Biology subjects

Chen, X. F.

Publications and source records attributed to Chen, X. F..

3 recordsLinked to original sources

bioRxiv2026

Deep learning based behavioral analysis in a neonatal rat model of hypoxic ischemic brain injury

Hypoxic-ischemic (HI) brain injury in neonates is one of the leading causes of lifelong neurological disability. Behavioral tests in preclinical rodent models are widely used to assess motor and cognitive outcomes after HI injury; however, these assays usually depend on subjective and labor-intensive manual scoring. Recent advances in markerless pose estimation offer new opportunities for automated and reproducible behavioral quantification in animal and infant recordings, but their use in neonatal HI preclinical studies remains limited. Wistar rat pups underwent HI injury using the Rice-Vannucci model at postnatal day 7 (P7). Three developmental behavioral tests included righting reflex (P8), negative geotaxis (P14), and wire hang (P16), were recorded and analyzed by both a human rater and an automated pipeline using DeepLabCut (DLC), an open source markerless pose estimation framework. Automated measurements were compared with manual scores using Intraclass Correlation Coefficients (ICC), Bland-Altman analysis, and Pearson correlation. DLC-derived measurements demonstrated strong agreement with manual scoring across all assays. ICC values were 0.929 (95% CI 0.648-0.971) for righting reflex, 0.965 (0.888-0.989) for negative geotaxis, and 0.958 (0.876-0.985) for wire hang. An automated behavioral analysis framework integrating DLC-based pose estimation with rule based quantification and supervised machine learning offers a reliable and objective alternative to manual scoring in neonatal HI models, enabling more efficient and reproducible behavioral assessment.

neuroscience↗

bioRxiv2025

TWIST1 mediated transcriptional activation of SPON2 drives colorectal peritoneal metastasis through activation of cancer-associated fibroblast signaling network

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related mortality in the United States. Peritoneal metastasis (PM), a malignant dissemination within the peritoneal cavity, affects approximately 20% of CRC patients and accounts for 25-35% of stage IV cases. CRC PM is associated with dismal outcomes, with a median overall survival of only 16 months on systemic chemotherapy and an almost 0% five-year survival rate, largely due to frequent treatment resistance and limited therapeutic options. Despite advances in understanding CRC metastasis, the molecular mechanisms driving CRC PM remain poorly defined. CRC heterogeneity is classified into four Consensus Molecular Subtypes (CMS1-4), with CRC PM tumors predominantly exhibiting the CMS4 signature--characterized by stromal enrichment, high mesenchymal gene expression, and enhanced cellular plasticity--features linked to aggressive disease progression and resistance to standard chemotherapy. In this study, we identify TWIST1, a basic helix-loop-helix transcription factor, as significantly upregulated in CRC PM. We establish TWIST1-SPON2 as a novel transcriptional axis driving CRC PM tumorigenesis, mediating tumor-stroma interactions between tumor epithelium and cancer-associated fibroblasts (CAFs). Additionally, we identify SPP1, secreted by CAFs, as an upstream regulator of the TWIST1-SPON2 cascade via AKT activation in tumor cells. This newly defined SPP1-TWIST1-SPON2 signaling circuit plays a pivotal role in shaping the tumor microenvironment and promoting CRC PM progression. The findings establish the SPP1-TWIST1-SPON2 axis as a potential biomarker and a promising therapeutic target in CRC PM. Keyword: Colorectal cancer, peritoneal metastasis, epithelial-mesenchymal transition, cancer-associated fibroblast

cancer biology↗

bioRxiv2024

Construction of Discrete Element Model of Different Moisture Hygroscopic Fertilizer Particles and Fertilizer Discharging Verification

The hygroscopic fertilizer particles were characterized based on discrete element simulation, and its accuracy was verified by the simulation of fertilizer discharge. Firstly, the repose angle of the fertilizer particles served as the key metric for assessment. A mathematical model characterizing the relationship between fertilizer moisture content and the repose angle was established, R2=0.9935; and a mathematical representation of the repose angle for fertilizer moisture content was established, R2=0.9933. Then, the moisture absorbing fertilizer particles was developed utilizing the integrated Hertz Mindlin with JKR model within EDEM system to account for particle adhesion; A robust correlation model between the repose angle of hygroscopic fertilizers and the discrete element method was established, p <0.0001; Finally, a correlation model between moisture content and significant parameters within the discrete element method was established, drawing upon the models for moisture content repose angle and repose angle discrete element parameters. The moisture-absorbing fertilizer particles model at moisture contents of 2% and 6% were resolved, with a relative error of the repose angle not exceeding 1.42% and a single circle fertilizer discharge error below 8.32%. The results show that the moisture content discrete element significant parameter correlation model can reliably and precisely forecast the discrete element parameters of various moisture content hygroscopic fertilizers. The hygroscopic fertilizer particle model can represent surface interaction characteristics of hygroscopic fertilizer granules. which has guiding significance for the design of precision fertilizer discharge technology device.

physiology↗