DFF40 (I18) Peptide
- Known as:
- DFF40 (I18) Peptide
- Catalog number:
- 2107P
- Product Quantity:
- 0.05 mg
- Category:
- -
- Supplier:
- Prosci
- Gene target:
- DFF40 (I18) Peptide
Related genes to: DFF40 (I18) Peptide
- Gene:
- DFFB NIH gene
- Name:
- DNA fragmentation factor subunit beta
- Previous symbol:
- -
- Synonyms:
- CAD, CPAN, DFF-40, DFF40
- Chromosome:
- 1p36.32
- Locus Type:
- gene with protein product
- Date approved:
- 1997-10-27
- Date modifiied:
- 2016-10-05
- Gene:
- ISM1 NIH gene
- Name:
- isthmin 1
- Previous symbol:
- C20orf82
- Synonyms:
- bA149I18.1
- Chromosome:
- 20p12.1
- Locus Type:
- gene with protein product
- Date approved:
- 2001-07-17
- Date modifiied:
- 2016-11-08
- Gene:
- LYSMD1 NIH gene
- Name:
- LysM domain containing 1
- Previous symbol:
- -
- Synonyms:
- SB145, MGC35223, RP11-68I18.5
- Chromosome:
- 1q21.3
- Locus Type:
- gene with protein product
- Date approved:
- 2005-07-15
- Date modifiied:
- 2016-10-05
- Gene:
- RGPD5 NIH gene
- Name:
- RANBP2 like and GRIP domain containing 5
- Previous symbol:
- -
- Synonyms:
- RGP5, BS-63, DKFZp686I1842
- Chromosome:
- 2q13
- Locus Type:
- gene with protein product
- Date approved:
- 2005-12-15
- Date modifiied:
- 2019-02-18
- Gene:
- TAF13 NIH gene
- Name:
- TATA-box binding protein associated factor 13
- Previous symbol:
- TAF2K
- Synonyms:
- TAFII18
- Chromosome:
- 1p13.3
- Locus Type:
- gene with protein product
- Date approved:
- 1995-07-13
- Date modifiied:
- 2018-08-07
Related products to: DFF40 (I18) Peptide
Related articles to: DFF40 (I18) Peptide
- Spinal cord injury (SCI) is a severe neurological disorder lacking early diagnostic biomarkers and precise therapeutic targets. PANoptosis, a combined cell death process involving apoptosis, necroptosis, and pyroptosis, is implicated in poor functional recovery after SCI, yet its biomarkers remain unexplored. This study aims to identify biomarkers associated with PANoptosis in SCI. - Source: PubMed
Publication date: 2026/04/29
Xu SongWang GuozhuZhang XiongwenDong XieLiang JinBai Tao - Cell-free DNA (cfDNA) consists of degraded DNA fragments released into body fluids. Its genetic and pathological information makes it useful for prenatal testing and early tumor detection. However, the mechanisms behind cfDNA biology are largely unknown. In this study, for the first time, we conduct a genome-wide association study (GWAS) to explore the genetic basis of cfDNA end motif frequencies, termed cfGWAS, in 28,016 pregnant women. We identify 15 study-wide significant loci, including the well-known cfDNA-related genes DFFB and DNASE1L3, as well as novel genes potentially involved in cfDNA biology, such as PANX1 and DNASE1L1. The findings are further verified through three independent GWAS studies and experimental validation in knockout mice and cell lines. Subsequent analyses reveal strong causal relationships of leukocytes, especially neutrophils, with cfDNA features. In summary, we introduce the cfGWAS, revealing the genetic basis of cfDNA biology on a genome-wide scale. Novel knowledge uncovered by this study promises to revolutionize liquid biopsy technology and lead to potential new drugs targeting certain diseases. Given that millions of cfDNA whole genome sequencing data have been generated from clinical testing, the potential of this paradigm is enormous. - Source: PubMed
Publication date: 2026/02/14
Zhu HuanhuanZhang YanLi LinxuanZeng ShuangZhang XinyiLin YingOu RijingWang LinLi XiameiziWang YuZeng JingyuLin YuXu ChuangZeng GuodanLi LingguoZhao RongkangJia YongjianLuo FeiYang MengHu YuxuanXiao HanXu XunWang JianZhou AifenZhang HaiqiangJin Xin - Fermentation of soybean meal has been shown to enhance duck performance. However, there are limited reports on the effects of duck-derived compound bacteria-fermented soybean meal (DFFB) on laying ducks. In this study, six strains of duck-derived bacteria were incorporated into compound fermented soybean meal, which significantly improved the quality of soybean meal by reducing pH and crude fiber content (P < 0.05), while increasing crude protein content (P < 0.05) and elevating levels of various unsaturated and saturated fatty acids (P < 0.05), alongside a reduction in certain trans fatty acids (P < 0.05). DFFB was used as an additive in the feed for 160 laying ducks, which significantly decreased the feed conversion ratio (FCR) and increased the Haugh unit of duck eggs (P < 0.05). Additionally, it enhanced the levels of various unsaturated fatty acids, trace elements, and amino acids in duck eggs (P < 0.05). Furthermore, it improved duodenal villus length and the villus-to-crypt ratio (P < 0.05), while promoting the expression of mucosal barrier proteins (Claudin, Muc2, and ZO-1), aquaporin (AQP4), oligopeptide transporter (PepT1), sIgA, cytokines (IL-2, IL-6, and IFN-α), and antioxidant factors (SOD and GSH-Px) (P < 0.05). It also significantly increased the abundance of beneficial bacteria Rothia and reduced the abundance of harmful bacteria Streptococcus in the intestinal microbiota of laying ducks. Correlation analysis revealed a significant positive correlation between Rothia and the intestinal villus-to-crypt ratio, villus length, intestinal barrier proteins and transporters, intestinal antioxidant enzyme SOD, the elevated differential metabolite 2‑hydroxy-β-naphthoflavone