Conectar
by Jing Liu, Junshuang Wang, Shuang Lv, Hengjiao Wang, Defu Yang, Ying Zhang, Ying Li, Huiling Qu, Ying Xu, Ying Yan
ObjectiveRadiation-induced brain injury (RIBI) is a significant complication following radiotherapy for brain tumors, leading to neurocognitive deficits and other neurological impairments. This study aims to identify potential biomarkers and therapeutic targets for RIBI by utilizing advanced proteomic techniques to explore the molecular mechanisms underlying RIBI.
MethodsA rat model of RIBI was established and subjected to whole-brain irradiation (30 Gy). Tandem mass tagging (TMT)-based quantitative proteomics, combined with high-resolution mass spectrometry, was used to identify differentially expressed proteins (DEPs) in the brain tissues of irradiated rats. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to identify the biological processes and pathways involved. Protein-protein interaction (PPI) networks were constructed to identify key hub proteins.
ResultsA total of 35 DEPs were identified, including PHLDA3, APOE and CPE. GO enrichment analysis revealed that the DEPs were mainly involved in lipid transport, cell adhesion, and metabolic processes. KEGG analysis highlighted the enrichment of pathways related to metabolism, tight junctions, and PPAR signaling. APOE was identified as a key hub protein through PPI network analysis, indicating its potential role in RIBI pathophysiology. Immunohistochemistry further validated the increased expression of PHLDA3, APOE, and CPE in the brain tissue of irradiated rats.
ConclusionThis study provides valuable insights into the molecular mechanisms of RIBI by identifying key proteins and their associated pathways. The findings suggest that these proteins, particularly APOE and PHLDA3, could serve as potential biomarkers and therapeutic targets for clinical intervention in RIBI. These results not only enhance our understanding of RIBI’s molecular pathology but also open new avenues for the development of targeted therapies to mitigate radiation-induced neurotoxicity.
Deep vein thrombosis (DVT) is a frequent complication following endovascular thrombectomy (EVT) in patients with acute ischaemic stroke (AIS), potentially leading to fatal pulmonary embolism (PE). Identifying patients early at high risk for DVT is clinically important. This study developed and validated a nomogram combining laboratory findings and clinical characteristics to predict the risk of lower-extremity DVT after EVT in patients with AIS.
This retrospective multicentre observational study was conducted in two tertiary hospitals in China, enrolling 640 patients who underwent ultrasonography for DVT diagnosis within 10 days following EVT. Data on medical history, examination and laboratory results were collected for logistic regression analyses to develop a DVT risk nomogram.
Logistic regression analyses identified critical predictors of DVT: lower limb National Institutes of Health Stroke Scale (NIHSS) score ≥ 2, elevated D-dimer levels (≥ 1.62 mg/L) and prolonged puncture-to-recanalization time (PRT ≥ 66 min). The nomogram demonstrated good discriminative ability (AUC 0.741–0.822) and clinical utility across internal and external validation cohorts. Additionally, the presence of DVT was significantly associated with reduced functional independence at 90 days post-EVT, highlighting the negative impact of DVT on patient recovery (OR = 3.85; 95% CI: 2.18–6.78; p < 0.001).
The study provides a practical clinical tool for early detection and intervention in patients with AIS at high risk for DVT following EVT. Early identification and intervention may help improve outcomes in patients with AIS undergoing EVT.
This nomogram helps in the early detection and proactive management of DVT in AIS patients, which can reduce severe complications and improve patient recovery outcomes.
No patient or public contributions were involved in this study due to its retrospective design, where data were utilised from existing medical records without direct patient interaction.
The objective of this study was to identify symptom clusters in lung cancer patients receiving immunotherapy and explore their impact on the quality of life of patients.
Immunotherapy is widely used in lung cancer; however, there is little understanding of symptom clusters and their impacts on the quality of life of this population.
Cross-sectional study.
The survey contained the Memorial Symptom Assessment Scale (MSAS), Quality of Life Questionnaire-Lung Cancer 43 and a self-designed General Information Evaluation Form. Symptom clusters were identified using exploratory factor analysis (EFA) based on the symptom scores. Spearman correlation analysis was performed to evaluate the associations between each symptom cluster and the patients' quality of life. Multiple linear regression analysis was employed to examine the impact of the symptom clusters on quality of life. This study adhered to the STROBE guidelines.
In total, 240 participants completed the survey. Five symptom clusters were identified and named according to their characteristics: emotional-related symptom cluster, lung cancer-related symptom cluster, physical symptom cluster, skin symptom cluster and neural symptom cluster. All symptom clusters, except for the neural symptom cluster, had a significantly detrimental impact on patient quality of life.
Lung cancer patients undergoing immunotherapy experience a range of symptoms, which can be categorized into five clusters. These symptom clusters have a negative impact on patients' quality of life. Future research should focus on developing interventions for each symptom cluster and their influencing factors.
In the data collection phase, lung cancer patients undergoing immunotherapy were recruited to participate in the survey.
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