BACKGROUND: Ferroptosis-related genes play a crucial role in regulating intracellular iron homeostasis and lipid peroxidation, and they are involved in the regulation of tumor growth and drug resistance. The expression of ferroptosis-related genes in tumor tissues can be used to predict patients' future survival times, aiding doctors and patients in anticipating disease progression. Based on the sequencing data of lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) database, this study identified genes involved in the regulation of ferroptosis, constructed a prognostic model, and evaluated the predictive performance of the model. METHODS: A total of 1467 ferroptosis-related genes were obtained from the GeneCards database. Gene expression profiles and clinical data from 541 LUAD patients were collected from the TCGA database. The expression data of all ferroptosis-related genes were extracted, and differentially expressed genes were identified using R software. Survival analysis was performed on these genes to screen for those with prognostic value. Subsequently, a prognostic risk scoring model for ferroptosis-related genes was constructed using LASSO regression model. Each LUAD patient sample was scored, and the patients were divided into high-risk and low-risk groups based on the median score. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. Kaplan-Meier survival curves were generated to assess model performance, followed by validation in an external dataset. Finally, univariate and multivariate Cox regression analyses were conducted to evaluate the independent prognostic value and clinical relevance of the model. RESULTS: Through survival analysis, 121 ferroptosis-related genes associated with prognosis were initially identified. Based on this, a LUAD prognostic risk scoring model was constructed using 12 ferroptosis-related genes (ALG3, C1QTNF6, CCT6A, GLS2, KRT6A, LDHA, NUPR1, OGFRP1, PCSK9, TRIM6, IGF2BP1 and MIR31HG). The results indicated that patients in the high-risk group had significantly shorter survival time than those in the low-risk group (P<0.001), and the model demonstrated good predictive performance in both the training set (1-yr AUC=0.721) and the external validation set (1-yr AUC=0.768). Risk scores were significantly associated with the prognosis of LUAD patients in both univariate and multivariate Cox regression analyses (P<0.001), suggesting that this score is an important prognostic factor for LUAD patients. CONCLUSIONS This study successfully established a LUAD risk scoring model composed of 12 ferroptosis-related genes. In the future, this model is expected to be used in conjunction with the tumor-node-metastasis (TNM) staging system for prognostic predictions in LUAD patients.
Humans ; Ferroptosis/genetics* ; Prognosis ; Adenocarcinoma of Lung/pathology* ; Lung Neoplasms/pathology* ; Male ; Female ; Gene Expression Regulation, Neoplastic ; Middle Aged ; ROC Curve

Ovarian tumor (OT) is the most lethal form of gynecologic malignancy, with minimal improvements in patient outcomes over the past several decades. Metastasis is the leading cause of ovarian cancer-related deaths, yet the underlying mechanisms remain poorly understood. Psychological stress is known to activate the glucocorticoid receptor (NR3C1), a factor associated with poor prognosis in OT patients. However, the precise mechanisms linking NR3C1 signaling and metastasis have yet to be fully elucidated. In this study, we demonstrate that chronic restraint stress accelerates epithelial-mesenchymal transition (EMT) and metastasis in OT through an NR3C1-dependent mechanism involving nuclear protein 1 (NUPR1). Mechanistically, NR3C1 directly regulates the transcription of NUPR1, which in turn increases the expression of snail family transcriptional repressor 2 (SNAI2), a key driver of EMT. Clinically, elevated NR3C1 positively correlates with NUPR1 expression in OT patients, and both are positively associated with poorer prognosis. Overall, our study identified the NR3C1/NUPR1 axis as a critical regulatory pathway in psychological stress-induced OT metastasis, suggesting a potential therapeutic target for intervention in OT metastasis.

OBJECTIVES: We propose a multimodal model integrating social media text and image data for automated assessment of psychological stress in college students to support the development of intelligent mental health services in higher education institutions. METHODS: Based on deep learning technology, we designed an evaluation framework comprising a text sentiment modeling module, an image sentiment modeling module, and a multimodal fusion prediction module. Text sentiment features were extracted using Bi-LSTM, and image semantic cues were extracted via U-Net. A feature concatenation strategy was used to enable cross-modal semantic collaboration to achieve automatic identification of 3 psychological stress levels: mild, moderate, and severe. We constructed a multimodal annotated dataset using social platform data from 1577 students across multiple universities in Guangdong Province. After data cleaning, 252 samples were randomly selected for model training and testing. RESULTS: In the 3-classification task, the model demonstrated outstanding performance on the test set, and achieved an accuracy of 92.86% and an F1 score of 0.9276, exhibiting excellent stability and consistency. Confusion matrix analysis further revealed the model's ability to effectively distinguish between different pressure levels. CONCLUSIONS The multimodal psychological stress assessment model developed in this study effectively integrates unstructured social behavior data to enhance the scientific rigor and practical applicability of psychological state recognition, and thus provides support for developing intelligent psychological service systems.
Humans ; Stress, Psychological/diagnosis* ; Students/psychology* ; Universities ; Social Media ; Deep Learning

Perineural invasion (PNI) by tumor cells is a key phenotype of highly-invasive oral squamous cell carcinoma (OSCC). Since Schwann cells (SCs) and fibroblasts maintain the physiological homeostasis of the peripheral nervous system, and we have focused on cancer-associated fibroblasts (CAFs) for decades, it's imperative to elucidate the impact of CAFs on SCs in PNI⁺ OSCCs. We describe a disease progression-driven shift of PNI^- towards PNI⁺ during the progression of early-stage OSCC (31%, n = 125) to late-stage OSCC (53%, n = 97), characterized by abundant CAFs and nerve demyelination. CAFs inhibited SC proliferation/migration and reduced neurotrophic factors and myelin in vitro, and this involved up-regulated ER stress and decreased MAPK signals. Moreover, CAFs also aggravated the paralysis of the hind limb and PNI in vivo. Unexpectedly, leukemia inhibitory factor (LIF) was exclusively expressed on CAFs and up-regulated in metastatic OSCC. The LIF inhibitor EC330 restored CAF-induced SC inactivation. Thus, OSCC-derived CAFs inactivate SCs to aggravate nerve injury and PNI development.
Schwann Cells/metabolism* ; Mouth Neoplasms/metabolism* ; Humans ; Cancer-Associated Fibroblasts/metabolism* ; Animals ; Carcinoma, Squamous Cell/metabolism* ; Neoplasm Invasiveness/pathology* ; Male ; Female ; Mice ; Cell Movement/physiology* ; Cell Proliferation/physiology* ; Cell Line, Tumor ; Leukemia Inhibitory Factor/metabolism* ; Middle Aged

Apical microsurgery is accurate and minimally invasive, produces few complications, and has a success rate of more than 90%. However, due to the lack of awareness and understanding of apical microsurgery by dental general practitioners and even endodontists, many clinical problems remain to be overcome. The consensus has gathered well-known domestic experts to hold a series of special discussions and reached the consensus. This document specifies the indications, contraindications, preoperative preparations, operational procedures, complication prevention measures, and efficacy evaluation of apical microsurgery and is applicable to dentists who perform apical microsurgery after systematic training.
Microsurgery/standards* ; Humans ; Apicoectomy ; Contraindications, Procedure ; Tooth Apex/diagnostic imaging* ; Postoperative Complications/prevention & control* ; Consensus ; Treatment Outcome