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Differentially Expressed Nedd4-binding Protein Ndfip1 Protects Neurons Against Methamphetamine-induced Neurotoxicity.
Neurotox Res
January 2025
Molecular Neuropsychiatry Section, Intramural Research Program, NIH/ NIDA, 21224, Baltimore, MD, U.S.A.
To identify factors involved in methamphetamine (METH) neurotoxicity, we comprehensively searched for genes which were differentially expressed in mouse striatum after METH administration using differential display (DD) reverse transcription-PCR method and sequent single-strand conformation polymorphism analysis, and found two DD cDNA fragments later identified as mRNA of Nedd4 (neural precursor cell expressed developmentally downregulated 4) WW domain-binding protein 5 (N4WBP5), later named Nedd4 family-interacting protein 1 (Ndfip1). It is an adaptor protein for the binding between Nedd4 of ubiquitin ligase (E3) and target substrate protein for ubiquitination. Northern blot analysis confirmed drastic increases in Ndfip1 mRNA in the striatum after METH injections, and in situ hybridization histochemistry showed that the mRNA expression was increased in the hippocampus and cerebellum at 2 h-2 days, in the cerebral cortex and striatum at 18 h-2 days after single METH administration.
View Article and Find Full Text PDFQuantifying Tumor Budding: Implications for Prognosis in Gastric Adenocarcinoma.
Am J Surg Pathol
January 2025
General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University.
The mechanism of tumor budding (TB) in gastric adenocarcinoma (GAC) and its relationship with biological indicators and prognostic significance, remains unclear. In this study, we conducted a comprehensive analysis using whole-slide imaging to evaluate TB in 75 cases of GAC. Our findings revealed the risk factors associated with TB in GAC and their impact on patient prognosis.
View Article and Find Full Text PDFDeep learning radiomics analysis for prediction of survival in patients with unresectable gastric cancer receiving immunotherapy.
Eur J Radiol Open
June 2025
Department of Medical Oncology, The Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, PR China.
Objective: Immunotherapy has become an option for the first-line therapy of advanced gastric cancer (GC), with improved survival. Our study aimed to investigate unresectable GC from an imaging perspective combined with clinicopathological variables to identify patients who were most likely to benefit from immunotherapy.
Method: Patients with unresectable GC who were consecutively treated with immunotherapy at two different medical centers of Chinese PLA General Hospital were included and divided into the training and validation cohorts, respectively.
The integration of self-efficacy and response-efficacy in decision making.
Sci Rep
January 2025
Department of Psychology, Rutgers University, 101 Warren Street, Smith Hall-Room 301, Newark, NJ, 07102, USA.
The belief that we can exert an influence in our environment is dependent on distinct components of perceived control. Here, we investigate the neural representations that differentially code for self-efficacy (belief in successfully executing a behavior) and response-efficacy (belief that the behavior leads to an expected outcome) and how such signals may be integrated to inform decision-making. Participants provided confidence ratings related to executing a behavior (self-efficacy), and the potential for a rewarding outcome (response-efficacy).
View Article and Find Full Text PDFApplication research of convolutional neural network and its optimization in lightning electric field waveform recognition.
Sci Rep
January 2025
Institute of Atmospheric Physics, LAGEO, Chinese Academy of Sciences, Beijing, China.
Quickly identifying and classifying lightning waveforms is the foundation of lightning forecasting and early warning. In this paper, based on the electric field observation of the Beijing lightning location website of the Institute of Atmospheric Physics, Chinese Academy of Sciences, a recognition and classification method of pulse signal waveform based on Convolutional Neural Network(CNN) algorithm is designed and implemented. The CNN network model and its parameters were optimized from three aspects: dataset, model parameters, and network structure, achieving a recognition rate of over 90%.
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