Targeting CDCP1 boost CD8+ T cells-mediated cytotoxicity in cervical cancer via the JAK/STAT signaling pathway.

Background: Cervical cancer remains a global health challenge. The identification of new immunotherapeutic targets may provide a promising platform for advancing cervical cancer treatment.

Objective: This study aims to investigate the role of CUB domain-containing protein 1 (CDCP1) in cervical cancer progression and evaluate its potential as a therapeutic target.

Terminal sequence consistency verification method for small diameter abreast optical fibers based on computer vision.

In a kind of precision industrial equipment, small diameter abreast optical fibers are used for high-speed communication among functional nodes. The arrangement order at both terminals of the abreast optical fibers need to comply with communication protocols. In this paper, we propose an automatic terminal sequence consistency verification method based on computer vision.

Impact of Pulmonary Ventilation Dysfunction on Prognosis of Patients with Coronary Artery Disease: A Single-Center, Observational Study.

Background: Patients with coronary artery disease (CAD) often experience pulmonary ventilation dysfunction following their initial event. However, there is insufficient research exploring the relationship between this dysfunction and CAD prognosis.

Methods: To address this gap, a retrospective observational study was conducted involving 3800 CAD patients without prior pulmonary ventilation disease who underwent cardiopulmonary exercise testing (CPET) during hospitalization between November 2015 and September 2021.

Drug-Target Interaction Prediction Based on an Interactive Inference Network.

Drug-target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug-target interactions (DTIs).

Hypoxia-induced Semaphorin 3A promotes the development of endometriosis through regulating macrophage polarization.

Background: Semaphorin 3A (Sema3A) is a member of neural guidance factor family well-known for inducing the collapse of nerve cell growth cone and regulating nerve redistribution. It also has been characterized as an immunoregulatory and tumor promoting factor. Our previous study showed that Sema3A was involved in the regulation of sympathetic innervation and neuropathic pain of endometriosis.

Impact of Combined Modulation of Two Potassium Ion Currents on Spiral Waves and Turbulent States in the Heart.

In the realm of cardiac research, the control of spiral waves and turbulent states has been a persistent focus for scholars. Among various avenues of investigation, the modulation of ion currents represents a crucial direction. It has been proved that the methods involving combined control of currents are superior to singular approaches.

MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information.

A contrastive learning approach to integrate spatial transcriptomics and histological images.

The rapid growth of spatially resolved transcriptomics technology provides new perspectives on spatial tissue architecture. Deep learning has been widely applied to derive useful representations for spatial transcriptome analysis. However, effectively integrating spatial multi-modal data remains challenging.

Acupuncture and tuina knowledge graph with prompt learning.

Introduction: Acupuncture and tuina, acknowledged as ancient and highly efficacious therapeutic modalities within the domain of Traditional Chinese Medicine (TCM), have provided pragmatic treatment pathways for numerous patients. To address the problems of ambiguity in the concept of Traditional Chinese Medicine (TCM) acupuncture and tuina treatment protocols, the lack of accurate quantitative assessment of treatment protocols, and the diversity of TCM systems, we have established a map-filling technique for modern literature to achieve personalized medical recommendations.

Methods: (1) Extensive acupuncture and tuina data were collected, analyzed, and processed to establish a concise TCM domain knowledge base.

Comprehensive mapping of lunar surface chemistry by adding Chang'e-5 samples with deep learning.

Lunar surface chemistry is essential for revealing petrological characteristics to understand the evolution of the Moon. Existing chemistry mapping from Apollo and Luna returned samples could only calibrate chemical features before 3.0 Gyr, missing the critical late period of the Moon.

An individualized criterion for left bundle branch capture in patients with a narrow QRS complex.

Background: Left bundle branch (LBB) pacing (LBBP) is a physiological pacing; however, the accuracy of current electrocardiographic criteria for LBBP remains inadequate.

Objective: The purpose of this study was to establish a novel individualized criterion to improve the accuracy of LBBP determination in patients with a narrow QRS complex.

Methods: Patients in whom both LBBP and left ventricular septal pacing (LVSP) were acquired during operation were enrolled.

Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods.

In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor.

HMGB1 induces macrophage pyroptosis in chronic endometritis.

Background: Chronic endometritis (CE) reflects the local imbalance in the endometrial immune microenvironment after inflammation. High mobility group box 1 (HMGB1) is highly involved in both immunity and inflammation. In this study, we aimed to explore the roles of HMGB1 in the endometrium of patients with CE.

Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain.

Managed pressure drilling (MPD) is the most effective means to ensure drilling safety, and MPD is able to avoid further deterioration of complex working conditions through precise control of the wellhead back pressure. The key to the success of MPD is the well control strategy, which currently relies heavily on manual experience, hindering the automation and intelligence process of well control. In response to this issue, an MPD knowledge graph is constructed in this paper that extracts knowledge from published papers and drilling reports to guide well control.

Multilayer Perceptron Network Optimization for Chaotic Time Series Modeling.

Chaotic time series are widely present in practice, but due to their characteristics-such as internal randomness, nonlinearity, and long-term unpredictability-it is difficult to achieve high-precision intermediate or long-term predictions. Multi-layer perceptron (MLP) networks are an effective tool for chaotic time series modeling. Focusing on chaotic time series modeling, this paper presents a generalized degree of freedom approximation method of MLP.

Efficacy and Safety of Moderate-Intensity Continuous Training on the Improvement of Cardiopulmonary Function in Patients After Transcatheter Aortic Valve Replacement (ENERGY): A Randomized Controlled Trial.

Objectives: To investigate the effect of moderate-intensity continuous training (MICT) on the improvement of cardiopulmonary function for patients undergoing transcatheter aortic valve replacement (TAVR).

Design: Randomized controlled study.

Setting And Participants: Between August 20, 2021, and February 28, 2022, a total of 66 patients after TAVR were screened for inclusion and randomly divided into the MICT and control groups at a ratio of 1:1.

LFDNN: A Novel Hybrid Recommendation Model Based on DeepFM and LightGBM.

Hybrid recommendation algorithms perform well in improving the accuracy of recommendation systems. However, in specific applications, they still cannot reach the requirements of the recommendation target due to the gap between the design of the algorithms and data characteristics. In this paper, in order to learn higher-order feature interactions more efficiently and to distinguish the importance of different feature interactions better on the prediction results of recommendation algorithms, we propose a light and FM deep neural network (LFDNN), a hybrid recommendation model including four modules.

Gut dysbiosis-derived β-glucuronidase promotes the development of endometriosis.

Objective: To explore the role of gut dysbiosis-derived β-glucuronidase (GUSB) in the development of endometriosis (EMs).

Design: 16S rRNA sequencing of stool samples from women with (n = 35) or without (n = 30) endometriosis and from a mouse model was conducted to assess gut microbiome changes and identify molecular factors influencing the development of endometriosis. Experiments in vivo in an endometriosis C57BL6 mouse model and in vitro verified the level of GUSB and its role in the development of EMs.

Daily exercise improves the long-term prognosis of patients with acute coronary syndrome.

Objective: To demonstrate the effect of daily exercise on the incidence of major adverse cardiovascular events (MACE) for patients with acute coronary syndrome (ACS).

Methods: A cohort of 9,636 patients with ACS were consecutively enrolled in our retrospective study between November 2015 and September 2017, which were used for model development. 6,745 patients were assigned as the derivation cohort and 2,891 patients were assigned as the validation cohort.