in the intestine, the Haugh unit of duck eggs, and the contents of magnesium, glutamic acid, and phenylalanine in duck eggs. In contrast, Streptococcus exhibited a significant negative correlation with the FCR and decreased differential metabolites in the intestine (Lysophosphatidylcholine, l-octanoylcarnitine, LysoPE, and l-hexanoylcarnitine), while being positively correlated with elevated harmine hydrochloride in the intestine. In conclusion, DFFB can enhance the quality and nutritional composition of soybean meal, and regulates intestinal microbiota, improves intestinal lipid metabolism and antioxidant capacity, strengthens the intestinal mucosal barrier, promotes the digestion, absorption, and utilization of nutrients in laying ducks, and then improves both the production performance of laying ducks and the quality of duck eggs. This study provides a scientific basis for the application of DFFB in laying duck production. - Source: PubMed
Publication date: 2025/12/29
Zhou HaiouChi ChunmeiLi CuitingZhang FengxiaPeng SongZhao MengshiZhong LulongLei SongboChen ChengChen XinZheng XueZhang XiaopingLin FengqiangLi Zhaolong - Gastric cancer (GC) is characterized by pronounced molecular and clinical heterogeneity, creating major challenges for therapeutic decision-making. Limitations in current molecular classification hinder the development of personalized therapies, underscoring the need for improved diagnostic and prognostic frameworks. we conducted an integrated multi-omics analysis of bulk, single-cell, and spatial transcriptomic data to systematically characterize three key programmed cell death pathways-pyroptosis, apoptosis, and necroptosis (collectively abbreviated as PAN). A scoring-based clustering framework integrating multiple machine learning algorithms was developed to define high-resolution molecular subtypes and construct a deep learning signature. A hybrid CNN+BiLSTM model with cross-fusion attention was applied for transcriptomic feature extraction and subtype classification, achieving superior performance compared with existing approaches. Validation in the TCGA cohort confirmed the robustness and biological relevance of our model. Among the identified subtypes, Subtype 2 showed the most favorable prognosis. We further established a nine-gene prognostic signature with strong predictive value. High-risk patients exhibited poor survival, enhanced immune infiltration, and potential sensitivity to AKT inhibitors, with several drugs, including gefitinib and paclitaxel, identified as promising candidates. Experimental validation was conducted using the Human Protein Atlas (HPA) and RT-qPCR in clinical samples. CFLAR and TNFSF13B were upregulated and PDK4 downregulated in GC, while UACA showed no significant change. Additional prognostic genes (DFFB, PSMB6, GLP1R, HDAC9, BACH2) displayed expression patterns largely consistent across HPA, TCGA, and RT-qPCR, with minor discrepancies likely due to sample size. This study integrates multi-omics and deep learning with experimental validation, providing insights into programmed cell death regulation and offering robust biomarkers and therapeutic targets for GC. - Source: PubMed
Publication date: 2025/11/10
Jin QiaoyingChang ZhaobinChen KangpingJiang NaChen GuoxiuLu Yonggang - Medical image segmentation is crucial in accurately diagnosing diseases and assisting physicians in examining relevant areas. Therefore, there is a pressing need for an artificial intelligence-based model that can facilitate the diagnostic process and reduce errors. Existing networks often have high parameters, high gigaflops, and low accuracy. This research addresses this gap by proposing a transformer-based architecture. We developed a Swin transformer as an encoder to improve segmentation accuracy in medical images by leveraging deep feature extraction. This model can extract key image features more accurately by employing sliding windows and an attention mechanism. The overarching goal of this research is to design an optimized architecture for medical image segmentation that maintains high accuracy while reducing the number of network parameters and minimizing computational costs. In the decoder section, we designed the dynamic feature fusion block (DFFB) to enhance the extracted features, enabling the extraction of multi-scale features. This capability enables the model to analyze the structural information of medical images at various levels, resulting in improved performance in segmenting complex regions. We also employed the dynamic attention enhancement block to further enhance the features extracted from the DFFB output. This block utilizes spatial and channel attention mechanisms to emphasize key areas in the images, thereby enhancing the model's overall accuracy. The proposed model achieved segmentation performance across three medical datasets, obtaining a mean intersection over union (mIoU) of 0.9125 and a Dice score of 0.9542 on GlaS, an mIoU of 0.9174 and a Dice score of 0.9569 on PH2, and an mIoU of 0.9085 and a Dice score of 0.9521 on Kvasir-SEG. The experiments illustrate that the proposed model outperforms previous methods, demonstrating its potential as an effective tool in medical image segmentation. - Source: PubMed
Publication date: 2025/11/06
Mirab Golkhatmi BenyaminHoushmand MahboobehHosseini Seyyed Abed