for exploring the diagnostic value of CLEC14A. Next, the expressions of CLEC14A in HCC cell lines and normal liver epithelial cells were compared, and the effects of knockdown of CLEC14A on the growth and apoptosis of HCC cells were examined. Results: The authors found that the expression of CLEC14A was markedly increased in hepatocellular carcinoma tumors in comparison with the adjacent tissue, and the expression level of CLEC14A was positively correlated with the size and differentiation of the tumor. Moreover, results of ROC analysis showed CLEC14A might function as a sensitive diagnostic biomarker for HCC. Furthermore, CLEC14A was up-regulated in HCC cell lines, and transient over-expression of CLEC14A decreased the proliferation and increased the apoptosis of HCC cells in vitro. Conclusions: Our results suggested that CLEC14A was up-regulated in HCC and might function as a potential diagnostic marker

GFlowCausal: Generative Flow Networks for Causal Discovery

Causal discovery aims to uncover causal structure among a set of variables. Score-based approaches mainly focus on searching for the best Directed Acyclic Graph (DAG) based on a predefined score function. However, most of them are not applicable on a large scale due to the limited searchability. Inspired by the active learning in generative flow networks, we propose a novel approach to learning a DAG from observational data called GFlowCausal. It converts the graph search problem to a generation problem, in which direct edges are added gradually. GFlowCausal aims to learn the best policy to generate high-reward DAGs by sequential actions with probabilities proportional to predefined rewards. We propose a plug-and-play module based on transitive closure to ensure efficient sampling. Theoretical analysis shows that this module could guarantee acyclicity properties effectively and the consistency between final states and fully-connected graphs. We conduct extensive experiments on both synthetic and real datasets, and results show the proposed approach to be superior and also performs well in a large-scale setting

Triplet Attention Transformer for Spatiotemporal Predictive Learning

Spatiotemporal predictive learning offers a self-supervised learning paradigm that enables models to learn both spatial and temporal patterns by predicting future sequences based on historical sequences. Mainstream methods are dominated by recurrent units, yet they are limited by their lack of parallelization and often underperform in real-world scenarios. To improve prediction quality while maintaining computational efficiency, we propose an innovative triplet attention transformer designed to capture both inter-frame dynamics and intra-frame static features. Specifically, the model incorporates the Triplet Attention Module (TAM), which replaces traditional recurrent units by exploring self-attention mechanisms in temporal, spatial, and channel dimensions. In this configuration: (i) temporal tokens contain abstract representations of inter-frame, facilitating the capture of inherent temporal dependencies; (ii) spatial and channel attention combine to refine the intra-frame representation by performing fine-grained interactions across spatial and channel dimensions. Alternating temporal, spatial, and channel-level attention allows our approach to learn more complex short- and long-range spatiotemporal dependencies. Extensive experiments demonstrate performance surpassing existing recurrent-based and recurrent-free methods, achieving state-of-the-art under multi-scenario examination including moving object trajectory prediction, traffic flow prediction, driving scene prediction, and human motion capture.Comment: Accepted to WACV 202

ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation

Pervasive mobile AI applications primarily employ one of the two learning paradigms: cloud-based learning (with powerful large models) or on-device learning (with lightweight small models). Despite their own advantages, neither paradigm can effectively handle dynamic edge environments with frequent data distribution shifts and on-device resource fluctuations, inevitably suffering from performance degradation. In this paper, we propose ECLM, an edge-cloud collaborative learning framework for rapid model adaptation for dynamic edge environments. We first propose a novel block-level model decomposition design to decompose the original large cloud model into multiple combinable modules. By flexibly combining a subset of the modules, this design enables the derivation of compact, task-specific sub-models for heterogeneous edge devices from the large cloud model, and the seamless integration of new knowledge learned on these devices into the cloud model periodically. As such, ECLM ensures that the cloud model always provides up-to-date sub-models for edge devices. We further propose an end-to-end learning framework that incorporates the modular model design into an efficient model adaptation pipeline including an offline on-cloud model prototyping and training stage, and an online edge-cloud collaborative adaptation stage. Extensive experiments over various datasets demonstrate that ECLM significantly improves model performance (e.g., 18.89% accuracy increase) and resource efficiency (e.g., 7.12x communication cost reduction) in adapting models to dynamic edge environments by efficiently collaborating the edge and the cloud models

The advantage of point-of-care ultrasound in central venous catheterization and related pericardial effusion in infants in the NICU

BackgroundCentral venous catheterization (CVC) is broadly used in neonatal intensive care units (NICUs) for efficient vascular access; however, its establishment and maintenance are associated with numerous risks and complications. Here, we focus on investigating the value of point-of-care ultrasound (POCUS) in the early diagnosis and treatment of pericardial effusion associated with CVC and compare the differences in ultrasound and radiography in CVC localization and monitoring in the NICU.MethodsTwenty-five infants with CVC-associated pericardial effusion (PCE) who were hospitalized in the NICU of Peking University Third Hospital between January 2013 and March 2023 were retrospectively selected for the study. Data concerning their catheterization characteristics, CVC tip position, clinical and imaging manifestations of PCE, treatments, and prognoses were analyzed.ResultsThe mean gestational age of our cohort was 29.3 ± 3.1 weeks, and the mean birth weight was 1,211 ± 237 g. The incidence of CVC-associated PCE was 0.65%, and 80% of PCE cases occurred within 4 days of CVC. After PCE, the most common symptoms were tachypnea (44%) and tachycardia (64%). Chest radiographs revealed cardiothoracic enlargement, and only 2 cases (9.10%) showed a “flask heart”. Cardiac ultrasound showed that the catheter tip extended deep into the heart in 72% of infants with PCE. Cardiac insufficiency was observed in 12 cases (48%). Overall, 8 infants (32%) had pericardial tamponade, 7 (87.5%) of whom underwent pericardiocentesis. Overall, 2 (8%) infants died, and the remaining 23 (92%) were cured.ConclusionCVC-associated PCE mostly occurs in the early post-catheterization stages (within 4 days) in infants. Some cases may have critical clinical manifestations and progress rapidly, with some even developing pericardial tamponade. A CVC tip being deep into the heart cavity is an important cause of PCE. Compared with chest radiography, point-of-care ultrasound is more accurate for CVC tip positioning and can detect PCE more quickly. Furthermore, it is more advantageous for locating and monitoring CVC-associated PCE. Early identification and diagnosis can effectively reduce fatality rates and improve the prognosis of infants with CVC-associated PCE

Transcatheter Aortic Valve Replacement for Aortic Regurgitation – A Review

Transcatheter aortic valve replacement (TAVR) is currently a widely used option for patients with severe symptomatic aortic stenosis with high to low surgical risk. However, aortic regurgitation (AR) remains an “off-label” indication for TAVR, particularly for patients with mild or absent leaflet calcification or aortic annulus dimensions beyond the size of the bioprosthesis, which increase the risk of dislocation. With advances in transcatheter heart valve devices, the safety and efficacy of TAVR in treating patients with severe pure native AR has gained acceptance. This review examines current evidence and clinical practice, and presents technological advancements in devices for AR

Laboratory-based Surveillance of Extensively Drug-Resistant Tuberculosis, China

To estimate the prevalence of extensively drug-resistant tuberculosis (XDR TB) in China, we retrospectively analyzed drug-resistance profiles of 989 clinical Mycobacterium tuberculosis isolates. We found 319 (32.3%) isolates resistant to >1 first-line drugs; 107 (10.8%) isolates were multidrug resistant, of which 20 (18.7%) were XDR. XDR TB is of major concern in China