Ask about this productRelated genes to: PSPN antibody
- Gene:
- PSPN NIH gene
- Name:
- persephin
- Previous symbol:
- -
- Synonyms:
- PSP
- Chromosome:
- 19p13.3
- Locus Type:
- gene with protein product
- Date approved:
- 1997-10-10
- Date modifiied:
- 2018-05-24
Related products to: PSPN antibody
Related articles to: PSPN antibody
- This study retrospectively analyzed the incidence and risk factors associated with platinum-induced sensory peripheral neuropathy (PSPN). - Source: PubMed
Publication date: 2026/01/21
Araujo Feitosa Aline CattiusseDos Santos Alexandre Francisca ThaysVianna Barbosa JenniferAparecida Vieira Barreto GiuliannaSilva Marques Araujo Ana BeatrizMont'Alverne Arruda LarissaFerreira Juaçaba SergioBarros Silva Paulo Goberlanio DeDa Silveira Bittencourt Flavio - Sepsis-associated acute kidney injury (S-AKI) is common and is associated with poor outcomes. This prospective observational study aimed to assess the predictive value of four novel biomarkers-syndecan-1 (SDC1), neutrophil gelatinase-associated lipocalin (NGAL), proenkephalin (PENK), and presepsin (PSPN)-for renal outcomes and mortality in septic ICU patients. Serum biomarker levels were measured in serum samples collected at the time of sepsis diagnosis on the basis of the Sepsis-3 criteria. Acute kidney injury (AKI) was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, and patients were grouped by the presence of AKI, renal replacement therapy requirement (RRT), and intensive care unit (ICU) survival. Demographic, clinical, laboratory, and severity score data were compared between groups to evaluate the predictive performance of biomarkers and clinical parameters. Of the 140 septic patients included, 55.0% developed AKI, 17.2% required RRT, and the ICU mortality rate was 50.0%. SDC1 was independently associated with both AKI (OR: 1.201; = 0.024) and RRT initiation (OR: 1.260; = 0.004). It also demonstrated the highest predictive performance for RRT (AUC: 0.715; = 0.001) and a significant AUC for AKI evaluation (AUC: 0.659; = 0.002). NGAL levels were significantly elevated in patients with AKI and higher SOFA scores but were not independently predictive. PENK and PSPN were not significantly associated with any renal outcome or mortality. The combined SOFA-SDC1 model improved discrimination for both AKI (AUC: 0.770) and RRT (AUC: 0.737), surpassing individual predictors. SDC1 emerged as the most reliable biomarker for assessing AKI and predicting the need for RRT, highlighting its potential role in early renal risk stratification among critically ill patients. - Source: PubMed
Publication date: 2025/07/30
Canbaz MertOrhun GünseliPolat ÖzlemAnaklı İlkayAydın Abdurrahman FatihKılınç SerhatErgin Özcan PerihanEsen Figen - Osteosarcoma (OS) is a rare and complex form of cancer that mostly affects children and adolescents. Pain is a common symptom for patients in OS which causes significant unhappiness and persistent aches. To date, there is minimal knowledge on the mechanisms underlying OS induced pain and few treatment options for patients. Previous genetic studies have demonstrated that the panel of four genes, artemin (), persephin (), glial cell line-derived neurotropic factor (), and neurturin () are associated with the regulation of pain processing in OS and analgesic responses. - Source: PubMed
Publication date: 2025/07/19
Feleke MesalieLin HaiyingjieLiu YunMo LiangRothzerg EmelSong DezhiZhao JinminFeng WenyuXu Jiake - The potential of optical pumped magnetometer magnetocardiography (OPM-MCG) for diagnosing coronary artery disease (CAD) has been initially shown, yet lacks large-scale prospective research.Using invasive coronary angiography (ICA) as a reference, we constructed three feature sets for the development of machine learning (ML) models: a 'Heart' feature set consisting only of OPM-MCG features, a 'Clinical' feature set, and a 'Heart + Clinical' combined feature set. We assessed the performance of 11 ML models with 10-fold cross-validation and conducted a feature importance analysis.. Among 1513 participants (mean age 58.2 ± 12.0 years, 75.5% male), 1194 (78.92%) tested positive for ICA. Significant differences were observed in 'Heart' and 'Clinical' features between ICA-positive and negative groups. ML models using only 'Heart' features (AUC 0.84-0.88) outperformed those using only 'Clinical' features (AUC 0.62-0.75). Combining both feature types improved diagnostic accuracy (AUC 0.75-0.90). Feature importance analysis highlighted that 'Significant change of Ar-PN' in OPM-MCG was key for ICA diagnosis (47.8%), along with 'Abnormal Sp-TT', 'Significant change of Ps-PN', and 'Abnormal Mg-TT'. OPM-MCG has high performance in diagnosing CAD, and the significant change of Ar-PN is the most important feature. Cat Boost and random forest are more suitable for OPM-MCG to build ML diagnostic models for CAD. - Source: PubMed
Publication date: 2025/08/11
Tu ChenchenYang ShuwenWang ZhixiangLiu LinqiMa ZhaoZhang HuanFeng LanxinCai BinZhang HongjiaDing MingSong Xiantao - To construct a multiphase contrast-enhanced CT-based radiomics nomogram that combines traditional CT features and radiomics signature for predicting the invasiveness of pancreatic solid pseudopapillary neoplasm (PSPN). - Source: PubMed
Publication date: 2025/04/07
Ren DabinLiu LiqiuSun AiyunWei YuguoWu TingfanWang YongtaoHe XiaxiaLiu ZishanZhu JieWang Guoyu