Induction of interleukin-6 by SPZ1-mediated Wnt5a signaling boosts progression of nasopharyngeal carcinoma cells.

Nasopharyngeal carcinoma (NPC) is a common malignancy in Southeast Asia, and in the Guangxi and Guangdong provinces of China. The spermatogenic transcription factor zip 1 (SPZ1) is a member of bHLH zip family, and promotes tumorigenesis in the liver, colon and breast tissues. However, the role of SPZ1 in the progression of NPC is unclear.

Visualizing plant salt stress with a NaCl-responsive fluorescent probe.

Salt stress is an adverse environmental condition that harms plant growth and development. The development of salt stress probes is critical for tracking the growth dynamics of plants, molecular breeding or screening of growth regulators. The sodium chloride (NaCl)-responsive fluorescent probe Aza-CyBz is designed based on the tenet that NaCl induces formation of ordered aggregates, and the sensitive fluorescence response can enable the visualization of plant salt stress in root tip tissues and live plants.

PreMLS: The undersampling technique based on ClusterCentroids to predict multiple lysine sites.

The translated protein undergoes a specific modification process, which involves the formation of covalent bonds on lysine residues and the attachment of small chemical moieties. The protein's fundamental physicochemical properties undergo a significant alteration. The change significantly alters the proteins' 3D structure and activity, enabling them to modulate key physiological processes.

Process and mechanism of termite impact on soil and plant.

Termites, as a kind of nesting social insects, are often confused as worldwide "pests" because some of their groups have great destructive effects. The vast majority of termites can regulate ecosystem functions and ser-vices by participating in biogeochemical cycles, known as "ecosystem engineers". We reviewed studies on the effects of termites on the physical, chemical and biological characteristics of mound soil ecosystems and the composition and diversity of plant communities.

Four-phase CT lesion recognition based on multi-phase information fusion framework and spatiotemporal prediction module.

Multiphase information fusion and spatiotemporal feature modeling play a crucial role in the task of four-phase CT lesion recognition. In this paper, we propose a four-phase CT lesion recognition algorithm based on multiphase information fusion framework and spatiotemporal prediction module. Specifically, the multiphase information fusion framework uses the interactive perception mechanism to realize the channel-spatial information interactive weighting between multiphase features.

Human umbilical cord mesenchymal stem cells on treating osteoarthritis in a rabbit model: Injection strategies.

Human umbilical cord mesenchymal stem cells (UCMSCs) are a novel stem-cell source to treat osteoarthritis (OA). Here we investigated the therapeutic effects of UCMSCs injection strategies on knee OA in a rabbit model. Thirty OA rabbits randomly received normal saline, a single dose of 1 × 10 UCMSCs, or three injections of 1 × 10 UCMSCs at 2, 4, 6 weeks.

Tremelimumab and durvalumab with chemotherapy in first-line treatment for metastatic non-small cell lung cancer: a US-based cost-effectiveness analysis.

Background: While the tremelimumab plus durvalumab combined with chemotherapy (T + D + CT) has shown promise in treating epidermal growth factor receptor/anaplastic lymphoma kinase (EGFR/ALK) wild-type metastatic non-small cell lung cancer (mNSCLC), particularly in patients with low or no programmed cell death ligand 1 (PD-L1) expression, the economic implications of its high cost remain poorly understood. This study fills a critical gap in knowledge by evaluating the cost-effectiveness of T + D + CT from a US health care perspective, offering valuable insights for clinical and policy decision-making.

Methods: A 10-year Markov model was crafted to track the disease progression, survival, and treatment-related toxicities of a patient cohort with EGFR/ALK wild-type mNSCLC.

Greenness on mortality of infant and under-5 child: A nationwide study in 147 Chinese cities.

Background: Higher greenness was associated with a lower risk of adult mortality. However, the effects of greenness on the mortality of infant and child under-5 have not been fully examined.

Objectives: The association of greenness on the infant mortality rate (IMR) and child under-5 mortality rate (U5MR) in 147 Chinese cities from 2009 to 2020 was evaluated.

Fate of arsenic in contaminated coastal soil induced by rising temperature and seawater intrusion.

Temperature rising and seawater intrusion are expected to influence the hydrologic regime and redox conditions in coastal soil, and the fate and mechanisms of biogeochemical cycling of Arsenic (As) in the specific environment are poorly understood. This work was carried out in an anaerobic operating chamber by adding sulfate to simulate seawater intrusion under various temperature. Results demonstrated the microbial community diversity was influenced by temperature and the highest Shannon and lowest Simpson index were found at 28 °C.

Causal relationship between immunophenotypes and mitral valve prolapse: a bidirectional Mendelian randomization study.

Background: Emerging evidence indicates a significant link between various immune cell types and the development of heart valve disorders. Mitral valve prolapse (MVP) is a common condition that can lead to heart failure, arrhythmias, and even sudden death. Currently, the role of immune cells in MVP is not well understood.

Knowledge-driven multi-graph convolutional network for brain network analysis and potential biomarker discovery.

In brain network analysis, individual-level data can provide biological features of individuals, while population-level data can provide demographic information of populations. However, existing methods mostly utilize either individual- or population-level features separately, inevitably neglecting the multi-level characteristics of brain disorders. To address this issue, we propose an end-to-end multi-graph neural network model called KMGCN.

Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor-immune crosstalk mechanisms.